Posts

Several times it’s come up that I’ve needed to visualize a time sequence for a collection of events across multiple sources. Unlike a normal time series, events don’t necessarily have a magnitude, e.g. a stock market series is a graph with a time and a price. Events simply have times, and possibly types. A one dimensional number line is still interesting in this case, because the frequency or density of events reveal patterns that might not easily be analyzed with non-visual methods. Moreover, if you have multiple sources, overlaying a timeline on each can show which is busier, when and possibly also demonstrate some effect or causality. ...

Applications like Git or Django’s management utility provide a rich interaction between a software library and their users by exposing many subcommands from a single root command. This style of what is essentially better argument parsing simplifies the user experience by only forcing them to remember one primary command, and allows the exploration of the utility hierarchy by using --help and other visibility mechanisms. Moreover, it allows the utility writer to decouple different commands or actions from each other. ...

I have a minor issue with freezing requirements, and so I put together a very complex solution. One that is documented here. Not 100% sure why this week is all about packaging, but there you go. First up, what is a requirement file? Basically they are a list of items that can be installed with pip using the following command: $ pip install -r requirements.txt The file therefore mostly serves as a list of arguments to the pip install command. The requirements file itself has a very specific format and can be created by hand, but generally the pip freeze command is used to dump out the requirements as follows: ...

Package deployment is something that is so completely necessary, but such a pain in the butt that I avoid it a little bit. However to reuse code in Python and to do awesome things like pip install mycode, you need to package it up and stick it on to PyPI (pronounced /pīˈpēˈī/ according to one site I read, though I still prefer /pīˈpī/). This process should be easy, but it’s detail oriented and there are only two good walk throughs (see links below). ...

The topic of the day is a simple one: JSON serialization. Here is my question, if you have a data structure like this: import json import datetime data = { "now": datetime.datetime.now(), "range": xrange(42), } Why can’t you do something as simple as: print json.dumps(data)? These are simple Python datetypes from the standard library. Granted serializing a datetime might have some complications, but JSON does have a datetime specification. Moreover, a generator is just an iterable, which can be put into memory as a list, which is exactly the kind of thing that JSON likes to serialize. It feels like this should just work. Luckily, there is a solution to the problem as shown in the Gist below: ...

Programming with databases is a fact of life for any seasoned programmer (read, “worth their salt”). From embedded databases like SQLite and LevelDB to server databases like PostgreSQL, data management is a fundamental part of any significant project. The first thing I should say here is skip the ORM and learn SQL. SQL is such a powerful tool to query and manage a database, and is far more performant thanks to 40 years of research and development. ...

If you’ve pair programmed with me, you might have seen me type something to the following effect on my terminal, particularly if I have just created a new file: $ codetime Then somehow I can magically paste a formatted timestamp into the file! Well it’s not a mystery, in fact, it’s just a simple alias: alias codetime="clock.py code | pbcopy" Oh, well that’s easy — why the blog post? Hey, what’s clock.py? A great question! This Python script is the dumbest thing that I have ever written, that has become the most useful tool that I use on a daily basis. Whenever there is a dumb to useful ratio like that, it’s blogging time. Here is clock.py: ...