scipy.signal.
qspline1d#
- 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 - qspline1d_eval
- 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() 