Android.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RoSRbBBe5DKJ"
   },
   "source": [
    "# Human Activity Recognition\n",
    "\n",
    "### Step 1: Download the dataset from link \n",
    "\n",
    "https://www.utwente.nl/en/eemcs/ps/dataset-folder/sensors-activity-recognition-dataset-shoaib.rar\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install tensorflow-gpu==2.3.0\n",
    "!pip install keras==2.4.3\n",
    "!pip install numpy==1.19.5\n",
    "!pip install matplotlib==3.3.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AqEuKfF55DKV",
    "outputId": "676e6b12-49a2-4499-aa04-bfb866f422b9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading dataset\n",
      "Download completed\n"
     ]
    }
   ],
   "source": [
    "import urllib.request\n",
    "\n",
    "print('Downloading dataset')\n",
    "\n",
    "url = 'https://www.utwente.nl/en/eemcs/ps/dataset-folder/sensors-activity-recognition-dataset-shoaib.rar'\n",
    "urllib.request.urlretrieve(url, 'HAR_dataset.rar')\n",
    "\n",
    "print('Download completed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ceompSvl5DKX"
   },
   "source": [
    "### Step 2: Extract in the working directory\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4rM0tfHD5DKh",
    "outputId": "ad8e079c-0ebd-471c-e37d-53c8b09fc978"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pyunpack\n",
      "  Downloading https://files.pythonhosted.org/packages/83/29/020436b1d8e96e5f26fa282b9c3c13a3b456a36b9ea2edc87c5fed008369/pyunpack-0.2.2-py2.py3-none-any.whl\n",
      "Collecting entrypoint2\n",
      "  Downloading https://files.pythonhosted.org/packages/8a/b0/8ef4b1d8be02448d164c52466530059d7f57218655d21309a0c4236d7454/entrypoint2-0.2.4-py3-none-any.whl\n",
      "Collecting easyprocess\n",
      "  Downloading https://files.pythonhosted.org/packages/48/3c/75573613641c90c6d094059ac28adb748560d99bd27ee6f80cce398f404e/EasyProcess-0.3-py2.py3-none-any.whl\n",
      "Installing collected packages: entrypoint2, easyprocess, pyunpack\n",
      "Successfully installed easyprocess-0.3 entrypoint2-0.2.4 pyunpack-0.2.2\n",
      "Collecting patool\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/43/94/52243ddff508780dd2d8110964320ab4851134a55ab102285b46e740f76a/patool-1.12-py2.py3-none-any.whl (77kB)\n",
      "\u001b[K     |████████████████████████████████| 81kB 5.5MB/s \n",
      "\u001b[?25hInstalling collected packages: patool\n",
      "Successfully installed patool-1.12\n"
     ]
    }
   ],
   "source": [
    "!pip3 install pyunpack\n",
    "!pip3 install patool\n",
    "from pyunpack import Archive\n",
    "Archive('HAR_dataset.rar').extractall('.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lLcjp6oH5DKi"
   },
   "source": [
    "### Step 3: Download Libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "DDFZRtEtHq2h",
    "outputId": "5e603f76-dc2f-4a12-9f3f-987bf781d9b1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.10.0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "tTjXayCr5DKj"
   },
   "outputs": [],
   "source": [
    "# import libs\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Vw1-G_Ou5DKk",
    "outputId": "441c01f7-74be-4db7-d1a6-ba5ea96aef15"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Participant_8.csv',\n",
       " 'Participant_9.csv',\n",
       " 'Participant_10.csv',\n",
       " 'Participant_7.csv',\n",
       " 'Participant_6.csv',\n",
       " 'Participant_4.csv',\n",
       " 'Participant_5.csv',\n",
       " 'Participant_1.csv',\n",
       " 'Participant_2.csv',\n",
       " 'Participant_3.csv']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path='dataset/DataSet/'\n",
    "files=[file for file in os.listdir(path) if file.endswith('.csv')]\n",
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 241
    },
    "id": "64gDWXL25DKl",
    "outputId": "99780937-211c-4bb4-86f5-545eee4a8002",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>Ax</th>\n",
       "      <th>Ay</th>\n",
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       "      <th>Lx</th>\n",
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       "      <th>Gy</th>\n",
       "      <th>Gz</th>\n",
       "      <th>...</th>\n",
       "      <th>Lx.4</th>\n",
       "      <th>Ly.4</th>\n",
       "      <th>Lz.4</th>\n",
       "      <th>Gx.4</th>\n",
       "      <th>Gy.4</th>\n",
       "      <th>Gz.4</th>\n",
       "      <th>Mx.4</th>\n",
       "      <th>My.4</th>\n",
       "      <th>Mz.4</th>\n",
       "      <th>Unnamed: 69</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.390000e+12</td>\n",
       "      <td>1.56630</td>\n",
       "      <td>-7.4640</td>\n",
       "      <td>2.50610</td>\n",
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       "      <td>1.56560</td>\n",
       "      <td>-1.53020</td>\n",
       "      <td>-0.090713</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.4679</td>\n",
       "      <td>1.539600</td>\n",
       "      <td>-1.89530</td>\n",
       "      <td>-0.33659</td>\n",
       "      <td>-0.156690</td>\n",
       "      <td>0.54978</td>\n",
       "      <td>24.00</td>\n",
       "      <td>2.82</td>\n",
       "      <td>-9.30</td>\n",
       "      <td>walking</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.390000e+12</td>\n",
       "      <td>2.23370</td>\n",
       "      <td>-8.0769</td>\n",
       "      <td>3.41870</td>\n",
       "      <td>-0.07754</td>\n",
       "      <td>1.36890</td>\n",
       "      <td>2.1517</td>\n",
       "      <td>1.13380</td>\n",
       "      <td>-0.37080</td>\n",
       "      <td>-0.376900</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.6108</td>\n",
       "      <td>-0.030106</td>\n",
       "      <td>-1.74350</td>\n",
       "      <td>-0.35064</td>\n",
       "      <td>-0.753500</td>\n",
       "      <td>0.67317</td>\n",
       "      <td>24.00</td>\n",
       "      <td>3.06</td>\n",
       "      <td>-8.82</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.390000e+12</td>\n",
       "      <td>0.19068</td>\n",
       "      <td>-10.3510</td>\n",
       "      <td>0.10896</td>\n",
       "      <td>-1.75790</td>\n",
       "      <td>-0.81255</td>\n",
       "      <td>-1.0668</td>\n",
       "      <td>1.18360</td>\n",
       "      <td>-0.66279</td>\n",
       "      <td>-0.554670</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.9025</td>\n",
       "      <td>-0.636090</td>\n",
       "      <td>-0.79292</td>\n",
       "      <td>-0.55528</td>\n",
       "      <td>-0.365600</td>\n",
       "      <td>0.83169</td>\n",
       "      <td>24.12</td>\n",
       "      <td>3.06</td>\n",
       "      <td>-8.70</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.390000e+12</td>\n",
       "      <td>-0.68102</td>\n",
       "      <td>-10.2290</td>\n",
       "      <td>-1.32120</td>\n",
       "      <td>-2.36530</td>\n",
       "      <td>-0.61226</td>\n",
       "      <td>-2.2454</td>\n",
       "      <td>1.20000</td>\n",
       "      <td>-1.45690</td>\n",
       "      <td>-0.396450</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.5825</td>\n",
       "      <td>-0.733360</td>\n",
       "      <td>-1.61230</td>\n",
       "      <td>-0.82711</td>\n",
       "      <td>-0.079107</td>\n",
       "      <td>0.85399</td>\n",
       "      <td>24.30</td>\n",
       "      <td>3.00</td>\n",
       "      <td>-8.58</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.390000e+12</td>\n",
       "      <td>-0.14982</td>\n",
       "      <td>-9.8884</td>\n",
       "      <td>-1.60720</td>\n",
       "      <td>-1.65250</td>\n",
       "      <td>-0.22073</td>\n",
       "      <td>-2.2773</td>\n",
       "      <td>0.93432</td>\n",
       "      <td>-0.97708</td>\n",
       "      <td>-0.450510</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.9397</td>\n",
       "      <td>-2.614700</td>\n",
       "      <td>-0.98619</td>\n",
       "      <td>-1.66770</td>\n",
       "      <td>-0.839630</td>\n",
       "      <td>0.62217</td>\n",
       "      <td>24.60</td>\n",
       "      <td>3.12</td>\n",
       "      <td>-8.40</td>\n",
       "      <td>walking</td>\n",
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       "<p>5 rows × 70 columns</p>\n",
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      "text/plain": [
       "     time_stamp       Ax       Ay       Az       Lx       Ly      Lz       Gx  \\\n",
       "0  1.390000e+12  1.56630  -7.4640  2.50610 -0.72489  2.01760  1.4955  1.56560   \n",
       "1  1.390000e+12  2.23370  -8.0769  3.41870 -0.07754  1.36890  2.1517  1.13380   \n",
       "2  1.390000e+12  0.19068 -10.3510  0.10896 -1.75790 -0.81255 -1.0668  1.18360   \n",
       "3  1.390000e+12 -0.68102 -10.2290 -1.32120 -2.36530 -0.61226 -2.2454  1.20000   \n",
       "4  1.390000e+12 -0.14982  -9.8884 -1.60720 -1.65250 -0.22073 -2.2773  0.93432   \n",
       "\n",
       "        Gy        Gz  ...    Lx.4      Ly.4     Lz.4     Gx.4      Gy.4  \\\n",
       "0 -1.53020 -0.090713  ... -3.4679  1.539600 -1.89530 -0.33659 -0.156690   \n",
       "1 -0.37080 -0.376900  ... -1.6108 -0.030106 -1.74350 -0.35064 -0.753500   \n",
       "2 -0.66279 -0.554670  ... -1.9025 -0.636090 -0.79292 -0.55528 -0.365600   \n",
       "3 -1.45690 -0.396450  ... -2.5825 -0.733360 -1.61230 -0.82711 -0.079107   \n",
       "4 -0.97708 -0.450510  ... -4.9397 -2.614700 -0.98619 -1.66770 -0.839630   \n",
       "\n",
       "      Gz.4   Mx.4  My.4  Mz.4  Unnamed: 69  \n",
       "0  0.54978  24.00  2.82 -9.30      walking  \n",
       "1  0.67317  24.00  3.06 -8.82      walking  \n",
       "2  0.83169  24.12  3.06 -8.70      walking  \n",
       "3  0.85399  24.30  3.00 -8.58      walking  \n",
       "4  0.62217  24.60  3.12 -8.40      walking  \n",
       "\n",
       "[5 rows x 70 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load a file into memory\n",
    "df=pd.read_csv(path+files[0],header=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QJiLb3oK5DKl",
    "outputId": "82e7ae91-1753-4ce6-9853-0deb9a125e36"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['walking', 'standing', 'jogging', 'sitting', 'biking', 'upsatirs',\n",
       "       'downstairs'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Unnamed: 69'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tOLwiHwf5DKm"
   },
   "source": [
    "## Visualize the difference in change in values w.r.t activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "i047xCCV5DKm",
    "outputId": "81b52e15-b008-4511-e9c5-19b3b4a3fc0c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x104066440>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# change in values while walking\n",
    "\n",
    "plt.plot(np.arange(0,100),df.Ax[df['Unnamed: 69']==\"walking\"][:100], label='X-axis')\n",
    "plt.plot(np.arange(0,100),df.Ay[df['Unnamed: 69']==\"walking\"][:100], label='Y-axis')\n",
    "plt.plot(np.arange(0,100),df.Az[df['Unnamed: 69']==\"walking\"][:100], label='Z-axis')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "iczJ5sLE5DKn",
    "outputId": "44485973-d1b1-4aaa-eb84-081714a6ee55"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hc/4_spfx051b5d36rrtssgcwl80000gn/T/ipykernel_79744/2446371698.py:3: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n",
      "  plt.plot(np.arange(0,100),df.Ax[df['Unnamed: 69']==\"standing\"][:100], label='X-axis')\n",
      "/var/folders/hc/4_spfx051b5d36rrtssgcwl80000gn/T/ipykernel_79744/2446371698.py:4: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n",
      "  plt.plot(np.arange(0,100),df.Ay[df['Unnamed: 69']==\"standing\"][:100], label='Y-axis')\n",
      "/var/folders/hc/4_spfx051b5d36rrtssgcwl80000gn/T/ipykernel_79744/2446371698.py:5: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n",
      "  plt.plot(np.arange(0,100),df.Az[df['Unnamed: 69']==\"standing\"][:100], label='Z-axis')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x176e30a00>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# change in values while standing\n",
    "\n",
    "plt.plot(np.arange(0,100),df.Ax[df['Unnamed: 69']==\"standing\"][:100], label='X-axis')\n",
    "plt.plot(np.arange(0,100),df.Ay[df['Unnamed: 69']==\"standing\"][:100], label='Y-axis')\n",
    "plt.plot(np.arange(0,100),df.Az[df['Unnamed: 69']==\"standing\"][:100], label='Z-axis')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "yc0ytU4v5DKn",
    "outputId": "7a2fd1a1-e299-46ef-fe79-9f516648b69c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hc/4_spfx051b5d36rrtssgcwl80000gn/T/ipykernel_79744/2867413791.py:3: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n",
      "  plt.plot(np.arange(0,100),df.Ax[df['Unnamed: 69']==\"biking\"][:100], label='X-axis')\n",
      "/var/folders/hc/4_spfx051b5d36rrtssgcwl80000gn/T/ipykernel_79744/2867413791.py:4: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n",
      "  plt.plot(np.arange(0,100),df.Ay[df['Unnamed: 69']==\"biking\"][:100], label='Y-axis')\n",
      "/var/folders/hc/4_spfx051b5d36rrtssgcwl80000gn/T/ipykernel_79744/2867413791.py:5: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n",
      "  plt.plot(np.arange(0,100),df.Az[df['Unnamed: 69']==\"biking\"][:100], label='Z-axis')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1778748e0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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k3XffZf78+Y16DUII22Za8RPm64K2kcuLLWn+FeGsOBTProh0dl1IY2QXH/N9pWV65v94mHUnk6o8z8Vew+ReAVzXP5DRXXxs4rWIpru8AhWVqsHTL7bEz88PPz+/Srft2rWLjh078sILL5hvi46OrvLcJ598ErVazdq1a5k2bRpXX301EydOrPFcISEhzJ07l7lz57JgwQK++OILCVSEaKPOpVi/PqWiIE8n7hgawne7o1m44SzLO7dDpVJRpjfwxM9HWHcyCbUKnCp0vy0zGMgvKWPF4XhWHI6nnYs91/YL5IlJXWvdvVnYvssrUGmDwsPDiYmJYdmyZQwZMoTVq1ezcuXKSo9ZvXo1X3/9Nbt372bgwIE8/fTTzJw5k2PHjuHlVXX+9/HHH2fq1Kl07dqVzMxMNm/eTI8ete+AKoRovUxLk7tV0zrfWuZN6MLPB2I5FJPFlrOpjOvqy/MrjrPqWCJ2GhVf3DOY8d3KP7gZDAYOxWTyx5EEVh1LJD2/hG93ReHmqOXJq7pZ8ZWIppK8WCt33XXX8cQTT/DII4/Qv39/du3axUsvvWS+PzU1ldmzZ/Pqq68ycOBAAF577TX8/f2ZO3dutccsKytj3rx59OjRgylTptC1a1cWL17cIq9HCNHyTFM/4U1s2mZJfu6OzBwRCsDCDWf59+pT/HwgFrUKFt0+oFKQAkoPqkEdvXn9+t7sff4Knp6sBCfbzqW29NCFhakMrbyrTk5ODh4eHmRnZ+Pu7l7pvqKiIiIjI+nUqROOjo5WGuHlQ/69hWh9yvQGery8jhKdni1PjSfUx3amxzPySxj7383kFevMty28pR83D6q76WVSdhHD3/wHlQoOvXglXi72zTlU0Qi1vX9XJBkVIYS4jEWn51Oi0+NopybE27orfi7l7WLPfaM7mb9//fpe9QpSAAI8HOnq74rBALsi0ptriKIFSI2KEEJcxkz9U7r4uaJR2942Gg+MDSMuo4Ahnby5Y2iHBj13dBdfziXnseNCKlf3bd9MIxTNTTIqQghxGTtnAx1pa+PqoOW92/o3OEgBZdNEgG3n0mTvoFZMAhUhhLiM2Xqg0hTDwryx06iIzyokKt1K26eIJpNARQghbEhqbjHf7Iwku7C0Rc5nClS6tcFAxdlea27Bv+O8rP5prSRQEUIIG/L5tghe++sUP+yp2rjR0kp0ei6m5gMQ7m87S5MtaUy4skfa9vNpVh6JaCwJVIQQwoYkZBUBENMCUxVR6fno9AZc7DUEeTo1+/mswVSnsjsiHV2Z3sqjEY0hgYoQQtiQ9PxiAJJzi5r9XPsiMwDoFeiBSmV7K34soVegB57OduQW6zgal2Xt4YhGkEBFCCFsSGa+UpuSlN38gcqWs0rdxrhuvs1+LmvRqFWM6qxkVWT6p3WSQEVUMn78eB5//HFrD0OIy1ZGQQkAKbnFzXqeYl0ZuyKUN+5xXdtuoALl0z8SqLROEqjYoC1btqBSqWr8mjBhQrOde8WKFfz73/9utuMLIWpmMBjIzFcClYz8Eop1Zc12roNRmRSUlOHj6kDP9jW3L28LRhsDlSOxWeQUtcxqKmE5EqjYoJEjR5KYmFjl67PPPkOlUvHwww8327m9vb1xc2t7yxSFaA1yinTo9OWNyVJymi+rssW4Wd+4rr6obbAjrSUFeznTyceFMr2BPdJOv9WRQMUG2dvbExAQUOkrMzOTp556iueff55bbrml2uft37+fK6+8Eh8fHzw8PBg3bhyHDh0y379lyxbs7e3Zvn27+bb//ve/+Pn5kZycDFSd+lm8eDHh4eE4Ojri7+/PzTff3DwvWghBhjGbYpLSjAW1W86mADC+DdenVDS6i5JV2XFBpn9am8tqrx+DwUChrtAq53bSOjW6qj4rK4vrr7+e8ePH1zotk5uby8yZM/nwww8xGAy8++67TJs2jfPnz+Pm5mYOQmbMmMHRo0e5ePEiL730Er/++iv+/v5VjnfgwAEeffRRvv/+e0aOHElGRkalIEcIYVmXBipJ2c2TUUnIKuRcch5qVXn9Rls3JtyH7/dEs+1cKgaDoc2ucmqLLqtApVBXyLAfh1nl3Hvv3IuzXcN3JtXr9dx5551otVqWLl1a6y/XxIkTK33/+eef4+npydatW7nmmmsAeOONN/j777954IEHOHHiBDNnzuS6666r9ngxMTG4uLhwzTXX4ObmRseOHRkwYECDX4MQon4yLwlUknOaJ6Oy1Tjt0z/EE09n+2Y5h60Z0bkd9lo1UekFrDwcz/SB9duFWVifTP3YuOeff57du3fzxx9/mGtHYmJicHV1NX/95z//ASA5OZn777+f8PBwPDw8cHd3Jy8vj5iYGPPx7O3tWbp0KcuXL6eoqIj//e9/NZ77yiuvpGPHjoSFhTFjxgyWLl1KQYHslyFEc7k0o9JcgUr5tI9fsxzfFrk52vHYFeEAvPrnyWb7txWWd1llVJy0Tuy9c6/Vzt1Qy5YtY+HChaxevZrw8HDz7YGBgRw5csT8vbe3NwAzZ84kPT2dDz74gI4dO+Lg4MCIESMoKan8x2/Xrl0AZGRkkJGRgYuLS7Xnd3Nz49ChQ2zZsoUNGzbw8ssv8+qrr7J//348PT0b/HqEELUzLU02aY430xKdnp0XlILSy6U+xeTBsWGsP5nEsbhsFqw4zlczB8sUUCtwWQUqKpWqUdMv1nDkyBFmz57NW2+9xeTJkyvdp9Vq6dKlS5Xn7Ny5k8WLFzNt2jQAYmNjSUurXDgWERHBE088wRdffMHPP//MzJkz2bhxI2p19ck1rVbLpEmTmDRpEq+88gqenp5s2rSJ6dOnW+iVCiFMTFM/vm4OpOYWk9QMgcrB6EzyinW0c7Gnd6CHxY9vy7QaNe/e0o+rF+1g05kUlh+K5+ZBMgVk62TqxwalpaVxww03MH78eO6++26SkpIqfaWmVr8LaHh4ON9//z2nT59m79693HXXXTg5lWdyysrKuPvuu5k8eTL33nsv33zzDceOHePdd9+t9nirVq1i0aJFHDlyhOjoaJYsWYJer6dbt27N8rqFuNylGwOVHsa+Js2xPNlUnzL2MliWXJ1wfzcev1LJUL/218kW6QAsmkYCFRu0evVqoqOjWbNmDe3bt6/yNWTIkGqf99VXX5GZmcnAgQOZMWMGjz76KH5+5XPQ//d//0d0dDSfffYZAO3bt+fzzz/nxRdf5OjRo1WO5+npyYoVK5g4cSI9evTg008/5aeffqJXr17N88KFuMxlmgMVpR4tKacIg8FQ21PMfjsYx1trz9T5+MttWXJ1HhgTRr8QT3KLdDy34li9/42FdagMrfx/KCcnBw8PD7Kzs3F3r9xdsaioiMjISDp16oSjo6OVRnj5kH9vIZrmxsU7ORyTxfu39efxn48AcPzVq3BztKvzuX1fXU9OkY7lD41gUEfvah+TlF3E8Df/QaWCgy9eibfL5bHipzrnk3O5+sMdlOj0vHdrP1kFZAW1vX9XJBkVIYSwEaZVP8FeTrg5KCWE9SmoLSjRkVOkA2B/VGaNj9t6Tsmm9Av2vKyDFFCmgB6dqNT6fbUj0sqjEbWxiUDl448/JjQ0FEdHR4YNG8a+ffusPSQhhGhxpkDFy8Uefw8lK5lcjzqV1AobGB6oNVApb5sv4K5hHbHXqDmZkMOphBxrD0fUwOqBys8//8y//vUvXnnlFQ4dOkS/fv2YPHkyKSkp1h6aEEK0mBKdnlxjVsTb2R5/dweAehV7Vtxp+WB0RrU1F2V6AzuMuwePu4zrUyrycrFnUk+lju/Xg7FWHo2oidUDlffee4/777+fe++9l549e/Lpp5/i7OzM119/be2hCSFEi8ky9lBRq8DDyQ5/d2NGpR77/VRcHZRZUEpEan6VxxyLyyKnSIe7o5Z+wZ6WGXQbcMugEAD+OJJAiU5v5dGI6lg1UCkpKeHgwYNMmjTJfJtarWbSpEns3r272ucUFxeTk5NT6UsIIVo7U7M3L2d71GqVOVCpzxLlSzcvPBCVUeUx243ZlFFdfNBchsuSazIm3Ac/Nwcy8kvYdCbZ2sMR1bBqoJKWlkZZWVmVDfH8/f1JSkqq9jlvvvkmHh4e5q+QkJA6z9PKFza1GvLvLETjZeSV16cABBgDlYZM/ZiarB6IrlqnYpr2GX2ZbEJYX1qN2rzi59cDcVYejaiO1ad+GmrBggVkZ2ebv2Jja55XtLNTlvTJ/jQtw/TvbPp3F0LUnymjYlqNY6pRacjUzxDjsuRLMyp5xToOxSjBy5guUp9yqVsGK4HKlnOpVbJTwvqs2kLfx8cHjUZDcnLldFtycjIBAQHVPsfBwQEHB4d6HV+j0eDp6WkuzHV2dpZ9HZqBwWCgoKCAlJQUPD090Wg01h6SEK2Oqdmbt7MpUDHWqNQro6I85qpe/uyPziAqvYDU3GJ83ZS/lXsi0tHpDXRs50yHdq1jG5GW1NnXlYEdPDkUk8XKQ/E8OK6ztYckKrBqoGJvb8+gQYP4559/uOGGGwDQ6/X8888/PPLIIxY5hyngkVVEzc/T07PGAFMIUbv0/MpTP+Yaldxi9HpDre3uTcuTu/i50s3fjTNJuRyMzmBK7/YA7LhgnPbpItM+NbllcAiHYrL49WAcD4wNkw+1NsTqmxL+61//YubMmQwePJihQ4fy/vvvk5+fz7333muR46tUKtq3b4+fnx+lpaUWOaaoys7OTjIpQjSBKaPSzhio+Lo5oFKBTm8gPb/EnB2pjilQ8XNzZFBHL84k5XIgKtMcqGw/r/RPGSP1KTW6pm97XvvrJBdS8jgSm8WADl41PlavN1BmMGCnaXXVE62S1QOV2267jdTUVF5++WWSkpLo378/69atq1Jg21QajUbeSIUQNuvSjIqdRk07FwfS8opJzimqMVApLdObn+vn7sCQUG+W7o1hv7GgNiGrkIjUfNQqGNFZApWauDnaMbV3e1YejueXA3E1BiplegMPLDnAjgtpzBjekYcndLnsu/w2N5sIBx955BGio6MpLi5m7969DBs2zNpDEkKIFpVZUDmjAhDgYSyoraWNflqekk3RqlV4O9szqKPyBnsyPpvCkjLzap9+IZ54OEmhe21uGaQU1a46mmD+d73UBxvP8c+ZFIp1er7cEcnY/27m/Y3nyCvWteRQLytWz6gIIYSAjHxlatqrQqDi7+bICXJqbaNvWvHj4+qAWq0i2MsJf3cHknOKORKbxXZjfcoYqU+p0/CwdnT2dSEiNZ8ZX+1j2f3D8XAuD+62nkvlw80XAJg3oTNbz6VyIj6H9zeeZ8nuaAZ28KJiaYubo5aZI0LpF+LZwq+kbbGJjIoQQlzuMvKVgMO06gcw7/eTVEtGxdRDxc+4nFmlUjE4VFmmvD8qg52mQEX296mTWq3iy5lD8HF14HRiDrO+3Ue+MVOSmF3IEz8fwWCAu4Z14OnJ3flz3mg+vnMgYT4uZOSXsPF0Mn+fKv9acSie6z/eyYPfH+B8cq6VX13rJRkVIYSwMoPBQKYxo+LtWjmjApBSa6Ci3OdXoYZlcEcvVh9LZNm+GDLyS3B10NJfPtXXSycfF36YM5TbPtvD4Zgs7l9ygM/vGcz8Hw+TkV9C7yB3XrqmJ6AENlf3bc/kXv78cybFvKmkyYGoTFYejmP9SSVwuXFAMI9PCifEW5aIN4QEKkIIYWX5JWWUlCn7zFTKqJg2JqwtUDFO/VQsth1izKgkGHuwDA/zlhUqDdA9wJ3v7hvKXV/sYVdEOle8u4XknGLcHLUsvnMQjnaVF2ZoNWom96ramuGOoR2YOy6MhRvOsv5kMssPxfHn0XjuGtaReRO61LqSq6m+3H6R7efT+OjOAbg5tu7aJPnJFUIIKzO1z3e0U+NkX/4maJr6qbVGJdcUqDiab+se4IZzheOMCZdpn4bqH+LJV7OG4KBVm//937m5X4Mb5oX7u/HZjMH8MW8Uo7v4UFpm4NtdUYx7ZzML158lp8jybTNKy/T87+9zbD2XyvqTrX//IglUhBDCyjLMK34qf8I2Tf3UtuqnvIdK+XO1GjUDOniav5f9fRpneFg7Pp0xiCBPJ568sitTeje+oWW/EE9+mDOMpXOG0S/Ek4KSMj7afIGx/91s7nNjKYdjssgvKQNgV0SaRY9tDRKoCJuUVVBCdHrVreqFaIsyzT1UKqfoA4wZlYz8Eop1ZdU+N7WaGhWAwcZ9f4I8nQjzcbHoeC8nE7r5sfO5icy/ItwixxvVxYffHx7Jp3cPItzPlayCUu5fcoD91ex43Vg7KgQ+uyPSW/2GsRKoNIBeb0Cvb93/4a2BwWBgxlf7mPTeVo7FZVl7OEI0O3OzN+fKjcO8nO2wN9aWpNQw/VO+6sex0u03DAgi2MuJ2aM7STt4G6NSqZjSO4DVj45hfDdfikr13PfNfo7HZVvk+NvOl2dRErOLiEpv3RvzSqBSB73ewN6L6Ty/8jiD3vibse9sNn/6Ec0jIjWf4/HZlJYZWLjhnLWHI0Szu7R9volKpTIvO65uV1+93lDt1A8oq1d2PDuR+0Z3ao4hCwuw16r59O5BDOvkTW6xjnu+3su5Ji5jzi4oNX/AM2XSTEvUWysJVGpwOjGHN9ecZtTbm7jt8z38uDeGzIJS4jILWbzlgrWH16b9faq8+GvbuVT2RVouJSqELbq0fX5F5l2Uq8moZBaUoDNmeX1cm28FiWg+jnYavpo1hH4hnmQWlHL3l3ubNO29+2IaegN09nXh+v5Bym0R6ZYarlVIoFKDb3ZG8tm2iyRmF+HmoOWWQcE8O6U7AN/tiiYus3Wn0mzZ36eSgPJPiAs3nG31c6xC1MaUUfF2rhqoBBgDlaTsqhkV07SPt4s99lr5c95auTpo+e7eIXQPcCMlt5h7v9lPWSPLDLYbp33GhPsyqks7AHZfTG/VZQvyk12DGwcEM6VXAJ/ePZD9L07inVv6MXdcGCPC2lFSpue9v1vPlERrepNPzS3mcGwWAJ/NGIS9Vs2+yAzzNvVCtEWmVT8Vm72ZmKZ+kquZ+kmpYdpHtD6ezvZ8P3sYTnYaLqblE9XIrEp5oOJD32BPnO01ZOSXcLYVd8aVQKUGIzory9Km9G5vbu6jUqlYME3Jqqw8HM+phBxrDrFWWQUl/LQvhts/303n59fwxbaL1h5SvWw6k4zBAH2CPBjQwYu7hnUAYOGGc60q4GoIXZmes0m5bfb1ibpl1COjklxdRsW4bLk5G4eJluPr5kDPQHcATsQ3vLA2Jr2AmIwCtGoVw8LaYa9Vm5v/7apm+udgdAav/XWSwpLqV5TZCglUGqhvsCfX9G2PwQBvrzvTYufNKSrljyPxHDRu3V6TA1EZzPnuAEP+byMLVhxnz8UM9Ab4dldUq3gjNNWnXNnTH4CHx3fByU7D0dgsNp5OsebQms3Tvx1j8vvb2HSmbb6+y8XF1Dz+PJrQqBR7ZiNrVMqbvUmg0lb0bkKgsv2Csix5YAcvXB2UxvMjOyvTP7suyUoXlOiY+8MhvtkZxYrDcU0ZcrOTQKURnrqqG1q1iq3nUqv851tSUWkZa48nMvf7gwx+YyOPLTvCHV/sITG7sNrHJ+cUcdeXe9l4OpnSMgPdA9x4enI3nOw0xGcVctKGM0AAhSVl5rSlKVDxdXNg1qhQAN7dcLZVz7NW50hsFisPxwOwz4J9FETL0usNzP7uAI/+dJgPNzW82L684VttgUrVjEr5ih/HKveJ1qlXkAcAJ+Ib/vd6+7nyaR+TkZ2V63sjM9AZt2kA+Gp7pPnn56hxut1WSaDSCKE+LuYpibfWnbHYm6fBYCAyLZ/v90Tz0A8HGfLGRh5aeoh1J5Mo0emx16op0en5dEtEtc//dGsExTo9fYI82PDEWNY9PpZ5E7owtqvyg7r+ZJJFxtlUBoOh2kKx7edTKdbpCfJ0onuAm/n2B8eG4eag5UxSLn8eTWjJoTaIwWDgz6MJ9S60NhgMvLnmtPn7qDRpcGdrDAYDuyLSmPv9QeZ8d4CCEl21j9t6PpVI4//f+/+cq3E5aFxmAb/sj6W0whuGrkxPVoHSRr36jIqxRqXWQEUyKm1F70BjoJKQ3aAseJneYO5CW7ETcc9Adzyc7Mgr1nHcmKVJzyvmswrlAMcs1L+luUig0kjzrwjHxV7Dsbhsvt4Z2aRgpUSn5/9Wn2LUW5uYsHALL/1+grUnksgt1hHo4ciD48JY8+gYvp01BICf9sdW+aOVklPEj3tjAHhmSje6+pe/0Zs2y7KFQKVEp+e6j3YyfuHmKpmhitM+FRtUeTrbM2dMGAD/+uUIr/91irzi6t8wrOnXg3E8+tNhHvrhUL3+wGw+m8LeCkuvo9JkJZmtKNaV8euBWKYt2sGdX+xl3ckkNp5O5vvd0dU+fsmuKAA8ne0wGOCxZYer/I4ejM7g2g938MzyY/y0L8Z8e1Zh+V4vnk5VN48zdafNLykjLa/y9I9552R3CVTainB/V+w1anKLdMRmVJ89r86xuCxyinS4O2rpG+xpvl2jVjE8rHKdyoebLpBXrKOTsc/KueTcGoNwWyCBSiP5uDrw4LjOALyx+jRXf7jDWAha9Q2qWFdW6xvX+xvP8cX2SBKyi7DTqBjWyZsnr+zKiodHsuPZiSyY2oOege6M6NyOIaFeSlZla+WsyufbLlKs0zOwgyeju1Te1+OK7v5o1SrOJeeZP/VZy3e7ojgen01sRiEPfn+QolKliKtMbzDXaFxlnPap6MFxYVzbLxC9Ab7eGcmkd7ey9niiTdXd/LBHeRM7Hp/N0To+oZTpDby99iwAU4yBZFR6vs1Mbf1zOpkLKXnWHoZVnErIYfTbm3n6t2OcTszByU5j/p36fNvFKoWH0en5bDmn1AYse2A43QPcSMsrYf5Ph82p9nUnErnzi71kGjMnfxwpzwya6lM8ne3QVrPDsbO91pxhvLSnUIpM/bQ5dho13dsr/9/HG1CnYpo2H9XFB426cidi0/TP7oh0YtILWLpX+Vv1xg298Xd3QG/ApksDJFBpgofHd+apq7ri5qDldGIO9317gFs/283P+2N4Z/0Z5ny3n9Fvb6Lbi+t48PuDlOj0VY6x52I6nxiDjn/f0Jujr1zFzw+OYP4V4Qzs4IW6wg+cSqXiUeN+Ez/ujTFX/KfmFvOD8Qfv0SvCq7TL9nC2Y3iYUlBlzaxKel4xizadB8BOo+JYXDYLVhzHYDBwOCaT9PwS3B21DOnkXeW5jnYaPrxjAN/dN5SO7ZxJyinioaWHuO/b/dV27DTZHZHOq3+erLGupzqlZXqu/2gHE9/dUu/MzbG4rErp06V7qv/kbbLiUBxnk3Nxd9Tyfzf2RqtWUazTk1jL5nMtZcf5NGZ/d4C5Pxy09lCsYs3xRFJzi/F1c+DZKd3ZvWAi39w7hA7ezqTnl5j/yJv8sCcagwHGdfWle4A7i+8aiKuDln2RGbz79zm+3hHJQ0sPUazTMybcB5UKDkZnmqcI02tZ8WMywlQQWWGDOYPBYG6rL1M/bUvvoPLpn/racb7qtI+JqaB2f1QG/1lzmtIyA2PCfRjVxcecfbHlOhUJVJpAq1HzyMRwtj0zgQfHhuGgVbM/KpNnlx/n480RbDydQlym8ga54VQyT/xypFJtRnZBKf/6+QgGA9w2OIQZwzvibK+t9Zyju/gwsIMnxTo9nxvnGL/cfpGiUj39QjwZ17X67dwn91KyFNUFKglZhdz5xZ5K6ejm8L+N58gt0tGzvTvf3jsUjVrFysPxfLk90jztM6G7H3bVfKo0GdfVl/WPj+XRiV2w16jZfDaVaz/cUWU1lMFg4JMtEdz15R6+3RXFXV/sNc/n12Xl4XiOxmVzMTWfn/bW79/ENO3WzTjl9texBLILqt++vai0zNyHZ96ELrRzdTBvHW8LdSrL9iuv5UJKnjkYvpwkZCm/s/eN6sRD4zvj6WyPnUbNvAlKBvXTrRfNmcDCkjJ+3h8LwMyRHQEI83XlrZv6APDJlgheX3UKgwHuHt6Bb+8dyjBjIL7qWCJQ+4ofE9Mn4opLTPOKdRQaxyFTP22LuU6lnhmVvGIdh2KUv4FjulR9D+ji54qvmwPFOj3rjO8Bz01VWm30C1bOZct1KhKoWICXiz0LpvVg69MTuGdER4aGenPH0BBevbYnP90/nE/uGoidRsXqY4ksWHEMvd6AwWDg+d+Pk5BdRGg7Z16+tme9zqVSqXhsUlcAftgbzbnkXJYY580fryabYnJlT2V64XBMVqW5c4PBwIIVx9kVkc7C9Wcb3Q2xLmeTcs1v5i9d05NRXXx4+RrlNb+59jS/HIg1jrPqtM+lHO00/Ouqbqx5bDRd/FxJzinm9s93s3RvNAaDgbxiHQ8vPcTb686gN4CLvdJAacZXe8kqqH2fJl2Zno83l6/a+HLHxRp3rTVRlo4rqfzXr+9F9wA3ikr1NS75+3ZXFInZRQR6ODJzZCgAndopc8UXrRyoZBeUsqHCFgYH6lgO3xbFGQOVIC+nSrdPHxhMkKcTaXnF5p/lP47Ek1Oko4O3M+O6+pkfe03fQGaO6Gj+/tkp3fn39b3RqFVc109pa/6XsTDcnFGpJVAZ2skbtQoupuabO9Sapn1cHbR1fsARrUvvIGWJ8smEnHpNb++JSEenN9DB29n8oacilUplzqoA3NA/kF7GYMiUUbHlDWAlULGgAA9HXr++N7/MHcGb0/sya1QnRnRux9Q+7Vl0+wDUKvjlQByvrzrF8kPxrD6WiFat4oPbB+DiUP8/NGPDfegX4klRqZ47v9hDYWkZfYM9GN+t+myKaWz9QzwBKr0R/XUska3G+fX0/JI6+7Q0hsFg4I3Vp9AblHoMUxr7nhEduW1wCHoDZBaUYqdR1ZgRqk4XPzd+nzeKqb0DKC0z8MLKE/zrl6Pc8PFO1p5Iwk6j4j839mH1o2PwdXPgTFIuM7/ZX+t0zh9HEohOL8DbxZ4Ad0eSc4pZcSi+1nH8fjiewtIywv1cGdrJ27wibOnemCp/ZFJzi1lsDISeuLKruZlgqLGozdoZlT+PxleaorTk1vOtRbwxCxrkWbnuQ8mqdAGUFXZFpWV8Z/yQMGN4xyp1Ac9f3YNnp3Tnm3uH8ND4zuYPEVN6B6BVqziZkENEal6t7fNNPJzs6GOcDjBN/8i0T9vV1d8NrVpFRn4JCdU0+rvUqmNK0Duxu1+NjzEFKvYaNU9e1c18u+nnKiq9oMYssLVJoNJCpvZpz39v7gcon6ifXX4MUN6s+hkDiPpSqVQ8bqxVSctT/sg9OrHmbIqJafXPBmPqL7uglNf/OgmAm6MSKDVHDcvmsylsP5+GvUZt7uwLyut4/YZeDOzgCcDwsHa4OVZd9VAbVwcti+8ayLNTuqNWKdM2F1Ly8Hd34OcHR3DnsA6E+rjww+xheDrbcTQ2iznf7Ten7ivSlen5yBhE3D8mjPvHKiuNPt0aUan/QEUGg8H86fquYR1QqVTcMCAIZ3sNF1Ly2B9VHviV6PQ89MNBcop0dA9wY/rAYPN9nWwkUPn1oJIFMgW1zRG42rIyvYEkY8YxyLPqJ9ObBylZlZTcYp4xFts6aNXcMji4ymMdtBoeGt+ZCd0qv3l4u9ib6whWHU0091CpbeoHYMQl0z+pxhVAPhKotDmOdhrCjdPIdU3/FJTozB8+r+sfWOPjru4byJReAbx+fS9CvMt/tr1c7Olg/P5YfFYTR948JFBpQTcPCubf1/cClD+IQ0O9mWtcOdRQ47v50tc4t9gr0J0retQcSZuY6lR2R6STXVjKm2tPk5ZXQhc/V/5zozKnvv5kkkVX0pSW6XljtdIr5N5RoXQ0TnGYOGg1fHHPYOZN6Mwr9Zz+upRKpeKh8Z357r6hBLg7Mibch7/mj2ZgBy/zY7oFuLHkvqG4OmjZczGDB74/WGU53qpjiUSm5ePlbMc9Izpyx9AQvJztiE4vYM2J6gO4QzGZnEnKxdFOzY3GwMPN0Y7rjX8wTIWXBoOBV/48wYHoTNwctXx818BKn8BNgYo1V2WdTcrlWFw2WrWKf1/fG1BSz7a8bNHSknOKKNMb0KpV1XZ7tdeqeWi88jtr6ulzQ/8gPGvJhlTn2r6BxmPEm9vnV9fsrSLzBnMR6cZCWuPSZAlU2qQ+pumfOgKVv08lU1BSRsd2zgyo5UOvq4OWT2cM4vahHarc19fG61QkUGlhM0aE8tb0Pkzu5c/7t/evki6uL5VKxRs39GZEWDv+78Y+dWZTQCnyC/dzRac38NbaMywzFgG+Ob0PV/Tww0GrJi6zkFOJllum9uuBOC6m5tPOxZ55E7tU+5h2rg48Pbk7Xfzcqr2/vsaE+7J7wUS+nz2s2uWafYM9+XrWEBzt1Gw7l8rtn+8xF9iW6Q3mFUlzxoThYpz3v3dUJwAWb75QbQC3dI+STbmuXyAeFXpg3DlUqU9YezyJ9Lxiftgbw0/7YlGpYNEdA+js61rpOKapn5iMghqzN83tV2Od0BU9/OgT7EF7D0fK9AaOxGRZZTzWEG+sT2nv6Vjj7+Ytg4Np71H+8zWjQi1KfV3Vyx97rZqI1Hz2XlSm1+rKqAzu6I2dRkV8ViExGQXSlbaNK1/5U/vf49+Nna2v7xdYr/eB6vSz8ZU/EqhYwe1DO/DZjMEEejrV/eBa9A325KcHhpvT9PVxlTGrYlrhc+ewDgwJ9cbZXstYY33I+pPJNT6/of4+pWQiZo/phHsDp3Uao65f1KGdvFk6ZzheznYci8tm+ic7iUjNY/XxRC6m5uPhpGRTTGaOCMXFXsOZpFw2n628F09mfgmrjisrN+4aVvnNqk+wB/2CPSgp0/PSHyd47U9liu3ZKd2rTAUAtHd3xEGrRqc3mN8sW1JpmZ7fjyh/8G4ZFALAYONmZpdTQa1pxU9QLb+bDloNDxtrVYaGepvfUBrCzdGOicafA9NUk7dL7b8fTvYaBhizhLsi0st7qMiKnzapVz1W/qTnFbPNuCz5+gFBjT6XKaPSkL4tLUkClcuMqU4FMPeJuPS+DRaqU9GV6c01GmPD618k29wGdfRi+UMj6eDtTGxGITd9sot31isbTM4e3alSnYyHsx13DVeCkMWby5vs6cr0/LAnmhKdnt5B7uZf9IpMwcua40no9Aau6xfIg8a6l0up1SpCrbjyZ/OZFNLySvBxdTAXZQ/uqLwpXk6BiqmdQF0fIu4e1oEv7hnMR3cNaPS5ru1XuZ7A26XugMO8wVxEenlXWpn6aZN6tHdDrVJWd9XUJmD18UTK9Ab6BHlUydI2RO8gD9QqSMwuqrUvlbVIoHKZ6RPkQUfj8rVXr+1VabpiUg8/NGoVZ5JyiU5v+pvliYQc8op1eDjZ0bO9e5OPZ0lhvq6seHgk/UI8ySooJTajEDdHrXkDxIpmj+6EvUbNgehMHvz+ANM+2E7PV9bzrrEXyl3DOlabybmmX3tzkXLvIHfevqlvrRmfUB/r9VIxFdFOHxhk7o46yBioHIrObLZl67bGlM0KriNQUalUXNnTv0nTLhO7++FirzF/X9uqH5PyDqNp5t2UZeqnbXK215qDj5oav5mnfWopoq0PFwctXfyUcx2Ltb2sigQqlxmVSsV39w5l6ZxhXN23faX7PJ3tzc2oLLH6Z7dxdcKwTt6VOuzaCh9XB366fxiTeijTYY9ODK92esrf3ZGbjas61p9M5lRiDiU6Pc72GiZ08+WG/tWnXJ3ttbx0TU8mdPPl8xmDcarwplSdTj7KH4qWDlTS8orZbNy+4JZB5atXuge44eqgJa9Yx9mk3BYdk7Uk1NBDpTk42Wsq9Q3yqmPqB5TVWE52GtLySohIVbY4kKmftqtPLTspx6QXcCgmC7VKqZFrKlvupyJdgi5DoT4u5uLNS03uFcCuiHQ2nEzmgbGNW5FksvuiEqiY2vfbImd7LV/cM4iknCLae9T85vTM5G442WnwcLKjW4AbPQLcCfZyqjMAu3VwCLcODqnXWDoZMyotPfXz++F4dHoD/UI8zUsiQem8PKCDJ9vPp3EgOoOegbaVFWsO8fWc+rGUa/sF8vuRBOy1alzr0UvJXqtmSCdvtp1LxVTbLVM/bVevIA9WHI6vtk7lD2NN2cjOPvi5Nz2r1i/Yg98OxtW5T5k1SEZFVGIqtj0Yk1nvlvPVKS3Tc8DYLGxEZ9sNVEDJMtUWpICSbXrpmp48ekU4k3sF0KGds8WzRKYalSgLTLs1hKmrbsVsisngjsaC2qi2X6diMJQXMtdWTGtJY7v6csfQEP51Zdd6r9io2GHUXquuNH17WctLgcIsa4/ConoHlneorchgMJiL35s67WNSMaNiS5u9ggQq4hLtPZzoF+yBwYB5/53GOBaXTUFJGV7Odub9b0TtOvkqgUp8ZmG1G1g2h9TcYnOlf8VCa5PBoUqdyuXQ+C27sJQC487ILZVRsdOoeXN63wb1U6oYqPi6OjR6SWqbUpABHw6Gb68BG3uTbQpTFjM+q9DcbwcwdjXOx0GrZkrvqr+3jdG9vRt2GhWZBaXmonJbIYGKqOIq4xtWU+pU9lw01ae0s8n6FFvk6+qAi70GvUHpp9ISdlxQtk/o2d692gZn/UM80aiV3h0JVlg23ZJMf5x9XO3NWxvYol6BHuYi7er+z9qknAQoqSXTmHQcirMh+bhyvY1wc7QzN4M8EZ9Nsa6M+KxCfjDuzj6ph3+Du3nXxEGroYdx0cNRG6tTkUBFVGHqYLsrIo2cosbt/WAqpLX1aR9bolKpzLVD9e1Qq2/iapxt55QeDGNr2GPJxUFrXrHV1pcp16eHii3QqFXmuq/Loj4l9Sx80A9WPljzY7Kiy6+fW9f8Y2pBpj49c747QLcX1zHqrU3mZp2WmvYxsdUOtRKoiCq6+LkR5utCaZnBvBqkIUp0eg5Et476FFtT3z1/9HoDN32yi+4vrWPqB9t54ucjfLY1gm3nUimtZ2dbvd7A9vNKRmVsV58aH2dapnygjW9QaKpPaalpn6YwvUGZpubatPMboKwEIrfX/JjMthuojDHuC1Vi/L2206gIcHdkau8AJtSyCWFj9LXRDrWy6kdUa3KvAD7ZEsGGk8lcX8Py25ocjcuiqFRPOxd7wv0a34TocmTe86eOgto9F9PNdSOnE3M4XWHbg37BHnxy96A633BPJeaQlleCs73GXDRbnSGh3ny7K6rNF9SW75ps+4HKNX0DGdDBC//LIaMSu0+5LMqCwkxwqiY4q5hRiT8Iucng5l/1ca3QLYOCzVOwPi4OuDtpm60uybQ/2r6oDLafT2WMjTTqlIyKqJapsHLL2ZRqdxqujWnaZ3hYOyn0ayDTyp/I1NoDleWHyiv+v7hnME9d1ZVr+rbH3VHL0bhsrv1wB7si0mo9xjZjNmVEWDvstTX/KTB9aj+TlENuI6cCm2p/VAYfbDxPsa5hP4sNkZDdcj1ULCHI08ncnK/NMhggbn/59xmR1T/OnFEx/r05v6FZh9WSVCoVXf3d6OzrioezXbP+Te3i58odQztgMMDjy46QlG0bXWrb+E+5aKy+QR4EuDuSX1JW5xvepcyBikz7NJhp5U9tS5QLSnSsPaHsMXTPiI5c2dOfRyaG89GdA1n96Bh6tncnPb+EGV/t48vtF2tcarjtnGnap/ZPTf7ujoR4O6E3wCErbVD40u8n+N/Gc3y/O7ruBzdSS/dQEfWQHQu5ieXfZ9YQqJgyKt2mKpdtbPqnJb1ybU/z35D5Px2y2iapFUmgIqqlVqvMPVXWn6j/MuWi0jIOxShTBCPCap5OENXrZMyoJGYXUVhSffZg/ckk87buplStSYi3M8sfGsn0AUGU6Q28sfo0j/98pMofm/xinXnqqK5ABZSsC8C6E4l1PNLySnR6LqQoXVi/2RlV7xqchmrpHiqiHkzTPibVZVRKCyHP+DdqmLHgNmITlNpGNqC1cbTTsPiugbg6aNkflcnCDeesPSQJVETNTNM/G08n13uvlyOxWRTr9Pi6OTRpk6zLlZeLvbmBV01ZlRXGaZ/pA4KrTQM72Wt499Z+vHptT7RqFX8cSWDp3phKj9kdkU5pmYEQbydCjXs/1eamgUozuD+PJJBfrGvQa2qqyLR8dMafv/isQtYct3ywVFRaRlqe0qdCAhUbYpr2URmXi1eXUcky/mw7uEOnceAWCKUFELWjZcbYBoX6uPDfm/sC8OnWCP453fieWpYggYqo0dBO3ng42ZGeX1LvFR9Sn9J0ta38ScouYscFZSruxlq2dVepVMwa1YlXru0JwMINZyt1GjbVp4wN963X/9PQTt6E+biQX1LGqmMJ9X8xFnA2ufI+Q59vq3k6q7FMS5Od7TV4OkunV5thyqh0maRcZkRVfYypPsWzI6hU0HWy8v25tc0+vLZsWp/2zBoZCsC/fjlKbAv1dqqOBCqiRnYaNVcYl7+tP1m/iHqPeX8fmfZprNpW/vx+JB6DAYaEetGhHpmQO4d1pHeQO7lFOt5ed8Z8e33rU0xUKhW3DVH2LDL1cGgp54wbIk7tHYCTnYaTCTnsMgbEllJx2kcCbBtRWghJx5TrfW9VLqvLqGRGKZdeHZXLrlOUy3Pr21SXWmt4floP+oV4kl1YysINZ602DglURK1MXWo3nEqq81Ps4ZhMDhuLLUfY8EaEtq6mlT8Gg4EVh+IAmD6w6r481dGoVbx2XW8AfjsYx8HoTGLSC4hKL0CrVlVqx16X6QOD0apVHI7JatHdlE0ZlWGdvLnVuIv159suWvQcCa2oh8plI+EI6HXg4gdh45XbchKq1p5kVcioAISNA62TUoibfLJ5xlZWCjktX6/V0uy1aj6+cwB3DA3h/27sY7VxSKAiajWuqy+OdmriMgs5lVh1q3GTH/fGcNtneygp0zOgg6c5KyAaLtS4i/KlNSonE3I4l5yHvVbNtD7t6328QR29zG/wL/9xgs1nlSZ+Azt4Naj9tq+bA5N6KAXWy/bH1PFoyzlnDFS6Brgxe3QYahVsPZfKmaSafx4bytxDpZUsTb4sxBmnfUKGgnM7sHcDDJV7pkDVjIqdU3lg0xzTP3kp8MlI+KAvRO20/PFtTLCXM29O71uv3b2biwQqolZO9hrGGpv+VDf9U6wr47nlx3h+5XFKyvRM7uXPkvuGSvq8CcJ8lCLkkwk5/HEk3twm31REe2VP/wbvmPvslO64O2o5mZBjTuHW1o22JrcPVaZ/Vh6Ob3B/ncYoKNGZ9z3q5u9Gh3bOTO2tBGlfbKthqWojxMmKH9tjqk8JHqLUnniHKt9fuvLn0owKVKhTWW/ZMRVkwPc3Qto5pVvu6ieV7IpoVhKoiDqZVv9suGSTwpj0Am79bA/L9seiUsHTk7vxyV2DLLZJ1uUq3N+VYC8nCkrKeGzZEa7+cAd/n0rmz6NKoHLTwIZ1CgZo5+rAU5O7AZBbpKzaqW99SkVjwn0J8nQiq6C0SZtW1tf55DwMBmWjwHauShfWOWM6AfDn0XiLNaRqLfv8XDYqNnoLGaZcein/72RcMu2XaczueVUTqMQdgLxUy4ypOBeW3gzJJ8DVX8nypJ6GvZ9Z5viiRhKoiDpd0cMPjVrFmaRcotPzSckp4uU/TnDFe1s4GpuFh5Md3947lHkTushOyRbgaKdh/eNjefLKrrg5aDmdmMP9Sw6QlleCj6t9o9ta3zWso3mDQW8Xe3oHejT4GBq1iluM00g/t0BRrak+pau/m/m2AR28GBrqTWmZgW92WiarYi6mlakf25AVo/RGUWshsL9ym7cxUKlYUFuYqeyaDJUzKu6B0L4fYICTK5s+ntJC+OkOpT2/kxfM+B0mvarct+UtyG3+oP1yJoGKqJOns715Fc+Tvxxl7DubWbI7mtIyA2PCffjrkdGMa8Snc1EzFwct868IZ9szE3hwbBgOxhb3Nw4Iwq6RbdM1ahVvTu+Dl7Mddw/r0Oig8pbBIahUsCsineg69iRqKtOKn4qBCsADY8MAWLo3hqyCkiado0xvIDFLycxIMa2NMGVTAvoqNSdQIaNSIVAxLU128QP7S1bB9b1dufzn9fI6lsYoLYJf7oGo7UqdzN0rwL8n9L8bggZDSS5seKnxxxd1slqgEhUVxezZs+nUqRNOTk507tyZV155hZKSpv3REc3jqp7K9M+B6EyKSvX0D/Hkx/uH8f3sYfVaJisax8vFngXTerD16Ql8cHt/nryqW5OO1y/Ek0MvXcm/mnCcIE8nc91Sc2dVTBmVbgGVA5WJ3f3oHuBGXrGOr3c0LauSmluMTm9Ao1ZdHpv8tQaxFQppTarLqJjqUypO+5gMexBChiuBxIoHoawRjQqTTsAXE5S9g7ROcNcvEDRQuU+thqsXAio4/os0mGtGVgtUzpw5g16v57PPPuPkyZP873//49NPP+X555+31pBELa7p256O7Zzp0d6dz2cMYuXDIxnZueHFmKJxAjwcub5/EI52miYfyxKFzncYi2p/ORBLQUnzdao9V0OgolarePSKcEBpq59d2PiCxvgspVg3wN2x7W/y11rE7lUug4eU32bKqGRGg76s/DpUnvYxUWtg+udKx9rYPbDjvaqPKcyC479B7P7KgYxeD7s+UoKUlFPg4gt3/gwdR1Z+fuAAGHyvcn3N01JY20ystt5oypQpTJkyxfx9WFgYZ8+e5ZNPPmHhwoXWGpaoQTtXB7Y+PcHawxA24ooe/gR7ORGXWcg3O6OYN6GLxc+RVVBCco7STTfcr+p2DFN6BdDN342zybl8szOSxyd1bdR54jKlkNamlBQoBatQOaPiEQxqO9CXQk48eHaoPaNiuv3qd2HF/UotSdgECBmiFOse/w3WPw/5ynJ9HNwhdLTShv/cWri4Rbm961S47kNwrWF6e+JLcPJ3JaDZ8haMXwAa6y3lbYts6uNDdnY23t61dzQtLi4mJyen0pcQomXZadQ8ZZw++nRLBBn5lp+yPZesbEQY5OlU7UoytVrF/CuUAOnrHZHkFDXu02yCsT5FCmltRMJhpdGbawB4hJTfrtaUBySmOpXaMiomfW+FPreAoQxWzFGOv+R65Xp+inIOR08ozoGza2Dds0qQYucM17wPd/xUc5AC4OxdXli7fSF8MkIJXKQrrsXYTKBy4cIFPvzwQx588MFaH/fmm2/i4eFh/goJCan18UKI5nFdv0B6BbqTW6zjw03nLX78mupTKprauz1d/FzJKdLx3c6oRp3HNPUT6OnYqOcLCzM3ejP2T6nI65I6lboyKibTFoJHB6Wo9vPxELkVtI4w8UWYfxCeuQgPbFECjrAJ0P0aeHC7Mq1Tn6nSgffAlLfAyVvpsfLrTOU8EZvr9ZJF7SweqDz33HOoVKpav86cOVPpOfHx8UyZMoVbbrmF+++/v9bjL1iwgOzsbPNXbGzL7jsihFCo1Sqem9odgB/2RBOTbtlNy2pa8VORRq1i/kQlq/LljkhyG5FVMXel9ZSicJsQa1zxEzy06n3eFVb+GAzlOyfXllEBcPKE6Z+ByviW12USPLwHxj4NWgclWxM4AEY/Aff8DrcvBZ8GTGeqVDD8IXjsKIx7FuxdIfEIfH8DRO+q/3FEtSw+kfbkk08ya9asWh8TFhZmvp6QkMCECRMYOXIkn3/+eZ3Hd3BwwMFBKvOFsAVjwn0ZE+7D9vNpvPv3WT64fYDFjl2eUalan1LRNX0D+eCf81xMzWfJ7ugG18vI1I+FHF4KpQUwZE79shDVKckvrw25tHAVKmdU8pJBV6QEHx712Puq40iYtRp0xUqL/ebonu3oDhOehyH3K3UxFzfDmdXVvxZRbxYPVHx9ffH1rV9Pjfj4eCZMmMCgQYP45ptvUKttZiZKCFFPz07pzvbzO/jjSAL3jwmjd1DDG8ldymAwmDc+rC2jAuVZlSd+PsqX2y9y08BgAjzqN41jMBgq7JwsUz+NlpsMf8wDDJByWplqaczf89OroDQfvEIhaFDV+ytmVEy9UdyDQVPPbtgtFTC4+kL/O5VAJWZ3y5yzDbNaZBAfH8/48ePp0KEDCxcuJDU1laSkJJKSpMOfEK1J7yAPru8fCMBba5VpXV2ZnpTcIk4l5JCaW9zgY6bkFpNdWIpGraKzb+0ZFYBr+wbSyceFzIJSRr71D3d9uYdf9sfWuWx51bFE8op1ONlpCPaSqZ9Gi9oOGItHD3wFf84vX0LcEEd/Ui773VF9xsOcUYkqL6Stqz7FWkyt/xOPKiuZRKNZbQ3V33//zYULF7hw4QLBwZXTdgaplhaiVXnqqm6sOZ7IjgtpDHh9A1mFpeZFD1q1ituHhvDoxHD83OuXtTBlU0LbOderd4xWo+bDOwbw8h8nOBSTxc4L6ey8kM6Lv59gxoiOvHh1jyr9Y4pKy8yB1dxxnS3So+ayFbVduWzfH5KOwZEfoKwYbvi0/kt1cxLKp3363lb9Y0xBSXGOsnoH6q5PsRbPDuAWCLkJSuv9TmOsPaJWy2oZlVmzZmEwGKr9EkK0LiHeztw7Svm0m1mgBCkqFXg626HTG/hhTwxj39nMW2vPkF1Qd8FrTY3eatM7yIMVD49i+zMTeHpyN7r6u1JSpuerHZF8uyuqyuO/2hFJfFYh7T0czS35RSNFGgOV8c/Bzd8oe/Qc/xV+uxd09Vy6fuwXwAAdRpRP8VzKzkl58wdlWgVsN6OiUkEHY1Yldo91x9LKSVcaIYRFPDO5G1N6B+Bsr8HH1QEvZ3s0ahV7Lqbz33VnOBSTxadbI1i6N5rHrghn1sjQGjvB1rc+pToh3s7Mm9CFeRO68PWOSF5fdYr/rDlN/xBPBnTwAiAlp4iPN18AlBobJ3srZlOidiq9OPx6WG8MTZGTABkRSlFrx5Hg6KGspPnlHjj9J2z+P7jytdqPYTDA0WXK9X631/5Y705KliLVuHrUVjMqoARdJ1dCjAQqTSHVq0IIi9Bq1Azs4EX3AHd8XB3QGDc9HB7WjuUPjeTLewbTPcCN3CIdb6w+zbUf7eRwTGa1xzJnVBoRqFR076hQru7TntIyA4/8eNi8geHCDWcpKCmjf4gn1/ULbNI5miQvFb67FpbeYr0xNJVpj5v2/ZQgBaDbVLjhE+X6wW+V3Ydrk3QMUk+DxgF63lD7Y70uybZ4hTZwwC3IVKcSu79xNTsCkEBFCNECVCoVk3r6s/rRMbw5vQ8eTnacTsxh+ie7eGHl8UrTQXq9wdyVtmsDpn5qOu9bN/UhtJ0z8VmF/OuXoxyPy+bXg3EAvHRNz0bvIm0ROXFKx9TsuNbbyTRym3IZOrry7b1uVJqsFWUpnVprY8qmdJ+m9DypjXdo5e9tdeoHwL832LlAcbayGko0igQqQogWo1GruGNoBzY9OY6bBgZjMMDSvTEM/c9Grv1wB0/9epT3N56jsLQMe62ajt5NX4nj5mjH4rsGYa9Vs+lMCjO+3ovBoHTWHdTRywKvqgkKTRklg9JDpDUyZVRCx1a+Xa2BQTOV6we+rvn5ZaVKPQsoq33qUjGjonUEV//6j7WlabRKh12QOpUmkEBFCNHi2rk68O6t/Vj2wHDC/Vwp1uk5Hp/NbwfjWLRJqR3p4utqsd2Mewa68/p1vQDIKijFQavmWWNXXasqrDD1VZxrvXE0Vnac0nxNpYEOw6veP2CGUlgbtw+Sjld/jIhNkJ+q7FDceWLd56xYaOvZoXkat1lSiPHfJWavdcfRikkxrRDCaoaHtWP942OJSs/nbFIuZ5JyOZuUS2xmAfePsexKnNuGhLA/KpPlh+J4aHxn29gtuWKgUpJnvXE0lmm1T2B/pSvrpdz8lX1zTv0OB76Ba96r+hhT75Q+t9SvcVvFjIotF9KamAI4KahtNAlUhBBWpVarCPN1JczXlal92jfbeVQqFe/c3Jd7R4XSK7CaN1VrqJRRaYU7wZv6p4TW0iNk8L1KoHLsF7jydXCo0MCvMAvOrFGu17Xax8TZWynaLcq27foUk+DByoqo7BjIjgePIGuPqNWRqR8hxGVDrVbRO8ijSvM3qynMKr/eGqd+TIFKbc3MQseCd2coyYUTv5XfritR2u6XFYNvDwjoW//zmrIqrSGj4uAGAX2U61Kn0igSqAghhLVUClRa2dRPZrSye7FaW16HUR21WsmqAOz/SlndVFoEP98NZ1aBxh4m/1/Dak26Xw12zhA2rmmvoaVInUqTSKAihBDW0pqLaU3ZlMCBladzqtPvTqVHStIxiN4JP90O59crq3buWAZdrmjYucc9A8/FKr1bWgNznYpsUNgYEqgIIYS1tOpAxbgsuT572Li0g143KNe/n660v7dzgbt+a3iQYlLfPYRsgSlQST7R+v6fbYAEKkIIYS1FWeXXS1rRG5jBUL7ip7ZC2ooG36dclhWDgzvMWHn5bNTnHqg0vzPoIW6/tUfT6kigIoQQ1tJaMyqZkUpXXbVdeZv4uoQMU4IaFz+45/fyDfsuFx2kTqWxWlHuTAgh2pjWGqiYpn2CB4N9PbsHq1Qw8y/Q6+rXL6Wt6TAMjv8iK38aQTIqQghhDaWFoCsq/741rfq58I9yWd9pHxOV6vIMUgA6jFQuo3dBbpJ1x9LKSKAihBDWUHFpMrSejEppEVzYqFzvNsW6Y2lN/HpA8FAoK4E9i609mlZFAhUhhLCGitM+0Ho6017corT7dw9SliaL+lGpYMy/lOv7v6r6/y9qJIGKEEJYw6VvVK1lr58zq5TL7lfb/oaAtiZ8Mvj1VP6v931p7dG0GhKoCCGENZgDFeObfWuY+tGXwdm1yvXuV1t3LK2RWg2jjVmVvZ9ASYF1x9NKSKAihBDWYOqh4hagXLaGQCVmDxSkgaMndBxl7dG0Tr1uBK9QKEiHQ0usPZpWQQIVIYSwBlNGxbODctkaVv2Ypn26Tb18V+80lUYLox5Tru/6UNmcUdRKAhUhhLAGU6DiEaJcluYrUyu2ymCA06b6lGusO5bWrt+d4OqvNM07/ou1R2PzJFARQghrMGdUQspvs+Xpn6RjkB0DWifoPNHao2nd7BxhxDzl+o73bTtAtQESqAghhDWY+qi4BoDGXrluyyt/TNmULlfUvxutqNng+8DRA9LPw9k11h6NTZNARQghrMGUUXHyAgc35botZ1TOyLSPRTm4wcCZyvVTf1p3LDZOAhUhhLAGc6DiCfauynVbDVTSIyDlFKg00HWytUfTdpj+LSM2gV5v3bHYMAlUhBDCGiplVNyV67YaqJiyKaGjwdnbumNpS4KHKkFqQZpSAySqJYGKEEJYg6mPSmuY+jHVp/S41rrjaGu09tBprHI94h/rjsWGSaAihBAtTV8GRdnKdScvcLDA1E/GRYjZ2/SxXSovFeL2K9elG63lmVZQXdjU+GOUFsHZdW12/yCttQcghBCXHVOQAsrKD1NGpSmrfr6/EbJi4PET4BHUtPFVFLcfMIBvD3APtNxxhaLLFcpl7B4lUDX9LNRHmQ6O/ghb3lZ6svj3gTl/g51T84zVSiSjIoQQLc30ydfeTenw2tSpn9xkyIwCg17JrDRE7H7ITar5/viDymXwoMaNTdTOOwy8OoFeB5Hb6/ccvR5OroTFw+HP+UqQApB8HNY913xjtRIJVIQQoqWZeqg4eSmX5lU/OY07XtLx8ut5yQ143gn46kr45Z6aH2MKVIIkUGk2pqxKfepUynSw9Gb4dZbSg8XJG676P7hjGaCCg9/C0Z+bcbAtTwIVIYRoaRWXJkPTV/0kNzJQSTwKGCB2LxRkVL1fr4eEQ8p1CVSaT2djoHKhHoHK9neVgEbrBOOeg8eOwshHlP2Xxj2rPGbV45ByptmG29IkUBFCiJZWJVAxTf00skalYkaltmmcS2VFl1+P2VP1/oyLSj2N1hH8ejZubKJuncaAWguZkbVP3cXug61vK9ev+xAmLABH9/L7xz0DncZBaYGSJSvJb95xtxAJVIQQoqVV7KECTV/109ipn8wKgUr0zqr3m7IpAX1lt+Tm5OAGIcOU6zVlVYpyYPkcMJRBn1uh7y1VH6PWwE1fKtsypJ2FVf9SNpNs5SRQEUKIllaxhwo0rZi2pADSL5R/39iMSvSuqvdLfUrLMS1TjqhhmfLaZ5X/L48OcPXCmo/j6gc3fw0qNRxbZpzea90kUBFCiJZmyqg4eiqX5uXJjQhUUk4rq31M8lLq/9zMqPLriUerBkoSqLQcU0Ft5DbQlVS+78RyZRmySg3TP1eWtNcmdBR0HKVcTztn+bG2MAlUhBCipV069WPfhIyKqfW6u7F3Sl49MyqlRZCbqFx39FSmFGL3ld+vK4FE47GDBjZ8XKJhAvqBs4/SSyeuwv9D7D5Y9YRyfcxT0HFE/Y7nEaJcZsdadpxWIIGKEEK0tCo1Kk0JVIz1KaZP5IWZoCuu+3mmNzA7l/LN8SpO/6SchLJiJYjxDmv4uETDqNXQeYJy/dx6OLECvpykLB8vyoagwUqxbH15BCuX2XGWH2sLk0BFCCFa2qV9VJqy6scUqISOBY29cr0+0z+mQlqvjuXTBBUDlYrTPipVw8clGs60THnXIvjtXqUrsMYe+t0