def test_correct_data_single_fit(self):
"""
Tests that correct data gives the expected result
"""
fitting_problem = FittingProblem(self.options)
x_data = np.array([-0.5, 0.0, 1.0, 0.5, 1.5, 2.0, 2.5, 3.0, 4.0])
y_data = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0])
e_data = np.array([1.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 9.0])
start_x = 0.5
end_x = 2.5
expected_x_data = np.array([0.5, 1.0, 1.5, 2.0, 2.5])
expected_y_data = np.array([3.0, 2.0, 4.0, 5.0, 6.0])
expected_e_data = np.array([40.0, 30.0, 50.0, 60.0, 70.0])
fitting_problem.data_x = x_data
fitting_problem.data_y = y_data
fitting_problem.data_e = e_data
fitting_problem.start_x = start_x
fitting_problem.end_x = end_x
fitting_problem.correct_data()
self.assertTrue(
np.isclose(fitting_problem.data_x[fitting_problem.sorted_index],
expected_x_data).all())
self.assertTrue(
np.isclose(fitting_problem.data_y[fitting_problem.sorted_index],
expected_y_data).all())
self.assertTrue(
np.isclose(fitting_problem.data_e[fitting_problem.sorted_index],
expected_e_data).all())
self.options.cost_func_type = "nlls"
fitting_problem.correct_data()
self.assertTrue(
np.isclose(fitting_problem.data_x[fitting_problem.sorted_index],
expected_x_data).all())
self.assertTrue(
np.isclose(fitting_problem.data_y[fitting_problem.sorted_index],
expected_y_data).all())
self.assertIs(fitting_problem.data_e, None)
def test_correct_data_multi_fit(self):
"""
Tests correct data on a multifit problem.
"""
fitting_problem = FittingProblem(self.options)
fitting_problem.multifit = True
x_data = [
np.array([-0.5, 0.0, 1.0, 0.5, 1.5, 2.0, 2.5, 3.0, 4.0]),
np.array([-0.5, 0.0, 1.0, 0.5, 1.4, 2.0, 2.5, 3.0, 4.0]),
np.array([-0.5, 0.0, 1.0, 0.5, 1.7, 2.0, 2.5, 3.0, 4.0])
]
y_data = [
np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]),
np.array([0.0, 1.0, 2.0, 3.0, 24.0, 5.0, 6.0, 7.0, 8.0]),
np.array([0.0, 1.0, 2.8, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0])
]
e_data = [
np.array([1.0, 20.0, 30.0, 40.0, 50.0, 60.0, 1.0, 6.0, 9.0]),
np.array([1.0, 20.0, 30.0, 40.0, 50.0, 60.0, 1.0, 6.0, 9.0]),
np.array([1.0, 20.0, 30.0, 40.0, 50.0, 60.0, 1.0, 6.0, 9.0])
]
start_x = [0.5, 1.1, 0.0]
end_x = [2.5, 2.6, 1.0]
expected_x_data = [
np.array([0.5, 1.0, 1.5, 2.0, 2.5]),
np.array([1.4, 2.0, 2.5]),
np.array([0.0, 0.5, 1.0])
]
expected_y_data = [
np.array([3.0, 2.0, 4.0, 5.0, 6.0]),
np.array([24.0, 5.0, 6.0]),
np.array([1.0, 3.0, 2.8])
]
expected_e_data = [
np.array([40.0, 30.0, 50.0, 60.0, 1.0]),
np.array([50.0, 60.0, 1.0]),
np.array([20.0, 40.0, 30.0])
]
fitting_problem.data_x = x_data
fitting_problem.data_y = y_data
fitting_problem.data_e = e_data
fitting_problem.start_x = start_x
fitting_problem.end_x = end_x
fitting_problem.correct_data()
for i in range(3):
self.assertTrue(
np.isclose(
fitting_problem.data_x[i][fitting_problem.sorted_index[i]],
expected_x_data[i]).all())
self.assertTrue(
np.isclose(
fitting_problem.data_y[i][fitting_problem.sorted_index[i]],
expected_y_data[i]).all())
self.assertTrue(
np.isclose(
fitting_problem.data_e[i][fitting_problem.sorted_index[i]],
expected_e_data[i]).all())
self.options.cost_func_type = "nlls"
fitting_problem.correct_data()
for i in range(3):
self.assertTrue(
np.isclose(
fitting_problem.data_x[i][fitting_problem.sorted_index[i]],
expected_x_data[i]).all())
self.assertTrue(
np.isclose(
fitting_problem.data_y[i][fitting_problem.sorted_index[i]],
expected_y_data[i]).all())
self.assertIs(fitting_problem.data_e[i], None)
