tracelyzer-to-csv.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Imports\" data-toc-modified-id=\"Imports-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Imports</a></span><ul class=\"toc-item\"><li><span><a href=\"#Json-to-csv---merge-all-jsons-into-one-csv\" data-toc-modified-id=\"Json-to-csv---merge-all-jsons-into-one-csv-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Json to csv - merge all jsons into one csv</a></span></li><li><span><a href=\"#Flattening-JSons---from-kaggle\" data-toc-modified-id=\"Flattening-JSons---from-kaggle-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Flattening JSons - from kaggle</a></span></li><li><span><a href=\"#In-a-loop-on-real-data-now\" data-toc-modified-id=\"In-a-loop-on-real-data-now-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>In a loop on real data now</a></span></li><li><span><a href=\"#Correlation-of-Columns\" data-toc-modified-id=\"Correlation-of-Columns-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Correlation of Columns</a></span></li><li><span><a href=\"#Regression-on-this-dataset\" data-toc-modified-id=\"Regression-on-this-dataset-1.5\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>Regression on this dataset</a></span><ul class=\"toc-item\"><li><span><a href=\"#With-ADAM-intead-of-RMSProp\" data-toc-modified-id=\"With-ADAM-intead-of-RMSProp-1.5.1\"><span class=\"toc-item-num\">1.5.1&nbsp;&nbsp;</span>With ADAM intead of RMSProp</a></span></li></ul></li></ul></li><li><span><a href=\"#Writing-a-flexible-hyperparameter-search-model\" data-toc-modified-id=\"Writing-a-flexible-hyperparameter-search-model-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Writing a flexible hyperparameter search model</a></span></li><li><span><a href=\"#End-bookmark\" data-toc-modified-id=\"End-bookmark-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>End bookmark</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import json\n",
    "from IPython.display import clear_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./tracelyzer\\\\1000-state-metrics_run_0.json',\n",
       " './tracelyzer\\\\1000-state-metrics_run_1.json',\n",
       " './tracelyzer\\\\1000-state-metrics_run_2.json',\n",
       " './tracelyzer\\\\1000-state-metrics_run_3.json',\n",
       " './tracelyzer\\\\1000-state-metrics_run_4.json',\n",
       " './tracelyzer\\\\10000-state-metrics_run_0.json',\n",
       " './tracelyzer\\\\10000-state-metrics_run_1.json',\n",
       " './tracelyzer\\\\10000-state-metrics_run_2.json',\n",
       " './tracelyzer\\\\10000-state-metrics_run_3.json',\n",
       " './tracelyzer\\\\10000-state-metrics_run_4.json']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace_files = glob.glob('./tracelyzer//*.json')\n",
    "trace_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "jsondf = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Timestamp</td>\n",
       "      <td>47416.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AudioHandlers</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "            name    value\n",
       "0      Timestamp  47416.6\n",
       "0  AudioHandlers        0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dataframe = pd.DataFrame.from_dict(a_json, orient=\"index\")\n",
    "testdf = pd.DataFrame.from_dict(jsonstr['metrics'][0],orient=\"index\").T\n",
    "testdf.append(pd.DataFrame.from_dict(jsonstr['metrics'][1],orient=\"index\").T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
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       "      <th>0</th>\n",
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       "      <th>4</th>\n",
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       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <td>Timestamp</td>\n",
       "      <td>AudioHandlers</td>\n",
       "      <td>Documents</td>\n",
       "      <td>Frames</td>\n",
       "      <td>JSEventListeners</td>\n",
       "      <td>LayoutObjects</td>\n",
       "      <td>MediaKeySessions</td>\n",
       "      <td>MediaKeys</td>\n",
       "      <td>Nodes</td>\n",
       "      <td>Resources</td>\n",
       "      <td>...</td>\n",
       "      <td>V8CompileDuration</td>\n",
       "      <td>TaskDuration</td>\n",
       "      <td>TaskOtherDuration</td>\n",
       "      <td>ThreadTime</td>\n",
       "      <td>ProcessTime</td>\n",
       "      <td>JSHeapUsedSize</td>\n",
       "      <td>JSHeapTotalSize</td>\n",
       "      <td>FirstMeaningfulPaint</td>\n",
       "      <td>DomContentLoaded</td>\n",
       "      <td>NavigationStart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>47416.6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>10121</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10402</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005253</td>\n",
       "      <td>1.24273</td>\n",
       "      <td>0.173812</td>\n",
       "      <td>1.1114</td>\n",
       "      <td>2.82812</td>\n",
       "      <td>1.9859e+07</td>\n",
       "      <td>3.69705e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>47415.5</td>\n",
       "      <td>47414.9</td>\n",
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       "</table>\n",
       "<p>2 rows × 35 columns</p>\n",
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       "              0              1          2       3                 4   \\\n",
       "name   Timestamp  AudioHandlers  Documents  Frames  JSEventListeners   \n",
       "value    47416.6              0          6       2               200   \n",
       "\n",
       "                  5                 6          7      8          9   ...  \\\n",
       "name   LayoutObjects  MediaKeySessions  MediaKeys  Nodes  Resources  ...   \n",
       "value          10121                 0          0  10402         36  ...   \n",
       "\n",
       "                      25            26                 27          28  \\\n",
       "name   V8CompileDuration  TaskDuration  TaskOtherDuration  ThreadTime   \n",
       "value           0.005253       1.24273           0.173812      1.1114   \n",
       "\n",
       "                29              30               31                    32  \\\n",
       "name   ProcessTime  JSHeapUsedSize  JSHeapTotalSize  FirstMeaningfulPaint   \n",
       "value      2.82812      1.9859e+07      3.69705e+07                     0   \n",
       "\n",
       "                     33               34  \n",
       "name   DomContentLoaded  NavigationStart  \n",
       "value           47415.5          47414.9  \n",
       "\n",
       "[2 rows x 35 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tempdf = pd.DataFrame(columns=['name','value'])\n",
    "for i in range(len(metrics)):\n",
    "#     print(metrics[i]['name'], metrics[i]['value'])\n",
    "    tempdf.loc[i] = [metrics[i]['name'], float(metrics[i]['value'])]#['name' + str(i)] + list(randint(10, size=2))\n",
    "tempdf.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "for file in trace_files:\n",
    "    with open(file) as f:\n",
    "        jsonstr = json.load(f)\n",
    "        metrics = jsonstr['metrics']\n",
    "        tempdf = pd.DataFrame(columns=['name','value'])\n",
    "        for i in range(len(metrics)):\n",
    "        #     print(metrics[i]['name'], metrics[i]['value'])\n",
    "            tempdf.loc[i] = [metrics[i]['name'], float(metrics[i]['value'])]#['name' + str(i)] + list(randint(10, size=2))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Timestamp</td>\n",
       "      <td>4.741659e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AudioHandlers</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Documents</td>\n",
       "      <td>6.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Frames</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JSEventListeners</td>\n",
       "      <td>2.000000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LayoutObjects</td>\n",
       "      <td>1.012100e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MediaKeySessions</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MediaKeys</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Nodes</td>\n",
       "      <td>1.040200e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Resources</td>\n",
       "      <td>3.600000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ContextLifecycleStateObservers</td>\n",
       "      <td>1.300000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>V8PerContextDatas</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>WorkerGlobalScopes</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>UACSSResources</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>RTCPeerConnections</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ResourceFetchers</td>\n",
       "      <td>6.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AdSubframes</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>DetachedScriptStates</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ArrayBufferContents</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LayoutCount</td>\n",
       "      <td>9.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>RecalcStyleCount</td>\n",
       "      <td>1.200000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>LayoutDuration</td>\n",
       "      <td>5.918200e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>RecalcStyleDuration</td>\n",
       "      <td>1.945240e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>DevToolsCommandDuration</td>\n",
       "      <td>3.792000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ScriptDuration</td>\n",
       "      <td>8.061640e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>V8CompileDuration</td>\n",
       "      <td>5.253000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>TaskDuration</td>\n",
       "      <td>1.242727e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>TaskOtherDuration</td>\n",
       "      <td>1.738120e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ThreadTime</td>\n",
       "      <td>1.111397e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ProcessTime</td>\n",
       "      <td>2.828125e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>JSHeapUsedSize</td>\n",
       "      <td>1.985900e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>JSHeapTotalSize</td>\n",
       "      <td>3.697050e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>FirstMeaningfulPaint</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>DomContentLoaded</td>\n",
       "      <td>4.741551e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>NavigationStart</td>\n",
       "      <td>4.741494e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              name         value\n",
       "0                        Timestamp  4.741659e+04\n",
       "1                    AudioHandlers  0.000000e+00\n",
       "2                        Documents  6.000000e+00\n",
       "3                           Frames  2.000000e+00\n",
       "4                 JSEventListeners  2.000000e+02\n",
       "5                    LayoutObjects  1.012100e+04\n",
       "6                 MediaKeySessions  0.000000e+00\n",
       "7                        MediaKeys  0.000000e+00\n",
       "8                            Nodes  1.040200e+04\n",
       "9                        Resources  3.600000e+01\n",
       "10  ContextLifecycleStateObservers  1.300000e+01\n",
       "11               V8PerContextDatas  2.000000e+00\n",
       "12              WorkerGlobalScopes  0.000000e+00\n",
       "13                  UACSSResources  0.000000e+00\n",
       "14              RTCPeerConnections  0.000000e+00\n",
       "15                ResourceFetchers  6.000000e+00\n",
       "16                     AdSubframes  0.000000e+00\n",
       "17            DetachedScriptStates  2.000000e+00\n",
       "18             ArrayBufferContents  0.000000e+00\n",
       "19                     LayoutCount  9.000000e+00\n",
       "20                RecalcStyleCount  1.200000e+01\n",
       "21                  LayoutDuration  5.918200e-02\n",
       "22             RecalcStyleDuration  1.945240e-01\n",
       "23         DevToolsCommandDuration  3.792000e-03\n",
       "24                  ScriptDuration  8.061640e-01\n",
       "25               V8CompileDuration  5.253000e-03\n",
       "26                    TaskDuration  1.242727e+00\n",
       "27               TaskOtherDuration  1.738120e-01\n",
       "28                      ThreadTime  1.111397e+00\n",
       "29                     ProcessTime  2.828125e+00\n",
       "30                  JSHeapUsedSize  1.985900e+07\n",
       "31                 JSHeapTotalSize  3.697050e+07\n",
       "32            FirstMeaningfulPaint  0.000000e+00\n",
       "33                DomContentLoaded  4.741551e+04\n",
       "34                 NavigationStart  4.741494e+04"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tempdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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'metrics': [{'name': 'Timestamp', 'value': 47416.589465}, {'name': 'AudioHandlers', 'value': 0}, {'name': 'Documents', 'value': 6}, {'name': 'Frames', 'value': 2}, {'name': 'JSEventListeners', 'value': 200}, {'name': 'LayoutObjects', 'value': 10121}, {'name': 'MediaKeySessions', 'value': 0}, {'name': 'MediaKeys', 'value': 0}, {'name': 'Nodes', 'value': 10402}, {'name': 'Resources', 'value': 36}, {'name': 'ContextLifecycleStateObservers', 'value': 13}, {'name': 'V8PerContextDatas', 'value': 2}, {'name': 'WorkerGlobalScopes', 'value': 0}, {'name': 'UACSSResources', 'value': 0}, {'name': 'RTCPeerConnections', 'value': 0}, {'name': 'ResourceFetchers', 'value': 6}, {'name': 'AdSubframes', 'value': 0}, {'name': 'DetachedScriptStates', 'value': 2}, {'name': 'ArrayBufferContents', 'value': 0}, {'name': 'LayoutCount', 'value': 9}, {'name': 'RecalcStyleCount', 'value': 12}, {'name': 'LayoutDuration', 'value': 0.059182}, {'name': 'RecalcStyleDuration', 'value': 0.194524}, {'name': 'DevToolsCommandDuration', 'value': 0.003792}, {'name': 'ScriptDuration', 'value': 0.806164}, {'name': 'V8CompileDuration', 'value': 0.005253}, {'name': 'TaskDuration', 'value': 1.242727}, {'name': 'TaskOtherDuration', 'value': 0.173812}, {'name': 'ThreadTime', 'value': 1.111397}, {'name': 'ProcessTime', 'value': 2.828125}, {'name': 'JSHeapUsedSize', 'value': 19859000}, {'name': 'JSHeapTotalSize', 'value': 36970496}, {'name': 'FirstMeaningfulPaint', 'value': 0}, {'name': 'DomContentLoaded', 'value': 47415.513677}, {'name': 'NavigationStart', 'value': 47414.937787}], 'connectStart': 1608062191221, 'navigationStart': 1608062190918, 'loadEventEnd': 1608062192547, 'domLoading': 1608062191227, 'secureConnectionStart': 0, 'fetchStart': 1608062190918, 'domContentLoadedEventStart': 1608062191494, 'responseStart': 1608062191223, 'responseEnd': 1608062191224, 'domInteractive': 1608062191494, 'domainLookupEnd': 1608062190919, 'redirectStart': 0, 'requestStart': 1608062191222, 'unloadEventEnd': 0, 'unloadEventStart': 0, 'domComplete': 1608062192547, 'domainLookupStart': 1608062190919, 'loadEventStart': 1608062192547, 'domContentLoadedEventEnd': 1608062191494, 'redirectEnd': 0, 'connectEnd': 1608062191222}\n"
     ]
    }
   ],
   "source": [
    "for file in trace_files:\n",
    "    with open(file) as f:\n",
    "        jsonstr = json.load(f)\n",
    "        print(jsonstr)\n",
    "        df = pd.DataFrame({'value': jsonstr})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>value</th>\n",
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       "      <td>1608062191222</td>\n",
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       "      <th>loadEventStart</th>\n",
       "      <td>1608062192547</td>\n",
       "    </tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>{'categories': {'scripting': 819.8820000141859...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>redirectEnd</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>redirectStart</th>\n",
       "      <td>0</td>\n",
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       "      <th>requestStart</th>\n",
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       "      <th>responseEnd</th>\n",
       "      <td>1608062191224</td>\n",
       "    </tr>\n",
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       "      <th>responseStart</th>\n",
       "      <td>1608062191223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "                                                                        value\n",
       "connectEnd                                                      1608062191222\n",
       "connectStart                                                    1608062191221\n",
       "domComplete                                                     1608062192547\n",
       "domContentLoadedEventEnd                                        1608062191494\n",
       "domContentLoadedEventStart                                      1608062191494\n",
       "domInteractive                                                  1608062191494\n",
       "domLoading                                                      1608062191227\n",
       "domainLookupEnd                                                 1608062190919\n",
       "domainLookupStart                                               1608062190919\n",
       "fetchStart                                                      1608062190918\n",
       "loadEventEnd                                                    1608062192547\n",
       "loadEventStart                                                  1608062192547\n",
       "metrics                     [{'name': 'Timestamp', 'value': 47416.589465},...\n",
       "navigationStart                                                 1608062190918\n",
       "profiling                   {'categories': {'scripting': 819.8820000141859...\n",
       "redirectEnd                                                                 0\n",
       "redirectStart                                                               0\n",
       "rendering                   {'firstPaint': 0.605076, 'forcedReflowEvents':...\n",
       "requestStart                                                    1608062191222\n",
       "responseEnd                                                     1608062191224\n",
       "responseStart                                                   1608062191223\n",
       "secureConnectionStart                                                       0\n",
       "unloadEventEnd                                                              0\n",
       "unloadEventStart                                                            0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for file in trace_files:\n",
    "#     with open(file,'r') as f:\n",
    "#         lines = f.read()\n",
    "#         runs = lines.split(\"--------------------\")\n",
    "#         for run in runs:\n",
    "#             if len(run) > 1:\n",
    "#                 submetrics = run.split('\\n')\n",
    "#                 jsons = []\n",
    "#                 for m in submetrics:\n",
    "#                     if m != '':\n",
    "#                         if json.loads(m):\n",
    "#                             metric = json.loads(m)\n",
    "#                             jsons.append(metric)\n",
    "#                             with pd.read_json(metric) as tempdf:\n",
    "#                                 print(tempdf)\n",
    "                            \n",
    "#                 jsons[0].update(jsons[1])\n",
    "#                 with open(f'{file}-merged.json','a') as fw:\n",
    "#                     json.dump(jsons[0],fw)\n",
    "#                     print(json.dumps(jsons[0]))\n",
    "#             clear_output(wait=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# len(run)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Json to csv - merge all jsons into one csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Life can and should be simpler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def flatten_json(y):\n",
    "    out = {}\n",
    "\n",
    "    def flatten(x, name=''):\n",
    "        if type(x) is dict:\n",
    "            for a in x:\n",
    "                flatten(x[a], name + a + '_')\n",
    "        elif type(x) is list:\n",
    "            i = 0\n",
    "            for a in x:\n",
    "                flatten(a, name + str(i) + '_')\n",
    "                i += 1\n",
    "        else:\n",
    "            out[name[:-1]] = x\n",
    "    flatten(y)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['profiling', 'rendering', 'metrics', 'connectStart', 'navigationStart', 'loadEventEnd', 'domLoading', 'secureConnectionStart', 'fetchStart', 'domContentLoadedEventStart', 'responseStart', 'responseEnd', 'domInteractive', 'domainLookupEnd', 'redirectStart', 'requestStart', 'unloadEventEnd', 'unloadEventStart', 'domComplete', 'domainLookupStart', 'loadEventStart', 'domContentLoadedEventEnd', 'redirectEnd', 'connectEnd'])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jsonstr.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'firstPaint': 0.605076,\n",
       " 'forcedReflowEvents': 5,\n",
       " 'fps_mean': 7.87,\n",
       " 'fps_variance': 474.21,\n",
       " 'fps_sd': 21.78,\n",
       " 'fps_lo': 4.38,\n",
       " 'fps_hi': 56.32}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flatten_json(jsonstr['rendering'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-32-f1573d73dadf>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-32-f1573d73dadf>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    list[flatten_json(jsonstr[g]) for g in jsonstr.keys() ]\u001b[0m\n\u001b[1;37m                                    ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "list[flatten_json(jsonstr[g]) for g in jsonstr.keys() ] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flattening JSons - from kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json \n",
    "import pandas as pd \n",
    "from pandas import json_normalize #package for flattening json in pandas df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>scripting</th>\n",
       "      <th>rendering</th>\n",
       "      <th>loading</th>\n",
       "      <th>painting</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>208.08</td>\n",
       "      <td>62.911</td>\n",
       "      <td>16.204</td>\n",
       "      <td>7.767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   scripting  rendering  loading  painting\n",
       "0     208.08     62.911   16.204     7.767"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load json object\n",
    "with open(trace_files[0]) as f:\n",
    "    d = json.load(f)\n",
    "\n",
    "#lets put the data into a pandas df\n",
    "#clicking on raw_nyc_phil.json under \"Input Files\"\n",
    "#tells us parent node is 'programs'\n",
    "nycphil = json_normalize(d['profiling']['categories'])\n",
    "nycphil.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>JS Frame</th>\n",
       "      <th>Layout</th>\n",
       "      <th>Recalculate Style</th>\n",
       "      <th>Parse HTML</th>\n",
       "      <th>Minor GC</th>\n",
       "      <th>Evaluate Script</th>\n",
       "      <th>Paint</th>\n",
       "      <th>Run Microtasks</th>\n",
       "      <th>Update Layer Tree</th>\n",
       "      <th>Compile Script</th>\n",
       "      <th>Composite Layers</th>\n",
       "      <th>Animation Frame Fired</th>\n",
       "      <th>Parse Stylesheet</th>\n",
       "      <th>XHR Load</th>\n",
       "      <th>Hit Test</th>\n",
       "      <th>XHR Ready State Change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>183.705</td>\n",
       "      <td>33.783</td>\n",
       "      <td>25.225</td>\n",
       "      <td>14.873</td>\n",
       "      <td>7.224</td>\n",
       "      <td>6.888</td>\n",
       "      <td>5.557</td>\n",
       "      <td>4.916</td>\n",
       "      <td>3.579</td>\n",
       "      <td>3.379</td>\n",
       "      <td>2.21</td>\n",
       "      <td>1.547</td>\n",
       "      <td>1.331</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0.031</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   JS Frame  Layout  Recalculate Style  Parse HTML  Minor GC  Evaluate Script  \\\n",
       "0   183.705  33.783             25.225      14.873     7.224            6.888   \n",
       "\n",
       "   Paint  Run Microtasks  Update Layer Tree  Compile Script  Composite Layers  \\\n",
       "0  5.557           4.916              3.579           3.379              2.21   \n",
       "\n",
       "   Animation Frame Fired  Parse Stylesheet  XHR Load  Hit Test  \\\n",
       "0                  1.547             1.331      0.39     0.324   \n",
       "\n",
       "   XHR Ready State Change  \n",
       "0                   0.031  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nycphil1 = json_normalize(d['profiling']['events'])\n",
    "nycphil1.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": false
   },
   "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>firstPaint</th>\n",
       "      <th>forcedReflowEvents</th>\n",
       "      <th>fps.mean</th>\n",
       "      <th>fps.variance</th>\n",
       "      <th>fps.sd</th>\n",
       "      <th>fps.lo</th>\n",
       "      <th>fps.hi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.068475</td>\n",
       "      <td>5</td>\n",
       "      <td>24.91</td>\n",
       "      <td>5841.6</td>\n",
       "      <td>76.43</td>\n",
       "      <td>2.74</td>\n",
       "      <td>205.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   firstPaint  forcedReflowEvents  fps.mean  fps.variance  fps.sd  fps.lo  \\\n",
       "0    3.068475                   5     24.91        5841.6   76.43    2.74   \n",
       "\n",
       "   fps.hi  \n",
       "0  205.63  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nycphil2 = json_normalize(d['rendering'])\n",
    "nycphil2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>name</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>AudioHandlers</th>\n",
       "      <th>Documents</th>\n",
       "      <th>Frames</th>\n",
       "      <th>JSEventListeners</th>\n",
       "      <th>LayoutObjects</th>\n",
       "      <th>MediaKeySessions</th>\n",
       "      <th>MediaKeys</th>\n",
       "      <th>Nodes</th>\n",
       "      <th>Resources</th>\n",
       "      <th>...</th>\n",
       "      <th>V8CompileDuration</th>\n",
       "      <th>TaskDuration</th>\n",
       "      <th>TaskOtherDuration</th>\n",
       "      <th>ThreadTime</th>\n",
       "      <th>ProcessTime</th>\n",
       "      <th>JSHeapUsedSize</th>\n",
       "      <th>JSHeapTotalSize</th>\n",
       "      <th>FirstMeaningfulPaint</th>\n",
       "      <th>DomContentLoaded</th>\n",
       "      <th>NavigationStart</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>149875.221675</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>1121.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1339.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003375</td>\n",
       "      <td>0.350691</td>\n",
       "      <td>0.083989</td>\n",
       "      <td>0.404158</td>\n",
       "      <td>3.453125</td>\n",
       "      <td>8585724.0</td>\n",
       "      <td>15736832.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149874.661378</td>\n",
       "      <td>149871.618395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "name       Timestamp  AudioHandlers  Documents  Frames  JSEventListeners  \\\n",
       "value  149875.221675            0.0        6.0     2.0             173.0   \n",
       "\n",
       "name   LayoutObjects  MediaKeySessions  MediaKeys   Nodes  Resources  ...  \\\n",
       "value         1121.0               0.0        0.0  1339.0       27.0  ...   \n",
       "\n",
       "name   V8CompileDuration  TaskDuration  TaskOtherDuration  ThreadTime  \\\n",
       "value           0.003375      0.350691           0.083989    0.404158   \n",
       "\n",
       "name   ProcessTime  JSHeapUsedSize  JSHeapTotalSize  FirstMeaningfulPaint  \\\n",
       "value     3.453125       8585724.0       15736832.0                   0.0   \n",
       "\n",
       "name   DomContentLoaded  NavigationStart  \n",
       "value     149874.661378    149871.618395  \n",
       "\n",
       "[1 rows x 35 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nycphil3 = json_normalize(data=d['metrics'], meta=['name','value'])#json_normalize(d['metrics'])\n",
    "nycphil3 = nycphil3.set_index('name')\n",
    "# nycphil3 = nycphil3.rename(columns={\"value\": \"0\"})\n",
    "nycphil3 = nycphil3.T\n",
    "nycphil3.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>connectEnd</th>\n",
       "      <th>connectStart</th>\n",
       "      <th>domComplete</th>\n",
       "      <th>domContentLoadedEventEnd</th>\n",
       "      <th>domContentLoadedEventStart</th>\n",
       "      <th>domInteractive</th>\n",
       "      <th>domLoading</th>\n",
       "      <th>domainLookupEnd</th>\n",
       "      <th>domainLookupStart</th>\n",
       "      <th>fetchStart</th>\n",
       "      <th>...</th>\n",
       "      <th>loadEventStart</th>\n",
       "      <th>navigationStart</th>\n",
       "      <th>redirectEnd</th>\n",
       "      <th>redirectStart</th>\n",
       "      <th>requestStart</th>\n",
       "      <th>responseEnd</th>\n",
       "      <th>responseStart</th>\n",
       "      <th>secureConnectionStart</th>\n",
       "      <th>unloadEventEnd</th>\n",
       "      <th>unloadEventStart</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>1608164646508</td>\n",
       "      <td>1608164646507</td>\n",
       "      <td>1608164649801</td>\n",
       "      <td>1608164649244</td>\n",
       "      <td>1608164649243</td>\n",
       "      <td>1608164649243</td>\n",
       "      <td>1608164646515</td>\n",
       "      <td>1608164646202</td>\n",
       "      <td>1608164646202</td>\n",
       "      <td>1608164646200</td>\n",
       "      <td>...</td>\n",
       "      <td>1608164649801</td>\n",
       "      <td>1608164646200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1608164646508</td>\n",
       "      <td>1608164646512</td>\n",
       "      <td>1608164646511</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          connectEnd   connectStart    domComplete  domContentLoadedEventEnd  \\\n",
       "value  1608164646508  1608164646507  1608164649801             1608164649244   \n",
       "\n",
       "       domContentLoadedEventStart  domInteractive     domLoading  \\\n",
       "value               1608164649243   1608164649243  1608164646515   \n",
       "\n",
       "       domainLookupEnd  domainLookupStart     fetchStart  ...  loadEventStart  \\\n",
       "value    1608164646202      1608164646202  1608164646200  ...   1608164649801   \n",
       "\n",
       "       navigationStart  redirectEnd  redirectStart   requestStart  \\\n",
       "value    1608164646200            0              0  1608164646508   \n",
       "\n",
       "         responseEnd  responseStart  secureConnectionStart  unloadEventEnd  \\\n",
       "value  1608164646512  1608164646511                      0               0   \n",
       "\n",
       "       unloadEventStart  \n",
       "value                 0  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nycphil4 = d['performanceTiming']\n",
    "nycphil4 = pd.DataFrame({'value':json.loads(nycphil4)})\n",
    "nycphil4 = nycphil4.T\n",
    "nycphil4.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1, 4), (1, 16), (1, 7), (1, 35))"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nycphil.shape, nycphil1.shape, nycphil2.shape, nycphil3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scripting</th>\n",
       "      <th>rendering</th>\n",
       "      <th>loading</th>\n",
       "      <th>painting</th>\n",
       "      <th>JS Frame</th>\n",
