Undocumented MATLAB has an in depth look at the next generation graphics handler for MATLAB which you can use today, although it’s not officially released yet. Use the command line option “-hgVersion 2″ when launching MATLAB. See the post for more details.
Posts archived in Software
Google Reader is going to be shut down on July 1.
If you use Reader, here’s what to do:
Step 1: Export all of your subscriptions from Google Reader
(takes less than 1 minute)
Try these directions. It’s easy.
Step 2: Start using an alternative, and import your old Google Reader stuff.
(can take as little as 2 minutes, once you decide on one)
I’m trying Bibliogo right now, and I like it so far. It’s geared towards academics, so it’s a good fit for the Labrigger audience. It opens webpages within the window in a nice way, making it fairly quick to flip back and forth between RSS entries and the actual webpages. Even Google Reader never did that very well.
Two-Photon Processor and SeNeCA – A freely available software package to process data from two-photon calcium imaging at speeds down to several ms per frame.
Jakub Tomek, Ondrej Novak, and Josef Syka
TJ Neurophysiol published 10 April 2013, 10.1152/jn.00087.2013
It’s notable in that it is an “all-in-one” package that’s freely available.
The image processing to detect cells and draw ROIs seems to work pretty good, even with poor S:N. I’d like to see it operate on GCaMP images, since those are more challenging in some ways. Cells labeled with Oregon Green BAPTA-1 tend to exhibit spherical patterns of somatic fluorescence, but GCaMP, when it’s working well, does not brightly label the nucleus, so the shape of the ideal ROI is quite different. Plus, it’s nice to pick up dendrites and other features, not just somata.
Hat tip to Christian Wilms
EDIT: The code became available shortly after this post.
Unenthusiastic about spending thousands of dollars on electrophysiology software like pCLAMP? Try the free and open source Strathclyde Electrophysiology Software. It’s a suite of programs for recording and analyzing signals from intracellular electrophysiology experiments. It’s for Windows. Many standard DAQs work right out of the box. Encouragingly, it has been updated routinely since 1997, including as recently as Jan 16, 2013.
The MATLAB-based Ephus, from the Svoboda lab, is another option. There are others as well (here’s a list), but these are the two open source, free options that seem to be regularly updated. Let me know if there are others.
Programming that happens in labs is typically rushed and sloppy. The goal is to get the job done right, and, almost as important, done ASAP. The goal is not to generate ultra-readable, conforming code that is full-featured and ready for distribution. This is similar to the “Worse is Better” philosophy of software development (which I’ve also seen called the “Carnegie Mellon” style, to contrast it with the “MIT” style that emphasizes completeness).
It sometimes takes discipline to stay focused and avoid the temptation to add in more features, improve the design, and make more bulletproof, elegant code. Particularly when working on very difficult biology experiments, when the control and reproducibility of programming can be a tremendous comfort and wonderful escape.
Here’s a recent, interesting discussion on the topic entitled “Do it right or do it ASAP?”.
The first priority is always accuracy. The code has to give the right answer. But as long as that’s taken care of, getting to version 1 as fast as possible is typically what I emphasize. Note that this isn’t the same as “Fail fast”, which I think encourages sloppiness. Rather it’s a prioritization of completion of a computation, ahead of the development of a mature piece of software.
What principles do you stick to when coding?
If you like Python, want to analyze data online, and are interested in a standardized environment that can be easily shared, read on:
If you’re an academic frustrated by setting up computing environments and annoyed that your colleagues can’t easily run your code, Wakari is made for you. Wakari handles all of the problems related to setting up a Python scientific computing environment. Because Wakari builds on Anaconda, useful libraries like SciKit, mpi4py and NumPy are right at your fingertips without compilation gymnastics.
Since you run code on our servers through a web browser, it is easy for your colleagues to re-run your code to repeat your analysis, or try out variations on their own. At Continuum, we understand that reproducibility is an important part of the scientific process that your results be consistent for reviewers and colleagues.