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# scipy.ndimage.measurements.variance

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

  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, or None, optional Labels defining sub-regions in input. If not None, must be same shape as input. index : None, int, or sequence of int, optional labels to include in output. If None, all values where labels is non-zero are used.
  Returns : | vars : float 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

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