Hi Matt, I solved it this way: In my code only the 1-5 reference component spectra for the LCF are permanently imported in IFEFFIT and one variable is reserved for all the spectra that I want to have fitted, which is done consecutively then. Each time a spectrum is fitted, the results are exported into arrays of my software and send to the plot pane. After that everything except the reference component spectra is resetted in IFEFFIT and the fitted spectrum is overwritten by the next one. Going that way IFEFFIT memory is never exceeded, neither when fitting 3000 spectra nor in principal with 3*10^6 spectra (although that would take more than a week I guess). Of course you need some time to code it but it works quite well. Nevertheless, a new IFEFFIT with faster fit routines would be most welcome of course :). Regards, Jan Zitat von Matt Newville :

Hi Edmund, Jan, Matthew,

On Thu, Apr 12, 2012 at 8:51 AM, Jan Stötzel wrote:

...

Hi Edmund,

in my opinion evaluating 3000 spectra is neither a heroic attempt nor brute force. The LCF of 3000 spectra yields one beautiful plot with 3000 * components points, where the information of an enormous Gbyte data set is included - for me that is indeed very economical. With such a plot each reader can decide (or be convinced) which spectra are "boring" and if you want to study high order kinetics during your reaction you will be happy to have enough points to perform the required fits. Of course, one can also write a program that preselects a few spectra by evaluating the differences between the spectra. But then I guess you need at least one (avoidable) parameter to adjust the tolerance to distinguish between differences caused by noise and those due to real variations in sample composition.

That´s why I´d prefer fitting all spectra and afterwards highlighting the interesting parts. That might take 2-3 hours for a few thosand spectra (extended lunch break) but it is still much more convenient than finding the "interesting" spectra manually by going through all spectra (or than finding the most suitable tolerance parameter). And the resulting plots are worth the time imo. I am rather going to check how much faster I can get by parallelizing the processes for multicores...

Best regards,

Jan Stötzel

Zitat von Edmund Welter :

...

Dear XAFS users

recently I read several mails on this list which were dealing with the problem of large data sets, as they are produced by Q-EXAFS scans or by dispersive XAFS. The question was if there are tools available to handle data sets of several 1000 spectra and perform a linear combination fit or even a full EXAFS evaluation on each of them. Evaluating 3000 spectra is a heroic attempt, but I wonder if it is also economical.

In most (that means not in ALL!) cases, the vast majority of these spectra is boring, because the spectrum with the number X looks exactly like the spectrum with the number X-1 looked and how the spectrum with the number X+1 will look and so on. Evaluating all these (basically identical) spectra is in principle a waste of time and working memory. The interesting spectra are those which were measured when something was happening in the sample. Since we do not always know at which time, or temperature or reactant concentration etc. interesting things will happen it is without any doubt justified to measure x-thousand spectra, but after that we should use a more sophisticated approach than brute force.

I think that it would be much more useful to find procedures (that means develop computer programs) that search for the (usually relatively small number of) interesting spectra. The most obvious parameter is how similar is a particular spectrum to the spectra measured before and after. The next step would probably be to identify clusters of related spectra using statistical methods. This is a problem which had to be solved in other areas like the automated analysis of images before and should also be possible with our kind of data.

Anyway, how to handle thousands of XAFS spectra will become a very important problem in the future. With all these beamlines that provide 10^12 photons per second we can measure a factor 100 -- 1000 faster than

we did with 10^9 photons per second. So, I wonder if anything beyond the brute force approach is going on in the EXAFS software universe to make effective and economical use of the measured data.

Best regards, Edmund Welter

-- -------------------------------------------------------- Dr. Edmund Welter      Deutsches Elektronen-Synchrotron DESY FS-Do

Notkestr. 85            Email: edmund.welter@desy.de D-22607 Hamburg         Phone: +49 40 8998 4510 Germany                 Fax  : +49 40 8998 2787 --------------------------------------------------------

Sorry for the delay, but I just wanted to add my agreement to this need to be able to work with larger data sets. Handling ~3000 XANES spectra for linear analysis is definitely something that we need to be able to do well, and it is not something that Ifeffit can do really at all. This is a definite weakness. For reference, 3000 EXAFS spectra can fit in well under 500 Mb of memory, and there really should be no problem handling this amount of data as a multi-dimensional array in memory on any modern machine -- in fact, expecting an order of magnitude larger is not really that absurd. The main problem is simply that the tools we have are not built with this is mind, and the hardware + data collection have outpaced analysis software.

I believe the Ifeffit2 approach will be much better suited for handling such large datasets, and can easily use any of the linear analysis tools from scipy (http://scipy.org/). If you or anyone else has some suggested algorithms to include (PCA, non-negative least-squares, etc), that would be very helpful.

--Matt

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