On 06/16/2009 02:18 PM, Robert wrote:
> >>> n = 10
> >>> xx = np.ones(n)
> >>> yy = np.arange(n)
> >>> aa = np.column_stack((xx,yy))
> >>> bb = np.column_stack((xx+1,yy))
> >>> aa
> array([[ 1., 0.],
> [ 1., 1.],
> [ 1., 2.],
> [ 1., 3.],
> [ 1., 4.],
> [ 1., 5.],
> [ 1., 6.],
> [ 1., 7.],
> [ 1., 8.],
> [ 1., 9.]])
> >>> bb
> array([[ 2., 0.],
> [ 2., 1.],
> [ 2., 2.],
> [ 2., 3.],
> [ 2., 4.],
> [ 2., 5.],
> [ 2., 6.],
> [ 2., 7.],
> [ 2., 8.],
> [ 2., 9.]])
> >>> np.column_stack((aa,bb))
> array([[ 1., 0., 2., 0.],
> [ 1., 1., 2., 1.],
> [ 1., 2., 2., 2.],
> [ 1., 3., 2., 3.],
> [ 1., 4., 2., 4.],
> [ 1., 5., 2., 5.],
> [ 1., 6., 2., 6.],
> [ 1., 7., 2., 7.],
> [ 1., 8., 2., 8.],
> [ 1., 9., 2., 9.]])
> >>> cc = _
> >>> cc.reshape((n*2,2))
> array([[ 1., 0.],
> [ 2., 0.],
> [ 1., 1.],
> [ 2., 1.],
> [ 1., 2.],
> [ 2., 2.],
> [ 1., 3.],
> [ 2., 3.],
> [ 1., 4.],
> [ 2., 4.],
> [ 1., 5.],
> [ 2., 5.],
> [ 1., 6.],
> [ 2., 6.],
> [ 1., 7.],
> [ 2., 7.],
> [ 1., 8.],
> [ 2., 8.],
> [ 1., 9.],
> [ 2., 9.]])
> >>>
>
>
> However I feel too, there is a intuitive abbrev function like
> 'interleave' or so missing in numpy shape_base or so.
Using fancy indexing, you can set strided portions of an array equal to
another array. So::
In [2]: aa = np.empty((10,2))
In [3]: aa[:, 0] = 1
In [4]: aa[:,1] = np.arange(10)
In [5]: bb = np.empty((10,2))
In [6]: bb[:,0] = 2
In [7]: bb[:,1] = aa[:,1] # this works
In [8]: cc = np.empty((20,2))
In [9]: cc[::2,:] = aa
In [10]: cc[1::2,:] = bb
In [11]: cc
Out[11]:
array([[ 1., 0.],
[ 2., 0.],
[ 1., 1.],
[ 2., 1.],
[ 1., 2.],
[ 2., 2.],
[ 1., 3.],
[ 2., 3.],
[ 1., 4.],
[ 2., 4.],
[ 1., 5.],
[ 2., 5.],
[ 1., 6.],
[ 2., 6.],
[ 1., 7.],
[ 2., 7.],
[ 1., 8.],
[ 2., 8.],
[ 1., 9.],
[ 2., 9.]])
Using this syntax, interleave could be a one-liner.
-Neil
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