def progress(self, res, tl_list, gt_list, it):
"""Write progress information
Cannot use this if using Dask
:param res: Residual image
:param tl_list: Theta list
:param gt_list: Gaintable list
:param it: iteration
:return:
"""
combined_model = calculate_skymodel_equivalent_image(tl_list)
import matplotlib.pyplot as plt
plt.clf()
for i in range(len(tl_list)):
plt.plot(
numpy.angle(tl_list[i].gaintable.gain[:, :, 0, 0,
0]).flatten(),
numpy.angle(gt_list[i]['T'].gain[:, :, 0, 0, 0]).flatten(),
'.')
plt.xlabel('Current phase')
plt.ylabel('Update to phase')
# plt.xlim([-numpy.pi, numpy.pi])
# plt.ylim([-numpy.pi, numpy.pi])
plt.title("MPCCal iteration%d: Change in phase" % (it))
plt.show()
return tl_list
def progress(res, tl_list, gt_list, it, context='MPCCAL'):
print('Iteration %d' % it)
print(
qa_image(res,
context='%s residual image: iteration %d' %
(context, it)))
export_image_to_fits(
res,
arl_path(
"test_results/low-sims-mpc-%s-residual_iteration%d_rmax%.1f.fits"
% (context, it, rmax)))
show_image(res,
title='%s residual image: iteration %d' % (context, it))
plt.show(block=block_plots)
combined_model = calculate_skymodel_equivalent_image(tl_list)
print(
qa_image(combined_model,
context='Combined model: iteration %d' % it))
export_image_to_fits(
combined_model,
arl_path(
"test_results/low-sims-mpc-%s-model_iteration%d_rmax%.1f.fits"
% (context, it, rmax)))
plt.clf()
for i in range(len(tl_list)):
plt.plot(
numpy.angle(tl_list[i].gaintable.gain[:, :, 0, 0,
0]).flatten(),
numpy.angle(gt_list[i]['T'].gain[:, :, 0, 0, 0]).flatten(),
'.')
plt.xlabel('Current phase')
plt.ylabel('Update to phase')
plt.title("%s iteration%d: Change in phase" % (context, it))
plt.savefig(
arl_path(
"test_results/low-sims-mpc-%s-skymodel-phase-change_iteration%d.jpg"
% (context, it)))
plt.show(block=block_plots)
return tl_list
def test_mpccal_MPCCAL_manysources_subimages(self):
self.actualSetup()
model = create_empty_image_like(self.theta_list[0].image)
if arlexecute.using_dask:
progress = None
else:
progress = self.progress
future_vis = arlexecute.scatter(self.all_skymodel_noniso_vis)
future_model = arlexecute.scatter(model)
future_theta_list = arlexecute.scatter(self.theta_list)
result = mpccal_skymodel_list_arlexecute_workflow(
future_vis,
future_model,
future_theta_list,
mpccal_progress=progress,
nmajor=5,
context='2d',
algorithm='hogbom',
scales=[0, 3, 10],
fractional_threshold=0.3,
threshold=0.2,
gain=0.1,
niter=1000,
psf_support=256,
deconvolve_facets=8,
deconvolve_overlap=8,
deconvolve_taper='tukey')
(self.theta_list, residual) = arlexecute.compute(result, sync=True)
combined_model = calculate_skymodel_equivalent_image(self.theta_list)
psf_obs = invert_list_arlexecute_workflow(
[self.all_skymodel_noniso_vis], [model], context='2d', dopsf=True)
result = restore_list_arlexecute_workflow([combined_model], psf_obs,
[(residual, 0.0)])
result = arlexecute.compute(result, sync=True)
if self.persist:
export_image_to_fits(
residual,
arl_path('test_results/test_mpccal_no_edge_residual.fits'))
if self.persist:
export_image_to_fits(
result[0],
arl_path('test_results/test_mpccal_no_edge_restored.fits'))
if self.persist:
export_image_to_fits(
combined_model,
arl_path('test_results/test_mpccal_no_edge_deconvolved.fits'))
recovered_mpccal_components = find_skycomponents(result[0],
fwhm=2,
threshold=0.32,
npixels=12)
def max_flux(elem):
return numpy.max(elem.flux)
recovered_mpccal_components = sorted(recovered_mpccal_components,
key=max_flux,
reverse=True)
assert recovered_mpccal_components[
0].name == 'Segment 8', recovered_mpccal_components[0].name
assert numpy.abs(recovered_mpccal_components[0].flux[0, 0] - 7.773751416364857) < 1e-7, \
recovered_mpccal_components[0].flux[0, 0]
newscreen = create_empty_image_like(self.screen)
gaintables = [th.gaintable for th in self.theta_list]
newscreen, weights = grid_gaintable_to_screen(
self.all_skymodel_noniso_blockvis, gaintables, newscreen)
if self.persist:
export_image_to_fits(
newscreen,
arl_path('test_results/test_mpccal_no_edge_screen.fits'))
if self.persist:
export_image_to_fits(
weights,
arl_path(
'test_results/test_mpccal_no_edge_screenweights.fits'))
arlexecute.close()
algorithm='hogbom',
fractional_threshold=0.3,
threshold=0.1,
gain=0.1,
niter=1000,
psf_support=512,
deconvolve_facets=8,
deconvolve_overlap=16,
deconvolve_taper='tukey')
(ical_skymodel, residual) = arlexecute.compute(result, sync=True)
print(qa_image(residual, context='ICAL residual image'))
print('ical finished')
combined_model = calculate_skymodel_equivalent_image(ical_skymodel)
print(qa_image(combined_model, context='ICAL combined model'))
psf_obs = invert_list_arlexecute_workflow([future_vis], [future_model],
context='2d',
dopsf=True)
result = restore_list_arlexecute_workflow([combined_model], psf_obs,
[(residual, 0.0)])
result = arlexecute.compute(result, sync=True)
ical_restored = result[0]
export_image_to_fits(
ical_restored,
arl_path('test_results/low-sims-mpc-ical-restored_%.1frmax.fits' %
rmax))
#######################################################################################################
