Dear Edward. I am working on a systematic investigations of dynamic parameters for hundreds of datasets.
For one example, a CPMG analysis is setup for: 17 variations of tau_cpmg The number of MC simulations is 50. 82 spins which are all clustered. There is no grid search, and only TSMFK01 is used. I do one grid search in the start, minimise this, copy over the parameters and take median, make a clustering analysis, and then repeat the last step 60 times. This would again would be needed to repeat 5-8 times for other datasets with variations. And then for other proteins. (Sigh..) I have setup relax to use 20 processors on our server, and a dispersion analysis takes between 2-6 Hours. That is a reasonable timeframe for an normal analysis of this type. But I have to squeeze hundreds of these analysis through relax, to get variation of the dynamic parameters. Our old Igor Pro scripts, could do a global fitting in 10 minutes. That does not include MC simulations. But I wonder if I could speed up relax by changing function tolerance and maximum number of iterations: minimise(min_algor='simplex', line_search=None, hessian_mod=None, hessian_type=None, func_tol=OPT_FUNC_TOL, grad_tol=None, max_iter=OPT_MAX_ITERATIONS, constraints=True, scaling=True, verbosity=1) where standard values of: OPT_FUNC_TOL = 1e-25 OPT_MAX_ITERATIONS = 10000000 Could you advise if this strategy is possible? What I hope for, is that an analysis come down to 10-20 minutes? Maybe I could cut away the MC simulations, since I am mostly interested in the fitted dynamic parameters, and not so much about their error? Thank you in advance! Best Troels _______________________________________________ relax (http://www.nmr-relax.com) This is the relax-users mailing list relax-users@gna.org 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-users
