numpy.broadcast¶

class numpy.broadcast[source]¶

Produce an object that mimics broadcasting.

Parameters:	in1, in2, … : array_like Input parameters.
Returns:	b : broadcast object Broadcast the input parameters against one another, and return an object that encapsulates the result. Amongst others, it has shape and nd properties, and may be used as an iterator.

See also

broadcast_arrays, broadcast_to

Examples

Manually adding two vectors, using broadcasting:

>>> x = np.array([[1], [2], [3]])
>>> y = np.array([4, 5, 6])
>>> b = np.broadcast(x, y)

>>> out = np.empty(b.shape)
>>> out.flat = [u+v for (u,v) in b]
>>> out
array([[ 5.,  6.,  7.],
       [ 6.,  7.,  8.],
       [ 7.,  8.,  9.]])

Compare against built-in broadcasting:

>>> x + y
array([[5, 6, 7],
       [6, 7, 8],
       [7, 8, 9]])

Attributes:

index current index in broadcasted result iters tuple of iterators along self’s “components.” nd Number of dimensions of broadcasted result. For code intended for NumPy 1.12.0 and later the more consistent ndim is preferred. >>> x = np.array([1, 2, 3]) >>> y = np.array([[4], [5], [6]]) >>> b = np.broadcast(x, y) >>> b.nd 2 ndim Number of dimensions of broadcasted result. Alias for nd. New in version 1.12.0. >>> x = np.array([1, 2, 3]) >>> y = np.array([[4], [5], [6]]) >>> b = np.broadcast(x, y) >>> b.ndim 2 numiter Number of iterators possessed by the broadcasted result. >>> x = np.array([1, 2, 3]) >>> y = np.array([[4], [5], [6]]) >>> b = np.broadcast(x, y) >>> b.numiter 2 shape Shape of broadcasted result. size Total size of broadcasted result.

Methods

reset()

Reset the broadcasted result’s iterator(s).