scipy.signal.
cspline1d#
- 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() 