`scipy.signal._spline_filters:cspline1d`

source: /scipy/signal/_spline_filters.py :305

Signature

def cspline1d ( signal , lamb = 0.0 )

Summary

Compute cubic spline coefficients for rank-1 array.

Extended Summary

Find the cubic spline coefficients for a 1-D signal assuming mirror-symmetric boundary conditions. To obtain the signal back from the spline representation mirror-symmetric-convolve these coefficients with a length 3 FIR window [1.0, 4.0, 1.0]/ 6.0 .

Parameters

signal : ndarray: A rank-1 array representing samples of a signal.
lamb : float, optional: Smoothing coefficient, default is 0.0.

Returns

c : ndarray: Cubic spline coefficients.

Notes

Array API Standard Support

cspline1d 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

We can filter a signal to reduce and smooth out high-frequency noise with a cubic spline:

import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import cspline1d, cspline1d_eval
rng = np.random.default_rng()
sig = np.repeat([0., 1., 0.], 100)
sig += rng.standard_normal(len(sig))*0.05  # add noise
time = np.linspace(0, len(sig))
filtered = cspline1d_eval(cspline1d(sig), time)

✓

plt.plot(sig, label="signal")
plt.plot(time, filtered, label="filtered")
plt.legend()

✗

plt.show()

✓

Aliases

scipy.signal.cspline1d