Data Visualize.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "hurcan= pd.read_csv('hurcan_data.csv')\n",
    "hurcan['warn']=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>class</th>\n",
       "      <th>accelX</th>\n",
       "      <th>accelY</th>\n",
       "      <th>accelZ</th>\n",
       "      <th>orientZ</th>\n",
       "      <th>warn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>hurcan</td>\n",
       "      <td>0.000177</td>\n",
       "      <td>-0.001576</td>\n",
       "      <td>-0.000742</td>\n",
       "      <td>0.999977</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hurcan</td>\n",
       "      <td>0.000177</td>\n",
       "      <td>-0.001576</td>\n",
       "      <td>-0.000742</td>\n",
       "      <td>0.999977</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hurcan</td>\n",
       "      <td>0.000338</td>\n",
       "      <td>-0.001330</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.999977</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hurcan</td>\n",
       "      <td>0.000338</td>\n",
       "      <td>-0.001330</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.999977</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hurcan</td>\n",
       "      <td>-0.001297</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>-0.000144</td>\n",
       "      <td>0.999977</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    class    accelX    accelY    accelZ   orientZ  warn\n",
       "0  hurcan  0.000177 -0.001576 -0.000742  0.999977     0\n",
       "1  hurcan  0.000177 -0.001576 -0.000742  0.999977     0\n",
       "2  hurcan  0.000338 -0.001330  0.000080  0.999977     0\n",
       "3  hurcan  0.000338 -0.001330  0.000080  0.999977     0\n",
       "4  hurcan -0.001297  0.000065 -0.000144  0.999977     0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hurcan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QRES8Y0oi4hN5tsAtYIiZ7QCWAyvMLNHMniiZ8ERERCQn+XWh30dg9Hln51xt51wtoCvQ3czuL+7gRES8UmqOgUuplV8CvwXo55xbe3SCc24NcFPwNRGRUsXI0IU+Y0bgVCyRCJRfAi/nnNuRdWLwOHi54glJRMR7bRZtgzPOgJdfzn9mKVoaQBiS/BL4kQK+JiLia7UT9wceLF3qbSBliQYQhiW/UejtzGxPDtMNiC2GeEo155xGuIpEuGzfUbUGJULldzOT6JIKREQkkjjtbEuEC/VCLiIiZYxa3hLZlMBFRDLINAod1IUuEUsJvATpvFIR/9C31UPaaQqJEriISA6UQjzgt3EHHu9oKIGLiGSgUegSNo92PJTAS5DubiQiIkVFCVxEJAfpY1a04y0RSglcRCSD9FHofjseW5popykkSuAiIjnQIS8PaKcpLErgJUinkYlEPg1iE7/wLIGbWWMzm2Jmy8xsiZkN8iqWUB1JPUKPUT34deOvXociIsXMqTEoofJoJ8/LFngK8DfnXEugG3C3mbXyMJ58rUpaxc8bfubOr+/0OhQRKSlqgZccv9W1x13+niVw59wW59y84OO9wDKgoVfxlAQdUxNP6HMXlqOD2NQC95COhYckIo6Bm1lT4DRgZg6vDTCzOWY2JzExscRjy4kSsfhSXj+Kv/4Kq1aVXCwiedFvbEg8T+BmVgX4HLjPOZft3uPOuRHOuU7OuU5169Yt+QAz0L28pdTq3h1atPA6ioiSvqOuZFJy9BsbFk8TuJmVI5C8P3LOfeFlLOEo6GhyjUIXiXzpO+pKJhLhvByFbsC7wDLn3CtexRGObLcZFBER8YiXLfDuwM3AOWYWH/y72MN4QqZj4CI+8/rrULNmWIuox0wiXYxXK3bOTQd/NWl1DFzEpwYODHlW9bRFADWSQuL5IDY/KvAxcH0oRfxH39uSo0ZSWJTARURyoLQtkU4JvADUxSalwc/rf8aeMtb8scbrUCKKroUufqEEXgA6jUxKg/fi3wNgytop3gYSofRtlUjn2SA2P1LLW0T8KulAErExsV6HIUVICbwANBhNpAwpJd/3Oi/WoXnN5l6HEZpSUufFTV3oYdBpZCJlR2m8mUnEj3fQb2xYlMALQKeRlT5r/1hLalqq12F4QmMzcpZeK/reSoRSAg+DjoGXTht2b6D50OY8OulRr0MpUepRyln691zVIxFOCbwA1JIuXbbt2wbA5HWTPY5EIom+5xLpNIitBKmrUiTylfbzwDtshhZJXkeRj1JW58VFCbwEGKbkLeIzrpQeYpg7wusI8lBK67y4qAs9DDpmKFKWaKdbIpsSeAnSMTWJRGXpc+mcgz594N57c50n22DVMlQ/4i9K4AUQbne4Wu6RTYc3ypgJE2DYsHxn8/Wn4o8/4IYbvI5CipkSeBh0GlnpVlbf3zK1gxlGazp9x86PLfAXXoDRo72OInx+rGvwLG4l8AIoS12OIqVJKL0tx3ZoytCOjRSMxzu/SuBhKGxLRV21HtqxA/bs8ToK8YGKG7bghkCX+du9DqXsKUu9QUVAp5EVQNjHwLUn7726daFq1TyTeFndwSpLPUqhbGuNhSsAOPvXzcUdjkihqAUuZcfevTlOPrqDNWfzHFbsWFGSEXmq5cJAS7Pahm1ehxJRLPizaH7erylDO2VlmRJ4CWiwx3HRyrLV0vGTjC3v9xe872EkJavr/1YDUH/eKo8jiSzZ7kKm761EKHWhh6GgXeG/vJ1G4z2wd2QRByRFTjtZpZtzafnPdPQ4rD4L3lHdh0Qt8BLQWGOnfKNsHgcvi9uch6hgF7rHYRSpKVN4ZJrXQYRAg9jCogReAGqllV5l6b11Wf5LUDCJRKVXkA9rKGsiPOccntXN9kodJfAw6DSy0i8tlC7W0qIMtnZC2kErDfXix50OCZsSeAEoEZdeZfG9Nf3YZ1aKbyf6p7leRyBFSQk8DDqfu3RyKSkMnQDNdpatLnTJRWlogefina+9jkCKkhJ4OJzj/l/huF0pBVxcySESVV60gntmwZixZawFXgY/j2GNQhfvlMHPZkEogYeh3Jp1vPIjvPneDq9DkWJSlnaynBJVzkpxF3qJiY6Gf/0r/OX0mQyLEng4UlMBqHawDA10KgMy/mSUpRZ4SL+VffrAI48UeywRpTQkEa93OtLSyt7nxgNK4OHIenpJmEpbcnArV7J10W9eh1GkylILPF1e2zxhQsFaUoWxZw/88kuxFF2gUegl9ZmIj4fdu0tmXVIqKIGHozRe4KGgHnwQO+UU6sWdTuLt18OhQ15HVHAZfrCLdCdr/35YvLjoyiuIw4fTT41LTss8dsOVxCc5NRWSk8Nb5tpr4cwzC5bMXn8d5s8Pf7mMiqoFvnVroCUaqtNOg4suKpp1h7IN4Xxn09Jg6FA4eDC8OHbtCm/+UL3+Oixdeuz51Kmwc2fu8zsHDz8MS5YUXQwHD8I2b+8joAQeBrPC3eTANm0OfJB+/x3OOw+SkoowuqDUVBgwACZODHyJZ80q+nUAvPxy+sO6o8bAN98Uz3pClZYG/frB9OmwZQt89VXIi7rgj6wjQwvtyy/hjTcCP6gZfyjCcc010LYtHD5csOUh8MO0aFEgqa1dG96yY8dCbCzJf7sPgHlbMp9DZOHsrLz0Evz4Y3jrnzoVWrWC8uXDW+5oAl66FBISwlt24EDo0CHXl/MbxJaalsqh1Fzer4MH4fnnISWEQawJCVC/PjzwQP7zBgIL/J8xA+65J7RlMvrPf2DYsPC+7xUrwrQQL8/26acwaBC0bAnr1uU9b8YdtlB7b5wL7MAc/Yzn1euRmnrsfV69Gnr1grPPhosvzn2ZxER44QVo0yb0bX7qqbzLvOCCwCEmLznnfPPXsWNH56XN86Y5B25t7ejwFgx8HAN/V13l3F//Gnj8zjtFH+TChZnXB0W/Dueyr+ODD4pnPaHauTMQR/Xqzp18cuBxSsqx1/Ooi0VfvescuJkNcHd/e3fm+cG5qKiCxRQbe6yMKVMKVkbGOPr0CW/Zm2/OtPz/ejbN9PK0808JTH/0xtzrpzCfpYIud/zxRbPOjMsGnx88uDfPcu/65i534Y1Zyjj3XOcuv9y5fv0Cz0eMyD+O558/tvynn+Y/f2pq4b6zWZf9+9+z10VOf127hlb+u+8eW6Z27bznXbz42LwPPRRa+fv3Z45r9erc573jjmPzXXHFsccVK+a+zPbtx+b785/zj2fDhvzfi4zxduiQf5kFBMxxueREtcDDcPQuRYW6zeAXX8CRI4HH4XSvhcqrATguxErZtg3GjSv69R+ty+hoWLUqvJgyFpNTC60o3qePPip8GYWMI+vhgfQu9Eg77B/l3c/SqPhR2atj0qRAj8zo0YHnoXQjHzhw7PHnn+c/fwE+q0Vi5szQ5sv4u5Jfz2HGbQl1u8L53XrnnWOPM34n8vp+ZPxM5deDsn49nHBC6PGAZ++fpwnczC40sxVmttrMBnsZSygsKjrwv9AFFWOSjfQE3qcPXHVVrvfmLrDgGQKZjoMVIOG5ovwiFvWX+ug2FlDWbct45L+o7T+yv+ALF+NnOL/392DKQdLyu51ouPFFR4cSWHhlFqPdh3Znr6fi/l0p4Panphw59iSv73vG+PNL4ImJBQikcN/NgvIsgZtZNPA6cBHQCuhnZq28iicnzjnW7Vp3bEIhR6EfteaPNQAkZ/zwFYGxS8fSanibbNPHLx9fZIkpOTUZeyr7l3nk/JFMXjs5//Vs2hT4v28fAEN+eozR8/+b/vKG3Rv4ZPEnzNoU3rH7e78dCMDuCsemHTgY3m3gumyGE1Zu4/edv4e1XE6mrZ/G4ZQMx1IzthpCtPdwlp2cMI9Bj1v2Rabn2VrgBfhNPpSSz8CnxERITqbKc1UyryuMz9/uI5m3e8/h0N7H3GKbt2Ve7gslJAQGcwV/1C9dDqO+zHs963dvyDeWIVOfSn982OU/iC8lNfd5ViWt4otlX7B65+rsnwkg6UD2FnE49b1oczxLNgTGR6z5Yw01nq/BiLkjMs2z/WDma1/kVf6Pvx/7nC5LWhFSDMu2ZR5cFmr8m3ZtDGmZ/y78MP3x8q25DyxNSUthe2r4t490HiVwK9IWRzgrNjsdGOKcuyD4/BEA59xzuS3TqVMnN2fOnCJZ/5bfF7Bj/TJS01JJI43KsxdyyqMvseCNIRysXY0KCVs59MFIHovbwbld+1GjdkOaz1rJha8EBkfNnTqa6NiKxFSsTMzva4myaI40OI40l0aF5atJJY3K8UvZ0asLHa/4a65xbPz4TY5Uq0xUwiaSK1bgcI0qpJDGml3rqBZbjZpbdjFx9Q8sq3aYP3UcQPPjWjB3+wLW7N1A5ZrH0fjrafSansCBzu2p/d+x/OkyeDfL+K13T4N6g/9J3YNRVF2TQKV6jUk9qTkuJgaio6mw8ndcuXIkN6yHi4nBlYvBxcRQZcov1H/k/9hzUW+qfTeJhLdfptGdf2N2A+i8OfM6hnWBF8+Aj064j/rN2xFdrjzR5coTVa48FdZvIqpceba2O5Gmp51N5WTY2qMD7vTTqf/86wDs69+P2Re35+bfHqbePpjzNjzfHbq37UPFM3uxeP9aJh9YTN9mfej86wYONWvE4ZpVqfPROGzbdtp1X0TCvzPH1OcGeOy8p6m5dRctH3kFgFW/fE39/7zLoauvwI6vR3R0DAk/T6BN8PXc7LroHH5yq3mp0QYu73gD2/Zs4ZTNh2nc83LqN27JhsPbmT36RTot2EHf6Tl3MY6//FQ21Iriq0b7GXb1SJI2riBp+XxaHq7G+9XX8unmH/nr0so8MD4wsnVe4xg6bMzcWvjkib58uW8OLZp3okfD7jSeFs+uHl0pV60GyeWjOeHld6g3fmKu23H/BdC18inMrbCDh75M4rgDMKtBYOcFYPsj93Lcc0MBmNy5LufMztwa+cfZ8Hv3lnQ8qQdzv3qLXU2O4/+ufI3Jm6bzt35D0+c7FA2xGX7T3rjndD7fNYONNaN495xXSf19Nb9W3UWTBi1pV6EJrc+7AYDD9epSYWvmdV7aD56q1IdXk74lsTK81u1pxrOccnXr0ebzabSauYZ6G3IegXzuzbC5KiwdHng+ctQgbr/tP7nWTygW14UDD91HjeNP4PO3H2BpXTi17qk0TK3Mre/mfLHxXdUrMOfjl9h5aBddV+yjws497Ox7ESmx5Wl612NUm5H7Rcp73wJNdgW2I24b3FShEw/UmsON24/jth+257jM5qpGg73h/bb/44rqNE5KZknlA9w/A9aefioHmjakz5uTss37yZk1WNyvNzcdPoVKuw9QZcEytjSrS+t/f5ht3g9Pr0Jasya0+2U17dYHdmqn9e9F1JKlxKYaneZnH8k9vsdxVD6tC3GLd5CwN4HnmiQwpOoltBmZ92DZ925sjV1yKfPf+xd9K3Wk6h/7OWnGCqoczlwX69s1YeIZ9Vi9ciZJJzdkxJub0l/bXAUa7Ds277A/tWVy1R30/+0QyU0asXPHRv48aVe2dW94/B6O1KpOcpfOtOx+WZ5xhsPM5jrnOuX4mocJ/GrgQufcHcHnNwNdnXMDc1umKBP4lJvP4uwPpxdJWSIiIunS0orssENeCTymSNZQMDltXba9CTMbAAwAOCHcgQV5aDrg78ztdCZRUVFEEUWteUuptH4Tm2+/lmgHtabOpOLajXxSdzs/J/9OrYNwze6GVNt3hIPdu1L55NakHTpI1J69VFq0jB29ulFrxXoO1a9L9ZXrOVz/OE4Y/S2HGtdny01XcOr9/5cthm9PhgN/vYN6lY6jyYRf2dfqRA6e2IRoi+LQkQMc3ruLE+b9zqRau6japAWHD+/nyyVf0KxKIwY0uJRdCavp8t5PgYozY+cZ7an9y/xM63itc6Ci59aHvx5uS50/DnOgQ1v+aNkMc47YHbuovnojiR1bkhpbHktJJSo5hXq/LqDZ+P+ll5MUdxK1F64m6aQG1F6dpfkN3HMRHL8PWiVCrfsfxXC45GQ4fJgWYyZSbuMm6m451jU1/ql+7KpdmRXLphO9dh13TTvEu5c15JofNnFqEiRHwQ8nQhOrzowqu1lTE/p2vZXNq+Zy+WeL2NekPsse/wtUqkTnGx/K8T3+8yVwU7mONKvRlGcSP6dCClx+2vW0H/crS67uxb6alUhNS+Vg8gHOevYj6u/LsRgAEitBfD2Yc1VX4tfNpMfB4+i5/BCzL4qjXkwN2k6YQ6OVW/P6yDGraXlOjKrNG+2OEFWpCo0XraeDq0fVE1sxqm4C105LouWK3AcIJVWEve1aMq9pBb6MWkmvyq3oPSuJjTddSpWYStSes4RGH+V9t4one8H1i+G4/TC0K/RbBIebNmJclQTaboe+y/JcnKlNAssdvx/+urIas3qcyLaoAzz2dt5dpT+dWp69DeqwuHllzoo5kdi5C/i+fRU6/VGRS8cuzHPZj9tAhy0wuRnUrNuIzgmOMadX47ERx4JNaF6HRmuyX+L4r31gX3m4eQH8Uck4q/xJNJi3Ku+NDMHzPaJoVK0RVVZtYGsV2FMBHvo172WmP3gdG7cs5/z4PdRauo75/7ybDo+9lu+63uoI9ffCgnrQZyU03xPF8A5pPJpH+2NlLWi2K9D79pe58FNzOG9N/tu1qlagpd9zPaypAc135T7vg+fB3w60I61mDRp+87/cZ8xgY+PqNN64mzlNytFpfe6HDcafAimNG3D1xMBvzeFoqBBCL/XGepU49ND9RP3reU5MzPtY96j20GAv/NamBk9+uSvX+ca2hOUnVGTwTweZ1AwuyOcI29L3XqRVSY1Fym14enH/AacDP2R4/gjwSF7LeHEaWVpamktNSy10Od+v+j7bKRyNX2lcBBFmNnXt1EzrYAjuhekvuEPJh8IrKDXVudmzc315dOvM2zJu2bj862nevGPLHDmSfZVpqW7r3q1uZsJMN2HlhPTpaWlp7khK9vkz+m3jb9nqN35LfPrrDAnURcgylNP1T4Flxy4ZG/ryaWmZyjh5IG7r3q2hL59DHOfdFN7pRV+//Of0ZbdUDmzD4m2Lc17N0fr5449A7EGpGd/jUwLz7D60O3sBqanO7T42PS3LaVF7D+8NOe5RVzbLtOyqpFUuJTXFpaSm5L3g229n++ynpKa4nQd2ukXbFmWed+fOTNvpDh92zjn3f2cGlk1NSXZfr/jaMQRX/bnqmcq99O1zcv48Hjni3Lp1zjnnflryTfr8V16L239kf/4bnmEdk778j3Pu2O/PnE1zHENwa3auyby+1NRsyybuT3SPTnzUMQT3z//9M/By8P1N3bPbzf91XKb5v1/1vavzQh33w+of3MHkg+78/57v7vrmrmPrWbnS/TLr8/T53+qA23d4nztw5ECOmzFt2Q/p81Z6NLTP7O6Du9KXqToYl5bxvXHOueRk51atcs45N/CiY7EPerRD+uM7L8l9XWkZvo/vx+VQfgark1Zn/i3Jat8+57Ztc0vqZP6sFRfyOI3MywQeA6wBmgHlgQVA67yW8fo88MJITctynie4WQmzimdlGdbxyq+vFMsqEnp3zjVZ5ik6OtcEXhhzNs3JVr8ZFSaBL/72vYIFlaGMhb+MK3QZ/V89O6xF077+On3ZDTXzvnZBbvWTZpZexv+6HB/W+vP8EczDjofvLfCyWRN4uNITXVqq+2r5V44huD4f9clU7uadG/ItJ+P3/acX/hJ27OEHnnnZXBN4Wqrbs/zYtSKW1Q5tXYfWrEpf5tsz8/4cJO5PTJ/3/A/OC3sbFi6dmuds91x4bFsPfvlF+uOBV1cOqfxXHjor5Fjyei+OtDgxfZ7Wr7fOv8wCyiuBezYK3TmXAgwEfgCWAZ8654rwOneRJcqyV3WnBjke1ihSN7S9oVjKbViudqbn0VEhnCoDx87HDOzEFZn29doXaXkZtW7QrtBltK0XV+gyena7Lqz5LcMV0Boed2KB1pmxI/DMk84pUBnhql2xVomsJ1SWpTu0fs3G+S6T8fve+4ReRR1SvrKecXCUYVQtf+wMgZfvCO3EnwpR5dIfn9ck789BnUp10h9f0uLSkMrPqG3tlnm+/uj9x86rj+1yevrjalVC+9z0O/Xq/Gdq0CDfWcpx7DevQkyFPOYsPp6eB+6cm+Cca+GcO9E5l/0gcSmX9YehOBxf5fjiKbhixUxPG1VrFNpy990X+B/KubFhCHkHoiDCvRRoMbmt0x3hLVDu2I9u1NmFT75RJ55U6DJCUsQ7dwVhRXideCvJ7Tn55MzrzvIbY2aZ6vftaz4IrdwmTaBjRwDKhXF5hZwaLvnK55SseudfleFJvcBlhoEHzno4pOLrxdbJf6ZQLlscAZ9TXYmtJD3yCHTpAo8/DgsWeB1N4fzzn5me1oitEdpyzz8fGKFZxAkcgGefDVx/HI79Lwot824RhKROCD8a+Qn3CmVNmx57/FyuZ2fm7ZZbAv8/+wyeeCK8ZatXD/wfPjy85TL+MM7L4xzuPFx2fYEWyxxGxlZsfDzcfz88+WT4BXXpEt78E3M/DTDcZV1OSSbjtHZh9C4dvb/AX3M/LTaryuUrh17+UbGx+c/z4IPHGgOPPQYnnkjtK/qFVn6tEFrq5cvDqFEwfnzu81QObNtj5xTtDl84vByFXvY8+2zJrOfpp6FqVbiheLrPgcBNKm64AT7+GEaMyH/+o4qz16Eo7z/84Ydw002By94WNOb//Q969gxcmrFatYKV0aFDIIktWRJ+HM2awcaNge7Agl6e9J134NVXoUaN8Jct7J2ohgwJ3OAiHE8/DampfG1P5T9viAwLJLpX8r5WQDZ790KFCpl6QvI0Zkzgc9K7d/hBfvFFYD3BM3XyTChHE/iJJ0JMGCmgQYOwW503xd0U+swHDwZu9FSzZv7zvvjiscdxcYGbmoTqggtCm+/WW/N+ffx4Eob/i2crvkknj66AqQReGv3jHyW7vizd6aXCjTcG/gqjR4/Cd7PNnBnoUqxQwGNsjUI8tJGbmJiCJe/CuOGGQCK+vgDN6KOf/acKl8CL5PBWlSr5z5NRsCu4QK68MvR569YN/L/ttoKvL0QxUWGkmNhYaN26+ILZsCGwc1BUybZJE7bcezu882bRlFcASuBScEeTk4c3nyj1YmLCayWVBqecEhHHF3PsfvaJ3AaxAYFDG0eOlL3PVeP8Bx+GK896LgFl7B2UInX0B86rG6iIlICSGGxaXHKNPdRufcnT0Z08r46Bq+kkBefjFoqISGEdPfulUrlKnqxfLXApOHWhSynmdfeoRL6O9TsypOcQ7ux4pyfrVwKXglMXupQBXnWPFgU/H8f3AzPjyV4FOLWwiKjpJAV39NSogo6QFhGRAlMLXArulVfgpJPg0vAvlygS6UpD69XPA/Akf2qBS8FVqwaDB+sYuJQaOV0SWElQIpVa4CIiQTP+NIM5m+d4HYb/xcXBwrzv8y6FpwQuIhLUqFqj9Fa4n0ehe979P3s2JCd7G0MZoAQuIpIHP49C9yz28uUj5i5+pZkOXoqIlFJ+7kWQ/CmBi4jkwPNu6ELQwLuyQQlcRCQPSoYSqZTARUTy4MeWuB9jlvApgYuIlFJ+HoAn+dModBEpdS466SLOOuGsIilLXegSqZTARaTUmXDjBK9DiAgahV66qQtdRCQHfk5+6jUoG5TARUTy4MfjyBrEVjYogYuI5KA0JEE/7nxI6JTARUTyoO5oiVRK4CIiIj6kBC4ikgM/D2KTskEJXEQkD348jtykRhOA9FujSumk88BFREqZAR0H0KR6Ey486UIAWtVtxdLEpR5HJUVNCVzKjIoxFb0OIWKd0+wcujXs5nUYEcXPo9CjLIqLTr4o/flvf/qNPw794WFEUhyUwKVM2PbgNspHl/c6jIg16ZZJXocQsUrDKPSqFapStUJVr8OQIqYELmXCcZWP8zoEEZEipUFsIiI50Ch0iXRK4CIiefDjKHQpG5TARUREfMiTBG5mL5rZcjNbaGbjzKyGF3GIiOTGz6PQpWzwqgX+E9DGORcHrAQe8SgOEZE8lYZR6FI6eZLAnXM/OudSgk9/A3S5IBERkTBEwjHw24HvvA5CRCQjjUKXSFds54Gb2USgXg4vPeac+zI4z2NACvBRHuUMAAYAnHDCCcUQqYhI7jQKXSJVsSVw59y5eb1uZv2BS4DeLo/RIs65EcAIgE6dOmmXWEREBI+uxGZmFwIPAz2dcwe8iEFERMTPvLqU6mtABeCn4AjP35xzfylIQcnJySQkJHDo0KGijE+kTIqNjaVRo0aUK1fO61BEJB+eJHDn3ElFVVZCQgJVq1aladOmOt1DpBCccyQlJZGQkECzZs28DkdE8hEJo9AL5dChQ9SuXVvJW6SQzIzatWurNytIF3KRSOf7BA660IJIUdF3KTvViUSqUpHAI8G4ceMwM5YvX16k5a5bt442bdoUWXnPPvtspudnnHFGkZWdkyFDhvDSSy8VW/mpqal07NiRadOmpU87//zz+eyzzwpd9h133MHSpUsLVUZiYiJdu3bltNNO4+eff870WtOmTdmxY0ehyheRsksJvIiMHj2aM888k08++cTTOFJTU/N8PWsC//XXX4sznGIXHR3N8OHDufvuu0lOTmb06NGYGddcc02hy37nnXdo1apVocqYNGkSp556KvPnz+ess84qdExHOedIS0srsvIkO13IRSKdEngR2LdvH7/88gvvvvtupgSemprKgw8+SNu2bYmLi2PYsGEAzJ49mzPOOIN27drRpUsX9u7dS2pqKg899BCdO3cmLi6Ot956K9t6cptn6tSpnH322dxwww20bdsWgCuuuIKOHTvSunVrRowYAcDgwYM5ePAg7du358YbbwSgSpUqQCAhPPTQQ7Rp04a2bdsyZsyY9LJ79erF1VdfzamnnsqNN96Y47HBt99+m86dO9OuXTv69u3LgQPZzw6Mj4+nW7duxMXFceWVV/LHH38A0KtXLx5++GG6dOlCixYt0luqBw4c4NprryUuLo7rrruOrl27MmfOnGzldu3alTPOOIMhQ4bw6KOP8vrrr+f4Pj399NN07tyZNm3aMGDAAJxzpKSk0LlzZ6ZOnQrAI488wmOPPZYe15w5c0hNTeXWW29Nr5t///vf2cpev349vXv3Ji4ujt69e7Nhwwbi4+P5+9//zoQJE2jfvj0HDx7MttywYcPo0KEDbdu2Te+9ydpr0aZNG9atW8e6deto2bIld911Fx06dGDjxo288MILtG3blnbt2jF48OA834tbb72Ve++9lzPOOIPmzZszduzYHOtJMtOFXCRSeXUaWbG47/v7iN8aX6Rltq/XnlcvfDXPecaPH8+FF15IixYtqFWrFvPmzaNDhw6MGDGCtWvXMn/+fGJiYti5cydHjhzhuuuuY8yYMXTu3Jk9e/ZQsWJF3n33XapXr87s2bM5fPgw3bt35/zzz890/C23eQBmzZrF4sWL00cPjxw5klq1anHw4EE6d+5M3759+de//sVrr71GfHz2Ovriiy+Ij49nwYIF7Nixg86dO9OjRw8A5s+fz5IlS2jQoAHdu3fnl19+4cwzz8y0/FVXXcWdd94JwOOPP867777LPffck2meW265hWHDhtGzZ0+eeOIJnnrqKV59NVC3KSkpzJo1iwkTJvDUU08xceJEhg8fTs2aNVm4cCGLFy+mffv2ub4Hzz33HI0bN+a+++7jpJNyPslh4MCBPPHEEwDcfPPNfPPNN1x66aW89957XH311QwdOpTvv/+emTNnZlouPj6eTZs2sXjxYgB27dqVY9m33HIL/fv3Z+TIkdx7772MHz+ep59+mjlz5vDaa6/lGFOdOnWYN28ew4cP56WXXuKdd97JdRsBVqxYwahRoxg+fDjfffcd48ePZ+bMmVSqVImdO3cCeb8XW7ZsYfr06SxfvpzLLruMq6++Os/1iUjkUgu8CIwePZrrr78egOuvv57Ro0