On Thu, Apr 26, 2007 at 12:06:56PM +0200, Zdeněk Hurák wrote: > But what makes Matlab difficult to be replaced is that lots of other > projects (commercial: Mathematica, Maple, ... and free: octave, maxima, > scipy, ...) only offer computation and visualization, while engineers in my > field also need INTERACTION OF THE SYSTEM WITH EXTERNAL WORLD. That is, > compatibility with a real-time operating system and MOST available > input-output (AD-DA) cards. Being able to acquire measurement data from an > external industrial system, process them computationally (for instance, > solving some Riccati matrix differential equations), visualize the data and > put the computed results back to the real system, this is what we need.
I am very surprised that you think Matlab is more suited for such a task. I have had the opposite experience. I work in an experimental lab where we do rather heavy experimental physics (Bose Einstein condensation). I am building an experiment from scratch and had to build a control framework for the experiment. It is fully automated and with need some reasonably sophisticated logics to scan parameters, do on the fly processing of the data, store it on the disk, display all this in a useful interface for the user, and feed all this back to the experiment. Due to legacy reasons I built the software in Matlab. It was a bad experience. First of all linking to C libraries to control hardware is really a pain. Writing Mex files is not that much fun and the memory management of MatLab is flawed. I have had segfaults for no obvious reasons, and the problems where neither in my code nor in the drivers (I worked with the engineer who wrote the driver to diagnose this). Matlab is single threaded so all your calls to the hardware are blocking (unless you are willing to add threads in you mex file, but this is very dangerous as you are playing with a loop-hole in matlab's mex loader which keeps the mex in the memory after it is executed). Finally the lack of proper object oriented programming and separated namespaces make it hard to write good code to control instruments. The lack of object oriented programming, passing by reference, threads, and proper control of the GUI event-loop also makes it very hard to write a complex interactive GUI. Python provides all this. What it does not provide are pre-linked libraries to control hardware, but if you are trying to control exotic hardware, all you have is a C or C++ SDK. It is even worse when the hardware is homemade, as you have to write the library yourself, and trust me, I'd much better write a hardware control library that speak to my custom made electronic over a bus in Python than in Matlab (GPIB, ethernet, serial, Python has bindings for all that). I have had a second choice to build the computer control framework of a similar experiment. I took my chance to build it in Python (it was a different lab, and I was replacing an existing C software that had become impossible to maintain). I never regretted it. Python has really been very good at it due to all the different libraries available, the ease to link to C, and the possibility to do proper OOP. I have written an article about this, that I submitted a month ago to CiSE. I have no news from them. The article is very poorly written (I had no hindsight about these matters when I started writing it, submitted it because I was tired to see it dragging along, and now I wish I had reworked it a bit), but I think I raise some points about what you need to build a control framework for an experiment, and how python can address those needs. Anyway, IANAL, and I am not to sure if releasing a preprint on a mailing list renders the article ineligible for CiSE or not but I just put a version on http://gael-varoquaux.info/computers/agile_computer_control_of_an_experiment.pdf . I hope it can help understanding what is needed to control an experiment from Python, what can be improved, and what already exists and is so incredibly useful. I am interested in comments. Keep in mind that I am working in a field where nobody is interested in computing and I sure could use some advice. I have written quite a few softwares to control experiments in different labs with different languages, and I think I have acquired some experience, and made a lot of progress, but I also have had to reinvent the wheel more than once. We all know that good software engineering books or article readable by some one who nows little about computing are hard to find. Let us address this issue ! Gaël _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
