def test__different_interferometer_without_mock_objects__customize_constructor_inputs(
self
):
primary_beam = aa.kernel.ones(shape_2d=(7, 7), pixel_scales=1.0)
interferometer = aa.interferometer(
visibilities=aa.visibilities.ones(shape_1d=(19,)),
primary_beam=primary_beam,
noise_map=2.0 * aa.visibilities.ones(shape_1d=(19,)),
uv_wavelengths=3.0 * np.ones((19, 2)),
)
visibilities_mask = np.full(fill_value=False, shape=(19, 2))
real_space_mask = aa.mask.unmasked(
shape_2d=(19, 19), pixel_scales=1.0, invert=True, sub_size=8
)
real_space_mask[9, 9] = False
masked_interferometer_7 = aa.masked.interferometer.manual(
interferometer=interferometer,
visibilities_mask=visibilities_mask,
real_space_mask=real_space_mask,
primary_beam_shape_2d=(7, 7),
)
assert (masked_interferometer_7.visibilities == np.ones((19, 2))).all()
assert (masked_interferometer_7.noise_map == 2.0 * np.ones((19, 2))).all()
assert (
masked_interferometer_7.interferometer.uv_wavelengths
== 3.0 * np.ones((19, 2))
).all()
assert (masked_interferometer_7.primary_beam.in_2d == np.ones((7, 7))).all()
assert masked_interferometer_7.primary_beam_shape_2d == (7, 7)
def make_interferometer_7(visibilities_7x2, noise_map_7x2, primary_beam_3x3,
uv_wavelengths_7x2):
return aa.interferometer(
visibilities=visibilities_7x2,
noise_map=noise_map_7x2,
uv_wavelengths=uv_wavelengths_7x2,
primary_beam=primary_beam_3x3,
)
def test__visibilities_and_model_are_identical__inversion_included__changes_certain_properties(
self):
visibilities_mask = np.full(fill_value=False, shape=(2, 2))
real_space_mask = aa.mask.manual(
mask_2d=np.array([[False, False], [False, False]]),
sub_size=1,
pixel_scales=(1.0, 1.0),
)
data = aa.visibilities.manual_1d(visibilities=[[1.0, 2.0], [3.0, 4.0]])
noise_map = aa.visibilities.manual_1d(
visibilities=[[2.0, 2.0], [2.0, 2.0]])
interferometer = aa.interferometer(visibilities=data,
noise_map=noise_map,
uv_wavelengths=np.ones(shape=(2,
2)))
masked_interferometer = aa.masked.interferometer(
interferometer=interferometer,
visibilities_mask=visibilities_mask,
real_space_mask=real_space_mask,
)
model_data = aa.visibilities.manual_1d(
visibilities=[[1.0, 2.0], [3.0, 4.0]])
inversion = mock_inversion.MockFitInversion(
regularization_term=2.0,
log_det_curvature_reg_matrix_term=3.0,
log_det_regularization_matrix_term=4.0,
)
fit = aa.fit(
masked_dataset=masked_interferometer,
model_data=model_data,
inversion=inversion,
)
assert fit.chi_squared == 0.0
assert fit.reduced_chi_squared == 0.0
assert fit.noise_normalization == np.sum(
np.log(2 * np.pi * noise_map**2.0))
assert fit.likelihood == -0.5 * (fit.chi_squared +
fit.noise_normalization)
assert fit.likelihood_with_regularization == -0.5 * (
fit.chi_squared + 2.0 + fit.noise_normalization)
assert fit.evidence == -0.5 * (fit.chi_squared + 2.0 + 3.0 - 4.0 +
fit.noise_normalization)
assert fit.figure_of_merit == fit.evidence
def test__interferometer(self):
visibilities_mask = np.full(fill_value=False, shape=(7, 2))
