Hi St�fan,

I understand the challenges of running on an older platform, but I also wonder: if a user can recompile scikits-image, what's stopping them from also building the latest (stable) numpy?

Well, it's just one more pain in the neck to build another package. And if you have to build numpy, (I think) you also have to build your own matplotlib, scipy, etc. Which is even a greater pain.

What's more, a large fraction of users want to use the packages installed by their IT managers, who like to have one standard version of all packages. People using Python(x,y) on Windows, for example, would have a hard time building all the packages. I think the example given by J�r�me is a good one: he works in a big institute where people might be interested in using skimage, but users typically don't install their own packages.

That said, we can of course tell people to install an older version skimage. However, I have the impression that an important part of the added value of new releases are in the algorithms/functions that the release provides, rather than in using newer numpy functionalities. This is by all means a biased point of view, since I've been more busy with the former point.

I certainly have the expectation that software should run with the available stable versions of dependencies on the date of release. That said, any benefit we can get with little effort is worth it, and there's no reason to purposefully make it harder for users than need be. So please, keep the PRs coming for backwards compatibility, as long as they are not too disruptive.

OK, I'll try to :-D.

Cheers, Emmanuelle