def extract_basic_feats_n_heads(series, modality, field, stat_type, trans_type):
"""
feature extraction with given time series data
:return: feature name list, feature value list
"""
heads = [] + stat_type
sig_heads = []
for sig_feat in feat.SIGNAL_FEATS:
if sig_feat in stat_type:
heads.remove(sig_feat)
sig_heads += [sig_feat]
stats = map(lambda x: comp.compute_basic_stat(series, x), heads)
stats += comp.compute_peak_stats(series) if feat.AVG_PEAK_INTERVAL in sig_heads else []
stats += comp.compute_fft_stats(series) if feat.FT_REL_FREQ_1ST in sig_heads else []
heads += sig_heads
if trans_type in feat.BIN_NEEDED_TR_TYPES:
eq_freq = True if feat.DISC_FQ in trans_type else False
bin_heads, bin_stats = extract_bin_feats_and_heads(series, modality, field, eq_freq)
heads += bin_heads
stats += bin_stats
return heads, stats
def get_feature_list(df_raw, c_name):
return [compute_basic_stat(df_raw[c_name], f)
for f in STATISTICS]