{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mockup Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To test the `FeatureExtractor` class from the `time_domain_features.py` script with a simple mockup dataset of 5 to 10 data points directly in a Python notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Modules\n",
    "\n",
    "Use relative imports or modify the path to include the directory where the module is stored. In this example, we’ll simulate the relative import setup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Mockup Data\n",
    "\n",
    "Create a small dataset with 5 to 10 data points. Simulate importing the `FeatureExtractor` from its relative path in the notebooks directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SampleData</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.496714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.138264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.647689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.523030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.234153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.234137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.579213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.767435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.469474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.542560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SampleData\n",
       "0    0.496714\n",
       "1   -0.138264\n",
       "2    0.647689\n",
       "3    1.523030\n",
       "4   -0.234153\n",
       "5   -0.234137\n",
       "6    1.579213\n",
       "7    0.767435\n",
       "8   -0.469474\n",
       "9    0.542560"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create mockup data\n",
    "np.random.seed(42)  # For reproducibility\n",
    "mock_data = np.random.randn(10)  # Generate 10 random data points\n",
    "\n",
    "# Convert to DataFrame (simulating processed data input)\n",
    "mock_df = pd.DataFrame(mock_data, columns=['SampleData'])\n",
    "mock_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize Data Points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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G3r17F1j/6+bmhnfeeQejRo1Cu3btMGTIkOx2ZuHh4XjppZdKFfPq1atx4sQJZGZm4vr169i4cSNiY2NRtWpVLF++PHtBnZ+fH9q2bYt3330XGRkZqFSpEtatW1fgDH5hr69Lly5wd3dH79698X//939ITk7G119/jeDg4OyzCaXVu3dvtG/fHm+88QbOnz+PBg0aYN26dVi2bBkmTJhQqhZvuTVp0gRffvkl3nrrLdSoUQPBwcHo0KEDoqKiEBMTgyZNmqBs2bLYt28flixZghdeeMEqz0tEYDszIrKO1atXK6NHj1YiIyMVHx8fxd3dXalRo4by4osvKtevX893+2+//VZp1KiR4uHhoQQGBirt2rVTYmNjs7++Y8cO5fHHH1e8vLyU0NDQ7PZoAJRNmzZl3y45OVkZOnSoEhAQoADI09ps2bJlSlRUlOLq6pqvfdjBgweVfv36KeXKlVM8PDyUqlWrKgMHDlQ2bNiQfRu1BdbNmzfN+h6o7cx++eWXIm9XUDszRVGU9evXK61bt1a8vLwUPz8/pXfv3sqxY8fy3X/GjBlKpUqVFBcXF7Namy1evDj7e122bFll2LBhyuXLl/PcxpJ2ZuqHu7u7EhISonTu3Fn5+OOPlaSkpHz3uXz5stK3b18lICBA8ff3V5566inl6tWrCgBlypQpZr2+5cuXK/Xr11c8PT2V8PBw5Z133lG+/fZbs74H5h7L+/fvKy+99JISGhqquLm5KTVr1lTee++9PC3KFKXwdmaPfv/Un4ncP7PXrl1Tevbsqfj6+ioAslubvfXWW0rz5s2VgIAAxcvLS4mMjFRmzpyZryUgEVnOoCgWdGMnIiIiItIY1vgSERERkS4w8SUiIiIiXWDiS0RERES6wMSXiIiIiHSBiS8RERER6QITXyIiIiLSBW5gUQyTyYSrV6/C19e3RFuFEhEREZF9KIqC+/fvIzQ0FC4uhc/rMvEtxtWrVxEWFiY7DCIiIiIqxqVLl1C5cuVCv87Etxi+vr4AxDfSz8/P5s+XkZGBdevWoUuXLnBzc7P585F8POb6w2OuTzzu+sNjbj9JSUkICwvLztsKw8S3GGp5g5+fn90SX29vb/j5+fGXRCd4zPWHx1yfeNz1h8fc/oorS+XiNiIiIiLSBSa+RERERKQLTHyJiIiISBdY40tERESaoCgKMjMzkZWVJTsUs2RkZMDV1RUPHz7UTMyOymg0wtXVtdStZZn4EhERkcNLT09HQkICUlNTZYdiNkVREBISgkuXLnEvACvw9vZGxYoV4e7ubvFjMPElIiIih2YymRAfHw+j0YjQ0FC4u7trIpE0mUxITk6Gj49PkZsqUNEURUF6ejpu3ryJ+Ph41KxZ0+LvJxNfIiIicmjp6ekwmUwICwuDt7e37HDMZjKZkJ6eDk9PTya+peTl5QU3NzdcuHAh+3tqCR4FIiIi0gQmj/pmjePPnyAiIiIi0gUmvkRERESkC0x8iYiISDeysoDNm4EffxSX7DJmmalTp6Jhw4aywygxJr5ERESkC0uXAuHhQPv2wNCh4jI8XFxvK//4xz9gNBphMBjg5uaGChUqoHPnzvj2229hMplK9Fjz589HQECAVeKKiYmBwWCAwWCAp6cnoqKi8MUXX5h9/3/+85/YsGFDiZ4zPDwcc+bMKWGk1sXEl4iIiJze0qXAgAHA5ct5r79yRVxvy+S3a9euSEhIwPnz57F69Wq0b98e48ePR69evZCZmWm7Jy7GmDFjkJCQgGPHjmHgwIEYO3YsfvzxR7Pu6+Pjg3Llytk4Qutj4ktERGRjWVnAli0GbN1aCVu2GHh63QoUBUhJMe8jKQkYN07cp6DHAYDx48XtzHm8gh6nKB4eHggJCUGlSpXQuHFjvP7661i2bBlWr16N+fPnZ9/uww8/RL169VCmTBmEhYXhH//4B5KTkwEAmzdvxqhRo5CYmJg9Uzt16lQAwMKFC9G0aVP4+voiJCQEQ4cOxY0bN4qNy9vbGyEhIahWrRqmTp2KmjVrYvny5QCAixcv4oknnoCPjw/8/PwwcOBAXL9+Pfu+j5Y6jBw5Ek8++STef/99VKxYEeXKlcPYsWORkZEBQMwwX7hwAS+99FJ2/ABw4cIF9O7dG4GBgShTpgwee+wxrFq1qmTf4BJg4ktERGRD6un1zp1d8eGHTdG5s6vNT6/rQWoq4ONj3oe/v5jZLYyiiJlgf3/zHs8am8d16NABDRo0wNJcPwguLi745JNPcPToUSxYsAAbN27Eq6++CgBo1aoV5syZAz8/PyQkJCAhIQH//Oc/AYitkWfMmIFDhw7h999/x/nz5zFy5MgSx+Tl5ZXdM/mJJ57AnTt3sGXLFsTGxuLcuXMYNGhQkffftGkTzp49i02bNmHBggWYP39+dmK/dOlSVK5cGdOnT8+OHwDGjh2LtLQ0bN26FYcPH8Y777wDHx+fEsduLm5gQUREZCPq6fVHZwjV0+tLlgD9+smJjeSLjIzEX3/9lf35hAkTssfh4eF466238Pzzz+OLL76Au7s7/P39YTAYEBISkudxRo8enT2uVq0aPvnkEzRr1ix717jiZGVl4ccff8Rff/2Fv/3tb9iwYQMOHz6M+Ph4hIWFAQC+++47PPbYY9i7dy+aNWtW4OMEBgbis88+g9FoRGRkJHr27IkNGzZgzJgxKFu2LIxGY/astOrixYvo378/6tWrlx2/LXHGl4iIyAayssTp86JOr0+YwK4ClvL2BpKTzfsw98z5qlXmPZ61No9TFCXP1svr169Hx44dUalSJfj6+uKZZ57B7du3kVrMFPP+/fvRu3dvVKlSBb6+vmjXrh0AkVQW5YsvvoCPjw+8vLwwZswYvPTSS/j73/+O48ePIywsLDvpBYCoqCgEBATg+PHjhT7eY489BqPRmP15xYoViy25GDduHN566y20bt0aU6ZMyfNGwBaY+BIREdnAtm35F1LlpijApUvidlRyBgNQpox5H126AJUri/sU9lhhYeJ25jxeYY9TUsePH0dERAQA4Pz58+jVqxfq16+PX3/9Ffv378fnn38OQGzZXJiUlBR07doVfn5++OGHH7B371789ttvxd4PAIYNG4a4uDjEx8cjJSUFH374Yal2R3Nzc8vzucFgKLZzxXPPPYdz587hmWeeweHDh9G0aVN8+umnFsdQHCa+RERENvC/Ekar3Y4sZzQCH38sxo8mrernc+aI29nLxo0bcfjwYfTv3x+AmLU1mUz44IMP8Pjjj6NWrVq4evVqnvu4u7sj65FTBCdOnMDt27fx9ttvIzo6GpGRkWYtbAMAf39/1KhRA5UqVcqT8NapUweXLl3CpUuXsq87duwY7t27h6ioKEtfcoHxA0BYWBief/55LF26FC+//DK+/vpri5+jOEx8iYiIbKBiRevejkqnXz9RU12pUt7rK1e2fa11Wloarl27hitXruDAgQOYNWsWnnjiCfTq1QvDhw8HANSoUQMZGRn49NNPce7cOSxcuBBz587N8zjh4eFITk7Ghg0bcOvWLaSmpqJKlSpwd3fPvt/y5csxY8aMUsXbqVMn1KtXD8OGDcOBAwewZ88eDB8+HO3atUPTpk0tftzw8HBs3boVV65cwa1btwCIuua1a9ciPj4eBw4cwKZNm1CnTp1SxV8UJr5EREQ2EB1t3un16Gj7xqVn/foB588DmzYBixaJy/h42y8wXLt2LSpWrIjw8HB069YNmzZtwieffIJly5Zl18Q2aNAAH374Id555x3UrVsXP/zwA2bPnp3ncVq1aoXnn38egwYNQvny5fHuu++ifPnymD9/Pn755RdERUXh7bffxvvvv1+qeA0GA5YtW4bAwEC0bdsWnTp1QrVq1bB48eJSPe706dNx/vx5VK9eHeXLlwcgFtaNHTsWderUQbdu3VCrVq0SbaRRUgZFKWk3On1JSkqCv78/EhMT4efnZ/Pny8jIwKpVq9CjR498tTLknHjM9YfHXD8K6+oAiMSXXR3M8/DhQ8THxyMiIgKenp6ywzGbyWRCUlIS/Pz8SlU7S0JRPwfm5ms8CkRERDbSrx/wySf5r/f2ZtJLJAMTXyIiIhtSa0pr1jRh8GDRCkpRgM6dJQZFpFNMfIlIiqwsYPNm4McfxSV7mZKzOnpUXLZoAQwadAo1ayp48EDM+BKRfTHxJSK7U7dwbd8eGDpUXHILV3JWauIbFaXAYACGDxd9Tf+3kysR2RETXyKyK3Wxz6ON/dUtXJn8krPJnfgCwLBhJri4AFu3AmfPSgxMg7geX9+scfyZ+BKR3XALV9KbzEzg5EkxVhPfypVz6nsXLJAUmMao3U+K27qXnJt6/EvTDcfVWsEQERWnJFu4xsTYLSwimzlzBkhPF9vcVqkCHDsmrh85Eli7ViS+U6cC7HRVNKPRiICAgOwdyby9vWGw1r7BNmQymZCeno6HDx+ynVkpKIqC1NRU3LhxAwEBAdm9jy3BxJeI7IZbuJLe5JQ55E1un3gC8PcHLl4Umyh07CgnPi0JCQkBALO343UEiqLgwYMH8PLy0kSi7ugCAgKyfw4spanEd+vWrXjvvfewf/9+JCQk4LfffsOTTz5Z6O03b96M9u3b57s+ISGh1N84Iio5buFKeqMmvo89lvd6Ly9gyBBg7lyxyI2Jb/EMBgMqVqyI4OBgZGRkyA7HLBkZGdi6dSvatm3LzWpKyc3NrVQzvSpNJb4pKSlo0KABRo8ejX4l6Pp98uTJPLt4BAcH2yI8IiqGuoXrlSuF72RVuTK3cCXnceSIuKxbN//XRo4Uie+vvwKffw7YYXNQp2A0Gq2SANmD0WhEZmYmPD09mfg6CE0lvt27d0f37t1LfL/g4GAEBARYPyAiKhGjEfj4Y6B///xfU88CzpkjbkfkDAqb8QWA5s2ByEjgxAng55+B556zb2xEeqSpxNdSDRs2RFpaGurWrYupU6eidevWhd42LS0NaWlp2Z8nJSUBEKcr7HFqRX0OrZzGodLT2zHv3RuoU8eI48fzLvSoVEnBBx9koXdvBc7+rdDbMder9HTg1ClXAAbUqpVR4HEfPtwFr79uxLx5JowYwXYmzoa/6/Zj7vfYqRPfihUrYu7cuWjatCnS0tLwzTffICYmBrt370bjxo0LvM/s2bMxbdq0fNevW7cO3t7etg45W2xsrN2eixyDXo75zZueOHGiCwCgV6+zWLGiOkJCkvHxxxtgNAKrVkkO0I70csz16uJFX2RmdoC3dwb++msVDh8W1+c+7iEhnnBx6YKdO13wzTcbERqaIilasiX+rtueua3uDIpGu0EbDIZiF7cVpF27dqhSpQoWLlxY4NcLmvENCwvDrVu38tQJ20pGRgZiY2PRuXNn1gPphN6O+YcfuuC114xo08aEb77JQmSkGzw8FNy9mwlXp34rnkNvx1yvfvnFgGHDXNGihQnbtmUVetz79DFizRoX/OtfWZgxwyQxYrI2/q7bT1JSEoKCgpCYmFhkvqaTfzM5mjdvju3btxf6dQ8PD3h4eOS73s3Nza4/tPZ+PpJPL8f855/F5bBhLqhZ0wVlygApKQZcuOCGyEi5sdmbXo65XqkbV9St6wI3t5zSnkeP++jRwJo1wA8/GDFzppE17k6Iv+u2Z+73V3fdlOPi4lCRvZKIpDh5EjhwAHB1FdsTu7jkLPpRV78TOYuiFrbl1rs3EBgoNnfZuNH2cRHpmaYS3+TkZMTFxSEuLg4AEB8fj7i4OFy8eBEAMGnSJAwfPjz79nPmzMGyZctw5swZHDlyBBMmTMDGjRsxduxYGeET6d6PP4rLzp2BoCAxrldPXKr1j0TOwtzE19MTGDpUjOfNs21MRHqnqVKHffv25dmQYuLEiQCAESNGYP78+UhISMhOggEgPT0dL7/8Mq5cuQJvb2/Ur18f69evL3BTCyKyLUXJSXzVf/JATn9TzviSM0lLA06fFuOCevg+auRI0cv3t9+Ae/cAduAksg1NJb4xMTEoai3e/Pnz83z+6quv4tVXX7VxVERkjoMHgVOnxOzWE0/kXM8ZX3JGJ08CWVkigTWnuq5JEzEzfPSoqIP/299sHiKRLmmq1IGItGvRInHZuzfg65tzvTobduYM8OCB/eMisoXcZQ7q5ixFMRiAUaPEmOUORLbDxJeIbM5kAhYvFuPcZQ4AUKECUL68KIU4dsz+sRHZgrn1vbkNGyZ2LfzzT7GbGxFZHxNfIrK57dvFinV/f6CgXcdZ50vOxpLENyQk5/djwQLrx0RETHyJyA7UMod+/YAC2mSzzpecjiWJL5BT7vDdd6JGmIisi4kvEdlURgawZIkYP1rmoOKMLzmThw+Bs2fFuKSJb69eQLlywNWrAHe5JbI+Jr5EZFOxscDt26KWt7BOgpzxJWdy4oSoay9bVvzcl4S7e84bxEcaFRGRFTDxJSKbUsscBg5EoVuxqrNiV68Cd+7YJy4iW1HPXNSta15Hh0ep5Q6//w7cvWu1sIgITHyJyIZSU8U/b6DwMgdAtDcLDxdjljuQ1lla36tq2BCoX19sgvHTT1YLi4jAxJeIbGjFCiAlBYiIAFq0KPq2rPMlZ1HaxNdgEDu5ASx3ILI2Jr5EZDNqmcPgwcWf8mWdLzmL0ia+gOjp6+oK7NnD/tZE1sTEl4hs4t49YPVqMS6qzEHFGV9yBqmpQHy8GJcm8Q0OBnr2FGPO+hJZDxNfIrKJpUuB9HSR0KpJbVFyz/gqim1jI7KV48fFz2/58uKjNNRyh4ULgczMUodGRGDiS0Q2opY5DBli3u1r1xandhMTgStXbBcXkS1Zo8xB1bOnSJ6vXQPWri394xERE18isoFr14BNm8TY3MTX3V0kvwDrfEm7rJn4urmJWl+A5Q5E1sLEl4is7uefRQP/xx8XHR3MxTpf0rrcPXytQS13WL5cbARDRKXDxJeIrK6kZQ4qdnYgrbPmjC8ANGgANGok6uV//NE6j0mkZ0x8iciqzp0Ddu8GXFzEbm0lwRlf0rLkZODCBTG2VuILsKcvkTUx8SUiq1J3murQAQgJKdl91RnfY8e4ip20R+23GxIClC1rvccdOlTU++7fz7MhRKXFxJeIrMrSMgdAbFtcpozYqvXsWauGRWRz1i5zUAUFAb17izFnfYlKh4kvEVnN4cPin7+7O9CvX8nv7+KSkzRwZou0xlaJL5BT7vD990BGhvUfn0gvmPgSkdWos709egABAZY9But8Satsmfh26yZ2c7txA1izxvqPT6QXTHyJyCoUJae+15IyBxU7O5BW2TLxdXMDnnlGjOfNs/7jE+kFE18isoo//wTOnwd8fIBevSx/HM74khYlJgKXLomxLRJfABgxQlz+8Qdw86ZtnoPI2THxJSKrUMscnnwS8Pa2/HHUGd8zZ4AHD0odFpFdqB0dKlWyvMynOPXqAU2aiI4n7OlLZBkmvkRUapmZYrc2oHRlDoCoYwwKEju/HT9e+tiI7MGWZQ65jRolLlnuQGQZJr5EVGqbNolFN+XKAZ07l+6xDAbW+ZL22CvxHTxYdE2JixMfRFQyTHyJqNTUMoennhKLcEqLdb6kNfZKfMuVA/r0EeMFC2z7XETOiIkvEZXKw4fA0qViXNoyBxVnfElr7JX4AjnlDt9/D6Sn2/75iJwJE18iKpXVq4GkJKByZaBNG+s8Jmd8SUvu3QOuXhXjqCjbP1+XLmJb5Fu3gFWrbP98RM6EiS8RlYpa5jB4sNh5zRrUWbMrV4C7d63zmES2os72hoUBfn62fz5X15yevtzCmKhkmPgSkcWSkoAVK8TYWmUOgEgeqlYVY876kqNTf0bVMxX2oG5hvHKlWFhKROZh4ktEFlu2TNT41q4NNGpk3cdmnS9phT3re1VRUUDz5qKV4A8/2O95ibSOiS8RWUwtcxgyRLQhsybW+ZJWyEh8gZxZ33nzxJbhRFQ8Jr5EZJGbN4HYWDG2ZpmDijO+pBWyEt/BgwEPD/E7cvCgfZ+bSKuY+BKRRZYsAbKyxBaqtWpZ//Fzz/hyNosc1e3bwPXrYlynjn2fOzBQbBEOcJEbkbmY+BKRRXKXOdhCZKRYvX7vnujuQOSI1Nne8HDAx8f+z6+WO/zwA5CWZv/nJ9IaJr5EVGIXLwLbt4u63kGDbPMc7u45M8ms8yVHJavMQdW5MxAaCty5k9NhhYgKx8SXiEps8WJx2bat2LjCVljnS45OduJrNALDh4sxyx2IisfEl4hKzNZlDip2diBHJ6OH76PUcofVq4Fr1+TFQaQFTHyJqEROnADi4kT97YABtn0uzviSo5M94wuIPtotW4rFpt9/Ly8OIi1g4ktEJfLjj+Kya1egXDnbPpc6i3bsmPinTuRIbtwAbt0Ste6RkXJjUWd9589nFxSiojDxJSKzKYr9yhwAICIC8PYWq9XPnLH98xGVhDrbW62a+DmVadAgwNNTxLR/v9xYiBwZE18iMtv+/SIB9fICnnjC9s/n4pJzCpl1vuRoHKHMQeXvD/TrJ8bz5smNhciRMfElIrOpZQ59+tivZynrfMlROVLiC+SUO/z4I/DwodRQiBwWE18iMktWFvDTT2JsjzIHlVrny8SXHI2jJb4dOoj2gnfvAn/8ITsaIsfExJeIzLJtG3D1KhAQAHTrZr/nVWd8WepAjkRRHC/xNRqBESPEmOUORAVj4ktEZlHLHPr3Bzw87Pe86ozvmTPAgwf2e16ioly7JnZLc3GR39EhNzXxXbtWvFEloryY+BJRsdLTgV9+EWN7ljkAQIUKQFAQYDIBx4/b97mJCqPO9taoIbopOIqaNYHWrcXvC3v6EuXHxJeIirVunagbDAkBYmLs+9wGA+t8yfE4WplDbqNGict589jTl+hRTHyJqFhqmcOgQaKO0N5Y50uOxpET36eeEi0HT5wA9uyRHQ2RY2HiS0RFSkkBfv9djO1d5qDijC85GkdOfP38RC0+IHZyI6IcTHyJqEh//AGkpordqZo3lxMDZ3zJkThiR4dHqeUOP/7IRaFEuTHxJaIiqWUOQ4aIelsZ1OTiyhVRa0wk09WrQGKiKPupVUt2NAWLiQGqVBFxLlsmOxoix8HEl4gKdecOsHq1GMsqcwDEqduqVcWYs74km/ozWLOmfVv7lYSLS05rM5Y7EOVg4ktEhVq6FMjIAOrXl39Kl3W+5CjUMgf1Z9JRqYnvunXA5ctyYyFyFEx8iahQixaJS5mzvSrW+ZKjcPT6XlX16kDbtqImeeFC2dEQOQYmvkRUoKtXgc2bxXjwYKmhAOCMLzkOrSS+ADBypLicP589fYkAJr5EVIiffxb/KFu1AsLDZUeTd8aX/8BJFkUBjh0TYy0kvk89BZQpA5w6BezaJTsaIvmY+BJRgRypzAEAatcWq+jv3RPdHYhkuHQJuH8fcHMTi9scnY8PMGCAGHORGxETXyIqwJkzwN69YmX4U0/Jjkbw8BDJL8A6X5JHLXOoVUskv1qgljssXix6chPpGRNfIsrnp5/EZadOQIUKcmPJjXW+JJuW6ntVbdsCERFAUhLw22+yoyGSi4kvEeWhKI5X5qBiZweSTf3Zc/RWZrmxpy9RDia+RJTHX38Bx4+L0oK+fWVHkxdnfEk2Lc74AsDw4eJywwbg4kW5sRDJxMSXiPJQtyju2RPw95cby6PUGd9jx4CsLLmxkP6YTNrq6JBbRATQvr04o/Pdd7KjIZKHiS8RZTOZchJfRytzAMQ/b29vIC1NLMAjsqcLF8TiMHd3sTmE1rCnLxETXyLKZdcucRrU11fM+DoaF5ecmTbW+ZK9qWUOkZGAq6vcWCzRv79ob3b2LLBjh+xoiORg4ktE2dTZ3r59AS8vubEUhnW+JItW63tVZcoAAweK8bx5cmMhkoWJLxEBADIzxW5tgGOWOajY2YFk0XriC+SUO/z8M5CSIjUUIimY+BIRALHa++ZNICgI6NhRdjSF44wvyeIMiW+bNqI+OTkZWLpUdjRE9sfEl4gA5JQ5DBzo2DtSqTO+Z84ADx7IjYX0Iysrp6ODlnr4PspgyJn1ZbkD6RETXyLCgwc5sz+OXOYAiJ3kypUTHSiOH5cdDelFfDzw8CHg6Sm6i2jZ8OEiAd60CTh/XnY0RPbFxJeIsGoVcP8+EBYGtGolO5qiGQys8yX7U8sc6tQBjEa5sZRWlSo55Uzs6Ut6w8SXiPL07nXRwF8F1vmSvTlDfW9uuXv6mkwyIyGyLw38iyMiW0pMBFasEGNHL3NQccaX7M3ZEt++fQE/P1HCsW2b7GiI7IeJL5HO/f672AktMhJo0EB2NObhjC/Zm7Mlvt7ewKBBYjx/vtRQiOyKiS+RzqllDkOHivpZLVAT3ytXgLt35cZCzi8rCzhxQoydJfEFcsodfvlFtDcj0gMmvkQ6duMGsH69GGulzAEQp2irVBFjljuQrZ09K86KeHsD4eGyo7Geli2BWrXERhZLlsiOhsg+mPgS6dgvv4jZrKZNgRo1ZEdTMqzzJXtRf8aiorSx+NNcuXv6styB9MKJfoWJqKRylzloDet8yV6crb43t2eeEQnwli3AuXOyoyGyPSa+RDp14QKwY4f4p6cuctESzviSvThz4lu5MtC5sxgvWCA3FiJ7YOJLpFM//SQu27UDQkPlxmKJ3DO+iiI3FnJuzpz4AsCoUeJywQL29CXnx8SXSKe0XOYAiPZrRiNw7x5w9arsaMhZZWQAJ0+KsbMmvk88Afj7i7NAmzfLjobItpj4EunQsWPAoUOAmxvQv7/saCzj4SFWpAOs8yXbOXNGJL8+PjmdRJyNlxcweLAYc5EbOTsmvkQ6pM72du0KlC0rN5bSYJ0v2Zpa5hAVpZ0+15ZQyx2WLAGSkuTGQmRLTHyJdEZRtF/moGJnB7I1Z6/vVTVvLsqHHjwQbQ6JnBUTXyKd2btXNOT39gb69JEdTelwxpdsTf3ZUt9kOSv29CW9YOJLpDPqbG+fPkCZMnJjKS01GTl2TGzEQWRtepnxBURPXxcXYPt2UdtM5IyY+BLpSFYWsHixGGu9zAEAqlUTC3MePhSz2ETWlJ4OnD4txnpIfENDRd0/wFlfcl5MfIl0ZMsWICEBCAzM+QenZS4uOQkJ63zJ2k6dAjIzAT8/oFIl2dHYh1rusGABz6KQc2LiS6QjaplD//6Au7vcWKyFdb5kK7nLHJy5o0NuffqIN8aXLwObNsmOhsj6mPgS6URaGvDrr2LsDGUOKnZ2IFvRU32vytMTGDJEjOfNkxsLkS0w8SXSibVrgbt3gYoVgbZtZUdjPZzxJVvRY+IL5JQ7LF0KJCZKDYXI6jSV+G7duhW9e/dGaGgoDAYDfv/992Lvs3nzZjRu3BgeHh6oUaMG5rNin3RKLXMYNEhs9ess1Bnf06dFD1Iia9Fr4tu0qXjNDx8CP/8sOxoi69JU4puSkoIGDRrg888/N+v28fHx6NmzJ9q3b4+4uDhMmDABzz33HNauXWvjSIkcS3IysGyZGDtTmQMAhIQA5coBJhNw4oTsaMhZPHyY09HB2Xv4Pip3T1+WO5CzcZUdQEl0794d3bt3N/v2c+fORUREBD744AMAQJ06dbB9+3Z89NFH6FrIkva0tDSkpaVlf570v70bMzIykJGRUYrozaM+hz2eixyDPY750qUGPHjgiurVFTRokAln+/F67DEjtm51wcGDmahbV5EdTrH4e+74jhwBTCY3BAYqKFfOOr8zWjrugwYBr73mil27DDhyJAO1a8uOSJu0dMy1ztzvsaYS35LatWsXOnXqlOe6rl27YsKECYXeZ/bs2Zg2bVq+69etWwdvb29rh1io2NhYuz0XOQZbHvNPP20BIASNG5/C6tXONy3q41MPQDX88Uc8ypU7Jjscs/H33HFt2VIJQFOEhNzB6tXbrfrYWjnujRq1wL59IZg6NR7PPHNcdjiappVjrmWpqalm3c6pE99r166hQoUKea6rUKECkpKS8ODBA3h5eeW7z6RJkzBx4sTsz5OSkhAWFoYuXbrAz8/P5jFnZGQgNjYWnTt3hpubm82fj+Sz9TG/fRuIixO/6m++WQ116lSz+nPIduWKC1atAh48qI4ePcJlh1Ms/p47vl27RCVgmzYB6NGjh1UeU2vH/eFDAwYPBv78sya+/z7CqdYG2IvWjrmWqWfoi+PUia8lPDw84OHhke96Nzc3u/7Q2vv5SD5bHfPly0UT/gYNgPr1nfNnqmFDcXn0qAvc3LSzdIG/545LrRevV88INzfrZnxaOe5PPgmULQtcuWLAli1uTrHpjSxaOeZaZu73Vzv/ISwQEhKC69ev57nu+vXr8PPzK3C2l8gZqd0c1N6czkhddX/5smjZRlRaeu3okJuHBzBsmBizIRI5C6dOfFu2bIkNGzbkuS42NhYtW7aUFBGRfV25IrYpBoDBg+XGYkv+/kCVKmKsJixElnrwADh7Voz1nPgCOd0dfvuNbyrJOWgq8U1OTkZcXBzi4uIAiHZlcXFxuHjxIgBRnzt8+PDs2z///PM4d+4cXn31VZw4cQJffPEFfv75Z7z00ksywieyu8WLAUUBWrcGqlaVHY1tcQc3spYTJ8TvTblyQHCw7GjkatRIbBKTlib+nhBpnaYS33379qFRo0Zo1KgRAGDixIlo1KgRJk+eDABISEjIToIBICIiAitXrkRsbCwaNGiADz74AN98802hrcyInI0eyhxU3MGNrEX9GapbV/S01TODARg1SoxZ7kDOQFOL22JiYqAohffoLGhXtpiYGBw8eNCGURE5ptOngX37xC5tTz0lOxrb44wvWQvre/MaNgx49VVg927g+HGgTh3ZERFZTlMzvkRkPnW2t1MnfZyuzT3jW8T7Y6JiMfHNKzgYUDu6cdaXtI6JL5ETUhRg0SIx1kOZAwBERorZ7bt3gatXZUdDWsbENz+13GHhQtEekUirmPgSOaG4OODkSdGOqG9f2dHYh4cHUKuWGLPOlyyVkgLEx4sxE98cPXoAQUFAQgKwbp3saIgsx8SXyAmpZQ69egF22HDQYbDOl0rr+P925g0OFokeCe7u7OlLzoGJL5GTMZmAn34SY72UOajY2YFKi2UOhVPLHZYtA+7ckRsLkaWY+BI5mR07gEuXxEyvuiBFLzjjS6XFxLdwDRqI7cHT03POKhFpDRNfIiej/kPq2xfQ287c6ozvsWNAVpbcWEibcvfwpfzUndxY7kBaxcSXyIlkZAC//CLGeitzAICICJHsP3yYs+UsUUlwxrdow4YBbm6iRzhLikiLmPgSOZH164Fbt4Dy5YGOHWVHY39GY07Cwn/KVFL37wPq5p9MfAsWFCQWzQKc9SVtYuJL5ETUMoeBAwFXTe3LaD2s8yVLHTsmLitWBAID5cbiyNRyh++/F2eZiLSEiS+Rk3jwAPjtNzHWY5mDip0dyFIsczBP9+6i3dv168DatbKjISoZJr5ETmLFCiA5GahaFWjZUnY08nDGlyzFxNc8bm7A00+L8bx5cmMhKikmvkROQi1zGDwYcNHxb7Y643v6tFjkRmQuJr7mU8sd/vhDrCsg0god/3skch737gGrVomxnsscACAkBChbVmzkoe7CRWQOtTyGiW/x6tUDmjQRNb7s6UtawsSXyAn89huQlgZERQH168uORi6DgXW+VHL37gFXrogxE1/zqLO+LHcgLWHiS+QE1BmXIUNE4qd3rPOlklI7OlSuDPj7y41FK4YMAdzdgYMHgUOHZEdDZB4mvkQad/06sGGDGA8eLDcWR8EZXyop1veWXLlyQJ8+YrxggdxYiMzFxJdI437+WdSzNm8O1KghOxrHwBlfKikmvpZhT1/SGia+RBqXu8yBBDXxvXxZ1G4SFYeJr2W6dhULSm/ezFlgS+TImPgSaVh8PLBrl6jrHThQdjSOw98fCAsTY5Y7kDmY+FrG1RV45hkx5hbGpAVMfIk07KefxGX79kBoqNxYHA3rfMlcd+4ACQliHBUlNxYtGjFCXK5YAdy4ITcWouIw8SXSMJY5FI51vmQudba3ShXA11duLFr02GNAs2ZAZiawaJHsaIiKxsSXSKOOHBFJnZsb0L+/7GgcD2d8yVxq4qu+WaKSGzVKXLLcgRwdE18ijVJne7t3BwID5cbiiHLP+CqK3FjIsbG+t/QGDxY9fQ8dEn19iRwVE18iDVKUnPpeljkULDISMBqBu3dz6jeJCsLEt/QCA4EnnxRjzvqSI2PiS6RBe/YA584B3t5A796yo3FMnp5AzZpizDpfKgoTX+tQyx1++AFIT5cbC1FhmPgSaZC6gOTJJ4EyZaSG4tBY50vFuXUrpxNBnTpyY9G6zp1Fd5nbt0WHByJHxMSXSGOysoDFi8WYZQ5FY2cHKo462xsRwTeRpWU0sqcvOT4mvkQas2kTcP26qKnr0kV2NI6NM75UHJY5WJe6hfGqVeLvFJGjYeJLpDFqN4ennhKrqKlw6ozv0aNippzoUeqbIia+1hEZCTz+uPh9+/572dEQ5cfEl0hD0tKAX38VY5Y5FK9aNcDLC3j4UCwGJHoUe/hanzrrO38+WwmS42HiS6Qhq1cDiYlApUpAdLTsaByf0Zgzk8c6X3qUorDUwRYGDRJdVY4cAQ4ckB0NUV5MfIk0RC1zGDRIJHVUPHUmj3W+9KgbN0QHAhcXcYqerCMgAOjbV4znzZMaClE+THyJNOL+feCPP8SYZQ7mUxe4ccaXHqXO9qolMWQ9arnDokWiRIvIUTDxJdKIZcuABw/EpgxNmsiORjs440uFYZmD7XTsCFSuLHZOVN+wEzkCJr5EGqGWOQwZAhgMcmPREnXG9/RpsciNSMXE13aMRmD4cDF+/33x92vzZnZXIfmY+BJpwK1bwLp1Yswyh5IJCQHKlhX/cE+ckB0NORImvrZVsaK43L0bGDoUaN8eCA8Hli6VGhbpHBNfIg1YsgTIzAQaNeIinJIyGFjnS/kpCnv42tLSpcC4cfmvv3IFGDCAyS/Jw8SXSANylzlQybHOlx6VkADcuydOydeuLTsa55KVBYwfX3APX/W6CRNY9kByMPElcnCXLgHbtonxoEFyY9EqzvjSo9Qyhxo1RM9Zsp5t24DLlwv/uqLk/btGZE9MfIkc3OLF4h9FdDRQpYrsaLRJnfFl4ksq1vfaTkKCdW9HZE1MfIkcHMscSk9NfC9fFqe3iZj42o66qM1atyOyJia+RA7s5Emx5afRKBaEkGX8/YGwMDFmnS8BTHxtKTpa9PAtrO2iwSB+H7ntOsnAxJfIgamzvV26AOXLy41F69Q6Xya+pChMfG3JaAQ+/liMC0t+58zhtuskh2tp7nzs2DFcvHgR6enpea7v06dPqYIiIvHPmWUO1lO3LrBqFet8SbTUSkoCXF2BWrVkR+Oc+vUTbRjHj8+70M3PD5g3T3ydSAaLEt9z586hb9++OHz4MAwGA5T/9Scx/O+tXRZ7lBCV2sGDwKlTYsX5k0/Kjkb7OONLKvVnoGZNwN1dbizOrF8/4IknRPeG774TCW+TJkx6SS6LSh3Gjx+PiIgI3LhxA97e3jh69Ci2bt2Kpk2bYvPmzVYOkUifFi0Sl717A76+cmNxBrk7OxTUX5T0Qy1zUH8myHaMRiAmBvjnP8Xnu3YBaWlSQyKdsyjx3bVrF6ZPn46goCC4uLjAxcUFbdq0wezZszGuoK1aiKhETCbRxgxgmYO1REaKf8J377KNkt6xvtf+6tQR6xQePgT27pUdDemZRYlvVlYWfP83BRUUFISrV68CAKpWrYqTJ09aLzoindq+XdTF+fkB3bvLjsY5eHqKU9sA63z1jomv/RkMQLt2Yrxli9xYSN8sSnzr1q2LQ4cOAQBatGiBd999Fzt27MD06dNRrVo1qwZIpEdqmUP//txVyppY50uKAhw7JsZMfO2LiS85AosS3zfffBMmkwkAMH36dMTHxyM6OhqrVq3Cx2oPEyKySEaGWA0NsMzB2riDG128CCQnA25uYrtish818d2xQ/ydI5LBoq4OXbt2zR7XqFEDJ06cwJ07dxAYGJjd2YGILBMbC9y+DQQHA+3by47GuXDGl9Qyh9q1RfJL9vPYY0C5cuLv2759QMuWsiMiPbJoxnf06NG4f/9+nuvKli2L1NRUjB492iqBEemVWuYwaJDoM0rWo874Hj0KsOuiPrG+Vx4XF6BtWzFmuQPJYlHiu2DBAjx48CDf9Q8ePMB3331X6qCI9Co1Ffj9dzFmmYP1VasGeHmJleXnzsmOhmRQZ/uZ+MrBOl+SrUTzSUlJSVAUBYqi4P79+/DMteomKysLq1atQnBwsNWDJNKLFSuAlBQgPBx4/HHZ0TgfoxGIigL27xd1vmqXB9IP9vCVS018t28HMjN5Vovsr0Q/cgEBATAYDDAYDKhVwD6PBoMB06ZNs1pwRHqjljkMGVL4HvdUOvXqicT3yBHuIKU3JhNw/LgYc8ZXjnr1gIAA4N494MABoHlz2RGR3pQo8d20aRMURUGHDh3w66+/omzZstlfc3d3R9WqVREaGmr1IIn04N49YPVqMWaZg+2ws4N+nT8vyok8PIDq1WVHo09Go6jzXb5clDsw8SV7K1Hi2+5/5yji4+MRFhYGFxeLSoSJqAC//25AerqYiVK7D5D1sbODfqllDuoufiRHu3Y5ie8rr8iOhvTGouqaqlWrAgBSU1Nx8eJFpKen5/l6/fr1Sx8Zkc789JN4Izl0qORAnJw643v6tFjkxg1C9IMdHRyDWue7bZvorsI3IWRPFiW+N2/exKhRo7BaPS/7iCz2CSIqkbt3PbB5syjqHTxYcjBOrmJFoGxZ4M4d4MQJoGFD2RGRvTDxdQwNG4rt2JOSgEOHgMaNZUdEemJRrcKECRNw79497N69G15eXlizZg0WLFiAmjVrYvny5daOkchpZWUBW7YY8N13UTCZDGjeXLTcItsxGFjnq1dMfB2D0QhER4vx5s1SQyEdsijx3bhxIz788EM0bdoULi4uqFq1Kp5++mm8++67mD17trVjJHJKS5eKtmWdO7ti06YqAIBTp8T1ZFus89WfrCx2dHAk7OdLsliU+KakpGT36w0MDMTNmzcBAPXq1cOBAwesFx2Rk1q6FBgwALh8Oe/1iYnieia/tsUZX/05d07UdHt5ARERsqOh3HW+JpPcWEhfLEp8a9eujZMnTwIAGjRogP/85z+4cuUK5s6di4oVK1o1QCJnk5UFjB8PKEr+r6nXTZjALXVtiTO++qOWOdSpw8VUjqBxY8DHB7h7l29Ayb4sSnzHjx+PhIQEAMCUKVOwevVqVKlSBZ988glmzZpl1QCJnM22bflnenNTFODSJXE7sg31VPelS6J/Mjk/1vc6FldXoHVrMWadL9mTRYnv008/jZEjRwIAmjRpggsXLmDv3r24dOkSBg0aZM34iJzO/94zWu12VHIBAUBYmBirCRE5Nya+jicmRlyyzpfsySo7UHh7e6Nx48YICgqyxsMROTVzq4FYNWRbrPPVFya+jket8926lXW+ZD8lTnxTUlIwefJk1K1bFz4+PvD19UX9+vUxffp0pKam2iJGIqcSHQ1UrizaahXEYBCzkWq7H7IN1vnqR2am6NkMMPF1JE2bAt7ewO3bwLFjsqMhvShR4pueno527drh3XffRc2aNfHiiy9i7NixiIiIwMyZM9GxY0dkZGTYKlYip2A0Ah9/XPDiNjUZnjOHC3BsjTO++nH2LJCeLpKs/208Sg7AzQ1o1UqMWe5A9lKindu+/PJLXL58GYcOHULt2rXzfO3EiROIiYnB3Llz8eKLL1o1SCJn068fUKuW6NubW+XKIunt109KWLqSe8ZXUQqfgSftU2f1o6IAF6sU+JG1xMQA69eLBW5jx8qOhvSgRH8Cli5din//+9/5kl4AiIyMxBtvvIElS5ZYLTgiZ3X0qEh6XVyAxYszMXHiPsTGZiI+nkmvvURGiln1O3e4kNDZqfW96iw/OY7cdb4FnQUjsrYSJb7Hjh1DjLoMswDt27fHMRbqEBXrv/8Vl336AH37Kmjb9gratVNY3mBHnp5AzZpizDpf58aFbY6rWTPxu3jjRk4dNpEtlSjxvXfvHsqVK1fo18uVK4fExMRSB0XkzNLSgIULxfjZZ+XGones89UHJr6Oy8MDaNlSjFnnS/ZQosTXZDLBWMSUlIuLC7K43RRRkZYvB27dAkJDgW7dZEejb+zs4PwyMnJq6Zn4Oia13IEbWZA9lGhxm6Io6NixI1xdC75bZmamVYIicmZqmcPIkWL3IjZCkYczvs7v9GnxO+brm7NpCTmW3BtZcKEp2VqJEt8pU6YUe5v+/ftbHAyRs7twAVi3ToxHj5YbC+XM+B47BmRlsYWcM1LLHKKimFA5qhYtRMnDtWvijUqtWrIjImdm9cSXiAo3f76Y0WjfHqheXXY0VK0a4OUFPHgAnDuXs9iNnAfrex2fp6dIfrduFbO+THzJlizuaJiZmYn169fjP//5D+7fvw8AuHr1KpKTk60WHJEzycoCvv1WjJ97Tm4sJBiNYiYQYJ2vs1KPKxNfx6bW+XKBG9maRYnvhQsXUK9ePTzxxBMYO3Ysbt68CQB455138M9//tOqARI5iw0bgIsXgYAAoG9f2dGQinW+zo09fLVBrfPdvJn9fMm2LEp8x48fj6ZNm+Lu3bvw8vLKvr5v377YsGGD1YIjcibffCMun35anF4nx8DODs4rLU3UjAKc8XV0jz8utjC+ckWUHRHZSolqfFXbtm3Dzp074e7unuf68PBwXLlyxSqBETmTW7eA338XY/budSyc8XVep06JEiN/f9E+kByXtzfQvDmwY4cod+AaCLIVi2Z8TSZTgf16L1++DF9f31IHReRsFi4ULZWaNAEaNpQdDeWmzviePg08fCg3FrKu3Avb2NHB8bHO13lkZYmylR9/FJeOtMWDRYlvly5dMGfOnOzPDQYDkpOTMWXKFPTo0cNasRE5BUXJ6d3L2V7HU7EiEBgo/jBzy1Tnwo4O2sKNLJzD0qVAeLjoXjR0qLgMDxfXOwKLEt8PPvgAO3bsQFRUFB4+fIihQ4dmlzm888471o6RSNN27xb/gL28gCFDZEdDjzIYWOfrrJj4akurVmJTn4sXgfPnZUdDlli6FBgwALh8Oe/1V66I6x0h+bUo8a1cuTIOHTqE119/HS+99BIaNWqEt99+GwcPHkRwcLC1YyTSNHW2d8AA0dGBHA/rfJ0TW5lpi48P0LSpGLPcQXuysoDx4wvuyqFeN2GC/LIHixa3AYCrqyuefvppa8ZC5HSSk4GffhJj9u51XJzxdT4PHwJnz4oxW5lpR7t2wJ9/isR3xAjZ0VBJbNuWf6Y3N0UBLl0St1Pb18lgduK7fPlysx+0T58+FgVD5Gx+/lkkvzVrAtHRsqOhwnDG1/mcOAGYTEDZskCFCrKjIXO1awe88w5nfLUoIcG6t7MVsxPfJ5980qzbGQyGAjs+EOmR2rv32We5qtyRqYnvpUtAYqJof0Xaxo4O2tS6tdhR8dw58fsYFiY7IjJXxYrWvZ2tmF3jazKZzPpg0kskHDsG7Nol/ojzlJ1jCwgAKlcWY5Y7OAcubNMmPz+gcWMx5qyvtkRHi7+jhb3RNBjEGxnZZz8tWtxGRMVTF7X16gWEhMiNhYrHOl/nwsRXu9jPV5uMRuDjjwte3KYmw3PmiNvJZHHiu2HDBvTq1QvVq1dH9erV0atXL6xfv96asRFpVno68N13YszevdrAOl/nwsRXu5j4ale/fmJNy6MqVwaWLBFfl82ixPeLL75At27d4Ovri/Hjx2P8+PHw8/NDjx498Pnnn1s7xjw+//xzhIeHw9PTEy1atMCePXsKve38+fNhMBjyfHh6eto0vtLIygK2bDFg69ZK2LLFIL3lB1lu+XKxTXHFikD37rKjIXNwxtd5pKaKGlGAia8WtWkjZghPnwauXpUdDZXE9evAmTNi/MsvwKJFwKZNQHy8YyS9gIXtzGbNmoWPPvoIL7zwQvZ148aNQ+vWrTFr1iyMHTvWagHmtnjxYkycOBFz585FixYtMGfOHHTt2hUnT54stH+wn58fTp48mf25wUFXOSxdKvrfXb7sCqApPvxQvEP6+GPH+WEh86llDiNHiobs5Phyz/gqChdEadnx4+IYBgUBbC2vPQEBQKNGwIEDYtaXG/9ox8qV4nevSRPRu94RWTTje+/ePXTr1i3f9V26dEFiYmKpgyrMhx9+iDFjxmDUqFGIiorC3Llz4e3tjW+//bbQ+xgMBoSEhGR/VHDAvjZa2OmEzHfxIrB2rRiPHi03FjJfnTqAiwtw5w5w7ZrsaKg01DIH9u/VLpY7aJPa+bZ3b7lxFMWiuag+ffrgt99+wyuvvJLn+mXLlqFXr15WCexR6enp2L9/PyZNmpR9nYuLCzp16oRdu3YVer/k5GRUrVoVJpMJjRs3xqxZs/BYEee+0tLSkJaWlv15UlISACAjIwMZGRlWeCV5ZWUB48a5/q8YPO8Uk5h1UjB+PNCjR6b0gnAyz3//6wJFMaJdOxOqVs1CcT826s+VLX6+yHxGI1CjhitOnTLg4MFMBAUVsELDSnjMbeuvv1wAGFGnThYyMkyyw8nG426+1q0N+OgjV2zerCAjI1N2OBbT0zF/+BCIjXUFYED37hnF/u+zNnO/xxYlvlFRUZg5cyY2b96Mli1bAgD+/PNP7NixAy+//DI++eST7NuOGzfOkqfI59atW8jKyso3Y1uhQgWcOHGiwPvUrl0b3377LerXr4/ExES8//77aNWqFY4ePYrKau+iR8yePRvTpk3Ld/26devg7e1d+hfyiMOHy+HKlTaFfl1RDLh8GXj//d2oV++21Z+frMtkAr78sjMAbzRufBCrVhWxjc0jYmNjbRcYmSUoqClOnaqEJUtOICPjrM2fj8fcNrZsaQEgBCbTEaxadV52OPnwuBfvwQM3GAzdcfKkAYsWbUBAQFrxd3Jgejjm+/YFIzW1JcqVe4CrV9fZfaOK1NRUs25nUJSCGk8ULSIiwrwHNxhwTl1hUEpXr15FpUqVsHPnzuxkGwBeffVVbNmyBbt37y72MTIyMlCnTh0MGTIEM2bMKPA2Bc34hoWF4datW/Dz8yv9C3nETz8ZMHx48e8/vvsuE4MH224Giqxj/XoDevRwhb+/gosXM+HlVfx9MjIyEBsbi86dO8PNzc32QVKhZsxwwYwZRgwfbsI339hudSmPuW3VquWK8+cN2LAhE9HRjvN3k8e9ZJo2dcVffxnwww+ZeOopxzmOJaGnY/7CCy746isj/va3LHz2mf3PtCQlJSEoKAiJiYlF5msWzfjGx8dbHJilgoKCYDQacf369TzXX79+HSFmNkl1c3NDo0aNcEZdclgADw8PeHh4FHhfW/zQmrsrTViYK5z8d8YpzJ8vLp9+2gA/v5IdMFv9jJH5GjYUl8eOucDNzfZtznnMrS85GTh/XowbNHDMv5s87uaJiQH++gvYscMVQ4fKjqZ0nP2YK4pY2AYATzxhhJub/Wszzf3+amYDC3d3dzRp0gQbNmzIvs5kMmHDhg15ZoCLkpWVhcOHD6Oi7P3yctHKTidUvFu3gN9/F2P27tUmdTHU0aNgO0GNOn5cXFaoAJQrJzcWKh0ucNOOgwfFgnxvb6BDB9nRFM2iGV9FUbBkyRJs2rQJN27cgMmUd0p7qY3aEEycOBEjRoxA06ZN0bx5c8yZMwcpKSkYNWoUAGD48OGoVKkSZs+eDQCYPn06Hn/8cdSoUQP37t3De++9hwsXLuC5556zSXyWUHc6GTBAJLkFFZ44wk4nVLzvvxcbVzRuLFrxkPZUrw54egIPHoi+kzVqyI6ISoobVziPtm3F5dGjwM2bQPnycuOhwv3xh7js0kX8DXVkFs34TpgwAc888wzi4+Ph4+MDf3//PB+2MmjQILz//vuYPHkyGjZsiLi4OKxZsyZ7wdvFixeRkKua+u7duxgzZgzq1KmDHj16ICkpCTt37kRUVJTNYrREv35iR5NKlfJ/bfhw9vHVAkXJ6d3L2V7tMhoB9c8Dd3DTJnUDEia+2hcUlHMct26VGwsVTU18HbmNmcqiGd+FCxdi6dKl6NGjh7XjKdYLL7yQZ+OM3DZv3pzn848++ggfffSRHaIqvX79gCeeADZtysTq1XEwGBrhgw+MWLtWtAhx9HdQerdnj/iH6+kJzdei6V29eqJx/pEjQN++sqOhkmIPX+cSEyOO6ZYtQP/+sqOhgly5AuzfL85a9+wpO5riWTTj6+/vj2rVqlk7Ft0zGoF27RS0bXsF06aZUKWKaKRfxP4c5CDU2d4BA8SuQ6RduXdwI+1hqYNzYZ2v41uxQly2aCFq6x2dRYnv1KlTMW3aNDx48MDa8dD/uLsDr74qxu++C7s3gibzJScDP/4oxg5UPk4WqldPXKqnzEk7kpKAS5fEmImvc1DrfA8fFrsqkuPRUpkDYGHiO3DgQNy9exfBwcGoV68eGjdunOeDrGP0aPHu6cIF4IcfZEdDhfnlF5H81qiR80eatEud8T11CkjTds983Tl2TFyGhvLMi7OoUAGIjBTrKLZtkx0NPSolBVi/Xoy1kvhaVOM7YsQI7N+/H08//TQqVKgAQ2G9uKhUvLyAl18WM7+zZwPPPMPuDo7om2/E5bPPFt6WjrQjNBQIDATu3gVOnAAaNJAdEZmLZQ7OKSZG/C5u3izWwpDjWL9eTBCEh2unrt6ixHflypVYu3Yt2rQpfKtdso7nnxdJ76lTwK+/AgMHyo6Icjt+HNi5U7whGTFCdjRkDQaD+AO+bZs4vcrEVzuY+Dqndu2AuXNZ5+uIcpc5aGXix6JSh7CwMJts30v5+foC48eL8axZBff5JXnURW09ewIOtC8KlRLrfLWJia9zUhe4xcUB9+7JjIRyM5lyFrZppcwBsDDx/eCDD/Dqq6/ivLovJNnUiy8CPj7AoUM5WwKSfOnpwHffiTF79zoXdnbQJvbwdU4VKwI1a4qJn+3bZUdDqr17gevXxQSd+uZECyxKfJ9++mls2rQJ1atXh6+vL8qWLZvng6yrbFlg7Fgxfustzvo6ij/+ELsJVawISGhpTTbEGV/tuXcPuHpVjJn4Oh81sXqkXT9JpJY5dOsmOlFphUU1vnPmzLFyGFScl14SWxvv3g1s3Ah07Cg7IlLLHEaMAFwt+k0iR6UmThcvAomJgA03pCQrUcscwsIAVuI5n5gYsZCYdb6OQ2ttzFQWd3Ug+6pQARgzBvj0U2DmTCa+sl26BKxZI8ajR8uNhawvMBCoXBm4fFkkVK1ayY6IisP6XuemzvgeOCD6NfPNjVwXLgB//QW4uGjvjKdFpQ65PXz4EElJSXk+yDZeeQVwcwM2bQJ27ZIdjb7Nny9KTtq1E7Vn5HxY56stTHydW+XKQLVqYkHVjh2yoyF1trd1a6BcObmxlJRFiW9KSgpeeOEFBAcHo0yZMggMDMzzQbYRFgYMHy7GM2fKjUXPTKacbaS5U5vzYp2vtjDxdX7cvthxaLXMAbAw8X311VexceNGfPnll/Dw8MA333yDadOmITQ0FN+py9zJJl57TZxaWLkSOHhQdjT6tHEjcP68qPvs3192NGQrnPHVFia+zo8L3BxDUpI48wzoKPH9448/8MUXX6B///5wdXVFdHQ03nzzTcyaNQs/cG9dm6pRAxg8WIxnzZIbi16pO7UNGyZ21yPnlHvGl51UHNvt28C1a2IcFSU3FrIdNfHdt09sE09yrFsHZGSIfKR2bdnRlJxFie+dO3dQrVo1AICfnx/u3LkDAGjTpg22bt1qveioQJMmictffxU7h5H93L4N/PabGLN3r3OLjBRnV3InVeSY1NneqlVFz3NyTuHh4hhnZYkdM0kOtcyhTx/t7NaWm0WJb7Vq1RAfHw8AiIyMxM8//wxAzAQHBARYLTgqWN26wJNPilmot9+WHY2+fP+92LiiUSOgcWPZ0ZAteXnlLFxkna9jUxNftTyFnBfrfOXKygJWrRJjLZY5ABYmvqNGjcKhQ4cAAK+99ho+//xzeHp64qWXXsIrr7xi1QCpYG+8IS5/+AH433sQsjFFySlz4GyvPrDOVxtY36sfrPOV688/gVu3gIAA0dFBiyzq4/vSSy9ljzt16oQTJ05g//79qFGjBurXr2+14KhwTZsCXbqIWpt33wW+/FJ2RM5v714x8+fpCQwdKjsasod69URJEWd8HRsTX/2IiRGXe/cCqamAt7fUcHRn+XJx2b27aK+qRSWa8d21axdWrFiR57rvvvsOMTExeP755/HZZ58hLS3NqgFS4dRZ32+/zdmqk2xH3amtf3+xwQE5P874agMTX/2IiBA9fTMy2M9ehtz1vVpVosR3+vTpOKr+hQFw+PBhPPvss+jUqRMmTZqEP/74A7Nnz7Z6kFSwtm2BNm1Ezen778uOxrmlpAA//ijG7N2rH2pnh6NHRf9mcjw3b4oPgwGoU0d2NGRrBgPrfGU5e1YsqHd1Bbp1kx2N5UqU+MbFxaFjrr1yf/rpJ7Ro0QJff/01XnrpJXzyySfZC93IPt58U1z+5z+i7oZs45dfgPv3gerVc/7okvOrXl2Utjx4AJw7JzsaKog6FxMRwdPeesE6XznU2d7oaFHjq1UlSnzv3r2LChUqZH++ZcsWdO/ePfvzZs2a4dKlS9aLjorVpQvQpImodZozR3Y0ziv3ojYttm8hyxiNOX1hWefrmFjmoD9q4rt7t3hTSvah1vdqucwBKGHiW6FChew2Zunp6Thw4AAef/zx7K/fv38fblqtdtYogyGn1vezz4DERLnxOKMTJ8Te8C4uwIgRsqMhe2Odr2NT35Aw8dWPmjWBihVFmd/u3bKj0Yd794Bt28RYq23MVCVKfHv06IHXXnsN27Ztw6RJk+Dt7Y3o6Ojsr//111+oXr261YOkoj3xhPijn5gIfP657Gicj7qorWdPIDRUbixkf7l3cCPHwx6++sM6X/tbswbIzBR19FpP80qU+M6YMQOurq5o164dvv76a3z99ddwd3fP/vq3336LLl26WD1IKpqLS85ubh99JBZikXWkpwPffSfG7N2rT5zxdVyKwlIHvWLia19qmYPWZ3uBEvbxDQoKwtatW5GYmAgfHx8YjcY8X//ll1/gw/0ipRg0CJg8WSzA+eorIFerZSqFFSuAGzeAkBCgRw/Z0ZAM6ozvqVNAWhrg4SE3Hspx/Tpw54548x8ZKTsasic18d21i7+XtpaRAaxeLcZar+8FLNy5zd/fP1/SCwBly5bNMwNM9uPqmjPr+/774g8BlZ5a5jBihHabdVPphIaKFcxZWaLemxyHOturdt8g/YiMBIKDgYcPgT17ZEfj3HbsEDW+QUFArmVdmmVR4kuOafhw0dj76lVg/nzZ0Wjf5cuirglgmYOeGQys83VULHPQL9b52o/axqxHD9HpRuuY+DoRd3fglVfE+J13RCE6WW7+fLFpQdu2YhUx6RfrfB0TE199Y+Jre4riXPW9ABNfp/Pcc0D58kB8fM5OY1RyJlNOmQN3aiPO+DomJr76pia+O3aIhchkfSdPAmfOiIm1rl1lR2MdTHydjLc3MHGiGM+axW1WLbVpE3D+PODnB/TvLzsako0zvo5HUdjDV++iooBy5cQmFvv2yY7GOallDjExgK+v1FCshomvE/rHP8RinBMngKVLZUejTepObcOGcRtUykl8L14EkpLkxkLC1auid7nRCNSuLTsaksHFheUOtqYmvs5S5gAw8XVKfn7AuHFiPGuWmBkh892+nfOGgYvaCAACA4FKlcSY5Q6OQS1zqFmTraz0jImv7dy+LcpIACa+pAHjxgFlygAHD+b03yPz/PCDqBdr2BBo3Fh2NOQoWOfrWFjfS0DeOt+MDLmxOJtVq0S5ZP36QNWqsqOxHia+TqpcOeDvfxfjmTM562suRckpc3j2WdEyhwhgna+jYeJLgHhDGhgIJCcDBw7Ijsa5OGOZA8DE16lNnChOAe7cydNA5tq3TyQ2Hh6ivpdIxRlfx8LElwBR59u2rRjz/5z1pKfn9LFn4kuaUbFiTo3qzJlyY9EKtYVZ//5iFoFIlXvGl2dQ5FIU4NgxMWbiS6zztb4tW4D794EKFYBmzWRHY11MfJ3cq6+K7YzXrwd275YdjWNLSQEWLRJj9u6lR9WpI2aXbt8Grl2THY2+Xb4sumu4unJzGcpJfLdv58ZN1qKWOfTqJf7uORMnezn0qKpVgaefFmPO+hZtyRLxDrdatZw/pEQqLy+gRg0xZrmDXOr3v1Yt0Vif9K1BA8DfX7wZiouTHY32KYrz1vcCTHx1YdIksUjrjz+Av/6SHY3jyr2ozdne4ZJ1qHW+XOAml1rfq5afkL4ZjUCbNmLMcofSO3JEbODk4QF06iQ7Guvjv3cdqFULGDhQjGfNkhuLozp5Upwmc3EBRoyQHQ05KjXR4oyvXFzYRo+KiRGXTHxLT53t7dRJtEV1Nkx8deL118Xlzz8Dp07JjcURqYvaevTI2aiA6FGc8XUMTHzpUWp52rZtQFaW3Fi0zpnLHAAmvrpRv774IVYU4O23ZUfjWDIygAULxJg7tVFR1Bnfo0dFY3eyP5OJHR0ov0aNAF9f4N49vjEtjevXcxbC9+olNxZbYeKrI2+8IS4XLgQuXJAbiyNZsQK4cUO0benZU3Y05Mhq1BB1bw8eAOfOyY5Gny5eFB1Y3N1zFhsSuboCrVuL8ebNUkPRtJUrxQRZkybOe/aTia+OtGgBdOwo2r28+67saByHWuYwYgTg5iY3FnJsRiMQFSXGrPOVQy1zqF1bJDtEKtb5lp6zlzkATHx15803xeV//wskJMiNxRFcuQKsXi3GLHMgc7DOVy7W91Jh1DrfrVtZimSJhw+BdevEmIkvOY127YBWrYC0NODDD2VHI9/8+eIPZHS06H5BVBx2dpBL/b4z8aVHNWkiuhDcuZPzBonMt3EjkJoqShwaNZIdje0w8dUZgyGn1vfLL8UuVHplMuWUOXCnNjIXZ3zlYg9fKoybm5jYAVjna4ncZQ4Gg9xYbImJrw517y7ezaWkAJ98IjsaeTZvBuLjAT8/YMAA2dGQVqgJ16lT4swJ2Y/JBBw/Lsac8aWCqOUOrPMtGUURC70B5y5zAJj46pLBkNPX95NPxDaPeqTu1DZ0KODtLTcW0o5KlYCAANEr9MQJ2dHoS3y86Kjh6Sm2Fid6lLrAbetWkcyReeLigMuXxf/CDh1kR2NbTHx1ql8/IDJS9Dz84gvZ0djfnTvA0qVizEVtVBIGA+t8ZVHLHCIjRYcNokc1awZ4eQE3b+acHaDiLV8uLjt3Fm8snRkTX51yccmZ9f3wQ1HQric//CBOUzdoIBZEEJUE63zlYEcHKo67O9CypRiz3MF8an1vnz5y47AHJr46NmQIEBEh3hmrp/31QFFyXu+zzzp3ET/ZBmd85WDiS+ZQ63y5wM08V68C+/eL/4V62MSJia+OuboC//qXGL/3HpCeLjcee9m/H/jrL7ED17BhsqMhLeKMrxxsZUbmyL2RBet8i6cuamveXOxg6uyY+OrcyJFAaKgoav/uO9nR2IfawqxfP6BsWbmxkDapM74XL+p3cai95V5MyMSXitK8uZjYuH5ddF+hoqn1vc7ezUHFxFfnPDyAf/5TjN9+W2xn7MxSU4FFi8SYvXvJUoGBOfvYs9zBPs6eFXX5Xl6iRIuoMJ6ewOOPizHrfIuWmgps2CDGeqjvBZj4EoC//Q0IChL/WBYvlh2NbS1ZImboIiJyTocRWYJ1vval1vdGRYnFuURFYZ2vedavF1sVV62qn01h+OeDUKYMMGGCGM+a5dx7nOde1MZ/nlQarPO1Ly5so5LIvZEF63wLl7vMQS8LvfmvnwAAL7wA+PsDx44By5bJjsY2Tp0Ctm0TCe/IkbKjIa3jjK99MfGlknj8cdHa7OpVcTaT8jOZcha26aXMAWDiS//j7y+SXwCYOdM53yGri9q6d8+pzySyVO4ZX2f8fXE0THypJLy9xSI3gHW+hdm3TywA9PXNmSHXAya+lG3CBPHHYv9+YN062dFYV0YGsGCBGHOnNrKGOnXE2YPbt8U/D7KdjAzg5EkxZuJL5spd7kD5qZtWdO0qZsf1gokvZQsKAv7v/8R45ky5sVjbypUiOQkOBnr1kh0NOQMvL6BGDTFmna9tnTkj+oyXKQNUqSI7GtKK3AvceFYmP721MVMx8aU8/vlP8c5v2zZg61bZ0ViPWuYwYgTg5iY3FnIerPO1D3Z0IEu0aiU2arp0CTh/XnY0juXCBbGRk4sL0KOH7Gjsi39CKI/QUGDUKDF2llnfK1eAVavEmGUOZE3s7GAfauKrl3ZLZB1lygDNmokxyx3yUhe1tWolzvbqCRNfyudf/wKMRlHnu3ev7GhKb8ECsXq1TRugdm3Z0ZAzURNfzvjaFhe2kaVY51swtb5Xb2UOABNfKkBEBDBsmBjPmiU3ltIymXLKHLhTG1mbOgN59Khz97+WjYkvWYobWeR3/z6waZMYM/El+p9Jk0Qz699/1/Zs1pYtwLlzol3LgAGyoyFnU6OG2PY7NRWIj5cdjXNKTxc9uAEmvlRyrVuLM5jnzwMXL8qOxjGsWyd+r2rUACIjZUdjf0x8qUCRkUD//mI8e7bcWEpD3alt6FBR70VkTUajWHAFsM7XVk6fBjIzAT8/oHJl2dGQ1vj6Ak2aiDHLHYTcZQ562a0tNya+VKjXXxeXP/0k2glpzd27wK+/ijEXtZGtsM7XtnJ3dNDjP2kqPdb55sjKEu09AX2WOQBMfKkIjRqJNicmE/D227KjKbkffgDS0oD69YGmTWVHQ85KrfPljK9tqG8oWOZAlmLim+PPP4Fbt4CAALHgW4+Y+FKR3nxTXH73neiFqBWKklPm8OyznCki2+GMr21xYRuVVps2ol/tmTOivaWeqWUO3bvrt6c9E18qUsuWQPv2YsvQ996THY35DhwADh0SC4+eflp2NOTM1BnfkyfFGQayLvbwpdLy9xdnMAHO+uq5jZmKiS8V6403xOXXX4ttf7VAbWHWty9QtqzcWMi5VaokThtmZYnkl6wnLS1nfQFnfKk0WO4AnD0LHDsmFuV26yY7GnmY+FKxOnQAWrQAHj4EPvpIdjTFS00V9b0Ae/eS7RkMrPO1lZMnxRuKgACgYkXZ0ZCWMfHNme1t2xYIDJQbi0xMfKlYBkPOrO/nnwN37siNpzi//gokJYmNONq3lx0N6QHrfG0jd30v6/SpNKKjxc/QyZNAQoLsaORgmYPAxJfM0quX6I6QnAx8+qnsaIqmLmobPVosaCCyNc742gYXtpG1BAaK/2EAsHWr3FhkuHcv53Uz8SUyg8GQ09f344/FloeO6NQp8cvt4gKMHCk7GtILzvjaBhNfsqaYGHGpx3KHNWvERjCRkWLHNj1j4ktmGzAAqFVLbAwxd67saAr27bfisls37vJE9qPO+F64IMpsyDrYw5esSc91vmqZQ58+cuNwBEx8yWxGIzBpkhh/8AHw4IHceB6VkQEsWCDG3KmN7CkwUHR3AHJmKal0HjwQq9ABJr5kHdHR4vLYMeDGDbmx2FNmJrB6tRjrvcwBYOJLJTRsGFC1qmhrps6uOopVq4Br14DgYFGTTGRPrPO1rhMnxEY05coBFSrIjoacQVBQzu+pnup8d+wQZ2rLlRO9+fWOiS+ViJsb8OqrYvzuu0B6utx4clN79w4fDri7y42F9Id1vtbFjg5kC3qs812+XFz27CnO3OodE18qsdGjgZAQ4OJF4PvvZUcjXL0KrFwpxixzIBk442tdXNhGtqDHOl+2McuLiS+VmKcn8PLLYvz226LBvGwLFgAmE9C6tVi1SmRv6ozv4cPiFD2VDhNfsoW2bcXl4cPA7dtyY7GHkyeB06fF2douXWRH4xiY+JJFnn9ebAV8+jTwyy9yYzGZcsocuFMbyVKnjmijd/u2drb2dmRMfMkWgoPF7yqgjzpftcwhJgbw85MaisNg4ksW8fEBJkwQ41mzRPIpy9atYvW3ry/w1FPy4iB98/LK6Y/JOt/SSUkB4uPFmIkvWZueyh3Yxiw/Jr5ksRdeEMnm4cPAihXy4lB3ahsyBChTRl4cRKzztY7jx0W5SPny4oPImvSywO32bdHRAWB9b25MfMligYHA2LFiPHOmnLrGu3eBX38VYy5qI9nY2cE6WOZAtqTO+B46JP6HOKvVq8XZ2Hr1RBtSEpj4Uqm89JI4xbtnD7B+vf2ff9Ei4OFD8YvdrJn9n58oN874Woea+KrfTyJrCgkRu5AqCrB9u+xobEet7+Vsb15MfKlUgoOBMWPEeOZM+z+/uqjt2WfZ65PkU2d8jx6VW/eudZzxJVtTZ303b5Yahs2kpwNr1ogx63vzYuJLpfbKK6JVypYtOfVE9nDgAHDwoNis4umn7fe8RIWpXh3w8ABSU3MWZ1HJMfElW3P2Ot+tW4H798WuhzwbmhcTXyq1ypWBkSPF2J6zvupsb9++YitGItlcXYGoKDFmna9lkpOBCxfEmIkv2Yo643vwIJCYKDcWW1C7OfTsKdosUg5+O8gq/vUv8cu1erWYibW1Bw+AH34QY/buJUfCOt/SOXZMXIaEiF7hRLZQqZI4Q2My2fdMpT0oSk59L8sc8tNc4vv5558jPDwcnp6eaNGiBfbs2VPk7X/55RdERkbC09MT9erVw6pVq+wUqb5Ury7aiQGir6+t/fqreJceHg506GD75yMyFzs7lA7LHMhenLXO9+hR4Px5UXbVqZPsaByPphLfxYsXY+LEiZgyZQoOHDiABg0aoGvXrrhx40aBt9+5cyeGDBmCZ599FgcPHsSTTz6JJ598Ekf4H8kmJk0Sl0uX5sza2Irau3f0aJ7GIcfCGd/SUf88M/ElW3PWjSzUMoeOHdnbviCusgMoiQ8//BBjxozBqFGjAABz587FypUr8e233+K1117Ld/uPP/4Y3bp1wyuvvAIAmDFjBmJjY/HZZ59h7ty5BT5HWloa0tLSsj9PSkoCAGRkZCAjI8PaLykf9Tns8VzWVqsW8MQTRixb5oKZM02YPz/LJs9z+jSwZYsbDAYFw4ZlQoPfqjy0fMwpv8hIAHDDqVMKkpMz4eGR/zY85oU7csQIwAWRkZnIyJDQHNyGeNwdS6tWAOCG/fsV3LmTCV9f6z+HjGO+bJn4HerRIwsZGfppL2Pu91gziW96ejr279+PSeq0IgAXFxd06tQJu3btKvA+u3btwsSJE/Nc17VrV/z++++FPs/s2bMxbdq0fNevW7cO3t7elgVvgdjYWLs9lzVFR/tj2bIY/PQT0LbtZoSEpFr9ORYurAOgFho1uoHDh/90mpk1rR5zyktRAG/vHkhNdcN//7sd4eFJhd6Wxzy//fu7APBCUtJOrFrlnLsL8Lg7juDgTrhxoww++WQvGjW6abPnsdcxv3fPHXv2dAMAeHqux6pVD+3yvI4gNdW8fEMzie+tW7eQlZWFChUq5Lm+QoUKOHHiRIH3uXbtWoG3v3btWqHPM2nSpDzJclJSEsLCwtClSxf4+fmV4hWYJyMjA7GxsejcuTPc3Nxs/ny2EBtrwtq1Lti3rwO++MK67zYzM4Hnnxc/tq++Wg49evSw6uPL4AzHnPJq1MiIHTuAwMBo9OiRf9aSx7xgiYnA7dvi+zF6dEsEBMiNx9p43B1P165GLFwIpKa2QI8e1p8dtfcxX7DAAEUxoFEjBcOH62sBjHqGvjiaSXztxcPDAx4FnJt0c3Oz6x8qez+fNb35JrB2LfDdd0ZMnWpEpUrWe+zVq4Fr14Dy5YG+fV2h0W9RgbR8zCmvevXESvHjx4v+GeUxz+v0aXFZqRJQvrzzfl943B1H+/bAwoXA9u1GuLkZbfY89jrm6vr9Pn0MuvsZM/f1amZZUFBQEIxGI65fv57n+uvXryMkJKTA+4SEhJTo9mQdbdoAbduKnWPef9+6j6327h0+XGxcQeSI2NnBMuzoQPambmSxdy+QkiI1lFJ7+FBMOgHcprgomkl83d3d0aRJE2zYsCH7OpPJhA0bNqBly5YF3qdly5Z5bg+IOpvCbk/W88Yb4vI//wFuWqlsKiEBWLlSjJ991jqPSWQL7OxgGSa+ZG/h4UBYmCijK2S5kGZs2iR2jQwNBRo3lh2N49JM4gsAEydOxNdff40FCxbg+PHj+Pvf/46UlJTsLg/Dhw/Ps/ht/PjxWLNmDT744AOcOHECU6dOxb59+/DCCy/Iegm60bkz0LSp2Gjio4+s85gLFgBZWWIlbp061nlMIltQE98LFwAzy84ITHzJ/gwG52lrprYx691bvC4qmKYS30GDBuH999/H5MmT0bBhQ8TFxWHNmjXZC9guXryIhISE7Nu3atUKixYtwldffYUGDRpgyZIl+P3331FX/a9ENmMw5Mz6fv45cO9e6R5PUXLKHLhTGzm6smXFrAuQk8xR8djDl2Rwho0sFCVv4kuF09zithdeeKHQGdvNBfzUPvXUU3jqqadsHBUVpE8fMfN15Ajw2Wdi0Zultm4FzpwBfHwAHk7Sgnr1gKtXxc8/q6uKd/euKGcCgKgoubGQvqiJ75494iyll5fceCwRFwdcvgx4e3M30+JoasaXtMXFBXj9dTGeMwdITrb8sdSd2oYMEckvkaNjnW/JqDPjVaoAdugcSZStRg1xhiY9HfjzT9nRWEad7e3cWZuJuz0x8SWbGjhQ/FG5fRv46ivLHuPePWDJEjHmojbSCnZ2KBnW95IszlDnyzIH8zHxJZsyGgF1N+n33xftVkpq0SJxv7p1gebNrRsfka3knvFVnGvnXZtg4ksyabnO9+pVYN8+Me7ZU24sWsDEl2zumWdEu5iEBGDevJLfX13U9uyzXKlK2hEVJX5eb90CbtyQHY3jY+JLMqmJ759/WjZBI9OKFeKyRQuA2xQUj4kv2Zy7O/DKK2L8zjtARob59z14EDhwQDzG00/bJj4iW/DyEmU+AOt8zcHEl2SqXRuoUAFISxOL3LSEZQ4lw8SX7OK554DgYNHXdNEi8++nzvY++SQQFGST0IhshnW+5rl1C1A32WSPbpJBq3W+qanA+vVizMTXPEx8yS68vICXXxbj2bPFRhTFefAA+P57MWbvXtIidnYwjzrbGx7Ori0kjxYT3/XrRWlGlSo5b7SpaEx8yW7+/ncgMBA4eRJYurT42y9dCiQmAlWrAh072j4+ImvjjK951MSXewuRTGriu3OnaG2mBWqZQ58+XANjLia+ZDe+vsC4cWI8c2bxK93V3r2jR4uewERaoyZyR48CJpPcWBwZ63vJEURFiZK6Bw+AvXtlR1M8kylnYRvLHMzHdILsatw4cSrz0CFg5crCb3fmjGgrYzAAI0faKzoi66pRA/DwAFJSgPPnZUfjuJj4kiPQWp3v/v3AtWvif6oaNxWPiS/ZVdmyouQBKHrW99tvxWXXrqJ2iUiLXF1zFmuxzrdwTHzJUWgp8V2+XFx26ybeYJN5mPiS3U2cKH5J//wT2LQp/9czM4H588WYO7WR1rHOt2g3boiuDgYDEBkpOxrSOzXx3bGjZK03ZWAbM8sw8SW7CwkBxowR45kz83999Wqx2UVQkCjYJ9IydnYomjrbW60a4O0tNxaiunXFmcmUFFFK4KguXhQlgy4uQI8esqPRFia+JMUrr4jTwBs3ipnf3NTevcOHi40riLSMM75FU78vLHMgR+DiArRtK8aOXO6gzva2bMke9yXFxJekqFJFJLZA3lnfhIScVaoscyBnoM74njypnRZJ9sT6XnI0Wqjzzd3GjEqGiS9J89pr4t31ihVAXJy47rvvxOYWLVuK1jJEWle5MuDvL2rXT56UHY3jYQ9fcjRq4rt9u/i9dTT37+esj2F9b8kx8SVpatYEBg4U45kzxS/ynDni89GjpYVFZFUGA+t8C6MonPElx1O/vnizev8+cPCg7Gjyi40VZ4+qV+eCUEsw8SWpXn9dXC5ZAnToIHoSAsDUqebt7kakBazzLdi1a8Ddu+LMT+3asqMhEoxGIDpajB2x3EFtY9a7N3drswQTX5Lq9OmCr796FRgwgMkvOQfO+BZMne2tUQPw9JQbC1FuMTHi0tES36ysnM2fWN9rGSa+JE1WFjB+fMFfUze2mDBB3I5IyzjjWzCWOZCjUut8t21zrP9Bu3eLvtf+/kCbNrKj0SYmviTNtm3A5cuFf11RgEuXxO2ItEyd8T1/XtQNksDElxxVw4aAry+QmCj65ToKtZtD9+6Am5vcWLSKiS9Jk5Bg3dsROaqyZYHQUDFWkz1iD19yXK6uOTOqjlTukLu+lyzDxJekqVjRurcjcmSs882LHR3I0Tlane+5c8CxY2LxXffusqPRLia+JE10tOhxWtiqVIMBCAvLWV1LpGWs883ryhUgKUnMrLGjAzmi3HW+JpPcWICcMofoaCAwUG4sWsbEl6QxGoGPPxbjR5Nf9fM5c8TtiLSOM755qbO9NWtya3JyTI0bA2XKAHfuOMYbVpY5WAcTX5KqXz/Rw7dSpbzXV64sru/XT05cRNbGGd+8WOZAjs7NDWjdWow3b5YaChITga1bxZiJb+kw8SXp+vUTq903bQIWLRKX8fFMesm51KkjzmTcvAlcvy47GvmY+JIWqOUOsut816wR2ydHRoqzJGQ5V9kBEAGinEFdSEDkjLy9xUYNp08DR49yuyUmvqQF6v+lrVvFgkxZO6Wp9b2c7S09zvgSEdmJWud75Ii+E192dCCtaNoU8PISm0YcOyYnhsxMYNUqMWbiW3pMfImI7ESt89X7jO/Fi0Bysqih5GlbcmTu7kCrVmIsq853xw7g7l2gXDmgZUs5MTgTJr5ERHaSM+MrNw7Z1NneWrW4+xQ5Ptl1vmqZQ48eov0flQ4TXyIiO1FnfI8dMzhEX1BZ1MRXfSNA5Mhyb2ShKPZ/ftb3WhcTXyIiO6lRA/DwAFJSDLhxw1t2ONKwvpe0pHlzwNMTuHEDOHnSvs998iRw6pQ4M9K1q32f21kx8SUishNXV9HWDAAuXPCTG4xETHxJSzw8gMcfF2N7lzuos70xMYCffv9kWBUTXyIiO1JP71+8qM//YiZTzup4Jr6kFWqdr70XuLHMwfqY+BIR2ZGa7O3bF4wtWwzIypIbjz1lZQGLFwOpqWL2OzxcdkRE5sm9wM1edb63bwPbt4sxE1/rYeJLRGQnS5cCH3wgxidPlkPnzq4IDxfXO7ulS0WiO3So+DwzU9Q86+G1k/Y9/rhobZaQAJw5Y5/nXL1anCGpV49vEq2JiS8RkR0sXQoMGCAa4ed25Yq43pkTQPW1X76c93o9vHZyDl5eQIsWYmyvOl+WOdgGE18iIhvLygLGjy/4FKl63YQJcMqyBz2/dnIu9qzzTU8H1qwRYya+1sVWyERENrZtW/7ZztwUBbh0CRg4EKhc2X5x2cPly+a99m3bcvqlEjmidu2At97KqfM12HADxq1bgaQkIDhYtFMj62HiS0RkYwkJ5t1Oz6f8zf0eEcnSsqXop3v5MhAfD1SrZrvnUsscevUCXHhu3qqY+BIR2VjFiubd7umngapVbRuLvV24AHz/ffG3M/d7RCRLmTJAs2bAzp1i1tdWia+isL7Xlpj4EhHZWHS0KGG4cqXgWleDQXx9/nzAaLR7eDaVlSVqIot77dHRdg+NqMTatctJfEeNss1zHDsmZpQ9PIDOnW3zHHrGCXQiIhszGoGPPxbjR+sC1c/nzHG+pBfQ92sn52OPBW7Ll4vLDh3ELDNZFxNfIiI76NcPWLIEqFQp7/WVK4vr+/WTE5c96Pm1k3Np1Uq8SbtwQXzYglrm0KePbR5f75j4EhHZSb9+wPnzQGxsJiZO3IfY2EzEx+sj8VNf+6ZNwKJF4lIvr52ch68v0LSpGNuin++NG8Cff4pxr17Wf3xijS8RkV0ZjUC7dgpSUq6gXbsGujrFbzSyZRlpX7t2wO7dIvEdPty6j71ypaiFb9TI+VobOgrO+BIRERGZyZZ1vixzsD0mvkRERERmatNG9NY9d67ozVlK6uFDYN06MWYbM9th4ktERERkJj8/oHFjMbZmne/mzUBKChAamvP4ZH1MfImIiIhKQC13sGbiq7Yx69XLttsh6x0TXyIiIqISsHbiqyjAihVizPpe22LiS0RERFQC0dFiVvbUKSAhofSPd+gQcOkS4OUlNq4g22HiS0RERFQCAQFAgwZibI1ZX7WbQ+fOIvkl22HiS0RERFRCak9qayS+an0vuznYHhNfIiIiohKyVp3v1avAvn1izN3abI+JLxEREVEJRUeLy+PHgevXLX+clSvFZfPmQEhI6eOiojHxJSIiIiqhcuWAevXEeOtWyx9Hre9lmYN9MPElIiIiskBp63xTU4HYWDFmGzP7YOJLREREZIHS1vlu2CC2Kq5SJWf2mGyLiS8RERGRBdq2FZdHjgC3bpX8/rnLHLhbm30w8SUiIiKyQPnyQFSUGJe0ztdkYn2vDEx8iYiIiCxkabnD/v3AtWuAj09OrTDZHhNfIiIiIgtZusBNne3t2hXw8LBqSFQEJr5EREREFlLrfP/6C7hzx/z7scxBDia+RERERBYKCQFq1wYUBdi2zbz7XLwIxMWJBW09etg0PHoEE18iIiKiUihpne+KFeKyVSuxQI7sh4kvERERUSmUtM6XZQ7yMPElIiIiKgV1xjcuDrh3r+jbJicDGzeKMRNf+2PiS0RERFQKoaFAjRqiN+/27UXfdt06ID0dqF4dqFPHPvFRDia+RERERKVkbp0vd2uTi4kvERERUSmZk/hmZQErV4oxyxzkYOJLREREVEpq4nvgAHD/fsG32b0buHkT8PcHoqPtFxvlYOJLREREVEpVqgAREWJWd8eOgm+jljl07w64udkvNsrBxJeIiIjICtRZ382bC/4625jJx8SXiIiIyAqKqvM9dw44ehQwGsWML8nBxJeIiIjICtSNLPbtA1JS8n5Nne1t0wYIDLRrWJQLE18iIiIiKwgPF7W+mZnAzp15v6Ymvn362D0syoWJLxEREZGVFFTnm5iYU/7A+l65mPgSERERWUlBdb7r1hmQmQnUrg3UrCknLhKY+BIRERFZiZr47tkDpKaK8YoVIt1imYN8THyJiIiIrKR6daBSJSAjA9i924CsLAPWrBF7E7PMQT4mvkRERERWYjDkzPpu3WrAiRNlcfeuAWXLAi1byo2NmPgSERERWVXuxHfPnhAAQI8egKurxKAIAMBDQERERGRFauL7558GeHlVBgD06iUxIMrGxJeIiIjIio4cAVxcgIwMAzIyPAEAEycCbm5Av36Sg9M5ljoQERERWcnSpcBTTwEmU97rExKAAQPE10keJr5EREREVpCVBYwfDyhK/q+p102YIG5HcjDxJSIiIrKCbduAy5cL/7qiAJcuiduRHEx8iYiIiKwgIcG6tyPrY+JLREREZAUVK1r3dmR9mkl879y5g2HDhsHPzw8BAQF49tlnkZycXOR9YmJiYDAY8nw8//zzdoqYiIiI9CQ6GqhcWWxiURCDAQgLE7cjOTST+A4bNgxHjx5FbGwsVqxYga1bt+Jvf/tbsfcbM2YMEhISsj/effddO0RLREREemM0Ah9/LMaPJr/q53PmiNuRHJpIfI8fP441a9bgm2++QYsWLdCmTRt8+umn+Omnn3D16tUi7+vt7Y2QkJDsDz8/PztFTURERHrTrx+wZAlQqVLe6ytXFtezj69cmtjAYteuXQgICEDTpk2zr+vUqRNcXFywe/du9O3bt9D7/vDDD/j+++8REhKC3r1749///je8vb0LvX1aWhrS0tKyP09KSgIAZGRkICMjwwqvpmjqc9jjucgx8JjrD4+5PvG460fv3mKL4s2bsxAbewSdO9dFTIwRRiPAw28b5v5eaSLxvXbtGoKDg/Nc5+rqirJly+LatWuF3m/o0KGoWrUqQkND8ddff+Ff//oXTp48iaVFdI+ePXs2pk2blu/6devWFZkwW1tsbKzdnoscA4+5/vCY6xOPu760bQukpV3B2rWyI3FuqampZt1OauL72muv4Z133inyNsePH7f48XPXANerVw8VK1ZEx44dcfbsWVSvXr3A+0yaNAkTJ07M/jwpKQlhYWHo0qWLXcokMjIyEBsbi86dO8PNzc3mz0fy8ZjrD4+5PvG46w+Puf2oZ+iLIzXxffnllzFy5Mgib1OtWjWEhITgxo0bea7PzMzEnTt3EBISYvbztWjRAgBw5syZQhNfDw8PeHh45Lvezc3Nrj+09n4+ko/HXH94zPWJx11/eMxtz9zvr9TEt3z58ihfvnyxt2vZsiXu3buH/fv3o0mTJgCAjRs3wmQyZSez5oiLiwMAVGQDPSIiIiLd0URXhzp16qBbt24YM2YM9uzZgx07duCFF17A4MGDERoaCgC4cuUKIiMjsWfPHgDA2bNnMWPGDOzfvx/nz5/H8uXLMXz4cLRt2xb169eX+XKIiIiISAJNJL6A6M4QGRmJjh07okePHmjTpg2++uqr7K9nZGTg5MmT2cXN7u7uWL9+Pbp06YLIyEi8/PLL6N+/P/744w9ZL4GIiIiIJNJEVwcAKFu2LBYtWlTo18PDw6EoSvbnYWFh2LJliz1CIyIiIiIN0MyMLxERERFRaTDxJSIiIiJdYOJLRERERLrAxJeIiIiIdIGJLxERERHpAhNfIiIiItIFJr5EREREpAua6eMri9obOCkpyS7Pl5GRgdTUVCQlJXFfb53gMdcfHnN94nHXHx5z+1HztNx7OhSEiW8x7t+/D0BsiEFEREREjuv+/fvw9/cv9OsGpbjUWOdMJhOuXr0KX19fGAwGmz9fUlISwsLCcOnSJfj5+dn8+Ug+HnP94THXJx53/eExtx9FUXD//n2EhobCxaXwSl7O+BbDxcUFlStXtvvz+vn58ZdEZ3jM9YfHXJ943PWHx9w+iprpVXFxGxERERHpAhNfIiIiItIFJr4OxsPDA1OmTIGHh4fsUMhOeMz1h8dcn3jc9YfH3PFwcRsRERER6QJnfImIiIhIF5j4EhEREZEuMPElIiIiIl1g4ktEREREusDE14F8/vnnCA8Ph6enJ1q0aIE9e/bIDolsaPbs2WjWrBl8fX0RHByMJ598EidPnpQdFtnR22+/DYPBgAkTJsgOhWzoypUrePrpp1GuXDl4eXmhXr162Ldvn+ywyIaysrLw73//GxEREfDy8kL16tUxY8YMsJ+AfEx8HcTixYsxceJETJkyBQcOHECDBg3QtWtX3LhxQ3ZoZCNbtmzB2LFj8eeffyI2NhYZGRno0qULUlJSZIdGdrB371785z//Qf369WWHQjZ09+5dtG7dGm5ubli9ejWOHTuGDz74AIGBgbJDIxt655138OWXX+Kzzz7D8ePH8c477+Ddd9/Fp59+Kjs03WM7MwfRokULNGvWDJ999hkAwGQyISwsDC+++CJee+01ydGRPdy8eRPBwcHYsmUL2rZtKzscsqHk5GQ0btwYX3zxBd566y00bNgQc+bMkR0W2cBrr72GHTt2YNu2bbJDITvq1asXKlSogP/+97/Z1/Xv3x9eXl74/vvvJUZGnPF1AOnp6di/fz86deqUfZ2Liws6deqEXbt2SYyM7CkxMREAULZsWcmRkK2NHTsWPXv2zPM7T85p+fLlaNq0KZ566ikEBwejUaNG+Prrr2WHRTbWqlUrbNiwAadOnQIAHDp0CNu3b0f37t0lR0ausgMg4NatW8jKykKFChXyXF+hQgWcOHFCUlRkTyaTCRMmTEDr1q1Rt25d2eGQDf300084cOAA9u7dKzsUsoNz587hyy+/xMSJE/H6669j7969GDduHNzd3TFixAjZ4ZGNvPbaa0hKSkJkZCSMRiOysrIwc+ZMDBs2THZousfEl8gBjB07FkeOHMH27dtlh0I2dOnSJYwfPx6xsbHw9PSUHQ7ZgclkQtOmTTFr1iwAQKNGjXDkyBHMnTuXia8T+/nnn/HDDz9g0aJFeOyxxxAXF4cJEyYgNDSUx10yJr4OICgoCEajEdevX89z/fXr1xESEiIpKrKXF154AStWrMDWrVtRuXJl2eGQDe3fvx83btxA48aNs6/LysrC1q1b8dlnnyEtLQ1Go1FihGRtFStWRFRUVJ7r6tSpg19//VVSRGQPr7zyCl577TUMHjwYAFCvXj1cuHABs2fPZuIrGWt8HYC7uzuaNGmCDRs2ZF9nMpmwYcMGtGzZUmJkZEuKouCFF17Ab7/9ho0bNyIiIkJ2SGRjHTt2xOHDhxEXF5f90bRpUwwbNgxxcXFMep1Q69at87UpPHXqFKpWrSopIrKH1NRUuLjkTbGMRiNMJpOkiEjFGV8HMXHiRIwYMQJNmzZF8+bNMWfOHKSkpGDUqFGyQyMbGTt2LBYtWoRly5bB19cX165dAwD4+/vDy8tLcnRkC76+vvlquMuUKYNy5cqxtttJvfTSS2jVqhVmzZqFgQMHYs+ePfjqq6/w1VdfyQ6NbKh3796YOXMmqlSpgsceewwHDx7Ehx9+iNGjR8sOTffYzsyBfPbZZ3jvvfdw7do1NGzYEJ988glatGghOyyyEYPBUOD18+bNw8iRI+0bDEkTExPDdmZObsWKFZg0aRJOnz6NiIgITJw4EWPGjJEdFtnQ/fv38e9//xu//fYbbty4gdDQUAwZMgSTJ0+Gu7u77PB0jYkvEREREekCa3yJiIiISBeY+BIRERGRLjDxJSIiIiJdYOJLRERERLrAxJeIiIiIdIGJLxERERHpAhNfIiIiItIFJr5EREREpAtMfImInNDIkSPx5JNPyg6DiMihuMoOgIiISqaw7a5VU6ZMwccffwxuzElElBcTXyIijUlISMgeL168GJMnT8bJkyezr/Px8YGPj4+M0IiIHBpLHYiINCYkJCT7w9/fHwaDIc91Pj4++UodYmJi8OKLL2LChAkIDAxEhQoV8PXXXyMlJQWjRo2Cr68vatSogdWrV+d5riNHjqB79+7w8fFBhQoV8Mwzz+DWrVt2fsVERNbBxJeISCcWLFiAoKAg7NmzBy+++CL+/ve/46mnnkKrVq1w4MABdOnSBc888wxSU1MBAPfu3UOHDh3QqFEj7Nu3D2vWrMH169cxcOBAya+EiMgyTHyJiHSiQYMGePPNN1GzZk1MmjQJnp6eCAoKwpgxY1CzZk1MnjwZt2/fxl9//QUA+Oyzz9CoUSPMmjULkZGRaNSoEb799lts2rQJp06dkvxqiIhKjjW+REQ6Ub9+/eyx0WhEuXLlUK9evezrKlSoAAC4ceMGAODQoUPYtGlTgfXCZ8+eRa1atWwcMRGRdTHxJSLSCTc3tzyfGwyGPNep3SJMJhMAIDk5Gb1798Y777yT77EqVqxow0iJiGyDiS8RERWocePG+PXXXxEeHg5XV/67ICLtY40vEREVaOzYsbhz5w6GDBmCvXv34uzZs1i7di1GjRqFrKws2eEREZUYE18iIipQaGgoduzYgaysLHTp0gX16tXDhAkTEBAQABcX/vsgIu0xKNzah4iIiIh0gG/ZiYiIiEgXmPgSERERkS4w8SUiIiIiXWDiS0RERES6wMSXiIiIiHSBiS8RERER6QITXyIiIiLSBSa+RERERKQLTHyJiIiISBeY+BIRERGRLjDxJSIiIiJd+H+XIYCF9yKfmgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plotting the data points\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(mock_df.index, mock_df['SampleData'], marker='o', color='blue', label='Data Points')\n",
