All, I'm slowly cleaning some code I had written a few months ago, porting SiZer to python. <educational break> SiZer, or Significant Zero crossing, is a graphical tool to estimate what features in a distribution are significant. It is based on gaussian kernel smoothing at different bandwidths: depending on the first derivative of the smooth, features are judged significant or not at a given confidence level. </educational break> The initial code was written in Matlab w/ pieces of Fortran, and I received the permission from the main author to port it to scipy and release it as BSD. The package introduces some gaussian-kernel smoothing functions, as well as some automatic bandwidth selectors. In a first step, I want to limit myself to 1D datasets, w/ gaussian kernels. * What has already been done in scipy concerning bandwith selectors ? There's a scipy.stats.kde, but the documentation is scarce... * What would be the best way to release the code ? Scikit ? Thanks a lot in advance for your inputs P.