data-processing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.3\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "print(pd.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading emg data & time marker from test-1 folder\n",
    "emg_data_path = 'test-1/0-New_Task-recording-0.csv'\n",
    "time_marker_path = 'test-1/time_marker.csv'\n",
    "\n",
    "emg_data = pd.read_csv(emg_data_path, skiprows=[0,1,3,4])\n",
    "time_marker = pd.read_csv(time_marker_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EMG data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<p>41854 rows × 22 columns</p>\n",
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      ],
      "text/plain": [
       "            1     2    3    4   5    6    7     8    9   10  ...   13  14  \\\n",
       "Channels                                                     ...            \n",
       "0          24    18   28  -12  17   29  -14     2   42  -16  ...   -2  42   \n",
       "2202       51     7   81  -47   3   63   47   -31   22  -24  ...  -13   9   \n",
       "4404       56    -2   86  -53 -21   39   56   -45    0  -27  ...  -35 -11   \n",
       "6606       35     0   55  -46  -7    5   25   -39    6  -16  ...  -42 -26   \n",
       "8808        3    29   23  -46  -7  -21  -36    -1    8  -19  ...  -36  -9   \n",
       "...       ...   ...  ...  ...  ..  ...  ...   ...  ...  ...  ...  ...  ..   \n",
       "92534528  397 -1928  -91   -7  -5  319   54  -893  135 -639  ...  254  70   \n",
       "92536730  354 -1996 -121 -183   7  262   15 -1008  111 -611  ...  248  67   \n",
       "92538932  282 -2043 -184 -336   2  184  -75 -1088    1 -573  ...  218  30   \n",
       "92541134  217 -2065 -212 -461  -5  161  -97 -1148  -58 -540  ...   85 -27   \n",
       "92543336  153 -2087 -240 -587 -12  139 -120 -1209 -117 -507  ...  -48 -84   \n",
       "\n",
       "           15   16   17   18   19    20   21   22  \n",
       "Channels                                           \n",
       "0          76  -48    1 -145   33    60  -97  -87  \n",
       "2202      117    4   34 -156   81    13 -172  -93  \n",
       "4404      119   14   20 -188   52   -78 -219 -121  \n",
       "6606       84  -14  -13 -195   21   -39 -218 -114  \n",
       "8808       70  -18    0 -182  -19   -25 -150 -104  \n",
       "...       ...  ...  ...  ...  ...   ...  ...  ...  \n",
       "92534528  119   20  209  203  200 -2388 -217  134  \n",
       "92536730   90  -21  172  125  144 -2357 -487  112  \n",
       "92538932   46  -62  132  186  102 -2320 -598   16  \n",
       "92541134  -67 -145   27  204   22 -2275 -701  -12  \n",
       "92543336 -180 -228  -78  222  -57 -2230 -805  -41  \n",
       "\n",
       "[41854 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reset emg data index with Channels\n",
    "emg_data = emg_data.set_index('Channels')\n",
    "emg_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 41854 entries, 0 to 92543336\n",
      "Data columns (total 22 columns):\n",
      " #   Column  Non-Null Count  Dtype\n",
      "---  ------  --------------  -----\n",
      " 0   1       41854 non-null  int64\n",
      " 1   2       41854 non-null  int64\n",
      " 2   3       41854 non-null  int64\n",
      " 3   4       41854 non-null  int64\n",
      " 4   5       41854 non-null  int64\n",
      " 5   6       41854 non-null  int64\n",
      " 6   7       41854 non-null  int64\n",
      " 7   8       41854 non-null  int64\n",
      " 8   9       41854 non-null  int64\n",
      " 9   10      41854 non-null  int64\n",
      " 10  11      41854 non-null  int64\n",
      " 11  12      41854 non-null  int64\n",
      " 12  13      41854 non-null  int64\n",
      " 13  14      41854 non-null  int64\n",
      " 14  15      41854 non-null  int64\n",
      " 15  16      41854 non-null  int64\n",
      " 16  17      41854 non-null  int64\n",
      " 17  18      41854 non-null  int64\n",
      " 18  19      41854 non-null  int64\n",
      " 19  20      41854 non-null  int64\n",
