Dear Edward. There is an explanation for everything, including user errors. :-)
I have analysed the 0.48 M GuaHCl dataset (the folded protein), and so this is not comparable to the 1M GuaHcl (intermediate between folded/unfolded) dataset, which is shown in the figures in the paper. So, I got the original datapoints for figure 3, which shows the ln(k_a) per GuaHCl. And now k_a fits perfect for 0.48 M. And I have today analysed the 1M dataset. Everything matches until first digit, so I am satisfied. So, I will soon send a swarm of patches to include this dataset. Thanks for looking! I will look into the tsmfk01 code to speed it up. To compare to the numerical methods, you mentioned that one could make auto conversion of the parameters? So could k_AB be calculated for the numerical methods, so: k_AB = kex*(1-pA) Best Troels Troels Emtekær Linnet 2013/9/9 Edward d'Auvergne <[email protected]>: > Hi Troels, > > I'm still not sure why the TSMFK01 model results do not match what you > expect (http://thread.gmane.org/gmane.science.nmr.relax.scm/18555/focus=4531). > The code is very clean and there is nothing obvious. The problems I > saw before with the k_AB parameter, you have now fixed. So I really > don't know what is happening. I would recommend looking at a spin > system with data at two fields, just in case the model is not stable > for single field strength data. In any case, more testing of data is > required to work out what is happening. If the motional parameters > are truly within the range of the TSMFK01 model, then comparison to > the numeric model should produce similar parameter values. This is > also a useful sanity check. > > Maybe you could write an email to Martin Tollinger about getting some > of the data used in the paper. This would include peak heights (or > R2eff) as well as the optimised parameter values. Having both is > important. Some of his code is in relax, so he is aware of the > dispersion branch. If you explain what you would like to do and how > you're in the process of implementing / debugging his model in relax, > I'm sure he'd be happy to help. He may even still have the synthetic > data he used in his paper (http://dx.doi.org/10.1021/ja011300z) - that > would be the best for the checks. > > On the subject of the subject line, I have a few points about the > lib.dispersion.tsmfk01 code to speed things up: > > 1) The dw * tau_CP mathematical operation occurs twice - this is a > waste and the result can be stored as a variable and reused. An easy > solution would be to put the denominator calculation before the > numerator, and the rest should be obvious. > > 2) The tau_CP value is re-calculated each time. These values could > be stored in data structure which is set up when the target function > class is initialised (in the __init__() method). That way this > calculation is avoided in the target function where it is much more > computationally expensive. > > 3) Also related to point 2), Python has to convert your integer '2' > into a float prior to the multiplication. If you use '2.0' instead, > then that avoids the time required for Python type conversions. > > Implementing these drop the number of mathematical operations per loop > per function call from 9 to 6, and removes a type conversion. So you > should get a good speed up. > > Regards, > > Edward > > > P. S. Be careful with the tau_CP to nu_CPMG calculation. In relax, > the factor of 1/2 rather than 1/4 is often used. This is the notation > used by CPMGFit. Different groups define nu_CPMG differently! > > _______________________________________________ > relax (http://www.nmr-relax.com) > > This is the relax-devel mailing list > [email protected] > > To unsubscribe from this list, get a password > reminder, or change your subscription options, > visit the list information page at > https://mail.gna.org/listinfo/relax-devel _______________________________________________ relax (http://www.nmr-relax.com) This is the relax-devel mailing list [email protected] To unsubscribe from this list, get a password reminder, or change your subscription options, visit the list information page at https://mail.gna.org/listinfo/relax-devel