Jt//YsIJmU6CS1fozKtJCXwghWlqV5cnGVT9lxUo2ROtQv+Po9ZB8Urke0Adc/ZVMSV4yeIbU/tysKOXSs0KgEn9QmRKyc4R46Z/S4jpPVJYp63XKNNCQOTD4PnDzb/ixTP//bSCjIoGKEEK0tJpqVEDJqtQ3UMmMhNJ8pc9Juy7lgUp9Vv5UzKi06wwufpCfoixJ7jhSCmmtwc0f7l4O+WnQ/RolYGysijUqBkOrzorJ1I8QQrQkg6FqoKLRgp2zcr0hK39MhbR+PZVjuBo/edenl4ppabJXqPIm1nGk8n30TqVnR+pZ5XsppG1ZYeOhz81NC1KgvLi6JK98OXwrJYGKEEK0pNIC0Jcq102BClTY76chgYqxPiWgj3Lp1oBAxZRR8eyoXFasU0k8AhiUnh2ufvUfj7Ad9s7K9BG0+ukfCVREm7YxeiNPbH6CpPwGNMESojmZsilqu/IsCjRu5U/SCeXSFKiYMir1mfrJqjD1A+UZlZi9yt4/AEED6j8WYXvayMofCVREm2QwGPjmxDc8seUJNsZs5KczP1l7SEIoKk77VKwbaMzKn0szKuapnzpW/RTllI/DlFHx66k0EivNh0NLlNukPqV1ayMrfyRQEW1Omb6M/+z9D+8dfM9827a4bVYckRAVXFqfYmIOVHLqd5z8dMhNUK7791Iu3QKUy7qavpmyKc7tylccqdXQwZhVyYpRLiVQad08OyiXrbzpm00EKsXFxfTv3x+VSsWRI0esPRzRihXqCnliyxMsO7sMFSoe7vcwapWaC1kXSMxLtPbwhKjaQ8WkoVM/ycZsindY+XNN9SS5ddSoXFqfYmKa/gGlXXv7/vUbi7BNMvVjOc888wyBgYHWHoZo5XJKcpizfg6bYzdjr7Zn4biFPNT/Ifr79gdge/x26w5QCKjaQ8XEvN9PPad+TNM+/r3Lb3M1ZlTyU5QeKzUx7fHjdWmgMqr8um/38myLaJ3MgYpkVJpk7dq1bNiwgYULa9kNUog6FJQW8PDGhzmWdgwPBw++nPwlV4VeBcCY4DGATP8IG1HT1E9DV/2Y61P6lt9myqjodVCYUfNzs2rIqLTvW17gK8uSWz+PttH0zaqBSnJyMvfffz/ff/89zs7OdT9BiGoUlxXz6OZHOZp6FHd7d7666isG+JWvVhgTpAQqexP3UqQrstYwhVDUWaPS0EClT/ltGjul7gRqX/mTecmKn4rPDx2tXO9Qz83vhO0yBSq5SVV3ZG5FrBaoGAwGZs2axdy5cxk8eHC9n1dcXExOTk6lL3H5KtWX8tSWp9ibuBdnrTOfTvqUbt7dKj2mq1dX/J39KSor4kDyASuNVAgjSwQqpUWQdk65XjFQgfLpn9oKamvKqABc/R5c+wH0u6PucQjb5uIDGgfAADnx1h5No1k8UHnuuedQqVS1fp05c4YPP/yQ3NxcFixY0KDjv/nmm3h4eJi/QkLq2M9CtFll+jJe2PECW+K24KBx4KMrPqKPb58qj1OpVDL9I2yHqUuoo2fl2xsSqKSeUaZ3nLzA/ZL6Prc6ligbDOWrerxCq97vGQKDZoFaU/c4hG1TqdpEQa3FA5Unn3yS06dP1/oVFhbGpk2b2L17Nw4ODmi1Wrp06QLA4MGDmTlzZo3HX7BgAdnZ2eav2NjWXSQkGu/jIx+zNnItWrWW98a/x5CAITU+dmzQWEAJVAwGQ0sNUYiqLJFRqTjtc+keLnU1fctPVbrjUuFNTLRdbWBzQotvSujr64uvr2+dj1u0aBFvvPGG+fuEhAQmT57Mzz//zLBhw2p8noODAw4O9dywS7Rpqy6uAuDl4S8zNnhsrY8d1n4Ydmo74vPiicyOJMwzrCWGKERVdRXT1mfVT8ZF5dK3e9X76trvx1Sf4h5Y/80PRevVBjIqVts9uUOHDpW+d3VVfkk7d+5McLBE+aJ2aYVpJOYnokJlXt1TG2c7Z4YEDGFXwi62x2+XQEVYjyX6qOQYG72ZNp6ryNz0rYZApeJmhKLtM6/8ibHuOJrA6suThWiME2nKHiedPTvjYudSr+eYVv9InYqwKnOg4ln5dgd35bJegYqxMLK6QKWupm+mHirVFdKKtqcNLFG2mUAlNDQUg8FA//79rT0U0QocT1Pm6Hu161Xv55imhw4lHyK3pAEbvwlhKWWlYPrZq5JRaUAfFXNGpZpGmXWt+rl0M0LRtrWBqR+bCVSEaAhTRqWPT9VVPjXp4N6BUPdQdAYdexL3NNfQhKiZKZsCygaAFVWc+qmt4NtgqD1QMU/91LDqp6b2+aJtqrgxYStdSCCBimh1DAaDOaPS27d3HY+ubHSQ0sxKpn+EVZgKaR09qi7/NQUqhjIoLaz9GDrj/W7tq95vmvopyat+J+aa2ueLtskUqOgKoaCWbsU2TAIV0erE5MaQW5KLvdqerp5dG/Rc0/TPtrhtlJS13k6NopWqqYcKQMVaq9pW/piyKc4+YOdY9X4Ht/JjXVpQW6YrnwKQjMrlQetQvhKsle75I4GKaHVM2ZTu7bpjp7Fr0HMH+w/G18mXjKIM/oj4ozmGJ0TNalqaDKBWg309Vv7UNu1j4lbDEuWceCVjo7GvPhsj2qZWvjmhBCqi1WlMfYqJncaO+3rfB8CXx76ktKzUomMTola1BSpQoU6llq1BalvxY1JT0zdTIa1HiBIYictDK1/5Iz+prcjmmM02VQQanRPNV8e/oqC0oEXPawpUevs0rD7F5OauN9POsR0J+Qn8dfEvSw5NiNrVGaiYVv7UY+qntoxKTU3fatqMULRtrXzljwQqrURSfhKPb3mchzc+TJZpntuKTqWf4u41d/P+ofdZfGRxi523VF/K6fTTQOMyKgCOWkfu7X0vAJ8f+5xSvWRVRDNJPApp58u/r6mHikl9mr7Va+qnhqZvtW1GKNouU0Ylq3U2fZNApZU4knIEvUFPqb6UTbGbrDqWY6nHmLNhDlnFWQD8cu4XsouzW+Tc5zPPU6Ivwc3ejQ5uHep+Qg1u7XYr3o7exOfFs/riaguOUAij+EPw2Tj4aDB8OgZ2LoJ0Y9BS59RPbYFKfaZ+amj6JhmVy1Mr3+9HApVW4mjqUfP1DVEbrDaOQ8mHeODvB8gtyaW/b3/CvcIp1BXy45kfW+T8FetTVJduxtYATlonZvWaBShZFZ1eZ4nhCVHu1O+AsW9F0jH4+yU4sVz5vqZAxbzfTxMzKjU1fTMFSpJRuby08qkfq+31IxqmYqCyJ3EPWUVZeFa3xLGRMosyicuNI70onfTCdDKKMijUFeLl6IW3ozftnNqRXZzNSztfolBXyNCAoXw48UO2xW3j6W1Ps/T0Umb2nImznbPFxlQdc/+URtanVHRbt9v45sQ3xObGsjZyLdd2vrbJxxTC7Ow65XLaQqVnyvHfIHqncptvj+qfU582+rXt82NiWvVTMaOSeBQSDoNKAyFD6x6/aDtMUz/5KVBaVP2ydhsmgUorUFxWzOkMpS7Dz8mPlMIU/on5h5u63mSR459MP8ndq+9GZ6hfVmFU4Cjen/A+jlpHrux4JR3cOhCTG8Nv537jnl73WGRMNWnKip9LOds5c0+ve/jg0Ad8fuxzpnWahubSJlxCNEbGRUg7qwQFfW5RalIG36d0B81NhOAh1T+vrqmfopzybIt7LcuLqyum3fWRctnrxvJP2OLy4OSl9NYpzVemDtt1tvaIGkSmflqBU+mn0Ol1tHNsx+3dbwdgQ7Tlpn+WnVmGzqDDw8GDXu16MSZoDDd0uYE7ut/B1NCpDA0YSmePzrRzbMcNXW5g0cRFOGqViFyj1pgLU7879V2zNlHLL80nIisCsExGBeCO7nfg4eBBVE4Ua6PWWuSYQnBuvXLZcWTlwlnPECWbUdO0ZV2rfkzZFEdPsK9lM07T1E9BmrK/UHZc+bTTyPn1eQWiLVGpWnUvFcmotAJHU5Rpn36+/ZgcOplFhxexN3EvmUWZeDnWMNddTwWlBayPUv6oLpqwiIH+Axt8jOs6X8cnRz4hpSCFVRdXMT18epPGVJNT6acwYKC9S3t8nHwsckwXOxfu6XkPHx7+kK+Of8W0TtNQqyR+F010zjjt03VKw55XV0alPoW0AM7tlGyOoUzZ82fPJ8r1TmMhsH/DxiTaBo9gJcuX1foCFfmL3AocST0CQD+/fnRw70AP7x6UGcr4J+afJh97Q/QGCnWFdHTvyAC/AY06hr3G3jzl8/WJrynTlzV5XNWxZH1KRbd3vx0XOxcuZF1ga+xWix5bXIaKciDKWIvS6EClhoZv9SmkBaWZm2nlT9o5OPitcn3kow0bj2g7WvHKHwlUbJzBYDAX0vbz7QfAVaFXAZgzIU3x+4XfAbi+8/VNWkVzS9db8HDwIDonmr9j/m7yuKpjyfqUitzt3bm9mzKl9uXxLzG00h1GhY2I+Af0pdCuC/h0adhzTS30a9rrp76BCpTXqWz9r3I8v57QZVLDxiPajla88kcCFRuXkJ9AWmEaWpWWXu16ATC542QA9iftJ6Oo8bthxubEcjD5IGqVuskrXpztnLmz+50A/O/A/1hzcY3FG6k1tSNtbe7ueTcOGgeOpR1jf9J+ix9fXEZM9SkNzaaA5aZ+oDxQidmlXI54pObaGNH2mZu+RVt3HI0ggYqNM9WndPPuZi5gDXEPscj0z+8RvwMwov0IAlwCmjzWO7vfia+TLwn5CTy7/VmmLJ/Cl8e/tEgzuLTCNBLzE1Gr1OaAzZJ8nHy4ocsNgJJVEaJR9GVw3ljo3iyBSgMyKqYlyqBsQNjnloaPR7Qd/sYPeDF7qjYCtHESqNi4S6d9TCaHKlmVxk7/lOnL+DPiTwDzG3RTeTp68tt1vzGv/zzaObYjpSCFDw59wKRfJzUpoCrVl/LBoQ8ACPMIa7ZeLff2vheNSsPuxN2cTDvZLOcQbVzcAShIB0cP6DC84c+v76qfek39VPjwMexB0No3fDyi7QjoDSHDlGnJA19ZezQNIoGKjaspUDHVqTR2+mdv0l6S8pNws3djQocJTR+okbejN3P7zWXDzRv4v9H/R3fv7hSVFfHSjpdIyEto8PEKSguYv2k+v1/4HbVKzQN9H7DYWC8V5BrEtE7TAMmqiEY6Z1zi3uVK0Ng1/Pl1NXxr0NSPsZjW3hUG3dvwsYi2Z/hDyuX+r5TGb62EBCo2rEhXxNmMswD09+tf6b4QtxB6tuuJ3qBnY/TGBh/bVEQ7rdM0HDQOTR1qFfYae67rfB0/Xf0TfX37kluay4LtCxq0Iii1IJVZ62axM34njhpH3h//PlM7TbX4WCu6r/d9APwT8w8Xsy/W+thDyYdYeX6lFN+KcmcbuSzZxDT1U5ILen3l+0rywbQhaX0yKl2ngE83uPK1mjdBFJeX7teCe7DSX+fEb9YeTb1JoGLDTqafRGfQ4evkS3uXql0op4Qqfwz/ivirQcfNKclhU4yyseGNXW5s+kBroVVreWvMWzhrnTmUcoivT3xdr+ddzLrI3Wvu5nTGabwdvfl68tcWzfzUpItXFyaETMCAga+P1zzWsxlneeDvB3h518vmoE9c5jKjIPW00r+kyxWNO4Zprx9QuohWlJNofIwbOLrXfSzPEHhkHwyZ07ixiLZHo4Vhxqz0nk+glXzIkkDFhlWc9qlu6fA1YdegUWk4knqEyOzIeh93XeQ6isuK6eLZhZ7telpsvDUJcQvhheEvALD4yGKOpx6v9fFFuiIe2vgQCfkJdHTvyA9Tf6CPr2WXJNdmTh/lD/uqi6vYErulyv15JXk8ufVJisuKAVh4YCHphektNj5ho0yrfToMB2fvxh3DzkkJdKDq9I952qce2RQhajLwHrBzhuQTELXd2qOpFwlUbFjFjrTV8XX2ZVTQKAD+uPBHvY9reuwNXW5oUu+Uhrg27FqmhE5BZ9Dx3PbnKCgtqPGxS04tISE/gQCXAL6f+j0h7iEtMkaTvr59ub7z9ZQZynhyy5PsSdxjvs9gMPDa7teIzokmwCWArl5dySnJ4b/7/9uiYxQ2pigbDn6nXG/stA8oy4drWvnTkEJaIWri5AX9lVYS7PnEumOpJwlUbFSlRm9+1QcqUL5i56+Iv+pV/xGbG8uxtGOoVWquDrvaImOtD5VKxYvDXyTAJYCY3Bje2vdWtY9LKUgxF7I+MfCJJm8R0FivjnyViSETKdGX8OimRzmScgSAn8/+zLqodWhVWt4Z+w6vjXwNtUrNmsg17Ijf0aJj3JO4h21x21r0nJet2H1weCnoiqveV5ABS66HlJPKap/eTdws1ByoXLLypyGFtELUZthc5fLsWkiPsO5Y6kECFRsVnxdPelE6WrW21umZ8cHj8XTwJKUwhV0Ju+o8rmk587CAYRbbL6e+PBw8+M/o/6BCxcoLK1l6emmVxyw6tIhCXSF9ffs2e+FsbbRqLe+Me4cR7UdQqCvk4Y0Ps/L8SnPm5IlBT9Dfrz+9fXqbG929seeNWjNFlrT83HLu33A/8/6Zx/a41pG+bbWyYuG76+CPh+HT0RBZ4d87LxW+uxYSDiv768xcBR5NDCRqaqMvGRVhKT7hEH4VYIB9n1t7NHWSQMVGmbIpPbx71Loqx05jZ86M1Keoc03kGgCrBQFDAobw6EBlv5G3971tLuoFpXjY1NvlmSHPtNi0VE3sNfa8P+F9BvoNJLc0l5d3vUypvpSJIROZ0XOG+XHzB8wnwCWA+Lx4Pj36abOPa23kWl7b/Zr5+1d3v0pOSQ17w4imW/886AqV62nn4LtrYMWDkHQcvr1amet39YdZq6F936afT6Z+REswLVU+/IMydWnDJFCpRWJeotXOXVP/lOqYpn82x26utQvshcwLnM88j1atZWKHiRYZZ2PM7j2bW7reggEDz257lmOpxzAYDPx3338xYODqsKvr9bpbgrOdMx9d8ZE5qxXkGsS/R/+7UhDlbOfMC8OUYuElp5ZwJuNMs41nS+wWnt/+PAYMTA+fTge3DqQUpPDO/nea7ZyXtQv/wOk/lQLXe9caV9Co4NgyJbuSdhbcAmHWGvDrYZlzmlb+XLrfj0z9CEsKmwC+3ZWfs6M/W3s0tZJApQZ/R//NtJXTmnU34NqYVsb09a37E1p37+509+5Oqb6U1RdX1/i4dVFKj4fRgaPxcPCwzEAbQaVS8fyw5xkTNIaisiLmb5rPtye/5VDKIRw1jjw+8HGrja06bvZufH7l5zw1+Cm+mvwV7vZVl4aODxnPVR2vosxQxvsH32+WcexJ3MOTW55EZ9AxrdM0Xh7+Mv8e9W9UqPj9wu9Sr2JpumJY+4xyfdiD0HEkXP0uzPkHAoy/l54d4N41Dd98sDaSUREtQaUqbwR45AfrjqUOEqjUYEf8DnR6Hf87+D/mbJjTotmVkrISzmYqjd7qu1OwKatS0/SPwWAwByrWrP0w0aq1LBy3kB7ePcgoyuC9g+8BMKv3LIvsO2RpHg4ezOw1kyDXmj/Nzh8wH4C9iXstPhVzMu0kj256lBJ9CRNCJvDG6DfQqDUM9B/I3T3vBuC1Xa9ZZF8lYbT7Y0i/AC5+MP658tuDB8H9m+Hu5fDAVvDuZNnzmgKV/NTy20qLlCZdIIGKsJw+t4DaDhKPQtIJa4+mRhKo1ODVEa/y+sjXcdI6cSD5ADf9eRNrLq5pkXOfyzxHqb4UTwfPWt8YK5rWaRpatZbTGafN3WwrOpVxiuicaBw1jowPGW/hETeOs50ziyctJtBF+cPr5+THvb1ab6vvUI9QwjzC0Bl07Iiz3AqgkrISnt/xPIW6Qoa3H87CcQuxU5e3Z58/YD4d3TuSUpgiy6QtJTsOthmn0676t7KapyKNFrpMany/lNoEDVIuD/8ApcbamFzjByWtk7K8VAhLcGkH3YwfXI9UXdxgKyRQqYFKpeLG8BtZfu1ycwv4Z7c/y4s7XkRv0Nd9gCY4nqZM+/T26V3vglIvRy8mhCidW6vLqqyLVLIp40LGNdumfo3h4+TDJ1d+whUdruD/xvyfTY2tMUz/B9U1imusr098zcXsi3g7erNw3ELsNZU3l3PSOpmngP6M+NP8fy2aYP0LUFoAHUZA39ta9tz97wSPECU42W/cPK7itI+Vi8xFGzNAychy7GfQlVh3LDWQQKUOIe4hfDflOx7u9zAalYY/Iv5ga+zWZj3niTQlBVffaR8T0/TPqourSMpPMt+uN+jLp31CrT/tc6kwjzDen/A+w9s3YrdZG2Nq8789fjulZaVNPl5kdiSfH1OWDz439Lkaa4sG+A3gnp73APDMtmf45OgnzR5Qt1lRO+DU70oB7bSFLR8YaB1gnLE2Zsd7Sj8VqU8RzaXzFcpO2wXpcH69tUdTLQlU6kGr1vJQ/4eY1WsWoOys25wb0VXMqDTEyMCRdHDrQFZxFnetucs8BXQk5QhJ+Um42rkyOni0xccryvXx6UM7x3bkleaxP3l/k46lN+h5bfdrlOpLGR002ry3U00eG/QYt3W7DQMGFh9ZzGObHiO3pIZdeEXNTv6uXPa/EwIa9jtoMf3uBO8w5c1j7yey4kc0H40W+hmzhodtc/pHApUGuLvn3ThoHDiWdoz9SU17E6pJbkmued+ehgYqWrWWL676gjCPMFIKUpi1bhZ7EvewNlLZen5ih4nNslOyKKdWqc01QE2d/vn9wu8cTD6Ik9aJF4e/WOc0oJ3ajheHv8i/R/0be7U9W+K2cMfqO7iQeaFJ47jsxO5VLhu7saAlaLQw/nnl+s4PIeWUcl0yKqI59DdO/5zfALnJ1h1LNSRQaQAfJx/zbsOmNu+WdjL9JKD06/B2bHihXqBrIEumLmGQ/yDySvN46O+HzE3UbGG1z+XAFKhsjt3c6MxbWmEa7x54F4B5/efVu6galCnAJVOXEOASQHRONHeuuVO619ZXcZ7SwA0gxMpTkb1vAr+eUJwNx35RbpNARTQH364QPAQMZUqPIBsjgUoDzeo9C41Kw+7E3ZxMO2nx4ze2PqUiDwcPPr/yc/MmgAW6ArwcvBjWfpilhilqMbz9cJy0TiTlJzW6+dt/9/+XnJIcenj34K4edzX4+b18evHzNT8zNGAohbpC5m+az/Jzyxs1lstK/EEw6MGjA7i3t+5Y1GqYYMyqYAx4ZepHNJf+xr8zh5dCM5Y2NIYEKg0U5BrEtE7TgObJqpgavTV02udS9hp73h77tnm57w3hN1Ra0iqaj6PWkRHtRwCNm/45k3GGtZFrUavUvDryVbRqbaPG4e3ozaeTPuW6ztdRZijj1d2v8tHhj5q1vqrVM037hAy17jhMul8D7fuXfy8ZFdFcek9Xlr+nnVUCdhsigUojzO4zG4B/Yv7hYtZFix7bEhkVE7VKzb8G/4vNt262uW6vbV3F6Z+G+unMTwBc1fGqWjekrA87jR1vjHqDB/s+CMBnxz7jxZ0vWmRFUptkDlRsJPuoUsHEl8q/l4yKaC6OHtDzOuX6ga9tKqsigUojdPbszMSQiRgw8NWJryx23OT8ZFIKU9CoNHT37m6x4/o4+aBWyX91SxoXMg61Ss3pjNMN6mqcXZxt3gbhzh53WmQsKpWKRwY8wqsjXkWj0vBnxJ88v+N5yaxcSq+HWGORvK1kVEAp6h37DIxfAK6+1h6NaMtMPVWOLIUvJ8HF5m3FUV/y7tVIc/rMAWDNxTUWa69vyqZ08ezS6hufXe68Hb3p79sfaFhWZeX5lRSXFdPdu7v5+ZZyU9ebWDRxEVqVlnVR6/gj4g+LHr/VSzurFK7auYC/lZYlV0elgokvVG7jL0RzCB2jZPDsnCH+ACy5DpZcb/WpIAlUGqmPbx+GtR+GzqDj1d2v1tivwmAwcDr9NAWlBXUes7H9U4Rtaugy5TJ9GcvOKhX3d3S/o95diRtibPBYHhnwCABv7n2TmJwYi5+j1TJN+wQPUpYHC3G5Ualg7FPw6BEY+oCyD9DFLfDFRPj7ZasNSwKVJpg/YD72ant2Jeyqtl9FdE40D/79ILeuupX71t+HTq+r9XiWrE8R1mdqp78/eX+9Gq/tTNhJfF487vbuzbqUfFavWQwJGEKBroAF2xdQqpd6FQBibKw+RQhrcfOHae/A/APQ7w5AZdXfCwlUmqCfb78q/SrWR62nuKyYT458wvQ/prM7cTeg9EdZerrmrn96g54T6UqgIhmVtiHUI5ROHp3Q6XXsTthd5+N/PPMjANPDp+OkdWq2cWnUGv4z+j+42btxLO0Ynx39rNnO1arYWiGtENbmFQo3fgrzD0K3aVYbhgQqTWTqVzEsYBiFukKe2voUU5dPZfHRxZToSxjRfgQP93sYgI+PfEx8Xny1x4nKjiK/NB9HjSOdPTu35EsQzci0THlf0r5aHxedE83O+J2oUHFrt1ubfVwBLgG8MuIVAL44/gWHkg81+zltWn4aZEQo14MHW3csQtiadp2tuhmmBCoW4O3ozadXfmreCyi1MBUfJx/eGfsOn135GXP7zWWw/2AKdYX8e8+/q11tYcqm9GzXs9F9M4TtGRqgrB6pa8uFZWeU2pQxwWMIcQtp9nEBTA6dzPWdr0dv0LNg+wLyS/Nb5Lw2KdYYSPr2ACcv645FCFGJBCoWolVreXLwk3w08SMeG/gYf97wJ1M6TUGlUqFSqXh5xMvYqe3YGb/TvJNxRZZq9CZsyyD/QahQcTH7ImmFadU+pqC0gD8uKCtw7uh+R0sOjwXDFhDkGkRCfgIboja06Lltiq01ehNCmEmgYmHjQsYxp88c3OzdKt3eyaMT9/e9H4C39r1FdnF2pfulkLZt8nT0pKtXVwAOJB2o9jGrLq4itzSXDm4dGBk4siWHh4udC1eHXQ1grqe6LEl9ihA2SwKVFjS792zCPMLIKMrgv/v/y4m0E2yL28bK8ys5k6nsCSMZlbZnSMAQoOY6FVM25dZut1qlMZ+pjmZv4l70Bn2Ln9/qdCUQb6zRkUBFCJtj9UBl9erVDBs2DCcnJ7y8vLjhhhusPaRmY6+xNxcw/hnxJ3esvoN5/8zj5V0vo9Pr8Hb0btAuuaJ1qK1OJS43jmNpx1Cr1ObMRkvr59sPZ60zGUUZnM04a5UxWFXSMSgrBidvpWhQCGFTrFq1uXz5cu6//37+85//MHHiRHQ6HSdOnLDmkJrdQP+BzO49m6Wnl+Lu4E47x3a0c2qHt6M3V4dd3SxNvoR1DfQfiAoVUTlRpBSk4OfsZ77PVK80NGAoPk4+VhmfncaOIQFD2Bq3ld2Ju+nRrodVxmE1Fad95PdPCJtjtUBFp9Px2GOP8c477zB79mzz7T17Nm0Tttbg8UGP8/igx609DNFCPBw86O7dndMZp9mftL9S5mRt5FqAZm3wVh8jAkcogUrCbu7rfZ9Vx9LiYvYolx1k2kcIW2S1qZ9Dhw4RHx+PWq1mwIABtG/fnqlTp9aZUSkuLiYnJ6fSlxC2rrrpn4isCM5lnkOr1nJFhyusNTRACVQADiUfokhXZNWxtKiyUimkFcLGWS1QuXjxIgCvvvoqL774IqtWrcLLy4vx48eTkZFR4/PefPNNPDw8zF8hIS3Tc0KIphjavmqgYpr2GRU4Cg8HD6uMy6STeyf8nf0p0ZdcXs3fNr4Kecng6AmBA6w9GiFENSweqDz33HPm3iE1fZ05cwa9Xlld8MILL3DTTTcxaNAgvvnmG1QqFb/++muNx1+wYAHZ2dnmr9jYWEu/BCEsboDfANQqNTG5MSTlJ2EwGMzTPlM6TbHy6EClUpmzKpfNMuXTq2D3R8r16z8Gu+bbtkAI0XgWr1F58sknmTVrVq2PCQsLIzExEahck+Lg4EBYWBgxMTXv6Org4ICDg4NFxipES3Gzd6Ond09OpJ9gf9J+Ont2JjonGkeNIxNDJlp7eACMDBzJ7xd+Z1fCLp7kSWsPp3llRMLvytYWjHgEelxj3fEIIWpk8UDF19cXX1/fOh83aNAgHBwcOHv2LKNHjwagtLSUqKgoOnbsaOlhCWF1Q9oPMQcq5zPPAzA2eCzOds5WHpliWHulRuNc5jnSCtOstgqp2ZUWwa8zoTgbgofCpFetPSIhRC2sVqPi7u7O3LlzeeWVV9iwYQNnz57loYceAuCWW26x1rCEaDZD/Msbv5nqU6y92qcib0dvengrS5P3JO6x8mia0YYXIPGo0jfllm9AY2ftEQkhamHVPirvvPMOWq2WGTNmUFhYyLBhw9i0aRNeXrIpmGh7BvoPRKPSmHfQdrFzYXTQaCuPqrIRgSM4nXGa3Qm7uSasDU6HnPoT9n+pXJ/+BXgEW3c8Qog6WbUzrZ2dHQsXLiQ5OZmcnBz+/vtvevXqZc0hCdFsXOxc6OVT/vM9MWQijlpHK46oKnNBbcLuanf5btX0etj0b+X6qMchfJJVhyOEqB+rt9AX4nJi6qcCtjXtYzLAbwAOGgdSC1OJyIqw9nAs68wqSDsHjh4w9ilrj0YIUU8SqAjRgkwFq54OngwPHG7l0VTloHFgsP9goI0tUzYYYPu7yvWhD4CDW+2PF0LYDAlUhGhBwwKG8dzQ5/jf+P9hp7bNIk7T9M/OhJ1WHokFXdwMiUdA6wTD5lp7NEKIBpBARYgWpFKpuKvHXQwOGGztodRoTNAYAPYm7CWtMM3Ko1EUlBYQm9OE5o7b31MuB80Clza67FqINkoCFSFEJWGeYfTz7YfOoGPl+ZXWHg4AT297mmkrp/HCjhfIK8lr2JNj90PUdlDbwchHmmeAQohmI4GKEKKKW7vdCsBv536jTF9m1bEk5SexLW4bAH9G/MnNf93csP2IdhizKX1vk+XIQrRCEqgIIaq4quNVuNu7k5CfYPValXWRSnO8zh6dCXINIj4vnnvX38uiQ4soLSut/cnJp+DsGkAFox9v9rEKISxPAhUhRBWOWkeu73I9AL+erXmT0JawNkrZvPHOHnfy27W/cX3n69Eb9Hxx/Aue3vZ07U/e+b5y2fM68Alv3oEKIZqFBCpCiGrd0lXZymJb/DYS8xKtMobonGhOpZ9Co9IwqeMkXO1deWP0G7w77l20Ki3/xPzD8dTjVZ9oMMDuj+G4Mcga/a+WHbgQwmIkUBFCVKuTRyeGBgxFb9Cz/Pxyq4zBNO0zPHA43o7e5tuvCr2KaWHTAPjy+JeVn1SSD8vnwPrnwaCHIXMgsH9LDVkIYWESqAghanRLNyWrsuL8Ckr1ddSDXMJgMPDqrld5ZtszDX6u6flrI5Vpn6mhU6FMV+n+2b1no0LFpthN5V10My7Cl1fCid9ArYWp78C0hQ0+txDCdkigIoSo0RUhV+Dt6E1qYSpbY7c26LmbYjex/Pxy1kauZcnJJQ0+9/ms80RkR2CntmPi6X/g7Y6w93NlWgdlGfXEDhMB+Pr4V3Dyd/h8PKScBBc/mPkXDHsAVKoGn1sIYTskUBFC1MhOY8f08OkA/HL2l3o/T2/Q89Hhj8zff3L0kwY3bDNN+4wJHIXbsV+hJA/WPg1Lb4bcZADm9JkDwOqLfxG/8j4oyobgofDgNug4skHnE0LYJglUhBC1uin8JlSo2J24m5icmHo9Z13kOi5kXcDNzo2BfgMpLivm9T2v13tH5krTPq6doLQA7F1B6wgXNsInI+Dk7/Q+vZ7hRSWUAd96eMLYp2HWKnBv38hXK4SwNRKoCCFqFewWzOig0QB8evTTOh+v0+tYfHQxADN7zeTfo/6Ng8aBPYl7WHVxVb3OeSLtBHF5cThpnRibZWzj3+NaeGALBPSBgnT4dSb88zpzMjMBWOnpRdqIuaB1aPiLFELYLAlUhBB1mtd/HgB/XfyLE2knan3sXxF/EZ0TjZeDF3f3vJsO7h2Y20/ZCPCd/e+QWZRZ5/lMvVPGh4zH+aLSlZbOV4BfD5jzD4x6DFCBiy9Dp35IH5/eFOtLWHp6aeNfpBDCJkmgIoSoUy+fXlzX+ToA3t73do1TOCVlJeasy+w+s3GxcwGUzEq4VziZxZksPFD7Khy9Qc/6yPUATPUfAcnHARV0nqA8QOsAV74Ojx+HR4+g6ncbc/rcD8CyM8vILclt6ssVQtgQCVSEEPXy6IBHcdI6cST1COuj1lf7mBXnV5CQn4Cvky+3dbvNfLud2o5XR7yKChV/RvzJroRdNZ5nS+wWUgpTcLNzY1S+cQPC9v2q7nrsGQIOroCSeeni2YW80jxzbYsQom2QQEUIUS/+Lv7c1/s+AP538H8U6Yoq3V+kK+LzY58D8EDfB3DUOla6v69vX+7ofgcAz2x7hqjsqCrniMmJ4cWdLwJwQ/gN2Ecal0R3uaLWsalVaq4KvQqAA0kHGvbChBA2TQIVIUS9zew1E39nfxLyE/j+1Pfm2y9kXuCJLU+QWphKoEugeUnzpR4f9Dh9fPqQXZzNQxsfIr0w3XxfXkke8zfNJ7ckl76+fXm8/6MQsUm5s3PtgQrAYP/BABxMPljv1UVCCNsngYoQot6ctE48MegJQGldfzT1KC/seIGb/rqJHfE7UKvUPDXkKew19jU+/8OJHxLsGkxcXhzzN82nUFeI3qBnwY4FXMy+iJ+TH++Pfx/71NPK6h57NwgZWufY+vj0QavWklKYQlxunEVftxDCeiRQEUI0yLRO0+jr05cCXQF3r7mbPyP+RG/QM6nDJFZct4IrO15Z6/PbObVj8aTFeDh4cDztOM9ue5aPDn/Eltgt2KvteX/C+/g6+8KFf5QndBoLGrs6x+WodaSPTx8ADiTL9I8QbYUEKkKIBlGpVDwz9Bnz98PbD+enq3/ifxP+R2fPzvU6RiePTiyasAh7tT2bYzfzxfEvAHhl5Cv08VWCDfO0T5eJ9R7bIP9BABxKOVTv51wqpSCFledX1msZtRCi+WmtPQAhROvTz7cf3035DrVKTX+//o06xkD/gfxnzH94autTAMzoOcO8BJqiHIjdq1yvR32KySD/QXx5/EsOJh9s8HhOpp/k+1Pfsz5yPTqDju7e3VkydQlOWqcGH0sIYTkSqAghGmWg/8AmH2Ny6GQcNA5EZUdxd8+7y++I2g56HXiHgXeneh+vv29/1Co1sbmxJOcn4+/iX+dzDiQd4MPDH1bKwtip7TiTcYaXd77Mf8f+F5VsbCiE1cjUjxDCqsaHjGdW71lo1RU+N5nqUxqQTQFwtXelu3d3oH7TP1lFWTy08SEOpRxCq9JyddjVLLt6GV9c9QValZZ1Uev46sRXDRqDEMKyJFARQtieCGOgUkf/lOoM9FMyPfWZ/tkYs5GisiLCPMJYd9M63hrzFr18ejHIfxALhi0AYNGhRWyL29bgcQghLEMCFSGEbUmPgMwoUNtB6JgGP71iP5W6rItcB8B1na+rMk10a7dbubXrrRgw8Oy2Z7mYfbHBYxFCNJ0EKkII21GUA8tnK9dDR5lb5DfEAP8BAFzIulDryp3UglT2Je0DYEqnKdU+5rmhzzHQbyB5pXk8tukxisuKGzweIUTTSKAihLANJQXw422QcBic28HU/zbqMN6O3nT2UJZJ11ansiF6AwYM9PXtS5BrULWPsdPY8d7492jn2I6onCh2xu9s1JiEEI0ngYoQoip9Gej1LXc+XTH8fDfE7AIHd7h7Bfh2a/ThTP1Uapv+MW1eODV0aq3HaufUjsmhkwHYGre10WMSQjSOLE8WQlSWeg6+mQKFWcqOxS6+ypdnCAy5H9r3tez5ynTKdE/EP2DnDHf9CoH9m3TIQf6D+OXcLzUGKvF58RxNPYoKlTkIqc24kHH8eOZHtsVtQ2/Qo1bJZzwhWooEKkKIcroSJWgoMG4WmJesfJkcWgK9psPEF6Fd/brQVpJyGn5/GEryym8rLYTsWNDYw+0/QofhTXsNlPd4OZNxhrySPFztK9e6rI9aD8CQgCFKu/46DPYfjLPWmbTCNE6ln6K3T+8mj1EIUT8SqAghym1+A5KOgZMXzPwLDHrIS4X8VLiwEU78BidXwKk/YMDdMGgWuAWAsw9oq9+IsJLt70JCNXUjai3c8i10nmCRlxHgEmDe+PBI6hFGB42udL9p2qemItpL2WvsGRU0ir+j/2ZL7BYJVIRoQRKoCCEUkdtg5yLl+nUfQkCfyvf3vwNGPw7//BvOr4dD3ylfJo6e4B4E134AIUOqHr8wC07/pVy/4VPw7FB+n3cncA+04ItRpn/i8uI4mHywUqByMfsiZzLOoFVpmdRhUr2PNy54HH9H/822uG08MuARi45VCFEzmWgVQkBBBqx4EDDAwJnQ49rqHxfQB+76Be5dp3SNdQ0AlUa5rygLUk7Cxleqf+7JFaArAt8e0O92Zfmx6cvCQQqUF9TujN9Joa7QfPv6SGXaZ3jgcLwcvep9vNFBo1Gh4nTGaZLykyw7WCFEjSRQEeJyZzDAqschNwHadYEpb9b9nI4jYMYKeOosvJQGz0TCnH+UJm3ROyGumiLWw0uVywF3QQvsnTO0/VA0Kg2nM05z9Yqr+fnMz5SWlbI2Spn2mdZpWoOO186pHX19lUJi6VQrRMuRQEWIy03KGTizGg58A1vfgZUPKjUnai1M/wLsXRp2PLUanL0heDD0uUW5bdeiyo9JPQvxB5TsS9/bLPM66hDkGsTCcQsJcg0itTCVN/a+wdQVU4nMjsRebc+EkIbXw4wLHgfIMmUhWpLUqAhxObm4FZZcV/19E16AoCbuiDxyPhz9EU7/CRmR5TsfH/5Buew6GVz9mnaOBpjUcRJjg8fy27nf+PzY5yQXKCuYxgaPrbISqD7GhYxj0eFF7E3cS6GuECetk8XGmlaYxp7EPexJ2ENcXhzPD3uerl5dLXZ8IVorCVSEuJyYil+9QsGvZ3mflIC+0KOGAKYh/HtCl0nKCqE9i2HaO0qflGM/K/cPuLvp52gge409d/a4kxu63MDS00vZnbibuf3mNupY4Z7hBLoEkpCfwN7EvYwPGW++b3vcdo6kHmFWr1m42bvV63gGg4GvT3zNqouruJB1odJ935/6nn+P+nejxilEWyKBihCXi+JcOLNGuX7z1xA0qHnOM3K+Eqgc/gHGL4DYfUovFhdfCL+qec5ZD852ztzf937u73t/o4+hUqkYGzyWZWeXsSV2C+NDxmMwGFhyagkLDywEYEvsFhZfsbjKJofVWXlhJe8fel85Niq6e3eno3tH1kWtY3fCbgwGA6oWqOcRbc+ZjDNEZEXQz7cfwW7B1h5Ok0igIsTl4sxq0BWCd2cIbOIUT206jVMyNEnHYP9XkHRUub3vbaCxa77ztpDxIeNZdnYZ2+K2odPreGf/O/x45kcAnLROnMs8x11r7mLxpMW1Tt2kFaaZg5vZvWczq9csPB09KdIVsSlmE8kFyURmRxLmGdYir0u0DRezLvLRkY/4O/pv821BrkEMbz+c4YHDGRYwrEGr3WyBBCpCXC6O/aJc9r21eVfdqFQw8lFYMQf2fqLsiAzQ/67mO2cLGhIwBCetE6mFqcxcN5NjqccAeGrwU0zqOImHNz7MxeyLzFw7k/cnvM+w9sOqPc5/9/+X3JJcenj34JEBj6BVK3+OHbWODPQfyJ7EPexO3C2BiqiXxLxEFh9dzJ8Rf6I36FGhoqtXVyKyIojPi2f5+eUsP78cgO7e3RkWMIxh7YcxyH8QznbOVh597WTVT03OrlU2STu7ztojEaLp8lLg4mblumllTnPqdQN4hCit+PWlEDhAqV9pA+w19owMHAnAsdRj2KnteGfcO8zsNZMg1yCWTF3CIP9B5JXmMXfjXFaeX4nBYKh0jO1x21kbuRa1Ss2rI181BykmIwJHALA7YXfLvCjRqh1PPc61v1/L7xd+R2/QMyFkAsuvW85v1/3Gjjt28PEVHzOj5wy6eHYBlGmh7059x8P/PMykXydxJOWIdV9AHSRQqUnUDqWL5smV1h6JEE13YoXSDj9oUOP26GkojR0Mf6j8+zaSTTG5osMVALjbu/PFVV8wJbS8Fb+HgwefXfkZU0KnoNPreHnXyzy97Wmyi7MBKCgt4I09bwBwd4+76dmuagBnCoT2Je2jtKy0uV+OaOU+PvoxxWXF9PXpyw/TfmDRxEWEe4UD4GLnwtjgsTwz5BlWXr+Szbdu5u0xbzM9fDoBLgHkluby/I7nKSgtsPKrqJkEKjXpfo1yeW4tyB8K0dodN0779Lm15c458B6lc62TN/S5ueXO2wKmdZrGf0b/h1+u/cXcAbciB40Db499m/kD5qNVaVkftZ7pf05nT+IeFh9ZTEJ+AoEugczrP6/a43f16oq3ozeFukKOph5t7pcjWrHzmefZGb8TtUrNW2Pfop9vv1of7+Pkw7Swabw28jVWXLcCf2d/YnNjWXR4Ua3PsyYJVGoSMlRZpVCUrWRXhGit0iMg/qDSbK339JY7r4MbzN0BD+9RNjlsQzRqDdd2vpYg16AaH6NWqXmg7wN8P+17Qt1DSSlI4f4N9/P96e8BeGH4CzXWBqhVanNty+5Emf651NbYrcz/Zz7J+cl1P7iN+/bktwBM6jCJELeQBj3Xzd6N10a+BsDS00vZn7Tf0sOzCAlUaqLWQDdji+0zq6w7FiGa4vivymXY+BZttgaAqy+41b1Mty3r7dObn6/5mVu7KtksvUHP1NCpjA0eW+vzTNM/UqdS1bcnv2VL3BY+O/aZtYdiVcn5yayJVFoO3Nv73kYdY1TQKG4KvwmAl3e+bJNTQFYNVM6dO8f111+Pj48P7u7ujB49ms2bN1tzSJWZpn/OrAa93rpjEaIxDIbKq32EVTjbOfPSiJf4ZNIn3NvrXl4Y/kKdzxnRXimoPZl+0lzfYg3J+cn8a8u/WHxksdXGcKmEvAQA/or4i6yiLOsO5hIGg4GEvAS2xm5lXdQ69Ibme+/48cyP6PQ6BvkPordP70Yf56nBTxHgEkBcXpy5r48tsWqgcs0116DT6di0aRMHDx6kX79+XHPNNSQl2cjOpGHjwN4NchMh4ZC1RyNEZcd/g92LoaSWT0AJhyAjArRO0P3qlhubqNbooNH8a/C/8HDwqPOx/i7+hHmEoTfo2Ze0rwVGV9XB5IPctuo2/o7+m0+PfmoTUy06vc68FUJRWRG/nf/NyiOCIl0RHxz6gBlrZjDyp5FMXj6ZRzY9wtNbn+anMz81yznzS/P59aySLZ3Va1aTjuVq78prI5QpoJ/O/MS2uG3NGmA1lNUClbS0NM6fP89zzz1H3759CQ8P56233qKgoIATJ05Ya1iVaR0g/Erl+um/rDsWISpKPgXLZ8P6BbB4OJzbUPUx+jI4aGyZ332aUjMiWhXT9M+uhF0tel6DwcDS00uZs34O6UXpym0YWB+1vkXHUZ2UghTKDGXm73868xOleusteCjTl/Hc9uf48viXHEk9Ql5pHlq11ly/9Pmxz5tlOmX5ueXklubSyaNTndOI9TEyaCQ3d1WK3uf9M48RP47grjV38equV1l6einnMs81+RyNZbVApV27dnTr1o0lS5aQn5+PTqfjs88+w8/Pj0GDam7tXVxcTE5OTqWvZtXDNP2zSkmjC2ELtv3XeEUFWdHw4y3w8wzIjof4Q7DueXivZ/nePi252kdYjDX6qRTqCnlhxwu8te8tdAYdU0On8vjAxwFYF2X9vlKmaZ8AlwDaObYjpSCFjdEbG3Usg8FAbG5slT43DbHwwEL+ifkHO7UdL494mRXXrWDfnfv468a/CHELIaMow9y52FJK9aXmouyZPWeiVlnmrfzJQU8yNngsdmo7CnQFHEs9xvLzy3lr31tsiKrmw1ALsVqgolKp2LhxI4cPH8bNzQ1HR0fee+891q1bh5dXzSsE3nzzTTw8PMxfISENq3JusC5XgsYe0i8oW9ULYW0pZ+Dk7//f3n2HR1WlDxz/zmTSQxJISEIggYRepYQSQDrSy+qCIioWLIhrXRRFf+6uC7iyWNa6siuiolgBQRAinZUaOoQaSCgJLYT0Ovf3x8lMEkiZSaYk4f08zzwzzL137uGIyTvnvOc96vUja9XeOjoXtWPxux1gwUDY/iFkJIOHH/R93ql77IiqiwqOwqA3cD7jPGfTzjrknu/EvsOK+BW46FyYETWDf/T7B+NajEOv03PwykHOpjumHeVJykwCoGm9ptzd5m4Avor7qkqftfTkUkb+NJJ39rxTpeu/PPKl+d5z+s5hQqsJtKzfElcXV1z1rky7TdUSWnhoIel56VW6R1nWnllLcmYyAR4BjG4+2maf6+Pmw4eDP2TH5B0sG7eMef3m8WjHRxkQNoDOQZ1tdh9r2TxQmTlzJjqdrsLH0aNH0TSN6dOnExQUxJYtW9i5cyfjx49nzJgxJCUllfv5L7/8MtevXzc/zp618/80Hr5qtQTAUZn+ETXA5nmABm3HqGX0d/wdHt8MYT1VUTeDJ7S/E+75Bv58Aoa8DnpZ4Fcbebl60blhZ8Bx0z/bk7YD8Lc+f+OB9g+g0+kI9Ayke0h3AKdP/5zPOA9AqE8oE1tNxFXvyoHLB6pUb8aUP/Ll4S+Jvx5v1bUxCTHM2zUPgOe7Pc/wiOE3nTMyYiSRfpGk5aXxxZEvrG5fWYyakYWHFgJwb9t7cXdxt8nnluSqd6W5f3OGRwzn6a5P8/6g9+nbuK/N72Mpm//0euGFF4iLi6vwERkZyfr161m5ciVLliyhT58+dO3alY8++ghPT08WLVpU7ue7u7vj6+tb6mF3ptU/cbJMWTjZ5eNwSO3XQb8Xi98P6QAP/QrTd8KMEzBhocpLMdj+h5hwLPP0jwPqqeQU5JCQlqDuW7TqyGRkhCrXsPr0aru3oyKmEZVGPo0I8AxgVKRKEl98ZLFVn3Ms5RhHU44CUKAVMH/3fIuuyynIISYhhpe3vIyGxt2t7y43mdVF72Iu6vflkS+5lnPNqjaWZcWpFRy7dgwfVx/zkve6zuaBSsOGDWnTpk2FDzc3N7KyVHKR/oZvenq9HmNNWwrcegSgg6R9kOrcYU9xi9vyT0CD1qOgUafSx/R6aNhakmbrGFPAsDNpJ4XGwkrOrp5TqacwakYaeDQg0DOw1LHB4YMx6A0cv3acU6mn7NqOiphyVEK9QwG1DQHA2gQ1HWKplfHqi2eHgA4YdAY2n9vM7+fLHrU6mnKU/xz8D1PXTKXPN314fuPz5BbmMqDJAGb2ULMI5RnSdAhtG7QlMz+ThYcXWty+smTlZ/GvPaqC7GOdHsPfw79an1dbOG08ODo6mvr16zNlyhT279/P8ePHmTFjBqdPn2bUqBq2jNInCMJ7qdfHVjm3LeLWdeVkcfG2/jOc2xbhMO0C2uGmdyM9P50LmRfsei/Tyo6W9Vve9MvXz92PPqF9AOeOqphGVEJ9VKDSukFruod0p1ArtDhptcBYYA5UpnaayqS2kwCYt3seBcYC83lGzcj83fOZsGIC7+15jx3JO8gz5hHkFcTdre/mH/3+cdOGkjfS6/Q81eUpAL6J+4bLWZet+wuX8Nmhz7iUfYkmPk2Y3LZu7Z9VEacFKoGBgfz6669kZGQwaNAgoqKi2Lp1K8uXL+e22yreq8ApzNM/kqcinGTLfJWD0mq42o1Y3BJc9C40qdcEwO6JrKZApVX9VmUeN+Vh/Hrm12qtlKkqo2YsHlEpClSgeFRl4aGF/OX3v1RaIG9H0g6uZF/Bz92Pfo378Xinx/F39+dk6kl+OvETALmFuby0+SVzifoBTQbwSs9XWD5+Ob/98Tde7fVquVsg3Oj2xrfTqWEncgpzWHBwgbV/bQCSMpLMbXkh6gXcXNyq9Dm1kVMz7KKiolizZg1Xr14lLS2Nbdu2MWLECGc2qXymZcoJv0PmFee2Rdx6UuLhwLfqdf8XKz5X1Dnh9cIB7L7yp7JAZVDYIDxcPEhISyAuJc6ubSnL1eyr5Bvz0ev0BHkVbwcxIGwAd7dWK4B+PPEjY5aOYfnJ5eUGUz+f+hmAEc1G4Oriip+7H092fhKAD/Z+wLn0czwe8zi/nvkVg87AnL5zeH/w+0xqM4lIv8gKp3rKotPp+FOXPwHw7bFv2ZlkfQG/d/e8S25hLlHBUebdu28VshTAUvWbqW+xWmFxMqMQjmA0wi8vqH97LYZC4/LrDIm6yREjKpqmVRqoeLl6mYuL/Xra8TVVTFNfwV7BuOpdze/rdXpe7fUqi4YvooV/C67lXuPV/73KQ2seuilvJTM/k/WJ6wEY23ys+f0JrSYQ6RfJtdxrjF8+ntiLsfi4+vDx0I8Z03xMtdveq1EvxjUfh1Ez8uLmF7mSbfkX3v2X97Pq9Cp06JjRfYbVgVJtJ4GKNW5T85jst09JZCHKtGU+nFqvlh0P/ZuzWyOcINxXjagkpifa7R6Xsi6RmpuKi86F5v7Nyz1vRIQa9V59ZrXDy6ybpn0aeTcq83jX4K58N+Y7nuv2HJ4GT2IvxjJ17dRSQUFMQgw5hTk0821Wan8cg97AjO4q9yu3MJcgryAWjVhEr0a9bNb+Wb1m0cK/BVdzrvLi5hctSo7WNI23dqoCj+NajKNdQDubtae2kEDFGh3uAr0BLuxVRbfErePsLngrErZ/7Nj7xm+CjXPU61HzIfjW+yElIKyeKmxpzxEV02hKM99mFdbm6Nu4L96u3iRnJlepdkl1lJWfciNXvSsPd3iYH8f+SKh3KAlpCTwW85g5b2XFKZVnOLb52JtGJvo27stDHR5iQNgAFo9cXO7IUlV5Gjx5e8DbeBm82JW8i4/2V77R49qEtRy4cgBPgydPd3napu2pLSRQsYZ3oBp6BziwxLltEY61/SPIugprX4WkA465Z3oy/DhVJdB2vg+63DpZ/qI0U47KufRzdktirWzax8TD4MHAsIEAbDy70S5tKY+5hko5IyolhdUL4z93/IeGng05ce0ET8Q8wclrJ80bPI6OLLui6/Pdnuf9Qe8T4h1iu4aXEOEXwevRrwNqH6Ct57dWeL5phdXktpNp6NXQLm2q6SRQsdZt96jnA9+pTd9E3ZeXBceLqnEaC2DZNCjIs+89Cwvgh0cg8xIEtYeR8+x7P1GjNfJphIvOhZzCHC5nV315a0XMgUqDykcRooKjADh85bBV93hlyyvcvfJuMvMzrW8gxVVpTRv+VSbMN4xPh36Kv7s/h64e4v7V9wPQPaQ7jXwqD3bsZWTkSHPy78tbXi63/kteYZ55n6ch4UMc1r6aRgIVa7UarvZPSTsPZ7Y4uzXCEU7GQH4m1AsFrwC4eKio8JodbZwLCVvBzQcmLgI3y5ZBirrJVe9qHkVITLNPnoqlIyqAObfj0NVDFuepXMi4wIr4FRy5eoQNZzdUqY1JGcVVaS3Von4LPhn6CT6uPmTkZwAwJrL6ybHVNaP7DNo2aEtqbmq55fX3XNpDVkEWAR4BtA1o6+AW1hwSqFjL1UPtowKw/1vntkU4xuGl6rnjXTCyKEDZ/E+4sM8+97t6SiXQAoz9FwS2tM99RK1izzyVvMI8zlw/A1gWqDT3b46nwZPM/EzzdZUpGZyYVt1YQ9M086ofU1VaS7UPaM+Hgz/E0+CJn7sfQ5sOtfr+tubu4m7etHDN6TVlBnybz20GVO6MrXZIro1u3b95dZhW/xxZDnlVG8IUtUTJaZ/2f4AOd0K7cWqp8LIn7TMFtPszQFP5UB3usv3ni1rJtPLHHoFK/PV4CrQCfN18CfYKrvR8g95A2wbqG/7BKwctukfJQGXr+a3kFORY1cbU3FSyC7IB60ZUTLoGd+Xn8T/z45gf8XHzsfp6e+jTuA/13OpxKfsSsRdjbzq+5ZwatTctCb9VSaBSFWE9oH6Emg6QjQprtj1fwj9bwfKn4OIR668/sRbys8A/HEK7qvdGzldTQJcOw+a3bNve/BzYV7S5Wveptv1sUavZc0Sl5LSPpTU6OgZ2BCwLVNLy0ohNVr+I67nWI7sg27xLs6VMoymBnoFV3jE4xDuEYO/KAzFHcXNxM+ee3FiXJjEtkTNpZzDoDOaNKW9VEqhUhU5XM2qq5GbA3sWw8R+qINi398NnI2D5dJWMKWDbh5BxEfZ+CR9Hwxfj4fhaVUTNEkeWqed249V/dwCfhmqpMMCWt9VUja0cWQ7Z18C3CbR0/vC0qDlMgYo9aqkcT7E8P8XEnKdy5VCl5249t5UCrYDmfs3NxdPWJa6zqo2m/BRrp31qOtO2BDEJMeQb883vbzmvRlO6BHehntutvdGoBCpV1aloe+34jZBmh43C9n0Db7eHnQugrOWIaUnw2XBY/qSqs7HrPxD3MyT+Dnu/gv2Wbc5Vp10/D5fjAJ3aq0mnh/gN8PUE+GwY5FS8Hwh5maWnfUpq/wdoPkhNAe0pOxGuSnZ/pp67PQh6F9t9rqj1zCMqaWdtvkTZNKLSukFri68xBSrHrh0jtzC3wnNN0z4Dwweay79vPLux1AaAlTGt+Kmohkpt1COkBw08GnAt91qp0vqm/JR+jW/taR+QQKXqGkRAeG9Ag302DgoK8uC31yHtHKz6sxohyc8uPn4pDv4zBC4eBO+G6pdavxkwYl7xdMGGOSq/oq7QNBU0pCVZfs2pooS9xl3hnsXw9D6Ifgrc6sG5nbB4YsU5RuZpn6ZlbwIY9bB63vc1FObffNxaFw/D2e2gc4Gu91f/80SdYiqjn56fXumme9ayZsWPSWOfxtR3r0+BsYBjKcfKPS+/MN9cK2RA2AC6BnfFz92P1NxU9l7aa/H9zDVUnLis2B4MeoM5uXfV6VUAZOVnsSt5FyD5KSCBSvV0UTt2svmfVct/KE/cz2q6wq2eGgXYt1iNAKQmwukt8N9hKogJaAlTf4Mx78GgV6HnYzBsDviFQ3oS7HBwFVV7ivsZvp4In/SBc7stu+ZU0dByi6L6A/WbwrDZ8NAqtcT87HZYMlnlhZTl8DL13H588bRPSa2Gg3eQqnVy3Ab7nuxeqJ7bjIJ69ik2JWovT4MnQZ5qIz5b5qlcyb7C1Zyr6NBVWDr/RjqdzqLpn13Ju8jIzyDQM5COgR0x6A30b9IfsG76x1yVto5N/QCMjBgJqNVQuYW57EjaQb4xn8Y+jYnwi3By65xPApXquG2SGv4vyIbvp6icEVvYWbQNeO8/wf3LVOJm0n74dz/46k7IvQ5hveCRtWqzxJIM7ipoAdj6LmSl2KZNzrb3K/WcdRU+Hw1Hf6n4fGMhnCpaZdD8hp1GG3WCyT+Cq7eaCvrh4ZtHRCqa9jFxcYXO96rX1Z3+yc2A/UXVjk0jNULcIMzX9nkqptGUpr5N8TR4WnWtKaG2okDFNO3Tv0l/8xJb0/TP+sT1Fk9jmUZU6trUD0DnoM4EewWTkZ/B1vNb2XxeTfvc3vj2W24DwrJIoFIdej3cuUAVArtyHFY+W3Y+iTWS9qtv+noDdJsCkf3hsU3QqLNKsizMg7Zj4YHl4NWg7M/oOAGCO0Jumhrtqe0yLsHJom9e4b1VYLhkcnFAV5YLeyEnFdz9yt5tOKw7TPoGXNzh2C+q2mzJaaDja9R96jdTfV+erg+o55O/wfVzVv7FSjj0I+SlQ4NIiOhf9c8RdVpFK3+yC7KrNCV04toJAFrWt75ej2lEpbyVP5qmsfHcRgBz2X2A3qG98TR4kpSZRFxKnEX3Mueo1MERFb1Oz/BmKql29enVsiz5BhKoVJd3IPzxM5VXcPB7iF1Yvc8z/fJtN654+N8/DB5eA/1eVLvnTlikCs+VR6+HoX9Rr3ctgGsJ1WuTsx38QSWtNo6CKStUTg6ayt9Z+1rZwaEpsInsDy6Gsj83sj9M/EIFhQe/hzmN4b3OKggyFVwrudqnLAHNodntaj+evYur/nc0J9E+pP77CVEG054/NwYqmqYxdc1URv40kouZF636THMibX3LE2lNTIHKmbQzpOWl3XQ8LiWO5MxkPA2e9GzU0/y+h8GDPqF9AMumfzLyMkjPSwfq5ogKFO9KHZMQw8Wsi3i4eNA9pLuTW1UzyE9EW2gaDUPUJlOsfqnqFUuzUtQvZYAej5U+5uoBg2ZBn2cs+0XWfLD6Zl6YBxtmV609NYVpCfht96igY/S7MOg19d7v/yqeMinJnJ8y+OZjJbUeDn9cqEbF0ODaaTi6UpXJB5WfUhnTqMreL6u2/9P5PZC0D1zcoLNsPCjKZ576uaGM/rFrxzhw5QBpeWn8crqSadEbVCWR1qS+R32a+Kgk37L2/TFN+/QO7Y2HofSXq0HhgwDLqtSaaqj4u/vj5Vo3t5NoF9CO8Hrh5gq1PRr1uKnPblUSqNhK9J+g1QgVGHw/xbrVKSb7FqvphpCOENaz8vMrotPB0L+q1we+c9yOv7Z28TAkHwC9a3GVVp0O+v0ZBryi/rxxbukck+zU4oTbG/NTytJuLLwQBzNOwQM/w/A3ocv9MOQvFU/7mLQdCx7+cP2synmxhtEIm/5R1I7x4B1g3fXillLe1E9MQoz59S/xlgcq+cZ8TqWqOkCWbEZYloryVEy7K5ec9jHp16QfBp2Bk6knSUireNTXlEhrya7JtZVOpzPXVAFZllySBCq2otfD+I9UBdNrZ+C/Q9UyYksZC1UtFFCjKbZIoArtAh3+CGjFvwxrG9NoSathN+fk9H5KrbpJTVCjGSanN6mposBWatrMUt6Bajqo1zQY9wH0fc6y/w6uHtBJ7YRqdVLt5rfUiiEXN5U8LUQFTIHK1ZyrpXYg/i3hN/Pr49eOm/NOKnP06lHyjfl4u3pXOfejvDyVCxkXOJpyFL1OX2auhZ+7H1EhUTe1vyzmFT91dNrHZESzEebXtze53YktqVkkULElrwbqG3lAC/Xt+rNhcGarZdee/E0FOB7+RcGFjfR/UT0fW6WWN9tSVgrELiqujPvdA7BwJPz2V9t8vrFQjQZBcSXgkty84fYX1OtN84qXGZvyUywZTbEV0/TP0VWQcdmya+JWqNEggNHvqNVIQlTA180Xf3d/AM6lq+Tt+NR44q/HY9Ab6BHSA7BsVCW7IJvXt6kp6+hG0VVeXdKxYdkjKovjVM5W54adqe9Rv8xrTfVDFhxcUOHKIXMNlTo8ogJqp+eZPWYyq+esOh+UWUMCFVtrEAGPxKipm5zr8OUfivNOKrLzU/Xc5T5ws+EcbMPWKldFMxYnbJYl7YIKZEoWlqvIuVj4uA+seLq4Mu6R5ZDwP9j6dvHS4OqI3wgZyeBZH1reUfY5UQ+pcvPpF1Qis6YVF3qrLD/FlkI6qNVFxnz4aSqsfL74se5vcOVk6fMvHoafHleve04rrskjRCVMCbWmJcqmaZ/oRtFMbK0qZq86varM3XhLmrtjLieunSDAI4BZvWZVuT1tGrTBRefC5ezL5kTeH47/wBdH1OjivW3vLffacS3G0T2kO5n5mTzx2xPljgSZVvw09mlc5XbWFpPbTuaeNvc4uxk1igQq9uDVQC0fbjtW5az8+Aj8/kH555/4TY2ooIPuj9i+PabE3D1flF3c7NQGeKc9vNsRZocUr375YpwKsm6sMRK7CBYOV8FB/QjoOqWoMu5bKs8CVGVdS/fTKc+Bb9Vzhz+Cwa3scwzu0H+Ger1lvkpKvX5WLTtu2qd697dWtwfVc/xG2P3f4seW+fBBFHx9N8RvUiNR30xSm1pG9IM7/u7YdopazVSh1pSn8luimjYZ2nQo/Zv0x9vVm6TMpAqrvi47uYylJ5ei1+l5q99bBHoGVrk9ngZP89LmQ1cOseXcFv6+Xf2bnnbbNIY1G1bute4u7rw/6H06BXbieu51Hl37aJn5KqZ9fupaVVphmXLWbYpqc/WECZ/D2ldh+0ewdpYarTD9UjU58z/4tmilR+fJqo6GrbUaDn5h6hf44aXQucQ0SkGumrbRjKoKrmaEvAz1uHZa/dJd+xr0eFS1b+MciP1cXdtmNIz/GDx8iz+vw11q6iVpPxz+CTpWcRorN11NjYBa7VORzpNh6ztq6uzHoi0EmkbbdmTKEp0nq/7MvFL6/aR9Kg/F9HDzUf3r31QtNS9v+bQQZQj3LRpRSUvkbNpZjqYcxUXnwsCwgXgYPBgSPoTlp5bzS/wvdAu+uYbQ8WvHmb1drQSc3nk6PRr1qHabOgR24GjKUZaeXMrO5J0UaoWMbT6WabdNq/Rab1dvPhryEQ+veZjj147z6NpHWTR8UamgxLTqpy7WUBGVkxEVe9K7wPC5xZViN/wd1r1RXPfjfKz6ll2QAy2HqTwFe3AxFFc7NU0xmWz7AFJOgU8wvJQAMxPhT3tU3ZaBs1SyavoFWPdXmN+qKEjRqeXBE78sHaSASkjt84x6ve5vat+iqohbofbZCWhRdsG2Un8/Vxjwsnp9tWiKxZH5KSZ6FxXQDXy59OPeb+GpWOj+KLh6qSDF1VsVnCuvaJ8Q5TAl1J5LP0dMopr26R7SHX8PfwBGRqpy7GsT1pJ/w2hoZn4mL2x8gZzCHPqE9mFqx6k2aZNp5c+mc5vILsimV6Ne/CX6Lxbnvfi5+/Hvof+mmW8zkjKTeGjNQ/x7/7/Zf3k/mfmZpOSoCtuSt3FrkkDFEfrNKB7e3/JPNcpy8TB8dZeqRtrsdpi4qPzpDVvo+oBaWXJhj8ovAUg9W1y5dugbKujw8FNFzMJ7qUTc5w7B+E/UkmlQyb6Tf1DLg8ur5xL9pAp8UhMqzoupSMnaKZb8sOs4AQJLFKxyZH6KJQJbwKh/wvNH1N5MD66E4PbObpWohUrmqJhWy5iSUgF6hvQk0DOQ67nXzZsBgiqa9udNf+ZM2hmCvIKYe/tcc0n76jKt/AFV4fbtAW/j6uJq1WcEegay4I4FhHqHcj7jPB/s+4D7Vt3HwO/U0mZvV2983Xwr+RRRF0mg4ii9/wQji4KCbR/ApwNVSfzGUeqbtat1e2xYzTuwuA6JaVRl7Sw1ahHeGzpNLPs6g7uaKnp8Czy2EZ7aBS2HVHwvN28YMFO93vwW5NxcsbJC6RfV5osAHctp1430LjCwqK6KXxgEtbPuno7iWV/lsjTu6uyWiFrKlKOSnJnMwSsH0aEzF08DcNG7mMuxm4q/xafGM+mXSWw9vxU3vRvz+88vdyVOVTT3a04L/xY08WnCR4M/op5bvSp9Toh3CN+O/pbXer3G0KZD8XXzJbsg23wP2ffm1iST447U41E1qrHiGSjMVfvx3PcDuFftf+oq3X//Nyp3pMUQtUpH5wIj51U+aqHTqbosluryAGz7CK6eUNVjTdNfloj7GdBUEFe/qeXXtRun8oIaNLdNHRohaqAAjwC8DF5kFWQB0CWoy03JsKMjR/NV3FdsPLuR5SeXM2fHHLIKsgj2Cubdge+WGgGxBRe9Cz+O/ZFCrRBXvXUjKTfy9/BnYuuJTGw9kUJjIUevHeXg5YPmpdfi1iMjKo7WbQrcsxi6T4X7l6pv2I7SuJt6FObB0qKVQD0eU0trbc3FULytwLYPIT3Z8msPL1PP5e1aXB6dTl0j9UhEHabT6cx5KlB62sekXUA7mvk2I7cwl1f/9ypZBVl0D+nOt6O/tXmQYqLX6asdpNzIRe9C+4D23NPmHiL97bDQQNQKEqg4Q5tRMGo++DR0/L1NS5U1o0qUHfiy/e7VZjQ06aGml/73nmXXpCerWiygRkiEEDcxrfwBGNL05qlYnU5nTqoFeKDdA3w69FMCPGWLBlH7SKByq2k3HryLAqShf1PJs/ai00H/l9TrvYshN6Pya+JWABo06W5d+XshbiGmPJVOgZ0I8Q4p85x729zL+BbjeXvA28zoPgODXmb6Re0k/3JvNa4eatXO1ZPFybX21HyQyhlJOQUHvyteJl2ew0vVs7XTPkLcQsZGjmX7he082fnJcs/xc/fjjT5vOLBVQtiHjKjcikI7q0Jsjkg41etVPg7AzgXFNWTKkp4MCb+r1zLtI0S5WtRvwXdjvqNPYwdXXxbCCSRQEfbX+V5V6OzSkeL8k7IcKVrt06QH+DVxWPOEEELUXBKoCPvz9IdOd6vXN1bGLUmmfYQQQtxAAhXhGD0eVc9xK+H6+ZuPpyVB4jb1WqZ9hBBCFJFARThGcHto2he0QohdePNxU5G3sJ7gV/e3chdCCGEZCVSE45hGVWI/V7sMlyTTPkIIIcoggYpwnDajoF4jyLxclDgL5OdAwrbiaZ+2Y53XPiGEEDWO1FERjuPiquqobJgNMa/B5nmqnotWqI6H9ZJpHyGEEKXIiIpwrK5T1MaM6Ulw5ZgKUjz8Vf7K0L86u3VCCCFqGBlREY5VL1hVxk0+AA3bQnA7NR0kux0LIYQogwQqwvEi+6uHEEIIUQmZ+hFCCCFEjSWBihBCCCFqLAlUhBBCCFFjSaAihBBCiBpLAhUhhBBC1FgSqAghhBCixrJboDJ79mx69+6Nl5cX/v7+ZZ6TmJjIqFGj8PLyIigoiBkzZlBQUGCvJgkhhBCilrFbHZW8vDwmTJhAdHQ0//3vf286XlhYyKhRowgJCeH3338nKSmJBx54AFdXV+bMmWOvZgkhhBCiFtFpmqbZ8waff/45zz77LKmpqaXeX716NaNHj+bChQsEBwcD8Mknn/DSSy9x+fJl3NzcLPr8tLQ0/Pz8uH79Or6+vrZuvhBCCCHswNLf307LUdm2bRsdO3Y0BykAw4YNIy0tjcOHD5d7XW5uLmlpaaUeQgghhKibnBaoJCcnlwpSAPOfk5OTy71u7ty5+Pn5mR9hYWF2bacQQgghnMeqQGXmzJnodLoKH0ePHrVXWwF4+eWXuX79uvlx9uxZu95PCCGEEM5jVTLtCy+8wIMPPljhOZGRkRZ9VkhICDt37iz13sWLF83HyuPu7o67u7tF9xBCCCFE7WZVoNKwYUMaNmxokxtHR0cze/ZsLl26RFBQEAAxMTH4+vrSrl07iz/HlAssuSpCCCFE7WH6vV3Zmh67LU9OTEwkJSWFxMRECgsL2bdvHwAtWrTAx8eHO+64g3bt2nH//ffz1ltvkZyczKuvvsr06dOtGjFJT08HkFwVIYQQohZKT0/Hz8+v3ON2W5784IMPsmjRopve37BhAwMGDAAgISGBadOmsXHjRry9vZkyZQpvvvkmBoPl8ZPRaOTChQvUq1cPnU5nq+YDKtoLCwvj7NmzsvTZzqSvHUf62nGkrx1H+tpxbNXXmqaRnp5OaGgoen35KbN2r6NSm0mNFseRvnYc6WvHkb52HOlrx3F0X8teP0IIIYSosSRQEUIIIUSNJYFKBdzd3Xn99ddlObQDSF87jvS140hfO470teM4uq8lR0UIIYQQNZaMqAghhBCixpJARQghhBA1lgQqQgghhKixJFARQgghRI0lgUo5PvzwQ5o1a4aHhwc9e/a8aQNFYb25c+fSvXt36tWrR1BQEOPHj+fYsWOlzsnJyWH69OkEBATg4+PDXXfdZd6sUlTdm2++iU6n49lnnzW/J31tO+fPn+e+++4jICAAT09POnbsyO7du83HNU3j//7v/2jUqBGenp4MGTKEEydOOLHFtVNhYSGvvfYaEREReHp60rx5c954441Se8VIX1fN5s2bGTNmDKGhoeh0OpYtW1bquCX9mpKSwuTJk/H19cXf359HHnmEjIyM6jdOEzdZsmSJ5ubmpn322Wfa4cOHtUcffVTz9/fXLl686Oym1WrDhg3TFi5cqB06dEjbt2+fNnLkSC08PFzLyMgwn/PEE09oYWFh2rp167Tdu3drvXr10nr37u3EVtd+O3fu1Jo1a6Z16tRJe+aZZ8zvS1/bRkpKita0aVPtwQcf1Hbs2KHFx8dra9as0U6ePGk+580339T8/Py0ZcuWafv379fGjh2rRUREaNnZ2U5see0ze/ZsLSAgQFu5cqV2+vRp7fvvv9d8fHy09957z3yO9HXVrFq1Sps1a5b2008/aYC2dOnSUsct6dfhw4drt912m7Z9+3Zty5YtWosWLbRJkyZVu20SqJShR48e2vTp081/Liws1EJDQ7W5c+c6sVV1z6VLlzRA27Rpk6Zpmpaamqq5urpq33//vfmcuLg4DdC2bdvmrGbWaunp6VrLli21mJgYrX///uZARfradl566SWtb9++5R43Go1aSEiINm/ePPN7qampmru7u/bNN984ool1xqhRo7SHH3641Ht33nmnNnnyZE3TpK9t5cZAxZJ+PXLkiAZou3btMp+zevVqTafTaefPn69We2Tq5wZ5eXnExsYyZMgQ83t6vZ4hQ4awbds2J7as7rl+/ToADRo0ACA2Npb8/PxSfd+mTRvCw8Ol76to+vTpjBo1qlSfgvS1Lf38889ERUUxYcIEgoKC6NKlCwsWLDAfP336NMnJyaX62s/Pj549e0pfW6l3796sW7eO48ePA7B//362bt3KiBEjAOlre7GkX7dt24a/vz9RUVHmc4YMGYJer2fHjh3Vur/l2xTfIq5cuUJhYSHBwcGl3g8ODubo0aNOalXdYzQaefbZZ+nTpw8dOnQAIDk5GTc3N/z9/UudGxwcTHJyshNaWbstWbKEPXv2sGvXrpuOSV/bTnx8PB9//DHPP/88r7zyCrt27eLpp5/Gzc2NKVOmmPuzrJ8p0tfWmTlzJmlpabRp0wYXFxcKCwuZPXs2kydPBpC+thNL+jU5OZmgoKBSxw0GAw0aNKh230ugIpxi+vTpHDp0iK1btzq7KXXS2bNneeaZZ4iJicHDw8PZzanTjEYjUVFRzJkzB4AuXbpw6NAhPvnkE6ZMmeLk1tUt3333HYsXL+brr7+mffv27Nu3j2effZbQ0FDp6zpMpn5uEBgYiIuLy02rHy5evEhISIiTWlW3PPXUU6xcuZINGzbQpEkT8/shISHk5eWRmppa6nzpe+vFxsZy6dIlunbtisFgwGAwsGnTJv71r39hMBgIDg6WvraRRo0a0a5du1LvtW3blsTERABzf8rPlOqbMWMGM2fO5J577qFjx47cf//9PPfcc8ydOxeQvrYXS/o1JCSES5culTpeUFBASkpKtfteApUbuLm50a1bN9atW2d+z2g0sm7dOqKjo53YstpP0zSeeuopli5dyvr164mIiCh1vFu3bri6upbq+2PHjpGYmCh9b6XBgwdz8OBB9u3bZ35ERUUxefJk82vpa9vo06fPTcvsjx8/TtOmTQGIiIggJCSkVF+npaWxY8cO6WsrZWVlodeX/rXl4uKC0WgEpK/txZJ+jY6OJjU1ldjYWPM569evx2g00rNnz+o1oFqpuHXUkiVLNHd3d+3zzz/Xjhw5oj322GOav7+/lpyc7Oym1WrTpk3T/Pz8tI0bN2pJSUnmR1ZWlvmcJ554QgsPD9fWr1+v7d69W4uOjtaio6Od2Oq6o+SqH02TvraVnTt3agaDQZs9e7Z24sQJbfHixZqXl5f21Vdfmc958803NX9/f2358uXagQMHtHHjxsmS2SqYMmWK1rhxY/Py5J9++kkLDAzUXnzxRfM50tdVk56eru3du1fbu3evBmhvv/22tnfvXi0hIUHTNMv6dfjw4VqXLl20HTt2aFu3btVatmwpy5Pt6f3339fCw8M1Nzc3rUePHtr27dud3aRaDyjzsXDhQvM52dnZ2pNPPqnVr19f8/Ly0v7whz9oSUlJzmt0HXJjoCJ9bTsrVqzQOnTooLm7u2tt2rTRPv3001LHjUaj9tprr2nBwcGau7u7NnjwYO3YsWNOam3tlZaWpj3zzDNaeHi45uHhoUVGRmqzZs3ScnNzzedIX1fNhg0byvz5PGXKFE3TLOvXq1evapMmTdJ8fHw0X19f7aGHHtLS09Or3TadppUo6SeEEEIIUYNIjooQQgghaiwJVIQQQghRY0mgIoQQQogaSwIVIYQQQtRYEqgIIYQQosaSQEUIIYQQNZYEKkIIIYSosSRQEUIIIUSNJYGKEEIIIWosCVSEEEIIUWNJoCKEEEKIGksCFSGEEELUWP8PL3bH1Zj2x7IAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# change in values while biking\n",
    "\n",
    "plt.plot(np.arange(0,100),df.Ax[df['Unnamed: 69']==\"biking\"][:100], label='X-axis')\n",
    "plt.plot(np.arange(0,100),df.Ay[df['Unnamed: 69']==\"biking\"][:100], label='Y-axis')\n",
    "plt.plot(np.arange(0,100),df.Az[df['Unnamed: 69']==\"biking\"][:100], label='Z-axis')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "GdppuvM35DKo",
    "outputId": "d09e2a00-3f83-4509-e0b2-f02a52d85a16"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hc/4_spfx051b5d36rrtssgcwl80000gn/T/ipykernel_79744/3628611996.py:3: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n",
      "  plt.plot(np.arange(0,100),df.Ax[df['Unnamed: 69']==\"jogging\"][:100], label='X-axis')\n",
      "/var/folders/hc/4_spfx051b5d36rrtssgcwl80000gn/T/ipykernel_79744/3628611996.py:4: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n",
      "  plt.plot(np.arange(0,100),df.Ay[df['Unnamed: 69']==\"jogging\"][:100], label='Y-axis')\n",
      "/var/folders/hc/4_spfx051b5d36rrtssgcwl80000gn/T/ipykernel_79744/3628611996.py:5: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n",
      "  plt.plot(np.arange(0,100),df.Az[df['Unnamed: 69']==\"jogging\"][:100], label='Z-axis')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28a2f7cd0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# change in values while jogging\n",
    "\n",
    "plt.plot(np.arange(0,100),df.Ax[df['Unnamed: 69']==\"jogging\"][:100], label='X-axis')\n",
    "plt.plot(np.arange(0,100),df.Ay[df['Unnamed: 69']==\"jogging\"][:100], label='Y-axis')\n",
    "plt.plot(np.arange(0,100),df.Az[df['Unnamed: 69']==\"jogging\"][:100], label='Z-axis')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CEF_btl05DKo"
   },
   "source": [
    "## Load and concatenate all files into one dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 456
    },
    "id": "M2lHIbnx5DKp",
    "outputId": "98f11845-5d91-4ed1-bd16-09700b7f18a6"
   },
   "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>time_stamp</th>\n",
       "      <th>Ax</th>\n",
       "      <th>Ay</th>\n",
       "      <th>Az</th>\n",
       "      <th>Lx</th>\n",
       "      <th>Ly</th>\n",
       "      <th>Lz</th>\n",
       "      <th>Gx</th>\n",
       "      <th>Gy</th>\n",
       "      <th>Gz</th>\n",
       "      <th>...</th>\n",
       "      <th>Lx.4</th>\n",
       "      <th>Ly.4</th>\n",
       "      <th>Lz.4</th>\n",
       "      <th>Gx.4</th>\n",
       "      <th>Gy.4</th>\n",
       "      <th>Gz.4</th>\n",
       "      <th>Mx.4</th>\n",
       "      <th>My.4</th>\n",
       "      <th>Mz.4</th>\n",
       "      <th>Unnamed: 69</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.390000e+12</td>\n",
       "      <td>1.566300</td>\n",
       "      <td>-7.4640</td>\n",
       "      <td>2.50610</td>\n",
       "      <td>-0.724890</td>\n",
       "      <td>2.01760</td>\n",
       "      <td>1.4955</td>\n",
       "      <td>1.56560</td>\n",
       "      <td>-1.53020</td>\n",
       "      <td>-0.090713</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.46790</td>\n",
       "      <td>1.539600</td>\n",
       "      <td>-1.89530</td>\n",
       "      <td>-0.33659</td>\n",
       "      <td>-0.156690</td>\n",
       "      <td>0.549780</td>\n",
       "      <td>24.00</td>\n",
       "      <td>2.82</td>\n",
       "      <td>-9.30</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.390000e+12</td>\n",
       "      <td>2.233700</td>\n",
       "      <td>-8.0769</td>\n",
       "      <td>3.41870</td>\n",
       "      <td>-0.077540</td>\n",
       "      <td>1.36890</td>\n",
       "      <td>2.1517</td>\n",
       "      <td>1.13380</td>\n",
       "      <td>-0.37080</td>\n",
       "      <td>-0.376900</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.61080</td>\n",
       "      <td>-0.030106</td>\n",
       "      <td>-1.74350</td>\n",
       "      <td>-0.35064</td>\n",
       "      <td>-0.753500</td>\n",
       "      <td>0.673170</td>\n",
       "      <td>24.00</td>\n",
       "      <td>3.06</td>\n",
