Project2_supriyakkushwaha.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "32db545d",
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#Importing libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "21c800d4",
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns',100)\n",
    "pd.set_option('display.max_rows',500)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e64ffef3",
   "metadata": {},
   "source": [
    "The Shannon entropy can measure the uncertainty of a random process.It is the average rate at which information is produced by a stochastic source of data. The higher the Shannon entropy, the bigger the information is given by a new value in the process. \n",
    "![image.png](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63e68468",
   "metadata": {},
   "source": [
    "Complexity measure is a measure derived from permutation entropy. It quantifies the complexity of a time series by considering the number of possible permutations relative to the observed permutations.\n",
    "![image.png](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "500881cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining functions\n",
    "\n",
    "# Shannon entropy (or just entropy) is a measure of uncertainty (or variability) associated with random variables.\n",
    "def s_entropy(freq_list):\n",
    "    freq_list = [element for element in freq_list if element != 0]\n",
    "    sh_entropy = 0.0\n",
    "    for freq in freq_list:\n",
    "        sh_entropy += freq * np.log(freq)\n",
    "    sh_entropy = -sh_entropy\n",
    "    return(sh_entropy)\n",
    "\n",
    "def _hash(x):\n",
    "    m, n = x.shape\n",
    "    if n == 1:\n",
    "        return np.zeros(m)\n",
    "    return np.sum(np.apply_along_axis(lambda y: y < x[:, 0], 0, x), axis=1) * np.math.factorial(n-1) + _hash(x[:, 1:]) \n",
    "\n",
    "def ordinal_patterns(ts, embdim, embdelay):\n",
    "    m, t = embdim, embdelay\n",
    "    x = ts if isinstance(ts, np.ndarray) else np.array(ts) \n",
    "\n",
    "    tmp = np.zeros((x.shape[0], m))\n",
    "    for i in range(m):\n",
    "        tmp[:, i] = np.roll(x, i*t)\n",
    "    partition = tmp[(t*(m-1)):, :] \n",
    "    permutation = np.argsort(partition)\n",
    "    idx = _hash(permutation)\n",
    "\n",
    "    counts = np.zeros(np.math.factorial(m))\n",
    "    for i in range(counts.shape[0]):\n",
    "        counts[i] = (idx == i).sum()\n",
    "    return list(counts[counts != 0].astype(int))\n",
    "    \n",
    "\n",
    "def p_entropy(op):\n",
    "    \n",
    "    ordinal_pat = op\n",
    "    max_entropy = np.log(len(ordinal_pat))\n",
    "    p = np.divide(np.array(ordinal_pat), float(sum(ordinal_pat)))\n",
    "    return(s_entropy(p)/max_entropy)\n",
    "\n",
    "def complexity(op):\n",
    "    \n",
    "    pe = p_entropy(op)\n",
    "    constant1 = (0.5+((1 - 0.5)/len(op)))* np.log(0.5+((1 - 0.5)/len(op)))\n",
    "    constant2 = ((1 - 0.5)/len(op))*np.log((1 - 0.5)/len(op))*(len(op) - 1)\n",
    "    constant3 = 0.5*np.log(len(op))\n",
    "    Q_o = -1/(constant1+constant2+constant3)\n",
    "\n",
    "    temp_op_prob = np.divide(op, sum(op))\n",
    "    temp_op_prob2 = (0.5*temp_op_prob)+(0.5*(1/len(op)))\n",
    "    JSdivergence = (s_entropy(temp_op_prob2) - 0.5 * s_entropy(temp_op_prob) - 0.5 * np.log(len(op)))\n",
    "    Comp_JS = Q_o * JSdivergence * pe\n",
    "    return(Comp_JS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78073c7e",
   "metadata": {},
   "source": [
    "#### 1. Importing Accelerometer Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e89fb050",
   "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>time</th>\n",
       "      <th>pid</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1970-01-01 00:00:00</td>\n",
       "      <td>JB3156</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1970-01-01 00:00:00</td>\n",
       "      <td>CC6740</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-05-02 14:04:42</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>0.0758</td>\n",
       "      <td>0.0273</td>\n",
       "      <td>-0.0102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-05-02 14:04:42</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.0359</td>\n",
       "      <td>0.0794</td>\n",
       "      <td>0.0037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-05-02 14:04:42</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.2427</td>\n",
       "      <td>-0.0861</td>\n",
       "      <td>-0.0163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-05-02 14:04:43</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.2888</td>\n",
       "      <td>0.0514</td>\n",
       "      <td>-0.0145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-05-02 14:04:43</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.0413</td>\n",
       "      <td>-0.0184</td>\n",
       "      <td>-0.0105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-05-02 14:04:43</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.0413</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>0.0017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017-05-02 14:04:43</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.0286</td>\n",
       "      <td>-0.0028</td>\n",
       "      <td>-0.0094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-05-02 14:04:43</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.0543</td>\n",
       "      <td>-0.0410</td>\n",
       "      <td>-0.0109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017-05-02 14:04:43</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>0.0070</td>\n",
       "      <td>-0.0050</td>\n",
       "      <td>-0.0849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2017-05-02 14:04:43</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.0561</td>\n",
       "      <td>-0.0226</td>\n",
       "      <td>0.0134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2017-05-02 14:04:43</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.0558</td>\n",
       "      <td>-0.0189</td>\n",
       "      <td>-0.0021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2017-05-02 14:04:44</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.0287</td>\n",
       "      <td>-0.0123</td>\n",
       "      <td>-0.0078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2017-05-02 14:04:44</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.0292</td>\n",
       "      <td>-0.0008</td>\n",
       "      <td>-0.0069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2017-05-02 14:04:44</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.0270</td>\n",
       "      <td>-0.0062</td>\n",
       "      <td>-0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2017-05-02 14:04:44</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.0174</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>-0.0032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017-05-02 14:04:44</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.0165</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>-0.0076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2017-05-02 14:04:44</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.0187</td>\n",
       "      <td>0.0113</td>\n",
       "      <td>-0.0096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2017-05-02 14:04:44</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.0180</td>\n",
       "      <td>0.0161</td>\n",
       "      <td>-0.0032</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  time     pid       x       y       z\n",
       "0  1970-01-01 00:00:00  JB3156  0.0000  0.0000  0.0000\n",
       "1  1970-01-01 00:00:00  CC6740  0.0000  0.0000  0.0000\n",
       "2  2017-05-02 14:04:42  SA0297  0.0758  0.0273 -0.0102\n",
       "3  2017-05-02 14:04:42  SA0297 -0.0359  0.0794  0.0037\n",
       "4  2017-05-02 14:04:42  SA0297 -0.2427 -0.0861 -0.0163\n",
       "5  2017-05-02 14:04:43  SA0297 -0.2888  0.0514 -0.0145\n",
       "6  2017-05-02 14:04:43  SA0297 -0.0413 -0.0184 -0.0105\n",
       "7  2017-05-02 14:04:43  SA0297 -0.0413 -0.0001  0.0017\n",
       "8  2017-05-02 14:04:43  SA0297 -0.0286 -0.0028 -0.0094\n",
       "9  2017-05-02 14:04:43  SA0297 -0.0543 -0.0410 -0.0109\n",
       "10 2017-05-02 14:04:43  SA0297  0.0070 -0.0050 -0.0849\n",
       "11 2017-05-02 14:04:43  SA0297 -0.0561 -0.0226  0.0134\n",
       "12 2017-05-02 14:04:43  SA0297 -0.0558 -0.0189 -0.0021\n",
       "13 2017-05-02 14:04:44  SA0297 -0.0287 -0.0123 -0.0078\n",
       "14 2017-05-02 14:04:44  SA0297 -0.0292 -0.0008 -0.0069\n",
       "15 2017-05-02 14:04:44  SA0297 -0.0270 -0.0062 -0.0004\n",
       "16 2017-05-02 14:04:44  SA0297 -0.0174  0.0050 -0.0032\n",
       "17 2017-05-02 14:04:44  SA0297 -0.0165  0.0052 -0.0076\n",
       "18 2017-05-02 14:04:44  SA0297 -0.0187  0.0113 -0.0096\n",
       "19 2017-05-02 14:04:44  SA0297 -0.0180  0.0161 -0.0032"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the accelerometer dataset\n",
    "\n",
    "df_acc = pd.read_csv('accelerometer.csv')\n",
    "df_acc['time'] = df_acc['time'].apply(str).str[:10].apply(int)\n",
    "df_acc['time'] = pd.to_datetime(df_acc['time'],unit='s')\n",
    "df_acc.head(20)\n",
    "\n",
    "# df_acc['time'] = [x[:8] for x in df_acc['time']]\n",
    "# df_acc = pd.read_csv('Data/accelerometer.csv')[lambda x: x['pid'] == 'SA0297'].sort_values(by='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "93731244",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1048575, 5)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_acc.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6c26cee",
   "metadata": {},
   "source": [
    "#### 2. Importing TAC Readings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b0579a7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a list of all TAC_Readings\n",
    "\n",
    "pid_list = ['BK7610','BU4707','CC6740','DC6359','DK3500','HV0618','JB3156','JR8022','MC7070','MJ8002','PC6771','SA0297','SF3079']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a620929e",
   "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>TAC_Reading</th>\n",
       "      <th>pid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-05-02 09:51:54</td>\n",
       "      <td>-0.000482</td>\n",
       "      <td>BK7610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-05-02 10:24:57</td>\n",
       "      <td>0.001573</td>\n",
       "      <td>BK7610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-05-02 10:30:27</td>\n",
       "      <td>0.002144</td>\n",
       "      <td>BK7610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-05-02 10:35:57</td>\n",
       "      <td>0.000877</td>\n",
       "      <td>BK7610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-05-02 10:41:26</td>\n",
       "      <td>-0.001145</td>\n",
       "      <td>BK7610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-05-02 10:46:56</td>\n",
       "      <td>-0.002159</td>\n",
       "      <td>BK7610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-05-02 10:52:25</td>\n",
       "      <td>-0.001033</td>\n",
       "      <td>BK7610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-05-02 10:57:54</td>\n",
       "      <td>0.001808</td>\n",
       "      <td>BK7610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017-05-02 11:03:23</td>\n",
       "      <td>0.004542</td>\n",
       "      <td>BK7610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-05-02 11:33:52</td>\n",
       "      <td>0.005185</td>\n",
       "      <td>BK7610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017-05-02 11:39:20</td>\n",
       "      <td>0.003094</td>\n",
       "      <td>BK7610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2017-05-02 11:44:34</td>\n",
       "      <td>-0.000291</td>\n",
       "      <td>BK7610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2017-05-02 11:50:04</td>\n",
       "      <td>-0.002025</td>\n",
       "      <td>BK7610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2017-05-02 12:20:36</td>\n",
       "      <td>0.000744</td>\n",
       "      <td>BK7610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2017-05-02 12:51:07</td>\n",
       "      <td>0.009188</td>\n",
       "      <td>BK7610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2017-05-02 13:21:36</td>\n",
       "      <td>0.022250</td>\n",
       "      <td>BK7610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2017-05-02 13:56:11</td>\n",
       "      <td>0.037422</td>\n",
       "      <td>BK7610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017-05-02 14:26:57</td>\n",
       "      <td>0.052238</td>\n",
       "      <td>BK7610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>0.065357</td>\n",
       "      <td>BK7610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2017-05-02 15:27:27</td>\n",
       "      <td>0.076462</td>\n",
       "      <td>BK7610</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  time  TAC_Reading     pid\n",
       "0  2017-05-02 09:51:54    -0.000482  BK7610\n",
       "1  2017-05-02 10:24:57     0.001573  BK7610\n",
       "2  2017-05-02 10:30:27     0.002144  BK7610\n",
       "3  2017-05-02 10:35:57     0.000877  BK7610\n",
       "4  2017-05-02 10:41:26    -0.001145  BK7610\n",
       "5  2017-05-02 10:46:56    -0.002159  BK7610\n",
       "6  2017-05-02 10:52:25    -0.001033  BK7610\n",
       "7  2017-05-02 10:57:54     0.001808  BK7610\n",
       "8  2017-05-02 11:03:23     0.004542  BK7610\n",
       "9  2017-05-02 11:33:52     0.005185  BK7610\n",
       "10 2017-05-02 11:39:20     0.003094  BK7610\n",
       "11 2017-05-02 11:44:34    -0.000291  BK7610\n",
       "12 2017-05-02 11:50:04    -0.002025  BK7610\n",
       "13 2017-05-02 12:20:36     0.000744  BK7610\n",
       "14 2017-05-02 12:51:07     0.009188  BK7610\n",
       "15 2017-05-02 13:21:36     0.022250  BK7610\n",
       "16 2017-05-02 13:56:11     0.037422  BK7610\n",
       "17 2017-05-02 14:26:57     0.052238  BK7610\n",
       "18 2017-05-02 14:57:26     0.065357  BK7610\n",
       "19 2017-05-02 15:27:27     0.076462  BK7610"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a dataframe from TAC_Readings\n",
    "\n",
    "df_tac = pd.DataFrame()\n",
    "\n",
    "for pid in pid_list:\n",
    "    temp = pd.read_csv('{0}.csv'.format(pid))\n",
    "    temp['timestamp'] = pd.to_datetime(temp['timestamp'],unit='s')\n",
    "    temp.rename(columns = {'timestamp':'time'}, inplace = True)\n",
    "    temp['pid'] = pid\n",
    "    df_tac = pd.concat([df_tac,temp])\n",
    "    \n",
    "\n",
    "df_tac.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7526ad13",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(715, 3)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tac.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92350fef",
   "metadata": {},
   "source": [
    "#### 3. Merging Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4e80306d",
   "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>pid</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>TAC_Reading</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-05-02 14:14:08</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>0.1312</td>\n",
       "      <td>0.0432</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-05-02 14:14:08</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>0.2734</td>\n",
       "      <td>0.1197</td>\n",
       "      <td>0.0968</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-05-02 14:14:08</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>0.1514</td>\n",
       "      <td>0.1886</td>\n",
       "      <td>0.0375</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-05-02 14:14:08</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.0914</td>\n",
       "      <td>0.0496</td>\n",
       "      <td>0.0534</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-05-02 14:14:08</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.7935</td>\n",
       "      <td>-0.5184</td>\n",
       "      <td>0.3967</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-05-02 14:14:08</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.3427</td>\n",
       "      <td>0.2482</td>\n",
       "      <td>-0.0175</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-05-02 14:14:08</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.3718</td>\n",
       "      <td>0.2592</td>\n",
       "      <td>-0.3961</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-05-02 14:14:08</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.2288</td>\n",
       "      <td>0.0383</td>\n",
       "      <td>-0.5308</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017-05-02 14:14:08</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.0053</td>\n",
       "      <td>-0.0141</td>\n",
       "      <td>-0.0991</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-05-02 14:14:08</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.1069</td>\n",
       "      <td>-0.2478</td>\n",
       "      <td>0.1142</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017-05-02 14:14:08</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>0.1439</td>\n",
       "      <td>-0.1099</td>\n",
       "      <td>0.1958</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2017-05-02 14:14:08</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>0.2280</td>\n",
       "      <td>0.1228</td>\n",
       "      <td>0.1888</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2017-05-02 14:14:08</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>0.2827</td>\n",
       "      <td>0.1882</td>\n",
       "      <td>0.1078</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2017-05-02 14:14:08</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>0.2677</td>\n",
       "      <td>0.1460</td>\n",
       "      <td>0.0752</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2017-05-02 14:14:08</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>0.0787</td>\n",
       "      <td>0.1604</td>\n",
       "      <td>-0.2288</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2017-05-02 14:14:08</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.0804</td>\n",
       "      <td>0.2447</td>\n",
       "      <td>-0.3525</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2017-05-02 14:14:08</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.0717</td>\n",
       "      <td>0.2824</td>\n",
       "      <td>-0.2578</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017-05-02 14:14:08</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.1858</td>\n",
       "      <td>0.1680</td>\n",
       "      <td>-0.6521</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2017-05-02 14:14:08</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>-0.1622</td>\n",
       "      <td>0.5699</td>\n",
       "      <td>-0.1518</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2017-05-02 14:14:08</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>0.0968</td>\n",
       "      <td>0.4312</td>\n",
       "      <td>0.1847</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2017-05-02 14:14:08</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>0.2174</td>\n",
       "      <td>0.2165</td>\n",
       "      <td>0.1514</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2017-05-02 14:14:08</td>\n",
       "      <td>SA0297</td>\n",
       "      <td>0.1672</td>\n",
       "      <td>0.0098</td>\n",
       "      <td>0.0518</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>-0.0012</td>\n",
       "      <td>-0.0008</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>-0.0010</td>\n",
       "      <td>0.0043</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0020</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>0.0026</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>-0.0006</td>\n",
       "      <td>-0.0006</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0025</td>\n",
       "      <td>-0.0006</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>-0.0006</td>\n",
       "      <td>0.0043</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>-0.0012</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>-0.0016</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>-0.0013</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0041</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0047</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>-0.0015</td>\n",
       "      <td>0.0038</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>-0.0023</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>-0.0006</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>-0.0008</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0037</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>-0.0009</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0008</td>\n",
       "      <td>-0.0009</td>\n",
       "      <td>0.0038</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>-0.0015</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>-0.0013</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>-0.0015</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>-0.0012</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0025</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0027</td>\n",
       "      <td>-0.0017</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>-0.0016</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>-0.0011</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0026</td>\n",
       "      <td>-0.0021</td>\n",
       "      <td>0.0037</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.0010</td>\n",
       "      <td>0.0051</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0026</td>\n",
       "      <td>-0.0009</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>-0.0008</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>-0.0006</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2017-05-02 14:49:03</td>\n",
       "      <td>PC6771</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>0.1410</td>\n",
       "      <td>-0.2686</td>\n",
       "      <td>-0.1372</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>0.1714</td>\n",
       "      <td>-0.2722</td>\n",
       "      <td>-0.1208</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>0.3328</td>\n",
       "      <td>-0.1353</td>\n",
       "      <td>-0.2548</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>0.2822</td>\n",
       "      <td>-0.0375</td>\n",
       "      <td>-0.2816</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>0.1847</td>\n",
       "      <td>0.1813</td>\n",
       "      <td>-0.2680</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>0.0392</td>\n",
       "      <td>0.3208</td>\n",
       "      <td>-0.2262</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>0.0139</td>\n",
       "      <td>0.2951</td>\n",
       "      <td>-0.1246</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>0.0213</td>\n",
       "      <td>0.3096</td>\n",
       "      <td>-0.0581</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>0.1511</td>\n",
       "      <td>0.2621</td>\n",
       "      <td>-0.0042</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>0.1721</td>\n",
       "      <td>0.1484</td>\n",
       "      <td>-0.0168</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>-0.0222</td>\n",
       "      <td>0.3686</td>\n",
       "      <td>0.3765</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>-0.0465</td>\n",
       "      <td>0.3835</td>\n",
       "      <td>0.2900</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>-0.0561</td>\n",
       "      <td>0.3402</td>\n",
       "      <td>0.1425</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>-0.0504</td>\n",
       "      <td>0.2679</td>\n",
       "      <td>0.2713</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>-0.0278</td>\n",
       "      <td>0.0938</td>\n",
       "      <td>0.1761</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>-0.0309</td>\n",
       "      <td>0.0405</td>\n",
       "      <td>0.0384</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>0.0344</td>\n",
       "      <td>-0.0436</td>\n",
       "      <td>0.1536</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>-0.1679</td>\n",
       "      <td>0.1382</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>-0.1889</td>\n",
       "      <td>-0.0497</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>0.0918</td>\n",
       "      <td>-0.2496</td>\n",
       "      <td>-0.0299</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>0.2005</td>\n",
       "      <td>-0.3317</td>\n",
       "      <td>-0.0685</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>0.2033</td>\n",
       "      <td>-0.3617</td>\n",
       "      <td>-0.1907</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>0.3330</td>\n",
       "      <td>-0.5314</td>\n",
       "      <td>-0.0481</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>0.3979</td>\n",
       "      <td>-0.2915</td>\n",
       "      <td>-0.2381</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>0.3511</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>-0.4670</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>0.2432</td>\n",
       "      <td>0.2708</td>\n",
       "      <td>-0.2488</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>0.1318</td>\n",
       "      <td>0.3401</td>\n",
       "      <td>0.0421</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>0.0871</td>\n",
       "      <td>0.2663</td>\n",
       "      <td>0.2028</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>0.0255</td>\n",
       "      <td>0.1523</td>\n",
       "      <td>0.2422</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>-0.0095</td>\n",
       "      <td>0.1462</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>0.2097</td>\n",
       "      <td>-0.0110</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>-0.0724</td>\n",
       "      <td>0.2590</td>\n",
       "      <td>-0.1375</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>-0.1875</td>\n",
       "      <td>0.3544</td>\n",
       "      <td>0.0051</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>-0.2379</td>\n",
       "      <td>0.2725</td>\n",
       "      <td>0.1978</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>-0.2184</td>\n",
       "      <td>0.1966</td>\n",
       "      <td>0.0509</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>-0.2598</td>\n",
       "      <td>0.3824</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>-0.1758</td>\n",
       "      <td>0.1868</td>\n",
       "      <td>0.1582</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2017-05-02 14:57:26</td>\n",
       "      <td>BK7610</td>\n",
       "      <td>-0.0940</td>\n",
       "      <td>0.2045</td>\n",
       "      <td>0.1455</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  time     pid       x       y       z  TAC_Reading\n",
       "0  2017-05-02 14:14:08  SA0297  0.1312  0.0432  0.0034     0.032672\n",
       "1  2017-05-02 14:14:08  SA0297  0.2734  0.1197  0.0968     0.032672\n",
       "2  2017-05-02 14:14:08  SA0297  0.1514  0.1886  0.0375     0.032672\n",
       "3  2017-05-02 14:14:08  SA0297 -0.0914  0.0496  0.0534     0.032672\n",
       "4  2017-05-02 14:14:08  SA0297 -0.7935 -0.5184  0.3967     0.032672\n",
       "5  2017-05-02 14:14:08  SA0297 -0.3427  0.2482 -0.0175     0.032672\n",
       "6  2017-05-02 14:14:08  SA0297 -0.3718  0.2592 -0.3961     0.032672\n",
       "7  2017-05-02 14:14:08  SA0297 -0.2288  0.0383 -0.5308     0.032672\n",
       "8  2017-05-02 14:14:08  SA0297 -0.0053 -0.0141 -0.0991     0.032672\n",
       "9  2017-05-02 14:14:08  SA0297 -0.1069 -0.2478  0.1142     0.032672\n",
       "10 2017-05-02 14:14:08  SA0297  0.1439 -0.1099  0.1958     0.032672\n",
       "11 2017-05-02 14:14:08  SA0297  0.2280  0.1228  0.1888     0.032672\n",
       "12 2017-05-02 14:14:08  SA0297  0.2827  0.1882  0.1078     0.032672\n",
       "13 2017-05-02 14:14:08  SA0297  0.2677  0.1460  0.0752     0.032672\n",
       "14 2017-05-02 14:14:08  SA0297  0.0787  0.1604 -0.2288     0.032672\n",
       "15 2017-05-02 14:14:08  SA0297 -0.0804  0.2447 -0.3525     0.032672\n",
       "16 2017-05-02 14:14:08  SA0297 -0.0717  0.2824 -0.2578     0.032672\n",
       "17 2017-05-02 14:14:08  SA0297 -0.1858  0.1680 -0.6521     0.032672\n",
       "18 2017-05-02 14:14:08  SA0297 -0.1622  0.5699 -0.1518     0.032672\n",
       "19 2017-05-02 14:14:08  SA0297  0.0968  0.4312  0.1847     0.032672\n",
       "20 2017-05-02 14:14:08  SA0297  0.2174  0.2165  0.1514     0.032672\n",
       "21 2017-05-02 14:14:08  SA0297  0.1672  0.0098  0.0518     0.032672\n",
       "22 2017-05-02 14:49:03  PC6771 -0.0012 -0.0008  0.0036     0.069712\n",
       "23 2017-05-02 14:49:03  PC6771 -0.0001 -0.0010  0.0043     0.069712\n",
       "24 2017-05-02 14:49:03  PC6771  0.0020 -0.0002  0.0026     0.069712\n",
       "25 2017-05-02 14:49:03  PC6771 -0.0006 -0.0006  0.0040     0.069712\n",
       "26 2017-05-02 14:49:03  PC6771  0.0025 -0.0006  0.0032     0.069712\n",
       "27 2017-05-02 14:49:03  PC6771  0.0018 -0.0006  0.0043     0.069712\n",
       "28 2017-05-02 14:49:03  PC6771  0.0003 -0.0012  0.0035     0.069712\n",
       "29 2017-05-02 14:49:03  PC6771 -0.0016  0.0003  0.0049     0.069712\n",
       "30 2017-05-02 14:49:03  PC6771 -0.0013  0.0004  0.0041     0.069712\n",
       "31 2017-05-02 14:49:03  PC6771  0.0014  0.0004  0.0047     0.069712\n",
       "32 2017-05-02 14:49:03  PC6771  0.0010  0.0004  0.0055     0.069712\n",
       "33 2017-05-02 14:49:03  PC6771  0.0018 -0.0015  0.0038     0.069712\n",
       "34 2017-05-02 14:49:03  PC6771  0.0004 -0.0023  0.0048     0.069712\n",
       "35 2017-05-02 14:49:03  PC6771 -0.0006 -0.0004  0.0044     0.069712\n",
       "36 2017-05-02 14:49:03  PC6771 -0.0008  0.0009  0.0037     0.069712\n",
       "37 2017-05-02 14:49:03  PC6771  0.0015 -0.0009  0.0033     0.069712\n",
       "38 2017-05-02 14:49:03  PC6771  0.0008 -0.0009  0.0038     0.069712\n",
       "39 2017-05-02 14:49:03  PC6771 -0.0001 -0.0015  0.0036     0.069712\n",
       "40 2017-05-02 14:49:03  PC6771  0.0013 -0.0013  0.0046     0.069712\n",
       "41 2017-05-02 14:49:03  PC6771 -0.0015  0.0000  0.0054     0.069712\n",
       "42 2017-05-02 14:49:03  PC6771  0.0035 -0.0012  0.0032     0.069712\n",
       "43 2017-05-02 14:49:03  PC6771  0.0009  0.0004  0.0036     0.069712\n",
       "44 2017-05-02 14:49:03  PC6771  0.0025 -0.0003  0.0034     0.069712\n",
       "45 2017-05-02 14:49:03  PC6771  0.0027 -0.0017  0.0048     0.069712\n",
       "46 2017-05-02 14:49:03  PC6771  0.0005  0.0014  0.0045     0.069712\n",
       "47 2017-05-02 14:49:03  PC6771  0.0022  0.0012  0.0034     0.069712\n",
       "48 2017-05-02 14:49:03  PC6771  0.0013 -0.0016  0.0049     0.069712\n",
       "49 2017-05-02 14:49:03  PC6771  0.0001  0.0000  0.0032     0.069712\n",
       "50 2017-05-02 14:49:03  PC6771 -0.0011  0.0001  0.0040     0.069712\n",
       "51 2017-05-02 14:49:03  PC6771  0.0026 -0.0021  0.0037     0.069712\n",
       "52 2017-05-02 14:49:03  PC6771  0.0014 -0.0002  0.0049     0.069712\n",
       "53 2017-05-02 14:49:03  PC6771  0.0005 -0.0014  0.0040     0.069712\n",
       "54 2017-05-02 14:49:03  PC6771  0.0005 -0.0010  0.0051     0.069712\n",
       "55 2017-05-02 14:49:03  PC6771  0.0026 -0.0009  0.0032     0.069712\n",
       "56 2017-05-02 14:49:03  PC6771  0.0001 -0.0003  0.0046     0.069712\n",
       "57 2017-05-02 14:49:03  PC6771 -0.0008  0.0005  0.0049     0.069712\n",
       "58 2017-05-02 14:49:03  PC6771  0.0002 -0.0006  0.0032     0.069712\n",
       "59 2017-05-02 14:49:03  PC6771  0.0014  0.0000  0.0031     0.069712\n",
       "60 2017-05-02 14:49:03  PC6771  0.0013  0.0007  0.0021     0.069712\n",
       "61 2017-05-02 14:49:03  PC6771 -0.0002  0.0004  0.0030     0.069712\n",
       "62 2017-05-02 14:57:26  BK7610  0.1410 -0.2686 -0.1372     0.065357\n",
       "63 2017-05-02 14:57:26  BK7610  0.1714 -0.2722 -0.1208     0.065357\n",
       "64 2017-05-02 14:57:26  BK7610  0.3328 -0.1353 -0.2548     0.065357\n",
       "65 2017-05-02 14:57:26  BK7610  0.2822 -0.0375 -0.2816     0.065357\n",
       "66 2017-05-02 14:57:26  BK7610  0.1847  0.1813 -0.2680     0.065357\n",
       "67 2017-05-02 14:57:26  BK7610  0.0392  0.3208 -0.2262     0.065357\n",
       "68 2017-05-02 14:57:26  BK7610  0.0139  0.2951 -0.1246     0.065357\n",
       "69 2017-05-02 14:57:26  BK7610  0.0213  0.3096 -0.0581     0.065357\n",
       "70 2017-05-02 14:57:26  BK7610  0.1511  0.2621 -0.0042     0.065357\n",
       "71 2017-05-02 14:57:26  BK7610  0.1721  0.1484 -0.0168     0.065357\n",
       "72 2017-05-02 14:57:26  BK7610 -0.0222  0.3686  0.3765     0.065357\n",
       "73 2017-05-02 14:57:26  BK7610 -0.0465  0.3835  0.2900     0.065357\n",
       "74 2017-05-02 14:57:26  BK7610 -0.0561  0.3402  0.1425     0.065357\n",
       "75 2017-05-02 14:57:26  BK7610 -0.0504  0.2679  0.2713     0.065357\n",
       "76 2017-05-02 14:57:26  BK7610 -0.0278  0.0938  0.1761     0.065357\n",
       "77 2017-05-02 14:57:26  BK7610 -0.0309  0.0405  0.0384     0.065357\n",
