playground.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Init a data list:\n",
    "data_list = []\n",
    "\n",
    "#Loop over all the files in the data folder:\n",
    "for file in os.listdir('data/'):\n",
    "    #Check if the file is a spectral csv file:\n",
    "    if 'dsc_fc' in file and file.endswith('.csv'):\n",
    "        #Read the csv file:\n",
    "        spec_data = pd.read_csv(\"data/\"+file, \\\n",
    "                                delimiter = ',', parse_dates=[0], \\\n",
    "                                    infer_datetime_format=True, na_values='0', \\\n",
    "                                        header = None)\n",
    "        data_list.append(spec_data)\n",
    "\n",
    "#Make a dataframe from the data list:\n",
    "spectral_data = pd.concat(data_list, axis=0, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    "spectral_data"
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  {
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   "execution_count": 4,
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    "spectral_data.columns = ['datetime'] + [i for i in range(spectral_data.shape[1] - 1)]\n",
    "spectral_data.columns"
   ]
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  {
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3298868</th>\n",
       "      <td>2019-06-27 23:55:00</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3298869</th>\n",
       "      <td>2019-06-27 23:56:00</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3298870</th>\n",
       "      <td>2019-06-27 23:57:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3298871</th>\n",
       "      <td>2019-06-27 23:58:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3298872</th>\n",
       "      <td>2019-06-27 23:59:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3298873 rows × 410 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    datetime        0         1         2   3   4   5   6   7  \\\n",
       "0        2020-02-25 00:00:00  4.04158 -0.337277  0.278823 NaN NaN NaN NaN NaN   \n",
       "1        2020-02-25 00:01:00  4.02179 -0.469653  0.141173 NaN NaN NaN NaN NaN   \n",
       "2        2020-02-25 00:02:00  3.98937 -0.655307  0.151470 NaN NaN NaN NaN NaN   \n",
       "3        2020-02-25 00:03:00  3.89345 -0.966260 -0.149979 NaN NaN NaN NaN NaN   \n",
       "4        2020-02-25 00:04:00  3.83742 -0.969013 -0.514656 NaN NaN NaN NaN NaN   \n",
       "...                      ...      ...       ...       ...  ..  ..  ..  ..  ..   \n",
       "3298868  2019-06-27 23:55:00      NaN       NaN       NaN NaN NaN NaN NaN NaN   \n",
       "3298869  2019-06-27 23:56:00      NaN       NaN       NaN NaN NaN NaN NaN NaN   \n",
       "3298870  2019-06-27 23:57:00      NaN       NaN       NaN NaN NaN NaN NaN NaN   \n",
       "3298871  2019-06-27 23:58:00      NaN       NaN       NaN NaN NaN NaN NaN NaN   \n",
       "3298872  2019-06-27 23:59:00      NaN       NaN       NaN NaN NaN NaN NaN NaN   \n",
       "\n",
       "          8  ...  399  400  401  402  403  404  405  406  407  408  \n",
       "0       NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "1       NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "2       NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "3       NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "4       NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "...      ..  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  \n",
       "3298868 NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "3298869 NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "3298870 NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "3298871 NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "3298872 NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "\n",
       "[3298873 rows x 410 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spectral_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Group by 3 hours and take the mean:\n",
    "spectral_data['datetime'] = pd.to_datetime(spectral_data['datetime'], errors='coerce')  \n",
    "agg_data = spectral_data.groupby(pd.Grouper(key='datetime', freq='3H')).mean().reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2016-01-01 00:00:00</td>\n",
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       "      <td>2016-01-01 03:00:00</td>\n",
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       "      <td>2016-01-01 06:00:00</td>\n",
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       "      <th>3</th>\n",
       "      <td>2016-01-01 09:00:00</td>\n",
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       "      <th>21427</th>\n",
       "      <td>2023-05-02 09:00:00</td>\n",
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       "      <th>21428</th>\n",
       "      <td>2023-05-02 12:00:00</td>\n",
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       "      <td>1.443822</td>\n",
       "      <td>1.263190</td>\n",
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       "      <th>21429</th>\n",
       "      <td>2023-05-02 15:00:00</td>\n",
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       "    <tr>\n",
       "      <th>21430</th>\n",
       "      <td>2023-05-02 18:00:00</td>\n",
       "      <td>3.445592</td>\n",
       "      <td>3.224468</td>\n",
       "      <td>-1.600414</td>\n",
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       "      <td>20.420421</td>\n",
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       "    <tr>\n",
       "      <th>21431</th>\n",
       "      <td>2023-05-02 21:00:00</td>\n",
       "      <td>4.232852</td>\n",
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       "      <td>-0.053075</td>\n",
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       "      <td>0.231726</td>\n",
       "      <td>11.961741</td>\n",
       "      <td>12.501766</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21432 rows × 410 columns</p>\n",
       "</div>"
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      "text/plain": [
       "                 datetime         0         1          2           3  \\\n",
       "0     2016-01-01 00:00:00  4.433160 -1.247880 -13.165854         NaN   \n",
       "1     2016-01-01 03:00:00  3.448769 -3.864323  -8.984068  139.338250   \n",
       "2     2016-01-01 06:00:00  4.407694 -5.729229  -6.420560  143.819967   \n",
       "3     2016-01-01 09:00:00  3.758710 -6.973925   3.080465         NaN   \n",
       "4     2016-01-01 12:00:00  6.514989 -6.250111  -0.786576  142.064460   \n",
       "...                   ...       ...       ...        ...         ...   \n",
       "21427 2023-05-02 09:00:00  4.527340 -0.300102   0.244972    1.264590   \n",
       "21428 2023-05-02 12:00:00  3.569858  1.563384   1.443822    1.263190   \n",
       "21429 2023-05-02 15:00:00  4.224378  0.180576  -0.906981    1.123050   \n",
       "21430 2023-05-02 18:00:00  3.445592  3.224468  -1.600414    0.354338   \n",
       "21431 2023-05-02 21:00:00  4.232852  3.290640  -0.053075    0.759813   \n",
       "\n",
       "                4           5           6           7           8  ...  399  \\\n",
       "0             NaN         NaN         NaN         NaN         NaN  ...  NaN   \n",
       "1      108.341150  131.678031  127.960636  124.528563  108.572063  ...  NaN   \n",
       "2      103.094027  126.340200  129.495920  124.643217   96.828230  ...  NaN   \n",
       "3             NaN         NaN         NaN         NaN         NaN  ...  NaN   \n",
       "4      102.750320  127.700920  130.539498  124.235998   73.482044  ...  NaN   \n",
       "...           ...         ...         ...         ...         ...  ...  ...   \n",
       "21427    0.231726    0.392370    0.231726    0.232078    0.231726  ...  NaN   \n",
       "21428    0.231726    0.354059    0.231726    0.231726    0.231726  ...  NaN   \n",
       "21429    0.231726    0.886440    0.353309    0.397666    0.231726  ...  NaN   \n",
       "21430    0.231726    3.591731   20.420421   12.804472    0.238711  ...  NaN   \n",
