Hi Edward. You are very productive! and I am stunned to see the development so fast. :-)
I think you are hitting "the nail" with the current development of relax. At the moment the "hardest/time consuming" part for students in our lab, is the analysis of the results. We have a good workflow from recording and peak assignment. But from there, it gets more tricky. Most of our analysis part is based on "home made" scripts for Igor Pro/Matlab. They come a long way for most of our analysis. The problem is that for a "beginner" there can be a "high energy barrier" to start using these scripts. Not mentioning "license problems", installation and such. And if you have to make some peak adjustments, then the whole analysis should be performed again. For a "trained" this takes some hours, but for un-trained, it can take a day or two. We have been looking for other software for CPMG. At least to "quickly" try to match our scripts values. (Here Nessy seemed very interesting, but very buggy) But concerns about installation, development, and even a "harder" interface than Igor Pro/Matlab, stopped us. We(as in me) are at the moment matching some test results at the Sherekhan server, after making a input converter to Sherekhan. That seems to be very easy, and promising. I am very impressed with relax. Mostly because the GUI can take care of novices, and there exist the possibility to script it all up for extensive analysis. It's free, and I was able to install it on Windows/Linux without to much fuss. If I want to try some of our data, is the GUI ready for trying? Best Troels Troels Emtekær Linnet 2013/5/29 Edward d'Auvergne <[email protected]> > Hi, > > Troels, you might be interested in the following comparison. The > reviving Sebastien Morin's relaxation dispersion branch in relax is > now complete. The relax_disp branch can now correctly optimise one of > the dispersion models, that of Luz and Meiboom 1963 for 2-site fast > exchange for CPMG-type experiments. This does not implement the > gradients or Hessians yet, and parameter constraints are still to be > added. But nevertheless relax can find the correct parameter values. > This should be a good test of the dispersion code in relax as the > addition of other models - such as that for R1rho data, more > complicated models for CPMG-type data, and numerical integration of > the Bloch-McConnell equations - should be trivial after that > (requiring only 20-50 lines of new code, not counting comments and > docstrings). > > Through relax user functions, I can now generate the input for CPMGFit > and NESSY. These are the relax_disp.cpmgfit_input and > relax_disp.nessy_input user functions. We can now also execute > CPMGFit within relax using relax_disp.cpmgfit_execute. This can be > completed later, but the idea is to follow the concept of model-free > user functions: > > dasha.create > dasha.execute > dasha.extract > palmer.create > palmer.execute > palmer.extract > > These are for the Dasha and Modelfree4 programs respectively. > Implementing the 3 user functions for creating input files, executing > the program in-line, and extracting the results from the program's > output will allow relax to use external programs as optimisation > engines. This is useful for comparing the results from different > programs and really eliminating all bugs from the dispersion field. > > Back to the comparison, I have used Flemming Hansen's 500 and 800 MHz > CPMG data from: > > Hansen, Vallurupalli & Kay, 2008, JPhysChemB, 112: 5898-5904. > > which he donated to Seb to be added to relax (located in > test_suite/shared_data/dispersion/Hansen/). Looking at a single > randomly chosen residue (number 70), I see different results from the > 3 programs: > > Param relax NESSY CPMGFit > R2 (500) 6.806 7.639 6.866 > R2 (800) 6.679 7.387 6.866 > phi 1.259e-13 0.259 1.226e-13 > phi (500) 31464.605 26292.031 30644.496 > phi (800) 80549.390 67307.598 78449.180 > kex 4763.249 3906.180 4.683 > tau 4.199e-05 5.120e-05 0.427 > chi2 106.393 156.446 106.471 > > tau = 2/kex (I think that CPMGFit works with ms units). > > Obviously NESSY is not doing so well (likely due to using the horribly > buggy scipy optimisation code) and relax and CPMGFit find the same > result for this model. The slight difference between relax and > CPMGFit is that it looks like CPMGFit assumes the same R2 value for > all static fields - which I think would be a little strange, > especially if fast internal motions are also present for that spin > system. > > Therefore I believe that this relax branch is in a state to accept > code for the other models. The backends for the > relax_disp.cpmgfit_input and relax_disp.nessy_input could be also > modified to handle these new models to allow for rigorous comparisons > and debugging. The dispersion infrastructure should no longer have > any large changes, so feel free to start playing. > > Regards, > > Edward >
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