import pandas as pd
import numpy as np
from datetime import datetime

import sys

def calculate_empatica_data_yield(features):
    # 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']
    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