cDMHHiRP7yl78QExPYT6pVqxYrVqygfv36dO7cGYBq1aoRExPDjz/+yAcffED79u3p2rUrSUlJrFq1KtN68pqnS5cumU79GTp0KO3ataNbt25s3LgxW1lZTZ8+nX79+hEdHc3xxx9Pz549mT17dnrZjRo1Iioqivbt27Nu3bpsyy9evJizzjqLtm3b8tFHH7FkyZJMr+/evZtdu3bRs2dPAPr375/puPVVV10FQMeOHdPLnz59enq9tmnThri4uFzjnzZtGtWrV09PsjmZMmUKXbt2pW3btkyePDk9xtatW3PzzTdz6aWXMnLkSMqXL59puebNm7NmzRruuecevv/+e6pVq5at7BkzZnDDDTcAgZ2D6dOn5xpHRjltd16aNGlCt27dgMDn67bbbqNSpUpA4PMFeb8XV1xxBVFRUbRq1Ypt27aFFGNZpVHoEulKVQs8v5ZycUhKSmLy5MksXrwYMyM1NRUz44UXXsA5l20Ea07Tjk4fNmwYF1xwQabpGX/Uc5tn6tSpVK5cOdPziRMnMmPGDCpVqkSvXr3yPTUorx+rChUqpD+Ojo4mJSUl2zy33nor48ePp127drz33nvpXdKhOrqOjOWH+gO6f/9+/v73vzN58mRuv/12JkyYwMUXX5xpnkOHDnHXXXcxZ84cGjduzJAhQzLVyaJFi6hRo0aOSa1mzZosWLCAH374gddff51PP/2UkSNH5hlTqCOXc9rumJiYTMe3M8aZ8X3O7bOU13uR8b1UggqNRqFLpFILvJDGjh3LLbfcwvr161m3bh0bN26kWbNmTJ8+nfPPP58333wz/Yd5586dnHrqqWzevDm9dbt3715SUlK44IILeOONN0hOTgZg5cqV7N+/P9O6QpkHAq3dmjVrUqlSJZYvX85vv/2W/lq5cuXSl8+oR48ejBkzhtTUVBITE5k2bRpdunQJuR727t1L/fr1SU5O5qOPst+bpnr16tSsWTP9+PZ///vf9NZ4bs4880w+/fRTAJYuXcqiRYtynO/pp5/m2muv5dRTT2X48OHcf//92XZYjj6vU6cO+/bty3T894svviApKYlp06Zx7733Zusi37FjB2lpafTt25dnnnmGefPmZYvhjDPOSB//8NFHH2U7xBCOpk2bpq9j3rx5rF27Nsf5zj//fEaOHJl+jPtoF3p+74WIlA6lqgXuhdGjR6cPHjqqb9++fPzxxwwbNoyVK1cSFxdHuXLluPPOOxk4cCBjxozhnnvu4eDBg1SsWJGJEydyxx13sG7dOjp06IBzjrp16zJ+/PhM5YYyD8CFF17Im2++SVxcHKecckp6lyvAgAEDiIuLo0OHDpl+3K+88kpmzJhBu3bt0nsQ6tWrF/Jpcc888wxdu3alSZMmtG3blr1792ab5/333+cvf/kLBw4coHnz5owaNSrPMu+66y769+9PXFwcp512GnFxcVSvXj3TPEuXLmXcuHEsWLAAgPbt23PBBRfw/PPP8+STT6bPV6NGDe68807atm1L06ZN0w9h7Nixg8GDBzNp0iQaN27MwIEDGTRoEO+//376sps2beK2225LbxU/99xz2WIdOnQot99+Oy+++CJ169bNd9vy0rdv3/RDJZ07d6ZFixY5znfhhRcSHx9Pp06dKF++PBdffDHPPvtsSO+F5E+j0CXSmZ+60Tp16uSyjkJetmwZLVu29CgiKU6pqakkJycTGxvL77//Tu/evVm5cmW2Y9RStPSdCvhgwQf0H9+fm+Nu5oMrP/A6HCmjzGyuc65TTq+pBS4R68CBA5x99tkkJyfjnOONN95Q8hYRCVICl4hVtWrVHM/7FikJfuqdlLJJg9hERPKgUegSqZTARUREfEgJXEQkBxqFLpFOCVxEJA+6FrpEKiXwIqLbieasuG8n+uijj/Lwww+nP1+/fj3NmzfP8Xrl4XjiiSeYOHFioco4fPgw5557Lu3bt0+/OcxRR2+UIiJSUErgRUS3E/XGP/7xD7788kuWLVsGwKBBg3jmmWeoUaNGocp9+umnOffccwtVxvz580lOTiY+Pp7rrruuUGVlld/7LIWnUegS6ZTAi4BuJ+rd7UQrVqzIK6+8wl133cV3333H3r1707ctlPguv/xyPvggcJGOt956K33ZW2+9Nf1yq4MHD6ZVq1bExcXx4IMPZit7586dXHHFFcTFxdGtWzcWLlzI9u3buemmm4iPj6d9+/b8/vvv2Zb77LPPsm3ze++9x8CBA9PnueSSS9KvZV6lShWeeOIJunbtyowZM/jggw+Ii4ujXbt23HzzzQB8/fXXdO3aldNOO41zzz03/druQ4YM4fbbb6dXr140b96coUOHZotHcqZR6BKxnHO++evYsaPLaunSpceeDBrkXM+eRfs3aFC2dWb13//+191+++3OOedOP/10N3fuXOecc8OHD3dXXXWVS05Ods45l5SU5A4fPuyaNWvmZs2a5Zxzbvfu3S45Odm99dZb7plnnnHOOXfo0CHXsWNHt2bNGrd27VrXunVr55zLdZ4pU6a4SpUquTVr1qTHlJSU5Jxz7sCBA65169Zux44dzjnnKleunCn2o8/Hjh3rzj33XJeSkuK2bt3qGjdu7DZv3uymTJniqlWr5jZu3OhSU1Ndt27d3M8//5ytDo6W75xzjz32mBs6dKhzzrknn3zSvfjii84559q2beumTp3qnHPuH//4hxsUrNuePXu6Bx54wDnn3Lfffut69+7tnHPuxRdfdAMGDHDOObdo0SIXHR3tZs+eneN7cNVVV7k6deq45cuX5/h6bvFt3brVnXjiiW7atGnu5JNPTq+3/v37u88++8wlJSW5Fi1auLS0NOecc3/88Ue2sgcOHOiGDBninHNu0qRJrl27ds4556ZMmeL69OmTYzy5bfOoUaPc3XffnT5fnz593JQpU5xzzgFuzJgxzjnnFi9e7Fq0aOESExOdc8fe7507d6bH+vbbb6ev48knn3Snn366O3TokEtMTHS1atVyR44cyTG2TN+pMmzU/FGOIbjbxt/mdShShgFzXC45URdyKQKjR4/mvvvuA47dTrRDhw453k500aJF2W4nCoFbhS5cuDC91bd7925WrVqV6TrYuc1Tvnz5HG8nOm7cOID024nWrl07123I7Xai1apVS7+dKJB+O9GsN+tYvHgxjz/+OLt27WLfvn3Z7piW0+1Er7nmmvTXc7ud6KBBg4D8byd69913c/DgQU455ZQcX88tvuOPP56nn36as88+m3HjxqXfkvOoatWqERsbyx133EGfPn245JJLcqy7zz//HIBzzjmHpKQkdu/enWuseW1zXqKjo+nbty8AkydP5uqrr6ZOnTrAsVuJJiQkcN1117FlyxaOHDmS6TPRp08fKlSoQIUKFTjuuOPYtm1b+vsq2fVr04/fEn7jn+f80+tQRHJUuhL4q6+W+Cp1O9EAL28nChAVFUVUVO5HhPKKb9GiRdSuXZvNmzdnWy4mJoZZs2YxadIkPvnkE1577TUmT56caZ6c4gyl2zXcW4nGxsYSHR2dvs6c1nHPPffwwAMPcNlllzF16lSGDBmSbX1Z1yk5qxBTgTcvedPrMERypWPghaTbiZK+HV7dTrQw8c2aNYvvvvuO+fPn89JLL2W7dee+ffvYvXs3F198Ma+++irx8fHZyu7Ro0d6mVOnTqVOnTrpPSvhatq0KfHx8aSlpbFx40ZmzZqV43y9e/fm008/JSkpCTh2K9Hdu3fTsGFDgEx3VBOR0qd0tcA9oNuJBnh1O9FQ5RTf4cOHufPOOxk1ahQNGjTg5Zdf5vbbb8/Uwt67dy+XX345hw4dwjnHv//972xlDxkyhNtuu424uDgqVapUqMTZvXt3mjVrRtu2bWnTpg0dOnTIcb7WrVvz2GOP0bNnT6KjoznttNN47733GDJkCNdccw0NGzakW7duud5LXET8T7cTlYil24l6Q98pkcih24mKL+l2oiIiuVMCl4il24mKiOROg9hERER8qFQkcD8dxxeJZPouifiH7xN4bGwsSUlJ+uERKSTnHElJScTGxnodioiEwPfHwBs1akRCQgKJiYlehyLie7Gxsbo6m4hP+D6BlytXLtPlIkVERMoC33ehi4iIlEVK4CIiIj6kBC4iIuJDvrqUqpklAuuLsMg6wI4iLE9Cp7r3jureO6p77/i17ps45+rm9IKvEnhRM7M5uV1jVoqX6t47qnvvqO69UxrrXl3oIiIiPqQELiIi4kNlPYGP8DqAMkx17x3VvXdU994pdXVfpo+Bi4iI+FVZb4GLiIj4UplN4GZ2oZmtMLPVZjbY63j8yMxGmtl2M1ucYVotM/vJzFYF/9fM8NojwfpeYWYXZJje0cwWBV8bamYWnF7BzMYEp880s6YluoERzMwam9kUM1tmZkvMbFBwuuq/mJlZrJnNMrMFwbp/KjhddV8CzCzazOab2TfB52W33p1zZe4PiAZ+B5oD5YEFQCuv4/LbH9AD6AAszjDtBWBw8PFg4Png41bBeq4ANAvWf3TwtVnA6YAB3wEXBaffBbwZfHw9MMbrbY6UP6A+0CH4uCqwMljHqv/ir3sDqgQflwNmAt1U9yVW/w8AHwPfBJ+X2Xr3PACPPgCnAz9keP4I8IjXcfnxD2iaJYGvAOoHH9cHVuRUx8APwfehPrA8w/R+wFsZ5wk+jiFwEQbzepsj8Q/4EjhP9V/i9V4JmAd0Vd2XSH03AiYB52RI4GW23stqF3pDYGOG5wnBaVJ4xzvntgAE/x8XnJ5bnTcMPs46PdMyzrkUYDdQu9gi96lgN99pBFqCqv8SEOzGjQe2Az8551T3JeNV4O9AWoZpZbbey2oCtxymaTh+8cqtzvN6L/Q+5cPMqgCfA/c55/bkNWsO01T/BeScS3XOtSfQIuxiZm3ymF11XwTM7BJgu3NubqiL5DCtVNV7WU3gCUDjDM8bAZs9iqW02WZm9QGC/7cHp+dW5wnBx1mnZ1rGzGKA6sDOYovcZ8ysHIHk/ZFz7ovgZNV/CXLO7QKmAheiui9u3YHLzGwd8Alwjpl9SBmu97KawGcDJ5tZMzMrT2Cwwlcex1RafAX0Dz7uT+DY7NHp1wdHeTYDTgZmBbu89ppZt+BI0FuyLHO0rKuByS54cKqsC9bVu8Ay59wrGV5S/RczM6trZjWCjysC5wLLUd0XK+fcI865Rs65pgR+syc7526iLNe71wfhvfoDLiYwcvd34DGv4/HjHzAa2AIkE9hz/ROB40WTgFXB/7UyzP9YsL5XEBz1GZzeCVgcfO01jl1gKBb4DFhNYNRoc6+3OVL+gDMJdO0tBOKDfxer/kuk7uOA+cG6Xww8EZyuui+596AXxwaxldl615XYREREfKisdqGLiIj4mhK4iIiIDymBi4iI+JASuIiIiA8pgYuIiPiQErhIGWRmtc0sPvi31cw2BR/vM7PhXscnIvnTaWQiZZyZDQH2Oede8joWEQmdWuAiks7MemW4z/IQM3vfzH40s3VmdpWZvRC8j/L3wUu5Hr238v/MbK6Z/XD0spYiUryUwEUkLycCfYDLgQ+BKc65tsBBoE8wiQ8DrnbOdQRGAv/nVbAiZUmM1wGISET7zjmXbGaLgGjg++D0RQTuBX8K0Ab4KXBZaaIJXF5XRIqZEriI5OUwgHMuzcyS3bFBM2kEfj8MWOKcO92rAEXKKnWhi0hhrADqmtnpELjFqZm19jgmkTJBCVxECsw5d4TAbRefN7MFBO6KdoanQYmUETqNTERExIfUAhcREfEhJXAREREfUgIXERHxISVwERERH1ICFxER8SElcBERER9SAhcREfEhJXAREREf+n9naay58P4DhgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "plt.plot(hurcan['accelX'], color= 'green', label='Acceleration along X axis of hurcan')\n",
    "plt.plot(hurcan['accelY'], color= 'red', label='Acceleration along Y axis of hurcan')\n",
    "#plt.plot(hurcan['orientZ'], color= 'black', label='Yaw')\n",
    "plt.title(\"Hurcan details\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Details\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>class</th>\n",
       "      <th>accelX</th>\n",
       "      <th>accelY</th>\n",
       "      <th>accelZ</th>\n",
       "      <th>orientZ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>42773</td>\n",
       "      <td>42773.000000</td>\n",
       "      <td>42773.000000</td>\n",
       "      <td>42773.000000</td>\n",
       "      <td>42773.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>hurcan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>42773</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000292</td>\n",
       "      <td>-0.000336</td>\n",
       "      <td>0.003927</td>\n",
       "      <td>0.052411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.057442</td>\n",
       "      <td>0.106547</td>\n",
       "      <td>13.443174</td>\n",
       "      <td>0.724268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.173815</td>\n",
       "      <td>-0.760674</td>\n",
       "      <td>-678.691589</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.001555</td>\n",
       "      <td>-0.004861</td>\n",
       "      <td>-0.013043</td>\n",
       "      <td>-0.704124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>-0.000476</td>\n",
       "      <td>-0.000317</td>\n",
       "      <td>-0.000953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001894</td>\n",
       "      <td>0.001008</td>\n",
       "      <td>0.000642</td>\n",
       "      <td>0.713005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.923969</td>\n",
       "      <td>10.189273</td>\n",
       "      <td>709.843140</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         class        accelX        accelY        accelZ       orientZ\n",
       "count    42773  42773.000000  42773.000000  42773.000000  42773.000000\n",
       "unique       1           NaN           NaN           NaN           NaN\n",
       "top     hurcan           NaN           NaN           NaN           NaN\n",
       "freq     42773           NaN           NaN           NaN           NaN\n",
       "mean       NaN      0.000292     -0.000336      0.003927      0.052411\n",
       "std        NaN      0.057442      0.106547     13.443174      0.724268\n",
       "min        NaN     -3.173815     -0.760674   -678.691589     -1.000000\n",
       "25%        NaN     -0.001555     -0.004861     -0.013043     -0.704124\n",
       "50%        NaN      0.000094     -0.000476     -0.000317     -0.000953\n",
       "75%        NaN      0.001894      0.001008      0.000642      0.713005\n",
