def test_crosssubtract_datamodel(self):
self.actualSetUp(zerow=True)
skymodel_vislist = predict_skymodel_list_serial_workflow(
self.vis_list[0], self.skymodel_list, context='2d', docal=True)
vobs = sum_predict_results(skymodel_vislist)
skymodel_vislist = crosssubtract_datamodels_skymodel_list_serial_workflow(
vobs, skymodel_vislist)
result_skymodel = [
SkyModel(components=None, image=self.skymodel_list[-1].image)
for v in skymodel_vislist
]
results = invert_skymodel_list_serial_workflow(skymodel_vislist,
result_skymodel,
context='2d',
docal=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.serial.image.operations import show_image
show_image(results[0][0],
title='Dirty image after cross-subtraction',
vmax=0.1,
vmin=-0.01)
plt.show()
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
if ix >= iy:
p = int(round(centre[0] + ix * spacing_pixels * numpy.sign(model.wcs.wcs.cdelt[0]))), \
int(round(centre[1] + iy * spacing_pixels * numpy.sign(model.wcs.wcs.cdelt[1])))
sc = pixel_to_skycoord(p[0], p[1], model.wcs)
log.info("Component at (%f, %f) [0-rel] %s" % (p[0], p[1], str(sc)))
flux = numpy.array([[100.0 + 2.0 * ix + iy * 20.0]])
comp = create_skycomponent(flux=flux, frequency=frequency, direction=sc,
polarisation_frame=PolarisationFrame('stokesI'))
original_comps.append(comp)
insert_skycomponent(model, comp)
predict_skycomponent_visibility(vt, original_comps)
cmodel = smooth_image(model)
show_image(cmodel)
plt.title("Smoothed model image")
plt.savefig('1.jpg')
#plt.show()
comps = find_skycomponents(cmodel, fwhm=1.0, threshold=10.0, npixels=5)
plt.clf()
for i in range(len(comps)):
ocomp, sep = find_nearest_skycomponent(comps[i].direction, original_comps)
plt.plot((comps[i].direction.ra.value - ocomp.direction.ra.value)/cmodel.wcs.wcs.cdelt[0],
(comps[i].direction.dec.value - ocomp.direction.dec.value)/cmodel.wcs.wcs.cdelt[1],
'.', color='r')
plt.xlabel('delta RA (pixels)')
plt.ylabel('delta DEC (pixels)')
plt.title("Recovered - Original position offsets")
def create_simulation_components(context,
phasecentre,
frequency,
pbtype,
offset_dir,
flux_limit,
pbradius,
pb_npixel,
pb_cellsize,
show=False):
""" Construct components for simulation
:param context:
:param phasecentre:
:param frequency:
:param pbtype:
:param offset_dir:
:param flux_limit:
:param pbradius:
:param pb_npixel:
:param pb_cellsize:
:return:
"""
HWHM_deg, null_az_deg, null_el_deg = find_pb_width_null(pbtype, frequency)
dec = phasecentre.dec.deg
ra = phasecentre.ra.deg
if context == 'singlesource':
log.info("create_simulation_components: Constructing single component")
offset = [HWHM_deg * offset_dir[0], HWHM_deg * offset_dir[1]]
log.info(
"create_simulation_components: Offset from pointing centre = %.3f, %.3f deg"
% (offset[0], offset[1]))
# The point source is offset to approximately the halfpower point
offset_direction = SkyCoord(
ra=(ra + offset[0] / numpy.cos(numpy.pi * dec / 180.0)) *
units.deg,
dec=(dec + offset[1]) * units.deg,
frame='icrs',
equinox='J2000')
original_components = [
Skycomponent(flux=[[1.0]],
direction=offset_direction,
frequency=frequency,
polarisation_frame=PolarisationFrame('stokesI'))
]
elif context == 'null':
log.info(
"create_simulation_components: Constructing single component at the null"
)
offset = [null_az_deg * offset_dir[0], null_el_deg * offset_dir[1]]
HWHM = HWHM_deg * numpy.pi / 180.0
log.info(
"create_simulation_components: Offset from pointing centre = %.3f, %.3f deg"
% (offset[0], offset[1]))
# The point source is offset to approximately the null point
offset_direction = SkyCoord(
ra=(ra + offset[0] / numpy.cos(numpy.pi * dec / 180.0)) *
units.deg,
dec=(dec + offset[1]) * units.deg,
frame='icrs',
equinox='J2000')
original_components = [
Skycomponent(flux=[[1.0]],
direction=offset_direction,
frequency=frequency,
polarisation_frame=PolarisationFrame('stokesI'))
]
else:
offset = [0.0, 0.0]
# Make a skymodel from S3
max_flux = 0.0
total_flux = 0.0
log.info("create_simulation_components: Constructing s3sky components")
from wrappers.serial.simulation.testing_support import create_test_skycomponents_from_s3
original_components = create_test_skycomponents_from_s3(
flux_limit=flux_limit / 100.0,
phasecentre=phasecentre,
polarisation_frame=PolarisationFrame("stokesI"),
frequency=numpy.array(frequency),
radius=pbradius)
log.info(
"create_simulation_components: %d components before application of primary beam"
% (len(original_components)))
pbmodel = create_image(npixel=pb_npixel,
cellsize=pb_cellsize,
phasecentre=phasecentre,
frequency=frequency,
polarisation_frame=PolarisationFrame("stokesI"))
pb = create_pb(pbmodel,
"MID_GAUSS",
pointingcentre=phasecentre,
use_local=False)
pb_feko = create_pb(pbmodel,
pbtype,
pointingcentre=phasecentre,
use_local=True)
pb.data = pb_feko.data[:, 0, ...][:, numpy.newaxis, ...]
pb_applied_components = [
copy_skycomponent(c) for c in original_components
]
pb_applied_components = apply_beam_to_skycomponent(
pb_applied_components, pb)
filtered_components = []
for icomp, comp in enumerate(pb_applied_components):
if comp.flux[0, 0] > flux_limit:
total_flux += comp.flux[0, 0]
if abs(comp.flux[0, 0]) > max_flux:
max_flux = abs(comp.flux[0, 0])
filtered_components.append(original_components[icomp])
log.info(
"create_simulation_components: %d components > %.3f Jy after application of primary beam"
% (len(filtered_components), flux_limit))
log.info(
"create_simulation_components: Strongest components is %g (Jy)" %
max_flux)
log.info(
"create_simulation_components: Total flux in components is %g (Jy)"
% total_flux)
original_components = [
copy_skycomponent(c) for c in filtered_components
]
if show:
plt.clf()
show_image(pb, components=original_components)
plt.show(block=False)
log.info("create_simulation_components: Created %d components" %
len(original_components))
# Primary beam points to the phasecentre
offset_direction = SkyCoord(ra=ra * units.deg,
dec=dec * units.deg,
frame='icrs',
equinox='J2000')
return original_components, offset_direction
channel_model = create_image_from_visibility(
vt,
cellsize=cellsize,
npixel=512,
nchan=1,
imagecentre=vis_list[0].phasecentre,
polarisation_frame=PolarisationFrame('stokesIQUV'))
beam = create_pb(channel_model,
telescope='VLA',
pointingcentre=vt.phasecentre,
use_local=False)
beam.data /= numpy.max(beam.data)
dirty, sumwt = invert_list_serial_workflow([vt], [channel_model])[0]
print(sumwt)
mosaic.data += dirty.data * beam.data
mosaicsens.data += beam.data**2
show_image(dirty)
plt.show()
show_image(mosaic, cm='Greys', title='Linear mosaic')
plt.show()
show_image(mosaicsens, cm='Greys', title='Linear mosaic sensitivity')
plt.show()
from wrappers.serial.image.operations import export_image_to_fits
export_image_to_fits(mosaic, "mosaics.fits")
export_image_to_fits(mosaicsens, "mosaicsens.fits")
# %%
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='.')