       "      <td>-8.82</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.390000e+12</td>\n",
       "      <td>0.190680</td>\n",
       "      <td>-10.3510</td>\n",
       "      <td>0.10896</td>\n",
       "      <td>-1.757900</td>\n",
       "      <td>-0.81255</td>\n",
       "      <td>-1.0668</td>\n",
       "      <td>1.18360</td>\n",
       "      <td>-0.66279</td>\n",
       "      <td>-0.554670</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.90250</td>\n",
       "      <td>-0.636090</td>\n",
       "      <td>-0.79292</td>\n",
       "      <td>-0.55528</td>\n",
       "      <td>-0.365600</td>\n",
       "      <td>0.831690</td>\n",
       "      <td>24.12</td>\n",
       "      <td>3.06</td>\n",
       "      <td>-8.70</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.390000e+12</td>\n",
       "      <td>-0.681020</td>\n",
       "      <td>-10.2290</td>\n",
       "      <td>-1.32120</td>\n",
       "      <td>-2.365300</td>\n",
       "      <td>-0.61226</td>\n",
       "      <td>-2.2454</td>\n",
       "      <td>1.20000</td>\n",
       "      <td>-1.45690</td>\n",
       "      <td>-0.396450</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.58250</td>\n",
       "      <td>-0.733360</td>\n",
       "      <td>-1.61230</td>\n",
       "      <td>-0.82711</td>\n",
       "      <td>-0.079107</td>\n",
       "      <td>0.853990</td>\n",
       "      <td>24.30</td>\n",
       "      <td>3.00</td>\n",
       "      <td>-8.58</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.390000e+12</td>\n",
       "      <td>-0.149820</td>\n",
       "      <td>-9.8884</td>\n",
       "      <td>-1.60720</td>\n",
       "      <td>-1.652500</td>\n",
       "      <td>-0.22073</td>\n",
       "      <td>-2.2773</td>\n",
       "      <td>0.93432</td>\n",
       "      <td>-0.97708</td>\n",
       "      <td>-0.450510</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.93970</td>\n",
       "      <td>-2.614700</td>\n",
       "      <td>-0.98619</td>\n",
       "      <td>-1.66770</td>\n",
       "      <td>-0.839630</td>\n",
       "      <td>0.622170</td>\n",
       "      <td>24.60</td>\n",
       "      <td>3.12</td>\n",
       "      <td>-8.40</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>62995</th>\n",
       "      <td>1.390000e+12</td>\n",
       "      <td>4.916900</td>\n",
       "      <td>-13.8380</td>\n",
       "      <td>-1.08960</td>\n",
       "      <td>3.521000</td>\n",
       "      <td>-5.19390</td>\n",
       "      <td>3.3259</td>\n",
       "      <td>-2.06110</td>\n",
       "      <td>0.92821</td>\n",
       "      <td>-0.350940</td>\n",
       "      <td>...</td>\n",
       "      <td>6.02740</td>\n",
       "      <td>-3.229600</td>\n",
       "      <td>0.97053</td>\n",
       "      <td>0.24923</td>\n",
       "      <td>0.394920</td>\n",
       "      <td>0.619420</td>\n",
       "      <td>-40.20</td>\n",
       "      <td>-31.26</td>\n",
       "      <td>-1.38</td>\n",
       "      <td>downstairs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62996</th>\n",
       "      <td>1.390000e+12</td>\n",
       "      <td>1.702500</td>\n",
       "      <td>-10.6100</td>\n",
       "      <td>-0.28603</td>\n",
       "      <td>0.062183</td>\n",
       "      <td>-2.18940</td>\n",
       "      <td>4.4646</td>\n",
       "      <td>-1.72910</td>\n",
       "      <td>1.46450</td>\n",
       "      <td>-0.048869</td>\n",
       "      <td>...</td>\n",
       "      <td>8.27400</td>\n",
       "      <td>-3.014200</td>\n",
       "      <td>2.44680</td>\n",
       "      <td>-0.19823</td>\n",
       "      <td>-0.669810</td>\n",
       "      <td>0.791070</td>\n",
       "      <td>-39.66</td>\n",
       "      <td>-30.72</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>downstairs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62997</th>\n",
       "      <td>1.390000e+12</td>\n",
       "      <td>-0.040861</td>\n",
       "      <td>-6.7421</td>\n",
       "      <td>-0.88532</td>\n",
       "      <td>-1.806600</td>\n",
       "      <td>1.57390</td>\n",
       "      <td>4.0031</td>\n",
       "      <td>-1.12220</td>\n",
       "      <td>1.57940</td>\n",
       "      <td>0.313070</td>\n",
       "      <td>...</td>\n",
       "      <td>4.39100</td>\n",
       "      <td>-2.695700</td>\n",
       "      <td>0.98136</td>\n",
       "      <td>-0.74862</td>\n",
       "      <td>-1.754100</td>\n",
       "      <td>0.481060</td>\n",
       "      <td>-39.18</td>\n",
       "      <td>-30.00</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>downstairs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62998</th>\n",
       "      <td>1.390000e+12</td>\n",
       "      <td>0.177060</td>\n",
       "      <td>-4.5764</td>\n",
       "      <td>-1.02150</td>\n",
       "      <td>-1.678600</td>\n",
       "      <td>3.65720</td>\n",
       "      <td>3.9718</td>\n",
       "      <td>-0.67592</td>\n",
       "      <td>0.95326</td>\n",
       "      <td>0.310320</td>\n",
       "      <td>...</td>\n",
       "      <td>0.36681</td>\n",
       "      <td>-0.724050</td>\n",
       "      <td>-1.05860</td>\n",
       "      <td>-1.25230</td>\n",
       "      <td>-1.614500</td>\n",
       "      <td>0.013744</td>\n",
       "      <td>-38.58</td>\n",
       "      <td>-29.10</td>\n",
       "      <td>0.06</td>\n",
       "      <td>downstairs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62999</th>\n",
       "      <td>1.390000e+12</td>\n",
       "      <td>1.321200</td>\n",
       "      <td>-4.8352</td>\n",
       "      <td>-1.52550</td>\n",
       "      <td>-0.585470</td>\n",
       "      <td>3.35630</td>\n",
       "      <td>3.5177</td>\n",
       "      <td>-0.30452</td>\n",
       "      <td>0.66768</td>\n",
       "      <td>0.125840</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.02400</td>\n",
       "      <td>0.554450</td>\n",
       "      <td>0.36056</td>\n",
       "      <td>-1.44440</td>\n",
       "      <td>-0.491750</td>\n",
       "      <td>-0.248620</td>\n",
       "      <td>-38.16</td>\n",
       "      <td>-28.68</td>\n",
       "      <td>0.42</td>\n",
       "      <td>downstairs</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>630000 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         time_stamp        Ax       Ay       Az        Lx       Ly      Lz  \\\n",
       "0      1.390000e+12  1.566300  -7.4640  2.50610 -0.724890  2.01760  1.4955   \n",
       "1      1.390000e+12  2.233700  -8.0769  3.41870 -0.077540  1.36890  2.1517   \n",
       "2      1.390000e+12  0.190680 -10.3510  0.10896 -1.757900 -0.81255 -1.0668   \n",
       "3      1.390000e+12 -0.681020 -10.2290 -1.32120 -2.365300 -0.61226 -2.2454   \n",
       "4      1.390000e+12 -0.149820  -9.8884 -1.60720 -1.652500 -0.22073 -2.2773   \n",
       "...             ...       ...      ...      ...       ...      ...     ...   \n",
       "62995  1.390000e+12  4.916900 -13.8380 -1.08960  3.521000 -5.19390  3.3259   \n",
       "62996  1.390000e+12  1.702500 -10.6100 -0.28603  0.062183 -2.18940  4.4646   \n",
       "62997  1.390000e+12 -0.040861  -6.7421 -0.88532 -1.806600  1.57390  4.0031   \n",
       "62998  1.390000e+12  0.177060  -4.5764 -1.02150 -1.678600  3.65720  3.9718   \n",
       "62999  1.390000e+12  1.321200  -4.8352 -1.52550 -0.585470  3.35630  3.5177   \n",
       "\n",
       "            Gx       Gy        Gz  ...     Lx.4      Ly.4     Lz.4     Gx.4  \\\n",
       "0      1.56560 -1.53020 -0.090713  ... -3.46790  1.539600 -1.89530 -0.33659   \n",
       "1      1.13380 -0.37080 -0.376900  ... -1.61080 -0.030106 -1.74350 -0.35064   \n",
       "2      1.18360 -0.66279 -0.554670  ... -1.90250 -0.636090 -0.79292 -0.55528   \n",
       "3      1.20000 -1.45690 -0.396450  ... -2.58250 -0.733360 -1.61230 -0.82711   \n",
       "4      0.93432 -0.97708 -0.450510  ... -4.93970 -2.614700 -0.98619 -1.66770   \n",
       "...        ...      ...       ...  ...      ...       ...      ...      ...   \n",
       "62995 -2.06110  0.92821 -0.350940  ...  6.02740 -3.229600  0.97053  0.24923   \n",
       "62996 -1.72910  1.46450 -0.048869  ...  8.27400 -3.014200  2.44680 -0.19823   \n",
       "62997 -1.12220  1.57940  0.313070  ...  4.39100 -2.695700  0.98136 -0.74862   \n",
       "62998 -0.67592  0.95326  0.310320  ...  0.36681 -0.724050 -1.05860 -1.25230   \n",
       "62999 -0.30452  0.66768  0.125840  ... -1.02400  0.554450  0.36056 -1.44440   \n",
       "\n",
       "           Gy.4      Gz.4   Mx.4   My.4  Mz.4  Unnamed: 69  \n",
       "0     -0.156690  0.549780  24.00   2.82 -9.30      walking  \n",
       "1     -0.753500  0.673170  24.00   3.06 -8.82      walking  \n",
       "2     -0.365600  0.831690  24.12   3.06 -8.70      walking  \n",
       "3     -0.079107  0.853990  24.30   3.00 -8.58      walking  \n",
       "4     -0.839630  0.622170  24.60   3.12 -8.40      walking  \n",
       "...         ...       ...    ...    ...   ...          ...  \n",
       "62995  0.394920  0.619420 -40.20 -31.26 -1.38   downstairs  \n",
       "62996 -0.669810  0.791070 -39.66 -30.72 -0.96   downstairs  \n",
       "62997 -1.754100  0.481060 -39.18 -30.00 -0.48   downstairs  \n",
       "62998 -1.614500  0.013744 -38.58 -29.10  0.06   downstairs  \n",
       "62999 -0.491750 -0.248620 -38.16 -28.68  0.42   downstairs  \n",
       "\n",
       "[630000 rows x 70 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame()\n",
    "\n",
    "for file in files:\n",
    "    df_temp=pd.read_csv(path+file,header=1)\n",
    "    df=pd.concat([df,df_temp], sort=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "szUYAOd05DKp",
    "outputId": "9b8a285d-cca4-4e1e-88b0-33a3874b1e9a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time_stamp\n",
      "Ax\n",
      "Ay\n",
      "Az\n",
      "Lx\n",
      "Ly\n",
      "Lz\n",
      "Gx\n",
      "Gy\n",
      "Gz\n",
      "Mx\n",
      "My\n",
      "Mz\n",
      "Unnamed: 13\n",
      "time_stamp.1\n",
      "Ax.1\n",
      "Ay.1\n",
      "Az.1\n",
      "Lx.1\n",
      "Ly.1\n",
      "Lz.1\n",
      "Gx.1\n",
      "Gy.1\n",
      "Gz.1\n",
      "Mx.1\n",
      "My.1\n",
      "Mz.1\n",
      "Unnamed: 27\n",
      "time_stamp.2\n",
      "Ax.2\n",
      "Ay.2\n",
      "Az.2\n",
      "Lx.2\n",
      "Ly.2\n",
      "Lz.2\n",
      "Gx.2\n",
      "Gy.2\n",
      "Gz.2\n",
      "Mx.2\n",
      "My.2\n",
      "Mz.2\n",
      "Unnamed: 41\n",
      "time_stamp.3\n",
      "Ax.3\n",
      "Ay.3\n",
      "Az.3\n",
      "Lx.3\n",
      "Ly.3\n",
      "Lz.3\n",
      "Gx.3\n",
      "Gy.3\n",
      "Gz.3\n",
      "Mx.3\n",
      "My.3\n",
      "Mz.3\n",
      "Unnamed: 55\n",
      "time_stamp.4\n",
      "Ax.4\n",
      "Ay.4\n",
      "Az.4\n",
      "Lx.4\n",
      "Ly.4\n",
      "Lz.4\n",
      "Gx.4\n",
      "Gy.4\n",
      "Gz.4\n",
      "Mx.4\n",
      "My.4\n",
      "Mz.4\n",
      "Unnamed: 69\n"
     ]
    }
   ],
   "source": [
    "for i in df.columns:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "EYTefRLS5DKq",
    "outputId": "4bcb7464-f65f-4300-d6de-07ed56411244"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "\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>Ax</th>\n",
       "      <th>Ay</th>\n",
       "      <th>Az</th>\n",
       "      <th>Lx</th>\n",
       "      <th>Ly</th>\n",
       "      <th>Lz</th>\n",
       "      <th>Gx</th>\n",
       "      <th>Gy</th>\n",
       "      <th>Gz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.566300</td>\n",
       "      <td>-7.4640</td>\n",
       "      <td>2.50610</td>\n",
       "      <td>-0.724890</td>\n",
       "      <td>2.01760</td>\n",
       "      <td>1.4955</td>\n",
       "      <td>1.56560</td>\n",
       "      <td>-1.53020</td>\n",
       "      <td>-0.090713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.233700</td>\n",
       "      <td>-8.0769</td>\n",
       "      <td>3.41870</td>\n",
       "      <td>-0.077540</td>\n",
       "      <td>1.36890</td>\n",
       "      <td>2.1517</td>\n",
       "      <td>1.13380</td>\n",
       "      <td>-0.37080</td>\n",
       "      <td>-0.376900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.190680</td>\n",
       "      <td>-10.3510</td>\n",
       "      <td>0.10896</td>\n",
       "      <td>-1.757900</td>\n",
       "      <td>-0.81255</td>\n",
       "      <td>-1.0668</td>\n",
       "      <td>1.18360</td>\n",
       "      <td>-0.66279</td>\n",
       "      <td>-0.554670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.681020</td>\n",
       "      <td>-10.2290</td>\n",
       "      <td>-1.32120</td>\n",
       "      <td>-2.365300</td>\n",
       "      <td>-0.61226</td>\n",
       "      <td>-2.2454</td>\n",
       "      <td>1.20000</td>\n",
       "      <td>-1.45690</td>\n",
       "      <td>-0.396450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.149820</td>\n",
       "      <td>-9.8884</td>\n",
       "      <td>-1.60720</td>\n",
       "      <td>-1.652500</td>\n",
       "      <td>-0.22073</td>\n",
       "      <td>-2.2773</td>\n",
       "      <td>0.93432</td>\n",
       "      <td>-0.97708</td>\n",
       "      <td>-0.450510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62995</th>\n",
       "      <td>4.916900</td>\n",
       "      <td>-13.8380</td>\n",
       "      <td>-1.08960</td>\n",
       "      <td>3.521000</td>\n",
       "      <td>-5.19390</td>\n",
       "      <td>3.3259</td>\n",
       "      <td>-2.06110</td>\n",
       "      <td>0.92821</td>\n",
       "      <td>-0.350940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62996</th>\n",
       "      <td>1.702500</td>\n",
       "      <td>-10.6100</td>\n",
       "      <td>-0.28603</td>\n",
       "      <td>0.062183</td>\n",
       "      <td>-2.18940</td>\n",
       "      <td>4.4646</td>\n",
       "      <td>-1.72910</td>\n",
       "      <td>1.46450</td>\n",
       "      <td>-0.048869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62997</th>\n",
       "      <td>-0.040861</td>\n",
       "      <td>-6.7421</td>\n",
       "      <td>-0.88532</td>\n",
       "      <td>-1.806600</td>\n",
       "      <td>1.57390</td>\n",
       "      <td>4.0031</td>\n",
       "      <td>-1.12220</td>\n",
       "      <td>1.57940</td>\n",
       "      <td>0.313070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62998</th>\n",
       "      <td>0.177060</td>\n",
       "      <td>-4.5764</td>\n",
       "      <td>-1.02150</td>\n",
       "      <td>-1.678600</td>\n",
       "      <td>3.65720</td>\n",
       "      <td>3.9718</td>\n",
       "      <td>-0.67592</td>\n",
       "      <td>0.95326</td>\n",
       "      <td>0.310320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62999</th>\n",
       "      <td>1.321200</td>\n",
       "      <td>-4.8352</td>\n",
       "      <td>-1.52550</td>\n",
       "      <td>-0.585470</td>\n",
       "      <td>3.35630</td>\n",
       "      <td>3.5177</td>\n",
       "      <td>-0.30452</td>\n",
       "      <td>0.66768</td>\n",
       "      <td>0.125840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>630000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Ax       Ay       Az        Lx       Ly      Lz       Gx  \\\n",
       "0      1.566300  -7.4640  2.50610 -0.724890  2.01760  1.4955  1.56560   \n",
       "1      2.233700  -8.0769  3.41870 -0.077540  1.36890  2.1517  1.13380   \n",
       "2      0.190680 -10.3510  0.10896 -1.757900 -0.81255 -1.0668  1.18360   \n",
       "3     -0.681020 -10.2290 -1.32120 -2.365300 -0.61226 -2.2454  1.20000   \n",
       "4     -0.149820  -9.8884 -1.60720 -1.652500 -0.22073 -2.2773  0.93432   \n",
       "...         ...      ...      ...       ...      ...     ...      ...   \n",
       "62995  4.916900 -13.8380 -1.08960  3.521000 -5.19390  3.3259 -2.06110   \n",
       "62996  1.702500 -10.6100 -0.28603  0.062183 -2.18940  4.4646 -1.72910   \n",
       "62997 -0.040861  -6.7421 -0.88532 -1.806600  1.57390  4.0031 -1.12220   \n",
       "62998  0.177060  -4.5764 -1.02150 -1.678600  3.65720  3.9718 -0.67592   \n",
       "62999  1.321200  -4.8352 -1.52550 -0.585470  3.35630  3.5177 -0.30452   \n",
       "\n",
       "            Gy        Gz  \n",
       "0     -1.53020 -0.090713  \n",
       "1     -0.37080 -0.376900  \n",
       "2     -0.66279 -0.554670  \n",
       "3     -1.45690 -0.396450  \n",
       "4     -0.97708 -0.450510  \n",
       "...        ...       ...  \n",
       "62995  0.92821 -0.350940  \n",
       "62996  1.46450 -0.048869  \n",
       "62997  1.57940  0.313070  \n",
       "62998  0.95326  0.310320  \n",
       "62999  0.66768  0.125840  \n",
       "\n",
       "[630000 rows x 9 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split out left and right pocket data\n",
    "\n",
    "left_pocket = df[df.columns[1:10]]\n",
    "left_pocket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "lMYEof545DKq",
    "outputId": "ccfad3df-b17b-4811-d4c1-ec022494dc0d"
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       "             Ax       Ay       Az       Lx       Ly        Lz       Gx  \\\n",
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       "4      2.192900  -9.9156  1.49820  1.98060 -0.11321  1.693900 -0.82345   \n",
       "...         ...      ...      ...      ...      ...       ...      ...   \n",
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       "\n",
       "             Gy        Gz  \n",
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       "2      0.177760 -1.522000  \n",
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       "...         ...       ...  \n",
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       "62999 -0.693640 -0.251070  \n",
       "\n",
       "[630000 rows x 9 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right_pocket = df[df.columns[15:24]]\n",
    "right_pocket.columns=left_pocket.columns\n",
    "right_pocket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "rULigNX_5DKq",
    "outputId": "aafa2506-bd50-475e-a1e9-8bba5c73a55e"
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   "outputs": [
    {
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       "             Ax       Ay       Az       Lx       Ly        Lz       Gx  \\\n",
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       "4     -0.149820  -9.8884 -1.60720 -1.65250 -0.22073 -2.277300  0.93432   \n",
       "...         ...      ...      ...      ...      ...       ...      ...   \n",
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       "\n",
       "            Gy        Gz  \n",
       "0     -1.53020 -0.090713  \n",
       "1     -0.37080 -0.376900  \n",
       "2     -0.66279 -0.554670  \n",
       "3     -1.45690 -0.396450  \n",
       "4     -0.97708 -0.450510  \n",
       "...        ...       ...  \n",
       "62995  0.43585  0.283750  \n",
