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_residual_list(self):
self.actualSetUp(zerow=True)
centre = self.freqwin // 2
residual_image_list = residual_list_arlexecute_workflow(self.vis_list, self.model_list, context='2d')
residual_image_list = arlexecute.compute(residual_image_list, sync=True)
qa = qa_image(residual_image_list[centre][0])
assert numpy.abs(qa.data['max'] - 0.35139716991480785) < 1.0, str(qa)
assert numpy.abs(qa.data['min'] + 0.7681701460717593) < 1.0, str(qa)
def test_sum_invert_list(self):
self.actualSetUp(zerow=True)
residual_image_list = residual_list_arlexecute_workflow(self.vis_list, self.model_list, context='2d')
residual_image_list = arlexecute.compute(residual_image_list, sync=True)
route2 = sum_invert_results(residual_image_list)
route1 = sum_invert_results_arlexecute(residual_image_list)
route1 = arlexecute.compute(route1, sync=True)
for r in route1, route2:
assert len(r) == 2
qa = qa_image(r[0])
assert numpy.abs(qa.data['max'] - 0.35139716991480785) < 1.0, str(qa)
assert numpy.abs(qa.data['min'] + 0.7681701460717593) < 1.0, str(qa)
assert numpy.abs(r[1]-415950.0) < 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()))
niter=1000, use_serial_clean=True,
fractional_threshold=0.01, scales=[0, 3, 10],
algorithm='msclean', gcfcf=gcfcf,
threshold=0.1, gain=0.7)
dec_imagelist = arlexecute.persist(dec_imagelist)
dec_imagelist = arlexecute.compute(dec_imagelist, sync=True)
deconvolved = dec_imagelist[0]
# export_image_to_fits(deconvolved,
# '%s/test_imaging_%s_deconvolved.fits' % (storedir, context))
show_image(deconvolved)
plt.title(context)
plt.savefig('%s/test_imaging_%s_clean.pdf' % (storedir, context))
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)
