r25236 - in /trunk/test_suite/shared_data/curve_fitting/profiling: profiling_relax_fit.py verify_error.py -- August 25, 2014 - 01:09

Author: tlinnet
Date: Mon Aug 25 01:09:00 2014
New Revision: 25236

URL: http://svn.gna.org/viewcvs/relax?rev=25236&view=rev
Log:
Small changes to verification scripts, to use chi2 function and use the 
scaling matrix correct.

task #7822(https://gna.org/task/index.php?7822): Implement user function to 
estimate R2eff and associated errors for exponential curve fitting.

Modified:
    
trunk/test_suite/shared_data/curve_fitting/profiling/profiling_relax_fit.py
    trunk/test_suite/shared_data/curve_fitting/profiling/verify_error.py

Modified: 
trunk/test_suite/shared_data/curve_fitting/profiling/profiling_relax_fit.py
URL: 
http://svn.gna.org/viewcvs/relax/trunk/test_suite/shared_data/curve_fitting/profiling/profiling_relax_fit.py?rev=25236&r1=25235&r2=25236&view=diff
==============================================================================
--- 
trunk/test_suite/shared_data/curve_fitting/profiling/profiling_relax_fit.py 
(original)
+++ 
trunk/test_suite/shared_data/curve_fitting/profiling/profiling_relax_fit.py 
Mon Aug 25 01:09:00 2014
@@ -57,6 +57,7 @@
 # relax module imports.
 from status import Status; status = Status()
 from target_functions.relax_fit import setup, func, dfunc, d2func, 
back_calc_I
+from target_functions.chi2 import chi2_rankN
 from target_functions.relax_disp_curve_fit import Exponential
 
 
@@ -289,6 +290,8 @@
         #param_vector = array([ 0.,  0.])
         self.param_vector = array([  8.800000000000001e+00,   
2.000000000800000e+05])
         self.scaling_list = [1.0, 1.0]
+        self.scaling_matrix = array( [ [self.scaling_list[0], 0], [0, 
self.scaling_list[1]] ])
+
         self.func_tol = 1e-25
         self.grad_tol = None
         #self.max_iterations = 10000000
@@ -372,7 +375,7 @@
 
     for values, errors, times, struct, num_times in C.loop_data():
         # Initialise the function to minimise.
-        exp_class = Exponential(num_params=len(C.param_vector), 
num_times=num_times, values=values, sd=errors, relax_times=times, 
scaling_matrix=C.scaling_list)
+        exp_class = Exponential(num_params=len(C.param_vector), 
num_times=num_times, values=values, sd=errors, relax_times=times, 
scaling_matrix=C.scaling_matrix)
 
         results = generic_minimise(func=exp_class.func_exp, args=(), 
x0=C.param_vector, min_algor=C.min_algor, min_options=C.min_options, 
func_tol=C.func_tol, grad_tol=C.grad_tol, maxiter=C.max_iterations, A=C.A, 
b=C.b, full_output=True, print_flag=C.verbosity)
 
@@ -394,7 +397,7 @@
 
     for values, errors, times, struct, num_times in C.loop_data():
         # Initialise the function to minimise.
-        exp_class = Exponential(num_params=len(C.param_vector), 
num_times=num_times, values=values, sd=errors, relax_times=times, 
scaling_matrix=C.scaling_list)
+        exp_class = Exponential()
 
         # Do optimisation with scipy.optimize.curve_fit
         # sigma : None or M-length sequence, optional. If not None, these 
values are used as weights in the least-squares problem.
@@ -478,7 +481,7 @@
         back_calc = exp_class.calc_exp(times=times, r2eff=popt[0], 
i0=popt[1])
 
         # Calculate chi2.
-        chi2 = exp_class.chi2_rankN(data=values, back_calc_vals=back_calc, 
errors=errors)
+        chi2 = chi2_rankN(data=values, back_calc_vals=back_calc, 
errors=errors)
 
         chi2_list.append(chi2)
 

Modified: trunk/test_suite/shared_data/curve_fitting/profiling/verify_error.py
URL: 
http://svn.gna.org/viewcvs/relax/trunk/test_suite/shared_data/curve_fitting/profiling/verify_error.py?rev=25236&r1=25235&r2=25236&view=diff
==============================================================================
--- trunk/test_suite/shared_data/curve_fitting/profiling/verify_error.py      
  (original)
+++ trunk/test_suite/shared_data/curve_fitting/profiling/verify_error.py      
  Mon Aug 25 01:09:00 2014
@@ -30,6 +30,7 @@
 #from specific_analyses.relax_disp.data import average_intensity, 
generate_r20_key, get_curve_type, has_exponential_exp_type, 
has_r1rho_exp_type, loop_exp_frq, loop_exp_frq_offset_point, 
loop_exp_frq_offset_point_time, loop_time, return_grace_file_name_ini, 
return_param_key_from_data
 from specific_analyses.relax_disp.data import average_intensity, 
find_intensity_keys, loop_exp_frq_offset_point, loop_time, 
return_param_key_from_data
 from status import Status; status = Status()
+from target_functions.chi2 import chi2_rankN
 
 # Initial try for Exponential class.
 from target_functions.relax_disp_curve_fit import Exponential
@@ -197,7 +198,7 @@
         back_calc = exp_class.calc_exp(times=times, r2eff=popt[0], 
i0=popt[1])
 
         # Calculate chi2.
-        chi2 = exp_class.chi2_rankN(data=values, back_calc_vals=back_calc, 
errors=errors)
+        chi2 = chi2_rankN(data=values, back_calc_vals=back_calc, 
errors=errors)
 
         # 'pcov': The estimated covariance of popt.
         # The diagonals provide the variance of the parameter estimate.