def _predict_base(self,
fluxthreshold=1.0,
gcf=None,
cf=None,
name='predict_2d',
gcfcf=None,
**kwargs):
vis = predict_2d(self.vis, self.model, gcfcf=gcfcf, **kwargs)
vis.data['vis'] = self.vis.data['vis'] - vis.data['vis']
dirty = invert_2d(vis,
self.model,
dopsf=False,
normalize=True,
gcfcf=gcfcf)
if self.persist:
export_image_to_fits(
dirty[0],
'%s/test_imaging_%s_residual.fits' % (self.dir, name))
assert numpy.max(numpy.abs(dirty[0].data)), "Residual image is empty"
maxabs = numpy.max(numpy.abs(dirty[0].data))
assert maxabs < fluxthreshold, "Error %.3f greater than fluxthreshold %.3f " % (
maxabs, fluxthreshold)
def 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
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.dir = arl_path('test_results')
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)
export_image_to_fits(beam,
"%s/test_deconvolve_mmclean_beam.fits" % self.dir)
self.test_model.data *= beam.data
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)
export_image_to_fits(
self.dirty, "%s/test_deconvolve_mmclean-dirty.fits" % self.dir)
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 test_insert_skycomponent_FFT(self):
self.model.data *= 0.0
self.sc = create_skycomponent(direction=self.phasecentre, flux=self.sc.flux,
frequency=self.component_frequency,
polarisation_frame=PolarisationFrame('stokesI'))
insert_skycomponent(self.model, self.sc)
npixel = self.model.shape[3]
# WCS is 1-relative
rpix = numpy.round(self.model.wcs.wcs.crpix).astype('int') - 1
assert rpix[0] == npixel // 2
assert rpix[1] == npixel // 2
# The phase centre is at rpix[0], rpix[1] in 0-relative pixels
assert self.model.data[2, 0, rpix[1], rpix[0]] == 1.0
# If we predict the visibility, then the imaginary part must be zero. This is determined entirely
# by shift_vis_to_image in libs.imaging.base
self.vis.data['vis'][...] = 0.0
self.vis = predict_2d(self.vis, self.model)
# The actual phase centre of a numpy FFT is at nx //2, nx //2 (0 rel).
assert numpy.max(numpy.abs(self.vis.vis.imag)) <1e-3
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'))
self.vis.data['vis'] *= 0.0
self.vis.data['uvw'][:, 2] = 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.vis = predict_2d(self.vis, self.model)
assert numpy.max(numpy.abs(self.vis.vis)) > 0.0
export_image_to_fits(
self.model, '%s/test_solve_skycomponent_model.fits' % (self.dir))
self.bigmodel = create_image_from_visibility(self.vis,
cellsize=0.0015,
npixel=512)
residual, sumwt = invert_2d(self.vis, self.bigmodel)
export_image_to_fits(
residual,
'%s/test_solve_skycomponent_msclean_dirty.fits' % (self.dir))
