This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.
- nilearn.signal.butterworth(signals, sampling_rate, low_pass=None, high_pass=None, order=5, copy=False)#
Apply a low-pass, high-pass or band-pass Butterworth filter.
Apply a filter to remove signal below the low frequency and above the high frequency.
numpy.ndarray(1D sequence or n_samples x n_sources)
Signals to be filtered. A signal is assumed to be a column of signals.
Number of samples per time unit (sample frequency).
floator None, optional
Low cutoff frequency in Hertz. If specified, signals above this frequency will be filtered out. If None, no low-pass filtering will be performed. Default=None.
High cutoff frequency in Hertz. If specified, signals below this frequency will be filtered out. Default=None.
Order of the Butterworth filter. When filtering signals, the filter has a decay to avoid ringing. Increasing the order sharpens this decay. Be aware that very high orders can lead to numerical instability. Default=5.
If False, signals is modified inplace, and memory consumption is lower than for
copy=True, though computation time is higher.
Signals filtered according to the given parameters.