       "      <th>Layout</th>\n",
       "      <th>Recalculate Style</th>\n",
       "      <th>Parse HTML</th>\n",
       "      <th>Minor GC</th>\n",
       "      <th>Evaluate Script</th>\n",
       "      <th>Paint</th>\n",
       "      <th>Run Microtasks</th>\n",
       "      <th>Update Layer Tree</th>\n",
       "      <th>Compile Script</th>\n",
       "      <th>Composite Layers</th>\n",
       "      <th>Animation Frame Fired</th>\n",
       "      <th>Parse Stylesheet</th>\n",
       "      <th>XHR Load</th>\n",
       "      <th>Hit Test</th>\n",
       "      <th>XHR Ready State Change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>208.08</td>\n",
       "      <td>62.911</td>\n",
       "      <td>16.204</td>\n",
       "      <td>7.767</td>\n",
       "      <td>183.705</td>\n",
       "      <td>33.783</td>\n",
       "      <td>25.225</td>\n",
       "      <td>14.873</td>\n",
       "      <td>7.224</td>\n",
       "      <td>6.888</td>\n",
       "      <td>5.557</td>\n",
       "      <td>4.916</td>\n",
       "      <td>3.579</td>\n",
       "      <td>3.379</td>\n",
       "      <td>2.21</td>\n",
       "      <td>1.547</td>\n",
       "      <td>1.331</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0.031</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   scripting  rendering  loading  painting  JS Frame  Layout  \\\n",
       "0     208.08     62.911   16.204     7.767   183.705  33.783   \n",
       "\n",
       "   Recalculate Style  Parse HTML  Minor GC  Evaluate Script  Paint  \\\n",
       "0             25.225      14.873     7.224            6.888  5.557   \n",
       "\n",
       "   Run Microtasks  Update Layer Tree  Compile Script  Composite Layers  \\\n",
       "0           4.916              3.579           3.379              2.21   \n",
       "\n",
       "   Animation Frame Fired  Parse Stylesheet  XHR Load  Hit Test  \\\n",
       "0                  1.547             1.331      0.39     0.324   \n",
       "\n",
       "   XHR Ready State Change  \n",
       "0                   0.031  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in nycphil1:\n",
    "    nycphil[col] = nycphil1[col]\n",
    "nycphil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scripting</th>\n",
       "      <th>rendering</th>\n",
       "      <th>loading</th>\n",
       "      <th>painting</th>\n",
       "      <th>JS Frame</th>\n",
       "      <th>Layout</th>\n",
       "      <th>Recalculate Style</th>\n",
       "      <th>Parse HTML</th>\n",
       "      <th>Minor GC</th>\n",
       "      <th>Evaluate Script</th>\n",
       "      <th>...</th>\n",
       "      <th>XHR Load</th>\n",
       "      <th>Hit Test</th>\n",
       "      <th>XHR Ready State Change</th>\n",
       "      <th>firstPaint</th>\n",
       "      <th>forcedReflowEvents</th>\n",
       "      <th>fps.mean</th>\n",
       "      <th>fps.variance</th>\n",
       "      <th>fps.sd</th>\n",
       "      <th>fps.lo</th>\n",
       "      <th>fps.hi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>208.08</td>\n",
       "      <td>62.911</td>\n",
       "      <td>16.204</td>\n",
       "      <td>7.767</td>\n",
       "      <td>183.705</td>\n",
       "      <td>33.783</td>\n",
       "      <td>25.225</td>\n",
       "      <td>14.873</td>\n",
       "      <td>7.224</td>\n",
       "      <td>6.888</td>\n",
       "      <td>...</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0.031</td>\n",
       "      <td>3.068475</td>\n",
       "      <td>5</td>\n",
       "      <td>24.91</td>\n",
       "      <td>5841.6</td>\n",
       "      <td>76.43</td>\n",
       "      <td>2.74</td>\n",
       "      <td>205.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   scripting  rendering  loading  painting  JS Frame  Layout  \\\n",
       "0     208.08     62.911   16.204     7.767   183.705  33.783   \n",
       "\n",
       "   Recalculate Style  Parse HTML  Minor GC  Evaluate Script  ...  XHR Load  \\\n",
       "0             25.225      14.873     7.224            6.888  ...      0.39   \n",
       "\n",
       "   Hit Test  XHR Ready State Change  firstPaint  forcedReflowEvents  fps.mean  \\\n",
       "0     0.324                   0.031    3.068475                   5     24.91   \n",
       "\n",
       "   fps.variance  fps.sd  fps.lo  fps.hi  \n",
       "0        5841.6   76.43    2.74  205.63  \n",
       "\n",
       "[1 rows x 27 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in nycphil2:\n",
    "    nycphil[col] = nycphil2[col]\n",
    "nycphil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>rendering</th>\n",
       "      <th>loading</th>\n",
       "      <th>painting</th>\n",
       "      <th>JS Frame</th>\n",
       "      <th>Layout</th>\n",
       "      <th>Recalculate Style</th>\n",
       "      <th>Parse HTML</th>\n",
       "      <th>Minor GC</th>\n",
       "      <th>Evaluate Script</th>\n",
       "      <th>...</th>\n",
       "      <th>V8CompileDuration</th>\n",
       "      <th>TaskDuration</th>\n",
       "      <th>TaskOtherDuration</th>\n",
       "      <th>ThreadTime</th>\n",
       "      <th>ProcessTime</th>\n",
       "      <th>JSHeapUsedSize</th>\n",
       "      <th>JSHeapTotalSize</th>\n",
       "      <th>FirstMeaningfulPaint</th>\n",
       "      <th>DomContentLoaded</th>\n",
       "      <th>NavigationStart</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>208.08</td>\n",
       "      <td>62.911</td>\n",
       "      <td>16.204</td>\n",
       "      <td>7.767</td>\n",
       "      <td>183.705</td>\n",
       "      <td>33.783</td>\n",
       "      <td>25.225</td>\n",
       "      <td>14.873</td>\n",
       "      <td>7.224</td>\n",
       "      <td>6.888</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003375</td>\n",
       "      <td>0.350691</td>\n",
       "      <td>0.083989</td>\n",
       "      <td>0.404158</td>\n",
       "      <td>3.453125</td>\n",
       "      <td>8585724.0</td>\n",
       "      <td>15736832.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149874.661378</td>\n",
       "      <td>149871.618395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   scripting  rendering  loading  painting  JS Frame  Layout  \\\n",
       "0     208.08     62.911   16.204     7.767   183.705  33.783   \n",
       "\n",
       "   Recalculate Style  Parse HTML  Minor GC  Evaluate Script  ...  \\\n",
       "0             25.225      14.873     7.224            6.888  ...   \n",
       "\n",
       "   V8CompileDuration  TaskDuration  TaskOtherDuration  ThreadTime  \\\n",
       "0           0.003375      0.350691           0.083989    0.404158   \n",
       "\n",
       "   ProcessTime  JSHeapUsedSize  JSHeapTotalSize  FirstMeaningfulPaint  \\\n",
       "0     3.453125       8585724.0       15736832.0                   0.0   \n",
       "\n",
       "   DomContentLoaded  NavigationStart  \n",
       "0     149874.661378    149871.618395  \n",
       "\n",
       "[1 rows x 62 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in nycphil3:\n",
    "#     print(nycphil3[col])\n",
    "    nycphil[col] = nycphil3.iloc[0][col]\n",
    "nycphil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scripting</th>\n",
       "      <th>rendering</th>\n",
       "      <th>loading</th>\n",
       "      <th>painting</th>\n",
       "      <th>JS Frame</th>\n",
       "      <th>Layout</th>\n",
       "      <th>Recalculate Style</th>\n",
       "      <th>Parse HTML</th>\n",
       "      <th>Minor GC</th>\n",
       "      <th>Evaluate Script</th>\n",
       "      <th>...</th>\n",
       "      <th>loadEventStart</th>\n",
       "      <th>navigationStart</th>\n",
       "      <th>redirectEnd</th>\n",
       "      <th>redirectStart</th>\n",
       "      <th>requestStart</th>\n",
       "      <th>responseEnd</th>\n",
       "      <th>responseStart</th>\n",
       "      <th>secureConnectionStart</th>\n",
       "      <th>unloadEventEnd</th>\n",
       "      <th>unloadEventStart</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>208.08</td>\n",
       "      <td>62.911</td>\n",
       "      <td>16.204</td>\n",
       "      <td>7.767</td>\n",
       "      <td>183.705</td>\n",
       "      <td>33.783</td>\n",
       "      <td>25.225</td>\n",
       "      <td>14.873</td>\n",
       "      <td>7.224</td>\n",
       "      <td>6.888</td>\n",
       "      <td>...</td>\n",
       "      <td>1608164649801</td>\n",
       "      <td>1608164646200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1608164646508</td>\n",
       "      <td>1608164646512</td>\n",
       "      <td>1608164646511</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 83 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   scripting  rendering  loading  painting  JS Frame  Layout  \\\n",
       "0     208.08     62.911   16.204     7.767   183.705  33.783   \n",
       "\n",
       "   Recalculate Style  Parse HTML  Minor GC  Evaluate Script  ...  \\\n",
       "0             25.225      14.873     7.224            6.888  ...   \n",
       "\n",
       "   loadEventStart  navigationStart  redirectEnd  redirectStart   requestStart  \\\n",
       "0   1608164649801    1608164646200            0              0  1608164646508   \n",
       "\n",
       "     responseEnd  responseStart  secureConnectionStart  unloadEventEnd  \\\n",
       "0  1608164646512  1608164646511                      0               0   \n",
       "\n",
       "   unloadEventStart  \n",
       "0                 0  \n",
       "\n",
       "[1 rows x 83 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in nycphil4:\n",
    "#     print(nycphil4[col])\n",
    "    nycphil[col] = nycphil4.iloc[0][col]\n",
    "nycphil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# perfkeys = [key for key in d.keys() if key not in ['profiling','rendering','metrics']]\n",
    "# nycphil4 = {\n",
    "#         key:d[key] for key in perfkeys\n",
    "#            }\n",
    "# nycphil4 = pd.DataFrame(nycphil4,index={'value'})\n",
    "# nycphil4.head()\n",
    "# perfkeys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 27)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nycphil.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# int(trace_file.split('\\\\')[1].split('-')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tempdf = mydf.copy()\n",
    "# for i in range(tempdf.count()[0]):\n",
    "#     tempdf[int(trace_file.split('\\\\')[1].split('-')[0])] = tempdf.iloc[i]\n",
    "# #     print(tempdf.iloc[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>name</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Timestamp</td>\n",
       "      <td>4.741659e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AudioHandlers</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Documents</td>\n",
       "      <td>6.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Frames</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JSEventListeners</td>\n",
       "      <td>2.000000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LayoutObjects</td>\n",
       "      <td>1.012100e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MediaKeySessions</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MediaKeys</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Nodes</td>\n",
       "      <td>1.040200e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Resources</td>\n",
       "      <td>3.600000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ContextLifecycleStateObservers</td>\n",
       "      <td>1.300000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>V8PerContextDatas</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>WorkerGlobalScopes</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>UACSSResources</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>RTCPeerConnections</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ResourceFetchers</td>\n",
       "      <td>6.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AdSubframes</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>DetachedScriptStates</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ArrayBufferContents</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LayoutCount</td>\n",
       "      <td>9.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>RecalcStyleCount</td>\n",
       "      <td>1.200000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>LayoutDuration</td>\n",
       "      <td>5.918200e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>RecalcStyleDuration</td>\n",
       "      <td>1.945240e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>DevToolsCommandDuration</td>\n",
       "      <td>3.792000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ScriptDuration</td>\n",
       "      <td>8.061640e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>V8CompileDuration</td>\n",
       "      <td>5.253000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>TaskDuration</td>\n",
       "      <td>1.242727e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>TaskOtherDuration</td>\n",
       "      <td>1.738120e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ThreadTime</td>\n",
       "      <td>1.111397e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ProcessTime</td>\n",
       "      <td>2.828125e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>JSHeapUsedSize</td>\n",
       "      <td>1.985900e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>JSHeapTotalSize</td>\n",
       "      <td>3.697050e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>FirstMeaningfulPaint</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>DomContentLoaded</td>\n",
       "      <td>4.741551e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>NavigationStart</td>\n",
       "      <td>4.741494e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              name         value\n",
       "0                        Timestamp  4.741659e+04\n",
       "1                    AudioHandlers  0.000000e+00\n",
       "2                        Documents  6.000000e+00\n",
       "3                           Frames  2.000000e+00\n",
       "4                 JSEventListeners  2.000000e+02\n",
       "5                    LayoutObjects  1.012100e+04\n",
       "6                 MediaKeySessions  0.000000e+00\n",
       "7                        MediaKeys  0.000000e+00\n",
       "8                            Nodes  1.040200e+04\n",
       "9                        Resources  3.600000e+01\n",
       "10  ContextLifecycleStateObservers  1.300000e+01\n",
       "11               V8PerContextDatas  2.000000e+00\n",
       "12              WorkerGlobalScopes  0.000000e+00\n",
       "13                  UACSSResources  0.000000e+00\n",
       "14              RTCPeerConnections  0.000000e+00\n",
       "15                ResourceFetchers  6.000000e+00\n",
       "16                     AdSubframes  0.000000e+00\n",
       "17            DetachedScriptStates  2.000000e+00\n",
       "18             ArrayBufferContents  0.000000e+00\n",
       "19                     LayoutCount  9.000000e+00\n",
       "20                RecalcStyleCount  1.200000e+01\n",
       "21                  LayoutDuration  5.918200e-02\n",
       "22             RecalcStyleDuration  1.945240e-01\n",
       "23         DevToolsCommandDuration  3.792000e-03\n",
       "24                  ScriptDuration  8.061640e-01\n",
       "25               V8CompileDuration  5.253000e-03\n",
       "26                    TaskDuration  1.242727e+00\n",
       "27               TaskOtherDuration  1.738120e-01\n",
       "28                      ThreadTime  1.111397e+00\n",
       "29                     ProcessTime  2.828125e+00\n",
       "30                  JSHeapUsedSize  1.985900e+07\n",
       "31                 JSHeapTotalSize  3.697050e+07\n",
       "32            FirstMeaningfulPaint  0.000000e+00\n",
       "33                DomContentLoaded  4.741551e+04\n",
       "34                 NavigationStart  4.741494e+04"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tempdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## In a loop on real data now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json \n",
    "import pandas as pd \n",
    "import glob\n",
    "import datetime\n",
    "from pandas import json_normalize #package for flattening json in pandas df\n",
    "from tqdm import trange, tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./tracelyzer\\\\1000-state-metrics_run_0.json',\n",
       " './tracelyzer\\\\1000-state-metrics_run_1.json',\n",
       " './tracelyzer\\\\1000-state-metrics_run_2.json',\n",
       " './tracelyzer\\\\1000-state-metrics_run_3.json',\n",
       " './tracelyzer\\\\1000-state-metrics_run_4.json',\n",
       " './tracelyzer\\\\1000-state-metrics_run_5.json',\n",
       " './tracelyzer\\\\1000-state-metrics_run_6.json',\n",
       " './tracelyzer\\\\1000-state-metrics_run_7.json',\n",
       " './tracelyzer\\\\1000-state-metrics_run_8.json',\n",
       " './tracelyzer\\\\1000-state-metrics_run_9.json',\n",
       " './tracelyzer\\\\2000-state-metrics_run_0.json',\n",
       " './tracelyzer\\\\2000-state-metrics_run_1.json',\n",
       " './tracelyzer\\\\2000-state-metrics_run_2.json',\n",
       " './tracelyzer\\\\2000-state-metrics_run_3.json',\n",
       " './tracelyzer\\\\2000-state-metrics_run_4.json',\n",
       " './tracelyzer\\\\2000-state-metrics_run_5.json',\n",
       " './tracelyzer\\\\2000-state-metrics_run_6.json',\n",
       " './tracelyzer\\\\2000-state-metrics_run_7.json',\n",
       " './tracelyzer\\\\2000-state-metrics_run_8.json',\n",
       " './tracelyzer\\\\2000-state-metrics_run_9.json',\n",
       " './tracelyzer\\\\3000-state-metrics_run_0.json',\n",
       " './tracelyzer\\\\3000-state-metrics_run_1.json',\n",
       " './tracelyzer\\\\3000-state-metrics_run_2.json',\n",
       " './tracelyzer\\\\3000-state-metrics_run_3.json',\n",
       " './tracelyzer\\\\3000-state-metrics_run_4.json',\n",
       " './tracelyzer\\\\3000-state-metrics_run_5.json',\n",
       " './tracelyzer\\\\3000-state-metrics_run_6.json',\n",
       " './tracelyzer\\\\3000-state-metrics_run_7.json',\n",
       " './tracelyzer\\\\3000-state-metrics_run_8.json',\n",
       " './tracelyzer\\\\3000-state-metrics_run_9.json',\n",
       " './tracelyzer\\\\4000-state-metrics_run_0.json',\n",
       " './tracelyzer\\\\4000-state-metrics_run_1.json',\n",
       " './tracelyzer\\\\4000-state-metrics_run_2.json',\n",
       " './tracelyzer\\\\4000-state-metrics_run_3.json',\n",
       " './tracelyzer\\\\4000-state-metrics_run_4.json',\n",
       " './tracelyzer\\\\4000-state-metrics_run_5.json',\n",
       " './tracelyzer\\\\4000-state-metrics_run_6.json',\n",
       " './tracelyzer\\\\4000-state-metrics_run_7.json',\n",
       " './tracelyzer\\\\4000-state-metrics_run_8.json',\n",
       " './tracelyzer\\\\4000-state-metrics_run_9.json',\n",
       " './tracelyzer\\\\5000-state-metrics_run_0.json',\n",
       " './tracelyzer\\\\5000-state-metrics_run_1.json',\n",
       " './tracelyzer\\\\5000-state-metrics_run_2.json',\n",
       " './tracelyzer\\\\5000-state-metrics_run_3.json',\n",
       " './tracelyzer\\\\5000-state-metrics_run_4.json',\n",
       " './tracelyzer\\\\5000-state-metrics_run_5.json',\n",
       " './tracelyzer\\\\5000-state-metrics_run_6.json',\n",
       " './tracelyzer\\\\5000-state-metrics_run_7.json',\n",
       " './tracelyzer\\\\5000-state-metrics_run_8.json',\n",
       " './tracelyzer\\\\5000-state-metrics_run_9.json',\n",
       " './tracelyzer\\\\6000-state-metrics_run_0.json',\n",
       " './tracelyzer\\\\6000-state-metrics_run_1.json',\n",
       " './tracelyzer\\\\6000-state-metrics_run_2.json',\n",
       " './tracelyzer\\\\6000-state-metrics_run_3.json',\n",
       " './tracelyzer\\\\6000-state-metrics_run_4.json',\n",
       " './tracelyzer\\\\6000-state-metrics_run_5.json',\n",
       " './tracelyzer\\\\6000-state-metrics_run_6.json',\n",
       " './tracelyzer\\\\6000-state-metrics_run_7.json',\n",
       " './tracelyzer\\\\6000-state-metrics_run_8.json',\n",
       " './tracelyzer\\\\6000-state-metrics_run_9.json']"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace_files = glob.glob('./tracelyzer//*.json')\n",
    "trace_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1, 1),\n",
       "       value\n",
       " 1000      5)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shape_data = [int(trace_file.split('\\\\')[1].split('-')[0]) for trace_file in trace_files]\n",
    "shape_data\n",
    "from collections import Counter\n",
    "sCounter = Counter(shape_data)\n",
    "shapesdf = pd.DataFrame().from_dict({'value':dict(sCounter)})\n",
    "shapesdf.shape, shapesdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>scripting</th>\n",
       "      <th>rendering</th>\n",
       "      <th>loading</th>\n",
       "      <th>painting</th>\n",
       "      <th>JS Frame</th>\n",
       "      <th>Layout</th>\n",
       "      <th>Recalculate Style</th>\n",
       "      <th>Parse HTML</th>\n",
       "      <th>Minor GC</th>\n",
       "      <th>Evaluate Script</th>\n",
       "      <th>...</th>\n",
       "      <th>V8CompileDuration</th>\n",
       "      <th>TaskDuration</th>\n",
       "      <th>TaskOtherDuration</th>\n",
       "      <th>ThreadTime</th>\n",
       "      <th>ProcessTime</th>\n",
       "      <th>JSHeapUsedSize</th>\n",
       "      <th>JSHeapTotalSize</th>\n",
       "      <th>FirstMeaningfulPaint</th>\n",
       "      <th>DomContentLoaded</th>\n",
       "      <th>NavigationStart</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>208.080</td>\n",
       "      <td>62.911</td>\n",
       "      <td>16.204</td>\n",
       "      <td>7.767</td>\n",
       "      <td>183.705</td>\n",
       "      <td>33.783</td>\n",
       "      <td>25.225</td>\n",
       "      <td>14.873</td>\n",
       "      <td>7.224</td>\n",
       "      <td>6.888</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003375</td>\n",
       "      <td>0.350691</td>\n",
       "      <td>0.083989</td>\n",
       "      <td>0.404158</td>\n",
       "      <td>3.453125</td>\n",
       "      <td>8585724.0</td>\n",
       "      <td>15736832.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149874.661378</td>\n",
       "      <td>149871.618395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>204.684</td>\n",
       "      <td>58.113</td>\n",
       "      <td>16.700</td>\n",
       "      <td>7.027</td>\n",
       "      <td>180.866</td>\n",
       "      <td>32.131</td>\n",
       "      <td>22.729</td>\n",
       "      <td>15.669</td>\n",
       "      <td>7.028</td>\n",
       "      <td>6.982</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003201</td>\n",
       "      <td>0.337871</td>\n",
       "      <td>0.080840</td>\n",
       "      <td>0.355027</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>8686260.0</td>\n",
       "      <td>15736832.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149877.891413</td>\n",
       "      <td>149877.369453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>199.802</td>\n",
       "      <td>63.662</td>\n",
       "      <td>16.221</td>\n",
       "      <td>9.171</td>\n",
       "      <td>173.666</td>\n",
       "      <td>33.012</td>\n",
       "      <td>25.885</td>\n",
       "      <td>14.619</td>\n",
       "      <td>6.482</td>\n",
       "      <td>8.994</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003121</td>\n",
       "      <td>0.348911</td>\n",
       "      <td>0.092350</td>\n",
       "      <td>0.371114</td>\n",
       "      <td>1.062500</td>\n",
       "      <td>9063880.0</td>\n",
       "      <td>15736832.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149880.200950</td>\n",
       "      <td>149879.114157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>195.818</td>\n",
       "      <td>61.070</td>\n",
       "      <td>15.136</td>\n",
       "      <td>9.415</td>\n",
       "      <td>172.945</td>\n",
       "      <td>32.358</td>\n",
       "      <td>25.881</td>\n",
       "      <td>14.073</td>\n",
       "      <td>6.689</td>\n",
       "      <td>6.901</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003809</td>\n",
       "      <td>0.324380</td>\n",
       "      <td>0.071626</td>\n",
       "      <td>0.347417</td>\n",
       "      <td>0.906250</td>\n",
       "      <td>8769704.0</td>\n",
       "      <td>15998976.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149882.516784</td>\n",
       "      <td>149881.975694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>188.158</td>\n",
       "      <td>58.982</td>\n",
       "      <td>17.560</td>\n",
       "      <td>7.318</td>\n",
       "      <td>164.618</td>\n",
       "      <td>33.720</td>\n",
       "      <td>22.338</td>\n",
       "      <td>16.288</td>\n",
       "      <td>6.513</td>\n",
       "      <td>6.856</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002927</td>\n",
       "      <td>0.324154</td>\n",
       "      <td>0.080299</td>\n",
       "      <td>0.346021</td>\n",
       "      <td>0.953125</td>\n",
       "      <td>8633188.0</td>\n",
       "      <td>15736832.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149884.413143</td>\n",
       "      <td>149883.891462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   scripting  rendering  loading  painting  JS Frame  Layout  \\\n",
       "0    208.080     62.911   16.204     7.767   183.705  33.783   \n",
       "0    204.684     58.113   16.700     7.027   180.866  32.131   \n",
       "0    199.802     63.662   16.221     9.171   173.666  33.012   \n",
       "0    195.818     61.070   15.136     9.415   172.945  32.358   \n",
       "0    188.158     58.982   17.560     7.318   164.618  33.720   \n",
       "\n",
       "   Recalculate Style  Parse HTML  Minor GC  Evaluate Script  ...  \\\n",
       "0             25.225      14.873     7.224            6.888  ...   \n",
       "0             22.729      15.669     7.028            6.982  ...   \n",
       "0             25.885      14.619     6.482            8.994  ...   \n",
       "0             25.881      14.073     6.689            6.901  ...   \n",
       "0             22.338      16.288     6.513            6.856  ...   \n",
       "\n",
       "   V8CompileDuration  TaskDuration  TaskOtherDuration  ThreadTime  \\\n",
       "0           0.003375      0.350691           0.083989    0.404158   \n",
       "0           0.003201      0.337871           0.080840    0.355027   \n",
       "0           0.003121      0.348911           0.092350    0.371114   \n",
       "0           0.003809      0.324380           0.071626    0.347417   \n",
       "0           0.002927      0.324154           0.080299    0.346021   \n",
       "\n",
       "   ProcessTime  JSHeapUsedSize  JSHeapTotalSize  FirstMeaningfulPaint  \\\n",
       "0     3.453125       8585724.0       15736832.0                   0.0   \n",
       "0     0.875000       8686260.0       15736832.0                   0.0   \n",
       "0     1.062500       9063880.0       15736832.0                   0.0   \n",
       "0     0.906250       8769704.0       15998976.0                   0.0   \n",
       "0     0.953125       8633188.0       15736832.0                   0.0   \n",
       "\n",
       "   DomContentLoaded  NavigationStart  \n",
       "0     149874.661378    149871.618395  \n",
       "0     149877.891413    149877.369453  \n",
       "0     149880.200950    149879.114157  \n",
       "0     149882.516784    149881.975694  \n",
       "0     149884.413143    149883.891462  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydf = pd.DataFrame()\n",
    "#load json object\n",
    "for trace_file in trace_files:\n",
    "    with open(trace_file) as f:\n",
    "        try:\n",
    "            d = json.load(f)\n",
    "        except:\n",
    "            print(f'file: {file} cannot be read')\n",
    "        \n",
    "        #lets put the data into a pandas df\n",
    "        #clicking on raw_nyc_phil.json under \"Input Files\"\n",
    "        #tells us parent node is 'programs'\n",
    "        nycphil = json_normalize(d['profiling']['categories'])\n",
    "#         print(nycphil.head(3))\n",
    "        \n",
    "        nycphil1 = json_normalize(d['profiling']['events'])\n",
    "#         print(nycphil1.head(3))\n",
    "        \n",
    "        nycphil2 = json_normalize(d['rendering'])\n",
    "#         print(nycphil2.head(3))\n",
    "        \n",
    "        nycphil3 = json_normalize(data=d['metrics'], meta=['name','value'])#json_normalize(d['metrics'])\n",
    "        nycphil3 = nycphil3.set_index('name')\n",
    "        # nycphil3 = nycphil3.rename(columns={\"value\": \"0\"})\n",
    "        nycphil3 = nycphil3.T\n",
    "#         print(nycphil3.head(3))\n",
    "        \n",
    "#         nycphil4 = d['performanceTiming']\n",
    "#         nycphil4 = pd.DataFrame({'value':json.loads(nycphil4)})\n",
    "#         nycphil4 = nycphil4.T\n",
    "#         nycphil4.head(3)\n",
    "#         perfkeys = [key for key in d.keys() if key not in ['profiling','rendering','metrics']]\n",
    "#         nycphil4 = {\n",
    "#                 key:d[key] for key in perfkeys\n",
    "#                    }\n",
    "#         nycphil4 = pd.DataFrame(nycphil4,index={'value'})\n",
    "#         nycphil4.head()\n",
    "\n",
    "#         print(nycphil.shape, nycphil1.shape, nycphil2.shape, nycphil3.shape)\n",
    "        \n",
    "        for col in nycphil1:\n",
    "            nycphil[col] = nycphil1[col]\n",
    "            \n",
    "        for col in nycphil2:\n",
    "            nycphil[col] = nycphil2[col]\n",
    "        \n",
    "        for col in nycphil3:\n",
    "        #     print(nycphil3[col])\n",
    "            nycphil[col] = nycphil3.iloc[0][col]\n",
    "        \n",
    "#         for col in nycphil4:\n",
    "#         #     print(nycphil4[col])\n",
    "#             nycphil[col] = nycphil4.iloc[0][col]\n",
    "        \n",
    "        \n",
    "#         print(nycphil.shape)\n",
    "    mydf = nycphil if not mydf.count().any() else mydf.append(nycphil)\n",
    "\n",
    "mydf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "    .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>scripting</th>\n",
       "      <th>rendering</th>\n",
       "      <th>loading</th>\n",
       "      <th>painting</th>\n",
       "      <th>JS Frame</th>\n",
       "      <th>Layout</th>\n",
       "      <th>Recalculate Style</th>\n",
       "      <th>Parse HTML</th>\n",
       "      <th>Minor GC</th>\n",
       "      <th>Evaluate Script</th>\n",
       "      <th>...</th>\n",
       "      <th>V8CompileDuration</th>\n",
       "      <th>TaskDuration</th>\n",
       "      <th>TaskOtherDuration</th>\n",
       "      <th>ThreadTime</th>\n",
       "      <th>ProcessTime</th>\n",
       "      <th>JSHeapUsedSize</th>\n",
       "      <th>JSHeapTotalSize</th>\n",
       "      <th>FirstMeaningfulPaint</th>\n",
       "      <th>DomContentLoaded</th>\n",
       "      <th>NavigationStart</th>\n",
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       "      <td>3.453125</td>\n",
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       "      <td>204.684</td>\n",
       "      <td>58.113</td>\n",
       "      <td>16.700</td>\n",
       "      <td>7.027</td>\n",
       "      <td>180.866</td>\n",
       "      <td>32.131</td>\n",
       "      <td>22.729</td>\n",
       "      <td>15.669</td>\n",
       "      <td>7.028</td>\n",
       "      <td>6.982</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003201</td>\n",
       "      <td>0.337871</td>\n",
       "      <td>0.080840</td>\n",
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       "      <td>0.875000</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>0</th>\n",
       "      <td>199.802</td>\n",
       "      <td>63.662</td>\n",
