def actualSetup(self,
sky_pol_frame='stokesIQUV',
data_pol_frame='linear',
f=[100.0, 50.0, -10.0, 40.0]):
f = numpy.array(f)
self.flux = numpy.array([f, 0.8 * f, 0.6 * f])
# The phase centre is absolute and the component is specified relative (for now).
# This means that the component should end up at the position phasecentre+compredirection
self.phasecentre = SkyCoord(ra=+180.0 * u.deg,
dec=-35.0 * u.deg,
frame='icrs',
equinox=2000.0)
self.compabsdirection = SkyCoord(ra=+181.0 * u.deg,
dec=-35.0 * u.deg,
frame='icrs',
equinox=2000.0)
self.comp = Skycomponent(
direction=self.compabsdirection,
frequency=self.frequency,
flux=self.flux,
polarisation_frame=PolarisationFrame(sky_pol_frame))
self.vis = create_blockvisibility(
self.lowcore,
self.times,
self.frequency,
phasecentre=self.phasecentre,
channel_bandwidth=self.channel_bandwidth,
weight=1.0,
polarisation_frame=PolarisationFrame(data_pol_frame))
self.vis = predict_skycomponent_blockvisibility(self.vis, self.comp)
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_timeslice, 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
:returns: Visibility
"""
for time in times:
actualtimes = time + numpy.arange(0, number_integrations) * integration_time * numpy.pi / 43200.0
vis = 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 = predict(vis, model, **kwargs)
if components is not None:
vis = predict_skycomponent_blockvisibility(vis, components)
# Add phase errors
if phase_error > 0.0 or amplitude_error > 0.0:
gt = create_gaintable_from_blockvisibility(vis)
gt = simulate_gaintable(gt=gt, phase_error=phase_error, amplitude_error=amplitude_error)
vis = apply_gaintable(vis, gt)
import time
time.sleep(sleep)
yield vis
def actualSetup(self, sky_pol_frame='stokesIQUV', data_pol_frame='linear'):
self.comp = Skycomponent(
direction=self.compabsdirection,
frequency=self.frequency,
flux=self.flux,
polarisation_frame=PolarisationFrame(sky_pol_frame))
self.vis = create_blockvisibility(
self.lowcore,
self.times,
self.frequency,
phasecentre=self.phasecentre,
channel_bandwidth=self.channel_bandwidth,
weight=1.0,
polarisation_frame=PolarisationFrame(data_pol_frame))
self.vis = predict_skycomponent_blockvisibility(self.vis, self.comp)
def rcal(vis: BlockVisibility, components, **kwargs):
""" Real-time calibration pipeline.
Reads visibilities through a BlockVisibility iterator, calculates model visibilities according to a
component-based sky model, and performans a calibration, writing a gaintable for each chunk of visibilities.
:param vis: Visibility or Union(Visibility, Iterable)
:param components: Component-based sky model
:param kwargs: Parameters
:returns: gaintable
"""
if not isinstance(vis, collections.Iterable):
vis = [vis]
for ichunk, vischunk in enumerate(vis):
vispred = copy_visibility(vischunk, zero=True)
vispred = predict_skycomponent_blockvisibility(vispred, components,
**kwargs)
solve_gain_graph = create_solve_gain_graph(vischunk, vispred, **kwargs)
gt = solve_gain_graph.compute()
yield gt
def ingest_visibility(self,
freq=1e8,
chan_width=1e6,
time=0.0,
reffrequency=[1e8],
add_errors=False):
lowcore = create_named_configuration('LOWBD2-CORE')
times = [time]
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=2000.0)
# 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=2000.0)
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
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)
comps.append(
create_skycomponent(
flux=flux,
frequency=vt.frequency,
direction=sc,
polarisation_frame=PolarisationFrame("stokesI")))
predict_skycomponent_blockvisibility(vt, comps)
insert_skycomponent(model, comps)
export_image_to_fits(
model, '%s/test_imaging_dask_model.fits' % (self.results_dir))
if add_errors:
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_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_blockvisibility(vis, peel)
# Make a gaintable and apply it to the visibility of the peeling source
gt = create_gaintable_from_blockvisibility(vis)
gt = simulate_gaintable(gt, phase_error=0.01, amplitude_error=0.01)
gt.data['gain'] *= 0.3
vis = apply_gaintable(vis, gt)
# 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_blockvisibility(vis, sc)
# 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 < 0.6, "Peak residual %.6f too large" % (residual)