variance#
- scipy.ndimage.variance(input, labels=None, index=None)[source]#
Calculate the variance of the values of an N-D image array, optionally at specified sub-regions.
- Parameters:
- inputarray_like
Nd-image data to process.
- labelsarray_like, optional
Labels defining 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:
- variancefloat or ndarray
Values of variance, for each sub-region if labels and index are specified.
See also
Notes
variance
has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variableSCIPY_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.variance(a) 7.609375
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)) array([ 2.1875, 2.25 , 9. ])
If no index is given, all non-zero labels are processed:
>>> ndimage.variance(a, lbl) 6.1875