cspline1d(signal, lamb=0.0)
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 .
A rank-1 array representing samples of a signal.
Smoothing coefficient, default is 0.0.
Cubic spline coefficients.
Compute cubic spline coefficients for rank-1 array.
cspline1d_eval
Evaluate a cubic spline at the new set of points.
We can filter a signal to reduce and smooth out high-frequency noise with a cubic spline:
>>> 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()
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.signal._bsplines.cspline1d
scipy.signal._bsplines.cspline1d_eval
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