33 lines
1.6 KiB
Python
33 lines
1.6 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, yaml
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def calculate_empatica_data_yield(features): # TODO
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# Get time segment duration in seconds from all segments in features dataframe
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datetime_start = pd.to_datetime(features['local_segment_start_datetime'], format='%Y-%m-%d %H:%M:%S')
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datetime_end = pd.to_datetime(features['local_segment_end_datetime'], format='%Y-%m-%d %H:%M:%S')
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tseg_duration = (datetime_end - datetime_start).dt.total_seconds()
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with open('config.yaml', 'r') as stream:
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config = yaml.load(stream, Loader=yaml.FullLoader)
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sensors = ["EMPATICA_ACCELEROMETER", "EMPATICA_TEMPERATURE", "EMPATICA_ELECTRODERMAL_ACTIVITY", "EMPATICA_INTER_BEAT_INTERVAL"]
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for sensor in sensors:
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features[f"{sensor.lower()}_data_yield"] = \
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(features[f"{sensor.lower()}_cr_SO_windowsCount"] * config[sensor]["PROVIDERS"]["CR"]["WINDOWS"]["WINDOW_LENGTH"]) / tseg_duration \
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if f'{sensor.lower()}_cr_SO_windowsCount' in features else 0
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empatica_data_yield_cols = [sensor.lower() + "_data_yield" for sensor in sensors]
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pd.set_option('display.max_rows', None)
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# Assigns 1 to values that are over 1 (in case of windows not being filled fully)
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features[empatica_data_yield_cols] = features[empatica_data_yield_cols].apply(lambda x: [y if y <= 1 or np.isnan(y) else 1 for y in x])
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features["empatica_data_yield"] = features[empatica_data_yield_cols].mean(axis=1).fillna(0)
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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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