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)
def test_skymodel_solve_fixed(self):
self.actualSetup(ntimes=1, doiso=True, fixed=True)
calskymodel, residual_vis = calskymodel_solve(self.vis,
self.skymodels,
niter=30,
gain=0.25)
# Check that the components are unchanged
calskymodel_skycomponents = list()
for sm in [csm[0] for csm in calskymodel]:
for comp in sm.components:
calskymodel_skycomponents.append(comp)
recovered_components = find_skycomponent_matches(
calskymodel_skycomponents, self.components, 1e-5)
for p in recovered_components:
assert numpy.abs(calskymodel_skycomponents[p[0]].flux[0, 0] -
self.components[p[1]].flux[0, 0]) < 1e-15
assert calskymodel_skycomponents[p[0]].direction.separation(
self.components[p[1]].direction).rad < 1e-15
residual_vis = convert_blockvisibility_to_visibility(residual_vis)
residual_vis, _, _ = weight_visibility(residual_vis, self.beam)
dirty, sumwt = invert_function(residual_vis, self.beam, context='2d')
export_image_to_fits(
dirty, "%s/test_skymodel-final-iso-residual.fits" % self.dir)
qa = qa_image(dirty)
assert qa.data['rms'] < 3.4e-3, qa
def test_coalesce_decoalesce_with_iter(self):
for rows in vis_timeslice_iter(self.blockvis):
visslice = create_visibility_from_rows(self.blockvis, rows)
cvisslice = convert_blockvisibility_to_visibility(visslice)
assert numpy.min(cvisslice.frequency) == numpy.min(self.frequency)
assert numpy.min(cvisslice.frequency) > 0.0
dvisslice = decoalesce_visibility(cvisslice)
assert dvisslice.nvis == visslice.nvis
def test_convert_decoalesce_zero(self):
cvis = convert_blockvisibility_to_visibility(self.blockvis)
assert numpy.min(cvis.frequency) == numpy.min(self.frequency)
assert numpy.min(cvis.frequency) > 0.0
dvis = decoalesce_visibility(cvis)
assert dvis.nvis == self.blockvis.nvis
dvis = decoalesce_visibility(cvis)
assert dvis.nvis == self.blockvis.nvis
def get_wf(bv):
v = convert_blockvisibility_to_visibility(bv)
return advise_wide_field(v,
guard_band_image=6.0,
delA=0.02,
facets=facets,
wprojection_planes=wprojection_planes,
oversampling_synthesised_beam=4.0)
def invert_serial(vis, im: Image, dopsf=False, normalize=True, context='2d', vis_slices=1,
facets=1, overlap=0, taper=None, **kwargs):
""" Invert using algorithm specified by context:
* 2d: Two-dimensional transform
* wstack: wstacking with either vis_slices or wstack (spacing between w planes) set
* wprojection: w projection with wstep (spacing between w places) set, also kernel='wprojection'
* timeslice: snapshot imaging with either vis_slices or timeslice set. timeslice='auto' does every time
* facets: Faceted imaging with facets facets on each axis
* facets_wprojection: facets AND wprojection
* facets_wstack: facets AND wstacking
* wprojection_wstack: wprojection and wstacking
:param vis:
:param im:
:param dopsf: Make the psf instead of the dirty image (False)
:param normalize: Normalize by the sum of weights (True)
:param context: Imaging context e.g. '2d', 'timeslice', etc.
:param kwargs:
:return: Image, sum of weights
"""
c = imaging_context(context)
vis_iter = c['vis_iterator']
invert = c['invert']
if not isinstance(vis, Visibility):
svis = convert_blockvisibility_to_visibility(vis)
else:
svis = vis
resultimage = create_empty_image_like(im)
totalwt = None
for rows in vis_iter(svis, vis_slices=vis_slices):
if numpy.sum(rows):
visslice = create_visibility_from_rows(svis, rows)
sumwt = 0.0
workimage = create_empty_image_like(im)
for dpatch in image_scatter_facets(workimage, facets=facets, overlap=overlap, taper=taper):
result, sumwt = invert(visslice, dpatch, dopsf, normalize=False, facets=facets,
vis_slices=vis_slices, **kwargs)
# Ensure that we fill in the elements of dpatch instead of creating a new numpy arrray
dpatch.data[...] = result.data[...]
