def setUp(self):
self.dir = './test_results'
self.lowcore = create_named_configuration('LOWBD2-CORE')
os.makedirs(self.dir, exist_ok=True)
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
# 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)
def setUp(self):
self.dir = './test_results'
os.makedirs(self.dir, exist_ok=True)
lowcore = create_named_configuration('LOWBD2-CORE')
times = numpy.arange(-3.0, +3.0, 1.0) * numpy.pi / 12.0
vnchan = 8
frequency = numpy.linspace(0.8e8, 1.20e8, vnchan)
channel_bandwidth = numpy.array(vnchan * [frequency[1] - frequency[0]])
# Define the component and give it some polarisation and spectral behaviour
f = numpy.array([100.0, 20.0, -10.0, 1.0])
self.flux = numpy.array(
[f, 0.9 * f, 0.8 * f, 0.7 * f, 0.6 * f, 0.5 * f, 0.4 * f, 0.3 * f])
self.phasecentre = SkyCoord(ra=+15.0 * u.deg,
dec=-35.0 * u.deg,
frame='icrs',
equinox='J2000')
self.compabsdirection = SkyCoord(ra=17.0 * u.deg,
dec=-36.5 * u.deg,
frame='icrs',
equinox='J2000')
self.comp = create_skycomponent(flux=self.flux,
frequency=frequency,
direction=self.compabsdirection)
self.image = create_test_image(
frequency=frequency,
phasecentre=self.phasecentre,
cellsize=0.001,
polarisation_frame=PolarisationFrame('stokesIQUV'))
self.blockvis = create_blockvisibility_iterator(
lowcore,
times=times,
frequency=frequency,
channel_bandwidth=channel_bandwidth,
phasecentre=self.phasecentre,
weight=1,
polarisation_frame=PolarisationFrame('linear'),
integration_time=1.0,
number_integrations=1,
predict=predict_2d,
components=self.comp,
phase_error=0.1,
amplitude_error=0.01,
sleep=1.0)
self.vis = create_blockvisibility(
lowcore,
times=times,
frequency=frequency,
channel_bandwidth=channel_bandwidth,
phasecentre=self.phasecentre,
weight=1,
polarisation_frame=PolarisationFrame('stokesIQUV'),
integration_time=1.0)
self.vis = predict_2d(self.vis, self.image)
def test_predict_2d(self):
# Test if the 2D prediction works
#
# Set w=0 so that the two-dimensional transform should agree exactly with the component transform.
# Good check on the grid correction in the image->vis direction
# Set all w to zero
self.actualSetUp()
self.componentvis = create_visibility(
self.lowcore,
self.times,
self.frequency,
channel_bandwidth=self.channel_bandwidth,
phasecentre=self.phasecentre,
weight=1.0)
self.componentvis.data['uvw'][:, 2] = 0.0
# Predict the visibility using direct evaluation
predict_skycomponent_visibility(self.componentvis, self.components)
self.modelvis = 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.modelvis.data['uvw'][:, 2] = 0.0
predict_2d(self.modelvis, self.model, **self.params)
self.residualvis = 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.residualvis.data['uvw'][:, 2] = 0.0
self.residualvis.data[
'vis'] = self.modelvis.data['vis'] - self.componentvis.data['vis']
self._checkdirty(self.residualvis,
'test_predict_2d',
fluxthreshold=4.0)
def test_insert_skycomponent(self):
sc = create_skycomponent(direction=self.phasecentre, flux=numpy.array([[1.0]]), frequency=self.frequency,
polarisation_frame = PolarisationFrame('stokesI'))
log.debug(self.model.wcs)
log.debug(str(sc))
# The actual phase centre of a numpy FFT is at nx //2, nx //2 (0 rel).
self.model.data *= 0.0
insert_skycomponent(self.model, sc)
npixel = self.model.shape[3]
rpix = numpy.round(self.model.wcs.wcs.crpix).astype('int')
assert rpix[0] == npixel // 2
assert rpix[1] == npixel // 2
assert self.model.data[0,0,rpix[1],rpix[0]] == 1.0
self.vis = predict_2d(self.vis, self.model)
assert self.vis.vis.imag.all() == 0.0
def test_insert_skycomponent(self):
sc = create_skycomponent(
direction=self.phasecentre,
flux=numpy.array([[1.0]]),
frequency=self.frequency,
polarisation_frame=PolarisationFrame('stokesI'))
self.model.data *= 0.0
insert_skycomponent(self.model, 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[0, 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 arl.imaging.base
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 self.vis.vis.imag.all() == 0.0
def test_predict_2d(self):
# Test if the 2D prediction works
#
# Set w=0 so that the two-dimensional transform should agree exactly with the component transform.
# Good check on the grid correction in the image->vis direction
# Set all w to zero
self.actualSetUp()
self.componentvis.data['uvw'][:, 2] = 0.0
# Predict the visibility using direct evaluation
self.componentvis.data['vis'][...] = 0.0
self.componentvis = predict_skycomponent_visibility(
self.componentvis, self.components)
self.modelvis = copy_visibility(self.componentvis, zero=True)
self.modelvis.data['uvw'][:, 2] = 0.0
self.modelvis = predict_2d(self.modelvis, self.model, **self.params)
self.residualvis = copy_visibility(self.componentvis, zero=True)
self.residualvis.data['uvw'][:, 2] = 0.0
self.residualvis.data[
'vis'] = self.modelvis.data['vis'] - self.componentvis.data['vis']
self._checkdirty(self.residualvis, 'predict_2d')
