# scipy.ndimage.standard_deviation

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

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

  Parameters
    input array_like

      N-D image data to process.

    labels array_like, optional

      Labels to identify 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
    standard_deviation float or ndarray

      Values of standard deviation, for each sub-region if labels and index are specified.

  See also

  `label`, `variance`, `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.standard_deviation(a)
      2.7585095613392387

  Features to process can be specified using labels and index:

      >>> lbl, nlbl = ndimage.label(a)
      >>> ndimage.standard_deviation(a, lbl, index=np.arange(1, nlbl+1))
      array([ 1.479,  1.5  ,  3.   ])

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

      >>> ndimage.standard_deviation(a, lbl)
      2.4874685927665499

scipy.ndimage.minimum_position scipy.ndimage.sum_labels