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_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_aterm_noover(self):
self.actualSetUp(zerow=True)
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_aterm_pb.fits" % self.dir)
gcf, cf = create_awterm_convolutionfunction(self.model,
make_pb=make_pb,
nw=1,
oversampling=1,
support=16,
use_aaf=True)
griddata = create_griddata_from_image(self.model)
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_aterm_noover.fits' % self.dir)
self.check_peaks(im, 97.10594988491549)
def invert_2d(vis: Visibility,
im: Image,
dopsf: bool = False,
normalize: bool = True,
gcfcf=None,
**kwargs) -> (Image, numpy.ndarray):
""" Invert using 2D convolution function, using the specified convolution function
Use the image im as a template. Do PSF in a separate call.
This is at the bottom of the layering i.e. all transforms are eventually expressed in terms
of this function. Any shifting needed is performed here.
:param vis: Visibility to be inverted
:param im: image template (not changed)
:param dopsf: Make the psf instead of the dirty image
:param normalize: Normalize by the sum of weights (True)
:param gcfcf: (Grid correction function i.e. in image space, Convolution function i.e. in uv space)
:return: resulting image
"""
assert isinstance(vis, Visibility), vis
svis = copy_visibility(vis)
if dopsf:
svis.data['vis'][...] = 1.0 + 0.0j
svis = shift_vis_to_image(svis, im, tangent=True, inverse=False)
if gcfcf is None:
gcf, cf = create_pswf_convolutionfunction(
im,
support=get_parameter(kwargs, "support", 6),
oversampling=get_parameter(kwargs, "oversampling", 128))
else:
gcf, cf = gcfcf
griddata = create_griddata_from_image(im)
griddata, sumwt = grid_visibility_to_griddata(svis,
griddata=griddata,
cf=cf)
imaginary = get_parameter(kwargs, "imaginary", False)
if imaginary:
result0, result1 = fft_griddata_to_image(griddata,
gcf,
imaginary=imaginary)
log.debug("invert_2d: retaining imaginary part of dirty image")
if normalize:
result0 = normalize_sumwt(result0, sumwt)
result1 = normalize_sumwt(result1, sumwt)
return result0, sumwt, result1
else:
result = fft_griddata_to_image(griddata, gcf)
if normalize:
result = normalize_sumwt(result, sumwt)
return result, sumwt
def test_griddata_invert_box(self):
self.actualSetUp(zerow=True)
gcf, cf = create_box_convolutionfunction(self.model)
griddata = create_griddata_from_image(self.model)
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_box.fits' % self.dir)
self.check_peaks(im, 97.10594988491546, tol=1e-7)
def test_griddata_invert_pswf_w(self):
self.actualSetUp(zerow=False)
gcf, cf = create_pswf_convolutionfunction(self.model)
griddata = create_griddata_from_image(self.model, self.vis)
griddata, sumwt = grid_visibility_to_griddata(self.vis,
griddata=griddata,
cf=cf)
cim = fft_griddata_to_image(griddata, gcf)
cim = normalize_sumwt(cim, sumwt)
im = convert_polimage_to_stokes(cim)
if self.persist:
export_image_to_fits(
im, '%s/test_gridding_dirty_pswf_w.fits' % self.dir)
self.check_peaks(im, 97.13240718331633, tol=1e-7)
def test_griddata_invert_pswf_stokesIQ(self):
self.actualSetUp(zerow=True, image_pol=PolarisationFrame("stokesIQ"))
gcf, cf = create_pswf_convolutionfunction(self.model)
griddata = create_griddata_from_image(self.model, self.vis)
griddata, sumwt = grid_visibility_to_griddata(self.vis,
griddata=griddata,
cf=cf)
cim = fft_griddata_to_image(griddata, gcf)
cim = normalize_sumwt(cim, sumwt)
im = convert_polimage_to_stokes(cim)
if self.persist:
export_image_to_fits(im,
'%s/test_gridding_dirty_pswf.fits' % self.dir)
self.check_peaks(im, 97.10594988491545, tol=1e-7)
def test_griddata_invert_pswf_w(self):
self.actualSetUp(zerow=False)
gcf, cf = create_pswf_convolutionfunction(self.model,
support=6,
oversampling=32)
griddata = create_griddata_from_image(self.model)
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_pswf_w.fits' % self.dir)
self.check_peaks(im, 97.01838776845877, tol=1e-7)
def test_griddata_weight(self):
self.actualSetUp(zerow=True)
gcf, cf = create_box_convolutionfunction(self.model)
gd = create_griddata_from_image(self.model)
gd_list = [
grid_weight_to_griddata(self.vis, gd, cf) for i in range(10)
]
gd, sumwt = griddata_merge_weights(gd_list, algorithm='uniform')
self.vis = griddata_reweight(self.vis, gd, cf)
gd, sumwt = grid_visibility_to_griddata(self.vis, griddata=gd, cf=cf)
im = fft_griddata_to_image(gd, gcf)
im = normalize_sumwt(im, sumwt)
if self.persist:
export_image_to_fits(
im, '%s/test_gridding_dirty_2d_uniform.fits' % self.dir)
self.check_peaks(im, 99.40822097133994)
def test_griddata_visibility_weight_IQ(self):
self.actualSetUp(zerow=True, image_pol=PolarisationFrame("stokesIQUV"))
gcf, cf = create_pswf_convolutionfunction(self.model)
gd = create_griddata_from_image(self.model, self.vis)
gd_list = [
grid_visibility_weight_to_griddata(self.vis, gd, cf)
for i in range(10)
]
gd, sumwt = griddata_merge_weights(gd_list, algorithm='uniform')
self.vis = griddata_visibility_reweight(self.vis, gd, cf)
gd, sumwt = grid_visibility_to_griddata(self.vis, griddata=gd, cf=cf)
cim = fft_griddata_to_image(gd, gcf)
cim = normalize_sumwt(cim, sumwt)
im = convert_polimage_to_stokes(cim)
if self.persist:
export_image_to_fits(
im, '%s/test_gridding_dirty_2d_IQ_uniform.fits' % self.dir)
self.check_peaks(im, 99.40822097133994)