Autarchy of the Private Cave

I’ve tried [briefly] Cantor (which also supports Octave and KAlgebra as backends), rkward, deducer/JGR, R Commander, and RStudio.

My personal choice was RStudio: it is good-looking, intuitive, easy-to-use, while powerful.

Next step would be using some R-equivalent of the excellent ipython’s Mathematica-like Notebook webinterface…

Sooner or later, everyone has to deal with graphs. Some people have to do programming with graphs, and a subset of those – do that in Python.

NetworkX is a pure Python implementation, where anything can be nodes. Both nodes and edges have attributes. NetworkX supports directed graphs and multigraphs (where there are multiple edges between nodes). It might be slower than other implementations, but you may even not notice that – especially when working with smaller graphs, or not applying computationally-intensive algorithms to your graphs.

graph-tool uses the Boost graph library (C++), so it should be really fast. It could be the only multi-threaded graph library for Python. It supports pickling the graphs, allows interactive graph drawing, and has well-illustrated documentation. If performance and efficiency are of utmost importance – could be the best choice.

igraph is also really fast – just like graph-tool when using 1 CPU; graph-tool only wins conclusively when it is run on multiple CPUs/cores. igraph has an R package bindings to C.

Pure python is also an option for really smaller cases.

Finally, there’s a discussion around Python Graph API to simplify the inter-changeability and inter-operability of various existing Python graph modules. It also has a list of some less-known Python graph libraries, so check it out.

beanstalkd: a simple, fast work queue.
Jack and the Beanstalkd: a web-app for basic work queue administration.
beanstalkc: a simple beanstalkd client library for Python.
queueit: a CLI interface tool which helps to integrate beanstalkd into shell scripts.

Sometimes there is a need to be sure that no identifier is processed twice – for example, when parsing a file into a database, with file potentially containing duplicate records. An obvious solution is to properly wrap the DB insertion code into try…except block, and process duplicate primary ID exceptions. Another, sometimes more desired solution is to maintain a set/list of processed IDs internally, and check against that list prior to attempting the insertion of anything. So is it a set or a list?

There are already quite a few internet resources discussing “python set vs list”, but probably the simplest while elegant way to test that is below.
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If you are a Python zealot, and Java doesn’t feel right, but the project you are working on is a Java project – try

• Jython – Python for the Java platform, compile your python scripts into Java bytecode
• Groovy – not Python, but still a scripting language which compiles to jars

This overview presentation is two years old, but still a highly valuable resource: modules and tools mentioned are alive and useful.
I think this is the second presentation by Giovanni I’m embedding (first one being about GNU/make for bioinformatics).

Imagine you need to install pycassa (which uses easy_install). Here are the 2 (at maximum) very simple steps to have it properly debianized and installed on your Debian/Ubuntu:

• if you don’t have the python-stdeb package: sudo aptitude install python-stdeb
• pypi-install pycassa

That’s it.

Refer to stdeb readme for more information. You will need that if there are dependencies – which might not be resolved automatically by stdeb.

Before stdeb, it wasn’t exactly trivial to make a .deb from python module.