r12626 - /1.3/specific_fns/relax_fit.py -- February 25, 2011 - 14:26

Author: bugman
Date: Fri Feb 25 14:26:23 2011
New Revision: 12626

URL: http://svn.gna.org/viewcvs/relax?rev=12626&view=rev
Log:
Added wrapper methods to the relaxation curve-fitting specific code for the 
new C modules.

These allow the parameter numpy array from minfx to be converted into a 
Python list prior to sending
it into the C module.


Modified:
    1.3/specific_fns/relax_fit.py

Modified: 1.3/specific_fns/relax_fit.py
URL: 
http://svn.gna.org/viewcvs/relax/1.3/specific_fns/relax_fit.py?rev=12626&r1=12625&r2=12626&view=diff
==============================================================================
--- 1.3/specific_fns/relax_fit.py (original)
+++ 1.3/specific_fns/relax_fit.py Fri Feb 25 14:26:23 2011
@@ -184,7 +184,7 @@
         setup(num_params=len(spin.params), num_times=len(cdp.relax_times), 
values=values, sd=errors, relax_times=times, scaling_matrix=scaling_matrix)
 
         # Make a single function call.  This will cause back calculation and 
the data will be stored in the C module.
-        func(param_vector)
+        self._func(param_vector)
 
         # Get the data back.
         results = back_calc_I()
@@ -227,6 +227,36 @@
             # Intensity at infinity.
             if cdp.curve_type == 'inv':
                 spin.iinf = param_vector[2]
+
+
+    def _func(self, params):
+        """Wrapper function for the C module, for converting numpy arrays.
+
+        @param params:  The parameter array from the minimisation code.
+        @type params:   numpy array
+        @return:        The function value generated by the C module.
+        @rtype:         float
+        """
+
+        # Call the C code.
+        chi2 = func(params.tolist())
+
+        # Return the chi2 value.
+        return chi2
+
+
+    def _dfunc(self, params):
+        """Wrapper function for the C module, for converting numpy arrays.
+
+        The currently does nothing.
+        """
+
+
+    def _d2func(self, params):
+        """Wrapper function for the C module, for converting numpy arrays.
+
+        The currently does nothing.
+        """
 
 
     def _grid_search_setup(self, spin=None, param_vector=None, lower=None, 
upper=None, inc=None, scaling_matrix=None):
@@ -762,7 +792,7 @@
                 # The relaxation times.
                 times.append(cdp.relax_times[key])
 
-            setup(num_params=len(spin.params), 
num_times=len(cdp.relax_times), values=values, sd=errors, relax_times=times, 
scaling_matrix=scaling_matrix)
+            setup(num_params=len(spin.params), 
num_times=len(cdp.relax_times), values=values, sd=errors, relax_times=times, 
scaling_matrix=scaling_matrix.tolist())
 
 
             # Setup the minimisation algorithm when constraints are present.
@@ -793,7 +823,7 @@
 
             # Grid search.
             if search('^[Gg]rid', min_algor):
-                results = grid(func=func, args=(), num_incs=inc, 
lower=lower, upper=upper, A=A, b=b, verbosity=verbosity)
+                results = grid(func=self._func, args=(), num_incs=inc, 
lower=lower, upper=upper, A=A, b=b, verbosity=verbosity)
 
                 # Unpack the results.
                 param_vector, chi2, iter_count, warning = results
@@ -803,7 +833,7 @@
 
             # Minimisation.
             else:
-                results = generic_minimise(func=func, dfunc=dfunc, 
d2func=d2func, args=(), x0=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=True, print_flag=verbosity)
+                results = generic_minimise(func=self._func, 
dfunc=self._dfunc, d2func=self._d2func, args=(), x0=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=True, 
print_flag=verbosity)
 
                 # Unpack the results.
                 if results == None: