def test_deconvolve_and_restore_cube_mmclean(self):
self.actualSetUp(add_errors=True)
dirty_imagelist = invert_list_serial_workflow(self.vis_list,
self.model_imagelist,
context='2d',
dopsf=False,
normalize=True)
psf_imagelist = invert_list_serial_workflow(self.vis_list,
self.model_imagelist,
context='2d',
dopsf=True,
normalize=True)
dec_imagelist = deconvolve_list_serial_workflow(
dirty_imagelist,
psf_imagelist,
self.model_imagelist,
niter=1000,
fractional_threshold=0.01,
scales=[0, 3, 10],
algorithm='mmclean',
nmoment=3,
nchan=self.freqwin,
threshold=0.1,
gain=0.7)
residual_imagelist = residual_list_serial_workflow(
self.vis_list, model_imagelist=dec_imagelist, context='2d')
restored = restore_list_serial_workflow(
model_imagelist=dec_imagelist,
psf_imagelist=psf_imagelist,
residual_imagelist=residual_imagelist,
empty=self.model_imagelist)[0]
export_image_to_fits(
restored,
'%s/test_imaging_serial_mmclean_restored.fits' % (self.dir))
def test_residual_list(self):
self.actualSetUp(zerow=True)
centre = self.freqwin // 2
residual_image_list = residual_list_serial_workflow(self.vis_list,
self.model_list,
context='2d')
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)