def createVis(self, config='MID', dec=-35.0, rmax=1e3, freq=1e9):
self.frequency = numpy.linspace(freq, 1.5 * freq, 3)
self.channel_bandwidth = numpy.array([2.5e7, 2.5e7, 2.5e7])
self.flux = numpy.array([[100.0], [100.0], [100.0]])
self.phasecentre = SkyCoord(ra=+15.0 * u.deg,
dec=-35.0 * u.deg,
frame='icrs',
equinox='J2000')
self.config = create_named_configuration(config)
self.times = numpy.linspace(-300.0, 300.0, 3) * numpy.pi / 43200.0
nants = self.config.xyz.shape[0]
assert nants > 1
assert len(self.config.names) == nants
assert len(self.config.mount) == nants
self.config = create_named_configuration(config, rmax=rmax)
self.phasecentre = SkyCoord(ra=+15 * u.deg,
dec=dec * u.deg,
frame='icrs',
equinox='J2000')
self.vis = create_visibility(
self.config,
self.times,
self.frequency,
channel_bandwidth=self.channel_bandwidth,
phasecentre=self.phasecentre,
weight=1.0,
polarisation_frame=PolarisationFrame('stokesI'))
def setUp(self):
from data_models.parameters import arl_path
self.dir = arl_path('test_results')
self.vnchan = 7
self.lowcore = create_named_configuration('LOWBD2', rmax=300.0)
self.times = (numpy.pi / 12.0) * numpy.linspace(-3.0, 3.0, 7)
self.frequency = numpy.linspace(8e7, 1.2e8, self.vnchan)
self.startfrequency = numpy.array([8e7])
self.channel_bandwidth = numpy.array(
self.vnchan * [(1.0 - 1.0e-7) *
(self.frequency[1] - self.frequency[0])])
self.phasecentre = SkyCoord(ra=+180.0 * u.deg,
dec=-60.0 * u.deg,
frame='icrs',
equinox='J2000')
self.vis = create_visibility(
self.lowcore,
times=self.times,
frequency=self.frequency,
phasecentre=self.phasecentre,
weight=1.0,
polarisation_frame=PolarisationFrame('stokesI'),
channel_bandwidth=self.channel_bandwidth)
self.model = create_image_from_visibility(
self.vis,
npixel=128,
cellsize=0.001,
nchan=self.vnchan,
frequency=self.startfrequency)
def actualSetup(self):
self.lowcore = create_named_configuration('LOWBD2-CORE')
self.times = (numpy.pi / 43200.0) * numpy.arange(0.0, 300.0, 30.0)
self.times = [0.0]
self.frequency = numpy.linspace(1.0e8, 1.1e8, 1)
self.channel_bandwidth = numpy.array([1e7])
# 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])
f = numpy.array([100.0])
self.flux = numpy.array([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.comp_actual_direction = SkyCoord(ra=+180.2 * u.deg,
dec=-35.1 * u.deg,
frame='icrs',
equinox='J2000')
self.comp_start_direction = SkyCoord(ra=+180.0 * u.deg,
dec=-35.0 * u.deg,
frame='icrs',
equinox='J2000')
self.comp = Skycomponent(
direction=self.comp_actual_direction,
frequency=self.frequency,
flux=self.flux,
polarisation_frame=PolarisationFrame("stokesI"))
def setUp(self):
client = get_dask_Client(memory_limit=4 * 1024 * 1024 * 1024,
n_workers=4,
dashboard_address=None)
global arlexecute
arlexecute = ARLExecuteBase(use_dask=True)
arlexecute.set_client(client, verbose=True)
from data_models.parameters import arl_path
self.dir = arl_path('test_results')
self.frequency = numpy.linspace(1e8, 1.5e8, 3)
self.channel_bandwidth = numpy.array([2.5e7, 2.5e7, 2.5e7])
self.flux = numpy.array([[100.0], [100.0], [100.0]])
self.phasecentre = SkyCoord(ra=+15.0 * u.deg,
dec=-35.0 * u.deg,
frame='icrs',
equinox='J2000')
self.config = create_named_configuration('LOWBD2-CORE')
self.times = numpy.linspace(-300.0, 300.0, 3) * numpy.pi / 43200.0
nants = self.config.xyz.shape[0]
assert nants > 1
assert len(self.config.names) == nants
assert len(self.config.mount) == nants
self.persist = False
def setUp(self):
from data_models.parameters import arl_path
self.dir = arl_path('test_results')
self.lowcore = create_named_configuration('LOWBD2-CORE')
self.times = (numpy.pi / (12.0)) * numpy.linspace(-3.0, 3.0, 7)
self.frequency = numpy.array([1e8])
