tlpipe.utils.np_util.unique

tlpipe.utils.np_util.unique(ar, return_index=False, return_inverse=False, return_counts=False)[source]

Find the unique elements of an array.

Returns the sorted unique elements of an array. There are two optional outputs in addition to the unique elements: the indices of the input array that give the unique values, and the indices of the unique array that reconstruct the input array.

Copied from newer version of numpy, as old version has no return_counts argument.

Parameters:
  • ar (array_like) – Input array. This will be flattened if it is not already 1-D.
  • return_index (bool, optional) – If True, also return the indices of ar that result in the unique array.
  • return_inverse (bool, optional) – If True, also return the indices of the unique array that can be used to reconstruct ar.
  • return_counts (bool, optional) –

    New in version 1.9.0.

    If True, also return the number of times each unique value comes up in ar.
Returns:

  • unique (ndarray) – The sorted unique values.
  • unique_indices (ndarray, optional) – The indices of the first occurrences of the unique values in the (flattened) original array. Only provided if return_index is True.
  • unique_inverse (ndarray, optional) – The indices to reconstruct the (flattened) original array from the unique array. Only provided if return_inverse is True.
  • unique_counts (ndarray, optional) – .. versionadded:: 1.9.0 The number of times each of the unique values comes up in the original array. Only provided if return_counts is True.

See also

numpy.lib.arraysetops()
Module with a number of other functions for performing set operations on arrays.

Examples

>>> np.unique([1, 1, 2, 2, 3, 3])
array([1, 2, 3])
>>> a = np.array([[1, 1], [2, 3]])
>>> np.unique(a)
array([1, 2, 3])

Return the indices of the original array that give the unique values:

>>> a = np.array(['a', 'b', 'b', 'c', 'a'])
>>> u, indices = np.unique(a, return_index=True)
>>> u
array(['a', 'b', 'c'],
       dtype='|S1')
>>> indices
array([0, 1, 3])
>>> a[indices]
array(['a', 'b', 'c'],
       dtype='|S1')

Reconstruct the input array from the unique values:

>>> a = np.array([1, 2, 6, 4, 2, 3, 2])
>>> u, indices = np.unique(a, return_inverse=True)
>>> u
array([1, 2, 3, 4, 6])
>>> indices
array([0, 1, 4, 3, 1, 2, 1])
>>> u[indices]
array([1, 2, 6, 4, 2, 3, 2])