Yes, setting the vertical sampling value to a high value (i.e. 10000) did the trick. It makes sense since it is going to sample the the next pixel in a vertical position that does not exist in the image. With the right distance matrix I could use Josh's idea, as he suggested on the first reply. That was simpler than implementing something with several loops.

Thank you both, those were very quick and effective answers. :)

On Wednesday, July 16, 2014 1:16:34 AM UTC+1, Juan Nunez-Iglesias wrote:

@JDWarner, actually, instead of iterating, setting one of the sampling dimensions to a very large number should produce the desired output, no? =)

On Tue, Jul 15, 2014 at 11:13 AM, Josh Warner <silvertr...@gmail.com javascript:> wrote:

...

I don’t believe sampling works like this. The sampling= kwarg tells the algorithm the spacing of each pixel/voxel in each spatial dimension. It then calculates the distance transform. So if you put a zero in, it will traverse in that dimension with a distance cost of zero for each step. This will generate a result that has mostly spurious zero distances, rather than select a particular axis to calculate the distance transform along.

To calculate the correct distance function along a specific axis probably requires repeated calls along that axis; since scipy.ndimage is by definition n-dimensional it should be able to efficiently handle rank-1 arrays without issue.

I use the super handy Chrome extension “Markdown Here” to write in Markdown and then have my free entry fields converted to pretty HTML prior to posting. At this point I pretty much think in Markdown…

Josh

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