import pandas as pd from scipy.stats import entropy from CalculatingFeatures.helper_functions import convert3DEmpaticaToArray, convertInputInto2d, accelerometerFeatureNames, frequencyFeatureNames from CalculatingFeatures.calculate_features import calculateFeatures import sys def getSampleRate(data): try: timestamps_diff = data['timestamp'].iloc[1] - data['timestamp'].iloc[0] except: raise Exception("Error occured while trying to get the sample rate from the first two sequential timestamps.") return 1000/timestamps_diff def extractAccFeaturesFromIntradayData(acc_intraday_data, features, time_segment, filter_data_by_segment): acc_intraday_features = pd.DataFrame(columns=["local_segment"] + features) if not acc_intraday_data.empty: sample_rate = getSampleRate(acc_intraday_data) acc_intraday_data = filter_data_by_segment(acc_intraday_data, time_segment) if not acc_intraday_data.empty: acc_intraday_features = pd.DataFrame() # apply methods from calculate features module acc_intraday_features = \ acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \ convertInputInto2d(x['double_values_0'], x.shape[0]), \ convertInputInto2d(x['double_values_1'], x.shape[0]), \ convertInputInto2d(x['double_values_2'], x.shape[0]), \ fs=int(sample_rate), featureNames=features)) acc_intraday_features.reset_index(inplace=True) return acc_intraday_features def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): eda_intraday_data = pd.read_csv(sensor_data_files["sensor_data"]) requested_intraday_features = provider["FEATURES"] # name of the features this function can compute base_intraday_features_names = accelerometerFeatureNames + frequencyFeatureNames # the subset of requested features this function can compute intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) # extract features from intraday data eda_intraday_features = extractAccFeaturesFromIntradayData(eda_intraday_data, intraday_features_to_compute, time_segment, filter_data_by_segment) return eda_intraday_features