I ran through my PyOhio presentation at tonight’s Clepy meeting.

I think I’ll spend more time talking about the material in the slides, rather than pausing just long enough to scan them with my eyes and move to the next. I’m anxious about boring people, so I think I go at a frenzied pace.

Also I need to learn how to tweak s5 (or at least rst2s5.py) so that I can have more control over how my content appears. A fair number of code samples had the last few lines truncated.

Anyway, I welcome comments on my presentation.

• reStructuredText file
• Slides
• Source code

It’s going to be a brain-melting conference with amazing swag. We got people you wouldn’t believe lined up to present. Google recruiters will be there with suitcases full of cash looking for new hires. Terminator robots will travel backwards in time to try to prevent all the amazing breakthroughs that will happen on this day. We’re gonna shake the foundations of science.

I already mentioned my topic but here it is again because I’m shameless.

Christian Dowski posts some uses for python’s enhanced generators. I tried to type a comment on that post, but I couldn’t figure out how to submit it successfully. Either comments are not allowed or I failed the CAPTCHA.

Anyhow, ever since I read about how common lisp handles exceptions, I’ve been daydreaming about how to do the same trick in python. In lisp, an exception jumps to some other place to get handled, just like in python. However, what is different is that the exception handler can repair the problem and then hand control back into the original block. For example, in the lisp toplevel, if you forget to define a variable before you try to copy its value to somebody else, the exception will propagate to the debugger. And in the debugger, you can then assign a value to that variable, and then resume your original program.

So that’s the background for my idea for generators. The generator that is trying the exception-raising code could yield the traceback to another generator when it hits an uncaught exception. Then the other generator, the exception-handling generator, could repair/log/do whatever, and then yield a value back to the original code.

For example, if the original code is iterating through a list of two-tuples, and for each two-tuple, it divides the first element by the second element, when it raises a ZeroDivisionError, it could catch that and yield it over to the exception handler. Then the exception handler could do whatever, like maybe prompt the programmer to choose a new denominator. After the programmer chose a new denominator, the exception handler could yield that back to the original generator and then the original generator could resume.

Lua calls generators that can receive values “coroutines” or “non-preemtible threads”. I think those are better labels because they hint that generators are way more than just iterators in drag.

I use defaultdicts a lot when I’m grouping elements into a dictionary of lists. Here’s a simple example:

>>> a = defaultdict(list)

>>> a
defaultdict(, {'y': ['yellow'], 'x': []})

Now here’s where I got silly. I used defaultdict.fromkeys to prepopulate the ‘x’ and ‘y’ key right away, because I know I needed those:

>>> b = defaultdict.fromkeys(['x', 'y'], list)

>>> b['z']
------------------------------------------------------------
Traceback (most recent call last):
File "", line 1, in
KeyError: 'z'

Wowsa! b calls itself a defaultdict, but it is not a defaultdict.

I haven’t really thought this through, but this behavior is so unexpected that I would prefer that defaultdict.fromkeys raised a NotImplementedError.