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 signal with every …

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- and 2-D …

Consult the Spectral Analysis section of the SciPy User Guide for a discussion of the scalings of a spectral density and an (amplitude) spectrum. Welch’s method may be interpreted as taking the …

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 matched filter using …

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 with …

To demonstrate this function’s usage we use a signal x supplied with SciPy (see scipy.misc.electrocardiogram). Let’s find all peaks (local maxima) in x whose amplitude lies above 0.

Determines the method for handling the edges of the signal, either “pad” or “gust”. When method is “pad”, the signal is padded; the type of padding is determined by padtype and padlen, and irlen is …

Consult the Spectral Analysis section of the SciPy User Guide for a discussion of the scalings of the power spectral density and the (squared) magnitude spectrum.

The plot’s colormap is logarithmically scaled as the power spectral density is in dB. The time extent of the signal x is marked by vertical dashed lines, and the shaded areas mark the presence of border …

Compute a spectrogram with consecutive Fourier transforms (legacy function). Spectrograms can be used as a way of visualizing the change of a nonstationary signal’s frequency content over time.