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 = [predict_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)
export_image_to_fits(model, '%s/test_imaging_serial_deconvolved_model.fits' % self.dir)
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,
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 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, -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)]
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 = [
predict_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, seed=180555)
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)]