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# numpy.broadcast

class numpy.broadcast(*arrays)

  Produce an object that mimics broadcasting.

  Parameters:
    in1, in2, …array_like

      Input parameters.

  Attributes:
    `index`

      current index in broadcasted result

    `iters`

      tuple of iterators along `self`’s “components.”

    `nd`

      Number of dimensions of broadcasted result.

    `ndim`

      Number of dimensions of broadcasted result.

    `numiter`

      Number of iterators possessed by the broadcasted result.

    `shape`

      Shape of broadcasted result.

    `size`

      Total size of broadcasted result.

  Methods

  `reset`() | Reset the broadcasted result's iterator(s).

  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`
  `broadcast_shapes`

  Examples

  Manually adding two vectors, using broadcasting:

      >>> import numpy as np
      >>> 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]])