This is a quick tutorial over the basics of what metaclasses do.

The Metaclass

Metaclasses, while seemingly a complex topic, really just do something very simple. They control what happens when you have code that turns into a class object. The normal place they are executed is right after the class statement. Let’s see that in action by using print as our metaclass.

I haven’t seen a great deal of practical documentation about using classmethods as factories in Python (which is arguably the most important use of a classmethod, IMO). This post hopes to fill in that gap.

I recently decided to try my hand at making an auto-load extension for Python and Plumbum. I was planning to suggest it as a new feature, then I thought it might be an experimental feature, and now it’s just a blog post. But it was an interesting idea and didn’t seem to be well documented process on the web. So, here it is.

The plan was to make commands like this:

Wouldn’t it be nice if we had uncertainty with a nice notation in IPython? The current method would be to use raw Python,

from uncertainties import ufloat

print(ufloat(12.34, 0.01))

12.340+/-0.010

Let’s use the infix library to make the notation easier. We’ll define |pm| to mean +/-.

I’ve been working on a color addition to Plumbum for a little while, and I’d like to share the basics of using it with you now. This library was originally built around a special str subclass, but now is built on the new Styles representation and is far more powerful than the first implementation. It safely does nothing if you do not have a color-compatible systems (posix + tty currently), but can be forced if need be. It is included with Plumbum, so you don’t have to add a requirement for your scripts that is non-essential (as color often is). It is integrated with plumbum.cli, too. Also, I’ve managed to accelerate the color selection algorithms about 8x, allowing near game-like speeds. (see the fullcolor.py example).

Scripting in Bash is a pain. Bash can do almost anything, and is unbeatable for small scripts, but it struggles when scaling up to doing anything close to a real world scripting problem. Python is a natural choice, especially for the scientist who already is using it for analysis. But, it’s much harder to do basic tasks in Python. So you are left with scripts starting out as Bash scripts, and then becoming a mess, then being (usually poorly) ported to Python, or even worse, being run by a Python script. I’ve seen countless Python scripts that run Bash scripts that run real programs. I’ve even written one or two. It’s not pretty.

I recently came (back) across a really powerful library for doing efficient command line scripts in Python. It contains a set of tools that makes the four (five with color) main tasks of command line scripts simple and powerful. I will also go over the one main drawback of the library (and the possible enhancement!).