[NumPy-Tickets] [NumPy] #1518: numpy.concat does not appear to work across broadcast axes

[NumPy Trac]

#1518: numpy.concat does not appear to work across broadcast axes
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 Reporter:  eob     |       Owner:  somebody
     Type:  defect  |      Status:  new     
 Priority:  normal  |   Milestone:  2.0.0   
Component:  Other   |     Version:  1.4.0   
 Keywords:          |  
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Comment(by pv):

 If you mean this,
 {{{
 >>> x = np.array([1,2,3,4])
 >>> y = np.array([[1,2],[3,4],[5,6],[7,8]])
 >>> np.concatenate((y, np.tile(x, (4,1))), axis=1)
 array([[1, 2, 1, 2, 3, 4],
        [3, 4, 1, 2, 3, 4],
        [5, 6, 1, 2, 3, 4],
        [7, 8, 1, 2, 3, 4]])
 }}}
 you can do it without copies:
 {{{
 def broadcast_view(x, ref):
     """Broadcast unit dimensions in `x` to match those in `ref` without
 copies"""
     strides = [0 if x.shape[j] == 1 else x.strides[j] for j in
 range(x.ndim)]
     shape = [ref.shape[j] if x.shape[j] == 1 else x.shape[j] for j in
 range(x.ndim)]
     from numpy.lib.stride_tricks import as_strided
     return as_strided(x, shape=shape, strides=strides)

 >>> x = np.array([1,2,3,4])
 >>> y = np.array([[1,2],[3,4],[5,6],[7,8]])
 >>> np.concatenate((y, broadcast_view(x[None,:], y)), axis=1)
 array([[1, 2, 1, 2, 3, 4],
        [3, 4, 1, 2, 3, 4],
        [5, 6, 1, 2, 3, 4],
        [7, 8, 1, 2, 3, 4]])
 >>> broadcast_view(x[None,:], y).base.base.base is x
 True
 }}}

 But yes, I guess `np.concatenate` should do this automatically.

-- 
Ticket URL: <http://projects.scipy.org/numpy/ticket/1518#comment:3>
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