def degrid_visibility_from_griddata(vis, griddata, cf, **kwargs):
"""Degrid Visibility from a GridData
:param vis: Visibility to be degridded
:param griddata: GridData containing image
:param cf: Convolution function (as GridData)
:param kwargs:
:return: Visibility
"""
nchan, npol, nz, oversampling, _, support, _ = cf.shape
pu_grid, pu_offset, pv_grid, pv_offset, pwg_grid, pwg_fraction, pwc_grid, pwc_fraction, pfreq_grid = \
convolution_mapping(vis, griddata, cf)
_, _, _, _, _, gv, gu = cf.shape
newvis = copy_visibility(vis, zero=True)
# coords = zip(pfreq_grid, pu_grid, pu_offset, pv_grid, pv_offset, pw_grid)
du = gu // 2
dv = gv // 2
nvis = vis.vis.shape[0]
for ivis in range(nvis):
chan, uu, uuf, vv, vvf, zzg, zzc = pfreq_grid[ivis], pu_grid[ivis], pu_offset[ivis], pv_grid[ivis], \
pv_offset[ivis], pwg_grid[ivis], pwc_grid[ivis]
# Use einsum to replace the following:
# newvis.vis[i,:] = numpy.sum(griddata.data[chan, :, zzg, (vv - dv):(vv + dv), (uu - du):(uu + du)] *
# cf.data[chan, :, zzc, vvf, uuf, :, :], axis=(1, 2))
newvis.vis[ivis, :] += numpy.einsum('ijk,ijk->i',
griddata.data[chan, :, zzg, (vv - dv):(vv + dv), (uu - du):(uu + du)],
cf.data[chan, :, zzc, vvf, uuf, :, :])
return newvis
def _predict_base(self, fluxthreshold=1.0, name='predict_ng', **kwargs):
from rascil.processing_components.imaging.ng import predict_ng, invert_ng
original_vis = copy_visibility(self.blockvis)
vis = predict_ng(self.blockvis,
self.model,
verbosity=self.verbosity,
**kwargs)
vis.data['vis'] = vis.data['vis'] - original_vis.data['vis']
dirty = invert_ng(vis,
self.model,
dopsf=False,
normalize=True,
verbosity=self.verbosity,
**kwargs)
# import matplotlib.pyplot as plt
# from rascil.processing_components.image.operations import show_image
# npol = dirty[0].shape[1]
# for pol in range(npol):
# plt.clf()
# show_image(dirty[0], pol=pol)
# plt.show(block=False)
if self.persist:
export_image_to_fits(
dirty[0],
'%s/test_imaging_ng_%s_residual.fits' % (self.dir, name))
# assert numpy.max(numpy.abs(dirty[0].data)), "Residual image is empty"
maxabs = numpy.max(numpy.abs(dirty[0].data))
assert maxabs < fluxthreshold, "Error %.3f greater than fluxthreshold %.3f " % (
maxabs, fluxthreshold)
def actualSetUp(self, nfreqwin=3, dospectral=True, dopol=False,
amp_errors=None, phase_errors=None, zerow=True):
if amp_errors is None:
amp_errors = {'T': 0.0, 'G': 0.1}
if phase_errors is None:
phase_errors = {'T': 1.0, 'G': 0.0}
self.npixel = 512
self.low = create_named_configuration('LOWBD2', rmax=750.0)
self.freqwin = nfreqwin
self.vis_list = list()
self.ntimes = 1
self.times = numpy.linspace(-3.0, +3.0, self.ntimes) * numpy.pi / 12.0
self.frequency = numpy.linspace(0.8e8, 1.2e8, self.freqwin)
if self.freqwin > 1:
self.channelwidth = numpy.array(self.freqwin * [self.frequency[1] - self.frequency[0]])
else:
self.channelwidth = numpy.array([1e6])
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 * 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=-60.0 * u.deg, frame='icrs', equinox='J2000')
self.blockvis_list = \
[rsexecute.execute(ingest_unittest_visibility, nout=1)(self.low,
[self.frequency[i]],
[self.channelwidth[i]],
self.times,
self.vis_pol,
self.phasecentre, block=True,
zerow=zerow)
for i in range(nfreqwin)]
self.blockvis_list = rsexecute.compute(self.blockvis_list, sync=True)
for v in self.blockvis_list:
v.data['vis'][...] = 1.0 + 0.0j
self.error_blockvis_list = [rsexecute.execute(copy_visibility(v)) for v in self.blockvis_list]
gt = rsexecute.execute(create_gaintable_from_blockvisibility)(self.blockvis_list[0])
gt = rsexecute.execute(simulate_gaintable)\
(gt, phase_error=0.1, amplitude_error=0.0, smooth_channels=1, leakage=0.0, seed=180555)
self.error_blockvis_list = [rsexecute.execute(apply_gaintable)(self.error_blockvis_list[i], gt)
for i in range(self.freqwin)]
self.error_blockvis_list = rsexecute.compute(self.error_blockvis_list, sync=True)
assert numpy.max(numpy.abs(self.error_blockvis_list[0].vis - self.blockvis_list[0].vis)) > 0.0
def actualSetUp(self, freqwin=1, block=True, dopol=False, zerow=False):
self.npixel = 1024
self.low = create_named_configuration('LOWBD2', rmax=550.0)
self.freqwin = freqwin
self.blockvis_list = list()
self.ntimes = 5
self.cellsize = 0.0005
# Choose the interval so that the maximum change in w is smallish
integration_time = numpy.pi * (24 / (12 * 60))
self.times = numpy.linspace(-integration_time * (self.ntimes // 2), integration_time * (self.ntimes // 2),