    "plt.title('Scatter Plot of Data Points')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('SampleData')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print Time-domain Features (Single Mockup Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Mean</th>\n",
       "      <th>Max</th>\n",
       "      <th>Peak (Pm)</th>\n",
       "      <th>Peak-to-Peak (Pk)</th>\n",
       "      <th>RMS</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Power</th>\n",
       "      <th>Crest Factor</th>\n",
       "      <th>Form Factor</th>\n",
       "      <th>Pulse Indicator</th>\n",
       "      <th>Margin</th>\n",
       "      <th>Kurtosis</th>\n",
       "      <th>Skewness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.448061</td>\n",
       "      <td>1.579213</td>\n",
       "      <td>1.579213</td>\n",
       "      <td>2.048687</td>\n",
       "      <td>0.819284</td>\n",
       "      <td>0.470467</td>\n",
       "      <td>0.723008</td>\n",
       "      <td>0.671226</td>\n",
       "      <td>1.927553</td>\n",
       "      <td>1.828509</td>\n",
       "      <td>3.524548</td>\n",
       "      <td>2.067638</td>\n",
       "      <td>1.917716</td>\n",
       "      <td>0.412307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Mean       Max  Peak (Pm)  Peak-to-Peak (Pk)       RMS  Variance  \\\n",
       "0  0.448061  1.579213   1.579213           2.048687  0.819284  0.470467   \n",
       "\n",
       "   Standard Deviation     Power  Crest Factor  Form Factor  Pulse Indicator  \\\n",
       "0            0.723008  0.671226      1.927553     1.828509         3.524548   \n",
       "\n",
       "     Margin  Kurtosis  Skewness  \n",
       "0  2.067638  1.917716  0.412307  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "import os\n",
    "# Assuming the src directory is one level up from the notebooks directory\n",
    "sys.path.append('../src/features')\n",
    "from time_domain_features import FeatureExtractor\n",
    "\n",
    "\n",
    "# Extract features\n",
    "extracted = FeatureExtractor(mock_df['SampleData'])\n",
    "\n",
    "# Format with pandas DataFramw\n",
    "features = pd.DataFrame(extracted.features, index=[0])\n",
    "features\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print Time-domain Features (Multiple CSV Mockup Data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "import os\n",
    "import re\n",
    "# Assuming the src directory is one level up from the notebooks directory\n",
    "sys.path.append('../src/features')\n",
    "from time_domain_features import ExtractTimeFeatures # use wrapper function instead of class for easy use\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to extract numbers from a filename that later used as labels features\n",
    "def extract_numbers(filename):\n",
    "    '''\n",
    "    Extract numbers from a filename\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    filename : str\n",
    "        The filename to extract numbers from\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    list\n",
    "        A list of extracted numbers: [damage_number, test_number, sensor_number]\n",
    "    '''\n",
    "    # Find all occurrences of one or more digits in the filename\n",
    "    numbers = re.findall(r'\\d+', filename)\n",
    "    # Convert the list of number strings to integers\n",
    "    numbers = [int(num) for num in numbers]\n",
    "    # Convert to a tuple and return\n",
    "    return numbers\n",
    "\n",
    "def build_features(input_dir:str, sensor:int=None):\n",
    "    all_features = []\n",
    "    for nth_damage in os.listdir(input_dir):\n",
    "        nth_damage_path = os.path.join(input_dir, nth_damage)\n",
    "        print(f'Extracting features from damage folder {nth_damage_path}')\n",
    "        if os.path.isdir(nth_damage_path):\n",
    "            for nth_test in os.listdir(nth_damage_path):\n",
    "                nth_test_path = os.path.join(nth_damage_path, nth_test)\n",
    "                if sensor is not None:\n",
    "                    # Check if the file has the specified sensor suffix\n",
    "                    if not nth_test.endswith(f'_{sensor}.csv'):\n",
    "                        continue\n",
    "                features = ExtractTimeFeatures(nth_test_path)  # return the one csv file feature in dictionary {}\n",
    "                features['label'] = extract_numbers(nth_test)[0]  # add labels to the dictionary\n",
    "                features['filename'] = nth_test  # add filename to the dictionary\n",
    "                all_features.append(features)\n",
    "\n",
    "    # Create a DataFrame from the list of dictionaries\n",
    "    df = pd.DataFrame(all_features)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Execute the automation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting features from damage folder ../../data/raw\\DAMAGE_1\n",
      "Extracting features from damage folder ../../data/raw\\DAMAGE_2\n",
      "Extracting features from damage folder ../../data/raw\\DAMAGE_3\n",
      "Extracting features from damage folder ../../data/raw\\DAMAGE_4\n",
      "Extracting features from damage folder ../../data/raw\\DAMAGE_5\n",
      "Extracting features from damage folder ../../data/raw\\README.md\n",
      "Extracting features from damage folder ../../data/raw\\DAMAGE_1\n",
      "Extracting features from damage folder ../../data/raw\\DAMAGE_2\n",
      "Extracting features from damage folder ../../data/raw\\DAMAGE_3\n",
      "Extracting features from damage folder ../../data/raw\\DAMAGE_4\n",
      "Extracting features from damage folder ../../data/raw\\DAMAGE_5\n",
      "Extracting features from damage folder ../../data/raw\\README.md\n"
     ]
    }
   ],
   "source": [
    "data_dir = \"../../data/raw\"\n",
    "# Extract features\n",
    "df1 = build_features(data_dir, sensor=1)\n",
    "df2 = build_features(data_dir, sensor=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Sensor 1 Extracted Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Sensor 2 Extracted Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Mean</th>\n",
       "      <th>Max</th>\n",
       "      <th>Peak (Pm)</th>\n",
       "      <th>Peak-to-Peak (Pk)</th>\n",
       "      <th>RMS</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Power</th>\n",
       "      <th>Crest Factor</th>\n",
       "      <th>Form Factor</th>\n",
       "      <th>Pulse Indicator</th>\n",
       "      <th>Margin</th>\n",
       "      <th>Kurtosis</th>\n",
       "      <th>Skewness</th>\n",
       "      <th>label</th>\n",
       "      <th>filename</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.161278</td>\n",
       "      <td>0.997084</td>\n",
       "      <td>2.045915</td>\n",
       "      <td>3.042999</td>\n",
       "      <td>0.908617</td>\n",
       "      <td>0.799573</td>\n",
       "      <td>0.942558</td>\n",
       "      <td>0.825584</td>\n",
       "      <td>2.251681</td>\n",
       "      <td>-5.633836</td>\n",
       "      <td>-12.685603</td>\n",
       "      <td>2.430726</td>\n",
       "      <td>2.530523</td>\n",
       "      <td>-0.741602</td>\n",
       "      <td>1</td>\n",
       "      <td>D1_TEST01_2.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.365151</td>\n",
       "      <td>1.926222</td>\n",
       "      <td>1.926222</td>\n",
       "      <td>2.968247</td>\n",
       "      <td>1.020321</td>\n",
       "      <td>0.907720</td>\n",
       "      <td>1.004280</td>\n",
       "      <td>1.041056</td>\n",
       "      <td>1.887858</td>\n",
       "      <td>2.794246</td>\n",
       "      <td>5.275138</td>\n",
       "      <td>2.051083</td>\n",
       "      <td>1.736563</td>\n",
       "      <td>-0.106945</td>\n",
       "      <td>1</td>\n",
       "      <td>D1_TEST02_2.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.111123</td>\n",
       "      <td>0.831170</td>\n",
       "      <td>1.762405</td>\n",
       "      <td>2.593575</td>\n",
       "      <td>0.820539</td>\n",
       "      <td>0.660937</td>\n",
       "      <td>0.856956</td>\n",
       "      <td>0.673285</td>\n",
       "      <td>2.147862</td>\n",
       "      <td>-7.384044</td>\n",
       "      <td>-15.859908</td>\n",
       "      <td>2.480844</td>\n",
       "      <td>2.672660</td>\n",
       "      <td>-1.046490</td>\n",
       "      <td>1</td>\n",
       "      <td>D1_TEST03_2.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.115486</td>\n",
       "      <td>0.872064</td>\n",
       "      <td>1.401001</td>\n",
       "      <td>2.273065</td>\n",
       "      <td>0.760087</td>\n",
       "      <td>0.564395</td>\n",
       "      <td>0.791900</td>\n",
       "      <td>0.577732</td>\n",
       "      <td>1.843212</td>\n",
       "      <td>-6.581648</td>\n",
       "      <td>-12.131370</td>\n",
       "      <td>1.898864</td>\n",
       "      <td>1.815395</td>\n",
       "      <td>-0.234579</td>\n",
       "      <td>1</td>\n",
       "      <td>D1_TEST04_2.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.482666</td>\n",
       "      <td>1.678422</td>\n",
       "      <td>1.678422</td>\n",
       "      <td>2.269074</td>\n",
       "      <td>0.901369</td>\n",
       "      <td>0.579500</td>\n",
       "      <td>0.802427</td>\n",
       "      <td>0.812466</td>\n",
       "      <td>1.862081</td>\n",
       "      <td>1.867479</td>\n",
       "      <td>3.477398</td>\n",
       "      <td>2.042134</td>\n",
       "      <td>1.703553</td>\n",
       "      <td>-0.045852</td>\n",
       "      <td>1</td>\n",
       "      <td>D1_TEST05_2.csv</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Mean       Max  Peak (Pm)  Peak-to-Peak (Pk)       RMS  Variance  \\\n",
       "0 -0.161278  0.997084   2.045915           3.042999  0.908617  0.799573   \n",
       "1  0.365151  1.926222   1.926222           2.968247  1.020321  0.907720   \n",
       "2 -0.111123  0.831170   1.762405           2.593575  0.820539  0.660937   \n",
       "3 -0.115486  0.872064   1.401001           2.273065  0.760087  0.564395   \n",
       "4  0.482666  1.678422   1.678422           2.269074  0.901369  0.579500   \n",
       "\n",
       "   Standard Deviation     Power  Crest Factor  Form Factor  Pulse Indicator  \\\n",
       "0            0.942558  0.825584      2.251681    -5.633836       -12.685603   \n",
       "1            1.004280  1.041056      1.887858     2.794246         5.275138   \n",
       "2            0.856956  0.673285      2.147862    -7.384044       -15.859908   \n",
       "3            0.791900  0.577732      1.843212    -6.581648       -12.131370   \n",
       "4            0.802427  0.812466      1.862081     1.867479         3.477398   \n",
       "\n",
       "     Margin  Kurtosis  Skewness  label         filename  \n",
       "0  2.430726  2.530523 -0.741602      1  D1_TEST01_2.csv  \n",
       "1  2.051083  1.736563 -0.106945      1  D1_TEST02_2.csv  \n",
       "2  2.480844  2.672660 -1.046490      1  D1_TEST03_2.csv  \n",
       "3  1.898864  1.815395 -0.234579      1  D1_TEST04_2.csv  \n",
       "4  2.042134  1.703553 -0.045852      1  D1_TEST05_2.csv  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Perform division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50 entries, 0 to 49\n",
      "Data columns (total 15 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   Mean_rel                50 non-null     float64\n",
      " 1   Max_rel                 50 non-null     float64\n",
      " 2   Peak (Pm)_rel           50 non-null     float64\n",
      " 3   Peak-to-Peak (Pk)_rel   50 non-null     float64\n",
      " 4   RMS_rel                 50 non-null     float64\n",
      " 5   Variance_rel            50 non-null     float64\n",
      " 6   Standard Deviation_rel  50 non-null     float64\n",
      " 7   Power_rel               50 non-null     float64\n",
      " 8   Crest Factor_rel        50 non-null     float64\n",
      " 9   Form Factor_rel         50 non-null     float64\n",
      " 10  Pulse Indicator_rel     50 non-null     float64\n",
      " 11  Margin_rel              50 non-null     float64\n",
      " 12  Kurtosis_rel            50 non-null     float64\n",
      " 13  Skewness_rel            50 non-null     float64\n",
      " 14  label                   50 non-null     int64  \n",
      "dtypes: float64(14), int64(1)\n",
      "memory usage: 6.0 KB\n"
     ]
    }
   ],
   "source": [
    "# Separate the label column\n",
    "label_column = df1.iloc[:, -2]\n",
    "\n",
    "# Perform the relative value by operate division on all the features\n",
    "df_relative = df2.iloc[:, :-2] / df1.iloc[:, :-2]\n",
    "\n",
    "# Add the label column back to the resulting DataFrame\n",
    "df_relative['label'] = label_column\n",
    "\n",
    "# Append a string to all column names\n",
    "suffix = '_rel'\n",
    "df_relative.columns = [col + suffix if col != 'label' else col for col in df_relative.columns]\n",
    "\n",
    "# Display the first 5 rows of the resulting DataFrame\n",
    "df_relative\n",
    "df_relative.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Subsetting DataFrame to see the pair plots due to many features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 808.875x750 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming your DataFrame is named 'df'\n",
    "\n",
    "# Subsetting the DataFrame to include only the first 3 columns and the label\n",
    "subset_df = df_relative[['Mean_rel', 'Max_rel', 'Peak (Pm)_rel', 'label']]\n",
    "\n",
    "# Plotting the pairplot\n",
    "g = sns.pairplot(subset_df, hue='label', diag_kind='kde')\n",
    "\n",
    "# Adjusting the axis limits\n",
    "for ax in g.axes.flatten():\n",
    "    ax.set_xlim(-10, 10)  # Adjust these limits based on your data\n",
    "    ax.set_ylim(-10, 10)  # Adjust these limits based on your data\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Standard Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\damar\\AppData\\Local\\Temp\\ipykernel_8200\\2762829106.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  subset_df[features] = scaler.fit_transform(subset_df[features])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 808.875x750 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Assuming 'subset_df' is your DataFrame\n",
