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scipy.ndimage.

# value_indices

scipy.ndimage.value_indices(arr, *, ignore_value=None)

  Find indices of each distinct value in given array.

  Parameters:
    arr ndarray of ints

      Array containing integer values.

    ignore_value int, optional

      This value will be ignored in searching the arr array. If not given, all values found will be included in output. Default is None.

  Returns:
    indices dictionary

      A Python dictionary of array indices for each distinct value. The dictionary is keyed by the distinct values, the entries are array index tuples covering all occurrences of the value within the array.

      This dictionary can occupy significant memory, usually several times the size of the input array.

  See also

  `label`, `maximum`, `median`, `minimum_position`, `extrema`, `sum`, `mean`, `variance`
  `standard_deviation`, `numpy.where`, `numpy.unique`

  Notes

  For a small array with few distinct values, one might use numpy.unique() to find all possible values, and `(arr == val)` to locate each value within that array. However, for large arrays, with many distinct values, this can become extremely inefficient, as locating each value would require a new search through the entire array. Using this function, there is essentially one search, with the indices saved for all distinct values.

  This is useful when matching a categorical image (e.g. a segmentation or classification) to an associated image of other data, allowing any per-class statistic(s) to then be calculated. Provides a more flexible alternative to functions like `scipy.ndimage.mean()` and `scipy.ndimage.variance()`.

  Some other closely related functionality, with different strengths and weaknesses, can also be found in `scipy.stats.binned_statistic()` and the scikit-image function `skimage.measure.regionprops()`.

  Note for IDL users: this provides functionality equivalent to IDL’s REVERSE_INDICES option (as per the IDL documentation for the HISTOGRAM function).

  Added in version 1.10.0.

  Examples

      >>> import numpy as np
      >>> from scipy import ndimage
      >>> a = np.zeros((6, 6), dtype=int)
      >>> a[2:4, 2:4] = 1
      >>> a[4, 4] = 1
      >>> a[:2, :3] = 2
      >>> a[0, 5] = 3
      >>> a
      array([[2, 2, 2, 0, 0, 3],
             [2, 2, 2, 0, 0, 0],
             [0, 0, 1, 1, 0, 0],
             [0, 0, 1, 1, 0, 0],
             [0, 0, 0, 0, 1, 0],
             [0, 0, 0, 0, 0, 0]])
      >>> val_indices = ndimage.value_indices(a)

  The dictionary val_indices will have an entry for each distinct value in the input array.

      >>> val_indices.keys()
      dict_keys([np.int64(0), np.int64(1), np.int64(2), np.int64(3)])

  The entry for each value is an index tuple, locating the elements with that value.

      >>> ndx1 = val_indices[1]
      >>> ndx1
      (array([2, 2, 3, 3, 4]), array([2, 3, 2, 3, 4]))

  This can be used to index into the original array, or any other array with the same shape.

      >>> a[ndx1]
      array([1, 1, 1, 1, 1])

  If the zeros were to be ignored, then the resulting dictionary would no longer have an entry for zero.

      >>> val_indices = ndimage.value_indices(a, ignore_value=0)
      >>> val_indices.keys()
      dict_keys([np.int64(1), np.int64(2), np.int64(3)])