Randal Schwartz recently gave a talk called “Dynamic Returns” dealing with misconceptions about dynamically typed languages (published as episode 135 of the Industry Misinterpretations podcast). His audience for the talk is composed of Smalltalk developers, but the points made are general and equally valid for other dynamic languages like IDL, Python, etc (at least in concept). The six myths are that statically typed languages like Java, C++, and C:

1. reduce development cost
2. increase speed of development
3. eliminate need for some tests
4. improve run-time stability
5. scale better
6. are faster

I agree that the first five points are indeed myths, but I’m not sure IDL has the tools for making IDL as fast or faster than a statically typed language. I know Python has a lot more tools in this area: Pyrex, Psyco, ctypes, f2py, and a bunch more. Of course, there are some things in IDL’s favor: the thread pool automatically uses multiple processors for array operations, there are libraries to make GPU computing and cluster computing easier, and a fairly straight-forward way to extend IDL using C when needed.

The Google Code project Mail Trends visualizes email in any IMAP mailbox (including Gmail if you have set it up for IMAP). It uses a Python interface to the Google Charts API to produce charts of sent and received email organized by time, sender, threads, mailing list, etc.

Link via Information Aesthetics.

I got back from PyCon about a week ago. This was the first software conference I have gone to (I haven’t gone to a conference since attending math conferences in grad school). I have been splitting my time fairly equally between IDL and Python recently and the conference really helped me feel more comfortable in Python and the Python scientific community. The conference was more about the Python community than Python itself, but I still got a lot of Python tips out of it.

Continue reading “PyCon 2007.”