def test_readwriteblockvisibility(self):
self.vis = create_blockvisibility(
self.mid,
self.times,
self.frequency,
channel_bandwidth=self.channel_bandwidth,
phasecentre=self.phasecentre,
polarisation_frame=PolarisationFrame("linear"),
weight=1.0)
self.vis = dft_skycomponent_visibility(self.vis, self.comp)
export_blockvisibility_to_hdf5(
self.vis,
'%s/test_data_model_helpers_blockvisibility.hdf' % self.dir)
newvis = import_blockvisibility_from_hdf5(
'%s/test_data_model_helpers_blockvisibility.hdf' % self.dir)
for key in self.vis.data.dtype.fields:
assert numpy.max(
numpy.abs(newvis.data[key] - self.vis.data[key])) < 1e-15
assert numpy.array_equal(newvis.frequency, self.vis.frequency)
assert newvis.data.shape == self.vis.data.shape
assert numpy.max(numpy.abs(self.vis.vis - newvis.vis)) < 1e-15
assert numpy.max(numpy.abs(self.vis.uvw - newvis.uvw)) < 1e-15
assert numpy.abs(newvis.configuration.location.x.value -
self.vis.configuration.location.x.value) < 1e-15
assert numpy.abs(newvis.configuration.location.y.value -
self.vis.configuration.location.y.value) < 1e-15
assert numpy.abs(newvis.configuration.location.z.value -
self.vis.configuration.location.z.value) < 1e-15
assert numpy.max(
numpy.abs(newvis.configuration.xyz -
self.vis.configuration.xyz)) < 1e-15
def test_fit_visibility(self):
# Sum the visibilities in the correct_visibility direction. This is limited by numerical precision
methods = [
'CG', 'BFGS', 'Powell', 'trust-ncg', 'trust-exact', 'trust-krylov'
]
for method in methods:
self.actualSetup()
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.vismodel = dft_skycomponent_visibility(self.vis, self.comp)
initial_comp = Skycomponent(
direction=self.comp_start_direction,
frequency=self.frequency,
flux=2.0 * self.flux,
polarisation_frame=PolarisationFrame("stokesI"))
sc, res = fit_visibility(self.vismodel,
initial_comp,
niter=200,
tol=1e-5,
method=method,
verbose=False)
assert sc.direction.separation(self.comp_actual_direction).to('rad').value < 1e-6, \
sc.direction.separation(self.comp_actual_direction).to('rad')
def ft_ift_sm(ov, sm, g):
assert isinstance(ov, Visibility) or isinstance(ov,
BlockVisibility), ov
assert isinstance(sm, SkyModel), sm
if g is not None:
assert len(g) == 2, g
assert isinstance(g[0], Image), g[0]
assert isinstance(g[1], ConvolutionFunction), g[1]
v = copy_visibility(ov)
v.data['vis'][...] = 0.0 + 0.0j
if len(sm.components) > 0:
if isinstance(sm.mask, Image):
comps = copy_skycomponent(sm.components)
comps = apply_beam_to_skycomponent(comps, sm.mask)
v = dft_skycomponent_visibility(v, comps)
else:
v = dft_skycomponent_visibility(v, sm.components)
if isinstance(sm.image, Image):
if numpy.max(numpy.abs(sm.image.data)) > 0.0:
if isinstance(sm.mask, Image):
model = copy_image(sm.image)
model.data *= sm.mask.data
else:
model = sm.image
v = predict_list_serial_workflow([v], [model],
context=context,
vis_slices=vis_slices,
facets=facets,
gcfcf=[g],
**kwargs)[0]
assert isinstance(sm.image, Image), sm.image
result = invert_list_serial_workflow([v], [sm.image],
context=context,
vis_slices=vis_slices,
facets=facets,
gcfcf=[g],
**kwargs)[0]
if isinstance(sm.mask, Image):
result[0].data *= sm.mask.data
return result
def test_phase_rotation_block(self):
self.vis = create_blockvisibility(self.lowcore, self.times, self.frequency,
channel_bandwidth=self.channel_bandwidth,
phasecentre=self.phasecentre, weight=1.0,
polarisation_frame=PolarisationFrame("stokesIQUV"))
self.vismodel = dft_skycomponent_visibility(self.vis, self.comp)
# Predict visibilities with new phase centre independently
ha_diff = -(self.compabsdirection.ra - self.phasecentre.ra).to(u.rad).value
vispred = create_blockvisibility(self.lowcore, self.times + ha_diff, self.frequency,
channel_bandwidth=self.channel_bandwidth,
phasecentre=self.compabsdirection, weight=1.0,
polarisation_frame=PolarisationFrame("stokesIQUV"))
vismodel2 = dft_skycomponent_visibility(vispred, self.comp)
