speasy.signal.filtering package

speasy.signal.filtering.apply_sos_filter(sos: ndarray, filter_function: Callable, var: SpeasyVariable | Collection[SpeasyVariable]) → SpeasyVariable | Collection[SpeasyVariable]

Apply an IIR filter to the variable(s) using the given filter function. This function just applies the filter to the values of the variable without any resampling, it assumes that the variable has a regular time axis.

Parameters:

sos: np.ndarray

Second-order sections representation of the filter, as returned by scipy.signal.iirfilter() with output=’sos’ for example.

filter_function: Callable

The filter function to use (e.g. scipy.signal.sosfiltfilt())

var: SpeasyVariable or Collection[SpeasyVariable]

The variable(s) to filter

Returns:

SpeasyVariable or Collection[SpeasyVariable]

The filtered variable(s)

Notes

It only supports 1D variables.

speasy.signal.filtering.sosfiltfilt(sos: ndarray, var: SpeasyVariable | Collection[SpeasyVariable]) → SpeasyVariable | Collection[SpeasyVariable]

Apply an IIR filter to the variable(s) using scipy.signal.sosfiltfilt(). This function just applies the filter to the values of the variable without any resampling, it assumes that the variable has a regular time axis.

Parameters:

sos: np.ndarray

Second-order sections representation of the filter

var: SpeasyVariable or Collection[SpeasyVariable]

The variable(s) to filter

Returns:

SpeasyVariable or Collection[SpeasyVariable]

The filtered variable(s)

Notes

It only supports 1D variables.