def test_create_image_from_visibility(self):
self.actualSetUp()
im = create_image_from_visibility(self.componentvis,
nchan=1,
npixel=128)
assert im.data.shape == (1, 1, 128, 128)
im = create_image_from_visibility(self.componentvis,
frequency=self.frequency,
npixel=128)
assert im.data.shape == (len(self.frequency), 1, 128, 128)
im = create_image_from_visibility(self.componentvis,
frequency=self.frequency,
npixel=128,
nchan=1)
assert im.data.shape == (1, 1, 128, 128)
def setUp(self):
self.dir = './test_results'
self.lowcore = create_named_configuration('LOWBD2-CORE')
os.makedirs(self.dir, exist_ok=True)
self.times = (numpy.pi / (12.0)) * numpy.linspace(-3.0, 3.0, 7)
self.frequency = numpy.array([1e8])
self.channel_bandwidth = numpy.array([1e6])
self.phasecentre = SkyCoord(ra=+180.0 * u.deg,
dec=-60.0 * u.deg,
frame='icrs',
equinox='J2000')
self.vis = create_visibility(
self.lowcore,
self.times,
self.frequency,
channel_bandwidth=self.channel_bandwidth,
phasecentre=self.phasecentre,
weight=1.0,
polarisation_frame=PolarisationFrame('stokesI'))
self.vis.data['vis'] *= 0.0
# Create model
self.model = create_test_image(cellsize=0.0015,
phasecentre=self.vis.phasecentre,
frequency=self.frequency)
self.model.data[self.model.data > 1.0] = 1.0
self.vis = predict_2d(self.vis, self.model)
assert numpy.max(numpy.abs(self.vis.vis)) > 0.0
export_image_to_fits(
self.model, '%s/test_solve_skycomponent_model.fits' % (self.dir))
self.bigmodel = create_image_from_visibility(self.vis,
cellsize=0.0015,
npixel=512)
def setUp(self):
self.dir = './test_results'
os.makedirs(self.dir, exist_ok=True)
self.vnchan = 5
self.lowcore = create_named_configuration('LOWBD2-CORE')
self.times = (numpy.pi / 12.0) * numpy.linspace(-3.0, 3.0, 7)
self.frequency = numpy.linspace(8e7, 1.2e8, self.vnchan)
self.startfrequency = numpy.array([8e7])
self.channel_bandwidth = numpy.array(
self.vnchan * [self.frequency[1] - self.frequency[0]])
self.phasecentre = SkyCoord(ra=+180.0 * u.deg,
dec=-60.0 * u.deg,
frame='icrs',
equinox='J2000')
self.vis = create_visibility(
self.lowcore,
times=self.times,
frequency=self.frequency,
phasecentre=self.phasecentre,
weight=1.0,
polarisation_frame=PolarisationFrame('stokesI'),
channel_bandwidth=self.channel_bandwidth)
self.model = create_image_from_visibility(
self.vis,
npixel=512,
cellsize=0.001,
nchan=self.vnchan,
frequency=self.startfrequency)
def ingest_visibility(self, freq=None, chan_width=None, times=None, reffrequency=None, add_errors=False,
block=True):
if freq is None:
freq = [1e8]
if times is None:
ntimes = 5
times = numpy.linspace(-numpy.pi / 3.0, numpy.pi / 3.0, ntimes)
if chan_width is None:
chan_width = [1e6]
if reffrequency is None:
reffrequency = [1e8]
lowcore = create_named_configuration('LOWBD2-CORE')
frequency = numpy.array([freq])
channel_bandwidth = numpy.array([chan_width])
phasecentre = SkyCoord(ra=+180.0 * u.deg, dec=-60.0 * u.deg, frame='icrs', equinox='J2000')
if block:
vt = create_blockvisibility(lowcore, times, frequency, channel_bandwidth=channel_bandwidth,
weight=1.0, phasecentre=phasecentre,
polarisation_frame=PolarisationFrame("stokesI"))
else:
vt = create_visibility(lowcore, times, frequency, channel_bandwidth=channel_bandwidth,
weight=1.0, phasecentre=phasecentre,
polarisation_frame=PolarisationFrame("stokesI"))
cellsize = 0.001
model = create_image_from_visibility(vt, npixel=self.npixel, cellsize=cellsize, npol=1,
frequency=reffrequency, phasecentre=phasecentre,
polarisation_frame=PolarisationFrame("stokesI"))
flux = numpy.array([[100.0]])
facets = 4
rpix = model.wcs.wcs.crpix - 1.0
spacing_pixels = self.npixel // facets
centers = [-1.5, -0.5, 0.5, 1.5]
comps = list()
for iy in centers:
for ix in centers:
p = int(round(rpix[0] + ix * spacing_pixels * numpy.sign(model.wcs.wcs.cdelt[0]))), \
int(round(rpix[1] + iy * spacing_pixels * numpy.sign(model.wcs.wcs.cdelt[1])))
sc = pixel_to_skycoord(p[0], p[1], model.wcs, origin=1)
comp = create_skycomponent(flux=flux, frequency=frequency, direction=sc,
polarisation_frame=PolarisationFrame("stokesI"))
comps.append(comp)
if block:
predict_skycomponent_blockvisibility(vt, comps)
else:
predict_skycomponent_visibility(vt, comps)
insert_skycomponent(model, comps)
self.model = copy_image(model)
self.empty_model = create_empty_image_like(model)
export_image_to_fits(model, '%s/test_pipeline_bags_model.fits' % self.dir)
if add_errors:
# These will be the same for all calls
numpy.random.seed(180555)
gt = create_gaintable_from_blockvisibility(vt)
gt = simulate_gaintable(gt, phase_error=1.0, amplitude_error=0.0)
vt = apply_gaintable(vt, gt)
return vt
def test_get_frequency_map_different_channel(self):
self.model = create_image_from_visibility(self.vis, npixel=512, cellsize=0.001,
frequency=self.startfrequency, nchan=3,
channel_bandwidth=2e7)
spectral_mode, vfrequency_map = get_frequency_map(self.vis, self.model)
assert numpy.max(vfrequency_map) == self.model.nchan - 1
assert spectral_mode == 'channel'
def ingest_visibility(self,
freq=1e8,
chan_width=1e6,
times=None,
reffrequency=None,
add_errors=False):
if times is None:
times = (numpy.pi / 12.0) * numpy.linspace(-3.0, 3.0, 5)
if reffrequency is None:
reffrequency = [1e8]
lowcore = create_named_configuration('LOWBD2-CORE')
frequency = numpy.array([freq])
channel_bandwidth = numpy.array([chan_width])
phasecentre = SkyCoord(ra=+180.0 * u.deg,
dec=-60.0 * u.deg,
frame='icrs',
equinox='J2000')
vt = create_visibility(lowcore,
times,
frequency,
channel_bandwidth=channel_bandwidth,
weight=1.0,
phasecentre=phasecentre,
polarisation_frame=PolarisationFrame("stokesI"))
cellsize = 0.001
model = create_image_from_visibility(
vt,
npixel=self.npixel,
cellsize=cellsize,
npol=1,
frequency=reffrequency,
phasecentre=phasecentre,
polarisation_frame=PolarisationFrame("stokesI"))
flux = numpy.array([[100.0]])
facets = 4
rpix = model.wcs.wcs.crpix
spacing_pixels = self.npixel // facets
centers = [-1.5, -0.5, 0.5, 1.5]
comps = list()
for iy in centers:
for ix in centers:
p = int(round(rpix[0] + ix * spacing_pixels * numpy.sign(model.wcs.wcs.cdelt[0]))), \
int(round(rpix[1] + iy * spacing_pixels * numpy.sign(model.wcs.wcs.cdelt[1])))
sc = pixel_to_skycoord(p[0], p[1], model.wcs, origin=0)
comp = create_skycomponent(
flux=flux,
frequency=frequency,
direction=sc,
polarisation_frame=PolarisationFrame("stokesI"))
comps.append(comp)
predict_skycomponent_visibility(vt, comps)
insert_skycomponent(model, comps)
self.model = copy_image(model)
export_image_to_fits(model,
'%s/test_bags_model.fits' % (self.results_dir))
return vt
def get_LSM(self, vt, cellsize=0.001, reffrequency=None, flux=0.0):
if reffrequency is None:
reffrequency = [1e8]
model = create_image_from_visibility(vt, npixel=self.npixel, cellsize=cellsize, npol=1,
frequency=reffrequency,
polarisation_frame=PolarisationFrame("stokesI"))
model.data[..., 32, 32] = flux
return model
def test_get_frequency_map_channel(self):
self.model = create_image_from_visibility(self.vis, npixel=512, cellsize=0.001,
nchan=self.vnchan,
frequency=self.startfrequency)
spectral_mode, vfrequency_map = get_frequency_map(self.vis, self.model)
assert numpy.max(vfrequency_map) == self.model.nchan - 1
assert numpy.min(vfrequency_map) == 0
assert spectral_mode == 'channel'
def test_create_image_from_visibility(self):
self.actualSetUp()
self.componentvis = create_visibility(
self.lowcore,
self.times,
self.frequency,
phasecentre=self.phasecentre,
weight=1.0,
polarisation_frame=self.vis_pol,
channel_bandwidth=self.channel_bandwidth)
im = create_image_from_visibility(self.componentvis,
nchan=1,
npixel=128)
assert im.data.shape == (1, 1, 128, 128)
im = create_image_from_visibility(self.componentvis,
frequency=self.frequency,
npixel=128)
assert im.data.shape == (len(self.frequency), 1, 128, 128)
im = create_image_from_visibility(self.componentvis,
frequency=self.frequency,
npixel=128,
nchan=1)
assert im.data.shape == (1, 1, 128, 128)
def create_unittest_model(vis, model_pol, npixel=None, cellsize=None, nchan=1):
advice = advise_wide_field(vis,
guard_band_image=2.0,
delA=0.02,
facets=1,
wprojection_planes=1,
oversampling_synthesised_beam=4.0)
if cellsize is None:
cellsize = advice['cellsize']
if npixel is None:
npixel = advice['npixels2']
model = create_image_from_visibility(vis,
npixel=npixel,
cellsize=cellsize,
nchan=nchan,
polarisation_frame=model_pol)
return model
def ingest_visibility(self, freq=1e8, chan_width=1e6, times=None, reffrequency=None, add_errors=False):
if times is None:
times = [0.0]
if reffrequency is None:
reffrequency = [1e8]
lowcore = create_named_configuration('LOWBD2-CORE')
frequency = numpy.array([freq])
channel_bandwidth = numpy.array([chan_width])
# phasecentre = SkyCoord(ra=+180.0 * u.deg, dec=-60.0 * u.deg, frame='icrs', equinox='J2000')
# Observe at zenith to ensure that timeslicing works well. We test that elsewhere.
phasecentre = SkyCoord(ra=+180.0 * u.deg, dec=-60.0 * u.deg, frame='icrs', equinox='J2000')
vt = create_blockvisibility(lowcore, times, frequency, channel_bandwidth=channel_bandwidth,
weight=1.0, phasecentre=phasecentre,
polarisation_frame=PolarisationFrame("stokesI"))
cellsize = 0.001
model = create_image_from_visibility(vt, npixel=self.npixel, cellsize=cellsize, npol=1,
frequency=reffrequency,
polarisation_frame=PolarisationFrame("stokesI"))
flux = numpy.array([[100.0]])
facets = 4
rpix = model.wcs.wcs.crpix - 1.0
spacing_pixels = self.npixel // facets
centers = [-1.5, -0.5, 0.5, 1.5]
comps = list()
for iy in centers:
for ix in centers:
p = int(round(rpix[0] + ix * spacing_pixels * numpy.sign(model.wcs.wcs.cdelt[0]))), \
int(round(rpix[1] + iy * spacing_pixels * numpy.sign(model.wcs.wcs.cdelt[1])))
sc = pixel_to_skycoord(p[0], p[1], model.wcs, origin=1)
comps.append(create_skycomponent(flux=flux, frequency=vt.frequency, direction=sc,
polarisation_frame=PolarisationFrame("stokesI")))
predict_skycomponent_blockvisibility(vt, comps)
insert_skycomponent(model, comps)
self.actualmodel = copy_image(model)
export_image_to_fits(model, '%s/test_imaging_model.fits' % (self.results_dir))
if add_errors:
# These will be the same for all calls
numpy.random.seed(180555)
gt = create_gaintable_from_blockvisibility(vt)
gt = simulate_gaintable(gt, phase_error=1.0, amplitude_error=0.0)
vt = apply_gaintable(vt, gt)
return vt
def wproject(vis, npixel_advice, cell_advice, channel, results_dir):
"""Do w-projected imaging of visibility data.
Args:
vis (obj): ARL visibility data.
npixel_advice (float): number of pixels in output image.
cell_advice (float): cellsize in output image.
channel (int): channel number to be imaged (affects output filename).
results_dir (str): directory to save results.
Returns:
dirty: dirty image.
psf: image of psf.
"""
try:
vis_slices = len(np.unique(vis.time))
print("There are %d timeslices" % vis_slices)
# Obtain advice on w-proj parameters:
advice = advise_wide_field(vis)
# Create a model image:
model = create_image_from_visibility(vis, cellsize=cell_advice,
npixel=npixel_advice,
polarisation_frame=PolarisationFrame('stokesIQUV'))
# Weight the visibilities:
vis, _, _ = weight_visibility(vis, model)
# Create a dirty image:
dirty, sumwt = create_invert_graph([vis], model, kernel='wprojection',
wstep=advice['w_sampling_primary_beam'],
oversampling=2).compute()
# Create the psf:
psf, sumwt = create_invert_graph([vis], model, dopsf=True, kernel='wprojection',
wstep=advice['w_sampling_primary_beam'],
oversampling=2).compute()
# Save to disk:
export_image_to_fits(dirty, '%s/imaging_dirty_WProj-%s.fits'
% (results_dir, channel))
export_image_to_fits(psf, '%s/imaging_psf_WProj-%s.fits'
% (results_dir, channel))
except:
print("Unexpected error:", sys.exc_info()[0])
raise
return dirty, psf
def test_peel_skycomponent_blockvisibility(self):
df = 1e6
frequency = numpy.array([1e8 - df, 1e8, 1e8 + df])
channel_bandwidth = numpy.array([df, df, df])
# Define the component and give it some spectral behaviour
f = numpy.array([100.0, 20.0, -10.0, 1.0])
flux = numpy.array([f, 0.8 * f, 0.6 * f])
phasecentre = SkyCoord(0 * u.deg, -60.0 * u.deg)
config = create_named_configuration('LOWBD2-CORE')
peeldirection = SkyCoord(+15 * u.deg, -60.0 * u.deg)
times = numpy.linspace(-3.0, 3.0, 7) * numpy.pi / 12.0
# Make the visibility
vis = create_blockvisibility(
config,
times,
frequency,
phasecentre=phasecentre,
weight=1.0,
polarisation_frame=PolarisationFrame('linear'),
channel_bandwidth=channel_bandwidth)
vis.data['vis'][...] = 0.0
# First add in the source to be peeled.
peel = Skycomponent(direction=peeldirection,
frequency=frequency,
flux=flux,
polarisation_frame=PolarisationFrame("stokesIQUV"))
vis = predict_skycomponent_visibility(vis, peel)
# Make a gaintable and apply it to the visibility of the peeling source
gt = create_gaintable_from_blockvisibility(vis, timeslice='auto')
gt = simulate_gaintable(gt,
phase_error=0.01,
amplitude_error=0.01,
timeslice='auto')
gt.data['gain'] *= 0.3
vis = apply_gaintable(vis, gt, timeslice='auto')
# Now create a plausible field using the GLEAM sources
model = create_image_from_visibility(
vis,
cellsize=0.001,
frequency=frequency,
polarisation_frame=PolarisationFrame('stokesIQUV'))
bm = create_low_test_beam(model=model)
sc = create_low_test_skycomponents_from_gleam(
flux_limit=1.0,
polarisation_frame=PolarisationFrame("stokesIQUV"),
frequency=frequency,
kind='cubic',
phasecentre=phasecentre,
radius=0.1)
sc = apply_beam_to_skycomponent(sc, bm)
# Add in the visibility due to these sources
vis = predict_skycomponent_visibility(vis, sc)
assert numpy.max(numpy.abs(vis.vis)) > 0.0
# Now we can peel
vis, peel_gts = peel_skycomponent_blockvisibility(vis, peel)
assert len(peel_gts) == 1
residual = numpy.max(peel_gts[0].residual)
assert residual < 0.7, "Peak residual %.6f too large" % (residual)
im, sumwt = invert_timeslice(vis, model, timeslice='auto')
qa = qa_image(im)
assert numpy.abs(qa.data['max'] - 14.2) < 1.0, str(qa)
def actualSetUp(self, time=None, dospectral=False, dopol=False):
self.lowcore = create_named_configuration('LOWBD2-CORE')
self.times = (numpy.pi / 12.0) * numpy.linspace(-3.0, 3.0, 5)
if time is not None:
self.times = time
log.info("Times are %s" % self.times)
if dospectral:
self.nchan = 3
self.frequency = numpy.array([0.9e8, 1e8, 1.1e8])
self.channel_bandwidth = numpy.array([1e7, 1e7, 1e7])
else:
self.frequency = numpy.array([1e8])
self.channel_bandwidth = numpy.array([1e7])
if dopol:
self.vis_pol = PolarisationFrame('linear')
self.image_pol = PolarisationFrame('stokesIQUV')
f = numpy.array([100.0, 20.0, -10.0, 1.0])
else:
self.vis_pol = PolarisationFrame('stokesI')
self.image_pol = PolarisationFrame('stokesI')
f = numpy.array([100.0])
if dospectral:
flux = numpy.array([f, 0.8 * f, 0.6 * f])
else:
flux = numpy.array([f])
self.phasecentre = SkyCoord(ra=+180.0 * u.deg,
dec=-60.0 * u.deg,
frame='icrs',
equinox='J2000')
self.componentvis = create_visibility(
self.lowcore,
self.times,
self.frequency,
channel_bandwidth=self.channel_bandwidth,
phasecentre=self.phasecentre,
weight=1.0,
polarisation_frame=self.vis_pol)
self.uvw = self.componentvis.data['uvw']
self.componentvis.data['vis'][...] = 0.0
# Create model
self.model = create_image_from_visibility(
self.componentvis,
npixel=self.params['npixel'],
cellsize=0.0005,
nchan=len(self.frequency),
polarisation_frame=self.image_pol)
# Fill the visibility with exactly computed point sources.
spacing_pixels = 512 // 8
log.info('Spacing in pixels = %s' % spacing_pixels)
centers = [(x, x) for x in numpy.linspace(-1.4, +1.4, 9)]
for x in numpy.linspace(-1.4, +1.4, 9):
centers.append((-x, x))
centers.append((0.5, 1.1))
centers.append((1e-7, 1e-7))
# Make the list of components
rpix = self.model.wcs.wcs.crpix
self.components = []
for center in centers:
ix, iy = center
# The phase center in 0-relative coordinates is n // 2 so we centre the grid of
# components on ny // 2, nx // 2. The wcs must be defined consistently.
p = int(round(rpix[0] + ix * spacing_pixels * numpy.sign(self.model.wcs.wcs.cdelt[0]))), \
int(round(rpix[1] + iy * spacing_pixels * numpy.sign(self.model.wcs.wcs.cdelt[1])))
sc = pixel_to_skycoord(p[0], p[1], self.model.wcs, origin=1)
log.info("Component at (%f, %f) [0-rel] %s" %
(p[0], p[1], str(sc)))
if ix != 0 and iy != 0:
# Channel images
comp = create_skycomponent(flux=flux,
frequency=self.frequency,
direction=sc,
polarisation_frame=self.image_pol)
self.components.append(comp)
# Predict the visibility from the components exactly
self.componentvis.data['vis'] *= 0.0
self.beam = create_low_test_beam(self.model)
self.components = apply_beam_to_skycomponent(self.components,
self.beam)
self.componentvis = predict_skycomponent_visibility(
self.componentvis, self.components)
self.model = insert_skycomponent(self.model, self.components)
