sdr.plot.impulse_response(filter: FIR | IIR | ArrayLike | tuple[ArrayLike, ArrayLike], N: int | None = None, offset: float = 0, **kwargs)

Plots the impulse response \(h[n]\) of a filter.

The impulse response \(h[n]\) is the filter output when the input is an impulse \(\delta[n]\).

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 \(a_j\).

N: int | None = None

The number of samples \(N\) to plot. If None, the length of b is used for FIR filters and 100 for IIR filters.

offset: float = 0

The x-axis offset to apply to the first sample. Can be useful for comparing the impulse response of filters with different lengths.

**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.impulse_response(h_srrc)
   ...:
../../_images/sdr_plot_impulse_response_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(); \
   ...: sdr.plot.impulse_response(iir, N=30)
   ...:
../../_images/sdr_plot_impulse_response_2.png