real_space_mask = np.array([
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
])
real_space_mask = aa.mask.manual(mask_2d=real_space_mask,
pixel_scales=0.1,
sub_size=1)
grid = aa.masked.grid.from_mask(mask=real_space_mask)
pix = aa.pix.VoronoiMagnification(shape=(7, 7))
sparse_grid = pix.sparse_grid_from_grid(grid=grid)
mapper = pix.mapper_from_grid_and_sparse_grid(
grid=grid, sparse_grid=sparse_grid, inversion_uses_border=False)
reg = aa.reg.Constant(coefficient=0.0)
visibilities = aa.visibilities.manual_1d(visibilities=[
[1.0, 0.0],
[1.0, 0.0],
[1.0, 0.0],
[1.0, 0.0],
[1.0, 0.0],
[1.0, 0.0],
[1.0, 0.0],
])
noise_map = aa.visibilities.ones(shape_1d=(7, ))
uv_wavelengths = np.ones(shape=(7, 2))
interferometer = aa.interferometer(
visibilities=visibilities,
noise_map=noise_map,
uv_wavelengths=uv_wavelengths,
)
masked_data = aa.masked.interferometer(
interferometer=interferometer,
visibilities_mask=visibilities_mask,
real_space_mask=real_space_mask,
)
inversion = aa.inversion(masked_dataset=masked_data,
mapper=mapper,
regularization=reg)
assert inversion.mapped_reconstructed_visibilities[:,
0] == pytest.approx(
np.ones(shape=(
7, )),
1.0e-4)
assert inversion.mapped_reconstructed_visibilities[:,
1] == pytest.approx(
np.zeros(shape=(
7, )),
1.0e-4)
def test__visibilities_and_model_are_different__no_masking__check_values_are_correct(
self):
visibilities_mask = np.full(fill_value=False, shape=(2, 2))
real_space_mask = aa.mask.manual(
mask_2d=np.array([[False, False], [False, False]]),
sub_size=1,
pixel_scales=(1.0, 1.0),
)
data = aa.visibilities.manual_1d(visibilities=[[1.0, 2.0], [3.0, 4.0]])
noise_map = aa.visibilities.manual_1d(
visibilities=[[2.0, 2.0], [2.0, 2.0]])
interferometer = aa.interferometer(visibilities=data,
noise_map=noise_map,
uv_wavelengths=np.ones(shape=(2,
2)))
masked_interferometer = aa.masked.interferometer(
interferometer=interferometer,
visibilities_mask=visibilities_mask,
real_space_mask=real_space_mask,
)
model_data = aa.visibilities.manual_1d(
visibilities=[[1.0, 2.0], [3.0, 3.0]])
fit = aa.fit(masked_dataset=masked_interferometer,
model_data=model_data)
assert (fit.visibilities_mask == np.array([[False, False],
[False, False]])).all()
assert (fit.visibilities.in_1d == np.array([[1.0, 2.0], [3.0,
4.0]])).all()
assert (fit.noise_map.in_1d == np.array([[2.0, 2.0], [2.0,
2.0]])).all()
assert (fit.signal_to_noise_map.in_1d == np.array([[0.5, 1.0],
[1.5, 2.0]])).all()
assert (fit.model_visibilities.in_1d == np.array([[1.0, 2.0],
[3.0, 3.0]])).all()
assert (fit.residual_map.in_1d == np.array([[0.0, 0.0], [0.0,
1.0]])).all()
assert (fit.normalized_residual_map.in_1d == np.array([[0.0, 0.0],
[0.0,
0.5]])).all()
assert (fit.chi_squared_map.in_1d == np.array([[0.0, 0.0],
[0.0, 0.25]])).all()
assert fit.chi_squared == 0.25
assert fit.reduced_chi_squared == 0.25 / 4.0
assert fit.noise_normalization == np.sum(
np.log(2 * np.pi * noise_map**2.0))
assert fit.likelihood == -0.5 * (fit.chi_squared +
fit.noise_normalization)