# Assume that sumwt is the same for all patches
if totalwt is None:
totalwt = sumwt
else:
totalwt += sumwt
resultimage.data += workimage.data
assert totalwt is not None, "No valid data found for imaging"
if normalize:
resultimage = normalize_sumwt(resultimage, totalwt)
return resultimage, totalwt
def test_convert_blockvisibility(self):
self.vis = create_blockvisibility(
self.lowcore,
self.times,
self.frequency,
phasecentre=self.phasecentre,
weight=1.0,
channel_bandwidth=self.channel_bandwidth)
vis = convert_blockvisibility_to_visibility(self.vis)
assert vis.nvis == len(vis.time)
assert numpy.unique(vis.time).size == self.vis.time.size # pylint: disable=no-member
def test_convert_weight(self):
cvis = convert_blockvisibility_to_visibility(self.blockvis)
model = create_image_from_visibility(vis=cvis, npixel=256, cellsize=0.001, phasecentre=self.phasecentre,
polarisation_frame=PolarisationFrame('stokesI'))
cvis = weight_visibility(cvis, model)
assert numpy.mean(cvis.data['imaging_weight']) < 1.0
assert numpy.std(cvis.data['imaging_weight']) > 0.0
dvis = decoalesce_visibility(cvis)
assert numpy.mean(dvis.data['imaging_weight']) < 1.0
assert numpy.std(dvis.data['imaging_weight']) > 0.0
assert dvis.nvis == self.blockvis.nvis
def actualSetUp(self, freqwin=1, block=True, dopol=False):
self.npixel = 512
self.low = create_named_configuration('LOWBD2', rmax=750.0)
self.freqwin = freqwin
self.vis = list()
self.ntimes = 5
self.times = numpy.linspace(-3.0, +3.0, self.ntimes) * numpy.pi / 12.0
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 freqwin > 1:
self.frequency = numpy.linspace(0.8e8, 1.2e8, self.freqwin)
self.channelwidth = numpy.array(
freqwin * [self.frequency[1] - self.frequency[0]])
flux = numpy.array(
[f * numpy.power(freq / 1e8, -0.7) for freq in self.frequency])
else:
self.frequency = numpy.array([1e8])
self.channelwidth = numpy.array([1e6])
flux = numpy.array([f])
self.phasecentre = SkyCoord(ra=+180.0 * u.deg,
dec=-60.0 * u.deg,
frame='icrs',
equinox='J2000')
self.bvis = ingest_unittest_visibility(self.low,
self.frequency,
self.channelwidth,
self.times,
self.vis_pol,
self.phasecentre,
block=block)
self.vis = convert_blockvisibility_to_visibility(self.bvis)
self.model = create_unittest_model(self.vis,
self.image_pol,
npixel=self.npixel,
nchan=freqwin)
self.components = create_unittest_components(self.model, flux)
self.model = insert_skycomponent(self.model, self.components)
self.bvis = predict_skycomponent_visibility(self.bvis, self.components)
def test_convert_weight(self):
cvis = convert_blockvisibility_to_visibility(self.blockvis)
cvis.data['imaging_weight'] = 10.0
model = create_image_from_visibility(
vis=cvis,
npixel=256,
cellsize=0.001,
phasecentre=self.phasecentre,
polarisation_frame=PolarisationFrame('stokesI'))
cvis = weight_visibility(cvis, model)
dvis = decoalesce_visibility(cvis, overwrite=True)
assert numpy.max(dvis.data['imaging_weight'] - 10.0) < 1e-7
assert dvis.nvis == self.blockvis.nvis
def create_visibility_from_uvfits(fitsname, channum=None, ack=False, antnum=None):
""" Minimal UVFITS to BlockVisibility converter
Creates a list of BlockVisibility's, split by field and spectral window
:param fitsname: File name of UVFITS file
:param channum: range of channels e.g. range(17,32), default is None meaning all
:param antnum: the number of antenna
:return:
"""
from processing_components.visibility.coalesce import convert_blockvisibility_to_visibility
return [convert_blockvisibility_to_visibility(v)
for v in create_blockvisibility_from_uvfits(fitsname=fitsname, channum=channum, ack=ack, antnum=antnum)]
def calskymodel_fit_skymodel(vis, calskymodel, gain=0.1, **kwargs):
"""Fit a single skymodel to a visibility
:param evis: Expected vis for this ssm
:param calskymodel: scm element being fit i.e. (skymodel, gaintable) tuple
:param gain: Gain in step
:param kwargs:
:return: skymodel
"""
if calskymodel[0].fixed:
return calskymodel[0]
else:
cvis = convert_blockvisibility_to_visibility(vis)
return solve_skymodel(cvis, calskymodel[0], **kwargs)
def create_visibility_from_ms(msname, channum=None, ack=False):
""" Minimal MS to BlockVisibility converter
The MS format is much more general than the ARL BlockVisibility so we cut many corners. This requires casacore to be
installed. If not an exception ModuleNotFoundError is raised.
