qspline1d — SciPy v1.15.2 Manual (original) (raw)
scipy.signal.
scipy.signal.qspline1d(signal, lamb=0.0)[source]#
Compute quadratic spline coefficients for rank-1 array.
Parameters:
signalndarray
A rank-1 array representing samples of a signal.
lambfloat, optional
Smoothing coefficient (must be zero for now).
Returns:
cndarray
Quadratic spline coefficients.
See also
Evaluate a quadratic spline at the new set of points.
Notes
Find the quadratic 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, 6.0, 1.0]/ 8.0 .
Examples
We can filter a signal to reduce and smooth out high-frequency noise with a quadratic spline:
import numpy as np import matplotlib.pyplot as plt from scipy.signal import qspline1d, qspline1d_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 = qspline1d_eval(qspline1d(sig), time) plt.plot(sig, label="signal") plt.plot(time, filtered, label="filtered") plt.legend() plt.show()
