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]
Using Matplotlib's "plot" function as the callable for :None:None:`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))
.
Numerator of a linear filter.
Denominator of a linear filter.
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 :None:None:`worN`
.
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
.
Compute frequency response of analog filter.
freqz
Compute the frequency response of a digital filter.
>>> from scipy.signal import freqs, iirfilter
>>> 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 pltSee :
... plt.semilogx(w, 20 * np.log10(abs(h)))
... plt.xlabel('Frequency')
... plt.ylabel('Amplitude response [dB]')
... plt.grid()
... plt.show()
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.signal._filter_design.freqs
scipy.signal._filter_design.butter
scipy.signal._filter_design.ellip
scipy.signal._filter_design.ellipord
scipy.signal._filter_design.iirfilter
scipy.signal._filter_design.cheby2
scipy.signal._filter_design.freqz_zpk
scipy.signal._filter_design.buttord
scipy.signal._filter_design.freqs_zpk
scipy.signal._filter_design.cheby1
scipy.signal._filter_design.bessel
scipy.signal._filter_design.bilinear
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