def test_restored_list_facet(self):
self.actualSetUp(zerow=True)
centre = self.freqwin // 2
psf_image_list = invert_list_arlexecute_workflow(self.vis_list, self.model_list, context='2d', dopsf=True)
residual_image_list = residual_list_arlexecute_workflow(self.vis_list, self.model_list, context='2d')
restored_4facets_image_list = restore_list_arlexecute_workflow(self.model_list, psf_image_list,
residual_image_list,
restore_facets=4, psfwidth=1.0)
restored_4facets_image_list = arlexecute.compute(restored_4facets_image_list, sync=True)
restored_1facets_image_list = restore_list_arlexecute_workflow(self.model_list, psf_image_list,
residual_image_list,
restore_facets=1, psfwidth=1.0)
restored_1facets_image_list = arlexecute.compute(restored_1facets_image_list, sync=True)
if self.persist: export_image_to_fits(restored_4facets_image_list[0],
'%s/test_imaging_invert_%s_restored_4facets.fits' %
(self.dir, arlexecute.type()))
qa = qa_image(restored_4facets_image_list[centre])
assert numpy.abs(qa.data['max'] - 99.43438263927833) < 1e-7, str(qa)
assert numpy.abs(qa.data['min'] + 0.6328915148563354) < 1e-7, str(qa)
restored_4facets_image_list[centre].data -= restored_1facets_image_list[centre].data
if self.persist: export_image_to_fits(restored_4facets_image_list[centre],
'%s/test_imaging_invert_%s_restored_4facets_error.fits' %
(self.dir, arlexecute.type()))
qa = qa_image(restored_4facets_image_list[centre])
assert numpy.abs(qa.data['max']) < 1e-10, str(qa)
def test_restored_list_noresidual(self):
self.actualSetUp(zerow=True)
centre = self.freqwin // 2
psf_image_list = invert_list_arlexecute_workflow(self.vis_list, self.model_list, context='2d', dopsf=True)
restored_image_list = restore_list_arlexecute_workflow(self.model_list, psf_image_list, psfwidth=1.0)
restored_image_list = arlexecute.compute(restored_image_list, sync=True)
if self.persist: export_image_to_fits(restored_image_list[centre],
'%s/test_imaging_invert_%s_restored_noresidual.fits' %
(self.dir, arlexecute.type()))
qa = qa_image(restored_image_list[centre])
assert numpy.abs(qa.data['max'] - 100.0) < 1e-7, str(qa)
assert numpy.abs(qa.data['min']) < 1e-7, str(qa)
def test_deconvolve_and_restore_cube_mmclean_facets(self):
self.actualSetUp(add_errors=True)
dirty_imagelist = invert_list_arlexecute_workflow(self.vis_list,
self.model_imagelist,
context='2d',
dopsf=False,
normalize=True)
psf_imagelist = invert_list_arlexecute_workflow(self.vis_list,
self.model_imagelist,
context='2d',
dopsf=True,
normalize=True)
dirty_imagelist = arlexecute.persist(dirty_imagelist)
psf_imagelist = arlexecute.persist(psf_imagelist)
dec_imagelist = deconvolve_list_arlexecute_workflow(
dirty_imagelist,
psf_imagelist,
self.model_imagelist,
niter=1000,
fractional_threshold=0.1,
scales=[0, 3, 10],
algorithm='mmclean',
nmoment=3,
nchan=self.freqwin,
threshold=0.01,
gain=0.7,
deconvolve_facets=8,
deconvolve_overlap=8,
deconvolve_taper='tukey')
dec_imagelist = arlexecute.persist(dec_imagelist)
residual_imagelist = residual_list_arlexecute_workflow(
self.vis_list, model_imagelist=dec_imagelist, context='2d')
residual_imagelist = arlexecute.persist(residual_imagelist)
restored_list = restore_list_arlexecute_workflow(
model_imagelist=dec_imagelist,
psf_imagelist=psf_imagelist,
residual_imagelist=residual_imagelist,
empty=self.model_imagelist)
restored = arlexecute.compute(restored_list, sync=True)[0]
if self.persist:
export_image_to_fits(
restored, '%s/test_imaging_%s_overlap_mmclean_restored.fits' %
(self.dir, arlexecute.type()))
def test_deconvolve_and_restore_cube_mmclean(self):
self.actualSetUp(add_errors=True)
dirty_imagelist = invert_list_arlexecute_workflow(self.vis_list, self.model_imagelist, context='2d',
dopsf=False, normalize=True)
psf_imagelist = invert_list_arlexecute_workflow(self.vis_list, self.model_imagelist, context='2d',
dopsf=True, normalize=True)
dec_imagelist, _ = deconvolve_list_arlexecute_workflow(dirty_imagelist, psf_imagelist, self.model_imagelist, niter=1000,
fractional_threshold=0.01, scales=[0, 3, 10],
algorithm='mmclean', nmoments=3, nchan=self.freqwin,
threshold=0.1, gain=0.7)
residual_imagelist = residual_list_arlexecute_workflow(self.vis_list, model_imagelist=dec_imagelist,
context='wstack', vis_slices=51)
restored = restore_list_arlexecute_workflow(model_imagelist=dec_imagelist, psf_imagelist=psf_imagelist,
residual_imagelist=residual_imagelist,
empty=self.model_imagelist)[0]
restored = arlexecute.compute(restored, sync=True)
export_image_to_fits(restored, '%s/test_imaging_%s_mmclean_restored.fits' % (self.dir, arlexecute.type()))
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()
psf_support=512,
deconvolve_facets=8,
deconvolve_overlap=16,
deconvolve_taper='tukey')
(ical_skymodel, residual) = arlexecute.compute(result, sync=True)
print(qa_image(residual, context='ICAL residual image'))
print('ical finished')
combined_model = calculate_skymodel_equivalent_image(ical_skymodel)
print(qa_image(combined_model, context='ICAL combined model'))
psf_obs = invert_list_arlexecute_workflow([future_vis], [future_model],
context='2d',
dopsf=True)
result = restore_list_arlexecute_workflow([combined_model], psf_obs,
[(residual, 0.0)])
result = arlexecute.compute(result, sync=True)
ical_restored = result[0]
export_image_to_fits(
ical_restored,
arl_path('test_results/low-sims-mpc-ical-restored_%.1frmax.fits' %
rmax))
#######################################################################################################
# Now set up the skymodels for MPCCAL. We find the brightest components in the ICAL image, remove
# sources that are too close to another stronger source, and then use these to set up
# a Voronoi tesselation to define the skymodel masks
ical_components = find_skycomponents(ical_restored,
fwhm=2,
residual_imagelist = residual_list_arlexecute_workflow(vt_list, model_imagelist=dec_imagelist,
context=context)
# residual_imagelist = arlexecute.persist(residual_imagelist)
residual_imagelist = arlexecute.compute(residual_imagelist, sync=True)
residualed = residual_imagelist[0][0]
show_image(residualed)
plt.title(context)
plt.savefig('%s/test_imaging_%s_residual.pdf' % (storedir, context))
# export_image_to_fits(residualed,
# '%s/test_imaging_%s_residualed.fits' % (storedir, context))
restored_list = restore_list_arlexecute_workflow(model_imagelist=dec_imagelist,
psf_imagelist=psf_imagelist, gcfcf=gcfcf,
residual_imagelist=residual_imagelist,
empty=targetimage_list)
restored_list = arlexecute.compute(restored_list, sync=True)
restored = restored_list[0]
show_image(restored)
plt.title(context)
plt.savefig('%s/test_imaging_%s_restored.pdf' % (storedir, context))
# export_image_to_fits(restored,
# '%s/test_imaging_%s_restored.fits' % (storedir, context))
comps = find_skycomponents(targetimage, fwhm=1.0, threshold=10.0, npixels=5)
plt.clf()
for comp in comps:
