scipy.signal.gauss_spline#

scipy.signal.gauss_spline(x, n)[source]#

Gaussian approximation to B-spline basis function of order n.

Parameters:

xarray_like: a knot vector
nint: The order of the spline. Must be non-negative, i.e., n >= 0

Returns:

resndarray: B-spline basis function values approximated by a zero-mean Gaussian function.

Notes

The B-spline basis function can be approximated well by a zero-mean Gaussian function with standard-deviation equal to \(\sigma=(n+1)/12\) for large n :

\[\frac{1}{\sqrt {2\pi\sigma^2}}exp(-\frac{x^2}{2\sigma})\]

References

[1]

Bouma H., Vilanova A., Bescos J.O., ter Haar Romeny B.M., Gerritsen F.A. (2007) Fast and Accurate Gaussian Derivatives Based on B-Splines. In: Sgallari F., Murli A., Paragios N. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2007. Lecture Notes in Computer Science, vol 4485. Springer, Berlin, Heidelberg

[2]

http://folk.uio.no/inf3330/scripting/doc/python/SciPy/tutorial/old/node24.html

Examples

We can calculate B-Spline basis functions approximated by a gaussian distribution:

>>> import numpy as np
>>> from scipy.signal import gauss_spline, bspline
>>> knots = np.array([-1.0, 0.0, -1.0])
>>> gauss_spline(knots, 3)
array([0.15418033, 0.6909883, 0.15418033])  # may vary

>>> bspline(knots, 3)
array([0.16666667, 0.66666667, 0.16666667])  # may vary