def test_deconvolution_channel_graph(self):
self.vis_graph_list = self.setupVis(freqwin=8)
self.model_graph = delayed(self.get_LSM)(self.vis_graph_list[self.nvis // 2], frequency=self.frequency)
model_graph = delayed(self.get_LSM)(self.vis_graph_list[self.nvis // 2],
flux=0.0)
dirty_graph = create_invert_wstack_graph(self.vis_graph_list, model_graph,
dopsf=False, vis_slices=self.vis_slices)
psf_model_graph = delayed(self.get_LSM)(self.vis_graph_list[self.nvis // 2],
flux=0.0)
psf_graph = create_invert_wstack_graph(self.vis_graph_list, psf_model_graph,
vis_slices=self.vis_slices,
dopsf=True)
channel_images = 4
clean_graph = create_deconvolve_channel_graph(dirty_graph, psf_graph, model_graph,
algorithm='hogbom', niter=1000,
fractional_threshold=0.02, threshold=2.0,
gain=0.1, subimages=channel_images)
if self.compute:
result = clean_graph.compute()
export_image_to_fits(result, '%s/test_imaging_deconvolution_channels%d.clean.fits' %
(self.results_dir, channel_images))
qa = qa_image(result)
assert numpy.abs(qa.data['max'] - 100.1) < 1.0, str(qa)
assert numpy.abs(qa.data['min'] + 1.8) < 1.0, str(qa)
def test_invert_wstack_graph(self):
dirty_graph = create_invert_wstack_graph(self.vis_graph_list, self.model_graph,
dopsf=False, normalize=True,
vis_slices=self.vis_slices)
if self.compute:
dirty = dirty_graph.compute()
export_image_to_fits(dirty[0], '%s/test_imaging_invert_wstack_dirty.fits' % (self.results_dir))
qa = qa_image(dirty[0])
assert numpy.abs(qa.data['max'] - 101.7) < 1.0, str(qa)
assert numpy.abs(qa.data['min'] + 3.5) < 1.0, str(qa)
def test_selfcal_global_graph(self):
corrupted_vis_graph_list = self.setupVis(add_errors=True)
selfcal_vis_graph_list = create_selfcal_graph_list(corrupted_vis_graph_list,
delayed(self.actualmodel),
global_solution=True,
c_predict_graph=create_predict_wstack_graph,
vis_slices=self.vis_slices)
dirty_graph = create_invert_wstack_graph(selfcal_vis_graph_list, self.model_graph,
dopsf=False, normalize=True,
vis_slices=self.vis_slices)
if self.compute:
dirty = dirty_graph.compute()
export_image_to_fits(dirty[0], '%s/test_imaging_graphs_global_selfcal_dirty.fits' % (self.results_dir))
qa = qa_image(dirty[0])
assert numpy.abs(qa.data['max'] - 101.7) < 1.0, str(qa)
assert numpy.abs(qa.data['min'] + 3.5) < 1.0, str(qa)
