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