       "      <td>16.221</td>\n",
       "      <td>9.171</td>\n",
       "      <td>173.666</td>\n",
       "      <td>33.012</td>\n",
       "      <td>25.885</td>\n",
       "      <td>14.619</td>\n",
       "      <td>6.482</td>\n",
       "      <td>8.994</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003121</td>\n",
       "      <td>0.348911</td>\n",
       "      <td>0.092350</td>\n",
       "      <td>0.371114</td>\n",
       "      <td>1.062500</td>\n",
       "      <td>9063880.0</td>\n",
       "      <td>15736832.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149880.200950</td>\n",
       "      <td>149879.114157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>195.818</td>\n",
       "      <td>61.070</td>\n",
       "      <td>15.136</td>\n",
       "      <td>9.415</td>\n",
       "      <td>172.945</td>\n",
       "      <td>32.358</td>\n",
       "      <td>25.881</td>\n",
       "      <td>14.073</td>\n",
       "      <td>6.689</td>\n",
       "      <td>6.901</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003809</td>\n",
       "      <td>0.324380</td>\n",
       "      <td>0.071626</td>\n",
       "      <td>0.347417</td>\n",
       "      <td>0.906250</td>\n",
       "      <td>8769704.0</td>\n",
       "      <td>15998976.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149882.516784</td>\n",
       "      <td>149881.975694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>188.158</td>\n",
       "      <td>58.982</td>\n",
       "      <td>17.560</td>\n",
       "      <td>7.318</td>\n",
       "      <td>164.618</td>\n",
       "      <td>33.720</td>\n",
       "      <td>22.338</td>\n",
       "      <td>16.288</td>\n",
       "      <td>6.513</td>\n",
       "      <td>6.856</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002927</td>\n",
       "      <td>0.324154</td>\n",
       "      <td>0.080299</td>\n",
       "      <td>0.346021</td>\n",
       "      <td>0.953125</td>\n",
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       "      <td>149883.891462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   scripting  rendering  loading  painting  JS Frame  Layout  \\\n",
       "0    208.080     62.911   16.204     7.767   183.705  33.783   \n",
       "0    204.684     58.113   16.700     7.027   180.866  32.131   \n",
       "0    199.802     63.662   16.221     9.171   173.666  33.012   \n",
       "0    195.818     61.070   15.136     9.415   172.945  32.358   \n",
       "0    188.158     58.982   17.560     7.318   164.618  33.720   \n",
       "\n",
       "   Recalculate Style  Parse HTML  Minor GC  Evaluate Script  ...  \\\n",
       "0             25.225      14.873     7.224            6.888  ...   \n",
       "0             22.729      15.669     7.028            6.982  ...   \n",
       "0             25.885      14.619     6.482            8.994  ...   \n",
       "0             25.881      14.073     6.689            6.901  ...   \n",
       "0             22.338      16.288     6.513            6.856  ...   \n",
       "\n",
       "   V8CompileDuration  TaskDuration  TaskOtherDuration  ThreadTime  \\\n",
       "0           0.003375      0.350691           0.083989    0.404158   \n",
       "0           0.003201      0.337871           0.080840    0.355027   \n",
       "0           0.003121      0.348911           0.092350    0.371114   \n",
       "0           0.003809      0.324380           0.071626    0.347417   \n",
       "0           0.002927      0.324154           0.080299    0.346021   \n",
       "\n",
       "   ProcessTime  JSHeapUsedSize  JSHeapTotalSize  FirstMeaningfulPaint  \\\n",
       "0     3.453125       8585724.0       15736832.0                   0.0   \n",
       "0     0.875000       8686260.0       15736832.0                   0.0   \n",
       "0     1.062500       9063880.0       15736832.0                   0.0   \n",
       "0     0.906250       8769704.0       15998976.0                   0.0   \n",
       "0     0.953125       8633188.0       15736832.0                   0.0   \n",
       "\n",
       "   DomContentLoaded  NavigationStart  \n",
       "0     149874.661378    149871.618395  \n",
       "0     149877.891413    149877.369453  \n",
       "0     149880.200950    149879.114157  \n",
       "0     149882.516784    149881.975694  \n",
       "0     149884.413143    149883.891462  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# is_NaN = mydf.isnull()\n",
    "# row_has_NaN = is_NaN.any(axis=1)\n",
    "# nans = mydf.loc[row_has_NaN == True]\n",
    "# nans.to_csv('nans.csv')\n",
    "mydf = mydf.fillna('0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydf.to_csv('metric-dataframe-'+ datetime.datetime.now().strftime(\"%d%b%Y-%I%M%p\") + '.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'16Dec2020-0552PM'"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime\n",
    "datetime.datetime.now().strftime(\"%d%b%Y-%I%M%p\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation of Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓Nodes\n",
      "✓JSHeapUsedSize\n",
      "✓JSHeapTotalSize\n",
      "✓scripting\n",
      "✓rendering\n",
      "✓painting\n",
      "✓loading\n",
      "✓TaskDuration\n",
      "✓ThreadTime\n",
      "✓ProcessTime\n",
      "✓RecalcStyleCount\n",
      "✓LayoutDuration\n",
      "✓RecalcStyleDuration\n",
      "✓DevToolsCommandDuration\n",
      "✓ScriptDuration\n",
      "✓V8CompileDuration\n",
      "✓TaskOtherDuration\n",
      "☐JS Frame\n",
      "☐Minor GC\n",
      "☐Recalculate Style\n",
      "☐Compile Script\n",
      "☐Layout\n",
      "☐Paint\n",
      "☐Update Layer Tree\n",
      "☐Composite Layers\n",
      "☐Evaluate Script\n",
      "☐Run Microtasks\n",
      "☐Parse HTML\n",
      "☐Hit Test\n",
      "☐Major GC\n",
      "☐Parse Stylesheet\n",
      "☐Animation Frame Fired\n",
      "☐firstPaint\n",
      "☐forcedReflowEvents\n",
      "☐fps.mean\n",
      "☐fps.variance\n",
      "☐fps.sd\n",
      "☐fps.lo\n",
      "☐fps.hi\n",
      "☐Timestamp\n",
      "☐AudioHandlers\n",
      "☐Documents\n",
      "☐Frames\n",
      "☐JSEventListeners\n",
      "☐LayoutObjects\n",
      "☐MediaKeySessions\n",
      "☐MediaKeys\n",
      "☐Resources\n",
      "☐ContextLifecycleStateObservers\n",
      "☐V8PerContextDatas\n",
      "☐WorkerGlobalScopes\n",
      "☐UACSSResources\n",
      "☐RTCPeerConnections\n",
      "☐ResourceFetchers\n",
      "☐AdSubframes\n",
      "☐DetachedScriptStates\n",
      "☐ArrayBufferContents\n",
      "☐LayoutCount\n",
      "☐FirstMeaningfulPaint\n",
      "☐DomContentLoaded\n",
      "☐NavigationStart\n"
     ]
    }
   ],
   "source": [
    "done = u'\\u2713'\n",
    "todo = u'\\u2610'\n",
    "done_list = [\n",
    "    'Nodes','JSHeapUsedSize','JSHeapTotalSize'\n",
    "     ,'scripting','rendering','painting','loading',\n",
    "     'TaskDuration','ThreadTime','ProcessTime'\n",
    "    ,'RecalcStyleCount'\n",
    "    ,'LayoutDuration'\n",
    "    ,'RecalcStyleDuration'\n",
    "    ,'DevToolsCommandDuration'\n",
    "    ,'ScriptDuration'\n",
    "    ,'V8CompileDuration'\n",
    "    ,'TaskOtherDuration'\n",
    "            ]\n",
    "for func in done_list:\n",
    "    funcname = func.split(\"(\")[0]\n",
    "    print(done + funcname)\n",
    "for func in [col for col in list(mydf.columns) if col not in done_list]:\n",
    "    funcname = func.split(\"(\")[0]\n",
    "    print(todo + funcname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11839.0</td>\n",
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       "      <td>3999.914</td>\n",
       "      <td>245.636</td>\n",
       "      <td>31.246</td>\n",
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       "      <td>5.571211</td>\n",
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       "      <td>0.173155</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11839.0</td>\n",
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       "\n",
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       "..             ...                  ...                      ...   \n",
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       "\n",
       "    ScriptDuration  V8CompileDuration  TaskOtherDuration  \n",
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       "0         3.806032           0.176742           1.312024  \n",
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       "..             ...                ...                ...  \n",
       "0         4.030683           0.190086           1.317737  \n",
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       "0         3.695307           0.180673           1.347687  \n",
       "0         3.708313           0.184432           1.396814  \n",
       "\n",
       "[508 rows x 17 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrdf = mydf[done_list] #'Major GC','Minor GC'\n",
    "corrdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sanke\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
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       "      Nodes  JSHeapUsedSize  scripting  rendering  painting  loading  \\\n",
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       "\n",
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       "..           ...         ...          ...               ...             ...   \n",
       "0       5.704033    5.132829    12.343750               6.0        0.038025   \n",
       "0       5.411753    4.984258    13.156250               6.0        0.036768   \n",
       "0       5.367911    4.934539    11.953125               7.0        0.037010   \n",
       "0       5.383438    4.955420    12.390625              14.0        0.035555   \n",
       "0       5.458940    5.032592    12.703125              12.0        0.036786   \n",
       "\n",
       "    RecalcStyleDuration  DevToolsCommandDuration  ScriptDuration  \\\n",
       "0              0.198569                 0.004323        3.844872   \n",
       "0              0.197637                 0.002995        3.806032   \n",
       "0              0.196332                 0.003197        3.839148   \n",
       "0              0.195420                 0.003201        3.817857   \n",
       "0              0.211065                 0.003047        4.314892   \n",
       "..                  ...                      ...             ...   \n",
       "0              0.124451                 0.003051        4.030683   \n",
       "0              0.121271                 0.003289        3.675823   \n",
       "0              0.126280                 0.003136        3.685465   \n",
       "0              0.120999                 0.003217        3.695307   \n",
       "0              0.129443                 0.003152        3.708313   \n",
       "\n",
       "    V8CompileDuration  TaskOtherDuration  JSHeapRatio  \n",
       "0            0.170650           1.295403     0.954521  \n",
       "0            0.176742           1.312024     0.955334  \n",
       "0            0.171951           1.318260     0.955750  \n",
       "0            0.173155           1.341521     0.954146  \n",
       "0            0.296786           1.407121     0.954930  \n",
       "..                ...                ...          ...  \n",
       "0            0.190086           1.317737     0.961700  \n",
       "0            0.192605           1.381997     0.961612  \n",
       "0            0.181253           1.334767     0.961970  \n",
       "0            0.180673           1.347687     0.961812  \n",
       "0            0.184432           1.396814     0.956826  \n",
       "\n",
       "[508 rows x 17 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrdf['JSHeapRatio'] = corrdf['JSHeapUsedSize']/corrdf['JSHeapTotalSize']\n",
    "corrdf = corrdf.drop(columns='JSHeapTotalSize')\n",
    "corrdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nodes</th>\n",
       "      <th>JSHeapUsedSize</th>\n",
       "      <th>scripting</th>\n",
       "      <th>rendering</th>\n",
       "      <th>painting</th>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>-0.102723</td>\n",
       "      <td>0.626864</td>\n",
       "      <td>0.004110</td>\n",
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       "    <tr>\n",
       "      <th>JSHeapUsedSize</th>\n",
       "      <td>0.950440</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.928467</td>\n",
       "      <td>0.953951</td>\n",
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       "      <td>0.927550</td>\n",
       "      <td>0.620309</td>\n",
       "      <td>0.498904</td>\n",
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       "      <td>0.949846</td>\n",
       "      <td>0.141101</td>\n",
       "      <td>0.929101</td>\n",
       "      <td>-0.115511</td>\n",
       "      <td>0.635386</td>\n",
       "      <td>0.235237</td>\n",
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       "      <th>scripting</th>\n",
       "      <td>0.989017</td>\n",
       "      <td>0.928467</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.981662</td>\n",
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       "      <td>0.996616</td>\n",
       "      <td>0.975711</td>\n",
       "      <td>0.628877</td>\n",
       "      <td>0.409934</td>\n",
       "      <td>0.963702</td>\n",
       "      <td>0.980194</td>\n",
       "      <td>0.141410</td>\n",
       "      <td>0.999951</td>\n",
       "      <td>-0.034587</td>\n",
       "      <td>0.635250</td>\n",
       "      <td>-0.022504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rendering</th>\n",
       "      <td>0.990200</td>\n",
       "      <td>0.953951</td>\n",
       "      <td>0.981662</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.932397</td>\n",
       "      <td>-0.018280</td>\n",
       "      <td>0.986298</td>\n",
       "      <td>0.972884</td>\n",
       "      <td>0.648341</td>\n",
       "      <td>0.446609</td>\n",
       "      <td>0.968028</td>\n",
       "      <td>0.998822</td>\n",
       "      <td>0.126447</td>\n",
       "      <td>0.981863</td>\n",
       "      <td>-0.093317</td>\n",
       "      <td>0.646135</td>\n",
       "      <td>0.012800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>painting</th>\n",
       "      <td>0.936726</td>\n",
       "      <td>0.895657</td>\n",
       "      <td>0.934783</td>\n",
       "      <td>0.932397</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.016113</td>\n",
       "      <td>0.938788</td>\n",
       "      <td>0.920663</td>\n",
       "      <td>0.606603</td>\n",
       "      <td>0.431073</td>\n",
       "      <td>0.911187</td>\n",
       "      <td>0.928800</td>\n",
       "      <td>0.109103</td>\n",
       "      <td>0.935199</td>\n",
       "      <td>-0.084483</td>\n",
       "      <td>0.643943</td>\n",
       "      <td>0.027387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loading</th>\n",
       "      <td>-0.007361</td>\n",
       "      <td>-0.012566</td>\n",
       "      <td>-0.032104</td>\n",
       "      <td>-0.018280</td>\n",
       "      <td>-0.016113</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.030145</td>\n",
       "      <td>-0.016526</td>\n",
       "      <td>0.025562</td>\n",
       "      <td>-0.003435</td>\n",
       "      <td>0.005695</td>\n",
       "      <td>-0.019890</td>\n",
       "      <td>-0.061373</td>\n",
       "      <td>-0.031863</td>\n",
       "      <td>-0.060940</td>\n",
       "      <td>-0.020786</td>\n",
       "      <td>-0.009649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TaskDuration</th>\n",
       "      <td>0.988679</td>\n",
       "      <td>0.936352</td>\n",
       "      <td>0.996616</td>\n",
       "      <td>0.986298</td>\n",
       "      <td>0.938788</td>\n",
       "      <td>-0.030145</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.988120</td>\n",
       "      <td>0.677497</td>\n",
       "      <td>0.457027</td>\n",
       "      <td>0.964886</td>\n",
       "      <td>0.982966</td>\n",
       "      <td>0.140324</td>\n",
       "      <td>0.996293</td>\n",
       "      <td>-0.040758</td>\n",
       "      <td>0.691936</td>\n",
       "      <td>0.008320</td>\n",
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       "    <tr>\n",
       "      <th>ThreadTime</th>\n",
       "      <td>0.974179</td>\n",
       "      <td>0.927550</td>\n",
       "      <td>0.975711</td>\n",
       "      <td>0.972884</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>0.944261</td>\n",
       "      <td>0.966637</td>\n",
       "      <td>0.134071</td>\n",
       "      <td>0.974991</td>\n",
       "      <td>-0.064183</td>\n",
       "      <td>0.764416</td>\n",
       "      <td>0.051303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProcessTime</th>\n",
       "      <td>0.634557</td>\n",
       "      <td>0.620309</td>\n",
       "      <td>0.628877</td>\n",
       "      <td>0.648341</td>\n",
       "      <td>0.606603</td>\n",
       "      <td>0.025562</td>\n",
       "      <td>0.677497</td>\n",
       "      <td>0.778809</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.660627</td>\n",
       "      <td>0.606594</td>\n",
       "      <td>0.629432</td>\n",
       "      <td>0.080507</td>\n",
       "      <td>0.626081</td>\n",
       "      <td>-0.082110</td>\n",
       "      <td>0.899969</td>\n",
       "      <td>0.205334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RecalcStyleCount</th>\n",
       "      <td>0.420848</td>\n",
       "      <td>0.498904</td>\n",
       "      <td>0.409934</td>\n",
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       "      <td>-0.125646</td>\n",
       "      <td>0.734472</td>\n",
       "      <td>0.291308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LayoutDuration</th>\n",
       "      <td>0.960327</td>\n",
       "      <td>0.921576</td>\n",
       "      <td>0.963702</td>\n",
       "      <td>0.968028</td>\n",
       "      <td>0.911187</td>\n",
       "      <td>0.005695</td>\n",
       "      <td>0.964886</td>\n",
       "      <td>0.944261</td>\n",
       "      <td>0.606594</td>\n",
       "      <td>0.402911</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.963039</td>\n",
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       "      <td>-0.047814</td>\n",
       "      <td>0.620969</td>\n",
       "      <td>0.016638</td>\n",
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       "    <tr>\n",
       "      <th>RecalcStyleDuration</th>\n",
       "      <td>0.989898</td>\n",
       "      <td>0.949846</td>\n",
       "      <td>0.980194</td>\n",
       "      <td>0.998822</td>\n",
       "      <td>0.928800</td>\n",
       "      <td>-0.019890</td>\n",
       "      <td>0.982966</td>\n",
       "      <td>0.966637</td>\n",
       "      <td>0.629432</td>\n",
       "      <td>0.411547</td>\n",
       "      <td>0.963039</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.121998</td>\n",
       "      <td>0.980463</td>\n",
       "      <td>-0.094737</td>\n",
       "      <td>0.622940</td>\n",
       "      <td>-0.002756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DevToolsCommandDuration</th>\n",
       "      <td>0.129243</td>\n",
       "      <td>0.141101</td>\n",
       "      <td>0.141410</td>\n",
       "      <td>0.126447</td>\n",
       "      <td>0.109103</td>\n",
       "      <td>-0.061373</td>\n",
       "      <td>0.140324</td>\n",
       "      <td>0.134071</td>\n",
       "      <td>0.080507</td>\n",
       "      <td>0.056997</td>\n",
       "      <td>0.149592</td>\n",
       "      <td>0.121998</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.141058</td>\n",
       "      <td>0.046736</td>\n",
       "      <td>0.105134</td>\n",
       "      <td>0.060193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ScriptDuration</th>\n",
       "      <td>0.989473</td>\n",
       "      <td>0.929101</td>\n",
       "      <td>0.999951</td>\n",
       "      <td>0.981863</td>\n",
       "      <td>0.935199</td>\n",
       "      <td>-0.031863</td>\n",
       "      <td>0.996293</td>\n",
       "      <td>0.974991</td>\n",
       "      <td>0.626081</td>\n",
       "      <td>0.408728</td>\n",
       "      <td>0.963809</td>\n",
       "      <td>0.980463</td>\n",
       "      <td>0.141058</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.042936</td>\n",
       "      <td>0.631854</td>\n",
       "      <td>-0.021828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V8CompileDuration</th>\n",
       "      <td>-0.102723</td>\n",
       "      <td>-0.115511</td>\n",
       "      <td>-0.034587</td>\n",
       "      <td>-0.093317</td>\n",
       "      <td>-0.084483</td>\n",
       "      <td>-0.060940</td>\n",
       "      <td>-0.040758</td>\n",
       "      <td>-0.064183</td>\n",
       "      <td>-0.082110</td>\n",
       "      <td>-0.125646</td>\n",
       "      <td>-0.047814</td>\n",
       "      <td>-0.094737</td>\n",
       "      <td>0.046736</td>\n",
       "      <td>-0.042936</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.006613</td>\n",
       "      <td>-0.062892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TaskOtherDuration</th>\n",
       "      <td>0.626864</td>\n",
       "      <td>0.635386</td>\n",
       "      <td>0.635250</td>\n",
       "      <td>0.646135</td>\n",
       "      <td>0.643943</td>\n",
       "      <td>-0.020786</td>\n",
       "      <td>0.691936</td>\n",
       "      <td>0.764416</td>\n",
       "      <td>0.899969</td>\n",
       "      <td>0.734472</td>\n",
       "      <td>0.620969</td>\n",
       "      <td>0.622940</td>\n",
       "      <td>0.105134</td>\n",
       "      <td>0.631854</td>\n",
       "      <td>0.006613</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.252181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JSHeapRatio</th>\n",
       "      <td>0.004110</td>\n",
       "      <td>0.235237</td>\n",
       "      <td>-0.022504</td>\n",
       "      <td>0.012800</td>\n",
       "      <td>0.027387</td>\n",
       "      <td>-0.009649</td>\n",
       "      <td>0.008320</td>\n",
       "      <td>0.051303</td>\n",
       "      <td>0.205334</td>\n",
       "      <td>0.291308</td>\n",
       "      <td>0.016638</td>\n",
       "      <td>-0.002756</td>\n",
       "      <td>0.060193</td>\n",
       "      <td>-0.021828</td>\n",
       "      <td>-0.062892</td>\n",
       "      <td>0.252181</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Nodes  JSHeapUsedSize  scripting  rendering  \\\n",
       "Nodes                    1.000000        0.950440   0.989017   0.990200   \n",
       "JSHeapUsedSize           0.950440        1.000000   0.928467   0.953951   \n",
       "scripting                0.989017        0.928467   1.000000   0.981662   \n",
       "rendering                0.990200        0.953951   0.981662   1.000000   \n",
       "painting                 0.936726        0.895657   0.934783   0.932397   \n",
       "loading                 -0.007361       -0.012566  -0.032104  -0.018280   \n",
       "TaskDuration             0.988679        0.936352   0.996616   0.986298   \n",
       "ThreadTime               0.974179        0.927550   0.975711   0.972884   \n",
       "ProcessTime              0.634557        0.620309   0.628877   0.648341   \n",
       "RecalcStyleCount         0.420848        0.498904   0.409934   0.446609   \n",
       "LayoutDuration           0.960327        0.921576   0.963702   0.968028   \n",
       "RecalcStyleDuration      0.989898        0.949846   0.980194   0.998822   \n",
       "DevToolsCommandDuration  0.129243        0.141101   0.141410   0.126447   \n",
       "ScriptDuration           0.989473        0.929101   0.999951   0.981863   \n",
       "V8CompileDuration       -0.102723       -0.115511  -0.034587  -0.093317   \n",
       "TaskOtherDuration        0.626864        0.635386   0.635250   0.646135   \n",
       "JSHeapRatio              0.004110        0.235237  -0.022504   0.012800   \n",
       "\n",
       "                         painting   loading  TaskDuration  ThreadTime  \\\n",
       "Nodes                    0.936726 -0.007361      0.988679    0.974179   \n",
       "JSHeapUsedSize           0.895657 -0.012566      0.936352    0.927550   \n",
       "scripting                0.934783 -0.032104      0.996616    0.975711   \n",
       "rendering                0.932397 -0.018280      0.986298    0.972884   \n",
       "painting                 1.000000 -0.016113      0.938788    0.920663   \n",
       "loading                 -0.016113  1.000000     -0.030145   -0.016526   \n",
       "TaskDuration             0.938788 -0.030145      1.000000    0.988120   \n",
       "ThreadTime               0.920663 -0.016526      0.988120    1.000000   \n",
       "ProcessTime              0.606603  0.025562      0.677497    0.778809   \n",
       "RecalcStyleCount         0.431073 -0.003435      0.457027    0.520482   \n",
       "LayoutDuration           0.911187  0.005695      0.964886    0.944261   \n",
       "RecalcStyleDuration      0.928800 -0.019890      0.982966    0.966637   \n",
       "DevToolsCommandDuration  0.109103 -0.061373      0.140324    0.134071   \n",
       "ScriptDuration           0.935199 -0.031863      0.996293    0.974991   \n",
       "V8CompileDuration       -0.084483 -0.060940     -0.040758   -0.064183   \n",
       "TaskOtherDuration        0.643943 -0.020786      0.691936    0.764416   \n",
       "JSHeapRatio              0.027387 -0.009649      0.008320    0.051303   \n",
       "\n",
       "                         ProcessTime  RecalcStyleCount  LayoutDuration  \\\n",
       "Nodes                       0.634557          0.420848        0.960327   \n",
       "JSHeapUsedSize              0.620309          0.498904        0.921576   \n",
       "scripting                   0.628877          0.409934        0.963702   \n",
       "rendering                   0.648341          0.446609        0.968028   \n",
       "painting                    0.606603          0.431073        0.911187   \n",
       "loading                     0.025562         -0.003435        0.005695   \n",
       "TaskDuration                0.677497          0.457027        0.964886   \n",
       "ThreadTime                  0.778809          0.520482        0.944261   \n",
       "ProcessTime                 1.000000          0.660627        0.606594   \n",
       "RecalcStyleCount            0.660627          1.000000        0.402911   \n",
       "LayoutDuration              0.606594          0.402911        1.000000   \n",
       "RecalcStyleDuration         0.629432          0.411547        0.963039   \n",
       "DevToolsCommandDuration     0.080507          0.056997        0.149592   \n",
       "ScriptDuration              0.626081          0.408728        0.963809   \n",
       "V8CompileDuration          -0.082110         -0.125646       -0.047814   \n",
       "TaskOtherDuration           0.899969          0.734472        0.620969   \n",
       "JSHeapRatio                 0.205334          0.291308        0.016638   \n",
       "\n",
       "                         RecalcStyleDuration  DevToolsCommandDuration  \\\n",
       "Nodes                               0.989898                 0.129243   \n",
       "JSHeapUsedSize                      0.949846                 0.141101   \n",
       "scripting                           0.980194                 0.141410   \n",
       "rendering                           0.998822                 0.126447   \n",
       "painting                            0.928800                 0.109103   \n",
       "loading                            -0.019890                -0.061373   \n",
       "TaskDuration                        0.982966                 0.140324   \n",
       "ThreadTime                          0.966637                 0.134071   \n",
       "ProcessTime                         0.629432                 0.080507   \n",
       "RecalcStyleCount                    0.411547                 0.056997   \n",
       "LayoutDuration                      0.963039                 0.149592   \n",
       "RecalcStyleDuration                 1.000000                 0.121998   \n",
       "DevToolsCommandDuration             0.121998                 1.000000   \n",
       "ScriptDuration                      0.980463                 0.141058   \n",
       "V8CompileDuration                  -0.094737                 0.046736   \n",
       "TaskOtherDuration                   0.622940                 0.105134   \n",
       "JSHeapRatio                        -0.002756                 0.060193   \n",
       "\n",
       "                         ScriptDuration  V8CompileDuration  TaskOtherDuration  \\\n",
       "Nodes                          0.989473          -0.102723           0.626864   \n",
       "JSHeapUsedSize                 0.929101          -0.115511           0.635386   \n",
       "scripting                      0.999951          -0.034587           0.635250   \n",
       "rendering                      0.981863          -0.093317           0.646135   \n",
       "painting                       0.935199          -0.084483           0.643943   \n",
       "loading                       -0.031863          -0.060940          -0.020786   \n",
       "TaskDuration                   0.996293          -0.040758           0.691936   \n",
       "ThreadTime                     0.974991          -0.064183           0.764416   \n",
       "ProcessTime                    0.626081          -0.082110           0.899969   \n",
       "RecalcStyleCount               0.408728          -0.125646           0.734472   \n",
       "LayoutDuration                 0.963809          -0.047814           0.620969   \n",
       "RecalcStyleDuration            0.980463          -0.094737           0.622940   \n",
       "DevToolsCommandDuration        0.141058           0.046736           0.105134   \n",
       "ScriptDuration                 1.000000          -0.042936           0.631854   \n",
       "V8CompileDuration             -0.042936           1.000000           0.006613   \n",
       "TaskOtherDuration              0.631854           0.006613           1.000000   \n",
       "JSHeapRatio                   -0.021828          -0.062892           0.252181   \n",
       "\n",
       "                         JSHeapRatio  \n",
       "Nodes                       0.004110  \n",
       "JSHeapUsedSize              0.235237  \n",
       "scripting                  -0.022504  \n",
       "rendering                   0.012800  \n",
       "painting                    0.027387  \n",
       "loading                    -0.009649  \n",
       "TaskDuration                0.008320  \n",
       "ThreadTime                  0.051303  \n",
       "ProcessTime                 0.205334  \n",
       "RecalcStyleCount            0.291308  \n",
       "LayoutDuration              0.016638  \n",
       "RecalcStyleDuration        -0.002756  \n",
       "DevToolsCommandDuration     0.060193  \n",
       "ScriptDuration             -0.021828  \n",
       "V8CompileDuration          -0.062892  \n",
       "TaskOtherDuration           0.252181  \n",
       "JSHeapRatio                 1.000000  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrdf.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "def correlation_heatmap(train, method='pearson'):\n",
    "    correlations = train.corr(method)\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(15,10))\n",
    "    sns.heatmap(correlations, vmax=1.0, center=0, fmt='.2f',\n",
    "                square=True, linewidths=.5, annot=True, cbar_kws={\"shrink\": .70},cmap='RdYlGn')\n",
    "    plt.title('Correlation Matrix', fontsize=16);\n",
    "    plt.show();\n",
    "    \n",