       "21431    0.231726   11.961741   12.501766    5.638664    0.300240  ...  NaN   \n",
       "\n",
       "       400  401  402  403  404  405  406  407  408  \n",
       "0      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "1      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "2      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "3      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "4      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "...    ...  ...  ...  ...  ...  ...  ...  ...  ...  \n",
       "21427  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "21428  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "21429  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "21430  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "21431  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "\n",
       "[21432 rows x 410 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dumping this data to a csv file:\n",
    "agg_data.to_csv('data/agg_data.csv', index=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sahirsahir/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "/Users/sahirsahir/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "/Users/sahirsahir/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the x, y, and z of the aggeragted magnetic field vector:\n",
    "sns.lineplot(agg_data['datetime'], agg_data[0], color='red')\n",
    "sns.lineplot(agg_data['datetime'], agg_data[1], color='blue')\n",
    "sns.lineplot(agg_data['datetime'], agg_data[2], color='black')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's apply Unsupervised Learning to Detect Anomaly "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9348410258631341"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Almost most of the data is missing\n",
    "np.mean(spectral_data.isna().sum()) / spectral_data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.04158</td>\n",
       "      <td>-0.337277</td>\n",
       "      <td>0.278823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.02179</td>\n",
       "      <td>-0.469653</td>\n",
       "      <td>0.141173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.98937</td>\n",
       "      <td>-0.655307</td>\n",
       "      <td>0.151470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.89345</td>\n",
       "      <td>-0.966260</td>\n",
       "      <td>-0.149979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.83742</td>\n",
       "      <td>-0.969013</td>\n",
       "      <td>-0.514656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3298868</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3298869</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3298870</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3298871</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3298872</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3298873 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0         1         2\n",
       "0        4.04158 -0.337277  0.278823\n",
       "1        4.02179 -0.469653  0.141173\n",
       "2        3.98937 -0.655307  0.151470\n",
       "3        3.89345 -0.966260 -0.149979\n",
       "4        3.83742 -0.969013 -0.514656\n",
       "...          ...       ...       ...\n",
       "3298868      NaN       NaN       NaN\n",
       "3298869      NaN       NaN       NaN\n",
       "3298870      NaN       NaN       NaN\n",
       "3298871      NaN       NaN       NaN\n",
       "3298872      NaN       NaN       NaN\n",
       "\n",
       "[3298873 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spectral_data.loc[:,0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.005292738814356e-07"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's check if the magnetic field vector valuesa are also missing - which is not the case:\n",
    "np.mean(spectral_data.loc[0:2].isna().sum()) / spectral_data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9348401253338603"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rest of the data is almost missing so it can be ignored\n",
    "np.mean(spectral_data.loc[3:].isna().sum()) / spectral_data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime        0\n",
       "0            3330\n",
       "1            3330\n",
       "2            3330\n",
       "3           12137\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for Nan values in the aggregated data:\n",
    "agg_data.loc[:,:3].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21432, 410)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>...</th>\n",
       "      <th>399</th>\n",
       "      <th>400</th>\n",
       "      <th>401</th>\n",
       "      <th>402</th>\n",
       "      <th>403</th>\n",
       "      <th>404</th>\n",
       "      <th>405</th>\n",
       "      <th>406</th>\n",
       "      <th>407</th>\n",
       "      <th>408</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>4.433160</td>\n",
       "      <td>-1.247880</td>\n",
       "      <td>-13.165854</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-01 03:00:00</td>\n",
       "      <td>3.448769</td>\n",
       "      <td>-3.864323</td>\n",
       "      <td>-8.984068</td>\n",
       "      <td>139.338250</td>\n",
       "      <td>108.341150</td>\n",
       "      <td>131.678031</td>\n",
       "      <td>127.960636</td>\n",
       "      <td>124.528563</td>\n",
       "      <td>108.572063</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-01 06:00:00</td>\n",
       "      <td>4.407694</td>\n",
       "      <td>-5.729229</td>\n",
       "      <td>-6.420560</td>\n",
       "      <td>143.819967</td>\n",
       "      <td>103.094027</td>\n",
       "      <td>126.340200</td>\n",
       "      <td>129.495920</td>\n",
       "      <td>124.643217</td>\n",
       "      <td>96.828230</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-01 09:00:00</td>\n",
       "      <td>3.758710</td>\n",
       "      <td>-6.973925</td>\n",
       "      <td>3.080465</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-01 12:00:00</td>\n",
       "      <td>6.514989</td>\n",
       "      <td>-6.250111</td>\n",
       "      <td>-0.786576</td>\n",
       "      <td>142.064460</td>\n",
       "      <td>102.750320</td>\n",
       "      <td>127.700920</td>\n",
       "      <td>130.539498</td>\n",
       "      <td>124.235998</td>\n",
       "      <td>73.482044</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21427</th>\n",
       "      <td>2023-05-02 09:00:00</td>\n",
       "      <td>4.527340</td>\n",
       "      <td>-0.300102</td>\n",
       "      <td>0.244972</td>\n",
       "      <td>1.264590</td>\n",
       "      <td>0.231726</td>\n",
       "      <td>0.392370</td>\n",
       "      <td>0.231726</td>\n",
       "      <td>0.232078</td>\n",
       "      <td>0.231726</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21428</th>\n",
       "      <td>2023-05-02 12:00:00</td>\n",
       "      <td>3.569858</td>\n",
       "      <td>1.563384</td>\n",
       "      <td>1.443822</td>\n",
       "      <td>1.263190</td>\n",
       "      <td>0.231726</td>\n",
       "      <td>0.354059</td>\n",
       "      <td>0.231726</td>\n",
       "      <td>0.231726</td>\n",
       "      <td>0.231726</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21429</th>\n",
       "      <td>2023-05-02 15:00:00</td>\n",
       "      <td>4.224378</td>\n",
       "      <td>0.180576</td>\n",
       "      <td>-0.906981</td>\n",
       "      <td>1.123050</td>\n",
       "      <td>0.231726</td>\n",
       "      <td>0.886440</td>\n",
       "      <td>0.353309</td>\n",
       "      <td>0.397666</td>\n",
       "      <td>0.231726</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21430</th>\n",
       "      <td>2023-05-02 18:00:00</td>\n",
       "      <td>3.445592</td>\n",
       "      <td>3.224468</td>\n",
       "      <td>-1.600414</td>\n",
       "      <td>0.354338</td>\n",
       "      <td>0.231726</td>\n",
       "      <td>3.591731</td>\n",
       "      <td>20.420421</td>\n",
       "      <td>12.804472</td>\n",
