scipy.signal.
qspline1d_eval#
- scipy.signal.qspline1d_eval(cj, newx, dx=1.0, x0=0)[source]#
- Evaluate a quadratic spline at the new set of points. - Parameters:
- cjndarray
- Quadratic spline coefficients 
- newxndarray
- New set of points. 
- dxfloat, optional
- Old sample-spacing, the default value is 1.0. 
- x0int, optional
- Old origin, the default value is 0. 
 
- Returns:
- resndarray
- Evaluated a quadratic spline points. 
 
 - See also - qspline1d
- Compute quadratic spline coefficients for rank-1 array. 
 - Notes - dx is the old sample-spacing while x0 was the old origin. In other-words the old-sample points (knot-points) for which the cj represent spline coefficients were at equally-spaced points of: - oldx = x0 + j*dx j=0...N-1, with N=len(cj) - Edges are handled using mirror-symmetric boundary conditions. - 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() 