self.channel_bandwidth = numpy.array([1e6])
self.phasecentre = SkyCoord(ra=+180.0 * u.deg,
dec=-60.0 * u.deg,
frame='icrs',
equinox='J2000')
self.vis = create_visibility(
self.lowcore,
self.times,
self.frequency,
channel_bandwidth=self.channel_bandwidth,
phasecentre=self.phasecentre,
weight=1.0,
polarisation_frame=PolarisationFrame('stokesI'),
zerow=True)
self.vis.data['vis'] *= 0.0
# Create model
self.test_model = create_test_image(cellsize=0.001,
phasecentre=self.vis.phasecentre,
frequency=self.frequency)
self.vis = predict_2d(self.vis, self.test_model)
assert numpy.max(numpy.abs(self.vis.vis)) > 0.0
self.model = create_image_from_visibility(
self.vis,
npixel=512,
cellsize=0.001,
polarisation_frame=PolarisationFrame('stokesI'))
self.dirty, sumwt = invert_2d(self.vis, self.model)
self.psf, sumwt = invert_2d(self.vis, self.model, dopsf=True)
def setUp(self):
from data_models.parameters import arl_path
dec = -40.0 * u.deg
self.lowcore = create_named_configuration('LOWBD2', rmax=300.0)
self.dir = arl_path('test_results')
self.times = numpy.linspace(-10.0, 10.0,
3) * numpy.pi / (3600.0 * 12.0)
self.frequency = numpy.array([1e8])
self.channel_bandwidth = numpy.array([1e6])
self.phasecentre = SkyCoord(ra=+0.0 * u.deg,
dec=dec,
frame='icrs',
equinox='J2000')
self.vis = create_blockvisibility(
self.lowcore,
self.times,
self.frequency,
channel_bandwidth=self.channel_bandwidth,
phasecentre=self.phasecentre,
weight=1.0,
polarisation_frame=PolarisationFrame('stokesI'))
self.vis.data['vis'] *= 0.0
# Create model
self.model = create_image(
npixel=512,
cellsize=0.000015,
polarisation_frame=PolarisationFrame("stokesI"),
frequency=self.frequency,
channel_bandwidth=self.channel_bandwidth,
phasecentre=self.phasecentre)
def createVis(self, config, dec=-35.0, rmax=None):
self.config = create_named_configuration(config, rmax=rmax)
self.phasecentre = SkyCoord(ra=+15 * u.deg, dec=dec * u.deg, frame='icrs', equinox='J2000')
self.vis = create_visibility(self.config, self.times, self.frequency,
channel_bandwidth=self.channel_bandwidth,
phasecentre=self.phasecentre, weight=1.0,
polarisation_frame=PolarisationFrame('stokesI'))
def actualSetUp(self, add_errors=False, freqwin=1, block=False, dospectral=True, dopol=False):
self.npixel = 256
self.low = create_named_configuration('LOWBD2', rmax=750.0)
self.freqwin = freqwin
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 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 = ingest_unittest_visibility(self.low, self.frequency, self.channelwidth, self.times,
self.vis_pol, self.phasecentre, block=block)
self.model = create_unittest_model(self.vis, self.image_pol, npixel=self.npixel)
def setUp(self):
from data_models.parameters import arl_path
self.dir = arl_path('test_results/')
self.midcore = create_named_configuration('MID', rmax=3000.0)
self.times = (numpy.pi / 43200.0) * numpy.arange(0.0, 300.0, 100.0)
self.frequency = numpy.linspace(1.0e9, 1.1e9, 3)
self.channel_bandwidth = numpy.array([1e7, 1e7, 1e7])
# 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])
# 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)
def setUp(self):
from data_models.parameters import arl_path
self.doplot = True
self.midcore = create_named_configuration('MID', rmax=100.0)
self.nants = len(self.midcore.names)
self.dir = arl_path('test_results')
self.ntimes = 100
interval = 10.0
self.times = numpy.arange(0.0, float(self.ntimes)) * interval
self.times *= numpy.pi / 43200.0
self.frequency = numpy.array([1.4e9])
self.channel_bandwidth = numpy.array([1e7])
self.phasecentre = SkyCoord(ra=+15.0 * u.deg, dec=-45.0 * u.deg, frame='icrs', equinox='J2000')
self.vis = create_blockvisibility(self.midcore, self.times, self.frequency,
channel_bandwidth=self.channel_bandwidth,
phasecentre=self.phasecentre, weight=1.0,
polarisation_frame=PolarisationFrame('stokesI'))
self.vis.data['vis'] *= 0.0
# Create model
self.model = create_image(npixel=512, cellsize=0.001, polarisation_frame=PolarisationFrame("stokesI"),
frequency=self.frequency, channel_bandwidth=self.channel_bandwidth,
phasecentre=self.phasecentre)
def actualSetUp(self,
time=None,
frequency=None,
dospectral=False,
dopol=False):
self.lowcore = create_named_configuration('LOWBD2', rmax=600)
self.times = (numpy.pi / 12.0) * numpy.linspace(-3.0, 3.0, 5)
if time is not None:
self.times = time
log.info("Times are %s" % (self.times))
if dospectral:
self.nchan = 3
self.frequency = numpy.array([0.9e8, 1e8, 1.1e8])
self.channel_bandwidth = numpy.array([1e7, 1e7, 1e7])
else:
self.frequency = numpy.array([1e8])
self.channel_bandwidth = numpy.array([1e7])
if dopol:
self.vis_pol = PolarisationFrame('linear')
self.image_pol = PolarisationFrame('stokesIQUV')
else:
self.vis_pol = PolarisationFrame('stokesI')
self.image_pol = PolarisationFrame('stokesI')
if dopol:
f = numpy.array([100.0, 20.0, -10.0, 1.0])
else:
f = numpy.array([100.0])
if dospectral:
numpy.array([f, 0.8 * f, 0.6 * f])
else:
numpy.array([f])
self.phasecentre = SkyCoord(ra=+180.0 * u.deg,
dec=-60.0 * u.deg,
frame='icrs',
equinox='J2000')
self.componentvis = create_visibility(
self.lowcore,
self.times,
self.frequency,
channel_bandwidth=self.channel_bandwidth,
phasecentre=self.phasecentre,
weight=1.0,
polarisation_frame=self.vis_pol)
self.uvw = self.componentvis.data['uvw']
self.componentvis.data['vis'] *= 0.0
# Create model
self.model = create_image_from_visibility(
self.componentvis,
npixel=self.npixel,
cellsize=0.0005,
nchan=len(self.frequency),
polarisation_frame=self.image_pol)
def setUp(self):
self.lowcore = create_named_configuration('LOWBD2-CORE')
self.times = numpy.linspace(-300.0, 300.0, 11) * numpy.pi / 43200.0
self.frequency = numpy.array([1e8])
self.channel_bandwidth = numpy.array([1e8])
self.phasecentre = SkyCoord(ra=+15.0 * u.deg, dec=-35.0 * u.deg, frame='icrs', equinox='J2000')
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 = [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)]
for v in self.blockvis_list:
v.data['vis'][...] = 1.0 + 0.0j
self.error_blockvis_list = [copy_visibility(v) for v in self.blockvis_list]
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.error_blockvis_list = [apply_gaintable(self.error_blockvis_list[i], gt)
for i in range(self.freqwin)]
assert numpy.max(numpy.abs(self.error_blockvis_list[0].vis - self.blockvis_list[0].vis)) > 0.0
def setUp(self):
self.lowcore = create_named_configuration('LOWBD2', rmax=300.0)
self.times = (numpy.pi / 43200.0) * numpy.arange(0.0, 30 * 3.76, 3.76)
df = 27343.75000
self.frequency = numpy.array([1e8 - df, 1e8, 1e8 + df])
self.channel_bandwidth = numpy.array([27343.75, 27343.75, 27343.75])
self.phasecentre = SkyCoord(ra=+0.0 * u.deg, dec=-35.0 * u.deg, frame='icrs', equinox='J2000')
self.blockvis = create_blockvisibility(self.lowcore, self.times, self.frequency, phasecentre=self.phasecentre,
weight=1.0, polarisation_frame=PolarisationFrame('stokesI'),
channel_bandwidth=self.channel_bandwidth, meta={"ARL":0.9})
def actualSetUp(self, freqwin=1, block=False, dospectral=True, dopol=False, zerow=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 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 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 = 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, npixel=self.npixel)
self.components = create_unittest_components(self.model, flux)
self.model = insert_skycomponent(self.model, self.components)
self.vis = predict_skycomponent_visibility(self.vis, self.components)
