Author: bugman
Date: Thu Aug 21 09:57:28 2014
New Revision: 25147
URL: http://svn.gna.org/viewcvs/relax?rev=25147&view=rev
Log:
The dispersion back_calc_r2eff() function can now handle the dynamic R1
parameter.
This required a call to r1_setup() to add or remove the parameter, and
is_r1_optimised() to obtain
the r1_fit flag to be sent into the target function class.
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=25147&r1=25146&r2=25147&view=diff
==============================================================================
--- trunk/specific_analyses/relax_disp/optimisation.py (original)
+++ trunk/specific_analyses/relax_disp/optimisation.py Thu Aug 21 09:57:28 2014
@@ -141,8 +141,10 @@
values, errors, missing, frqs, frqs_H, exp_types, relax_times =
return_r2eff_arrays(spins=[spin], spin_ids=[spin_id], fields=fields,
field_count=field_count)
# The offset and R1 data.
+ r1_setup()
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 dispersion data.
recalc_tau = True
@@ -206,7 +208,7 @@
missing[ei][si][mi].append(zeros(num, int32))
# Initialise the relaxation dispersion fit functions.
- model = Dispersion(model=spin.model, num_params=param_num(spins=[spin]),
num_spins=1, 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)
+ model = Dispersion(model=spin.model, num_params=param_num(spins=[spin]),
num_spins=1, 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)
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