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scipy.ndimage.

# extrema

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

  Calculate the minimums and maximums of the values of an array at labels, along with their positions.

  Parameters:
    inputndarray

      N-D image data to process.

    labelsndarray, optional

      Labels of features 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 non-zero labels are used.

  Returns:
    minimums, maximumsint or ndarray

      Values of minimums and maximums in each feature.

    min_positions, max_positionstuple or list of tuples

      Each tuple gives the N-D coordinates of the corresponding minimum or maximum.

  See also

  `maximum`, `minimum`, `maximum_position`, `minimum_position`, `center_of_mass`

  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.extrema(a)
      (0, 9, (0, 2), (3, 0))

  Features to process can be specified using labels and index:

      >>> lbl, nlbl = ndimage.label(a)
      >>> ndimage.extrema(a, lbl, index=np.arange(1, nlbl+1))
      (array([1, 4, 3]),
       array([5, 7, 9]),
       [(0, 0), (1, 3), (3, 1)],
       [(1, 0), (2, 3), (3, 0)])

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

      >>> ndimage.extrema(a, lbl)
      (1, 9, (0, 0), (3, 0))