       "      <td>0.238711</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21431</th>\n",
       "      <td>2023-05-02 21:00:00</td>\n",
       "      <td>4.232852</td>\n",
       "      <td>3.290640</td>\n",
       "      <td>-0.053075</td>\n",
       "      <td>0.759813</td>\n",
       "      <td>0.231726</td>\n",
       "      <td>11.961741</td>\n",
       "      <td>12.501766</td>\n",
       "      <td>5.638664</td>\n",
       "      <td>0.300240</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21432 rows × 410 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 datetime         0         1          2           3  \\\n",
       "0     2016-01-01 00:00:00  4.433160 -1.247880 -13.165854         NaN   \n",
       "1     2016-01-01 03:00:00  3.448769 -3.864323  -8.984068  139.338250   \n",
       "2     2016-01-01 06:00:00  4.407694 -5.729229  -6.420560  143.819967   \n",
       "3     2016-01-01 09:00:00  3.758710 -6.973925   3.080465         NaN   \n",
       "4     2016-01-01 12:00:00  6.514989 -6.250111  -0.786576  142.064460   \n",
       "...                   ...       ...       ...        ...         ...   \n",
       "21427 2023-05-02 09:00:00  4.527340 -0.300102   0.244972    1.264590   \n",
       "21428 2023-05-02 12:00:00  3.569858  1.563384   1.443822    1.263190   \n",
       "21429 2023-05-02 15:00:00  4.224378  0.180576  -0.906981    1.123050   \n",
       "21430 2023-05-02 18:00:00  3.445592  3.224468  -1.600414    0.354338   \n",
       "21431 2023-05-02 21:00:00  4.232852  3.290640  -0.053075    0.759813   \n",
       "\n",
       "                4           5           6           7           8  ...  399  \\\n",
       "0             NaN         NaN         NaN         NaN         NaN  ...  NaN   \n",
       "1      108.341150  131.678031  127.960636  124.528563  108.572063  ...  NaN   \n",
       "2      103.094027  126.340200  129.495920  124.643217   96.828230  ...  NaN   \n",
       "3             NaN         NaN         NaN         NaN         NaN  ...  NaN   \n",
       "4      102.750320  127.700920  130.539498  124.235998   73.482044  ...  NaN   \n",
       "...           ...         ...         ...         ...         ...  ...  ...   \n",
       "21427    0.231726    0.392370    0.231726    0.232078    0.231726  ...  NaN   \n",
       "21428    0.231726    0.354059    0.231726    0.231726    0.231726  ...  NaN   \n",
       "21429    0.231726    0.886440    0.353309    0.397666    0.231726  ...  NaN   \n",
       "21430    0.231726    3.591731   20.420421   12.804472    0.238711  ...  NaN   \n",
       "21431    0.231726   11.961741   12.501766    5.638664    0.300240  ...  NaN   \n",
       "\n",
       "       400  401  402  403  404  405  406  407  408  \n",
       "0      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "1      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "2      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "3      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "4      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "...    ...  ...  ...  ...  ...  ...  ...  ...  ...  \n",
       "21427  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "21428  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "21429  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "21430  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "21431  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "\n",
       "[21432 rows x 410 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop nan values:\n",
    "#agg_data = agg_data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>datetime</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>4.433160</td>\n",
       "      <td>-1.247880</td>\n",
       "      <td>-13.165854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-01 03:00:00</td>\n",
       "      <td>3.448769</td>\n",
       "      <td>-3.864323</td>\n",
       "      <td>-8.984068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-01 06:00:00</td>\n",
       "      <td>4.407694</td>\n",
       "      <td>-5.729229</td>\n",
       "      <td>-6.420560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-01 09:00:00</td>\n",
       "      <td>3.758710</td>\n",
       "      <td>-6.973925</td>\n",
       "      <td>3.080465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-01 12:00:00</td>\n",
       "      <td>6.514989</td>\n",
       "      <td>-6.250111</td>\n",
       "      <td>-0.786576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21427</th>\n",
       "      <td>2023-05-02 09:00:00</td>\n",
       "      <td>4.527340</td>\n",
       "      <td>-0.300102</td>\n",
       "      <td>0.244972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21428</th>\n",
       "      <td>2023-05-02 12:00:00</td>\n",
       "      <td>3.569858</td>\n",
       "      <td>1.563384</td>\n",
       "      <td>1.443822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21429</th>\n",
       "      <td>2023-05-02 15:00:00</td>\n",
       "      <td>4.224378</td>\n",
       "      <td>0.180576</td>\n",
       "      <td>-0.906981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21430</th>\n",
       "      <td>2023-05-02 18:00:00</td>\n",
       "      <td>3.445592</td>\n",
       "      <td>3.224468</td>\n",
       "      <td>-1.600414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21431</th>\n",
       "      <td>2023-05-02 21:00:00</td>\n",
       "      <td>4.232852</td>\n",
       "      <td>3.290640</td>\n",
       "      <td>-0.053075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21432 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 datetime         0         1          2\n",
       "0     2016-01-01 00:00:00  4.433160 -1.247880 -13.165854\n",
       "1     2016-01-01 03:00:00  3.448769 -3.864323  -8.984068\n",
       "2     2016-01-01 06:00:00  4.407694 -5.729229  -6.420560\n",
       "3     2016-01-01 09:00:00  3.758710 -6.973925   3.080465\n",
       "4     2016-01-01 12:00:00  6.514989 -6.250111  -0.786576\n",
       "...                   ...       ...       ...        ...\n",
       "21427 2023-05-02 09:00:00  4.527340 -0.300102   0.244972\n",
       "21428 2023-05-02 12:00:00  3.569858  1.563384   1.443822\n",
       "21429 2023-05-02 15:00:00  4.224378  0.180576  -0.906981\n",
       "21430 2023-05-02 18:00:00  3.445592  3.224468  -1.600414\n",
       "21431 2023-05-02 21:00:00  4.232852  3.290640  -0.053075\n",
       "\n",
       "[21432 rows x 4 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_data.loc[:,:2]#.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.433160</td>\n",
       "      <td>-1.247880</td>\n",
       "      <td>-13.165854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.448769</td>\n",
       "      <td>-3.864323</td>\n",
       "      <td>-8.984068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.407694</td>\n",
       "      <td>-5.729229</td>\n",
       "      <td>-6.420560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.758710</td>\n",
       "      <td>-6.973925</td>\n",
       "      <td>3.080465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.514989</td>\n",
       "      <td>-6.250111</td>\n",
       "      <td>-0.786576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21427</th>\n",
       "      <td>4.527340</td>\n",
       "      <td>-0.300102</td>\n",
       "      <td>0.244972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21428</th>\n",
       "      <td>3.569858</td>\n",
       "      <td>1.563384</td>\n",
       "      <td>1.443822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21429</th>\n",
       "      <td>4.224378</td>\n",
       "      <td>0.180576</td>\n",
       "      <td>-0.906981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21430</th>\n",
       "      <td>3.445592</td>\n",
       "      <td>3.224468</td>\n",
       "      <td>-1.600414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21431</th>\n",
       "      <td>4.232852</td>\n",
       "      <td>3.290640</td>\n",
       "      <td>-0.053075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21432 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0         1          2\n",