# Calculate the model convolved with a Gaussian.
self.cmodel = smooth_image(self.model)
export_image_to_fits(self.model,
'%s/test_imaging_functions_model.fits' % self.dir)
export_image_to_fits(
self.cmodel, '%s/test_imaging_functions_cmodel.fits' % self.dir)
#print("uvw", block_vis.uvw, numpy.sum(block_vis.uvw))
#print("vis", block_vis.vis, numpy.sum(block_vis.vis))
#print("weight", block_vis.weight, numpy.sum(block_vis.weight))
#print("time", block_vis.time, numpy.sum(block_vis.time))
#print("integration_time", block_vis.integration_time, numpy.sum(block_vis.integration_time))
#print("nvis, size", block_vis.nvis, block_vis.size())
gt = create_gaintable_from_blockvisibility(block_vis)
#print("np.sum(gt.data): ", numpy.sum(gt.data['gain']))
gt = simulate_gaintable(gt, phase_error=1.0)
#print("np.sum(gt.data): ", numpy.sum(gt.data['gain']))
blockvis = apply_gaintable(block_vis, gt)
#print("np.sum(blockvis.data): ", numpy.sum(blockvis.data['vis']))
model = create_image_from_visibility(block_vis, npixel=npixel, frequency=[numpy.average(frequency)], nchan=1,
channel_bandwidth=[numpy.sum(channel_bandwidth)], cellsize=cellsize, phasecentre=phasecentre)
#print("model sum, min, max, shape: ", numpy.sum(model.data), numpy.amin(model.data), numpy.amax(model.data), model.shape)
print(qa_image(model, context='Blockvis model image'))
export_image_to_fits(model, '%s/imaging-blockvis_model.fits'
% (results_dir))
dirty, sumwt = invert_function(predicted_vis, model, vis_slices=vis_slices, dopsf=False, context='wstack')
print(qa_image(dirty, context='Dirty image'))
export_image_to_fits(dirty, '%s/imaging-dirty.fits'
% (results_dir))
def actualSetup(self, sky_pol_frame='stokesI', data_pol_frame='stokesI', f=None, vnchan=1, doiso=True,
ntimes=1, flux_limit=18.0):
nfreqwin = vnchan
ntimes = ntimes
rmax = 300.0
npixel = 1024
cellsize = 0.001
frequency = numpy.linspace(0.8e8, 1.2e8, nfreqwin)
if nfreqwin > 1:
channel_bandwidth = numpy.array(nfreqwin * [frequency[1] - frequency[0]])
else:
channel_bandwidth = [0.4e8]
times = numpy.linspace(-numpy.pi / 3.0, numpy.pi / 3.0, ntimes)
phasecentre = SkyCoord(ra=+0.0 * u.deg, dec=-45.0 * u.deg, frame='icrs', equinox='J2000')
lowcore = create_named_configuration('LOWBD2', rmax=rmax)
block_vis = create_blockvisibility(lowcore, times, frequency=frequency, channel_bandwidth=channel_bandwidth,
weight=1.0, phasecentre=phasecentre,
polarisation_frame=PolarisationFrame("stokesI"))
block_vis.data['uvw'][..., 2] = 0.0
self.beam = create_image_from_visibility(block_vis, npixel=npixel, frequency=[numpy.average(frequency)],
nchan=nfreqwin,
channel_bandwidth=[numpy.sum(channel_bandwidth)], cellsize=cellsize,
phasecentre=phasecentre)
self.components = create_low_test_skycomponents_from_gleam(flux_limit=flux_limit, phasecentre=phasecentre,
frequency=frequency,
polarisation_frame=PolarisationFrame('stokesI'),
radius=npixel * cellsize)
self.beam = create_low_test_beam(self.beam)
self.components = apply_beam_to_skycomponent(self.components, self.beam, flux_limit=flux_limit / 100.0)
print("Number of components %d" % len(self.components))
self.vis = copy_visibility(block_vis, zero=True)
gt = create_gaintable_from_blockvisibility(block_vis, timeslice='auto')
for i, sc in enumerate(self.components):
if sc.flux[0, 0] > 10:
sc.flux[...] /= 10.0
print('Component %d, flux = %s' % (i, str(sc.flux[0, 0])))
component_vis = copy_visibility(block_vis, zero=True)
gt = simulate_gaintable(gt, amplitude_error=0.0, phase_error=0.1, seed=None)
component_vis = predict_skycomponent_visibility(component_vis, sc)
component_vis = apply_gaintable(component_vis, gt)
self.vis.data['vis'][...] += component_vis.data['vis'][...]
# Do an isoplanatic selfcal
self.model_vis = copy_visibility(self.vis, zero=True)
self.model_vis = predict_skycomponent_visibility(self.model_vis, self.components)
if doiso:
gt = solve_gaintable(self.vis, self.model_vis, phase_only=True, timeslice='auto')
self.vis = apply_gaintable(self.vis, gt, inverse=True)
self.model_vis = convert_blockvisibility_to_visibility(self.model_vis)
self.model_vis, _, _ = weight_visibility(self.model_vis, self.beam)
self.dirty_model, sumwt = invert_function(self.model_vis, self.beam, context='2d')
export_image_to_fits(self.dirty_model, "%s/test_sagecal-model_dirty.fits" % self.dir)
lvis = convert_blockvisibility_to_visibility(self.vis)
lvis, _, _ = weight_visibility(lvis, self.beam)
dirty, sumwt = invert_function(lvis, self.beam, context='2d')
print(qa_image(dirty))
export_image_to_fits(dirty, "%s/test_sagecal-initial_dirty.fits" % self.dir)
