Author: tlinnet
Date: Mon Oct 6 02:39:50 2014
New Revision: 26154
URL: http://svn.gna.org/viewcvs/relax?rev=26154&view=rev
Log:
Made back_calc_r2eff() in optimisation module use the spin and id list
instead.
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=26154&r1=26153&r2=26154&view=diff
==============================================================================
--- trunk/specific_analyses/relax_disp/optimisation.py (original)
+++ trunk/specific_analyses/relax_disp/optimisation.py Mon Oct 6 02:39:50
2014
@@ -112,13 +112,13 @@
return results
-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):
+def back_calc_r2eff(spins=None, spin_ids=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.
- @type spin: SpinContainer instance
- @keyword spin_id: The spin ID string for the spin container.
- @type spin_id: str
+ @keyword spins: The list of specific spin data container for
cluster.
+ @type spins: List of SpinContainer instances
+ @keyword spin_ids: The list of spin ID strings for the spin
containers in cluster.
+ @type spin_ids: list of str
@keyword cpmg_frqs: The CPMG frequencies to use instead of the
user loaded values - to enable interpolation.
@type cpmg_frqs: list of lists of numpy rank-1 float arrays
@keyword spin_lock_offset: The spin-lock offsets to use instead of the
user loaded values - to enable interpolation.
@@ -134,11 +134,12 @@
"""
# Skip protons for MMQ data.
- if spin.model in MODEL_LIST_MMQ and spin.isotope == '1H':
- return
+ for spin in spins:
+ if spin.model in MODEL_LIST_MMQ and spin.isotope == '1H':
+ return
# Create the initial parameter vector.
- param_vector = assemble_param_vector(spins=[spin])
+ param_vector = assemble_param_vector(spins=spins)
# Number of spectrometer fields.
fields = [None]
@@ -148,12 +149,12 @@
field_count = cdp.spectrometer_frq_count
# Initialise the data structures for the target function.
- 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)
+ values, errors, missing, frqs, frqs_H, exp_types, relax_times =
return_r2eff_arrays(spins=spins, spin_ids=spin_ids, 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)
+ 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)
# The relaxation times.
@@ -179,7 +180,7 @@
values.append([])
errors.append([])
missing.append([])
- for si in range(1):
+ for si in range(len(spins)):
values[ei].append([])
errors[ei].append([])
missing[ei].append([])
@@ -204,7 +205,7 @@
values.append([])
errors.append([])
missing.append([])
- for si in range(1):
+ for si in range(len(spins)):
values[ei].append([])
errors[ei].append([])
missing[ei].append([])
@@ -222,14 +223,15 @@
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, r1_fit=r1_fit)
+ 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)
# 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)
# Store the chi-squared value.
if store_chi2:
- spin.chi2 = chi2
+ for spin in spins:
+ spin.chi2 = chi2
# Return the structure.
return model.get_back_calc()
r26154 - /trunk/specific_analyses/relax_disp/optimisation.py