       "78 2017-05-02 14:57:26  BK7610  0.0344 -0.0436  0.1536     0.065357\n",
       "79 2017-05-02 14:57:26  BK7610  0.0009 -0.1679  0.1382     0.065357\n",
       "80 2017-05-02 14:57:26  BK7610  0.0700 -0.1889 -0.0497     0.065357\n",
       "81 2017-05-02 14:57:26  BK7610  0.0918 -0.2496 -0.0299     0.065357\n",
       "82 2017-05-02 14:57:26  BK7610  0.2005 -0.3317 -0.0685     0.065357\n",
       "83 2017-05-02 14:57:26  BK7610  0.2033 -0.3617 -0.1907     0.065357\n",
       "84 2017-05-02 14:57:26  BK7610  0.3330 -0.5314 -0.0481     0.065357\n",
       "85 2017-05-02 14:57:26  BK7610  0.3979 -0.2915 -0.2381     0.065357\n",
       "86 2017-05-02 14:57:26  BK7610  0.3511  0.1343 -0.4670     0.065357\n",
       "87 2017-05-02 14:57:26  BK7610  0.2432  0.2708 -0.2488     0.065357\n",
       "88 2017-05-02 14:57:26  BK7610  0.1318  0.3401  0.0421     0.065357\n",
       "89 2017-05-02 14:57:26  BK7610  0.0871  0.2663  0.2028     0.065357\n",
       "90 2017-05-02 14:57:26  BK7610  0.0255  0.1523  0.2422     0.065357\n",
       "91 2017-05-02 14:57:26  BK7610 -0.0095  0.1462  0.1250     0.065357\n",
       "92 2017-05-02 14:57:26  BK7610  0.0071  0.2097 -0.0110     0.065357\n",
       "93 2017-05-02 14:57:26  BK7610 -0.0724  0.2590 -0.1375     0.065357\n",
       "94 2017-05-02 14:57:26  BK7610 -0.1875  0.3544  0.0051     0.065357\n",
       "95 2017-05-02 14:57:26  BK7610 -0.2379  0.2725  0.1978     0.065357\n",
       "96 2017-05-02 14:57:26  BK7610 -0.2184  0.1966  0.0509     0.065357\n",
       "97 2017-05-02 14:57:26  BK7610 -0.2598  0.3824  0.0869     0.065357\n",
       "98 2017-05-02 14:57:26  BK7610 -0.1758  0.1868  0.1582     0.065357\n",
       "99 2017-05-02 14:57:26  BK7610 -0.0940  0.2045  0.1455     0.065357"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging the accelerometer dataframe and the TAC_Readings dataframe\n",
    "\n",
    "df_all = df_acc.merge(df_tac, on = ['pid','time'])\n",
    "df_all.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6ab872b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(444, 6)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f9507302",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 444 entries, 0 to 443\n",
      "Data columns (total 6 columns):\n",
      " #   Column       Non-Null Count  Dtype         \n",
      "---  ------       --------------  -----         \n",
      " 0   time         444 non-null    datetime64[ns]\n",
      " 1   pid          444 non-null    object        \n",
      " 2   x            444 non-null    float64       \n",
      " 3   y            444 non-null    float64       \n",
      " 4   z            444 non-null    float64       \n",
      " 5   TAC_Reading  444 non-null    float64       \n",
      "dtypes: datetime64[ns](1), float64(4), object(1)\n",
      "memory usage: 20.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_all.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18434389",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_normalized = df_all.groupby(['time','pid']).agg('mean').reset_index()\n",
    "# df_normalized"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba0c0b11",
   "metadata": {},
   "source": [
    "#### 4. Plotting Data Points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "203bf021",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\supri\\AppData\\Local\\Temp\\ipykernel_14220\\4160664643.py:3: RuntimeWarning: Second argument 'x' is ambiguous: could be a format string but is in 'data'; using as data.  If it was intended as data, set the format string to an empty string to suppress this warning.  If it was intended as a format string, explicitly pass the x-values as well.  Alternatively, rename the entry in 'data'.\n",
      "  ax[0].plot('time','x',data = df_all)\n",
      "C:\\Users\\supri\\AppData\\Local\\Temp\\ipykernel_14220\\4160664643.py:4: RuntimeWarning: Second argument 'y' is ambiguous: could be a format string but is in 'data'; using as data.  If it was intended as data, set the format string to an empty string to suppress this warning.  If it was intended as a format string, explicitly pass the x-values as well.  Alternatively, rename the entry in 'data'.\n",
      "  ax[1].plot('time','y',data = df_all)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1a0b0736510>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(4, figsize = (15,8))\n",
    "plt.xlabel('time')\n",
    "ax[0].plot('time','x',data = df_all)\n",
    "ax[1].plot('time','y',data = df_all)\n",
    "ax[2].plot('time','z',data = df_all)\n",
    "ax[3].plot('time','TAC_Reading',data = df_all)\n",
    "ax[3].axhline(y=0.08, color = 'r', linestyle = '-')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7dd0ce38",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# fig,ax = plt.subplots(4, figsize = (15,8))\n",
    "# plt.xlabel('time')\n",
    "# ax[0].plot('time','x',data = df_normalized)\n",
    "# ax[1].plot('time','y',data = df_normalized)\n",
    "# ax[2].plot('time','z',data = df_normalized)\n",
    "# ax[3].plot('time','TAC_Reading',data = df_normalized)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b940fa6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Components for acceleration in x direction\n",
    "\n",
    "# Following the convention for segment size T>=5D\n",
    "segment_length_x = int(round(df_all.shape[0]/5,0))\n",
    "pe_results_x = []\n",
    "comp_results_x = []\n",
    "\n",
    "\n",
    "for i in range(0,5):\n",
    "    start_index = i*segment_length_x\n",
    "    end_index = start_index + segment_length_x\n",
    "    \n",
    "    input_list = list(df_all['x'][start_index:end_index])\n",
    "    \n",
    "    op = ordinal_patterns(input_list, 3, 1)\n",
    "    pe = p_entropy(op)\n",
    "    comp = complexity(op)\n",
    "    \n",
    "    pe_results_x.append(pe)\n",
    "    comp_results_x.append(comp)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1a1a3196",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>x</th>\n",
       "      <th>TAC_Reading</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.131200</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.273400</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.151400</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.091400</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.793500</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.342700</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.371800</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.228800</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.005300</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.106900</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.143900</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.228000</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.282700</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.267700</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.078700</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.080400</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.071700</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.185800</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.162200</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.096800</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.217400</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.167200</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.001200</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-0.000100</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-0.000600</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.001800</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-0.001600</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>-0.001300</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.001400</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.001800</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>-0.000600</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>-0.000800</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.001500</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>-0.000100</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.001300</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>-0.001500</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.003500</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.000900</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.002700</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.002200</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.001300</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>-0.001100</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.002600</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0.001400</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0.002600</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>-0.000800</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0.001400</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0.001300</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>-0.000200</td>\n",
       "      <td>0.069712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>0.141000</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0.171400</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.332800</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0.184700</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>0.039200</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>0.013900</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0.021300</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0.151100</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0.172100</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>-0.022200</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>-0.046500</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>-0.056100</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>-0.050400</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>-0.027800</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>-0.030900</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>0.034400</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0.000900</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>0.091800</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>0.200500</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>0.203300</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>0.333000</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>0.397900</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0.351100</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>0.243200</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>0.131800</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>0.087100</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>0.025500</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>-0.009500</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>0.007100</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>-0.072400</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>-0.187500</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>-0.237900</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>-0.218400</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>-0.259800</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>-0.175800</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>-0.094000</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.128300</td>\n",
       "      <td>0.065357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>0.000700</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>-0.004200</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>-0.003600</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>0.002900</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>-0.005000</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>-0.004700</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>-0.002500</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>0.001800</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>-0.002700</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>-0.001200</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>0.001700</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>0.002800</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>0.000600</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>-0.003400</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>0.001900</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>-0.000800</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>-0.003500</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>0.001200</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>-0.004100</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>0.002300</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>0.002700</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>-0.002900</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>-0.003000</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>0.000600</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>-0.001500</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>0.001200</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>-0.001800</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>-0.000500</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>-0.002300</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>-0.000900</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>-0.001500</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>0.000600</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.074726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>-0.000200</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>-0.000700</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>-0.000100</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>-0.000300</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>-0.000500</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>-0.000200</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>-0.000200</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>-0.000200</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>-0.000500</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>-0.000400</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>-0.000200</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>-0.000300</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>-0.000200</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>-0.000100</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>-0.000500</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>-0.000100</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>-0.000200</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>-0.000100</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>-0.000400</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>0.000600</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>-0.000900</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>0.000900</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>0.001500</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>-0.000500</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>-0.000200</td>\n",
       "      <td>0.076462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>-0.000400</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>-0.000800</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>-0.001100</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>0.000600</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>0.000900</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>-0.000400</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>-0.000600</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>0.000700</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>0.001200</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>-0.000700</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>-0.000900</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>0.000600</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>-0.001800</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>-0.000200</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>-0.000100</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>-0.001400</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>-0.000300</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>-0.001800</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>-0.000300</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>0.002100</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>0.000700</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>0.001600</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>-0.001100</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>0.001500</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>-0.000600</td>\n",
       "      <td>0.088046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>-0.000400</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>0.001800</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>0.004600</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>0.010400</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>0.009800</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>-0.007700</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>-0.000800</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>0.004200</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>0.001100</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>0.001100</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>0.001700</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>-0.001500</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>0.002900</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>0.004200</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>0.003000</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>-0.000500</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>0.003100</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>0.001400</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>0.000600</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>0.003600</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>0.001700</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>0.002300</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>0.002800</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>0.001700</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>0.002600</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>0.001900</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>0.001700</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>0.002100</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>-0.000900</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>0.003400</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>-0.000300</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>0.004900</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>-0.001000</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>0.001900</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>0.004300</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>-0.003200</td>\n",
       "      <td>0.077807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>1.829285</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>1.981330</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>1.193751</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>1.087926</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>-0.092390</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>0.893237</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>0.508302</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>-3.015564</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>12.345151</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>5.630081</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>-3.207831</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>-6.072485</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>-10.960404</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>9.608539</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>9.408048</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>-0.274466</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>11.652537</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>19.596716</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>7.915498</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>-0.683910</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>-6.537205</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>-6.315897</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>-3.101944</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>-3.772891</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>-2.990885</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>-2.042745</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>-1.981279</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>-1.022672</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>1.031924</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>1.100014</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>5.660532</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>7.039517</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>3.190054</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>-0.469684</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>-5.300287</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>-8.186913</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>-5.963913</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>-1.689659</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>-4.628882</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>-3.232278</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>0.051773</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>5.418577</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>6.904035</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>8.029737</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>8.250082</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>4.412470</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>4.905080</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>1.360132</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>0.366935</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>-1.207362</td>\n",
       "      <td>0.035549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>-0.000700</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>-0.000200</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>-0.000100</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>-0.000800</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>-0.000100</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>-0.001200</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>-0.000600</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>-0.000400</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>-0.000500</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>0.000600</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>-0.000400</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>0.000700</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>-0.000800</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>-0.000700</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>0.000600</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>0.001200</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>-0.000200</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>-0.000200</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>-0.000100</td>\n",
       "      <td>0.085197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>-0.000400</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>-0.000700</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>-0.000100</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>0.001300</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>0.001600</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>0.001500</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>-0.000500</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>0.001800</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>-0.001000</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>-0.000900</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>-0.000500</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>-0.001400</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>-0.001100</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>-0.001200</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>-0.001200</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>0.002600</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>0.000900</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>0.001200</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>-0.001600</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>0.003900</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>-0.000300</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>0.002100</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>-0.000800</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>-0.001500</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>-0.001300</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>-0.000200</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>-0.002400</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>-0.000900</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>-0.001200</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>-0.000500</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>-0.001400</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>-0.001200</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>-0.001200</td>\n",
       "      <td>0.171436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>-4.696118</td>\n",
       "      <td>0.041870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>-8.342324</td>\n",
       "      <td>0.041870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>-6.992756</td>\n",
       "      <td>0.041870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>-6.556036</td>\n",
       "      <td>0.041870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>-7.352158</td>\n",
       "      <td>0.041870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>-10.326719</td>\n",
       "      <td>0.041870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>-11.090120</td>\n",
       "      <td>0.041870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>-4.385909</td>\n",
       "      <td>0.041870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>7.609210</td>\n",
       "      <td>0.041870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>17.517378</td>\n",
       "      <td>0.041870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>5.410182</td>\n",
       "      <td>0.041870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>-4.176830</td>\n",
       "      <td>0.041870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>-9.735878</td>\n",
       "      <td>0.041870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>0.519900</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>0.560900</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>0.424700</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>0.280700</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>0.260600</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>0.257400</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>0.234400</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>0.147000</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>-0.291600</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>-0.960700</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>-0.490800</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>-0.181900</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>0.025500</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>0.027700</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>0.042200</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>0.130100</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>0.221900</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>0.134100</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>0.043700</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>0.113200</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>0.153300</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>0.087700</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>0.097500</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>0.142800</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>0.158000</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>-0.077700</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>-0.155300</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>-0.056300</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>0.017200</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>0.120100</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>0.146900</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>0.144400</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>0.175700</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>0.235900</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>0.294000</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>0.275100</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>0.263600</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>0.288500</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>0.366300</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>0.428800</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             x  TAC_Reading\n",
       "0     0.131200     0.032672\n",
       "1     0.273400     0.032672\n",
       "2     0.151400     0.032672\n",
       "3    -0.091400     0.032672\n",
       "4    -0.793500     0.032672\n",
       "5    -0.342700     0.032672\n",
       "6    -0.371800     0.032672\n",
       "7    -0.228800     0.032672\n",
       "8    -0.005300     0.032672\n",
       "9    -0.106900     0.032672\n",
       "10    0.143900     0.032672\n",
       "11    0.228000     0.032672\n",
       "12    0.282700     0.032672\n",
       "13    0.267700     0.032672\n",
       "14    0.078700     0.032672\n",
       "15   -0.080400     0.032672\n",
       "16   -0.071700     0.032672\n",
       "17   -0.185800     0.032672\n",
       "18   -0.162200     0.032672\n",
       "19    0.096800     0.032672\n",
       "20    0.217400     0.032672\n",
       "21    0.167200     0.032672\n",
       "22   -0.001200     0.069712\n",
       "23   -0.000100     0.069712\n",
       "24    0.002000     0.069712\n",
       "25   -0.000600     0.069712\n",
       "26    0.002500     0.069712\n",
       "27    0.001800     0.069712\n",
       "28    0.000300     0.069712\n",
       "29   -0.001600     0.069712\n",
       "30   -0.001300     0.069712\n",
       "31    0.001400     0.069712\n",
       "32    0.001000     0.069712\n",
       "33    0.001800     0.069712\n",
       "34    0.000400     0.069712\n",
       "35   -0.000600     0.069712\n",
       "36   -0.000800     0.069712\n",
       "37    0.001500     0.069712\n",
       "38    0.000800     0.069712\n",
       "39   -0.000100     0.069712\n",
       "40    0.001300     0.069712\n",
       "41   -0.001500     0.069712\n",
       "42    0.003500     0.069712\n",
       "43    0.000900     0.069712\n",
       "44    0.002500     0.069712\n",
       "45    0.002700     0.069712\n",
       "46    0.000500     0.069712\n",
       "47    0.002200     0.069712\n",
       "48    0.001300     0.069712\n",
       "49    0.000100     0.069712\n",
       "50   -0.001100     0.069712\n",
       "51    0.002600     0.069712\n",
       "52    0.001400     0.069712\n",
       "53    0.000500     0.069712\n",
       "54    0.000500     0.069712\n",
       "55    0.002600     0.069712\n",
       "56    0.000100     0.069712\n",
       "57   -0.000800     0.069712\n",
       "58    0.000200     0.069712\n",
       "59    0.001400     0.069712\n",
       "60    0.001300     0.069712\n",
       "61   -0.000200     0.069712\n",
       "62    0.141000     0.065357\n",
       "63    0.171400     0.065357\n",
       "64    0.332800     0.065357\n",
       "65    0.282200     0.065357\n",
       "66    0.184700     0.065357\n",
       "67    0.039200     0.065357\n",
       "68    0.013900     0.065357\n",
       "69    0.021300     0.065357\n",
       "70    0.151100     0.065357\n",
       "71    0.172100     0.065357\n",
       "72   -0.022200     0.065357\n",
       "73   -0.046500     0.065357\n",
       "74   -0.056100     0.065357\n",
       "75   -0.050400     0.065357\n",
       "76   -0.027800     0.065357\n",
       "77   -0.030900     0.065357\n",
       "78    0.034400     0.065357\n",
       "79    0.000900     0.065357\n",
       "80    0.070000     0.065357\n",
       "81    0.091800     0.065357\n",
