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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.2.1\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": 44,
"metadata": {},
"outputs": [],
"source": [
"# loading emg data & time marker from test-1 folder\n",
"emg_data_path = 'test-new/0-New_Task-recording-0.csv'\n",
"time_marker_path = 'test-new/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": 45,
"metadata": {},
"outputs": [
{
"data": {
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" <th>18</th>\n",
" <th>19</th>\n",
" <th>20</th>\n",
" <th>21</th>\n",
" <th>22</th>\n",
" <th>23</th>\n",
" <th>24</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Channels</th>\n",
" <th></th>\n",
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" <td>-1143</td>\n",
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" <th>1600</th>\n",
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" <td>-769</td>\n",
" <td>255</td>\n",
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" <td>0</td>\n",
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" <tr>\n",
" <th>95198110</th>\n",
" <td>0</td>\n",
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" 17 18 19 20 21 22 23 24\n",
"Channels \n",
"0 2508 1311 2601 1099 1212 1028 -1143 -1249\n",
"1600 1865 1182 754 94 68 11 -1138 -1130\n",
"3200 382 961 -327 240 -462 -75 -445 -66\n",
"4800 -493 -87 -1505 -375 -872 -558 -722 -211\n",
"6400 -1565 -666 -2092 -724 -1142 -809 -769 255\n",
"... ... ... ... ... ... ... ... ...\n",
"95198400 -2717 -2701 -2697 -2706 -2692 -2691 -2680 -2703\n",
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"\n",
"[59504 rows x 8 columns]"
]
},
"execution_count": 45,
"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": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 59504 entries, 0 to 95199710\n",
"Data columns (total 8 columns):\n",
" # Column Non-Null Count Dtype\n",
"--- ------ -------------- -----\n",
" 0 17 59504 non-null int64\n",
" 1 18 59504 non-null int64\n",
" 2 19 59504 non-null int64\n",
" 3 20 59504 non-null int64\n",
" 4 21 59504 non-null int64\n",
" 5 22 59504 non-null int64\n",
" 6 23 59504 non-null int64\n",
" 7 24 59504 non-null int64\n",
"dtypes: int64(8)\n",
"memory usage: 4.1 MB\n"
]
}
],
"source": [
"emg_data.info()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"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": 46,
"metadata": {},
"outputs": [],
"source": [
"# Get signal data from difference of emg_data\n",
"signal_left_lateral = emg_data['17'] - emg_data['18']\n",
"signal_left_medial = emg_data['19'] - emg_data['20']\n",
"\n",
"signal_right_lateral = emg_data['23'] - emg_data['24']\n",
"signal_right_medial = emg_data['21'] - emg_data['22']"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean of RMS Signal 1: 414.735, Std Dev of RMS Signal 1: 702.679\n",
"Mean of RMS Signal 2: 443.660, Std Dev of RMS Signal 2: 578.622\n",
"Mean of RMS Signal 3: 440.785, Std Dev of RMS Signal 3: 622.244\n",
"Mean of RMS Signal 4: 483.905, Std Dev of RMS Signal 4: 758.514\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": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Channels\n",
"0 NaN\n",
"1600 NaN\n",
"3200 NaN\n",
"4800 NaN\n",
"6400 NaN\n",
"8000 NaN\n",
"9600 NaN\n",
"11200 NaN\n",
"12800 NaN\n",
"14400 NaN\n",
"16000 NaN\n",
"17600 NaN\n",
"19200 NaN\n",
"20800 NaN\n",
"22400 NaN\n",
"24000 NaN\n",
"25600 NaN\n",
"27200 NaN\n",
"28800 NaN\n",
"30400 NaN\n",
"32000 NaN\n",
"33600 NaN\n",
"35200 NaN\n",
"36800 NaN\n",
"38400 101.80\n",
"40000 187.36\n",
"41600 257.64\n",
"43200 258.04\n",
"44800 249.12\n",
"46400 213.04\n",
"dtype: float64"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"signal_left_lateral_RMS.head(30)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"131.65497112394493"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.mean(signal_left_lateral_RMS.loc[:10000000])"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"101.73584628144596"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.std(signal_left_lateral_RMS.loc[:10000000])"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"90.46881927178374"
]
},
"execution_count": 49,
"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": 50,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>event_time</th>\n",
" <th>description</th>\n",
" <th>tag</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>78357902.0</td>\n",
" <td>Cough</td>\n",
" <td>start</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>79670999.0</td>\n",
" <td>Cough</td>\n",
" <td>end</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>81227489.0</td>\n",
" <td>Bite</td>\n",
" <td>start</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>82465323.0</td>\n",
" <td>Bite</td>\n",
" <td>end</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>84239727.0</td>\n",
" <td>Swallow</td>\n",
" <td>start</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>85434346.0</td>\n",
" <td>Swallow</td>\n",
" <td>end</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>86628547.0</td>\n",
" <td>Swallow</td>\n",
" <td>start</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>87951834.0</td>\n",
" <td>Swallow</td>\n",
" <td>end</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>89673825.0</td>\n",
" <td>Swallow</td>\n",
" <td>start</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>91158663.0</td>\n",
" <td>Swallow</td>\n",
" <td>end</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>92257779.0</td>\n",
" <td>Cough</td>\n",
" <td>start</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>93714668.0</td>\n",
" <td>Cough</td>\n",
" <td>end</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" event_time description tag\n",
"0 78357902.0 Cough start\n",
"1 79670999.0 Cough end\n",
"2 81227489.0 Bite start\n",
"3 82465323.0 Bite end\n",
"4 84239727.0 Swallow start\n",
"5 85434346.0 Swallow end\n",
