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 …

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 …

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 …

Find peaks inside a signal based on peak properties. This function takes a 1-D array and finds all local maxima by simple comparison of neighboring values. Optionally, a subset of these peaks can be …

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 very long. Also, …

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 …

For the default Hann window an overlap of 50% is a reasonable trade-off between accurately estimating the signal power, while not over counting any of the data.

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 type I …

Discrete Fourier transforms (scipy.fft) # Fast Fourier Transforms (FFTs) #Discrete Sin and Cosine Transforms (DST and DCT) #