dask.array.broadcast_arrays
dask.array.broadcast_arrays¶
- dask.array.broadcast_arrays(*args, subok=False)[source]¶
Broadcast any number of arrays against each other.
This docstring was copied from numpy.broadcast_arrays.
Some inconsistencies with the Dask version may exist.
- Parameters
- *argsarray_likes
The arrays to broadcast.
- subokbool, 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
- broadcastedtuple 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 version 1.17: The output is currently marked so that if written to, a deprecation warning will be emitted. A future version will set the
writable
flag False so writing to it will raise an error.
See also
broadcast
broadcast_to
broadcast_shapes
Examples
>>> import numpy as np >>> x = np.array([[1,2,3]]) >>> y = np.array([[4],[5]]) >>> np.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.
>>> [np.array(a) for a in np.broadcast_arrays(x, y)] [array([[1, 2, 3], [1, 2, 3]]), array([[4, 4, 4], [5, 5, 5]])]