def test_fill_awterm_to_convolutionfunction(self):
make_pb = functools.partial(create_pb_generic,
diameter=35.0,
blockage=0.0,
use_local=False)
pb = make_pb(self.image)
if self.persist:
export_image_to_fits(
pb, "%s/test_convolutionfunction_awterm_pb.fits" % self.dir)
gcf, cf = create_awterm_convolutionfunction(self.image,
make_pb=make_pb,
nw=201,
wstep=8,
oversampling=8,
support=60,
use_aaf=True)
assert numpy.max(numpy.abs(cf.data)) > 0.0
if self.persist:
export_image_to_fits(
gcf, "%s/test_convolutionfunction_awterm_gcf.fits" % self.dir)
cf_image = convert_convolutionfunction_to_image(cf)
cf_image.data = numpy.real(cf_image.data)
if self.persist:
export_image_to_fits(
cf_image,
"%s/test_convolutionfunction_awterm_cf.fits" % self.dir)
peak_location = numpy.unravel_index(numpy.argmax(numpy.abs(cf.data)),
cf.shape)
assert peak_location == (0, 0, 100, 4, 4, 30, 30), peak_location
assert numpy.abs(cf.data[peak_location] - (0.07761529943522588-0j)) < 1e-7, \
cf.data[peak_location]
u_peak, v_peak = cf.grid_wcs.sub([1, 2]).wcs_pix2world(
peak_location[-2], peak_location[-1], 0)
assert numpy.abs(u_peak) < 1e-7, u_peak
assert numpy.abs(v_peak) < 1e-7, u_peak
bboxes = calculate_bounding_box_convolutionfunction(cf)
assert len(bboxes) == 201, len(bboxes)
assert len(bboxes[0]) == 3, len(bboxes[0])
assert bboxes[-1][0] == 200, bboxes[-1][0]
peak_location = numpy.unravel_index(numpy.argmax(numpy.abs(cf.data)),
cf.shape)
assert peak_location == (0, 0, 100, 4, 4, 30, 30), peak_location
cf_clipped = apply_bounding_box_convolutionfunction(
cf, fractional_level=1e-3)
peak_location = numpy.unravel_index(
numpy.argmax(numpy.abs(cf_clipped.data)), cf_clipped.shape)
assert peak_location == (0, 0, 100, 4, 4, 21, 21), peak_location
cf_image = convert_convolutionfunction_to_image(cf_clipped)
cf_image.data = numpy.real(cf_image.data)
if self.persist:
export_image_to_fits(
cf_image,
"%s/test_convolutionfunction_awterm_clipped_cf.fits" %
self.dir)
def test_griddata_predict_aterm(self):
self.actualSetUp(zerow=True)
make_pb = functools.partial(create_pb_generic, diameter=35.0, blockage=0.0)
griddata = create_griddata_from_image(self.model)
gcf, cf = create_awterm_convolutionfunction(self.model, make_pb=make_pb, nw=1,
oversampling=16, support=16,
use_aaf=True)
griddata = fft_image_to_griddata(self.model, griddata, gcf)
newvis = degrid_visibility_from_griddata(self.vis, griddata=griddata, cf=cf)
qa = qa_visibility(newvis)
assert qa.data['rms'] < 120.0, str(qa)
def test_fill_wterm_to_convolutionfunction(self):
gcf, cf = create_awterm_convolutionfunction(self.image,
make_pb=None,
nw=201,
wstep=8.0,
oversampling=8,
support=60,
use_aaf=True)
export_image_to_fits(
gcf, "%s/test_convolutionfunction_wterm_gcf.fits" % self.dir)
cf_image = convert_convolutionfunction_to_image(cf)
cf_image.data = numpy.real(cf_image.data)
if self.persist:
export_image_to_fits(
cf_image,
"%s/test_convolutionfunction_wterm_cf.fits" % self.dir)
peak_location = numpy.unravel_index(numpy.argmax(numpy.abs(cf.data)),
cf.shape)
assert peak_location == (0, 0, 100, 4, 4, 30, 30), peak_location
assert numpy.abs(cf.data[peak_location] - (0.18704481681878257-0j)) < 1e-7, \
cf.data[peak_location]
u_peak, v_peak = cf.grid_wcs.sub([1, 2]).wcs_pix2world(
peak_location[-2], peak_location[-1], 0)
assert numpy.abs(u_peak) < 1e-7, u_peak
assert numpy.abs(v_peak) < 1e-7, u_peak
cf_clipped = apply_bounding_box_convolutionfunction(cf, 1e-3)
cf_image = convert_convolutionfunction_to_image(cf_clipped)
cf_image.data = numpy.real(cf_image.data)
if self.persist:
export_image_to_fits(
cf_image,
"%s/test_convolutionfunction_wterm_clipped_real_cf.fits" %
self.dir)