    "correlation_heatmap(corrdf) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Gw1CkcUzyWs51y1Vm+ebM+basdlmmMZXPOoeJmfI9i7jC3nM60hdBwXwFiPBFUCh/FOu3bQ56DsdjwsS5XN+qnneurnkO27fvIjl5W6YxzjmmT19M8+YXAtC6VX0mTJgDwJlnFqZ6tfJERkYEPXYJnLxUZB8APgSyTfWaWTkzm2Bmc/3fz/YvP8fMppnZDDPrmWWbx/3L55rZC/5lZ5jZGDObY2bzzeyWYCSWW6VjSrBma3L67bVbkykdU+Kwy08FScnbiY+PSb8dX7IoScmZi+ytKbsoUrhg+skqpzG7d+9jyq9LaNa46kmP+UQkbdrJWXGF02/HlyhMUg5F9uG338GPicto26rGyQgv4EoVLc6alEPPzXUpmyhdNPNzc+76ZVxfzStSapdN4OzYkulj+rR6iKdHv0eaSwte0CegdNESrM2Sb6mimecF5q1fxvXVvMKq9tkJlIstmf776pxj7H1vML3LR9xTv0XwAj9OpWNKsDbjuScl+7ln7rpl3FCjEQB1ylWmXLF4ysTEMX/9ci47vybFzihCwXwFuLpKA8rGZm+dCjd5LWfv9SUp/fa6lE3Z8p2zbimt0/NNoFyxkpSJiWP9ts28PuErVvYYzroXR7Jt905+/Ov3YIZ/3JKSUoiPP/TmNz4+lqTklExjtqb8S5EihQ69NsXHkJSUeYyc2vJSkQ3wLtDOzLJO8b0DDHTOVQcGAW/5l/cD3nPO1QE2HhxsZs2ACkBdoCZwkZldDlwFrHfO1XDOVQV+OJnJHCvDsi1zOMxyWO6CEdGJczkEmi2dXIz5OXERtWqWC+tWEeAwuWQ/fofT+61JPPbAZUREnBq/+jk+Z7M8Bq9NHERMocL83uVj/nPpDcxev5QDaalck9CATTu3MmvdkmCFe8JyOpRZD/mrE74gtlBhZjw6gAcvvZHZ65aSmpYKQKO3/0O9N+6hRf/HeODSG7j03PB+M5Wb4/vy+IHEFCrMn09/RudGNzFr7RIOpKXyV9IqXv3xC8Z3fovvO/dl7rplHPA/DuEsr+Wc09kpa76v/Pg5sYUK88eTn9L58jbMWuv9DscULEzL6pdxXvebKPNsS84oUJB2tZsHJ/ATlPNrk2UddPQxckrLUx98dM5tN7OBwP8BuzOsagDc4P/5c+BV/8+XADdmWP6K/+dm/q9Z/tvReEX3FKCPmb0CjHbOTckag5l1AjoBfPDBBwHIKvfWpiRTNjYu/XaZ2DjWp2wmf0S+7Mu3bQpqbMdi0NfTGPKtN5tRrUoZNm5MSV+3MWkbcSUy9yHHxp7B9h27OXAglcjIiBzHjBk3h2vDtFVk0Lez+GbUPACqVYpnQ/KO9HUbN+0g7swzcr2v+Ys30rW71zqRsm03idP/JjLCaHJ5hcAGHSDrtm2ibMyh52bpmBKs3575z8U79u6i09cvp99e3O1rVm7ZwM01G3Nt5UtoXqk+UZH5KRJ1Bp/c+ix3f/Vi0OI/VmtTNlEmS74bcsj33sEvpd9e8uwQVvyzAYAN2/8BYNPOFEbMS6TO2QlM/XtOECI/PmtTkimT8dzjn73MaMeeXdzzRa/023/3+JYV/6wHYMC0UQyYNgqAXi3vzzRDHK7yWs5rUzZlmm0vHVMi53wHHcp3efdhrPhnPc0r1WPlP+vZvDMFgOFzJtHg3GoMmjkuKLEfq0GDJjNk6C8AVKtajo0bt6av27hxK3ElMs/vxcZGs337rkOvTRtTiIvLuc1PTk2nxnRWYL0J3AMcqTJxh/n5IANecs7V9H+d75z72Dm3BLgImAe8ZGb/zbZj5z50ztV2ztXu1KnT8WdxHEbOncKd9a8BoN45Vdi2eycbt//DjFWLqBBXlvJnnkW+iEja1m7KyLnZ3h+EjXa3NEj/sGKTK6rw3eg/cc4xe+5qCkdHZSugzYx6tc9j3E/zARg+6k+ubFQ5ff2OHXuY8ccKGmdYFk7a3XAh331yJ999cieNLzufET8s9PJdsJ7C0QWIKx6d631NGHIvE7/xvpo1rMh/uzYJ2wIbYOaavzi/eBnKF/OemzfXbMzoBb9kGlM0Kpp8Ed58QYd61zH17zns2LuL577/kPNevIkLet/CHYNeYNKyP8O6wAZ/viUy5HthY0bPn5ppTKZ867dg6nIv30L5o4guUBCAQvmjaFKxDgs2/h30HI5F1nPPLRc1YeS8zOeeogUP5dvx4pYkLpvNjj27ACjh70UvG1uS1jUa8dXMH4ObwHHIaznPWL3Ie05nyHfUvCzP6Sz5Tlnu5bt6axL1ylehYL4CAFxZsTaLNq4Mdgq51q5dQ0YM78aI4d1o0rg63434zTtXz15B4cIFsxXQZka9ehUZN86brxs+YjpXXlk9FKHLSZKnZrIBnHNbzGwIXqE9wL/4V6At3mx1O+DgGeAX//Iv/MsPGgf0NLNBzrmdZlYa2I/3eG5xzn3hvxJJ+5OdT0ZfduhBo4q1KB4dw5reI3l+dP/0E9cHU4Yzdv6vXFP1Ypb1GMqufXu4e6BXcKSmpdJ5cB/GPdTPu0zar6NZuGHFke4qbDS89AImT/2Lpi1fo2BUPnp3b5O+7t7On/Dif2+kZFwRHn/4Kro89RVv/m88CReUos31ddLH/fjzfC6pX4FCBfOHIoVj0rDBOSRO/5tmbT8mKiofvZ8+9KfTTo9/S88nm1GyeDQDh/7Jx1/OYPOWf2nZfiAN65/Di0+dGn9mzSg1LZVHhr/J6Hv7EGE+Pp0xlkVJK7m3QUsA+k8bSaWS5RjQ9hlSXSqLklZx35CXj7LX8JWalsoj3/ZlTKfX8fl8fPb7GBYmreTeBq0A6D9thJfvbc+QlpbGoqSV6bP4JaNj+aZDb8D7sNjgP39kfJj3r6ampfLQkNf54cE3ifD5+GSad+6579LWAHwwdTgJ8eX57M7/kpqWxsKNK+j4Re/07Yfe25szzyjK/tQDdB7Sh5TdOw53V2Ejr+WcmpbK/33zBt//py8RFsEn00ezcOMK7rvkegA++OU7EkqW59M7niPVpbFo4wo6DvL+UvP7qoUMm/0zM5/8lAOpqcxeu4T+v44IYTa517BhVSYnLqBp8+cpGJWf3r3vSF93b6d3efHFdpSMi+HxR1vT5dGPefOtUSQklKHNTRcDsGnTNm5s8wo7d+7B5zM+G/gzY0c/R3R0wVClJMfBcuobOh2Z2U7nXLT/55LACuBV51x3MyuPV3AXBzYBdzvnVpvZOcCXeMXzMODZDPt4GOjo3/1O4HbgfOA1IA2v6H7AOTfzCGE5e6B+YBMNU+696d4Pu4aHNpBgKtQal/xhqKMIGovz/jJT4LFT48o0gbC3TyL5u1529IGniX1veDOuvgcbhDiS4Eh7dxqQd/IFL+eIhy4OdRhBk/q2d8k80iaENpBg8jVW43eQ5JmZ7IPFsf/nJKBQhtsrgStz2GYFXr/2QS9nWNcP74ORGS3Hm+UWERERkTwsL/Zki4iIiIicVCqyRUREREQCTEW2iIiIiEiAqcgWEREREQkwFdkiIiIiIgGmIltEREREJMBUZIuIiIiIBJiKbBERERGRAFORLSIiIiISYCqyRUREREQCTEW2iIiIiEiAqcgWEREREQkwFdkiIiIiIgGmIltEREREJMBUZIuIiIiIBJiKbBERERGRAFORLSIiIiISYCqyRUREREQCTEW2iIiIiEiAqcgWEREREQkwFdkiIiIiIgGmIltEREREJMBUZIuIiIiIBJiKbBERERGRADPnXKhjyMv04IuIiEgwWagDyCs0ky0iIiIiEmCRoQ4gz9s1PNQRBEeh1gDYA/VDHEjwuPemc/eP94Q6jKD5pOnHAKS5CSGOJHh81hjS8k6++Bp733ePCG0cwVKwFQAu5YsQBxI8FnN73jm+kH6MOTAutHEEU2TzUEeQZ2gmW0REREQkwFRki4iIiIgEmIpsEREREZEAU5EtIiIiIhJgKrJFRERERAJMRbaIiIiISICpyBYRERERCTAV2SIiIiIiAaYiW0REREQkwFRki4iIiIgEmIpsEREREZEAU5EtIiIiIhJgKrJFRERERAJMRbaIiIiISICpyBYRERERCTAV2SIiIiIiAaYiW0RERETyLDMbYGbJZjb/MOvNzN4ys2VmNtfMauVmvyqyRURERCQv+xS46gjrrwYq+L86Ae/lZqcqskVEREQkz3LOJQJbjjCkFTDQeaYDMWZ21tH2GxmoAEVEREREAsEeqO8CtrP3f7sPbwb6oA+dcx8ewx5KA2sy3F7rX7bhSBupyBYRERGR05a/oD6Wojory2m3R9tI7SIiIiIiIoe3Fiib4XYZYP3RNlKRLSIiIiJyeCOBO/1XGakPbHPOHbFVBNQuIiIiIiJhxnw5dWicpPsy+wpoBBQ3s7XA80A+AOfc+8BY4BpgGbALuDs3+1WRfZpxztHr1VFM/mUxUVH5ePmFNlRJKJ1t3Jp1W+j61Fds27aLygmlefXFm8mfL5KPPpvMqLGzAUhNTWP5imSmTXyOmKKFgpzJ0X18xzNcV+0SkndspVrPdjmO6XdzV66p0oBd+/bSfmBPZq1ZDEDzyvXpd3MXIszHR7+M5JXxnwcz9ONW9cyq3HbBrfjMSFw3hbErv8825oLYC7jtgrZEWAQ79u/klZmvAtCh8t3UKFGd7ft28Ny0/wY79OM2ZcoCevf6hrQ0x003Xcy9nZpnWu+co3evb0hMXEBUVD56v3QnVaqcDcAz3T5n0qR5FDuzMKNGPReK8I+Zc45evb9hsj+fl3sfyiejNWs30/XRAWxL+ZfKlcvy6ivtyZ8/kuV/b6Rbt89ZsHANXR5pwT0dmoYgi2PjnbdGMnnqX17OPW6mSkKZbOPWrNtC1ycHHTpv9WpL/nyR/DZjOf/p8hllSsUC0LRxVTrfF755O+fo9cY4En9dRlRUPl56riVVKmW/UMEX38xg4ODfWL12K9PGPUpsTKFj2j5cnOjxBfhtxnJ6vzaSAwfSiI0txBcfPxDsNI6Jc45eLw1jcuJCogrm5+Ve7ahSuWy2cWvW/kPXxz71cq5chldfuoP8+SP5aeJc+r09Fp8ZEZE+uj15A7UvOi8EmeQNzrlbj7LeAQ8e635D3i5iZqlmNtvMFpjZHDPramYhjys3zKymmV0T6jgySpy6mJWrNzN+xGP0fPYGuvf+Lsdxffp9T/t2lzJ+5OMUKVyQocNnAtDxroaM+PphRnz9MF0fak6di84JywIb4NNpY7jq7S6HXX91lQZUiCtLhefb0OnLl3jv1icA8JmPd9s+xtXvdKFyj1u5tU4zEuLLBynq42cYd1RqR99ZfXnm1+eoF1+PUmdkfmEtGFmQOyrdTr/Zb/PstP/yvzmHLuU5df0vvPFn32CHfUJSU9Po2eNrPuzfmVGjn2PMmJksW5b5L3SJiQtYtSqZH8Z154Ue7ejxwuD0dde3rs+H/TsHO+wTkpi4gJWrkhn/Q3d6vtCO7j0G5ziuz+vf0f7OKxk/7gWKFC3E0GG/AhBT9AyeeaYN93RoHMywT0ji1L+889bIJ+j53I107zU8x3F93hxL+9svY/yoJylSpCBDh89IX1f7wvKMGNKFEUO6hHWBDZD46zJWrdnCuKEP0uOpa3nh1bE5jqtVvQwD3r6dUmcVPa7tw8WJHt/t23fzwkvDea9fe8Z8+yj9XrsjmOEfl8QpC1m5ahPjv3+Ont1voXuPITmO6/PGCNrf2Yjx3z9HkSKFGPrtNAAa1LuAkd8+yYhvn6R3z9t49vmvghm+BEg4FLO7nXM1nXNVgKZ40/HPhzim3KqJF2/YmDB5IddfVwszo2b1s9m+YzfJm7ZnGuOcY/qM5TRvUhWA1i1qMWHSgmz7GvPDHK67qmYwwj4uU5bNZsu/2w+7vlWNyxk43Xvx+W3FAmIKRRNf5Ezqlq/Msk1rWbF5PftTDzB45o+0qnF5sMI+bucWPZfkXcls2r2ZVJfK7xt/58ISF2YaUz++Pn8m/8mWPd7lPnfs35G+bknKEnbu/zeoMZ+ouXNXcvbZJShbtjj580dyzTUXMXHCnExjJk6YS6tW9bznfM1z2L59F8nJ2wCoU6cCMUXPCEXox23CxLlcf5h8DnLOMX36Ypo3945/61b1meB/XM48szDVq5UnMjIi6LEfrwmTMp63yh3hvLWM5k2qAdC6RW0m/Jz9vHUqmJC4hFZXV/fyrVaG7Tv2kLx5R7ZxlS84izKlYo57+3Bxosd31PezaHplVUqd5f2l4sxi0cFN4DhMmDiP61vW9XKucY4/5xx+j39bSvNmNQFo3aouEybMA+CMMwpg5rVL7N69L/3nvMR8FrCvUAmHIjudcy4Z7zqGnf3N5VFm9omZzTOzWWZ2BYCZRZhZH//yuWb2kH/5SjMr7v+5tplN8v/c3cw+M7Px/jE3mNmr/u1/MLN8/nEXmdlkM/vDzMYdvNC4mU0ys1fM7HczW2Jml5lZfqAHcIt/Jv4WM2vo/3m2P97CwX4Mk5K3Ex8fk347vmRRkpIzn8y2puyiSOGC6S/COY3ZvXsfU35dQrPGVU96zCdL6ZgSrNmanH577dZkSseUOOzycBdbIIYtew9dK3/L3q3EFojJNCa+UEkK5SvEkxc9zvP1nuPisxoEOcrASk5KId7/wgpQMj6WpKTML1RJWcbEx8eSnJQSrBADLikphfj4zPkkJadkGrM15V+KFCl06Hc4PoakUznn5G1ZzlsxJGV5Y5HzeevQmNlzV9Py5r50fPBjli7bGJS4j1fSph2cVbJI+u34uCIkbcp9kXyi2wfbiR7flas2s337bu64531uuLUf3436I2ixH68cc07KmvO/WXLO/Lj8+NMcrrruRe574AN697wtKHFLYIVdT7Zz7m9/u0gccLt/WTUzqwSMN7OKeA3n5wAXOucOmFmxXOz6POAKoDIwDbjROfeEmQ0HrjWzMcDbQCvn3CYzuwXoBXTwbx/pnKvrbw953jnXxMz+C9R2znUGMLNRwIPOuV/MLBrYkzUIM+uE/4LoH3zwAZ1uD2xx57UNZb3PbIOOOubnxEXUqlkubFtFcsNyuKylw+U4I5DDQxKGcsonswjzUb5wOV79ow/5I/LzbN1uLN/2N0m7koITYoDldFiyHj6Xw6hTedYn59/hrEmfbjlnX3YsOVdJKM3E75/mjEIFmDxlEQ92+Yzxo548GaEGRk655HgZ3pO0fZCd6PFNTU1jwaJ1fPphJ/bs2U/bO9+hRvWzOadc+E6OBOK1uGmTGjRtUoMZM5fR7+0xfPrxqdX6JmFYZPsdfJpdilf44pz7y8xWARWBJsD7zrkD/nVH+leYB33vnNtvZvOACOAH//J5QHngAqAq8KP/FzuCzP/J51v/9z/843PyC/CGmQ0CvnXOrc06IMsF0R27cu5NOxaDvp7GkG9/B6BalTJs3JiSvm5j0jbiShTJND429gy279jNgQOpREZG5DhmzLg5XBvGrSK5sTYlmbKxcem3y8TGsT5lM/kj8mVfvm1TKEI8Jlv3bqVYgUPvJ4sViCVlb0qmMVv2bmXHPzvZl7aPfWn7WLx1CWWjy56yRXbJkjFs3LA1/XbSxq3ExWXuT40vGZtpzMaNWymRZUy4GzRoMkOG/gJAtarl2Lgxcz5xJTLnExsbzfbtuw79Dm9Myfa4hLtBg39lyLe/AVCtStks562UYzpvRUdHpY9reFkCL/T+ji1b/6VYbPi0Cg36ZgbfjJgFQLXKpdiQdOivhxuTtxNXIvctECXjipzQ9sEQyOMbX7IosTGFKFQwP4UK5qf2Refy1+INYVdkD/oykSFDvZ7qalXPzp5zXA6/x5lyTsn2uw5Qp/b5rF6zmS1bd1IsNryO88kUyjaPQAmrdhEAMzsXSAWSyfk/7OBfntMk1wEO5RSVZd1eAOdcGrDfHXqbmYb3ZsOABf7+8JrOuWrOuWZZt/fHluObE+fcy0BHoCAw3T/7ftK1u6VB+ocVm1xRhe9G/4lzjtlzV1M4OirbyczMqFf7PMb9NB+A4aP+5MpGldPX79ixhxl/rKBxhmWnopFzp3Bnfa9lvt45Vdi2eycbt//DjFWLqBBXlvJnnkW+iEja1m7KyLlTQhzt0a3YvoK4QiUpHlWcCIugbnxdZm2anWnMrE2zqRhTEZ/5yO/Lz7lFz2XDv0e9lGfYqlatHKtWJbN27Wb27TvA2LF/cMWV1TONueLKaowY8Zv3nJ+9gsKFC55yBWe7dg0ZMbwbI4Z3o0nj6nx3lHzMjHr1KjJunFe0DR8xnSuzPC7hrl3bi9M/qJj5vLWKwtEFj3De8npWh4+amX7e2rR5R/rM4dx5q0lzLv1KHOGiXZs6fPdFJ777ohONL7+AEd/P9fKdt9Y7TxfPfXfhlZdVPKHtgyGQx7dxo8rMnLWSAwdS2b17H3Pnrea8c+Oy3Weotbvtckb4P6zYpHF1vhv5u5fznBX+1+Icfo/rVmDc+NkADB/xO1de6fWkr1q1Kf05vWDhGvbvTyU2JnzeNAbD6dCTHVYz2WZWAngfeMc558wsEWgHTPS3iZwNLAbGA/eb2aSD7SL+2eyVwEXA98CNx3j3i4ESZtbAOTfN36dd0Tl3pE/W7ADSz2xmdp5zbh4wz8waAJWAv44xjhPS8NILmDz1L5q2fI2CUfno3b1N+rp7O3/Ci/+9kZJxRXj84avo8tRXvPm/8SRcUIo219dJH/fjz/O5pH4FChXMH8zQj9mXHXrQqGItikfHsKb3SJ4f3Z98Ed5T+oMpwxk7/1euqXoxy3oMZde+Pdw98EUAUtNS6Ty4D+Me6keEz8eAX0ezcMOKUKaSK2kujUGLB/ForS74zMeU9VNZ/+96GpVpCMCktZPZ8O8G5v0zjx71X8DhSFyXyLp/1wFwX7VOVIq9gOh80bx+2Wt8t3wEU9ZPDWVKRxUZGcGzz91Cx3veIS0tjRtubECFCqUYPDgRgLZtL6dhw6okJi6gebPniYrKT+/eh6488GjXAfw+YwkpW3fSqGE3Oj90LTfddEmo0smVhg2rMjlxAU2bP0/BLPnc2+ldXnyxHSXjYnj80dZ0efRj3nxrFAkJZWhz08UAbNq0jRvbvMLOnXvw+YzPBv7M2NHPER1dMFQpHVXDyyp5560Wr3g5v5DhvPXgx7z4/E2UjCvK449cQ5cnv+TNd8d5563WdQEY99NcvhoynYhIH1EF8vHGy7eFdftMw0vOJ/HXZTS78V2ioiLp/VzL9HWdHvmKns9cR8kShRn49e98/PmvbN6yk5btPqDhxefz4jMtjrh9ODrR43veuSW57OKKtLy5Lz4zbmpdl4rnx4cqnVxpeHll7/f46h5ezi8euszsvfe/z4s9bvVy7tqSLo99yptvjfF+j2+sD8C4H2czYuQMIiMjiIrKR98+7cP6OS05s5z6hoIagFkqXstGPryZ6M+BN5xzaWYWhVd0X+Rf19U597OZRQKvAlcB+4H+zrl3zOwy4GMgCfgNr1+6kZl1B3Y65/r473Oncy7a/3P6OjOrCbwFFMV7A/Kmc66//wOUjznnZvo/WDnTOVfe3ws+zh/7S3jtLVfgzXYvBNo75w7OgOckIO0ip4RCrQGwB+qHOJDgce9N5+4f7wl1GEHzSdOPAUhzE0IcSfD4rDGk5Z188fkvC7h7RGjjCJaCrQBwKV+EOJDgsZjb887xhfRjzIFxoY0jmCKbnxLVer4ulwasQN3fd2pIcg75TLZz7rDXmXLO7QHa57D8ANDV/5Vx+RS8nu2s47tnuR2d0zrn3Gwg27XcnHONMvy8GX9Ptn/2vE6GoV8fLhcRERERyZ3TYeY+7HqyRUREREROdSqyRUREREQCLOTtIiIiIiIiGekSfiIiIiIiko2KbBERERGRAFO7iIiIiIiEldOhXURFtoiIiIiEldOhyFa7iIiIiIhIgKnIFhEREREJMLWLiIiIiEhYUbuIiIiIiIhkoyJbRERERCTA1C4iIiIiImFF7SIiIiIiIpKNimwRERERkQBTu4iIiIiIhBW1i4iIiIiISDaayRYRERGRsKKZbBERERERyUZFtoiIiIhIgKldRERERETCipnaRUREREREJAsV2SIiIiIiAaZ2EREREREJK6fD1UXMORfqGPIyPfgiIiISTKdE9Vq0Z7OA1Ujbnhsfkpw1kx1iLvnDUIcQFBbXCYC7f7wnxJEEzydNP8YeqB/qMILGvTfd+755QIgjCR4r3iHP5QvgNn0U4kiCw0p0BPLOeRq8c3VeOb5w6Bizd0xoAwmmAteGOoI8Q0W2iIiIiISV06FdREW2iIiIiISV06HI1tVFREREREQCTEW2iIiIiEiAqV1ERERERMKK2kVERERERCQbFdkiIiIiIgGmdhERERERCStqFxERERERkWxUZIuIiIiIBJjaRUREREQkrKhdREREREREstFMtoiIiIiEFc1ki4iIiIhINiqyRUREREQCTO0iIiIiIhJW1C4iIiIiIiLZqMgWEREREQkwtYuIiIiISFgxU7uIiIiIiIhkoSJbRERERCTA1C4iIiIiImFFVxcREREREQkw81nAvnJ1f2ZXmdliM1tmZk/lsL6omY0yszlmtsDM7j7aPlVki4iIiEieZWYRwLvA1UBl4FYzq5xl2IPAQudcDaAR8LqZ5T/SflVkn2acc7z45kSatf2Ylnd9xoLFSTmO+2LYLJq1/ZhKl73O1pRd2dbPW7SRyg3f4Iefl5zskE9I1TOr0vviXrx8SW+uKX91jmMuiL2AF+o/z4sNevBk7SfSl3eofDf9GvalZ4MewQr3hH18xzMkvTqWec8NOuyYfjd3ZekL3zDnmS+4sOwF6cubV67PX92/ZukL3/BkszuCEW5AOOd4se9PNLv5A1reOYAFizfmOO6LoX/Q7OYPqHTJK5me0xOmLKXlnQO4/q5PuLHDZ/wxZ22wQj8ueS1fOHjemkCzW/rT8q5PjnDe+pNmt/Sn0qWvHea8tYHKl/fhh58Xn+yQT0heO0+f6PGdMGUpLe/6hOvbf8qN9ww8dZ7TL39L02t70eLG11iwMOeY16z9hza3vUmz63rzyOMD2bf/QKb1c+evJqHmo/wwfk4wws7L6gLLnHN/O+f2AYOBVlnGOKCweZc9iQa2AAc4grAtss0s1cxmm9l8//R8TKhjOhUkTl/BqrVbGfdVB3o80ZQXXv8px3G1qpViQN+bKBVfJNu61NQ0+ryfyKV1y5/kaE+MYdxRqR19Z/XlmV+fo158PUqdcVamMQUjC3JHpdvpN/ttnp32X/435730dVPX/8Ibf/YNdtgn5NNpY7jq7S6HXX91lQZUiCtLhefb0OnLl3jvVu9Nhc98vNv2Ma5+pwuVe9zKrXWakRBfPkhRn5jEaX+zau0Wxn3diR5PNOeFPuNzHFerehkG9Gub7Tld/6JyjPjsbr777G56d7uaZ1/+PhhhH7e8li/4z1trtjJucEd6PN6cF/r8mOO4WtVKM+DNmw9/3nov/M9bkLfO03Dix7f+ReUY8Wl7vvu0Pb2fvopnXxkXjLBPSOLURaxctZnxo7vR879t6P7i0BzH9XlzNO3vaMj40d0oUqQgQ7/9LX1damoaffqO5tKLL8hx29NdkNtFSgNrMtxe61+W0TtAArAemAc87JxLO9JOw7bIBnY752o656rivVt4MNgB+P98cEqZMHU5ra6qjJlRs0optu/cS/LmndnGVa5YkjJnFc1xH18Mm0WzhhUoFlPoZId7Qs4tei7Ju5LZtHszqS6V3zf+zoUlLsw0pn58ff5M/pMte7YAsGP/jvR1S1KWsHP/v0GN+URNWTabLf9uP+z6VjUuZ+D0sQD8tmIBMYWiiS9yJnXLV2bZprWs2Lye/akHGDzzR1rVuDxYYZ+QCVOX0uqqqt5zumpptu84tuf0GYXyp19vddee/YT7pVfzWr7gzVS2uqqKP+dSbN+55zjOW396563Y8D5vQd46T8OJH99T8jn983yub1Hby7lGebbv2E3ypsznbucc039fRvOm1QFo3bIOE36en77+8y+n0Lxpdc4sVjiosZ+OzKyTmc3M8NUp65AcNnNZbjcHZgOlgJrAO2aW/R1wBuFcZGc0Df87CjM7z8x+MLM/zGyKmVXyL2/jn/WeY2aJ/mVRZvaJmc0zs1lmdoV/eXsze+fgzs1stJk18v+808x6mNlvQAMzu9PM5vr3+7l/TAkzG2ZmM/xfl/iXN/TPvs/231/QfzOSNu3krLhDdxtfojBJOZzMDr/9Dn5MXEbbVjVORngBFVsghi17t6Tf3rJ3K7EFYjKNiS9UkkL5CvHkRY/zfL3nuPisBkGOMrhKx5Rgzdbk9NtrtyZTOqbEYZefCrzn9KHzWHxcYZI27TjCFtn9OHkJV9/an/sfG0qvbtcEOsSAymv5AiRtznLeijue89ZS2l5f8yREF3h56TwNJ358wf+cvu1j7n/8W3o9fVWgQwy4pOTtxMfHpN+OLxlDUvK2TGO2pvxLkcJRREZG+McUJSnJG5OUlMJPE+fRts3FQYv5dOac+9A5VzvD14dZhqwFyma4XQZvxjqju4FvnWcZsAKodKT7DftL+PlnkxsDH/sXfQjc75xbamb1gP8BVwL/BZo759ZlaC15EMA5V81fjI83s4pHucszgPnOuf+aWRXgGeAS59xmMyvmH9MP6Oucm2pmZwPj8P6E8BjwoHPuFzOLBvac+CNwjFzWN17H9l+Ter81icceuIyIiFPh/Vf2vLJmH2E+yhcux6t/9CF/RH6erduN5dv+JmlXzj2BpzrL8TFxOT4HcniqhKcTfE4DNG1YkaYNKzJj9hre6j+FT/q1DVR0gZfX8oXsv7jkPK10OL37TeSx+xueIuct8th5mhM+vpD1OT2VT/rdEpDQThaX4zHOOij7dgfH9Hp1BI89ct2pc4xPAl9wU58BVDCzc4B1QFvgtixjVuPVo1PMrCRwAfD3kXYazkV2QTObDZQH/gB+9BeuFwPfZDghFfB//wX41MyGAN/6l10KvA3gnPvLzFYBRyuyU4Fh/p+vBIY65zb793Fw2rQJUDlDDEX8s9a/AG+Y2SC8dzvZPung/xNFJ4APPviAe68/SjS5MOjbWXwzah4A1SrFsyH50KzXxk07iDvzjFzva/7ijXTtPgaAlG27SZz+N5ERRpPLK5x4oAG2de9WihUoln67WIFYUvamZBqzZe9Wdvyzk31p+9iXto/FW5dQNrrsaVtkr01JpmxsXPrtMrFxrE/ZTP6IfNmXb9sUihBzZdCwP/lmpPdBn2oJ8WxIPvRn1o3JO4grHn1c+61Tsyyr16WwNWUXsWH0Z/a8li/4cx41F4BqCWdlPm8dY87zFyfRtfsowH/emraCyAhfWJ238tp5OpDHN6M6Ncuyev228HxOD57KkGHTAahWpSwbN6akr9uYlEJcicytMLGxZ7B9xx4OHEglMjKCjUnbiIvzxsxfsIauT34OwNat/zJ5yiIiI300ubJacJIJAxFB7Atyzh0ws854k6YRwADn3AIzu9+//n2gJ16dOQ/vfeKTB+vDwwnnInu3c66mmRUFRuPNSn8KpDjnamYd7Jy73z+zfS0w28xqcvg3ywfI3CoTleHnPc65VP/PRo7vNfEBDZxzu7Msf9nMxgDXANPNrIlz7q8scX6INxvv3UzO+heLY9fuhgtpd4PXizzp178Z9O0srm1ciTkLN1A4usAxncwmDLk3/eenev1Ao4vPDasTd0Yrtq8grlBJikcVZ+verdSNr8sH8zI/nrM2zeb2C9rhMx+RFsm5Rc9l/KqcP3RzOhg5dwqdG7Vh8MwfqXdOFbbt3snG7f+waWcKFeLKUv7Ms1iXsom2tZty24D/hjrcw2p3Yy3a3VgLgEm/LmfQsD+4tkkCcxasP+bn9Kq1Wzm7dAxmxoLFG9m/P5WYogVPVujHJa/lCznlPItrm1RizoLjOG99c6i98qleY2l08Xlhd97Ka+fpQB7fzM/ppPB9Tre9lHZtLwVgUuJCvvhqKtdefSFz5q6icOEo4kpkbt81M+rVOZ9xP87l2qsvZPjIGVzZqCoAE394Nn3cU89+RaPLK+epAjsUnHNjgbFZlr2f4ef1QLNj2Wc4F9kAOOe2mdn/ASOA94AVZtbGOfeN/zIq1Z1zc8zsPOfcb8BvZtYCr7cmEWgHTPS3iZwNLAaKAP8xMx9er3fdw9z9BGC4mfV1zv1jZsX8s9njgc7AawBmVtM5N9sfwzxgnpk1wOvV+esw+z4pGjY4h8Tpf9Os7cdEReWj99PN09d1evxbej7ZjJLFoxk49E8+/nIGm7f8S8v2A2lY/xxefKr5EfYcftJcGoMWD+LRWl3wmY8p66ey/t/1NCrTEIBJayez4d8NzPtnHj3qv4DDkbgukXX/rgPgvmqdqBR7AdH5onn9stf4bvkIpqyfGsqUjurLDj1oVLEWxaNjWNN7JM+P7k++CO/X+IMpwxk7/1euqXoxy3oMZde+Pdw98EUAUtNS6Ty4D+Me6keEz8eAX0ezcMOKUKaSaw0bnEvitOU0u/lDoqIi6Z2hx7jTo9/Q86mrKFmiMAO/mcnHg37zntN3fkLDBufy4tNXM37SYkZ8P5/IyAgKFIikb49Wx9x+EUx5LV84mPPfNLulv3fe6nbocpydHhvq5Vw8moHf/MHHX/7u5XzXp17OT4V/f25Week8DSd+fMdPWsKIHxYQGenzntMvtAj/5/RlCUyesoim1/amYFQ+eve8NX3dvf/5kBe730LJuKI83uU6ujwxkDffGUtCpTK0uaFeCKOWQLOc+obCgZntdM5FZ7g9ChgCTMUrts8C8gGDnXM9zOxboALe7PME4BG8VpL3gYvwZq+7Oud+9hfnX+B9OnQ+UBLo7pyblMP93gU8jtdGMss5197MiuNdtDwB741Kon8m/W3gCv/YhUB759zeI6QZkJnsU4HFeTNNd/94T4gjCZ5Pmn6MPVA/1GEEjXvP+zOp2zwgxJEEjxXvkOfyBXCbPgpxJMFhJToCkFfO0+Cdq/PK8YVDx5i9Y0IbSDAVuDa836H4nfvxjQErUP++Z1hIcg7bmeyMha7/dosMN7NNXTjnbshhN3uA9jmMdXgz3Lm538+Az7Is2wxk+9SFc+6hnPYpIiIiInlL3v3YqoiIiIjISRK2M9kiIiIikjcF8+oiJ4tmskVEREREAkxFtoiIiIhIgKldRERERETCyunwzy5PgxRERERERMKLZrJFREREJKzog48iIiIiIpKNimwRERERkQBTu4iIiIiIhBW1i4iIiIiISDYqskVEREREAkztIiIiIiISViJ8ahcREREREZEsVGSLiIiIiASY2kVEREREJKxEnPrdIiqyRURERCS8qCdbRERERESyUZEtIiIiIhJgahcRERERkbCi//goIiIiIiLZqMgWEREREQkwtYuIiIiISFjR1UVERERERCQbc86FOoa8TA++iIiIBNMpMUV82ZDbAlYjTbn5y5DkrHYREREREQkr+o+PcsIKPHZ5qEMIir19EgFIcxNCHEnw+KwxbvOAUIcRNFa8g/f9gfohjiR43HvT81y+AIW6XRHiSIJjV++fAcjf9bIQRxI8+96YkmeOLxw6xrifQxtIMFneOb6hpiJbRERERMKKPvgoIiIiIiLZqMgWEREREQkwtYuIiIiISFjRv1UXEREREZFsVGSLiIiIiASY2kVEREREJKyoXURERERERLJRkS0iIiIiEmBqFxERERGRsBJxGkwDq8gWERERkbCinmwREREREclGRbaIiIiISICpXUREREREwkqET+0iIiIiIiKShYpsEREREZEAU7uIiIiIiIQVXV1ERERERESyUZEtIiIiIhJgahcRERERkbCi//goIiIiIhJg6skWEREREZFsVGSLiIiIiASY2kVEREREJKycDv/xUUX2aaTZBXV5vdX/EeHzMeC3MfT5eVCm9TEFo/nw5qc498zS7Dmwj05DXmbhxhXp633mY9ojH7J+22ZaD3gq2OEflylTFtC71zekpTluuuli7u3UPNN65xy9e31DYuICoqLy0fulO6lS5WwAnun2OZMmzaPYmYUZNeq5UIR/zJxz9HpzAonTlhMVlY+XnrmGKhfEZxv3xdA/GDhkJqvXpTBtzEPExhQCYMKUpfTrPwWfGRERPro93JiLapQJdhq59vEdz3BdtUtI3rGVaj3b5Tim381duaZKA3bt20v7gT2ZtWYxAM0r16ffzV2IMB8f/TKSV8Z/HszQj1tey7lphTq8dl1nInwRfDpjDK8nfpVpfUxUNO/f+ATnFCvF3gP7uP/bV1mYtJLSRUvwUZunKRldjDTnGDBjNP/7dViIsjg2zSrV5Y3rH8bn8/HJ9NG8NjH7ubp/26f95+q9dBr8Mgv85+olzw5h595dpKalcSAtlQZ97w1FCsckLx5j5xy9eg1hcuJ8oqLy8/JLd6W/9mS0Zu1munb9iG3b/qVy5bN59ZW7yZ8/MtfbS3jLVbuIme00M5+ZvWVm881snpnNMLNz/OtXmlnxDOMbmdnoQAdrZjuz3G5vZu8EYL+fmtlN/p+vM7NZZjbHzBaa2X3+5feb2Z0nel8ni8989GvdhZYfPU6N1+7klgsbU6lkuUxjnmx8B3PWL6P2G3dzz1e9eKPV/2Va/9BlN/FX0qpghn1CUlPT6Nnjaz7s35lRo59jzJiZLFu2IdOYxMQFrFqVzA/juvNCj3b0eGFw+rrrW9fnw/6dgx32CUmc9jer1m5h3Ned6PFEc17oMz7HcbWql2FAv7aUii+SaXn9i8ox4rO7+e6zu+nd7Wqeffn7YIR93D6dNoar3u5y2PVXV2lAhbiyVHi+DZ2+fIn3bn0C8H4f3m37GFe/04XKPW7l1jrNSIgvH6SoT0xeytlnPvq2fJjrP32KWm+2p02NxlSKy3zeerxRO+ZuWEa9tzvScehLvHbdQwCkpqXy9Nj3qPVmexq99x/uq98q27bhyGc++t3QlRYfPkaNV+7gllpNSChZPtOYJ5vcyZx1S7moT3s6fNmL169/ONP6pv97mDqvdzglCuy8eIwBEhPns3JVMuPH9aBnj3Z0f+HLHMf16fMt7e9qzPhxPSlSpBBDh/1yTNtLeDuWnuxbgFJAdedcNaA1kHIyggoVM8sHfAi0cM7VAC4EJgE45953zg0MYXhHVOfsBJb/s44VWzawP/UAQ2ZPoEWVSzONSShZnp+X/gHA4k2rKRcbT1x0LACli5bg6oQGfPL7mKDHfrzmzl3J2WeXoGzZ4uTPH8k111zExAlzMo2ZOGEurVrVw8yoWfMctm/fRXLyNgDq1KlATNEzQhH6cZswdSmtrqrq5VO1NNt37CV5885s4ypXLEmZs4pmW35GofyY/xPbu/bsJ9w/vD1l2Wy2/Lv9sOtb1bicgdPHAvDbigXEFIomvsiZ1C1fmWWb1rJi83r2px5g8MwfaVXj8mCFfULyUs61y1Ri+T/rWbnVO28NnTuR6xIuyTQmIa48Py//E4Alm9ZQLqYkcdGxbNyxhdnrlwKwc99uFievplSR4tnuI9zUOTuB5ZsznKtnTaBF1ezn6okHz9XJqylX7NC5+lSTF48xwIQJc7m+VX3/a8+5bN++O/215yDnHNOnL6Z581oAtL6+ARN+mpPr7U93EWYB+wqVYymyzwI2OOfSAJxza51zW4+2kZmdYWYD/DPfs8yslX95eTObYmZ/+r8u9i9vZGaJZjbcP5P8vpkdNU4za+OfZZ9jZon+ZRFm9pr/vudmmJU2M3vHv/8xQJx/N4XxWmj+8ee41zm32L9NdzN7zMxKmdnsDF+pZlbOzEqY2TD/fc0ws0uyR3nylCpanDUpyem316VsonTREpnGzF2/jOureS+6tcsmcHZsyfQxfVo9xNOj3yPNO7ynhOSkFOLPOvTCUzI+lqSkzCehpCxj4uNjSU5KCVaIAZe0aSdnxR2anY6PK0zSph3HtI8fJy/h6lv7c/9jQ+nV7ZpAhxhUpWNKsGbroef92q3JlI4pcdjlp4PTKedSRYuzbluG89a2TdmKqHkbl9Oqiv+8VaYSZ8fEU7pI5rzOjilJjVLnM2PNopMf9AkqXbQEa7Ocq0sVzZLz+mVcX60hALXPTqBcbMn0Y+mcY+x9bzC9y0fcU79F8AI/TnnxGENOrz0xJGV57dma8i9FihQiMjLi0JjklFxvL+HvWIrsIUALf2H5upldmGX9zwcLT+CjDMufASY65+oAVwCvmdkZQDLQ1DlXC2+W/K0M29QFHgWqAecBN+Qivv8Czf0z0C39y+4Btvnvuw5wr7/FpTVwgX//9wIXAzjntgAjgVVm9pWZtcta4Dvn1jvnajrnagL9gWHOuVVAP6Cv/75uzPIYpDOzTmY208xmfvjhh7lIK3eM7O/UnHOZbr82cRAxhQrze5eP+c+lNzB7/VIOpKVyTUIDNu3cyqx1SwIWTzC4HJZlfcPqchhl4T59eyTuxPNp2rAi3391L++8fANv9Z8SqMhCIsfnPS7HxySHh+6UdDrlfLhcMuoz+UtiC0YzvXN/7m/QmjkbvPPWQWfkj+Krdj14Ysy77Ni766THfKJy+nXNepxenfAFsYUKM+PRATx46Y3MXreUVH/Ojd7+D/XeuIcW/R/jgUtv4NJzawQh6uOXF48xHO61J+ugHMYcy/YSUGZ2lZktNrNlZpbjB9P8E8GzzWyBmU0+2j5z/cFH59xaM7sAuNL/NcHM2jjnJviHXOGc23wwCOAx//JmQEszO3g7CjgbWA+8Y2Y1gVSgYoa7+90597d/X18BlwJDDxea//svwKdmNgT4NsN9Vz/Ybw0UBSoAlwNfOedSgfVmNjFDnh3NrBrQxJ9DU6B91jv1z1R3BC7zL2oCVM7wQlfEzAo75zJNMzrnPsRrSQFwDz32xWHSOjbrtm2ibExc+u3SMSVYv31zpjE79u6i09cvp99e3O1rVm7ZwM01G3Nt5UtoXqk+UZH5KRJ1Bp/c+ix3f/ViQGI7WUqWjGHjhkN/TEnauJW4uMwtEvElYzON2bhxKyXisrdRhLNBw/7km5HenxCrJcSzIflQK8HG5B3EFY8+rv3WqVmW1etS2JqyK/2DkaeatSnJlI099LwvExvH+pTN5I/Il335tk2hCDHgTqec123bROmiGc5bRUuwYfs/mcbs2LuL+4a9mn570eNfsXKr99mLSF8EX97Wg8Gzf2LEglPjDePalE2UyXKu3pDDufrewS+l317y7BBW/OPlfPDx2bQzhRHzEqlzdgJT/87cJhdO8tIxHjRoEkO+mQpAtWrlsrz2pBAXF5NpfGxsNNu37+LAgVQiIyMyjcn+2pV9+9NdMP/jo5lFAO/i1XxrgRlmNtI5tzDDmBjgf8BVzrnVZhaX484yOKYU/O0T3zvnHgd6A9fnJnbgxoOzv865s51zi4AuQBJQA6gN5M94V1nv2v99t5llHFcM2OyP7X7gWaAsMNvMzvTf90MZ7vsc59z4LPvMKc95zrm+eA/2jdkSMjsL+Bi4xTl3sCHWBzTIcF+lsxbYJ9PMNX9xfvEylC92FvkiIrm5ZmNGL/gl05iiUdHki/DeV3Wodx1T/57Djr27eO77DznvxZu4oPct3DHoBSYt+zPsC2zwTmKrViWzdu1m9u07wNixf3DFldUzjbniymqMGPEbzjlmz15B4cIFsxXi4a7djbX4zv9hxcaXV2TED/O9fOavo3B0gWMqslet3Zr+F44Fizeyf38qMUULnqzQT7qRc6dwZ32v5aXeOVXYtnsnG7f/w4xVi6gQV5byZ3q/D21rN2Xk3PB+gc6t0ynnP9b9xfnFS1MuNp58EZHcVP1Kxiz6NdOYolFnpJ+37q59LVNXzE2fzXzvhidYvGkVb//yTdBjP14z1/zF+SUynKsvbMzo+VMzjcl0rq7fgqnLvXN1ofxRRBfwfl8L5Y+iScU6LNj4d9BzOBZ56Ri3a9eIEd89y4jvnqVJ45p8N2K6/7XnbwoXjsr22mNm1Kt3AePGef3ow7+bxpWNvdewK6+sftTtJaDqAsucc3875/YBg4FWWcbcBnzrnFsN4JxL5ihyPZNtZrWAjc659f4WiurA3FxsOg54yMwecs45M7vQOTcLb1Z5rXMuzczuAiIybFPX39axCq+V5ODM72TgdmCAmRUEbgae8Md3nnPuN+A3M2uBV2yPAx4ws4nOuf1mVhFYByQC95nZQLx+7CuAL80sGqjtnJvkv7+a/hgyPg758FpnnnTOZeyvGA90Bl7zj6vpnJudi8cnIFLTUnlk+JuMvrcPEebj0xljWZS0knsbeJ0z/aeNpFLJcgxo+wypLpVFSau4b8jLR9lreIuMjODZ526h4z3vkJaWxg03NqBChVIMHpwIQNu2l9OwYVUSExfQvNnzREXlp3fvO9K3f7TrAH6fsYSUrTtp1LAbnR+6lptuCmor/TFr2OBcEqctp9nNHxIVFUnvDD3VnR79hp5PXUXJEoUZ+M1MPh70G5u3/EvLOz+hYYNzefHpqxk/aTEjvp9PZGQEBQpE0rdHq7Bun/myQw8aVaxF8egY1vQeyfOj+6e/GH8wZThj5//KNVUvZlmPoezat4e7B3pvDlPTUuk8uA/jHurnXdLy19Es3LDiSHcVNvJSzqlpaXQd+RYj736VCPMx8I/vWZS8ko51vV7jj34fxQUlyvFRm6dJdWn8lbySB4a9BkCDclVpV6sZ8zYsZ3rn/gA8P/4jxi35LWT55EZqWiqPfNuXMZ1ex+fz8dnvY1iYtJJ7G3iv5/2njfDO1bc9Q1paGouSVqb/BbJkdCzfdOgNeDO8g//8kfF//R6yXHIjLx5jgIYNqzI5cT5Nmz1Hwaj89O59V/q6ezu9zYs976BkyRgef6w1Xbp+xJv9RpKQUJY2/tegI22fVwTyA4tm1gnolGHRh/7OgoNKA2sy3F4L1Muym4pAPjObhPcZvn5HuyCGZe3bzSGwSLwZ53ZAL6CAf9XvwH+cc3vMbCVecZqpXcQ5d52/GH4Tr+/ZgJX+5RWAYcAu4Ge8Gedo/7b/BTbh9Uwn+u8nzcxKAx8AZfz7Guice91/n9/itYIYMAF4xP/zi0AL/8+b8GbftwNv47W9HCyUv8Aryr/G6wPfDfwLPOycm2lm3YGdwAz/uL8yPEzXAPvw/tSQgPfmJdE/u34krsBj4f3p/0DZ28crfNPSu4tOfz5rjNs8INRhBI0V7+B9f6B+iCMJHvfe9DyXL0ChbleEOJLg2NX7ZwDyd73sKCNPH/vemJJnji8cOsa4n0MbSDDZFeE7s5LBfRM7BuxTJR9c+dERczazNnif6+vov30HUNc591CGMe/gdV40BgoC04Brs0y4ZpKbmewqwHLn3A/ADzkNcM6Vz3J7EocufbcbuC+HbZbizYYf9HSGn3c5527JYZt1wHWHiSGnD0c6oJv/K6vDXSA5x8stOOe6Z7gZdZhts8UsIiIiImFtLV4HxEFl8D47mHXMZufcv8C//ivZ1eDQZG02R+zJNrP7ga/wep1FRERERE66CAvcVy7MACqY2Tn+z/61xbvaXEYjgMvMLNLMCuG1kxzxmpJHnMl2zr0PvJ+r8AIk4yy4iIiIiMjJ5Jw7YGad8dqBI4ABzrkF/snmg/+QcJGZ/YD3ecQ04CPn3Pwj7TfXH3wUERERETkdOefGAmOzLHs/y+3X8F/gIjdUZIuIiIhIWPGF8ZWvcktFtoiIiIiElVz2Uoe1IP4/HRERERGRvEFFtoiIiIhIgKldRERERETCik/tIiIiIiIikpWKbBERERGRAFO7iIiIiIiEFV1dREREREREslGRLSIiIiISYGoXEREREZGw4jsNLi+imWwRERERkQDTTLaIiIiIhBV98FFERERERLJRkS0iIiIiEmBqFxERERGRsHIafO5RM9kiIiIiIoGmIltEREREJMDULiIiIiIiYUVXFxERERERkWzMORfqGPIyPfgiIiISTKfEHPF/p3cKWI3Uo/6HIclZ7SIiIiIiElZ8dkq8FzgiFdkhlr/rZaEOISj2vTHF+yFtQmgDCSZfY9zmAaGOImiseAfv+wP1QxxJ8Lj3pue5fAEKPtUotIEEye6XJwGQr8uloQ0kiPb3nUqhbleEOoyg2dX7Z+8H93NoAwkmOzWOr3qyRUREREQkGxXZIiIiIiIBpnYREREREQkr+o+PIiIiIiKSjYpsEREREZEAU7uIiIiIiISViNPgEn6ayRYRERERCTAV2SIiIiIiAaZ2EREREREJK7q6iIiIiIiIZKOZbBEREREJK/q36iIiIiIiko2KbBERERGRAFO7iIiIiIiEFd9pMA18GqQgIiIiIhJeNJMtIiIiImFF//FRRERERESyUZEtIiIiIhJgahcRERERkbCi//goIiIiIiLZaCZbRERERMKK/uOjiIiIiIhkoyJbRERERCTA1C4iIiIiImHldPjgo4rs00izSnV54/qH8fl8fDJ9NK9NHJRpfUzBaPq3fZpzzyzNngN76TT4ZRZsXAHAkmeHsHPvLlLT0jiQlkqDvveGIoVj5pyjV+9vmJy4gKiofLzc+06qVDk727g1azfT9dEBbEv5l8qVy/LqK+3Jnz+S5X9vpFu3z1mwcA1dHmnBPR2ahiCL3HPO0evNCSROW05UVD5eeuYaqlwQn23cF0P/YOCQmaxel8K0MQ8RG1MIgAlTltKv/xR8ZkRE+Oj2cGMuqlEm2Gnk2sd3PMN11S4hecdWqvVsl+OYfjd35ZoqDdi1by/tB/Zk1prFADSvXJ9+N3chwnx89MtIXhn/eTBDP255LeemFevSp0VnIiyCT2eMoc/kLzOtjykYzQc3Pck5xUqx98A+7hv6KguTVlAgMj8/3deP/JH5iPRFMHzeZF786dPQJHGMmlWqxxutHybCfAz4bTSvTfgi0/qYgoXp3/Zpziteij3793Hv4JfSz9VFo6L5oO2TVIk/F4ej01cvMX3VglCkkWtNK9Thtes6E+HzjvHriV9lWh8TFc37Nz6Rfozv//ZVFiatpEBkPn68tx/5I/MT6Yvgu/mTeXHCp6FJ4hg55+jVawiTE+cTFZWfl1+66/CvTV0/Ytu2f6lc+WxefeVu8uePzPX2Et6OqV3EzHaamc/M3jKz+WY2z8xmmNk5/vUrzax4hvGNzGx0IAM2s9/MbLaZrTazTf6fZ5tZ+RzGtjezUrnY56dmdpP/5+vMbJaZzTGzhWZ2n3/5/WZ2ZyBzCSSf+eh3Q1dafPgYNV65g1tqNSGhZPlMY55scidz1i3loj7t6fBlL16//uFM65v+72HqvN7hlCmwARITF7ByVTLjf+hOzxfa0b3H4BzH9Xn9O9rfeSXjx71AkaKFGDrsVwBiip7BM8+04Z4OjYMZ9nFLnPY3q9ZuYdzXnejxRHNe6DM+x3G1qpdhQL+2lIovkml5/YvKMeKzu/nus7vp3e1qnn35+2CEfdw+nTaGq97uctj1V1dpQIW4slR4vg2dvnyJ9259AvB+H95t+xhXv9OFyj1u5dY6zUiILx+kqE9MXsrZZz7ebPUwrT55kgv73kWbmldSKa5cpjFPNLqdOeuXUbffPdwz5CX6tOgMwN4D+7iqf1fq9etIvX4daVaxLnXLVg5FGsfEZz7eutE7V1d/5XbaXpj9XP1UkzuYs34ptV5rz91fvsgbrQ+dq/ve8DDjF/1GtZfbcdFr7VmUtCrIGRwbn/no2/Jhrv/0KWq92Z42NRpnO8aPN2rH3A3LqPd2RzoOfYnXrnsIgL0H9nP1x12p/3ZH6r/dkaYV61KnbEIo0jhmiYnzvdemcT3o2aMd3V/4Msdxffp8S/u7GjN+XE+KFCnE0GG/HNP2Et6Opyf7FqAUUN05Vw1oDaQEMqgjcc7Vc87VBP4LfO2cq+n/WpnD8PZ4seaKmeUDPgRaOOdqABcCk/z3+75zbuCJRX/y1Dk7geWb17Fiywb2px5gyKwJtKh6aaYxCSXLM3HpHwAsTl5NuWLxxEXHhiLcgJkwcS7Xt6qHmVGz5jls376L5ORtmcY455g+fTHNm18IQOtW9ZkwYQ4AZ55ZmOrVyhMZGRH02I/HhKlLaXVVVS/fqqXZvmMvyZt3ZhtXuWJJypxVNNvyMwrlx/z/qnbXnv2E+3+tnbJsNlv+3X7Y9a1qXM7A6WMB+G3FAmIKRRNf5Ezqlq/Msk1rWbF5PftTDzB45o+0qnF5sMI+IXkp5zplK7H8n3Ws9J+3vpkzkesqX5JpTKWS5Zi07E8AlmxaTbnYQ+etf/ftBiBfRCSREZE4XHATOA51z05g+ea1rPjHO05fz/op+7k6vjw/L8l4rj6LuOhYChcoxKXn1mDAb97c1f7UA2zbk/33P5zULlOJ5f+sZ+VW7xgPnTuR6xIyH+OEuPL8vPzgMV5DuZiSGY7xHsA7xvl8EZwChxiACRPmcn2r+v7XpnPZvn33EV6bagHQ+voGTPhpTq63P91FmAXsK1SOp8g+C9jgnEsDcM6tdc5tPdpGZnaGmQ3wz3zPMrNW/uXlzWyKmf3p/7rYv7yRmSWa2XD/jPL7ZpZjvGZW08ymm9lc//hY/8x0bWCQf6a7oJn913//883sQ7Nsj3xhvBaaf/y57XXOLfbfR3cze8zMSmWYPZ9tZqlmVs7MSpjZMP/+Z5jZJQRR6aIlWJuSnH57XcomShUtnmnMvPXLuL5aQwBqn51AudiSlI4pAXi/7GPve4PpXT7invotghf4CUpKSiE+/tAbhfj4WJKSUzKN2ZryL0WKFEovpOPjY0hKyjzmVJG0aSdnxR2anY6PK0zSph3HtI8fJy/h6lv7c/9jQ+nV7ZpAhxhUpWNKsGbroef92q3JlI4pcdjlp4PTKedSRUqwdtum9Nvrtm2idJHMMc/bsJxWVS8DvILt7Jh4Shf1xvjMx/T/+4jVz37HxKUzmbFmUfCCP06lYrKcq7dtSs/noLnrlnF9de8NUh3/ubpMTBznnlmKzTtT+PjWbsx4dAAf3PIkhfJHBTX+Y1WqaHHWbcucb6kiWV6bNi6nVRUv3/RjXCTDMe7cn1XdhjNh2R/MWBv+xxj8r01nZXxtyv66k+Nrk//1KzfbS/g7niJ7CNDCX2C+bmYXZln/88ECFPgow/JngInOuTrAFcBrZnYGkAw0dc7VwpslfyvDNnWBR4FqwHnADYeJaSDwpHOuOjAPeN45NxSYCbTzz3TvBt5xztVxzlUFCgLXZdyJc24LMBJYZWZfmVm7rIW9c279wdlzoD8wzDm3CugH9PXnd2OW3NOZWSczm2lmMz/88MPDpHPscnqj5rK84391whfEFirMjEcH8OClNzJ73VJS01IBaPT2f6j3xj206P8YD1x6A5eeWyNgsZ1MLmuSQLb3TrkZc6oIQC5NG1bk+6/u5Z2Xb+Ct/lMCFVlIGNlzd7gcH5McHrpT0umUc47nrSxTlX0mfUlMwcJM/7+PeODiG5izfikH/OetNJdG/bc6cv5LbahdNoHKJc8JRtgn5HDHL6NXJ3xBbMHCzHzsEx68zDtXH0hLJTIiggvLVOSDX76jzusd+HffHp5ofHuwQj8uucm3z+QviS0YzfTO/bm/QWvmbMhyjN+5lwqvtKF22UpUztJaE65y+qtKtud7TufzY9lewt4xf/DRObfWzC4ArvR/TTCzNs65Cf4hVzjnNoM3Gw085l/eDGhpZgdvRwFnA+uBd8ysJpAKVMxwd7875/727+sr4FJgaMZ4zKwoEOOcm+xf9BnwzWHCv8LMngAKAcWABcCoLPl1NLNqQBN/7E3x2k4y8c9UdwQu8y9qAlTO8EJXxMwKO+cyTTM65z7Ea0kBcJ27BuaDSWtTNlEmJi79dumYEmzYvjnTmB17d3Hv4JfSby95dggr/tkAwIbt/wCwaWcKI+YlUufsBKb+PScgsQXaoEGTGTLU61urVrUcGzce+kPKxo1biSuRuU0iNjaa7dt3ceBAKpGREWzcmEJcXPZWinA1aNiffDPSOxbVEuLZkHyolWBj8g7iikcf137r1CzL6nUpbE3Zlf7ByFPN2pRkysYeet6XiY1jfcpm8kfky748w4zpqex0ynndtk2UyTCLW7poCdbncN66b+gr6bf/enIwK7dsyDRm256dJP49m2YV67IwacXJDfoErUtJznyuLlqC9duy59wxw7l66XPfsOKf9RTKH8XabZv4ffVCAIbN+Tnsi2xvpj5zvgdfbw7asXcX9w17Nf32ose/YuXWrMf4X6b8PZumFeqyMGnlSY35eA0aNIkh30wFoFq1cmzckPG1KYW4uJhM43N+bfLGxJeMPer2p7vT4eoix3WdbH8bxffOuceB3sD1udjMgBsz9FCf7ZxbBHQBkoAaeO0d+TPeVda7Pp54AcwsCvgfcJO/l7w/XqGfjXNunnOuL16BfWMO+zoL+Bi4xTl3sCHOBzTIkF/prAX2yTRzzV+cX6IM5YudRb6ISG6+sDGj50/NNKZoVDT5Irz3VR3qt2Dq8jns2LuLQvmjiC5QEIBC+aNoUrEOCzb+HazQj1m7dg0ZMbwbI4Z3o0nj6nw34jecc8yevYLChQtmK6DNjHr1KjJu3CwAho+YzpVXVg9F6Mel3Y21+M7/YcXGl1dkxA/zvXznr6NwdIFjKrJXrd2aPvu/YPFG9u9PJaZowZMV+kk3cu4U7qzvtbzUO6cK23bvZOP2f5ixahEV4spS/kzv96Ft7aaMnHtqz9ofdDrlPHPtYs4/swzlYuPJFxFJmxpXMmbhr5nGZDxv3V3nWqau8M5bxc8oStEo77kfFZmfK8+/iMWbVgc9h2M1Y81fnF+ibPq5+pYLmzB6wS+ZxmTM+Z4M5+qkHVtYm5JMxRJlAbiyQm0WbVwZ7BSOyR/r/uL84qXTj/FN1a9kzKKsx/iMQ8e49rVMXTE3wzE+A/CO8RXnX8SSMD7G7do1YsR3zzLiu2dp0rgm342Y7n9t+pvChaMO89p0AePGef3ow7+bxpWNvdemK6+sftTtT3cRFrivUDnmmWwzqwVsdM6t97dSVAfm5mLTccBDZvaQc86Z2YXOuVlAUWCtcy7NzO4CMn4Cra7/yiWr8FpJsvVXOOe2mdlWM7vMOTcFuAM4OKu9A6/PGg4V1JvNLBq4ieyz4tFAbefcJP+imv77zjgmH17LzJPOuSUZVo0HOgOv+cfVdM7NzsXjEhCpaak88m1fxnR6HZ/Px2e/j2Fh0krubdAKgP7TRlCpZDkG3PYMaWlpLEpaSaevXwagZHQs33ToDUCkL4LBf/7I+L9+D1boJ6Rhw6pMTlxA0+bPUzAqP71735G+7t5O7/Lii+0oGRfD44+2psujH/PmW6NISChDm5suBmDTpm3c2OYVdu7cg89nfDbwZ8aOfo7o6PAsPBs2OJfEactpdvOHREVF0jtDT3WnR7+h51NXUbJEYQZ+M5OPB/3G5i3/0vLOT2jY4FxefPpqxk9azIjv5xMZGUGBApH07dEqrFtnvuzQg0YVa1E8OoY1vUfy/Oj+6S/GH0wZztj5v3JN1YtZ1mMou/bt4e6BLwLe70PnwX0Y91A/Inw+Bvw6moUbwnuG86C8lHNqWipdRvZjVIfXiPD5+Gzm9yxKXknHei0B+Oi3kVSKO5uPbu5GaloafyWv5H7/jGd84TPpf/PTRJgPn/kYNu9nvv9rWijTyZXUtFQeHvYGY+57gwifj09/G8PCjSvodLF3rv7w1xEklCzHgHbPknrwXD345fTtHxnWl4F3PE/+iEj+/mc9Hb966XB3FRZS09LoOvItRt79KhHmY+Af/mNc1/vsz0e/j+KCEuX4qM3TpDrvGD8w7DXAf4xvegqf+fD5fHw7bxLfL54ewmxyz3ttmk/TZs/5X5vuSl93b6e3ebHnHZQsGcPjj7WmS9ePeLPfSBISytLmpkuOur2cOiynntYcB5pF4s04twN6AQX8q34H/uOc22NmK/GK1EztIs6568ysIPAmcDHerPZK//IKwDBgF/Az8JBzLtq/7X+BTXg92Yn++0nz77u9/746+1tN3sdrA/kbuNs5t9XMbsSbad8NNMDrC28LrATWAKucc93N7FNgNN4bga/x+r93A/8CDzvnZppZd2AnMMM/7q8MD881wD7gXSAB781LonPu/qM8rC5/18uOMuT0sO8N/4xa2oQjDzyd+BrjNg8IdRRBY8U7eN8fqB/iSILHvTc9z+ULUPCpRqENJEh2vzwJgHxdLj3ywNPI/r5TKdTtilCHETS7ev/s/eB+Dm0gwWRXhO/MSgafLfpPwD5VclfC/0KS87HMZFcBljvnfgB+yGmAc658ltuTOHQJvN3AfTlssxRvNvygpzP8vMs5d8th7utT4FP/z7OBbK90zrlheAX8Qc/6v7KOa5/hZo6XW3DOdc9w83Af584xVhERERHJPV8Y/5U1t3LVk21m9wNfkUOBKiIiIiJyKjOzq8xssZktM7OnjjCujv/yzTcdbZ+5msl2zr2P144RNBlnwUVERERETgYzi8Br+W0KrAVmmNlI59zCHMa9gtc2fFTH/MFHEREREZGTKchXBakLLMtw2ejBQCtgYZZxD+G1IdfJzU6P6xJ+IiIiIiKnidJ4F8Q4aK1/WTozKw205hg6O1Rki4iIiMhpK+N/2/Z/dco6JIfNsl7d5E28yzen5vZ+1S4iIiIiImElkFcXyfLftnOyFiib4XYZvP9InlFtYLD/f0sUB64xswPOue8Ot1MV2SIiIiKSl80AKvj/AeI6vP+pclvGAc65cw7+fPD/qxypwAYV2SIiIiISZoJ5nWzn3AEz64x31ZAIYIBzboH/EtYHr7J3zFRki4iIiEie5pwbC4zNsizH4jrLPzE8LH3wUUREREQkwDSTLSIiIiJhJc/8W3UREREREck9FdkiIiIiIgGmdhERERERCSs+O/XngVVki4iIiEhYUU+2iIiIiIhkoyJbRERERCTA1C4iIiIiImFF7SIiIiIiIpKNimwRERERkQBTu4iIiIiIhBW1i4iIiIiISDYqskVEREREAkztIiIiIiISVnynwTywOedCHUNepgdfREREgumUaHYeu/LxgNVI15R/LSQ5ayY7xHwPNgh1CEGR9u4074fdI0IbSDAVbIXb9FGoowgaK9ERgELdrghxJMGzq/fPFHyqUajDCJrdL08CwB6oH9pAgsS9Nx3IO+dp8M7VEQ9dHOowgib17V+9H/Z/H9pAginf1aGOIFf0wUcREREREclGRbaIiIiISICpXUREREREworaRUREREREJBsV2SIiIiIiAaZ2EREREREJKz479eeBT/0MRERERETCjIpsEREREZEAU7uIiIiIiISV0+HqIiqyRURERCSsnA5FttpFREREREQCTEW2iIiIiEiAqV1ERERERMKK2kVERERERCQbFdkiIiIiIgGmdhERERERCSv6j48iIiIiIpKNimwRERERkQBTu4iIiIiIhBUfp/7VRVRki4iIiEhY0SX8REREREQkGxXZIiIiIiIBpnYREREREQkruoSfiIiIiIhko5ns00jzyvV586ZHiPBF8PEvI3nlx88zrY8pWJiPb3+G80qUZs/+fdzzRS8WbPgbgP9rdDMdL2mJmfHRLyPp9/PXoUjhmDnn6PXqSCZP/YuoqHy83ONmqiSUyTZuzbotdH1yENu27aJyQmle7dWW/Pki+W3Gcv7T5TPKlIoFoGnjqnS+r2mw08g15xy9+k0kcdrfREVF8lK3a6hyQcls474Y9icDh/zB6nUpTBv9ILExhTKtn7doA7fcN4g3XmjBVVdcEKzwj0vTCnV47brORPgi+HTGGF5P/CrT+pioaN6/8QnOKVaKvQf2cf+3r7IwaSWli5bgozZPUzK6GGnOMWDGaP7367AQZZF7TSvWpU+LzkSYl2+fyV9mWh9TMJoPbnoyPd/7hr7KwqQVFIjMz0/39SN/ZD4ifREMnzeZF3/6NDRJHIOP73iG66pdQvKOrVTr2S7HMf1u7so1VRqwa99e2g/syaw1iwHvnNfv5i5EmI+PfhnJK+M/z3H7cHMi5+pHrmjLPZe0wDnHvPXL6fB5L/Ye2BeKNHKteUI9+t7oz3faKF7NKd923Ti3eGn2HNhHx0G90/N9+IpbuKdBC5yD+RuW0+GL8M8X/Ofql75l8pRF3mtTr9uoUrlstnFr1v5D18c/8782leHVl28nf75Ifpo4j35vj8XnMyIiIuj2VGtq1zo3BJnIichTM9lmVsrMhh5lTIyZ/edYtgkHPvPxzs2Pcs27XanS81ba1m5KQnz5TGO6XXUXc9YtoWbvO7hrYA/ebNMFgCpnnUvHS1pS79V7qNn7Tq6tegnnl8heqIajxKl/sXL1ZsaPfIKez91I917DcxzX582xtL/9MsaPepIiRQoydPiM9HW1LyzPiCFdGDGkS1gX2ACJ01ewas1Wxg3uSI/Hm/NCnx9zHFerWmkGvHkzpeKLZFuXmppGn/cSubRu+ZMc7YnzmY++LR/m+k+fotab7WlTozGV4splGvN4o3bM3bCMem93pOPQl3jtuocASE1L5emx71HrzfY0eu8/3Fe/VbZtw43PfLzZ6mFaffIkF/a9izY1r8wW8xONbmfO+mXU7XcP9wx5iT4tOgOw98A+rurflXr9OlKvX0eaVaxL3bKVQ5HGMfl02hiuervLYddfXaUBFeLKUuH5NnT68iXeu/UJwHus3m37GFe/04XKPW7l1jrNsp3zwtGJnKtLFS3BQ43aUOeVDlTvdTsRvgja1m4Sgixyz2c+3m7zGNe+9yhVe91G24uaZMv36WZ3MnvdUi58+U7af96Tvjc+AkCposV5qGEb6r7WgRov3U6E+Wh7UXjne1DilEWsXL2J8WOfoWf3W+je85scx/XpO4r2dzRi/NhnKVKkEEOHTQegQf2KjPz2CUYMe4LePW/l2ecHBzP8sOAzC9hXyHII2T0HmZlFOufWO+duOsrQGCC9yM7lNiFXt3xllm1ay4p/1rM/9QBf//ETrapfnmlMQnx5JiyeCcDipFWULxZPXOFYEuLL89uKBezev5fUtFQSl86idY2GoUjjmE2YtJDrr6uFmVGzejm279hN8qbtmcY455g+YxnNm1QDoHWL2kz4eUEowj1hE6YspdVVVbx8q5Zi+849JG/emW1c5YolKXNW0Rz38cWwP2nWsALFYgvluD6c1C5TieX/rGfl1g3sTz3A0LkTuS7hkkxjEuLK8/PyPwFYsmkN5WJKEhcdy8YdW5i9fikAO/ftZnHyakoVKR70HI5FnbKVWP7POlZu8fL9Zs5ErqucOd9KJcsxadnBfFdTLjaeuGjvLzH/7tsNQL6ISCIjInG44CZwHKYsm82Wf7cfdn2rGpczcPpYAH5bsYCYQtHEFznz0Dlvs3fOGzzzR1rVuPyw+wkXJ3KuBoiMiKBgvgJE+CIolC+K9Smbg57DsahbrjLLN2fOt2W1yzKNqXzWOUzMlO9Zh/L1Zcg3fxTrt4V3vgdN+Hke17es452ra5T3vzZtyzTGOcf035bSvFkNAFq3qsOEifMAOKNQAcxfHO7evfc0uGJ03nRKFtlmdoaZjTGzOWY238xuMbM6Zvarf9nvZlbYzNqb2TdmNgoYb2blzWy+fx/tzWyEmf1gZovN7Hn/7l8GzjOz2Wb2Wg7bfOvfZqmZvZohpnvMbImZTTKz/mb2TjAfk9IxJVi7NTn99tqUZErHlMg0Zu66ZdxQoxEAdcpVplyxeMrExDF//XIuO78mxc4oQsF8Bbi6SgPKxmZvQQhHScnbiI+PSb8dXzKGpOTMJ7KtKbsoUrggkZER/jFFM42ZPXc1LW/uS8cHP2bpso1Bift4JW3eyVlxhdNvx8cVJimHIvuw22/awY+JS2l7fc2TEF3glSpanHXbDj2v123blK1QnrdxOa2qeEVK7TKVODsmntJFMj/3z44pSY1S5zNjzaKTH/QJKFWkBGu3bUq/vW7bpmy5zNuwnFZVvSIlPd+i3hif+Zj+fx+x+tnvmLh0ZtjnmxulY0qwJuO5bat3bjvc8nB3Iufq9ds28fpPX7LqxeGs7z2KbXt28uNfvwcz/GPmHaek9NvrUjZly3fOuqW0Ts83gXLFSvrz3czrE75iZY/hrHtxJNt2h3++ByUlbSM+Pjb9dnzJGJKSsr42/ZvltSnz69ePP83lqha9ue8//end89bgBC4BdUoW2cBVwHrnXA3nXFXgB+Br4GHnXA2gCbDbP7YBcJdz7soc9lMXaAfUBNqYWW3gKWC5c66mc+7xHLapCdwCVANuMbOyZlYKeA6oDzQFKgUmzdyzHN7nOpd5Fuvl8QOJKVSYP5/+jM6NbmLW2iUcSEvlr6RVvPrjF4zv/Bbfd+7L3HXLOJCWGqzQT4jLYaLOsv5pKIdBB8dUSSjNxO+fZuSQLtzR9mIe7PLZyQgzcHLK9xg2791vIo/d35CIiFPjVz/H53WWB6HP5C+JLRjN9M79ub9Ba+ZsWJrp+XtG/ii+ateDJ8a8y469u056zCcip79qZst30pfEFCzM9P/7iAcuvoE56w/lm+bSqP9WR85/qQ21yyZQueQ5wQj7pDrccyDb7zk5nw/CzYmcq2MKFqZl9cs49783UrpbC87IH0W7Os2DFfpxyen8lDXfV378nNhChfnjyU/pfHkbZq1dminf87rfRJlnW3JGgYK0qx3e+R6Uu9emI49p2qQ6P4zqxrtv3UO/d74PcITh73RoFzlVP/g4D+hjZq8Ao4EUYINzbgaAc247pD9Zf3TObTnMfn50zv3jH/stcCnw3VHue4Jzbpt/m4VAOaA4MPng/ZjZN0DFnDY2s05AJ4APPvggF6nmztqUZMrExqXfPjgLkNGOPbu454te6bf/7vEtK/5ZD8CAaaMYMG0UAL1a3p9ppiXcDBr8K0O+/Q2AalXKsnFjSvq6jUkpxJXI3IccG3sG23fs5sCBVCIjI9iYtC19THR0VPq4hpcl8ELv79iy9V+KxZ5x8hPJpUHD/uSbUXMBqJZwFhuSd6Sv25i8g7ji0bne1/zFSXTt7h3nlG27SZy2gsgIH00urxDYoANk3bZNlC566HldumgJNmz/J9OYHXt3cd+w9D8qsejxr1i5dQPg/an5y9t6MHj2T4xYMCU4QZ+Adds2UabooVm+0kVLsH57lt/jvbu4b+gr6bf/enIwK7dsyDRm256dJP49m2YV67IwacXJDfokW5uSTNmM57bYONanbCZ/RL7syzP8FSBcnci5unlCfVb+s4HNO1MAGD57MhefW41BM8YFJfbjsTZlU6a/jJaOKZFzvoMO5bu8+zAv30r1WPnP+kP5zplEg3OrMWhmeOY76KspDBk6DYBqVc9m48at6es2JqUQF3e016bsr18AdWqfx+o1m9mydSfFYnN/vpfQOzWms7Jwzi0BLsIrtl8CWpPje0IA/j3Sro5yOyd7M/ycivdGJddvk5xzHzrnajvnanfq1Cm3mx3VjFWLqBBXlvJnnkW+iEhuuagJI+dlLiqKFowmX4T3vqrjxS1JXDabHXu8mb0S/p7OsrElaV2jEV/NzPkDdeGgXduL0z+o2OSKKnw3+k+cc8yeu4rC0QWznaTMjHq1z2PcT16v2/BRM7mykfeBsE2bd6TPqsydt5o057JdiSPU2t1Yi+8+bc93n7an8WXnM+KHBV6+89dTOLrAMRXZE77pxMSh9zFx6H00a1SR/z7aJGwLbIA/1v3F+cVLUy42nnwRkdxU/UrGLPo105iiUWekP6/vrn0tU1fMTZ+xfu+GJ1i8aRVv/5Lzh47Czcy1izn/zDLp+bapcSVjFmbN99Dv8d11rmXqijns2LuL4mcUpWiU91yIiszPledfxOJNq4OeQ6CNnDuFO+tfA0C9c6qwbfdONm7/J9s5r23tpoycG/5vpE7kXL1660bqnVOFgvkKAHDlBbVZtHFlsFM4JjNWL+L8EmUy5Ttq3tRMY7LmO2X5wXyTqFc+Q74Vwzvfdrdexohh3ocVm1xZje9GzvDO1XNW+l+bMn9OxsyoV/d8xo2fA8DwETO48krvs0OrVm9Kf21asHAN+/enEhsTPpM/weAzX8C+QuWUnMn2t2dscc59YWY78WaGS5lZHefcDDMrzKF2kSNpambF/GOvBzoAO4DCR9ooB78Dfc0s1r/9jXhvAIImNS2Vh4a8zg8PvkmEz8cn00azcMMK7ru0NQAfTB1OQnx5Prvzv6SmpbFw4wo6ftE7ffuh9/bmzDOKsj/1AJ2H9CFl947D3VVYaXhZJSZP/YumLV6hYFR+er/QJn3dvQ9+zIvP30TJuKI8/sg1dHnyS958dxwJF5SiTeu6AIz7aS5fDZlORKSPqAL5eOPl23L8M3S4aNjgXBKn/U2zW/oTFZWP3t2uTl/X6bGh9HzqKkoWj2bgN3/w8Ze/s3nLv7S861MaNjiXF5+6KoSRH5/UtDS6jnyLkXe/SoT5GPjH9yxKXknHui0A+Oj3UVxQohwftXmaVJfGX8kreWDYawA0KFeVdrWaMW/DcqZ37g/A8+M/YtyS30KWz9GkpqXSZWQ/RnV4jQifj89m+vOt1xKAj34bSaW4s/no5m6kpnn53u+fxY8vfCb9b36aCP+LyrB5P/P9X9NCmU6ufNmhB40q1qJ4dAxreo/k+dH90wuuD6YMZ+z8X7mm6sUs6zGUXfv2cPfAFwHvseo8uA/jHupHhM/HgF+9c164O5Fz9e8rFzJs1s/88dRnHEg7wKy1S/jwlxGhTOeoUtNS+b9v3uD7//QlwiL4ZPpoFm5cwX2XXA/AB798R0LJ8nx6x3OkujQWbVxBx0EvAfD7qoUMm/0zM5/8lAOpqcxeu4T+v4Z3vgc1vLwyk6csounVL1KwYP5MPdX3PvABL77Q1ntt6tKCLo8P5M23x5KQUJo2N9QHYNyPcxgxciaRkT6iovLRt89dYf3aJDmzrL1RpwIzaw68BqQB+4EH8GaT3wYK4hXNTYCbgNrOuc7+7coDo51zVc2sPXANcAZwPvClc+4F/7gvgerA98C7WbbJuL/RQB/n3CR/G8hjwHpgEd6bgGeOkorzPdjgxB+QU0Dau/4X+92nxgkyIAq2wm36KNRRBI2V6AhAoW5XhDiS4NnV+2cKPtUo1GEEze6XJwFgD9QPbSBB4t7zLqeWV87T4J2rIx66ONRhBE3q2/6/FO3PQz3P+a4+Jar12Zt7B6xArVm8W0hyPiVnsp1z44CcmrKynvk/9X8d3G4lUDXD+uSDBXOW/d+WZVFV//Ks+7suw5gvnXMfmlkkMBwYf+QsRERERCQnofzAYqCckj3ZYaq7mc0G5gMrOPoHKEVERETkNHVKzmQHQtZZ6QDs77FA7UtERERETm15tsgWERERkfDkOw3+z6XaRUREREREAkxFtoiIiIhIgKldRERERETCyulwdREV2SIiIiISVkL5nxoD5dTPQERERETkBJjZVWa22MyWmdlTOaxvZ2Zz/V+/mlmNo+1TRbaIiIiI5FlmFoH3H76vBioDt5pZ5SzDVgANnXPVgZ7Ah0fbr9pFRERERCSsBLknuy6wzDn3N4CZDQZaAQsPDnDO/Zph/HSgzNF2qplsERERETltmVknM5uZ4atTliGlgTUZbq/1Lzuce4Dvj3a/mskWERERkdOWc+5DjtzekdO0uctxoNkVeEX2pUe7XxXZIiIiIhJWLLhXF1kLlM1wuwywPusgM6sOfARc7Zz752g7VbuIiIiIiORlM4AKZnaOmeUH2gIjMw4ws7OBb4E7nHNLcrNTzWSLiIiISJ7lnDtgZp2BcUAEMMA5t8DM7vevfx/4L3Am8D/zPpR5wDlX+0j7VZEtIiIiImHFF+RmC+fcWGBslmXvZ/i5I9DxWPapdhERERERkQDTTLaIiIiIhJUgf/DxpDj1MxARERERCTMqskVEREREAkztIiIiIiISVnxqFxERERERkaxUZIuIiIiIBJjaRUREREQkrNhpMA9szrlQx5CX6cEXERGRYLJQB5Aba3a+H7AaqWz0/SHJ+dR/myAiIiIiEmbULhJivgcbhDqEoEh7dxoALuWLEEcSPBZzOy75w1CHETQW1wmA/F0vC3EkwbPvjSnk63JpqMMImv19pwJ577xlD9QPcSTB496bTsRDF4c6jKBJfftX/w8/hjaQYIpoGuoI8gwV2SIiIiISVk6HnuxTPwMRERERkTCjIltEREREJMDULiIiIiIiYUX/8VFERERERLJRkS0iIiIiEmBqFxERERGRsGKnQbuIimwRERERCSu+06DZ4tTPQEREREQkzKjIFhEREREJMLWLiIiIiEhYOR16sk/9DEREREREwoyKbBERERGRAFO7iIiIiIiEFf3HRxERERERyUZFtoiIiIhIgKldRERERETCihER6hBOmGayRUREREQCTDPZIiIiIhJW9MFHERERERHJRkW2iIiIiEiAqV1ERERERMKKnQbzwKd+BiIiIiIiYUZFtoiIiIhIgKld5DTSvHJ93rzpESJ8EXz8y0he+fHzTOtjChbm49uf4bwSpdmzfx/3fNGLBRv+BuD/Gt1Mx0taYmZ89MtI+v38dShSOGbOOXq9MY7EX5cRFZWPl55rSZVKZ2Ub98U3Mxg4+DdWr93KtHGPEhtT6Ji2DxfOOXr1+5nE6SuIKhDJS92uosoFJbON+2LYLAZ+8yer16UwbdQD6fkeNG/RRm65/0ve6H4dV11RMVjhH5dmleryxvUP4/P5+GT6aF6bOCjT+piC0fRv+zTnnlmaPQf20mnwyyzYuAKAJc8OYefeXaSmpXEgLZUGfe8NRQrHpFmlerzR+mEizMeA30bz2oQvMq2PKViY/m2f5rzipdizfx/3Dn4pPd+iUdF80PZJqsSfi8PR6auXmL5qQSjSyLUTOW89ckVb7rmkBc455q1fTofPe7H3wL5QpJFrH9/xDNdVu4TkHVup1rNdjmP63dyVa6o0YNe+vbQf2JNZaxYD3mPV7+YuRJiPj34ZySvjP89x+3DTPKEefW/0H+Npo3g1p2PcrhvnFi/NngP76DioNws2/E3FuLP56u4e6ePOPbM0z4/tz1uThgQ7hWPmnKNX76FMTlxAVMH8vNz7DqpULptt3Jq1m+n66Cds27aLypXL8urLd5I/fyQjR82g/8c/AnBGoQJ0/+8tVKpUJthphJSuLnKKMbPyZjb/OLb79WTEE0g+8/HOzY9yzbtdqdLzVtrWbkpCfPlMY7pddRdz1i2hZu87uGtgD95s0wWAKmedS8dLWlLv1Xuo2ftOrq16CeeXODV+mRN/XcaqNVsYN/RBejx1LS+8OjbHcbWql2HA27dT6qyix7V9uEicvoJVa7cy7qsO9HiiKS+8/lOO42pVK8WAvjdRKr5ItnWpqWn0eT+RS+uWP8nRnjif+eh3Q1dafPgYNV65g1tqNSGhZPlMY55scidz1i3loj7t6fBlL16//uFM65v+72HqvN7hlCiwfebjrRu9fKu/cjttL8ye71NN7mDO+qXUeq09d3/5Im+0PpRv3xseZvyi36j2cjsueq09i5JWBTmDY3Mi561SRUvwUKM21HmlA9V73U6EL4K2tZuEIItj8+m0MVz1dpfDrr+6SgMqxJWlwvNt6PTlS7x36xOA91i92/Yxrn6nC5V73MqtdZple6zCkc98vN3mMa5971Gq9rqNthc1yRb3083uZPa6pVz48p20/7wnfW98BIAlyau56JX2XPRKe+q82oFd+/fw3ZzE4CdxHBITF7Jy1SbG//A8PV+4le4vDM5xXJ/XR9D+risY/8PzFClSkKHfTgOgTJkz+eKzRxj1XTceuP8qnnv+q2CGLwFyShXZ5glazGYWAeCcuzhY93m86pavzLJNa1nxz3r2px7g6z9+olX1yzONSYgvz4TFMwFYnLSK8sXiiSscS0J8eX5bsYDd+/eSmpZK4tJZtK7RMBRpHLMJiUtodXV1zIya1cqwfccekjfvyDau8gVnUaZUzHFvHy4mTF1Oq6sqe/FWKcX2nXtJ3rwz27jKFUtSJssbioO+GDaLZg0rUCzL7HY4qnN2Ass3r2PFlg3sTz3AkFkTaFH10kxjEkqWZ+LSPwBYnLyacsXiiYuODUW4J6zu2Qks35zh93jWT9nzjS/Pz0sy5nsWcdGxFC5QiEvPrcGA30YDsD/1ANv2ZH9uhJMTOW8BREZEUDBfASJ8ERTKF8X6lM1Bz+FYTVk2my3/bj/s+lY1LmfgdO/N/m8rFhBTKJr4Imceeqw2e4/V4Jk/0qrG5YfdT7ioW65y5uf0Hz/RstplmcZUPuscJmY6xmelH+ODGl9Qm+Wb17F668agxX4iJkycy/Wt6nrn6hrnsH3HbpI3bcs0xjnH9N+W0LzZhQC0vr4eEybMAaDWhedStKh3jq5Z4xw2JqUENX4JjLAvsv2zz4vM7H/An8BzZjbDzOaa2QtZxvQ3swVmNt7MCvrXXWRmc8xsGvBghv1GmNlrGfZ1n395IzP72cy+BOb5l+3MsG6SmQ01s7/MbJCZmX/dNf5lU83sLTMbHczHqXRMCdZuTU6/vTYlmdIxJTKNmbtuGTfUaARAnXKVKVcsnjIxccxfv5zLzq9JsTOKUDBfAa6u0oCysdlbEMJR0qYdnFXy0GxtfFwRkjblvkg+0e2DLWnTTs6KK5x+O75EYf6/vfsOk6LK+jj+PTMDAhJVggKKrqKAGEHAhCJBfVXErJgDZteA64oZEJXVVWTVRQwYUBeQbABFBQwoqAQBMyAIDKAMQYIynPePqhlmhiF3d81U/z48/XRX6OGc7urq07du3coupsje9PNX8N64Hzmv/cHJCC/halepzrycDdv1rzmL2aPKboXWmTb/R05vHPwobLJnA/aqVjN/23d33r7630y45TmuaH5q6gLfTntULZLvssXUrrLx5/j0sBBtGuZbp2oN9tl1D5aszOH587sw8bYX6HPuHVQoWy6l8W+rHdlvzV+2mMfef4053Ycwv8cIlq1ZyXvffpHK8JOidtXqzC34miwNXpNNzS/pgriz86d/zVm8UdxTfv2BDvnvcQP22iXYpgs697DWvPHle0mPN1GyF+VQq9aGHwq1alYlu0ihvDTnDypXKk9WVma4TjWyswsX4gCD3vyUY49pmNR4SyKzjITdolLii+zQ/sDLwB1AbeAI4BDgcDPL+ym/H/CUuzcCcoAzw/kvAje5e4sif/MKYJm7NwWaAleZ2d7hsiOAu9y9uK36UOBmoCGwD3CUmZUD+gAnufvRwCb3fGbWycwmmdmkZ599divT3zLDNprn7oWmHx79MlUrVOKrO1/ihuPO4ut537NufS7fZs+h53uvMvqGJ3nnhseZ+uuPrFufm7DYkqpIjlD8a5G056dacfHa1sfb48mP6HztMWRmlo6PfnGpFX0Jeo55lWoVKjHxthe4/ugzmfzrD+SG2+9xva+j2b+v4NS+nbn26DM4ep+S/eOi2M8xhRPuOeZVqpWvxKTOL3L9MUG+69bnkpWZyaF16tPnk6E0fexy/vhzDf844cJUhb5ddmS/VbV8JU476Bj2ufdManc5lZ3LlqNj03apCj1pNrUNFPc5L2Z3UOIUt3cq+h4/8t4rVKtQiS/v6McNx57N1/N+KPQdVCYzi1MbH82grz9IcrSJU9x7s9F7WOz+vPD0hM+/Z9Dgz+h8W/sERlc6ZCTwX1RKy4mPc9x9gpk9CrQFvg7nVyQorn8BZrn75HD+l0A9M6sCVHX3seH8V4CTwsdtgYPM7Kxwukr4t/4EvnD3WZuI5Qt3nwdgZpOBesBK4OcCz3kd6FTck939WSCvuvZrrn9xy9lvhXk5i6hTbcMv/6Clp/Ch0xVrVnHFqw/mT//cdTCzfpsPwAufjeCFz0YA8OBp1xRqXSpp+g+cyMBhwSbQuOEeLMjecOh14aLl1Khecav/Vs0alXfo+anQf/DXDBwxDYDGB9RiwaINLe0LF6+gxq47b/Xf+ua7hdx6/1sA5CxbzbgJP5OVabQ+dr/EBp0g83IWF2rRql21OguWF9mu167iqjceyp/+/u4BzPptAQALlv8GwOKVOQybNo6mezbg45+npCDy7fNrzqLC+VapvvHneO0qriyQ7w/3DGTWb/OpULYc85Yt5otfZgDw5pQPS3yRvSP7rXYNmjP7twUsWZkDwJDJYzlyn8b0nzgqJbEny7ycRdQt+JpUq8H8nCWUzSyz8fxli6MIcZvMy1lc6Mho7arFbNNrVnFF/w3v8U/3v5n/3QRwUsMWfD33exatWJr8gHdA/9fGMmBgcApX48Z7sXDhhngXZudQo0bhLnzVqlVk+YrVrFuXS1ZWJguzlxZa59vvfuXue1+jb59rqVa1ZH0vydYpHc1Z8Ed4b8BD7n5IeNvX3Z8Pl60tsH4uwQ8IAzb1W9+AGwv8rb3dfXSR/684m/p/IjVxzkz2q1GXervuTpnMLM49vDXDp40vtE6V8hUpkxn8rrryyNMY9+NkVqxZBUD1sA9r3Wo16XDwcbw+qeQelut4dlOGvtqJoa924oRj92fYO1NxdyZPm0eliuWosVulLf+RUKtj6u/Q81Oh4xmHMvTFixn64sWccMy+DHt3RhDv9PlUqrgTNXbb+p3vmAFX8cHA4Na2ZX3uvbV1iS2wASbN/ZZ9q9eh3i7Bdn3OoScw8puPC61TpdyG7fry5qfy8U9TWLF2FRXKlqPiTuUBqFC2HK3rN2X6wp9TnsO2mDj3W/atXjc/33MPbc3I6Z8UWqdgvlcUyDd7xe/My1lE/erBCAat9mvCzIWzU53CNtmR/dYvSxfSbO9GlC+zEwCt9i/5+W6N4VPHc3HzkwFotncjlq1eycLlv230Wp3XpA3Dp47fwl+L3sRfZgaf4QLv8YhpRT7DRd7j8T9t+G4COO/wNqWiq0jHC1oybMidDBtyJ61POIihw74I9tVTZlGpUnlqVC9cZJsZzY6oz6jRQaPRkKGf06rVQQDMn/87N97Ul54PX8ze9UpH903ZWGlpyc4zCuhmZv3dfaWZ1Qb+2tTK7p5jZsvM7Gh3/xgoOF7SKOBaM/vA3f8ys/rAr9sZ17fAPmZWz91nA+du59/Zbrnrc7lxwGO8e/0TZGZk8OJnI5mxYBZXH90BgD4fD6FBrXq8dPG95K5fz4yFs7jy1R75zx90VQ923bkKf+Wu44YBj5KzuuT2Sy6o5VH7Mu7TH2l75lOUK5dFj3tOy1/W6ebX6XbXKdSsXomX//cFz7/yKUt+X8lpHfvQ8sh96X7XqZt9fknUssXejJvwM23Pe55y5crQ484Nh8c73T6Ybne0peZuFXl50Fc8/9pElvz+B6dd+jItm+9N93+WvkPpuetzuXnw47zV6TEyMjJ46Yu3mJE9m6taBIdO+342jANq7sULF9zF+vXrmZk9m07/exiAmhWrMfDyYBvPysjkja/eY3QJ77Obuz6Xv7/5b966+t9kZmTQ7/O3mLFwFp2ODPJ99tNhNKi5Fy90vJvcvHzfeDj/+Te/+TgvX3QfZTOz+Pm3+Vz5+kOb+q9KhB3Zb30xewZvfv0hX/7zJdatX8fX877n2U+GRZnOVnnt8q4cV/8wdqtYlbk9hnPfyL75BWaf8UN4+5tPOfnAI/mx6yBW/bmGy17uDgSv1Q1vPMqoG3uRmZHBC58Gr1VJl7s+l5sG/pt3rnucTMvkxQkjmbFwFlcfdToAfT4ZSoOa9eh30T3k+npmLpzFlf03bLfly+xE6wOacs0bj0SUwfZpeWwjxo6bTpsTH6B8uTL0eHDDUaWrrn6a7t0uoGaNqtx+W3tu6fwiT/QaSYMGdTn7zKBn61PPvEPOsj94oGswnG5mVgaDB94RSS5RibIvdaJY0b5RJY2Z1QNGuvuB4fTfgSvDxSuBCwlalAuu0xmo6O73m9nhwAvAKoLC+ix3PzAcpaQ7cCpBS/Ri4HSCPted3f2UAjGsdPeKZnZcwWVm9h9gkrv3M7NTgX8BS4AvgJruXvwgqBt4xvVFu4rH0/qngmGJPOfVLawZH1b1QnxR4vrdl3RWI+ghVfbWY7awZnz8+e/xlLnl6C2vGBN/PR60QKbbfsuubR5xJKnjz0wg88YSP6BWwuT2DkfozS35LeUJk9km8qPvW2PVuhEJK1ArZJ0aSc4lviU7bBk+sMB0L6BXMasWXOfRAo+/BAqe7XR/OH890CW8FfRReCsYQ8XwvtAyd7+hwGofuvsB4WgjTwGTNpuYiIiIiMRWiS+yS5GrzOwSoCzBiZl9Io5HREREpFSKwxUfVWQniLs/DjwedRwiIiIiEr3S/zNBRERERKSEUUu2iIiIiJQoFoN24NKfgYiIiIhICaOWbBEREREpUeJw4mPpz0BEREREpIRRkS0iIiIikmDqLiIiIiIiJYpOfBQRERERkY2oyBYRERERSTB1FxERERGREkWji4iIiIiIlHJmdqKZfWdmP5rZP4tZbmb2ZLh8qpkdtqW/qSJbRERERNKWmWUCTwEnAQ2B882sYZHVTgL2C2+dgGe29HfVXUREREREShRLbXeRI4Af3f3n4P+2N4D2wIwC67QHXnZ3ByaYWVUz293dF2zqj6olW0RERERKFPPE3bZCbWBugel54bxtXacQFdkiIiIiEltm1snMJhW4dSq6SjFPK1qeb806hai7iIiIiIjElrs/Czy7mVXmAXULTNcB5m/HOoWoJVtEREREShZfn7jblk0E9jOzvc2sLHAeMLzIOsOBi8NRRpoDyzbXHxvUki0iIiIiaczd15nZDcAoIBN4wd2nm9k14fL/Am8DJwM/AquAy7b0d1Vki4iIiEhac/e3CQrpgvP+W+CxA9dvy99UkS0iIiIiJcvWdfMo0dQnW0REREQkwVRki4iIiIgkmAVdTCQievFFREQklYob77nkWTcqcTVSVrtIclaf7Ihl3nhk1CGkRG7vT4MHq4dFG0gqlW+PL34u6ihSxqpfCUCFLsdHHEnqrOrxYdrlC+m330qXfCHI2a5tHnUYKePPTAge5L4XbSCplNkm6gi2jvpki4iIiIhIUSqyRUREREQSTN1FRERERKRkWa/uIiIiIiIiUoSKbBERERGRBFN3EREREREpWTS6iIiIiIiIFKWWbBEREREpWdSSLSIiIiIiRanIFhERERFJMHUXEREREZGSRd1FRERERESkKBXZIiIiIiIJpu4iIiIiIlKy6LLqIiIiIiJSlIpsEREREZEEU3cRERERESlZYjC6iIpsERERESlZYlBkq7uIiIiIiEiCqcgWEREREUkwdRcRERERkZJF3UVERERERKQoFdkiIiIiIgmm7iIiIiIiUqK45ybsb1nC/tK2UZEdI+0aNOPxM28mMyOT5z8bQc/3Xim0vGr5SjzfsQv77FabNev+5Mr+PZi+4GcA/n78uVzR4lTc4ZsFP3H5qw+ydt2fUaSxTdydB3sOZ+zH31KuXBke7noOjRrU2Wi9ub/+zq139GfZslU0bFCbng+eR9kyweb/+cSf6PGv4axbt55q1Srw6vPXpjqNrebuPNjrA8Z99jPlymXxUJeTabR/zY3We/XNr3h5wJf88msOn428nmpVKwAwZvwP9HruYzLMyMzMoMtNrTj84I1fr5KkzX5N+dcpN5CZkUm/iW/x2LjXCy2vWq4i/z3zH+y9yx6sXfcn1wzuyYzs2dSuUp3nzr6TmhV3Yb07L0wcydOfvhlRFltve/PdKasM713Vi7JZZcnKyGToN2PpPqZfNElsg3Tcb21vzvVr7Mnrl3XNX2+fXWtz39t9efKjAalOYZs8f9FdnNL4KBatWErjbh2LXafXObdycqMWrPpzLZe+3I2v534HQLuGzel1zi1kWgbPfTKcR0a/UuzzSxp358Eegxg7bjrlypfl4R4X0ahh3Y3WmztvCbfe9mLw3dSwLj0fvpiyZbMYPmIifZ9/D4CdK+zE/feeywEHlOx9tWws7bqLmFlXM2u9hXWOM7MjC0xfY2YXJz+67ZdhGfQ+uzP/98xtHPjgBZx3eGsa1KpXaJ07217M5F9/4NCHL+bSV7rx+Jk3A7BHld24seXZHPGvyzn4oQvJtAzOO3yzL1GJMe7jb5n9yxJGD/8H3e45k/sfHFLseo8+8TaXXngMo0fcQeXK5Rk0ZCIAy5ev5oGHhvBMr0t5a/Bt9PrXRakMf5uNmzCLOXOXMuqNK+l6ezseePS9Ytc7rHFtXnjiHPaoVbnQ/OaH78WwfpcytN+l9LjzRO5+ZFQqwt5uGZbB46f9ndP7/ZPDnriUsw8+gQNq7FVonduP68jUBT/SrPeVXDnoIf51yo0A5K7P5c63n+GwJy7luGeu4+rm7Td6bkmzI/muXfcXJz1/K817X0nz3lfSpv4RNK3bIIo0tlo67rd2JOfvF/3C4Y9cyuGPXErTnpez6q81DJ0yLvVJbKN+n73Fib1v2eTykxq1YL8addnvvrPp9NpDPHP+P4DgtXrqvM6c9J9baNj1fM5v2naj16qkGjduBrPnLGb0u/fR7YHzuf+BN4pd79HHhnHpJccz+t37gu+mwZ8BUKfOrrz60s2MGNqFa685kXvue73Y50vJlnZFtrvf6+7vb2G144D8Itvd/+vuLyc1sB10xF4N+WnJPGb9Np+/ctfxvy/f57TGxxRap+Hue/PBd5MA+C57DvV22Z0alaoBkJWRSfkyO5GZkUmFsuWYv2xJynPYHmM+msHppxyGmXHIQXuxfMVqFi1eXmgdd2fCxB9p17oxAB1ObcKYD6cDMOKdr2nT6kD22D14HXbdpWJqE9hGY8b/QPsTGwX5HrgHy1euYdGSlRut17B+TersXmWj+TtXKItZcOBs1Zq/sKiOoW2lJnUO4Kff5jN76QL+yl3HoKkfcEqDowqt06BGPT786SsAvl88l72q1qRGxWosXPE7k+f/AMDKP1fz3aJf2KPybinPYVvsSL4Af/y5BoAymVmUycgET2382yod91s7mnOeE/Zvwk9LfuWXpQtTFvv2Gv/jZH7/Y/kml7c/+FhenvA2AJ/Pmk7VChWpVXlXjqjXkB8Xz2PWkuC1emPSe7Q/+NhUhb1DxnwwldPbHxHsqw/eO/xuWlZoHXdnwuff067toQB0OL0ZY8ZMAeCwQ/ehSpXgCOQhB+/NwuyclMZfIqxfn7hbREp9kW1m9czsWzN7ycymmtkgM6tgZvea2UQz+8bMnrWwsjCzfmZ2Vvh4tpk9YGZfmdk0MzvAzOoB1wC3mNlkMzvGzO43s87hcz4ys0fM7Asz+97MjgnnVzCzAWEM/zOzz82sSapeh9pVqzN3aXb+9K85i6ldtXqhdab8+gMdDj4OgKZ7NWCvXWpSp2oN5i9bwmNjXmd21yH82n04y1av5L1vv0hV6Dske9EyatWqmj9dq2ZVshcV3pEtzVlF5UrlycrKDNepkr/O7DlLWL58NRdd8V/OOL8XQ0d8mbLYt0f2kpXsXqNS/nStGpXILqbI3pz3xn7PSRc8zzW3D+bBO09MdIgJtUeV3fh12aL86V+XLd6oUJ628CfaNwq+eJvUOYA9q9aiduXC2/6eVWty8B77MnHuzOQHvQN2NN8My2DCDX2Z02UIY378konzSna+6bjf2pGcCzr3sNa88WXxR7JKm+A12bDdz1u6iNpVq29yfmmQvSiHWrU2/DCqVbMq2UUK5aU5fxT5bqpGdnbh7y+AQW9+yrHHNExqvJIcpb7IDu0PPOvuBwHLgeuA/7h7U3c/ECgPnLKJ5y5x98OAZ4DO7j4b+C/wuLsf4u7ji3lOlrsfAdwM3BfOuw5YGsbQDTg8MaltneIaJN0LN2M98t4rVKtQiS/v6McNx57N1/N+YN36XKqWr8RpBx3D3+4/izp3n8bOO5WnY5N2qQl8B3kxLXVWtHm2mJXy1snNXc/0mb/S5z+X89zTV/L0s+8za87iZISaGMXlu41/ok3L+rzz2hX856HTebLvxwkJK1msmOy8yIvw6NjXqFa+IhNu6Ms1LTowZUGwXefZuWw5Xu/YlX+89RQr1q5Kesw7YkfzXe/raf6fq9jvkbNpUvcAGtasl4qwt1s67rd2JOc8ZTKzOLXx0Qz6+oMkR5sam9ruN9qXU/w+vyTa/u+mwtMTPv+eQYM/o/Nt7RMYXSnh6xN3i0hcTnyc6+6fhI9fBW4CZpnZP4AKwC7AdGBEMc8dHN5/CZyxlf9fwefUCx8fDfQCcPdvzGxqcU80s05AJ4A+ffps5X+3ZfNyFlO32oYT4GpXrb7RodMVa1ZxRf8H86d/uv9NZv02n3YHNGP2b/NZsjIHgCFTPqLFPo3pP6lk9tft/8anDBj8OQCNG9Vl4cKc/GULs3OoUb1wP+Rq1XZm+YrVrFuXS1ZWJguzl+WvU6tmFapVrUCF8mWpUL4sTQ7fh2+/W8Dee5Wc1pL+b37FwBHB5tS4we4sWLQif9nCRSuosdv2dXFpekhdfpm/jKU5q/JPjCxpfl22mNpVNrTg1a5SnQXLfyu0zoq1q7j6zZ750zNvf53ZSxcAQXeC1y7oyhuT32fY9OJ+L5csO5pvnmVr/mD8z5Nps98RzMiendSYd0Q67bfy7EjOeU5q2IKv537PohVLkx9wCszLWUTdahu2+zrVajA/ZwllM8tsPH9ZyW0E6f/aWAYM/BSAxo33YuHCDe/PwuwcatQo3IWvWrWKRb6blhZa59vvfuXue1+jb59rqVa1ZHdllOLFpSW76M9BB54GznL3xkBfoNwmnrs2vM9l6390FPecrWpQdPdn3b2Juzfp1KnTVv53Wzbxl5nsW70O9XbdnTKZWZx7eGtGTCvcSlmlfEXKZAbhXnnkaYz/aTIr1qzil6XZNKvXiPJldgKgVf0mzFw4O2GxJVrH845k2IBbGDbgFlof34ihI7/C3Zk8dQ6VKpbfqMg2M5o1+Ruj3p8GwJARk2h1XHDo7YTjGjLp69msW5fL6tV/MnXaL/xtnxob/Z9R6njmYQwNT1Y84Zh9Gfbu9CDfb+ZTqeJO21Rkz5m3NL/VbPp32fz1Vy5Vq5RPVug77Mtfv2Xf3WqzV7ValMnM4qyDWvHWzE8LrVOl3M752/VlTf6Pj2dNzW+xfuaMf/Dd4jn0/mRgymPfHjuS7247V6FKuZ0BKJdVluP3PZzvF/+S8hy2RTrtt/LsSM55zju8TWy6igAMnzqei5ufDECzvRuxbPVKFi7/jYlzZrJfjbr5r9V5TdowfGrJ/bHc8YKWDBtyJ8OG3EnrEw5i6LAvgn31lFlUqlSeGtULF9lmRrMj6jNq9NcADBn6Oa1aHQTA/Pm/c+NNfen58MXsXW/jEaSkdIhLS/aeZtbC3T8Dzgc+JjhxcYmZVQTOAgZtw99bAVTe4lqFfQycA3xoZg2Bxtv4/B2Suz6Xmwb+m3eue5xMy+TFCSOZsXAWVx91OgB9PhlKg5r16HfRPeT6emYunMWV/R8C4Is5M3hz8odMuqMf63JzmTzve/p+OiyV4W+3lsccwNiPv6XNqY9QvlxZejxwdv6yq65/nu73nUXNGlW4/eaTueWO13jiqVE02H8Pzu5wBAB/26cmxxxZn9POeZwMM87qcAT1960VVTpb1LLFPoz77GfantuXcuXK0KPLSfnLOnUeRLd/nkjN3Sry8sAvef61L1jy+x+cdkk/WrbYh+7/PJHRH33PsHenk5WVwU47ZfH4A6cWe0i2pMhdv55bhz/J8Mt6kmkZvPzlO8xcNJsrjzgVgOe+GMH+1ffiubPvJNfX8+2i2Vz75r8AaLHXgXQ8rC3TFvzEhBv6AnDf6OcY9f3nkeWzJTuSb61Ku9L3rH+SYRlkZGQweNpHvPPdhAiz2bJ03G/tSM4A5cvsROsDmnLNG49ElMG2e+3yrhxX/zB2q1iVuT2Gc9/Ivvk/IvqMH8Lb33zKyQceyY9dB7HqzzVc9nJ3IHitbnjjUUbd2IvMjAxe+HQkMxbMijKVrdby2EaMHTedNic+QPlyZejx4IX5y666+mm6d7uAmjWqcvtt7bml84s80WskDRrU5ewzWwDw1DPvkLPsDx7o+j8AMrMyGDzwjkhyiUwMLqtuRfuClTbhiYpvA+MICusfgIuALsB5wGxgLjDH3e83s37ASHcfZGazgSbuviQ8SfFRdz/OzOoTFOXrgRuBE4CV7v6omX1E0Hd7kpntBkxy93pmtjPwElAf+Bo4EDjP3X/YTPieeeORm1kcH7m9w9a41SX/SzBhyrfHFz8XdRQpY9WvBKBCl+MjjiR1VvX4MO3yBUi3/Va65AtBznZt86jDSBl/JvwhmhufIwNblNmm5LasFOBLXkhYgWq7XR5JznFpyV7v7tcUmXd3eCvE3S8t8LhegceTCIbuw92/Bw4q8LTxBdY7rsDjJWzok70GuNDd15jZ34AxwJztyEVERERESrm4FNklQQWCriJlCPpnX+vuJf/SYyIiIiIlTQy6i5T6Ijsccu/AEhDHCiBl42KLiIiISMkVl9FFRERERERKjFLfki0iIiIiMaPuIiIiIiIiiWU1OpWKUVA2R91FREREREQSTEW2iIiIiEiCqcgWEREREUkwFdkiIiIiIgmmIltEREREJMFUZIuIiIiIJJiKbBERERGRBFORLSIiIiKSYCqyRUREREQSTEW2iIiIiEiCqcgWEREREUkwFdkiIiIiIgmmIltEREREJMFUZIuIiIiIJJiKbBERERGRBFORLSIiIiKSYCqyRUREREQSTEW2iIiIiEiCqcgWEREREUkwc/eoY0hnevFFREQklSzqANKFWrJFRERERBIsK+oA0t76MVFHkBoZJwT360ZFG0cqZbWDtW9FHUXq7PR/wb1/GG0cqWTHp1++AH+9E20cqVLmpOA+971o40ilzDbply9g1zaPOJDU8WcmRB1C2lBLtoiIiIhIgqnIFhERERFJMBXZIiIiIiIJpiJbRERERCTBVGSLiIiIiCSYimwRERERkQRTkS0iIiIikmAqskVEREREEkxFtoiIiIhIgqnIFhERERFJMBXZIiIiIiIJpiJbRERERCTBVGSLiIiIiCSYimwRERERkQRTkS0iIiIikmAqskVEREREEkxFtoiIiIhIgqnIFhERERFJMBXZIiIiIiIJpiJbRERERCTBVGSLiIiIiCSYimwRERERkQRTkS0iIiIikmAqskVEREREEiwr6gAksdydB3sMZOy46ZQrV4aHe1xMo0Z7brTe3HlLuPW2F1iW8wcNG9al5yOXUrZsFj/9vJAuXV5h+oy53HLzqVxxeZsIsth67s6DD73J2HEzKFe+LA8/2JFGDetutN7ceb9xa+d+LFu2ioYN69DzoYsoWzaL9z+YSq/eb5NhRmZWBl3uOIMmh/8tgky2jrvz4CNDGDt+JuXKleXhbufTqGGdjdabO+83bv3HKyxbvoqGDerQs8cFlC2z4eM+9ZtfOPfCXjze82JObHtwKlPYZu7Ogw8OYOy4b4KcH7pk09v0rc+xbNkfNGy4Jz0fuYyyZbO2+vklRbrlC3mf48Hhdl2Ghx+8YNOf49tfCj7HDerQ8+ELKVsmi/c/mBZ8jjOMzMxMuvyzA00O2yeCTLZOsJ8eFOyny5fl4R4XbSLfJdx624vhfqsuPR++mLJlsxg+YiJ9n38PgJ0r7MT9957LAQdsvB8oKdIt3+cvuotTGh/FohVLadytY7Hr9DrnVk5u1IJVf67l0pe78fXc7wBo17A5vc65hUzL4LlPhvPI6FdSGbokWOxass1sZYL+zv1m1jl83NXMWifi7ybbuHHTmT1nEaPfvZ9uD3Tk/q5vFLveo48N5dKLWzF61ANUrlKBQW9+CkDVKjtz111nc8XlJ6Qy7O02bvwMZs9ZzOh37qHb/edyf9cBxa736L+HcenFxzH6nXuoXLkCgwZ/BkCLZvszfPAdDBt8Bz26XcDd972eyvC32biPZzJ7zhJGj+xCt3vP5v7ug4pd79EnRnLpRS0ZPbILlSuXZ9Dgz/OX5eau59HHR3L0kfunKuwdMm7cN8E2Paor3bp25P4HXit2vUcfHcyll5zA6FHdgvf4zU+26fklRbrlCzBu/Exm/7KY0W/fFXyOuw0sdr1HHx/BpRcdx+i37w5zngBAi+b1GT74Hwx78x/06HY+d99X/H6vpBg3LtxvvXsf3R44n/sf2NR+ehiXXnI8o9+9L/wcB/utOnV25dWXbmbE0C5ce82J3FPS91tplm+/z97ixN63bHL5SY1asF+Nuux339l0eu0hnjn/HwBkWAZPndeZk/5zCw27ns/5TdvSoFa9FEUtyRC7IjsZ3P1ed38/6ji2xpgPpnJ6+2aYGYccsjfLl69i0aJlhdZxdyZM+I527Q4FoEP75owZMwWAXXetxEGN65GVlZny2LfHmA+mcfppRwT5Hrw3y1esZtHiYvL9/AfatT0EgA7tj2DMmGkA7LzzTpgZAKtX/5n/uKQa8+E3nH5qkzDfemG+ywut4+5M+OJH2rU5CIAOpzVlzIff5C9/5bXxtGtzELvuUimlsW+vMWOmcnr75uE2vQ/Ll6/ezDZ9GAAdTm/BmPenbPXzS5J0yxdgzIfTOP20pkW26019joMjLx3aN2XMB+HnuELBz/FaSvanOG8/vTX7re9p1zbcT5/eLH8/fdih+1ClSgUADjl4bxZm56Q0/m2VbvmO/3Eyv/+xfJPL2x98LC9PeBuAz2dNp2qFitSqvCtH1GvIj4vnMWvJfP7KXccbk96j/cHHpipsSYLYFtkW+JeZfWNm08zs3HB+RTMbY2ZfhfPbF3jOXWb2nZm9D+xfYH4/MzsrfDzbzB4o8PwDwvnVzey9cH4fM5tjZrulOG2ys3OoVata/nStWtXIXpRTaJ2lOX9QuXKF/EK6Vq2qZJfwndamZC9aRq1aVfOna9WsSnZ24Z330pw/qFyp/IZ8a1Ylu0DR8d77UzjxlO5cfW0fenS7ICVxb6/sRcs3zndRcfmWK5BvlfzXJDs7h/c/mMZ5Zx+Zsph3VHZ2DrV2L7hNb7y9FrtNh9v91jy/JEm3fAGys5cV3m9t1+d4Kiee2oOrr+tLj27npybw7ZS9KKeYfHMKrbNxvtU2ek0ABr35Kcce0zCp8e6odMt3S2pXrc7cpYvyp+ctXUTtqtU3OV9Kr9gW2cAZwCHAwUBr4F9mtjuwBujg7ocBxwOPhQX54cB5wKHhc5tu5m8vCZ//DNA5nHcf8EE4fwhQbCdIM+tkZpPMbNKzzz67ozluxN2L+z+LrrTldUqJ4vPdaKXNrtOm9cG8O/Junup9Jb16v5XgCBNr6/Ld+Hl56zzYcxidbz6FzMzS89H3YhLaqvd4W55fgqRbvlBsOsXstza/TpvWB/HuiC489eQV9PrPOwmOMLG2Lt8tv48TPv+eQYM/o/Nt7TdatyRJt3y3xIo51uJ4sd/Dxb12UnrE+cTHo4HX3T0XyDazsQSF8ztADzM7FlgP1AZqAscAQ9x9FYCZDd/M3x4c3n9JUJDn/X8dANz9XTNbWtwT3f1ZIK+6dtaP2c70NujffywDBgX9MRsfuBcLF274rxcuXEqN6lUKrV+tWkWWL1/FunW5ZGVlsnBhDjVqFF6nJOv/2jgGDAr66jU+cE8WLszJX7Ywe+NcqlWryPIVqzfkm52z0WsC0LTJvvwydwm/L13JLtUqJjWHbdH/jY8ZEPY9bdyo7sb5bvT+7szyFWsK5Lss/zX5Zvpcbr0jOJFm6dI/GDt+JllZGbRu1Tg1yWyl/v0/YsDAjwFo3HgvFi4ouE3nUKNG1ULrF79NB+vUqllti8+PWrrlC9D/9fFFPscFYs7OoUaNyoXWD7brop/jwusANG3yt5L5OX5tLAMGBue+NG68VzH5bmm/tbTQOt9+9yt33/safftcS7WqJSfPPOmW77aYl7OIutVq5E/XqVaD+TlLKJtZZuP5yxZHEaIkSOlpztp2m2q76QhUBw5390OAbKBcuGxrfzOuDe9z2fBDJbK2oo4dWzJsSBeGDelC6xMOYuiwz3F3Jk+eRaVK5TfamZkZzZrVZ9SorwEYMmwCrVodFEXo26XjBccyLDxZsfUJBzF0+BdBvlNmUaliuY2KTjOj2RH7MWr0ZACGDPuCVmFROWfO4vzW4ekz5vLXX7lUq7pzSvPZko7nHc2wgZ0ZNrAzrVs1ZuiISWG+s6lUqdxGhYaZ0azpvox6byoAQ4ZPpNVxBwLwwbt388G79/DBu/fQrs3B3HfXmSWuwAbo2PE4hg29m2FD76b1CYcwdNiEcJv+Oci52G16f0aN+gqAIUM/o9UJwTbdqtVBW3x+1NItX4CO5x/DsDeDkxVbt2rM0OETN2zXFctv4nO8L6NGB/10hwybuOFz/Esp+Bxf0JJhQ+5k2JA7w/10gf1WpU3lW59Ro8P99NDP8/fT8+f/zo039aXnwxezd72aKc9la6Rbvtti+NTxXNz8ZACa7d2IZatXsnD5b0ycM5P9atSl3q67UyYzi/OatGH41PERRys7Is4t2eOAq83sJWAX4FjgduBcYJG7/2VmxwN7FVi/n5k9TPC6nAr02Yb/72PgHOARM2sLVNvC+knRsuWBjB03nTbt7qN8ubL06HFR/rKrOj1F9+4dqVmjKrff1oFbbnueJ54cQYMGdTj7rKCP7uLFyzjz7EdYuXINGRnGSy9/yNsj76FixfJRpLNFLY9tGOR7Utcg3+4bhku66pr/0r3r+dSsUYXbbz2NWzr344kn3wryPbM5AKPem8yw4RPJysqkXLkyPP7opSW660zLYxowdvxM2vxfD8qXK1Oo7+lV1z1L9/vPDfK95RRu+cfLPPGft2lwQB3OPqNZhFHvmGCb/oY2be8Jt+lL8pdd1ak33btdRM2aVbm9cwduufU5nug1nAYN6nL2WUdt8fklUbrlC+HnePxM2pzUnfLlyxberq/tQ/cHzgu361O55faXeaL32zRoUJuzz8j7HE9h2PBJZGVlhJ/jS0r25/jYRsF+68QHgs/xgxfmL7vq6qfp3u2CcD/dnls6v8gTvUYG7/GZLQB46pl3yFn2Bw90/R8AmVkZDB54RyS5bI10y/e1y7tyXP3D2K1iVeb2GM59I/tSJjMot/qMH8Lb33zKyQceyY9dB7HqzzVc9nJ3AHLX53LDG48y6sZeZGZk8MKnI5mxYFaUqcgOsuL6eJZmZrbS3StasIftCZxE0ELd3d3/F56MOAIoA0wGjgJOcvfZZnYXcDEwB5gHzHD3R82sHzDS3QeZ2WygibsvMbMmwKPufpyZ1QBeJyiuxxIU83u7+1o2LSHdRUqFjHBIwHWjoo0jlbLawdqS3cc7oXb6v+DeP4w2jlSy49MvX4C/Snaf54Qpc1Jwn/tetHGkUmab9MsXsGubRxxI6vgzE0ruL9CYiV1LtrtXDO+doOX69iLLlwAtNvHcB4EHi5l/aYHH9Qo8ngQcF04uA9q5+zozawEcv4UCW0RERERiKnZFdoT2BAaYWQbwJ3BVxPGIiIiISERUZCeIu/9AMPyfiIiIiKS5OI8uIiIiIiISCRXZIiIiIiIJpiJbRERERCTBVGSLiIiIiCSYimwRERERkQRTkS0iIiIikmAqskVEREREEkxFtoiIiIhIgqnIFhERERFJMBXZIiIiIiIJpiJbRERERCTBVGSLiIiIiCSYimwRERERkQRTkS0iIiIikmAqskVEREREEkxFtoiIiIhIgqnIFhERERFJMBXZIiIiIiIJpiJbRERERCTBVGSLiIiIiCSYimwRERERkQQzd486hnSmF19ERERSyaIOIF2oJTtaFsXNzK6O6v9WvspZ+Spn5at8lXOk+UqKqMhOT52iDiDF0i1fSL+c0y1fSL+clW/8pVvO6ZZv2lGRLSIiIiKSYCqyRUREREQSTEV2eno26gBSLN3yhfTLOd3yhfTLWfnGX7rlnG75ph2NLiIiIiIikmBqyRYRERERSTAV2SIiIiIiCaYiW0RKLTPbOeoYUind8hURKc1UZItIqWNmR5rZDGBmOH2wmT0dcVhJk275iojEgU58TBNmdjbwrruvMLO7gcOA7u7+VcShJUXY4rfa3debWX3gAOAdd/8r4tCSJp1yNrPPgbOA4e5+aDjvG3c/MNrIkiPd8s1jZk8WM3sZMMndh6U6nmQzsxFA0S/lZcAkoI+7r0l9VMllZocVM3sZMMfd16U6nmQzs12Kmb0ijvtpUUt2OrknLLCPBtoBLwHPRBxTMo0DyplZbWAMcBnQL9KIki+tcnb3uUVm5UYSSIqkW76hcsAhwA/h7SBgF+AKM3siurCS5mdgJdA3vC0HsoH64XQcPQ1MIBjOri/wGfAG8L2ZtY0ysCT5ClgMfE+wTS8GZpnZV2Z2eKSRScKpyE4feV/I/wc8E7YClY0wnmQzd18FnAH0dvcOQMOIY0q2dMp5rpkdCbiZlTWzzoRdKWIq3fLNsy/Qyt17u3tvoDXQAOgAxLEAO9TdL3D3EeHtQuAId7+e4OhjHM0myLuJux8OHAp8Q/Be94wysCR5FzjZ3Xdz912Bk4ABwHUEPzgkRlRkp49fzawPcA7wtpntRLzffzOzFkBH4K1wXlaE8aRCOuV8DXA9UBuYR9DaeX2UASVZuuWbpzZQ8GTPnYE93D0XWBtNSElV3cz2zJsIH+8WTv4ZTUhJd4C7T8+bcPcZBEX3zxHGlExN3H1U3oS7jwaOdfcJwE7RhSXJENcvYNnYOcCJwKPunmNmuwO3RxxTMv0duBMY4u7TzWwf4MOIY0q2m0mfnMu7e8eCM8ysVlTBpEC65ZunJzDZzD4CDDgW6BGef/B+lIElyW3Ax2b2E0G+ewPXhfm+FGlkyfOdmT1D0EUE4FyCriI7AXHsp/y7md1B4XyXmlkmsD66sCQZdOJjGgn7Y+/n7i+aWXWgorvPijquZDCzs9194JbmxZGZ7ezuf0QdRzKZ2TpgIHC5u68O533l7rE8pJ5u+RYUNggcQVB0fuHu8yMOKanC4vIAgny/jePJjgWZWXmCrhJHE+T8MUG3iTVABXdfGWF4CWdmuwH3UTjfBwhO9tzT3X+MMDxJMBXZacLM7gOaAPu7e30z2wMY6O5HRRxaUhRXgMS9KAm7ijxP8ONpTzM7GLja3a+LOLSEM7OvCU6SugI4x91/MrOv80beiJt0y7eg8ETevShw5NXdx0UXUXKFfe/rUTjflyMLSES2m7qLpI8OBCeUfAXg7vPNrFK0ISWemZ0EnAzULjL8V2UgdsNBFfEEwcgxwwHcfYqZHRtpRMnj7v60mU0BRoSHX+PcYpBu+QJgZo8QHE6fzoZD6U4wkk7smNkrwN+AyWw4Wd2B2BbZZnYUcD8b/5DaJ6qYkikcXrUzG/+QahVVTJI8KrLTx5/u7mbmEOsrx80nGFP2NODLAvNXALdEElEKuftcMys4K67DvBmAu39iZicA/yM4xB5X6ZZvntMJjr7F8STH4jQBGnp6HWJ+nmDf/CXx3V8VNBD4L/Ac6ZFvWlORnT4GhKOLVDWzq4DLieG4q+4+BZhiZq+l4eD+hYZ5A24ivsO8nZz3wN0XmFkr4MgI40m2dMs3z89AGeI5kkhxvgFqAQuiDiSFlrn7O1EHkULr3D3O16iQAlRkpwl3f9TM2hBc3GB/4F53fy/isJLpCDO7nw2HII3gkHssD0GGrgF6sWGYt9HEbJg3M7vQ3V8Fzi/SYp8nVt0I0i3fYqwiGF1kDAUKbXe/KbqQkmo3YIaZfUHhfE+LLqSk+9DM/gUMpnDOsbwaMUF3r+uAIRTO9/foQpJkUZGdRsKiOs6FdUHpdggSd19CMEZ2nOV1c4rd+QSbkG75FjU8vKWL+6MOIALNwvsmBeY5ENc+ypeE9wWH0HUgzg1AaUuji8Scma1gMydIuXvlFIaTMmb2ubs32/Ka8WFmLwF/d/eccLoa8Ji7Xx5pYCIiImlILdkx5+6VAMysK7AQeIWg60RH4t06lm6HIAEOyiuwAdx9qZnFaoi38HyCj9z9Bwv6TzwPnAnMAS5x968jDTDB0i3fPGY2wN3PMbNpFNNI4O4HRRBW0pjZx+5+dDGNInnd3GLXGJLXFcrMbi1uubv/O9UxJZOZtXL3D8zsjOKWu/vgVMckyaciO320K9Ky+4yZfU5wRbU4SrdDkAAZZlbN3ZcCmNkuxO8z/negX/j4fOBggsOshwJPAsdEE1bSpFu+ef4e3p8SaRQp4u5Hh/dxbvgoanNdoeJ4iL0l8AFwajHLnKBBSGImbl/Asmm5ZtaR4FKuTvCFHdu+yu5+fNQxROAx4FMzG0TwHp8DPBhtSAm3rsCoMacAL7v7b8D7ZhbHH4zpli8QjKASPrzO3e8ouCwcO/uOjZ9VuplZBjDV3Q+MOpZUcPc+4cP33f2TgsvCsbNjxd3vC+8vizoWSR31yU4TZlaPYOSJvJ3Xx8DN7j47qpiSycxqAj2APdz9JDNrCLRw9+cjDi0pwi/o5kAOQWu9AWPcfUaUcSWamX0F/B+wlKDLRCt3nx4um+nuDaKML9HSLd+iNnHl1qlx6y6Sx8z6A3e6+y9Rx5Iq6XJ13k11i8kTt+4xElBLdpoIi+n2UceRQv2AF4G7wunvCS7gEcsi293Xm9lj7t4CiFVhXcS9BBcbygSGFyg4WxKMqRw36ZYvAGZ2LXAdsI+ZTS2wqBLwSfHPioXdgenhEH5/5M2M4xB+ZtaCYKz36kUK0MoE23vc5HWL2R9oyoZRc04l/kNxpi21ZKcJM6sD9CZoyXaCluy/u/u8SANLEjOb6O5Nzexrdz80nDfZ3Q+JOLSkMbMHgKnA4DhfMc7MsoBKeX3Pw3k7E+zPVkYXWXKkW74AZlYFqAY8BPyzwKIVcR5POPzxtBF3H5vqWJItzPU4gvH9/1tg0QpghLv/EEVcyWZmo4Ez3X1FOF0JGOjuJ0YbmSSDWrLTx4vAa8DZ4fSF4bw2kUWUXH+Y2a6EJ9CYWXNgWbQhJd2tBCcTrTOzNcRwZIKCZ+Zv4uIssTp5KN3yzePuywg+r+eb2WHA0QSf5U+A2BbZ7j7WzPYC9nP3982sAvFs1c374TDWzPq5+5yo40mhPYE/C0z/CdSLJhRJNhXZ6aO6u79YYLqfmd0cVTApcCvB4bi/mdknQHXgrGhDSq40GZkg78z8GgSHmj8Ip48HPiJ+RWdxIxHkif2IBGZ2D8EJvHl5vmhmA929e4RhJU04ZGMnYBfgbwRXb/0vcEKUcSXZTmb2LEGhmV+TuHtcR4J6BfjCzIYQfIY7AC9HG5Iki7qLpAkze5+gn/Lr4azzgcvcPbY77/Aw+/4ELbrfFRilIZbM7Nji5rt77Pr7mdlI4Kq8USjMbHfgKXcvdgxaKZ3MbCZwqLuvCafLA1/F9YRPM5sMHAF8XqCb2zR3bxxpYElkZlMIfkgUujqvu38ZWVBJZmaHExydARgX1/HuRS3Z6eRy4D/A4wS/nj8N58WSmWUCJ7OhdaStmcX9DO6Cl+ktR/Bl/SXxHBu8XoFh3gCygfpRBZMKZvZ/QCOC9xYAd+8aXUQpMZsg3zXh9E7AT5FFk3xr3f3PvK5BYUNB3FvC1rn7M1EHkUru/qWZzSX8LJvZnuk0okw6UZGdJsIPcOzOUN+MEQRfzNOA9RHHkhLuXqhrgZnVJb4XG/rIzEYRHJlx4Dzgw2hDSh4z+y9QgaBbzHMEXZ++iDSo1FhLMNrGewTvcxvgYzN7EsDdb4oyuCQYa2ZdgPJm1oZghJUREceUbCPM7DpgCIWvzhvLvvdmdhrBNQ32ABYR9NH+luAHtMSMuovEnJndu5nF7u7dUhZMCsV5LN2tFV6Ge2pcDzWbWQcgr4vMOHcfEmU8yZS3PRe4r0gwikzbqGNLJjO7ZHPL3f2lVMWSCuF491cAbQm6uY0Cnov5aEGzipnt7r5PyoNJgbB7TCuCi/AcambHA+e7e6eIQ5MkUEt2/P1RzLydCXbkuwKxLLKBd8ysrbuPjjqQVDGz3mw4tJwBHAJMiSyg5PuKYEi3982sgplVyhsWK4ZWh/erzGwP4Ddg7wjjSYm4FdFb4u7rgb7hLS24e+y34yL+cvffzCzDzDLc/cPwKqYSQyqyY87dH8t7HI7H+XfgMoLLqz+2qefFwARgSNgy9BcxHM6uGJMKPF4HvF70csVxkYajMIw0s6rAvwh+XDhBt5FYMrMB7n6OmU2jmD7JcTtKtak888QtXwAza+XuHxQcprIgd4/ryDk54ZGo8UB/M1tEsL+WGFJ3kTRgZrsQDGnXEXgJ6FXwwhZxZGY/A6cD0+J8qLUoMyvLhhMAYzuiSrqNwmBmO7n72rzHhCcD5s2LGzPb3d0XhGNGbyRu4yoXyPP68P6V8L4jsCqOJ7ia2QPufp+ZvVjMYnf3WJ6YH15IKu86Bh2BKkB/d/8t0sAkKVRkx5yZ/Qs4A3iWYIizWF4hrqjwpLiTwsOvacHMjiP4ETWbYAdeF7gkpkP4fe7uzfKu6BmOwvBVHFv8AMzsK3c/bEvz4iQcIWiUu7eOOpZUMbNP3P2oLc2T0s3MahJcWh3gC3dfFGU8kjzqLhJ/txGcsX03cFeBq8bFvfvEAoIRKN6h8BnrcR7C7zGgrbt/B2Bm9QlG3zg80qiSIy1GYTCzWgRdYcqb2aEEn1uAygSjjcSWu+ea2SozqxJeATId7GxmR7v7xwBmdiTBOTSxlk7DU5rZOQTdvj4i+Dz3NrPb3X1QpIFJUqjIjjl3z4g6hojMCm9lw1s6KJNXYAO4+/dmVibKgJLonwQn704DrgbeJp59lNsBlwJ1gII/EFcAXaIIKMXWANPCIfzyT+KO4dB9ea4AXjCzKuF0DjG+ngGk5fCUdwFN81qvzaw68D6gIjuG1F1EJCbM7AWCk6fy+nNeCGS6+2XRRZV84TkHddx9atSxJIuZnenub0YdR6ptagi/uI86YmaVCb6fY9+Cn27DUxY9dyQ8OX9KXM8nSXdqyZZYMrMPKX5Ugjhe/TDPtQQnTt1EcBhyHPB0pBEliZl9RHBxpSxgMrDYzMa6+61RxpVEY8zs32wYF3ws0DUNirBBBCd45kJ+P+2dog0pecKTWs8kvFJtXve+uHadCKXb8JTvFriQFsC5BEfiJIZUZEtcdS7wuBzBF1esh0kKR5r4N/DvAq27sRx9Aqji7svN7ErgxXCUgti2ZAPPA98A54TTFwEvEpzUHGdjgNZA3gnb5YHRwJGRRZRcw4BlwJcUOJck5vKGp+xJkDfEs+sXAO5+u5mdCRxF0BjybJwvpJXu1F1E0kbY0tky6jiSpbjWXSCWrbvhuMJtCUZTucvdJ8b5Kp9mNtndD9nSvLhJt7zN7Bt3PzDqOFLJzMoTHIU7huDo43jgGXdfE2lgIgmQrifFScyZ2S4FbruZWTugVtRxJVkVd19O0Lr5orsfTtAKGEddCS45/WNYYO8D/BBxTAlnZnuGD1eb2dEF5h/FhsPscfaHmeUPU2hmTYh33p+aWbr1zX2JYGSRJ4HeQAPg5UgjSgIzW2Fmy4u5rTCz5VHHJ8mhlmyJJTObRdAqYgTdRGYR9GH9ONLAkijdWnfTQd5Y2GZ2CMH7WoVgm/4duNTdp0QZX7KFRfX/gPkEn+c9gHPd/cvNPrGUMrMZwL4E+6u1bBhqNbafYTOb4u4Hb2meSGmkPtkSS+4e5xNnNiWvdfeTuLbumllvCp/Q6sAS4MOY/oAyAHefDBwcjjpBeMQiHewNHArsCXQAmrOZy4/HwElRBxCBr82subtPADCzZsAnEcckkhBqyZZYMrPrCS5VmxNOVwPOd/dYjraRLjYxpNsuBCcE/s/dn0htRMllZouANza1PMbjRQOFhnc7GuhBcMGlLu7eLOLQkibMdT93fzEcQ7miu8+KOq5kMbOZwP7AL+GsPYGZwHpi3oov8aciW2JpEydMfe3uh0YUUtKFV3h8Bqjp7gea2UHAae7ePeLQki48eerTuL2/ZjYHuHdTy9NgvOiv3f1QM3sImObur8X5c2xm9wFNgP3dvX44pN3AOF9W3cz22txyd5+TqlhEEk3dRSSuMszMPPwVGY6vG/crP/YFbgf6ALj7VDN7DYh9ke3uq/PGFI6Z3+JeSG/Br2bWh+AE3kfCcaTjfMJ+B4LuMV8BuPt8M6sUbUjJpSJa4izOOytJb6OAAWZ2gpm1Ihj4/92IY0q2Cu5e9HLEsR4bHMDMsszsMmBe1LEkwZ9RBxCxcwg+yyeGXb92IfghGVd/hg0DeY0DO0ccj4jsALVkS1zdAVxNMP6qEVzAIrYXOAgtMbO/seEL+ixgQbQhJZaZrWDDiW95TderCK6AeHUkQSWRuzfPe2xmZwBHE+T/cTpcwMLdVwGDC0wvIGbbdB4LDsWMDFvuq5rZVcDlBEeoRKQUUp9siS0zK0twQo0D37n7XxGHlFThaCLPElwNbynBMGAddTi29DOzpwmGdit4Keaf3P366KKSRDOzrwgaCNoS/Igc5e7vRRuViGwvFdkSS2Z2HMG4wrMJvqzqApe4+7jookqN8BBzBsFFO8519/4Rh5QURVp2x7v70GgjSh4zmw4cWOAcgwyCEwEbRRuZJJKZPQX0c/eJUcciIjtOfbIlrh4D2rp7S3c/FmgHPB5xTElhZpXN7E4z+4+ZtSHoPnEJ8CNBn9bYCVt2rwGmAd8A14QFSlx9RzC0WZ66wNSIYpHkOR74zMx+MrOpebeogxKR7aOWbIml4q50GNerH5rZMILuIZ8BJwDVCEZS+Xt4EZPYSbeWXTMbCzQF8k5sbUrwfq8CcPfTIgpNEmhTw9mpy5dI6aQTHyWuJpnZ88Ar4XRHIJaXYgb2cffGAGb2HMEVEPd09xXRhpVUeS27ecVH3Ft2NzlWtsSHimmReFFLtsRSOJ7u9QR9dg0YBzzt7msjDSwJzOwrdz9sU9NxpJZdEREp6VRkS2yFlyTG3RdHHUsymVku8EfeJFCeoNg0gssSV44qtmQxs5abW+7uY1MVSyqYWXOgN9CAoCtQJvBHHN9bEZG4UJEtsRKONXsfcANBkWlALtDb3btGGZvI9jKzScB5wECCy25fDOzn7l0iDUxERDZJo4tI3NwMHAU0dfdd3X0XoBlwlJndEmlkkjBm1tzMJprZSjP708xyzWx51HElk7v/CGS6e667vwgcF3FIIiKyGTrxUeLmYqCNuy/Jm+HuP5vZhQRXfYzlMH5p6D8U07IbaUTJtSq8uNJkM+tJcNVDXXJbRKQEU0u2xE2ZggV2nrBfdpkI4pEkSbOW3YsI9tc3EPS/rwucGWlEIiKyWWrJlrj5czuXSemSVi277j7HzMoDu7v7A1HHIyIiW6aWbImbg81seTG3FUDjqIOThEmrll0zOxWYDLwbTh9iZsMjDUpERDZLo4uISKkUtuzu6e7fRR1LspnZl0Ar4CN3PzScF8srmIqIxIVaskWk1EnDlt117r4s6iBERGTrqcgWkdLofuAIIAfA3ScD9SKLJvm+MbMLgEwz28/MegOfRh2UiIhsmopsESmN0q1l90agEbAWeB1YTjAmvIiIlFDqky0ipY6ZPQ+MAf5JcMLjTQTDN14TaWAiIiIhFdkiUuqYWQXgLqAtYMAooJu7r4k0sAQzsxHAJnfS7n5aCsMREZFtoCJbRKSEMrOWeQ+BvsCVBZe7+9iUByUiIltFRbaIlBrp3LJrZl/nDd8nIiIln674KCKlyaPhfbEtuzGnFhERkVJERbaIlBoFu0eY2cq4d5cws10KTGaaWTWCHxgAuPvvqY9KRES2hopsESmt0qFl90uCPPMK668KLHNgn5RHJCIiW0VFtoiUGunWsuvue0cdg4iIbB+d+CgipYaZzaJwy25B7u6xatk1s72AnLwL75jZ8cDpwGzgKXf/M7roRERkc1Rki4iUUGb2OdDB3eeb2SHA+8BDwEHAX+6eTid+ioiUKuouIiKlRhq27JZ39/nh4wuBF9z9MTPLACZHF5aIiGxJRtQBiIhsgwHAzgBhy+5A4BfgEODpyKJKnoLdYloRXEoed18fTTgiIrK11JItIqVJurXsfmhmA4CFQDXgAwAz2x2IW6u9iEisqCVbREqTtGrZdfebgMHAz8BR7v5XuKgm0CWywEREZIvUki0ipUlateya2Qo2jKbS1Sz/N4YBa83sR+Audx8TUYgiIrIJGl1EREoVMzsPqAUMyOs6EvbPruHuo6OMLZXMLBM4EOjv7gdGHY+IiBSmIltESo0iLbsFd14GrAXSrmXXzK529z5RxyEiIoWpyBaRWFDLroiIlCQ68VFEYsHdc919CtA76lhERETUki0iIiIikmBqyRYRERERSTAV2SIiIiIiCaYiW0REREQkwVRki4iIiIgkmIpsEREREZEE+3+Qmjo1ClnaagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_heatmap(corrdf,'spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_heatmap(corrdf,'kendall')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cmap = ListedColormap(['r','g'])\n",
    "f = plt.figure(figsize=(13, 10))\n",
    "plt.matshow(corrdf.corr(), fignum=f.number, cmap='RdYlGn') #cmap=cmap\n",
    "plt.xticks(range(corrdf.shape[1]), corrdf.columns, fontsize=14, rotation=45)\n",
    "plt.yticks(range(corrdf.shape[1]), corrdf.columns, fontsize=14)\n",
    "cb = plt.colorbar()\n",
    "cb.ax.tick_params(labelsize=14)\n",
    "plt.title('Correlation Matrix', fontsize=16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "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>Nodes</th>\n",
       "      <th>Documents</th>\n",
       "      <th>Resources</th>\n",
       "      <th>JSHeapUsedSize</th>\n",
       "      <th>JSHeapTotalSize</th>\n",
       "      <th>scripting</th>\n",
       "      <th>rendering</th>\n",
       "      <th>painting</th>\n",
       "      <th>loading</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Nodes</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.067920</td>\n",
       "      <td>-0.144152</td>\n",
       "      <td>0.943421</td>\n",
       "      <td>0.951788</td>\n",
       "      <td>0.988398</td>\n",
       "      <td>0.989157</td>\n",
       "      <td>0.981410</td>\n",
       "      <td>-0.010527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Documents</th>\n",
       "      <td>-0.067920</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.607221</td>\n",
       "      <td>0.011050</td>\n",
       "      <td>-0.013311</td>\n",
       "      <td>-0.053927</td>\n",
       "      <td>-0.100121</td>\n",
       "      <td>-0.056887</td>\n",
       "      <td>-0.026283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Resources</th>\n",
       "      <td>-0.144152</td>\n",
       "      <td>0.607221</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.130308</td>\n",
