import pandas as pd from scipy.stats import entropy from cr_features.helper_functions import convert_to2d, accelerometer_features, frequency_features from cr_features.calculate_features_old import calculateFeatures from cr_features.calculate_features import calculate_features from cr_features_helper_methods import extract_second_order_features import sys def extract_acc_features_from_intraday_data(acc_intraday_data, features, window_length, time_segment, filter_data_by_segment): acc_intraday_features = pd.DataFrame(columns=["local_segment"] + features) if not acc_intraday_data.empty: sample_rate = 32 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 # if window_length is None: # acc_intraday_features = \ # acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \ # convert_to2d(x['double_values_0'], x.shape[0]), \ # convert_to2d(x['double_values_1'], x.shape[0]), \ # convert_to2d(x['double_values_2'], x.shape[0]), \ # fs=sample_rate, featureNames=features)) # else: # acc_intraday_features = \ # acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \ # convert_to2d(x['double_values_0'], window_length*sample_rate), \ # convert_to2d(x['double_values_1'], window_length*sample_rate), \ # convert_to2d(x['double_values_2'], window_length*sample_rate), \ # fs=sample_rate, featureNames=features)) # apply methods from calculate features module if window_length is None: acc_intraday_features = \ acc_intraday_data.groupby('local_segment').apply(lambda x: calculate_features( \ convert_to2d(x['double_values_0'], x.shape[0]), \ convert_to2d(x['double_values_1'], x.shape[0]), \ convert_to2d(x['double_values_2'], x.shape[0]), \ fs=sample_rate, feature_names=features)) else: acc_intraday_features = \ acc_intraday_data.groupby('local_segment').apply(lambda x: calculate_features( \ convert_to2d(x['double_values_0'], window_length*sample_rate), \ convert_to2d(x['double_values_1'], window_length*sample_rate), \ convert_to2d(x['double_values_2'], window_length*sample_rate), \ fs=sample_rate, feature_names=features)) acc_intraday_features.reset_index(inplace=True) return acc_intraday_features def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): acc_intraday_data = pd.read_csv(sensor_data_files["sensor_data"]) requested_intraday_features = provider["FEATURES"] calc_windows = kwargs.get('calc_windows', False) if provider["WINDOWS"]["COMPUTE"] and calc_windows: requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"] else: requested_window_length = None # name of the features this function can compute base_intraday_features_names = accelerometer_features + frequency_features # 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 acc_intraday_features = extract_acc_features_from_intraday_data(acc_intraday_data, intraday_features_to_compute, requested_window_length, time_segment, filter_data_by_segment) if calc_windows: so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"] acc_second_order_features = extract_second_order_features(acc_intraday_features, so_features_names) return acc_intraday_features, acc_second_order_features return acc_intraday_features