0001 global global_opt_model global_spectra ...
0002     global_bvecs DistEseManager
0003 
0004 % Load Experimental Data
0005 filter_options.filter_range = 150:600;
0006 filter_options.data_range = 10:600;
0007 filter_options.var_range = 500:600;
0008 
0009 spectrum = filterRaw( 'n14_demo.mat', ...
0010     0.3030, filter_options );
0011 
0012 global_spectra = {spectrum};
0013 
0014 % Setup system model and register Variables
0015 optmodel = eseOptModel({'N14'},'1');
0016 
0017 optmodel.nuclei{1}.type = 'tensor';
0018 
0019 optmodel = registerOptModelVariable(optmodel, ...
0020             'Axx', 1, 1, -10, 10, ...
0021             'Ayy', 1, 2, -10, 10, ...
0022             'Azz', 1, 3, -10, 10);
0023 
0024 optmodel = registerOptModelVariable(optmodel, ...
0025             'eeqQ', 1, 4, -10, 10, ...
0026             'eta', 1, 5, 0, 1);
0027                                           
0028 optmodel = registerOptModelVariable(optmodel, ...
0029             'QEuler1', 1, 6, -360, 360, ...
0030             'QEuler2', 1, 7, -360, 360, ...
0031             'QEuler3', 1, 8, -360, 360);
0032 
0033 global_opt_model = optmodel;
0034 
0035 % Setup poweder average method
0036 % and field vectors for averaging
0037 global_ese_routine = @powderAveragerDistributed;
0038 global_bvecs = gensphrser(4);
0039 
0040 % Setup node manager for use
0041 % in powderAveragerDistributed.m
0042 DistEseManager = ...
0043     java.rmi.Naming.lookup('//##IP##/eseman');
0044 
0045 % Parameter Optimization
0046 x = [2.291, 2.165, 1.645, 1.736, 0.599, 52.69, 8.32, 13.03];
0047 options = optimset('OutputFcn', @esePlot, 'Display','iter');
0048 [ x_new, fval ] = fminsearch(@eseOptObjective, x, options);
0049 
0050 % Estimate optimal parameter uncertainties
0051 bounds = eseOptCftInterval(x_new, fval, 0.99);
0052 
0053 % Generate a report
0054 eseReport(x_new,fval,bounds);