       "      <td>-0.125145</td>\n",
       "      <td>-0.138509</td>\n",
       "      <td>-0.144584</td>\n",
       "      <td>-0.133953</td>\n",
       "      <td>-0.040095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JSHeapUsedSize</th>\n",
       "      <td>0.943421</td>\n",
       "      <td>0.011050</td>\n",
       "      <td>-0.130308</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998306</td>\n",
       "      <td>0.920304</td>\n",
       "      <td>0.954334</td>\n",
       "      <td>0.919593</td>\n",
       "      <td>-0.002569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JSHeapTotalSize</th>\n",
       "      <td>0.951788</td>\n",
       "      <td>-0.013311</td>\n",
       "      <td>-0.125145</td>\n",
       "      <td>0.998306</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.929805</td>\n",
       "      <td>0.963354</td>\n",
       "      <td>0.928262</td>\n",
       "      <td>-0.003902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scripting</th>\n",
       "      <td>0.988398</td>\n",
       "      <td>-0.053927</td>\n",
       "      <td>-0.138509</td>\n",
       "      <td>0.920304</td>\n",
       "      <td>0.929805</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.982625</td>\n",
       "      <td>0.976474</td>\n",
       "      <td>-0.031957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rendering</th>\n",
       "      <td>0.989157</td>\n",
       "      <td>-0.100121</td>\n",
       "      <td>-0.144584</td>\n",
       "      <td>0.954334</td>\n",
       "      <td>0.963354</td>\n",
       "      <td>0.982625</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.973846</td>\n",
       "      <td>-0.019867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>painting</th>\n",
       "      <td>0.981410</td>\n",
       "      <td>-0.056887</td>\n",
       "      <td>-0.133953</td>\n",
       "      <td>0.919593</td>\n",
       "      <td>0.928262</td>\n",
       "      <td>0.976474</td>\n",
       "      <td>0.973846</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.020827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loading</th>\n",
       "      <td>-0.010527</td>\n",
       "      <td>-0.026283</td>\n",
       "      <td>-0.040095</td>\n",
       "      <td>-0.002569</td>\n",
       "      <td>-0.003902</td>\n",
       "      <td>-0.031957</td>\n",
       "      <td>-0.019867</td>\n",
       "      <td>-0.020827</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Nodes  Documents  Resources  JSHeapUsedSize  \\\n",
       "Nodes            1.000000  -0.067920  -0.144152        0.943421   \n",
       "Documents       -0.067920   1.000000   0.607221        0.011050   \n",
       "Resources       -0.144152   0.607221   1.000000       -0.130308   \n",
       "JSHeapUsedSize   0.943421   0.011050  -0.130308        1.000000   \n",
       "JSHeapTotalSize  0.951788  -0.013311  -0.125145        0.998306   \n",
       "scripting        0.988398  -0.053927  -0.138509        0.920304   \n",
       "rendering        0.989157  -0.100121  -0.144584        0.954334   \n",
       "painting         0.981410  -0.056887  -0.133953        0.919593   \n",
       "loading         -0.010527  -0.026283  -0.040095       -0.002569   \n",
       "\n",
       "                 JSHeapTotalSize  scripting  rendering  painting   loading  \n",
       "Nodes                   0.951788   0.988398   0.989157  0.981410 -0.010527  \n",
       "Documents              -0.013311  -0.053927  -0.100121 -0.056887 -0.026283  \n",
       "Resources              -0.125145  -0.138509  -0.144584 -0.133953 -0.040095  \n",
       "JSHeapUsedSize          0.998306   0.920304   0.954334  0.919593 -0.002569  \n",
       "JSHeapTotalSize         1.000000   0.929805   0.963354  0.928262 -0.003902  \n",
       "scripting               0.929805   1.000000   0.982625  0.976474 -0.031957  \n",
       "rendering               0.963354   0.982625   1.000000  0.973846 -0.019867  \n",
       "painting                0.928262   0.976474   0.973846  1.000000 -0.020827  \n",
       "loading                -0.003902  -0.031957  -0.019867 -0.020827  1.000000  "
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrdf.corr('spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(15, 10))\n",
    "plt.matshow(corrdf.corr('spearman'), fignum=f.number, cmap='coolwarm') #cmap=cmap\n",
    "plt.xticks(range(corrdf.shape[1]), corrdf.columns, fontsize=14, rotation=45)\n",
    "plt.yticks(range(corrdf.shape[1]), corrdf.columns, fontsize=14)\n",
    "cb = plt.colorbar()\n",
    "cb.ax.tick_params(labelsize=14)\n",
    "plt.title('Correlation Matrix', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression on this dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "#INSERT YOUR CODE TO BUILD AND COMPILE THE MODEL HERE\n",
    "def reg_model():\n",
    "    model = keras.Sequential([\n",
    "        layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]),\n",
    "        layers.Dense(32, activation='relu'),\n",
    "        layers.Dense(1)\n",
    "    ])\n",
    "\n",
    "    optimizer = tf.keras.optimizers.RMSprop(0.001)\n",
    "\n",
    "    model.compile(loss='mse',\n",
    "                optimizer=optimizer,\n",
    "                metrics=['mae', 'mse'])\n",
    "    return model\n",
    "    #YOU CAN ALSO CHANGE THE MODEL TRAINING PARAMETERS BELOW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nodes</th>\n",
       "      <th>JSHeapUsedSize</th>\n",
       "      <th>JSHeapTotalSize</th>\n",
       "      <th>scripting</th>\n",
       "      <th>rendering</th>\n",
       "      <th>painting</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11839.0</td>\n",
       "      <td>578982240.0</td>\n",
       "      <td>606568448.0</td>\n",
       "      <td>4024.107</td>\n",
       "      <td>258.527</td>\n",
       "      <td>29.821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11839.0</td>\n",
       "      <td>578974656.0</td>\n",
       "      <td>606044160.0</td>\n",
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       "      <td>246.942</td>\n",
       "      <td>34.918</td>\n",
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       "      <th>0</th>\n",
       "      <td>11839.0</td>\n",
       "      <td>578975932.0</td>\n",
       "      <td>605782016.0</td>\n",
       "      <td>4019.479</td>\n",
       "      <td>245.804</td>\n",
       "      <td>29.737</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11839.0</td>\n",
       "      <td>579004920.0</td>\n",
       "      <td>606830592.0</td>\n",
       "      <td>3999.914</td>\n",
       "      <td>245.636</td>\n",
       "      <td>31.246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11839.0</td>\n",
       "      <td>578979804.0</td>\n",
       "      <td>606306304.0</td>\n",
       "      <td>4620.789</td>\n",
       "      <td>270.111</td>\n",
       "      <td>33.084</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",
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       "      <th>0</th>\n",
       "      <td>7331.0</td>\n",
       "      <td>567883836.0</td>\n",
       "      <td>590499840.0</td>\n",
       "      <td>4224.692</td>\n",
       "      <td>169.569</td>\n",
       "      <td>16.675</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7331.0</td>\n",
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       "      <td>590499840.0</td>\n",
       "      <td>3872.961</td>\n",
       "      <td>166.847</td>\n",
       "      <td>23.788</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7331.0</td>\n",
       "      <td>568042836.0</td>\n",
       "      <td>590499840.0</td>\n",
       "      <td>3870.943</td>\n",
       "      <td>171.404</td>\n",
       "      <td>18.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7331.0</td>\n",
       "      <td>567949748.0</td>\n",
       "      <td>590499840.0</td>\n",
       "      <td>3879.705</td>\n",
       "      <td>167.397</td>\n",
       "      <td>36.460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7548.0</td>\n",
       "      <td>568768256.0</td>\n",
       "      <td>594432000.0</td>\n",
       "      <td>3902.371</td>\n",
       "      <td>175.860</td>\n",
       "      <td>27.163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>508 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Nodes  JSHeapUsedSize  JSHeapTotalSize  scripting  rendering  painting\n",
       "0   11839.0     578982240.0      606568448.0   4024.107    258.527    29.821\n",
       "0   11839.0     578974656.0      606044160.0   3992.477    246.942    34.918\n",
       "0   11839.0     578975932.0      605782016.0   4019.479    245.804    29.737\n",
       "0   11839.0     579004920.0      606830592.0   3999.914    245.636    31.246\n",
       "0   11839.0     578979804.0      606306304.0   4620.789    270.111    33.084\n",
       "..      ...             ...              ...        ...        ...       ...\n",
       "0    7331.0     567883836.0      590499840.0   4224.692    169.569    16.675\n",
       "0    7331.0     567831628.0      590499840.0   3872.961    166.847    23.788\n",
       "0    7331.0     568042836.0      590499840.0   3870.943    171.404    18.046\n",
       "0    7331.0     567949748.0      590499840.0   3879.705    167.397    36.460\n",
       "0    7548.0     568768256.0      594432000.0   3902.371    175.860    27.163\n",
       "\n",
       "[508 rows x 6 columns]"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_data = corrdf.drop(columns=['loading','Documents','Resources'])\n",
    "model_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = model_data.iloc[:400]\n",
    "test_dataset = model_data.iloc[400:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Nodes</th>\n",
       "      <td>400.0</td>\n",
       "      <td>2.725721e+04</td>\n",
       "      <td>1.516037e+04</td>\n",
       "      <td>1.419000e+03</td>\n",
       "      <td>1.408075e+04</td>\n",
       "      <td>2.788800e+04</td>\n",
       "      <td>3.856675e+04</td>\n",
       "      <td>5.400600e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JSHeapUsedSize</th>\n",
       "      <td>400.0</td>\n",
       "      <td>6.410953e+08</td>\n",
       "      <td>8.308513e+07</td>\n",
       "      <td>4.877208e+08</td>\n",
       "      <td>5.930578e+08</td>\n",
       "      <td>6.283301e+08</td>\n",
       "      <td>6.980581e+08</td>\n",
       "      <td>8.129797e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JSHeapTotalSize</th>\n",
       "      <td>400.0</td>\n",
       "      <td>6.712553e+08</td>\n",
       "      <td>8.681762e+07</td>\n",
       "      <td>5.092352e+08</td>\n",
       "      <td>6.207652e+08</td>\n",
       "      <td>6.602998e+08</td>\n",
       "      <td>7.309793e+08</td>\n",
       "      <td>8.496783e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scripting</th>\n",
       "      <td>400.0</td>\n",
       "      <td>5.474613e+03</td>\n",
       "      <td>1.261110e+03</td>\n",
       "      <td>3.170390e+03</td>\n",
       "      <td>4.420377e+03</td>\n",
       "      <td>5.535136e+03</td>\n",
       "      <td>6.473174e+03</td>\n",
       "      <td>7.795689e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rendering</th>\n",
       "      <td>400.0</td>\n",
       "      <td>5.512475e+02</td>\n",
       "      <td>2.746815e+02</td>\n",
       "      <td>5.819000e+01</td>\n",
       "      <td>2.924992e+02</td>\n",
       "      <td>5.949010e+02</td>\n",
       "      <td>7.368715e+02</td>\n",
       "      <td>1.069410e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>painting</th>\n",
       "      <td>400.0</td>\n",
       "      <td>9.107540e+01</td>\n",
       "      <td>5.464218e+01</td>\n",
       "      <td>7.037000e+00</td>\n",
       "      <td>4.226625e+01</td>\n",
       "      <td>8.612450e+01</td>\n",
       "      <td>1.310863e+02</td>\n",
       "      <td>3.011640e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count          mean           std           min  \\\n",
       "Nodes            400.0  2.725721e+04  1.516037e+04  1.419000e+03   \n",
       "JSHeapUsedSize   400.0  6.410953e+08  8.308513e+07  4.877208e+08   \n",
       "JSHeapTotalSize  400.0  6.712553e+08  8.681762e+07  5.092352e+08   \n",
       "scripting        400.0  5.474613e+03  1.261110e+03  3.170390e+03   \n",
       "rendering        400.0  5.512475e+02  2.746815e+02  5.819000e+01   \n",
       "painting         400.0  9.107540e+01  5.464218e+01  7.037000e+00   \n",
       "\n",
       "                          25%           50%           75%           max  \n",
       "Nodes            1.408075e+04  2.788800e+04  3.856675e+04  5.400600e+04  \n",
       "JSHeapUsedSize   5.930578e+08  6.283301e+08  6.980581e+08  8.129797e+08  \n",
       "JSHeapTotalSize  6.207652e+08  6.602998e+08  7.309793e+08  8.496783e+08  \n",
       "scripting        4.420377e+03  5.535136e+03  6.473174e+03  7.795689e+03  \n",
       "rendering        2.924992e+02  5.949010e+02  7.368715e+02  1.069410e+03  \n",
       "painting         4.226625e+01  8.612450e+01  1.310863e+02  3.011640e+02  "
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_stats = train_dataset.describe()\n",
    "train_stats = train_stats.transpose()\n",
    "train_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels = train_dataset.pop('Nodes')\n",
    "test_labels = test_dataset.pop('Nodes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 108)"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_dataset),len(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>JSHeapTotalSize</th>\n",
       "      <th>JSHeapUsedSize</th>\n",
       "      <th>ProcessTime</th>\n",
       "      <th>TaskDuration</th>\n",
       "      <th>ThreadTime</th>\n",
       "      <th>painting</th>\n",
       "      <th>rendering</th>\n",
       "      <th>scripting</th>\n",
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       "      <th>0</th>\n",
       "      <td>-0.745089</td>\n",
       "      <td>-0.747583</td>\n",
       "      <td>-1.187922</td>\n",
       "      <td>-1.188393</td>\n",
       "      <td>-1.161712</td>\n",
       "      <td>-1.121009</td>\n",
       "      <td>-1.065672</td>\n",
       "      <td>-1.150182</td>\n",
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       "      <th>0</th>\n",
       "      <td>-0.751128</td>\n",
       "      <td>-0.747674</td>\n",
       "      <td>-1.155884</td>\n",
       "      <td>-1.203583</td>\n",
       "      <td>-1.157991</td>\n",
       "      <td>-1.027730</td>\n",
       "      <td>-1.107848</td>\n",
       "      <td>-1.175263</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.754147</td>\n",
       "      <td>-0.747659</td>\n",
       "      <td>-1.107828</td>\n",
       "      <td>-1.182386</td>\n",
       "      <td>-1.147685</td>\n",
       "      <td>-1.122547</td>\n",
       "      <td>-1.111991</td>\n",
       "      <td>-1.153852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.742069</td>\n",
       "      <td>-0.747310</td>\n",
       "      <td>-1.277626</td>\n",
       "      <td>-1.180870</td>\n",
       "      <td>-1.149408</td>\n",
       "      <td>-1.094931</td>\n",
       "      <td>-1.112603</td>\n",
       "      <td>-1.169366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.748108</td>\n",
       "      <td>-0.747612</td>\n",
       "      <td>-0.992494</td>\n",
       "      <td>-0.736069</td>\n",
       "      <td>-0.804087</td>\n",
       "      <td>-1.061294</td>\n",
       "      <td>-1.023500</td>\n",
       "      <td>-0.677042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   JSHeapTotalSize  JSHeapUsedSize  ProcessTime  TaskDuration  ThreadTime  \\\n",
       "0        -0.745089       -0.747583    -1.187922     -1.188393   -1.161712   \n",
       "0        -0.751128       -0.747674    -1.155884     -1.203583   -1.157991   \n",
       "0        -0.754147       -0.747659    -1.107828     -1.182386   -1.147685   \n",
       "0        -0.742069       -0.747310    -1.277626     -1.180870   -1.149408   \n",
       "0        -0.748108       -0.747612    -0.992494     -0.736069   -0.804087   \n",
       "\n",
       "   painting  rendering  scripting  \n",
       "0 -1.121009  -1.065672  -1.150182  \n",
       "0 -1.027730  -1.107848  -1.175263  \n",
       "0 -1.122547  -1.111991  -1.153852  \n",
       "0 -1.094931  -1.112603  -1.169366  \n",
       "0 -1.061294  -1.023500  -0.677042  "
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normed_train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [],
   "source": [
    "def norm(x):\n",
    "    return (x - train_stats['mean']) / train_stats['std']\n",
    "normed_train_data = norm(train_dataset)\n",
    "normed_train_data = normed_train_data.drop(columns='Nodes')\n",
    "normed_test_data = norm(test_dataset)\n",
    "normed_test_data = normed_test_data.drop(columns='Nodes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = reg_model()\n",
    "early_stop=keras.callbacks.EarlyStopping(monitor='val_loss',patience=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      " 1/10 [==>...........................] - ETA: 0s - loss: 776004736.0000 - mae: 25481.0469 - mse: 776004736.0000WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0009s vs `on_train_batch_end` time: 0.0020s). Check your callbacks.\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 697936832.0000 - mae: 23567.9023 - mse: 697936832.0000 - val_loss: 2069055488.0000 - val_mae: 42010.2461 - val_mse: 2069055488.0000\n",
      "Epoch 2/1000\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 697902080.0000 - mae: 23567.0527 - mse: 697902080.0000 - val_loss: 2068889600.0000 - val_mae: 42008.3828 - val_mse: 2068889600.0000\n",
      "Epoch 3/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 697866624.0000 - mae: 23566.2051 - mse: 697866624.0000 - val_loss: 2068715520.0000 - val_mae: 42006.3945 - val_mse: 2068715520.0000\n",
      "Epoch 4/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 697825728.0000 - mae: 23565.2285 - mse: 697825728.0000 - val_loss: 2068509056.0000 - val_mae: 42004.0586 - val_mse: 2068509056.0000\n",
      "Epoch 5/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 697775104.0000 - mae: 23564.0176 - mse: 697775104.0000 - val_loss: 2068244480.0000 - val_mae: 42001.0703 - val_mse: 2068244480.0000\n",
      "Epoch 6/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 697715712.0000 - mae: 23562.5938 - mse: 697715712.0000 - val_loss: 2067955328.0000 - val_mae: 41997.7617 - val_mse: 2067955328.0000\n",
      "Epoch 7/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 697646528.0000 - mae: 23560.9570 - mse: 697646528.0000 - val_loss: 2067595648.0000 - val_mae: 41993.7070 - val_mse: 2067595648.0000\n",
      "Epoch 8/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 697566592.0000 - mae: 23559.0195 - mse: 697566592.0000 - val_loss: 2067191040.0000 - val_mae: 41989.1445 - val_mse: 2067191040.0000\n",
      "Epoch 9/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 697476288.0000 - mae: 23556.8965 - mse: 697476288.0000 - val_loss: 2066741632.0000 - val_mae: 41984.0234 - val_mse: 2066741632.0000\n",
      "Epoch 10/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 697375040.0000 - mae: 23554.4043 - mse: 697375040.0000 - val_loss: 2066241152.0000 - val_mae: 41978.3281 - val_mse: 2066241152.0000\n",
      "Epoch 11/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 697261440.0000 - mae: 23551.6680 - mse: 697261440.0000 - val_loss: 2065678592.0000 - val_mae: 41971.9414 - val_mse: 2065678592.0000\n",
      "Epoch 12/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 697133440.0000 - mae: 23548.6680 - mse: 697133440.0000 - val_loss: 2065033600.0000 - val_mae: 41964.6328 - val_mse: 2065033600.0000\n",
      "Epoch 13/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 696993664.0000 - mae: 23545.2051 - mse: 696993664.0000 - val_loss: 2064359168.0000 - val_mae: 41956.9766 - val_mse: 2064359168.0000\n",
      "Epoch 14/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 696840704.0000 - mae: 23541.6562 - mse: 696840704.0000 - val_loss: 2063597952.0000 - val_mae: 41948.3672 - val_mse: 2063597952.0000\n",
      "Epoch 15/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 696671616.0000 - mae: 23537.5566 - mse: 696671616.0000 - val_loss: 2062757888.0000 - val_mae: 41938.8516 - val_mse: 2062757888.0000\n",
      "Epoch 16/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 696488256.0000 - mae: 23533.1836 - mse: 696488256.0000 - val_loss: 2061870336.0000 - val_mae: 41928.7695 - val_mse: 2061870336.0000\n",
      "Epoch 17/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 696291072.0000 - mae: 23528.4727 - mse: 696291072.0000 - val_loss: 2060928000.0000 - val_mae: 41918.0000 - val_mse: 2060928000.0000\n",
      "Epoch 18/1000\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 696075136.0000 - mae: 23523.2578 - mse: 696075136.0000 - val_loss: 2059871872.0000 - val_mae: 41906.0312 - val_mse: 2059871872.0000\n",
      "Epoch 19/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 695846528.0000 - mae: 23517.7559 - mse: 695846528.0000 - val_loss: 2058771072.0000 - val_mae: 41893.5000 - val_mse: 2058771072.0000\n",
      "Epoch 20/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 695600320.0000 - mae: 23511.7344 - mse: 695600320.0000 - val_loss: 2057575680.0000 - val_mae: 41879.8945 - val_mse: 2057575680.0000\n",
      "Epoch 21/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 695334656.0000 - mae: 23505.3574 - mse: 695334656.0000 - val_loss: 2056278784.0000 - val_mae: 41865.1680 - val_mse: 2056278784.0000\n",
      "Epoch 22/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 695052672.0000 - mae: 23498.5527 - mse: 695052672.0000 - val_loss: 2054930048.0000 - val_mae: 41849.8047 - val_mse: 2054930048.0000\n",
      "Epoch 23/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 694752640.0000 - mae: 23491.3965 - mse: 694752640.0000 - val_loss: 2053468160.0000 - val_mae: 41833.1914 - val_mse: 2053468160.0000\n",
      "Epoch 24/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 694431296.0000 - mae: 23483.5977 - mse: 694431296.0000 - val_loss: 2051909632.0000 - val_mae: 41815.4922 - val_mse: 2051909632.0000\n",
      "Epoch 25/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 694085568.0000 - mae: 23475.5273 - mse: 694085568.0000 - val_loss: 2050227584.0000 - val_mae: 41796.3711 - val_mse: 2050227584.0000\n",
      "Epoch 26/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 693722752.0000 - mae: 23466.7148 - mse: 693722752.0000 - val_loss: 2048481664.0000 - val_mae: 41776.4805 - val_mse: 2048481664.0000\n",
      "Epoch 27/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 693341376.0000 - mae: 23457.4414 - mse: 693341376.0000 - val_loss: 2046638080.0000 - val_mae: 41755.5078 - val_mse: 2046638080.0000\n",
      "Epoch 28/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 692941440.0000 - mae: 23447.7891 - mse: 692941440.0000 - val_loss: 2044747776.0000 - val_mae: 41733.9453 - val_mse: 2044747776.0000\n",
      "Epoch 29/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 692518336.0000 - mae: 23438.0566 - mse: 692518336.0000 - val_loss: 2042683392.0000 - val_mae: 41710.4258 - val_mse: 2042683392.0000\n",
      "Epoch 30/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 692076992.0000 - mae: 23427.1680 - mse: 692076992.0000 - val_loss: 2040589312.0000 - val_mae: 41686.4766 - val_mse: 2040589312.0000\n",
      "Epoch 31/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 691610752.0000 - mae: 23415.9512 - mse: 691610752.0000 - val_loss: 2038337920.0000 - val_mae: 41660.8047 - val_mse: 2038337920.0000\n",
      "Epoch 32/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 691126912.0000 - mae: 23404.0820 - mse: 691126912.0000 - val_loss: 2036047488.0000 - val_mae: 41634.5742 - val_mse: 2036047488.0000\n",
      "Epoch 33/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 690611008.0000 - mae: 23391.6504 - mse: 690611008.0000 - val_loss: 2033474304.0000 - val_mae: 41605.3672 - val_mse: 2033474304.0000\n",
      "Epoch 34/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 690071552.0000 - mae: 23378.7695 - mse: 690071552.0000 - val_loss: 2030939392.0000 - val_mae: 41576.3008 - val_mse: 2030939392.0000\n",
      "Epoch 35/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 689507648.0000 - mae: 23365.0938 - mse: 689507648.0000 - val_loss: 2028183808.0000 - val_mae: 41544.8867 - val_mse: 2028183808.0000\n",
      "Epoch 36/1000\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 688912256.0000 - mae: 23350.8535 - mse: 688912256.0000 - val_loss: 2025274368.0000 - val_mae: 41511.7305 - val_mse: 2025274368.0000\n",
      "Epoch 37/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 688298240.0000 - mae: 23335.8418 - mse: 688298240.0000 - val_loss: 2022362368.0000 - val_mae: 41478.3594 - val_mse: 2022362368.0000\n",
      "Epoch 38/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 687658624.0000 - mae: 23320.7773 - mse: 687658624.0000 - val_loss: 2019296896.0000 - val_mae: 41443.2188 - val_mse: 2019296896.0000\n",
      "Epoch 39/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 686992704.0000 - mae: 23304.6035 - mse: 686992704.0000 - val_loss: 2016071296.0000 - val_mae: 41406.3555 - val_mse: 2016071296.0000\n",
      "Epoch 40/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 686301312.0000 - mae: 23287.9805 - mse: 686301312.0000 - val_loss: 2012780160.0000 - val_mae: 41368.5000 - val_mse: 2012780160.0000\n",
      "Epoch 41/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 685581696.0000 - mae: 23270.5000 - mse: 685581696.0000 - val_loss: 2009289984.0000 - val_mae: 41328.4883 - val_mse: 2009289984.0000\n",
      "Epoch 42/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 684823552.0000 - mae: 23252.4199 - mse: 684823552.0000 - val_loss: 2005589632.0000 - val_mae: 41286.1016 - val_mse: 2005589632.0000\n",
      "Epoch 43/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 684050240.0000 - mae: 23233.2207 - mse: 684050240.0000 - val_loss: 2001977728.0000 - val_mae: 41244.3672 - val_mse: 2001977728.0000\n",
      "Epoch 44/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 683260480.0000 - mae: 23213.7559 - mse: 683260480.0000 - val_loss: 1998188544.0000 - val_mae: 41200.7852 - val_mse: 1998188544.0000\n",
      "Epoch 45/1000\n",
      "10/10 [==============================] - 0s 3ms/step - loss: 682442496.0000 - mae: 23193.8945 - mse: 682442496.0000 - val_loss: 1994344064.0000 - val_mae: 41156.2070 - val_mse: 1994344064.0000\n",
      "Epoch 46/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 681581952.0000 - mae: 23173.1035 - mse: 681581952.0000 - val_loss: 1990124800.0000 - val_mae: 41107.6484 - val_mse: 1990124800.0000\n",
      "Epoch 47/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 680690304.0000 - mae: 23151.6777 - mse: 680690304.0000 - val_loss: 1985950464.0000 - val_mae: 41059.3086 - val_mse: 1985950464.0000\n",
      "Epoch 48/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 679794560.0000 - mae: 23128.9531 - mse: 679794560.0000 - val_loss: 1981758464.0000 - val_mae: 41010.5391 - val_mse: 1981758464.0000\n",
      "Epoch 49/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 678861376.0000 - mae: 23105.9648 - mse: 678861376.0000 - val_loss: 1977219840.0000 - val_mae: 40957.9961 - val_mse: 1977219840.0000\n",
      "Epoch 50/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 677877376.0000 - mae: 23082.3379 - mse: 677877376.0000 - val_loss: 1972434176.0000 - val_mae: 40902.5820 - val_mse: 1972434176.0000\n",
      "Epoch 51/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 676855936.0000 - mae: 23057.5840 - mse: 676855936.0000 - val_loss: 1967569920.0000 - val_mae: 40846.1484 - val_mse: 1967569920.0000\n",
      "Epoch 52/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 675802944.0000 - mae: 23031.4316 - mse: 675802944.0000 - val_loss: 1962470144.0000 - val_mae: 40787.0000 - val_mse: 1962470144.0000\n",
      "Epoch 53/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 674719744.0000 - mae: 23004.9551 - mse: 674719744.0000 - val_loss: 1957246976.0000 - val_mae: 40726.3750 - val_mse: 1957246976.0000\n",
      "Epoch 54/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 673620608.0000 - mae: 22977.9102 - mse: 673620608.0000 - val_loss: 1952089856.0000 - val_mae: 40666.2148 - val_mse: 1952089856.0000\n",
      "Epoch 55/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 672500864.0000 - mae: 22950.0820 - mse: 672500864.0000 - val_loss: 1946744448.0000 - val_mae: 40603.5938 - val_mse: 1946744448.0000\n",
      "Epoch 56/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 671323840.0000 - mae: 22921.9648 - mse: 671323840.0000 - val_loss: 1940983040.0000 - val_mae: 40536.5391 - val_mse: 1940983040.0000\n",
      "Epoch 57/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 670126976.0000 - mae: 22893.7656 - mse: 670126976.0000 - val_loss: 1935368192.0000 - val_mae: 40470.6055 - val_mse: 1935368192.0000\n",
      "Epoch 58/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 668906560.0000 - mae: 22868.2344 - mse: 668906560.0000 - val_loss: 1929585920.0000 - val_mae: 40402.6797 - val_mse: 1929585920.0000\n",
      "Epoch 59/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 667654848.0000 - mae: 22840.3047 - mse: 667654848.0000 - val_loss: 1923607936.0000 - val_mae: 40332.2188 - val_mse: 1923607936.0000\n",
      "Epoch 60/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 666353216.0000 - mae: 22814.5137 - mse: 666353216.0000 - val_loss: 1917389184.0000 - val_mae: 40259.0391 - val_mse: 1917389184.0000\n",
      "Epoch 61/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 665024960.0000 - mae: 22790.1211 - mse: 665024960.0000 - val_loss: 1911064192.0000 - val_mae: 40184.4414 - val_mse: 1911064192.0000\n",
      "Epoch 62/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 663668544.0000 - mae: 22765.1055 - mse: 663668544.0000 - val_loss: 1904596736.0000 - val_mae: 40108.0234 - val_mse: 1904596736.0000\n",
      "Epoch 63/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 662272192.0000 - mae: 22738.7617 - mse: 662272192.0000 - val_loss: 1897862784.0000 - val_mae: 40028.3633 - val_mse: 1897862784.0000\n",
      "Epoch 64/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 660837120.0000 - mae: 22712.5488 - mse: 660837120.0000 - val_loss: 1891099392.0000 - val_mae: 39947.8789 - val_mse: 1891099392.0000\n",
      "Epoch 65/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 659368640.0000 - mae: 22685.6191 - mse: 659368640.0000 - val_loss: 1884012928.0000 - val_mae: 39863.6602 - val_mse: 1884012928.0000\n",
      "Epoch 66/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 657863296.0000 - mae: 22657.2090 - mse: 657863296.0000 - val_loss: 1876832896.0000 - val_mae: 39778.0703 - val_mse: 1876832896.0000\n",
      "Epoch 67/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 656318336.0000 - mae: 22628.1719 - mse: 656318336.0000 - val_loss: 1869360384.0000 - val_mae: 39689.0312 - val_mse: 1869360384.0000\n",
      "Epoch 68/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 654725632.0000 - mae: 22598.0508 - mse: 654725632.0000 - val_loss: 1861792384.0000 - val_mae: 39598.5000 - val_mse: 1861792384.0000\n",
      "Epoch 69/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 653111808.0000 - mae: 22568.7402 - mse: 653111808.0000 - val_loss: 1854111360.0000 - val_mae: 39506.2422 - val_mse: 1854111360.0000\n",
      "Epoch 70/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 651440000.0000 - mae: 22536.9219 - mse: 651440000.0000 - val_loss: 1845927680.0000 - val_mae: 39408.3359 - val_mse: 1845927680.0000\n",
      "Epoch 71/1000\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 649750336.0000 - mae: 22504.8730 - mse: 649750336.0000 - val_loss: 1837934208.0000 - val_mae: 39312.1406 - val_mse: 1837934208.0000\n",
