You don’t like FX-only movies, right? You can guess the plot from the trailer, and it then falls apart from the very first minutes – you just find it fairly hard to believe, without first turning off your brain – either willfully, if you are well-trained in mental/djedi techniques, or with the help of beer/other alcohol. Then, all you get are the special effects thrown in your face in the post-processing-added, ugly and eye-hurting 3D. There are a few “wow” moments, but that’s it – you leave the cinema with the mixed feelings of emptiness, lost time and money, and disappointment.

If that sounds familiar – go watch Pacific Rim. Go with the above-described expectations. Do watch the HD trailer. Try not to read anything revealing the plot – that could be a serious spoiler. Just one hint: you may want to sit through the 3D credits (about 2 minutes) for one final movie scene. I’ve heard there’s also something after the non-3D credits, but wasn’t patient enough to verify that.

Pacific Rim leaves a pleasant double-sided impression. Read the rest of this entry »

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.

MultiParanoid

Here we present a new proteome-scale analysis program called MultiParanoid that can automatically find orthology relationships between proteins in multiple proteomes. The software is an extension of the InParanoid program that identifies orthologs and inparalogs in pairwise proteome comparisons. MultiParanoid applies a clustering algorithm to merge multiple pairwise ortholog groups from InParanoid into multi-species ortholog groups.

QuickParanoid

QuickParanoid is a suite of programs for automatic ortholog clustering and analysis. It takes as input a collection of files produced by InParanoid and finds ortholog clusters among multiple species. For a given dataset, QuickParanoid first preprocesses each InParanoid output file and then computes ortholog clusters. It also provides a couple of programs qa1 and qa2 for analyzing the result of ortholog clustering.

So… both use InParanoid… Are there any differences? Let me list those which I’ve found.

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LinuxAteMyRAM (also as a PDF: Linux disk caching effects)

To examine the behavior of your Linux box disk caching under specific loads, see Linux write cache mystery (PDF).

To understand what is going on, see also The Linux Page Cache and pdflush (PDF) by the same author, Gregory Smith.

Another useful resource is OpenSUSE’s Tuning the Memory Management Subsystem, which nicely explains some of the kernel cache/memory-related configuration options.

On Monday, the 24th of June 2013, at 05:15 in the morning we (me and my bicycle-maniac coworker) started from Dudweiler, Saarbrücken towards Baden-Baden – on the bicycles.

We thoroughly planned this trip:
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