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qspline1d_eval(cj, newx, dx=1.0, x0=0)

Notes

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.

Parameters

cj : ndarray

Quadratic spline coefficients

newx : ndarray

New set of points.

dx : float, optional

Old sample-spacing, the default value is 1.0.

x0 : int, optional

Old origin, the default value is 0.

Returns

res : ndarray

Evaluated a quadratic spline points.

Evaluate a quadratic spline at the new set of points.

See Also

qspline1d

Compute quadratic spline coefficients for rank-1 array.

Examples

We can filter a signal to reduce and smooth out high-frequency noise with a quadratic spline:

>>> 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()
See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy.signal._bsplines.qspline1d scipy.signal._bsplines.qspline1d_eval

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GitHub : /scipy/signal/_bsplines.py#602
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