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Add custom range to flirt #3

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Add custom range to flirt #3

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55fb2b4
Merge pull request #1 from im-ethz/master
adrian-lison Apr 21, 2021
1e4199e
Add custom range to stat features
adrian-lison Apr 21, 2021
4f57acf
Add custom range to hrv features
adrian-lison Apr 21, 2021
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12 changes: 8 additions & 4 deletions flirt/hrv/feature_calculation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -39,7 +39,7 @@ def __generate__(self, data: np.array) -> dict:
}


def get_hrv_features(data: pd.Series, window_length: int = 180, window_step_size: int = 1,
def get_hrv_features(data: pd.Series, window_length: int = 180, window_step_size: int = 1, custom_range = None,
domains: List[str] = ['td', 'fd', 'stat'], threshold: float = 0.2,
clean_data: bool = True, num_cores: int = 0):
"""
Expand Down Expand Up @@ -118,9 +118,13 @@ def get_hrv_features(data: pd.Series, window_length: int = 180, window_step_size

first_index = clean_data.index[0].floor('s')
last_index = clean_data.index[-1].ceil('s')
target_index = pd.date_range(start=first_index,
end=max(first_index, last_index - window_length_timedelta),
freq=window_step_size_timedelta)

if custom_range is None:
target_index = pd.date_range(start=first_index,
end=max(first_index, last_index - window_length_timedelta),
freq=window_step_size_timedelta)
else:
target_index = first_index + pd.to_timedelta(custom_range, unit='s')

def process(memmap_data) -> pd.DataFrame:
with Parallel(n_jobs=num_cores, max_nbytes=None) as parallel:
Expand Down
12 changes: 8 additions & 4 deletions flirt/stats/feature_calculation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,14 +3,14 @@

import pandas as pd
from joblib import Parallel, delayed
from tqdm.autonotebook import trange
from tqdm.autonotebook import trange, tqdm
from ..util import processing

from .common import get_stats


def get_stat_features(data: pd.DataFrame, window_length: int = 60, window_step_size: int = 1, data_frequency: int = 32,
entropies: bool = True, num_cores: int = 0):
custom_range = None, entropies: bool = True, num_cores: int = 0):
"""
Computes several statistical and entropy-based time series features for each column in the provided DataFrame.

Expand Down Expand Up @@ -49,8 +49,12 @@ def get_stat_features(data: pd.DataFrame, window_length: int = 60, window_step_s
num_cores = multiprocessing.cpu_count()

input_data = data.copy()
# advance by window_step_size * data_frequency
inputs = trange(0, len(input_data) - 1, window_step_size * data_frequency, desc="Stat features")

if custom_range is None:
# advance by window_step_size * data_frequency
inputs = trange(0, len(input_data) - 1, window_step_size * data_frequency, desc="Stat features")
else:
inputs = tqdm(custom_range, desc="Stat features")

def process(memmap_data):
with Parallel(n_jobs=num_cores, max_nbytes=None) as parallel:
Expand Down