# Calculate the model convolved with a Gaussian.
self.cmodel = smooth_image(self.model)
export_image_to_fits(self.model, '%s/test_imaging_model.fits' % self.dir)
export_image_to_fits(self.cmodel, '%s/test_imaging_cmodel.fits' % self.dir)
def actualSetUp(self, zerow=True):
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.vis_pol = PolarisationFrame('linear')
self.image_pol = PolarisationFrame('stokesIQUV')
f = numpy.array([100.0, 20.0, -10.0, 1.0])
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=False,
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 = predict_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)
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 setUp(self):
from data_models.parameters import arl_path
self.dir = arl_path('test_results')
self.frequency = numpy.linspace(0.8e8, 1.2e8, 5)
self.channel_bandwidth = numpy.array([1e7, 1e7, 1e7, 1e7, 1e7])
self.flux = numpy.array([[100.0], [100.0], [100.0], [100.0], [100.0]])
self.phasecentre = SkyCoord(ra=+15.0 * u.deg, dec=-35.0 * u.deg, frame='icrs', equinox='J2000')
self.config = create_named_configuration('LOWBD2-CORE')
self.times = numpy.linspace(-300.0, 300.0, 3) * numpy.pi / 43200.0
nants = self.config.xyz.shape[0]
assert nants > 1
assert len(self.config.names) == nants
assert len(self.config.mount) == nants
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 = predict_skycomponent_visibility(self.vis, self.comp)
def setUp(self):
self.lowcore = create_named_configuration('LOWBD2-CORE')
self.times = (numpy.pi / 43200.0) * numpy.arange(0.0, 300.0, 30.0)
self.frequency = numpy.linspace(1.0e8, 1.1e8, 3)
self.channel_bandwidth = numpy.array([1e7, 1e7, 1e7])
# 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])
# 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')
pcof = self.phasecentre.skyoffset_frame()
self.compreldirection = self.compabsdirection.transform_to(pcof)
self.comp = Skycomponent(direction=self.compreldirection, frequency=self.frequency, flux=self.flux)
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 test_rfi(self):
sample_freq = 3e4
nchannels = 1000
frequency = 170.5e6 + numpy.arange(nchannels) * sample_freq
ntimes = 100
integration_time = 0.5
times = numpy.arange(ntimes) * integration_time
# Perth from Google for the moment
perth = EarthLocation(lon="115.8605", lat="-31.9505", height=0.0)
rmax = 1000.0
low = create_named_configuration('LOWR3', rmax=rmax)
antskip = 33
low.data = low.data[::antskip]
nants = len(low.names)
# Calculate the power spectral density of the DTV station: Watts/Hz
emitter = simulate_DTV(frequency,
times,
power=50e3,
timevariable=False)
numpy.testing.assert_almost_equal(numpy.max(numpy.abs(emitter)),
0.00166834)
assert emitter.shape == (ntimes, nchannels)
# Calculate the propagators for signals from Perth to the stations in low
# These are fixed in time but vary with frequency. The ad hoc attenuation
# is set to produce signal roughly equal to noise at LOW
attenuation = 1.0
propagators = create_propagators(low,
perth,
frequency=frequency,
attenuation=attenuation)
assert propagators.shape == (nants, nchannels), propagators.shape
# Now calculate the RFI at the stations, based on the emitter and the propagators
rfi_at_station = calculate_rfi_at_station(propagators, emitter)
assert rfi_at_station.shape == (ntimes, nants,
nchannels), rfi_at_station.shape
# Calculate the rfi correlation
# [nants, nants, ntimes, nchan]
correlation = calculate_station_correlation_rfi(rfi_at_station)