       "62996  0.44104  0.271840  \n",
       "62997  0.35430  0.077580  \n",
       "62998 -0.41417 -0.083689  \n",
       "62999 -0.69364 -0.251070  \n",
       "\n",
       "[1260000 rows x 9 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concatenate left and right split in one data frame\n",
    "\n",
    "full_data=pd.concat([left_pocket,right_pocket],sort=False)\n",
    "full_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "m5rdhgd_5DKr",
    "outputId": "716f0325-fbd1-4bd3-8fb9-bf0edeb83f98"
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   "outputs": [
    {
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       "      <td>2.50610</td>\n",
       "      <td>-0.72489</td>\n",
       "      <td>2.01760</td>\n",
       "      <td>1.495500</td>\n",
       "      <td>1.56560</td>\n",
       "      <td>-1.53020</td>\n",
       "      <td>-0.090713</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.233700</td>\n",
       "      <td>-8.0769</td>\n",
       "      <td>3.41870</td>\n",
       "      <td>-0.07754</td>\n",
       "      <td>1.36890</td>\n",
       "      <td>2.151700</td>\n",
       "      <td>1.13380</td>\n",
       "      <td>-0.37080</td>\n",
       "      <td>-0.376900</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.190680</td>\n",
       "      <td>-10.3510</td>\n",
       "      <td>0.10896</td>\n",
       "      <td>-1.75790</td>\n",
       "      <td>-0.81255</td>\n",
       "      <td>-1.066800</td>\n",
       "      <td>1.18360</td>\n",
       "      <td>-0.66279</td>\n",
       "      <td>-0.554670</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.681020</td>\n",
       "      <td>-10.2290</td>\n",
       "      <td>-1.32120</td>\n",
       "      <td>-2.36530</td>\n",
       "      <td>-0.61226</td>\n",
       "      <td>-2.245400</td>\n",
       "      <td>1.20000</td>\n",
       "      <td>-1.45690</td>\n",
       "      <td>-0.396450</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.149820</td>\n",
       "      <td>-9.8884</td>\n",
       "      <td>-1.60720</td>\n",
       "      <td>-1.65250</td>\n",
       "      <td>-0.22073</td>\n",
       "      <td>-2.277300</td>\n",
       "      <td>0.93432</td>\n",
       "      <td>-0.97708</td>\n",
       "      <td>-0.450510</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62995</th>\n",
       "      <td>-0.081722</td>\n",
       "      <td>-9.6977</td>\n",
       "      <td>-3.60940</td>\n",
       "      <td>1.00890</td>\n",
       "      <td>-0.25132</td>\n",
       "      <td>-1.212100</td>\n",
       "      <td>-0.44685</td>\n",
       "      <td>0.43585</td>\n",
       "      <td>0.283750</td>\n",
       "      <td>downstairs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62996</th>\n",
       "      <td>0.068102</td>\n",
       "      <td>-11.1010</td>\n",
       "      <td>-5.58430</td>\n",
       "      <td>1.18720</td>\n",
       "      <td>-1.69020</td>\n",
       "      <td>-3.062100</td>\n",
       "      <td>-0.66676</td>\n",
       "      <td>0.44104</td>\n",
       "      <td>0.271840</td>\n",
       "      <td>downstairs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62997</th>\n",
       "      <td>1.484600</td>\n",
       "      <td>-13.5930</td>\n",
       "      <td>-2.62870</td>\n",
       "      <td>2.57360</td>\n",
       "      <td>-4.22070</td>\n",
       "      <td>0.043749</td>\n",
       "      <td>-1.03300</td>\n",
       "      <td>0.35430</td>\n",
       "      <td>0.077580</td>\n",
       "      <td>downstairs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62998</th>\n",
       "      <td>1.539100</td>\n",
       "      <td>-15.1870</td>\n",
       "      <td>-5.96570</td>\n",
       "      <td>2.56300</td>\n",
       "      <td>-5.91120</td>\n",
       "      <td>-2.951300</td>\n",
       "      <td>-1.55470</td>\n",
       "      <td>-0.41417</td>\n",
       "      <td>-0.083689</td>\n",
       "      <td>downstairs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62999</th>\n",
       "      <td>-0.585670</td>\n",
       "      <td>-15.8000</td>\n",
       "      <td>-5.12130</td>\n",
       "      <td>0.40361</td>\n",
       "      <td>-6.64580</td>\n",
       "      <td>-1.744900</td>\n",
       "      <td>-2.24830</td>\n",
       "      <td>-0.69364</td>\n",
       "      <td>-0.251070</td>\n",
       "      <td>downstairs</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1260000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Ax       Ay       Az       Lx       Ly        Lz       Gx  \\\n",
       "0      1.566300  -7.4640  2.50610 -0.72489  2.01760  1.495500  1.56560   \n",
       "1      2.233700  -8.0769  3.41870 -0.07754  1.36890  2.151700  1.13380   \n",
       "2      0.190680 -10.3510  0.10896 -1.75790 -0.81255 -1.066800  1.18360   \n",
       "3     -0.681020 -10.2290 -1.32120 -2.36530 -0.61226 -2.245400  1.20000   \n",
       "4     -0.149820  -9.8884 -1.60720 -1.65250 -0.22073 -2.277300  0.93432   \n",
       "...         ...      ...      ...      ...      ...       ...      ...   \n",
       "62995 -0.081722  -9.6977 -3.60940  1.00890 -0.25132 -1.212100 -0.44685   \n",
       "62996  0.068102 -11.1010 -5.58430  1.18720 -1.69020 -3.062100 -0.66676   \n",
       "62997  1.484600 -13.5930 -2.62870  2.57360 -4.22070  0.043749 -1.03300   \n",
       "62998  1.539100 -15.1870 -5.96570  2.56300 -5.91120 -2.951300 -1.55470   \n",
       "62999 -0.585670 -15.8000 -5.12130  0.40361 -6.64580 -1.744900 -2.24830   \n",
       "\n",
       "            Gy        Gz    Activity  \n",
       "0     -1.53020 -0.090713     walking  \n",
       "1     -0.37080 -0.376900     walking  \n",
       "2     -0.66279 -0.554670     walking  \n",
       "3     -1.45690 -0.396450     walking  \n",
       "4     -0.97708 -0.450510     walking  \n",
       "...        ...       ...         ...  \n",
       "62995  0.43585  0.283750  downstairs  \n",
       "62996  0.44104  0.271840  downstairs  \n",
       "62997  0.35430  0.077580  downstairs  \n",
       "62998 -0.41417 -0.083689  downstairs  \n",
       "62999 -0.69364 -0.251070  downstairs  \n",
       "\n",
       "[1260000 rows x 10 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# restore labels in dataframe\n",
    "labels=pd.concat([df['Unnamed: 69'],df['Unnamed: 69']],axis=0, sort=False)\n",
    "labels.columns=['Activity']\n",
    "full_data['Activity']=labels\n",
    "full_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "En2g6nbX5DKr",
    "outputId": "4e763dba-9d84-465a-b961-cbb30ebad920"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['walking', 'standing', 'jogging', 'sitting', 'biking', 'upsatirs',\n",
       "       'downstairs', 'upstairs'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for unique labels\n",
    "full_data.Activity.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qjT9xMdc5DKr",
    "outputId": "d6d95148-b0e1-4f9e-b01d-4f8afaa5cb41"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hc/4_spfx051b5d36rrtssgcwl80000gn/T/ipykernel_79744/3208768380.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "  full_data.Activity.loc[(full_data.Activity == 'upsatirs')] = 'upstairs'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['walking', 'standing', 'jogging', 'sitting', 'biking', 'upstairs',\n",
       "       'downstairs'], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_data.Activity.loc[(full_data.Activity == 'upsatirs')] = 'upstairs'\n",
    "full_data.Activity.unique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "KkyxbF9o5DKr"
   },
   "source": [
    "# Preprocessing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Ax</th>\n",
       "      <th>Ay</th>\n",
       "      <th>Az</th>\n",
       "      <th>Lx</th>\n",
       "      <th>Ly</th>\n",
       "      <th>Lz</th>\n",
       "      <th>Gx</th>\n",
       "      <th>Gy</th>\n",
       "      <th>Gz</th>\n",
       "      <th>Activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.5663</td>\n",
       "      <td>-7.464</td>\n",
       "      <td>2.5061</td>\n",
       "      <td>-0.72489</td>\n",
       "      <td>2.0176</td>\n",
       "      <td>1.4955</td>\n",
       "      <td>1.5656</td>\n",
       "      <td>-1.5302</td>\n",
       "      <td>-0.090713</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Ax     Ay      Az       Lx      Ly      Lz      Gx      Gy        Gz  \\\n",
       "0  1.5663 -7.464  2.5061 -0.72489  2.0176  1.4955  1.5656 -1.5302 -0.090713   \n",
       "\n",
       "  Activity  \n",
       "0  walking  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_data.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           walking\n",
       "1           walking\n",
       "2           walking\n",
       "3           walking\n",
       "4           walking\n",
       "            ...    \n",
       "62995    downstairs\n",
       "62996    downstairs\n",
       "62997    downstairs\n",
       "62998    downstairs\n",
       "62999    downstairs\n",
       "Name: Activity, Length: 1260000, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_data[full_data.columns[-1]]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaled = scaler.fit_transform(full_data[full_data.columns[:-1]])\n",
    "\n",
    "scaled = pd.DataFrame(data = scaled, columns =  full_data[full_data.columns[:-1]].columns)\n",
    "scaled['Label'] = full_data[full_data.columns[-1]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>Ax</th>\n",
       "      <th>Ay</th>\n",
       "      <th>Az</th>\n",
       "      <th>Lx</th>\n",
       "      <th>Ly</th>\n",
       "      <th>Lz</th>\n",
       "      <th>Gx</th>\n",
       "      <th>Gy</th>\n",
       "      <th>Gz</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.227397</td>\n",
       "      <td>0.022322</td>\n",
       "      <td>1.208985</td>\n",
       "      <td>-0.264575</td>\n",
       "      <td>0.554205</td>\n",
       "      <td>0.457299</td>\n",
       "      <td>1.037012</td>\n",
       "      <td>-1.135640</td>\n",
       "      <td>-0.103532</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.394274</td>\n",
       "      <td>-0.087468</td>\n",
       "      <td>1.416520</td>\n",
       "      <td>-0.035456</td>\n",
       "      <td>0.397925</td>\n",
       "      <td>0.675807</td>\n",
       "      <td>0.752615</td>\n",
       "      <td>-0.282753</td>\n",
       "      <td>-0.434180</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.116563</td>\n",
       "      <td>-0.494833</td>\n",
       "      <td>0.663849</td>\n",
       "      <td>-0.630190</td>\n",
       "      <td>-0.127613</td>\n",
       "      <td>-0.395921</td>\n",
       "      <td>0.785415</td>\n",
       "      <td>-0.497549</td>\n",
       "      <td>-0.639568</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.334523</td>\n",
       "      <td>-0.472979</td>\n",
       "      <td>0.338615</td>\n",
       "      <td>-0.845169</td>\n",
       "      <td>-0.079361</td>\n",
       "      <td>-0.788383</td>\n",
       "      <td>0.796216</td>\n",
       "      <td>-1.081719</td>\n",
       "      <td>-0.456768</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.201702</td>\n",
       "      <td>-0.411966</td>\n",
       "      <td>0.273576</td>\n",
       "      <td>-0.592886</td>\n",
       "      <td>0.014963</td>\n",
       "      <td>-0.799005</td>\n",
       "      <td>0.621231</td>\n",
       "      <td>-0.728750</td>\n",
       "      <td>-0.519226</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Ax        Ay        Az        Lx        Ly        Lz        Gx  \\\n",
       "0  0.227397  0.022322  1.208985 -0.264575  0.554205  0.457299  1.037012   \n",
       "1  0.394274 -0.087468  1.416520 -0.035456  0.397925  0.675807  0.752615   \n",
       "2 -0.116563 -0.494833  0.663849 -0.630190 -0.127613 -0.395921  0.785415   \n",
       "3 -0.334523 -0.472979  0.338615 -0.845169 -0.079361 -0.788383  0.796216   \n",
       "4 -0.201702 -0.411966  0.273576 -0.592886  0.014963 -0.799005  0.621231   \n",
       "\n",
       "         Gy        Gz    Label  \n",
       "0 -1.135640 -0.103532  walking  \n",
       "1 -0.282753 -0.434180  walking  \n",
       "2 -0.497549 -0.639568  walking  \n",
       "3 -1.081719 -0.456768  walking  \n",
       "4 -0.728750 -0.519226  walking  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-Ks288pZ5DKt"
   },
   "source": [
    "## Encode labels into number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sV5ImXBM5DKt",
    "outputId": "fba854a0-92bc-43e6-9f9e-b39f4d72c5ba",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          6\n",
       "1          6\n",
       "2          6\n",
       "3          6\n",
       "4          6\n",
       "          ..\n",
       "1259995    1\n",
       "1259996    1\n",
       "1259997    1\n",
       "1259998    1\n",
       "1259999    1\n",
       "Name: Label, Length: 1260000, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "encoder=LabelEncoder()\n",
    "scaled['Label']=encoder.fit_transform(scaled['Label'])\n",
    "scaled['Label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['biking', 'downstairs', 'jogging', 'sitting', 'standing',\n",
       "       'upstairs', 'walking'], dtype=object)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.classes_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RKXX0oyr5DKu"
   },
   "source": [
    "## Convert dataset into time series sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stats\n",
    "frame_size = 100 # how many samples to be used for the prediction window (150 samples = 3 seconds)\n",
    "hop_size = 50 # how many samples to skip before proceeding to next window (100)\n",
    "N_FEATURES = scaled.columns.size -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N_FEATURES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "id": "hkrm1v8E5DKu"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hc/4_spfx051b5d36rrtssgcwl80000gn/T/ipykernel_79744/447050504.py:17: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  label = stats.mode(df['Label'][i: i + frame_size])[0][0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((25198, 100, 9), (25198,))"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "def get_frames(df, frame_size, hop_size):\n",
    "\n",
    "    frames = []\n",
    "    labels = []\n",
    "    for i in range(0, len(df) - frame_size, hop_size):\n",
    "        Ax = df['Ax'].values[i: i + frame_size]\n",
    "        Ay = df['Ay'].values[i: i + frame_size]\n",
    "        Az = df['Az'].values[i: i + frame_size]\n",
    "        Lx\t = df['Lx'].values[i: i + frame_size]\n",
    "        Ly\t = df['Ly'].values[i: i + frame_size]\n",
    "        Lz\t = df['Lz'].values[i: i + frame_size]\n",
    "        Gx  = df['Gx'].values[i: i + frame_size]\n",
    "        Gy  = df['Gy'].values[i: i + frame_size]\n",
    "        Gz  = df['Gz'].values[i: i + frame_size]\n",
    "        \n",
    "        # Retrieve the most often used label in this segment\n",
    "        label = stats.mode(df['Label'][i: i + frame_size])[0][0]\n",
    "        frames.append([\n",
    "            Ax,\n",
    "            Ay,\n",
    "            Az,\n",
    "            Lx,\n",
    "            Ly,\n",
    "            Lz,\n",
    "            Gx,\n",
    "            Gy,\n",
    "            Gz\n",
    "            ])\n",
    "        labels.append(label)\n",
    "\n",
    "    # Bring the segments into a better shape\n",
    "    frames = np.asarray(frames).reshape(-1, frame_size, N_FEATURES)\n",
    "    labels = np.asarray(labels)\n",
    "\n",
    "    return frames, labels\n",
    "\n",
    "X, y = get_frames(scaled, frame_size, hop_size)\n",
    "\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bqi9V9_G5DKt"
   },
   "source": [
    "## Split training data into training and validation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "suXMrR6F5DKt",
    "outputId": "a3adf7cc-d852-4f98-fafc-cc620f0593e1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17638, 100, 9), (7560, 100, 9), (17638,), (7560,))"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, stratify = y, random_state= 24, shuffle=True)\n",
    "\n",
    "#This will be used by the train_test_split() function to ensure that both the train and test sets \n",
    "# have the proportion of examples in each class that is present in the provided “y” array.\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SGLDQRkx5DKu"
   },
   "source": [
    "## Create and compile LSTM model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "id": "pnknIcpl5DKu"
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Flatten, LSTM\n",
    "from keras.regularizers import l2\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.unique(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xD8i5T3b5DKv",
    "outputId": "7397eda8-bf0c-46ad-b9e2-07aae10ab78f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm_1 (LSTM)               (None, 100, 32)           5376      \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 3200)              0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 64)                204864    \n",
      "                                                                 \n",
      " output (Dense)              (None, 7)                 455       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 210,695\n",
      "Trainable params: 210,695\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model=Sequential()\n",
    "model.add(LSTM(32, return_sequences=True, input_shape = (frame_size, N_FEATURES),\n",
    "              kernel_regularizer = l2(0.000001), bias_regularizer = l2(0.000001), name='lstm_1'))\n",
    "model.add(Flatten(name='flatten'))\n",
    "model.add(Dense(64, activation='relu',kernel_regularizer = l2(0.000001), bias_regularizer = l2(0.000001), name='dense_1' ))\n",
    "model.add(Dense(len(np.unique(y_train)), activation='softmax', \n",
    "                kernel_regularizer = l2(0.000001), bias_regularizer = l2(0.000001), name='output'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, LSTM, Bidirectional\n",
    "from keras.optimizers import Adam\n",
    "from keras.datasets import imdb\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras import callbacks\n",
    "\n",
    "from config import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N_FEATURES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.unique(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17638,)"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7560,)"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "def createBidirectionalLSTM(segment_time_size,\n",
    "                            learning_rate,\n",
    "                            n_hidden_neurons,\n",
    "                            droput_rate,\n",
    "                            n_epochs,\n",
    "                            batch_size,\n",