self.ntimes)
if freqwin > 1:
self.frequency = numpy.linspace(0.8e8, 1.2e8, self.freqwin)
self.channelwidth = numpy.array(freqwin * [self.frequency[1] - self.frequency[0]])
else:
self.frequency = numpy.array([1.0e8])
self.channelwidth = numpy.array([4e7])
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])
self.phasecentre = SkyCoord(ra=+0.0 * u.deg, dec=-40.0 * u.deg, frame='icrs', equinox='J2000')
self.blockvis_list = [rsexecute.execute(ingest_unittest_visibility)(self.low,
[self.frequency[freqwin]],
[self.channelwidth[freqwin]],
self.times,
self.vis_pol,
self.phasecentre, block=block,
zerow=zerow)
for freqwin, _ in enumerate(self.frequency)]
self.blockvis_list = rsexecute.compute(self.blockvis_list, sync=True)
self.vis_list = [rsexecute.execute(convert_blockvisibility_to_visibility)(bv) for bv in self.blockvis_list]
self.vis_list = rsexecute.compute(self.vis_list, sync=True)
self.skymodel_list = [rsexecute.execute(create_low_test_skymodel_from_gleam)
(npixel=self.npixel, cellsize=self.cellsize, frequency=[self.frequency[f]],
phasecentre=self.phasecentre,
polarisation_frame=PolarisationFrame("stokesI"),
flux_limit=0.6,
flux_threshold=1.0,
flux_max=5.0) for f, freq in enumerate(self.frequency)]
self.skymodel_list = rsexecute.compute(self.skymodel_list, sync=True)
assert isinstance(self.skymodel_list[0].image, Image), self.skymodel_list[0].image
assert isinstance(self.skymodel_list[0].components[0], Skycomponent), self.skymodel_list[0].components[0]
assert len(self.skymodel_list[0].components) == 35, len(self.skymodel_list[0].components)
self.skymodel_list = expand_skymodel_by_skycomponents(self.skymodel_list[0])
assert len(self.skymodel_list) == 36, len(self.skymodel_list)
assert numpy.max(numpy.abs(self.skymodel_list[-1].image.data)) > 0.0, "Image is empty"
self.vis_list = [copy_visibility(self.vis_list[0], zero=True) for i, _ in enumerate(self.skymodel_list)]
def invert_2d(vis: Visibility,
im: Image,
dopsf: bool = False,
normalize: bool = True,
gcfcf=None,
**kwargs) -> (Image, numpy.ndarray):
""" Invert using 2D convolution function, using the specified convolution function
Use the image im as a template. Do PSF in a separate call.
This is at the bottom of the layering i.e. all transforms are eventually expressed in terms
of this function. Any shifting needed is performed here.
:param vis: Visibility to be inverted
:param im: image template (not changed)
:param dopsf: Make the psf instead of the dirty image
:param normalize: Normalize by the sum of weights (True)
:param gcfcf: (Grid correction function i.e. in image space, Convolution function i.e. in uv space)
:return: resulting image
"""
assert isinstance(vis, Visibility), vis
svis = copy_visibility(vis)
if dopsf:
svis.data['vis'][...] = 1.0 + 0.0j
svis = shift_vis_to_image(svis, im, tangent=True, inverse=False)
if gcfcf is None:
gcf, cf = create_pswf_convolutionfunction(
im,
support=get_parameter(kwargs, "support", 6),
oversampling=get_parameter(kwargs, "oversampling", 128))
else:
gcf, cf = gcfcf
griddata = create_griddata_from_image(im)
griddata, sumwt = grid_visibility_to_griddata(svis,
griddata=griddata,
cf=cf)
imaginary = get_parameter(kwargs, "imaginary", False)
if imaginary:
result0, result1 = fft_griddata_to_image(griddata,
gcf,
imaginary=imaginary)
log.debug("invert_2d: retaining imaginary part of dirty image")
if normalize:
result0 = normalize_sumwt(result0, sumwt)
result1 = normalize_sumwt(result1, sumwt)
return result0, sumwt, result1
else:
result = fft_griddata_to_image(griddata, gcf)
if normalize:
result = normalize_sumwt(result, sumwt)
return result, sumwt
def test_copy_visibility(self):
self.vis = create_visibility(self.lowcore, self.times, self.frequency,
channel_bandwidth=self.channel_bandwidth, phasecentre=self.phasecentre, weight=1.0,
polarisation_frame=PolarisationFrame("stokesIQUV"))
vis = copy_visibility(self.vis)
self.vis.data['vis'] = 0.0
vis.data['vis'] = 1.0
assert (vis.data['vis'][0, 0].real == 1.0)
assert (self.vis.data['vis'][0, 0].real == 0.0)
def test_apply_gaintable_null(self):
for spf, dpf in[('stokesI', 'stokesI'), ('stokesIQUV', 'linear'), ('stokesIQUV', 'circular')]:
self.actualSetup(spf, dpf)
gt = create_gaintable_from_blockvisibility(self.vis, timeslice='auto')
gt.data['gain']*=0.0
original = copy_visibility(self.vis)
vis = apply_gaintable(self.vis, gt, inverse=True)
error = numpy.max(numpy.abs(vis.vis[:,0,1,...] - original.vis[:,0,1,...]))
