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freqs(b, a, worN=200, plot=None)

Given the M-order numerator b and N-order denominator a of an analog filter, compute its frequency response

        b[0]*(jw)**M + b[1]*(jw)**(M-1) + ... + b[M]
H(w) = ----------------------------------------------
        a[0]*(jw)**N + a[1]*(jw)**(N-1) + ... + a[N]

Notes

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. Try lambda w, h: plot(w, abs(h)).

Parameters

b : array_like

Numerator of a linear filter.

a : array_like

Denominator of a linear filter.

worN : {None, int, array_like}, 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, then compute at that many frequencies. Otherwise, compute the response at the angular frequencies (e.g., rad/s) given in worN.

plot : callable, optional

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 angular frequencies at which h was computed.

h : ndarray

The frequency response.

Compute frequency response of analog filter.

See Also

freqz

Compute the frequency response of a digital filter.

Examples

from scipy.signal import freqs, iirfilter
import numpy as np
b, a = iirfilter(4, [1, 10], 1, 60, analog=True, ftype='cheby1')
w, h = freqs(b, a, worN=np.logspace(-1, 2, 1000))
import matplotlib.pyplot as plt
plt.semilogx(w, 20 * np.log10(abs(h)))
plt.xlabel('Frequency')
plt.ylabel('Amplitude response [dB]')
plt.grid(True)
plt.show()
See :

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

freqzfreqz

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them


GitHub : /scipy/signal/_filter_design.py#119
type: <class 'function'>
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