def test_pad_extract(self):
for npixel, N2 in [(100, 128), (128, 256), (126, 128)]:
# Make a 2D complex image of size (npixel, npixel) centred on (npixel//2, npixel//2)
cs = 1 + self._pattern(npixel)
# Pad it and extract npixel pixels around the centre
cs_pad = pad_mid(cs, N2)
# Now create the pattern we expect directly
cs2 = 1 + self._pattern(N2) * N2 / npixel
# At this point all fields in cs2 and cs_pad should either
# be equal or zero.
equal = numpy.abs(cs_pad - cs2) < 1e-15
zero = numpy.abs(cs_pad) < 1e-15
assert (equal + zero).all(), "Pad (%d, %d) failed" % (npixel, N2)
# And extracting the middle should recover the original data
assert_allclose(extract_mid(cs_pad, npixel), cs)
def predict_2d_base(vis: Union[BlockVisibility, Visibility], model: Image,
**kwargs) -> Union[BlockVisibility, Visibility]:
""" Predict using convolutional degridding.
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 predicted
:param model: model image
:return: resulting visibility (in place works)
"""
if isinstance(vis, BlockVisibility):
log.debug("imaging.predict: coalescing prior to prediction")
avis = coalesce_visibility(vis, **kwargs)
else:
avis = vis
assert isinstance(avis, Visibility), avis
_, _, ny, nx = model.data.shape
padding = {}
if get_parameter(kwargs, "padding", False):
padding = {'padding': get_parameter(kwargs, "padding", False)}
spectral_mode, vfrequencymap = get_frequency_map(avis, model)
polarisation_mode, vpolarisationmap = get_polarisation_map(avis, model)
uvw_mode, shape, padding, vuvwmap = get_uvw_map(avis, model, **padding)
kernel_name, gcf, vkernellist = get_kernel_list(avis, model, **kwargs)
uvgrid = fft((pad_mid(model.data, int(round(padding * nx))) *
gcf).astype(dtype=complex))
avis.data['vis'] = convolutional_degrid(vkernellist,
avis.data['vis'].shape, uvgrid,
vuvwmap, vfrequencymap,
vpolarisationmap)
# Now we can shift the visibility from the image frame to the original visibility frame
svis = shift_vis_to_image(avis, model, tangent=True, inverse=True)
if isinstance(vis, BlockVisibility) and isinstance(svis, Visibility):
log.debug("imaging.predict decoalescing post prediction")
return decoalesce_visibility(svis)
else:
return svis
def predict_2d_base(vis: Visibility, model: Image, **kwargs) -> Visibility:
""" Predict using convolutional degridding.
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 predicted
:param model: model image
:return: resulting visibility (in place works)
"""
if type(vis) is not Visibility:
avis = coalesce_visibility(vis, **kwargs)
else:
avis = vis
_, _, ny, nx = model.data.shape
# print(model.shape)
spectral_mode, vfrequencymap = get_frequency_map(avis, model) # 可以并行
polarisation_mode, vpolarisationmap = get_polarisation_map(
avis, model, **kwargs) # 可以并行
uvw_mode, shape, padding, vuvwmap = get_uvw_map(avis, model,
**kwargs) # 可以并行
kernel_name, gcf, vkernellist = get_kernel_list(avis, model, **kwargs)
uvgrid = fft((pad_mid(model.data, int(round(padding * nx))) *
gcf).astype(dtype=complex))
avis.data['vis'] = convolutional_degrid(vkernellist,
avis.data['vis'].shape, uvgrid,
vuvwmap, vfrequencymap,
vpolarisationmap)
# Now we can shift the visibility from the image frame to the original visibility frame
svis = shift_vis_to_image(avis, model, tangent=True, inverse=True)
if type(vis) is not Visibility:
return decoalesce_visibility(svis)
else:
return svis
def predict_2d_base_timing(vis: Visibility, model: Image,
**kwargs) -> (Visibility, tuple):
""" Predict using convolutional degridding.
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 predicted
:param model: model image
:return: resulting visibility (in place works)
"""
if not isinstance(vis, Visibility):
avis = coalesce_visibility(vis, **kwargs)
else:
avis = vis
_, _, ny, nx = model.data.shape
opt = get_parameter(kwargs, 'opt', False)
if not opt:
log.debug('Using original algorithm')
else:
log.debug('Using optimized algorithm')
padding = {}
if get_parameter(kwargs, "padding", False):
padding = {'padding': get_parameter(kwargs, "padding", False)}
spectral_mode, vfrequencymap = get_frequency_map(avis, model, opt)
polarisation_mode, vpolarisationmap = get_polarisation_map(avis, model)
uvw_mode, shape, padding, vuvwmap = get_uvw_map(avis, model, **padding)
kernel_name, gcf, vkernellist = get_kernel_list(avis, model, **kwargs)
inarr = (pad_mid(model.data, int(round(padding * nx))) *
gcf).astype(dtype=complex)
# Use original algorithm
if not opt:
time_fft = -time.time()
uvgrid = fft(inarr)
time_fft += time.time()
time_degrid = -time.time()
vt = convolutional_degrid(vkernellist, avis.data['vis'].shape, uvgrid,
vuvwmap, vfrequencymap, vpolarisationmap)
time_degrid += time.time()
# Use optimized algorithm
else:
time_fft = -time.time()
uvgrid = numpy.zeros(inarr.shape, dtype=inarr.dtype)
fft_c(uvgrid, inarr)
time_fft += time.time()
time_degrid = -time.time()
kernel_indices, kernels = vkernellist
ks0, ks1, ks2, ks3 = kernels[0].shape
kernels_c = numpy.zeros((len(kernels), ks0, ks1, ks2, ks3),
dtype=kernels[0].dtype)
for i in range(len(kernels)):
kernels_c[i, ...] = kernels[i]
vfrequencymap_c = numpy.array(vfrequencymap, dtype=numpy.int32)
vt = numpy.zeros(avis.data['vis'].shape, dtype=numpy.complex128)
convolutional_degrid_c(vt, native_order(kernels_c),
native_order(kernel_indices),
native_order(uvgrid), native_order(vuvwmap),
native_order(vfrequencymap_c))
time_degrid += time.time()
avis.data['vis'] = vt
# Now we can shift the visibility from the image frame to the original visibility frame
svis = shift_vis_to_image(avis, model, tangent=True, inverse=True)
if not isinstance(vis, Visibility):
svis = decoalesce_visibility(svis)
return svis, (time_degrid, time_fft)
