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bundles / scipy 1.17.1 / scipy / signal / _spline_filters / gauss_spline

function

scipy.signal._spline_filters:gauss_spline

source: /scipy/signal/_spline_filters.py :88

Signature

def   gauss_spline ( x ,  n )

Summary

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

Parameters

x : array_like

a knot vector

n : int

The order of the spline. Must be non-negative, i.e., n >= 0

Returns

res : ndarray

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 for large n :

Array API Standard Support

gauss_spline has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

====================  ====================  ====================
Library               CPU                   GPU
====================  ====================  ====================
NumPy                 ✅                     n/a                 
CuPy                  n/a                   ✅                   
PyTorch               ✅                     ✅                   
JAX                   ✅                     ✅                   
Dask                  ✅                     n/a                 
====================  ====================  ====================

See dev-arrayapi for more information.

Examples

We can calculate B-Spline basis functions approximated by a Gaussian distribution: 
import numpy as np
from scipy.signal import gauss_spline
knots = np.array([-1.0, 0.0, -1.0])
✓
gauss_spline(knots, 3)
✗

Aliases

  • scipy.signal.gauss_spline

Referenced by

This package