def test_optimal_central_epoch_raises_error(self):
astrometric_data = generate_astrometric_data()
fitter = AstrometricFitter(
inverse_covariance_matrices=astrometric_data[
'inverse_covariance_matrix'],
epoch_times=astrometric_data['epoch_delta_t'])
with pytest.raises(ValueError):
fitter.find_optimal_central_epoch(
'incorrect_choice'), fitter.find_optimal_central_epoch('dec')
def test_optimal_central_epoch_on_linear_data(self):
astrometric_data = generate_astrometric_data()
fitter = AstrometricFitter(
inverse_covariance_matrices=astrometric_data[
'inverse_covariance_matrix'],
epoch_times=astrometric_data['epoch_delta_t'])
central_epoch_ra, central_epoch_dec = fitter.find_optimal_central_epoch(
'ra'), fitter.find_optimal_central_epoch('dec')
cov_matrix = fitter.evaluate_cov_matrix(central_epoch_ra,
central_epoch_dec)
assert np.allclose([cov_matrix[0, 2], cov_matrix[1, 3]], 0)
def test_optimal_central_epoch_on_cubic_data(self):
astrometric_data = generate_astrometric_data(acc=True, jerk=True)
fitter = AstrometricFitter(
inverse_covariance_matrices=astrometric_data[
'inverse_covariance_matrix'],
epoch_times=astrometric_data['epoch_delta_t'],
use_parallax=False,
fit_degree=3)
central_epoch_ra, central_epoch_dec = fitter.find_optimal_central_epoch(
'ra'), fitter.find_optimal_central_epoch('dec')
cov_matrix = fitter.evaluate_cov_matrix(central_epoch_ra,
central_epoch_dec)
assert np.allclose([cov_matrix[0, 2], cov_matrix[1, 3]], 0)