cf_image = convert_convolutionfunction_to_image(cf_clipped)
cf_image.data = numpy.imag(cf_image.data)
if self.persist:
export_image_to_fits(
cf_image,
"%s/test_convolutionfunction_wterm_clipped_imag_cf.fits" %
self.dir)
peak_location = numpy.unravel_index(
numpy.argmax(numpy.abs(cf_clipped.data)), cf_clipped.shape)
assert peak_location == (0, 0, 100, 4, 4, 27, 27), peak_location
assert numpy.abs(cf_clipped.data[peak_location] - (0.18704481681878257-0j)) < 1e-7, \
cf_clipped.data[peak_location]
u_peak, v_peak = cf_clipped.grid_wcs.sub([1, 2]).wcs_pix2world(
peak_location[-2], peak_location[-1], 0)
assert numpy.abs(u_peak) < 1e-7, u_peak
assert numpy.abs(v_peak) < 1e-7, u_peak
def test_fill_aterm_to_convolutionfunction_noover(self):
make_pb = functools.partial(create_pb_generic,
diameter=35.0,
blockage=0.0,
use_local=False)
pb = make_pb(self.image)
if self.persist:
export_image_to_fits(
pb, "%s/test_convolutionfunction_aterm_pb.fits" % self.dir)
gcf, cf = create_awterm_convolutionfunction(self.image,
make_pb=make_pb,
nw=1,
wstep=1e-7,
oversampling=1,
support=32,
use_aaf=True)
cf_image = convert_convolutionfunction_to_image(cf)
cf_image.data = numpy.real(cf_image.data)
if self.persist:
export_image_to_fits(
cf_image,
"%s/test_convolutionfunction_aterm_noover_cf.fits" % self.dir)
peak_location = numpy.unravel_index(numpy.argmax(numpy.abs(cf.data)),
cf.shape)
assert numpy.abs(cf.data[peak_location] - 0.0776153022780847 +
0j) < 1e-7, cf.data[peak_location]
assert peak_location == (0, 0, 0, 0, 0, 16, 16), peak_location
u_peak, v_peak = cf.grid_wcs.sub([1, 2]).wcs_pix2world(
peak_location[-2], peak_location[-1], 0)
assert numpy.abs(u_peak) < 1e-7, u_peak
assert numpy.abs(v_peak) < 1e-7, u_peak
if self.persist:
export_image_to_fits(
gcf,
"%s/test_convolutionfunction_aterm_noover_gcf.fits" % self.dir)
cf_clipped = apply_bounding_box_convolutionfunction(
cf, fractional_level=0.001)
cf_image = convert_convolutionfunction_to_image(cf_clipped)
cf_image.data = numpy.real(cf_image.data)
if self.persist:
export_image_to_fits(
cf_image,
"%s/test_convolutionfunction_aterm_clipped_noover_cf.fits" %
self.dir)
peak_location = numpy.unravel_index(
numpy.argmax(numpy.abs(cf_clipped.data)), cf_clipped.shape)
assert peak_location == (0, 0, 0, 0, 0, 5, 5), peak_location
def test_invert_awterm(self):
self.actualSetUp(zerow=False)
make_pb = functools.partial(create_pb_generic,
diameter=35.0,
blockage=0.0,
use_local=False)
gcfcf = create_awterm_convolutionfunction(self.model,
make_pb=make_pb,
nw=100,
wstep=8.0,
oversampling=4,
support=100,
use_aaf=True)
self._invert_base(name='invert_awterm',
positionthreshold=35.0,
check_components=False,
gcfcf=gcfcf)
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 = [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.model_list = [arlexecute.execute(create_unittest_model, nout=freqwin)(self.vis_list[freqwin],
self.image_pol,
cellsize=self.cellsize,
npixel=self.npixel)
for freqwin, _ in enumerate(self.frequency)]
self.components_list = [arlexecute.execute(create_unittest_components)(self.model_list[freqwin],
flux[freqwin, :][numpy.newaxis, :],
single=True)
for freqwin, _ in enumerate(self.frequency)]
self.components_list = arlexecute.compute(self.components_list, sync=True)
self.model_list = [arlexecute.execute(insert_skycomponent, nout=1)(self.model_list[freqwin],
self.components_list[freqwin])
for freqwin, _ in enumerate(self.frequency)]
self.model_list = arlexecute.compute(self.model_list, sync=True)
self.vis_list = [arlexecute.execute(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 = [arlexecute.execute(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
