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broadcast_arrays#

ivy.broadcast_arrays(*arrays)[source]#

Broadcasts one or more arrays against one another.

Parameters:

arrays (Union[Array, NativeArray]) – an arbitrary number of arrays to-be broadcasted.

Return type:

List[Array]

Returns:

ret – A list containing broadcasted arrays of type ivy.Array Each array must have the same shape, and each array must have the same dtype as its corresponding input array.

Examples

With ivy.Array input:

>>> x1 = ivy.array([1, 2, 3])
>>> x2 = ivy.array([4, 5, 6])
>>> y = ivy.broadcast_arrays(x1, x2)
>>> print(y)
[ivy.array([1, 2, 3]), ivy.array([4, 5, 6])]

With ivy.NativeArray inputs:

>>> x1 = ivy.native_array([0.3, 4.3])
>>> x2 = ivy.native_array([3.1, 5])
>>> x3 = ivy.native_array([2, 0])
>>> y = ivy.broadcast_arrays(x1, x2, x3)
[ivy.array([0.3, 4.3]), ivy.array([3.1, 5.]), ivy.array([2, 0])]

With mixed ivy.Array and ivy.NativeArray inputs:

>>> x1 = ivy.array([1, 2])
>>> x2 = ivy.native_array([0.3, 4.3])
>>> y = ivy.broadcast_arrays(x1, x2)
>>> print(y)
[ivy.array([1, 2]), ivy.array([0.3, 4.3])]

With ivy.Container inputs:

>>> x1 = ivy.Container(a=ivy.array([3, 1]), b=ivy.zeros(2))
>>> x2 = ivy.Container(a=ivy.array([4, 5]), b=ivy.array([2, -1]))
>>> y = ivy.broadcast_arrays(x1, x2)
>>> print(y)
[{
    a: ivy.array([3, 1]),
    b: ivy.array([0., 0.])
}, {
    a: ivy.array([4, 5]),
    b: ivy.array([2, -1])
}]

With mixed ivy.Array and ivy.Container inputs:

>>> x1 = ivy.zeros(2)
>>> x2 = ivy.Container(a=ivy.array([4, 5]), b=ivy.array([2, -1]))
>>> y = ivy.broadcast_arrays(x1, x2)
>>> print(y)
[{
    a: ivy.array([0., 0.]),
    b: ivy.array([0., 0.])
}, {
    a: ivy.array([4, 5]),
    b: ivy.array([2, -1])
}]
Array.broadcast_arrays(self, *arrays)[source]#

ivy.Array instance method variant of ivy.broadcast_arrays. This method simply wraps the function, and so the docstring for ivy.broadcast_arrays also applies to this method with minimal changes.

Parameters:

  • self (Array) – An input array to be broadcasted against other input arrays.

  • arrays (Union[Array, NativeArray]) – an arbitrary number of arrays to-be broadcasted. Each array must have the same shape. Each array must have the same dtype as its corresponding input array.

Return type:

List[Array]

Returns:

ret – A list containing broadcasted arrays of type ivy.Array

Examples

With ivy.Array inputs:

>>> x1 = ivy.array([1, 2])
>>> x2 = ivy.array([0.2, 0.])
>>> x3 = ivy.zeros(2)
>>> y = x1.broadcast_arrays(x2, x3)
>>> print(y)
[ivy.array([1, 2]), ivy.array([0.2, 0. ]), ivy.array([0., 0.])]

With mixed ivy.Array and ivy.NativeArray inputs:

>>> x1 = ivy.array([-1., 3.4])
>>> x2 = ivy.native_array([2.4, 5.1])
>>> y = x1.broadcast_arrays(x2)
>>> print(y)
[ivy.array([-1., 3.4]), ivy.array([2.4, 5.1])]
Container.broadcast_arrays(self, *arrays, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

ivy.Container instance method variant of ivy.broadcast_arrays. This method simply wraps the function, and so the docstring for ivy.broadcast_arrays also applies to this method with minimal changes.

Parameters:

  • self (Container) – A container to be broadcatsed against other input arrays.

  • arrays (Union[Container, Array, NativeArray]) – an arbitrary number of containers having arrays to-be broadcasted. Each array must have the same shape. Each array must have the same dtype as its corresponding input array.

  • key_chains (Optional[Union[List[str], Dict[str, str], Container]], default: None) – The key-chains to apply or not apply the method to. Default is None.

  • to_apply (Union[bool, Container], default: True) – If True, the method will be applied to key_chains, otherwise key_chains will be skipped. Default is True.

  • prune_unapplied (Union[bool, Container], default: False) – Whether to prune key_chains for which the function was not applied. Default is False.

  • map_sequences (Union[bool, Container], default: False) – Whether to also map method to sequences (lists, tuples). Default is False.

Return type:

Container

Examples

With ivy.Container inputs:

>>> x1 = ivy.Container(a=ivy.array([1, 2]), b=ivy.array([3, 4]))
>>> x2 = ivy.Container(a=ivy.array([-1.2, 0.4]), b=ivy.array([0, 1]))
>>> y = x1.broadcast_arrays(x2)
>>> print(y)
[{
    a: ivy.array([1, 2]),
    b: ivy.array([3, 4])
}, {
    a: ivy.array([-1.2, 0.4]),
    b: ivy.array([0, 1])
}]

With mixed ivy.Container and ivy.Array inputs:

>>> x1 = ivy.Container(a=ivy.array([4, 5]), b=ivy.array([2, -1]))
>>> x2 = ivy.zeros(2)
>>> y = x1.broadcast_arrays(x2)
>>> print(y)
[{
    a: ivy.array([4, 5]),
    b: ivy.array([2, -1])
}, {
    a: ivy.array([0., 0.]),
    b: ivy.array([0., 0.])
}]
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