work_vis = arlexecute.compute(result, sync=True)
for w in work_vis:
all_skymodel_vis.data['vis'] += w.data['vis']
assert numpy.max(numpy.abs(all_skymodel_vis.data['vis'])) > 0.0
all_skymodel_blockvis = convert_visibility_to_blockvisibility(all_skymodel_vis)
#######################################################################################################
# Now proceed to run MPCCAL in ICAL mode i.e. with only one skymodel
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))
for ix in locations:
if True: # ix >= iy:
p = int(round(centre[0] + ix * spacing_pixels * numpy.sign(model.wcs.wcs.cdelt[0]))), \
int(round(centre[1] + iy * spacing_pixels * numpy.sign(model.wcs.wcs.cdelt[1])))
sc = pixel_to_skycoord(p[0], p[1], model.wcs)
# log.info("Component at (%f, %f) [0-rel] %s" % (p[0], p[1], str(sc)))
flux = numpy.array([[200.0]]) # numpy.array([[100.0 + 2.0 * ix + iy * 20.0]])
comp = create_skycomponent(flux=flux, frequency=frequency, direction=sc,
polarisation_frame=PolarisationFrame('stokesI'))
original_comps.append(comp)
insert_skycomponent(model, comp)
predict_skycomponent_visibility(vt, original_comps)
cmodel = smooth_image(model)
show_image(cmodel)
plt.title("Smoothed model image")
plt.savefig('%s/%s_smoothed.pdf' % (storedir, str(freq)))
# plt.show()
comps = find_skycomponents(cmodel, fwhm=1.0, threshold=10.0, npixels=5)
plt.clf()
log.info('Total components = %s' % len(comps))
for i in range(len(comps)):
ocomp, sep = find_nearest_skycomponent(comps[i].direction, original_comps)
log.info('Position offset %d %f %f' % (i, comps[i].direction.ra.value - ocomp.direction.ra.value,
comps[i].direction.dec.value - ocomp.direction.dec.value))
plt.plot((comps[i].direction.ra.value - ocomp.direction.ra.value),
(comps[i].direction.dec.value - ocomp.direction.dec.value),
'.', color='r')
# plt.plot((comps[i].direction.ra.value - ocomp.direction.ra.value) / cmodel.wcs.wcs.cdelt[0],
flux_limit=flux_limit,
phasecentre=phasecentre,
frequency=frequency,
polarisation_frame=PolarisationFrame('stokesI'),
radius=0.2)
pb_gleam_components = apply_beam_to_skycomponent(original_gleam_components,
beam)
bright_components = filter_skycomponents_by_flux(pb_gleam_components,
flux_min=0.3)
all_components = filter_skycomponents_by_flux(pb_gleam_components,
flux_min=flux_limit)
show_image(beam,
components=bright_components,
cm='Greys',
title='Primary beam plus bright components',
vmax=3.0)
lprint("Number of bright components %d" % len(bright_components))
# In[7]:
idx, filtered_bright_components = remove_neighbouring_components(
bright_components, 0.01)
show_image(beam,
components=filtered_bright_components,
cm='Greys',
title='Primary beam plus filtered bright components',
vmax=3.0)
comp = create_skycomponent(flux=flux, frequency=frequency, direction=sc,
polarisation_frame=PolarisationFrame("stokesI"))
comps.append(comp)
predict_skycomponent_visibility(vt, comps)
arlexecute.set_client(use_dask=True)
dirty = create_image_from_visibility(vt, npixel=npixel, cellsize=cellsize,
polarisation_frame=PolarisationFrame("stokesI"))
vt = weight_visibility(vt, dirty)
future = invert_list_arlexecute_workflow([vt], [dirty], context='2d')
dirty, sumwt = arlexecute.compute(future, sync=True)[0]
if doplot:
show_image(dirty,cm='hot')
plt.title("Dirty image")
plt.savefig('%s/%s_dirty.pdf' % (results_dir, str(freq)))
print("Max, min in dirty image = %.6f, %.6f, sumwt = %f" % (dirty.data.max(), dirty.data.min(), sumwt))
export_image_to_fits(dirty, '%s/imaging-wterm_dirty.fits' % (results_dir))
dirtyFacet = create_image_from_visibility(vt, npixel=npixel, npol=1, cellsize=cellsize)
future = invert_list_arlexecute_workflow([vt], [dirtyFacet], facets=4, context='facets')
dirtyFacet, sumwt = arlexecute.compute(future, sync=True)[0]
dirtyFacet = smooth_image(dirtyFacet)
if doplot:
show_image(dirtyFacet,cm='hot')
plt.title("Smoothed model image")
plt.savefig('%s/%s_smooth.pdf' % (results_dir, str(freq)))
# plt.show()
arlexecute.set_client(use_dask=True)
dirty = create_image_from_visibility(
vt,
npixel=npixel,
cellsize=cellsize,
polarisation_frame=PolarisationFrame("stokesI"))
vt = weight_visibility(vt, dirty)
future = invert_list_arlexecute_workflow([vt], [dirty], context='2d')
dirty, sumwt = arlexecute.compute(future, sync=True)[0]
cmodel = smooth_image(dirty)
show_image(cmodel)
plt.title("Smoothed model image")
plt.savefig('%s/%s_smoothed.pdf' % ('./', str(freq)))
plt.show()
comps = find_skycomponents(cmodel,
fwhm=1.0,
threshold=500.0,
npixels=npixel // 60)
plt.clf()
log.info('Total components = %s' % len(comps))
discomp.append(len(comps))
x_offset = []
y_offset = []
offset = []