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.
"""
# 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_chi2_exp(params)
# The real Jacobian.
real = [[ 0.00000000e+00, -1.00000000e+01],
[ 4.06292489e+02, -8.12584978e-01],
[ 4.62204173e+01, -4.62204173e-02],
[ 3.61013094e+00, -2.40675396e-03],
[ 2.43517791e-01, -1.21758895e-04]]
# Numpy conversion.
matrix = array(matrix)
real = transpose(array(real))
# Printout.
print("The chi-squared Jacobian at %s is:\n%s" % (params, matrix))
print("The real chi-squared Jacobian at the minimum is:\n%s" % real)
# Check that the Jacobian matches the numerically derived values.
for i in range(len(matrix)):
for j in range(len(matrix[i])):
self.assertAlmostEqual(matrix[i, j], real[i, j], 3)
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.
"""
# 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_chi2_exp(params)
# The real Jacobian.
real = [[0.00000000e+00, -1.00000000e+01],
[4.06292489e+02, -8.12584978e-01],
[4.62204173e+01, -4.62204173e-02],
[3.61013094e+00, -2.40675396e-03],
[2.43517791e-01, -1.21758895e-04]]
# Numpy conversion.
matrix = array(matrix)
real = transpose(array(real))
# Printout.
print("The chi-squared Jacobian at %s is:\n%s" % (params, matrix))
print("The real chi-squared Jacobian at the minimum is:\n%s" % real)
# Check that the Jacobian matches the numerically derived values.
for i in range(len(matrix)):
for j in range(len(matrix[i])):
self.assertAlmostEqual(matrix[i, j], real[i, j], 3)
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_exp(self.params)
# The real Jacobian.
real = [[ 0.00000000e+00, 0.00000000e+00],
[ -3.25440806e-09, 3.25446070e-12],
[ 2.09660266e-09, -1.04831421e-12],
[ 1.07707223e-10, -3.58994022e-14],
[ -5.00778448e-11, 1.25201612e-14]]
# Numpy conversion.
matrix = array(matrix)
real = transpose(array(real))
# Printouts.
print("The chi-squared Jacobian at the minimum is:\n%s" % matrix)
print("The real chi-squared Jacobian at the minimum is:\n%s" % real)
# Check that the Jacobian matches the numerically derived values.
for i in range(len(matrix)):
for j in range(len(matrix[i])):
self.assertAlmostEqual(matrix[i, j], real[i, j], 3)
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_exp(self.params)
# The real Jacobian.
real = [[0.00000000e+00, 0.00000000e+00],
[-3.25440806e-09, 3.25446070e-12],
[2.09660266e-09, -1.04831421e-12],
[1.07707223e-10, -3.58994022e-14],
[-5.00778448e-11, 1.25201612e-14]]
# Numpy conversion.
matrix = array(matrix)
real = transpose(array(real))
# Printouts.
print("The chi-squared Jacobian at the minimum is:\n%s" % matrix)
print("The real chi-squared Jacobian at the minimum is:\n%s" % real)
# Check that the Jacobian matches the numerically derived values.
for i in range(len(matrix)):
for j in range(len(matrix[i])):
self.assertAlmostEqual(matrix[i, j], real[i, j], 3)
