cspline1d_eval(cj, newx, dx=1.0, x0=0)
dx
is the old sample-spacing while :None:None:`x0` was the old origin. In other-words the old-sample points (knot-points) for which the :None:None:`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.
cublic spline coefficients
New set of points.
Old sample-spacing, the default value is 1.0.
Old origin, the default value is 0.
Evaluated a cubic spline points.
Evaluate a cubic spline at the new set of points.
cspline1d
Compute cubic spline coefficients for rank-1 array.
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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