grains.utils.non_unique

grains.utils.non_unique(array, axis=None)

Finds indices of non-unique elements in a 1D or 2D ndarray.

Parameters

• array (ndarray) – Array in which the non-unique elements are searched.

• axis ({None, 0, 1}, optional) – The axis to operate on. If None, array will be flattened. If an integer, the subarrays indexed by the given axis will be flattened and treated as the elements of a 1-D array with the dimension of the given axis. Object arrays or structured arrays that contain objects are not supported if the axis kwarg is used. The default is None.

Returns

• nonunique_values (list) – Unique (individual, row or column) entries.

• nonunique_indices (list) – Each element of the list corresponds to non-unique elements, whose indices are given in a 1D numpy array.

Examples

In a 1D array, the repeated values and their indices are found by

>>> val, idx = non_unique(np.array([1, -1, 0, -1, 2, 5, 0, -1]))
>>> val
[-1, 0]
>>> idx
[array([1, 3, 7]), array([2, 6])]

In the matrix below, we can see that rows 0 and 2 are identical, as well as rows 1 and 4.

>>> val, idx = non_unique(np.array([[1, 3], [2, 4], [1, 3], [-1, 0], [2, 4]]), axis=0)
>>> val
[array([1, 3]), array([2, 4])]
>>> idx
[array([0, 2]), array([1, 4])]

By transposing the matrix above, the same holds for the columns.

>>> val, idx = non_unique(np.array([[1, 2, 1, -1, 2], [3, 4, 3, 0, 4]]), axis=1)
>>> val
[array([1, 3]), array([2, 4])]
>>> idx
[array([0, 2]), array([1, 4])]

If the dimensions along which to find the duplicates are not given, the input is flattened and the indexing happens in C-order (row-wise).

>>> val, idx = non_unique(np.array([[1, 2, 1, -1, 2], [3, 4, 3, 0, 4]]))
>>> val
[1, 2, 3, 4]
>>> idx
[array([0, 2]), array([1, 4]), array([5, 7]), array([6, 9])]