def test__furthest_separation_of_source_plane_positions(self):
positions = al.GridIrregularGrouped(grid=[[(0.0, 0.0), (0.0, 1.0)]])
noise_map = al.ValuesIrregularGrouped([[1.0, 1.0]])
tracer = MockTracerPositions(positions=positions)
fit = al.FitPositionsSourceMaxSeparation(
positions=positions, noise_map=noise_map, tracer=tracer
)
assert fit.furthest_separations_of_source_plane_positions.in_grouped_list == [
[1.0, 1.0]
]
assert fit.max_separation_of_source_plane_positions == 1.0
assert fit.max_separation_within_threshold(threshold=2.0) == True
assert fit.max_separation_within_threshold(threshold=0.5) == False
positions = al.GridIrregularGrouped(
grid=[[(0.0, 0.0), (0.0, 1.0), (0.0, 3.0)], [(0.0, 0.0)]]
)
noise_map = al.ValuesIrregularGrouped([[1.0, 1.0], [1.0]])
tracer = MockTracerPositions(positions=positions)
fit = al.FitPositionsSourceMaxSeparation(
positions=positions, noise_map=noise_map, tracer=tracer
)
assert fit.furthest_separations_of_source_plane_positions.in_grouped_list == [
[3.0, 2.0, 3.0],
[0.0],
]
assert fit.max_separation_of_source_plane_positions == 3.0
assert fit.max_separation_within_threshold(threshold=3.5) == True
assert fit.max_separation_within_threshold(threshold=2.0) == False
assert fit.max_separation_within_threshold(threshold=0.5) == False
def test__same_as_above_with_real_tracer(self):
tracer = al.Tracer.from_galaxies(
galaxies=[
al.Galaxy(
redshift=0.5, mass=al.mp.SphericalIsothermal(einstein_radius=1.0)
),
al.Galaxy(redshift=1.0),
]
)
noise_map = al.ValuesIrregularGrouped([[1.0, 1.0]])
positions = al.GridIrregularGrouped([[(1.0, 0.0), (-1.0, 0.0)]])
fit = al.FitPositionsSourceMaxSeparation(
positions=positions, noise_map=noise_map, tracer=tracer
)
assert fit.max_separation_within_threshold(threshold=0.01)
positions = al.GridIrregularGrouped([[(1.2, 0.0), (-1.0, 0.0)]])
fit = al.FitPositionsSourceMaxSeparation(
positions=positions, noise_map=noise_map, tracer=tracer
)
assert fit.max_separation_within_threshold(threshold=0.3)
assert not fit.max_separation_within_threshold(threshold=0.15)
def test__two_sets_of_positions__residuals_likelihood_correct(self):
tracer = MockTracerPositions(positions=None)
positions = al.GridIrregularGrouped([[(0.0, 0.0), (3.0, 4.0)], [(3.0, 3.0)]])
noise_map = al.ValuesIrregularGrouped([[0.5, 1.0], [1.0]])
model_positions = al.GridIrregularGrouped(
[[(3.0, 1.0), (2.0, 3.0)], [(3.0, 3.0)]]
)
positions_solver = mock.MockPositionsSolver(model_positions=model_positions)
fit = al.FitPositionsImage(
positions=positions,
noise_map=noise_map,
tracer=tracer,
positions_solver=positions_solver,
)
assert fit.model_positions_all.in_grouped_list == [
[(3.0, 1.0), (2.0, 3.0)],
[(3.0, 3.0)],
]
assert fit.model_positions.in_grouped_list == [
[(3.0, 1.0), (2.0, 3.0)],
[(3.0, 3.0)],
]
assert fit.noise_map.in_grouped_list == [[0.5, 1.0], [1.0]]
assert fit.residual_map.in_grouped_list == [
[np.sqrt(10.0), np.sqrt(2.0)],
[0.0],
]
assert fit.normalized_residual_map.in_grouped_list == [
[np.sqrt(10.0) / 0.5, np.sqrt(2.0) / 1.0],
[0.0],
]
assert fit.chi_squared_map.in_grouped_list == [
[(np.sqrt(10.0) / 0.5) ** 2, np.sqrt(2.0) ** 2.0],
[0.0],
]
assert fit.chi_squared == pytest.approx(42.0, 1.0e-4)
assert fit.noise_normalization == pytest.approx(4.12733, 1.0e-4)
assert fit.log_likelihood == pytest.approx(-23.06366, 1.0e-4)
def test__more_model_positions_than_data_positions__pairs_closest_positions(self):
tracer = MockTracerPositions(positions=None)
positions = al.GridIrregularGrouped([[(0.0, 0.0), (3.0, 4.0)], [(3.0, 3.0)]])
noise_map = al.ValuesIrregularGrouped([[0.5, 1.0], [1.0]])
model_positions = al.GridIrregularGrouped(
[[(3.0, 1.0), (2.0, 3.0), (1.0, 0.0), (0.0, 1.0)], [(3.0, 3.0), (4.0, 4.0)]]
)
positions_solver = mock.MockPositionsSolver(model_positions=model_positions)
fit = al.FitPositionsImage(
positions=positions,
noise_map=noise_map,
tracer=tracer,
positions_solver=positions_solver,
)
assert fit.model_positions_all.in_grouped_list == [
[(3.0, 1.0), (2.0, 3.0), (1.0, 0.0), (0.0, 1.0)],
[(3.0, 3.0), (4.0, 4.0)],
]
assert fit.model_positions.in_grouped_list == [
[(1.0, 0.0), (2.0, 3.0)],
[(3.0, 3.0)],
]
assert fit.noise_map.in_grouped_list == [[0.5, 1.0], [1.0]]
assert fit.residual_map.in_grouped_list == [[1.0, np.sqrt(2.0)], [0.0]]
assert fit.normalized_residual_map.in_grouped_list == [
[2.0, np.sqrt(2.0) / 1.0],
[0.0],
]
assert fit.chi_squared_map.in_grouped_list == [
[4.0, np.sqrt(2.0) ** 2.0],
[0.0],
]
assert fit.chi_squared == pytest.approx(6.0, 1.0e-4)
assert fit.noise_normalization == pytest.approx(4.12733, 1.0e-4)
assert fit.log_likelihood == pytest.approx(-5.06366, 1.0e-4)
def make_fluxes_noise_map_x2():
return al.ValuesIrregularGrouped(values=[[1.0, 1.0]])
def make_fluxes_x2():
return al.ValuesIrregularGrouped(values=[[1.0, 2.0]])