broadcast_arrays(*args, subok=False)
The arrays to broadcast.
If True, then sub-classes will be passed-through, otherwise the returned arrays will be forced to be a base-class array (default).
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.
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.
Broadcast any number of arrays against each other.
>>> 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]])]See :
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.broadcast_to
numpy.broadcast_shapes
numpy.broadcast
dask.array.core.broadcast_arrays
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