Peekaboo

Last week I was at Pycon DE , the German Python conference. After hacking on scikit-learn a lot last week, I decided to to something different on my way back, that I had planned for quite a while: doing a wordl -like word cloud . I know, word clouds are a bit out of style but I kind of like them any way. My motivation to think about word clouds was that I thought these could be combined with topic-models to give somewhat more interesting visualizations. So I looked around to find a nice open-source implementation of word-clouds ... only to find none. (This has been a while, maybe it has changed since). While I was bored in the train last week, I came up with this code . A little today-themed taste:

I started working with spanning trees for euclidean distance graphs today. The first think I obviously needed to do was compute the spanning tree. There are MST algorithms in Python, for example in pygraph and networkx . These use their native graph formats, though, which would have meant I'd have to construct a graph from my point set. I didn't see a way on how to do this and set the edge weights without iterating over all edges. That would probably take longer than the computation of the MST, so I decided to do my own small implementation using numpy. This is an instantiation of Prim's algorithm based on numpy matrices. The input is a dense matrix of distances, the output a list of edges. It is not as pretty as I would have hoped but still reasonably short. If any one has suggestions how to make this prettier, I'd love that. [edit] Using line_profiler I had a quick look a the code and made some minor improvements. It's now significantly faster than net...