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_exp(self.params)
# Printout.
print("The gradient at the minimum is:\n%s" % grad)
# Assert that the elements must be 0.0.
self.assertAlmostEqual(grad[0], 0.0, 6)
self.assertAlmostEqual(grad[1], 0.0, 6)
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_exp(self.params)
# Printout.
print("The gradient at the minimum is:\n%s" % grad)
# Assert that the elements must be 0.0.
self.assertAlmostEqual(grad[0], 0.0, 6)
self.assertAlmostEqual(grad[1], 0.0, 6)
def dfunc_exp(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 gradient generated by the C module converted to numpy format.
@rtype: numpy float64 array
"""
# Convert if necessary.
if isinstance(params, ndarray):
params = params.tolist()
# Call the C code.
dchi2 = dfunc_exp(params)
# Return the chi2 gradient as a numpy array.
return array(dchi2, float64)
def dfunc_exp(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 gradient generated by the C module converted to numpy format.
@rtype: numpy float64 array
"""
# Convert if necessary.
if isinstance(params, ndarray):
params = params.tolist()
# Call the C code.
dchi2 = dfunc_exp(params)
# Return the chi2 gradient as a numpy array.
return array(dchi2, float64)
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.
"""
# The off-minimum parameter values.
I0 = 500.0
R = 2.0
params = [R/self.scaling_list[0], I0/self.scaling_list[1]]
# Get the chi-squared gradient.
grad = dfunc_exp(params)
# Printout.
print("The gradient at %s is:\n %s" % (params, grad))
# Check that the gradient matches the numerically derived values.
self.assertAlmostEqual(grad[0], 456.36655522098829*self.scaling_list[0], 3)
self.assertAlmostEqual(grad[1], -10.8613338920982*self.scaling_list[1], 3)
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.
"""
# The off-minimum parameter values.
I0 = 500.0
R = 2.0
params = [R / self.scaling_list[0], I0 / self.scaling_list[1]]
# Get the chi-squared gradient.
grad = dfunc_exp(params)
# Printout.
print("The gradient at %s is:\n %s" % (params, grad))
# Check that the gradient matches the numerically derived values.
self.assertAlmostEqual(grad[0],
456.36655522098829 * self.scaling_list[0], 3)
self.assertAlmostEqual(grad[1],
-10.8613338920982 * self.scaling_list[1], 3)
