signal_filter

SignalFilter(features, filter_type, filter_frequency, *, order=5)

Bases: Transform

Applies a Butterworth filter to time series features.

Applies a Butterworth lowpass or highpass filter to time series features. For this transform to work, the time series must already have a uniform sampling rate. Use a `Resample' transform to uniformly sample the points of a time series.

Initializes the Filter transform.

Parameters:

Name	Type	Description	Default
features	Iterable[str]	Features that shall be filtered.	required
filter_type	SignalFilterType	Type of the filter to apply. Valid options are "lowpass" and "highpass".	required
filter_frequency	float	Characteristic frequency of the filter in Hz. For high- and lowpass this is the cutoff frequency.	required
order	int	Order of the Butterworth filter to uses. Defaults to 5.	5

Source code in src/flowcean/transforms/signal_filter.py

def __init__(
    self,
    features: Iterable[str],
    filter_type: SignalFilterType,
    filter_frequency: float,
    *,
    order: int = 5,
) -> None:
    """Initializes the Filter transform.

    Args:
        features: Features that shall be filtered.
        filter_type: Type of the filter to apply. Valid options are
            "lowpass" and "highpass".
        filter_frequency: Characteristic frequency of the filter in Hz. For
            high- and lowpass this is the cutoff frequency.
        order: Order of the Butterworth filter to uses. Defaults to 5.
    """
    self.features = features
    self.filter_type = filter_type
    self.frequency = filter_frequency
    self.order = order