assert correlation.shape == (ntimes, nants, nants, nchannels,
1), correlation.shape
def actualSetUp(self, freqwin=1, block=False, dopol=False, zerow=False):
self.npixel = 1024
self.low = create_named_configuration('LOWBD2', rmax=750.0)
self.freqwin = freqwin
self.vis_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=+30.0 * u.deg,
dec=-60.0 * u.deg,
frame='icrs',
equinox='J2000')
self.vis_list = [
arlexecute.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)
]
def setUp(self):
from data_models.parameters import arl_path
self.lowcore = create_named_configuration('LOWBD2-CORE')
self.dir = arl_path('test_results')
self.times = (numpy.pi / 12.0) * numpy.linspace(-3.0, 3.0, 7)
self.image_frequency = numpy.linspace(0.9e8, 1.1e8, 5)
self.component_frequency = numpy.linspace(0.8e8, 1.2e8, 7)
self.channel_bandwidth = numpy.array(5 * [1e7])
self.phasecentre = SkyCoord(ra=+180.0 * u.deg,
dec=-60.0 * u.deg,
frame='icrs',
equinox='J2000')
self.vis = create_visibility(
self.lowcore,
self.times,
self.image_frequency,
channel_bandwidth=self.channel_bandwidth,
phasecentre=self.phasecentre,
weight=1.0,
polarisation_frame=PolarisationFrame('stokesI'),
zerow=True)
self.vis.data['vis'] *= 0.0
# Create model
self.model = create_test_image(cellsize=0.0015,
phasecentre=self.vis.phasecentre,
frequency=self.image_frequency)
self.model.data[self.model.data > 1.0] = 1.0
self.vis = predict_2d(self.vis, self.model)
assert numpy.max(numpy.abs(self.vis.vis)) > 0.0
dphasecentre = SkyCoord(ra=+181.0 * u.deg,
dec=-58.0 * u.deg,
frame='icrs',
equinox='J2000')
flux = [[numpy.power(f / 1e8, -0.7)] for f in self.component_frequency]
self.sc = create_skycomponent(
direction=dphasecentre,
flux=flux,
frequency=self.component_frequency,
polarisation_frame=PolarisationFrame('stokesI'))
def setUp(self):
from data_models.parameters import arl_path
self.lowcore = create_named_configuration('LOWBD2', rmax=300.0)
self.dir = arl_path('test_results')
self.times = (numpy.pi / 12.0) * numpy.linspace(-3.0, 3.0, 7)
self.frequency = numpy.array([1e8])
self.channel_bandwidth = numpy.array([1e6])
self.phasecentre = SkyCoord(ra=+180.0 * u.deg, dec=-60.0 * u.deg, frame='icrs', equinox='J2000')
self.vis = create_blockvisibility(self.lowcore, self.times, self.frequency,
channel_bandwidth=self.channel_bandwidth,
phasecentre=self.phasecentre, weight=1.0,
polarisation_frame=PolarisationFrame('stokesI'))
self.vis.data['vis'] *= 0.0
# Create model
self.model = create_test_image(cellsize=0.0015, phasecentre=self.vis.phasecentre,
frequency=self.frequency)
self.model.data[self.model.data > 1.0] = 1.0
self.mask = create_test_image(cellsize=0.0015, phasecentre=self.vis.phasecentre,
frequency=self.frequency)
self.mask.data[self.mask.data > 0.1] = 1.0
self.mask.data[self.mask.data <= 0.1] = 0.0
def actualSetup(self, sky_pol_frame='stokesIQUV', data_pol_frame='linear'):
self.lowcore = create_named_configuration('LOWBD2', rmax=300.0)
self.times = (numpy.pi / 43200.0) * numpy.arange(0.0, 3000.0, 30.0)
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 = predict_skycomponent_visibility(self.vis, self.comp)
def setUp(self):
from data_models.parameters import arl_path
self.dir = arl_path('test_results')
self.persist = False
self.niter = 1000
self.lowcore = create_named_configuration('LOWBD2-CORE')
self.nchan = 5
self.times = (numpy.pi / 12.0) * numpy.linspace(-3.0, 3.0, 7)
self.frequency = numpy.linspace(0.9e8, 1.1e8, self.nchan)
self.channel_bandwidth = numpy.array(self.nchan * [self.frequency[1] - self.frequency[0]])
