def test__fit_figure_of_merit__matches_correct_fit_given_galaxy_profiles(
self, imaging_data_7x7, mask_function_7x7):
lens_galaxy = al.Galaxy(
redshift=0.5,
light=al.light_profiles.EllipticalSersic(intensity=0.1))
phase_imaging_7x7 = al.PhaseImaging(
mask_function=mask_function_7x7,
galaxies=[lens_galaxy],
cosmology=cosmo.FLRW,
phase_name="test_phase",
)
analysis = phase_imaging_7x7.make_analysis(data=imaging_data_7x7)
instance = phase_imaging_7x7.variable.instance_from_unit_vector([])
fit_figure_of_merit = analysis.fit(instance=instance)
mask = phase_imaging_7x7.meta_data_fit.mask_function(
image=imaging_data_7x7.image, sub_size=2)
lens_data = al.LensImagingData(imaging_data=imaging_data_7x7,
mask=mask)
tracer = analysis.tracer_for_instance(instance=instance)
fit = al.LensImagingFit.from_lens_data_and_tracer(lens_data=lens_data,
tracer=tracer)
assert fit.likelihood == fit_figure_of_merit
def test__fit_figure_of_merit__includes_hyper_image_and_noise__matches_fit(
self, imaging_data_7x7, mask_function_7x7):
hyper_image_sky = al.HyperImageSky(sky_scale=1.0)
hyper_background_noise = al.HyperBackgroundNoise(noise_scale=1.0)
lens_galaxy = al.Galaxy(
redshift=0.5,
light=al.light_profiles.EllipticalSersic(intensity=0.1))
phase_imaging_7x7 = al.PhaseImaging(
mask_function=mask_function_7x7,
galaxies=[lens_galaxy],
hyper_image_sky=hyper_image_sky,
hyper_background_noise=hyper_background_noise,
cosmology=cosmo.FLRW,
phase_name="test_phase",
)
analysis = phase_imaging_7x7.make_analysis(data=imaging_data_7x7)
instance = phase_imaging_7x7.variable.instance_from_unit_vector([])
fit_figure_of_merit = analysis.fit(instance=instance)
mask = phase_imaging_7x7.meta_data_fit.mask_function(
image=imaging_data_7x7.image, sub_size=2)
lens_data = al.LensImagingData(imaging_data=imaging_data_7x7,
mask=mask)
tracer = analysis.tracer_for_instance(instance=instance)
fit = al.LensImagingFit.from_lens_data_and_tracer(
lens_data=lens_data,
tracer=tracer,
hyper_image_sky=hyper_image_sky,
hyper_background_noise=hyper_background_noise,
)
assert fit.likelihood == fit_figure_of_merit
def test__pixel_scale_interpolation_grid_input__grids_include_interpolator_on_blurring_grid(
self, imaging_data_7x7, sub_mask_7x7):
lens_imaging_data_7x7 = al.LensImagingData(
imaging_data=imaging_data_7x7,
mask=sub_mask_7x7,
pixel_scale_interpolation_grid=1.0,
)
grid = al.Grid.from_mask(mask=sub_mask_7x7)
new_grid = grid.new_grid_with_interpolator(
pixel_scale_interpolation_grid=1.0)
blurring_grid = al.Grid.blurring_grid_from_mask_and_psf_shape(
mask=sub_mask_7x7, psf_shape=(3, 3))
new_blurring_grid = blurring_grid.new_grid_with_interpolator(
pixel_scale_interpolation_grid=1.0)
assert (lens_imaging_data_7x7.grid == new_grid).all()
assert (lens_imaging_data_7x7.grid.interpolator.vtx ==
new_grid.interpolator.vtx).all()
assert (lens_imaging_data_7x7.grid.interpolator.wts ==
new_grid.interpolator.wts).all()
assert (lens_imaging_data_7x7.blurring_grid == new_blurring_grid).all()
assert (lens_imaging_data_7x7.blurring_grid.interpolator.vtx ==
new_blurring_grid.interpolator.vtx).all()
assert (lens_imaging_data_7x7.blurring_grid.interpolator.wts ==
new_blurring_grid.interpolator.wts).all()
def test__default_mask_function(self, phase_imaging_7x7, imaging_data_7x7):
lens_data = al.LensImagingData(
imaging_data=imaging_data_7x7,
mask=phase_imaging_7x7.meta_data_fit.mask_function(
image=imaging_data_7x7.image, sub_size=1
),
)
assert len(lens_data._image_1d) == 9
def test__lens_data_is_binned_up(self, imaging_data_7x7, mask_7x7_1_pix,
mask_function_7x7_1_pix):
binned_up_imaging_data = imaging_data_7x7.new_imaging_data_with_binned_up_arrays(
bin_up_factor=2)
binned_up_mask = mask_7x7_1_pix.binned_up_mask_from_mask(
bin_up_factor=2)
phase_imaging_7x7 = al.PhaseImaging(
phase_name="phase_imaging_7x7",
bin_up_factor=2,
mask_function=mask_function_7x7_1_pix,
)
analysis = phase_imaging_7x7.make_analysis(data=imaging_data_7x7)
assert (analysis.lens_data.image(
return_in_2d=True,
return_masked=False) == binned_up_imaging_data.image).all()
assert (analysis.lens_data.psf == binned_up_imaging_data.psf).all()
assert (analysis.lens_data.noise_map(
return_in_2d=True,
return_masked=False) == binned_up_imaging_data.noise_map).all()
assert (analysis.lens_data.mask == binned_up_mask).all()
lens_data = al.LensImagingData(imaging_data=imaging_data_7x7,
mask=mask_7x7_1_pix)
binned_up_lens_data = lens_data.new_lens_imaging_data_with_binned_up_imaging_data_and_mask(
bin_up_factor=2)
assert (analysis.lens_data.image(
return_in_2d=True) == binned_up_lens_data.image(
return_in_2d=True)).all()
assert (analysis.lens_data.psf == binned_up_lens_data.psf).all()
assert (analysis.lens_data.noise_map(
return_in_2d=True) == binned_up_lens_data.noise_map(
return_in_2d=True)).all()
assert (analysis.lens_data.mask == binned_up_lens_data.mask).all()
assert (analysis.lens_data._image_1d == binned_up_lens_data._image_1d
).all()
assert (analysis.lens_data._noise_map_1d ==
binned_up_lens_data._noise_map_1d).all()
def test__different_imaging_data_without_mock_objects__customize_constructor_inputs(
self):
psf = al.PSF(np.ones((7, 7)), 1)
imaging_data = al.ImagingData(
np.ones((19, 19)),
pixel_scale=3.0,
psf=psf,
noise_map=2.0 * np.ones((19, 19)),
)
mask = al.Mask.unmasked_from_shape_pixel_scale_and_sub_size(
shape=(19, 19), pixel_scale=1.0, invert=True, sub_size=8)
mask[9, 9] = False
lens_imaging_data_7x7 = al.LensImagingData(
imaging_data=imaging_data,
mask=mask,
trimmed_psf_shape=(7, 7),
positions=[np.array([[1.0, 1.0]])],
positions_threshold=1.0,
)
assert (lens_imaging_data_7x7.image(return_in_2d=True,
return_masked=False) == np.ones(
(19, 19))).all()
assert (lens_imaging_data_7x7.noise_map(return_in_2d=True,
return_masked=False) == 2.0 *
np.ones((19, 19))).all()
assert (lens_imaging_data_7x7.psf == np.ones((7, 7))).all()
assert lens_imaging_data_7x7.sub_size == 8
assert lens_imaging_data_7x7.convolver.psf.shape == (7, 7)
assert (lens_imaging_data_7x7.positions[0] == np.array([[1.0,
1.0]])).all()
assert lens_imaging_data_7x7.positions_threshold == 1.0
assert lens_imaging_data_7x7.trimmed_psf_shape == (7, 7)
def make_lens_imaging_data_7x7(imaging_data_7x7, sub_mask_7x7):
return al.LensImagingData(imaging_data=imaging_data_7x7, mask=sub_mask_7x7)
def make_lens_imaging_data_6x6(imaging_data_6x6, mask_6x6):
return al.LensImagingData(imaging_data=imaging_data_6x6, mask=mask_6x6)
def test__simulate_lensed_source_and_fit__include_psf_blurring__chi_squared_is_0__noise_normalization_correct(
):
psf = al.PSF.from_gaussian(shape=(3, 3), pixel_scale=0.2, sigma=0.75)
grid = al.Grid.from_shape_pixel_scale_and_sub_size(shape=(11, 11),
pixel_scale=0.2,
sub_size=1)
lens_galaxy = al.Galaxy(
redshift=0.5,
light=al.light_profiles.EllipticalSersic(centre=(0.1, 0.1),
intensity=0.1),
mass=al.mass_profiles.EllipticalIsothermal(centre=(0.1, 0.1),
einstein_radius=1.8),
)
source_galaxy = al.Galaxy(
redshift=1.0,
light=al.light_profiles.EllipticalExponential(centre=(0.1, 0.1),
intensity=0.5),
)
tracer = al.Tracer.from_galaxies(galaxies=[lens_galaxy, source_galaxy])
imaging_simulated = al.SimulatedImagingData.from_image_and_exposure_arrays(
image=tracer.padded_profile_image_2d_from_grid_and_psf_shape(
grid=grid, psf_shape=psf.shape),
pixel_scale=0.2,
exposure_time=300.0,
psf=psf,
background_sky_level=0.0,
add_noise=False,
)
imaging_simulated.noise_map = np.ones(imaging_simulated.image.shape)
path = "{}/data_temp/simulate_and_fit".format(
os.path.dirname(os.path.realpath(
__file__))) # Setup path so we can output the simulated image.
try:
shutil.rmtree(path)
except FileNotFoundError:
pass
if os.path.exists(path) is False:
os.makedirs(path)
array_util.numpy_array_2d_to_fits(array_2d=imaging_simulated.image,
file_path=path + "/image.fits")
array_util.numpy_array_2d_to_fits(array_2d=imaging_simulated.noise_map,
file_path=path + "/noise_map.fits")
array_util.numpy_array_2d_to_fits(array_2d=psf,
file_path=path + "/psf.fits")
imaging_data = al.load_imaging_data_from_fits(
image_path=path + "/image.fits",
noise_map_path=path + "/noise_map.fits",
psf_path=path + "/psf.fits",
pixel_scale=0.2,
)
mask = al.Mask.circular(shape=imaging_data.image.shape,
pixel_scale=0.2,
sub_size=1,
radius_arcsec=0.8)
lens_data = al.LensImagingData(imaging_data=imaging_data, mask=mask)
tracer = al.Tracer.from_galaxies(galaxies=[lens_galaxy, source_galaxy])
fitter = al.LensImagingFit.from_lens_data_and_tracer(lens_data=lens_data,
tracer=tracer)
assert fitter.chi_squared == pytest.approx(0.0, 1e-4)
path = "{}/data_temp".format(os.path.dirname(os.path.realpath(
__file__))) # Setup path so we can output the simulated image.
if os.path.exists(path) == True:
shutil.rmtree(path)
