r21319 - /branches/relax_disp/specific_analyses/relax_disp/api.py -- October 29, 2013 - 14:33

Author: bugman
Date: Tue Oct 29 14:33:57 2013
New Revision: 21319

URL: http://svn.gna.org/viewcvs/relax?rev=21319&view=rev
Log:
Fixes for the Monte Carlo simulations in the dispersion analysis when R2eff 
data has been read.

As peak intensity data has not been read, the relaxation time period will not 
have been set.  The
_back_calc_r2eff() method can now handle this.


Modified:
    branches/relax_disp/specific_analyses/relax_disp/api.py

Modified: branches/relax_disp/specific_analyses/relax_disp/api.py
URL: 
http://svn.gna.org/viewcvs/relax/branches/relax_disp/specific_analyses/relax_disp/api.py?rev=21319&r1=21318&r2=21319&view=diff
==============================================================================
--- branches/relax_disp/specific_analyses/relax_disp/api.py (original)
+++ branches/relax_disp/specific_analyses/relax_disp/api.py Tue Oct 29 
14:33:57 2013
@@ -137,8 +137,13 @@
             chemical_shifts, offsets, tilt_angles = 
return_offset_data(spins=[spin], spin_ids=[spin_id], fields=fields, 
field_count=field_count)
             r1 = return_r1_data(spins=[spin], spin_ids=[spin_id], 
fields=fields, field_count=field_count)
 
+        # The relaxation time period.
+        relax_time = None
+        if hasattr(cdp, 'relax_time_list'):
+            relax_time = cdp.relax_time_list[0]
+
         # Initialise the relaxation dispersion fit functions.
-        model = Dispersion(model=spin.model, 
num_params=param_num(spins=[spin]), num_spins=1, num_frq=field_count, 
num_disp_points=cdp.dispersion_points, exp_types=exp_types, values=values, 
errors=errors, missing=missing, frqs=frqs, 
cpmg_frqs=return_cpmg_frqs(ref_flag=False), 
spin_lock_nu1=return_spin_lock_nu1(ref_flag=False), 
chemical_shifts=chemical_shifts, spin_lock_offsets=offsets, 
tilt_angles=tilt_angles, r1=r1, relax_time=cdp.relax_time_list[0], 
scaling_matrix=scaling_matrix)
+        model = Dispersion(model=spin.model, 
num_params=param_num(spins=[spin]), num_spins=1, num_frq=field_count, 
num_disp_points=cdp.dispersion_points, exp_types=exp_types, values=values, 
errors=errors, missing=missing, frqs=frqs, 
cpmg_frqs=return_cpmg_frqs(ref_flag=False), 
spin_lock_nu1=return_spin_lock_nu1(ref_flag=False), 
chemical_shifts=chemical_shifts, spin_lock_offsets=offsets, 
tilt_angles=tilt_angles, r1=r1, relax_time=relax_time, 
scaling_matrix=scaling_matrix)
 
         # 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)