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_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_griddata_invert_awterm(self):
self.actualSetUp(zerow=False)
make_pb = functools.partial(create_pb_generic,
diameter=35.0,
blockage=0.0,
use_local=False)
pb = make_pb(self.model)
if self.persist:
export_image_to_fits(pb,
"%s/test_gridding_awterm_pb.fits" % self.dir)
gcf, cf = create_awterm_convolutionfunction(self.model,
make_pb=make_pb,
nw=100,
wstep=8.0,
oversampling=16,
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_gridding_awterm_cf.fits" % self.dir)
griddata = create_griddata_from_image(self.model, nw=100, wstep=8.0)
griddata, sumwt = grid_visibility_to_griddata(self.vis,
griddata=griddata,
cf=cf)
im = fft_griddata_to_image(griddata, gcf)
im = normalize_sumwt(im, sumwt)
if self.persist:
export_image_to_fits(
im, '%s/test_gridding_dirty_awterm.fits' % self.dir)
self.check_peaks(im, 97.13240677427714)
def test_griddata_invert_wterm(self):
self.actualSetUp(zerow=False)
gcf, cf = create_awterm_convolutionfunction(self.model,
nw=100,
wstep=8.0,
oversampling=8,
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_gridding_wterm_cf.fits" % self.dir)
griddata = create_griddata_from_image(self.model, nw=1)
griddata, sumwt = grid_visibility_to_griddata(self.vis,
griddata=griddata,
cf=cf)
im = fft_griddata_to_image(griddata, gcf)
im = normalize_sumwt(im, sumwt)
if self.persist:
export_image_to_fits(
im, '%s/test_gridding_dirty_wterm.fits' % self.dir)
self.check_peaks(im, 97.13215242859648)
def test_fill_pswf_to_convolutionfunction_nooversampling(self):
oversampling = 1
support = 6
gcf, cf = create_pswf_convolutionfunction(self.image,
oversampling=oversampling,
support=support)
assert numpy.max(numpy.abs(cf.data)) > 0.0
export_image_to_fits(
gcf, "%s/test_convolutionfunction_pswf_nooversampling_gcf.fits" %
self.dir)
cf_image = convert_convolutionfunction_to_image(cf)
cf_image.data = numpy.real(cf_image.data)
export_image_to_fits(
cf_image,
"%s/test_convolutionfunction_pwsf_nooversampling_cf.fits" %
self.dir)
peak_location = numpy.unravel_index(numpy.argmax(numpy.abs(cf.data)),
cf.shape)
assert numpy.abs(cf.data[peak_location] - 0.18712109669890536 +
0j) < 1e-7, cf.data[peak_location]
assert peak_location == (0, 0, 0, 0, 0, 3, 3), 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
def test_compare_wterm_kernels(self):
_, cf = create_awterm_convolutionfunction(self.image,
nw=110,
wstep=8,
oversampling=8,
support=60,
use_aaf=True)
cf.data = numpy.real(cf.data)
peak_location = numpy.unravel_index(numpy.argmax(numpy.abs(cf.data)),
cf.shape)
assert peak_location == (0, 0, 55, 4, 4, 30, 30), peak_location
assert numpy.abs(cf.data[peak_location] - (0.18704481681878257 - 0j)) < 1e-7, \
cf.data[peak_location]
_, cf_noover = create_awterm_convolutionfunction(self.image,
nw=111,
wstep=8,
oversampling=1,
support=60,
use_aaf=True)
cf_noover.data = numpy.real(cf_noover.data)
cf.data[...] -= cf_noover.data[0, 0, 0, 0, 0]
cf_image = convert_convolutionfunction_to_image(cf)
cf_image.data = numpy.real(cf_image.data)
export_image_to_fits(
cf_image,
"%s/test_convolutionfunction_compare_wterm_kernels.fits" %
self.dir)
assert numpy.abs(cf.data[0, 0, 0, 4, 4, 30, 30]) < 5e-6, cf.data[0, 0,
0, 4,
4, 30,
30]
def test_create_convolutionfunction(self):
cf = create_convolutionfunction_from_image(self.image, nz=1)
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_cf.fits" % self.dir)
def test_fill_box_to_convolutionfunction(self):
gcf, cf = create_box_convolutionfunction(self.image)
assert numpy.max(numpy.abs(cf.data)) > 0.0
export_image_to_fits(
gcf, "%s/test_convolutionfunction_box_gcf.fits" % self.dir)
cf_image = convert_convolutionfunction_to_image(cf)
cf_image.data = numpy.real(cf_image.data)
export_image_to_fits(
cf_image, "%s/test_convolutionfunction_box_cf.fits" % self.dir)
peak_location = numpy.unravel_index(numpy.argmax(numpy.abs(cf.data)),
cf.shape)
assert numpy.abs(cf.data[peak_location] -
1.0) < 1e-15, "Peak is incorrect %s" % str(
cf.data[peak_location] - 1.0)
assert peak_location == (0, 0, 0, 0, 0, 2, 2), peak_location
def test_fill_wterm_to_convolutionfunction_nopswf(self):
gcf, cf = create_awterm_convolutionfunction(self.image,
make_pb=None,
nw=201,
wstep=8.0,
oversampling=8,
support=60,
use_aaf=False)
export_image_to_fits(
gcf,
"%s/test_convolutionfunction_wterm_nopswf_gcf.fits" % self.dir)
cf_image = convert_convolutionfunction_to_image(cf)
cf_image.data = numpy.real(cf_image.data)
export_image_to_fits(
cf_image,
"%s/test_convolutionfunction_wterm_nopswf_cf.fits" % self.dir)