       "max        NaN      5.923969     10.189273    709.843140      1.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hurcan.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jbEygqg519/6z8d47ie24fbbh9tmG22cbbt+53Ibny2H2P05yRVVdXlV/M8mBJPeveE4AMMJ5sWfe3ceq6ueS/LckFyX5UHc/tuJpAcAI50XMk6S7P5nkkyucwlk5fL8D2Y7bZxtun224fbbh9p2zbVjdLzjPDAAY5Hz5zhwA2CIx30BV/WJVdVVduuq5TFNV/6aq/ryq/qyqPlFV37PqOU3hksbbU1WXVdXvV9XjVfVYVb171XOaqqouqqo/qar/uuq5TFVV31NV9y7/Hj5eVT98Nn+emJ+gqi5L8k+TfHXVcxnqwSRXdfcPJPmfSW5Z8XxGcEnjM+JYkl/o7u9Pck2Sm2zDLXt3ksdXPYnhfj3J73b330vygznL21PMX+i2JP86G1y0hs119+9197Hl6R9l7ZoBbM4ljbepu5/u7s8uj5/N2j+eriT5IlXV3iQ/luQ3Vj2XqarqkiT/MMmdSdLdf9Xd//ds/kwxX6eqfjzJ17r7T1c9lwvETyd5YNWTGMIljc+gqtqX5IeSPLziqUz077K2Q/PXK57HZN+b5GiS/7R8XfEbVfXSs/kDz5s/TTtXquq/J/k7G7z0S0nem+RHzu2M5jnVNuzu+5Zlfilrhz0/di7nNthpXdKYzVXVdyf5rSTv6e6/XPV8JqmqtyR5prsfqao3rXg6k+1K8g+S/Hx3P1xVv57k5iS/fDZ/4I7S3f9ko/Gq+vtJLk/yp1WVrB0e/mxVXd3d//scTvG8d7JteFxV3ZDkLUmubX/7eLpO65LGnFpVfVfWQv6x7v7tVc9noDcm+fGq+tEkfyvJJVX1n7v7J1Y8r2mOJDnS3cePDN2btZifNf7O/CSq6itJ9ne3/2jgRaiq65L8WpJ/1N1HVz2fKapqV9ZOGLw2ydeydonjf+FKiKev1n4LvyvJN7r7PSueznjLnvkvdvdbVjyVkarqD5P8THc/UVW/muSl3f2vztbP23F75px1/z7JxUkeXI5w/FF3/+xqp3T+c0njM+KNSX4yyaNV9bll7L3L1SXhXPv5JB9b/r+RLyX5qbP5w+yZA8BwzmYHgOHEHACGE3MAGE7MAWA4MQeA4cQcAIYTcwAYTswBYLj/D5c9xD7dl8vFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.hist(hurcan['accelX'], bins=[-4, -3, -2, -1, 0,1,2,3,4,5,6])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.hist(hurcan['accelY'], bins=[-4, -3, -2, -1, 0,1,2,3,4,5,6,7,8,9,10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.hist(hurcan['orientZ'], bins=[-1, -0.75, -0.5, -0.25, 0, 0.25, 0.5, 0.75, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "plt.plot(hurcan['orientZ'], color= 'black', label='Yaw')\n",
    "plt.title(\"Hurcan details\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Details\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "countAcclXneg10= countAcclXneg8= countAcclXneg6= countAcclXneg4= countAcclXneg2= countAcclX0= countAcclX2= countAcclX4= countAcclX6= countAcclX8= 0\n",
    "countAcclYneg10= countAcclYneg8= countAcclYneg6= countAcclYneg4= countAcclYneg2= countAcclY0= countAcclY2= countAcclY4= countAcclY6= countAcclY8= 0\n",
    "countOrientZneg10= countOrientZneg8= countOrientZneg6= countOrientZneg4= countOrientZneg2= countOrientZ0= countOrientZ2= countOrientZ4= countOrientZ6= countOrientZ8= 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(hurcan)):\n",
    "    if(float(hurcan['accelX'].iloc[i])>=-1 and float(hurcan['accelX'].iloc[i])<-0.8):\n",
    "        countAcclXneg10+=1\n",
    "    if(float(hurcan['accelX'].iloc[i])>=-0.8 and float(hurcan['accelX'].iloc[i])<=-0.6):\n",
    "        countAcclXneg8+=1\n",
    "    if(float(hurcan['accelX'].iloc[i])>=-0.6 and float(hurcan['accelX'].iloc[i])<=-0.4):\n",
    "        countAcclXneg6+=1\n",
    "    if(float(hurcan['accelX'].iloc[i])>=-0.4 and float(hurcan['accelX'].iloc[i])<=-0.2):\n",
    "        countAcclXneg4+=1\n",
    "    if(float(hurcan['accelX'].iloc[i])>=-0.2 and float(hurcan['accelX'].iloc[i])<=0):\n",
    "        countAcclXneg2+=1\n",
    "    if(float(hurcan['accelX'].iloc[i])>=0 and float(hurcan['accelX'].iloc[i])<=0.2):\n",
    "        countAcclX0+=1\n",
    "    if(float(hurcan['accelX'].iloc[i])>=0.2 and float(hurcan['accelX'].iloc[i])<=0.4):\n",
    "        countAcclX2+=1\n",
    "    if(float(hurcan['accelX'].iloc[i])>=0.4 and float(hurcan['accelX'].iloc[i])<=0.6):\n",
    "        countAcclX4+=1\n",
    "    if(float(hurcan['accelX'].iloc[i])>=0.6 and float(hurcan['accelX'].iloc[i])<=0.8):\n",
    "        countAcclX6+=1\n",
    "    if(float(hurcan['accelX'].iloc[i])>=0.8 and float(hurcan['accelX'].iloc[i])<=1):\n",
    "        countAcclX8+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(hurcan)):\n",
    "    if(float(hurcan['accelY'].iloc[i])>=-1 and float(hurcan['accelY'].iloc[i])<-0.8):\n",
    "        countAcclYneg10+=1\n",
    "    if(float(hurcan['accelY'].iloc[i])>=-0.8 and float(hurcan['accelY'].iloc[i])<=-0.6):\n",
    "        countAcclYneg8+=1\n",
    "    if(float(hurcan['accelY'].iloc[i])>=-0.6 and float(hurcan['accelY'].iloc[i])<=-0.4):\n",
    "        countAcclYneg6+=1\n",
    "    if(float(hurcan['accelY'].iloc[i])>=-0.4 and float(hurcan['accelY'].iloc[i])<=-0.2):\n",
    "        countAcclYneg4+=1\n",
    "    if(float(hurcan['accelY'].iloc[i])>=-0.2 and float(hurcan['accelY'].iloc[i])<=0):\n",
    "        countAcclYneg2+=1\n",
    "    if(float(hurcan['accelY'].iloc[i])>=0 and float(hurcan['accelY'].iloc[i])<=0.2):\n",
    "        countAcclY0+=1\n",
    "    if(float(hurcan['accelY'].iloc[i])>=0.2 and float(hurcan['accelY'].iloc[i])<=0.4):\n",
    "        countAcclY2+=1\n",
    "    if(float(hurcan['accelY'].iloc[i])>=0.4 and float(hurcan['accelY'].iloc[i])<=0.6):\n",
    "        countAcclY4+=1\n",
    "    if(float(hurcan['accelY'].iloc[i])>=0.6 and float(hurcan['accelY'].iloc[i])<=0.8):\n",
    "        countAcclY6+=1\n",
    "    if(float(hurcan['accelY'].iloc[i])>=0.8 and float(hurcan['accelY'].iloc[i])<=1):\n",
    "        countAcclY8+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(hurcan)):\n",
    "    if(float(hurcan['orientZ'].iloc[i])>=-1 and float(hurcan['orientZ'].iloc[i])<-0.8):\n",
    "        countOrientZneg10+=1\n",
    "    if(float(hurcan['orientZ'].iloc[i])>=-0.8 and float(hurcan['orientZ'].iloc[i])<=-0.6):\n",
    "        countOrientZneg8+=1\n",
    "    if(float(hurcan['orientZ'].iloc[i])>=-0.6 and float(hurcan['orientZ'].iloc[i])<=-0.4):\n",
    "        countOrientZneg6+=1\n",
    "    if(float(hurcan['orientZ'].iloc[i])>=-0.4 and float(hurcan['orientZ'].iloc[i])<=-0.2):\n",
    "        countOrientZneg4+=1\n",
    "    if(float(hurcan['orientZ'].iloc[i])>=-0.2 and float(hurcan['orientZ'].iloc[i])<=0):\n",
    "        countOrientZneg2+=1\n",
    "    if(float(hurcan['orientZ'].iloc[i])>=0 and float(hurcan['orientZ'].iloc[i])<=0.2):\n",
    "        countOrientZ0+=1\n",
    "    if(float(hurcan['orientZ'].iloc[i])>=0.2 and float(hurcan['orientZ'].iloc[i])<=0.4):\n",
    "        countOrientZ2+=1\n",
    "    if(float(hurcan['orientZ'].iloc[i])>=0.4 and float(hurcan['orientZ'].iloc[i])<=0.6):\n",
    "        countOrientZ4+=1\n",
    "    if(float(hurcan['orientZ'].iloc[i])>=0.6 and float(hurcan['orientZ'].iloc[i])<=0.8):\n",
    "        countOrientZ6+=1\n",
    "    if(float(hurcan['orientZ'].iloc[i])>=0.8 and float(hurcan['orientZ'].iloc[i])<=1):\n",
    "        countOrientZ8+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "countAcclYneg10  0\n",
      "countAcclYneg8  4\n",
      "countAcclYneg6  2\n",
      "countAcclYneg4 109\n",
      "countAcclYneg2  25334\n",
      "countAcclY0  17176\n",
      "countAcclY2  121\n",
      "countAcclY4  6\n",
      "countAcclY6  3\n",
      "countAcclY8  4\n"
     ]
    }
   ],
   "source": [
    "print(\"countAcclYneg10  \"+ str(countAcclYneg10))\n",
    "print(\"countAcclYneg8  \"+ str(countAcclYneg8))\n",
    "print(\"countAcclYneg6  \"+ str(countAcclYneg6))\n",
    "print(\"countAcclYneg4 \"+ str(countAcclYneg4))\n",
    "print(\"countAcclYneg2  \"+ str(countAcclYneg2))\n",
    "print(\"countAcclY0  \"+ str(countAcclY0))\n",
    "print(\"countAcclY2  \"+ str(countAcclY2))\n",
    "print(\"countAcclY4  \"+ str(countAcclY4))\n",
    "print(\"countAcclY6  \"+ str(countAcclY6))\n",
    "print(\"countAcclY8  \"+ str(countAcclY8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "countAcclXneg10  0\n",
      "countAcclXneg8  3\n",
      "countAcclXneg6  5\n",
      "countAcclXneg4 12\n",
      "countAcclXneg2  20428\n",
      "countAcclX0  22285\n",
      "countAcclX2  18\n",
      "countAcclX4  0\n",
      "countAcclX6  1\n",
      "countAcclX8  1\n"
     ]
    }
   ],
   "source": [
    "print(\"countAcclXneg10  \"+ str(countAcclXneg10))\n",
    "print(\"countAcclXneg8  \"+ str(countAcclXneg8))\n",
    "print(\"countAcclXneg6  \"+ str(countAcclXneg6))\n",
    "print(\"countAcclXneg4 \"+ str(countAcclXneg4))\n",
    "print(\"countAcclXneg2  \"+ str(countAcclXneg2))\n",
    "print(\"countAcclX0  \"+ str(countAcclX0))\n",
    "print(\"countAcclX2  \"+ str(countAcclX2))\n",
    "print(\"countAcclX4  \"+ str(countAcclX4))\n",
    "print(\"countAcclX6  \"+ str(countAcclX6))\n",
    "print(\"countAcclX8  \"+ str(countAcclX8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "countOrientZneg10  5202\n",
      "countOrientZneg8  9405\n",
      "countOrientZneg6  1009\n",
      "countOrientZneg4 464\n",
      "countOrientZneg2  6786\n",
      "countOrientZ0  2031\n",
      "countOrientZ2  436\n",
      "countOrientZ4  659\n",
      "countOrientZ6  8506\n",
      "countOrientZ8  8275\n"
     ]
    }
   ],
   "source": [
    "print(\"countOrientZneg10  \"+ str(countOrientZneg10))\n",
    "print(\"countOrientZneg8  \"+ str(countOrientZneg8))\n",
    "print(\"countOrientZneg6  \"+ str(countOrientZneg6))\n",
    "print(\"countOrientZneg4 \"+ str(countOrientZneg4))\n",
    "print(\"countOrientZneg2  \"+ str(countOrientZneg2))\n",
    "print(\"countOrientZ0  \"+ str(countOrientZ0))\n",
    "print(\"countOrientZ2  \"+ str(countOrientZ2))\n",
    "print(\"countOrientZ4  \"+ str(countOrientZ4))\n",
    "print(\"countOrientZ6  \"+ str(countOrientZ6))\n",
    "print(\"countOrientZ8  \"+ str(countOrientZ8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_inc= 0.2 \n",
    "max_alpha_inc = 0.5\n",
    "max_speed= 0.143\n",
    "speed_warn_lim= 40 #3 min\n",
    "acc_warn_lim= 40 #3 min\n",
    "alpha_warn_lim= 240 #3 min\n",
    "total_lim= 60 #2 sec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_speed(speed_1, a, t):\n",
    "    #speed= speed_1 + a*(t+0.005)\n",