       "0      4.433160 -1.247880 -13.165854\n",
       "1      3.448769 -3.864323  -8.984068\n",
       "2      4.407694 -5.729229  -6.420560\n",
       "3      3.758710 -6.973925   3.080465\n",
       "4      6.514989 -6.250111  -0.786576\n",
       "...         ...       ...        ...\n",
       "21427  4.527340 -0.300102   0.244972\n",
       "21428  3.569858  1.563384   1.443822\n",
       "21429  4.224378  0.180576  -0.906981\n",
       "21430  3.445592  3.224468  -1.600414\n",
       "21431  4.232852  3.290640  -0.053075\n",
       "\n",
       "[21432 rows x 3 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B_vector = agg_data.loc[:,0:2]#.values\n",
    "B_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "B_vector = B_vector.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(max_iter=1000, n_clusters=2, n_init=100, random_state=0)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "#Fit the kmeans model to the magnetic field vector:\n",
    "kmeans = KMeans(n_clusters=2, random_state=0, max_iter=1000, n_init=100)\n",
    "kmeans.fit(B_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot the clusters:\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=300, c='red', label = 'Centroids')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZXJExEdKG+VoKDWjnDKDzV5I7oKYMd0aFZ89CTRmBmnY/qnWHryWrleg2eTdQbQch9TaQJ3wrSP5KCCjtO64oCoQ/CuGPombNh5ynfSNbdVAzcSfDoWKQHNkbiJQgfL4M4Q81dGtA2EsoIdei5nwNWY/6WppykGDfAplbinfZgfzvUQkF08UQfj+KqamP5Ktd6Eq+ElTL35B+o6/FcCJCKjl8DbI2KfmgwWAxFtjkwWoRGE0SQ4mr0m6D9WvCEUotV67+QMwKFFNBql9LBeUG/ZpssK2A0ytQUXA+wQowdoPIZ1CMzX0toN+hK/kKUO3J/qPgARFYcRZAmbeowuN+hYhEhNyEDBoKKZeDTCUgSJKTqaAYJEEhktxshZxMAx882ZDhk0dgqD8KADX/b0i7A60CzM4WhBJZvGGrC6YPlaInK/t6SLkClSCKYkdETEHkdCxkvweOo2DuiwgehlD8McmeZ9CVfEWk3lKDkwMg+B7IfQtNPLfNlyJMbSpuYz9c83G8RcwqpOMY5M6HkPHOEnc572AKOclvy2xkZ8DuLSH8uSqGW567laH3DSo6VZjaImu9N7wAAvDmjUra9iKKSvHV1TwxJYID5WlIOyPWwroOmfMuxK5AGBp4VzQfoXvXVICzwIUfEDgCEfFs6UAoF6h5qyBjfIVt/IdgyhbJjoKYL1BMLSo9W816HXI+8IBcCt6xuwcgYpdC/joQwciAAQj7LqT9ACj1wNgUTg+hdOGNmo/pTHURUvDfrmHftQxzf5Toj3wthWZU5F2jz+T9gsIfX6GCKViMFMFg6oqIeLpSBQ8gAi9BZhT2pTX1gFMa9nemggdIg9NXIuP+QBhiXBwvRoRORIpIyC4bal+acCrM6glAEIQ9jBIyGgD1VDs8rugD/4cwtgRjS6AgrM3QCxHQq6iJau4D1p81HLTwuvCHcoc+xvqbryXwGloV8j5UULR7ixBiY8G+aCHEGiHE3oL/UZX14084n3A87GEaMBTi1iMinoGgYRD6IETNgaBREHg1IuJlRNRshHDPPVIIE8SsADwQJmrWqvCGi2jJUkhkxtOV9iKEQAm9HWJ/B4OrACXhtMmSVX4n4W+i1NuDUm9rkYIHIOzZSsevGQIR8XzlrSJe9LAcHsLY3dcSuIH/WDA8jZYz+YullCklticDP0opXxJCTC7Y9lefrTLI3C/x+GzO+jekjkSKaAibiDD3dM7YA3pXu0vF1AzqbUK1bgPbTrD8DLYfay5r6ChIXUbNfxxuPGVYVyOlihAV32TVvF8hYyyuzQ7SaZOtiMwnkEFXln1KyltYuYw1IWKme09mhhikeRRYv/CcLKI9yJ2adqnEfoFq3Q45nwCq0/XX3zC0c7lbSom0/ASWlWBsDSF3ukgnUrvw5FR1CFCYG/VT4BoPjqU9OXM8P4Y8Bo5DzkyJaTchE9uh5mjj2qaYu6CEjICod6n516wgjC3w3uxHIvOWo9r2oqquTQuq5XfIuJOa2ZWzkdbSSd7U/B1g16rykSsUMLhIUXEG0nEa9dSFnlXwADKj4uOhL0HQpCp0GAiAYu6EEvUaImJq9WXzJNGflNml5u9FJraFjHucabtz3oCkjqi5tchrzQWaLLwKIQ7iXK6XwIdSyplCiHQpZWSJNmlSyjImGyHEGGAMQJMmTbofPuwfHiJqYh9nRKuvCJ+JEnxRjbtR89ZARg3L34U+4XR3TOyCb3LUB0PsKhRjQtEe9dQ5aJZmWTSFqI8LvKm8VE814GpE5GsuZ/RSOpxFZlyuW/g54S+gBBenHZZSIhO74l8VxWIh8FIImwQi3Dlrtx2AvBnlniHi1latwpiX8UZagz5Sym7AlcA4IUQ/d0+UUs6UUvaQUvaIi4vTSBwNMLv8vLxH5hhnCbjEC1Cz33cWdC4HKSXS9i8y/+eyJdRsf9ZMDtESEXKTUxmF3F2zvqpNLqT0Q1WtSMuPqFkfoKnSkIch9VK8puAB8r9Bpt7qWhzLd9Q+Ba9AyIOlFDw4104I85WVtrxqYClg+RKSz4ek9s56yxUoeACZ9Z724nkJTZS8lM54fyllEvA10BNIFELUByj4750EL1oRep+vJXAiUyH7LWTqKKQs606nZryETGyHPH0dMm0sMqkvanaJC1LW4Cs290PEryyabYrQeys5wZNISOqETL8Hcl73oRwaYvsTNfeHsvtzPbwmoDWxa1Hq7UIJcz0JUEJu8LJAQOBolHrrIXoJmuR1yvdUdlnPU2MlL4QIEUKEFb4GLge2A8uBwmiiW4BlNR2rJqiOU6inb0NNuhQ1802krLgIhmJqDfjLk4UK9kNgWV20R0oV9VRXyPuYYlu5Ctgg+wOkZS1qxjOQ91k1xlMg7EWU6FmlFj+FUEBxp+6qjttkPoCqnmECE7UsGlO4478R7nExilCaoUQ+6XxtXY8mBW/kUdRcn6qwaqPFTD4B+E0IsRX4C1gppfweeAkYIITYCwwo2PYJasZrkNwPbL+DehRyZyATO6DaS/t9S2lH5m9A5v/qNI/EfgmYqzGiJ8IP8pDW4lJ4zqLH5T3S5yPTp0LevCqOEQxRixAJ21FCXKfTFTF1ZBbtN1ghqQtq7oriGrVhD/pWpKqS93O5h9Tseain2lN5rIKGRLxd/Nr6t3b9Zj6Mmlf7cv7UWBtJKQ8A57jYfxq4tKb91wQpJTL9Kchf4OoopPRHLQotN1IqEAkKqtRUwY1SxELYY5D5WLVl3rU5iN9WRmIwSi6+Jp1m7QrD3k1gKFE1x76ukp6qYx2zgf04IqDM11mEMDRExnwPqTf5dmG6TiEh80Fk5mRk+PMowdegmq8A6ypfC+Ye0vX6iHp6DNh+9q4sAML5+1WtO8C6Vtu+M5+DoKu17dPD1OmIV5l0EciTFbWgOHeIC1e8qqYWjl2GYohDVZMh+xWq6mf/4dP1WTk3FqtFIBRYMjOOmx8+xbB7kkEYEUGeLlZhg6wHUGUKSujN5bZSTC0g4XekzAM1DZm/FzLH4rU6sHUWG2Q+gmpoghL9DmreD5DzvrMoDQZw7Km0B58QNLDMLjXvV98oeIDUG5AJGyDrOe37lrUv50+dLRqiZn1WiYL3ADmzAJyRmJEzqMrHu3dbECvnxpKfpyClQHUIrBaFT1+px+4tQYjImYiCmbzHiydkv+BWMyGCEIYGKMH9UertRCRsR8T94ozarY1FTPyFLGc0rBJ0GUrsYpS47yDwYvyzpm8QirEeqmpBzXgdNbEv6qk+BTEMviIHmXQZ2DyRByvQA316ljqp5KWUkPOK9we2H0DadgGgBF4CgYMrbF4yROG3byOw5pf9EdutgvsHtUYEnF+8M2+xJuKWj+rSk6cyhDAjDPUR5l5gqq4LqgKYAF/5JEeAiPTR2AU4ShfFlta/IafkAruGGPvgnhpwFfVpgrifUNNfg6QukPdBQZU0PzDjSS3zLJUg5K6yQ/lRkkdX1E1zjcwBfFAP1PoL8vQ6pLEVImoOhD0Olm9dylJY5UhKZ7EMS45AurDuSOli9mbwRkWc6l8aQgiI/hiZ+RrkfYrbZpzAkSiRxXljpO0/ZOodTjdSr5EBIc9A9sv4zFfdWFyaT1VtkHoH2majLIH9d4iaC4a2kNIXl7+b6JUIdb/TGcF+GNRTEDAAJegyVMvPYPnQM7L5G0E3gKEl6qluQHapQ5IACL4JETbRmUPKj6iTM3lEIL67f6lg34NMH49iiIS4tWDqh/MxLxSM5wFxHNvv9No5sCOQcZe3YensOMp7HA8KLX5EVNPGQbYHbI0lEfXdyq1SYRfChBIxGZHwHyJmhXsnWRag5hcHbwlTR2eeeW+T/RQ+TWAVXiIVQFIPPJ5zPu1mUIIh7k8wdqHoOlQaQ8xSFHNrROBAlOBrUcInokS+jBJ0mbNN+iOelQ0TBHk6YZwbRH4CSiRkTuBMBe8kH3JnIdN8GUvimjo5kxfCiAwaDXlzfCeEbSvScQrFUA9iZpU6JGU+p9b2Ia6BjYbN80k6Yaasgi9UMoJXfnD6/KrJ14PDk3lVCodWUNU8FCWo4mZqltO0YKiPUKIKIm+3geMEwnwOEIC07wG1Co/vafdCvRIFy6Wv0uL6Igw/CCLfRTG1B0DN/tRLckjIm48ScivEfuX8Hu27QM1EGFshpd35PYjwUjd/1bIDSPewbDbIe9LDY7hBxosg3Vj4tv6CmrcGJWiA52Vykzqp5AFE+MNIcsBnJfEkqFml3R4LECKAbkNXsHnpELr2TefWR08y5+V65Oc5FyuL6pkKhZdWTaVdz9aoapZ3FDwAxyHpHFQSIHYBirG0fVx1ZDldKB3FC8DyjGIb1Z8H5yClLFYmxlbg2Fvt3mpO5YXGa04wxK9DUc5IEZ07x8PjliD/N2TwDUjbVki9ncIcRaXfeRQyfApK8OCCQLuqxmEUEgDYQESAzELb4iUe+r7cUfCFZIxHzSj8LQRC+FSU4GHay+Qmdb4ylGo/CimX4f3H7wBEwmZEJdGAR7b/TtaxJ0k7lc6SmfGknDTRqJWFgTc1pd/NxZVr1LSHIH+5p4V2TdQilALfedWRBMkX4zEbMQDBiKgZiIDeqLZdcLriBWyPEvmhM2oydx4eW+eJ+xPFRZEUNfkKcBz0zJguUXD+Tir5rQSMgvzqZMcMhOj5KOZOqDmLIWsqZ011qqAxKBEPeaz7ihKU1XklD4WZGO/Dq4o+dBpK6Ai3m6v2XEi/xzk7VppD1FsoxvrOY3lLIGOypyR1gzBEwkak7V9IHY53yuMBMd+imFqhpj0D+dWdNdYEgUjYiRAK6un7wfa99kOEPoIS6trdUM1bCxm+SgrnIQKuBVMbNyp61T1Ewu4ar3WV2/fZruSlIwWZ3B/Pzj7PIGQiStg9FTZRbbsg7T5Qj7huoJyLEv8lalJfUH2c383QAhwHvDxoPZR6vwKgWvdC+t2gejFTpKkfGOLBtqOUaUo7glDqbS33qDNNby/Am95FOh4j7ncUg2fyYek1Xu0eDh5yRc4MZOhtCFHCM0ZVIX2C++Hq6mbUUz2psISdt/C6ggc4hWo/gWJsgGJuDfE/odqPQcoAPB9dawDbr56dFygVJ+0SQiCFcjZVqtMYBSLnQvqNvhakgIodGTxF3XShPBMlHu/b/gQ4jhdtSSkhqXs18pGkc1anC0gvXZVIMTaCuD8oP1e4VnjhMw+p5EnPfqDyEoY6FaD6kYIHxeCb7KJnhZIXpna4jtjzJGrBzcWJzJkD+ModsBZj34IsESUmpYTU26j9JoxQMPct96h0JEHKQPRpfF2hrJedtzgrlDwAijdzwxshaDCipEtcbtmakjrucMZClf0/D9nHvU02nB6EVF0F1oBMHetlefyVWpZbvzxiPVyrtwLOHiUfNMhLA5kheBQi/Okz9gd4aXwtMQNhlbbyLA5k9gfFm3b/qAGsDVZkSln/aSnzwLHdB/L4I150lvAICkSvKBNr4mUJzg5EyBg8vs4cswKR8A9K+FSEOKPYSJinw789QbOChEw+zn6Y8ybqqe6oqgNMbX0ri9ao+1HTS0d0Stt+HwnjZ4gYfFM4vhoE3QemPjjNwgYQIRA0ChH/B4rZt9fs2eFdAwglAhk1C9Ju9dgYSgUKSAkagJpzDtjLd5nzP/YU1FMNxveFpbOcNV4Db/exHB7AsgApn0EIgZq3FDKe8rVE/kHQdZA709dSVIIBkbDDY/7vWnDWzOQBlIDeEOGh8nUBwysfP3YRBPtJgfAq4WsFX4gDLB9V3sxvaOd2S5m7BjV/HWQ8gm/y5vgZ5sHeTetQLSIRCVv9WsGDNoW8Gwsh1gohdgoh/hNCTCjY/7QQ4rgQYkvBn7eM4hWiBF0NcRvB1JMK337oExC/FRG/AcLfLr9dASJyWtFrKSWqbS+qvWxIugi7l8rrxp5ZcMO/LyIdF4Q+B+xyv33WfZDhB9kW/QXrcnySLtxdwj5AqfdXWbOsH6KFucYOPCil/EcIEQZsEkKsKTj2hpRyugZjaIpiCIeYz5HS4fRisP1SuoGId+YwV084U92WLNjhEkPR3VzNWw0ZD1Dol6+iQNg0lBDnApsQRmTMYjg9klIulQFXISJfLzUrkLYd4DiMVJpC6l34RTEGHRcYQISBdDi9qsIeRFZHYXszmlenehjOh+iPUAy1p0KUFoW8TwInC15nCSF24ruyPlVCpj9SVsGDs7pNzgxn1Ko7HQUMcYag530DmQ+ecVCFrCnOup2B5yOlDeE4hAy5yzlBVxpCwEUohogy3QpTB6SxOST2wXUOaz9BaQjq8crb1VkcTjOgbQNCiQOsYN1UjX4UzurAN38kfDooYWDujaLURg85jRdehRDNgHOBDUAf4D4hxM3ARpyzfb+ogivtR5BpD2jnphY+AZl8BaiHym+TfhOqsTc49hekV80DgsEQhQjoU76sWW/g1woenEFfagZ+L6cnSb8DkM4KQdmvAbFV70NpAqoGnjVKezB1rWamSJ1SyGyUSsp4lmou7Ug1DyEMIIL8wl6v2cKrECIUWAw8IKXMBN4HWgJdcc70XyvnvDFCiI1CiI3JyZ43R6iOTGTKFRopeAHRv0LG8xUr+ELsf4BMxLmQKYEccBxDpo1HWlahpk5APX07au7K4ihPy2oN5Kw5Jw4ZkLJ0XdoiAi7lrFbwQHFkaj7IPJDVuJbN50PY0zUXRd1ZoOAja96Xu4Q8gPejygsx4DFHQYd7tWJVRzrq6dHIxA6Q3B2Z1BWZ2A418w2kzEfadiIdiZ6RsRI0yUIpnEUNvwFWSSnLuK8UzPC/kVJ2qqgfT2WhLIma2B/kyRr00ALMrcDcCxFyHWBCJrbXSrwSmCHyI0gfg699hVMTjdhtENewdP4fIYCgOyFvlusTz2qq4XZqOh9CH4S0yj21dEpgugiCB0PGpEqbVpnQ51FCSwesSWkDxxEkZnBkQsbDoO6roJOSefoLi+soYOqEiJiOMDarsZgezUIpnM8js4GdJRW8EKJ+gb0eYCjg8xA+VVVrqOCBgPPAEAz53yJz3gKZrolsZbFC+i0e6tt9VBUO7jLTrV8uZz55Sgki31vVqmobKigtQK1C9k5HKqS5X4NAp4DwB1FMbVEJhwzXufmriwjqh5QOsG1y1lPIXQGOXVStpoLq4rUKtm3IlEEQ9xPCRQU5rdDiGacPcBPwrxBiS8G+x4FRQoiuOG9fhwDfVz+wbah5H/mLOWuq2QA/LqlP3ytPlVHwRaibyzlwtmOB4FsKioK7iXoIPSFZFTF0gfw/UNPuBfWoxp1HIJP/h2fr2NqRma8hol712AhaeNf8hmtH7m9r2rf21PQHpHA2KXiAb+Z14tJrXT/9OBW/kdqfX8RD2DbgvtlGwbOfY0OgjnlAKV1BTYXsFz00QIaH+j2D/BWA55R8nY54lVIirVuRljWo9pPUKGl/8CS8VvbOj+g7tDvpya7nAlICgbr9uFysvyOi54CouDgIGCD2d8/KYm6OSNgNQTdRZ7KZyJMgy6mqVqtQUe2eczysI9+2EzXvW8h6GdR0MLYBmQlqojNIpSaLl6b/QUBP/4nu9yLXP3gjr46az0NvHEEpEYgrJdjtRkyWhb4Tzu9REOauEL8Bad0G9n2Q8ymoe0q0CQIskFJ+bnltCAT1FMLYBBkyFnLeo9abhqRvvFU8gmUhhHrGol1nlLya/jRY5hfv0DIRmG0V5GoRCBGP07JVSy5O0RqDIYBxM5fx7sRR3PboHkIjVFQVTp1oRsPGR9HzrFRA0LUACGFABJwLAeeimrpC9ttg/RGn6a/w8/NwEJSxGTL5soIxa7lyr4tYPbe2VSeUvGrPLK3gNccK+V9p0E8SEArhnzkDZuRxZ51Pn9RPrQSlLcQuAyAsMoQHPlmOlBKH3YHJZKRh0H1gPeRbGf0ZpQkidBxSWpFZ7xYk27L4SBgT5H7M2WhuBEBp7v+L2orn6jbUDZv86WvKPaRBGIDGZEPOqyjxi1AS/oCwKb4WqDRKAkR/CYZISLoANXk00uY0LwghUORe1PTJYPWPIC2/JPB6RNx3IMzIxL6Q+wFVV/BaRkraOGsVPMEFOYH8ThGUIBARdJ3Heq/1M3nVkQiy/MROfhBVXBbHv0gpnSHPhsa+lqY0qglSS/hqO/5Cnr4aGfoC5H0Fjn98J5u/ozSCyI9QzC0BULPep/rudwH4buZfl/D3hbQACL4ZEXCBx0ao9Uoe69++lqCa2AAzwtjUz+YY5dwwsx/3rhi1kbCHUMwtUe0nnMXG1bKppt0m5EHIeYmzJWGZlK4nZOXtrxOYhyDCxyOMTTw6TO031xg7VKm5fyhUU3EeatsmKs8vXwcxVJa+uRYiQlCt+yHlopopeABjPES+o4lYtYHyFLwr/M8EWw1MV6JEv+pxBQ91YCavmFqgigS33KmkBIQRnwc0hT5R/Npx1CmT9OMCCVpi6AeOX8GhQfSxX6GAqQskl59RtEpkTKAuzMGqQ+HsvaIZfK2e4Qc9iBLhvQQAdeMqivveaQ+FcjMlFu5TI34EEepF4c7AdAlK6KjibWN7kGfRopjj10oaKNTKSljBD0Hy+Wg7gTiLrosSVKa8z7wB1LqZfditXh2uTih5RQlBif8J4jYjYpbwz/r+qGrxly+lM9HWigXXYwqqD9FfuxGF6CFsP6Fai8vCCVM7MPekViq2KlPRewyEuD+cOdX9xKhWioCBEL0SDL1xpiowAIFg6g9xWyD3Fd/K54d4QvlWxYTjn8o/0OvFRzRJNawVWqYa/ub9z8k+NZPGLXPYuSkMc9SNjH7yDoR6DHl6PMidZ5wR6Kz7atsOpGoiQ7mIRigJPxVtSmlFZr4OeR97dlyfEkKpcoeuCLwGLEu9IEs1EGEoCa6rPamW3yH9Ni8L5O8YkNLhlkmlpOmlogVYuw0MRlDOmJrabJCVZiAq1lFqHlHe2P9tDCLpmJkLLs8kIFCSnycICvGSHox4CyXoSs279WiqYX9l0F0XQW4iqvUAvYZ2QAQPQlr/RqbfVM4ZFrD9DcZmYPewkpfHUB2JKIYEAIQwIyImo4pQyK28aHjtJJJKlbxlhTcEqR4VTYYsP3tNjJoRiFMLejJKOQBCxpJ/ehbmgEq+b4qfsgsVt+oAxeBaQRtNrverdsGDQ1sTEOig+0XZZKUrPPDqcZdtt/4RQvIJE30HZRAY7PxOg0IkDgcYDGXba4qxp0cUfKXDen1EL6CeHu9MRUCBPcq+xpn7vVLywF5Drwh3SbsXYheX3ufBnNJFhExABA0CQxNnoA6nPT8mZtzLgOjP7oItyj9kbF7j3tNTFKz5BmLr2UrlCKopUkJmukJwqIrJpIXffSAu/fdFLMQsRDE618YSd+ygcZM15cokREOUemsBp+Frz8bt/L1yFdfe8hEBxrJrEeXN7q0WwVuPNOLEQacJ5NCuIIJCHNw19SRhkWX7mfdGAs/NPYg50NsWDIGIckcHaU+dsMkXIqUV9VTfIgVfPbyUNtfuooaKTYP6npURcgfC2NxZgzLwUs+PB0Ad8Bwyua7ZKqWEnE+q3W12pmBU1/aM6NKJW3u1Y9ufIdXuyxV2G6z4JJaNP4VpY6M2Xw5xmyF4LAQMgpD7IPB2IApSRqGmTUZ12AlvOrVcO/mxAwlFCr6QNj06ccMjbTGaqqaS3n+qPj8uji7sHcUgiYq3ExrhetHaahHYrGXvGAbDmQ9rWs5/TRC9FGGI0bBP96lbSj59Ms78MDUhWAtR3MDVc2dFJcQ0IHAkihJYvB3+NJ5/mKtGQWt/xHyuy93S+hfIwxWeqqqwd1sQqckBZGc3xlHCAef2Pu1ITTIBAodd4bv5MVjzKzBkh02hKj9bm1Vhz5YQdmwM0UbJCwOKIQQlfBIicjrk/wSWj0HuBRIhfwkkdyAy+BvWrb6k1HuVEnKyDES2/rQ8aRFV8E4SAsY+fYKYBCvmQJXgUAdtz81l+pLyf0e52QaM5rIfhKPUsMKZ76a6xG2CiLch5GGI/gal3n8oZk+UCHWPumWuyf++hh0Y8FpBamMHpP0oMm855C4BWZ2qNgbcM3EYIPQxlNCbS+1VFCNq3G9w+hZQd1dj/MoIwzvmIM8jQm4seq1a1kLWC+BIojKFa7PCnFfq8e3cWGxWQcuOeTRu3YD7XjhJToZCxmmngi/kj+/Defit8rSxASXkFlRpLqo4JVXKzBcKTRs2K5w+ZeLvn8L459dQ8vMU7n3uRIXyWvIgILACN8aggcWv89eAfUeZJqoKauYr9LvhB/76phMhxnlEROdx6kQ7WvaeRmy8a9PXH98Fcn4Vo/sDg2H+5uNkZjdmx5a+7N+ayOZ1yfS4KIfQCEeRu6XEafcffFsKq7+MYsCwtCKbPIAqwVDwni2OC9j243a2b6hHVLydi4emERnjrinRjGIIK/05+Zg65V2jnmqjoTSeRMFp4/XwzB0AgVKvrALfv/UAi6e/R70GG7nmjkRCwlSNgkuMYO4PqhXs67ToEFDA0B5UO8iS76U+Tvuw5wouAM7oxBinPVXNWQBZTxYdKvz5CAFH9xlRVYXGLa0IxbmIuOyTGGY+3RApnR+u0aRitwkat7bQ64p0Fr5bj5JautP52Tz76UFCwsvzka8PFFfqcjjg28+j+Hd9GM3b59K+ex4dz8tFqrBuZQTvP9mQrDTnXM4coPLFlv9cmjKkhIO7Avj0lQSe/vhIOddCA0TCj9jzk0n+bzSx8UcwGIvff0nyLYI/VjXm0rt/KO9TLcW8577kv58/5t7nj1G/ia2oP4cdvp4Vy4o5sVhyFc6/LJObHzlFdJwdxeD8jIVSUgZBcsY9GCwfceKwCZNJEtfiQqKbjgJhRyrt+PihuzEaTjP4ttOERjg4nWgkvqENRTFwMvkqHrlqD5mpRiy5CuZAiaJInpt3kM7nV76QTNRKlIDWbr1nLanIu6bOKHkpVWRiO40l8hcEBAwBVMhfXrVToxagBHQr2jy+7xRjzpmENa/YTi4UePf73bTqVIPCKgCGnihxnyNte5Cnr65ZX0UICL4HETYBIQRS2pDWfyD98Wo+/VSVSETChoKxJTKxC5DPv+tDWPZJDFnpRnpemkmHc3N44JrWICG2vpWPf9vFhKtbc3BneeY/SaOWFo7tL/R4cdKqcy6vLt5PcKj7gVBShRfuacKvK6IqbBcc6uCVr/bTukuxd42U8ONXkcx5pR7Jx81cdn0aDZrnM3xcUilvlnwLqFE/sOXHvXTrdi/mAFnhpCAnU+G1SY1p1+deRj42tNx2Vksen025n00/nODw7kBCIx0MvSuJ68akYDTBi/c04c/V4eTnOVejhVKoryRRsXYuuiada8ekENfAhpTOm4LRFIhSr/wC81LNxZaxmLTjPxARVx9z9N0IYzOEEFwZOArVYUV1lH5Ci2+Uz2cbdpX/nkUCxCxHMVb8HXgKn7pQCiEGAm/htC3MklK+5JGBVA+7PfqSmDUopiaoKddW3lZpDjgg8DIIfRilhKuGNd/GHR0fwGFzUFKxOANuXV+97oePN0LEzi0Y6Dd3TnATCbkzkHlfQPyvIPMgbSyVumNWdzRZPCO0K/148+H27PjrAeIbRpOZcoq3V+Qz9eZm/PNLYTCdYMu6UOdsUjq3U04GMP2BJiQeqyjoRXDycAAxDaycPmGm8PPf928QWWkGgkKq8GQlYNLrRypV8jabIKFR6UVwuw2OHQgg+bhT1p+XRWK3KSz+KJZel2dx3/PHCAqRmMxwddwkRow/RdrBKGLr2ejeP6toJn8mJrNk9+Yg/ly1oFwlb8u3suLVq1jyXhQ2q/NmmJakMO/1eqQnmbjxwUT++D4Ca36xwpVq4YciSE0y8/VHcXw3P4bXvt5Hy44WjCaw2ywY7ceKPH1KItUMDvw2hK3r8qnX2Mq5F65HyiUQOgUZfBN2qw1XJjhbReskgJKg1VOr9nhUyQshDMB7wACc6Q3/FkIsl1KWNeTVFMXTqQqCgDx2bQ5k58ZQQiMd9B2UQVCIF0LPLavAdJd77p1qNkS9jxLQpcyhdYvXFyj4sphcLEaB83H4hXuacPtjp4hOsBMUorpU/CJhtTN1MoDQ1kMEAJmGTL4RQgbjSQ8oVYUNPzZm+7+jWfzat8CfABzffQJFkXw3P4Yt68IofVMUSFWW2vfriijMgQ6cmt+1glAd8O6aRrxx/17++jECgCatLZw6ZiKhsfvvUQinbbpJm1ySjweQl2MoM67BqNLv6nTCo0t//4oCRlPxd2+3ORVcTrqJX5ZGEptg47bHTpGbpeCww1czEhCKRDE4nwxe+3of9ZvasFnBVJBnLy9H4YdFUaScDABU0pMziIyLKCP3ltVzWfR+KHZ76c8nP8/AN3Njue6e5EoDwaUU5GUrvDO5EW+ucJo/bVaBMW8JhN1fop3k54V/8OGk1zl9srQzgMmsctHQT7n1pV7ljpOXYyDfIggMcvE7EdFl9/kRnp7J9wT2SSkPAAghFgBDAM2VvBCBSHM/sFaWG6V6qKrCE6Obs319KKrq/GHMeKIhL35xgHbd3MtZLdVi+2HVKLiBiSCQ5c9g8/MEf/1kJS/7Lpp3yOXAjgR6jXqXyHinGWvf5vJvEmu/juSmBxPLzMzSko2cd2km8Y2sRT9iIc6Y4Yd/jBAlTgy8AjKnVvVNVo66DRxdqapLptUCR/YG0qKDpYwPesn3YckTrF8dzov3RAErOVPDqKpg8YdxqKqrL7GsNrJaKnZ4FwrEtH2JJxd+j8ycSr5FEBCkYjKVdudzd0Y/7bNDpCVHsmbFzaycWdIWLgkJdzDu+bJppG1WwS/LI132Z7MqLJ0dy8/LIjEYnTeNkp4/llyFaXc2Y8aavaz+Mppu/bLJyVJYOiuONQuLnypys/JcKvmMk2tJTzGVmJ0XYzRKko+bsVrc+cEIdv0TXBTQpBhAlcEoOJX77Mfn8eXLy4ranvld2awG1nwZzW/fTMZpcCh7YzYYJacTDTRs5sL7J+pzN2T0HZ52oWwIlDScHivYV4QQYowQYqMQYmNycnLNRgsYUrPzK2DRjIb891cI+RYFm1UhL8dAbpaBp29rhlrOZD7pmIn//g5my+/BvHRfY/5cE1Zu2woJ/p/zv/nycpv893cwo7p25LWJjXlvSkMmDm5D0jELhoxrOLLd6ZPcoVf5C9NLZsbhOGOSn28RvHhPUy4clFmk4AspVPSEPI0SXLoItVAiIew1d99d1ZBVc3G12+Hr2XE8OLQV7zzWEKul9I/3+AEzmWkGEo+Z+Py1erx8X1NcKYJCTh7SKi20JDgsFKGEExA1HHOjPwlJuIH0tJ58Pedy5r73ANnWYTgcAkte5XlYhIB6TWy0Py+UBz64m6XpnxJdv1CxCjJTTSx4JwFLnsBhdy7YWnIFKz6N4cieoHL7teQqnDoSwPEDZU1PUhUc3RdI4jETaclGxlzclnGXt2XNwmiKPj8BMfVdm5Ek0YRHua45a7cJDu4MLHtSOZjMJdYIJBiC+gPw+bSvSih4KP/RQJCXU1IdSoQotv9Hxlpp2PExMF4MKGSmGcnNbQkx61DMrdyW0xd4eibv6hMt9Y1KKWcCM8G58FrdgdT0J8GyoLqnV8q386Kx5JYNB7fkKuzZGkS7c4uPpZ828Mj1LTm8J7Do3QoF/lwVQVScnde+3kdMgpv+wEojFEMoqm0f5P/ssonNKnjy5ubkZJWeOS6aEU/XvtlI9WGadNpI7yHnYTQZsNvsFH81TgH7D0nHfMbveOLgVuzfHlyxggksreDVrDcg5wNc/XA1Ie+DKjU3GuGq0af5+PkGfPt5LKoDbnvsFBExDlQHTBzSisxUUxV6FFRkgqlKP0PuH1S0pRiiUSKfpF4kXNtZ5avXV3BT51/Iy+5Mr8szeOqTin3xi1CPAPDjvF9JPZlR6tDC9xL4+6dw+g9Jw2iEdd9EsntLZTdNccb/M44qkrwshYM7zYRF2klLNqE6itv3HXo+AUGu1yZaXjCWsc/cwovjmpa6XMyBKr0HZvDZq+5GgEu6X5yBojhNRUlJ59G8udPD5bOnF7rZh1NmU6CR6+8+yuhJzoVnKZ0TBSTY06ex798gXh7fmlOHne+pwwWPMvWr14iu55tAJ3fw9Ez+GFCyvl0joGJH3WqgOtI8quAJHo/E9YUqBDjsJR9hBbf1bsfh3YEgC2eEAqkKLDkGko6aeXVCcaGAChWoUh9il5F5+CFS/hvM6cQkl+ds+T0U1eHKXKCwekE07btnAWAwGPh037sEhAQUzFKcj/GTXj/Kg6+XfZSPjHVe3T8viyoToOOww7Y/Q7AcHYhqT0PN+sTpwprzPv6WRdLpjuiU6fsvYhnRpSM3n9+aQU26kJlanXmONhlDb3ri+jL7fpi3jitMI/jokc/Jy3YukDdqVbHXU+nrwXmj//Chz1y2PbgziDkvNWDWcw3cUPCVo6qCey5vxeZ14Zw+VaDghUAxKJx3ZVce+fS+cs9t0aUDgfHjGHV/IgaD8zsymlXqNcnnt28jSEt2/+ZryVXY+HMoyz67kKbnO4OtrPlVXbuRhEXkcssjTgWP861gMjnXG7LSjTxyfStOHAxEVQWqKtj+RzajGt/N3s1eiFavJp6eyf8NtBZCNMeZvGQkcIPmo2Q+o3GHgRD5Ccg0yF8PuTMZcW8IM55oWLQwVYjBKGl3brFN/utZseRmGShPETgcgm1/hJCaZCAkXOXE0WY0P/9tp3K0H3R6xwReCOZOKKa2zJjwMuu+2svpRGcFrPMuyeSZOYdK2WnPNEEUIqXAkquQl6MU3aLiG8fyTdY8ju4+TubxebRtP6coyrDQBc1gBGu+YPi4JP5dH8rHL9SnU89s4hvaMAeqWC0KBqMkrr6V5JMKjYMvoHLF7m7glvbs/6+0myIIko55K7LZNc+tmIzBWPrJa+sv//HyTSUT1DllXr86jDumnAKK6yWoKmSnGzi8J4D23