import pandas as pd from scipy.stats import entropy from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, genericFeatureNames from CalculatingFeatures.calculate_features import calculateFeatures 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 extractTempFeaturesFromIntradayData(temperature_intraday_data, features, time_segment, filter_data_by_segment): temperature_intraday_features = pd.DataFrame(columns=["local_segment"] + features) if not temperature_intraday_data.empty: sample_rate = getSampleRate(temperature_intraday_data) temperature_intraday_data = filter_data_by_segment(temperature_intraday_data, time_segment) if not temperature_intraday_data.empty: temperature_intraday_features = pd.DataFrame() # apply methods from calculate features module temperature_intraday_features = \ temperature_intraday_data.groupby('local_segment').apply(\ lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=int(sample_rate), featureNames=features)) temperature_intraday_features.reset_index(inplace=True) return temperature_intraday_features def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): temperature_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 = genericFeatureNames # 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 temperature_intraday_features = extractTempFeaturesFromIntradayData(temperature_intraday_data, intraday_features_to_compute, time_segment, filter_data_by_segment) return temperature_intraday_features