Even though I have always seemed to fall back to Vim in a terminal for my coding, I have been using Atom (with SSHFS) for some time now and for reasons I don’t quite understand yet, I’ve not really felt the urge to stop. My initial impressions were that Atom is a Sublime knock-off with some more flexibility, so I expected after a short time of playing with my invite, to go back to my tried and true Vim since that’s what I did with Sublime.  But here I am, and I’m more convinced to stick with it some more now that it has been open sourced.  The ‘gq’ wrap lines feature was one I sorely missed, so I thought I’d try to script it.

I was surprised at how easy and quick it was to code a package for it, and to subsequently make available to everyone else through their package management system (the image above takes you to the package).     On a slightly related note, my friend Rob pointed me to this interesting Vim modernization project.

More specifically, he suggests some of the ‘thought’ before coding should be formalized as a specification (like a blueprint) written in the language of mathematics.   The TLA specification language uses satisfiability of boolean formulas as a mathematical way to express initial states and the possibilities for a state to transition to other states.   The framework provides the programmer a way to  (1) think in depth about the possible states before coding and running test cases, (2) better document what an implemented piece of code does making it more convenient for updates later, and (3) automatically find bugs from logical inconsistencies.

This video provides more motivation and examples for what I said above, including a couple of anecdotes that mentioning the use of TLA by Amazon and Microsoft.  Overall, I think he made a good argument that putting more effort in the specification can lead to better design decisions down the line.

A couple of notes:

(1) This approach is mostly about ‘functional’ specification, so it provides little assistance in implementing such a specification. In this way, it is largely independent of programming language choice/design, compilation, and optimization in practice.  Many specific algorithms could satisfy a functional specification. In his talk, I got the impression that these other issues were trivial in comparison to specification, which I don’t think is often the case.  However, I certainly agree that more thought should be spent at the specification stage, if possible.

(2) Large-scale adoption of this formalism seems like it would be difficult.  There is the obvious overhead in teaching people to understand and implement this formalism in their work.  Even if knowledge of TLA was part of a programmer’s toolkit, this type of formalism may be far more beneficial for specific, more ‘predictable’ problems.  By predictable, I mean that a programmer (or computer) can consider the possibilities of desirable and undesirable states a priori.  For example, the task “write a program to produce a pleasing sound” is not only a larger/more general problem, but it also very difficult to specify it exactly.  This is an extreme example, but less extreme versions of it are a very common and practical form of programming (e.g. programs that are highly dependent on human satisfaction during use).  For these applications, past intuition, seeing how you, a test group of people, and even your customers respond to your program may provide the most valuable information for improvement.

Leslie Lamport is a computer scientist working at Microsoft Research.  He recently won the Turing Award (one of the highest honors in computer science), and ever since I learned about Lamport clocks, he has been one of my favorite CS academics.

Want an individual color from a color map in matplotlib?  You would think this would be easy (and it is as you will see below), but as I traversed the documentation, I could not find for the life of me how to do this.  After looking at various matplotlib functions, Rob and I finally found that it’s really simple (and apparently undocumented as far as we can tell).  Just choose your colormap (e.g. ‘jet’) and call it like a function with a value between 0 and 1: e.g. pylab.cm.jet(.5).  This will return you the rgba 4-tuple that you can use for whatever.

I used to use R all the time until I got into Python, NumPy, and SciPy.

In general, however, R has much more sophisticated packages for statistics (I don’t think there’s too much argument here).  Unfortunately, R, in my opinion, is a cumbersome language for string manipulations or more general programming (though it is fairly capable).

You can get the best of both worlds with RPy.

I recently wanted to calculate the rank of a matrix via a QR decomposition as an alternative to computing the SVD.  I haven’t yet seen code to do this in numpy.  R, on the other hand, computes this automatically in its ‘qr’ function.  To get this running in my numpy/scipy code, I didn’t even have to worry much about matrix format conversions between the languages.   After a couple of ‘imports’ and within 5 minutes I calculated the rank in Python via the R code:

rqr = robjects.r[‘qr’]
print rqr(R)[1][0]

Mendeley is pretty cool.

I’ve been using it for a week or so and it’s definitely integrated itself well without being obtrusive.

Some things I really like:

• Cross-platform: OS  X, Linux, Windows
• Drag-and-drop your PDFs and it figures out the metadata as best as it can.  If it gets something slightly off, you press a button and a Google scholar check usually fixes it all up
• PDF viewing is quite speedy and natural for me with their tabbed interface
• Notes, highlights, etc: syncs with the cloud and can be integrated with other users that you share your documents with
• I find storing associated URLs to be a very handy feature (links to the web site, supplementary info, etc.)
• Cleanly store your PDFs in a directory of your choice (e.g. I use Dropbox to view them on my iPad)

Requests (maybe I can request some of these?):

• iPad app (supposedly on the way)*
• Sometimes highlighting/selecting text isn’t as accurate and intuitive as it is in OS X’s Preview application, which does selection brilliantly
• Parse PDFs for their citations and do *cool* things with this.  What can you tell me about papers I may be interested in by looking at the citation network of my collection?
• If Mendeley is like iTunes for music, it would be nice to have a slick storefront to browse related papers of interest and import.

* Related posts