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],
[ -6.76676416e+01, 1.35335283e-01],
[ -1.83156389e+01, 1.83156389e-02],
[ -3.71812826e+00, 2.47875218e-03],
[ -6.70925256e-01, 3.35462628e-04]]
# Numpy conversion.
matrix = array(matrix)
real = transpose(array(real))
# Printout.
print("The Jacobian at %s is:\n%s" % (params, matrix))
print("The real 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_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],
[-6.76676416e+01, 1.35335283e-01],
[-1.83156389e+01, 1.83156389e-02],
[-3.71812826e+00, 2.47875218e-03],
[-6.70925256e-01, 3.35462628e-04]]
# Numpy conversion.
matrix = array(matrix)
real = transpose(array(real))
# Printout.
print("The Jacobian at %s is:\n%s" % (params, matrix))
print("The real 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_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],
[ -3.67879441e+02, 3.67879441e-01],
[ -2.70670566e+02, 1.35335283e-01],
[ -1.49361205e+02, 4.97870684e-02],
[ -7.32625556e+01, 1.83156389e-02]]
# Numpy conversion.
matrix = array(matrix)
real = transpose(array(real))
# Printouts.
print("The Jacobian at the minimum is:\n%s" % matrix)
print("The real 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_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],
[-3.67879441e+02, 3.67879441e-01],
[-2.70670566e+02, 1.35335283e-01],
[-1.49361205e+02, 4.97870684e-02],
[-7.32625556e+01, 1.83156389e-02]]
# Numpy conversion.
matrix = array(matrix)
real = transpose(array(real))
# Printouts.
print("The Jacobian at the minimum is:\n%s" % matrix)
print("The real 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)
