cspline1d — SciPy v1.15.3 Manual (original) (raw)

scipy.signal.

scipy.signal.cspline1d(signal, lamb=0.0)[source]#

Compute cubic spline coefficients for rank-1 array.

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:

signalndarray

A rank-1 array representing samples of a signal.

lambfloat, optional

Smoothing coefficient, default is 0.0.

Returns:

cndarray

Cubic spline coefficients.

See also

cspline1d_eval

Evaluate a cubic spline at the new set of points.

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()