    "features = subset_df.columns[:-1]  # Select all columns except the label\n",
    "scaler = StandardScaler()\n",
    "subset_df[features] = scaler.fit_transform(subset_df[features])\n",
    "\n",
    "# Plotting the pairplot\n",
    "sns.pairplot(subset_df, hue='label', diag_kind='kde')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Min-Max Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\damar\\AppData\\Local\\Temp\\ipykernel_8200\\3995040898.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  subset_df[features] = scaler.fit_transform(subset_df[features])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 808.875x750 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Assuming 'subset_df' is your DataFrame\n",
    "features = subset_df.columns[:-1]  # Select all columns except the label\n",
    "scaler = MinMaxScaler()\n",
    "subset_df[features] = scaler.fit_transform(subset_df[features])\n",
    "\n",
    "# Plotting the pairplot\n",
    "sns.pairplot(subset_df, hue='label', diag_kind='kde')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## QUGS Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To test the `FeatureExtractor` class from the `time_domain_features.py` script with real data from QUGS that has been converted purposed for the thesis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Modules\n",
    "\n",
    "Use relative imports or modify the path to include the directory where the module is stored. In this example, we’ll simulate the relative import setup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Real DataFrame\n",
    "\n",
    "Create one DataFrame from one of the raw data file. Simulate importing the `FeatureExtractor` from its relative path in the notebooks directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Time</th>\n",
       "      <th>Real.4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.002501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000977</td>\n",
       "      <td>-0.005371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001953</td>\n",
       "      <td>-0.003811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.002930</td>\n",
       "      <td>0.005464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.003906</td>\n",
       "      <td>-0.029412</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Time    Real.4\n",
       "0  0.000000 -0.002501\n",
       "1  0.000977 -0.005371\n",
       "2  0.001953 -0.003811\n",
       "3  0.002930  0.005464\n",
       "4  0.003906 -0.029412"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert to DataFrame (simulating processed data input)\n",
    "single_data_dir = \"D:/thesis/data/converted/raw/DAMAGE_2/D2_TEST05_01.csv\"\n",
    "df = pd.read_csv(single_data_dir)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize Data Points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plotting the data points\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(df['Time'], df[df.columns[-1]], marker='o', color='blue', label='Data Points')\n",
    "plt.title('Scatter Plot of Data Points')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Amp')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Downsampled Plot with Alpha Blending\n",
    "\n",
    "Reduce the number of data points by sampling a subset of the data and use transparency to help visualize the density of overlapping points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Downsample the data by taking every nth point\n",
    "n = 1  # Adjust this value as needed\n",
    "downsampled_df = df.iloc[::n, :]\n",
    "\n",
    "# Plotting the downsampled data points with alpha blending\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(downsampled_df['Time'], downsampled_df[downsampled_df.columns[-1]], alpha=0.5, color='blue', label='Data Points')\n",
    "plt.title('Scatter Plot of Downsampled Data Points')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Amp')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Line Plot with Rolling Avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Calculate the rolling average\n",
    "window_size = 50  # Adjust this value as needed\n",
    "rolling_avg = df[df.columns[-1]].rolling(window=window_size).mean()\n",
    "\n",
    "# Plotting the original data points and the rolling average\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(df['Time'], df[df.columns[-1]], alpha=0.3, color='blue', label='Original Data')\n",
    "plt.plot(df['Time'], rolling_avg, color='red', label='Rolling Average')\n",
    "plt.title('Line Plot with Rolling Average')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Amp')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print Time-domain Features (Single CSV Real Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Mean</th>\n",
       "      <th>Max</th>\n",
       "      <th>Peak (Pm)</th>\n",
       "      <th>Peak-to-Peak (Pk)</th>\n",
       "      <th>RMS</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Power</th>\n",
       "      <th>Crest Factor</th>\n",
       "      <th>Form Factor</th>\n",
       "      <th>Pulse Indicator</th>\n",
       "      <th>Margin</th>\n",
       "      <th>Kurtosis</th>\n",
       "      <th>Skewness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-4.594330e-11</td>\n",
       "      <td>0.691769</td>\n",
       "      <td>0.691769</td>\n",
       "      <td>1.375404</td>\n",
       "      <td>0.150915</td>\n",
       "      <td>0.022775</td>\n",
       "      <td>0.150916</td>\n",
       "      <td>0.022775</td>\n",
       "      <td>4.583819</td>\n",
       "      <td>-3.284819e+09</td>\n",
       "      <td>-1.505702e+10</td>\n",
       "      <td>2.16734</td>\n",
       "      <td>3.012459</td>\n",
       "      <td>0.008173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Mean       Max  Peak (Pm)  Peak-to-Peak (Pk)       RMS  Variance  \\\n",
       "0 -4.594330e-11  0.691769   0.691769           1.375404  0.150915  0.022775   \n",
       "\n",
       "   Standard Deviation     Power  Crest Factor   Form Factor  Pulse Indicator  \\\n",
       "0            0.150916  0.022775      4.583819 -3.284819e+09    -1.505702e+10   \n",
       "\n",
       "    Margin  Kurtosis  Skewness  \n",
       "0  2.16734  3.012459  0.008173  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "import os\n",
    "# Assuming the src directory is one level up from the notebooks directory\n",
    "sys.path.append('../src/features')\n",
    "from time_domain_features import FeatureExtractor\n",
    "\n",
    "\n",
    "# Extract features\n",
    "extracted = FeatureExtractor(df[df.columns[-1]])\n",
    "\n",
    "# Format with pandas DataFramw\n",
    "features = pd.DataFrame(extracted.features, index=[0])\n",
    "features\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print Time-domain Features (Multiple CSV Real Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "import os\n",
    "import re\n",
    "# Assuming the src directory is one level up from the notebooks directory\n",
    "sys.path.append('../src/features')\n",
    "from time_domain_features import ExtractTimeFeatures # use wrapper function instead of class for easy use\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to extract numbers from a filename that later used as labels features\n",
    "def extract_numbers(filename):\n",
    "    '''\n",
    "    Extract numbers from a filename\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    filename : str\n",
    "        The filename to extract numbers from\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    list\n",
    "        A list of extracted numbers: [damage_number, test_number, sensor_number]\n",
    "    '''\n",
    "    # Find all occurrences of one or more digits in the filename\n",
    "    numbers = re.findall(r'\\d+', filename)\n",
    "    # Convert the list of number strings to integers\n",
    "    numbers = [int(num) for num in numbers]\n",
    "    # Convert to a tuple and return\n",
    "    return numbers\n",
    "\n",
    "def build_features(input_dir:str, sensor:int=None, verbose:bool=False):\n",
    "    all_features = []\n",
    "    for nth_damage in os.listdir(input_dir):\n",
    "        nth_damage_path = os.path.join(input_dir, nth_damage)\n",
    "        if verbose:\n",
    "            print(f'Extracting features from damage folder {nth_damage_path}')\n",
    "        if os.path.isdir(nth_damage_path):\n",
    "            for nth_test in os.listdir(nth_damage_path):\n",
    "                nth_test_path = os.path.join(nth_damage_path, nth_test)\n",
    "                # if verbose:\n",
    "                #     print(f'Extracting features from {nth_test_path}')\n",
    "                if sensor is not None:\n",
    "                    # Check if the file has the specified sensor suffix\n",
    "                    if not nth_test.endswith(f'_{sensor:02}.csv'):\n",
    "                        continue\n",
    "                # if verbose:\n",
    "                #     print(f'Extracting features from {nth_test_path}')\n",
    "                features = ExtractTimeFeatures(nth_test_path)  # return the one csv file feature in dictionary {}\n",
    "                if verbose:\n",
    "                    print(features)\n",
    "                features['label'] = extract_numbers(nth_test)[0]  # add labels to the dictionary\n",
    "                features['filename'] = nth_test  # add filename to the dictionary\n",
    "                all_features.append(features)\n",
    "\n",
    "    # Create a DataFrame from the list of dictionaries\n",
    "    df = pd.DataFrame(all_features)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Execute the automation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting features from damage folder D:/thesis/data/converted/raw\\DAMAGE_1\n",
      "{'Mean': 4.935794427560776e-09, 'Max': 0.5858288, 'Peak (Pm)': 0.6626305, 'Peak-to-Peak (Pk)': 1.2484593, 'RMS': 0.1362702190299897, 'Variance': 0.01856957259448133, 'Standard Deviation': 0.13627047894654068, 'Power': 0.018569572594481366, 'Crest Factor': 4.862621523006222, 'Form Factor': 27608568.596187092, 'Pulse Indicator': 134250019.87521306, 'Margin': 2.1849538435504186, 'Kurtosis': 3.022962308835951, 'Skewness': 0.0012958106685132709}\n",
      "{'Mean': 1.0670429728482285e-07, 'Max': 0.7143209, 'Peak (Pm)': 0.7143209, 'Peak-to-Peak (Pk)': 1.2992979, 'RMS': 0.14557249700238117, 'Variance': 0.021191351883496887, 'Standard Deviation': 0.14557277466169877, 'Power': 0.021191351883508274, 'Crest Factor': 4.906977036935182, 'Form Factor': 1364260.8658375633, 'Pulse Indicator': 6694396.741054232, 'Margin': 2.2755535142432146, 'Kurtosis': 2.9896062310785814, 'Skewness': 0.0016697103771502017}\n",
      "{'Mean': 1.9706693736568082e-08, 'Max': 0.7278727, 'Peak (Pm)': 0.7278727, 'Peak-to-Peak (Pk)': 1.4333739, 'RMS': 0.165006915629019, 'Variance': 0.027227282205401805, 'Standard Deviation': 0.1650072303568338, 'Power': 0.027227282205402194, 'Crest Factor': 4.411164812246163, 'Form Factor': 8373140.509248861, 'Pulse Indicator': 36935302.78239149, 'Margin': 2.1792802291177393, 'Kurtosis': 2.9812895758530917, 'Skewness': -0.006938824931332053}\n",
      "{'Mean': 4.6400798331387123e-08, 'Max': 0.637569, 'Peak (Pm)': 0.637569, 'Peak-to-Peak (Pk)': 1.1512296000000002, 'RMS': 0.12090799377123415, 'Variance': 0.01461874295778264, 'Standard Deviation': 0.12090822438646137, 'Power': 0.014618742957784795, 'Crest Factor': 5.273174916840671, 'Form Factor': 2605730.8951395294, 'Pulse Indicator': 13740474.796286557, 'Margin': 2.226963805106291, 'Kurtosis': 2.9826863333977065, 'Skewness': -0.00442221557241219}\n",
      "{'Mean': 2.7488388806731582e-08, 'Max': 0.5571427, 'Peak (Pm)': 0.5852444, 'Peak-to-Peak (Pk)': 1.1423871, 'RMS': 0.13144029417868638, 'Variance': 0.017276550933778858, 'Standard Deviation': 0.1314405448828225, 'Power': 0.017276550933779614, 'Crest Factor': 4.452549377319485, 'Form Factor': 4781666.000973408, 'Pulse Indicator': 21290603.975183897, 'Margin': 1.9649866646054637, 'Kurtosis': 3.015813008716952, 'Skewness': -0.000659001044647282}\n",
      "Extracting features from damage folder D:/thesis/data/converted/raw\\DAMAGE_2\n",
      "{'Mean': 1.3934612310944543e-08, 'Max': 0.7251477, 'Peak (Pm)': 0.7776651, 'Peak-to-Peak (Pk)': 1.5028128, 'RMS': 0.15703024768091647, 'Variance': 0.024658498686729775, 'Standard Deviation': 0.15703054719434353, 'Power': 0.024658498686729976, 'Crest Factor': 4.952326774521848, 'Form Factor': 11269079.051275905, 'Pulse Indicator': 55808161.909836926, 'Margin': 2.3914494217651483, 'Kurtosis': 3.065793079040563, 'Skewness': -0.011438874839155505}\n",
      "{'Mean': -1.6040696913631207e-08, 'Max': 0.7163731, 'Peak (Pm)': 0.7801023, 'Peak-to-Peak (Pk)': 1.4964754, 'RMS': 0.1629113530481945, 'Variance': 0.026540108951993217, 'Standard Deviation': 0.1629116637790146, 'Power': 0.026540108951993477, 'Crest Factor': 4.788507893426066, 'Form Factor': -10156126.876866193, 'Pulse Indicator': -48632693.71651039, 'Margin': 2.3491353605498797, 'Kurtosis': 3.0095153805004067, 'Skewness': 0.0035459331148647265}\n",
      "{'Mean': -3.6031959580527945e-08, 'Max': 0.7914127, 'Peak (Pm)': 0.8284533, 'Peak-to-Peak (Pk)': 1.619866, 'RMS': 0.18490495831144427, 'Variance': 0.034189843608155635, 'Standard Deviation': 0.18490531099201457, 'Power': 0.03418984360815694, 'Crest Factor': 4.480427715759771, 'Form Factor': -5131693.098683672, 'Pulse Indicator': -22992179.988115467, 'Margin': 2.343746706467838, 'Kurtosis': 3.002473556285487, 'Skewness': 0.016718729830412823}\n",
      "{'Mean': 9.172816008257008e-09, 'Max': 0.6702775, 'Peak (Pm)': 0.7604433, 'Peak-to-Peak (Pk)': 1.4307208, 'RMS': 0.14168733498902872, 'Variance': 0.02007530089629316, 'Standard Deviation': 0.1416876052379772, 'Power': 0.020075300896293242, 'Crest Factor': 5.367052037917739, 'Form Factor': 15446438.134318551, 'Pulse Indicator': 82901837.26736467, 'Margin': 2.457804091403889, 'Kurtosis': 3.0023394577627944, 'Skewness': -0.01140764905780411}\n",
      "{'Mean': 9.495013175985419e-09, 'Max': 0.6917691, 'Peak (Pm)': 0.6917691, 'Peak-to-Peak (Pk)': 1.375404, 'RMS': 0.1509157231480295, 'Variance': 0.022775555493292594, 'Standard Deviation': 0.15091601099884902, 'Power': 0.02277555549329269, 'Crest Factor': 4.583810656504364, 'Form Factor': 15894208.923240073, 'Pulse Indicator': 72856044.2390546, 'Margin': 2.167332528548143, 'Kurtosis': 3.012447483653337, 'Skewness': 0.008172550552215142}\n",
