numpy.broadcast_arrays
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numpy.broadcast_arrays(*args, **kwargs)
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Broadcast any number of arrays against each other.
Parameters: -
`*args` : array_likes
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The arrays to broadcast.
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subok : bool, optional
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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 : list of arrays
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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.
Examples
>>> 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]])]
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Licensed under the 3-clause BSD License.
https://docs.scipy.org/doc/numpy-1.17.0/reference/generated/numpy.broadcast_arrays.html