Creates a list of BlockVisibility's, split by field and spectral window
:param msname: File name of MS
:param channum: range of channels e.g. range(17,32), default is None meaning all
:return:
"""
from processing_components.visibility.coalesce import convert_blockvisibility_to_visibility
return [convert_blockvisibility_to_visibility(v)
for v in create_blockvisibility_from_ms(msname=msname, channum=channum, ack=ack)]
def test_skymodel_solve_noiso(self):
self.actualSetup(ntimes=1, doiso=False)
calskymodel, residual_vis = calskymodel_solve(self.vis,
self.skymodels,
niter=30,
gain=0.25)
residual_vis = convert_blockvisibility_to_visibility(residual_vis)
residual_vis, _, _ = weight_visibility(residual_vis, self.beam)
dirty, sumwt = invert_function(residual_vis, self.beam, context='2d')
export_image_to_fits(
dirty, "%s/test_skymodel-final-noiso-residual.fits" % self.dir)
qa = qa_image(dirty)
assert qa.data['rms'] < 3.8e-3, qa
def test_predict_sky_components_coalesce(self):
sc = create_low_test_skycomponents_from_gleam(flux_limit=10.0,
polarisation_frame=PolarisationFrame("stokesI"),
frequency=self.frequency, kind='cubic',
phasecentre=SkyCoord("17h20m31s", "-00d58m45s"),
radius=0.1)
self.config = create_named_configuration('LOWBD2-CORE')
self.phasecentre = SkyCoord("17h20m31s", "-00d58m45s")
sampling_time = 3.76
self.times = numpy.arange(0.0, + 300 * sampling_time, sampling_time)
self.vis = create_blockvisibility(self.config, self.times, self.frequency, phasecentre=self.phasecentre,
weight=1.0, polarisation_frame=PolarisationFrame('stokesI'),
channel_bandwidth=self.channel_bandwidth)
self.vis = predict_skycomponent_visibility(self.vis, sc)
cvt = convert_blockvisibility_to_visibility(self.vis)
assert cvt.cindex is not None
def predict_serial(vis, model: Image, context='2d', vis_slices=1, facets=1, overlap=0, taper=None,
**kwargs) -> Visibility:
"""Predict visibilities using algorithm specified by context
* 2d: Two-dimensional transform
* wstack: wstacking with either vis_slices or wstack (spacing between w planes) set
* wprojection: w projection with wstep (spacing between w places) set, also kernel='wprojection'
* timeslice: snapshot imaging with either vis_slices or timeslice set. timeslice='auto' does every time
* facets: Faceted imaging with facets facets on each axis
* facets_wprojection: facets AND wprojection
* facets_wstack: facets AND wstacking
* wprojection_wstack: wprojection and wstacking
:param vis:
:param model: Model image, used to determine image characteristics
:param context: Imaging context e.g. '2d', 'timeslice', etc.
:param inner: Inner loop 'vis'|'image'
:param kwargs:
:return:
"""
c = imaging_context(context)
vis_iter = c['vis_iterator']
predict = c['predict']
if not isinstance(vis, Visibility):
svis = convert_blockvisibility_to_visibility(vis)
else:
svis = vis
result = copy_visibility(vis, zero=True)
for rows in vis_iter(svis, vis_slices=vis_slices):
if numpy.sum(rows):
visslice = create_visibility_from_rows(svis, rows)
visslice.data['vis'][...] = 0.0
for dpatch in image_scatter_facets(model, facets=facets, overlap=overlap, taper=taper):
result.data['vis'][...] = 0.0
result = predict(visslice, dpatch, **kwargs)
svis.data['vis'][rows] += result.data['vis']
if not isinstance(vis, Visibility):
svis = convert_visibility_to_blockvisibility(svis)
return svis
def test_modelpartition_solve_arlexecute(self):
self.actualSetup(doiso=True)
self.skymodel_list = [arlexecute.execute(SkyModel, nout=1)(components=[cm])
for cm in self.components]
modelpartition_list = solve_modelpartition_arlexecute(self.vis, skymodel_list=self.skymodel_list, niter=30,
gain=0.25)
skymodel, residual_vis = arlexecute.compute(modelpartition_list, sync=True)
residual_vis = convert_blockvisibility_to_visibility(residual_vis)
residual_vis, _, _ = weight_visibility(residual_vis, self.beam)
dirty, sumwt = invert_arlexecute(residual_vis, self.beam, context='2d')
export_image_to_fits(dirty, "%s/test_modelpartition-%s-final-iso-residual.fits" % (self.dir, arlexecute.type()))
qa = qa_image(dirty)
assert qa.data['rms'] < 3.2e-3, qa
def create_visibility_from_ms(msname, channum=None, start_chan=None, end_chan=None, ack=False):
""" Minimal MS to BlockVisibility converter
The MS format is much more general than the ARL BlockVisibility so we cut many corners. This requires casacore to be
installed. If not an exception ModuleNotFoundError is raised.
Creates a list of BlockVisibility's, split by field and spectral window
Reading of a subset of channels is possible using either start_chan and end_chan or channnum. Using start_chan
and end_chan is preferred since it only reads the channels required. Channum is more flexible and can be used to
read a random list of channels.
:param msname: File name of MS
:param channum: range of channels e.g. range(17,32), default is None meaning all
:param start_chan: Starting channel to read
:param end_chan: End channel to read
:return:
"""
from processing_components.visibility.coalesce import convert_blockvisibility_to_visibility
return [convert_blockvisibility_to_visibility(v)
for v in create_blockvisibility_from_ms(msname=msname, channum=channum,
start_chan=start_chan, end_chan=end_chan, ack=ack)]
def test_export_ms(self):
if run_ms_tests == False:
return
msoutfile = arl_path("data/vis/Test_output.ms")
from astropy.coordinates import SkyCoord
from astropy import units as u
from wrappers.serial.image.operations import show_image, export_image_to_fits
from wrappers.serial.simulation.configurations import create_named_configuration
from wrappers.serial.simulation.testing_support import create_test_image
from wrappers.serial.imaging.base import create_image_from_visibility
from wrappers.serial.imaging.base import advise_wide_field
from workflows.serial.imaging.imaging_serial import invert_list_serial_workflow, predict_list_serial_workflow
from data_models.polarisation import PolarisationFrame
lowr3 = create_named_configuration('LOWBD2', rmax=750.0)
times = numpy.zeros([1])
frequency = numpy.array([1e8])
channelbandwidth = numpy.array([1e6])
phasecentre = SkyCoord(ra=+15.0 * u.deg, dec=-45.0 * u.deg, frame='icrs', equinox='J2000')
bvis = create_blockvisibility(lowr3, times, frequency, phasecentre=phasecentre,
weight=1.0, polarisation_frame=PolarisationFrame('stokesI'),
channel_bandwidth=channelbandwidth)
vt = convert_blockvisibility_to_visibility(bvis)
advice = advise_wide_field(vt, guard_band_image=3.0, delA=0.1, facets=1, wprojection_planes=1,
oversampling_synthesised_beam=4.0)
cellsize = advice['cellsize']
m31image = create_test_image(frequency=frequency, cellsize=cellsize)
nchan, npol, ny, nx = m31image.data.shape
m31image.wcs.wcs.crval[0] = vt.phasecentre.ra.deg
m31image.wcs.wcs.crval[1] = vt.phasecentre.dec.deg
m31image.wcs.wcs.crpix[0] = float(nx // 2)
m31image.wcs.wcs.crpix[1] = float(ny // 2)
vt = predict_list_serial_workflow([vt], [m31image], context='2d')[0]
# uvdist = numpy.sqrt(vt.data['uvw'][:, 0] ** 2 + vt.data['uvw'][:, 1] ** 2)
#
# model = create_image_from_visibility(vt, cellsize=cellsize, npixel=512)
# dirty, sumwt = invert_list_serial_workflow([vt], [model], context='2d')[0]
# psf, sumwt = invert_list_serial_workflow([vt], [model], context='2d', dopsf=True)[0]
#
# show_image(dirty)
# 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))
# results_dir="/Users/f.wang"
# export_image_to_fits(dirty, '%s/imaging_dirty.fits' % (results_dir))
# export_image_to_fits(psf, '%s/imaging_psf.fits' % (results_dir))
v = convert_visibility_to_blockvisibility(vt)
vis_list=[]
vis_list.append(v)
export_blockvisility_to_ms(msoutfile, vis_list,source_name='M31')
def create_blockvisibility_iterator(
config: Configuration,
times: numpy.array,
frequency: numpy.array,
channel_bandwidth,
phasecentre: SkyCoord,
weight: float = 1,
polarisation_frame=PolarisationFrame('stokesI'),
integration_time=1.0,
number_integrations=1,
predict=predict_2d,
model=None,
components=None,
phase_error=0.0,
amplitude_error=0.0,
sleep=0.0,
**kwargs):
""" Create a sequence of Visibilities and optionally predicting and coalescing
This is useful mainly for performing large simulations. Do something like::
vis_iter = create_blockvisibility_iterator(config, times, frequency, channel_bandwidth, phasecentre=phasecentre,
weight=1.0, integration_time=30.0, number_integrations=3)
for i, vis in enumerate(vis_iter):
if i == 0:
fullvis = vis
else:
fullvis = append_visibility(fullvis, vis)
:param config: Configuration of antennas
:param times: hour angles in radians
:param frequency: frequencies (Hz] Shape [nchan]
:param weight: weight of a single sample
:param phasecentre: phasecentre of observation
:param npol: Number of polarizations
:param integration_time: Integration time ('auto' or value in s)
:param number_integrations: Number of integrations to be created at each time.
:param model: Model image to be inserted
:param components: Components to be inserted
:param sleep_time: Time to sleep between yields
:return: Visibility
"""
for time in times:
actualtimes = time + numpy.arange(
0, number_integrations) * integration_time * numpy.pi / 43200.0
bvis = create_blockvisibility(config,
actualtimes,
frequency=frequency,
phasecentre=phasecentre,
weight=weight,
polarisation_frame=polarisation_frame,
integration_time=integration_time,
channel_bandwidth=channel_bandwidth)
if model is not None:
vis = convert_blockvisibility_to_visibility(bvis)
vis = predict(vis, model, **kwargs)
bvis = convert_visibility_to_blockvisibility(vis)
if components is not None:
vis = predict_skycomponent_visibility(bvis, components)
# Add phase errors
if phase_error > 0.0 or amplitude_error > 0.0:
gt = create_gaintable_from_blockvisibility(bvis)
gt = simulate_gaintable(gt=gt,
phase_error=phase_error,
amplitude_error=amplitude_error)
bvis = apply_gaintable(bvis, gt)
import time
time.sleep(sleep)
yield bvis
def to_vis(v):
if isinstance(v, BlockVisibility):
av = convert_blockvisibility_to_visibility(v)
return av
else:
return v
def actualSetUp(self,
freqwin=1,
block=True,
dospectral=True,
dopol=False,
zerow=False,
do_shift=False):
self.npixel = 512
self.low = create_named_configuration('LOWBD2', rmax=750.0)
self.freqwin = freqwin
self.blockvis = list()
self.ntimes = 5
self.times = numpy.linspace(-3.0, +3.0, self.ntimes) * numpy.pi / 12.0
if freqwin > 1:
self.frequency = numpy.linspace(0.99e8, 1.01e8, self.freqwin)
self.channelwidth = numpy.array(
freqwin * [self.frequency[1] - self.frequency[0]])
else:
self.frequency = numpy.array([1e8])
self.channelwidth = numpy.array([1e6])
if dopol:
self.blockvis_pol = PolarisationFrame('linear')
self.image_pol = PolarisationFrame('stokesIQUV')
f = numpy.array([100.0, 20.0, -10.0, 1.0])
else:
self.blockvis_pol = PolarisationFrame('stokesI')
self.image_pol = PolarisationFrame('stokesI')
f = numpy.array([100.0])
if dospectral:
flux = numpy.array(
[f * numpy.power(freq / 1e8, -0.7) for freq in self.frequency])
else:
flux = numpy.array([f])
self.phasecentre = SkyCoord(ra=+180.0 * u.deg,
dec=-45.0 * u.deg,
frame='icrs',
equinox='J2000')
self.blockvis = ingest_unittest_visibility(self.low,
self.frequency,
self.channelwidth,
self.times,
self.blockvis_pol,
self.phasecentre,
block=block,
zerow=zerow)
self.vis = convert_blockvisibility_to_visibility(self.blockvis)
self.model = create_unittest_model(self.vis,
self.image_pol,
npixel=self.npixel,
nchan=freqwin)
self.components = create_unittest_components(self.model, flux)
self.model = insert_skycomponent(self.model, self.components)
self.blockvis = predict_skycomponent_visibility(
self.blockvis, self.components)
# Calculate the model convolved with a Gaussian.
self.cmodel = smooth_image(self.model)
if self.persist:
export_image_to_fits(self.model,
'%s/test_imaging_ng_model.fits' % self.dir)
if self.persist:
export_image_to_fits(self.cmodel,
'%s/test_imaging_ng_cmodel.fits' % self.dir)
def actualSetup(self,
vnchan=1,
doiso=True,
ntimes=5,
flux_limit=2.0,
zerow=True,
fixed=False):
nfreqwin = vnchan
rmax = 300.0
npixel = 512
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=-60.0 * u.deg,
dec=-60.0 * 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"),
zerow=zerow)
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)
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
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_skymodel-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')
if doiso:
export_image_to_fits(
dirty, "%s/test_skymodel-initial-iso-residual.fits" % self.dir)
else:
export_image_to_fits(
dirty,
"%s/test_skymodel-initial-noiso-residual.fits" % self.dir)
self.skymodels = [
SkyModel(components=[cm], fixed=fixed) for cm in self.components
]
def ical_serial(block_vis: BlockVisibility,
model: Image,
components=None,
context='2d',
controls=None,
**kwargs):
""" Post observation image, deconvolve, and self-calibrate
:param vis:
:param model: Model image
:param components: Initial components
:param context: Imaging context
:param controls: calibration controls dictionary
:return: model, residual, restored
"""
nmajor = get_parameter(kwargs, 'nmajor', 5)
log.info("ical_serial: Performing %d major cycles" % nmajor)
do_selfcal = get_parameter(kwargs, "do_selfcal", False)
if controls is None:
controls = create_calibration_controls(**kwargs)
# The model is added to each major cycle and then the visibilities are
# calculated from the full model
vis = convert_blockvisibility_to_visibility(block_vis)
block_vispred = copy_visibility(block_vis, zero=True)
vispred = convert_blockvisibility_to_visibility(block_vispred)
vispred.data['vis'][...] = 0.0
visres = copy_visibility(vispred)
vispred = predict_serial(vispred, model, context=context, **kwargs)
if components is not None:
vispred = predict_skycomponent_visibility(vispred, components)
if do_selfcal:
vis, gaintables = calibrate_function(vis,
vispred,
'TGB',
controls,
iteration=-1)
visres.data['vis'] = vis.data['vis'] - vispred.data['vis']
dirty, sumwt = invert_serial(visres, model, context=context, **kwargs)
log.info("Maximum in residual image is %.6f" %
(numpy.max(numpy.abs(dirty.data))))
psf, sumwt = invert_serial(visres,
model,
dopsf=True,
context=context,
**kwargs)
thresh = get_parameter(kwargs, "threshold", 0.0)
for i in range(nmajor):
log.info("ical_serial: Start of major cycle %d of %d" % (i, nmajor))
cc, res = deconvolve_cube(dirty, psf, **kwargs)
model.data += cc.data
vispred.data['vis'][...] = 0.0
vispred = predict_serial(vispred, model, context=context, **kwargs)
if do_selfcal:
vis, gaintables = calibrate_function(vis,
vispred,
'TGB',
controls,
iteration=i)
visres.data['vis'] = vis.data['vis'] - vispred.data['vis']
dirty, sumwt = invert_serial(visres, model, context=context, **kwargs)
log.info("Maximum in residual image is %s" %
(numpy.max(numpy.abs(dirty.data))))
if numpy.abs(dirty.data).max() < 1.1 * thresh:
log.info("ical_serial: Reached stopping threshold %.6f Jy" %
thresh)
break
log.info("ical_serial: End of major cycle")
log.info("ical_serial: End of major cycles")
restored = restore_cube(model, psf, dirty, **kwargs)
return model, dirty, restored