def test_grid_gaintable_to_screen(self):
screen = import_image_from_fits(
arl_path('data/models/test_mpc_screen.fits'))
beam = create_test_image(cellsize=0.0015,
phasecentre=self.vis.phasecentre,
frequency=self.frequency)
beam = create_low_test_beam(beam, use_local=False)
gleam_components = create_low_test_skycomponents_from_gleam(
flux_limit=1.0,
phasecentre=self.phasecentre,
frequency=self.frequency,
polarisation_frame=PolarisationFrame('stokesI'),
radius=0.2)
pb_gleam_components = apply_beam_to_skycomponent(
gleam_components, beam)
actual_components = filter_skycomponents_by_flux(pb_gleam_components,
flux_min=1.0)
gaintables = create_gaintable_from_screen(self.vis, actual_components,
screen)
assert len(gaintables) == len(actual_components), len(gaintables)
assert gaintables[0].gain.shape == (3, 94, 3, 1,
1), gaintables[0].gain.shape
newscreen = create_empty_image_like(screen)
newscreen, weights = grid_gaintable_to_screen(self.vis, gaintables,
newscreen)
assert numpy.max(numpy.abs(screen.data)) > 0.0
if self.persist:
export_image_to_fits(
newscreen,
arl_path('test_results/test_mpc_screen_gridded.fits'))
if self.persist:
export_image_to_fits(
weights,
arl_path('test_results/test_mpc_screen_gridded_weights.fits'))
def test_mpccal_MPCCAL_manysources_subimages(self):
self.actualSetup()
model = create_empty_image_like(self.theta_list[0].image)
if arlexecute.using_dask:
progress = None
else:
progress = self.progress
future_vis = arlexecute.scatter(self.all_skymodel_noniso_vis)
future_model = arlexecute.scatter(model)
future_theta_list = arlexecute.scatter(self.theta_list)
result = mpccal_skymodel_list_arlexecute_workflow(
future_vis,
future_model,
future_theta_list,
mpccal_progress=progress,
nmajor=5,
context='2d',
algorithm='hogbom',
scales=[0, 3, 10],
fractional_threshold=0.3,
threshold=0.2,
gain=0.1,
niter=1000,
psf_support=256,
deconvolve_facets=8,
deconvolve_overlap=8,
deconvolve_taper='tukey')
(self.theta_list, residual) = arlexecute.compute(result, sync=True)
combined_model = calculate_skymodel_equivalent_image(self.theta_list)
psf_obs = invert_list_arlexecute_workflow(
[self.all_skymodel_noniso_vis], [model], context='2d', dopsf=True)
result = restore_list_arlexecute_workflow([combined_model], psf_obs,
[(residual, 0.0)])
result = arlexecute.compute(result, sync=True)
if self.persist:
export_image_to_fits(
residual,
arl_path('test_results/test_mpccal_no_edge_residual.fits'))
if self.persist:
export_image_to_fits(
result[0],
arl_path('test_results/test_mpccal_no_edge_restored.fits'))
if self.persist:
export_image_to_fits(
combined_model,
arl_path('test_results/test_mpccal_no_edge_deconvolved.fits'))
recovered_mpccal_components = find_skycomponents(result[0],
fwhm=2,
threshold=0.32,
npixels=12)
def max_flux(elem):
return numpy.max(elem.flux)
recovered_mpccal_components = sorted(recovered_mpccal_components,
key=max_flux,
reverse=True)
assert recovered_mpccal_components[
0].name == 'Segment 8', recovered_mpccal_components[0].name
assert numpy.abs(recovered_mpccal_components[0].flux[0, 0] - 7.773751416364857) < 1e-7, \
recovered_mpccal_components[0].flux[0, 0]
newscreen = create_empty_image_like(self.screen)
gaintables = [th.gaintable for th in self.theta_list]
newscreen, weights = grid_gaintable_to_screen(
self.all_skymodel_noniso_blockvis, gaintables, newscreen)
if self.persist:
export_image_to_fits(
newscreen,
arl_path('test_results/test_mpccal_no_edge_screen.fits'))
if self.persist:
export_image_to_fits(
weights,
arl_path(
'test_results/test_mpccal_no_edge_screenweights.fits'))
arlexecute.close()
