35 lines
2.0 KiB
Python
35 lines
2.0 KiB
Python
import pandas as pd
|
|
import numpy as np
|
|
from datetime import datetime
|
|
|
|
import sys
|
|
|
|
def calculate_empatica_data_yield(features): # TODO
|
|
|
|
# Get time segment duration in seconds from dataframe
|
|
datetime_start = datetime.strptime(features.loc[0, 'local_segment_start_datetime'], '%Y-%m-%d %H:%M:%S')
|
|
datetime_end = datetime.strptime(features.loc[0, 'local_segment_end_datetime'], '%Y-%m-%d %H:%M:%S')
|
|
tseg_duration = (datetime_end - datetime_start).total_seconds()
|
|
|
|
features["acc_data_yield"] = (features['empatica_accelerometer_cr_SO_windowsCount'] * 15) / tseg_duration if 'empatica_accelerometer_cr_SO_windowsCount' in features else 0
|
|
features["temp_data_yield"] = (features['empatica_temperature_cr_SO_windowsCount'] * 300) / tseg_duration if 'empatica_temperature_cr_SO_windowsCount' in features else 0
|
|
features["eda_data_yield"] = (features['empatica_electrodermal_activity_cr_SO_windowsCount'] * 60) / tseg_duration if 'empatica_electrodermal_activity_cr_SO_windowsCount' in features else 0
|
|
features["ibi_data_yield"] = (features['empatica_inter_beat_interval_cr_SO_windowsCount'] * 300) / tseg_duration if 'empatica_inter_beat_interval_cr_SO_windowsCount' in features else 0
|
|
|
|
empatica_data_yield_cols = ['acc_data_yield', 'temp_data_yield', 'eda_data_yield', 'ibi_data_yield']
|
|
pd.set_option('display.max_rows', None)
|
|
|
|
print(tseg_duration)
|
|
print(features['empatica_accelerometer_cr_SO_windowsCount'])
|
|
print(features['empatica_temperature_cr_SO_windowsCount'])
|
|
print(features['empatica_electrodermal_activity_cr_SO_windowsCount'])
|
|
print(features['empatica_inter_beat_interval_cr_SO_windowsCount'])
|
|
# print((features['empatica_inter_beat_interval_cr_SO_windowsCount'] * 300) / tseg_duration)
|
|
sys.exit()
|
|
|
|
features["empatica_data_yield"] = features[empatica_data_yield_cols].mean(axis=1).fillna(0)
|
|
features.drop(empatica_data_yield_cols, axis=1, inplace=True) # In case of if the advanced operations will later not be needed (e.g., weighted average)
|
|
|
|
|
|
return features
|