r26828 - in /trunk: specific_analyses/relax_fit/ target_functions/ -- November 29, 2014 - 10:18

Author: bugman
Date: Sat Nov 29 10:18:50 2014
New Revision: 26828

URL: http://svn.gna.org/viewcvs/relax?rev=26828&view=rev
Log:
The exponential model name is now being passed into the target function class.

The model as specified by the relax_fit.select_model user function is now 
finally being sent into
the target function, in this case the Relax_fit_opt class in 
target_functions.relax_fit_wrapper.


Modified:
    trunk/specific_analyses/relax_fit/api.py
    trunk/specific_analyses/relax_fit/optimisation.py
    trunk/target_functions/relax_fit_wrapper.py

Modified: trunk/specific_analyses/relax_fit/api.py
URL: 
http://svn.gna.org/viewcvs/relax/trunk/specific_analyses/relax_fit/api.py?rev=26828&r1=26827&r2=26828&view=diff
==============================================================================
--- trunk/specific_analyses/relax_fit/api.py    (original)
+++ trunk/specific_analyses/relax_fit/api.py    Sat Nov 29 10:18:50 2014
@@ -135,7 +135,7 @@
 
         # Initialise data in C code.
         scaling_list = [1.0, 1.0]
-        model = Relax_fit_opt(num_params=len(param_vector), values=values, 
errors=errors, relax_times=times, scaling_matrix=scaling_list)
+        model = Relax_fit_opt(model=spin.model, 
num_params=len(param_vector), values=values, errors=errors, 
relax_times=times, scaling_matrix=scaling_list)
 
         # Use the direct Jacobian from function.
         jacobian_matrix_exp = transpose(asarray( 
model.jacobian(param_vector) ) )
@@ -364,7 +364,7 @@
                     scaling_list.append(scaling_matrix[model_index][i, i])
 
             # Set up the target function.
-            model = Relax_fit_opt(num_params=len(spin.params), 
values=values, errors=errors, relax_times=times, scaling_matrix=scaling_list)
+            model = Relax_fit_opt(model=spin.model, 
num_params=len(spin.params), values=values, errors=errors, relax_times=times, 
scaling_matrix=scaling_list)
 
 
             # Setup the minimisation algorithm when constraints are present.

Modified: trunk/specific_analyses/relax_fit/optimisation.py
URL: 
http://svn.gna.org/viewcvs/relax/trunk/specific_analyses/relax_fit/optimisation.py?rev=26828&r1=26827&r2=26828&view=diff
==============================================================================
--- trunk/specific_analyses/relax_fit/optimisation.py   (original)
+++ trunk/specific_analyses/relax_fit/optimisation.py   Sat Nov 29 10:18:50 
2014
@@ -60,7 +60,7 @@
         scaling_list.append(1.0)
 
     # Initialise the relaxation fit functions.
-    model = Relax_fit_opt(num_params=len(spin.params), values=values, 
errors=errors, relax_times=times, scaling_matrix=scaling_list)
+    model = Relax_fit_opt(model=spin.model, num_params=len(spin.params), 
values=values, errors=errors, relax_times=times, scaling_matrix=scaling_list)
 
     # Make a single function call.  This will cause back calculation and the 
data will be stored in the C module.
     model.func(param_vector)

Modified: trunk/target_functions/relax_fit_wrapper.py
URL: 
http://svn.gna.org/viewcvs/relax/trunk/target_functions/relax_fit_wrapper.py?rev=26828&r1=26827&r2=26828&view=diff
==============================================================================
--- trunk/target_functions/relax_fit_wrapper.py (original)
+++ trunk/target_functions/relax_fit_wrapper.py Sat Nov 29 10:18:50 2014
@@ -38,9 +38,11 @@
 class Relax_fit_opt:
     """The exponential curve-fitting Python to C wrapper target function 
class."""
 
-    def __init__(self, num_params=None, values=None, errors=None, 
relax_times=None, scaling_matrix=None):
+    def __init__(self, model=None, num_params=None, values=None, 
errors=None, relax_times=None, scaling_matrix=None):
         """Set up the target function class and alias the target functions.
 
+        @keyword model:             The exponential curve type.  This can be 
'exp' for the standard two parameter exponential curve, 'inv' for the 
inversion recovery experiment, and 'sat' for the saturation recovery 
experiment.
+        @type model:                str
         @keyword num_params:        The number of parameters in the model.
         @type num_params:           int
         @keyword values:            The peak intensities.
@@ -52,6 +54,9 @@
         @keyword scaling_matrix:    The scaling matrix in a diagonalised 
list form.
         @type scaling_matrix:       list of float
         """
+
+        # Store the args.
+        self.model = model
 
         # Initialise the C code.
         setup(num_params=num_params, num_times=len(relax_times), 
values=values, sd=errors, relax_times=relax_times, 
scaling_matrix=scaling_matrix)