assert error < 1e-12, "Error = %s" % (error)
def test_apply_gaintable_only(self):
for spf, dpf in[('stokesI', 'stokesI'), ('stokesIQUV', 'linear'), ('stokesIQUV', 'circular')]:
self.actualSetup(spf, dpf)
gt = create_gaintable_from_blockvisibility(self.vis, timeslice='auto')
log.info("Created gain table: %s" % (gaintable_summary(gt)))
gt = simulate_gaintable(gt, phase_error=0.1, amplitude_error=0.01)
original = copy_visibility(self.vis)
vis = apply_gaintable(self.vis, gt)
error = numpy.max(numpy.abs(vis.vis - original.vis))
assert error > 10.0, "Error = %f" % (error)
def test_solve_gaintable_scalar_timeslice(self):
self.actualSetup('stokesI', 'stokesI', f=[100.0], ntimes=10)
gt = create_gaintable_from_blockvisibility(self.vis, timeslice=120.0)
log.info("Created gain table: %s" % (gaintable_summary(gt)))
gt = simulate_gaintable(gt, phase_error=10.0, amplitude_error=0.0)
original = copy_visibility(self.vis)
self.vis = apply_gaintable(self.vis, gt)
gtsol = solve_gaintable(self.vis, original, phase_only=True, niter=200)
residual = numpy.max(gtsol.residual)
assert residual < 3e-8, "Max residual = %s" % (residual)
assert numpy.max(numpy.abs(gtsol.gain - 1.0)) > 0.1
def test_solve_gaintable_stokesI_pointsource(self):
self.actualSetup('stokesI', 'stokesI', f=[100.0])
gt = create_gaintable_from_blockvisibility(self.vis)
log.info("Created gain table: %s" % (gaintable_summary(gt)))
gt = simulate_gaintable(gt, phase_error=10.0, amplitude_error=0.0)
original = copy_visibility(self.vis)
self.vis = apply_gaintable(self.vis, gt)
point_vis = divide_visibility(self.vis, original)
gtsol = solve_gaintable(point_vis, phase_only=False, niter=200)
residual = numpy.max(gtsol.residual)
assert residual < 3e-8, "Max residual = %s" % (residual)
assert numpy.max(numpy.abs(gtsol.gain - 1.0)) > 0.1
def predict_wstack_single(vis,
model,
remove=True,
gcfcf=None,
**kwargs) -> Visibility:
""" Predict using a single w slices.
This processes a single w plane, rotating out the w beam for the average w
The w-stacking or w-slicing approach is to partition the visibility data by slices in w. The measurement equation is
approximated as:
.. math::
V(u,v,w) =\\sum_i \\int \\frac{ I(l,m) e^{-2 \\pi j (w_i(\\sqrt{1-l^2-m^2}-1))})}{\\sqrt{1-l^2-m^2}} e^{-2 \\pi j (ul+vm)} dl dm
If images constructed from slices in w are added after applying a w-dependent image plane correction, the w term will be corrected.
