Author: bugman Date: Sat Nov 29 19:40:35 2014 New Revision: 26857 URL: http://svn.gna.org/viewcvs/relax?rev=26857&view=rev Log: Fixes for the unit tests of the target_functions.relax_fit C module. This is for the recent renaming of all the C functions based on the model type. Modified: trunk/test_suite/unit_tests/_target_functions/test_relax_fit.py Modified: trunk/test_suite/unit_tests/_target_functions/test_relax_fit.py URL: http://svn.gna.org/viewcvs/relax/trunk/test_suite/unit_tests/_target_functions/test_relax_fit.py?rev=26857&r1=26856&r2=26857&view=diff ============================================================================== --- trunk/test_suite/unit_tests/_target_functions/test_relax_fit.py (original) +++ trunk/test_suite/unit_tests/_target_functions/test_relax_fit.py Sat Nov 29 19:40:35 2014 @@ -27,7 +27,7 @@ from dep_check import C_module_exp_fn from status import Status; status = Status() if C_module_exp_fn: - from target_functions.relax_fit import setup, func, dfunc, d2func, jacobian, jacobian_chi2 + from target_functions.relax_fit import setup, func_exp, dfunc_exp, d2func_exp, jacobian_exp, jacobian_chi2_exp class Test_relax_fit(TestCase): @@ -73,21 +73,21 @@ setup(num_params=2, num_times=len(relax_times), values=I, sd=errors, relax_times=relax_times, scaling_matrix=self.scaling_list) - def test_func(self): - """Unit test for the value returned by the func() function at the minimum.""" + def test_func_exp(self): + """Unit test for the value returned by the func_exp() function at the minimum.""" # Get the chi-squared value. - val = func(self.params) + val = func_exp(self.params) # Assert that the value must be 0.0. self.assertAlmostEqual(val, 0.0) - def test_dfunc(self): - """Unit test for the gradient returned by the dfunc() function at the minimum.""" + def test_dfunc_exp(self): + """Unit test for the gradient returned by the dfunc_exp() function at the minimum.""" # Get the chi-squared gradient. - grad = dfunc(self.params) + grad = dfunc_exp(self.params) # Printout. print("The gradient at the minimum is:\n%s" % grad) @@ -97,8 +97,8 @@ self.assertAlmostEqual(grad[1], 0.0, 6) - def test_dfunc_off_minimum(self): - """Unit test for the gradient returned by the dfunc() function at a position away from the minimum. + def test_dfunc_exp_off_minimum(self): + """Unit test for the gradient returned by the dfunc_exp() function at a position away from the minimum. This uses the data from test_suite/shared_data/curve_fitting/numeric_gradient/integrate.log. """ @@ -109,7 +109,7 @@ params = [R/self.scaling_list[0], I0/self.scaling_list[1]] # Get the chi-squared gradient. - grad = dfunc(params) + grad = dfunc_exp(params) # Printout. print("The gradient at %s is:\n %s" % (params, grad)) @@ -119,14 +119,14 @@ self.assertAlmostEqual(grad[1], -10.8613338920982*self.scaling_list[1], 3) - def test_d2func(self): - """Unit test for the Hessian returned by the d2func() function at the minimum. + def test_d2func_exp(self): + """Unit test for the Hessian returned by the d2func_exp() function at the minimum. This uses the data from test_suite/shared_data/curve_fitting/numeric_gradient/Hessian.log. """ # Get the chi-squared Hessian. - hess = d2func(self.params) + hess = d2func_exp(self.params) # Printout. print("The Hessian at the minimum is:\n%s" % hess) @@ -138,8 +138,8 @@ self.assertAlmostEqual(hess[1][1], 2.31293027e-02*self.scaling_list[1]**2, 3) - def test_d2func_off_minimum(self): - """Unit test for the Hessian returned by the d2func() function at a position away from the minimum. + def