scipy.ndimage.variance

scipy.ndimage.variance(input, labels=None, index=None)

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

label, standard_deviation, maximum, minimum, extrema

Examples

>>> 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