      "Extracting features from damage folder D:/thesis/data/converted/raw\\DAMAGE_3\n",
      "{'Mean': -5.125203652084519e-09, 'Max': 0.7967038, 'Peak (Pm)': 0.7967038, 'Peak-to-Peak (Pk)': 1.5589914999999999, 'RMS': 0.16884091104965937, 'Variance': 0.028507253244078955, 'Standard Deviation': 0.16884123309028998, 'Power': 0.028507253244078986, 'Crest Factor': 4.718665606854455, 'Form Factor': -32943258.943669196, 'Pulse Indicator': -155448222.95519227, 'Margin': 2.3556475344978147, 'Kurtosis': 3.0014048735916217, 'Skewness': 0.00016304631330188324}\n",
      "{'Mean': -7.222874873547567e-09, 'Max': 0.8287752, 'Peak (Pm)': 0.8287752, 'Peak-to-Peak (Pk)': 1.5514018, 'RMS': 0.16929145862040224, 'Variance': 0.028659597961823314, 'Standard Deviation': 0.16929178152038984, 'Power': 0.02865959796182336, 'Crest Factor': 4.895552361317535, 'Form Factor': -23438237.76324863, 'Pulse Indicator': -114743120.22699365, 'Margin': 2.446085789562267, 'Kurtosis': 2.9631006088008798, 'Skewness': 0.010084718218560516}\n",
      "{'Mean': -7.725628435660411e-09, 'Max': 0.7944632, 'Peak (Pm)': 0.7944632, 'Peak-to-Peak (Pk)': 1.5331682, 'RMS': 0.1625625135017677, 'Variance': 0.026426570796012346, 'Standard Deviation': 0.16256282356722532, 'Power': 0.02642657079601241, 'Crest Factor': 4.887124238463261, 'Form Factor': -21041979.284352075, 'Pulse Indicator': -102834766.98579885, 'Margin': 2.3967788349063155, 'Kurtosis': 3.0213181211381293, 'Skewness': 0.004488735052518127}\n",
      "{'Mean': 9.120998409253091e-09, 'Max': 0.7135901, 'Peak (Pm)': 0.7135901, 'Peak-to-Peak (Pk)': 1.4117066, 'RMS': 0.16207233854213446, 'Variance': 0.026267442920516162, 'Standard Deviation': 0.16207264767265123, 'Power': 0.02626744292051624, 'Crest Factor': 4.402911110056487, 'Form Factor': 17769144.480687, 'Pulse Indicator': 78235963.6502157, 'Margin': 2.1564501310909345, 'Kurtosis': 2.998678121046115, 'Skewness': 0.006172057448616143}\n",
      "{'Mean': -3.128234370585584e-08, 'Max': 0.6531574, 'Peak (Pm)': 0.7488455, 'Peak-to-Peak (Pk)': 1.4020029, 'RMS': 0.15292709505145338, 'Variance': 0.023386696400875272, 'Standard Deviation': 0.15292738673868306, 'Power': 0.02338669640087626, 'Crest Factor': 4.896748347623067, 'Form Factor': -4888607.339955366, 'Pulse Indicator': -23938279.914104436, 'Margin': 2.328524868575274, 'Kurtosis': 3.000210053435982, 'Skewness': -0.0029119433234809813}\n",
      "Extracting features from damage folder D:/thesis/data/converted/raw\\DAMAGE_4\n",
      "{'Mean': 1.55488128252111e-08, 'Max': 0.7442828, 'Peak (Pm)': 0.7442828, 'Peak-to-Peak (Pk)': 1.459784, 'RMS': 0.1690262994991522, 'Variance': 0.02856988992237687, 'Standard Deviation': 0.169026621893385, 'Power': 0.028569889922377106, 'Crest Factor': 4.403354993899828, 'Form Factor': 10870688.418416755, 'Pulse Indicator': 47867500.13436445, 'Margin': 2.2012696204227113, 'Kurtosis': 2.99056612619621, 'Skewness': -0.018603955930243943}\n",
      "{'Mean': 1.292989200929343e-08, 'Max': 0.8217714, 'Peak (Pm)': 0.8217714, 'Peak-to-Peak (Pk)': 1.6252129000000002, 'RMS': 0.18214987905659388, 'Variance': 0.033178578440331605, 'Standard Deviation': 0.1821502264822355, 'Power': 0.03317857844033178, 'Crest Factor': 4.511512191258036, 'Form Factor': 14087501.96255875, 'Pulse Indicator': 63555936.8484553, 'Margin': 2.3449707893701555, 'Kurtosis': 3.0280857310588734, 'Skewness': 0.0009546518963361565}\n",
      "{'Mean': -9.007574774122792e-09, 'Max': 0.8003945, 'Peak (Pm)': 0.8320401, 'Peak-to-Peak (Pk)': 1.6324345999999998, 'RMS': 0.1758869327443254, 'Variance': 0.03093621311020678, 'Standard Deviation': 0.17588726822426548, 'Power': 0.03093621311020685, 'Crest Factor': 4.730539597330285, 'Form Factor': -19526558.164092984, 'Pulse Indicator': -92371156.5948148, 'Margin': 2.4119301890810863, 'Kurtosis': 2.9844842979116764, 'Skewness': -0.011957880697579701}\n",
      "{'Mean': 1.3813648390246642e-09, 'Max': 0.6532164, 'Peak (Pm)': 0.6532164, 'Peak-to-Peak (Pk)': 1.2857231, 'RMS': 0.14361795378567382, 'Variance': 0.020626116649583946, 'Standard Deviation': 0.14361822771701033, 'Power': 0.020626116649583946, 'Crest Factor': 4.54829206782056, 'Form Factor': 103968155.06544794, 'Pulse Indicator': 472877534.99011487, 'Margin': 2.096257507768814, 'Kurtosis': 2.9947520336358764, 'Skewness': -0.0031834515529559487}\n",
      "{'Mean': -4.778003436297728e-08, 'Max': 0.6669507, 'Peak (Pm)': 0.6669507, 'Peak-to-Peak (Pk)': 1.3241321, 'RMS': 0.15547548985491688, 'Variance': 0.024172627945624078, 'Standard Deviation': 0.1554757864028522, 'Power': 0.02417262794562636, 'Crest Factor': 4.289748182317162, 'Form Factor': -3253984.4712918047, 'Pulse Indicator': -13958773.971012289, 'Margin': 2.0572986524988806, 'Kurtosis': 2.998294672107473, 'Skewness': -0.005900011198990793}\n",
      "{'Mean': -1.4860702017497662e-08, 'Max': 0.5918152, 'Peak (Pm)': 0.5918152, 'Peak-to-Peak (Pk)': 1.1695822, 'RMS': 0.1273105747526182, 'Variance': 0.016207982443841768, 'Standard Deviation': 0.12731081757988907, 'Power': 0.016207982443841983, 'Crest Factor': 4.648594204762468, 'Form Factor': -8566928.70920344, 'Pulse Indicator': -39824175.150216326, 'Margin': 2.0169392631404075, 'Kurtosis': 3.02420106022748, 'Skewness': 0.0074253093438661905}\n",
      "{'Mean': -1.7241842698455592e-08, 'Max': 0.6988725, 'Peak (Pm)': 0.6988725, 'Peak-to-Peak (Pk)': 1.3169399, 'RMS': 0.1422862926904231, 'Variance': 0.020245389087584442, 'Standard Deviation': 0.14228656408179957, 'Power': 0.020245389087584748, 'Crest Factor': 4.911734551412901, 'Form Factor': -8252383.180781955, 'Pulse Indicator': -40533515.60054543, 'Margin': 2.2528929044415698, 'Kurtosis': 3.006112425097173, 'Skewness': 0.0020457966428877265}\n",
      "{'Mean': -4.1197091090896294e-09, 'Max': 0.9292531, 'Peak (Pm)': 0.9292531, 'Peak-to-Peak (Pk)': 1.7662965000000002, 'RMS': 0.17837248381965012, 'Variance': 0.03181674298399133, 'Standard Deviation': 0.17837282404043445, 'Power': 0.03181674298399135, 'Crest Factor': 5.209621350227732, 'Form Factor': -43297349.180817954, 'Pulse Indicator': -225562794.70065442, 'Margin': 2.6794066535741035, 'Kurtosis': 3.059551564691877, 'Skewness': -0.009642173835931916}\n",
      "{'Mean': -1.1934879643152173e-08, 'Max': 0.707527, 'Peak (Pm)': 0.755228, 'Peak-to-Peak (Pk)': 1.462755, 'RMS': 0.15931024345213565, 'Variance': 0.025379753668778586, 'Standard Deviation': 0.15931054731433875, 'Power': 0.025379753668778728, 'Crest Factor': 4.7406116746466855, 'Form Factor': -13348290.742382344, 'Pulse Indicator': -63279062.92991602, 'Margin': 2.3013726266794055, 'Kurtosis': 2.9869035740941716, 'Skewness': 0.014359917561322237}\n",
      "{'Mean': 7.395535928902504e-09, 'Max': 0.6920146, 'Peak (Pm)': 0.6920146, 'Peak-to-Peak (Pk)': 1.3443711, 'RMS': 0.1412843411260445, 'Variance': 0.019961265047420452, 'Standard Deviation': 0.14128461060633815, 'Power': 0.019961265047420507, 'Crest Factor': 4.898027583839816, 'Form Factor': 19104003.074867226, 'Pulse Indicator': 93571934.02246033, 'Margin': 2.236725107476516, 'Kurtosis': 2.9968041695990943, 'Skewness': 0.0034724073879618903}\n",
      "{'Mean': 1.0422456716329221e-08, 'Max': 0.5713457, 'Peak (Pm)': 0.5797505, 'Peak-to-Peak (Pk)': 1.1510962, 'RMS': 0.12968834598537562, 'Variance': 0.016819067084422386, 'Standard Deviation': 0.12968859334791594, 'Power': 0.016819067084422497, 'Crest Factor': 4.470336140036637, 'Form Factor': 12443164.746579224, 'Pulse Indicator': 55625129.06306293, 'Margin': 1.9590883401151484, 'Kurtosis': 3.016023674055137, 'Skewness': 0.00852173949170516}\n",
      "{'Mean': 8.107469372403859e-09, 'Max': 0.6199987, 'Peak (Pm)': 0.6756481, 'Peak-to-Peak (Pk)': 1.2956468, 'RMS': 0.1402657999449104, 'Variance': 0.019674494634185562, 'Standard Deviation': 0.14026606748247794, 'Power': 0.01967449463418563, 'Crest Factor': 4.816912606389881, 'Form Factor': 17300811.572886854, 'Pulse Indicator': 83336497.36621463, 'Margin': 2.1975471976374075, 'Kurtosis': 3.037578810042664, 'Skewness': -0.013613810169722728}\n",
      "{'Mean': 4.262961079273146e-08, 'Max': 0.814953, 'Peak (Pm)': 0.814953, 'Peak-to-Peak (Pk)': 1.5565602, 'RMS': 0.16625709306754788, 'Variance': 0.027641420995269454, 'Standard Deviation': 0.1662574101798985, 'Power': 0.02764142099527128, 'Crest Factor': 4.901763798245265, 'Form Factor': 3900037.7900681063, 'Pulse Indicator': 19117064.05114431, 'Margin': 2.4308595525011776, 'Kurtosis': 3.0190362730354328, 'Skewness': 0.0022508235385028208}\n",
      "{'Mean': 9.371075913619966e-10, 'Max': 0.7196078, 'Peak (Pm)': 0.7196078, 'Peak-to-Peak (Pk)': 1.3686654, 'RMS': 0.15299253723022288, 'Variance': 0.023406716448141136, 'Standard Deviation': 0.15299282904227765, 'Power': 0.023406716448141133, 'Crest Factor': 4.703548375808263, 'Form Factor': 163260375.47925827, 'Pulse Indicator': 767903073.9193124, 'Margin': 2.235990216835229, 'Kurtosis': 2.981834630537439, 'Skewness': 0.0007909124000460926}\n",
      "{'Mean': 1.4493594366489167e-08, 'Max': 0.6528962, 'Peak (Pm)': 0.6528962, 'Peak-to-Peak (Pk)': 1.290172, 'RMS': 0.14220565540682817, 'Variance': 0.020222448429685333, 'Standard Deviation': 0.14220592664440052, 'Power': 0.020222448429685555, 'Crest Factor': 4.591211215420132, 'Form Factor': 9811621.038299773, 'Pulse Indicator': 45047224.552494034, 'Margin': 2.1038145838382705, 'Kurtosis': 2.9857388812869656, 'Skewness': -0.007918264333781077}\n",
      "{'Mean': 1.031987296632552e-08, 'Max': 0.595708, 'Peak (Pm)': 0.6369753, 'Peak-to-Peak (Pk)': 1.2326833000000001, 'RMS': 0.12773594252827294, 'Variance': 0.01631647101358614, 'Standard Deviation': 0.12773618616687432, 'Power': 0.016316471013586246, 'Crest Factor': 4.986656749794699, 'Form Factor': 12377666.173322521, 'Pulse Indicator': 61723172.569904275, 'Margin': 2.166322112675606, 'Kurtosis': 3.012298419893727, 'Skewness': 0.004319132036356966}\n",
      "{'Mean': 1.5924574521904461e-09, 'Max': 0.6533219, 'Peak (Pm)': 0.6533219, 'Peak-to-Peak (Pk)': 1.2685596000000001, 'RMS': 0.1471860602460746, 'Variance': 0.021663736330761102, 'Standard Deviation': 0.1471863409830795, 'Power': 0.021663736330761102, 'Crest Factor': 4.438748471884747, 'Form Factor': 92426997.05641633, 'Pulse Indicator': 410260191.945064, 'Margin': 2.0719719014709344, 'Kurtosis': 2.968632188312844, 'Skewness': -0.005134453231454551}\n",
      "{'Mean': -2.47821254392403e-08, 'Max': 1.001901, 'Peak (Pm)': 1.001901, 'Peak-to-Peak (Pk)': 1.8490419, 'RMS': 0.19846256347765465, 'Variance': 0.03938738910212148, 'Standard Deviation': 0.19846294201747922, 'Power': 0.0393873891021221, 'Crest Factor': 5.048312298519747, 'Form Factor': -8008294.68659725, 'Pulse Indicator': -40428372.55651924, 'Margin': 2.736851348421886, 'Kurtosis': 3.0049833880728696, 'Skewness': 0.00472936527535124}\n",
      "{'Mean': 1.1718091881137134e-08, 'Max': 0.6872381, 'Peak (Pm)': 0.764375, 'Peak-to-Peak (Pk)': 1.4516130999999999, 'RMS': 0.16163067307431705, 'Variance': 0.026124474478456623, 'Standard Deviation': 0.161630981362418, 'Power': 0.02612447447845676, 'Crest Factor': 4.729145684176814, 'Form Factor': 13793258.724528132, 'Pulse Indicator': 65230329.9678364, 'Margin': 2.3146302669231273, 'Kurtosis': 3.018968361591216, 'Skewness': -0.0027038961477170074}\n",
      "{'Mean': 4.483916744688538e-09, 'Max': 0.5636284, 'Peak (Pm)': 0.6170025, 'Peak-to-Peak (Pk)': 1.1806309000000001, 'RMS': 0.13617995217916362, 'Variance': 0.018544979375519265, 'Standard Deviation': 0.1361802119235431, 'Power': 0.01854497937551929, 'Crest Factor': 4.530788050125378, 'Form Factor': 30370758.409035303, 'Pulse Indicator': 137603469.27290198, 'Margin': 2.033717161471145, 'Kurtosis': 2.9918743365839853, 'Skewness': -0.0004150140042344471}\n",
      "{'Mean': 2.9766394349442996e-09, 'Max': 0.5311777, 'Peak (Pm)': 0.5506368, 'Peak-to-Peak (Pk)': 1.0818145000000001, 'RMS': 0.12818522987371672, 'Variance': 0.01643145315777759, 'Standard Deviation': 0.12818547436927183, 'Power': 0.0164314531577776, 'Crest Factor': 4.29563375236341, 'Form Factor': 43063741.06614474, 'Pulse Indicator': 184986059.62676963, 'Margin': 1.8701294186586008, 'Kurtosis': 2.9729630742257265, 'Skewness': -0.0017016004702273704}\n",
      "{'Mean': -1.7561047037294235e-08, 'Max': 0.7885605, 'Peak (Pm)': 0.7885605, 'Peak-to-Peak (Pk)': 1.5711632, 'RMS': 0.1717757508437296, 'Variance': 0.029506908577926764, 'Standard Deviation': 0.17177607848215945, 'Power': 0.02950690857792707, 'Crest Factor': 4.590639226588979, 'Form Factor': -9781634.914987188, 'Pulse Indicator': -44903956.94091254, 'Margin': 2.313529340377086, 'Kurtosis': 2.999345507398793, 'Skewness': -0.0039902823350783535}\n",
      "{'Mean': -7.485887050165297e-09, 'Max': 0.8665391, 'Peak (Pm)': 0.8665391, 'Peak-to-Peak (Pk)': 1.6968147, 'RMS': 0.19506420041417172, 'Variance': 0.03805004228322011, 'Standard Deviation': 0.19506457247209133, 'Power': 0.038050042283220155, 'Crest Factor': 4.442327696010408, 'Form Factor': -26057593.322873402, 'Pulse Indicator': -115756368.50957641, 'Margin': 2.385612230408672, 'Kurtosis': 2.9914497228644032, 'Skewness': -0.006119251854077776}\n",
      "{'Mean': -1.065039840858949e-09, 'Max': 0.7413595, 'Peak (Pm)': 0.7413595, 'Peak-to-Peak (Pk)': 1.428194, 'RMS': 0.1555196869879253, 'Variance': 0.02418637304082225, 'Standard Deviation': 0.15551998362016786, 'Power': 0.024186373040822256, 'Crest Factor': 4.766981688032589, 'Form Factor': -146022412.51604208, 'Pulse Indicator': -696086166.5063134, 'Margin': 2.2912655142745573, 'Kurtosis': 3.0331285787830486, 'Skewness': -0.013338525795229992}\n",
      "{'Mean': 2.033986356306842e-08, 'Max': 0.5774453, 'Peak (Pm)': 0.6074951, 'Peak-to-Peak (Pk)': 1.1849404, 'RMS': 0.13797500828702497, 'Variance': 0.019037102911804195, 'Standard Deviation': 0.1379752714552237, 'Power': 0.019037102911804608, 'Crest Factor': 4.402935774689337, 'Form Factor': 6783477.571479364, 'Pulse Indicator': 29867216.076269235, 'Margin': 1.9889851026106096, 'Kurtosis': 2.986310918941125, 'Skewness': 0.006731218798193905}\n"
     ]
    }
   ],
   "source": [
    "data_dir = \"D:/thesis/data/converted/raw\"\n",
    "# Extract features\n",
    "df1 = build_features(data_dir, sensor=1, verbose=True)\n",
    "df2 = build_features(data_dir, sensor=2, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Mean</th>\n",
       "      <th>Max</th>\n",
       "      <th>Peak (Pm)</th>\n",
       "      <th>Peak-to-Peak (Pk)</th>\n",
       "      <th>RMS</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Power</th>\n",