test_d2func_exp_off_minimum(self): + """Unit test for the Hessian returned by the d2func_exp() function at a position away from the minimum. This uses the data from test_suite/shared_data/curve_fitting/numeric_gradient/Hessian.log. """ @@ -150,7 +150,7 @@ params = [R/self.scaling_list[0], I0/self.scaling_list[1]] # Get the chi-squared Hessian. - hess = d2func(params) + hess = d2func_exp(params) # Printout. print("The Hessian at %s is:\n%s" % (params, hess)) @@ -162,14 +162,14 @@ self.assertAlmostEqual(hess[1][1], 2.03731472e-02*self.scaling_list[1]**2, 3) - def test_jacobian(self): - """Unit test for the Jacobian returned by the jacobian() function at the minimum. - - This uses the data from test_suite/shared_data/curve_fitting/numeric_gradient/Hessian.log. - """ - - # Get the exponential curve Jacobian. - matrix = jacobian(self.params) + def test_jacobian_exp(self): + """Unit test for the Jacobian returned by the jacobian_exp() function at the minimum. + + This uses the data from test_suite/shared_data/curve_fitting/numeric_gradient/Hessian.log. + """ + + # Get the exponential curve Jacobian. + matrix = jacobian_exp(self.params) # The real Jacobian. real = [[ 0.00000000e+00, 1.00000000e+00], @@ -192,14 +192,14 @@ self.assertAlmostEqual(matrix[i, j], real[i, j], 3) - def test_jacobian_chi2(self): - """Unit test for the Jacobian returned by the jacobian_chi2() function at the minimum. + def test_jacobian_chi2_exp(self): + """Unit test for the Jacobian returned by the jacobian_chi2_exp() function at the minimum. This uses the data from test_suite/shared_data/curve_fitting/numeric_gradient/jacobian_chi2.log. """ # Get the exponential curve Jacobian. - matrix = jacobian_chi2(self.params) + matrix = jacobian_chi2_exp(self.params) # The real Jacobian. real = [[ 0.00000000e+00, 0.00000000e+00], @@ -222,8 +222,8 @@ self.assertAlmostEqual(matrix[i, j], real[i, j], 3) - def test_jacobian_chi2_off_minimum(self): - """Unit test for the Jacobian returned by the jacobian_chi2() function at a position away from the minimum. + def test_jacobian_chi2_exp_off_minimum(self): + """Unit test for the Jacobian returned by the jacobian_chi2_exp() function at a position away from the minimum. This uses the data from test_suite/shared_data/curve_fitting/numeric_gradient/jacobian_chi2.log. """ @@ -234,7 +234,7 @@ params = [R/self.scaling_list[0], I0/self.scaling_list[1]] # Get the exponential curve Jacobian. - matrix = jacobian_chi2(params) + matrix = jacobian_chi2_exp(params) # The real Jacobian. real = [[ 0.00000000e+00, -1.00000000e+01], @@ -257,19 +257,19 @@ self.assertAlmostEqual(matrix[i, j], real[i, j], 3) - def test_jacobian_off_minimum(self): - """Unit test for the Jacobian returned by the jacobian() function at a position away from the minimum. - - This uses the data from test_suite/shared_data/curve_fitting/numeric_gradient/Hessian.log. - """ - - # The off-minimum parameter values. - I0 = 500.0 - R = 2.0 - params = [R/self.scaling_list[0], I0/self.scaling_list[1]] - - # Get the exponential curve Jacobian. - matrix = jacobian(params) + def test_jacobian_exp_off_minimum(self): + """Unit test for the Jacobian returned by the jacobian_exp() function at a position away from the minimum. + + This uses the data from test_suite/shared_data/curve_fitting/numeric_gradient/Hessian.log. + """ + + # The off-minimum parameter values. + I0 = 500.0 + R = 2.0 + params = [R/self.scaling_list[0], I0/self.scaling_list[1]] + + # Get the exponential curve Jacobian. + matrix = jacobian_exp(params) # The real Jacobian. real = [[ 0.00000000e+00, 1.00000000e+00],