`scipy.ndimage._measurements:standard_deviation`

source: /scipy/ndimage/_measurements.py :866

Signature

def standard_deviation ( input , labels = None , index = None )

Summary

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

Parameters

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

Returns

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

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 dev-arrayapi 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)

✗

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))

✗

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

ndimage.standard_deviation(a, lbl)

✗

Aliases

scipy.ndimage.standard_deviation