r4499 - /branches/N_state_model/specific_fns/n_state_model.py -- January 08, 2008 - 17:21

Author: bugman
Date: Tue Jan  8 17:21:32 2008
New Revision: 4499

URL: http://svn.gna.org/viewcvs/relax?rev=4499&view=rev
Log:
Fixed up the minimise() method to use the target function framework properly.


Modified:
    branches/N_state_model/specific_fns/n_state_model.py

Modified: branches/N_state_model/specific_fns/n_state_model.py
URL: 
http://svn.gna.org/viewcvs/relax/branches/N_state_model/specific_fns/n_state_model.py?rev=4499&r1=4498&r2=4499&view=diff
==============================================================================
--- branches/N_state_model/specific_fns/n_state_model.py (original)
+++ branches/N_state_model/specific_fns/n_state_model.py Tue Jan  8 17:21:32 
2008
@@ -25,7 +25,7 @@
 
 # relax module imports.
 from data import Data as relax_data_store
-from maths_fns.n_state_model import setup, func
+from maths_fns.n_state_model import N_state_model
 from specific_fns.base_class import Common_functions
 
 
@@ -96,8 +96,8 @@
         @type sim_index:        None or int
         """
 
-        # Set up the target function.
-        setup()
+        # Set up the class instance containing the target function.
+        model = N_state_model()
 
         # Setup the minimisation algorithm when constraints are present.
         if constraints and not match('^[Gg]rid', min_algor):
@@ -107,9 +107,9 @@
 
         # Minimisation.
         if constraints:
-            results = generic_minimise(func=func, args=(), 
x0=self.param_vector, min_algor=min_algor, min_options=min_options, 
func_tol=func_tol, grad_tol=grad_tol, maxiter=max_iterations, A=A, b=b, 
full_output=1, print_flag=print_flag)
+            results = generic_minimise(func=model.func, args=(), 
x0=self.param_vector, min_algor=min_algor, min_options=min_options, 
func_tol=func_tol, grad_tol=grad_tol, maxiter=max_iterations, A=A, b=b, 
full_output=1, print_flag=print_flag)
         else:
-            results = generic_minimise(func=func, args=(), 
x0=self.param_vector, min_algor=min_algor, min_options=min_options, 
func_tol=func_tol, grad_tol=grad_tol, maxiter=max_iterations, full_output=1, 
print_flag=print_flag)
+            results = generic_minimise(func=model.func, args=(), 
x0=self.param_vector, min_algor=min_algor, min_options=min_options, 
func_tol=func_tol, grad_tol=grad_tol, maxiter=max_iterations, full_output=1, 
print_flag=print_flag)
         if results == None:
             return
         self.param_vector, self.func, self.iter_count, self.f_count, 
self.g_count, self.h_count, self.warning = results