86 lines
3.9 KiB
Python
86 lines
3.9 KiB
Python
import pandas as pd
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import numpy as np
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from cr_features.helper_functions import convert_ibi_to2d_time, hrv_features, hrv_freq_features
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from cr_features.hrv import extract_hrv_features_2d_wrapper
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import math
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import sys
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pd.set_option('display.max_rows', 1000)
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pd.set_option('display.max_columns', None)
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def get_sample_rate(data):
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try:
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timestamps_diff = data['timestamp'].diff().dropna().mean()
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except:
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raise Exception("Error occured while trying to get the mean sample rate from the data.")
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return int(1000/timestamps_diff)
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def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_length, time_segment, filter_data_by_segment):
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ibi_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
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if not ibi_intraday_data.empty:
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sample_rate = get_sample_rate(ibi_intraday_data)
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ibi_intraday_data = filter_data_by_segment(ibi_intraday_data, time_segment)
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if not ibi_intraday_data.empty:
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ibi_intraday_features = pd.DataFrame()
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# np.set_printoptions(threshold=sys.maxsize)
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# print(ibi_intraday_data.groupby('local_segment').apply(lambda x: math.ceil(x['timings'].iloc[-1])))
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# nekaj = ibi_intraday_data.groupby('local_segment').apply(lambda x: \
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# convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], window_length)[1])
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# sys.exit()
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# apply methods from calculate features module
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if window_length is None:
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ibi_intraday_features = \
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ibi_intraday_data.groupby('local_segment').apply(\
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lambda x:
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extract_hrv_features_2d_wrapper(
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signal_2D = \
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convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], math.ceil(x['timings'].iloc[-1]))[0],
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ibi_timings = \
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convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], math.ceil(x['timings'].iloc[-1]))[1],
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sampling=None, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features))
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else:
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ibi_intraday_features = \
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ibi_intraday_data.groupby('local_segment').apply(\
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lambda x:
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extract_hrv_features_2d_wrapper(
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signal_2D = convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], window_length)[0],
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ibi_timings = convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], window_length)[1],
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sampling=None, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features))
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ibi_intraday_features.reset_index(inplace=True)
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return ibi_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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ibi_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
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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 = hrv_features + hrv_freq_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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ibi_intraday_features = extract_ibi_features_from_intraday_data(ibi_intraday_data, intraday_features_to_compute,
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requested_window_length, time_segment, filter_data_by_segment)
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return ibi_intraday_features |