Plots the frequency response \(H(e^{j\omega})\) of the filter.

Parameters:¶

b: ArrayLike¶

The feedforward coefficients \(b_i\).

a: ArrayLike = 1¶

The feedback coefficients \(a_j\). For FIR filters, this is set to 1.

sample_rate: float = 1.0¶

The sample rate \(f_s\) of the filter in samples/s.

N: int = 1024¶

The number of samples \(N\) in the frequency response.

x_axis: Literal[one - sided] | typing.Literal[two - sided] | typing.Literal[log] = 'two-sided'¶

The x-axis scaling. Options are to display a one-sided spectrum, a two-sided spectrum, or one-sided spectrum with a logarithmic frequency axis.

decades: int = 4¶

The number of decades to plot when x_axis="log".

**kwargs

Additional keyword arguments to pass to matplotlib.pyplot.plot().

Examples¶

See the FIR filters example.

In [1]: h_srrc = sdr.root_raised_cosine(0.5, 10, 10)

In [2]: plt.figure(figsize=(8, 4)); \
   ...: sdr.plot.frequency_response(h_srrc); \
   ...: plt.show()
   ...: 

See the IIR filters example.

In [3]: zero = 0.6; \
   ...: pole = 0.8 * np.exp(1j * np.pi / 8); \
   ...: iir = sdr.IIR.ZerosPoles([zero], [pole, pole.conj()])
   ...: 

In [4]: plt.figure(figsize=(8, 4)); \
   ...: sdr.plot.frequency_response(iir.b_taps, iir.a_taps); \
   ...: plt.show()
   ...: 

In [5]: plt.figure(figsize=(8, 4)); \
   ...: sdr.plot.frequency_response(h_srrc, x_axis="one-sided"); \
   ...: plt.show()
   ...: 

In [6]: plt.figure(figsize=(8, 4)); \
   ...: sdr.plot.frequency_response(iir.b_taps, iir.a_taps, x_axis="log", decades=3); \
   ...: plt.show()
   ...: