def test_invert(self):
uvfitsfile = arl_path("data/vis/ASKAP_example.fits")
nchan_ave = 32
nchan = 192
for schan in range(0, nchan, nchan_ave):
max_chan = min(nchan, schan + nchan_ave)
bv = create_blockvisibility_from_uvfits(uvfitsfile,
range(schan, max_chan))[0]
vis = convert_blockvisibility_to_visibility(bv)
from processing_components.visibility.operations import convert_visibility_to_stokesI
vis = convert_visibility_to_stokesI(vis)
model = create_image_from_visibility(
vis,
npixel=256,
polarisation_frame=PolarisationFrame('stokesI'))
dirty, sumwt = invert_2d(vis, model, context='2d')
assert (numpy.max(numpy.abs(dirty.data))) > 0.0
assert dirty.shape == (nchan_ave, 1, 256, 256)
import matplotlib.pyplot as plt
from processing_components.image.operations import show_image
show_image(dirty)
plt.show()
if self.persist:
export_image_to_fits(
dirty, '%s/test_visibility_uvfits_dirty.fits' % self.dir)
global_solution=False,
psf_support=64,
do_selfcal=True)
# In[ ]:
log.info('About to run ical')
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_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()
export_image_to_fits(restored,
vis_list = arlexecute.persist(vis_list)
cellsize = 0.001
npixel = 1024
pol_frame = PolarisationFrame("stokesI")
model_list = [
arlexecute.execute(create_image_from_visibility)(
v, npixel=npixel, cellsize=cellsize, polarisation_frame=pol_frame)
for v in vis_list
]
model_list = arlexecute.persist(model_list)
dirty_list = invert_list_arlexecute_workflow(
vis_list,
template_model_imagelist=model_list,
context='wstack',
vis_slices=51)
log.info('About to run invert_list_arlexecute_workflow')
result = arlexecute.compute(dirty_list, sync=True)
dirty, sumwt = result[centre]
arlexecute.close()
show_image(dirty, title='Dirty image', cm='Greys', vmax=0.1, vmin=-0.01)
print(qa_image(dirty, context='Dirty image'))
export_image_to_fits(
dirty, '%s/ska-imaging_arlexecute_dirty.fits' % (results_dir))
def test_create_gradient(self):
real_vp = import_image_from_fits(
arl_path('data/models/MID_GRASP_VP_real.fits'))
gradx, grady = image_gradients(real_vp)
gradxx, gradxy = image_gradients(gradx)
gradyx, gradyy = image_gradients(grady)
gradx.data *= real_vp.data
grady.data *= real_vp.data
gradxx.data *= real_vp.data
gradxy.data *= real_vp.data
gradyx.data *= real_vp.data
gradyy.data *= real_vp.data
import matplotlib.pyplot as plt
plt.clf()
show_image(gradx, title='gradx')
plt.show()
plt.clf()
show_image(grady, title='grady')
plt.show()
export_image_to_fits(gradx,
"%s/test_image_gradients_gradx.fits" % (self.dir))
export_image_to_fits(grady,
"%s/test_image_gradients_grady.fits" % (self.dir))
plt.clf()
show_image(gradxx, title='gradxx')
plt.show()
plt.clf()
show_image(gradxy, title='gradxy')
plt.show()
plt.clf()
show_image(gradyx, title='gradyx')
plt.show()
plt.clf()
show_image(gradyy, title='gradyy')
plt.show()
export_image_to_fits(
gradxx, "%s/test_image_gradients_gradxx.fits" % (self.dir))
export_image_to_fits(
gradxy, "%s/test_image_gradients_gradxy.fits" % (self.dir))
export_image_to_fits(
gradyx, "%s/test_image_gradients_gradyx.fits" % (self.dir))
export_image_to_fits(
gradyy, "%s/test_image_gradients_gradyy.fits" % (self.dir))