    "                            X_train, y_train,\n",
    "                            X_val, y_val,\n",
    "                            visualize=False):\n",
    "    \"\"\"\n",
    "    Create a bidirectional Low-short term memory recurrent\n",
    "    neural network for activity recognition.\n",
    "    Input:\n",
    "    - segment_time_size: size of a sliding window,\n",
    "    - n_hidden_neurons: number of hidden neurons (nodes) in one layer,\n",
    "    - \n",
    "    \n",
    "    _rate: learning rate used by the optimizer (Adam),\n",
    "    - droput_rate: dropout rate during training,\n",
    "    - n_epochs: number of epochs for training,\n",
    "    - batch_size: batch size,\n",
    "    - X_train, y_train, X_val, y_val: training and valuation data and labels.\n",
    "    Returns sequential keras model.\n",
    "    \"\"\"\n",
    "    # Build a model\n",
    "    model = Sequential()\n",
    "    model.add(Bidirectional(LSTM(n_hidden_neurons,\n",
    "                            return_sequences=True,\n",
    "                            activation=\"tanh\"), input_shape=(segment_time_size, N_FEATURES),\n",
    "                            merge_mode='sum'))\n",
    "    model.add(Bidirectional(LSTM(n_hidden_neurons)))\n",
    "    model.add(Dropout(droput_rate))\n",
    "    # model.add(Flatten(name='flatten'))\n",
    "    model.add(Dense(len(np.unique(y_train)), activation='sigmoid'))\n",
    "    adam_optimizer = Adam(lr=learning_rate, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)\n",
    "    model.compile(optimizer=adam_optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "    history = model.fit(X_train, y_train,\n",
    "                        batch_size=batch_size,\n",
    "                        epochs=n_epochs,\n",
    "                        validation_data=[X_val, y_val])\n",
    "\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEGMENT_TIME_SIZE = frame_size\n",
    "LEARNING_RATE = 0.0003\n",
    "N_HIDDEN_NEURONS = 64\n",
    "DROPOUT_RATE = 0.5\n",
    "N_EPOCHS = 30\n",
    "BATCH_SIZE = 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "276/276 [==============================] - 25s 81ms/step - loss: 0.7413 - accuracy: 0.7586 - val_loss: 0.1377 - val_accuracy: 0.9697\n",
      "Epoch 2/30\n",
      "276/276 [==============================] - 22s 79ms/step - loss: 0.1307 - accuracy: 0.9675 - val_loss: 0.0834 - val_accuracy: 0.9763\n",
      "Epoch 3/30\n",
      "276/276 [==============================] - 21s 77ms/step - loss: 0.0924 - accuracy: 0.9765 - val_loss: 0.0639 - val_accuracy: 0.9823\n",
      "Epoch 4/30\n",
      "276/276 [==============================] - 21s 77ms/step - loss: 0.0766 - accuracy: 0.9790 - val_loss: 0.0580 - val_accuracy: 0.9848\n",
      "Epoch 5/30\n",
      "276/276 [==============================] - 28s 103ms/step - loss: 0.0652 - accuracy: 0.9824 - val_loss: 0.0591 - val_accuracy: 0.9827\n",
      "Epoch 6/30\n",
      "276/276 [==============================] - 29s 105ms/step - loss: 0.0690 - accuracy: 0.9813 - val_loss: 0.0567 - val_accuracy: 0.9829\n",
      "Epoch 7/30\n",
      "276/276 [==============================] - 22s 79ms/step - loss: 0.0583 - accuracy: 0.9846 - val_loss: 0.0421 - val_accuracy: 0.9890\n",
      "Epoch 8/30\n",
      "276/276 [==============================] - 21s 75ms/step - loss: 0.0556 - accuracy: 0.9846 - val_loss: 0.0418 - val_accuracy: 0.9881\n",
      "Epoch 9/30\n",
      "276/276 [==============================] - 21s 77ms/step - loss: 0.0529 - accuracy: 0.9847 - val_loss: 0.0581 - val_accuracy: 0.9861\n",
      "Epoch 10/30\n",
      "276/276 [==============================] - 23s 83ms/step - loss: 0.0484 - accuracy: 0.9858 - val_loss: 0.0448 - val_accuracy: 0.9868\n",
      "Epoch 11/30\n",
      "276/276 [==============================] - 35s 126ms/step - loss: 0.0422 - accuracy: 0.9881 - val_loss: 0.0445 - val_accuracy: 0.9872\n",
      "Epoch 12/30\n",
      "276/276 [==============================] - 24s 87ms/step - loss: 0.0412 - accuracy: 0.9878 - val_loss: 0.0415 - val_accuracy: 0.9890\n",
      "Epoch 13/30\n",
      "276/276 [==============================] - 21s 76ms/step - loss: 0.0386 - accuracy: 0.9891 - val_loss: 0.0543 - val_accuracy: 0.9848\n",
      "Epoch 14/30\n",
      "276/276 [==============================] - 21s 77ms/step - loss: 0.0418 - accuracy: 0.9878 - val_loss: 0.0418 - val_accuracy: 0.9886\n",
      "Epoch 15/30\n",
      "276/276 [==============================] - 21s 76ms/step - loss: 0.0435 - accuracy: 0.9871 - val_loss: 0.0392 - val_accuracy: 0.9890\n",
      "Epoch 16/30\n",
      "276/276 [==============================] - 21s 76ms/step - loss: 0.0352 - accuracy: 0.9894 - val_loss: 0.0384 - val_accuracy: 0.9897\n",
      "Epoch 17/30\n",
      "276/276 [==============================] - 22s 79ms/step - loss: 0.0395 - accuracy: 0.9878 - val_loss: 0.0604 - val_accuracy: 0.9847\n",
      "Epoch 18/30\n",
      "276/276 [==============================] - 21s 76ms/step - loss: 0.0413 - accuracy: 0.9878 - val_loss: 0.0409 - val_accuracy: 0.9888\n",
      "Epoch 19/30\n",
      "276/276 [==============================] - 22s 78ms/step - loss: 0.0296 - accuracy: 0.9905 - val_loss: 0.0391 - val_accuracy: 0.9890\n",
      "Epoch 20/30\n",
      "276/276 [==============================] - 22s 79ms/step - loss: 0.0288 - accuracy: 0.9906 - val_loss: 0.0438 - val_accuracy: 0.9882\n",
      "Epoch 21/30\n",
      "276/276 [==============================] - 24s 85ms/step - loss: 0.0314 - accuracy: 0.9905 - val_loss: 0.0371 - val_accuracy: 0.9899\n",
      "Epoch 22/30\n",
      "276/276 [==============================] - 22s 81ms/step - loss: 0.0266 - accuracy: 0.9918 - val_loss: 0.0419 - val_accuracy: 0.9881\n",
      "Epoch 23/30\n",
      "276/276 [==============================] - 22s 81ms/step - loss: 0.0255 - accuracy: 0.9923 - val_loss: 0.0354 - val_accuracy: 0.9909\n",
      "Epoch 24/30\n",
      "276/276 [==============================] - 22s 81ms/step - loss: 0.0231 - accuracy: 0.9926 - val_loss: 0.0408 - val_accuracy: 0.9890\n",
      "Epoch 25/30\n",
      "276/276 [==============================] - 22s 80ms/step - loss: 0.0255 - accuracy: 0.9917 - val_loss: 0.0395 - val_accuracy: 0.9890\n",
      "Epoch 26/30\n",
      "276/276 [==============================] - 24s 87ms/step - loss: 0.0236 - accuracy: 0.9929 - val_loss: 0.0401 - val_accuracy: 0.9878\n",
      "Epoch 27/30\n",
      "276/276 [==============================] - 22s 81ms/step - loss: 0.0207 - accuracy: 0.9937 - val_loss: 0.0361 - val_accuracy: 0.9911\n",
      "Epoch 28/30\n",
      "276/276 [==============================] - 23s 83ms/step - loss: 0.0287 - accuracy: 0.9917 - val_loss: 0.0415 - val_accuracy: 0.9897\n",
      "Epoch 29/30\n",
      "276/276 [==============================] - 22s 79ms/step - loss: 0.0244 - accuracy: 0.9918 - val_loss: 0.0408 - val_accuracy: 0.9894\n",
      "Epoch 30/30\n",
      "276/276 [==============================] - 24s 88ms/step - loss: 0.0182 - accuracy: 0.9933 - val_loss: 0.0361 - val_accuracy: 0.9905\n"
     ]
    }
   ],
   "source": [
    " # Build a model\n",
    "earlystopping = callbacks.EarlyStopping(monitor =\"val_loss\", \n",
    "                                        mode =\"min\", patience = 5, \n",
    "                                        restore_best_weights = True)\n",
    "  \n",
    "\n",
    "model = createBidirectionalLSTM(SEGMENT_TIME_SIZE,\n",
    "                                    LEARNING_RATE,\n",
    "                                    N_HIDDEN_NEURONS,\n",
    "                                    DROPOUT_RATE,\n",
    "                                    N_EPOCHS,\n",
    "                                    BATCH_SIZE,\n",
    "                                    X_train, y_train,\n",
    "                                    X_test, y_test,\n",
    "                                    visualize=True,\n",
    "                                    callbacks =[earlystopping])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "237/237 [==============================] - 3s 11ms/step\n",
      "[3 1 4 0 3]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hc/4_spfx051b5d36rrtssgcwl80000gn/T/ipykernel_79744/917384041.py:11: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\n",
      "  plt.show()\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.metrics import ConfusionMatrixDisplay, f1_score\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "# print(y_pred[0:5])\n",
    "new_pred = np.argmax(y_pred,axis=1)\n",
    "print(new_pred[0:5])\n",
    "\n",
    "ConfusionMatrixDisplay.from_predictions( y_test, new_pred,display_labels=[\"class 0\", \"class 1\", \"class 2\",\"class 3\",\"4\",\"5\",\"6\"], )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "id": "4zTYz_nS5DKv"
   },
   "outputs": [],
   "source": [
    "#compile\n",
    "model.compile(loss='sparse_categorical_crossentropy', optimizer=Adam(), metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "id": "br8l8yJA5DKv"
   },
   "outputs": [],
   "source": [
    "# prepare callbacks\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "\n",
    "callbacks= [ModelCheckpoint('model.h5', save_weights_only=False, save_best_only=True, verbose=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17638, 100, 9)"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RTAXFD5E5DKv"
   },
   "source": [
    "## Start training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "71Z_1I-k5DKw",
    "outputId": "166366c1-f79e-4dcb-c8f7-07ed890c84f6",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "552/552 [==============================] - 9s 14ms/step - loss: 0.1353 - accuracy: 0.9605 - val_loss: 0.0452 - val_accuracy: 0.9862\n",
      "Epoch 2/10\n",
      "552/552 [==============================] - 7s 13ms/step - loss: 0.0306 - accuracy: 0.9910 - val_loss: 0.0349 - val_accuracy: 0.9906\n",
      "Epoch 3/10\n",
      "552/552 [==============================] - 7s 12ms/step - loss: 0.0171 - accuracy: 0.9948 - val_loss: 0.0333 - val_accuracy: 0.9926\n",
      "Epoch 4/10\n",
      "552/552 [==============================] - 7s 12ms/step - loss: 0.0123 - accuracy: 0.9960 - val_loss: 0.0377 - val_accuracy: 0.9909\n",
      "Epoch 5/10\n",
      "552/552 [==============================] - 7s 12ms/step - loss: 0.0103 - accuracy: 0.9967 - val_loss: 0.0283 - val_accuracy: 0.9929\n",
      "Epoch 6/10\n",
      "552/552 [==============================] - 7s 12ms/step - loss: 0.0038 - accuracy: 0.9993 - val_loss: 0.0311 - val_accuracy: 0.9937\n",
      "Epoch 7/10\n",
      "552/552 [==============================] - 7s 12ms/step - loss: 0.0021 - accuracy: 0.9996 - val_loss: 0.0330 - val_accuracy: 0.9933\n",
      "Epoch 8/10\n",
      "552/552 [==============================] - 7s 12ms/step - loss: 0.0099 - accuracy: 0.9967 - val_loss: 0.0613 - val_accuracy: 0.9866\n",
      "Epoch 9/10\n",
      "552/552 [==============================] - 7s 13ms/step - loss: 0.0076 - accuracy: 0.9983 - val_loss: 0.0419 - val_accuracy: 0.9927\n",
      "Epoch 10/10\n",
      "552/552 [==============================] - 7s 13ms/step - loss: 0.0026 - accuracy: 0.9994 - val_loss: 0.0341 - val_accuracy: 0.9933\n"
     ]
    }
   ],
   "source": [
    "# All features\n",
    "\n",
    "epochNumber = 10\n",
    "history = model.fit(X_train, \n",
    "                    y_train, \n",
    "                    epochs = epochNumber, \n",
    "                    validation_data= (X_test, y_test), \n",
    "                    verbose=1,\n",
    "                    # shuffle=True,\n",
    "                    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x_SRv9C85DKw"
   },
   "source": [
    "# Exporting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>Ax</th>\n",
       "      <th>Ay</th>\n",
       "      <th>Az</th>\n",
       "      <th>Lx</th>\n",
       "      <th>Ly</th>\n",
       "      <th>Lz</th>\n",
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       "      <th>Gy</th>\n",
       "      <th>Gz</th>\n",
       "      <th>Activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.56630</td>\n",
       "      <td>-7.4640</td>\n",
       "      <td>2.50610</td>\n",
       "      <td>-0.72489</td>\n",
       "      <td>2.01760</td>\n",
       "      <td>1.4955</td>\n",
       "      <td>1.56560</td>\n",
       "      <td>-1.53020</td>\n",
       "      <td>-0.090713</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.23370</td>\n",
       "      <td>-8.0769</td>\n",
       "      <td>3.41870</td>\n",
       "      <td>-0.07754</td>\n",
       "      <td>1.36890</td>\n",
       "      <td>2.1517</td>\n",
       "      <td>1.13380</td>\n",
       "      <td>-0.37080</td>\n",
       "      <td>-0.376900</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.19068</td>\n",
       "      <td>-10.3510</td>\n",
       "      <td>0.10896</td>\n",
       "      <td>-1.75790</td>\n",
       "      <td>-0.81255</td>\n",
       "      <td>-1.0668</td>\n",
       "      <td>1.18360</td>\n",
       "      <td>-0.66279</td>\n",
       "      <td>-0.554670</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.68102</td>\n",
       "      <td>-10.2290</td>\n",
       "      <td>-1.32120</td>\n",
       "      <td>-2.36530</td>\n",
       "      <td>-0.61226</td>\n",
       "      <td>-2.2454</td>\n",
       "      <td>1.20000</td>\n",
       "      <td>-1.45690</td>\n",
       "      <td>-0.396450</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.14982</td>\n",
       "      <td>-9.8884</td>\n",
       "      <td>-1.60720</td>\n",
       "      <td>-1.65250</td>\n",
       "      <td>-0.22073</td>\n",
       "      <td>-2.2773</td>\n",
       "      <td>0.93432</td>\n",
       "      <td>-0.97708</td>\n",
       "      <td>-0.450510</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Ax       Ay       Az       Lx       Ly      Lz       Gx       Gy  \\\n",
       "0  1.56630  -7.4640  2.50610 -0.72489  2.01760  1.4955  1.56560 -1.53020   \n",
       "1  2.23370  -8.0769  3.41870 -0.07754  1.36890  2.1517  1.13380 -0.37080   \n",
       "2  0.19068 -10.3510  0.10896 -1.75790 -0.81255 -1.0668  1.18360 -0.66279   \n",
       "3 -0.68102 -10.2290 -1.32120 -2.36530 -0.61226 -2.2454  1.20000 -1.45690   \n",
       "4 -0.14982  -9.8884 -1.60720 -1.65250 -0.22073 -2.2773  0.93432 -0.97708   \n",
       "\n",
       "         Gz Activity  \n",
       "0 -0.090713  walking  \n",
       "1 -0.376900  walking  \n",
       "2 -0.554670  walking  \n",
       "3 -0.396450  walking  \n",
       "4 -0.450510  walking  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Ay</th>\n",
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       "      <th>Ly</th>\n",
       "      <th>Lz</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.56630</td>\n",
       "      <td>-7.4640</td>\n",
       "      <td>2.50610</td>\n",
       "      <td>-0.72489</td>\n",
       "      <td>2.01760</td>\n",
       "      <td>1.4955</td>\n",
       "      <td>1.56560</td>\n",
       "      <td>-1.53020</td>\n",
       "      <td>-0.090713</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.23370</td>\n",
       "      <td>-8.0769</td>\n",
       "      <td>3.41870</td>\n",
       "      <td>-0.07754</td>\n",
       "      <td>1.36890</td>\n",
       "      <td>2.1517</td>\n",
       "      <td>1.13380</td>\n",
       "      <td>-0.37080</td>\n",
       "      <td>-0.376900</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.19068</td>\n",
       "      <td>-10.3510</td>\n",
       "      <td>0.10896</td>\n",
       "      <td>-1.75790</td>\n",
       "      <td>-0.81255</td>\n",
       "      <td>-1.0668</td>\n",
       "      <td>1.18360</td>\n",
       "      <td>-0.66279</td>\n",
       "      <td>-0.554670</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.68102</td>\n",
       "      <td>-10.2290</td>\n",
       "      <td>-1.32120</td>\n",
       "      <td>-2.36530</td>\n",
       "      <td>-0.61226</td>\n",
       "      <td>-2.2454</td>\n",
       "      <td>1.20000</td>\n",
       "      <td>-1.45690</td>\n",
       "      <td>-0.396450</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.14982</td>\n",
       "      <td>-9.8884</td>\n",
       "      <td>-1.60720</td>\n",
       "      <td>-1.65250</td>\n",
       "      <td>-0.22073</td>\n",
       "      <td>-2.2773</td>\n",
       "      <td>0.93432</td>\n",
       "      <td>-0.97708</td>\n",
       "      <td>-0.450510</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>5.82950</td>\n",
       "      <td>-3.2008</td>\n",
       "      <td>0.32689</td>\n",
       "      <td>0.85714</td>\n",
       "      <td>5.01500</td>\n",
       "      <td>-1.6598</td>\n",
       "      <td>2.22290</td>\n",
       "      <td>0.42669</td>\n",
       "      <td>-0.160050</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>5.06680</td>\n",
       "      <td>-8.6625</td>\n",
       "      <td>-4.01800</td>\n",
       "      <td>-0.22936</td>\n",
       "      <td>-0.45554</td>\n",
       "      <td>-4.8935</td>\n",
       "      <td>3.05400</td>\n",
       "      <td>-0.32895</td>\n",
       "      <td>-0.624000</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>1.15770</td>\n",
       "      <td>-14.3290</td>\n",
       "      <td>-8.44460</td>\n",
       "      <td>-3.42400</td>\n",
       "      <td>-5.66400</td>\n",
       "      <td>-8.1241</td>\n",
       "      <td>3.23760</td>\n",
       "      <td>-0.82833</td>\n",
       "      <td>-0.840550</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>-0.80360</td>\n",
       "      <td>-18.9320</td>\n",
       "      <td>-16.34400</td>\n",
       "      <td>-5.51210</td>\n",
       "      <td>-10.33100</td>\n",
       "      <td>-16.4510</td>\n",
       "      <td>1.98530</td>\n",
       "      <td>-0.39340</td>\n",
       "      <td>-0.465780</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>-2.15200</td>\n",
       "      <td>-16.9440</td>\n",
       "      <td>-10.10600</td>\n",
       "      <td>-4.00560</td>\n",
       "      <td>-7.98960</td>\n",
       "      <td>-6.5625</td>\n",
       "      <td>-1.98200</td>\n",
       "      <td>2.06750</td>\n",
       "      <td>0.424550</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Ax       Ay        Az       Lx        Ly       Lz       Gx       Gy  \\\n",
       "0    1.56630  -7.4640   2.50610 -0.72489   2.01760   1.4955  1.56560 -1.53020   \n",
       "1    2.23370  -8.0769   3.41870 -0.07754   1.36890   2.1517  1.13380 -0.37080   \n",
       "2    0.19068 -10.3510   0.10896 -1.75790  -0.81255  -1.0668  1.18360 -0.66279   \n",
       "3   -0.68102 -10.2290  -1.32120 -2.36530  -0.61226  -2.2454  1.20000 -1.45690   \n",
       "4   -0.14982  -9.8884  -1.60720 -1.65250  -0.22073  -2.2773  0.93432 -0.97708   \n",
       "..       ...      ...       ...      ...       ...      ...      ...      ...   \n",