"6 86628547.0 Swallow start\n",
"7 87951834.0 Swallow end\n",
"8 89673825.0 Swallow start\n",
"9 91158663.0 Swallow end\n",
"10 92257779.0 Cough start\n",
"11 93714668.0 Cough end"
]
},
"execution_count": 50,
"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)', 'description', 'tag']]\n",
"time_marker = time_marker.rename(columns={'0-New_Task-recording_time(us)': 'event_time'})\n",
"time_marker"
]
},
{
"cell_type": "code",
"execution_count": 35,
"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": 35,
"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": 51,
"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]['description'].values"
]
},
{
"cell_type": "code",
"execution_count": 36,
"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": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"signal LL basic 10s std : 90.469\n",
"signal RL basic 10s std : 64.398\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": 18,
"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": 68,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"78357902"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"event_start_times[0]"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"79670999"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"event_end_times[0]"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"numpy.int32"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(event_start_times[0])"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.series.Series"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(signal_left_lateral_RMS)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Channels\n",
"78358400 369.00\n",
"78360000 380.12\n",
"78361600 285.88\n",
"78363200 238.68\n",
"78364800 215.96\n",
" ... \n",
"79664000 36.44\n",
"79665600 34.52\n",
"79667200 32.32\n",
"79668800 29.92\n",
"79670400 14.52\n",
"Length: 821, dtype: float64"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mask = (signal_left_lateral_RMS.index >= event_start_times[0]) & (signal_left_lateral_RMS.index <= event_end_times[0])\n",
"signal_left_lateral_RMS.iloc[mask]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "78357902",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3805\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
"File \u001b[1;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
"File \u001b[1;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
"File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:2606\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
"File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:2630\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mKeyError\u001b[0m: 78357902",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[76], line 6\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m series\u001b[38;5;241m.\u001b[39mindex[series\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mget_loc(timestamp)]\n\u001b[0;32m 5\u001b[0m \u001b[38;5;66;03m# Then use:\u001b[39;00m\n\u001b[1;32m----> 6\u001b[0m start_idx \u001b[38;5;241m=\u001b[39m \u001b[43mget_nearest_index\u001b[49m\u001b[43m(\u001b[49m\u001b[43msignal_left_lateral_RMS\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevent_start_times\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m \n\u001b[0;32m 7\u001b[0m end_idx \u001b[38;5;241m=\u001b[39m get_nearest_index(signal_left_lateral_RMS, event_end_times[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m 8\u001b[0m event_data \u001b[38;5;241m=\u001b[39m signal_left_lateral_RMS\u001b[38;5;241m.\u001b[39mloc[start_idx:end_idx]\n",
"Cell \u001b[1;32mIn[76], line 3\u001b[0m, in \u001b[0;36mget_nearest_index\u001b[1;34m(series, timestamp)\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_nearest_index\u001b[39m(series, timestamp):\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m series\u001b[38;5;241m.\u001b[39mindex[\u001b[43mseries\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimestamp\u001b[49m\u001b[43m)\u001b[49m]\n",
"File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3807\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m 3808\u001b[0m \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[0;32m 3809\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[0;32m 3810\u001b[0m ):\n\u001b[0;32m 3811\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[1;32m-> 3812\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m 3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m 3814\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m 3815\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m 3816\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[0;32m 3817\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
"\u001b[1;31mKeyError\u001b[0m: 78357902"
]
}
],
"source": [
"# Convert microsecond timestamps to array indices\n",
"def get_nearest_index(series, timestamp):\n",
" return series.index[series.index.get_loc(timestamp)]\n",
"\n",
"# Then use:\n",
"start_idx = get_nearest_index(signal_left_lateral_RMS, event_start_times[0]) \n",
"end_idx = get_nearest_index(signal_left_lateral_RMS, event_end_times[0])\n",
"event_data = signal_left_lateral_RMS.loc[start_idx:end_idx]"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "78357902",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3805\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
"File \u001b[1;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
"File \u001b[1;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
"File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:2606\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
"File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:2630\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mKeyError\u001b[0m: 78357902",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[70], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m start_time \u001b[38;5;241m=\u001b[39m \u001b[43msignal_left_lateral_RMS\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mevent_start_times\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2\u001b[0m start_time\n",