:param vis: Visibility to be predicted
:param model: model image
:return: resulting visibility (in place works)
"""
assert isinstance(
vis,
Visibility), "wstack requires Visibility format not BlockVisibility"
assert image_is_canonical(model)
vis.data['vis'][...] = 0.0
log.debug("predict_wstack_single: predicting using single w slice")
# We might want to do wprojection so we remove the average w
w_average = numpy.average(vis.w)
if remove:
vis.data['uvw'][..., 2] -= w_average
tempvis = copy_visibility(vis)
# Calculate w beam and apply to the model. The imaginary part is not needed
workimage = convert_stokes_to_polimage(model, vis.polarisation_frame)
w_beam = create_w_term_like(model, w_average, vis.phasecentre)
workimage.data = numpy.conjugate(w_beam.data) * workimage.data
gcf, cf = gcfcf
griddata = create_griddata_from_image(model, vis)
griddata = fft_image_to_griddata(workimage, griddata, gcf)
vis = degrid_visibility_from_griddata(vis, griddata=griddata, cf=cf)
if remove:
vis.data['uvw'][..., 2] += w_average
return vis
def test_create_gaintable_from_visibility_interval(self):
for timeslice in [10.0, 'auto', 1e5]:
for spf, dpf in[('stokesIQUV', 'linear')]:
self.actualSetup(spf, dpf)
gt = create_gaintable_from_blockvisibility(self.vis, timeslice=timeslice)
log.info("Created gain table: %s" % (gaintable_summary(gt)))
gt = simulate_gaintable(gt, phase_error=1.0)
original = copy_visibility(self.vis)
vis = apply_gaintable(self.vis, gt)
assert numpy.max(numpy.abs(original.vis)) > 0.0
assert numpy.max(numpy.abs(vis.vis)) > 0.0
assert numpy.max(numpy.abs(vis.vis - original.vis)) > 0.0
def test_addnoise_blockvisibility(self):
self.vis = create_blockvisibility(
self.config,
self.times,
self.frequency,
phasecentre=self.phasecentre,
weight=1.0,
polarisation_frame=PolarisationFrame('stokesIQUV'),
channel_bandwidth=self.channel_bandwidth)
original = copy_visibility(self.vis)
self.vis = addnoise_visibility(self.vis)
actual = numpy.std(numpy.abs(self.vis.vis - original.vis))
assert abs(actual - 0.01077958403015586) < 1e-4, actual
def test_solve_gaintable_stokesI_small_n_large_t(self):
# Select only 6 stations
self.actualSetup('stokesI', 'stokesI', f=[100.0], ntimes=4000, rmax=83)
gt = create_gaintable_from_blockvisibility(self.vis)
log.info("Created gain table: %s" % (gaintable_summary(gt)))
gt = simulate_gaintable(gt, phase_error=10.0, amplitude_error=0.0)
gt.data['gain'] = gt.gain[1, ...]
original = copy_visibility(self.vis)
self.vis = apply_gaintable(self.vis, gt)
gtsol = solve_gaintable(self.vis, original, phase_only=True, niter=200)
self.vis = apply_gaintable(self.vis, gtsol)
residual = numpy.max(gtsol.residual)
assert residual < 3e-8, "Max residual = %s" % (residual)
assert numpy.max(numpy.abs(gtsol.gain - 1.0)) > 0.1
def test_solve_gaintable_scalar_normalise(self):
self.actualSetup('stokesI', 'stokesI', f=[100.0])
gt = create_gaintable_from_blockvisibility(self.vis)
log.info("Created gain table: %s" % (gaintable_summary(gt)))
gt = simulate_gaintable(gt, phase_error=0.0, amplitude_error=0.1)
gt.data['gain'] *= 2.0
original = copy_visibility(self.vis)
self.vis = apply_gaintable(self.vis, gt)
gtsol = solve_gaintable(self.vis,
original,
phase_only=False,
niter=200,
normalise_gains=True)
residual = numpy.max(gtsol.residual)
assert residual < 3e-8, "Max residual = %s" % (residual)
assert numpy.max(numpy.abs(gtsol.gain - 1.0)) > 0.1
def test_calibrate_G_function(self):
self.actualSetup('stokesIQUV', 'linear', f=[100.0, 0.0, 0.0, 50.0])
# Prepare the corrupted visibility data_models
gt = create_gaintable_from_blockvisibility(self.vis)
log.info("Created gain table: %s" % (gaintable_summary(gt)))
gt = simulate_gaintable(gt, phase_error=0.0, amplitude_error=0.1)
original = copy_visibility(self.vis)
self.vis = apply_gaintable(self.vis, gt)
# Now get the control dictionary and calibrate
controls = create_calibration_controls()
controls['G']['first_selfcal'] = 0
calibrated_vis, gaintables = calibrate_chain(self.vis,
original,
calibration_context='G',
controls=controls)
residual = numpy.max(gaintables['G'].residual)
assert residual < 1e-8, "Max T residual = %s" % residual
def test_solve_gaintable_stokesI_bandpass(self):
self.actualSetup('stokesI', 'stokesI', f=[100.0], vnchan=128)
gt = create_gaintable_from_blockvisibility(self.vis)
log.info("Created gain table: %s" % (gaintable_summary(gt)))
gt = simulate_gaintable(gt,
phase_error=10.0,
amplitude_error=0.01,
smooth_channels=8)
original = copy_visibility(self.vis)
self.vis = apply_gaintable(self.vis, gt)
gtsol = solve_gaintable(self.vis,
original,
phase_only=False,
niter=200,
damping=0.5)
residual = numpy.max(gtsol.residual)
assert residual < 3e-8, "Max residual = %s" % (residual)
assert numpy.max(numpy.abs(gtsol.gain - 1.0)) > 0.1
def decoalesce_visibility(vis: Visibility, **kwargs) -> BlockVisibility:
""" Decoalesce the visibilities to the original values (opposite of coalesce_visibility)
This relies upon the block vis and the index being part of the vis. Needs the index generated by coalesce_visibility
:param vis: (Coalesced visibility)
:return: BlockVisibility with vis and weight columns overwritten
"""
assert isinstance(vis, Visibility), "vis is not a Visibility: %r" % vis
assert isinstance(vis.blockvis,
BlockVisibility), "No blockvisibility in vis %r" % vis
assert vis.cindex is not None, "No reverse index in Visibility %r" % vis
log.debug(
'decoalesce_visibility: Created new Visibility for decoalesced data_models'
)
decomp_vis = copy_visibility(vis.blockvis)
vshape = decomp_vis.data['vis'].shape
npol = vshape[-1]
dvis = numpy.zeros(vshape, dtype='complex')
assert numpy.max(vis.cindex) < dvis.size
assert numpy.max(
vis.cindex
) < vis.vis.shape[0], "Incorrect template used in decoalescing"
for i in range(dvis.size // npol):
decomp_vis.data['vis'].flat[i:i +
npol] = vis.data['vis'][vis.cindex[i]]
decomp_vis.data['flags'].flat[i:i +
npol] = vis.data['flags'][vis.cindex[i]]
decomp_vis.data['weight'].flat[i:i + npol] = vis.data['weight'][
vis.cindex[i]]
decomp_vis.data['imaging_weight'].flat[i:i + npol] = vis.data[
'imaging_weight'][vis.cindex[i]]
log.debug('decoalesce_visibility: Coalesced %s, decoalesced %s' %
(vis_summary(vis), vis_summary(decomp_vis)))
return decomp_vis
def rcal(vis: BlockVisibility, components, **kwargs) -> GainTable:
""" Real-time calibration pipeline.