      " 20  21      41854 non-null  int64\n",
      " 21  22      41854 non-null  int64\n",
      "dtypes: int64(22)\n",
      "memory usage: 7.3 MB\n"
     ]
    }
   ],
   "source": [
    "emg_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get signal data from difference of emg_data\n",
    "signal_left_lateral = emg_data['21'] - emg_data['3']\n",
    "signal_left_medial = emg_data['22'] - emg_data['2']\n",
    "\n",
    "signal_right_lateral = emg_data['16'] - emg_data['6']\n",
    "signal_right_medial = emg_data['17'] - emg_data['5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of RMS Signal 1:  442.605, Std Dev of RMS Signal 1:  628.329\n",
      "Mean of RMS Signal 2:  366.791, Std Dev of RMS Signal 2:  681.991\n",
      "Mean of RMS Signal 3:  135.842, Std Dev of RMS Signal 3:  212.442\n",
      "Mean of RMS Signal 4:  206.513, Std Dev of RMS Signal 4:  403.897\n"
     ]
    }
   ],
   "source": [
    "# RMS caculation\n",
    "\n",
    "# Define the moving average window size\n",
    "N = 25\n",
    "\n",
    "# Function to calculate moving RMS\n",
    "def moving_rms(signal, window_size):\n",
    "    rms = np.sqrt(pd.Series(signal).rolling(window=window_size).mean()**2)\n",
    "    rms.index = signal.index  # Ensure the index matches the original signal\n",
    "    return rms\n",
    "\n",
    "# Calculate moving RMS for each channel\n",
    "signal_left_lateral_RMS = moving_rms(signal_left_lateral, N)\n",
    "signal_left_medial_RMS = moving_rms(signal_left_medial, N)\n",
    "signal_right_lateral_RMS = moving_rms(signal_right_lateral, N)\n",
    "signal_right_medial_RMS = moving_rms(signal_right_medial, N)\n",
    "\n",
    "# Calculate mean and standard deviation of the RMS signals\n",
    "mean_ch1_rms = np.mean(signal_left_lateral_RMS)\n",
    "std_ch1_rms = np.std(signal_left_lateral_RMS)\n",
    "\n",
    "mean_ch2_rms = np.mean(signal_left_medial_RMS)\n",
    "std_ch2_rms = np.std(signal_left_medial_RMS)\n",
    "\n",
    "mean_ch3_rms = np.mean(signal_right_lateral_RMS)\n",
    "std_ch3_rms = np.std(signal_right_lateral_RMS)\n",
    "\n",
    "mean_ch4_rms = np.mean(signal_right_medial_RMS)\n",
    "std_ch4_rms = np.std(signal_right_medial_RMS)\n",
    "\n",
    "# Print mean and standard deviation values\n",
    "print(f'Mean of RMS Signal 1: {mean_ch1_rms: .3f}, Std Dev of RMS Signal 1: {std_ch1_rms: .3f}')\n",
    "print(f'Mean of RMS Signal 2: {mean_ch2_rms: .3f}, Std Dev of RMS Signal 2: {std_ch2_rms: .3f}')\n",
    "print(f'Mean of RMS Signal 3: {mean_ch3_rms: .3f}, Std Dev of RMS Signal 3: {std_ch3_rms: .3f}')\n",
    "print(f'Mean of RMS Signal 4: {mean_ch4_rms: .3f}, Std Dev of RMS Signal 4: {std_ch4_rms: .3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Channels\n",
       "0           NaN\n",
       "2202        NaN\n",
       "4404        NaN\n",
       "6606        NaN\n",
       "8808        NaN\n",
       "11010       NaN\n",
       "13212       NaN\n",
       "15414       NaN\n",
       "17616       NaN\n",
       "19818       NaN\n",
       "22020       NaN\n",
       "24222       NaN\n",
       "26424       NaN\n",
       "28626       NaN\n",
       "30828       NaN\n",
       "33030       NaN\n",
       "35232       NaN\n",
       "37434       NaN\n",
       "39636       NaN\n",
       "41838       NaN\n",
       "44040       NaN\n",
       "46242       NaN\n",
       "48444       NaN\n",
       "50646       NaN\n",
       "52848    254.96\n",
       "55050    259.68\n",
       "57252    261.56\n",
       "59454    256.00\n",
       "61656    248.76\n",
       "63858    243.28\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signal_left_lateral_RMS.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "131.65497112394493"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(signal_left_lateral_RMS.loc[:10000000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101.73584628144596"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(signal_left_lateral_RMS.loc[:10000000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101.7471471252777"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signal_left_lateral_RMS.loc[:10000000].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time Marker Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>event_time</th>\n",
