r25515 - /branches/est_par_error/specific_analyses/relax_disp/estimate_r2eff.py -- September 01, 2014 - 20:51

Author: tlinnet
Date: Mon Sep  1 20:51:48 2014
New Revision: 25515

URL: http://svn.gna.org/viewcvs/relax?rev=25515&view=rev
Log:
Further extended the function, to call the co-variance function.

There are some issues with dimensionality of arrays.

Maybe an initial looping will be good.

task #7824(https://gna.org/task/index.php?7824): Model parameter ERROR 
estimation from Jacobian and Co-variance matrix of dispersion models.

Modified:
    branches/est_par_error/specific_analyses/relax_disp/estimate_r2eff.py

Modified: 
branches/est_par_error/specific_analyses/relax_disp/estimate_r2eff.py
URL: 
http://svn.gna.org/viewcvs/relax/branches/est_par_error/specific_analyses/relax_disp/estimate_r2eff.py?rev=25515&r1=25514&r2=25515&view=diff
==============================================================================
--- branches/est_par_error/specific_analyses/relax_disp/estimate_r2eff.py     
  (original)
+++ branches/est_par_error/specific_analyses/relax_disp/estimate_r2eff.py     
  Mon Sep  1 20:51:48 2014
@@ -39,7 +39,7 @@
 from pipe_control.mol_res_spin import generate_spin_string, spin_loop
 from specific_analyses.relax_disp.checks import check_model_type
 from specific_analyses.relax_disp.data import average_intensity, 
is_r1_optimised, loop_exp_frq_offset_point, loop_time, return_cpmg_frqs, 
return_offset_data, return_param_key_from_data, return_r1_data, 
return_r1_err_data, return_r2eff_arrays, return_spin_lock_nu1
-from specific_analyses.relax_disp.parameters import 
disassemble_param_vector, param_num
+from specific_analyses.relax_disp.parameters import assemble_param_vector, 
disassemble_param_vector, param_num
 from specific_analyses.relax_disp.variables import MODEL_CR72, MODEL_R2EFF, 
MODEL_TSMFK01
 from target_functions.chi2 import chi2_rankN, dchi2
 from target_functions.relax_disp import Dispersion
@@ -234,8 +234,17 @@
         scaling_matrix = assemble_scaling_matrix(scaling=True)
 
         # Init the Dispersion clas with data, so we can call functions in it.
-        tfunc = Dispersion(model=model, num_params=model_param_num, 
num_spins=num_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, scaling_matrix=scaling_matrix, r1_fit=r1_fit)
-
+        tfunc = Dispersion(model=model, num_params=model_param_num, 
num_spins=num_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, r1_fit=r1_fit)
+
+        # Create the parameter vector.
+        param_vector = assemble_param_vector(spins=[cur_spin])
+
+        ## Make a single function call.
+        jacobian = tfunc.jacobian(param_vector)
+        weights = 1. / tfunc.errors**2
+
+        # Get the co-variance
+        pcov = multifit_covar(J=jacobian, weights=weights)
 
 
 #### This class is only for testing.