def test_ical_pipeline(self):
self.actualSetUp(add_errors=True)
controls = create_calibration_controls()
controls['T']['first_selfcal'] = 1
controls['T']['timeslice'] = 'auto'
ical_list = \
ical_list_arlexecute_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=1, gain=0.1,
deconvolve_facets=4, deconvolve_overlap=32,
deconvolve_taper='tukey', psf_support=64,
restore_facets=4, psfwidth=1.0,
calibration_context='T', controls=controls, do_selfcal=True,
global_solution=False)
clean, residual, restored, gt_list = arlexecute.compute(ical_list, sync=True)
centre = len(clean) // 2
if self.persist:
export_image_to_fits(clean[centre], '%s/test_pipelines_ical_pipeline_arlexecute_clean.fits' % self.dir)
export_image_to_fits(residual[centre][0], '%s/test_pipelines_ical_pipeline_arlexecute_residual.fits' % self.dir)
export_image_to_fits(restored[centre], '%s/test_pipelines_ical_pipeline_arlexecute_restored.fits' % self.dir)
export_gaintable_to_hdf5(gt_list[centre]['T'], '%s/test_pipelines_ical_pipeline_arlexecute_gaintable.hdf5' %
self.dir)
qa = qa_image(restored[centre])
assert numpy.abs(qa.data['max'] - 99.32729396999524) < 1.0, str(qa)
assert numpy.abs(qa.data['min'] + 0.6501547522800477) < 1.0, str(qa)
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_readwritegaintable(self):
self.vis = create_blockvisibility(self.lowcore, self.times, self.frequency,
channel_bandwidth=self.channel_bandwidth,
phasecentre=self.phasecentre,
polarisation_frame=PolarisationFrame("linear"),
weight=1.0)
gt = create_gaintable_from_blockvisibility(self.vis, timeslice='auto')
gt = simulate_gaintable(gt, phase_error=1.0, amplitude_error=0.1)
export_gaintable_to_hdf5(gt, '%s/test_data_model_helpers_gaintable.hdf' % self.dir)
newgt = import_gaintable_from_hdf5('%s/test_data_model_helpers_gaintable.hdf' % self.dir)
assert gt.data.shape == newgt.data.shape
assert numpy.max(numpy.abs(gt.gain - newgt.gain)) < 1e-15