"File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3807\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m 3808\u001b[0m \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[0;32m 3809\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[0;32m 3810\u001b[0m ):\n\u001b[0;32m 3811\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[1;32m-> 3812\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m 3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m 3814\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m 3815\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m 3816\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[0;32m 3817\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
"\u001b[1;31mKeyError\u001b[0m: 78357902"
]
}
],
"source": [
"start_time = signal_left_lateral_RMS.index.get_loc(int(event_start_times[0]))\n",
"start_time"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "78357902",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3805\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
"File \u001b[1;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
"File \u001b[1;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
"File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:2606\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
"File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:2630\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mKeyError\u001b[0m: 78357902",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[72], line 5\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# Use nearest method to find the closest index values\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;66;03m# start_time = signal_left_lateral_RMS.index.get_loc(event_start_times[0], method='nearest')\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;66;03m# end_time = signal_left_lateral_RMS.index.get_loc(event_end_times[0], method='nearest')\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m \u001b[43msignal_left_lateral_RMS\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43mevent_start_times\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mevent_end_times\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\n",
"File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexing.py:1191\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1189\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[0;32m 1190\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_deprecated_callable_usage(key, maybe_callable)\n\u001b[1;32m-> 1191\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmaybe_callable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexing.py:1411\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 1409\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mslice\u001b[39m):\n\u001b[0;32m 1410\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(key, axis)\n\u001b[1;32m-> 1411\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_slice_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1412\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m com\u001b[38;5;241m.\u001b[39mis_bool_indexer(key):\n\u001b[0;32m 1413\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getbool_axis(key, axis\u001b[38;5;241m=\u001b[39maxis)\n",
"File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexing.py:1443\u001b[0m, in \u001b[0;36m_LocIndexer._get_slice_axis\u001b[1;34m(self, slice_obj, axis)\u001b[0m\n\u001b[0;32m 1440\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m 1442\u001b[0m labels \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m_get_axis(axis)\n\u001b[1;32m-> 1443\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[43mlabels\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mslice_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mslice_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mslice_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mslice_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1445\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(indexer, \u001b[38;5;28mslice\u001b[39m):\n\u001b[0;32m 1446\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_slice(indexer, axis\u001b[38;5;241m=\u001b[39maxis)\n",
"File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexes\\base.py:6662\u001b[0m, in \u001b[0;36mIndex.slice_indexer\u001b[1;34m(self, start, end, step)\u001b[0m\n\u001b[0;32m 6618\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mslice_indexer\u001b[39m(\n\u001b[0;32m 6619\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 6620\u001b[0m start: Hashable \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m 6621\u001b[0m end: Hashable \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m 6622\u001b[0m step: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m 6623\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mslice\u001b[39m:\n\u001b[0;32m 6624\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 6625\u001b[0m \u001b[38;5;124;03m Compute the slice indexer for input labels and step.\u001b[39;00m\n\u001b[0;32m 6626\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 6660\u001b[0m \u001b[38;5;124;03m slice(1, 3, None)\u001b[39;00m\n\u001b[0;32m 6661\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 6662\u001b[0m start_slice, end_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mslice_locs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 6664\u001b[0m \u001b[38;5;66;03m# return a slice\u001b[39;00m\n\u001b[0;32m 6665\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_scalar(start_slice):\n",
