def predict_wstack_single(vis,
model,
remove=True,
facets=1,
vis_slices=1,
**kwargs) -> Visibility:
""" Predict using a single w slices.
This processes a single w plane, rotating out the w beam for the average w
:param vis: Visibility to be predicted
:param model: model image
:return: resulting visibility (in place works)
"""
if not isinstance(vis, Visibility):
log.debug("predict_wstack_single: Coalescing")
avis = coalesce_visibility(vis, **kwargs)
else:
avis = vis
log.debug("predict_wstack_single: predicting using single w slice")
avis.data['vis'] *= 0.0
# We might want to do wprojection so we remove the average w
w_average = numpy.average(avis.w)
avis.data['uvw'][..., 2] -= w_average
tempvis = copy_visibility(avis)
# Calculate w beam and apply to the model. The imaginary part is not needed
workimage = copy_image(model)
w_beam = create_w_term_like(model, w_average, vis.phasecentre)
# Do the real part
workimage.data = w_beam.data.real * model.data
avis = predict_2d(avis, workimage, facets=1, vis_slices=1, **kwargs)
# and now the imaginary part
workimage.data = w_beam.data.imag * model.data
tempvis = predict_2d(tempvis,
workimage,
facets=facets,
vis_slices=vis_slices,
**kwargs)
avis.data['vis'] -= 1j * tempvis.data['vis']
if not remove:
avis.data['uvw'][..., 2] += w_average
if isinstance(vis, BlockVisibility) and isinstance(avis, Visibility):
log.debug("imaging.predict decoalescing post prediction")
return decoalesce_visibility(avis)
else:
return avis
def test_create_w_term_image(self):
m31image = create_test_image(cellsize=0.001)
im = create_w_term_like(m31image, w=20000.0, remove_shift=True)
im.data = im.data.real
for x in [64, 64 + 128]:
for y in [64, 64 + 128]:
self.assertAlmostEqual(im.data[0, 0, y, x],
0.84946344276442431, 7)
export_image_to_fits(im, '%s/test_wterm.fits' % self.dir)
assert im.data.shape == (1, 1, 256, 256)
self.assertAlmostEqual(numpy.max(im.data.real), 1.0, 7)
def test_convert_image_to_kernel(self):
m31image = create_test_image(cellsize=0.001,
frequency=[1e8],
canonical=True)
screen = create_w_term_like(m31image, w=20000.0, remove_shift=True)
screen_fft = fft_image(screen)
converted = convert_image_to_kernel(screen_fft, 8, 8)
assert converted.shape == (1, 1, 8, 8, 8, 8)
with self.assertRaises(AssertionError):
converted = convert_image_to_kernel(m31image, 15, 1)
with self.assertRaises(AssertionError):
converted = convert_image_to_kernel(m31image, 15, 1000)
def invert_wstack_single(vis: Visibility,
im: Image,
dopsf,
normalize=True,
remove=True,
facets=1,
vis_slices=1,
**kwargs) -> (Image, numpy.ndarray):
"""Process single w slice
: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)
"""
log.debug("invert_wstack_single: predicting using single w slice")
kwargs['imaginary'] = True
assert isinstance(vis, Visibility), vis
# We might want to do wprojection so we remove the average w
w_average = numpy.average(vis.w)
vis.data['uvw'][..., 2] -= w_average
reWorkimage, sumwt, imWorkimage = invert_2d(vis,
im,
dopsf,
normalize=normalize,
facets=facets,
vis_slices=vis_slices,
**kwargs)
if not remove:
vis.data['uvw'][..., 2] += w_average
# Calculate w beam and apply to the model. The imaginary part is not needed
w_beam = create_w_term_like(im, w_average, vis.phasecentre)
reWorkimage.data = w_beam.data.real * reWorkimage.data - w_beam.data.imag * imWorkimage.data
return reWorkimage, sumwt