Reads visibilities through a BlockVisibility iterator, calculates model visibilities according to a
component-based sky model, and performs calibration solution, writing a gaintable for each chunk of
visibilities.
:param vis: Visibility or Union(Visibility, Iterable)
:param components: Component-based sky model
:param kwargs: Parameters
:return: gaintable
"""
if not isinstance(vis, collections.abc.Iterable):
vis = [vis]
for ichunk, vischunk in enumerate(vis):
vispred = copy_visibility(vischunk, zero=True)
vispred = dft_skycomponent_visibility(vispred, components)
gt = solve_gaintable(vischunk, vispred, **kwargs)
yield gt
def core_solve(self,
spf,
dpf,
phase_error=0.1,
amplitude_error=0.0,
leakage=0.0,
phase_only=True,
niter=200,
crosspol=False,
residual_tol=1e-6,
f=None,
vnchan=3,
timeslice='auto'):
if f is None:
f = [100.0, 50.0, -10.0, 40.0]
self.actualSetup(spf, dpf, f=f, vnchan=vnchan)
gt = create_gaintable_from_blockvisibility(self.vis,
timeslice=timeslice)
log.info("Created gain table: %s" % (gaintable_summary(gt)))
gt = simulate_gaintable(gt,
phase_error=phase_error,
amplitude_error=amplitude_error,
leakage=leakage)
original = copy_visibility(self.vis)
vis = apply_gaintable(self.vis, gt)
gtsol = solve_gaintable(self.vis,
original,
phase_only=phase_only,
niter=niter,
crosspol=crosspol,
tol=1e-6)
vis = apply_gaintable(vis, gtsol, inverse=True)
residual = numpy.max(gtsol.residual)
assert residual < residual_tol, "%s %s Max residual = %s" % (spf, dpf,
residual)
log.debug(qa_gaintable(gt))
assert numpy.max(numpy.abs(gtsol.gain - 1.0)) > 0.1
def test_create_vis_iter(self):
vis_iter = create_blockvisibility_iterator(
self.config,
self.times,
self.frequency,
channel_bandwidth=self.channel_bandwidth,
phasecentre=self.phasecentre,
weight=1.0,
polarisation_frame=PolarisationFrame('stokesI'),
integration_time=30.0,
number_integrations=3)
fullvis = None
totalnvis = 0
for i, vis in enumerate(vis_iter):
assert vis.nvis
if i == 0:
fullvis = copy_visibility(vis)
totalnvis = vis.nvis
else:
fullvis = append_visibility(fullvis, vis)
totalnvis += vis.nvis
assert fullvis.nvis == totalnvis
blockvis_pol,
phasecentre,
block=block,
zerow=zerow)
vis = convert_blockvisibility_to_visibility(blockvis)
model = create_unittest_model(vis, image_pol, npixel=npixel, nchan=freqwin)
components = create_unittest_components(model, flux)
model = insert_skycomponent(model, components)
blockvis = predict_skycomponent_visibility(blockvis, components)
#blockvis = dft_skycomponent_visibility(blockvis, components)
blockvis1 = copy_visibility(blockvis)
vis1 = convert_blockvisibility_to_visibility(blockvis1)
# Calculate the model convolved with a Gaussian.
cmodel = smooth_image(model)
if persist: export_image_to_fits(model, '%s/test_imaging_2d_model.fits' % rdir)
if persist:
export_image_to_fits(cmodel, '%s/test_imaging_2d_cmodel.fits' % rdir)
# In[4]:
print(qa_image(model))
# In[5]:
def invert_ng(bvis: BlockVisibility,
model: Image,
dopsf: bool = False,
normalize: bool = True,
**kwargs) -> (Image, numpy.ndarray):
""" Invert using nifty-gridder module
https://gitlab.mpcdf.mpg.de/ift/nifty_gridder
Use the image im as a template. Do PSF in a separate call.
