def test_ical_pipeline_global(self):
self.actualSetUp(add_errors=True)
controls = create_calibration_controls()
controls['T']['first_selfcal'] = 1
controls['T']['timescale'] = 'auto'
clean, residual, restored, gt_list = \
ical_list_serial_workflow(self.vis_list,
model_imagelist=self.model_imagelist,
context='2d',
algorithm='mmclean', facets=1,
scales=[0, 3, 10],
niter=1000, fractional_threshold=0.1, threshold=0.1,
nmoment=3,
nmajor=5, gain=0.1,
deconvolve_facets=4, deconvolve_overlap=32,
deconvolve_taper='tukey', psf_support=64,
calibration_context='T', controls=controls, do_selfcal=True,
global_solution=True)
centre = len(clean) // 2
if self.persist:
export_image_to_fits(clean[centre], '%s/test_pipelines_ical_global_pipeline_serial_clean.fits' % self.dir)
export_image_to_fits(residual[centre][0], '%s/test_pipelines_ical_global_pipeline_serial_residual.fits' % self.dir)
export_image_to_fits(restored[centre], '%s/test_pipelines_ical_global_pipeline_serial_restored.fits' % self.dir)
export_gaintable_to_hdf5(gt_list[0]['T'],
'%s/test_pipelines_ical_global_pipeline_serial_gaintable.hdf5' %
self.dir)
qa = qa_image(restored[centre])
assert numpy.abs(qa.data['max'] - 98.92656340122159) < 1.0, str(qa)
assert numpy.abs(qa.data['min'] + 0.7024492707920869) < 1.0, str(qa)
def test_ical_pipeline(self):
amp_errors = {'T': 0.0, 'G': 0.00, 'B': 0.0}
phase_errors = {'T': 0.1, 'G': 0.0, 'B': 0.0}
self.actualSetUp(add_errors=True,
block=True,
amp_errors=amp_errors,
phase_errors=phase_errors)
controls = create_calibration_controls()
controls['T']['first_selfcal'] = 1
controls['G']['first_selfcal'] = 3
controls['B']['first_selfcal'] = 4
controls['T']['timescale'] = 'auto'
controls['G']['timescale'] = 'auto'
controls['B']['timescale'] = 1e5
clean, residual, restored = \
ical_list_serial_workflow(self.vis_list, model_imagelist=self.model_imagelist, context='2d',
calibration_context='T', controls=controls, do_selfcal=True,
global_solution=False,
algorithm='mmclean',
facets=1,
scales=[0, 3, 10],
niter=1000, fractional_threshold=0.1,
nmoments=2, nchan=self.freqwin,
threshold=2.0, nmajor=5, gain=0.1,
deconvolve_facets=8, deconvolve_overlap=16, deconvolve_taper='tukey')
centre = len(clean) // 2
export_image_to_fits(
clean[centre],
'%s/test_pipelines_ical_pipeline_clean.fits' % self.dir)
export_image_to_fits(
residual[centre][0],
'%s/test_pipelines_ical_pipeline_residual.fits' % self.dir)
export_image_to_fits(
restored[centre],
'%s/test_pipelines_ical_pipeline_restored.fits' % self.dir)
qa = qa_image(restored[centre])
assert numpy.abs(qa.data['max'] - 100.13739440876233) < 1.0, str(qa)
assert numpy.abs(qa.data['min'] + 0.03644435471804354) < 1.0, str(qa)
start = time.time()
original_ical = False
if original_ical:
if rank == 0:
ical_list = ical_list_serial_workflow(predicted_vislist,
model_imagelist=model_list,
context='wstack',
calibration_context='TG',
controls=controls,
scales=[0, 3, 10],
algorithm='mmclean',
nmoment=3,
niter=1000,
fractional_threshold=0.1,
threshold=0.1,
nmajor=5,
gain=0.25,
deconvolve_facets=8,
deconvolve_overlap=16,
deconvolve_taper='tukey',
vis_slices=ntimes,
timeslice='auto',
global_solution=False,
psf_support=64,
do_selfcal=True)
else:
ical_list = ical_list_mpi_workflow(predicted_vislist,
model_imagelist=model_list,
print(qa_image(model, context='Blockvis model image'))
export_image_to_fits(model, '%s/imaging-blockvis_model.fits'
% (results_dir))
dirty, sumwt = invert_list_serial_workflow(predicted_vis, model, vis_slices=vis_slices, dopsf=False, context='wstack')
print(qa_image(dirty, context='Dirty image'))
export_image_to_fits(dirty, '%s/imaging-dirty.fits'
% (results_dir))
deconvolved, residual, restored = ical_list_serial_workflow(vis_list=[blockvis],
model_imagelist=[model], vis_slices=vis_slices,
timeslice='auto',
algorithm='hogbom', niter=1000, fractional_threshold=0.1, threshold=0.1,
context='wstack', nmajor=5, gain=0.1, first_selfcal=1,
global_solution=False)
print(qa_image(deconvolved, context='Clean image'))
export_image_to_fits(deconvolved, '%s/imaging-dask_ical_deconvolved.fits'
% (results_dir))
print(qa_image(residual, context='Residual clean image'))
export_image_to_fits(residual, '%s/imaging-dask_ical_residual.fits'
% (results_dir))
print(qa_image(restored, context='Restored clean image'))
export_image_to_fits(restored, '%s/imaging-dask_ical_restored.fits'
% (results_dir))
print(qa_image(model, context='Blockvis model image1'))