# Should yield the same results as rotation
rotatedvis = phaserotate_visibility(self.vismodel, newphasecentre=self.compabsdirection, tangent=False)
assert_allclose(rotatedvis.vis, vismodel2.vis, rtol=3e-6)
assert_allclose(rotatedvis.uvw, vismodel2.uvw, rtol=3e-6)
def ft_cal_sm(obv, sm):
assert isinstance(obv, BlockVisibility), obv
bv = copy_visibility(obv)
bv.data['vis'][...] = 0.0 + 0.0j
assert len(sm.components) > 0
if isinstance(sm.mask, Image):
comps = copy_skycomponent(sm.components)
comps = apply_beam_to_skycomponent(comps, sm.mask)
bv = dft_skycomponent_visibility(bv, comps)
else:
bv = dft_skycomponent_visibility(bv, sm.components)
if docal and isinstance(sm.gaintable, GainTable):
bv = apply_gaintable(bv, sm.gaintable, inverse=True)
return bv
def test_qa(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"))
self.vismodel = dft_skycomponent_visibility(self.vis, self.comp)
qa = qa_visibility(self.vis, context='test_qa')
self.assertAlmostEqual(qa.data['maxabs'], 100.0, 7)
self.assertAlmostEqual(qa.data['medianabs'], 11.0, 7)
assert qa.context == 'test_qa'
def ft_cal_sm(ov, sm, g):
assert isinstance(ov, Visibility), ov
assert isinstance(sm, SkyModel), sm
if g is not None:
assert len(g) == 2, g
assert isinstance(g[0], Image), g[0]
assert isinstance(g[1], ConvolutionFunction), g[1]
v = copy_visibility(ov)
v.data['vis'][...] = 0.0 + 0.0j
if len(sm.components) > 0:
if isinstance(sm.mask, Image):
comps = copy_skycomponent(sm.components)
comps = apply_beam_to_skycomponent(comps, sm.mask)
v = dft_skycomponent_visibility(v, comps)
else:
v = dft_skycomponent_visibility(v, sm.components)
if isinstance(sm.image, Image):
if numpy.max(numpy.abs(sm.image.data)) > 0.0:
if isinstance(sm.mask, Image):
model = copy_image(sm.image)
model.data *= sm.mask.data
else:
model = sm.image
v = predict_list_serial_workflow([v], [model],
context=context,
vis_slices=vis_slices,
facets=facets,
gcfcf=[g],
**kwargs)[0]
if docal and isinstance(sm.gaintable, GainTable):
bv = convert_visibility_to_blockvisibility(v)
bv = apply_gaintable(bv, sm.gaintable, inverse=True)
v = convert_blockvisibility_to_visibility(bv)
return v
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 = dft_skycomponent_visibility(self.bvis, self.components)
def test_phase_rotation_inverse_block(self):
self.vis = create_blockvisibility(self.lowcore, self.times, self.frequency,
channel_bandwidth=self.channel_bandwidth,
phasecentre=self.phasecentre, weight=1.0,
polarisation_frame=PolarisationFrame("stokesIQUV"))
self.vismodel = dft_skycomponent_visibility(self.vis, self.comp)
there = SkyCoord(ra=+250.0 * u.deg, dec=-60.0 * u.deg, frame='icrs', equinox='J2000')
# Phase rotating back should not make a difference
original_vis = self.vismodel.vis
original_uvw = self.vismodel.uvw
rotatedvis = phaserotate_visibility(phaserotate_visibility(self.vismodel, there, tangent=False,
inverse=True),
self.phasecentre, tangent=False, inverse=True)
assert_allclose(rotatedvis.uvw, original_uvw, rtol=1e-7)
assert_allclose(rotatedvis.vis, original_vis, rtol=1e-7)
def actualSetup(self,
sky_pol_frame='stokesIQUV',
data_pol_frame='linear',
f=None,
vnchan=1):
self.lowcore = create_named_configuration('LOWBD2-CORE')
self.times = (numpy.pi / 43200.0) * numpy.linspace(0.0, 30.0, 3)
self.frequency = numpy.linspace(1.0e8, 1.1e8, vnchan)
if vnchan > 1:
self.channel_bandwidth = numpy.array(
vnchan * [self.frequency[1] - self.frequency[0]])
else:
self.channel_bandwidth = numpy.array([2e7])
if f is None:
f = [100.0, 50.0, -10.0, 40.0]
if sky_pol_frame == 'stokesI':
f = [100.0]
self.flux = numpy.outer(
numpy.array(
[numpy.power(freq / 1e8, -0.7) for freq in self.frequency]), 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='J2000')
self.compabsdirection = SkyCoord(ra=+181.0 * u.deg,
dec=-35.0 * u.deg,
frame='icrs',
equinox='J2000')
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 = dft_skycomponent_visibility(self.vis, self.comp)
