r26770 - in /trunk: specific_analyses/relax_disp/ specific_analyses/relax_fit/ target_functions/ -- November 26, 2014 - 19:42

Author: bugman
Date: Wed Nov 26 19:42:20 2014
New Revision: 26770

URL: http://svn.gna.org/viewcvs/relax?rev=26770&view=rev
Log:
Shifted the C code Jacobian functions into the new 
target_functions.relax_fit_wrapper module.

This shifts all of the relaxation curve-fitting C code access into the
target_functions.relax_fit_wrapper module so that the rest of relax does not 
need to handle the C
code.  This will allow for new models to be very easily supported, as they 
would all be set up in
this target function module.


Modified:
    trunk/specific_analyses/relax_disp/estimate_r2eff.py
    trunk/specific_analyses/relax_fit/api.py
    trunk/target_functions/relax_fit_wrapper.py

Modified: trunk/specific_analyses/relax_disp/estimate_r2eff.py
URL: 
http://svn.gna.org/viewcvs/relax/trunk/specific_analyses/relax_disp/estimate_r2eff.py?rev=26770&r1=26769&r2=26770&view=diff
==============================================================================
--- trunk/specific_analyses/relax_disp/estimate_r2eff.py        (original)
+++ trunk/specific_analyses/relax_disp/estimate_r2eff.py        Wed Nov 26 
19:42:20 2014
@@ -44,10 +44,6 @@
 from target_functions.chi2 import chi2_rankN, dchi2
 from target_functions.relax_fit_wrapper import Relax_fit_opt
 
-# C modules.
-if C_module_exp_fn:
-    from target_functions.relax_fit import jacobian, jacobian_chi2, setup
-
 # Scipy installed.
 if scipy_module:
     # Import leastsq.
@@ -123,10 +119,10 @@
 
             # Initialise data in C code.
             scaling_list = [1.0, 1.0]
-            setup(num_params=len(param_vector), num_times=len(times), 
values=values, sd=errors, relax_times=times, scaling_matrix=scaling_list)
+            model = Relax_fit_opt(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( jacobian(param_vector) 
) )
+            jacobian_matrix_exp = transpose(asarray( 
model.jacobian(param_vector) ) )
             weights = 1. / errors**2
 
             # Get the co-variance
@@ -823,12 +819,12 @@
     if E.c_code == True:
         if E.chi2_jacobian:
             # Use the chi2 Jacobian from C.
-            jacobian_matrix_exp = transpose(asarray( 
jacobian_chi2(param_vector) ) )
+            jacobian_matrix_exp = transpose(asarray( 
model.jacobian_chi2(param_vector) ) )
             weights = ones(E.errors.shape)
 
         else:
             # Use the direct Jacobian from C.
-            jacobian_matrix_exp = transpose(asarray( jacobian(param_vector) 
) )
+            jacobian_matrix_exp = transpose(asarray( 
model.jacobian(param_vector) ) )
             weights = 1. / E.errors**2
 
     elif E.c_code == False:

Modified: trunk/specific_analyses/relax_fit/api.py
URL: 
http://svn.gna.org/viewcvs/relax/trunk/specific_analyses/relax_fit/api.py?rev=26770&r1=26769&r2=26770&view=diff
==============================================================================
--- trunk/specific_analyses/relax_fit/api.py    (original)
+++ trunk/specific_analyses/relax_fit/api.py    Wed Nov 26 19:42:20 2014
@@ -45,10 +45,6 @@
 from specific_analyses.relax_fit.parameters import assemble_param_vector, 
disassemble_param_vector, linear_constraints
 from target_functions.relax_fit_wrapper import Relax_fit_opt
 
-# C modules.
-if C_module_exp_fn:
-    from target_functions.relax_fit import jacobian, setup
-
 
 class Relax_fit(API_base, API_common):
     """Class containing functions for relaxation curve fitting."""
@@ -139,10 +135,10 @@
 
         # Initialise data in C code.
         scaling_list = [1.0, 1.0]
-        setup(num_params=len(param_vector), num_times=len(times), 
values=values, sd=errors, relax_times=times, scaling_matrix=scaling_list)
+        model = Relax_fit_opt(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( jacobian(param_vector) ) )
+        jacobian_matrix_exp = transpose(asarray( 
model.jacobian(param_vector) ) )
         weights = 1. / errors**2
 
         # Return the matrices.

Modified: trunk/target_functions/relax_fit_wrapper.py
URL: 
http://svn.gna.org/viewcvs/relax/trunk/target_functions/relax_fit_wrapper.py?rev=26770&r1=26769&r2=26770&view=diff
==============================================================================
--- trunk/target_functions/relax_fit_wrapper.py (original)
+++ trunk/target_functions/relax_fit_wrapper.py Wed Nov 26 19:42:20 2014
@@ -32,7 +32,7 @@
 
 # C modules.
 if C_module_exp_fn:
-    from target_functions.relax_fit import setup, func, dfunc, d2func, 
back_calc_I
+    from target_functions.relax_fit import jacobian, jacobian_chi2, setup, 
func, dfunc, d2func, back_calc_I
 
 
 class Relax_fit_opt:
@@ -60,6 +60,10 @@
         self.func = self.func_wrapper
         self.dfunc = self.dfunc_wrapper
         self.d2func = self.d2func_wrapper
+
+        # Alias the Jacobian C functions.
+        self.jacobian = jacobian
+        self.jacobian_chi2 = jacobian_chi2
 
 
     def back_calc_data(self):