`scipy.signal._filter_design:lp2lp`

source: /scipy/signal/_filter_design.py:1955

Signature

def lp2lp(b, a, wo = 1.0)

Summary

Transform a lowpass filter prototype to a different frequency.

Extended Summary

Return an analog low-pass filter with cutoff frequency wo from an analog low-pass filter prototype with unity cutoff frequency, in transfer function ('ba') representation.

Parameters

b : array_like, shape (M,): Numerator polynomial coefficients. Must be 1-D.
a : array_like, shape (N,): Denominator polynomial coefficients. Must be 1-D.
wo : float: Desired cutoff, as angular frequency (e.g. rad/s). Defaults to no change.

Returns

b : array_like: Numerator polynomial coefficients of the transformed low-pass filter.
a : array_like: Denominator polynomial coefficients of the transformed low-pass filter.

Notes

This is derived from the s-plane substitution

s \to \frac{s}{ω _{0}}

Array API Standard Support

lp2lp has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

====================  ====================  ====================
Library               CPU                   GPU
====================  ====================  ====================
NumPy                 ✅                     n/a                 
CuPy                  n/a                   ✅                   
PyTorch               ✅                     ✅                   
JAX                   ⚠️ no JIT             ⛔                   
Dask                  ⚠️ computes graph     n/a                 
====================  ====================  ====================

See dev-arrayapi for more information.

Examples

from scipy import signal
import matplotlib.pyplot as plt

✓

lp = signal.lti([1.0], [1.0, 1.0])
lp2 = signal.lti(*signal.lp2lp(lp.num, lp.den, 2))
w, mag_lp, p_lp = lp.bode()
w, mag_lp2, p_lp2 = lp2.bode(w)

✓

plt.plot(w, mag_lp, label='Lowpass')
plt.plot(w, mag_lp2, label='Transformed Lowpass')
plt.semilogx()

✗

[]

plt.grid(True)

✓

plt.xlabel('Frequency [rad/s]')
plt.ylabel('Amplitude [dB]')
plt.legend()

✗

<matplotlib.legend.Legend object at 0x7fa8e7e07a10>

Aliases

scipy.signal.lp2lp