    "    speed= speed_1 + a*t\n",
    "    return speed\n",
    "\n",
    "def speed_check(speed):\n",
    "    if(abs(speed)> max_speed):\n",
    "        return True\n",
    "    return False\n",
    "\n",
    "def check_alpha(alpha1, alpha2):\n",
    "    if (abs(alpha1-alpha2)>max_alpha_inc):\n",
    "        return True\n",
    "    return False\n",
    "\n",
    "def high_speed_check(speed):\n",
    "    if(speed> (max_speed*1.5)):\n",
    "        return True\n",
    "    return False\n",
    "\n",
    "def acc_check(acc1, acc2):\n",
    "    if(abs(acc2-acc1)>acc_inc):\n",
    "        return True\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_warn=[] \n",
    "total_warn= []\n",
    "speed_warn=[]\n",
    "speed=[]\n",
    "alpha_warn=[]\n",
    "def control_loop():\n",
    "    speed1= speed2= alpha1= alpha2= acc1= acc2= 0\n",
    "    ac= al= sp= hs= to=0\n",
    "    tic= time.time()\n",
    "    for i in range (len(hurcan[0:2000])):\n",
    "        time.sleep(0.005) #imitate actual reading of data (considering data is read in every 5 millisec)\n",
    "        accX= hurcan['accelX'].iloc[i]\n",
    "        accY= hurcan['accelY'].iloc[i]\n",
    "        alpha2= hurcan['orientZ'].iloc[i]\n",
    "        acc2= ((accX**2+ accY**2)**0.5)*(abs(accY)/ accY)\n",
    "        tac= time.time()\n",
    "        t= tac-tic\n",
    "        speed2= calc_speed(speed1, acc2, t)\n",
    "        speed.append(speed2)\n",
    "        if(check_alpha(alpha1, alpha2)):\n",
    "            alpha_warn.append(time.time())\n",
    "            total_warn.append(time.time())\n",
    "            hurcan.warn[i]=1\n",
    "            al+=1\n",
    "            #break\n",
    "        if(acc_check(acc1, acc2)):\n",
    "            acc_warn.append(time.time())\n",
    "            total_warn.append(time.time())\n",
    "            if(len(acc_warn)>3 and (acc_warn[len(acc_warn)-1]- acc_warn[len(acc_warn)-4])< acc_warn_lim and len(acc_warn)>(ac+2) ):\n",
    "                hurcan.warn[i]=1\n",
    "                ac+=1\n",
    "                #break\n",
    "            elif(len(total_warn)>7 and (total_warn[len(total_warn)-1]- total_warn[len(total_warn)-8])< total_lim and len(total_warn)>(to+7) ):\n",
    "                hurcan.warn[i]=1\n",
    "                to+=1\n",
    "                #break\n",
    "        if(speed_check(speed2)):\n",
    "            speed_warn.append(time.time())\n",
    "            total_warn.append(time.time())\n",
    "            if(len(speed_warn)>5 and (speed_warn[len(speed_warn)-1]- speed_warn[len(speed_warn)-6])< speed_warn_lim and len(speed_warn)>(sp+5) ):\n",
    "                hurcan.warn[i]=1\n",
    "                sp+=1\n",
    "                #break\n",
    "            elif(len(total_warn)>7 and (total_warn[len(total_warn)-1]- total_warn[len(total_warn)-8])< total_lim and len(total_warn)>(to+7) ):\n",
    "                hurcan.warn[i]=1\n",
    "                to+=1\n",
    "                #break\n",
    "        \n",
    "        if (high_speed_check(speed2)):\n",
    "            hurcan.warn[i]=1\n",
    "            hs+=1\n",
    "            #break\n",
    "        \n",
    "        speed1=speed2\n",
    "        alpha1= alpha2\n",
    "        acc1= acc2\n",
    "        tic= time.time()\n",
    "    print (\"alpha warns  \"+ str(al))\n",
    "    print (\"speed warns  \"+ str(sp))\n",
    "    print (\"accel warns  \"+ str(ac))\n",
    "    print (\"high speed warns  \"+ str(hs))\n",
    "    print (\"total warns  \"+ str(to))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Program Data\\anaconda3\\envs\\tensorflow_env\\lib\\site-packages\\ipykernel_launcher.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha warns  2\n",
      "speed warns  0\n",
      "accel warns  0\n",
      "high speed warns  0\n",
      "total warns  0\n"
     ]
    }
   ],
   "source": [
    "control_loop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2)\n",
    "axs[0, 0].plot(hurcan.accelY[0:2000])\n",
    "axs[0, 0].set_title('Acceleration')\n",
    "axs[0, 1].plot(hurcan.orientZ[0:2000], 'tab:orange')\n",
    "axs[0, 1].set_title('Rotation')\n",
    "axs[1, 0].plot(speed, 'tab:green')\n",
    "axs[1, 0].set_title('speed')\n",
    "axs[1, 1].plot(hurcan.accelX[0:2000], 'tab:red')\n",
    "axs[1, 1].set_title('Warning')\n",
    "\n",
    "for ax in axs.flat:\n",
    "    ax.set(xlabel='x-label', ylabel='y-label')\n",
    "\n",
    "# Hide x labels and tick labels for top plots and y ticks for right plots.\n",
    "for ax in axs.flat:\n",
    "    ax.label_outer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}