Firefox: TINA

Becoming increasingly infuriated by firefox eating up a disproportionate amount of my computer’s time. Alternatives, though, seem limited:

– Chromium: best of the alternatives, but has largely-dysfunctional text searching.

– Opera: still around, still not very good on a small screen

– flock: built on firefox, but with more stuff on top of it

– galeon: not even installable in ubuntu, for some reason

– uzbl: nice idea, gaping usability/discoverability problems

..and so I return grudgingly to firefox
:(

Untitled

I doubt very much _why is a Randian, but something in his self-annihilation reminds me of The Fountainhead. There’s something scary about the vigorous assertion that your creations are yours to destroy — something that’s uncomfortably on the border of right and wrong, in an intense and personal way.

Untitled

JSFiddle is a useful tool for editing javascript in a browser window. Raphael puts pretty pictures there. together, they make for a pretty decent learning interface.

Saving firefox

further to earlier grumbling about firefox, it seems the main culprit is the restore-session facility. This is something I hated anyway, even without realising that it was shutting down my hdd every 10 seconds to churn through all my tabs. Solution: go to about:config. browser.sessionstore.interval controls how often firefox stores its tab data. The default is 10 seconds; setting it to a long string of nines has sped up my computer no end.

and so, firefox is saved for another day.

also of note: the vimkeys plugin, providing j/k scrolling.

Webmontag 19.7.10

At Web Monday. Presentations:

  • First Trimester, blogging for doctors. Apparently while there are a lot of web projects targetting patients, there aren’t many blogs aimed at providing professional information for doctors.
  • Yourcent, a micropayments system
  • Feed Magazine a free german-language (paper) magazine about the online world. Now at issue 0

Nested dictionaries in python

Python’s defaultdict is perfect for making nested dictionaries — especially useful if you’re doing any kind of work with json or nosql. It provides a dict which returns a default value when a key isn’t found. Set that default value an empty dict, and you have a convenient dict of dicts:


>>> from collections import defaultdict
>>> foo = defaultdict(dict)
>>> foo['x']
{}

But it breaks down when you go more than one layer deep:


>>> foo['x']['y']
Traceback (most recent call last):
File "", line 1, in 
KeyError: 'y'

You can get another layer by passing in a defaultdict of dicts as the default:


>>> bar = defaultdict(lambda: defaultdict(dict))
>>> bar['x']['y']
{}

But suppose you want deeply-nesting dictionaries. This means you can refer as deeply into the hierarchy as you want, without needing to check whether the intermediate dictionaries have already been created. You do need to be sure that intervening levels aren’t anything other than a recursive defaultdict, mind. But if you know you’re going to have your content filed away inside, say, quadruple-nested dicts, this isn’t necessarily a problem.

One approach would be to extend the method above, with lambdas inside lambdas:


>>> baz = defaultdict(lambda: defaultdict(lambda:defaultdict(dict)))
>>> baz[1][2][3]
{}
>>> baz[1][2][3][4]
Traceback (most recent call last):
File "", line 1, in 
KeyError: 4
>>>

It’s marginally more readable if we use partial rather than lambda:


>>> thud = defaultdict(partial(defaultdict, partial(defaultdict, dict)))
>>> thud[1][2][3]
{}

But still pretty ugly, and non-extending. Want infinite nesting instead? You can do it with a recursive function:


>>> def infinite_defaultdict():
...     return defaultdict(infinite_defaultdict)
...
>>> spam = infinite_defaultdict() #defaultdict(infinite_defaultdict) is equivalent
>>> spam['x']['y']['z']['l']['m']
defaultdict(<function infinite_defaultdict at 0x7fe4fb0c9de8>, {})

This works fine. The __repr__ output is annoyingly convoluted, though:



>>> spam = infinite_defaultdict()
>>> spam['x']['y']['z']['l']['m']
defaultdict(, {})
>>> spam
defaultdict(<function infinite_defaultdict at 0x7fe4fb0c9de8>, {'x':
defaultdict(<function infinite_defaultdict at 0x7fe4fb0c9de8>, {'y':
defaultdict(<function infinite_defaultdict at 0x7fe4fb0c9de8>, {'z':
defaultdict(<function infinite_defaultdict at 0x7fe4fb0c9de8>, {'l':
defaultdict(<function infinite_defaultdict at 0x7fe4fb0c9de8>, {'m':
defaultdict(<function infinite_defaultdict at 0x7fe4fb0c9de8>, {})})})})})})




A cleaner way of achieving the same effect is to ignore defaultdict entirely, and make a direct subclass of dict. This is based on Peter Norvig’s original implementation of defaultdict:


>>> class NestedDict(dict):
...     def __getitem__(self, key):
...         if key in self: return self.get(key)
...         return self.setdefault(key, NestedDict())
>>> eggs = NestedDict()
>>> eggs[1][2][3][4][5]
{}
>>> eggs
{1: {2: {3: {4: {5: {}}}}}}

Kyrgz miltias

Not a good sign:

Amid the early April tumult that brought down former President Kurmanbek Bakiyev’s administration, young men in Bishkek and other cities began forming druzhiniki groups to patrol the streets and restore order. These groups were originally envisioned as a temporary solution to security challenges. But in the ongoing unrest that has plagued Kyrgyzstan since April, militia groups have kept on amassing influence. [Eurasianet

Temperature and clothing: a little project I’ll never find time for

I hate hot weather passionately. Or more accurately, lethargically: when the temperature goes above 25, I find myself unable to concentrate on anything.

But now I find myself wondering: what makes people dress down in the heat? Do they choose their clothing based on today’s weather, yesterday’s weather, or some combination of the two?

Fortunately, we have the data and technology to answer this. I’m not going to

implement

it (see: lethargy). But here’s what I would do, if I had the time/energy — and perhaps I will when autumn comes and I start to wake up.

Skin-detection algorithms already exist. This is the only freely-available code I could find for the purpose; I haven’t tested it.

You’d also need a source of images, tagged by date and location. Flickr will probably give you that, if you choose the right tags to narrow it down to full-body portraits of people. You can get weather information from The US National Weather Service, although it’s not clear what historical data is available. Failing that, you could limit photos to a particular group of dates/locations, for which you manually look up the historical weather. Then just assemble the data, and run some regressions.

Oxford comma

“For my parents, God and Damien Katz.”

— Noah Slater’s dedicaiton, in the O’Reilly couchdb book

git pull –rebase

Useful post on git by Yehuda Katz. inter alia, strongly suggests using the

--rebase

flag when merging