scipy.signal.

spline_filter#

scipy.signal.spline_filter(Iin, lmbda=5.0)[source]#

Smoothing spline (cubic) filtering of a rank-2 array.

Filter an input data set, Iin, using a (cubic) smoothing spline of fall-off lmbda.

Parameters:

Iinarray_like: input data set
lmbdafloat, optional: spline smoothing fall-off value, default is 5.0.

Returns:

resndarray: filtered input data

Notes

Array API Standard Support

spline_filter 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	⚠️ no JIT	⛔
Dask	⚠️ computes graph	n/a

See Support for the array API standard for more information.

Examples

We can filter an multi dimensional signal (ex: 2D image) using cubic B-spline filter:

>>> import numpy as np
>>> from scipy.signal import spline_filter
>>> import matplotlib.pyplot as plt
>>> orig_img = np.eye(20)  # create an image
>>> orig_img[10, :] = 1.0
>>> sp_filter = spline_filter(orig_img, lmbda=0.1)
>>> f, ax = plt.subplots(1, 2, sharex=True)
>>> for ind, data in enumerate([[orig_img, "original image"],
...                             [sp_filter, "spline filter"]]):
...     ax[ind].imshow(data[0], cmap='gray_r')
...     ax[ind].set_title(data[1])
>>> plt.tight_layout()
>>> plt.show()

../../_images/scipy-signal-spline_filter-1.png