Indexing Routines¶

cupy.
take
(a, indices, axis=None, out=None)[source]¶ Takes elements of an array at specified indices along an axis.
This is an implementation of “fancy indexing” at single axis.
This function does not support
mode
option.Parameters:  a (cupy.ndarray) – Array to extract elements.
 indices (int or arraylike) – Indices of elements that this function takes.
 axis (int) – The axis along which to select indices. The flattened input is used by default.
 out (cupy.ndarray) – Output array. If provided, it should be of appropriate shape and dtype.
Returns: The result of fancy indexing.
Return type: See also

cupy.
diagonal
(a, offset=0, axis1=0, axis2=1)[source]¶ Returns specified diagonals.
This function extracts the diagonals along two specified axes. The other axes are not changed. This function returns a writable view of this array as NumPy 1.10 will do.
Parameters:  a (cupy.ndarray) – Array from which the diagonals are taken.
 offset (int) – Index of the diagonals. Zero indicates the main diagonals, a positive value upper diagonals, and a negative value lower diagonals.
 axis1 (int) – The first axis to take diagonals from.
 axis2 (int) – The second axis to take diagonals from.
Returns: A view of the diagonals of
a
.Return type: See also

cupy.
ix_
(*args)[source]¶ Construct an open mesh from multiple sequences.
This function takes N 1D sequences and returns N outputs with N dimensions each, such that the shape is 1 in all but one dimension and the dimension with the nonunit shape value cycles through all N dimensions.
Using ix_ one can quickly construct index arrays that will index the cross product.
a[cupy.ix_([1,3],[2,5])]
returns the array[[a[1,2] a[1,5]], [a[3,2] a[3,5]]]
.Parameters: *args – 1D sequences Returns: N arrays with N dimensions each, with N the number of input sequences. Together these arrays form an open mesh. Return type: tuple of ndarrays Examples
>>> a = cupy.arange(10).reshape(2, 5) >>> a array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]) >>> ixgrid = cupy.ix_([0,1], [2,4]) >>> ixgrid (array([[0], [1]]), array([[2, 4]]))
See also

cupy.
fill_diagonal
(a, val, wrap=False)[source]¶ Fill the main diagonal of the given array of any dimensionality.
For an array a with
a.ndim > 2
, the diagonal is the list of locations with indicesa[i, i, ..., i]
all identical. This function modifies the input array inplace, it does not return a value.Parameters:  a (cupy.ndarray) – The array, at least 2D.
 val (scalar) – The value to be written on the diagonal. Its type must be compatible with that of the array a.
 wrap (bool) – If specified, the diagonal is “wrapped” after N columns. This affects only tall matrices.
Examples
>>> a = cupy.zeros((3, 3), int) >>> cupy.fill_diagonal(a, 5) >>> a array([[5, 0, 0], [0, 5, 0], [0, 0, 5]])
See also