For those who use Snow Leopard you have probably noticed how much snappier the OS is than its predecessor. Much of the optimizations in Snow Leopard is thanks to several new and emerging tools called LLVM, Clang, and OpenCL. What’s more is that we can use these tools to speed up some of our basic command line executable files and our own code (if it’s written in C, C++, and Python). This means we can see speed improvements for our coding projects.

LLVM is the next stage in the evolutionary chain of C and C++ compilers. It can compile about any bit of code that is currently compiled by GCC now. For gutsy details I suggest reading the overview section on the LLVM website. One caveat: LLVM has the ability to do array optimization, but it is turned off by default by the LLVM and Apple group. If you plan to compile code that depends on auto arrays I’d suggest using GNU compilers at the moment.

Clang is front-end to LLVM. It can only compile C code and is much faster than GCC and LLVM. On top of that it optimizes the compiled code even more than LLVM. It has better diagnostics to why a compiling job has failed. Almost as good as Python’s Traceback! If you can use Clang you’re golden.

Then there’s OpenCL. Ever heard of CUDA? It’s like CUDA gone agnostic and can work with both NVIDIA and ATI graphics cards for scientific parallel processing.  Like CUDA, OpenCL can work with a cluster of GPU. It can also harness multiple CPU as well. Imagine writing a program that can harness your graphics card for small jobs. If a super computer were ever needed and it supported OpenCL, your code would only require almost no change and it could harness all the CPU cores.

If you read this far, then I think you may be convinced that using these tools is necessary for your work. Documentation on how to setup your Mac to use these tools is still sparse on the web, so here’s some instructions on how to link your environment.

Linking to Apple Developer Bin and Lib

In  your .bash_profile put in the following to use LLVM, Clang, and OpenCL.

export PATH=/Developer/usr/bin/:$PATH
export PATH=/Developer/usr/lib/:$PATH
export PATH=/Developer/usr/libexec/:$PATH

Using LLVM or Clang

Add the following text to your .bash_profile to use LLVM and Clang for compiling code when you use Make.

export CC=’clang -w’
export CXX=’llvm-g++

OpenCL

OpenCL is a completely different beast from LLVM and Clang. At this point it’s learning how to program C code using OpenCL. Since this would take quite a few more lines to document, follow this link at MacResearch to learn more about it and how to use it. For Python users there is a module called PyOpenCL, written by Andreas Klockner, that harnesses OpenCL’s power.

Enjoy!

Folks have been clamoring for a Papers vs. BibDesk post and here it is, finally. Since I don’t actually use Papers, this post is more of a listing of my reasons for sticking with BibDesk for the time being. This topic has been discussed elsewhere and is sure to be visited many times in the future on AstroBetter, so consider this post just one part of a much bigger discussion.

There are different things to look for in an article management tool. I want my app to be as one-stop shopping as possible: I want to be able to search, import, organize, assign BibTeX cite keys, and annotate all in one place. BibDesk does most of those things while, as far as I can tell, Papers really only excels at searching and importing. Papers gurus, I’m counting on you to speak up in the comments if I’ve missed something. Below, I’ve listed the main functionalities, in rough order of importance to me, and which application I think handles them best.

Keywords/Tags: Bibdesk

Keywords play a starring role in BibDesk, occupying the left column in the main page while in Papers they are hidden in Notes. With BibDesk I can highlight multiple articles and quickly specify if I only want articles with ALL of those keywords, or papers with ANY of the selected keywords. This ability to easily tag articles with multiple keywords and then search based on them is a huge advantage of BibDesk over Papers but I think Papers will catch up soon.

PDF Annotations: Bibdesk + Skim

Annotations added in the PDF reader app Skim show up as Notes in BibDesk so you don’t even have to open the PDF to see the text you’ve highlighted or underlined. (Skim vs. Acrobat vs. Preview is a can of worms that I choose to leave for another day.) Improved annotation functionality and integration with Skim is likely for future versions of Papers.

Search and Import: Papers

Papers auto-magically imports articles and metadata and downloads the PDF while importing an article into BibDesk is (usually) a multistep process. The way I do it is browse to the article’s ADS entry, scroll down, clink on “Bibtex entry for this abstract”, highlight the bibtex, switch to BibDesk, use alt-command-L to add a new publication from the clipboard. BibDesk does have a Papers-like single-click import for articles on arxiv.org, but not for ADS. Looks like this ADS to BibDesk app is on the right track, but it only works with Safari, not Firefox. Getting this functionality built into BibDesk would rock.

Cite Keys: BibDesk

BibTex + AstroNat is what you should be using to include references in LaTeX articles prepared with AASTeX. Again, a full discussion of BibTeX is left for another day, but suffice it to say, being able to assign memorable cite keys to articles is important. To be honest, I’m not quite sure how Papers assigns cite keys to new articles. BibDesk autogenerates cite keys based on a user-defined format. I’m partial to first author + two digit year (Custom format string:%a1%y). And then I just add a sequential number to the cite key for the autonaming and autofiling of the associated PDFs.

