def test_crosssubtract_datamodel(self):
self.actualSetUp(zerow=True)
future_vis = arlexecute.scatter(self.vis_list[0])
future_skymodel_list = arlexecute.scatter(self.skymodel_list)
skymodel_vislist = predict_skymodel_list_arlexecute_workflow(
future_vis, future_skymodel_list, context='2d', docal=True)
skymodel_vislist = arlexecute.compute(skymodel_vislist, sync=True)
vobs = sum_predict_results(skymodel_vislist)
future_vobs = arlexecute.scatter(vobs)
skymodel_vislist = crosssubtract_datamodels_skymodel_list_arlexecute_workflow(
future_vobs, skymodel_vislist)
skymodel_vislist = arlexecute.compute(skymodel_vislist, sync=True)
result_skymodel = [
SkyModel(components=None, image=self.skymodel_list[-1].image)
for v in skymodel_vislist
]
self.vis_list = arlexecute.scatter(self.vis_list)
result_skymodel = invert_skymodel_list_arlexecute_workflow(
skymodel_vislist, result_skymodel, context='2d', docal=True)
results = arlexecute.compute(result_skymodel, sync=True)
assert numpy.max(numpy.abs(results[0][0].data)) > 0.0
assert numpy.max(numpy.abs(results[0][1])) > 0.0
if self.plot:
import matplotlib.pyplot as plt
from wrappers.arlexecute.image.operations import show_image
show_image(results[0][0],
title='Dirty image after cross-subtraction',
vmax=0.1,
vmin=-0.01)
plt.show()
print("Display power spectrum of an image")
im = import_image_from_fits(args.image)
nchan, npol, ny, nx = im.shape
if args.signal_channel is None:
signal_channel = nchan // 2
else:
signal_channel = args.signal_channels
noise_channel = args.noise_channel
resolution = args.resolution
plt.clf()
show_image(im, chan=signal_channel)
plt.title('Signal image %s' % (basename))
plt.savefig('simulation_image_channel_%d.png' % signal_channel)
plt.show()
plt.clf()
show_image(im, chan=noise_channel)
plt.title('Noise image %s' % (basename))
plt.savefig('simulation_noise_channel_%d.png' % signal_channel)
plt.show()
print(im)
imfft = fft_image(im)
print(imfft)
omega = numpy.pi * resolution**2 / (4 * numpy.log(2.0))
wavelength = consts.c / numpy.average(im.frequency)
global_solution=False,
psf_support=64,
do_selfcal=True)
# In[ ]:
log.info('About to run ical_list_serial_workflow')
result = arlexecute.compute(ical_list, sync=True)
deconvolved = result[0][0]
residual = result[1][0]
restored = result[2][0]
arlexecute.close()
show_image(deconvolved,
title='Clean image',
cm='Greys',
vmax=0.1,
vmin=-0.01)
print(qa_image(deconvolved, context='Clean image'))
plt.show()
export_image_to_fits(
deconvolved,
'%s/gleam_ical_arlexecute_deconvolved.fits' % (results_dir))
show_image(restored,
title='Restored clean image',
cm='Greys',
vmax=0.1,
vmin=-0.01)
print(qa_image(restored, context='Restored clean image'))
plt.show()
# Now construct and run a graph to sum the results from the summations over each chunk
final_result = sum_invert_results_arlexecute(results)
dirty, sumwt = arlexecute.compute(final_result, sync=True)
# We are done! make plots and fits files
type = 'dirty'
if dopsf:
type = 'psf'
if args.write_fits == "True":
imagename = "simulate_rfi_%.1f_%s.fits" % (declination, type)
export_image_to_fits(dirty, imagename)
plt.clf()
show_image(dirty, chan=len(frequency) // channel_average // 2)
plotname = "simulate_rfi_%.1f_%s.png" % (declination, type)
plt.title('Image of the target field')
plt.savefig(plotname)
plt.show(block=False)
final_pole_result = sum_invert_results_arlexecute(pole_results)
pole_dirty, pole_sumwt = arlexecute.compute(final_pole_result, sync=True)
if args.write_fits == "True":
imagename = "simulate_rfi_pole_%s.fits" % (type)
export_image_to_fits(pole_dirty, imagename)
plt.clf()
show_image(pole_dirty, chan=len(frequency) // channel_average // 2)
plotname = "simulate_rfi_pole_%s.png" % (type)