This is at the bottom of the layering i.e. all transforms are eventually expressed in terms
of this function. . Any shifting needed is performed here.
:param bvis: BlockVisibility to be inverted
:param im: image template (not changed)
:param normalize: Normalize by the sum of weights (True)
:return: (resulting image, sum of the weights for each frequency and polarization)
"""
assert image_is_canonical(model)
assert isinstance(bvis, BlockVisibility), bvis
im = copy_image(model)
nthreads = get_parameter(kwargs, "threads", 4)
epsilon = get_parameter(kwargs, "epsilon", 1e-12)
do_wstacking = get_parameter(kwargs, "do_wstacking", True)
verbosity = get_parameter(kwargs, "verbosity", 0)
sbvis = copy_visibility(bvis)
sbvis = shift_vis_to_image(sbvis, im, tangent=True, inverse=False)
vis = bvis.vis
freq = sbvis.frequency # frequency, Hz
nrows, nants, _, vnchan, vnpol = vis.shape
uvw = sbvis.uvw.reshape([nrows * nants * nants, 3])
ms = vis.reshape([nrows * nants * nants, vnchan, vnpol])
wgt = sbvis.imaging_weight.reshape(
[nrows * nants * nants, vnchan, vnpol])
if dopsf:
ms[...] = 1.0 + 0.0j
if epsilon > 5.0e-6:
ms = ms.astype("c8")
wgt = wgt.astype("f4")
# Find out the image size/resolution
npixdirty = im.nwidth
pixsize = numpy.abs(numpy.radians(im.wcs.wcs.cdelt[0]))
fuvw = uvw.copy()
# We need to flip the u and w axes.
fuvw[:, 0] *= -1.0
fuvw[:, 2] *= -1.0
nchan, npol, ny, nx = im.shape
im.data[...] = 0.0
sumwt = numpy.zeros([nchan, npol])
ms = convert_pol_frame(ms,
bvis.polarisation_frame,
im.polarisation_frame,
polaxis=2)
# There's a latent problem here with the weights.
# wgt = numpy.real(convert_pol_frame(wgt, bvis.polarisation_frame, im.polarisation_frame, polaxis=2))
# Set up the conversion from visibility channels to image channels
vis_to_im = numpy.round(model.wcs.sub([4]).wcs_world2pix(
freq, 0)[0]).astype('int')
for vchan in range(vnchan):
ichan = vis_to_im[vchan]
for pol in range(npol):
# Nifty gridder likes to receive contiguous arrays
ms_1d = numpy.array([
ms[row, vchan:vchan + 1, pol]
for row in range(nrows * nants * nants)
],
dtype='complex')
ms_1d.reshape([ms_1d.shape[0], 1])
wgt_1d = numpy.array([
wgt[row, vchan:vchan + 1, pol]
for row in range(nrows * nants * nants)
])
wgt_1d.reshape([wgt_1d.shape[0], 1])
dirty = ng.ms2dirty(fuvw,
freq[vchan:vchan + 1],
ms_1d,
wgt_1d,
npixdirty,
npixdirty,
pixsize,
pixsize,
epsilon,
do_wstacking=do_wstacking,
nthreads=nthreads,
verbosity=verbosity)
sumwt[ichan, pol] += numpy.sum(wgt[:, vchan, pol])
im.data[ichan, pol] += dirty.T
if normalize:
im = normalize_sumwt(im, sumwt)
return im, sumwt
def predict_ng(bvis: BlockVisibility, model: Image,
**kwargs) -> BlockVisibility:
""" Predict using convolutional degridding.
Nifty-gridder version. https://gitlab.mpcdf.mpg.de/ift/nifty_gridder
In the imaging and pipeline workflows, this may be invoked using context='ng'.
:param bvis: BlockVisibility to be predicted
:param model: model image
:return: resulting BlockVisibility (in place works)
"""
assert isinstance(bvis, BlockVisibility), bvis
assert image_is_canonical(model)
if model is None:
return bvis
nthreads = get_parameter(kwargs, "threads", 4)
epsilon = get_parameter(kwargs, "epsilon", 1e-12)
do_wstacking = get_parameter(kwargs, "do_wstacking", True)
verbosity = get_parameter(kwargs, "verbosity", 0)
newbvis = copy_visibility(bvis, zero=True)
# Extracting data from BlockVisibility
freq = bvis.frequency # frequency, Hz
nrows, nants, _, vnchan, vnpol = bvis.vis.shape
uvw = newbvis.data['uvw'].reshape([nrows * nants * nants, 3])
vist = numpy.zeros([vnpol, vnchan, nants * nants * nrows],
dtype='complex')
# Get the image properties
m_nchan, m_npol, ny, nx = model.data.shape
# Check if the number of frequency channels matches in bvis and a model
# assert (m_nchan == v_nchan)
assert (m_npol == vnpol)
fuvw = uvw.copy()
# We need to flip the u and w axes. The flip in w is equivalent to the conjugation of the
# convolution function grid_visibility to griddata
fuvw[:, 0] *= -1.0
fuvw[:, 2] *= -1.0
# Find out the image size/resolution
pixsize = numpy.abs(numpy.radians(model.wcs.wcs.cdelt[0]))
# Make de-gridding over a frequency range and pol fields
vis_to_im = numpy.round(model.wcs.sub([4]).wcs_world2pix(
freq, 0)[0]).astype('int')
mfs = m_nchan == 1
if mfs:
for vpol in range(vnpol):
vist[vpol, :, :] = ng.dirty2ms(
fuvw.astype(numpy.float64),
bvis.frequency.astype(numpy.float64),
model.data[0, vpol, :, :].T.astype(numpy.float64),
pixsize_x=pixsize,
pixsize_y=pixsize,
epsilon=epsilon,
do_wstacking=do_wstacking,
nthreads=nthreads,
verbosity=verbosity).T
else:
for vpol in range(vnpol):
for vchan in range(vnchan):
imchan = vis_to_im[vchan]
vist[vpol, vchan, :] = ng.dirty2ms(
fuvw.astype(numpy.float64),
numpy.array(freq[vchan:vchan + 1]).astype(
numpy.float64),
model.data[imchan, vpol, :, :].T.astype(numpy.float64),
pixsize_x=pixsize,
pixsize_y=pixsize,
epsilon=epsilon,
do_wstacking=do_wstacking,
nthreads=nthreads,
verbosity=verbosity)[:, 0]
vis = convert_pol_frame(vist.T,
model.polarisation_frame,
bvis.polarisation_frame,
polaxis=2)
newbvis.data['vis'] = vis.reshape([nrows, nants, nants, vnchan, vnpol])
# Now we can shift the visibility from the image frame to the original visibility frame
return shift_vis_to_image(newbvis, model, tangent=True, inverse=True)
def invert_ng(bvis: BlockVisibility,
model: Image,
dopsf: bool = False,
normalize: bool = True,
**kwargs) -> (Image, numpy.ndarray):
""" Invert using nifty-gridder module
https://gitlab.mpcdf.mpg.de/ift/nifty_gridder
Use the image im as a template. Do PSF in a separate call.
In the imaging and pipeline workflows, this may be invoked using context='ng'.
:param dopsf: Make the PSF instead of the dirty image
:param bvis: BlockVisibility to be inverted
:param im: image template (not changed)
:param normalize: Normalize by the sum of weights (True)
:return: (resulting image, sum of the weights for each frequency and polarization)
"""
assert image_is_canonical(model)
assert isinstance(bvis, BlockVisibility), bvis
im = copy_image(model)
nthreads = get_parameter(kwargs, "threads", 4)
epsilon = get_parameter(kwargs, "epsilon", 1e-12)
do_wstacking = get_parameter(kwargs, "do_wstacking", True)
verbosity = get_parameter(kwargs, "verbosity", 0)
sbvis = copy_visibility(bvis)
sbvis = shift_vis_to_image(sbvis, im, tangent=True, inverse=False)
freq = sbvis.frequency # frequency, Hz
nrows, nants, _, vnchan, vnpol = sbvis.vis.shape
# if dopsf:
# sbvis = fill_vis_for_psf(sbvis)
ms = sbvis.vis.reshape([nrows * nants * nants, vnchan, vnpol])
ms = convert_pol_frame(ms,
bvis.polarisation_frame,
im.polarisation_frame,
polaxis=2)
uvw = sbvis.uvw.reshape([nrows * nants * nants, 3])
wgt = sbvis.flagged_imaging_weight.reshape(
[nrows * nants * nants, vnchan, vnpol])
if epsilon > 5.0e-6:
ms = ms.astype("c8")
wgt = wgt.astype("f4")
# Find out the image size/resolution
npixdirty = im.nwidth
pixsize = numpy.abs(numpy.radians(im.wcs.wcs.cdelt[0]))
fuvw = uvw.copy()
# We need to flip the u and w axes.
fuvw[:, 0] *= -1.0
fuvw[:, 2] *= -1.0
nchan, npol, ny, nx = im.shape
im.data[...] = 0.0
sumwt = numpy.zeros([nchan, npol])
