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_calibrate_serial(self):
amp_errors = {'T': 0.0, 'G': 0.0}
phase_errors = {'T': 1.0, 'G': 0.0}
self.actualSetUp(amp_errors=amp_errors, phase_errors=phase_errors)
controls = create_calibration_controls()
controls['T']['first_selfcal'] = 0
controls['T']['timeslice'] = 'auto'
calibrate_list = \
calibrate_list_serial_workflow(self.error_blockvis_list, self.blockvis_list,
calibration_context='T', controls=controls, do_selfcal=True,
global_solution=False)
assert len(calibrate_list) == 2
assert numpy.max(calibrate_list[1][0]['T'].residual) < 7e-6, numpy.max(calibrate_list[1][0]['T'].residual)
assert numpy.max(numpy.abs(calibrate_list[0][0].vis - self.blockvis_list[0].vis)) < 2e-6
def test_calibrate_serial_global_empty(self):
amp_errors = {'T': 0.0, 'G': 0.0}
phase_errors = {'T': 1.0, 'G': 0.0}
self.actualSetUp(amp_errors=amp_errors, phase_errors=phase_errors)
for v in self.blockvis_list:
v.data['vis'][...] = 0.0 + 0.0j
controls = create_calibration_controls()
controls['T']['first_selfcal'] = 0
controls['T']['timeslice'] = 'auto'
calibrate_list = \
calibrate_list_serial_workflow(self.error_blockvis_list, self.blockvis_list,
calibration_context='T', controls=controls, do_selfcal=True,
global_solution=True)
assert len(calibrate_list[1][0]) == 1
assert numpy.max(calibrate_list[1][0]['T'].residual) == 0.0, numpy.max(calibrate_list[1][0]['T'].residual)
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)
print(qa_image(residual[0], context='Residual clean image - no selfcal'))
#plt.show()
export_image_to_fits(
residual[0],
'%s/imaging-dask_continuum_imaging_residual.fits' % (results_dir))
if rank == 0:
for chan in range(nfreqwin):
residual = continuum_imaging_list[1][chan]
#show_image(residual[0], title='Channel %d' % chan, cm='Greys',
# vmax=0.1, vmin=-0.01)
#plt.show()
# In[ ]:
controls = create_calibration_controls()
controls['T']['first_selfcal'] = 1
controls['G']['first_selfcal'] = 3
controls['B']['first_selfcal'] = 4
controls['T']['timeslice'] = 'auto'
controls['G']['timeslice'] = 'auto'
controls['B']['timeslice'] = 1e5
pp.pprint(controls)
# In[ ]:
# TODO I change this to predicted_vislist to make it deterministic, I hope it makes
# sense :)
