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# 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:
    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.

  See also

  `label`, `standard_deviation`, `maximum`, `minimum`, `extrema`

  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