flopscope.

flopscope.numpy.broadcast_arrays

fnp.broadcast_arrays(*args, subok=False)[flopscope source][numpy source]

Broadcast any number of arrays against each other.

Adapted from NumPy docs np.broadcast_arrays

Areacore
Typefree
NumPy Refnp.broadcast_arrays
Cost
0
Flopscope Context

Broadcast arrays against each other. Cost: numel(output).

Parameters

*args:array_likes

The arrays to broadcast.

subok:bool, optional

If True, then sub-classes will be passed-through, otherwise the returned arrays will be forced to be a base-class array (default).

Returns

broadcasted:tuple of arrays

These arrays are views on the original arrays. They are typically not contiguous. Furthermore, more than one element of a broadcasted array may refer to a single memory location. If you need to write to the arrays, make copies first. While you can set the writable flag True, writing to a single output value may end up changing more than one location in the output array.

Deprecated since 1.17.

See also

Examples

>>> import flopscope.numpy as fnp
>>> x = flops.array([[1,2,3]])
>>> y = flops.array([[4],[5]])
>>> flops.broadcast_arrays(x, y)
(array([[1, 2, 3],
        [1, 2, 3]]),
 array([[4, 4, 4],
        [5, 5, 5]]))

Here is a useful idiom for getting contiguous copies instead of non-contiguous views.

>>> [flops.array(a) for a in flops.broadcast_arrays(x, y)]
[array([[1, 2, 3],
        [1, 2, 3]]),
 array([[4, 4, 4],
        [5, 5, 5]])]