def test_predict_wprojection_graph(self):
flux_model_graph = delayed(self.get_LSM)(self.vis_graph_list[self.nvis // 2], flux=100.0)
zero_vis_graph_list = create_zero_vis_graph_list(self.vis_graph_list)
predicted_vis_graph_list = create_predict_graph(zero_vis_graph_list, flux_model_graph, wstep=4.0,
kernel='wprojection')
residual_vis_graph_list = create_subtract_vis_graph_list(self.vis_graph_list, predicted_vis_graph_list)
if self.compute:
qa = qa_visibility(self.vis_graph_list[0].compute())
numpy.testing.assert_almost_equal(qa.data['maxabs'], 1600.0, 0)
qa = qa_visibility(predicted_vis_graph_list[0].compute())
numpy.testing.assert_almost_equal(qa.data['maxabs'], 111.8, 0)
qa = qa_visibility(residual_vis_graph_list[0].compute())
numpy.testing.assert_almost_equal(qa.data['maxabs'], 1644.3, 0)
def test_predict_wstack_graph_wprojection(self):
flux_model_graph = delayed(self.get_LSM)(self.vis_graph_list[self.nvis // 2], flux=100.0)
zero_vis_graph_list = create_zero_vis_graph_list(self.vis_graph_list)
predicted_vis_graph_list = create_predict_wstack_graph(zero_vis_graph_list, flux_model_graph,
vis_slices=11, wstep=10.0, kernel='wprojection')
residual_vis_graph_list = create_subtract_vis_graph_list(self.vis_graph_list, predicted_vis_graph_list)
if self.compute:
qa = qa_visibility(self.vis_graph_list[0].compute())
numpy.testing.assert_almost_equal(qa.data['maxabs'], 1600.0, 0)
qa = qa_visibility(predicted_vis_graph_list[0].compute())
numpy.testing.assert_almost_equal(qa.data['maxabs'], 100.064844507, 0)
qa = qa_visibility(residual_vis_graph_list[0].compute())
numpy.testing.assert_almost_equal(qa.data['maxabs'], 1668.3018405354974, 0)
def test_predict_facet_wstack_graph(self):
flux_model_graph = delayed(self.get_LSM)(self.vis_graph_list[self.nvis // 2], flux=100.0)
zero_vis_graph_list = create_zero_vis_graph_list(self.vis_graph_list)
predicted_vis_graph_list = create_predict_facet_wstack_graph(zero_vis_graph_list, flux_model_graph,
facets=2, vis_slices=self.vis_slices)
residual_vis_graph_list = create_subtract_vis_graph_list(self.vis_graph_list, predicted_vis_graph_list)
if self.compute:
qa = qa_visibility(self.vis_graph_list[0].compute())
numpy.testing.assert_almost_equal(qa.data['maxabs'], 1600.0, 0)
qa = qa_visibility(predicted_vis_graph_list[0].compute())
numpy.testing.assert_almost_equal(qa.data['maxabs'], 100.064844507, 0)
qa = qa_visibility(residual_vis_graph_list[0].compute())
numpy.testing.assert_almost_equal(qa.data['maxabs'], 1643.1, 0)
def test_predict_timeslice_graph(self):
flux_model_graph = delayed(self.get_LSM)(self.vis_graph_list[self.nvis // 2], flux=100.0)
zero_vis_graph_list = create_zero_vis_graph_list(self.vis_graph_list)
predicted_vis_graph_list = create_predict_timeslice_graph(zero_vis_graph_list, flux_model_graph,
vis_slices=3)
residual_vis_graph_list = create_subtract_vis_graph_list(self.vis_graph_list, predicted_vis_graph_list)
if self.compute:
qa = qa_visibility(self.vis_graph_list[0].compute())
numpy.testing.assert_almost_equal(qa.data['maxabs'], 1600.0, 0)
qa = qa_visibility(predicted_vis_graph_list[0].compute())
numpy.testing.assert_almost_equal(qa.data['maxabs'], 152.1, 0)
qa = qa_visibility(residual_vis_graph_list[0].compute())
numpy.testing.assert_almost_equal(qa.data['maxabs'], 1749.2, 0)
def test_predict_facet_timeslice_graph_wprojection(self):
flux_model_graph = delayed(self.get_LSM)(self.vis_graph_list[self.nvis // 2], flux=100.0)
zero_vis_graph_list = create_zero_vis_graph_list(self.vis_graph_list)