self.phasecentre = SkyCoord(ra=+0.0 * u.deg, dec=-45.0 * u.deg, frame='icrs', equinox='J2000')
self.vis = create_visibility(self.lowcore, self.times, self.frequency, self.channel_bandwidth,
phasecentre=self.phasecentre, weight=1.0,
polarisation_frame=PolarisationFrame('stokesI'), zerow=True)
self.vis.data['vis'] *= 0.0
# Create model
self.test_model = create_low_test_image_from_gleam(npixel=512, cellsize=0.001,
phasecentre=self.vis.phasecentre,
frequency=self.frequency,
channel_bandwidth=self.channel_bandwidth,
flux_limit=1.0)
beam = create_low_test_beam(self.test_model)
if self.persist: export_image_to_fits(beam, "%s/test_deconvolve_mmclean_beam.fits" % self.dir)
self.test_model.data *= beam.data
if self.persist: export_image_to_fits(self.test_model, "%s/test_deconvolve_mmclean_model.fits" % self.dir)
self.vis = predict_2d(self.vis, self.test_model)
assert numpy.max(numpy.abs(self.vis.vis)) > 0.0
self.model = create_image_from_visibility(self.vis, npixel=512, cellsize=0.001,
polarisation_frame=PolarisationFrame('stokesI'))
self.dirty, sumwt = invert_2d(self.vis, self.model)
self.psf, sumwt = invert_2d(self.vis, self.model, dopsf=True)
if self.persist: export_image_to_fits(self.dirty, "%s/test_deconvolve_mmclean-dirty.fits" % self.dir)
if self.persist: export_image_to_fits(self.psf, "%s/test_deconvolve_mmclean-psf.fits" % self.dir)
window = numpy.ones(shape=self.model.shape, dtype=numpy.bool)
window[..., 129:384, 129:384] = True
self.innerquarter = create_image_from_array(window, self.model.wcs, polarisation_frame=PolarisationFrame('stokesI'))
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 = [
arlexecute.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 = arlexecute.compute(self.blockvis_list, sync=True)
self.vis_list = [
arlexecute.execute(convert_blockvisibility_to_visibility)(bv)
for bv in self.blockvis_list
]
self.vis_list = arlexecute.compute(self.vis_list, sync=True)
self.skymodel_list = [
arlexecute.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 = arlexecute.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 actualSetup(self, nsources=None, nvoronoi=None):
n_workers = 8
# Set up the observation: 10 minutes at transit, with 10s integration.
# Skip 5/6 points to avoid outstation redundancy
nfreqwin = 1
ntimes = 3
self.rmax = 2500.0
dec = -40.0 * u.deg
frequency = [1e8]
channel_bandwidth = [0.1e8]
times = numpy.linspace(-10.0, 10.0,
ntimes) * numpy.pi / (3600.0 * 12.0)
phasecentre = SkyCoord(ra=+0.0 * u.deg,
dec=dec,
frame='icrs',
equinox='J2000')
low = create_named_configuration('LOWBD2', rmax=self.rmax)
centre = numpy.mean(low.xyz, axis=0)
distance = numpy.hypot(low.xyz[:, 0] - centre[0],
low.xyz[:, 1] - centre[1],
low.xyz[:, 2] - centre[2])
lowouter = low.data[distance > 1000.0][::6]
lowcore = low.data[distance < 1000.0][::3]
low.data = numpy.hstack((lowcore, lowouter))
blockvis = create_blockvisibility(
low,
times,
frequency=frequency,
channel_bandwidth=channel_bandwidth,
weight=1.0,
phasecentre=phasecentre,
polarisation_frame=PolarisationFrame("stokesI"),
zerow=True)
vis = convert_blockvisibility_to_visibility(blockvis)
advice = advise_wide_field(vis, guard_band_image=2.0, delA=0.02)
cellsize = advice['cellsize']
npixel = advice['npixels2']
small_model = create_image_from_visibility(blockvis,
npixel=512,
frequency=frequency,
nchan=nfreqwin,
cellsize=cellsize,
phasecentre=phasecentre)
vis.data['imaging_weight'][...] = vis.data['weight'][...]