       "82    0.200500     0.065357\n",
       "83    0.203300     0.065357\n",
       "84    0.333000     0.065357\n",
       "85    0.397900     0.065357\n",
       "86    0.351100     0.065357\n",
       "87    0.243200     0.065357\n",
       "88    0.131800     0.065357\n",
       "89    0.087100     0.065357\n",
       "90    0.025500     0.065357\n",
       "91   -0.009500     0.065357\n",
       "92    0.007100     0.065357\n",
       "93   -0.072400     0.065357\n",
       "94   -0.187500     0.065357\n",
       "95   -0.237900     0.065357\n",
       "96   -0.218400     0.065357\n",
       "97   -0.259800     0.065357\n",
       "98   -0.175800     0.065357\n",
       "99   -0.094000     0.065357\n",
       "100   0.128300     0.065357\n",
       "101   0.000700     0.074726\n",
       "102   0.000400     0.074726\n",
       "103  -0.004200     0.074726\n",
       "104  -0.003600     0.074726\n",
       "105   0.002900     0.074726\n",
       "106  -0.005000     0.074726\n",
       "107  -0.004700     0.074726\n",
       "108  -0.002500     0.074726\n",
       "109   0.001800     0.074726\n",
       "110  -0.002700     0.074726\n",
       "111  -0.001200     0.074726\n",
       "112   0.001700     0.074726\n",
       "113   0.002800     0.074726\n",
       "114   0.000600     0.074726\n",
       "115  -0.003400     0.074726\n",
       "116   0.001900     0.074726\n",
       "117  -0.000800     0.074726\n",
       "118  -0.003500     0.074726\n",
       "119   0.001200     0.074726\n",
       "120  -0.004100     0.074726\n",
       "121   0.000000     0.074726\n",
       "122   0.002300     0.074726\n",
       "123   0.002700     0.074726\n",
       "124  -0.002900     0.074726\n",
       "125  -0.003000     0.074726\n",
       "126   0.000400     0.074726\n",
       "127   0.000400     0.074726\n",
       "128   0.000500     0.074726\n",
       "129   0.000600     0.074726\n",
       "130   0.000800     0.074726\n",
       "131  -0.001500     0.074726\n",
       "132   0.001200     0.074726\n",
       "133  -0.001800     0.074726\n",
       "134  -0.000500     0.074726\n",
       "135  -0.002300     0.074726\n",
       "136  -0.000900     0.074726\n",
       "137   0.000800     0.074726\n",
       "138  -0.001500     0.074726\n",
       "139   0.000600     0.074726\n",
       "140   0.000200     0.074726\n",
       "141   0.000100     0.076462\n",
       "142   0.000300     0.076462\n",
       "143   0.000200     0.076462\n",
       "144  -0.000200     0.076462\n",
       "145   0.000200     0.076462\n",
       "146  -0.000700     0.076462\n",
       "147  -0.000100     0.076462\n",
       "148  -0.000300     0.076462\n",
       "149  -0.000500     0.076462\n",
       "150  -0.000200     0.076462\n",
       "151  -0.000200     0.076462\n",
       "152  -0.000200     0.076462\n",
       "153  -0.000500     0.076462\n",
       "154   0.000000     0.076462\n",
       "155  -0.000400     0.076462\n",
       "156  -0.000200     0.076462\n",
       "157   0.000000     0.076462\n",
       "158   0.000100     0.076462\n",
       "159  -0.000300     0.076462\n",
       "160   0.000200     0.076462\n",
       "161   0.000300     0.076462\n",
       "162   0.000100     0.076462\n",
       "163  -0.000200     0.076462\n",
       "164  -0.000100     0.076462\n",
       "165   0.000200     0.076462\n",
       "166  -0.000500     0.076462\n",
       "167  -0.000100     0.076462\n",
       "168  -0.000200     0.076462\n",
       "169  -0.000100     0.076462\n",
       "170  -0.000400     0.076462\n",
       "171   0.000500     0.076462\n",
       "172   0.000600     0.076462\n",
       "173   0.000500     0.076462\n",
       "174   0.000100     0.076462\n",
       "175   0.000200     0.076462\n",
       "176  -0.000900     0.076462\n",
       "177   0.000900     0.076462\n",
       "178   0.001500     0.076462\n",
       "179  -0.000500     0.076462\n",
       "180  -0.000200     0.076462\n",
       "181   0.000400     0.088046\n",
       "182  -0.000400     0.088046\n",
       "183  -0.000800     0.088046\n",
       "184   0.000800     0.088046\n",
       "185  -0.001100     0.088046\n",
       "186   0.000600     0.088046\n",
       "187   0.000400     0.088046\n",
       "188   0.000900     0.088046\n",
       "189  -0.000400     0.088046\n",
       "190   0.000300     0.088046\n",
       "191  -0.000600     0.088046\n",
       "192   0.000700     0.088046\n",
       "193   0.001000     0.088046\n",
       "194   0.001200     0.088046\n",
       "195  -0.000700     0.088046\n",
       "196  -0.000900     0.088046\n",
       "197   0.000300     0.088046\n",
       "198   0.000600     0.088046\n",
       "199  -0.001800     0.088046\n",
       "200   0.000500     0.088046\n",
       "201  -0.000200     0.088046\n",
       "202   0.000800     0.088046\n",
       "203  -0.000100     0.088046\n",
       "204  -0.001400     0.088046\n",
       "205  -0.000300     0.088046\n",
       "206   0.000100     0.088046\n",
       "207   0.000000     0.088046\n",
       "208  -0.001800     0.088046\n",
       "209   0.000300     0.088046\n",
       "210  -0.000300     0.088046\n",
       "211   0.001000     0.088046\n",
       "212   0.001000     0.088046\n",
       "213   0.002100     0.088046\n",
       "214   0.001000     0.088046\n",
       "215   0.000700     0.088046\n",
       "216   0.001600     0.088046\n",
       "217  -0.001100     0.088046\n",
       "218   0.001500     0.088046\n",
       "219   0.000100     0.088046\n",
       "220  -0.000600     0.088046\n",
       "221  -0.000400     0.077807\n",
       "222   0.001800     0.077807\n",
       "223   0.004600     0.077807\n",
       "224   0.010400     0.077807\n",
       "225   0.009800     0.077807\n",
       "226  -0.007700     0.077807\n",
       "227  -0.000800     0.077807\n",
       "228   0.004200     0.077807\n",
       "229   0.001100     0.077807\n",
       "230   0.001000     0.077807\n",
       "231   0.001100     0.077807\n",
       "232   0.001700     0.077807\n",
       "233   0.000800     0.077807\n",
       "234  -0.001500     0.077807\n",
       "235   0.002900     0.077807\n",
       "236   0.004200     0.077807\n",
       "237   0.003000     0.077807\n",
       "238   0.000000     0.077807\n",
       "239  -0.000500     0.077807\n",
       "240   0.003100     0.077807\n",
       "241   0.001400     0.077807\n",
       "242   0.000600     0.077807\n",
       "243   0.003600     0.077807\n",
       "244   0.000300     0.077807\n",
       "245   0.001700     0.077807\n",
       "246   0.002300     0.077807\n",
       "247   0.002800     0.077807\n",
       "248   0.001700     0.077807\n",
       "249   0.002600     0.077807\n",
       "250   0.001900     0.077807\n",
       "251   0.001700     0.077807\n",
       "252   0.002100     0.077807\n",
       "253  -0.000900     0.077807\n",
       "254   0.003400     0.077807\n",
       "255  -0.000300     0.077807\n",
       "256   0.004900     0.077807\n",
       "257  -0.001000     0.077807\n",
       "258   0.001900     0.077807\n",
       "259   0.004300     0.077807\n",
       "260  -0.003200     0.077807\n",
       "261   1.829285     0.035549\n",
       "262   1.981330     0.035549\n",
       "263   1.193751     0.035549\n",
       "264   1.087926     0.035549\n",
       "265  -0.092390     0.035549\n",
       "266   0.893237     0.035549\n",
       "267   0.508302     0.035549\n",
       "268  -3.015564     0.035549\n",
       "269  12.345151     0.035549\n",
       "270   5.630081     0.035549\n",
       "271  -3.207831     0.035549\n",
       "272  -6.072485     0.035549\n",
       "273 -10.960404     0.035549\n",
       "274   9.608539     0.035549\n",
       "275   9.408048     0.035549\n",
       "276  -0.274466     0.035549\n",
       "277  11.652537     0.035549\n",
       "278  19.596716     0.035549\n",
       "279   7.915498     0.035549\n",
       "280  -0.683910     0.035549\n",
       "281  -6.537205     0.035549\n",
       "282  -6.315897     0.035549\n",
       "283  -3.101944     0.035549\n",
       "284  -3.772891     0.035549\n",
       "285  -2.990885     0.035549\n",
       "286  -2.042745     0.035549\n",
       "287  -1.981279     0.035549\n",
       "288  -1.022672     0.035549\n",
       "289   1.031924     0.035549\n",
       "290   1.100014     0.035549\n",
       "291   5.660532     0.035549\n",
       "292   7.039517     0.035549\n",
       "293   3.190054     0.035549\n",
       "294  -0.469684     0.035549\n",
       "295  -5.300287     0.035549\n",
       "296  -8.186913     0.035549\n",
       "297  -5.963913     0.035549\n",
       "298  -1.689659     0.035549\n",
       "299  -4.628882     0.035549\n",
       "300  -3.232278     0.035549\n",
       "301   0.051773     0.035549\n",
       "302   5.418577     0.035549\n",
       "303   6.904035     0.035549\n",
       "304   8.029737     0.035549\n",
       "305   8.250082     0.035549\n",
       "306   4.412470     0.035549\n",
       "307   4.905080     0.035549\n",
       "308   1.360132     0.035549\n",
       "309   0.366935     0.035549\n",
       "310  -1.207362     0.035549\n",
       "311   0.000400     0.085197\n",
       "312   0.000000     0.085197\n",
       "313   0.000100     0.085197\n",
       "314   0.000200     0.085197\n",
       "315   0.000500     0.085197\n",
       "316  -0.000700     0.085197\n",
       "317   0.000100     0.085197\n",
       "318  -0.000200     0.085197\n",
       "319  -0.000100     0.085197\n",
       "320   0.000000     0.085197\n",
       "321   0.000100     0.085197\n",
       "322  -0.000800     0.085197\n",
       "323   0.000000     0.085197\n",
       "324   0.000200     0.085197\n",
       "325   0.000100     0.085197\n",
       "326  -0.000100     0.085197\n",
       "327  -0.001200     0.085197\n",
       "328  -0.000600     0.085197\n",
       "329  -0.000400     0.085197\n",
       "330  -0.000500     0.085197\n",
       "331   0.000500     0.085197\n",
       "332   0.000600     0.085197\n",
       "333  -0.000400     0.085197\n",
       "334   0.000700     0.085197\n",
       "335  -0.000800     0.085197\n",
       "336   0.000100     0.085197\n",
       "337   0.000400     0.085197\n",
       "338  -0.000700     0.085197\n",
       "339   0.000800     0.085197\n",
       "340   0.000300     0.085197\n",
       "341   0.000600     0.085197\n",
       "342   0.000300     0.085197\n",
       "343   0.000300     0.085197\n",
       "344   0.000500     0.085197\n",
       "345   0.000400     0.085197\n",
       "346   0.000300     0.085197\n",
       "347   0.001200     0.085197\n",
       "348  -0.000200     0.085197\n",
       "349  -0.000200     0.085197\n",
       "350  -0.000100     0.085197\n",
       "351  -0.000400     0.171436\n",
       "352  -0.000700     0.171436\n",
       "353  -0.000100     0.171436\n",
       "354   0.001300     0.171436\n",
       "355   0.000500     0.171436\n",
       "356   0.001600     0.171436\n",
       "357   0.001500     0.171436\n",
       "358  -0.000500     0.171436\n",
