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.

ax: plt.Axes | None = None¶

The axis to plot on. If None, the current axis is used.

x_axis: 'auto' | 'one-sided' | 'two-sided' | 'log' = 'auto'¶

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. The default is "auto" which selects "one-sided" for real-valued filters and "two-sided" for complex-valued filters.

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(); \
   ...: sdr.plot.group_delay(h_srrc);
   ...: 

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(); \
   ...: sdr.plot.group_delay(iir)
   ...: 

In [5]: plt.figure(); \
   ...: sdr.plot.group_delay(h_srrc, x_axis="two-sided");
   ...: 

In [6]: plt.figure(); \
   ...: sdr.plot.group_delay(iir, x_axis="log", decades=3)
   ...: