def test_deconvolve_spectral(self):
self.actualSetUp(add_errors=True)
dirty_imagelist = invert_component(self.vis_list, self.model_imagelist,
context='2d',
dopsf=False, normalize=True)
psf_imagelist = invert_component(self.vis_list, self.model_imagelist,
context='2d',
dopsf=True, normalize=True)
deconvolved, _ = deconvolve_component(dirty_imagelist, psf_imagelist, self.model_imagelist, niter=1000,
fractional_threshold=0.1, scales=[0, 3, 10],
threshold=0.1, gain=0.7)
deconvolved = arlexecute.compute(deconvolved, sync=True)
export_image_to_fits(deconvolved[0], '%s/test_imaging_%s_deconvolve_spectral.fits' %
(self.dir, arlexecute.type()))
def _invert_base(self,
context,
extra='',
fluxthreshold=1.0,
positionthreshold=1.0,
check_components=True,
facets=1,
vis_slices=1,
**kwargs):
dirty = invert_component(self.vis_list,
self.model_graph,
context=context,
dopsf=False,
normalize=True,
facets=facets,
vis_slices=vis_slices,
**kwargs)[0]
dirty = arlexecute.compute(dirty, sync=True)
export_image_to_fits(
dirty[0], '%s/test_imaging_invert_%s%s_%s_dirty.fits' %
(self.dir, context, extra, arlexecute.type()))
assert numpy.max(numpy.abs(dirty[0].data)), "Image is empty"
if check_components:
self._checkcomponents(dirty[0], fluxthreshold, positionthreshold)
def _predict_base(self,
context='2d',
extra='',
fluxthreshold=1.0,
facets=1,
vis_slices=1,
**kwargs):
vis_list = zero_vislist_component(self.vis_list)
vis_list = predict_component(vis_list,
self.model_graph,
context=context,
vis_slices=vis_slices,
facets=facets,
**kwargs)
vis_list = subtract_vislist_component(self.vis_list, vis_list)[0]
vis_list = arlexecute.compute(vis_list, sync=True)
dirty = invert_component([vis_list], [self.model_graph[0]],
context='2d',
dopsf=False,
normalize=True)[0]
dirty = arlexecute.compute(dirty, sync=True)
assert numpy.max(numpy.abs(dirty[0].data)), "Residual image is empty"
export_image_to_fits(
dirty[0], '%s/test_imaging_predict_%s%s_%s_dirty.fits' %
(self.dir, context, extra, arlexecute.type()))
maxabs = numpy.max(numpy.abs(dirty[0].data))
assert maxabs < fluxthreshold, "Error %.3f greater than fluxthreshold %.3f " % (
maxabs, fluxthreshold)
def test_deconvolve_and_restore_cube_mmclean(self):
self.actualSetUp(add_errors=True)
dirty_imagelist = invert_component(self.vis_list, self.model_imagelist, context='2d',
dopsf=False, normalize=True)
psf_imagelist = invert_component(self.vis_list, self.model_imagelist, context='2d',
dopsf=True, normalize=True)
dec_imagelist, _ = deconvolve_component(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_component(self.vis_list, model_imagelist=dec_imagelist,
context='wstack', vis_slices=51)
restored = restore_component(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()))
def test_weighting(self):
self.actualSetUp()
context = 'wstack'
vis_slices = 41
facets = 1
dirty_graph = invert_component(self.vis_list,
self.model_graph,
context=context,
dopsf=False,
normalize=True,
facets=facets,
vis_slices=vis_slices)
dirty = arlexecute.compute(dirty_graph[0], sync=True)
export_image_to_fits(
dirty[0], '%s/test_imaging_noweighting_%s_dirty.fits' %
(self.dir, arlexecute.type()))