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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:
    input ndarray

      N-D image data to process.

    labels ndarray, optional

      Labels of features 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 non-zero labels are used.

  Returns:
    minimums, maximums int or ndarray

      Values of minimums and maximums in each feature.

    min_positions, max_positions tuple 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`

  Notes

  Array API Standard Support

  `extrema` has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable `SCIPY_ARRAY_API=1` and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

  Library | CPU | GPU
  --- | --- | ---
  NumPy | ✅ | n/a
  CuPy | n/a | ✅
  PyTorch | ✅ | ⛔
  JAX | ⚠️ no JIT | ⛔
  Dask | ⚠️ computes graph | n/a

  > See Support for the array API standard for more information.

  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))