def counter():
    a = 0
    def inc():
        nonlocal a
        a+=1
        return a
    return inc
 
c = counter()
print(c())
print(c())
print(c())
 
print(c.__closure__[0].cell_contents)

I’d guess the locations of cells in the closure tuple are determined at compile time (i.e. they won’t change between runs), but I haven’t investigated this.

But maybe there’s another way.  This article is mostly focussed on relaxing the durability constraints of postgres but also mentions a possible mixing of relational and non-relational systems.  What a cool idea!  Use the somewhat batch-oriented, distributed, and non-relational system as a large backing store, and populate a traditional relational database (almost) on demand by transforming and moving (ETLing) the data there.  With the right transforms, we get all the ad-hoc queries of our beloved star schema data warehouses without the expense of a big-honkin’ database server.  Certainly these ‘transforms’ are the tricky part (and slicing the data out of the non-relational store along a particular dimension (probably time) will almost always be necessary), but the gains seems pretty great.  Maybe we’ll call this approach a “pattern” someday.

Isn’t big data exciting?

(+ 1 2)

And converts it into this:

[Symbol(+), Number(1), Number(2)]

A pretty-printed python list consisting of objects representing symbols and numbers.

I think the design is fairly readable if a bit unusual. There are classes for each token (instances of which are used for fully and partially formed tokens) and we “add” these tokens together to form a larger token and ultimately invoke a next_token_exception allowing the lexer to yield the largest, greediest, most fully-formed token possible.

Here’s an example of the lexer in action:

>>> symbol_token("h")
<parse_sexp.symbol_token object at 0x7fb9e2d253d0>
>>> _ + symbol_token("i")
<parse_sexp.symbol_token object at 0x7fb9e2d25710>
>>> formed = _
>>> formed + " "
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "parse_sexp.py", line 33, in __add__
    raise next_token_exception()
parse_sexp.next_token_exception
>>> formed.value
'hi'

After lexing, we’re left with a stream of fully-formed tokens; it’s a simple matter to filter out the whitespace tokens and start yielding instances of Symbol() and Number() classes with proper, pythonic types for the data.

Although I used regexes (quite unnecessarily) and generators, this little thing could probably be ported to C. It’d be the fastest, most useless parser/lexer around

Happy Hacking!

I rode alone with dreams of solving my problems and divining my future in sunbaked introspection.  I carried all my gear (too much of it), camped 4 nights (hotel-camped 2 nights ) and navigated my poorly planned route with two guide books and my G1.

Riding alone has its perks.  Plenty of time to think, and plenty of time to think about getting lost.  Fortunately my navigating skills seemed to improve after the second day (I nearly burst into tears that day after the GPS put my location in the middle of a lake, not, as I expected, between the Hennepin and I&M Canals).

Camping alone is different.  There are very few perks.  All my nights were fairly restless, and the one night I didn’t have to hiss at raccoons to shoo them away, a large storm came through at 2:00am.  My tent held out, though, and I can sleep when I’m dead.  But still, I’ve no shame now admitting to being afraid of the dark.

`

Being alone in the middle of Illinois is an odd thing.  It’s possible to ride for hours without seeing another human, but at no point are you truly in the middle of nowhere.  Everything has the imprint of humanity.  The road itself is a man-made scar through the land, but moreover, the land is far from natural.  Almost every inch is cultivated, pruned, or tilled for our use.  I suppose this is the trade-off  of a civilized society (possibly the definition of it), but it still makes me glad there are areas we have set aside for the exclusive non-use by humans.

In general the trip is somewhat hazy.  I probably rode too fast and did not stop at enough towns to really soak everything in.  But still I find myself thinking of very specific things about the ride, whether it be pushing my bike up a giant hill or resting for a bit on a bench.  I will do this again.  Someday I will find the time to ride farther west or maybe even ride the entire American Discovery Trail.

class LazyDictionary(object):
 
    def __init__(self, callback=dict):
        self.data = None
        self.callback = callback
 
    def evaluate_callback(self):
        self.data = self.callback()
 
    def __getitem__(self, name):
        if(self.data is None):
            self.evaluate_callback()
        return self.data.__getitem__(name)
 
    def __setitem__(self, name, value):
        if(self.data is None):
            self.evaluate_callback()
        return self.data.__setitem__(name, value)
 
    def __getattr__(self, name):
        if(self.data is None):
            self.evaluate_callback()
        return getattr(self.data, name)

The real version I started to use (and then threw out because it was not needed) inherited from a dict instead of using delegation. At the time I thought this was necessary to get it to play nice with the Django templating system (I may have been wrong about this). Anyways, I like this version much better, and I view inheritance for the composition of behavior with growing skepticism each day.

I also have unittests for this but they made this post unappealing long (I should really start using that github gist thingie. Is there a mercurial equivalent? (also I fixed my blog comments)).

I created this image using matplotlib and the data from my CPS Racial Data Warehouse project.  Circle size represents school population.  The axes are latitude and longitude (as a reference, the loop is the empty rectangle just below the left corner of the legend).  Also I should note the Native American population was excluded from this image because of a technical issue.

Full size image here, more on this project soon (in the meantime, the code is here).

1. Words without punctuation, numbers or accents (i.e. only a-z in regex).
2. Longer than four characters.
3. Consisting of three or more distinct characters.
4. Not in a set of stop words I decided I was not interested in.

The astute reader will notice that these rules actual mean all valid palindromes will be 5 or more characters. Now for the results:

“Level” is the clear winner with 662 occurrences. The venerable palindrome “racecar” sped into the #30 spot with a mere 8 occurrences.

Unfortunately I did not keep track of how many tweets I scanned, but it appears a very small percentage of tweets contain this type of palindrome.  I would like to convert this to a full-on palindrome tracking twitter bot that will annoyingly tweet at you if you tweet something palindrome-ish, but I am slightly afraid someone would take that code and make streaming-twitter-pesterbot-v1™.  Hunting for palindromic sentences would be fun, too (a palindromic 144 character tweet would be pure genius).  Most importantly, I’d like to search for sarahpalindromes which I’ve defined as words that become palindromes with one transposition (sarahpalindrome detection algorithms are much appreciated…you could probably get a paper out of something like that).