# There's a latent problem here with the weights.
# wgt = numpy.real(convert_pol_frame(wgt, bvis.polarisation_frame, im.polarisation_frame, polaxis=2))
# Set up the conversion from visibility channels to image channels
vis_to_im = numpy.round(model.wcs.sub([4]).wcs_world2pix(
freq, 0)[0]).astype('int')
# Nifty gridder likes to receive contiguous arrays so we transpose
# at the beginning
mfs = nchan == 1
if dopsf:
mst = ms.T
mst[...] = 0.0
mst[0, ...] = 1.0
wgtt = wgt.T
if mfs:
dirty = ng.ms2dirty(fuvw.astype(numpy.float64),
bvis.frequency.astype(numpy.float64),
numpy.ascontiguousarray(mst[0, :, :].T),
numpy.ascontiguousarray(wgtt[0, :, :].T),
npixdirty,
npixdirty,
pixsize,
pixsize,
epsilon,
do_wstacking=do_wstacking,
nthreads=nthreads,
verbosity=verbosity)
sumwt[0, :] += numpy.sum(wgtt[0, 0, :].T, axis=0)
im.data[0, :] += dirty.T
else:
for vchan in range(vnchan):
ichan = vis_to_im[vchan]
frequency = numpy.array(freq[vchan:vchan + 1]).astype(
numpy.float64)
dirty = ng.ms2dirty(
fuvw.astype(numpy.float64),
frequency.astype(numpy.float64),
numpy.ascontiguousarray(mst[0,
vchan, :][...,
numpy.newaxis]),
numpy.ascontiguousarray(wgtt[0,
vchan, :][...,
numpy.newaxis]),
npixdirty,
npixdirty,
pixsize,
pixsize,
epsilon,
do_wstacking=do_wstacking,
nthreads=nthreads,
verbosity=verbosity)
sumwt[ichan, :] += numpy.sum(wgtt[0, ichan, :].T, axis=0)
im.data[ichan, :] += dirty.T
else:
mst = ms.T
wgtt = wgt.T
for pol in range(npol):
if mfs:
dirty = ng.ms2dirty(
fuvw.astype(numpy.float64),
bvis.frequency.astype(numpy.float64),
numpy.ascontiguousarray(mst[pol, :, :].T),
numpy.ascontiguousarray(wgtt[pol, :, :].T),
npixdirty,
npixdirty,
pixsize,
pixsize,
epsilon,
do_wstacking=do_wstacking,
nthreads=nthreads,
verbosity=verbosity)
sumwt[0, pol] += numpy.sum(wgtt[pol, 0, :].T, axis=0)
im.data[0, pol] += dirty.T
else:
for vchan in range(vnchan):
ichan = vis_to_im[vchan]
frequency = numpy.array(freq[vchan:vchan + 1]).astype(
numpy.float64)
dirty = ng.ms2dirty(fuvw.astype(numpy.float64),
frequency.astype(numpy.float64),
numpy.ascontiguousarray(
mst[pol,
vchan, :][...,
numpy.newaxis]),
numpy.ascontiguousarray(
wgtt[pol,
vchan, :][...,
numpy.newaxis]),
npixdirty,
npixdirty,
pixsize,
pixsize,
epsilon,
do_wstacking=do_wstacking,
nthreads=nthreads,
verbosity=verbosity)
sumwt[ichan, pol] += numpy.sum(wgtt[pol, ichan, :].T,
axis=0)
im.data[ichan, pol] += dirty.T
if normalize:
im = normalize_sumwt(im, sumwt)
return im, sumwt
def degrid_blockvisibility_from_griddata(vis, griddata, cf, **kwargs):
"""Degrid blockVisibility from a GridData
:param vis: Visibility to be degridded
:param griddata: GridData containing image
:param cf: Convolution function (as GridData)
:param kwargs:
:return: Visibility
"""
assert vis.polarisation_frame == griddata.polarisation_frame
newvis = copy_visibility(vis, zero=True)
nchan, npol, nz, oversampling, _, support, _ = cf.shape
vis_to_im = numpy.round(
griddata.grid_wcs.sub([5]).wcs_world2pix(vis.frequency, 0)[0]).astype('int')
nrows, nants, _, nvchan, nvpol = vis.vis.shape
fvist = numpy.zeros([nvpol, nvchan, nrows * nants * nants], dtype='complex')
_, _, _, _, _, gv, gu = cf.shape
du = gu // 2
dv = gv // 2
for vchan in range(nvchan):
imchan = vis_to_im[vchan]
frequency = vis.frequency[vchan]
pu_grid, pu_offset, pv_grid, pv_offset, pwg_grid, pwg_fraction, pwc_grid, pwc_fraction = \
convolution_mapping_blockvisibility(vis, griddata, frequency, cf)
for pol in range(nvpol):
for row in range(nrows * nants * nants):
subgrid = griddata.data[imchan, \
pol, \
pwg_grid[row], \
(pv_grid[row] - dv):(pv_grid[row] + dv), \
(pu_grid[row] - du):(pu_grid[row] + du)]
subcf = cf.data[imchan,
pol,
pwc_grid[row],
pv_offset[row],
pu_offset[row],
:, :]
fvist[pol, vchan, row] = numpy.einsum('ij,ij', subgrid, subcf)# / numpy.sum(subcf.real)
# import matplotlib.pyplot as plt
# plt.clf()
# plt.plot(pu_offset[::10], numpy.abs(fvist[0, 0, ::10]), '.')
# plt.title("U offset")
# plt.show(block=False)
# plt.clf()
# plt.plot(pv_offset[::10], numpy.abs(fvist[0, 0, ::10]), '.')
# plt.title("V offset")
# plt.show(block=False)
# plt.clf()
# plt.plot(pu_offset[::10], pv_offset[::10], '.')
# plt.title("U vs V offset")
# plt.show(block=False)
newvis.data['vis'][...] = fvist.T.reshape([nrows, nants, nants, nvchan, nvpol])
return newvis