"File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexes\\base.py:6879\u001b[0m, in \u001b[0;36mIndex.slice_locs\u001b[1;34m(self, start, end, step)\u001b[0m\n\u001b[0;32m 6877\u001b[0m start_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 6878\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m start \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 6879\u001b[0m start_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_slice_bound\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mleft\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 6880\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m start_slice \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 6881\u001b[0m start_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
"File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexes\\base.py:6804\u001b[0m, in \u001b[0;36mIndex.get_slice_bound\u001b[1;34m(self, label, side)\u001b[0m\n\u001b[0;32m 6801\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_searchsorted_monotonic(label, side)\n\u001b[0;32m 6802\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m:\n\u001b[0;32m 6803\u001b[0m \u001b[38;5;66;03m# raise the original KeyError\u001b[39;00m\n\u001b[1;32m-> 6804\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m err\n\u001b[0;32m 6806\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(slc, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[0;32m 6807\u001b[0m \u001b[38;5;66;03m# get_loc may return a boolean array, which\u001b[39;00m\n\u001b[0;32m 6808\u001b[0m \u001b[38;5;66;03m# is OK as long as they are representable by a slice.\u001b[39;00m\n\u001b[0;32m 6809\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m is_bool_dtype(slc\u001b[38;5;241m.\u001b[39mdtype)\n",
"File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexes\\base.py:6798\u001b[0m, in \u001b[0;36mIndex.get_slice_bound\u001b[1;34m(self, label, side)\u001b[0m\n\u001b[0;32m 6796\u001b[0m \u001b[38;5;66;03m# we need to look up the label\u001b[39;00m\n\u001b[0;32m 6797\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 6798\u001b[0m slc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 6799\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m 6800\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
"File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3807\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m 3808\u001b[0m \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[0;32m 3809\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[0;32m 3810\u001b[0m ):\n\u001b[0;32m 3811\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[1;32m-> 3812\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m 3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m 3814\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m 3815\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m 3816\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[0;32m 3817\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
"\u001b[1;31mKeyError\u001b[0m: 78357902"
]
}
],
"source": [
"# Use nearest method to find the closest index values\n",
"# start_time = signal_left_lateral_RMS.index.get_loc(event_start_times[0], method='nearest')\n",
"# end_time = signal_left_lateral_RMS.index.get_loc(event_end_times[0], method='nearest')\n",
"\n",
"signal_left_lateral_RMS.loc[event_start_times[0] : event_end_times[0]]"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Event 1: Cough\n",
"Start time: 78.358 sec, End time: 79.671 sec\n",
"left std ratio: 1.008, right std ratio: 2.445\n",
"LM_max_index: 79.310, LL_max_index: 78.360, left delta t: 0.950\n",
"RM_max_index: 78.814, RL_max_index: 79.126, right delta t: -0.312\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: Bite\n",
"Start time: 81.227 sec, End time: 82.465 sec\n",
"left std ratio: 1.518, right std ratio: 6.700\n",
"LM_max_index: 81.672, LL_max_index: 81.954, left delta t: -0.282\n",
"RM_max_index: 81.611, RL_max_index: 81.656, right delta t: -0.045\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: 84.240 sec, End time: 85.434 sec\n",
"left std ratio: 1.512, right std ratio: 5.680\n",
"LM_max_index: 84.914, LL_max_index: 85.347, left delta t: -0.434\n",
"RM_max_index: 85.064, RL_max_index: 85.357, right delta t: -0.293\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: 86.629 sec, End time: 87.952 sec\n",
"left std ratio: 1.689, right std ratio: 4.807\n",
"LM_max_index: 87.749, LL_max_index: 87.533, left delta t: 0.216\n",
"RM_max_index: 87.331, RL_max_index: 87.098, right delta t: 0.234\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: Swallow\n",
"Start time: 89.674 sec, End time: 91.159 sec\n",
"left std ratio: 1.318, right std ratio: 4.252\n",
"LM_max_index: 90.611, LL_max_index: 90.832, left delta t: -0.221\n",
"RM_max_index: 90.851, RL_max_index: 90.176, right delta t: 0.675\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: 92.258 sec, End time: 93.715 sec\n",
"left std ratio: 1.773, right std ratio: 4.874\n",
"LM_max_index: 93.262, LL_max_index: 93.605, left delta t: -0.342\n",
"RM_max_index: 93.386, RL_max_index: 92.814, right delta t: 0.571\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",
" mask_LL = (signal_left_lateral_RMS.index >= event_start_time) & (signal_left_lateral_RMS.index <= event_end_time)\n",
" event_signal_LL = signal_left_lateral_RMS.iloc[mask_LL]\n",
" \n",
" mask_LM = (signal_left_medial_RMS.index >= event_start_time) & (signal_left_medial_RMS.index <= event_end_time)\n",
" event_signal_LM = signal_left_medial_RMS.iloc[mask_LM]\n",
" \n",
" mask_RL = (signal_right_lateral_RMS.index >= event_start_time) & (signal_right_lateral_RMS.index <= event_end_time)\n",
" event_signal_RL = signal_right_lateral_RMS.iloc[mask_RL]\n",
" \n",
" mask_RM = (signal_right_medial_RMS.index >= event_start_time) & (signal_right_medial_RMS.index <= event_end_time)\n",
" event_signal_RM = signal_right_medial_RMS.iloc[mask_RM]\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": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|