/
test_phase_imaging.py
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/
test_phase_imaging.py
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import os
from os import path
import numpy as np
import pytest
from astropy import cosmology as cosmo
import autofit as af
import autolens as al
from autolens import exc
from test.unit.mock.pipeline import mock_pipeline
pytestmark = pytest.mark.filterwarnings(
"ignore:Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of "
"`arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result "
"either in an error or a different result."
)
directory = path.dirname(path.realpath(__file__))
@pytest.fixture(scope="session", autouse=True)
def do_something():
af.conf.instance = af.conf.Config(
"{}/../test_files/config/phase_imaging_7x7".format(directory)
)
def clean_images():
try:
os.remove("{}/source_lens_phase/source_image_0.fits".format(directory))
os.remove("{}/source_lens_phase/lens_image_0.fits".format(directory))
os.remove("{}/source_lens_phase/model_image_0.fits".format(directory))
except FileNotFoundError:
pass
af.conf.instance.data_path = directory
class TestPhase(object):
def test__make_analysis(
self, phase_imaging_7x7, imaging_data_7x7, lens_imaging_data_7x7
):
analysis = phase_imaging_7x7.make_analysis(data=imaging_data_7x7)
assert (
analysis.lens_imaging_data.image(return_in_2d=True, return_masked=False)
== imaging_data_7x7.image
)
assert (
analysis.lens_imaging_data.noise_map(return_in_2d=True, return_masked=False)
== imaging_data_7x7.noise_map
)
def test__make_analysis__phase_info_is_made(
self, phase_imaging_7x7, imaging_data_7x7
):
phase_imaging_7x7.make_analysis(data=imaging_data_7x7)
file_phase_info = "{}/{}".format(
phase_imaging_7x7.optimizer.phase_output_path, "phase.info"
)
phase_info = open(file_phase_info, "r")
optimizer = phase_info.readline()
sub_size = phase_info.readline()
psf_shape = phase_info.readline()
positions_threshold = phase_info.readline()
cosmology = phase_info.readline()
auto_link_priors = phase_info.readline()
phase_info.close()
assert optimizer == "Optimizer = MockNLO \n"
assert sub_size == "Sub-grid size = 2 \n"
assert psf_shape == "PSF shape = None \n"
assert positions_threshold == "Positions Threshold = None \n"
assert (
cosmology
== 'Cosmology = FlatLambdaCDM(name="Planck15", H0=67.7 km / (Mpc s), Om0=0.307, Tcmb0=2.725 K, '
"Neff=3.05, m_nu=[0. 0. 0.06] eV, Ob0=0.0486) \n"
)
assert auto_link_priors == "Auto Link Priors = False \n"
def test__fit_using_imaging(self, imaging_data_7x7, mask_function_7x7):
clean_images()
phase_imaging_7x7 = al.PhaseImaging(
optimizer_class=mock_pipeline.MockNLO,
galaxies=dict(
lens=al.GalaxyModel(
redshift=0.5, light=al.light_profiles.EllipticalSersic
),
source=al.GalaxyModel(
redshift=1.0, light=al.light_profiles.EllipticalSersic
),
),
mask_function=mask_function_7x7,
phase_name="test_phase_test_fit",
)
result = phase_imaging_7x7.run(data=imaging_data_7x7)
assert isinstance(result.constant.galaxies[0], al.Galaxy)
assert isinstance(result.constant.galaxies[0], al.Galaxy)
def test_modify_image(self, mask_function_7x7, imaging_data_7x7):
class MyPhase(al.PhaseImaging):
def modify_image(self, image, results):
assert imaging_data_7x7.image.shape == image.shape
image = 20.0 * np.ones(shape=(5, 5))
return image
phase_imaging_7x7 = MyPhase(
phase_name="phase_imaging_7x7", mask_function=mask_function_7x7
