def write_image(im, im_type="restored", index=0, axis='spw'):
filename_root = \
"{results_directory}/{project:s}_{source:s}_{pipeline}_{im_type:s}_{axis}{index:d}".format(
results_directory=results_directory,
project=erp_params["configure"]["project"],
source=erp_params["ingest"]["source"],
im_type=im_type,
pipeline=pipeline,
axis=axis,
index=index)
log.info(qa_image(im, context=filename_root))
plt.clf()
show_image(im, title=filename_root)
plotfile = "{0}.png".format(filename_root)
plt.savefig(plotfile)
plt.show(block=False)
filename = "{0}.fits".format(filename_root)
export_image_to_fits(im, filename)
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,
rascil_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,
rascil_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(
rascil_path(
"test_results/low-sims-mpc-%s-skymodel-phase-change_iteration%d.jpg"
% (context, it)))
plt.show(block=block_plots)
return tl_list
npixels=12)
for comp in all_components[:args.ninitial]:
ical_components.append(comp)
# ### Remove weaker of components that are too close (0.02 rad)
idx, ical_components = remove_neighbouring_components(
ical_components, 0.02)
ical_components = sorted(ical_components,
key=lambda comp: numpy.max(comp.flux),
reverse=True)
print("Voronoi decomposition based on %d point sources" %
len(ical_components))
print(qa_image(ical_restored, context='ICAL restored image'))
show_image(ical_restored,
title='ICAL restored image',
vmax=0.3,
vmin=-0.03)
show_image(ical_restored,
title='ICAL restored image',
components=ical_components,
vmax=0.3,
vmin=-0.03)
plt.show(block=block_plots)
gaintable = create_gaintable_from_blockvisibility(block_vis)
mpccal_skymodel = initialize_skymodel_voronoi(model, ical_components,
ical_skymodel[0].gaintable)
ical_fluxes = [comp.flux[0, 0] for comp in ical_components]
plt.clf()
plt.semilogy(numpy.arange(len(all_fluxes)), all_fluxes, marker='.')
dopsf=True,
verbosity=0,
nthreads=nthreads,
do_wstacking=not zerow)
psf_list = rsexecute.compute(psf_list, sync=True)
psf, sumwt = sum_invert_results(psf_list)
print("PSF sumwt ", sumwt)
print(qa_image(psf, context='PSF'))
if export_images:
export_image_to_fits(psf, 'PSF_rascil.fits')
if show:
show_image(psf,
cm='gray_r',
title='%s PSF' % basename,
vmin=-0.01,
vmax=0.1)
plt.savefig('PSF_rascil.png')
plt.show(block=False)
del psf_list
del future_psf_list
if context == "s3sky":
pb_list = [
rsexecute.execute(create_image_from_visibility)(
bv,
npixel=pb_npixel,
frequency=frequency,
nchan=nfreqwin,
cellsize=pb_cellsize,
uvdist = numpy.sqrt(vt.data['uvw'][:, 0]**2 + vt.data['uvw'][:, 1]**2)
plt.clf()
plt.plot(uvdist, numpy.abs(vt.data['vis']), '.')
plt.xlabel('uvdist')
plt.ylabel('Amp Visibility')
plt.show()
model = create_image_from_visibility(
vt,
cellsize=cellsize,
npixel=1024,
polarisation_frame=PolarisationFrame('stokesIV'))
dirty, sumwt = invert_2d(vt, model, context='2d')
psf, sumwt = invert_2d(vt, model, context='2d', dopsf=True)
dirty = show_image(dirty)
dirty.show()
show_image(psf)
# print("Max, min in dirty image = %.6f, %.6f, sumwt = %f" % (dirty.data.max(), dirty.data.min(), sumwt))
# print("Max, min in PSF = %.6f, %.6f, sumwt = %f" % (psf.data.max(), psf.data.min(), sumwt))
# export_image_to_fits(dirty, '%s/imaging_dirty.fits'%(results_dir))
# export_image_to_fits(psf, '%s/imaging_psf.fits'%(results_dir))
comp, residual = deconvolve_cube(dirty,
psf,
niter=100,
threshold=0.001,
fractional_threshold=0.001,
gain=0.2,
del bvis_list
print("Inverting to get PSF")
psf_list = invert_list_rsexecute_workflow(future_vis_list,
future_psf_list,
args.imaging_context,
dopsf=True)
psf_list = rsexecute.compute(psf_list, sync=True)
psf, sumwt = sum_invert_results(psf_list)
print("PSF sumwt ", sumwt)
if export_images:
export_image_to_fits(psf, 'PSF_rascil.fits')
if show:
show_image(psf,
cm='gray_r',
title='%s PSF' % basename,
vmin=-0.01,
vmax=0.1)
plt.savefig('PSF_rascil.png')
plt.show(block=False)
del psf_list
del future_psf_list
# ### Calculate the voltage pattern without errors
vp_list = [
rsexecute.execute(create_image_from_visibility)(
bv,
npixel=pb_npixel,
frequency=frequency,
nchan=nfreqwin,
cellsize=pb_cellsize,
log.info("Processing took %.2f (s)" % run_time)
pp.pprint(result)
deconvolved = result[0][0]
residual = result[1][0][0]
restored = result[2][0]
print(qa_image(restored))
restored_name = target_ms.split('/')[-1].replace(
msext, '_cip_restored.fits')
log.info("Writing restored image to %s" % restored_name)
export_image_to_fits(restored, restored_name)
title = target_ms.split('/')[-1].replace(msext, ' cip restored image')
show_image(restored, vmax=0.03, vmin=-0.003, title=title)
plot_name = target_ms.split('/')[-1].replace(msext,
'_cip_restored.jpg')
plt.savefig(plot_name)
plt.show(block=False)
deconvolved_name = target_ms.split('/')[-1].replace(
msext, '_cip_deconvolved.fits')
log.info("Writing cip deconvolved image to %s" % deconvolved_name)
export_image_to_fits(deconvolved, deconvolved_name)
residual_name = target_ms.split('/')[-1].replace(
msext, '_cip_residual.fits')
log.info("Writing residual image to %s" % residual_name)
export_image_to_fits(residual, residual_name)