XM5ui+QwCAH4Q36EwHYLN4pjGPLVwCFh+c8TJ9repJ5OovDO46R0DSW+CZxlZ7f+9pbadD2Yha8/RAgsVtFheYj1wg2/xrB5l8jGDLufJSCbGd/LKt6adCAIJXfVoaDELQ5J5eERsU3imWzY8gv81sTqA7Jvd0nsyhxlsu1B1/jUSUvpbQLIe4DVuH8lX8spfxPyzEsudnkHF9FZIx7C1SFPxKhVDAfi/0RRCCbFl3NH6sUgkOjuPS6NLr0ymbHxhAsuQqmAGeQxGPvHy61mLfum0gqm+mpDsEnL9Zn65+hzNg8D8UcBuY3y7Rbv3ITS9/9GymLoyL/+iGcKTc044UvihX9Ob2zy3goAAQGO+h1RQY/fd2Qa8+Iqm7ctiG0fYTMQ/sICfgZcH5+QoFtfwZz/GAAycdNhIbbSE0K4J7L2tL9okyad7BwzgXZ9LgkmwbNbcWfZ2Wffci9kLsC5CGPVvUp2beqOm+AM55oWPFJHsZoNhLXOAaHzcG5l3bm7uk3ExZV1hvsuRGvuzz/6L4g1i6NoFu/LD58qgFxDe0c2B7IP+tCURRw2BXMQSqqXaAYTjJlwUYiE8JJO5Xhsj8tUUwKb66bRvuezvWe8JgwOl9YcQj/X99t5qvXV5CWmE6Lc5ry07xCl9uaXRRBoYGce2mxV5mo8CJzZXITnDwUwHNjmhEcqmKzCS69Lo0JrxxDUWDL78VeVdEJNs6/LBMp4c9V4WScNrLyg4Xc8MQtxaU9/QSP+8lLKb8FvvVE36mn0rijw1i++MdR1uOjHPLzBG9O7sHkWedC7syyDaJ/RNoP88zQp9i8LgZLrnPWunR2HLc/foLr70lmy2+hREQ7uPjaNGIS7KSfNvDHtxEYTJLgsMpnq1LC6i9jaN65CeFRYWWOJx1NISc9hzfv/rDgUbz0LPSfX8P5bn4Ug250RnqGRqjc+9wx3n+iETabQHUIAoMddOqZgxCCi27/olxZbA7nwnFhmlWDAc7pnUun83P54q14UpOczwCqKvj7pwg2rwtDAD0ucaZ/cFtZK9EoCatR8/dDqvbpVqWEjNMG8vPAaIaoOAeWXMHk4S01MUvUhDGv3sTQ8YMqbGPNt5GelFnOUcHL45oSFmknK92IUIr9xQuvNktO4UxD8tTQV+k1uAe/f/2XJvK7ostFHeh2aWeuvvtyImLdL8Dz+XNf8fm0r4pceQ9t1y6grWnHxlxwdfei7d5DXMYGFVD+Aiw4c9yA0+usbddcBo1OJb6hlZ2bghl002nGPnMCqTqflu+ddpy3HmnIrj8WIhOnIwOuQkROR1TPlU5zanXummnDXycn00G+ReHDZ+px7ZgUouLsSNW52p543ETD5k53O5vVGdDwzmMtGPHkcyjhzZGB/ZDZ74IjEQIuR4TegbQn8dvn49i8riGWXOcX7bA7PRJmPt2QL7dtp3v/4vw2c6cnMO+NhFLh7RUvzDmPNe/ShA83Ty91JC0xnSeHTGXPppPOGXIFQUpbfgsrUvIAV96QRvvuuXz/RQw7NkZgMhto328QF989vujxtRDV+i9YvgNDE6IaDcd68lMMhtLmFlu+4M9VZR897TaFnZtcK00p4a8fQ7HkGjinT3bpfB9G50xPCWjpEaONqsLzYxuz7Y8zFY6HHhncxBxgqlTBr5i5irfHzqqkJ0FWuhHn+k7F7VSHSl6WZ3LGBwSbeXzeBHoP6VlpW1VVyc+zEhgcgBCC5TO+59Mnv/SIXADTf3qqlAnMZDYxadY9vH7n+9XuMz/PwLKPYxk0OpWR4xPZszWIsU+fIKAoVbHz/4RXjrN6YaRzO/8bZOK3yNBnECHDK3mi8Dy1Wsn/98dupCqYcmNzdm8O4ZtPY2nfPZfIWDs7NwWTnlL49gQIaNKuPtO+mUKD5s5iHY78fRz5dwcmk4UGzWeC4wBHdp/m1xVhRQq+JEKRbP4tjP6DnY/BW38P4fPXEygVPSrB+cWXp+idsszcUtbFcGK/hzm+N624XTn9BAarXHpdiQhf0RXkbpq1zWPs0yeAExAwECVqQqnzpHQgU64DR+kwhX9+S6Br7yRMBaHq+XkKaxZGse9fV8q8/HB2m1Xw2sQmZJx2fu7d+mUxddZhAoJMKIbOJXtAuLk4W7KEo6vizoW26YBglUffPcbCd+NZ9nEsvlbuAO3Ob8XrvzxbYZtZj8/jy5eWutmj+++p/QWt+eeHf91uX9GQ0396mibtG2HNsxLfJLZCpZWXY2HdV3+y+I1vOLj9KFKVhEQG0+Wijvy5tOo2cndp3aOlSy+eK2+/hO6XdWHOkwtIPJxMeEwYv329oUq+AYWz+hYd8xk1IRHF4PrkQaNL5lBSIXsqMncexC3zqaKv1Uq+8IvavTmEwh/Azk3lRFtKOLLzJHd3eRCDURISmkWLDhY6nm/moiE52PIlRrmGhARBQFBDhJBFSaWKulBFkdLZuy2IySPLKyRRWZgejGpyN7P/e4PgMKciPbDt8BkKvvB/2QvKHChLPU0gt5FyUhAaKTAHSGeVnfzvURMHQtQ7kPcFSAWsf4K6t0x/zdqk8djIllx0TRqKAX5eGsn2DeVFrQqna6gLMk4bChS8U/5/fg3j+bubgBRs/PkmoupF8sHmV4kMvgmZ+1m5xZhL7s+3COZOTyA/T+GiIal0uuwZpKE9xzdej9UqaNDMSliU01wXUM/OPdNOcP6ATB4f1bIc+b1H2/NacWDbYZp1bOxSAf33+64qKPgqIKD75V1ZOH0FNkv1o4MVg+D6Sf/jnP4dK22bdDSF50a+wc4/95Q5lpOe61EFD7B3436WvvstRpOJuCYx9Bx4bpFijW8SyyNznF4+aYnpbFi5CVu+ewvTBqPTnbOQK0amF7iIlsYcWM7kR92FtKxDBPWr8nvSilqdoGzMOQ9y8N8j1RhJYjBIHA6FDj2yeXHBgYJsc86j/24IYcoNzYuSIhUhJK065fHKov2MvbQtScdrtsDSsmszPvjnVQBm3Duarz+wUF5oQXCYM52AOUDlhfkHadXZ+Tiek6mweV0oPS7OKkqdqqpORZmZJgiPksWBS7i2oTsXj0J55vYW5YxfVp7vjm0rmlkXLnC+eG9T1q8+08RT+kkkMCSA5ZlzsZ8ej2JbXbRfVSE7O44D/+bSvlsu5kDJvn+DePfxhgU3cejeP5OsnHNJObKHnCwY9/xxLrs+rUyUrpTw1K3N2LDGPzwdhCIY9dhQbn12ZKkZ3fhej7NrQ9mbrq8JiQqm/7DeDLrzUk7uT+SbD9dgtdi45Ia+DLrrMswBxa6NDoeDZ657lT+Xu65/60va925DSFgQ5iAzF1zVnUtH98McYGL9N5t4cshLbuTXl8Q1tPHe93uIKGF6tNud8RduY+qJEuPZqNg6m4UyNSmdEfXuqtGY0xfvo3OvsqkCPpuewKL34p15OhSw20WBuxgYjSqqFC5906vKasdCNn77JU9etwi71XWkZVScjftfPkZwqErnC7JLKbW3HmnIPc+eKFPOzGGH00lGcrMMNGtb7GdtswpSTpqIjLWXyrtz/KCZ2/u0pXToRPlrC+26ZXP9Pck0aW1h37YgZj7bkPQU9/yan/76YfoM6enM6uhIByUMRTEipeT62KFkpTtdUEs/SUkaNs/j+MFiE9KHa3eVem+F5OUI9m0P4qGh3k/5WhH1msdjNBkwB5oYePslfPnKMk6f8HCa5Cry4dbptOjcFIA3xnzAT1/8hiXH+RkHBJtpeU4zXv/lWQxGA5Y8C4PDbi7I21NDXD+0akpYdAiztr9BdL0oUo6nMKrxPRW0llx1czIdezrNv+f0zgEJ701pQOcLcuj3vwznArh0RuduXhdKz0vKSdhm6osS87Gn3hZQsZKv1eaa6PhI7nnrVt6fMKfafbTs5HqBavSkRC67PpU/V0ewfUMIf35fPCu027VbNXc4VL6aPg8hizMRlsQcoDJgeCq9B5b1vLBZBScOmbHmizJK3mAEJDxwdWveW7WHhi2sLJ0dy6cv13MWdXAI6jXOJy3FhMEo6do3s2j8Gx6/lm3rdrB93S7KU/S7/gnlubucboBO05b773nVnJ/pdlkXgkICESVSswoh6DvsSr4tlXelmJIKHuDY/gCatM7njHVlFAMoiv9MXgo5dbC4atmMB+YQGFJRlkrvc/XYAUUK/uju4/wwb12plNT5uVYO/nuEP5ZvpO/QngyNvk0bBQ8gYfaON2jSrhFH9hznjnYPaNNvCbJSc5h++3u88O0TxDaMpc/QnuzbuA67DVITTUgpEEISHhfM60s2MXl4a9YsjMFqUWjWLo+j+wJx2AXffh7Lkg9z6Xt1BqrDmfvn5GEzQ25PKYpnKEXI3Zq/l6pQq2fyhWz49h+euPrFao350S+7aNLa1WxQ4dp2nTSZrVdE6+4tOLR9L7b8Mxd6JYHBKk3bWnhl0QECg52z7lIJtXIV7r6kDTN/3l1itd+Jww6/fhPBK+Ob0On8HBKPmkk8euaNpKQCL37d9rxWvLP+BWZN/pyFry6nohl9sYKv4uck4NoJVzH2tVvKLEoNq38n6YkZlJ7ale2/dRdnqbeSFX7y8wR/fB/Ojr+DWT4nvmoy+YDCPPX+RIPWCVw74Wo+euRz8nPL/jauGjOAXRv2sH+rm6kW3OTC6y/gyYUPAnD6VCojG3hGOc47NIP4JnGoqRNw5H6HYoDTp4w4HIK4BjYcDpg6ujlbfg9zmTytPAKDHSzZvb3IHRkApRNK/BLt38QZVDST9w9Hzhpy/qBurFEX8cnutxgx+RpanNOk8pMKmP9mPJbc0l+kJVewdHaMxxU8wN5NBwoU/Jk/dEGztnk0bWPhoeta8vqDjTi400x2hkJ+nsCWL8jJUshMM7BlXWiZSDybVfDFWwmoDoV//wwl8WgAroI/XL0+eTARIQR3vXwT3+Z/wZBxhX7tZ8goZMFp1ficJCx5cyVzniybjmLRyVk8s+wRGrdtSKN2DV0GDgHs3RbM+EGtWbs0Amu+ID9PsGZhFK9NbMyGn9z33S5Jjyu6EBxR1YjL6lO/RQIxDaIqb+hFTuxN5N37ZpfrRbX1l+2aK3iAg9uK+4ypF803uZ/TtGPZnPA1ZXTLcdzcahybVv+KYnBOmmLr20loZGP28/UY2rozm9dVTcEDWPOV0nnnQ5/1ioKvjDoxk3fFFy8u4eMp5QcBlWTI7cnc/PApZ4FfCSvmxPLxC/VRq/glewbnLFpRZEEeb4lUBUGhKllpzpDygCCVcc8f4+Jr0lEMksSjZt56tBFbfw+joll4eUTVi2DhidJ+2ynHT3NPj0eLZthGkySmvpXEI65uHlVDMQiad2nK0PuupHX3ljTv3KTU7H7utEV89lRFiaYkQnGaaBx2QUCw5OvUOXzxwnfMfXZRjWTzNK27t2DU5KFMG/6aNoW2azHte7Xh7d+fd3lMSokQgk+mfsH857VQnJL6Ta28+/2eonKIjw5vzpbfzqwV4D4Jjax8umFn0c1RJOwonYLbg9TZhdfKyErL4u17Z/Hzl39U2tZglETG2slMMxQtsPqWqitno0nFHCjJzVJwZYZxlzY9WvDeXy9XLF3Bj25Q0Ci33dHcJSAkgNn/vUFCQe6T/Lx8bmgylszTlRdZNwUYmbn1NRq1aYDVYuXq0NHa2Y09wBMLJtJryHlcG30r+XlVy5Nf11CMCjdOuZZhDw4mKLT8p6mVs37gnXs/wmGvWcEeo0nl2ruSueOJU2SmGhjWqSPVVfABQSqPv3+YCy4vXjsTCbu95h9f58015REWFcaULybSb1j5FV8KcdgFp0+ZihR8cEQQQWHu57PWnqpfHHabUpAcDYpNK1XvZ/x7lXssFV68b66bVuX+KyM/J5+bW44r2g4ICuCdv9yrGtm2ZysatWkAgDnQzPPfPu4PcVEuOf9/3ek/vDfmABMvrfZAkZVahmpXmfvMVwwOv5kPH3GdRRPgqjsv45pKoojdwW5T+PWbSAD++L465j1JZKyNcy/M4vn5B0opeFB8HulaSJ1W8oXc+eKNBARVzae9wwVtWJb+GS+tmUqrbs09llDLM7h2xXQHY4CRph0bV96wgDY9WrEocRbte2nrrqg6JJ9NKzbRGA3uXarbf9sFgCU3n1taj+fxgc/7RebjuIb5PP7hQT7dsIPXl+5hxloH05bcVHS8U592zD/yAY3a1PfI+M+ueJR6zf1/IbqQr6av4OrQG7kq5AYGKMO4MmgUIxrcxbQRr3Nw+xE2rdmqyTh2mzPAsV6TitI5l3cBCYbde4oX5h+g8/lnuGGHPKSJfFpQp801JUk5fpqpg18qUQKvYiX4yJxxDLj5oqJtm9XGrg17eX/SHPZuOlj+iW5iDDBir4GZY+zrt/DBpE9rLEd5BISYadm1GecNPJeGLevT7vxW1GsWj81qx2Q2lpml/L70L1648a1SLndacMu04dzw2HX8/vVfPDvMvWpTa9RFTOw/tcAF1PdccHk6T80+jM0m+HVFJOtXh5OXrZCRZiAooj0TZ05i2y872PzjvyAEvyys3LxYFUIiglma9ikrP1rDm3e7SMqnMZHx4dz50mgCgs08P/JN7QcQ0LBVPY7vdeGuWA2Cw+wMuTOZBW/Vc5EvymnujGscTW6WhcCQQCw5FoLCAhkybiCnT5wiJnQ+YdFWAoNU2pyTR2DsCGJbP+HVmfxZa5N3xeave/PIdQ2oTMl/b11QJt93IacOJfHXt//w8RNfkJPuXn3XQqISIjjnkk70HtqTpW+sZIeLMHB3eOWHJ3n08mcrSValLUIRRfZto8lAbKMYbn9+FBeP7MvJA4nc3mECdqtncsbfOm04c6ZWXuWneeemvPX7NAaH3+wROarCeQPb8eTsw5hYT36eYOLg1hw/aC6IpC5cK6n6mklVCAwN4I1fp9Gqa3NUVeXWtvdzcn+iZv0/Onc8L9/8jsvJbnTDKFKPeybYSygCg6Jgt2t1vUmCw2zkZhUG9FX1Oynt7vvWH8/R4YK2GslWObqSL8H+X/sy9qJ6VPYlrlEr98rY9MNWJl/+nEaSuY/JbMRms/uFGQIgMDSQl1ZNYfEbK1n31XqPjRMQbCY/t/wnBWEQzNn1FuZAM6Maj/WYHO7QrGNjPvzVDvnfA7BkZiyfvFSv0uLe1WXugfeY++witv78H4oiiG8aR9eLOjLorsuIrlfsoqmqKtOGTee3r2ueS0YxKKgOL84yzqBeiwROHdDuhlWTdSxXvLhqCj0GdNWkr8rQlXwJ7Okv8r+Ev7HbKv6xTV04iR3r99CodX0uvfHCclf7ByjD3BvYC2Hb/kB8sziSDtWwIHs5CEUQGV+2GEZIdDDnXdGNpMNJ7Pijek9GroiqF8ktzw7nzTHumTiEQXDJqL4Me3AwLTqHI5MuBJwJwib8rxW7ykueV0PuenU0wx8c4nZ7h8PB/0Jvwlbl8nj+RYdebar9JOwtwmNDeX/Tq8Q3jvXoOLqSL4FUc1k7ezQv3l24kOfiru1CIcc0jCI8Ooxul3bm1udGEhjs9LxZ/dnPvHrrex6VubYRVS/CY1WJHp4zju4DziEnI4dGbRqgKAp7Nh9gXPdHNR3n4lF9GPfOHdzW9n6y3HDdLGThyY+ISohE2nYjT18POBf0HhvZgn9+LVsgpqZExIbxVVLV86JkZ+TwQO8pHN55XHOZvMXURZOYNsx1NS1/IjI+nC9PfFSmroOW6Er+DKSUzHrkJRa+tolqP5oJ6DW4B08smMSqT9fy9tiPNJWxttOlXwe2/bqj8oY1oGHr+lxyY1/mPq1twJPBqKCqslr+9cERQeRm5hEcKlj473+YzM7Z8h/fh/PCPU01jcGIrh/FgmMfurXAt2nNFl644S1ys/IIj4uga/8ODLlvIMvfX82Pc3/VTCZv0WtwD575+hGuMI3w6ziIQp5Z9gi9/3eex/r3mJIXQrwK/A+wAvuB26SU6UKIZsBOYHdB0/VSykqNpN5S8pmnM9n19z62/vwfC19ZXqO+AoMDWHz6EyzZFt64+wP+WPE3qs3/LzpPU695HKcOesZsU1u4dkwStz12CnOAM7/Pg0Nb8N9foWhl873mvoGMe/uOco//uWIjy979jm3rdtYor7w/stIyH7PZxKI3ljPzwbm+FsctZu94kybtPFNv2JPBUGuATlLKLsAe4LESx/ZLKbsW/Pl2FawAVVWZ2G8q18XdwZRBL7LwleXEN43l5TVP0n945QFTrrDk5vPFC0sIjwnjqa8eZvoPT2srdC0lOz2Xu6f73sPFlyyZGc/L45pweG84+7cH0vPSLE37v/mZES73H917nCtMI3hyyMtsWrOtzin4Rz4dh9ns9IK5/oH/0ahdA83HuOXZYXTu156gMO0yhY4550FU1fsL1ZqZa4QQQ4HrpZQ3Fszkv5FSdqpKH56eyT8zbDq/Ld5QZr850IS1Bj+E+CaxzDv0PhmpmYw952FSjqdWftJZgFAEbc9vxb5/DtYoJkDHBQqsti0sY6pRVZUrzCPAd04v1aJlt2acOpCMJTuP4PBgLhvdj0ZtG7Dw1WUkHkpGKIIOvVrz4qonCAou6wTx8i3v8MMZZidTgJGeg7pVq6B5r8HdeXbpZPcdK9wkOCKY6T8+Retu5VWVqx7eyid/O1CySm9zIcRmIBN4Qkq5TsOxqoUrBQ/USMEDhESGcH+fx9j5574a9VPXkKpk15/Oyke9Bnf3efWghm3rY7PYiYwPx2FzsH/LIZ/KUyNUZ376+i0SSu3+4MFPa5WCNxgNjH39Fq6570rSkzP46vUVrF/5Dzs37KHThe2Zu/89Fr/5DZt/+Jeul3QiIND1zPrRT8fz8Cfj2Ll+Lwf/PUzzzk1od74zCnugeWSVPdv+XL6JfIv2uYRyM3K5t8ejDL5vIOMrMLVpSaUzeSHED0A9F4emSCmXFbSZAvQArpVSSiFEABAqpTwthOgOLAU6SinLVL4QQowBxgA0adKk++HD2qcwLUTru3Ihrbq3YN+mAx7pW0c7Hps3gUtG9S3a3v77Lh4d8GyNb/K+4vOD75HQtHSqgiFRt5CbUbUAPV9jNBsIjQohPbFsYZwzUQwKn+x+iwYtXKkk1wxvcGe1vL3e+PVZJvZ7ssrnuUuhBUALamSTl1JeJqXs5OKvUMHfAlwN3CgL7hhSynwp5emC15twLsq2Kaf/mVLKHlLKHnFxcdV7hz5GV/DVRzGIctchTYFGzFXMOVQR85//CoC0pAxWzlzDm3d/WGsVPMDNrcez6PUVRduqqtY6BQ9gtzrcUvAAqkPl3h5l3WWTjibz7PWvOnPdGIYxwDiM0S3GsemHbdV2541pGF2t89wl6UgKt7QZ79ExoIbmGiHEQOBRoL+UMrfE/jggVUrpEEK0AFoDPteEbXq0YM9Gn4vhNkIBo9mIaldxFEYWarCEUq95PHP3v8cDfabwnw+CSYQieOWHJ4mICSM/L59vP/6JLT9uJ/FQElKFiPgw7n3zds4f1I2kIykkHUlm/otLOLT9KAEhZlKPp1dr3MM7jjN54DS2/LS9xmlq/QHVrjLzoc/Y8uM2nl85BUVRiIgLJyPZPYVZW8lJz8Vus2M0OdXXoteWM/PhMzxsJCQeSmLy5dXLkmoMMFK/eQK3PDOcTyusZVAzTuw7xT9r/6XbxZ09NkZNXSj3AQHA6YJd66WUY4UQ1wHPAnbAATwlpVxRTjdFeHrh1Wa1MSjwBo/1rxUBwWaWpn1adBED/L1qC9OGv0ZelqXG/c/c9hrNOzVh54a9PHTJU1jz3J/NhkSFkJNWtvC5lghF0KBVAsf3aJOA6mygMA3HkrdW8vGU+RWmf/A2BqPCyz88ScqxVF4a/bYmfS7P+oygkCDeHT+bZe99r0mfJWl3QWve+eMFAOY9/xVznvzSoxHrt79wA6MmD632+R5zoZRStpJSNj7TVVJKuVhK2VFKeY6Usps7Ct4bmMwmXlr1hK/FqJT8XCtfvLy0aHvL2u08dc0rmih4gPkvLUFKSfvzWzNt+WMYze4/0DlsDu6fcRdNOzXBFGiq/IRqIFWpK/gCWndrzsUj+/DUkocrbLd24e8ADL1/EDc9NZyAYO3MXNUlKCyQRz4dx/fWLzmnX0catnbfjl5p3yFB7Nt8kG8+XK1ZnyXZtX4v2enOycyNU65n6peTPDJOIR8/Pt9jwYNnRT75knQfcA5PLfGfXM/l8fmzi8g87fSrnvnwXE3zjPw8/3c+e9r5CNr14o7Yre67Nzpsdq6+ewD3vnErA2+/hIBQ7fyIdcry8pqpPD7/AYzlZEQtxFbgCSKEYMTDQ1iW8RkNWmmnVKuMgM/2vcuAmy4q2tW2RytNuh499ToAfl74h0fNbv/9sbvo9bezfvDYOIU8eNFTjOn6kOa+9N4pQOhnaJnEylOodpUJfabwya63ObzjqOb9z3t+Mbc8M4LXxnxQpfM6X9ieF258i9+/3qB52T+dsrw4+m0uG92P6XfMqLCd1WLlzs4TSU/KICohiszTWaSe9EyaX7eQEBpVOiGbEAKDWcFhrZkS63FF1xKd4jEzSnC40x//5IFENq3e5plBzuDgtsMMq3cHi5M+0azPs24mD/i4rJ/7HNtzkhMHTnnELCJVyZWBI1n98Vr3TxLQ97oLWPfVn7qC9xJb1v7H+5M+xWap+PN+a+wsDv93jIzkLA5tP+JbBV/Ayg/WuNhb85QOD/Sdyj8/bOWiEb09WrHtl4V/MHfaIm5pc5/nBnFBZko2f66oeSroQs5KJX/13Zf7WgS3ubPTJFqe06xa5waFV3wzq2qBj0mz7mHBS1/XCc+U2oLJbCQ90TMZPT3N78tLK6rkY6dxaFRU5tHLn+Oebo94tGjOsne/57OnFnq1ME8hS976VrO+zkolHxUfwVVjLvP6uNc+cFWVz7FZbPy7bme1xut33QXVOs8VF1zdjXVfrSfpcIpmfZZEMQjim3g253ZtJDcrz9ciVBtTQGlr8KH/jvhIktpHTP2oyhu5yVmp5AEmvD/Ga2NFxIbx/j+vcM5FHat1vlSlM2ioKgi49dmRXD/papfHqsKVd1zCzg17+fu7zVU7sQp06NWWeYfe5543b/XYGLWNVt2a1+pCM3+t3ExWejaOghJ9rc5t7mOJag/j3tUu5cFZq+S9VWT3opG9+SrpY1p1bU7nC9tXu5+QyNAyM6NyEdC4bQNWfLCa21+8ke9tC7j3rdu4ddoI5h/5gMBg9z1imnRqxKo5a8lI1jaD4pkEhzkXuS4a0duj49QG6rdMYO6Bdxl428W+FqXGXBt9G1eH3MjtHR/g7XGzCY/VvnBKXWPM9JsJi9CuithZq+TBqQg9zSNzihdtwqJCuXrsgGr107htfZ5f+TjNOzepsJ3RbAAJR3edYP7zSxgUMIqBppHMmPAJS976ln2bD5ZJalURzTs2QXV4fjqpGJ2XYskAsLMRU4CJz/a+S71mCVx6Uz9fi6MJdpuDozuP89vi9WSmeHayUFsxBhoZ9fhQvs2fz7BJ/9O077Nayb/x23NFysUTxDeLRTE4+7fk5vPlq8vY9utOAqowkwZAwJBxV3LuJZ2ZufU1Vtm/pEt/108FFS2mZqZk8eSQl8nPy3d76E2rtlZN1mpSmG4iPDpMq5oa1SIw1LeeV5PnTSh6HRoeQsuuTX0ojU5lTFuhTdnJ0PBgbn/uBkwm7T3pzmolHxETxneWLxj+8GBMgdrPIJMOp/Dn8o0kHUlmeIM7mfXo5xzZcYz8XPeVrGJQuHDoBaXMGIqi8NraZ5l74D0uGX1hleU6sc+9Cvctz22maYKwigiLLn487XutdgvGlRFdP7LUtiVbm6ji6jBk/BX0u/b8UvtiG+mL0f5KcHgQ6xZvcD4915A+Q3tqIJFrzu5nY5wKc+8/Byv1Q64WEj6e+gVpJ9PJy3RPeQSHB9GpTzu69O+Aw65y7qWdadezlcs1hISmcfz8xW9aS13E/s2HiGmo3Sp/eZgCTVw/0fmImnQ0hZP7T3p8TICo+pGk+Yl74vgZdzJ47BWl9m39+T82fu+5xe7aQv/hvVi3eL1XzIZVQVEEq+f8XPN+DIK7Xhpdc4HK4axX8rlZeWz+8d8a9aEYFdRyfMeP7jheZXmmfDmR4NCy1W/OJD050+MX/unjng2qMRgVhowbyBW3XYzD4WDihVNJOuIZN80zad+zNX8s0y7opCa8e99szAEmBt52SdG+WY/NOytiEhSDqPA6/mPZ336n4MFZ4rJKuIjONZgUlpye49bvvbqc1eYagD2b9te4jwuudpn8rXpIilwV8y1W1nz2Cx88OIenhr7C41c9z7IZ32O3O586lJo/JfqcL098xN2v3owQgk2rt5GZ6r2FuZCIYK+NVRlSlbx+1wdY84uzRx7eccyHEnmPMdNvqvB4nYmuljDq8aFExIcTHhPGiMnXsDxjrkcVPGhY41ULPJ1q2BW/L/+Lp6951atjVkaH3q3Z8cfeCtsMvf9KVn70Q5XSBPsbLc9pygebp2Oz2nj1tvf49as/cdi8N3Odd/h9Rre4F+lns8RzL+vCK6uncleXSRzarn3eoop48fsnaNOjBbmZeSyavpzlM1Z5dLzG7RvywT+vMjh8tFe/e1+xOOVjp3OBxngs1XBd4NA2/4vCq0zBA3z99nc+VfAxDaP5zvoFH/zzcqVtXeUKEorgwdn3kpOZy1XBN7L2i9+9+iO/8vZLiG8cy9OLK07h6ws2/7CNW9rcx63PjvR6yuDo+pGER4eR0DSOFR94Jo1vIcHhwdz2/CiGRNys2XdvMCl06N2GsJhQTfrTmpBw7z89nvVKvnG7hr4WodZx/UP/45U1TyKEoGXXFnxx9EOEUnZhWDEqfLh1Ossz5jLqsaEEhgSgGASRCRE88MFdtDq3OQ/2fxKpencmfc39VzJp1j0A9B58HqYg/7N7ndiXyA+f/8LY128hul4kBg+6+pbkp/nOhfzczFzNv5fRT17H/+69gv/dcznPLnuUz/a9w/PD36hSquvKuODq7ryy5kmWJH/ClIWTCIkKxhhgJCDE9/n1u13WGUMlKaM9wVlvrpFScnXoaKx5/lNJp7ZgMCk89vkE+g9zunf+8PkvrJz5A+GxYdz58o00bu28gTocDv5Y/jfTrn+Nkpebwaj4ZGGxsIoSQHpyBsMS7vS6DFXhtudGMnTCVSQeTuauTp4tXnHelefyzNJHePGGN1m3eINm/Q4acxkTP7i71L7lM77nnftmazZGSSpbzPU2bc9rwdt/voiieOZmXZG55qxX8gD7tx3i3u6Pojrqvk3QE8zY9DKtz23h8tiWtduZes3LWDSqaqUFQ+67klufHc6hHceY2Heqr8Vxi//dezn3v3sXX7+zkhkT5nh0LKPZqOnsut/w3jzxxQNl3IA/fWoBn09brNk41WX2jjfIOJ3JQxc97fLGIBSB0WSkzzXn8efKjeRnuz8hNAUamXf4faLiIjWUuCy6kneD7b/vYuKFteMH72806dCI2dvfKLM/5UQqt7Qerz8lacTCU7OIjAvnCtMIr5u4qkpMw2i69OvApFljCQxyHeG9e+M+7uv5mJclc02X/h14aslDLHt3Fas+/gm73U6b7i05tuckWanZ9LjiHG57bhTxjWNRVZV/1mxl18b9/DRvHUd3nXDZ54jJ13DzU8MxB3imTGZJPKbkhRBPA3cByQW7HpdSfltw7DHgDpyFvO+XUla6TO9LJQ9wS+vxnNiv1xatDmOm38SwSYNL7Vvw8tfMfmy+jySqe1x2c38enXMfB/49xN3n+N+CcSGh0SF8nTLHrbbDEu4gPTnTswK5SXyTWOYdeh+AOU8tYPFr32ApiE43GBVCIkKYue21MmmAU0+l8d6Ej/l71VZUh0q7Hi15dO79xDWK8ZrsnvaueaNEIe9CBd8BGAl0BAYCM4QQ/re6dQaz/nu9KNfM2YirxVN3mfnQXDas3FRqX4qHA6nONtJOpQPQonMzVtm/ZMwrN5brRWIOMnH+1d28KF0x4VHueba8fOu7fqPgAZKOpLD9911kp+ew6NXlRQoewGFXycvKY/Eb35Q5L7peFFO/fJDl6Z/xTdbnTF/7jFcVfGV4SqMNARZIKfOllAeBfYDnkjNohMls4tu8+bQ9r2XRPsWg0KFPa4QGn5Qp0ETXAZ05/+ruRd4SIeHBPPHlRDpd2L5GSlYLamq6e/u+WaW2z72kU4360ynN9Q8WZydUFIVhD13DkuRP+NYyn+/yv+DDrdOZ+tWDLDg+k5U587lkZF8CQ7xfaL2/G+miU06k8sNnv3hBmqqx+cdtHNp+xGVab5vVzpa1230gVc3QIq3BfUKIm4GNwINSyjSgIbC+RJtjBfvKIIQYA4wBaNKk4jS63sBgNPDuhpewWqxkpeUQGR+OweB8CJk2/DV+/Wp9JT2Uj81iY8ua0ikUcjJzeW7EGzw69z6GPfg/nrrmlRrJXyNqaOZNLZhpFnLB1d09Wmj5bCKucQw9BpzD0d3HeeSyZ0k5nuqyzbUTr6LVOc04dSiJz59fjCXH/WR4WrH0ne/ocXlXuvTrUG6b2Y/P86JE7tOmRytiGka7jLIVQlC/ebwPpKoZlc5PhRA/CCG2u/gbArwPtAS6AieB1wpPc9GVy5+6lHKmlLKHlLJHXFxc9d6FBzAHmompH1Wk4AH+WrXFY+NNv+19ev2vB5Hx4R4bw9NEJUSU2jYYDRhMfm+l81+Esz7AgJv7M+/Q+xz49xC3t3/ApYIHSD56mg8nfcYtrcdzU4txHN1ZtbxJ5dGgZb0qtc/LsvD4oBdIOX663DbrajBZ8hShUSH0vPJc6jdPoEOvtpjMpefA5iATwx4aXM7Z/kulM3kppVvFUIUQHwGFBqtjQOMShxsBrpegawkOu8OjboAOu4ObW91HepL/2CirygMz7iqzr0nbhhz81/+iimsDaxyLSm3f002b3OVVIbp+JJbcql/3VouV7z9Zy+gnri9zTEpJfq5/eVw1aFWP1395tsjN8+klD/HSTe+wac02DEaFgKAA7p9xJ+16tvaxpFWnRpZmIUT9EptDgUKD1XJgpBAiQAjRHGgN/FWTsXyNJcfzft6nDiZ5fAxPIQyCnoO6l9k//r2aBRoNuLl/jc73FabAmrvNORzOAjC/L/2LgYEjvB7Hce5lnZl/5AO6X35Olc+VquTYHtfzOiGEz9efCkloFsuyjM/4dM87pbxmQiJCmLZ8MguOf8jMra/x5cmZ9Lu+lw8lrT41XU58RQjxrxBiG3AxMBFASvkfsBDYAXwPjJNSll+yqBYQHB7s2apBtdyp5/F595OXYylSTIV07tueS0dXr4zdZTf145E59zF14USvFS+pKUGhgTy95CHOu6LqivFMDv57hMmDnuPpa1/FYfW8gjeYDKxRFxX9vbL6SQwGA+PfvbNa1bp+/HwdP83/1eWx86px4/AERpOpqL6wK8Kjw6jfIqGU2ba2oQdDVYGVM9fw5tiZHun7nIs7snXtfx7p29OYAg3Y8h0gndGSQ++/kjteuLEoT4fdZuexgc+xpZL3d82EK7ny9kvIycijVddmBJVIwSqlZHj9O/3WnHXN/Vdy6Q0X0rB1fQ5tP8qjlz9boxS5ikEhtmEUSUfKt2trzcSP7mbQHWWts1+9voIPH/qs2v2+vf552vdsU2pfviWf/4Xe5POgrusmXc3Y6bf4VAYt0CNeNeTnL3/n/UmfknrStQ+4waRUnlFPgDnQhOqQNO3QiCe+nEhweBAj6o/xgMTeRzEqXH33AMa/U9pUs3Lmat4c+1GF55bMK3MmO9bv5oG+U91WDA1b12fwuIF8/Ng88j0Qddvu/Nace3Enhj08mNDIED6eMp/Fb3yD3eaosfIKiQgmJ6OKRSmqiTAIHp83gYuG9ylzbOPqrTx25XM19pBy9b16ctLkLktOf0KYm379/oyu5D3ExtVbeOe+2WSmZnHewHOZMOOuolSiMx6Yzddvf1/UNjQqmAnvj6H34PMwB5Y1PVgtVq4KvtFrsnsaU4CJr5Jml3kUvir4BqyW8lMkv7X+eTqcMesrxG6zs3/LQT59ehG7/9pL5unsSuXoP7wXfy7fWOGY1WHIfQO57+07irZXzVnL2/d+pPk43mBx8seEx7jOcT6hzxR2/LmnxmNM+OBurh5T+ilBSsn0299j9afe95dXjApv/fY87Xq28vrYnkBX8rWAzNNZXBd3u6/F0JSbnhzGTU8NK5WY6t/fdjKp35PlnjPysaHc8fwNpfZJKZn3/FfMe24xdmvVl3b6De/FhhWbsObbNDMPtDinKV