      "Epoch 72/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 648029632.0000 - mae: 22474.0430 - mse: 648029632.0000 - val_loss: 1829697920.0000 - val_mae: 39213.0547 - val_mse: 1829697920.0000\n",
      "Epoch 73/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 646254272.0000 - mae: 22440.4727 - mse: 646254272.0000 - val_loss: 1820996224.0000 - val_mae: 39108.4453 - val_mse: 1820996224.0000\n",
      "Epoch 74/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 644445376.0000 - mae: 22405.6934 - mse: 644445376.0000 - val_loss: 1812358400.0000 - val_mae: 39003.6875 - val_mse: 1812358400.0000\n",
      "Epoch 75/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 642608768.0000 - mae: 22370.5195 - mse: 642608768.0000 - val_loss: 1803500800.0000 - val_mae: 38896.2617 - val_mse: 1803500800.0000\n",
      "Epoch 76/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 640740352.0000 - mae: 22335.3965 - mse: 640740352.0000 - val_loss: 1794626176.0000 - val_mae: 38788.0234 - val_mse: 1794626176.0000\n",
      "Epoch 77/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 638861376.0000 - mae: 22298.9570 - mse: 638861376.0000 - val_loss: 1785562752.0000 - val_mae: 38677.4609 - val_mse: 1785562752.0000\n",
      "Epoch 78/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 636920320.0000 - mae: 22261.5957 - mse: 636920320.0000 - val_loss: 1776318720.0000 - val_mae: 38564.0234 - val_mse: 1776318720.0000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 79/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 634948864.0000 - mae: 22223.9746 - mse: 634948864.0000 - val_loss: 1766750592.0000 - val_mae: 38446.7773 - val_mse: 1766750592.0000\n",
      "Epoch 80/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 632948864.0000 - mae: 22186.2305 - mse: 632948864.0000 - val_loss: 1757245056.0000 - val_mae: 38329.3867 - val_mse: 1757245056.0000\n",
      "Epoch 81/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 630913408.0000 - mae: 22152.7422 - mse: 630913408.0000 - val_loss: 1747330304.0000 - val_mae: 38207.3672 - val_mse: 1747330304.0000\n",
      "Epoch 82/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 628849600.0000 - mae: 22120.3730 - mse: 628849600.0000 - val_loss: 1737499264.0000 - val_mae: 38085.4922 - val_mse: 1737499264.0000\n",
      "Epoch 83/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 626753152.0000 - mae: 22084.9883 - mse: 626753152.0000 - val_loss: 1727293696.0000 - val_mae: 37958.6797 - val_mse: 1727293696.0000\n",
      "Epoch 84/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 624589184.0000 - mae: 22050.3672 - mse: 624589184.0000 - val_loss: 1716891264.0000 - val_mae: 37829.0547 - val_mse: 1716891264.0000\n",
      "Epoch 85/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 622418880.0000 - mae: 22014.5508 - mse: 622418880.0000 - val_loss: 1706554624.0000 - val_mae: 37699.7422 - val_mse: 1706554624.0000\n",
      "Epoch 86/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 620197376.0000 - mae: 21977.8828 - mse: 620197376.0000 - val_loss: 1695765632.0000 - val_mae: 37564.3594 - val_mse: 1695765632.0000\n",
      "Epoch 87/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 617926208.0000 - mae: 21940.5586 - mse: 617926208.0000 - val_loss: 1684731904.0000 - val_mae: 37425.9453 - val_mse: 1684731904.0000\n",
      "Epoch 88/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 615612736.0000 - mae: 21903.7637 - mse: 615612736.0000 - val_loss: 1673534976.0000 - val_mae: 37284.8750 - val_mse: 1673534976.0000\n",
      "Epoch 89/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 613280960.0000 - mae: 21868.2344 - mse: 613280960.0000 - val_loss: 1662312960.0000 - val_mae: 37142.6758 - val_mse: 1662312960.0000\n",
      "Epoch 90/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 610909056.0000 - mae: 21836.5371 - mse: 610909056.0000 - val_loss: 1650735744.0000 - val_mae: 36996.4102 - val_mse: 1650735744.0000\n",
      "Epoch 91/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 608494400.0000 - mae: 21799.6855 - mse: 608494400.0000 - val_loss: 1639098624.0000 - val_mae: 36847.5820 - val_mse: 1639098624.0000\n",
      "Epoch 92/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 606011520.0000 - mae: 21765.3242 - mse: 606011520.0000 - val_loss: 1626885376.0000 - val_mae: 36691.6953 - val_mse: 1626885376.0000\n",
      "Epoch 93/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 603509824.0000 - mae: 21729.2109 - mse: 603509824.0000 - val_loss: 1614927488.0000 - val_mae: 36537.8672 - val_mse: 1614927488.0000\n",
      "Epoch 94/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 601016192.0000 - mae: 21694.8906 - mse: 601016192.0000 - val_loss: 1603086976.0000 - val_mae: 36384.6445 - val_mse: 1603086976.0000\n",
      "Epoch 95/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 598470144.0000 - mae: 21658.7070 - mse: 598470144.0000 - val_loss: 1590443264.0000 - val_mae: 36221.6172 - val_mse: 1590443264.0000\n",
      "Epoch 96/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 595879936.0000 - mae: 21622.1172 - mse: 595879936.0000 - val_loss: 1578053632.0000 - val_mae: 36060.8008 - val_mse: 1578053632.0000\n",
      "Epoch 97/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 593249472.0000 - mae: 21584.1211 - mse: 593249472.0000 - val_loss: 1565093248.0000 - val_mae: 35908.5625 - val_mse: 1565093248.0000\n",
      "Epoch 98/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 590606848.0000 - mae: 21546.7734 - mse: 590606848.0000 - val_loss: 1552377984.0000 - val_mae: 35769.2383 - val_mse: 1552377984.0000\n",
      "Epoch 99/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 587925952.0000 - mae: 21506.0703 - mse: 587925952.0000 - val_loss: 1539360128.0000 - val_mae: 35628.9219 - val_mse: 1539360128.0000\n",
      "Epoch 100/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 585207936.0000 - mae: 21469.8066 - mse: 585207936.0000 - val_loss: 1525873664.0000 - val_mae: 35482.1289 - val_mse: 1525873664.0000\n",
      "Epoch 101/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 582471616.0000 - mae: 21429.0000 - mse: 582471616.0000 - val_loss: 1512686720.0000 - val_mae: 35341.5234 - val_mse: 1512686720.0000\n",
      "Epoch 102/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 579729152.0000 - mae: 21388.1387 - mse: 579729152.0000 - val_loss: 1499148032.0000 - val_mae: 35205.6758 - val_mse: 1499148032.0000\n",
      "Epoch 103/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 576935616.0000 - mae: 21347.6777 - mse: 576935616.0000 - val_loss: 1485640576.0000 - val_mae: 35070.4609 - val_mse: 1485640576.0000\n",
      "Epoch 104/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 574109632.0000 - mae: 21304.6191 - mse: 574109632.0000 - val_loss: 1471673728.0000 - val_mae: 34929.6992 - val_mse: 1471673728.0000\n",
      "Epoch 105/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 571245184.0000 - mae: 21263.9336 - mse: 571245184.0000 - val_loss: 1457719040.0000 - val_mae: 34786.9844 - val_mse: 1457719040.0000\n",
      "Epoch 106/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 568344960.0000 - mae: 21218.0977 - mse: 568344960.0000 - val_loss: 1443586304.0000 - val_mae: 34642.9453 - val_mse: 1443586304.0000\n",
      "Epoch 107/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 565379968.0000 - mae: 21173.3184 - mse: 565379968.0000 - val_loss: 1429004544.0000 - val_mae: 34492.7266 - val_mse: 1429004544.0000\n",
      "Epoch 108/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 562439552.0000 - mae: 21128.6562 - mse: 562439552.0000 - val_loss: 1414905216.0000 - val_mae: 34347.2578 - val_mse: 1414905216.0000\n",
      "Epoch 109/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 559453248.0000 - mae: 21084.4160 - mse: 559453248.0000 - val_loss: 1399977216.0000 - val_mae: 34191.0273 - val_mse: 1399977216.0000\n",
      "Epoch 110/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 556474112.0000 - mae: 21037.0410 - mse: 556474112.0000 - val_loss: 1385643904.0000 - val_mae: 34041.0000 - val_mse: 1385643904.0000\n",
      "Epoch 111/1000\n",
      "10/10 [==============================] - 0s 3ms/step - loss: 553485952.0000 - mae: 20989.4551 - mse: 553485952.0000 - val_loss: 1371190016.0000 - val_mae: 33888.6328 - val_mse: 1371190016.0000\n",
      "Epoch 112/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 550402816.0000 - mae: 20943.0801 - mse: 550402816.0000 - val_loss: 1355689344.0000 - val_mae: 33722.4297 - val_mse: 1355689344.0000\n",
      "Epoch 113/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 547308096.0000 - mae: 20894.4121 - mse: 547308096.0000 - val_loss: 1340518528.0000 - val_mae: 33559.1055 - val_mse: 1340518528.0000\n",
      "Epoch 114/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 544207104.0000 - mae: 20844.3789 - mse: 544207104.0000 - val_loss: 1325522688.0000 - val_mae: 33398.0859 - val_mse: 1325522688.0000\n",
      "Epoch 115/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 541084288.0000 - mae: 20795.8555 - mse: 541084288.0000 - val_loss: 1310365696.0000 - val_mae: 33232.8438 - val_mse: 1310365696.0000\n",
      "Epoch 116/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 537913280.0000 - mae: 20744.1133 - mse: 537913280.0000 - val_loss: 1294732544.0000 - val_mae: 33060.9961 - val_mse: 1294732544.0000\n",
      "Epoch 117/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 534692160.0000 - mae: 20693.7891 - mse: 534692160.0000 - val_loss: 1278596352.0000 - val_mae: 32881.3516 - val_mse: 1278596352.0000\n",
      "Epoch 118/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 531441856.0000 - mae: 20641.3867 - mse: 531441856.0000 - val_loss: 1262620288.0000 - val_mae: 32701.6562 - val_mse: 1262620288.0000\n",
      "Epoch 119/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 528145216.0000 - mae: 20586.0742 - mse: 528145216.0000 - val_loss: 1246285824.0000 - val_mae: 32517.0684 - val_mse: 1246285824.0000\n",
      "Epoch 120/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 524838464.0000 - mae: 20532.0371 - mse: 524838464.0000 - val_loss: 1230365312.0000 - val_mae: 32336.5820 - val_mse: 1230365312.0000\n",
      "Epoch 121/1000\n",
      "10/10 [==============================] - 0s 3ms/step - loss: 521533088.0000 - mae: 20477.7266 - mse: 521533088.0000 - val_loss: 1214093696.0000 - val_mae: 32149.4688 - val_mse: 1214093696.0000\n",
      "Epoch 122/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 518163552.0000 - mae: 20419.8828 - mse: 518163552.0000 - val_loss: 1197592320.0000 - val_mae: 31958.3496 - val_mse: 1197592320.0000\n",
      "Epoch 123/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 514807648.0000 - mae: 20362.4023 - mse: 514807648.0000 - val_loss: 1181224832.0000 - val_mae: 31767.2754 - val_mse: 1181224832.0000\n",
      "Epoch 124/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 511407424.0000 - mae: 20306.6934 - mse: 511407424.0000 - val_loss: 1164351232.0000 - val_mae: 31567.7246 - val_mse: 1164351232.0000\n",
      "Epoch 125/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 507977888.0000 - mae: 20247.9648 - mse: 507977888.0000 - val_loss: 1147574912.0000 - val_mae: 31366.9160 - val_mse: 1147574912.0000\n",
      "Epoch 126/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 504481184.0000 - mae: 20186.8027 - mse: 504481184.0000 - val_loss: 1130077824.0000 - val_mae: 31155.2930 - val_mse: 1130077824.0000\n",
      "Epoch 127/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 500998560.0000 - mae: 20128.5273 - mse: 500998560.0000 - val_loss: 1113264000.0000 - val_mae: 30949.2559 - val_mse: 1113264000.0000\n",
      "Epoch 128/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 497527456.0000 - mae: 20066.0859 - mse: 497527456.0000 - val_loss: 1096112384.0000 - val_mae: 30737.9746 - val_mse: 1096112384.0000\n",
      "Epoch 129/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 494007648.0000 - mae: 20006.0918 - mse: 494007648.0000 - val_loss: 1078945408.0000 - val_mae: 30523.3496 - val_mse: 1078945408.0000\n",
      "Epoch 130/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 490438848.0000 - mae: 19941.3223 - mse: 490438848.0000 - val_loss: 1061240192.0000 - val_mae: 30300.0410 - val_mse: 1061240192.0000\n",
      "Epoch 131/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 486835808.0000 - mae: 19877.6543 - mse: 486835808.0000 - val_loss: 1043622400.0000 - val_mae: 30074.8242 - val_mse: 1043622400.0000\n",
      "Epoch 132/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 483239328.0000 - mae: 19812.3574 - mse: 483239328.0000 - val_loss: 1026164096.0000 - val_mae: 29850.0996 - val_mse: 1026164096.0000\n",
      "Epoch 133/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 479557632.0000 - mae: 19747.0879 - mse: 479557632.0000 - val_loss: 1007770816.0000 - val_mae: 29609.5996 - val_mse: 1007770816.0000\n",
      "Epoch 134/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 475898304.0000 - mae: 19680.2871 - mse: 475898304.0000 - val_loss: 990429504.0000 - val_mae: 29380.8379 - val_mse: 990429504.0000\n",
      "Epoch 135/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 472311648.0000 - mae: 19613.8027 - mse: 472311648.0000 - val_loss: 973017920.0000 - val_mae: 29148.4121 - val_mse: 973017920.0000\n",
      "Epoch 136/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 468664768.0000 - mae: 19547.7012 - mse: 468664768.0000 - val_loss: 955409536.0000 - val_mae: 28910.4883 - val_mse: 955409536.0000\n",
      "Epoch 137/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 465026240.0000 - mae: 19480.7188 - mse: 465026240.0000 - val_loss: 937617536.0000 - val_mae: 28666.6758 - val_mse: 937617536.0000\n",
      "Epoch 138/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 461270112.0000 - mae: 19409.1152 - mse: 461270112.0000 - val_loss: 919400960.0000 - val_mae: 28415.4277 - val_mse: 919400960.0000\n",
      "Epoch 139/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 457518528.0000 - mae: 19340.4688 - mse: 457518528.0000 - val_loss: 901527680.0000 - val_mae: 28165.3965 - val_mse: 901527680.0000\n",
      "Epoch 140/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 453798400.0000 - mae: 19269.0117 - mse: 453798400.0000 - val_loss: 883738944.0000 - val_mae: 27912.4102 - val_mse: 883738944.0000\n",
      "Epoch 141/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 450064992.0000 - mae: 19197.2500 - mse: 450064992.0000 - val_loss: 865590080.0000 - val_mae: 27649.6562 - val_mse: 865590080.0000\n",
      "Epoch 142/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 446270144.0000 - mae: 19125.0215 - mse: 446270144.0000 - val_loss: 847484288.0000 - val_mae: 27384.9004 - val_mse: 847484288.0000\n",
      "Epoch 143/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 442484320.0000 - mae: 19051.1211 - mse: 442484320.0000 - val_loss: 829320064.0000 - val_mae: 27115.6523 - val_mse: 829320064.0000\n",
      "Epoch 144/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 438661568.0000 - mae: 18977.2949 - mse: 438661568.0000 - val_loss: 811210112.0000 - val_mae: 26842.2383 - val_mse: 811210112.0000\n",
      "Epoch 145/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 434876608.0000 - mae: 18903.9492 - mse: 434876608.0000 - val_loss: 793429184.0000 - val_mae: 26570.6660 - val_mse: 793429184.0000\n",
      "Epoch 146/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 431017152.0000 - mae: 18827.3945 - mse: 431017152.0000 - val_loss: 775158080.0000 - val_mae: 26288.4277 - val_mse: 775158080.0000\n",
      "Epoch 147/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 427146720.0000 - mae: 18751.0977 - mse: 427146720.0000 - val_loss: 757725824.0000 - val_mae: 26016.2148 - val_mse: 757725824.0000\n",
      "Epoch 148/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 423315392.0000 - mae: 18673.9258 - mse: 423315392.0000 - val_loss: 739811520.0000 - val_mae: 25732.0996 - val_mse: 739811520.0000\n",
      "Epoch 149/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 419377216.0000 - mae: 18594.3496 - mse: 419377216.0000 - val_loss: 721600128.0000 - val_mae: 25439.6914 - val_mse: 721600128.0000\n",
      "Epoch 150/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 415490624.0000 - mae: 18515.7969 - mse: 415490624.0000 - val_loss: 704096768.0000 - val_mae: 25153.1367 - val_mse: 704096768.0000\n",
      "Epoch 151/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 411541824.0000 - mae: 18435.2070 - mse: 411541824.0000 - val_loss: 685905664.0000 - val_mae: 24851.7031 - val_mse: 685905664.0000\n",
      "Epoch 152/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 407608032.0000 - mae: 18354.3906 - mse: 407608032.0000 - val_loss: 668133056.0000 - val_mae: 24550.6289 - val_mse: 668133056.0000\n",
      "Epoch 153/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 403655808.0000 - mae: 18272.8789 - mse: 403655808.0000 - val_loss: 650386944.0000 - val_mae: 24245.5469 - val_mse: 650386944.0000\n",
      "Epoch 154/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 399715968.0000 - mae: 18189.7617 - mse: 399715968.0000 - val_loss: 632939904.0000 - val_mae: 23941.2988 - val_mse: 632939904.0000\n",
      "Epoch 155/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 395758784.0000 - mae: 18107.1504 - mse: 395758784.0000 - val_loss: 615329984.0000 - val_mae: 23628.8242 - val_mse: 615329984.0000\n",
      "Epoch 156/1000\n",
      "10/10 [==============================] - 0s 3ms/step - loss: 391662304.0000 - mae: 18020.9980 - mse: 391662304.0000 - val_loss: 596956160.0000 - val_mae: 23296.7793 - val_mse: 596956160.0000\n",
      "Epoch 157/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 387574720.0000 - mae: 17934.7422 - mse: 387574720.0000 - val_loss: 579374976.0000 - val_mae: 22973.5605 - val_mse: 579374976.0000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 158/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 383498528.0000 - mae: 17847.1602 - mse: 383498528.0000 - val_loss: 562068864.0000 - val_mae: 22650.3789 - val_mse: 562068864.0000\n",
      "Epoch 159/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 379400000.0000 - mae: 17758.0254 - mse: 379400000.0000 - val_loss: 544692544.0000 - val_mae: 22320.8125 - val_mse: 544692544.0000\n",
      "Epoch 160/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 375354048.0000 - mae: 17669.8594 - mse: 375354048.0000 - val_loss: 527685120.0000 - val_mae: 21990.6250 - val_mse: 527685120.0000\n",
      "Epoch 161/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 371304544.0000 - mae: 17580.9336 - mse: 371304544.0000 - val_loss: 510643872.0000 - val_mae: 21654.3652 - val_mse: 510643872.0000\n",
      "Epoch 162/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 367185472.0000 - mae: 17490.6719 - mse: 367185472.0000 - val_loss: 493305344.0000 - val_mae: 21303.7500 - val_mse: 493305344.0000\n",
      "Epoch 163/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 363041216.0000 - mae: 17398.3008 - mse: 363041216.0000 - val_loss: 476391008.0000 - val_mae: 20954.7891 - val_mse: 476391008.0000\n",
      "Epoch 164/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 358943584.0000 - mae: 17306.4375 - mse: 358943584.0000 - val_loss: 459621984.0000 - val_mae: 20601.9258 - val_mse: 459621984.0000\n",
      "Epoch 165/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 354825664.0000 - mae: 17213.5859 - mse: 354825664.0000 - val_loss: 443318208.0000 - val_mae: 20252.5195 - val_mse: 443318208.0000\n",
      "Epoch 166/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 350717088.0000 - mae: 17120.2148 - mse: 350717088.0000 - val_loss: 427264672.0000 - val_mae: 19900.2949 - val_mse: 427264672.0000\n",
      "Epoch 167/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 346498048.0000 - mae: 17022.7637 - mse: 346498048.0000 - val_loss: 410582656.0000 - val_mae: 19525.7109 - val_mse: 410582656.0000\n",
      "Epoch 168/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 342265024.0000 - mae: 16925.4121 - mse: 342265024.0000 - val_loss: 394255616.0000 - val_mae: 19150.9434 - val_mse: 394255616.0000\n",
      "Epoch 169/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 338051840.0000 - mae: 16826.8242 - mse: 338051840.0000 - val_loss: 378300352.0000 - val_mae: 18775.4844 - val_mse: 378300352.0000\n",
      "Epoch 170/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 333852096.0000 - mae: 16727.8789 - mse: 333852096.0000 - val_loss: 362623072.0000 - val_mae: 18397.6543 - val_mse: 362623072.0000\n",
      "Epoch 171/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 329617312.0000 - mae: 16627.5215 - mse: 329617312.0000 - val_loss: 347014496.0000 - val_mae: 18011.0566 - val_mse: 347014496.0000\n",
      "Epoch 172/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 325425664.0000 - mae: 16526.1602 - mse: 325425664.0000 - val_loss: 331774528.0000 - val_mae: 17623.2246 - val_mse: 331774528.0000\n",
      "Epoch 173/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 321211456.0000 - mae: 16424.2344 - mse: 321211456.0000 - val_loss: 317094560.0000 - val_mae: 17240.4473 - val_mse: 317094560.0000\n",
      "Epoch 174/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 317010752.0000 - mae: 16321.4395 - mse: 317010752.0000 - val_loss: 302270496.0000 - val_mae: 16842.1367 - val_mse: 302270496.0000\n",
      "Epoch 175/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 312876416.0000 - mae: 16219.2949 - mse: 312876416.0000 - val_loss: 288467552.0000 - val_mae: 16461.7012 - val_mse: 288467552.0000\n",
      "Epoch 176/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 308619200.0000 - mae: 16115.0107 - mse: 308619200.0000 - val_loss: 273797952.0000 - val_mae: 16044.8594 - val_mse: 273797952.0000\n",
      "Epoch 177/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 304377696.0000 - mae: 16006.8652 - mse: 304377696.0000 - val_loss: 259986048.0000 - val_mae: 15639.3252 - val_mse: 259986048.0000\n",
      "Epoch 178/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 300159040.0000 - mae: 15898.7871 - mse: 300159040.0000 - val_loss: 246302416.0000 - val_mae: 15223.7500 - val_mse: 246302416.0000\n",
      "Epoch 179/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 295944160.0000 - mae: 15791.5518 - mse: 295944160.0000 - val_loss: 233032448.0000 - val_mae: 14806.7422 - val_mse: 233032448.0000\n",
      "Epoch 180/1000\n",
      "10/10 [==============================] - 0s 3ms/step - loss: 291734080.0000 - mae: 15683.4199 - mse: 291734080.0000 - val_loss: 219836768.0000 - val_mae: 14377.5176 - val_mse: 219836768.0000\n",
      "Epoch 181/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 287503392.0000 - mae: 15570.8926 - mse: 287503392.0000 - val_loss: 207280000.0000 - val_mae: 13952.8232 - val_mse: 207280000.0000\n",
      "Epoch 182/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 283247200.0000 - mae: 15461.0410 - mse: 283247200.0000 - val_loss: 194672032.0000 - val_mae: 13510.5879 - val_mse: 194672032.0000\n",
      "Epoch 183/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 279004032.0000 - mae: 15345.5576 - mse: 279004032.0000 - val_loss: 182681888.0000 - val_mae: 13071.7051 - val_mse: 182681888.0000\n",
      "Epoch 184/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 274863040.0000 - mae: 15233.8311 - mse: 274863040.0000 - val_loss: 171448800.0000 - val_mae: 12642.9199 - val_mse: 171448800.0000\n",
      "Epoch 185/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 270652864.0000 - mae: 15118.7139 - mse: 270652864.0000 - val_loss: 159936928.0000 - val_mae: 12183.1230 - val_mse: 159936928.0000\n",
      "Epoch 186/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 266413152.0000 - mae: 15001.0674 - mse: 266413152.0000 - val_loss: 149086208.0000 - val_mae: 11728.0420 - val_mse: 149086208.0000\n",
      "Epoch 187/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 262203680.0000 - mae: 14881.4043 - mse: 262203680.0000 - val_loss: 138663696.0000 - val_mae: 11286.2676 - val_mse: 138663696.0000\n",
      "Epoch 188/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 258030080.0000 - mae: 14764.7422 - mse: 258030080.0000 - val_loss: 128879344.0000 - val_mae: 10860.2666 - val_mse: 128879344.0000\n",
      "Epoch 189/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 253888976.0000 - mae: 14646.4814 - mse: 253888976.0000 - val_loss: 119545296.0000 - val_mae: 10439.4238 - val_mse: 119545296.0000\n",
      "Epoch 190/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 249765456.0000 - mae: 14524.6846 - mse: 249765456.0000 - val_loss: 110617920.0000 - val_mae: 10011.7061 - val_mse: 110617920.0000\n",
      "Epoch 191/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 245582816.0000 - mae: 14403.0156 - mse: 245582816.0000 - val_loss: 102003488.0000 - val_mae: 9570.9658 - val_mse: 102003488.0000\n",
      "Epoch 192/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 241430304.0000 - mae: 14276.8594 - mse: 241430304.0000 - val_loss: 94031880.0000 - val_mae: 9132.9902 - val_mse: 94031880.0000\n",
      "Epoch 193/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 237340768.0000 - mae: 14155.1924 - mse: 237340768.0000 - val_loss: 86669944.0000 - val_mae: 8696.1270 - val_mse: 86669944.0000\n",
      "Epoch 194/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 233274080.0000 - mae: 14027.6328 - mse: 233274080.0000 - val_loss: 79706128.0000 - val_mae: 8247.5273 - val_mse: 79706128.0000\n",
      "Epoch 195/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 229224032.0000 - mae: 13900.2314 - mse: 229224032.0000 - val_loss: 73347952.0000 - val_mae: 7797.5273 - val_mse: 73347952.0000\n",
      "Epoch 196/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 225180720.0000 - mae: 13772.8027 - mse: 225180720.0000 - val_loss: 67475368.0000 - val_mae: 7351.6689 - val_mse: 67475368.0000\n",
      "Epoch 197/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 221188128.0000 - mae: 13643.5186 - mse: 221188128.0000 - val_loss: 62362740.0000 - val_mae: 6936.0068 - val_mse: 62362740.0000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 198/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 217245280.0000 - mae: 13514.0986 - mse: 217245280.0000 - val_loss: 57817896.0000 - val_mae: 6536.1479 - val_mse: 57817896.0000\n",
      "Epoch 199/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 213307424.0000 - mae: 13384.7090 - mse: 213307424.0000 - val_loss: 53749024.0000 - val_mae: 6215.2891 - val_mse: 53749024.0000\n",
      "Epoch 200/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 209447200.0000 - mae: 13252.0488 - mse: 209447200.0000 - val_loss: 50488212.0000 - val_mae: 5973.6743 - val_mse: 50488212.0000\n",
      "Epoch 201/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 205583264.0000 - mae: 13120.0547 - mse: 205583264.0000 - val_loss: 47669864.0000 - val_mae: 5764.4854 - val_mse: 47669864.0000\n",
      "Epoch 202/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 201702368.0000 - mae: 12984.5840 - mse: 201702368.0000 - val_loss: 45573128.0000 - val_mae: 5601.0171 - val_mse: 45573128.0000\n",
      "Epoch 203/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 197912240.0000 - mae: 12850.0908 - mse: 197912240.0000 - val_loss: 44185928.0000 - val_mae: 5496.5161 - val_mse: 44185928.0000\n",
      "Epoch 204/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 194154336.0000 - mae: 12710.3525 - mse: 194154336.0000 - val_loss: 43423576.0000 - val_mae: 5470.6279 - val_mse: 43423576.0000\n",
      "Epoch 205/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 190440416.0000 - mae: 12578.3438 - mse: 190440416.0000 - val_loss: 43393848.0000 - val_mae: 5499.5586 - val_mse: 43393848.0000\n",
      "Epoch 206/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 186662928.0000 - mae: 12441.2490 - mse: 186662928.0000 - val_loss: 44140852.0000 - val_mae: 5586.2407 - val_mse: 44140852.0000\n",
      "Epoch 207/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 182994736.0000 - mae: 12298.3730 - mse: 182994736.0000 - val_loss: 45546568.0000 - val_mae: 5672.6670 - val_mse: 45546568.0000\n",
      "Epoch 208/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 179417248.0000 - mae: 12163.0693 - mse: 179417248.0000 - val_loss: 47848024.0000 - val_mae: 5792.6123 - val_mse: 47848024.0000\n",
      "Epoch 209/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 175832672.0000 - mae: 12023.3027 - mse: 175832672.0000 - val_loss: 50855888.0000 - val_mae: 5925.3271 - val_mse: 50855888.0000\n",
      "Epoch 210/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 172340624.0000 - mae: 11879.7236 - mse: 172340624.0000 - val_loss: 54525268.0000 - val_mae: 6106.1509 - val_mse: 54525268.0000\n",
      "Epoch 211/1000\n",
      "10/10 [==============================] - 0s 3ms/step - loss: 168875536.0000 - mae: 11741.7695 - mse: 168875536.0000 - val_loss: 58945528.0000 - val_mae: 6323.6709 - val_mse: 58945528.0000\n",
      "Epoch 212/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 165446688.0000 - mae: 11593.7959 - mse: 165446688.0000 - val_loss: 64239540.0000 - val_mae: 6595.8101 - val_mse: 64239540.0000\n",
      "Epoch 213/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 162067680.0000 - mae: 11452.8291 - mse: 162067680.0000 - val_loss: 70413680.0000 - val_mae: 6893.1064 - val_mse: 70413680.0000\n",
      "Epoch 214/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 158742416.0000 - mae: 11301.6943 - mse: 158742416.0000 - val_loss: 77341056.0000 - val_mae: 7237.6045 - val_mse: 77341056.0000\n",
      "Epoch 215/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 155483600.0000 - mae: 11152.5723 - mse: 155483600.0000 - val_loss: 85162160.0000 - val_mae: 7653.0093 - val_mse: 85162160.0000\n"
     ]
    }
   ],
   "source": [
    "EPOCHS = 1000\n",
    "\n",
    "history = model.fit(\n",
    "  normed_train_data, train_labels,\n",
    "  epochs=EPOCHS, validation_split = 0.2,\n",
    "  callbacks=[early_stop]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 - 0s - loss: 804862912.0000 - mae: 24988.5488 - mse: 804862912.0000\n",
      "Testing set Mean Abs Error: 24988.55 Nodes\n"
     ]
    }
   ],
   "source": [
    "loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=2)\n",
    "\n",
    "print(\"Testing set Mean Abs Error: {:5.2f} Nodes\".format(mae))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_predictions = model.predict(normed_test_data).flatten()\n",
    "\n",
    "a = plt.axes(aspect='equal')\n",
    "plt.scatter(test_labels, test_predictions)\n",
    "plt.xlabel('True Values [Nodes]')\n",
    "plt.ylabel('Predictions [Nodes]')\n",
    "lims = [3000, 15000]\n",
    "plt.xlim(lims)\n",
    "plt.ylim(lims)\n",
    "plt.gcf().set_size_inches((9,9))\n",
    "_ = plt.plot(lims, lims)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5815.0\n",
       "0    5815.0\n",
       "0    5815.0\n",
       "0    5815.0\n",
       "0    5815.0\n",
       "      ...  \n",
       "0    7331.0\n",
       "0    7331.0\n",
       "0    7331.0\n",
       "0    7331.0\n",
       "0    7548.0\n",
       "Name: Nodes, Length: 108, dtype: float64"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 14030.975,  13958.536,  13741.924,  13890.784,  14019.522,\n",
       "        77448.65 ,  73453.49 ,  99093.68 ,  78157.88 ,  92078.664,\n",
       "        79668.86 ,  84932.82 ,  72869.68 ,  71711.375,  73042.48 ,\n",
       "        69598.26 ,  73121.92 ,  69333.555,  70452.055,  70636.305,\n",
       "        69843.06 ,  85496.86 ,  68599.34 ,  69103.016,  71473.555,\n",
       "        78429.805,  77066.34 ,  94785.51 ,  80699.82 ,  74896.75 ,\n",
       "        75229.484,  76919.67 ,  77765.9  ,  79002.95 ,  80660.164,\n",
       "        77559.086,  81584.89 ,  79841.06 ,  77500.555,  88276.71 ,\n",
       "        76029.69 ,  77378.9  ,  77094.055,  83549.266,  78234.74 ,\n",
       "        87221.68 ,  81503.24 ,  83026.48 ,  88315.27 ,  85835.94 ,\n",
       "        82444.445,  80802.44 , 122857.64 ,  83580.805,  81341.71 ,\n",
       "        12987.675,  13067.254,  13195.288,  13128.304,  13189.628,\n",
