scipy.signal.freqs¶

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

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, then compute at that many frequencies. Otherwise, compute the response at the angular frequencies (e.g. rad/s) 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. w : ndarray The angular frequencies at which h was computed. h : ndarray The frequency response.

freqz
Compute the frequency response of a digital filter.

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.

Examples

```>>> 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 plt
>>> plt.semilogx(w, abs(h))
>>> plt.xlabel('Frequency')
>>> plt.ylabel('Amplitude response')
>>> plt.grid()
>>> plt.show()
```

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