36dWDlR2s8Ux/YS9zy7AiXHjCF3NhsrCYmo9hG0Xxx5EOXx1JOpPLNh2v49sPVpGlsjhOKoEWXJmSm5qAogk592nHlHZfSpX8Hl3WTaysVKfmzvsarvxAWHUpQWCB5fpStsabMfXYR2ek53PvmbUX7OvdtX+E5zTs1ZuVHawDBxSP7EBwWxBt3f8B3s36qthy/LvyTPtf0pGHr+tisNuIaxTDz4bnV7g/gwNbDHNh6uEZ9+IqA4ACad2nMwx+Po0m7RhW2PfeyLqz6eG2NxzRWEC8R2yCaW58ZwaJXl9V4nEICgs0MHX8lt04b5ZMc7v6EPpP3I377egPPXDdds/5CIkNoeU5Ttv2yQ7M+q8OZJc+uib6FHDeLIDdsXY/je7VJGhdVL5KeV51Lx15tef3ODzTpszYxdPyV3PPmbVWawSYeTuaOjg/U2K89INjMN9kVR7kOUIZVud+eV3fj5L5EMpIzad6pMWOm30yb7i0rP7GOoZtrahG7/t7Lw5c9q33glQ/TC7z+y7N0vrB4Bn/iwCluaTXeN8Kc5QiDoPuAc3huxWS33QJPHUpixgMfs3HVNmz51V9z+HDzq7Q4p1m5x68wVS0WwGgy8q1lfp0yu1QXvcZrLaLdea1ZkTGXUY8PLXPsvCu7Vr/jAgXfsmszoupFVNxWY85M1dCgRT2+SppN32vPJyIunBbnNKVVt2ZelelsRTokG7/fwkDTSL6b/WOpY9/N/oG7z32Iu899iAUvfU1ORg4A9ZrF8+zSyXybN5/QqJBqj/3r4opTGVxz38Aq9XfxDX10Be8G+kzej7Fabaydtw6HKqnfPI5HLpumSb9jpt/MJ098gc0HniDtzm/NtOWPEhlX+kZza9v7Ob73pNflcQeDSUEoCiaTkbzsurNmAs6ZvSynTF5801hmbplOSESxYq9JoY9Js+7hytsvKfe43WbnwYufZscfuyvtyxxkYmn6Z5hM+rIi6OaaOsH1CXeQ4Ue5t2vKiEeHcOeLo4u2X739PVbP+dl3AlVCxz5teeLLSYxqdHfljesQl93Uj0c/LW1ayzidyQuj3uK/P3ZVyVb/rWU+JnPl6R5OHkxk0fTlpJ9KZ/D4K2l7Xiteve09/v5uM8IguHhEX8a/dyfGs3xBtSS6kq/lZKVlc23MbZU39DABwWY69GrLQ7PvZfNP/zL99hnV70zA6z8/Q+cLnelok4+lcEOTezSS1DMMHncFy9+rtMBZnaPPNT15avFDZUwj//y4lWkj3iA7NafSPh765F6uuOViT4l41qO7UNZyKnI/8zQhEcHc/vworh57ealK81fcejHb1u1g9Sc/V69jCV+8tLRIycc1iiUqIYK0xAwNpPYM3836keDwIHIz83wtilf5felf3Nl5Ih9tex1FUdi9cR+v3/WBWy6kbc9ryUMfj6NZx8aVttXxDPrCay0gKDSIph0q9mf2FDkZuQy667JSCr6QPX/tr1HfWaezSm0/9413ijqHRgRX6zy71cG9b96qrTC1hCM7jnOFcQQjGo7hvp6PuaXgX/npSd7d8JKu4H2MruRrCS+vmeqyJJk3eG7EGy73Jx5OdrnfXQbcfFGp7TbdWzLv8PtEJmjr/ROZEE6X/h148fsprHYsZGHirMpPckHTjo244tZLuGHKtZrKV5soL2eTKx4dMI19Ww55Thgdt9CVfC0hpn40K7I/94mi/3N52XWSnMxcVLVm6W+vuO2iMvviGzs9OrTknfUv8NraZ+hxeVd2b9zHoMAbKj/pDIxmIw984Fx0vW3aKL63LuDKOy/VVM66hnRIJvR+vEz6aR3voiv5WoTBYODzgzOIbhDp1XFdKfNpw1+vUWbH8TPuICDIdZHpqITIavd7Ju3Ob0W9pgkAZKRkMv78x6vcR59re/Lhlul07N22aJ/BaODet24rKsiu4xqrxcZvizf4WoyzGn3htZYRXS+KL499xNoFv/HCDW95ZcyQyNI27HVL1rNp9dZq93fhsAsYPLZqgS9VJSA4gKH3X8mIR69h7rRF7Pl7X7XTI6QlZtCkXdk69Pk5+TRqW5/D/1WvlmpEXDgZpzNB43ogBqOBT3a/Rf3mCRzcfoT7ez+OJdv7Bb0LObzjmM/G1tGVfK3l4pF96TmoG89cN53NP/7r0bEmz72/6PWO9bt59vrXKmjtmuDwIIbcN5DrJ/2vVB6b8jCajditVc/umNA8ls/3vw/ArMc/Z2jUrVXu40x2/L6b5OOpxDWMLtqXk5HD6Ob3YsmpXHneOm04Lbs248f5v5N6Mo2wyFD6DbuA/sN7s2j6cj55YkGNS/uFRYcQHB5M/2G9uWHKtUUpr+s1i8NoMgK+U/K9h7j07NPxErqSr8WEhAfzyponq5XYyV2iEsK54Kri2q1v3VNx0Q+XCIhvHEN8o1hCI90Li49pGE1iFWveFpYLBPjru3/48iXtsho+dsU0Zm0vXoD+ZuYPbin4K267mBunOL+fC646r8zxkY8O5YrbLmHbLzsICDbx93dbWP7+qirnGRIGA58fKB238M+P//L00Fd8GqUb3zSWVue28Nn4OrpNvtaTb/HsDC0ksnTVnKO7qmGakHDov2O8de9H3N31IbcWbKOrmF8nLDqURz8dXxSw80w1njYq4kyTw8ZVW9w6b9Unaxnd8l52/rW33DZR8RH0H9aLC67qwfh37+Tx+RMQxqrlZMlMziQ3u9h/Py/H4nMFHxBi5pNdb/tsfB0nNVLyQogvhRBbCv4OCSG2FOxvJoTIK3Hs7Mvr6iWEC/91LTm2+wQ3txrH0ne+w2qxEhjserHUXQ5tP8qqTyrOT/7H8r/Y+Wf5StEVWanFVaJO7D+F1QPl/kpSr1m8220TDyZz/wWPc2Ozeyr1NEk6lsILo95C2qseiT6xzxNFN9CN32/xafKubgM6szxjLuaAytMY6HiWGmkIKeUIKWVXKWVXYDGwpMTh/YXHpJRjazKOTvmY3cgFUlNOHkjivQc+Ztz5k8lOrzyEvTI+efKLCo+/MKoaC8ol9FlWWo7mCq7Vuc1KbV/7wFVV7iPpSAp3dpwElPVY+uv7f3ig7xOM7fZItWU88O8RFry8FID8PCtWa80T0EXEhvHs8sl8uvedSkvlRTeI5P4Zd7Eybz4vr3rSZQCdjvfRxCYvnL+o4UD5KeZ0PMYDH47hzbvLFkQ2Bhmw52nkoyzh0L9HNekqPbHiRGvVcc2MbxJT9Lp55yYYAwzaleUT8MzS0sq3eacmTJo1tsrFR47tOVG0hhIUFkj9Fgkc2n60xguvhSx8ZRmjJg/l1OEk7Pk1f/9PLHqQrv07AvDO+hex5tvY988BpJS0Orc5uZl55GTkUr9lgtv56XW8i1a32guBRCllyWfs5kKIzUKIX4QQF5Z3ohBijBBioxBiY3JyzSIoz1auumsAT3/9EObAglm9gAuu7s7C47MRfjiZ0qrOakmSDp/m0H/Om5A5wOSWB4879BzYlUWnZhHfOK7MsStvv5QHPqr+Q2peloUDWw9rpuAB8rItHNh2mE+nfqlJf8vf/R6HvXiiYA4w0aFXWzr2bkdAUABRCZE0atNAV/B+TKVZKIUQPwD1XByaIqVcVtDmfWCflPK1gu0AIFRKeVoI0R1YCnSUUlY4hdOzUGrPqUNJ3NRinK/FKEVwRBDL0j4r9/itbcdXy6e924AuvLxqKgADDMM0rYRlCjBy89PD6T2kJ6FRIYTHhDK83l2l1gL8AYPJQLOOjdi/Rbv6s+0uaMXbv7+gF+jwYzyaalgIYQSOA92llC6jHoQQPwMPSSkr1OC6kvcMDoeDOVMXsGbuL6SeTPfITLoq3PHSjYx85BqXx3Kz8rgm6pZqy9j1kk48Muc+bmx6D/6URttreKjM423Pj+KGx87enD3+jqfL/10G7Cqp4IUQcUIIQ8HrFkBr4IAGY+lUA4PBwB0v3MiCozMZ+eg1KAbf2XCad27CsAf/V+7xdYvX1+gmtOWn7dzQZOzZqeDBY3V8F7z0dY1zFen4Bi1+7SOBM90l+gHbhBBbga+AsVLKVA3G0qkhtz9/A3P3v0vb8yr2lNCS4PBAOvVtxzNLH+HDLdPLtd+qqsrCV7QLYNLRDmuejdRT6b4WQ6ca1Ni7Rkp5q4t9i3G6VOr4IfFN4nh3w4vk5+Uz/Y4Z/LzgD4+NNeCWi3jkE/fWBL58eSlHqhNspeNxhCIICQ/ytRg61cAPfS90vEVAUAATZozx6Bj3vXO7222/1GfxfsuF111AUKiu5GsjupI/ywmpZpUkd5j8+f0EV0Ex5GadXWX1agvnX9WNiR96djKg4zn0BGVnOXZ75QEzkXHhWHLzixJyCUXQuF1DrhpzGZff3J9xPR/jxL7SLo8XjezDpTeUGx7hkqDQQL+un2owGzAaDEXBWkIIDEYDdlvxZxjfJJbbnxvJSze/6ysxq01EXBiz/3uT5GOnObLzOCERwbTo0pS4RjGVn6zjt+hK/izHZDIhFFGuR8uUBQ/Q7/peFYaoz9n9Nj/NX8fyGaswB5oZOfkaug84p8qytD2vJZt/3F7l87zBeYPO5YVviguOpJ5KIy/bQv0WCS4/m40/bOOHz371pog1IrZhNF8c/RCAiNhwWnVt7mOJdLRCN9focPPTw13u7z+iFxcN71NpDhIhBJfe2I+3fn+eV398qloKHmD4w9dgCnSdiyeucQx3vHgDjds1qFbfNcFgMvDc8sml9kXXi6Jhq/rlfjaPzhnPLc8ML5VTp/OF7T0pZo3wpVutjmfRZ/I6jH7iesKjQ5k1eT552XmYA03c88atXH335V6Vo8fl53DdA1ex5M2VGIwKQigYTAZeXj2V1t2cOcm7XtSJ8b2qXsJPCOh3fS/sNjvmoACum3gVkfERrPxwDcve+75CM9HLq56oVrKt0VOHMXpq6Vz/W9Zu55EBz/o8IO1MOvVt52sRdDxEjSNetUSPeNUBSDqawpafthMaFcJ5A7tiOiPT5tqFv/HCyKplqgwMDeTpxQ9V+pSRlZbN+xPnsHfTAdqc15K7XrmJyNjwKr+HipBSsuTtlcyePB9bfs0zRWrBguMziakf5WsxdKqJR9MaaImu5HXc5ciuY0y8cCpZqTluRbe279WGN9dN87v0t+nJGeRlW6jXLJ5HL5/m8VKOZyIUweu/PEOnPv5rStKpHE+nNdDR8TpN2jXiyxMfMWnWWBRjxZdxi3Oa8eoP/pnfPDIugvrNExBCcNVdl3lnUAH9h/fm0c/Gs8r2pa7g6zj+d9Xr6LiJ0WRk4G2X8NZvzxGZ4LpcYFBoIG/9Po2AoJpVtPIG/Yb1opcXil4HhQRy01PDuGx0Pz2z5FmAruR1aj3terZm0clZLDz1ESMfH0pUvUiiEiIY8eg1fJU0m8DgQF+L6BZCCJ79+lFeXDWF8Fht8uG7wmA00LCVq+zhOnUR3Savo1MLsOTm88mU+ayZ+yu5WXk4bNWr+GUOMjNx5t1cdmM/jSXU8SX6wquOTh1n98b9vHzz25w6mITBbMCSlV+mTacL23Hni6Pp2LutDyTU8SQVKXndT15Hpw7QtkdLPt5R7FYqpeSPFX+zfsUm2p3XiivvvNQvF551PI+u5HV06iBCCPoM7kmfwT19LYqOj9Fv7To6Ojp1GF3J6+jo6NRhdCWvo6OjU4fRlbyOjo5OHUZX8jo6Ojp1GL/ykxdCJAOH3WweC6R4UJzq4q9ygf/K5q9ygf/K5q9ygf/K5q9yQc1layqljHN1wK+UfFUQQmwsz/nfl/irXOC/svmrXOC/svmrXOC/svmrXOBZ2XRzjY6Ojk4dRlfyOjo6OnWY2qzkZ/pagHLwV7nAf2XzV7nAf2XzV7nAf2XzV7nAg7LVWpu8jo6Ojk7l1OaZvI6Ojo5OJehKXkdHR6cOU6uUvBCiqxBivRBiixBioxCiZ4ljjwkh9gkhdgshrvCRfOMLxv9PCPGKn8n2kBBCCiFi/UUuIcSrQohdQohtQoivhRCRfiTbwIKx9wkhJnt7/DNkaSyEWCuE2FlwbU0o2B8thFgjhNhb8D/KR/IZhBCbhRDf+JlckUKIrwqusZ1CiF7+IJsQYmLB97hdCPGFECLQo3JJKWvNH7AauLLg9SDg54LXHYCtQADQHNgPGLws28XAD0BAwXa8H8nWGFiFM9As1o/kuhwwFrx+GXjZH2QDDAVjtgDMBbJ08OZnc4Y89YFuBa/DgD0Fn9ErwOSC/ZMLPz8fyDcJmA98U7DtL3J9CtxZ8NoMRPpaNqAhcBAIKtheCNzqSblq1UwekEB4wesI4ETB6yHAAillvpTyILAP8HYi7XuAl6SU+QBSyiQ/ku0N4BGcn18hPpdLSrlaSmkv2FwPNPIT2XoC+6SUB6SUVmBBgUw+QUp5Ukr5T8HrLGAnTmUxBKcio+D/Nd6WTQjRCLgKmFVitz/IFQ70A2YDSCmtUsp0f5ANZx2PICGEEQjGqcc8JldtU/IPAK8KIY4C04HHCvY3BI6WaHesYJ83aQNcKITYIIT4RQhxnj/IJoQYDByXUm4945A/fGYluR34ruC1r2Xz9fjlIoRoBpwLbAASpJQnwXkjAOJ9INKbOCcQaol9/iBXCyAZ+KTAlDRLCBHia9mklMdx6q4jwEkgQ0q52pNy+V1lKCHED4CrUvJTgEuBiVLKxUKI4Tjv0pcBwkV7zX1DK5HNCEQBFwDnAQuFEC28IVslcj2O0yxS5jRPywUVyyalXFbQZgpgB+Z5U7YK8PX4LhFChAKLgQeklJlCuBLTq/JcDSRJKTcJIS7yqTBlMQLdgPFSyg1CiLdwmkF8SoGtfQhOM2Q6sEgIMdqTY/qdkpdSXlbeMSHEZ8CEgs1FFD8iHsNpdy6kEcWmHG/Jdg+wRDqNan8JIVScSYc8Llt5cgkhOuO8mLYWKIRGwD8FC9Y+/8wKZLwFuBq4tOCzw1uyVYCvxy+DEMKEU8HPk1IuKdidKISoL6U8KYSoDySV34NH6AMMFkIMAgKBcCHE534gFzi/w2NSyg0F21/hVPK+lu0y4KCUMhlACLEE6O1JuWqbueYE0L/g9SXA3oLXy4GRQogAIURzoDXwl5dlW1ogE0KINjgXelJ8KZuU8l8pZbyUspmUshnOC7+blPKUL+UqRAgxEHgUGCylzC1xyNey/Q20FkI0F0KYgZEFMvkE4bxDzwZ2SilfL3FoOXBLwetbgGXelEtK+ZiUslHBtTUS+ElKOdrXchXIdgo4KoRoW7DrUmCHH8h2BLhACBFc8L1einONxXNyeXNlWYOV6b7AJpzeDhuA7iWOTcHpEbGbAg8cL8tmBj4HtgP/AJf4i2wl5DhEgXeNP8iFc0H1KLCl4O8DP5JtEE4vlv04TUs++c4KZOmL01y0rcRnNQiIAX7EOdn5EYj2oYwXUexd4xdyAV2BjQWf21Kc5lSfywY8A+wq0BVzcXqReUwuPa2Bjo6OTh2mtplrdHR0dHSqgK7kdXR0dOowupLX0dHRqcPoSl5HR0enDqMreR0dHZ06jK7kdXR0dOowupLX0dHRqcP8H8YoWLu2mZf4AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import TSNE\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "# Create a TSNE instance: model\n",
    "model = TSNE(learning_rate=200)\n",
    "# Apply fit_transform to samples: tsne_features\n",
    "tsne_features = model.fit_transform(B_vector)\n",
    "# Select the 0th feature: xs\n",
    "xs = tsne_features[:,0]\n",
    "# Select the 1st feature: ys\n",
    "ys = tsne_features[:,1]\n",
    "# Scatter plot, coloring by variety_numbers\n",
    "plt.scatter(xs,ys, c=kmeans.labels_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}