sample_load_file.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('sample/mindMonitor_2020-03-01--12-50-38.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.set_index(pd.DatetimeIndex(df['TimeStamp']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Delta_TP9</th>\n",
       "      <th>Delta_AF7</th>\n",
       "      <th>Delta_AF8</th>\n",
       "      <th>Delta_TP10</th>\n",
       "      <th>Theta_TP9</th>\n",
       "      <th>Theta_AF7</th>\n",
       "      <th>Theta_AF8</th>\n",
       "      <th>Theta_TP10</th>\n",
       "      <th>Alpha_TP9</th>\n",
       "      <th>Alpha_AF7</th>\n",
       "      <th>...</th>\n",
       "      <th>Gyro_X</th>\n",
       "      <th>Gyro_Y</th>\n",
       "      <th>Gyro_Z</th>\n",
       "      <th>HeadBandOn</th>\n",
       "      <th>HSI_TP9</th>\n",
       "      <th>HSI_AF7</th>\n",
       "      <th>HSI_AF8</th>\n",
       "      <th>HSI_TP10</th>\n",
       "      <th>Battery</th>\n",
       "      <th>Elements</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TimeStamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2020-03-01 12:50:39.029</td>\n",
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       "      <td>4.515991</td>\n",
       "      <td>-2.721558</td>\n",
       "      <td>-3.035583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-01 12:50:41.028</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.929108</td>\n",
       "      <td>6.990814</td>\n",
       "      <td>-2.340240</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-01 12:50:42.029</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.201965</td>\n",
       "      <td>-4.089813</td>\n",
       "      <td>-2.915955</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-01 12:50:43.029</td>\n",
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       "      <td>5.353394</td>\n",
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       "      <td>-2.437439</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Delta_TP9  Delta_AF7  Delta_AF8  Delta_TP10  \\\n",
       "TimeStamp                                                              \n",
       "2020-03-01 12:50:39.029        0.0        0.0        0.0         0.0   \n",
       "2020-03-01 12:50:40.029        0.0        0.0        0.0         0.0   \n",
       "2020-03-01 12:50:41.028        0.0        0.0        0.0         0.0   \n",
       "2020-03-01 12:50:42.029        0.0        0.0        0.0         0.0   \n",
       "2020-03-01 12:50:43.029        0.0        0.0        0.0         0.0   \n",
       "\n",
       "                         Theta_TP9  Theta_AF7  Theta_AF8  Theta_TP10  \\\n",
       "TimeStamp                                                              \n",
       "2020-03-01 12:50:39.029        0.0        0.0        0.0         0.0   \n",
       "2020-03-01 12:50:40.029        0.0        0.0        0.0         0.0   \n",
       "2020-03-01 12:50:41.028        0.0        0.0        0.0         0.0   \n",
       "2020-03-01 12:50:42.029        0.0        0.0        0.0         0.0   \n",
       "2020-03-01 12:50:43.029        0.0        0.0        0.0         0.0   \n",
       "\n",
       "                         Alpha_TP9  Alpha_AF7  ...    Gyro_X    Gyro_Y  \\\n",
       "TimeStamp                                      ...                       \n",
       "2020-03-01 12:50:39.029        0.0        0.0  ...  4.740295 -3.043060   \n",
       "2020-03-01 12:50:40.029        0.0        0.0  ...  4.515991 -2.721558   \n",
       "2020-03-01 12:50:41.028        0.0        0.0  ...  5.929108  6.990814   \n",
       "2020-03-01 12:50:42.029        0.0        0.0  ...  4.201965 -4.089813   \n",
       "2020-03-01 12:50:43.029        0.0        0.0  ...  5.353394 -3.648682   \n",
       "\n",
       "                           Gyro_Z  HeadBandOn  HSI_TP9  HSI_AF7  HSI_AF8  \\\n",
       "TimeStamp                                                                  \n",
       "2020-03-01 12:50:39.029 -2.512207         0.0      4.0      4.0      4.0   \n",
       "2020-03-01 12:50:40.029 -3.035583         0.0      4.0      4.0      4.0   \n",
       "2020-03-01 12:50:41.028 -2.340240         0.0      4.0      4.0      4.0   \n",
       "2020-03-01 12:50:42.029 -2.915955         0.0      4.0      4.0      4.0   \n",
       "2020-03-01 12:50:43.029 -2.437439         0.0      4.0      4.0      4.0   \n",
       "\n",
       "                         HSI_TP10  Battery  Elements  \n",
       "TimeStamp                                             \n",
       "2020-03-01 12:50:39.029       4.0     25.0       NaN  \n",
       "2020-03-01 12:50:40.029       4.0     25.0       NaN  \n",
       "2020-03-01 12:50:41.028       4.0     25.0       NaN  \n",
       "2020-03-01 12:50:42.029       4.0     25.0       NaN  \n",
       "2020-03-01 12:50:43.029       4.0     25.0       NaN  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pop('TimeStamp')\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b9db1d0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(df.index.to_period('Min'))['Theta_TP9'].apply(lambda x: np.mean(x)).plot()"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}