vis = weight_list_serial_workflow([vis], [small_model])[0]
vis = taper_list_serial_workflow([vis], 3 * cellsize)[0]
blockvis = convert_visibility_to_blockvisibility(vis)
# ### Generate the model from the GLEAM catalog, including application of the primary beam.
beam = create_image_from_visibility(blockvis,
npixel=npixel,
frequency=frequency,
nchan=nfreqwin,
cellsize=cellsize,
phasecentre=phasecentre)
beam = create_low_test_beam(beam, use_local=False)
flux_limit = 0.5
original_gleam_components = create_low_test_skycomponents_from_gleam(
flux_limit=flux_limit,
phasecentre=phasecentre,
frequency=frequency,
polarisation_frame=PolarisationFrame('stokesI'),
radius=0.15)
all_components = apply_beam_to_skycomponent(original_gleam_components,
beam)
all_components = filter_skycomponents_by_flux(all_components,
flux_min=flux_limit)
voronoi_components = filter_skycomponents_by_flux(all_components,
flux_min=1.5)
def max_flux(elem):
return numpy.max(elem.flux)
voronoi_components = sorted(voronoi_components,
key=max_flux,
reverse=True)
if nsources is not None:
all_components = [all_components[0]]
if nvoronoi is not None:
voronoi_components = [voronoi_components[0]]
self.screen = import_image_from_fits(
arl_path('data/models/test_mpc_screen.fits'))
all_gaintables = create_gaintable_from_screen(blockvis, all_components,
self.screen)
gleam_skymodel_noniso = [
SkyModel(components=[all_components[i]],
gaintable=all_gaintables[i])
for i, sm in enumerate(all_components)
]
# ### Now predict the visibility for each skymodel and apply the gaintable for that skymodel,
# returning a list of visibilities, one for each skymodel. We then sum these to obtain
# the total predicted visibility. All images and skycomponents in the same skymodel
# get the same gaintable applied which means that in this case each skycomponent has a separate gaintable.
self.all_skymodel_noniso_vis = convert_blockvisibility_to_visibility(
blockvis)
ngroup = n_workers
future_vis = arlexecute.scatter(self.all_skymodel_noniso_vis)
chunks = [
gleam_skymodel_noniso[i:i + ngroup]
for i in range(0, len(gleam_skymodel_noniso), ngroup)
]
for chunk in chunks:
result = predict_skymodel_list_arlexecute_workflow(future_vis,
chunk,
context='2d',
docal=True)
work_vis = arlexecute.compute(result, sync=True)
for w in work_vis:
self.all_skymodel_noniso_vis.data['vis'] += w.data['vis']
assert numpy.max(
numpy.abs(self.all_skymodel_noniso_vis.data['vis'])) > 0.0
self.all_skymodel_noniso_blockvis = convert_visibility_to_blockvisibility(
self.all_skymodel_noniso_vis)
# ### Remove weaker of components that are too close (0.02 rad)
idx, voronoi_components = remove_neighbouring_components(
voronoi_components, 0.02)
model = create_image_from_visibility(blockvis,
npixel=npixel,
frequency=frequency,
nchan=nfreqwin,
cellsize=cellsize,
phasecentre=phasecentre)
# Use the gaintable for the brightest component as the starting gaintable
all_gaintables[0].gain[...] = numpy.conjugate(
all_gaintables[0].gain[...])