)
analysis = phase_imaging_7x7.make_analysis(data=imaging_data_7x7)
assert (
analysis.lens_data.image(return_in_2d=True, return_masked=False)
== 20.0 * np.ones(shape=(5, 5))
).all()
assert (analysis.lens_data._image_1d == 20.0 * np.ones(shape=9)).all()
def test__lens_data_signal_to_noise_limit(
self, imaging_data_7x7, mask_7x7_1_pix, mask_function_7x7_1_pix
):
imaging_data_snr_limit = imaging_data_7x7.new_imaging_data_with_signal_to_noise_limit(
signal_to_noise_limit=1.0
)
phase_imaging_7x7 = al.PhaseImaging(
phase_name="phase_imaging_7x7",
signal_to_noise_limit=1.0,
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)
== imaging_data_snr_limit.image
).all()
assert (
analysis.lens_data.noise_map(return_in_2d=True, return_masked=False)
== imaging_data_snr_limit.noise_map
).all()
imaging_data_snr_limit = imaging_data_7x7.new_imaging_data_with_signal_to_noise_limit(
signal_to_noise_limit=0.1
)
phase_imaging_7x7 = al.PhaseImaging(
phase_name="phase_imaging_7x7",
signal_to_noise_limit=0.1,
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)
== imaging_data_snr_limit.image
).all()
assert (
analysis.lens_data.noise_map(return_in_2d=True, return_masked=False)
== imaging_data_snr_limit.noise_map
).all()
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__fit_figure_of_merit__matches_correct_fit_given_galaxy_profiles(
self, imaging_data_7x7, mask_function_7x7
):
# noinspection PyTypeChecker
lens_galaxy = al.Galaxy(
redshift=0.5, light=al.light_profiles.EllipticalSersic(intensity=0.1)
)
phase_imaging_7x7 = al.PhaseImaging(
galaxies=[lens_galaxy],
mask_function=mask_function_7x7,
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.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__phase_can_receive_hyper_image_and_noise_maps(self):
phase_imaging_7x7 = al.PhaseImaging(
galaxies=dict(
lens=al.GalaxyModel(redshift=al.Redshift),
lens1=al.GalaxyModel(redshift=al.Redshift),
),
hyper_image_sky=al.HyperImageSky,
hyper_background_noise=al.HyperBackgroundNoise,
optimizer_class=af.MultiNest,
phase_name="test_phase",
)
instance = phase_imaging_7x7.optimizer.variable.instance_from_physical_vector(
[0.1, 0.2, 0.3, 0.4]
)
assert instance.galaxies[0].redshift == 0.1
assert instance.galaxies[1].redshift == 0.2
assert instance.hyper_image_sky.sky_scale == 0.3
assert instance.hyper_background_noise.noise_scale == 0.4
def test__extended_with_hyper_and_pixelizations(self, phase_imaging_7x7):
phase_extended = phase_imaging_7x7.extend_with_multiple_hyper_phases(
hyper_galaxy=False, inversion=False
)
assert phase_extended == phase_imaging_7x7
phase_extended = phase_imaging_7x7.extend_with_multiple_hyper_phases(
inversion=True
)
assert type(phase_extended.hyper_phases[0]) == al.InversionPhase
phase_extended = phase_imaging_7x7.extend_with_multiple_hyper_phases(
hyper_galaxy=True, inversion=False
)
assert type(phase_extended.hyper_phases[0]) == al.HyperGalaxyPhase
phase_extended = phase_imaging_7x7.extend_with_multiple_hyper_phases(
hyper_galaxy=False, inversion=True
)
assert type(phase_extended.hyper_phases[0]) == al.InversionPhase
phase_extended = phase_imaging_7x7.extend_with_multiple_hyper_phases(
hyper_galaxy=True, inversion=True
)
assert type(phase_extended.hyper_phases[0]) == al.HyperGalaxyPhase
assert type(phase_extended.hyper_phases[1]) == al.InversionPhase
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