def test_readwriteskymodel(self):
im = create_test_image()
sm = SkyModel(components=[self.comp], images=[im, im])
export_skymodel_to_hdf5(sm, '%s/test_skymodel.hdf' % self.dir)
newsm = import_skymodel_from_hdf5('%s/test_skymodel.hdf' % self.dir)
assert newsm.components[0].flux.shape == self.comp.flux.shape
assert newsm.images[0].data.shape == im.data.shape
assert numpy.max(numpy.abs(newsm.images[0].data - im.data)) < 1e-15
def test_readwriteskymodel(self):
self.vis = create_blockvisibility(self.lowcore, self.times, self.frequency,
channel_bandwidth=self.channel_bandwidth,
phasecentre=self.phasecentre,
polarisation_frame=PolarisationFrame("linear"),
weight=1.0)
im = create_test_image()
gt = create_gaintable_from_blockvisibility(self.vis, timeslice='auto')
sm = SkyModel(components=[self.comp], image=im, gaintable=gt)
export_skymodel_to_hdf5(sm, '%s/test_data_model_helpers_skymodel.hdf' % self.dir)
newsm = import_skymodel_from_hdf5('%s/test_data_model_helpers_skymodel.hdf' % self.dir)
assert newsm.components[0].flux.shape == self.comp.flux.shape
assert newsm.image.data.shape == im.data.shape
assert numpy.max(numpy.abs(newsm.image.data - im.data)) < 1e-15
gleam_model = [arlexecute.execute(create_low_test_image_from_gleam)(npixel=npixel,
frequency=[frequency[f]],
channel_bandwidth=[channel_bandwidth[f]],
cellsize=cellsize,
phasecentre=phasecentre,
polarisation_frame=PolarisationFrame("stokesI"),
flux_limit=1.0,
applybeam=True)
for f, freq in enumerate(frequency)]
print('About to make GLEAM model')
gleam_model = arlexecute.persist(gleam_model)
print('About to run predict to get predicted visibility')
vis_list = arlexecute.scatter(vis_list)
predicted_vislist = predict_list_arlexecute_workflow(vis_list, gleam_model, context='wstack',
vis_slices=vis_slices)
predicted_vislist = arlexecute.persist(predicted_vislist)
corrupted_vislist = corrupt_list_arlexecute_workflow(predicted_vislist, phase_error=1.0)
print('About to run corrupt to get corrupted visibility')
corrupted_vislist = arlexecute.persist(corrupted_vislist)
corrupted_vislist = arlexecute.compute(corrupted_vislist, sync=True)
export_blockvisibility_to_hdf5(corrupted_vislist, 'gleam_simulation_vislist.hdf')
gleam_model = arlexecute.compute(gleam_model, sync=True)
gleam_skymodel = SkyModel(image=gleam_model)
export_skymodel_to_hdf5(gleam_skymodel, 'gleam_simulation_skymodel.hdf')
arlexecute.close()
phasecentre=phasecentre,
polarisation_frame=PolarisationFrame("stokesI"),
flux_limit=3.0,
applybeam=True) for f, freq in enumerate(frequency)
]
# Put the model on the cluster
dprepb_model = arlexecute.persist(dprepb_model)
print('About to make GLEAM model')
zero_model = [
arlexecute.execute(create_empty_image_like)(im) for im in dprepb_model
]
zero_model = arlexecute.compute(zero_model, sync=True)
zero_skymodel = SkyModel(images=zero_model)
export_skymodel_to_hdf5(
zero_skymodel,
arl_path('%s/ska-pipeline_simulation_skymodel.hdf' % results_dir))
# In[ ]:
# vis_list = arlexecute.scatter(vis_list)
wstack = True
if wstack:
print('Using w stack with %d slices' % vis_slices)
predicted_vislist = predict_list_arlexecute_workflow(
vis_list, dprepb_model, context='wstack', vis_slices=vis_slices)
else:
print('Using timeslicing with %d slices' % ntimes)
predicted_vislist = predict_list_arlexecute_workflow(
vis_list, dprepb_model, context='timeslice', vis_slices=ntimes)
