sdr.plot.group_delay(filter: FIR | IIR | ArrayLike | tuple[ArrayLike, ArrayLike], sample_rate: float | None = None, N: int = 1024, x_axis: 'one-sided' | 'two-sided' | 'log' = 'two-sided', decades: int = 4, **kwargs)

Plots the group delay \(\tau_g(\omega)\) of the IIR filter.

Parameters:
filter: FIR | IIR | ArrayLike | tuple[ArrayLike, ArrayLike]

The filter definition.

  • sdr.FIR, sdr.IIR: The filter object.

  • npt.ArrayLike: The feedforward coefficients \(b_i\).

  • tuple[npt.ArrayLike, npt.ArrayLike]: The feedforward coefficients \(b_i\) and feedback coefficients \(a_j\).

sample_rate: float | None = None

The sample rate \(f_s\) of the signal in samples/s. If None, the x-axis will be labeled as “Normalized Frequency”.

N: int = 1024

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

x_axis: 'one-sided' | 'two-sided' | '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.group_delay(h_srrc); \
   ...: plt.ylim(48, 52)
   ...: 
Out[2]: (48.0, 52.0)
../../_images/sdr_plot_group_delay_1.png

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.group_delay(iir)
   ...:
../../_images/sdr_plot_group_delay_2.png