       "      <th>name</th>\n",
       "      <th>tag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32030195.0</td>\n",
       "      <td>bite</td>\n",
       "      <td>start</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56294235.0</td>\n",
       "      <td>bite</td>\n",
       "      <td>end</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60284534.0</td>\n",
       "      <td>swallow</td>\n",
       "      <td>start</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>62478843.0</td>\n",
       "      <td>swallow</td>\n",
       "      <td>end</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>67892676.0</td>\n",
       "      <td>swallow</td>\n",
       "      <td>start</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>69216432.0</td>\n",
       "      <td>swallow</td>\n",
       "      <td>end</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>71896644.0</td>\n",
       "      <td>swallow</td>\n",
       "      <td>start</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>73034917.0</td>\n",
       "      <td>swallow</td>\n",
       "      <td>end</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>77098837.0</td>\n",
       "      <td>cough</td>\n",
       "      <td>start</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>79341557.0</td>\n",
       "      <td>cough</td>\n",
       "      <td>end</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>82717865.0</td>\n",
       "      <td>cough</td>\n",
       "      <td>start</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>83992269.0</td>\n",
       "      <td>cough</td>\n",
       "      <td>end</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>86344529.0</td>\n",
       "      <td>cough</td>\n",
       "      <td>start</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>88152623.0</td>\n",
       "      <td>cough</td>\n",
       "      <td>end</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    event_time     name    tag\n",
       "0   32030195.0     bite  start\n",
       "1   56294235.0     bite    end\n",
       "2   60284534.0  swallow  start\n",
       "3   62478843.0  swallow    end\n",
       "4   67892676.0  swallow  start\n",
       "5   69216432.0  swallow    end\n",
       "6   71896644.0  swallow  start\n",
       "7   73034917.0  swallow    end\n",
       "8   77098837.0    cough  start\n",
       "9   79341557.0    cough    end\n",
       "10  82717865.0    cough  start\n",
       "11  83992269.0    cough    end\n",
       "12  86344529.0    cough  start\n",
       "13  88152623.0    cough    end"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_marker = pd.read_csv(time_marker_path)\n",
    "time_marker = time_marker[['0-New_Task-recording_time(us)', 'name', 'tag']]\n",
    "time_marker = time_marker.rename(columns={'0-New_Task-recording_time(us)': 'event_time'})\n",
    "time_marker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select column value with odd/even index\n",
    "event_start_times = time_marker.loc[0::2]['event_time'].values.astype(int)\n",
    "event_end_times = time_marker.loc[1::2]['event_time'].values.astype(int)\n",
    "event_names = time_marker.loc[0::2]['name'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "signal LL basic 10s std :  101.747\n",
      "signal RL basic 10s std :  14.723\n"
     ]
    }
   ],
   "source": [
    "# Get signal basic 10s std\n",
    "signal_left_lateral_basics_10s_std = signal_left_lateral_RMS.loc[: 10000000].std()\n",
    "print(f\"signal LL basic 10s std : {signal_left_lateral_basics_10s_std: .3f}\")\n",
    "\n",
    "signal_right_lateral_basics_10s_std = signal_right_lateral_RMS.loc[: 10000000].std()\n",
    "print(f\"signal RL basic 10s std : {signal_right_lateral_basics_10s_std: .3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def emg_plot(event_index, event_plot_name, left_std_ratio, left_delta_t, right_std_ratio, right_delta_t):\n",
    "    \"\"\"\n",
    "    Plots a 2D quadrant chart for EMG signal analysis with colored squares indicating the quadrant.\n",
    "\n",
    "    Parameters:\n",
    "    std (float): Standard deviation value of the EMG signal.\n",
    "    delta_t (float): Delta T value of the EMG signal.\n",
    "    \"\"\"\n",
    "    # Create a new figure\n",
    "    fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "    # Determine the quadrant and plot the colored square\n",
    "    if left_std_ratio > 3 and left_delta_t > 0:\n",
    "        # First quadrant\n",
    "        ax.add_patch(plt.Rectangle((2, 2), 6, 6, color='blue', alpha=0.5))\n",
    "    elif left_std_ratio <= 3 and left_delta_t > 0:\n",
    "        # Second quadrant\n",
    "        ax.add_patch(plt.Rectangle((-8, 2), 6, 6, color='blue', alpha=0.5))\n",
    "    elif left_std_ratio <= 3 and left_delta_t <= 0:\n",
    "        # Third quadrant\n",
    "        ax.add_patch(plt.Rectangle((-8, -8), 6, 6, color='blue', alpha=0.5))\n",
    "    elif left_std_ratio > 3 and left_delta_t <= 0:\n",
    "        # Fourth quadrant\n",
    "        ax.add_patch(plt.Rectangle((2, -8), 6, 6, color='blue', alpha=0.5))\n",
    "        \n",
    "    # Determine the quadrant and plot the colored square\n",
    "    if right_std_ratio > 3 and right_delta_t > 0:\n",
    "        # First quadrant\n",
    "        ax.add_patch(plt.Rectangle((1.5, 1.5), 6, 6, color='green', alpha=0.5))\n",
    "    elif right_std_ratio <= 3 and right_delta_t > 0:\n",
    "        # Second quadrant\n",
    "        ax.add_patch(plt.Rectangle((-8.5, 1.5), 6, 6, color='green', alpha=0.5))\n",
    "    elif right_std_ratio <= 3 and right_delta_t <= 0:\n",
    "        # Third quadrant\n",
    "        ax.add_patch(plt.Rectangle((-8.5, -8.5), 6, 6, color='green', alpha=0.5))\n",
    "    elif right_std_ratio > 3 and right_delta_t <= 0:\n",
    "        # Fourth quadrant\n",
    "        ax.add_patch(plt.Rectangle((1.5, -8.5), 6, 6, color='green', alpha=0.5))\n",
    "\n",
    "    # Add horizontal and vertical lines to create quadrants\n",
    "    plt.axhline(y=0, color='black', linestyle='--')\n",
    "    plt.axvline(x=0, color='black', linestyle='--')\n",
    "\n",
    "    # Add title and axis labels\n",
    "    plt.title(f'Muscle Coordination Analysis - {event_index}:{event_plot_name}', fontsize=14)\n",
    "    plt.xlabel('Std Ratio > 3', fontsize=12)\n",
    "    plt.ylabel('Delta T > 0', fontsize=12)\n",
    "\n",
    "    # Remove axis numbers and labels\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.set_xticklabels([])\n",
    "    ax.set_yticklabels([])\n",
    "    \n",
    "    # Set plot legend with color\n",
    "    plt.legend(['Left', 'Right'], loc='upper left', fontsize=10)\n",
    "\n",
    "    # Set the limits of the plot\n",
    "    plt.xlim(-10, 10)\n",
    "    plt.ylim(-10, 10)\n",
    "\n",
    "    # Display the plot\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Event 1: bite\n",
      "Start time:  32.030 sec, End time:  56.294 sec\n",
      "left std ratio:  6.765, right std ratio:  18.278\n",
      "LM_max_index:  51.882, LL_max_index:  51.882, left delta t:  0.000\n",
      "RM_max_index:  51.900, RL_max_index:  52.496, right delta t: -0.596\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Event 2: swallow\n",
      "Start time:  60.285 sec, End time:  62.479 sec\n",
      "left std ratio:  7.153, right std ratio:  13.522\n",
      "LM_max_index:  61.289, LL_max_index:  60.473, left delta t:  0.815\n",
      "RM_max_index:  61.280, RL_max_index:  61.289, right delta t: -0.009\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Event 3: swallow\n",
      "Start time:  67.893 sec, End time:  69.216 sec\n",
      "left std ratio:  7.229, right std ratio:  17.350\n",
      "LM_max_index:  67.902, LL_max_index:  67.920, left delta t: -0.018\n",
      "RM_max_index:  67.967, RL_max_index:  68.663, right delta t: -0.696\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Event 4: swallow\n",
      "Start time:  71.897 sec, End time:  73.035 sec\n",
      "left std ratio:  11.192, right std ratio:  16.181\n",
      "LM_max_index:  71.898, LL_max_index:  71.944, left delta t: -0.046\n",
      "RM_max_index:  71.946, RL_max_index:  71.898, right delta t:  0.048\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Event 5: cough\n",
      "Start time:  77.099 sec, End time:  79.342 sec\n",
      "left std ratio:  4.604, right std ratio:  9.801\n",
      "LM_max_index:  78.709, LL_max_index:  78.628, left delta t:  0.081\n",
      "RM_max_index:  77.962, RL_max_index:  79.038, right delta t: -1.075\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Event 6: cough\n",
      "Start time:  82.718 sec, End time:  83.992 sec\n",
      "left std ratio:  1.160, right std ratio:  1.392\n",
      "LM_max_index:  82.867, LL_max_index:  83.569, left delta t: -0.702\n",
      "RM_max_index:  82.953, RL_max_index:  83.553, right delta t: -0.601\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Event 7: cough\n",
      "Start time:  86.345 sec, End time:  88.153 sec\n",
      "left std ratio:  0.575, right std ratio:  1.769\n",
      "LM_max_index:  86.460, LL_max_index:  87.226, left delta t: -0.766\n",
      "RM_max_index:  86.672, RL_max_index:  87.228, right delta t: -0.555\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analyze event data \n",
    "event_number = len(event_names)\n",
    "\n",
    "for i in range(1, 2*event_number, 2):\n",
    "    event_name = event_names[i//2]\n",
    "    event_start_time = event_start_times[i//2]\n",
    "    event_end_time = event_end_times[i//2]\n",
    "    \n",
    "    print(f\"Event {i//2+1}: {event_name}\")\n",
    "    print(f\"Start time: {float(event_start_time)/1000000: .3f} sec, End time: {float(event_end_time)/1000000: .3f} sec\")\n",
    "    \n",
    "    # Get event signal data with event time duration\n",
    "    event_signal_LL = signal_left_lateral_RMS.loc[event_start_time:event_end_time]\n",
    "    event_signal_LM = signal_left_medial_RMS.loc[event_start_time:event_end_time]\n",
    "    \n",
    "    event_signal_RL = signal_right_lateral_RMS.loc[event_start_time:event_end_time]\n",
    "    event_signal_RM = signal_right_medial_RMS.loc[event_start_time:event_end_time]\n",
    "    \n",
    "    # Calculate std ratio \n",
    "    left_event_std = event_signal_LL.std()\n",
    "    left_std_ratio = left_event_std / signal_left_lateral_basics_10s_std\n",
    "    \n",
    "    right_event_std = event_signal_RL.std()\n",
    "    right_std_ratio = right_event_std / signal_right_lateral_basics_10s_std\n",
    "    \n",
    "    print(f\"left std ratio: {left_std_ratio: .3f}, right std ratio: {right_std_ratio: .3f}\")\n",
    "    \n",
    "    # Get signal max value index\n",
    "    LL_max_index = event_signal_LL.idxmax()\n",
    "    LM_max_index = event_signal_LM.idxmax()\n",
    "    left_delta_t = LM_max_index - LL_max_index\n",
    "    print(f\"LM_max_index: {float(LM_max_index)/1000000: .3f}, LL_max_index: {float(LL_max_index)/1000000: .3f}, left delta t: {float(left_delta_t)/1000000: .3f}\")\n",
    "    \n",
    "    RL_max_index = event_signal_RL.idxmax()\n",
    "    RM_max_index = event_signal_RM.idxmax()\n",
    "    right_delta_t = RM_max_index - RL_max_index\n",
    "    print(f\"RM_max_index: {float(RM_max_index)/1000000: .3f}, RL_max_index: {float(RL_max_index)/1000000: .3f}, right delta t: {float(right_delta_t)/1000000: .3f}\")\n",
    "    \n",
    "    # Plot with each event data\n",
    "    emg_plot(i//2+1, event_name, left_std_ratio, left_delta_t, right_std_ratio, right_delta_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "snomed",
   "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": 2
}