all_gaintables[0].gain[...] = 1.0 + 0.0j
self.theta_list = initialize_skymodel_voronoi(model,
voronoi_components,
all_gaintables[0])
def actualSetUp(self,
add_errors=False,
freqwin=3,
block=False,
dospectral=True,
dopol=False,
zerow=False,
makegcfcf=False):
self.npixel = 256
self.low = create_named_configuration('LOWBD2', rmax=750.0)
self.freqwin = freqwin
self.vis_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.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_list = [
create_unittest_model(self.vis_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=True)
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.vis_list = [
predict_skycomponent_visibility(self.vis_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)
export_image_to_fits(self.model,
'%s/test_imaging_model.fits' % self.dir)
export_image_to_fits(self.cmodel,
'%s/test_imaging_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)
]
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 simulate_list_arlexecute_workflow(
config='LOWBD2',
phasecentre=SkyCoord(ra=+15.0 * u.deg,
dec=-60.0 * u.deg,
frame='icrs',
equinox='J2000'),
frequency=None,
channel_bandwidth=None,
times=None,
polarisation_frame=PolarisationFrame("stokesI"),
order='frequency',
format='blockvis',
rmax=1000.0,
zerow=False):
""" A component to simulate an observation
The simulation step can generate a single BlockVisibility or a list of BlockVisibility's.
The parameter keyword determines the way that the list is constructed.
If order='frequency' then len(frequency) BlockVisibility's with all times are created.
If order='time' then len(times) BlockVisibility's with all frequencies are created.
If order = 'both' then len(times) * len(times) BlockVisibility's are created each with
a single time and frequency. If order = None then all data are created in one BlockVisibility.
The output format can be either 'blockvis' (for calibration) or 'vis' (for imaging)
:param config: Name of configuration: def LOWBDS-CORE
:param phasecentre: Phase centre def: SkyCoord(ra=+15.0 * u.deg, dec=-60.0 * u.deg, frame='icrs', equinox='J2000')
:param frequency: def [1e8]
:param channel_bandwidth: def [1e6]
:param times: Observing times in radians: def [0.0]
:param polarisation_frame: def PolarisationFrame("stokesI")
:param order: 'time' or 'frequency' or 'both' or None: def 'frequency'
:param format: 'blockvis' or 'vis': def 'blockvis'
:return: vis_list with different frequencies in different elements
"""
if format == 'vis':
create_vis = create_visibility
else:
create_vis = create_blockvisibility
if times is None:
times = [0.0]
if channel_bandwidth is None:
channel_bandwidth = [1e6]
if frequency is None:
frequency = [1e8]
conf = create_named_configuration(config, rmax=rmax)
if order == 'time':
log.debug(
"simulate_list_arlexecute_workflow: Simulating distribution in %s"
% order)
vis_list = list()
for i, time in enumerate(times):
vis_list.append(
arlexecute.execute(create_vis, nout=1)(
conf,
numpy.array([times[i]]),
frequency=frequency,
channel_bandwidth=channel_bandwidth,
weight=1.0,
phasecentre=phasecentre,
polarisation_frame=polarisation_frame,
zerow=zerow))
elif order == 'frequency':
log.debug(
"simulate_list_arlexecute_workflow: Simulating distribution in %s"
% order)
vis_list = list()
for j, _ in enumerate(frequency):
vis_list.append(
arlexecute.execute(create_vis, nout=1)(
conf,
times,
frequency=numpy.array([frequency[j]]),
channel_bandwidth=numpy.array([channel_bandwidth[j]]),
weight=1.0,
phasecentre=phasecentre,
polarisation_frame=polarisation_frame,
zerow=zerow))
elif order == 'both':
log.debug(
"simulate_list_arlexecute_workflow: Simulating distribution in time and frequency"
)
vis_list = list()
for i, _ in enumerate(times):
for j, _ in enumerate(frequency):
vis_list.append(
arlexecute.execute(create_vis, nout=1)(
conf,
numpy.array([times[i]]),
frequency=numpy.array([frequency[j]]),
channel_bandwidth=numpy.array([channel_bandwidth[j]]),
weight=1.0,
phasecentre=phasecentre,
polarisation_frame=polarisation_frame,
zerow=zerow))
elif order is None:
log.debug(
"simulate_list_arlexecute_workflow: Simulating into single %s" %
format)
vis_list = list()
vis_list.append(
arlexecute.execute(create_vis,
nout=1)(conf,
times,
frequency=frequency,
channel_bandwidth=channel_bandwidth,
weight=1.0,
phasecentre=phasecentre,
polarisation_frame=polarisation_frame,
zerow=zerow))
else:
raise NotImplementedError("order $s not known" % order)
return vis_list