       "      <th>Crest Factor</th>\n",
       "      <th>Form Factor</th>\n",
       "      <th>Pulse Indicator</th>\n",
       "      <th>Margin</th>\n",
       "      <th>Kurtosis</th>\n",
       "      <th>Skewness</th>\n",
       "      <th>label</th>\n",
       "      <th>filename</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.935794e-09</td>\n",
       "      <td>0.585829</td>\n",
       "      <td>0.662631</td>\n",
       "      <td>1.248459</td>\n",
       "      <td>0.136270</td>\n",
       "      <td>0.018570</td>\n",
       "      <td>0.136270</td>\n",
       "      <td>0.018570</td>\n",
       "      <td>4.862622</td>\n",
       "      <td>2.760857e+07</td>\n",
       "      <td>1.342500e+08</td>\n",
       "      <td>2.184954</td>\n",
       "      <td>3.022962</td>\n",
       "      <td>0.001296</td>\n",
       "      <td>1</td>\n",
       "      <td>D1_TEST01_01.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.067043e-07</td>\n",
       "      <td>0.714321</td>\n",
       "      <td>0.714321</td>\n",
       "      <td>1.299298</td>\n",
       "      <td>0.145572</td>\n",
       "      <td>0.021191</td>\n",
       "      <td>0.145573</td>\n",
       "      <td>0.021191</td>\n",
       "      <td>4.906977</td>\n",
       "      <td>1.364261e+06</td>\n",
       "      <td>6.694397e+06</td>\n",
       "      <td>2.275554</td>\n",
       "      <td>2.989606</td>\n",
       "      <td>0.001670</td>\n",
       "      <td>1</td>\n",
       "      <td>D1_TEST02_01.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.970669e-08</td>\n",
       "      <td>0.727873</td>\n",
       "      <td>0.727873</td>\n",
       "      <td>1.433374</td>\n",
       "      <td>0.165007</td>\n",
       "      <td>0.027227</td>\n",
       "      <td>0.165007</td>\n",
       "      <td>0.027227</td>\n",
       "      <td>4.411165</td>\n",
       "      <td>8.373141e+06</td>\n",
       "      <td>3.693530e+07</td>\n",
       "      <td>2.179280</td>\n",
       "      <td>2.981290</td>\n",
       "      <td>-0.006939</td>\n",
       "      <td>1</td>\n",
       "      <td>D1_TEST03_01.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.640080e-08</td>\n",
       "      <td>0.637569</td>\n",
       "      <td>0.637569</td>\n",
       "      <td>1.151230</td>\n",
       "      <td>0.120908</td>\n",
       "      <td>0.014619</td>\n",
       "      <td>0.120908</td>\n",
       "      <td>0.014619</td>\n",
       "      <td>5.273175</td>\n",
       "      <td>2.605731e+06</td>\n",
       "      <td>1.374047e+07</td>\n",
       "      <td>2.226964</td>\n",
       "      <td>2.982686</td>\n",
       "      <td>-0.004422</td>\n",
       "      <td>1</td>\n",
       "      <td>D1_TEST04_01.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.748839e-08</td>\n",
       "      <td>0.557143</td>\n",
       "      <td>0.585244</td>\n",
       "      <td>1.142387</td>\n",
       "      <td>0.131440</td>\n",
       "      <td>0.017277</td>\n",
       "      <td>0.131441</td>\n",
       "      <td>0.017277</td>\n",
       "      <td>4.452549</td>\n",
       "      <td>4.781666e+06</td>\n",
       "      <td>2.129060e+07</td>\n",
       "      <td>1.964987</td>\n",
       "      <td>3.015813</td>\n",
       "      <td>-0.000659</td>\n",
       "      <td>1</td>\n",
       "      <td>D1_TEST05_01.csv</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Mean       Max  Peak (Pm)  Peak-to-Peak (Pk)       RMS  Variance  \\\n",
       "0  4.935794e-09  0.585829   0.662631           1.248459  0.136270  0.018570   \n",
       "1  1.067043e-07  0.714321   0.714321           1.299298  0.145572  0.021191   \n",
       "2  1.970669e-08  0.727873   0.727873           1.433374  0.165007  0.027227   \n",
       "3  4.640080e-08  0.637569   0.637569           1.151230  0.120908  0.014619   \n",
       "4  2.748839e-08  0.557143   0.585244           1.142387  0.131440  0.017277   \n",
       "\n",
       "   Standard Deviation     Power  Crest Factor   Form Factor  Pulse Indicator  \\\n",
       "0            0.136270  0.018570      4.862622  2.760857e+07     1.342500e+08   \n",
       "1            0.145573  0.021191      4.906977  1.364261e+06     6.694397e+06   \n",
       "2            0.165007  0.027227      4.411165  8.373141e+06     3.693530e+07   \n",
       "3            0.120908  0.014619      5.273175  2.605731e+06     1.374047e+07   \n",
       "4            0.131441  0.017277      4.452549  4.781666e+06     2.129060e+07   \n",
       "\n",
       "     Margin  Kurtosis  Skewness  label          filename  \n",
       "0  2.184954  3.022962  0.001296      1  D1_TEST01_01.csv  \n",
       "1  2.275554  2.989606  0.001670      1  D1_TEST02_01.csv  \n",
       "2  2.179280  2.981290 -0.006939      1  D1_TEST03_01.csv  \n",
       "3  2.226964  2.982686 -0.004422      1  D1_TEST04_01.csv  \n",
       "4  1.964987  3.015813 -0.000659      1  D1_TEST05_01.csv  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean</th>\n",
       "      <th>Max</th>\n",
       "      <th>Peak (Pm)</th>\n",
       "      <th>Peak-to-Peak (Pk)</th>\n",
       "      <th>RMS</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Power</th>\n",
       "      <th>Crest Factor</th>\n",
       "      <th>Form Factor</th>\n",
       "      <th>Pulse Indicator</th>\n",
       "      <th>Margin</th>\n",
       "      <th>Kurtosis</th>\n",
       "      <th>Skewness</th>\n",
       "      <th>label</th>\n",
       "      <th>filename</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.486070e-08</td>\n",
       "      <td>0.591815</td>\n",
       "      <td>0.591815</td>\n",
       "      <td>1.169582</td>\n",
       "      <td>0.127311</td>\n",
       "      <td>0.016208</td>\n",
       "      <td>0.127311</td>\n",
       "      <td>0.016208</td>\n",
       "      <td>4.648594</td>\n",
       "      <td>-8.566929e+06</td>\n",
       "      <td>-3.982418e+07</td>\n",
       "      <td>2.016939</td>\n",
       "      <td>3.024201</td>\n",
       "      <td>0.007425</td>\n",
       "      <td>1</td>\n",
       "      <td>D1_TEST1_02.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.724184e-08</td>\n",
       "      <td>0.698873</td>\n",
       "      <td>0.698873</td>\n",
       "      <td>1.316940</td>\n",
       "      <td>0.142286</td>\n",
       "      <td>0.020245</td>\n",
       "      <td>0.142287</td>\n",
       "      <td>0.020245</td>\n",
       "      <td>4.911735</td>\n",
       "      <td>-8.252383e+06</td>\n",
       "      <td>-4.053352e+07</td>\n",
       "      <td>2.252893</td>\n",
       "      <td>3.006112</td>\n",
       "      <td>0.002046</td>\n",
       "      <td>1</td>\n",
       "      <td>D1_TEST2_02.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-4.119709e-09</td>\n",
       "      <td>0.929253</td>\n",
       "      <td>0.929253</td>\n",
       "      <td>1.766297</td>\n",
       "      <td>0.178372</td>\n",
       "      <td>0.031817</td>\n",
       "      <td>0.178373</td>\n",
       "      <td>0.031817</td>\n",
       "      <td>5.209621</td>\n",
       "      <td>-4.329735e+07</td>\n",
       "      <td>-2.255628e+08</td>\n",
       "      <td>2.679407</td>\n",
       "      <td>3.059552</td>\n",
       "      <td>-0.009642</td>\n",
       "      <td>1</td>\n",
       "      <td>D1_TEST3_02.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.193488e-08</td>\n",
       "      <td>0.707527</td>\n",
       "      <td>0.755228</td>\n",
       "      <td>1.462755</td>\n",
       "      <td>0.159310</td>\n",
       "      <td>0.025380</td>\n",
       "      <td>0.159311</td>\n",
       "      <td>0.025380</td>\n",
       "      <td>4.740612</td>\n",
       "      <td>-1.334829e+07</td>\n",
       "      <td>-6.327906e+07</td>\n",
       "      <td>2.301373</td>\n",
       "      <td>2.986904</td>\n",
       "      <td>0.014360</td>\n",
       "      <td>1</td>\n",
       "      <td>D1_TEST4_02.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.395536e-09</td>\n",
       "      <td>0.692015</td>\n",
       "      <td>0.692015</td>\n",
       "      <td>1.344371</td>\n",
       "      <td>0.141284</td>\n",
       "      <td>0.019961</td>\n",
       "      <td>0.141285</td>\n",
       "      <td>0.019961</td>\n",
       "      <td>4.898028</td>\n",
       "      <td>1.910400e+07</td>\n",
       "      <td>9.357193e+07</td>\n",
       "      <td>2.236725</td>\n",
       "      <td>2.996804</td>\n",
       "      <td>0.003472</td>\n",
       "      <td>1</td>\n",
       "      <td>D1_TEST5_02.csv</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Mean       Max  Peak (Pm)  Peak-to-Peak (Pk)       RMS  Variance  \\\n",
       "0 -1.486070e-08  0.591815   0.591815           1.169582  0.127311  0.016208   \n",
       "1 -1.724184e-08  0.698873   0.698873           1.316940  0.142286  0.020245   \n",
       "2 -4.119709e-09  0.929253   0.929253           1.766297  0.178372  0.031817   \n",
       "3 -1.193488e-08  0.707527   0.755228           1.462755  0.159310  0.025380   \n",
       "4  7.395536e-09  0.692015   0.692015           1.344371  0.141284  0.019961   \n",
       "\n",
       "   Standard Deviation     Power  Crest Factor   Form Factor  Pulse Indicator  \\\n",
       "0            0.127311  0.016208      4.648594 -8.566929e+06    -3.982418e+07   \n",
       "1            0.142287  0.020245      4.911735 -8.252383e+06    -4.053352e+07   \n",
       "2            0.178373  0.031817      5.209621 -4.329735e+07    -2.255628e+08   \n",
       "3            0.159311  0.025380      4.740612 -1.334829e+07    -6.327906e+07   \n",
       "4            0.141285  0.019961      4.898028  1.910400e+07     9.357193e+07   \n",
       "\n",
       "     Margin  Kurtosis  Skewness  label         filename  \n",
       "0  2.016939  3.024201  0.007425      1  D1_TEST1_02.csv  \n",
       "1  2.252893  3.006112  0.002046      1  D1_TEST2_02.csv  \n",
       "2  2.679407  3.059552 -0.009642      1  D1_TEST3_02.csv  \n",
       "3  2.301373  2.986904  0.014360      1  D1_TEST4_02.csv  \n",
       "4  2.236725  2.996804  0.003472      1  D1_TEST5_02.csv  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Perform division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>Mean_rel</th>\n",
       "      <th>Max_rel</th>\n",
       "      <th>Peak (Pm)_rel</th>\n",
       "      <th>Peak-to-Peak (Pk)_rel</th>\n",
       "      <th>RMS_rel</th>\n",
       "      <th>Variance_rel</th>\n",
       "      <th>Standard Deviation_rel</th>\n",
       "      <th>Power_rel</th>\n",
       "      <th>Crest Factor_rel</th>\n",
       "      <th>Form Factor_rel</th>\n",
       "      <th>Pulse Indicator_rel</th>\n",
       "      <th>Margin_rel</th>\n",
       "      <th>Kurtosis_rel</th>\n",
       "      <th>Skewness_rel</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-3.010802</td>\n",
       "      <td>1.010219</td>\n",
       "      <td>0.893130</td>\n",
       "      <td>0.936820</td>\n",
       "      <td>0.934251</td>\n",
       "      <td>0.872825</td>\n",
       "      <td>0.934251</td>\n",
       "      <td>0.872825</td>\n",
       "      <td>0.955985</td>\n",
       "      <td>-0.310300</td>\n",
       "      <td>-0.296642</td>\n",
       "      <td>0.923104</td>\n",
       "      <td>1.000410</td>\n",
       "      <td>5.730242</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.161585</td>\n",
       "      <td>0.978373</td>\n",
       "      <td>0.978373</td>\n",
       "      <td>1.013578</td>\n",
       "      <td>0.977426</td>\n",
       "      <td>0.955361</td>\n",
       "      <td>0.977426</td>\n",
       "      <td>0.955361</td>\n",
       "      <td>1.000970</td>\n",
       "      <td>-6.048977</td>\n",
       "      <td>-6.054842</td>\n",
       "      <td>0.990042</td>\n",
       "      <td>1.005521</td>\n",
       "      <td>1.225240</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.209051</td>\n",
       "      <td>1.276670</td>\n",
       "      <td>1.276670</td>\n",
       "      <td>1.232265</td>\n",
       "      <td>1.081000</td>\n",
       "      <td>1.168561</td>\n",
       "      <td>1.081000</td>\n",
       "      <td>1.168561</td>\n",
       "      <td>1.181008</td>\n",
       "      <td>-5.170981</td>\n",
       "      <td>-6.106970</td>\n",
       "      <td>1.229492</td>\n",
       "      <td>1.026251</td>\n",
       "      <td>1.389598</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.257213</td>\n",
       "      <td>1.109726</td>\n",
       "      <td>1.184543</td>\n",
       "      <td>1.270602</td>\n",
       "      <td>1.317615</td>\n",
       "      <td>1.736111</td>\n",
       "      <td>1.317615</td>\n",
       "      <td>1.736111</td>\n",
       "      <td>0.899005</td>\n",
       "      <td>-5.122667</td>\n",
       "      <td>-4.605304</td>\n",
       "      <td>1.033413</td>\n",
       "      <td>1.001414</td>\n",
       "      <td>-3.247222</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.269042</td>\n",
       "      <td>1.242078</td>\n",
       "      <td>1.182437</td>\n",
       "      <td>1.176809</td>\n",
       "      <td>1.074894</td>\n",
       "      <td>1.155396</td>\n",
       "      <td>1.074894</td>\n",
       "      <td>1.155396</td>\n",
       "      <td>1.100050</td>\n",
       "      <td>3.995261</td>\n",
       "      <td>4.394987</td>\n",
       "      <td>1.138290</td>\n",
       "      <td>0.993697</td>\n",
       "      <td>-5.269199</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.747955</td>\n",
       "      <td>0.787903</td>\n",
       "      <td>0.745502</td>\n",
       "      <td>0.765961</td>\n",
       "      <td>0.825881</td>\n",
       "      <td>0.682080</td>\n",
       "      <td>0.825881</td>\n",
       "      <td>0.682080</td>\n",
       "      <td>0.902674</td>\n",
       "      <td>1.104186</td>\n",
       "      <td>0.996720</td>\n",
       "      <td>0.819205</td>\n",
       "      <td>0.983766</td>\n",
       "      <td>-0.744981</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.505431</td>\n",
       "      <td>0.865469</td>\n",
       "      <td>0.866102</td>\n",
       "      <td>0.865799</td>\n",
       "      <td>0.860995</td>\n",
       "      <td>0.741312</td>\n",
       "      <td>0.860995</td>\n",
       "      <td>0.741312</td>\n",
       "      <td>1.005932</td>\n",
       "      <td>-1.703485</td>\n",
       "      <td>-1.713590</td>\n",
       "      <td>0.935471</td>\n",
       "      <td>1.009325</td>\n",
       "      <td>-3.839274</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.183106</td>\n",
