scipy.ndimage.

standard_deviation#

scipy.ndimage.standard_deviation(input, labels=None, index=None)[source]#

Calculate the standard deviation of the values of an N-D image array, optionally at specified sub-regions.

Parameters:

inputarray_like: N-D image data to process.
labelsarray_like, optional: Labels to identify sub-regions in input. If not None, must be same shape as input.
indexint or sequence of ints, optional: labels to include in output. If None (default), all values where labels is non-zero are used.

Returns:

standard_deviationfloat or ndarray: Values of standard deviation, for each sub-region if labels and index are specified.

See also

label, variance, maximum, minimum, extrema

Notes

Array API Standard Support

standard_deviation 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

>>> import numpy as np
>>> a = np.array([[1, 2, 0, 0],
...               [5, 3, 0, 4],
...               [0, 0, 0, 7],
...               [9, 3, 0, 0]])
>>> from scipy import ndimage
>>> ndimage.standard_deviation(a)
2.7585095613392387

Features to process can be specified using labels and index:

>>> lbl, nlbl = ndimage.label(a)
>>> ndimage.standard_deviation(a, lbl, index=np.arange(1, nlbl+1))
array([ 1.479,  1.5  ,  3.   ])

If no index is given, non-zero labels are processed:

>>> ndimage.standard_deviation(a, lbl)
2.4874685927665499