predicted_vis_graph_list = create_predict_facet_timeslice_graph(zero_vis_graph_list, flux_model_graph,
vis_slices=3, facets=2,
wstep=4.0, kernel='wprojection')
simple_vis(predicted_vis_graph_list[0], filename='predict_facet_timeslice_graph_wprojection', format='png')
residual_vis_graph_list = create_subtract_vis_graph_list(self.vis_graph_list, predicted_vis_graph_list)
if self.compute:
qa = qa_visibility(self.vis_graph_list[0].compute())
numpy.testing.assert_almost_equal(qa.data['maxabs'], 1600.0, 0)
qa = qa_visibility(predicted_vis_graph_list[0].compute())
numpy.testing.assert_almost_equal(qa.data['maxabs'], 94.2, 0)
qa = qa_visibility(residual_vis_graph_list[0].compute())
numpy.testing.assert_almost_equal(qa.data['maxabs'], 1656.6, 0)
def create_ical_pipeline_graph(vis_graph_list,
model_graph: delayed,
c_deconvolve_graph=create_deconvolve_graph,
c_invert_graph=create_invert_graph,
c_residual_graph=create_residual_graph,
c_predict_graph=create_predict_graph,
c_calibrate_graph=create_calibrate_graph_list,
first_selfcal=None,
**kwargs) -> delayed:
"""Create graph for ICAL pipeline
:param vis_graph_list:
:param model_graph:
:param c_deconvolve_graph: Default: create_deconvolve_graph
:param c_invert_graph: Default: create_invert_graph,
:param c_residual_graph: Default: Default: create_residual_graph
:param kwargs: Parameters for functions in graphs
:return:
"""
psf_graph = c_invert_graph(vis_graph_list,
model_graph,
dopsf=True,
**kwargs)
if first_selfcal is not None and first_selfcal == 0:
# Make the predicted visibilities, selfcalibrate against it correcting the gains, then
# form the residual visibility, then make the residual image
model_vis_graph_list = create_zero_vis_graph_list(vis_graph_list)
model_vis_graph_list = c_predict_graph(model_vis_graph_list,
model_graph, **kwargs)
vis_graph_list = c_calibrate_graph(vis_graph_list,
model_vis_graph_list, **kwargs)
residual_vis_graph_list = create_subtract_vis_graph_list(
vis_graph_list, model_vis_graph_list)
residual_graph = c_invert_graph(residual_vis_graph_list,
model_graph,
dopsf=True,
**kwargs)
else:
# If we are not selfcalibrating it's much easier and we can avoid an unnecessary round of gather/scatter
# for visibility partitioning such as timeslices and wstack.
residual_graph = c_residual_graph(vis_graph_list, model_graph,
**kwargs)
deconvolve_model_graph = c_deconvolve_graph(residual_graph, psf_graph,
model_graph, **kwargs)
nmajor = get_parameter(kwargs, "nmajor", 5)
if nmajor > 1:
for cycle in range(nmajor):
if first_selfcal is not None and cycle >= first_selfcal:
model_vis_graph_list = create_zero_vis_graph_list(
vis_graph_list)
model_vis_graph_list = c_predict_graph(model_vis_graph_list,
deconvolve_model_graph,
**kwargs)
vis_graph_list = c_calibrate_graph(vis_graph_list,
model_vis_graph_list,
**kwargs)
residual_vis_graph_list = create_subtract_vis_graph_list(
vis_graph_list, model_vis_graph_list)
residual_graph = c_invert_graph(residual_vis_graph_list,
model_graph,
dopsf=False,
**kwargs)
else:
residual_graph = c_residual_graph(vis_graph_list,
deconvolve_model_graph,
**kwargs)
deconvolve_model_graph = c_deconvolve_graph(
residual_graph, psf_graph, deconvolve_model_graph, **kwargs)
residual_graph = c_residual_graph(vis_graph_list, deconvolve_model_graph,
**kwargs)
restore_graph = delayed(restore_cube, pure=True,
nout=1)(deconvolve_model_graph, psf_graph[0],
residual_graph[0], **kwargs)
return delayed((deconvolve_model_graph, residual_graph, restore_graph))
