I just found a good introductory video by the creator of the language, and if you’re curious whether this is a language you’re interested in playing with, I think it is a good place to start:

http://www.youtube.com/watch?v=zqFryHC018k

1. Open an application in full screen mode (e.g. Terminal)
2. Use three fingers on the trackpad and move slightly to the right or left so you are transitioning into the respective space (the ‘transition position’). Hold this position.
3. While maintaining the transition position, launch another application.  For example, use spotlight to launch TextEdit by pressing the apple key + space and search for TextEdit. Choose it, and press enter.
4. Still holding the transition position, wait until your application loads.
5. Let go of the transition position by lifting your three fingers.

I stumbled across this by accident while trying to open system preferences when in full screen mode as you can see in the attached picture.

Now your second screen is not a paperweight

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]

I was not too surprised, a little disappointed, and pretty excited to find out that this has been done.  The most comical creation of a Wikipedia book has to go to Rob Matthews for actually attempting to bind all the featured articles.  On a more serious front, I was happy to see that PediaPress, wiki-to-print publishing service, is partnered with the Wikimedia foundation to do exactly this.  It’s integrated quite well with Wikipedia so you can click on the articles you want to include in your own book or you can select books that have already been compiled by PediaPress.

As an alternative to purchasing a book, you can also generate a PDF that you can print at your own leisure.  My only gripe when immediately trying this is that scalable content Latex equations are embedded in the PDFs as if they are images.  It still looks decent, though, and is pretty cool!

The image to the right is a configuration at my friend’s house where my laptop (bottom) is controlling his dual-monitor computer setup (one regular monitor and an HD TV on top).

It works great as a remote and, yes, you can watch Hulu on it without being charged for watching Hulu on a TV device.

There are a lot of new internet TV solutions, but with just a little bit of extra work a very easy-to-control TV setup works that’s far more flexible than what’s on the market today.

That being said, I do own an Apple TV and what’s nice about these types of devices is that they are small and focus on a simple user interface to get access to content.

I recently created an application (I won’t go into the details now) that had a portion of it that could be significantly sped up if compiled as a C module.  This spawned a whole exploration into speeding up my Python code (ideally while making minimal modifications to it).

I created a C module directly, used the Shedskin compiler to compile my Python code into C++, and tried the JIT solutions PyPy and Unladen Swallow.  The time results for running the first few iteration for this application were surprising to me:

cpython:        59.174s
shedskin: 1m18.428s
c-stl:             12.515s
pypy:           10.316s
unladen:       44.050s
cython:         39.824

While this is not an exhaustive test, PyPy consistently beats a handwritten module using C++ and STL!  Moreover, PyPy required little modification to my source (itertools had some issues) [1].  I’m surprised that Uladen and Shedskin took so long (all code was compiled at O3 optimization and run on multiple systems to make sure the performance numbers were relatively consistent).

Apparently out-of-the-box solutions these days can offer nearly a 10x improvement over default Python for a particular app. and I wonder what aspects of PyPy’s system accounts for this large performance improvement (their JIT implementation?).

[1] Uladen required no modifications to my program to run properly and Shedskin required quite a few to get going.  Of course, creating a C-based version took a moment .

Update 1: Thanks for the comments below.  I added Cython, re-ran the analysis, and emailed off the source to those who were interested.

Update 2: The main meat of the code is a nested for loop that does string slicing and comparisons and it turns out that it’s in the slicing and comparisons that was the bottleneck for Shedskin.  The new numbers are below with a faster matching function for all tests (note that this kind of addition requires call ‘code twiddling’, where we find ourselves fiddling with a very straightforward, readable set of statements to gain efficiency).

cpython:       59.593s

shedskin0.6:   8.602s

shedskin0.7:   3.332s

c-stl:              1.423s

pypy:             8.947s

unladen:       29.163s

cython:         26.486s (3.5s after adding a few types)

So C comes out the winner here, but Shedskin and Cython are quite competitive.  PyPy’s JIT performance is impressive and I’ve been scrolling through some of the blog entries on their website to learn more about why this could be. Thanks to Mark (Shedskin) and Maciej (PyPy) for their comments in general and and to Mark for profling the various Shedskin versions himself and providing a matching function. It would be interesting to see if the developers of Unladen and Cython have some suggestions for improvement.