Slick UI: Papers

You gotta admit, Papers sure is pretty while BibDesk leaves something to be desired. The most recent update to BibDesk (1.3.22) helped a little, but it’s still not as clean as Papers even though they both use the same 3-column layout.

Cost: Bibdesk

Bibdesk is free and open source. Papers costs $42 for non-students, $26 for students.

Reading Papers: Papers

Papers is great for using as a browser to read PDFs. Papers even has an iPhone app ($10) which enables you to sync a Smart Collection so that the PDFs are at your fingertips when you have some dead time for reading. BibDesk is great for browsing notes or annotations, but not for reading the original papers.
(Updated Nov 2, 2009: Thanks to Marshall for pointing out this functionality that I had overlooked.)

Also consider Mendeley, which is both a desktop and online article organizer.

Alright, there’s by BibDesk biased view, let’s hear from the Paper’s lovers in the comments.

BibDesk, free
Papers, $42 or $26 for students

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  looking forward to giving this a try.

Given the useful responses to Jane’s question about spectral line analysis, here’s another query for the community. This one is about making large image mosaics and it comes from Adam Ginsburg, a grad student at the University of Colorado, Boulder.

I want to make a large-scale mosaic of the Galactic Plane covering 90-180 degrees x a few degrees. There are a few ways to do this:

• IRAF’s imcombine with offset=wcs is a quick and dirty path to a mosaic. It is very fast, but very inaccurate over large scales and it sometimes does things flat-out wrong.
• IDL’s hastrom and related astrolib utils generally work, but for large arrays (96000×4000 pixels) I’ve seen IDL just crash.
• IRSA’s Montage works well, but is slow and consumes a lot of hard drive space in the process of making mosaics. The setup is also kind of a pain; you need to make a lot of subdirectories and .tbl files in the process.
• SSC’s mopex must work quite well, but I just discovered it today and, like Montage, it has a learning curve.

So, I’m curious, what do others use? For example, what did GigaGalaxy Zoom use? If you’re trying to build a large near-IR finder chart using 2MASS, do you just go to montage? Does anyone have convenient wrapper scripts written up? Let’s hear it in the comments.

Here’s a guest post from Mark Westmoquette at the University College London.

Do you currently work with, or are you thinking about applying for integral field unit (IFU) observations? If so, then the Integral Field Spectroscopy wiki is for you.

With IFS instruments installed on all the main optical telescope facilities around the world, it is likely that you will come across IFS data sooner or later. Unfortunately IFS continues to be avoided by large sections of the astronomical community due to perceived difficulties with data handling, reduction and analysis. There is no doubt that dealing with IFS data is more complicated than simple imaging or long-slit spectroscopy, but many of the problems that arise could benefit from the experience and knowledge of others. We have set up the IFS wiki in order to facilitate exchange of IFS knowledge, and to be a repository of information, tips, codes, tools, references, etc., accessible and editable by the whole community. The wiki is intended for use by IFS beginners for any questions or issues with IFS data and for more expert users. All are invited to contribute – tips, experience of particular instrumental quirks, pieces of code, etc, are particularly welcome.

Topics covered by the wiki are:
• current and future integral field spectrographs;
• observational techniques and planning;
• data reduction, including overview of procedures for different types of IFS;
• more advanced tasks like mosaicing or differential atmospheric refraction (DAR) correction;
• analysis techniques, from visualisation to line fitting and source extraction.

The founders and maintainers of this site are currently Mark Westmoquette (University College London, UK) and Katrina Exter (Katholieke Universiteit Leuven, Belgium). To access the wiki go to: http://ifs.wikidot.com.

I really need to post these more often…

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I’ve been to 4 winter AAS meetings.  Twice, I came back with the “AAS Ick”, a nasty cold that lingered and certainly did NOT make my Astro Better.

It’s understandable: the big meeting is right after the holidays — children are home from daycare & school, relatives have traveled from afar, and germs are zinging overhead like bullets in South Tucson on New Year’s.  Once the microfauna are sufficiently diverse, everyone hops onto a plane, spreads the new germs, picks up new ones, and travels to the conference.  Then we shake hands for several days and pass on our newly-acquired germs.

So, in the interest of post-AAS meeting productivity, what can we all do to avoid the AAS Ick?

Our department just had free flu shots (1 nurse, 1 conference room, 2 hours, very popular.)  If you can talk your department into doing it, it’s a GREAT idea, and builds herd immunity.  Else, both pharmacies on my walk to work have ~$30 shots.

And, I learned from NPR that I’ve been sneezing wrong all my life — I should sneeze into my elbow, not my hand.

What else?  Not to be too Howard Hughes-ian, but the AAS Ick is a productivity-killer.

This is an embarrassing post, but I’m going to forge ahead.  Time was, we used IRAF and we hated it, but what else was there?  Now, there are many choices, lots of them buggy and badly documented, some of them superb.

Say I have a one-dimensional, flux-calibrated, wavelength-calibrated spectrum.  (So, all the hard work of calibration is already done.) I want to measure equivalent widths, fluxes, and wavelengths of lines, interactively and then non-interactively.  What software packages can people can recommend?