On Thu, Mar 22, 2007 at 04:33:53PM -0700, Travis Oliphant wrote: > >I would rather opt for changing the spline fitting algorithm than for > >padding with zeros. > > > > > From what I understand, the splines used in ndimage have the implicit > mirror-symmetric boundary condition which also allows them to be > computed rapidly. There may be ways to adapt other boundary conditions > and maintain rapid evaluation, but it is not trivial as far as I know. > Standard spline-fitting allows multiple boundary conditions because > matrix inversion is used. I think the spline-fitting done in ndimage > relies on equal-spacing and mirror-symmetry to allow simple IIR filters > to be used to compute the spline coefficients very rapidly.
Thanks, Travis. I wasn't aware of these restrictions. Would it be possible to call fitpack to do the spline fitting? I noticed that it doesn't exhibit the same mirror-property: In [24]: z = scipy.interpolate.splrep([0,1,2,3,4],[0,4,3,2,1]) In [25]: scipy.interpolate.splev([0,1,2,3,4,5],z) Out[25]: array([ -1.32724622e-16, 4.00000000e+00, 3.00000000e+00, 2.00000000e+00, 1.00000000e+00, -1.25000000e+00]) Regards Stéfan _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion