`scipy.ndimage._measurements:variance`

source: /scipy/ndimage/_measurements.py :813

Signature

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

Summary

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

Parameters

input : array_like: Nd-image data to process.
labels : array_like, optional: Labels defining 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

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

Notes

Array API Standard Support

variance 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.variance(a)

✗

Features to process can be specified using `labels` and `index`:

lbl, nlbl = ndimage.label(a)

✓

ndimage.variance(a, lbl, index=np.arange(1, nlbl+1))

✗

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

ndimage.variance(a, lbl)

✗

Aliases

scipy.ndimage.variance