diff --git a/test_autolens/analysis/test_result.py b/test_autolens/analysis/test_result.py index e57e4caf0..faeac185a 100644 --- a/test_autolens/analysis/test_result.py +++ b/test_autolens/analysis/test_result.py @@ -215,10 +215,12 @@ def test__positions_threshold_from(analysis_imaging_7x7): # x = 0), so the point solver's branch is decided by sub-ULP tie-breaking; PyAutoArray#519's # exact identity transform at angle 0 selected the other branch (positions_threshold # 0.0019501455 -> 0.0019534291, positions[1].x sign flip, magnitude bit-identical). Values - # are the exact-transform branch; a 1e-15 centre nudge flips them. See PyAutoLens#721. - assert result.positions_threshold_from() == pytest.approx(0.0019534291, 1.0e-4) + # are the exact-transform branch; a 1e-15 centre nudge flips them. The value moved again + # (0.0019534291 -> 0.00098983199) when the Isothermal q <= 0.99999 clamp was removed + # (PyAutoGalaxy#631), as the fixture is now exactly circular. See PyAutoLens#721. + assert result.positions_threshold_from() == pytest.approx(0.00098983199, 1.0e-4) assert result.positions_threshold_from(factor=5.0) == pytest.approx( - 0.0097671455155, 1.0e-4 + 0.0049491599483, 1.0e-4 ) assert result.positions_threshold_from(minimum_threshold=10.0) == pytest.approx( 10.0, 1.0e-4 @@ -346,12 +348,14 @@ def test__positions_likelihood_from__mass_centre_radial_distance_min( # x = 0), so the point solver's branch is decided by sub-ULP tie-breaking; PyAutoArray#519's # exact identity transform at angle 0 selected the other branch (positions_threshold # 0.0019501455 -> 0.0019534291, positions[1].x sign flip, magnitude bit-identical). Values - # are the exact-transform branch; a 1e-15 centre nudge flips them. See PyAutoLens#721. + # are the exact-transform branch; a 1e-15 centre nudge flips them. The positions moved + # again (branch and order) when the Isothermal q <= 0.99999 clamp was removed + # (PyAutoGalaxy#631), as the fixture is now exactly circular. See PyAutoLens#721. assert positions_likelihood.positions[0] == pytest.approx( - (-1.00097656e00, 5.63818622e-04), 1.0e-4 + (9.99023438e-01, 5.63818622e-04), 1.0e-4 ) assert positions_likelihood.positions[1] == pytest.approx( - (1.00097656e00, 5.63818622e-04), 1.0e-4 + (-1.00000000e00, 1.12763724e-03), 1.0e-4 ) diff --git a/test_autolens/imaging/test_simulate_and_fit_imaging.py b/test_autolens/imaging/test_simulate_and_fit_imaging.py index 7c5e88eba..8ee3a486f 100644 --- a/test_autolens/imaging/test_simulate_and_fit_imaging.py +++ b/test_autolens/imaging/test_simulate_and_fit_imaging.py @@ -494,7 +494,7 @@ def test__simulate_imaging_data_and_fit__linear_light_profiles_and_pixelization( assert fit_linear.inversion.reconstruction[0:2] == pytest.approx( np.array( [ - 99.993449641, 0.114213814, + 99.993447113, 0.114229460, ] ), 1.0e-4, @@ -617,7 +617,7 @@ def test__simulate_imaging_data_and_fit__linear_light_profiles_and_pixelization_ assert fit_linear.inversion.reconstruction[0:2] == pytest.approx( np.array( [ - 99.974078996, 0.251635768, + 99.974074623, 0.251662353, ] ), 1.0e-4, @@ -757,13 +757,13 @@ def test__simulate_imaging_data_and_fit__linear_light_profiles_and_pixelization_ assert fit_linear.inversion.reconstruction[0:3] == pytest.approx( np.array( [ - 1.00179579e+02, 5.35321466e-01, 8.55754143e-01 + 1.00179521e+02, 2.48877703e-01, 9.28951186e-01 ] ), 1.0e-4, ) - assert fit_linear.figure_of_merit == pytest.approx(-190.564548990939, 1.0e-4) + assert fit_linear.figure_of_merit == pytest.approx(-190.664392317177, 1.0e-4) lens_galaxy_image = lens_galaxy.blurred_image_2d_from( grid=masked_dataset.grids.lp, @@ -779,15 +779,15 @@ def test__simulate_imaging_data_and_fit__linear_light_profiles_and_pixelization_ ) assert fit_linear.galaxy_model_image_dict[source_galaxy_pix][0] == pytest.approx( - 0.1667703826, 1.0e-4 + 0.1579572359, 1.0e-4 ) assert fit_linear.model_images_of_planes_list[1][0] == pytest.approx( - 0.166757208736973, 1.0e-4 + 0.157957235866697, 1.0e-4 ) assert fit_linear.subtracted_images_of_planes_list[1][0] == pytest.approx( - 0.180018267146, 1.0e-4 + 0.180065151300, 1.0e-4 ) @@ -804,13 +804,13 @@ def test__simulate_imaging_data_and_fit__linear_light_profiles_and_pixelization_ assert fit_linear.inversion.reconstruction[0:2] == pytest.approx( np.array( [ - 99.9785287998059, - 0.8958653625423 + 99.9796865598131, + 0.7557985549100 ] ), 1.0e-4, ) - assert fit_linear.figure_of_merit == pytest.approx(-190.6935526756, 1.0e-4) + assert fit_linear.figure_of_merit == pytest.approx(-190.7901143681, 1.0e-4) def test__fit_figure_of_merit__mge_mass_model(masked_imaging_7x7, masked_imaging_covariance_7x7): diff --git a/test_autolens/interferometer/test_simulate_and_fit_interferometer.py b/test_autolens/interferometer/test_simulate_and_fit_interferometer.py index f9f532856..b113fd975 100644 --- a/test_autolens/interferometer/test_simulate_and_fit_interferometer.py +++ b/test_autolens/interferometer/test_simulate_and_fit_interferometer.py @@ -299,16 +299,16 @@ def test__simulate_interferometer_data_and_fit__linear_light_profiles_and_pixeli assert fit_linear.inversion.reconstruction == pytest.approx( np.array( [ - 101.76664331, - 0.49639672, - 0.49531196, - 0.49854243, - 0.44661417, - 0.44782337, - 0.44844437, - 0.39942579, - 0.40320996, - 0.40104302, + 102.21688734, + 0.61507474, + 0.61369673, + 0.61727889, + 0.55279219, + 0.55418069, + 0.55482003, + 0.49375700, + 0.49858010, + 0.49588652, ] ), 1.0e-2,