numpy.broadcast_arrays()

numpy.broadcast_arrays(*args, **kwargs) [source]

Broadcast any number of arrays against each other.

Parameters:

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 : list 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.

`*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 : list 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.

Examples

>>> x = np.array([[1,2,3]])
>>> y = np.array([[1],[2],[3]])
>>> np.broadcast_arrays(x, y)
[array([[1, 2, 3],
       [1, 2, 3],
       [1, 2, 3]]), array([[1, 1, 1],
       [2, 2, 2],
       [3, 3, 3]])]

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],
       [1, 2, 3]]), array([[1, 1, 1],
       [2, 2, 2],
       [3, 3, 3]])]

Links:

https://docs.scipy.org/doc/numpy-1.11.0/reference/generated/numpy.broadcast_arrays.html

doc_NumPy

2025-01-10 15:47:30

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