       "359   0.001800     0.171436\n",
       "360   0.000800     0.171436\n",
       "361   0.000300     0.171436\n",
       "362  -0.001000     0.171436\n",
       "363  -0.000900     0.171436\n",
       "364  -0.000500     0.171436\n",
       "365  -0.001400     0.171436\n",
       "366  -0.001100     0.171436\n",
       "367   0.000800     0.171436\n",
       "368  -0.001200     0.171436\n",
       "369  -0.001200     0.171436\n",
       "370   0.002600     0.171436\n",
       "371   0.000500     0.171436\n",
       "372   0.000900     0.171436\n",
       "373   0.001200     0.171436\n",
       "374  -0.001600     0.171436\n",
       "375   0.002000     0.171436\n",
       "376   0.003900     0.171436\n",
       "377  -0.000300     0.171436\n",
       "378   0.002100     0.171436\n",
       "379   0.000800     0.171436\n",
       "380  -0.000800     0.171436\n",
       "381  -0.001500     0.171436\n",
       "382  -0.001300     0.171436\n",
       "383  -0.000200     0.171436\n",
       "384  -0.002400     0.171436\n",
       "385  -0.000900     0.171436\n",
       "386  -0.001200     0.171436\n",
       "387  -0.000500     0.171436\n",
       "388  -0.001400     0.171436\n",
       "389  -0.001200     0.171436\n",
       "390  -0.001200     0.171436\n",
       "391  -4.696118     0.041870\n",
       "392  -8.342324     0.041870\n",
       "393  -6.992756     0.041870\n",
       "394  -6.556036     0.041870\n",
       "395  -7.352158     0.041870\n",
       "396 -10.326719     0.041870\n",
       "397 -11.090120     0.041870\n",
       "398  -4.385909     0.041870\n",
       "399   7.609210     0.041870\n",
       "400  17.517378     0.041870\n",
       "401   5.410182     0.041870\n",
       "402  -4.176830     0.041870\n",
       "403  -9.735878     0.041870\n",
       "404   0.519900     0.040862\n",
       "405   0.560900     0.040862\n",
       "406   0.424700     0.040862\n",
       "407   0.280700     0.040862\n",
       "408   0.260600     0.040862\n",
       "409   0.257400     0.040862\n",
       "410   0.234400     0.040862\n",
       "411   0.147000     0.040862\n",
       "412  -0.291600     0.040862\n",
       "413  -0.960700     0.040862\n",
       "414  -0.490800     0.040862\n",
       "415  -0.181900     0.040862\n",
       "416   0.025500     0.040862\n",
       "417   0.027700     0.040862\n",
       "418   0.042200     0.040862\n",
       "419   0.130100     0.040862\n",
       "420   0.221900     0.040862\n",
       "421   0.134100     0.040862\n",
       "422   0.043700     0.040862\n",
       "423   0.113200     0.040862\n",
       "424   0.153300     0.040862\n",
       "425   0.087700     0.040862\n",
       "426   0.097500     0.040862\n",
       "427   0.142800     0.040862\n",
       "428   0.158000     0.040862\n",
       "429  -0.077700     0.040862\n",
       "430  -0.155300     0.040862\n",
       "431  -0.056300     0.040862\n",
       "432   0.017200     0.040862\n",
       "433   0.120100     0.040862\n",
       "434   0.146900     0.040862\n",
       "435   0.144400     0.040862\n",
       "436   0.175700     0.040862\n",
       "437   0.235900     0.040862\n",
       "438   0.294000     0.040862\n",
       "439   0.275100     0.040862\n",
       "440   0.263600     0.040862\n",
       "441   0.288500     0.040862\n",
       "442   0.366300     0.040862\n",
       "443   0.428800     0.040862"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all[['x','TAC_Reading']][0:1229]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3a8e5f77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9582255574452221,\n",
       " 0.9864227750651389,\n",
       " 0.9983229134958873,\n",
       " 0.9731950251479947,\n",
       " 0.9438221028956725]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing the permutation entropy result of x\n",
    "\n",
    "pe_results_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9ed8ed1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0405029836740981,\n",
       " 0.01370088881414036,\n",
       " 0.0016618078012607727,\n",
       " 0.024664715400325166,\n",
       " 0.051315827153278995]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Printing the complexity result of x\n",
    "\n",
    "comp_results_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1a349771",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Components for acceleration in y direction\n",
    "\n",
    "segment_length_y = int(round(df_all.shape[0]/5,0))\n",
    "pe_results_y = []\n",
    "comp_results_y = []\n",
    "\n",
    "for i in range(0,5):\n",
    "    start_index = i*segment_length_y\n",
    "    end_index = start_index + segment_length_y\n",
    "    \n",
    "    input_list = list(df_all['y'][start_index:end_index])\n",
    "    \n",
    "    op = ordinal_patterns(input_list, 3, 1)\n",
    "    pe = p_entropy(op)\n",
    "    comp = complexity(op)\n",
    "    \n",
    "    pe_results_y.append(pe)\n",
    "    comp_results_y.append(comp)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3025baab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.918896090777148,\n",
       " 0.998781522840396,\n",
       " 0.9891892880257012,\n",
       " 0.9559635284262776,\n",
       " 0.9333038969345313]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing the permutation entropy result of y\n",
    "\n",
    "pe_results_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0fac1953",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0405029836740981,\n",
       " 0.01370088881414036,\n",
       " 0.0016618078012607727,\n",
       " 0.024664715400325166,\n",
       " 0.051315827153278995]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing the complexity result of y\n",
    "\n",
    "comp_results_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "965a069d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Components for acceleration in z direction\n",
    "\n",
    "segment_length_z = int(round(df_all.shape[0]/5,0))\n",
    "pe_results_z = []\n",
    "comp_results_z = []\n",
    "\n",
    "for i in range(0,5):\n",
    "    start_index = i*segment_length_z\n",
    "    end_index = start_index + segment_length_z\n",
    "    \n",
    "    input_list = list(df_all['z'][start_index:end_index])\n",
    "    \n",
    "    op = ordinal_patterns(input_list, 3, 1)\n",
    "    pe = p_entropy(op)\n",
    "    comp = complexity(op)\n",
    "    \n",
    "    pe_results_z.append(pe)\n",
    "    comp_results_z.append(comp)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b113892a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9905513371530683,\n",
       " 0.9743882882881202,\n",
       " 0.9923904985003168,\n",
       " 0.9638455957044966,\n",
       " 0.9746415164209047]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing the permutation entropy result of z\n",
    "\n",
    "pe_results_z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9a231e77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.009171158592067637,\n",
       " 0.024995969778616633,\n",
       " 0.007350840210279693,\n",
       " 0.035815605862475446,\n",
       " 0.023935907899817404]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing the complexity result of z\n",
    "\n",
    "comp_results_z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fc7ebb57",
   "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>pe_results_x</th>\n",
       "      <th>pe_results_y</th>\n",
       "      <th>pe_results_z</th>\n",
       "      <th>comp_results_x</th>\n",
       "      <th>comp_results_y</th>\n",
       "      <th>comp_results_z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.958226</td>\n",
       "      <td>0.918896</td>\n",
       "      <td>0.990551</td>\n",
       "      <td>0.040503</td>\n",
       "      <td>0.070869</td>\n",
       "      <td>0.009171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.986423</td>\n",
       "      <td>0.998782</td>\n",
       "      <td>0.974388</td>\n",
       "      <td>0.013701</td>\n",
       "      <td>0.001202</td>\n",
       "      <td>0.024996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.998323</td>\n",
       "      <td>0.989189</td>\n",
       "      <td>0.992390</td>\n",
       "      <td>0.001662</td>\n",
       "      <td>0.010109</td>\n",
       "      <td>0.007351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.973195</td>\n",
       "      <td>0.955964</td>\n",
       "      <td>0.963846</td>\n",
       "      <td>0.024665</td>\n",
       "      <td>0.041147</td>\n",
       "      <td>0.035816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.943822</td>\n",
       "      <td>0.933304</td>\n",
       "      <td>0.974642</td>\n",
       "      <td>0.051316</td>\n",
       "      <td>0.060876</td>\n",
       "      <td>0.023936</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pe_results_x  pe_results_y  pe_results_z  comp_results_x  comp_results_y  \\\n",
       "0      0.958226      0.918896      0.990551        0.040503        0.070869   \n",
       "1      0.986423      0.998782      0.974388        0.013701        0.001202   \n",
       "2      0.998323      0.989189      0.992390        0.001662        0.010109   \n",
       "3      0.973195      0.955964      0.963846        0.024665        0.041147   \n",
       "4      0.943822      0.933304      0.974642        0.051316        0.060876   \n",
       "\n",
       "   comp_results_z  \n",
       "0        0.009171  \n",
       "1        0.024996  \n",
       "2        0.007351  \n",
       "3        0.035816  \n",
       "4        0.023936  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_metrics = pd.DataFrame([pe_results_x,pe_results_y,pe_results_z,\n",
    "                        comp_results_x,comp_results_y,comp_results_z\n",
    "                        ]).transpose()\n",
    "\n",
    "\n",
    "df_metrics = df_metrics.rename(columns = {0:'pe_results_x',\n",
    "                                          1:'pe_results_y',\n",
    "                                          2:'pe_results_z',\n",
    "                                          3:'comp_results_x',\n",
    "                                          4:'comp_results_y',\n",
    "                                          5:'comp_results_z'})\n",
    "\n",
    "df_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "38b2bb18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot((pe_results_x,pe_results_y,pe_results_z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "879ca067",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot((comp_results_x,comp_results_y,comp_results_z))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "298f3b0a",
   "metadata": {},
   "source": [
    "Following the above approach with permutation entropy and complexity method for all 3 components-x,y & z , We cannot see any kind of correlation betwen permutation entropy and complexity method. Therefore, permutation entropy and complexity method is not reliable in differentiating heavy drinking vs. sober cases."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}