mars.tensor.nonzero#

mars.tensor.nonzero(a)[source]#

Return the indices of the elements that are non-zero.

Returns a tuple of tensors, one for each dimension of a, containing the indices of the non-zero elements in that dimension. The values in a are always tested and returned. The corresponding non-zero values can be obtained with:

a[nonzero(a)]

To group the indices by element, rather than dimension, use:

transpose(nonzero(a))

The result of this is always a 2-D array, with a row for each non-zero element.

Parameters: a (array_like) – Input tensor.
Returns: tuple_of_arrays – Indices of elements that are non-zero.
Return type: tuple

See also

flatnonzero: Return indices that are non-zero in the flattened version of the input tensor.
Tensor.nonzero: Equivalent tensor method.
count_nonzero: Counts the number of non-zero elements in the input tensor.

Examples

>>> import mars.tensor as mt

>>> x = mt.array([[1,0,0], [0,2,0], [1,1,0]])
>>> x.execute()
array([[1, 0, 0],
       [0, 2, 0],
       [1, 1, 0]])
>>> mt.nonzero(x).execute()
(array([0, 1, 2, 2]), array([0, 1, 0, 1]))

>>> x[mt.nonzero(x)].execute()  # TODO(jisheng): accomplish this after fancy indexing is supported

>>> mt.transpose(mt.nonzero(x)).execute() # TODO(jisheng): accomplish this later

A common use for nonzero is to find the indices of an array, where a condition is True. Given an array a, the condition a > 3 is a boolean array and since False is interpreted as 0, np.nonzero(a > 3) yields the indices of the a where the condition is true.

>>> a = mt.array([[1,2,3],[4,5,6],[7,8,9]])
>>> (a > 3).execute()
array([[False, False, False],
       [ True,  True,  True],
       [ True,  True,  True]])
>>> mt.nonzero(a > 3).execute()
(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))

The nonzero method of the boolean array can also be called.

>>> (a > 3).nonzero().execute()
(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))