def test_insert_skycomponent_dft(self):
self.actualSetup()
self.sc = create_skycomponent(
direction=self.phasecentre,
flux=self.sc.flux,
frequency=self.component_frequency,
polarisation_frame=PolarisationFrame('stokesI'))
self.vis.data['vis'][...] = 0.0
self.vis = dft_skycomponent_visibility(self.vis, self.sc)
im, sumwt = invert_2d(self.vis, self.model)
if self.persist:
export_image_to_fits(im,
'%s/test_skycomponent_dft.fits' % self.dir)
assert numpy.max(numpy.abs(self.vis.vis.imag)) < 1e-3
def test_phase_rotation_identity(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"))
self.vismodel = dft_skycomponent_visibility(self.vis, self.comp)
newphasecenters = [SkyCoord(182, -35, unit=u.deg), SkyCoord(182, -30, unit=u.deg),
SkyCoord(177, -30, unit=u.deg), SkyCoord(176, -35, unit=u.deg),
SkyCoord(216, -35, unit=u.deg), SkyCoord(180, -70, unit=u.deg)]
for newphasecentre in newphasecenters:
# Phase rotating back should not make a difference
original_vis = self.vismodel.vis
original_uvw = self.vismodel.uvw
rotatedvis = phaserotate_visibility(phaserotate_visibility(self.vismodel, newphasecentre, tangent=False),
self.phasecentre, tangent=False)
assert_allclose(rotatedvis.uvw, original_uvw, rtol=1e-7)
assert_allclose(rotatedvis.vis, original_vis, rtol=1e-7)
def test_readwriteblockvisibility(self):
self.vis = create_blockvisibility(
self.midcore,
self.times,
self.frequency,
channel_bandwidth=self.channel_bandwidth,
phasecentre=self.phasecentre,
polarisation_frame=PolarisationFrame("linear"),
weight=1.0,
meta={"RASCIL": 0.0})
self.vis = dft_skycomponent_visibility(self.vis, self.comp)
config = {
"buffer": {
"directory": self.dir
},
"vislist": {
"name": "test_bufferblockvisibility.hdf",
"data_model": "BlockVisibility"
}
}
bdm = BufferBlockVisibility(config["buffer"], config["vislist"],
self.vis)
bdm.sync()
new_bdm = BufferBlockVisibility(config["buffer"], config["vislist"])
new_bdm.sync()
newvis = bdm.memory_data_model
assert isinstance(newvis, BlockVisibility)
assert numpy.array_equal(newvis.frequency, self.vis.frequency)
assert newvis.data.shape == self.vis.data.shape
assert numpy.max(numpy.abs(self.vis.vis - newvis.vis)) < 1e-15
assert numpy.max(numpy.abs(self.vis.uvw - newvis.uvw)) < 1e-15
assert numpy.abs(newvis.configuration.location.x.value -
self.vis.configuration.location.x.value) < 1e-15
assert numpy.abs(newvis.configuration.location.y.value -
self.vis.configuration.location.y.value) < 1e-15
assert numpy.abs(newvis.configuration.location.z.value -
self.vis.configuration.location.z.value) < 1e-15
assert numpy.max(
numpy.abs(newvis.configuration.xyz -
self.vis.configuration.xyz)) < 1e-15
assert newvis.meta == self.vis.meta
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 test_zero_list(self):
self.actualSetUp()
centre = self.freqwin // 2
vis_list = zero_list_serial_workflow(self.bvis_list)
assert numpy.max(numpy.abs(vis_list[centre].vis)) < 1e-15, numpy.max(
numpy.abs(vis_list[centre].vis))
predicted_vis_list = [
dft_skycomponent_visibility(vis_list[freqwin],
self.components_list[freqwin])
for freqwin, _ in enumerate(self.frequency)
]
assert numpy.max(numpy.abs(predicted_vis_list[centre].vis)) > 0.0, \
numpy.max(numpy.abs(predicted_vis_list[centre].vis))
diff_vis_list = subtract_list_serial_workflow(self.bvis_list,
predicted_vis_list)
assert numpy.max(numpy.abs(
diff_vis_list[centre].vis)) < 1e-15, numpy.max(
numpy.abs(diff_vis_list[centre].vis))
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 = dft_skycomponent_visibility(self.vis, sc)
cvt = convert_blockvisibility_to_visibility(self.vis)
assert cvt.cindex is not None
def actualSetup(self, sky_pol_frame='stokesIQUV', data_pol_frame='linear'):
self.lowcore = create_named_configuration('LOWBD2', rmax=100.0)
self.times = (numpy.pi / 43200.0) * numpy.arange(0.0, 3000.0, 60.0)
vnchan = 3
self.frequency = numpy.linspace(1.0e8, 1.1e8, vnchan)
self.channel_bandwidth = numpy.array(
vnchan * [self.frequency[1] - self.frequency[0]])
# Define the component and give it some spectral behaviour
f = numpy.array([100.0, 20.0, -10.0, 1.0])
self.flux = numpy.array([f, 0.8 * f, 0.6 * f])
self.phasecentre = SkyCoord(ra=+180.0 * u.deg,
dec=-35.0 * u.deg,
frame='icrs',
equinox='J2000')
self.compabsdirection = SkyCoord(ra=+181.0 * u.deg,
dec=-35.0 * u.deg,
frame='icrs',
equinox='J2000')
if sky_pol_frame == 'stokesI':
self.flux = self.flux[:, 0][:, numpy.newaxis]
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 = dft_skycomponent_visibility(self.vis, self.comp)
def actualSetUp(self,
add_errors=False,
freqwin=3,
block=True,
dospectral=True,
dopol=False,
zerow=False,
makegcfcf=False):
self.npixel = 256
self.low = create_named_configuration('LOWBD2', rmax=750.0)
self.freqwin = freqwin
self.bvis_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])
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.bvis_list = [
ingest_unittest_visibility(
self.low,
numpy.array([self.frequency[freqwin]]),
numpy.array([self.channelwidth[freqwin]]),
self.times,
self.vis_pol,
self.phasecentre,
block=block,
zerow=zerow) for freqwin, _ in enumerate(self.frequency)
]
self.model_list = [
create_unittest_model(self.bvis_list[freqwin],
self.image_pol,
cellsize=self.cellsize,
npixel=self.npixel)
for freqwin, _ in enumerate(self.frequency)
]
self.components_list = [
create_unittest_components(self.model_list[freqwin],
flux[freqwin, :][numpy.newaxis, :],
single=False)
for freqwin, _ in enumerate(self.frequency)
]
self.model_list = [
insert_skycomponent(self.model_list[freqwin],
self.components_list[freqwin])
for freqwin, _ in enumerate(self.frequency)
]
self.bvis_list = [
dft_skycomponent_visibility(self.bvis_list[freqwin],
self.components_list[freqwin])
for freqwin, _ in enumerate(self.frequency)
]
centre = self.freqwin // 2
# Calculate the model convolved with a Gaussian.
self.model = self.model_list[centre]
self.cmodel = smooth_image(self.model)
if self.persist:
export_image_to_fits(self.model,
'%s/test_imaging_model.fits' % self.dir)
if self.persist:
export_image_to_fits(self.cmodel,
'%s/test_imaging_cmodel.fits' % self.dir)
if add_errors and block:
self.bvis_list = [
insert_unittest_errors(self.bvis_list[i])
for i, _ in enumerate(self.frequency)
]
self.components = self.components_list[centre]
if makegcfcf:
self.gcfcf = [
create_awterm_convolutionfunction(self.model,
nw=61,
wstep=16.0,
oversampling=8,
support=64,
use_aaf=True)
]
self.gcfcf_clipped = [
(self.gcfcf[0][0],
apply_bounding_box_convolutionfunction(self.gcfcf[0][1],
fractional_level=1e-3))
]
self.gcfcf_joint = [
create_awterm_convolutionfunction(self.model,
nw=11,
wstep=16.0,
oversampling=8,
support=64,
use_aaf=True)
]
else:
self.gcfcf = None
self.gcfcf_clipped = None
self.gcfcf_joint = None
def actualSetUp(self,
add_errors=False,
freqwin=7,
block=False,
dospectral=True,
dopol=False,
zerow=True):
self.npixel = 256
self.low = create_named_configuration('LOWBD2', rmax=750.0)
self.freqwin = freqwin
self.vis_list = list()
self.ntimes = 5
cellsize = 0.001
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 freqwin > 1:
self.channelwidth = numpy.array(
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.vis_list = [
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.model_imagelist = [
create_unittest_model(self.vis_list[freqwin],
self.image_pol,
cellsize=cellsize,
npixel=self.npixel)
for freqwin, _ in enumerate(self.frequency)
]
self.componentlist = [
create_unittest_components(self.model_imagelist[freqwin],
flux[freqwin, :][numpy.newaxis, :])
for freqwin, _ in enumerate(self.frequency)
]
self.model_imagelist = [
insert_skycomponent(self.model_imagelist[freqwin],
self.componentlist[freqwin])
for freqwin, _ in enumerate(self.frequency)
]
self.vis_list = [
dft_skycomponent_visibility(self.vis_list[freqwin],
self.componentlist[freqwin])
for freqwin, _ in enumerate(self.frequency)
]
# Calculate the model convolved with a Gaussian.
model = self.model_imagelist[0]
self.cmodel = smooth_image(model)
if self.persist:
export_image_to_fits(
model,
'%s/test_imaging_serial_deconvolved_model.fits' % self.dir)
if self.persist:
export_image_to_fits(
self.cmodel,
'%s/test_imaging_serial_deconvolved_cmodel.fits' % self.dir)
if add_errors and block:
self.vis_list = [
insert_unittest_errors(self.vis_list[i])
for i, _ in enumerate(self.frequency)
]
def actualSetUp(self,
zerow=True,
block=False,
image_pol=PolarisationFrame("stokesIQUV")):
self.doplot = False
self.npixel = 256
self.cellsize = 0.0009
self.low = create_named_configuration('LOWBD2', rmax=750.0)
self.freqwin = 1
self.vis_list = list()
self.ntimes = 3
self.times = numpy.linspace(-2.0, +2.0, self.ntimes) * numpy.pi / 12.0
if self.freqwin == 1:
self.frequency = numpy.array([1e8])
self.channelwidth = numpy.array([4e7])
else:
self.frequency = numpy.linspace(0.8e8, 1.2e8, self.freqwin)
self.channelwidth = numpy.array(
self.freqwin * [self.frequency[1] - self.frequency[0]])
self.image_pol = image_pol
if image_pol == PolarisationFrame("stokesI"):
self.vis_pol = PolarisationFrame("stokesI")
f = numpy.array([100.0])
elif image_pol == PolarisationFrame("stokesIQUV"):
self.vis_pol = PolarisationFrame("linear")
f = numpy.array([100.0, 20.0, -10.0, 1.0])
elif image_pol == PolarisationFrame("stokesIQ"):
self.vis_pol = PolarisationFrame("linearnp")
f = numpy.array([100.0, 20.0])
elif image_pol == PolarisationFrame("stokesIV"):
self.vis_pol = PolarisationFrame("circularnp")
f = numpy.array([100.0, 20.0])
else:
raise ValueError("Polarisation {} not supported".format(image_pol))
flux = numpy.array(
[f * numpy.power(freq / 1e8, -0.7) for freq in self.frequency])
self.phasecentre = SkyCoord(ra=+180.0 * u.deg,
dec=-60.0 * u.deg,
frame='icrs',
equinox='J2000')
self.vis = ingest_unittest_visibility(self.low,
self.frequency,
self.channelwidth,
self.times,
self.vis_pol,
self.phasecentre,
block=block,
zerow=zerow)
self.model = create_unittest_model(self.vis,
self.image_pol,
cellsize=self.cellsize,
npixel=self.npixel,
nchan=self.freqwin)
self.components = create_unittest_components(self.model,
flux,
applypb=False,
scale=0.5,
single=False,
symmetric=True)
self.model = insert_skycomponent(self.model, self.components)
self.vis = dft_skycomponent_visibility(self.vis, 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_gridding_model.fits' % self.dir)
export_image_to_fits(self.cmodel,
'%s/test_gridding_cmodel.fits' % self.dir)
pb = create_pb_generic(self.model,
diameter=35.0,
blockage=0.0,
use_local=False)
self.cmodel.data *= pb.data
if self.persist:
export_image_to_fits(self.cmodel,
'%s/test_gridding_cmodel_pb.fits' % self.dir)
self.peak = numpy.unravel_index(
numpy.argmax(numpy.abs(self.cmodel.data)), self.cmodel.shape)
def actualSetUp(self,
freqwin=1,
block=True,
dospectral=True,
image_pol=PolarisationFrame('stokesI'),
zerow=False,
mfs=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(0.8e8, 1.2e8, 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 image_pol == PolarisationFrame('stokesIQUV'):
self.blockvis_pol = PolarisationFrame('linear')
self.image_pol = image_pol
f = numpy.array([100.0, 20.0, -10.0, 1.0])
elif image_pol == PolarisationFrame('stokesIQ'):
self.blockvis_pol = PolarisationFrame('linearnp')
self.image_pol = image_pol
f = numpy.array([100.0, 20.0])
elif image_pol == PolarisationFrame('stokesIV'):
self.blockvis_pol = PolarisationFrame('circularnp')
self.image_pol = image_pol
f = numpy.array([100.0, 20.0])
