The signal processing toolbox currently contains some filtering functions, a limited set of filter design tools, and a few B-spline interpolation algorithms for 1- and 2-D data.

The functions are simpler to use than the classes, but are less efficient when using the same transform on many arrays of the same length, since they repeatedly generate the same chirp …

When using mode='same' with even-length inputs, the outputs of correlate and correlate2d differ: There is a 1-index offset between them. Examples Try it in your browser! Implement a …

To demonstrate this function’s usage we use a signal x supplied with SciPy (see scipy.datasets.electrocardiogram). Let’s find all peaks (local maxima) in x whose amplitude …

Signal Processing (scipy.signal) # The signal processing toolbox currently contains some filtering functions, a limited set of filter design tools, and a few B-spline interpolation algorithms for 1- …

The example below uses a Blackman window from scipy.signal and shows the effect of windowing (the zero component of the FFT has been truncated for illustrative purposes).

scipy.signal. decimate # decimate(x, q, n=None, ftype='iir', axis=-1, zero_phase=True) [source] # Downsample the signal after applying an anti-aliasing filter. By default, an order 8 Chebyshev …

Design a digital high-pass filter at 15 Hz to remove the 10 Hz tone, and apply it to the signal. (It’s recommended to use second-order sections format when filtering, to avoid numerical error …

The following example depicts a signal being up-sampled from 20 samples to 100 samples. The ringing at the beginning of the up-sampled signal is due to interpreting the signal being periodic.

The signal is assumed to be periodic. Discontinuities between signal start and end can lead to unwanted results due to Gibbs phenomenon. The FFT is slow if the signal length is prime or …