rapids/src/features/empatica_electrodermal_acti.../cr/main.py

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import pandas as pd
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import numpy as np
from scipy.stats import entropy
from cr_features.helper_functions import convert_to2d, gsr_features
from cr_features.calculate_features import calculate_features
from cr_features.gsr import extractGsrFeatures2D
from cr_features_helper_methods import extract_second_order_features
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import sys
#pd.set_option('display.max_columns', None)
#pd.set_option('display.max_rows', None)
#np.seterr(invalid='ignore')
def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_length, time_segment, filter_data_by_segment):
eda_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
if not eda_intraday_data.empty:
sample_rate = 4
eda_intraday_data = filter_data_by_segment(eda_intraday_data, time_segment)
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if not eda_intraday_data.empty:
eda_intraday_features = pd.DataFrame()
# apply methods from calculate features module
if window_length is None:
eda_intraday_features = \
eda_intraday_data.groupby('local_segment').apply(\
lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], x.shape[0]), sampleRate=sample_rate, featureNames=features,
threshold=.01, offset=1, riseTime=5, decayTime=15))
else:
eda_intraday_features = \
eda_intraday_data.groupby('local_segment').apply(\
lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], window_length*sample_rate), sampleRate=sample_rate, featureNames=features,
threshold=.01, offset=1, riseTime=5, decayTime=15))
eda_intraday_features.reset_index(inplace=True)
return eda_intraday_features
def cr_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"]
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 = gsr_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
eda_intraday_features = extract_eda_features_from_intraday_data(eda_intraday_data, intraday_features_to_compute,
requested_window_length, time_segment, filter_data_by_segment)
if calc_windows:
if provider["WINDOWS"]["IMPUTE_NANS"]:
eda_intraday_features[eda_intraday_features["numPeaks"] == 0] = \
eda_intraday_features[eda_intraday_features["numPeaks"] == 0].fillna(0)
pd.set_option('display.max_columns', None)
so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
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
return eda_intraday_features