       "        99754.3  , 124709.414,  99318.61 , 100729.29 ,  96922.71 ,\n",
       "        97576.13 ,  95832.9  ,  96474.11 ,  98302.   , 101386.54 ,\n",
       "       112119.086, 110249.05 , 111203.64 , 109611.03 , 115850.8  ,\n",
       "       105035.22 , 103295.586, 101843.   , 100184.72 , 102901.53 ,\n",
       "       104002.35 , 104524.22 , 100745.77 , 105510.98 , 110063.73 ,\n",
       "       125125.14 , 104202.55 , 111835.195, 105813.67 , 106178.64 ,\n",
       "       107521.11 , 101919.695, 104563.67 , 105231.266,  99909.04 ,\n",
       "       101333.54 , 101314.92 , 100739.63 ,  82050.69 , 110327.72 ,\n",
       "        14092.407,  13767.99 ,  12686.803,  12629.581,  12709.018,\n",
       "        12874.389,  12316.239,  12442.271], dtype=float32)"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With ADAM intead of RMSProp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [],
   "source": [
    "#INSERT YOUR CODE TO BUILD AND COMPILE THE MODEL HERE\n",
    "def reg_adam_model():\n",
    "    model = keras.Sequential([\n",
    "        layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]),\n",
    "        layers.Dense(32, activation='relu'),\n",
    "        layers.Dense(1)\n",
    "    ])\n",
    "\n",
    "    optimizer = tf.keras.optimizers.Adam(0.01)\n",
    "\n",
    "    model.compile(loss='mse',\n",
    "                optimizer=optimizer,\n",
    "                metrics=['mae', 'mse'])\n",
    "    return model\n",
    "    #YOU CAN ALSO CHANGE THE MODEL TRAINING PARAMETERS BELOW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = reg_adam_model()\n",
    "early_stop=keras.callbacks.EarlyStopping(monitor='val_loss',patience=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 697871424.0000 - mae: 23566.2969 - mse: 697871424.0000 - val_loss: 2067930368.0000 - val_mae: 41997.6172 - val_mse: 2067930368.0000\n",
      "Epoch 2/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 697301504.0000 - mae: 23551.8906 - mse: 697301504.0000 - val_loss: 2062955904.0000 - val_mae: 41939.7930 - val_mse: 2062955904.0000\n",
      "Epoch 3/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 695368384.0000 - mae: 23502.3945 - mse: 695368384.0000 - val_loss: 2048836352.0000 - val_mae: 41773.8398 - val_mse: 2048836352.0000\n",
      "Epoch 4/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 690539584.0000 - mae: 23375.1133 - mse: 690539584.0000 - val_loss: 2017589504.0000 - val_mae: 41402.3633 - val_mse: 2017589504.0000\n",
      "Epoch 5/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 680534464.0000 - mae: 23115.6172 - mse: 680534464.0000 - val_loss: 1959570688.0000 - val_mae: 40701.4414 - val_mse: 1959570688.0000\n",
      "Epoch 6/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 663283264.0000 - mae: 22731.4395 - mse: 663283264.0000 - val_loss: 1861085568.0000 - val_mae: 39486.2344 - val_mse: 1861085568.0000\n",
      "Epoch 7/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 635117888.0000 - mae: 22197.1523 - mse: 635117888.0000 - val_loss: 1712681216.0000 - val_mae: 37591.3516 - val_mse: 1712681216.0000\n",
      "Epoch 8/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 596057344.0000 - mae: 21624.5977 - mse: 596057344.0000 - val_loss: 1507888896.0000 - val_mae: 35584.8906 - val_mse: 1507888896.0000\n",
      "Epoch 9/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 547171136.0000 - mae: 20869.4961 - mse: 547171136.0000 - val_loss: 1248134912.0000 - val_mae: 32911.7422 - val_mse: 1248134912.0000\n",
      "Epoch 10/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 490915648.0000 - mae: 19870.7637 - mse: 490915648.0000 - val_loss: 952258176.0000 - val_mae: 29251.7695 - val_mse: 952258176.0000\n",
      "Epoch 11/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 428620864.0000 - mae: 18651.3398 - mse: 428620864.0000 - val_loss: 639819200.0000 - val_mae: 24359.8594 - val_mse: 639819200.0000\n",
      "Epoch 12/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 360561856.0000 - mae: 17185.3691 - mse: 360561856.0000 - val_loss: 348385632.0000 - val_mae: 18207.0781 - val_mse: 348385632.0000\n",
      "Epoch 13/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 286711584.0000 - mae: 15417.6328 - mse: 286711584.0000 - val_loss: 138462272.0000 - val_mae: 11300.8281 - val_mse: 138462272.0000\n",
      "Epoch 14/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 220602832.0000 - mae: 13556.8076 - mse: 220602832.0000 - val_loss: 45470820.0000 - val_mae: 5548.0439 - val_mse: 45470820.0000\n",
      "Epoch 15/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 156796544.0000 - mae: 11296.2266 - mse: 156796544.0000 - val_loss: 94184016.0000 - val_mae: 8111.6768 - val_mse: 94184016.0000\n",
      "Epoch 16/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 112810256.0000 - mae: 9311.5469 - mse: 112810256.0000 - val_loss: 251361696.0000 - val_mae: 14019.3564 - val_mse: 251361696.0000\n",
      "Epoch 17/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 87163496.0000 - mae: 8048.6924 - mse: 87163496.0000 - val_loss: 414306496.0000 - val_mae: 18374.7539 - val_mse: 414306496.0000\n",
      "Epoch 18/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 77338656.0000 - mae: 7617.1104 - mse: 77338656.0000 - val_loss: 478619552.0000 - val_mae: 19895.3750 - val_mse: 478619552.0000\n",
      "Epoch 19/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 70569912.0000 - mae: 7278.0024 - mse: 70569912.0000 - val_loss: 448549888.0000 - val_mae: 19354.0195 - val_mse: 448549888.0000\n",
      "Epoch 20/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 62957320.0000 - mae: 6796.3945 - mse: 62957320.0000 - val_loss: 326650368.0000 - val_mae: 16591.8164 - val_mse: 326650368.0000\n",
      "Epoch 21/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 55613736.0000 - mae: 6372.5640 - mse: 55613736.0000 - val_loss: 281926816.0000 - val_mae: 15386.6953 - val_mse: 281926816.0000\n",
      "Epoch 22/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 49728128.0000 - mae: 6082.8882 - mse: 49728128.0000 - val_loss: 249820288.0000 - val_mae: 14460.7480 - val_mse: 249820288.0000\n",
      "Epoch 23/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 44394696.0000 - mae: 5751.9712 - mse: 44394696.0000 - val_loss: 214646864.0000 - val_mae: 13408.0371 - val_mse: 214646864.0000\n",
      "Epoch 24/1000\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 39587560.0000 - mae: 5433.8296 - mse: 39587560.0000 - val_loss: 188391360.0000 - val_mae: 12565.8848 - val_mse: 188391360.0000\n"
     ]
    }
   ],
   "source": [
    "EPOCHS = 1000\n",
    "\n",
    "history = model.fit(\n",
    "  normed_train_data, train_labels,\n",
    "  epochs=EPOCHS, validation_split = 0.2,\n",
    "  callbacks=[early_stop]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 - 0s - loss: 837124928.0000 - mae: 25937.2656 - mse: 837124928.0000\n",
      "Testing set Mean Abs Error: 25937.27 Nodes\n"
     ]
    }
   ],
   "source": [
    "loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=2)\n",
    "\n",
    "print(\"Testing set Mean Abs Error: {:5.2f} Nodes\".format(mae))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DxwDnNo/PBY7tGz+/qu6rqpuA7cDzkxwA7FlVl1dVAR9asM78e10IHDm/90WS2sBYkYZn0Mua50PgHwEfrKqvrjEOnlxVtwNU1e1J9m/GNwNf6nvdjmbs/ubxwvH5dW5p3uuBJHcB+wLf3WXyycn09tJw4IEHrmHa0uT5bczdYqxIwzXoHpYrk3yGXrBcmuSJwENDnMdi8VPLjC+3zq6DVWdV1VxVzW3atGmNU5Qmx29j7hZjRRq+QYPlJHrnmzyvqn4EPJbeYaHV+k5zmIfm9x3N+A7gKX2v2wLc1oxvWWT8Uesk2Q3Yi10PQUlTwW9j7g5jRRqNQYOlgEOB1zXPHw/ssYa/dzFwYvP4ROCTfePHNVf+HETv5NormsNH9yQ5ojkEdcKCdebf6xXA55rzXKSp47cxd4OxIo3OoMHyXuAFwKua5/cA/3G5FZKcB1wOHJJkR5KT6J2o+ytJbgR+pXlOVV0HXABcD/wl8Nqqmv/PyVOAs+mdiPu3wCXN+PuBfZNsB95Ac8WRNI38Nub2M1ak0Rr0pNtfqKrDklwFUFU/SPLY5VaoqlctsejIJV7/DuAdi4xvA561yPj/BF650sSlaXDqUYdw2kXXPOqwkN/G3B7GijR6gwbL/c19UQogySaGe9KtpGX4bcztZaxI4zFosJwB/AWwf5J30Dtn5G0jm5WkXfhtzO1jrEjjM1CwVNVHk1xJ73BOgGOr6usjnZkktZixIo3XssGSZJ++p3cA5/UvqyovI5Y0c4wVafxW2sNyJY/cwO1A4AfN472Bm4GDRjk5SWobY0WajJW+rfmgqnoacCnwj6tqv6raF3gZcNE4JihJbWGsSJMz6H1YnldVn55/UlWXAP9wNFOSpPYxVqTJGvQqoe8meRvwEXqHiI4HvjeyWUlSixgr0uQNuoflVcAmepc2fwLYn0fueitJU8tYkdph0Muavw/8bpI9gYeq6u9GOy1JmjxjRWqPgfawJHl2c1v+a4DrklyZZJfb5UvStDBWpHYZ9JDQ+4A3VNVTq+qpwBuBs0Y3LUmaHGNFap9Bg+XxVfVX80+q6vPA40cyI0maIGNFaqdBrxL6H0n+FfDh5vnxwE2jmZIkTYaxIrXXoHtYfoveVUIX0btSaBPw6lFNSpLGzViR2m3Qq4R+ALxuxHORpIkwVqT2W+nLDz9I70Zxi6mqOmn4U5Kk8TFWpG5YaQ/LpxYZOxD4PWDD0GcjSWNkrEjdsWywVNWfzz9O8jTgXwIvAt4JvH+0U5Ok0TFWpG5Z8aTbJD+b5CPAfwb+G3BoVZ1ZVT8e+ewkaQSMFal7VjqH5ePAHPBu4PXAg8CeSYCHb9kvSZ1hrEjdtNI5LM+jd9Lt79O7uy1Amt8F+L90SZ1hrEjdtdI5LFvHNA9JGiljReq2Zc9hSfKTK73BIK+RpEkyVqTuW+mk208P8B6DvEaSJsJYkabDSuewPCfJ3cssD7DcckmaGGNFmh4rncPizeEkdZKxIk2XQb/8UJI6w1iRpo/BImmqGCvSdDJYJE0NY0WaXgMFS5KnJ9m9efyLSV6XZO+RzkySVsFYkabboHtY/hx4MMkz6H3p4UHAn41sVpK0CsaKNP0GDZaHquoB4NeAP6mq1wMHjG5akjQYY0WaDYMGy/1JXgWcCHyqGXvMaKYkSYMxVqTZMWiwvBp4AfCOqropyUHAR0Y3LUlanrEizZaV7nQLQFVdD7yu7/lNwDtHNSlJWo6xIs2egYIlyQuBPwCe2qwToKrK/5eQNFbGijSbBgoWelcGvR64EnhwdNORpKUZK9LsGjRY7qqqS0Y6E0lahrEizbZBg+WvkpwOXATcNz9YVV8ZyawkqY+xImnQYPmF5vdc31gBvzTc6UjSoxkrkmDwq4RePOqJSNJCxoqkeYN+l9BeSf44ybbm598m2WvUk5M0u4wVSf0GvXHcB4B7gH/a/NwNfHBUk5I024wVSQsNeg7L06vqn/Q9/8MkV49gPpJmnLEiaTGD7mG5N8nfn3/S3Eju3tFMSdKsMlYkLWXQPSynAOc2560E+D7wm6OalKTZY6xIWs6gVwldDTwnyZ7N87tHOSlJs8VYkbSSZYMlyfFV9ZEkb1gwDkBV/fEI5yZpBhgrkgax0h6Wxze/n7jIshryXCTNGGNF0qCWDZaqel/z8L9W1V/3L2tOvJWkNTFWJK3GoFcJ/fsBxyRpRcaKpNVa6RyWFwB/D9i04DyWPYENo5yYpOlkrEhai5XOYXks8ITmdf3nsdwNvGJUk5I0nYwVSWu10jksXwC+kOScqvrWmOYkaQoZK5LWY9BzWM5Osvf8kyRPSnLpaKYkadoYK5LWa9Bg2a+q7px/UlU/APYfyYwkTRVjRdIwDBosDyU5cP5JkqfifVgkrcBYkTQsg36X0FuB/5bkC83zFwEnj2ZKkqaBsSJpmAb9LqG/THIYcAS9Lz98fVV9d6Qzk9RZxoqkYVv2kFCSn2l+HwYcCNwG3Aoc2IxJ0qMYK5JGYaU9LG8EXgP820WWFfBLQ5+RpM4yViSNykr3YXlN8/vF45mOpK4yViSN0kq35v/15ZZX1UXDnY6kLjJWJI3aSoeE/nHze3963yn0ueb5i4HPAwaLNOOMFUnjsNIhoVcDJPkUcGhV3d48PwD4j6OfnqQ2M1YkjcugN47bOh8rje8APz2C+UjqCGNF0jgNeuO4zzffHXQevauDjgP+amSzktRqxoqkcRv0xnG/neTX6N3hFuCsqvqL0U1LUlsZK5ImYdA9LABfAe6pqv+a5HFJnlhV94xqYpLax1iRNCkDncOS5DXAhcD7mqHNwCdGNCdJLWSsSJqkQU+6fS3wQuBugKq6kd6lzpJmgLEiadIGDZb7qurH80+S7Ebv5FtJU85YkdQGgwbLF5L8S2Bjkl8BPg7857X+0SSvT3JdkmuTnJdkjyT7JPlskhub30/qe/1pSbYnuSHJUX3jhye5pll2RpKsdU6SdmWsSGqLQYPlzcBO4BrgXwCfBt62lj+YZDPwOmCuqp4FbKB3mfRbgMuq6mDgsuY5SQ5tlj8TOBp4b5INzdudCZwMHNz8HL2WOUnalbEiqU1WvEooyU8AX2vi4j8N8e9uTHI/8DjgNuA04Beb5efSu/X/m4FjgPOr6j7gpiTbgecn+SawZ1Vd3szzQ8CxwCVDmqM0s4wVSW2z4h6WqnoI+GqSA4fxB6vqVuDdwM3A7cBdVfUZ4Mnzd9Ntfs+f1LsZuKXvLXY0Y5ubxwvHd5Hk5CTbkmzbuXPnMD6GNLWMFUltNOh9WA4ArktyBfDD+cGqevlq/2BzbsoxwEHAncDHkxy/3CqLjNUy47sOVp0FnAUwNzfnycLSEowVSW01aLD84RD/5i8DN1XVToAkF9H7JujvJDmgqm5vvlzxjub1O4Cn9K2/hd4hpB3N44XjktbAWJHUZsseEmqu3vk94JXAzwB/XVVfmP9Z49+8GTiiuVtugCOBrwMXAyc2rzkR+GTz+GLguCS7JzmI3sm1VzSHje5JckTzPif0rSNpFYwVSW230h6Wc4H7gf8P+FXgUOB31/MHq+rLSS6kd6v/B4Cr6B2ueQJwQZKT6EXNK5vXX5fkAuD65vWvraoHm7c7BTgH2EjvZFtPuJVWyViR1AWpWvqUjiTXVNWzm8e70duzcdi4JjcKc3NztW3btklPQ2oFY0VSmyS5sqrmFlu20lVC988/qKoHhjorSRNlrEjqkpUOCT0nyd3N49C7d8rdzeOqqj1HOjtJI2GsSOqaZYOlqjYst1xS9xgrkrpo0FvzS5oCxoqkrjJYpBlhrEjqMoNFmgHGiqSuM1ikKWesSJoGBos0xYwVSdPCYJGmlLEiaZoYLNIUMlYkTRuDRZoyxoqkaWSwSFPEWJE0rQwWaUoYK5KmmcEiTQFjRdK0M1ikjjNWJM0Cg0XqMGNF0qwwWKSOMlYkzRKDReogY0XSrDFYpI4xViTNIoNF6hBjRdKsMlikjjBWJM0yg0XqAGNF0qwzWKSWM1YkyWCRWs1YkaQeg0VqKWNFkh5hsEgtZKxI0qMZLFLLGCuStCuDRWoRY0WSFmewSC1hrEjS0gwWqQWMFUlansEiTZixIkkrM1ikCTJWJGkwBos0IcaKJA3OYJEmwFiRpNUxWKQxM1YkafUMFmmMjBVJWhuDRRoTY0WS1s5gkcbAWJGk9TFYpBEzViRp/QwWaYSMFUkaDoNFGhFjRZKGx2CRRsBYkaThMlikITNWJGn4DBZpiIwVSRoNg0UaEmNFkkbHYJGGwFiRpNEyWKR1MlYkafQMFmkdjBVJGg+DRVojY0WSxsdgkdbAWJGk8TJYpFUyViRp/AwWaRWMFUmaDINFGpCxIkmTY7BIAzBWJGmyDBZpBcaKJE2ewSItw1iRpHYwWKQlGCuS1B4Gi7QIY0WS2sVgkRYwViSpfQwWqY+xIkntZLBIDWNFktrLYJEwViSp7QwWzTxjRZLaz2DRTDNWJKkbDBbNLGNFkrrDYNFMMlYkqVsMFs0cY0WSusdg0UwxViSpmwwWzQxjRZK6y2DRTDBWJKnbDBZNPWNFkrrPYNFUM1YkaTpMJFiS7J3kwiTfSPL1JC9Isk+Szya5sfn9pL7Xn5Zke5IbkhzVN354kmuaZWckySQ+j9rJWJGk6TGpPSz/DvjLqvoZ4DnA14G3AJdV1cHAZc1zkhwKHAc8EzgaeG+SDc37nAmcDBzc/Bw9zg+h9jJWJGm6jD1YkuwJvAh4P0BV/biq7gSOAc5tXnYucGzz+Bjg/Kq6r6puArYDz09yALBnVV1eVQV8qG8dzTBjRZKmzyT2sDwN2Al8MMlVSc5O8njgyVV1O0Dze//m9ZuBW/rW39GMbW4eLxzfRZKTk2xLsm3nzp3D/TRqFWNFkqbTJIJlN+Aw4Myqei7wQ5rDP0tY7LyUWmZ818Gqs6pqrqrmNm3atNr5qiOMFUmaXpMIlh3Ajqr6cvP8QnoB853mMA/N7zv6Xv+UvvW3ALc141sWGdcMMlYkabqNPViq6tvALUkOaYaOBK4HLgZObMZOBD7ZPL4YOC7J7kkOondy7RXNYaN7khzRXB10Qt86miHGiiRNv90m9Hd/B/hokscC/wN4Nb14uiDJScDNwCsBquq6JBfQi5oHgNdW1YPN+5wCnANsBC5pfjRDjBVJmg3pXWAzO+bm5mrbtm2TnoaGwFiRpOmS5MqqmltsmXe6VScZK5I0WwwWdY6xIkmzx2BRpxgrkjSbDBZ1hrEiSbPLYFEnGCuSNNsMFrWesSJJMljUasaKJAkMFrWYsSJJmmewqJWMFUlSP4NFrWOsSJIWMljUKsaKJGkxBotaw1iRJC3FYFErGCuSpOUYLJo4Y0WStBKDRRNlrEiSBmGwaGKMFUnSoAwWTYSxIklaDYNFY2esSJJWy2DRWBkrkqS1MFg0NsaKJGmtDBaNhbEiSVoPg0UjZ6xIktbLYNFIGSuSpGEwWDQyxookaVgMFo2EsSJJGiaDRUNnrEiShs1g0VAZK5KkUTBYNDTGiiRpVAwWDYWxIkkaJYNF62asSJJGzWDRuhgrkqRxMFi0ZsaKJGlcDBatibEiSRong0WrZqxIksbNYNGqGCuSpEkwWDQwY0WSNCkGiwZirEiSJslg0YqMFUnSpBksWpaxIklqA4NFSzJWJEltYbBoUcaKJKlNDBbtwliRJLWNwaJHMVYkSW1ksOhhxookqa0MFgHGiiSp3QwWGSuSpNYzWGacsSJJ6gKDZYYZK5KkrjBYZpSxIknqEoNlBhkrkqSuMVhmjLEiSeoig2WGGCuSpK4yWGaEsSJJ6jKDZQYYK5KkrjNYppyxIkmaBgbLFDNWJEnTwmCZUsaKJGmaGCxTyFiRJE0bg2XKGCuSpGlksEwRY0WSNK0MlilhrEiSppnBMgWMFUnStDNYOs5YkSTNAoOlw4wVSdKsMFg6yliRJM0Sg6WDjBVJ0qwxWDrGWJEkzSKDpUOMFUnSrDJYOsJYkSTNMoOlA4wVSdKsM1hazliRJMlgaTVjRZKkHoOlpYwVSZIeMbFgSbIhyVVJPtU83yfJZ5Pc2Px+Ut9rT0uyPckNSY7qGz88yTXNsjOSZBKfZdiMFUmSHm2Se1h+F/h63/O3AJdV1cHAZc1zkhwKHAc8EzgaeG+SDc06ZwInAwc3P0ePZ+qjY6xIkrSriQRLki3AS4Gz+4aPAc5tHp8LHNs3fn5V3VdVNwHbgecnOQDYs6our6oCPtS3TicZK5IkLW5Se1j+BHgT8FDf2JOr6naA5vf+zfhm4Ja+1+1oxjY3jxeO7yLJyUm2Jdm2c+fOoXyAYTNWJEla2tiDJcnLgDuq6spBV1lkrJYZ33Ww6qyqmququU2bNg34Z8fHWJEkaXm7TeBvvhB4eZJ/BOwB7JnkI8B3khxQVbc3h3vuaF6/A3hK3/pbgNua8S2LjHeKsSJJ0srGvoelqk6rqi1VtZXeybSfq6rjgYuBE5uXnQh8snl8MXBckt2THETv5NormsNG9yQ5ork66IS+dTrBWJEkaTCT2MOylHcCFyQ5CbgZeCVAVV2X5ALgeuAB4LVV9WCzzinAOcBG4JLmpxOMFUmSBpfeBTazY25urrZt2zbRORgrkiTtKsmVVTW32DLvdDtmxookSatnsIyRsSJJ0toYLGNirEiStHYGyxgYK5IkrY/BMmLGiiRJ62ewjJCxIknScBgsI2KsSJI0PAbLCBgrkiQNl8EyZMaKJEnDZ7AMkbEiSdJoGCxDYqxIkjQ6BssQGCuSJI2WwbJOxookSaNnsKyDsSJJ0ngYLGtkrEiSND4GyxoYK5IkjZfBskrGiiRJ42ewrIKxIknSZBgsAzJWJEmaHINlAMaKJEmTZbCswFiRJGnyDJZlGCuSJLWDwbIEY0WSpPYwWBZhrEiS1C4GywLGiiRJ7WOw9DFWJElqJ4OlYaxIktReBgvGiiRJbTfzwWKsSJLUfjMdLMaKJEndMLPBYqxIktQdMxksxookSd0yc8FSYKxIktQxu016AuN2y/d/xPeMFUmSOmXmguWue+/n3xgrkiR1Sqpq0nMYqyQ7gW9Neh4r2A/47qQn0XFuw+FwO66f23A43I7D0fbt+NSq2rTYgpkLli5Isq2q5iY9jy5zGw6H23H93IbD4XYcji5vx5k76VaSJHWPwSJJklrPYGmnsyY9gSngNhwOt+P6uQ2Hw+04HJ3djp7DIkmSWs89LJIkqfUMljFKsiHJVUk+1TzfJ8lnk9zY/H5S32tPS7I9yQ1JjuobPzzJNc2yM5JkEp9lEpLsneTCJN9I8vUkL3Abrl6S1ye5Lsm1Sc5LsofbcWVJPpDkjiTX9o0Nbbsl2T3Jx5rxLyfZOtYPOAZLbMPTm/9Nfy3JXyTZu2+Z23ARi23HvmW/n6SS7Nc3Nh3bsar8GdMP8Abgz4BPNc//X+AtzeO3AO9qHh8KfBXYHTgI+FtgQ7PsCuAFQIBLgF+d9Oca4/Y7F/jnzePHAnu7DVe9DTcDNwEbm+cXAL/pdhxo270IOAy4tm9saNsN+D+BP20eHwd8bNKfeUzb8CXAbs3jd7kN17Ydm/GnAJfSu9fYftO2Hd3DMiZJtgAvBc7uGz6G3r+EaX4f2zd+flXdV1U3AduB5yc5ANizqi6v3j9JH+pbZ6ol2ZPe/0jfD1BVP66qO3EbrsVuwMYkuwGPA27D7biiqvoi8P0Fw8Pcbv3vdSFw5LTttVpsG1bVZ6rqgebpl4AtzWO34RKW+GcR4D3Am+h9bd68qdmOBsv4/Am9f5Ae6ht7clXdDtD83r8Z3wzc0ve6Hc3Y5ubxwvFZ8DRgJ/DB9A6rnZ3k8bgNV6WqbgXeDdwM3A7cVVWfwe24VsPcbg+v0/wL/C5g35HNvJ1+i95/6YPbcFWSvBy4taq+umDR1GxHg2UMkrwMuKOqrhx0lUXGapnxWbAbvV2gZ1bVc4Ef0tsFvxS34SKacyyOobdr+KeAxyc5frlVFhmb+e04gLVst5nepkneCjwAfHR+aJGXuQ0XkeRxwFuBf73Y4kXGOrkdDZbxeCHw8iTfBM4HfinJR4DvNLvlaH7f0bx+B71jkfO20Nttv4NHdpf2j8+CHcCOqvpy8/xCegHjNlydXwZuqqqdVXU/cBHw93A7rtUwt9vD6zSH6/Zi8d3+UyfJicDLgN9oDk+A23A1nk7vP0K+2vx7ZgvwlSQ/yRRtR4NlDKrqtKraUlVb6Z3A9LmqOh64GDixedmJwCebxxcDxzVnah8EHAxc0exyvifJEc3xxBP61plqVfVt4JYkhzRDRwLX4zZcrZuBI5I8rvn8RwJfx+24VsPcbv3v9Qp6/z8x8f+qHbUkRwNvBl5eVT/qW+Q2HFBVXVNV+1fV1ubfMzuAw5r/35ye7Tjps35n7Qf4RR65Smhf4DLgxub3Pn2veyu9s7lvoO/qC2AOuLZZ9h9obv43Cz/AzwPbgK8BnwCe5DZc03b8Q+AbzTb4ML2rB9yOK2+38+id93M/vX8hnDTM7QbsAXyc3kmRVwBPm/RnHtM23E7vfImrm58/dRuufjsuWP5NmquEpmk7eqdbSZLUeh4SkiRJrWewSJKk1jNYJElS6xkskiSp9QwWSZLUegaLJElqPYNFmlFJ9k1ydfPz7SS39j1/7BDe/w+S/NGCsZ9P8vUV1vn99f7tZd7/m0muSTLXPP98km19y+eSfH6V7/n5+fdb5XqvT3Jzkv+w2nWlWbTbpCcgaTKq6nv0bsZHkj8A/q6q3j2/PMlu9ci36K7FefS+yO60vrHjgD9bx3sOw4ur6rt9z/dP8qtVdcmSa4xAVb0nyQ/o3bxL0grcwyLpYUnOSfLHSf4KeNfCPR5Jrk2ytXl8fJIrmj0y70uyof+9quoG4M4kv9A3/E+B85O8JsnfJPlqkj9vvrxt4Vwe3nORZL/mO1JIsiHJ6c36X0vyL5rxA5J8sZnPtUn+wYAf+3TgbYv8/T2SfLDZI3NVkhc34xuTnN/87Y8BG/vWeUmSy5N8JcnHkzyhGX9nkuubdd698G9JWpnBImmhnwZ+uareuNQLkvws8M+AF1bVzwMPAr+xyEvPo7dXhSRHAN+rqhuBi6rqeVX1HHrfZXTSKuZ3EnBXVT0PeB7wmuY7Uv434NJmPs+hd5v3QVwO3DcfJH1eC1BVzwZeBZybZA/gFOBHVfVzwDuAw5vPtx+98PnlqjqM3tdIvCHJPsCvAc9s1nn7Kj6rpIaHhCQt9PGqenCF1xxJ71/Uf9P73jQ28sg3Ffc7H/j/k7yRXric14w/K8nbgb2BJwCXrmJ+LwF+Lskrmud70ftCt78BPpDkMcAnqurqVbzn2+nFxpv7xv4+8O8BquobSb5FL+ZeBJzRjH8tydea1x8BHAr8dbNNHksvhu4G/idwdpL/AnxqFfOS1DBYJC30w77HD/DoPbF7NL8DnFtV/een7KKqbmkO5fxD4J8AL2gWnQMcW1VfTfKb9L4UdKH+v71H33iA36mqXSInyYuAlwIfTnJ6VX1oufn1zfNzSf4fetHR/3eWXGWRsQCfrapXLTKv59OLvOOA3wZ+aZB5SXqEh4QkLeebwGEASQ4DDmrGLwNekWT/Ztk+SZ66xHucB7wH+Nuq2tGMPRG4vdkbstihpPm/fXjz+BV945cCpzTrkuSnkzy++ft3VNV/At4/P+9VeAfwpr7nX5yfW5KfBg6k9223/ePPAn6uef2XgBcmeUaz7HHN3J4A7FVVnwZ+j+ZEZ0mr4x4WScv5c+CEJFfTO+Ty3wGq6vokbwM+k+Qn6H3N/WuBby3yHh8H/h3wO31j/wr4cvP6a+gFzELvBi5I8r8Dn+sbPxvYCnwlvWMvO4Fj6e2lOTXJ/cDfASes5oNW1aeT7Owbei/wp0muobe35zer6r4kZwIfbA4FXQ1c0ay/s9lbdF6S3Zv3eBtwD/DJ5vyXAK9fzbwk9aRqsT2bkjR9msNTcwsua56YJnDmquq3Jz0Xqe08JCRpluwELlvLjd6GLcnr6d2j5u5Jz0XqAvewSJKk1nMPiyRJaj2DRZIktZ7BIkmSWs9gkSRJrWewSJKk1vtfaMt4/f+ipGgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_predictions = model.predict(normed_test_data).flatten()\n",
    "\n",
    "a = plt.axes(aspect='equal')\n",
    "plt.scatter(test_labels, test_predictions)\n",
    "plt.xlabel('True Values [Nodes]')\n",
    "plt.ylabel('Predictions [Nodes]')\n",
    "lims = [3000, 15000]\n",
    "plt.xlim(lims)\n",
    "plt.ylim(lims)\n",
    "plt.gcf().set_size_inches((9,9))\n",
    "_ = plt.plot(lims, lims)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Writing a flexible hyperparameter search model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [],
   "source": [
    "#INSERT YOUR CODE TO BUILD AND COMPILE THE MODEL HERE\n",
    "def flex_reg_model(optimizer='Adam',activation='relu'):\n",
    "    model = keras.Sequential([\n",
    "        layers.Dense(64, activation=activation, input_shape=[len(train_dataset.keys())]),\n",
    "        layers.Dense(32,activation=activation ),\n",
    "        layers.Dense(1)\n",
    "    ])\n",
    "\n",
    "    optimizer = tf.keras.optimizers.Adam(0.01)\n",
    "\n",
    "    model.compile(loss='mse',\n",
    "                optimizer=optimizer,\n",
    "                metrics=['mae', 'mse'])\n",
    "    return model\n",
    "    #YOU CAN ALSO CHANGE THE MODEL TRAINING PARAMETERS BELOW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = flex_reg_model()\n",
    "early_stop=keras.callbacks.EarlyStopping(monitor='val_loss',patience=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 697937536.0000 - mae: 23567.7539 - mse: 697937536.0000 - val_loss: 2068981760.0000 - val_mae: 42009.2578 - val_mse: 2068981760.0000\n",
      "Epoch 2/1000\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 697873216.0000 - mae: 23566.1211 - mse: 697873216.0000 - val_loss: 2068595968.0000 - val_mae: 42004.7695 - val_mse: 2068595968.0000\n",
      "Epoch 3/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 697770112.0000 - mae: 23563.5293 - mse: 697770112.0000 - val_loss: 2067934208.0000 - val_mae: 41997.1055 - val_mse: 2067934208.0000\n",
      "Epoch 4/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 697606016.0000 - mae: 23559.3438 - mse: 697606016.0000 - val_loss: 2066888704.0000 - val_mae: 41985.0000 - val_mse: 2066888704.0000\n",
      "Epoch 5/1000\n",
      "3/3 [==============================] - 0s 7ms/step - loss: 697348992.0000 - mae: 23552.8730 - mse: 697348992.0000 - val_loss: 2065330432.0000 - val_mae: 41966.9609 - val_mse: 2065330432.0000\n",
      "Epoch 6/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 696974592.0000 - mae: 23543.1445 - mse: 696974592.0000 - val_loss: 2063105792.0000 - val_mae: 41941.1680 - val_mse: 2063105792.0000\n",
      "Epoch 7/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 696446976.0000 - mae: 23529.9883 - mse: 696446976.0000 - val_loss: 2060018432.0000 - val_mae: 41905.3398 - val_mse: 2060018432.0000\n",
      "Epoch 8/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 695710400.0000 - mae: 23511.4824 - mse: 695710400.0000 - val_loss: 2055873280.0000 - val_mae: 41857.1562 - val_mse: 2055873280.0000\n",
      "Epoch 9/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 694728320.0000 - mae: 23486.2129 - mse: 694728320.0000 - val_loss: 2050448000.0000 - val_mae: 41793.9570 - val_mse: 2050448000.0000\n",
      "Epoch 10/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 693452800.0000 - mae: 23453.3340 - mse: 693452800.0000 - val_loss: 2043443968.0000 - val_mae: 41712.1953 - val_mse: 2043443968.0000\n",
      "Epoch 11/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 691788288.0000 - mae: 23411.7012 - mse: 691788288.0000 - val_loss: 2034572032.0000 - val_mae: 41608.3750 - val_mse: 2034572032.0000\n",
      "Epoch 12/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 689769472.0000 - mae: 23357.5098 - mse: 689769472.0000 - val_loss: 2023529088.0000 - val_mae: 41478.7266 - val_mse: 2023529088.0000\n",
      "Epoch 13/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 687180672.0000 - mae: 23290.7148 - mse: 687180672.0000 - val_loss: 2010061568.0000 - val_mae: 41319.8516 - val_mse: 2010061568.0000\n",
      "Epoch 14/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 684007936.0000 - mae: 23209.1855 - mse: 684007936.0000 - val_loss: 1993773312.0000 - val_mae: 41126.7812 - val_mse: 1993773312.0000\n",
      "Epoch 15/1000\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 680200576.0000 - mae: 23110.1543 - mse: 680200576.0000 - val_loss: 1974214272.0000 - val_mae: 40893.8320 - val_mse: 1974214272.0000\n",
      "Epoch 16/1000\n",
      "3/3 [==============================] - 0s 7ms/step - loss: 675612928.0000 - mae: 22990.0918 - mse: 675612928.0000 - val_loss: 1951033728.0000 - val_mae: 40616.1523 - val_mse: 1951033728.0000\n",
      "Epoch 17/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 670344896.0000 - mae: 22867.5586 - mse: 670344896.0000 - val_loss: 1923826944.0000 - val_mae: 40288.1992 - val_mse: 1923826944.0000\n",
      "Epoch 18/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 664104128.0000 - mae: 22750.4199 - mse: 664104128.0000 - val_loss: 1892424064.0000 - val_mae: 39905.9805 - val_mse: 1892424064.0000\n",
      "Epoch 19/1000\n",
      "3/3 [==============================] - 0s 7ms/step - loss: 656880128.0000 - mae: 22607.8008 - mse: 656880128.0000 - val_loss: 1856329088.0000 - val_mae: 39462.5625 - val_mse: 1856329088.0000\n",
      "Epoch 20/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 648749760.0000 - mae: 22453.5137 - mse: 648749760.0000 - val_loss: 1815200512.0000 - val_mae: 38952.0078 - val_mse: 1815200512.0000\n",
      "Epoch 21/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 639501760.0000 - mae: 22275.7871 - mse: 639501760.0000 - val_loss: 1768862336.0000 - val_mae: 38369.2695 - val_mse: 1768862336.0000\n",
      "Epoch 22/1000\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 629074240.0000 - mae: 22103.7715 - mse: 629074240.0000 - val_loss: 1717181184.0000 - val_mae: 37708.7305 - val_mse: 1717181184.0000\n",
      "Epoch 23/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 618084864.0000 - mae: 21949.5469 - mse: 618084864.0000 - val_loss: 1659756288.0000 - val_mae: 36971.8867 - val_mse: 1659756288.0000\n",
      "Epoch 24/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 605000128.0000 - mae: 21757.5605 - mse: 605000128.0000 - val_loss: 1597544192.0000 - val_mae: 36345.5195 - val_mse: 1597544192.0000\n",
      "Epoch 25/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 592029504.0000 - mae: 21578.2891 - mse: 592029504.0000 - val_loss: 1529271680.0000 - val_mae: 35706.2109 - val_mse: 1529271680.0000\n",
      "Epoch 26/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 577022464.0000 - mae: 21352.4746 - mse: 577022464.0000 - val_loss: 1456172416.0000 - val_mae: 34999.3203 - val_mse: 1456172416.0000\n",
      "Epoch 27/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 562094848.0000 - mae: 21125.1445 - mse: 562094848.0000 - val_loss: 1377383936.0000 - val_mae: 34206.2539 - val_mse: 1377383936.0000\n",
      "Epoch 28/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 545962432.0000 - mae: 20874.8086 - mse: 545962432.0000 - val_loss: 1294723328.0000 - val_mae: 33338.8047 - val_mse: 1294723328.0000\n",
      "Epoch 29/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 529026464.0000 - mae: 20578.5254 - mse: 529026464.0000 - val_loss: 1207369344.0000 - val_mae: 32367.6504 - val_mse: 1207369344.0000\n",
      "Epoch 30/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 511336704.0000 - mae: 20278.4434 - mse: 511336704.0000 - val_loss: 1116597632.0000 - val_mae: 31299.4785 - val_mse: 1116597632.0000\n",
      "Epoch 31/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 492717632.0000 - mae: 19928.8867 - mse: 492717632.0000 - val_loss: 1022144000.0000 - val_mae: 30109.2871 - val_mse: 1022144000.0000\n",
      "Epoch 32/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 474615392.0000 - mae: 19589.7461 - mse: 474615392.0000 - val_loss: 924675968.0000 - val_mae: 28797.4805 - val_mse: 924675968.0000\n",
      "Epoch 33/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 454852672.0000 - mae: 19192.4746 - mse: 454852672.0000 - val_loss: 825948544.0000 - val_mae: 27360.6621 - val_mse: 825948544.0000\n",
      "Epoch 34/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 435232960.0000 - mae: 18811.4883 - mse: 435232960.0000 - val_loss: 727079936.0000 - val_mae: 25810.0859 - val_mse: 727079936.0000\n",
      "Epoch 35/1000\n",
      "3/3 [==============================] - 0s 7ms/step - loss: 415348128.0000 - mae: 18387.5059 - mse: 415348128.0000 - val_loss: 629549952.0000 - val_mae: 24139.7031 - val_mse: 629549952.0000\n",
      "Epoch 36/1000\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 394043648.0000 - mae: 17936.0117 - mse: 394043648.0000 - val_loss: 535604544.0000 - val_mae: 22373.3867 - val_mse: 535604544.0000\n",
      "Epoch 37/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 373021760.0000 - mae: 17471.6895 - mse: 373021760.0000 - val_loss: 445010432.0000 - val_mae: 20478.7656 - val_mse: 445010432.0000\n",
      "Epoch 38/1000\n",
      "3/3 [==============================] - 0s 7ms/step - loss: 350710912.0000 - mae: 16971.4961 - mse: 350710912.0000 - val_loss: 360621376.0000 - val_mae: 18503.3887 - val_mse: 360621376.0000\n",
      "Epoch 39/1000\n",
      "3/3 [==============================] - 0s 7ms/step - loss: 328944640.0000 - mae: 16460.4551 - mse: 328944640.0000 - val_loss: 282531648.0000 - val_mae: 16409.3730 - val_mse: 282531648.0000\n",
      "Epoch 40/1000\n",
      "3/3 [==============================] - 0s 7ms/step - loss: 306380128.0000 - mae: 15908.5957 - mse: 306380128.0000 - val_loss: 213183792.0000 - val_mae: 14225.8652 - val_mse: 213183792.0000\n",
      "Epoch 41/1000\n",
      "3/3 [==============================] - 0s 7ms/step - loss: 284810592.0000 - mae: 15357.4893 - mse: 284810592.0000 - val_loss: 153079904.0000 - val_mae: 11917.0449 - val_mse: 153079904.0000\n",
      "Epoch 42/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 262895264.0000 - mae: 14769.6582 - mse: 262895264.0000 - val_loss: 105276216.0000 - val_mae: 9723.3350 - val_mse: 105276216.0000\n",
      "Epoch 43/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 241486496.0000 - mae: 14163.7637 - mse: 241486496.0000 - val_loss: 70537576.0000 - val_mae: 7518.1836 - val_mse: 70537576.0000\n",
      "Epoch 44/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 221066368.0000 - mae: 13547.9951 - mse: 221066368.0000 - val_loss: 49635320.0000 - val_mae: 5891.8203 - val_mse: 49635320.0000\n",
      "Epoch 45/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 201279152.0000 - mae: 12903.1641 - mse: 201279152.0000 - val_loss: 43727296.0000 - val_mae: 5546.0186 - val_mse: 43727296.0000\n",
      "Epoch 46/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 182117280.0000 - mae: 12240.3379 - mse: 182117280.0000 - val_loss: 52640328.0000 - val_mae: 6016.2354 - val_mse: 52640328.0000\n",
      "Epoch 47/1000\n",
      "3/3 [==============================] - 0s 7ms/step - loss: 164555472.0000 - mae: 11566.5830 - mse: 164555472.0000 - val_loss: 75908944.0000 - val_mae: 7169.2905 - val_mse: 75908944.0000\n",
      "Epoch 48/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 147991920.0000 - mae: 10865.8672 - mse: 147991920.0000 - val_loss: 111937424.0000 - val_mae: 8995.5605 - val_mse: 111937424.0000\n",
      "Epoch 49/1000\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 132703768.0000 - mae: 10155.3926 - mse: 132703768.0000 - val_loss: 158887056.0000 - val_mae: 10910.9561 - val_mse: 158887056.0000\n",
      "Epoch 50/1000\n",
      "3/3 [==============================] - 0s 7ms/step - loss: 120509352.0000 - mae: 9535.7070 - mse: 120509352.0000 - val_loss: 214044512.0000 - val_mae: 12794.6953 - val_mse: 214044512.0000\n",
      "Epoch 51/1000\n",
      "3/3 [==============================] - 0s 7ms/step - loss: 109243776.0000 - mae: 9006.7559 - mse: 109243776.0000 - val_loss: 273260128.0000 - val_mae: 14586.8184 - val_mse: 273260128.0000\n",
      "Epoch 52/1000\n",
      "3/3 [==============================] - 0s 7ms/step - loss: 101038728.0000 - mae: 8620.9658 - mse: 101038728.0000 - val_loss: 336229728.0000 - val_mae: 16347.8799 - val_mse: 336229728.0000\n",
      "Epoch 53/1000\n",
      "3/3 [==============================] - 0s 7ms/step - loss: 94776472.0000 - mae: 8337.2041 - mse: 94776472.0000 - val_loss: 400335680.0000 - val_mae: 17964.9609 - val_mse: 400335680.0000\n",
      "Epoch 54/1000\n",
      "3/3 [==============================] - 0s 7ms/step - loss: 90408592.0000 - mae: 8160.3022 - mse: 90408592.0000 - val_loss: 459167328.0000 - val_mae: 19320.6152 - val_mse: 459167328.0000\n",
      "Epoch 55/1000\n",
      "3/3 [==============================] - 0s 7ms/step - loss: 86466728.0000 - mae: 8021.5508 - mse: 86466728.0000 - val_loss: 503728640.0000 - val_mae: 20290.2188 - val_mse: 503728640.0000\n"
     ]
    }
   ],
   "source": [
    "EPOCHS = 1000\n",
    "\n",
    "history = model.fit(\n",
    "  normed_train_data, train_labels,\n",
    "  epochs=EPOCHS, validation_split = 0.2,batch_size=128,\n",
    "  callbacks=[early_stop]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 - 0s - loss: 179142013296968158284873728.0000 - mae: 13228499271680.0000 - mse: 179142013296968158284873728.0000\n",
      "Testing set Mean Abs Error: 13228499271680.00 Nodes\n"
     ]
    }
   ],
   "source": [
    "loss, mae, mse = model.evaluate(test_dataset, test_labels, verbose=2)\n",
    "\n",
    "print(\"Testing set Mean Abs Error: {:5.2f} Nodes\".format(mae))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_predictions = model.predict(normed_test_data).flatten()\n",
    "\n",
    "a = plt.axes(aspect='equal')\n",
    "plt.scatter(test_labels, test_predictions)\n",
    "plt.xlabel('True Values [Nodes]')\n",
    "plt.ylabel('Predictions [Nodes]')\n",
    "lims = [0, 15000]\n",
    "plt.xlim(lims)\n",
    "plt.ylim(lims)\n",
    "plt.gcf().set_size_inches((9,9))\n",
    "_ = plt.plot(lims, lims)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.42030595, -0.37776802, -0.97555406, -0.98704312, -0.95867842]])"
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_test = np.array(normed_train_data.iloc[50]).reshape((1,5))\n",
    "sample_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((108, 5), (1, 5))"
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normed_test_data.shape, sample_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[9239.249]], dtype=float32), 13856.0)"
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(sample_test), train_labels.iloc[50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# End bookmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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