On Fri, Mar 16, 2007 at 11:49:56AM -0400, James Turner wrote:
> Hi Stefan,
>
> > I'd like to confirm that you see the same results when running your
> > script:
> >
> > [[ 4. 3. 2. 1.]
> > [ 4. 3. 2. 1.]]
> > [[-1. 3.12520003 2.11439991 1.01719999 1.87479997 -1. ]
> > [-1. 3.12520003 2.11439991 1.01719999 1.87479997 -1. ]]
> > [[-1. 3.0996666 2.0999999 1.34300005 1.90033329 -1. ]
> > [-1. 3.0996666 2.0999999 1.34300005 1.90033329 -1. ]]
>
> Yes, I get exactly the same numbers with numarray on a PC running Linux.
> I just rounded the numbers off in my explanatory text to simplify the
> discussion; sorry if that was confusing.
Not at all, just wanted to make sure. I am starting to form an idea
of what is happening here. Check out the following result:
In [25]: import numpy as N
In [26]: x = N.array([[4,3,8,1],[4,3,8,1.]])
In [27]:
ndi.geometric_transform(x,shift,output_shape=(2,6),prefilter=False,order=0,cval=-1)
Out[27]:
array([[-1., 3., 8., 1., 8., -1.],
[-1., 3., 8., 1., 8., -1.]])
Looks like the spline fit is done on mirrored input data, instead of
padding the input with the cval.
Regards
Stéfan
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