Source Code for Module specific_analyses.relax_fit.optimisation

 1  ############################################################################### 
 2  #                                                                             # 
 3  # Copyright (C) 2004-2014 Edward d'Auvergne                                   # 
 4  # Copyright (C) 2014 Troels E. Linnet                                         # 
 5  #                                                                             # 
 6  # This file is part of the program relax (http://www.nmr-relax.com).          # 
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 8  # This program is free software: you can redistribute it and/or modify        # 
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16  # GNU General Public License for more details.                                # 
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19  # along with this program.  If not, see <http://www.gnu.org/licenses/>.       # 
20  #                                                                             # 
21  ############################################################################### 
22   
23  # Module docstring. 
24  """The R1 and R2 exponential relaxation curve fitting optimisation functions.""" 
25   
26  # relax module imports. 
27  from specific_analyses.relax_fit.parameters import assemble_param_vector 
28  from target_functions.relax_fit_wrapper import Relax_fit_opt 
29   
30   
31 -def back_calc(spin=None, relax_time_id=None): 
32      """Back-calculation of peak intensity for the given relaxation time. 
33   
34      @keyword spin:              The spin container. 
35      @type spin:                 SpinContainer instance 
36      @keyword relax_time_id:     The ID string for the desired relaxation time. 
37      @type relax_time_id:        str 
38      @return:                    The peak intensity for the desired relaxation time. 
39      @rtype:                     float 
40      """ 
41   
42      # Create the initial parameter vector. 
43      param_vector = assemble_param_vector(spin=spin) 
44   
45      # The keys. 
46      keys = list(spin.peak_intensity.keys()) 
47   
48      # The peak intensities and times. 
49      values = [] 
50      errors = [] 
51      times = [] 
52      for key in keys: 
53          values.append(spin.peak_intensity[key]) 
54          errors.append(spin.peak_intensity_err[key]) 
55          times.append(cdp.relax_times[key]) 
56   
57      # A fake scaling matrix in a diagonalised list form. 
58      scaling_list = [] 
59      for i in range(len(param_vector)): 
60          scaling_list.append(1.0) 
61   
62      # Initialise the relaxation fit functions. 
63      model = Relax_fit_opt(model=spin.model, num_params=len(spin.params), values=values, errors=errors, relax_times=times, scaling_matrix=scaling_list) 
64   
65      # Make a single function call.  This will cause back calculation and the data will be stored in the C module. 
66      model.func(param_vector) 
67   
68      # Get the data back. 
69      results = model.back_calc_data() 
70   
71      # Return the correct peak height. 
72      return results[keys.index(relax_time_id)] 
73