Processing

def notch_filter_signals(path_names: dict, sampling_rate: float = 1000.0, notch_f0: float = 60.0)

def bandpass_filter_signals(path_names: dict, low: float = 20.0, high: float = 450.0, sampling_rate: float = 1000.0)

def rectify_signals(path_names: dict)

def screen_artefact_signals(path_names: dict, n_std: float = 3.0, window: int = 50)

def fill_missing_signals(path_names: dict, method: str = 'spline')

def smooth_signals(path_names: dict, method: str = 'rms', window: int = 100)

import ability as ab

data = ab.read_file("trial.csv")
filtered = ab.bandpass(data, sampling_rate=250, low=1.0, high=50.0)
print(filtered.shape)

import ability as ab

data = ab.read_file("trial.csv")
clean = ab.notch(data, sampling_rate=250, freq=50.0)
print(clean.shape)

import ability as ab

data = ab.read_file("trial.csv")
quiet = ab.denoise(data, method="wavelet")
print(quiet.shape)