24 lines
1.4 KiB
Python
24 lines
1.4 KiB
Python
import pandas as pd
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import numpy as np
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from datetime import datetime
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import sys
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def calculate_empatica_data_yield(features):
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# Get time segment duration in seconds from dataframe
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datetime_start = datetime.strptime(features.loc[0, 'local_segment_start_datetime'], '%Y-%m-%d %H:%M:%S')
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datetime_end = datetime.strptime(features.loc[0, 'local_segment_end_datetime'], '%Y-%m-%d %H:%M:%S')
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tseg_duration = (datetime_end - datetime_start).total_seconds()
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features["acc_data_yield"] = (features['empatica_accelerometer_cr_SO_windowsCount'] * 15) / tseg_duration
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features["temp_data_yield"] = (features['empatica_temperature_cr_SO_windowsCount'] * 300) / tseg_duration
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features["eda_data_yield"] = (features['empatica_electrodermal_activity_cr_SO_windowsCount'] * 60) / tseg_duration
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features["ibi_data_yield"] = (features['empatica_inter_beat_interval_cr_SO_windowsCount'] * 300) / tseg_duration
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# TODO: boljša nastavitev delovnih ur sedaj je od 4:00 do 4:00... to povzroči veliko manjkajočih podatkov in posledično nizek (telefonski in E4) data_yield ...
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empatica_data_yield_cols = ['acc_data_yield', 'temp_data_yield', 'eda_data_yield', 'ibi_data_yield']
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features["empatica_data_yield"] = features[empatica_data_yield_cols].mean(axis=1)
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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)
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return features
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