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scipy.ndimage.

# maximum_position

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

  Find the positions of the maximums of the values of an array at labels.

  For each region specified by labels, the position of the maximum value of input within the region is returned.

  Parameters:
    input array_like

      Array_like of values.

    labels array_like, optional

      An array of integers marking different regions over which the position of the maximum value of input is to be computed. labels must have the same shape as input. If labels is not specified, the location of the first maximum over the whole array is returned.

      The labels argument only works when index is specified.

    index array_like, optional

      A list of region labels that are taken into account for finding the location of the maxima. If index is None, the first maximum over all elements where labels is non-zero is returned.

      The index argument only works when labels is specified.

  Returns:
    output list of tuples of ints

      List of tuples of ints that specify the location of maxima of input over the regions determined by labels and whose index is in index.

      If index or labels are not specified, a tuple of ints is returned specifying the location of the `first` maximal value of input.

  See also

  `label`, `minimum`, `median`, `maximum_position`, `extrema`, `sum`, `mean`, `variance`
  `standard_deviation`

  Notes

  Array API Standard Support

  `maximum_position` 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

      >>> from scipy import ndimage
      >>> import numpy as np
      >>> a = np.array([[1, 2, 0, 0],
      ...               [5, 3, 0, 4],
      ...               [0, 0, 0, 7],
      ...               [9, 3, 0, 0]])
      >>> ndimage.maximum_position(a)
      (3, 0)

  Features to process can be specified using labels and index:

      >>> lbl = np.array([[0, 1, 2, 3],
      ...                 [0, 1, 2, 3],
      ...                 [0, 1, 2, 3],
      ...                 [0, 1, 2, 3]])
      >>> ndimage.maximum_position(a, lbl, 1)
      (1, 1)

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

      >>> ndimage.maximum_position(a, lbl)
      (2, 3)

  If there are no maxima, the position of the first element is returned:

      >>> ndimage.maximum_position(a, lbl, 2)
      (0, 2)