I also think it’s important not to look at this comparison as a ‘bake-off’ to see which one is better.  PyPy is doing some very different things than Shedskin, for example.  Which one you use at this point will likely be highly dependent on the application and your urge to create more optimized code.  I think in general hand-writing C code and code-twiddling it will almost always get faster results, but this comes at the cost of time and headache.  In the meanwhile, the folks behind these tools are making it more feasible to take our Python code and optimize it right out of the box.

Update 3: I also added (per request below ) just a few basic ‘cdef’s and types to my Cython version.  It does a lot better, getting about 3.5s on average per run!

While I still use C for the nitty-gritty stuff that needs to be fast, I’m finding that a lot of stuff can get done, and get done very fast in a scripting language.  Over a year ago I learned the wonders of Ruby (which, to me, is basically a superior replacement to Perl from a ‘it’s fun to code in’ perspective  and is easy to transition to from Perl).

But overwhelmingly, I’ve found myself enjoying and using Python.  The biggest selling point from my perspective is the high quality scientific computing and plotting support (which in many cases has replaced my use of R-project for these types of things).

Here are three little tidbits that I’ve recently found handy and take virtually no effort to begin using:

(1) First,to speed up the things that need the speed, calling C functions from Python is super-handy.  I really like the ctypes module because in many cases, as long as your C functions take the default types as inputs, you can simply expose your function via a dynamic library with no real extra effort.

(2) List comprehensions are a fun and very useful feature in Python.  But many times, you don’t want to create the whole list before iterating through it.  Here’s where generator expressions come in.  Instead of brackets around your comprehension, just put parens, and suddenly you can iterate over these elements without creating the entire list in advance (especially useful with a module like itertools).

(3) Finally, one thing that had bugged me a little in Python, especially since I’m used to C, is that there really is no real scanf equivalent [1].  What I end up doing is parsing out my variables from a string and on separate lines explicitly setting their type.  This just takes too many lines (and I could often do it in fewer lines in C)!   After some thought, a lab mate and I converged on:

>>> types = [float, str, int]

>>> fields = ['3.14159', 'Hello', 10]

>>> pi, hi, ten = [f(x) for f,x in zip(types,fields)]

[1] Note that while there is a suggestion in the Python docs on how to do this, it just suggests how to extract different types with regular expressions, not concisely convert them.

Well, this actually happens quite a bit, and after a recent lunch conversation, I was motivated to look more into the sources of “screencasts for papers” that I have watched in the past.

While I think many students and researchers are familiar with video lectures (such as MIT’s opencourseware or videolectures.net), one form of screencasts that focuses on research articles stood out to me as having technical enough content to properly motivate a full research paper: SciVee.

This website promotes YouTube-like screencasts of research papers and has coined the term “PubCast“.

I’m definitely all for this, so what’s the problem?

Thankfully, due in large part to the open access movement, I find myself visiting the journal sites themselves more and more for the content and supplementary information.  Especially for open access journals like PLoS, researchers have less of a reason to post a PDF version of their article on their web page.  Rather, why not just link directly to the open access content which makes the article visible in HTML and PDF?

While SciVee is partnered with PLoS (awesome), I noticed that the associated PLoS pages never link to the SciVee video (as far as I can tell).  Moreover, PLoS Bio and Comp Bio videos seem to stop existing on SciVee after around 2008 or so.

This is a shame, because even if there’s a lot of great video content out there associated with these papers, no one knows about if they visit the journal’s home page.  Why not just stick the video link here?:

I think the basic idea is out there and there are some early-adopters of this technique (not necessarily limited to SciVee’s services, of course).  What I think it needs is better marketing and accessibility from journal websites.  Intuitively, it seems like well-done screencasts promoted by the journals themselves (perhaps even made a part of the editorial process) could really be good for getting the journal and its papers more attention.

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