82 lines
3.6 KiB
Python
82 lines
3.6 KiB
Python
import pandas as pd
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import numpy as np
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from scipy.stats import entropy
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from cr_features.helper_functions import convert_to2d, gsr_features
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from cr_features.calculate_features import calculate_features
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from cr_features.gsr import extractGsrFeatures2D
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from cr_features_helper_methods import extract_second_order_features
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import sys
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#pd.set_option('display.max_columns', None)
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#pd.set_option('display.max_rows', None)
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#np.seterr(invalid='ignore')
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def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_length, time_segment, filter_data_by_segment):
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eda_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
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if not eda_intraday_data.empty:
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sample_rate = 4
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eda_intraday_data = filter_data_by_segment(eda_intraday_data, time_segment)
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if not eda_intraday_data.empty:
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eda_intraday_features = pd.DataFrame()
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# apply methods from calculate features module
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if window_length is None:
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eda_intraday_features = \
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eda_intraday_data.groupby('local_segment').apply(\
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lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], x.shape[0]), sampleRate=sample_rate, featureNames=features,
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threshold=.01, offset=1, riseTime=5, decayTime=15))
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else:
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eda_intraday_features = \
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eda_intraday_data.groupby('local_segment').apply(\
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lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], window_length*sample_rate), sampleRate=sample_rate, featureNames=features,
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threshold=.01, offset=1, riseTime=5, decayTime=15))
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eda_intraday_features.reset_index(inplace=True)
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return eda_intraday_features
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def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
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data_types = {'local_timezone': 'str', 'device_id': 'str', 'timestamp': 'int64', 'electrodermal_activity': 'float64', 'local_date_time': 'str',
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'local_date': "str", 'local_time': "str", 'local_hour': "str", 'local_minute': "str", 'assigned_segments': "str"}
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eda_intraday_data = pd.read_csv(sensor_data_files["sensor_data"], dtype=data_types)
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requested_intraday_features = provider["FEATURES"]
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calc_windows = kwargs.get('calc_windows', False)
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if provider["WINDOWS"]["COMPUTE"] and calc_windows:
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requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"]
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else:
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requested_window_length = None
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# name of the features this function can compute
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base_intraday_features_names = gsr_features
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# the subset of requested features this function can compute
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intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))
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# extract features from intraday data
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eda_intraday_features = extract_eda_features_from_intraday_data(eda_intraday_data, intraday_features_to_compute,
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requested_window_length, time_segment, filter_data_by_segment)
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if calc_windows:
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if provider["WINDOWS"]["IMPUTE_NANS"]:
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eda_intraday_features[eda_intraday_features["numPeaks"] == 0] = \
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eda_intraday_features[eda_intraday_features["numPeaks"] == 0].fillna(0)
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pd.set_option('display.max_columns', None)
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so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
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eda_second_order_features = extract_second_order_features(eda_intraday_features, so_features_names)
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return eda_intraday_features, eda_second_order_features
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return eda_intraday_features |