       "145  5.82950  -3.2008   0.32689  0.85714   5.01500  -1.6598  2.22290  0.42669   \n",
       "146  5.06680  -8.6625  -4.01800 -0.22936  -0.45554  -4.8935  3.05400 -0.32895   \n",
       "147  1.15770 -14.3290  -8.44460 -3.42400  -5.66400  -8.1241  3.23760 -0.82833   \n",
       "148 -0.80360 -18.9320 -16.34400 -5.51210 -10.33100 -16.4510  1.98530 -0.39340   \n",
       "149 -2.15200 -16.9440 -10.10600 -4.00560  -7.98960  -6.5625 -1.98200  2.06750   \n",
       "\n",
       "           Gz Activity  \n",
       "0   -0.090713  walking  \n",
       "1   -0.376900  walking  \n",
       "2   -0.554670  walking  \n",
       "3   -0.396450  walking  \n",
       "4   -0.450510  walking  \n",
       "..        ...      ...  \n",
       "145 -0.160050  walking  \n",
       "146 -0.624000  walking  \n",
       "147 -0.840550  walking  \n",
       "148 -0.465780  walking  \n",
       "149  0.424550  walking  \n",
       "\n",
       "[150 rows x 10 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_data[full_data['Activity'] == 'walking'].head(150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['walking', 'standing', 'jogging', 'sitting', 'biking', 'upstairs',\n",
       "       'downstairs'], dtype=object)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_data['Activity'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['biking', 'downstairs', 'jogging', 'sitting', 'standing',\n",
       "       'upstairs', 'walking'], dtype=object)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <td>2.1517</td>\n",
       "      <td>1.13380</td>\n",
       "      <td>-0.37080</td>\n",
       "      <td>-0.376900</td>\n",
       "      <td>walking</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.19068</td>\n",
       "      <td>-10.3510</td>\n",
       "      <td>0.10896</td>\n",
       "      <td>-1.75790</td>\n",
       "      <td>-0.81255</td>\n",
       "      <td>-1.0668</td>\n",
       "      <td>1.18360</td>\n",
       "      <td>-0.66279</td>\n",
       "      <td>-0.554670</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.68102</td>\n",
       "      <td>-10.2290</td>\n",
       "      <td>-1.32120</td>\n",
       "      <td>-2.36530</td>\n",
       "      <td>-0.61226</td>\n",
       "      <td>-2.2454</td>\n",
       "      <td>1.20000</td>\n",
       "      <td>-1.45690</td>\n",
       "      <td>-0.396450</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.14982</td>\n",
       "      <td>-9.8884</td>\n",
       "      <td>-1.60720</td>\n",
       "      <td>-1.65250</td>\n",
       "      <td>-0.22073</td>\n",
       "      <td>-2.2773</td>\n",
       "      <td>0.93432</td>\n",
       "      <td>-0.97708</td>\n",
       "      <td>-0.450510</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.85714</td>\n",
       "      <td>5.01500</td>\n",
       "      <td>-1.6598</td>\n",
       "      <td>2.22290</td>\n",
       "      <td>0.42669</td>\n",
       "      <td>-0.160050</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>5.06680</td>\n",
       "      <td>-8.6625</td>\n",
       "      <td>-4.01800</td>\n",
       "      <td>-0.22936</td>\n",
       "      <td>-0.45554</td>\n",
       "      <td>-4.8935</td>\n",
       "      <td>3.05400</td>\n",
       "      <td>-0.32895</td>\n",
       "      <td>-0.624000</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>1.15770</td>\n",
       "      <td>-14.3290</td>\n",
       "      <td>-8.44460</td>\n",
       "      <td>-3.42400</td>\n",
       "      <td>-5.66400</td>\n",
       "      <td>-8.1241</td>\n",
       "      <td>3.23760</td>\n",
       "      <td>-0.82833</td>\n",
       "      <td>-0.840550</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>-0.80360</td>\n",
       "      <td>-18.9320</td>\n",
       "      <td>-16.34400</td>\n",
       "      <td>-5.51210</td>\n",
       "      <td>-10.33100</td>\n",
       "      <td>-16.4510</td>\n",
       "      <td>1.98530</td>\n",
       "      <td>-0.39340</td>\n",
       "      <td>-0.465780</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>-2.15200</td>\n",
       "      <td>-16.9440</td>\n",
       "      <td>-10.10600</td>\n",
       "      <td>-4.00560</td>\n",
       "      <td>-7.98960</td>\n",
       "      <td>-6.5625</td>\n",
       "      <td>-1.98200</td>\n",
       "      <td>2.06750</td>\n",
       "      <td>0.424550</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Ax       Ay        Az       Lx        Ly       Lz       Gx       Gy  \\\n",
       "0    1.56630  -7.4640   2.50610 -0.72489   2.01760   1.4955  1.56560 -1.53020   \n",
       "1    2.23370  -8.0769   3.41870 -0.07754   1.36890   2.1517  1.13380 -0.37080   \n",
       "2    0.19068 -10.3510   0.10896 -1.75790  -0.81255  -1.0668  1.18360 -0.66279   \n",
       "3   -0.68102 -10.2290  -1.32120 -2.36530  -0.61226  -2.2454  1.20000 -1.45690   \n",
       "4   -0.14982  -9.8884  -1.60720 -1.65250  -0.22073  -2.2773  0.93432 -0.97708   \n",
       "..       ...      ...       ...      ...       ...      ...      ...      ...   \n",
       "145  5.82950  -3.2008   0.32689  0.85714   5.01500  -1.6598  2.22290  0.42669   \n",
       "146  5.06680  -8.6625  -4.01800 -0.22936  -0.45554  -4.8935  3.05400 -0.32895   \n",
       "147  1.15770 -14.3290  -8.44460 -3.42400  -5.66400  -8.1241  3.23760 -0.82833   \n",
       "148 -0.80360 -18.9320 -16.34400 -5.51210 -10.33100 -16.4510  1.98530 -0.39340   \n",
       "149 -2.15200 -16.9440 -10.10600 -4.00560  -7.98960  -6.5625 -1.98200  2.06750   \n",
       "\n",
       "           Gz Activity  \n",
       "0   -0.090713  walking  \n",
       "1   -0.376900  walking  \n",
       "2   -0.554670  walking  \n",
       "3   -0.396450  walking  \n",
       "4   -0.450510  walking  \n",
       "..        ...      ...  \n",
       "145 -0.160050  walking  \n",
       "146 -0.624000  walking  \n",
       "147 -0.840550  walking  \n",
       "148 -0.465780  walking  \n",
       "149  0.424550  walking  \n",
       "\n",
       "[150 rows x 10 columns]"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_data[full_data['Activity'] == 'walking'].head(150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>20000</th>\n",
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       "      <th>20001</th>\n",
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       "      <th>20002</th>\n",
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       "      <td>0.380632</td>\n",
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       "    <tr>\n",
       "      <th>20003</th>\n",
       "      <td>-2.214444</td>\n",
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       "      <td>-3.334933</td>\n",
       "      <td>-4.568079</td>\n",
       "      <td>-1.939310</td>\n",
       "      <td>-6.323208</td>\n",
       "      <td>-1.776467</td>\n",
       "      <td>1.392493</td>\n",
       "      <td>1.180665</td>\n",
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       "      <th>20004</th>\n",
       "      <td>-0.767037</td>\n",
       "      <td>-0.294850</td>\n",
       "      <td>-0.379980</td>\n",
       "      <td>-0.701402</td>\n",
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       "      <td>-0.225464</td>\n",
       "      <td>-2.116848</td>\n",
       "      <td>-2.895411</td>\n",
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       "    <tr>\n",
       "      <th>20145</th>\n",
       "      <td>-0.365173</td>\n",
       "      <td>-2.012441</td>\n",
       "      <td>1.828474</td>\n",
       "      <td>-1.209259</td>\n",
       "      <td>-2.188847</td>\n",
       "      <td>1.787294</td>\n",
       "      <td>-0.030353</td>\n",
       "      <td>-2.944403</td>\n",
       "      <td>1.672155</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>20146</th>\n",
       "      <td>-1.049707</td>\n",
       "      <td>-1.395152</td>\n",
       "      <td>1.930696</td>\n",
       "      <td>-1.932344</td>\n",
       "      <td>-1.320262</td>\n",
       "      <td>1.725058</td>\n",
       "      <td>-1.087274</td>\n",
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       "    <tr>\n",
       "      <th>20147</th>\n",
       "      <td>0.220596</td>\n",
       "      <td>0.163836</td>\n",
       "      <td>1.596174</td>\n",
       "      <td>-0.018835</td>\n",
       "      <td>0.784059</td>\n",
       "      <td>1.088748</td>\n",
       "      <td>-1.592642</td>\n",
       "      <td>2.760174</td>\n",
       "      <td>1.284763</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>20148</th>\n",
       "      <td>1.276340</td>\n",
       "      <td>1.769255</td>\n",
       "      <td>1.438192</td>\n",
       "      <td>1.202295</td>\n",
       "      <td>2.889946</td>\n",
       "      <td>0.676373</td>\n",
       "      <td>-0.710273</td>\n",
       "      <td>2.128563</td>\n",
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       "      <th>20149</th>\n",
       "      <td>1.320622</td>\n",
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       "      <td>1.045397</td>\n",
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       "      <td>-0.180226</td>\n",
       "      <td>1.756703</td>\n",
       "      <td>-0.522404</td>\n",
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       "             Ax        Ay        Az        Lx        Ly        Lz        Gx  \\\n",
       "20000  0.312536  2.001052  0.512077 -1.628563  2.458472 -1.468715  2.090165   \n",
       "20001 -0.395823  0.859192  0.001001 -2.552256  1.047762 -2.023476  1.520448   \n",
       "20002 -3.188474 -1.509975 -2.947652 -5.984517 -1.751687 -5.712171  0.380632   \n",
       "20003 -2.214444 -1.656326 -3.334933 -4.568079 -1.939310 -6.323208 -1.776467   \n",
       "20004 -0.767037 -0.294850 -0.379980 -0.701402  0.005609 -0.225464 -2.116848   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
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       "20146 -1.049707 -1.395152  1.930696 -1.932344 -1.320262  1.725058 -1.087274   \n",
       "20147  0.220596  0.163836  1.596174 -0.018835  0.784059  1.088748 -1.592642   \n",
       "20148  1.276340  1.769255  1.438192  1.202295  2.889946  0.676373 -0.710273   \n",
       "20149  1.320622  2.408489  1.351480  1.045397  3.694111  0.439018 -0.180226   \n",
       "\n",
       "             Gy        Gz  Label  \n",
       "20000 -1.021030 -1.028797      2  \n",
       "20001 -0.722460 -0.998804      2  \n",
       "20002  0.943098 -0.103885      2  \n",
       "20003  1.392493  1.180665      2  \n",
       "20004 -2.895411  1.238087      2  \n",
       "...         ...       ...    ...  \n",
       "20145 -2.944403  1.672155      2  \n",
       "20146  2.625995  2.272826      2  \n",
       "20147  2.760174  1.284763      2  \n",
       "20148  2.128563  0.214773      2  \n",
       "20149  1.756703 -0.522404      2  \n",
       "\n",
       "[150 rows x 10 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled.iloc[20000:20150]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>300</th>\n",
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       "      <th>301</th>\n",
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       "      <td>-0.110805</td>\n",
       "      <td>0.303591</td>\n",
       "      <td>-0.738660</td>\n",
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       "      <th>302</th>\n",
       "      <td>0.496440</td>\n",
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       "      <td>0.390693</td>\n",
       "      <td>0.225542</td>\n",
       "      <td>0.670679</td>\n",
       "      <td>-0.859585</td>\n",
       "      <td>1.359684</td>\n",
       "      <td>1.853893</td>\n",
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       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>0.448782</td>\n",
       "      <td>0.129676</td>\n",
       "      <td>1.382454</td>\n",
       "      <td>0.215560</td>\n",
       "      <td>0.727830</td>\n",
       "      <td>0.781132</td>\n",
       "      <td>-1.278804</td>\n",
       "      <td>2.235082</td>\n",
       "      <td>1.784802</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>0.326163</td>\n",
       "      <td>0.222395</td>\n",
       "      <td>1.205892</td>\n",
       "      <td>0.019314</td>\n",
       "      <td>0.845564</td>\n",
       "      <td>0.463426</td>\n",
       "      <td>-1.190877</td>\n",
       "      <td>2.305408</td>\n",
       "      <td>1.551189</td>\n",
       "      <td>6</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <th>445</th>\n",
       "      <td>-0.218732</td>\n",
       "      <td>-0.580279</td>\n",
       "      <td>0.834207</td>\n",
       "      <td>-0.625943</td>\n",
       "      <td>-0.212571</td>\n",
       "      <td>0.018635</td>\n",
       "      <td>0.532513</td>\n",
       "      <td>-0.085251</td>\n",
       "      <td>-0.972682</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>-0.021205</td>\n",
       "      <td>-0.714449</td>\n",
       "      <td>0.605000</td>\n",
       "      <td>-0.303691</td>\n",
       "      <td>-0.387160</td>\n",
       "      <td>-0.285838</td>\n",
       "      <td>0.557258</td>\n",
       "      <td>-0.106599</td>\n",
       "      <td>-0.991745</td>\n",
       "      <td>6</td>\n",
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       "      <th>447</th>\n",
       "      <td>0.046908</td>\n",
       "      <td>-0.763352</td>\n",
       "      <td>0.121802</td>\n",
       "      <td>-0.163261</td>\n",
       "      <td>-0.444618</td>\n",
       "      <td>-0.844658</td>\n",
       "      <td>0.532717</td>\n",
       "      <td>0.112022</td>\n",
       "      <td>-1.097611</td>\n",
       "      <td>6</td>\n",
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       "      <th>448</th>\n",
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       "      <td>-0.895014</td>\n",
       "      <td>0.357195</td>\n",
       "      <td>-0.290033</td>\n",
       "      <td>-0.617954</td>\n",
       "      <td>-0.455926</td>\n",
       "      <td>0.432934</td>\n",
       "      <td>0.200776</td>\n",
       "      <td>-1.168873</td>\n",
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       "      <th>449</th>\n",
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       "      <td>0.462518</td>\n",
       "      <td>-0.013982</td>\n",
       "      <td>-0.807070</td>\n",
       "      <td>-0.277953</td>\n",
       "      <td>0.159148</td>\n",
       "      <td>-0.055818</td>\n",
       "      <td>-0.903868</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Ax        Ay        Az        Lx        Ly        Lz        Gx  \\\n",
       "300 -0.412853 -0.673069  0.865183 -0.898294 -0.331244  0.279146 -0.278191   \n",
       "301 -0.109750 -0.512030  0.902344 -0.429758 -0.110805  0.303591 -0.738660   \n",
       "302  0.496440 -0.258253  1.196613  0.390693  0.225542  0.670679 -0.859585   \n",
       "303  0.448782  0.129676  1.382454  0.215560  0.727830  0.781132 -1.278804   \n",
       "304  0.326163  0.222395  1.205892  0.019314  0.845564  0.463426 -1.190877   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "445 -0.218732 -0.580279  0.834207 -0.625943 -0.212571  0.018635  0.532513   \n",
       "446 -0.021205 -0.714449  0.605000 -0.303691 -0.387160 -0.285838  0.557258   \n",
       "447  0.046908 -0.763352  0.121802 -0.163261 -0.444618 -0.844658  0.532717   \n",
       "448 -0.072289 -0.895014  0.357195 -0.290033 -0.617954 -0.455926  0.432934   \n",
       "449  0.111603 -1.036529  0.462518 -0.013982 -0.807070 -0.277953  0.159148   \n",
       "\n",
       "           Gy        Gz  Label  \n",
       "300  0.094271  2.134068      6  \n",
       "301  0.963769  1.907964      6  \n",
       "302  1.359684  1.853893      6  \n",
       "303  2.235082  1.784802      6  \n",
       "304  2.305408  1.551189      6  \n",
       "..        ...       ...    ...  \n",
       "445 -0.085251 -0.972682      6  \n",
       "446 -0.106599 -0.991745      6  \n",
       "447  0.112022 -1.097611      6  \n",
       "448  0.200776 -1.168873      6  \n",
       "449 -0.055818 -0.903868      6  \n",
       "\n",
       "[150 rows x 10 columns]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled.iloc[300:450]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "walking = full_data[full_data['Activity'] == 'walking'].head(100)\n",
    "walking = walking[walking.columns[:-1]].values\n",
    "walking = walking.reshape(1,100,9)\n",
    "\n",
    "\n",
    "standing = full_data[full_data['Activity'] == 'standing'].tail(100)\n",
    "standing = standing[standing.columns[:-1]].values\n",
    "standing = standing.reshape(1,100,9)\n",
    "\n",
    "\n",
    "jogging = full_data[full_data['Activity'] == 'jogging'].tail(100)\n",
    "jogging = jogging[jogging.columns[:-1]].values\n",
    "jogging = jogging.reshape(1,100,9)\n",
    "\n",
    "\n",
    "biking = full_data[full_data['Activity'] == 'biking'].tail(100)\n",
    "biking = biking[biking.columns[:-1]].values\n",
    "biking = biking.reshape(1,100,9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute 'head'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [255], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m biking\u001b[39m.\u001b[39;49mhead()\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'head'"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['biking', 'downstairs', 'jogging', 'sitting', 'standing',\n",
       "       'upstairs', 'walking'], dtype=object)"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 16ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[2]])"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_results = model.predict(biking)\n",
    "prediction_results = np.argmax(prediction_results, axis=1, keepdims=True)\n",
    "prediction_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 12ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[2]])"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_results = model.predict(walking)\n",
    "prediction_results = np.argmax(prediction_results, axis=1, keepdims=True)\n",
    "prediction_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 13ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1]])"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_results = model.predict(standing)\n",
    "prediction_results = np.argmax(prediction_results, axis=1, keepdims=True)\n",
    "prediction_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 12ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[2]])"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_results = model.predict(jogging)\n",
    "prediction_results = np.argmax(prediction_results, axis=1, keepdims=True)\n",
    "prediction_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "Human_Activity_Recognition.ipynb",
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  "kernelspec": {
   "display_name": "Python 3",
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