Author: tlinnet Date: Fri Aug 22 14:48:18 2014 New Revision: 25215 URL: http://svn.gna.org/viewcvs/relax?rev=25215&view=rev Log: Modified back_calc_r2eff() to accept interpolated timepoints. bug #22461(https://gna.org/bugs/?22461): NS R1rho 2-site_fit_r1 has extremely high chi2 value in systemtest Relax_disp.test_r1rho_kjaergaard_missing_r1. Modified: trunk/specific_analyses/relax_disp/optimisation.py Modified: trunk/specific_analyses/relax_disp/optimisation.py URL: http://svn.gna.org/viewcvs/relax/trunk/specific_analyses/relax_disp/optimisation.py?rev=25215&r1=25214&r2=25215&view=diff ============================================================================== --- trunk/specific_analyses/relax_disp/optimisation.py (original) +++ trunk/specific_analyses/relax_disp/optimisation.py Fri Aug 22 14:48:18 2014 @@ -104,7 +104,7 @@ return results -def back_calc_r2eff(spin=None, spin_id=None, cpmg_frqs=None, spin_lock_offset=None, spin_lock_nu1=None, store_chi2=False): +def back_calc_r2eff(spin=None, spin_id=None, cpmg_frqs=None, spin_lock_offset=None, spin_lock_nu1=None, relax_times_new=None, store_chi2=False): """Back-calculation of R2eff/R1rho values for the given spin. @keyword spin: The specific spin data container. @@ -117,6 +117,8 @@ @type spin_lock_offset: list of lists of numpy rank-1 float arrays @keyword spin_lock_nu1: The spin-lock field strengths to use instead of the user loaded values - to enable interpolation. @type spin_lock_nu1: list of lists of numpy rank-1 float arrays + @keyword relax_times_new: The interpolated experiment specific fixed time period for relaxation (in seconds). The dimensions are {Ei, Mi, Oi, Di, Ti}. + @type relax_times_new: rank-4 list of floats @keyword store_chi2: A flag which if True will cause the spin specific chi-squared value to be stored in the spin container. @type store_chi2: bool @return: The back-calculated R2eff/R1rho value for the given spin. @@ -145,6 +147,10 @@ offsets, spin_lock_fields_inter, chemical_shifts, tilt_angles, Delta_omega, w_eff = return_offset_data(spins=[spin], spin_ids=[spin_id], field_count=field_count, spin_lock_offset=spin_lock_offset, fields=spin_lock_nu1) r1 = return_r1_data(spins=[spin], spin_ids=[spin_id], field_count=field_count) r1_fit = is_r1_optimised(spin.model) + + # The relaxation times. + if relax_times_new != None: + relax_times = relax_times_new # The dispersion data. recalc_tau = True