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],
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