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.model = create_unittest_model(self.blockvis,
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 = dft_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)
if mfs:
self.model = create_unittest_model(self.blockvis,
self.image_pol,
npixel=self.npixel,
nchan=1)
def ingest_visibility(self,
freq=None,
chan_width=None,
times=None,
add_errors=False,
block=True,
bandpass=False):
if freq is None:
freq = [1e8]
if chan_width is None:
chan_width = [1e6]
if times is None:
times = (numpy.pi / 12.0) * numpy.linspace(-3.0, 3.0, 5)
lowcore = create_named_configuration('LOWBD2', rmax=750.0)
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=frequency,
phasecentre=phasecentre,
polarisation_frame=PolarisationFrame("stokesI"))
nchan = len(self.frequency)
flux = numpy.array(nchan * [[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(
direction=sc,
flux=flux,
frequency=frequency,
polarisation_frame=PolarisationFrame("stokesI"))
comps.append(comp)
if block:
dft_skycomponent_visibility(vt, comps)
else:
dft_skycomponent_visibility(vt, comps)
insert_skycomponent(model, comps)
self.comps = comps
self.model = copy_image(model)
self.empty_model = create_empty_image_like(model)
export_image_to_fits(
model, '%s/test_pipeline_functions_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)
if bandpass:
bgt = create_gaintable_from_blockvisibility(vt, timeslice=1e5)
bgt = simulate_gaintable(bgt,
phase_error=0.01,
amplitude_error=0.01,
smooth_channels=4)
vt = apply_gaintable(vt, bgt)
return vt
def actualSetUp(self, add_errors=False, nfreqwin=7, dospectral=True, dopol=False, zerow=True):
self.npixel = 512
self.low = create_named_configuration('LOWBD2', rmax=750.0)
self.freqwin = nfreqwin
self.vis_list = list()
self.ntimes = 5
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, 0.0, 0.0])
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 = [ingest_unittest_visibility(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.vis_list = [convert_blockvisibility_to_visibility(bv) for bv in self.blockvis_list]
self.model_imagelist = [
create_unittest_model(self.vis_list[i], self.image_pol, npixel=self.npixel, cellsize=0.0005)
for i in range(nfreqwin)]
self.components_list = [create_unittest_components(self.model_imagelist[freqwin],
flux[freqwin, :][numpy.newaxis, :])
for freqwin, m in enumerate(self.model_imagelist)]
self.blockvis_list = [
dft_skycomponent_visibility(self.blockvis_list[freqwin], self.components_list[freqwin])
for freqwin, _ in enumerate(self.blockvis_list)]
self.model_imagelist = [insert_skycomponent(self.model_imagelist[freqwin], self.components_list[freqwin])
for freqwin in range(nfreqwin)]
model = self.model_imagelist[0]
self.cmodel = smooth_image(model)
if self.persist:
export_image_to_fits(model, '%s/test_imaging_serial_model.fits' % self.dir)
export_image_to_fits(self.cmodel, '%s/test_imaging_serial_cmodel.fits' % self.dir)
if add_errors:
gt = create_gaintable_from_blockvisibility(self.blockvis_list[0])
gt = simulate_gaintable(gt, phase_error=0.1, amplitude_error=0.0, smooth_channels=1, leakage=0.0)
self.blockvis_list = [apply_gaintable(self.blockvis_list[i], gt)
for i in range(self.freqwin)]
self.vis_list = [convert_blockvisibility_to_visibility(bv) for bv in self.blockvis_list]
self.model_imagelist = [
create_unittest_model(self.vis_list[i], self.image_pol, npixel=self.npixel, cellsize=0.0005)
for i in range(nfreqwin)]
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 = dft_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
