scipy.signal.freqs(b, a, worN=None, plot=None)

Compute frequency response of analog filter.

Given the numerator (b) and denominator (a) of a filter compute its frequency response:

        b[0]*(jw)**(nb-1) + b[1]*(jw)**(nb-2) + ... + b[nb-1]
H(w) = -------------------------------------------------------
        a[0]*(jw)**(na-1) + a[1]*(jw)**(na-2) + ... + a[na-1]
Parameters :

b : ndarray

Numerator of a linear filter.

a : ndarray

Denominator of a linear filter.

worN : {None, int}, optional

If None, then compute at 200 frequencies around the interesting parts of the response curve (determined by pole-zero locations). If a single integer, the compute at that many frequencies. Otherwise, compute the response at frequencies given in worN.

plot : callable

A callable that takes two arguments. If given, the return parameters w and h are passed to plot. Useful for plotting the frequency response inside freqs.

Returns :

w : ndarray

The frequencies at which h was computed.

h : ndarray

The frequency response.

See also

Compute the frequency response of a digital filter.


Using Matplotlib’s “plot” function as the callable for plot produces unexpected results, this plots the real part of the complex transfer function, not the magnitude.

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