bundles / scipy 1.17.1 / scipy / ndimage / _interpolation / spline_filter
function
scipy.ndimage._interpolation:spline_filter
Signature
def spline_filter ( input , order = 3 , output = <class 'numpy.float64'> , mode = mirror ) Summary
Multidimensional spline filter.
Parameters
input: array_likeThe input array.
order: int, optionalThe order of the spline, default is 3.
output: ndarray or dtype, optionalThe array in which to place the output, or the dtype of the returned array. Default is
numpy.float64.mode: {'reflect', 'grid-mirror', 'constant', 'grid-constant', 'nearest', 'mirror', 'grid-wrap', 'wrap'}, optionalThe
modeparameter determines how the input array is extended beyond its boundaries. Default is 'mirror'. Behavior for each valid value is as follows (see additional plots and details onboundary modes <ndimage-interpolation-modes>):'reflect' (
d c b a | a b c d | d c b a)The input is extended by reflecting about the edge of the last pixel. This mode is also sometimes referred to as half-sample symmetric.
'grid-mirror'
This is a synonym for 'reflect'.
'constant' (
k k k k | a b c d | k k k k)The input is extended by filling all values beyond the edge with the same constant value, defined by the
cvalparameter. No interpolation is performed beyond the edges of the input.'grid-constant' (
k k k k | a b c d | k k k k)The input is extended by filling all values beyond the edge with the same constant value, defined by the
cvalparameter. Interpolation occurs for samples outside the input's extent as well.'nearest' (
a a a a | a b c d | d d d d)The input is extended by replicating the last pixel.
'mirror' (
d c b | a b c d | c b a)The input is extended by reflecting about the center of the last pixel. This mode is also sometimes referred to as whole-sample symmetric.
'grid-wrap' (
a b c d | a b c d | a b c d)The input is extended by wrapping around to the opposite edge.
'wrap' (
d b c d | a b c d | b c a b)The input is extended by wrapping around to the opposite edge, but in a way such that the last point and initial point exactly overlap. In this case it is not well defined which sample will be chosen at the point of overlap.
Returns
spline_filter: ndarrayFiltered array. Has the same shape as
input.
Notes
The multidimensional filter is implemented as a sequence of 1-D spline filters. The intermediate arrays are stored in the same data type as the output. Therefore, for output types with a limited precision, the results may be imprecise because intermediate results may be stored with insufficient precision.
For complex-valued input, this function processes the real and imaginary components independently.
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
dev-arrayapifor more information.
Examples
We can filter an image using multidimensional splines:from scipy.ndimage import spline_filter import numpy as np import matplotlib.pyplot as plt orig_img = np.eye(20) # create an image orig_img[10, :] = 1.0 sp_filter = spline_filter(orig_img, order=3) 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()✓

See also
- spline_filter1d
Calculate a 1-D spline filter along the given axis.
Aliases
-
scipy.ndimage.spline_filter