84 lines
4.0 KiB
Python
84 lines
4.0 KiB
Python
import pandas as pd
|
|
from sklearn.preprocessing import StandardScaler
|
|
import numpy as np
|
|
|
|
from cr_features.helper_functions import convert_ibi_to2d_time, hrv_features
|
|
from cr_features.hrv import extract_hrv_features_2d_wrapper, get_HRV_features
|
|
from cr_features_helper_methods import extract_second_order_features
|
|
|
|
import math
|
|
import sys
|
|
|
|
# pd.set_option('display.max_rows', 1000)
|
|
pd.set_option('display.max_columns', None)
|
|
|
|
|
|
def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_length, time_segment, filter_data_by_segment):
|
|
ibi_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
|
|
|
|
if not ibi_intraday_data.empty:
|
|
|
|
ibi_intraday_data = filter_data_by_segment(ibi_intraday_data, time_segment)
|
|
|
|
if not ibi_intraday_data.empty:
|
|
|
|
ibi_intraday_features = pd.DataFrame()
|
|
|
|
# apply methods from calculate features module
|
|
if window_length is None:
|
|
ibi_intraday_features = \
|
|
ibi_intraday_data.groupby('local_segment').apply(\
|
|
lambda x:
|
|
extract_hrv_features_2d_wrapper(
|
|
signal_2D = \
|
|
convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], math.ceil(x['timings'].iloc[-1]))[0],
|
|
ibi_timings = \
|
|
convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], math.ceil(x['timings'].iloc[-1]))[1],
|
|
sampling=None, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features))
|
|
else:
|
|
ibi_intraday_features = \
|
|
ibi_intraday_data.groupby('local_segment').apply(\
|
|
lambda x:
|
|
extract_hrv_features_2d_wrapper(
|
|
signal_2D = convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], window_length)[0],
|
|
ibi_timings = convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], window_length)[1],
|
|
sampling=None, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features))
|
|
|
|
ibi_intraday_features.reset_index(inplace=True)
|
|
|
|
return ibi_intraday_features
|
|
|
|
|
|
def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
|
|
|
|
data_types = {'local_timezone': 'str', 'device_id': 'str', 'timestamp': 'int64', 'inter_beat_interval': 'float64', 'timings': 'float64', 'local_date_time': 'str',
|
|
'local_date': "str", 'local_time': "str", 'local_hour': "str", 'local_minute': "str", 'assigned_segments': "str"}
|
|
|
|
temperature_intraday_data = pd.read_csv(sensor_data_files["sensor_data"], dtype=data_types)
|
|
ibi_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 = hrv_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
|
|
ibi_intraday_features = extract_ibi_features_from_intraday_data(ibi_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"]
|
|
ibi_second_order_features = extract_second_order_features(ibi_intraday_features, so_features_names)
|
|
|
|
return ibi_intraday_features, ibi_second_order_features
|
|
|
|
return ibi_intraday_features |