       "      <td>1.029745</td>\n",
       "      <td>0.983704</td>\n",
       "      <td>0.960919</td>\n",
       "      <td>0.899149</td>\n",
       "      <td>0.808469</td>\n",
       "      <td>0.899149</td>\n",
       "      <td>0.808469</td>\n",
       "      <td>1.094039</td>\n",
       "      <td>-0.759990</td>\n",
       "      <td>-0.831459</td>\n",
       "      <td>1.037168</td>\n",
       "      <td>1.005516</td>\n",
       "      <td>0.134629</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.102161</td>\n",
       "      <td>1.073597</td>\n",
       "      <td>0.946300</td>\n",
       "      <td>0.956626</td>\n",
       "      <td>1.079790</td>\n",
       "      <td>1.165946</td>\n",
       "      <td>1.079790</td>\n",
       "      <td>1.165946</td>\n",
       "      <td>0.876375</td>\n",
       "      <td>10.569451</td>\n",
       "      <td>9.262799</td>\n",
       "      <td>0.909751</td>\n",
       "      <td>0.993170</td>\n",
       "      <td>-0.069332</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.526443</td>\n",
       "      <td>0.943807</td>\n",
       "      <td>0.943807</td>\n",
       "      <td>0.938031</td>\n",
       "      <td>0.942285</td>\n",
       "      <td>0.887901</td>\n",
       "      <td>0.942285</td>\n",
       "      <td>0.887901</td>\n",
       "      <td>1.001614</td>\n",
       "      <td>0.617308</td>\n",
       "      <td>0.618305</td>\n",
       "      <td>0.970693</td>\n",
       "      <td>0.991134</td>\n",
       "      <td>-0.968885</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-2.013554</td>\n",
       "      <td>0.747716</td>\n",
       "      <td>0.799513</td>\n",
       "      <td>0.790693</td>\n",
       "      <td>0.756546</td>\n",
       "      <td>0.572362</td>\n",
       "      <td>0.756546</td>\n",
       "      <td>0.572362</td>\n",
       "      <td>1.056794</td>\n",
       "      <td>-0.375727</td>\n",
       "      <td>-0.397066</td>\n",
       "      <td>0.919629</td>\n",
       "      <td>1.003629</td>\n",
       "      <td>26.490216</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.220474</td>\n",
       "      <td>0.788298</td>\n",
       "      <td>0.788298</td>\n",
       "      <td>0.817686</td>\n",
       "      <td>0.869424</td>\n",
       "      <td>0.755898</td>\n",
       "      <td>0.869424</td>\n",
       "      <td>0.755898</td>\n",
       "      <td>0.906690</td>\n",
       "      <td>-3.943428</td>\n",
       "      <td>-3.575467</td>\n",
       "      <td>0.847056</td>\n",
       "      <td>1.001867</td>\n",
       "      <td>-0.509132</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3.207781</td>\n",
       "      <td>1.261104</td>\n",
       "      <td>1.261104</td>\n",
       "      <td>1.206027</td>\n",
       "      <td>1.220838</td>\n",
       "      <td>1.490446</td>\n",
       "      <td>1.220838</td>\n",
       "      <td>1.490446</td>\n",
       "      <td>1.032982</td>\n",
       "      <td>0.380587</td>\n",
       "      <td>0.393139</td>\n",
       "      <td>1.141887</td>\n",
       "      <td>0.994594</td>\n",
       "      <td>1.053608</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.284738</td>\n",
       "      <td>0.963071</td>\n",
       "      <td>1.071168</td>\n",
       "      <td>1.028268</td>\n",
       "      <td>0.997275</td>\n",
       "      <td>0.994557</td>\n",
       "      <td>0.997275</td>\n",
       "      <td>0.994557</td>\n",
       "      <td>1.074095</td>\n",
       "      <td>0.776248</td>\n",
       "      <td>0.833764</td>\n",
       "      <td>1.073352</td>\n",
       "      <td>1.006766</td>\n",
       "      <td>-0.438087</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.143337</td>\n",
       "      <td>0.862929</td>\n",
       "      <td>0.823938</td>\n",
       "      <td>0.842103</td>\n",
       "      <td>0.890489</td>\n",
       "      <td>0.792971</td>\n",
       "      <td>0.890489</td>\n",
       "      <td>0.792971</td>\n",
       "      <td>0.925265</td>\n",
       "      <td>-6.212558</td>\n",
       "      <td>-5.748261</td>\n",
       "      <td>0.873393</td>\n",
       "      <td>0.997222</td>\n",
       "      <td>0.142521</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.191438</td>\n",
       "      <td>0.713677</td>\n",
       "      <td>0.739822</td>\n",
       "      <td>0.741078</td>\n",
       "      <td>0.758374</td>\n",
       "      <td>0.575132</td>\n",
       "      <td>0.758374</td>\n",
       "      <td>0.575132</td>\n",
       "      <td>0.975537</td>\n",
       "      <td>3.961455</td>\n",
       "      <td>3.864544</td>\n",
       "      <td>0.849569</td>\n",
       "      <td>0.994114</td>\n",
       "      <td>0.091464</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-1.358174</td>\n",
       "      <td>0.959586</td>\n",
       "      <td>0.959586</td>\n",
       "      <td>0.966743</td>\n",
       "      <td>0.943046</td>\n",
       "      <td>0.889336</td>\n",
       "      <td>0.943046</td>\n",
       "      <td>0.889336</td>\n",
       "      <td>1.017539</td>\n",
       "      <td>-0.694348</td>\n",
       "      <td>-0.706527</td>\n",
       "      <td>0.986592</td>\n",
       "      <td>0.990509</td>\n",
       "      <td>-4.179830</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.831066</td>\n",
       "      <td>1.082640</td>\n",
       "      <td>1.041463</td>\n",
       "      <td>1.039438</td>\n",
       "      <td>1.109032</td>\n",
       "      <td>1.229952</td>\n",
       "      <td>1.109032</td>\n",
       "      <td>1.229952</td>\n",
       "      <td>0.939074</td>\n",
       "      <td>1.334469</td>\n",
       "      <td>1.253166</td>\n",
       "      <td>0.989088</td>\n",
       "      <td>1.002334</td>\n",
       "      <td>0.511734</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.771005</td>\n",
       "      <td>1.134937</td>\n",
       "      <td>1.134937</td>\n",
       "      <td>1.110810</td>\n",
       "      <td>1.082871</td>\n",
       "      <td>1.172609</td>\n",
       "      <td>1.082871</td>\n",
       "      <td>1.172609</td>\n",
       "      <td>1.048082</td>\n",
       "      <td>-1.404492</td>\n",
       "      <td>-1.472022</td>\n",
       "      <td>1.093027</td>\n",
       "      <td>1.012815</td>\n",
       "      <td>4.189957</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.425698</td>\n",
       "      <td>0.865799</td>\n",
       "      <td>0.910855</td>\n",
       "      <td>0.894881</td>\n",
       "      <td>0.887439</td>\n",
       "      <td>0.787548</td>\n",
       "      <td>0.887439</td>\n",
       "      <td>0.787548</td>\n",
       "      <td>1.026386</td>\n",
       "      <td>-2.084668</td>\n",
       "      <td>-2.139673</td>\n",
       "      <td>0.966795</td>\n",
       "      <td>0.996003</td>\n",
       "      <td>-1.140882</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mean_rel   Max_rel  Peak (Pm)_rel  Peak-to-Peak (Pk)_rel   RMS_rel  \\\n",
       "0  -3.010802  1.010219       0.893130               0.936820  0.934251   \n",
       "1  -0.161585  0.978373       0.978373               1.013578  0.977426   \n",
       "2  -0.209051  1.276670       1.276670               1.232265  1.081000   \n",
       "3  -0.257213  1.109726       1.184543               1.270602  1.317615   \n",
       "4   0.269042  1.242078       1.182437               1.176809  1.074894   \n",
       "5   0.747955  0.787903       0.745502               0.765961  0.825881   \n",
       "6  -0.505431  0.865469       0.866102               0.865799  0.860995   \n",
       "7  -1.183106  1.029745       0.983704               0.960919  0.899149   \n",
       "8   0.102161  1.073597       0.946300               0.956626  1.079790   \n",
       "9   1.526443  0.943807       0.943807               0.938031  0.942285   \n",
       "10 -2.013554  0.747716       0.799513               0.790693  0.756546   \n",
       "11 -0.220474  0.788298       0.788298               0.817686  0.869424   \n",
       "12  3.207781  1.261104       1.261104               1.206027  1.220838   \n",
       "13  1.284738  0.963071       1.071168               1.028268  0.997275   \n",
       "14 -0.143337  0.862929       0.823938               0.842103  0.890489   \n",
       "15  0.191438  0.713677       0.739822               0.741078  0.758374   \n",
       "16 -1.358174  0.959586       0.959586               0.966743  0.943046   \n",
       "17  0.831066  1.082640       1.041463               1.039438  1.109032   \n",
       "18 -0.771005  1.134937       1.134937               1.110810  1.082871   \n",
       "19 -0.425698  0.865799       0.910855               0.894881  0.887439   \n",
       "\n",
       "    Variance_rel  Standard Deviation_rel  Power_rel  Crest Factor_rel  \\\n",
       "0       0.872825                0.934251   0.872825          0.955985   \n",
       "1       0.955361                0.977426   0.955361          1.000970   \n",
       "2       1.168561                1.081000   1.168561          1.181008   \n",
       "3       1.736111                1.317615   1.736111          0.899005   \n",
       "4       1.155396                1.074894   1.155396          1.100050   \n",
       "5       0.682080                0.825881   0.682080          0.902674   \n",
       "6       0.741312                0.860995   0.741312          1.005932   \n",
       "7       0.808469                0.899149   0.808469          1.094039   \n",
       "8       1.165946                1.079790   1.165946          0.876375   \n",
       "9       0.887901                0.942285   0.887901          1.001614   \n",
       "10      0.572362                0.756546   0.572362          1.056794   \n",
       "11      0.755898                0.869424   0.755898          0.906690   \n",
       "12      1.490446                1.220838   1.490446          1.032982   \n",
       "13      0.994557                0.997275   0.994557          1.074095   \n",
       "14      0.792971                0.890489   0.792971          0.925265   \n",
       "15      0.575132                0.758374   0.575132          0.975537   \n",
       "16      0.889336                0.943046   0.889336          1.017539   \n",
       "17      1.229952                1.109032   1.229952          0.939074   \n",
       "18      1.172609                1.082871   1.172609          1.048082   \n",
       "19      0.787548                0.887439   0.787548          1.026386   \n",
       "\n",
       "    Form Factor_rel  Pulse Indicator_rel  Margin_rel  Kurtosis_rel  \\\n",
       "0         -0.310300            -0.296642    0.923104      1.000410   \n",
       "1         -6.048977            -6.054842    0.990042      1.005521   \n",
       "2         -5.170981            -6.106970    1.229492      1.026251   \n",
       "3         -5.122667            -4.605304    1.033413      1.001414   \n",
       "4          3.995261             4.394987    1.138290      0.993697   \n",
       "5          1.104186             0.996720    0.819205      0.983766   \n",
       "6         -1.703485            -1.713590    0.935471      1.009325   \n",
       "7         -0.759990            -0.831459    1.037168      1.005516   \n",
       "8         10.569451             9.262799    0.909751      0.993170   \n",
       "9          0.617308             0.618305    0.970693      0.991134   \n",
       "10        -0.375727            -0.397066    0.919629      1.003629   \n",
       "11        -3.943428            -3.575467    0.847056      1.001867   \n",
       "12         0.380587             0.393139    1.141887      0.994594   \n",
       "13         0.776248             0.833764    1.073352      1.006766   \n",
       "14        -6.212558            -5.748261    0.873393      0.997222   \n",
       "15         3.961455             3.864544    0.849569      0.994114   \n",
       "16        -0.694348            -0.706527    0.986592      0.990509   \n",
       "17         1.334469             1.253166    0.989088      1.002334   \n",
       "18        -1.404492            -1.472022    1.093027      1.012815   \n",
       "19        -2.084668            -2.139673    0.966795      0.996003   \n",
       "\n",
       "    Skewness_rel  label  \n",
       "0       5.730242      1  \n",
       "1       1.225240      1  \n",
       "2       1.389598      1  \n",
       "3      -3.247222      1  \n",
       "4      -5.269199      1  \n",
       "5      -0.744981      2  \n",
       "6      -3.839274      2  \n",
       "7       0.134629      2  \n",
       "8      -0.069332      2  \n",
       "9      -0.968885      2  \n",
       "10     26.490216      3  \n",
       "11     -0.509132      3  \n",
       "12      1.053608      3  \n",
       "13     -0.438087      3  \n",
       "14      0.142521      3  \n",
       "15      0.091464      4  \n",
       "16     -4.179830      4  \n",
       "17      0.511734      4  \n",
       "18      4.189957      4  \n",
       "19     -1.140882      4  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Separate the label column\n",
    "label_column = df1.iloc[:, -2]\n",
    "\n",
    "# Perform the relative value by operate division on all the features\n",
    "df_relative = df2.iloc[:, :-2] / df1.iloc[:, :-2]\n",
    "\n",
    "# Add the label column back to the resulting DataFrame\n",
    "df_relative['label'] = label_column\n",
    "\n",
    "# Append a string to all column names\n",
    "suffix = '_rel'\n",
    "df_relative.columns = [col + suffix if col != 'label' else col for col in df_relative.columns]\n",
    "\n",
    "# Display the first 5 rows of the resulting DataFrame\n",
    "df_relative"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Subsetting DataFrame to see the pair plots due to many features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 808.875x750 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming your DataFrame is named 'df'\n",
    "\n",
    "# Subsetting the DataFrame to include only the first 3 columns and the label\n",
    "subset_df = df_relative[['Mean_rel', 'Max_rel', 'Peak (Pm)_rel', 'label']]\n",
    "\n",
    "# Plotting the pairplot\n",
    "g = sns.pairplot(subset_df, hue='label', diag_kind='kde')\n",
    "\n",
    "# Adjusting the axis limits\n",
    "for ax in g.axes.flatten():\n",
    "    ax.set_xlim(-10, 10)  # Adjust these limits based on your data\n",
    "    ax.set_ylim(-10, 10)  # Adjust these limits based on your data\n",
    "\n",
    "plt.show()"
   ]
  }
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