r26172 - /trunk/specific_analyses/relax_disp/optimisation.py -- October 06, 2014 - 18:03

Author: tlinnet
Date: Mon Oct  6 18:03:28 2014
New Revision: 26172

URL: http://svn.gna.org/viewcvs/relax?rev=26172&view=rev
Log:
Fix for references to "spin" in optimisation.back_calc_r2eff().

Bug #22754 (https://gna.org/bugs/index.php?22754): The minimise.calculate() 
does not calculate chi2 value for clustered residues.

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=26172&r1=26171&r2=26172&view=diff
==============================================================================
--- trunk/specific_analyses/relax_disp/optimisation.py  (original)
+++ trunk/specific_analyses/relax_disp/optimisation.py  Mon Oct  6 18:03:28 
2014
@@ -150,7 +150,7 @@
     r1_setup()
     offsets, spin_lock_fields_inter, chemical_shifts, tilt_angles, 
Delta_omega, w_eff = return_offset_data(spins=spins, spin_ids=spin_ids, 
field_count=field_count, spin_lock_offset=spin_lock_offset, 
fields=spin_lock_nu1)
     r1 = return_r1_data(spins=spins, spin_ids=spin_ids, 
field_count=field_count)
-    r1_fit = is_r1_optimised(spin.model)
+    r1_fit = is_r1_optimised(spins[0].model)
 
     # The relaxation times.
     if relax_times_new != None:
@@ -218,7 +218,7 @@
                         missing[ei][si][mi].append(zeros(num, int32))
 
     # Initialise the relaxation dispersion fit functions.
-    model = Dispersion(model=spin.model, num_params=param_num(spins=spins), 
num_spins=len(spins), num_frq=field_count, exp_types=exp_types, 
values=values, errors=errors, missing=missing, frqs=frqs, frqs_H=frqs_H, 
cpmg_frqs=cpmg_frqs, spin_lock_nu1=spin_lock_nu1, 
chemical_shifts=chemical_shifts, offset=offsets, tilt_angles=tilt_angles, 
r1=r1, relax_times=relax_times, recalc_tau=recalc_tau, r1_fit=r1_fit)
+    model = Dispersion(model=spins[0].model, 
num_params=param_num(spins=spins), num_spins=len(spins), num_frq=field_count, 
exp_types=exp_types, values=values, errors=errors, missing=missing, 
frqs=frqs, frqs_H=frqs_H, cpmg_frqs=cpmg_frqs, spin_lock_nu1=spin_lock_nu1, 
chemical_shifts=chemical_shifts, offset=offsets, tilt_angles=tilt_angles, 
r1=r1, relax_times=relax_times, recalc_tau=recalc_tau, r1_fit=r1_fit)
 
     # Make a single function call.  This will cause back calculation and the 
data will be stored in the class instance.
     chi2 = model.func(param_vector)