def sum_visibility(vis: Visibility, direction: SkyCoord) -> numpy.array:
""" Direct Fourier summation in a given direction
:param vis: Visibility to be summed
:param direction: Direction of summation
:return: flux[nch,npol], weight[nch,pol]
"""
# TODO: Convert to Visibility or remove?
svis = copy_visibility(vis)
l, m, n = skycoord_to_lmn(direction, svis.phasecentre)
phasor = numpy.conjugate(simulate_point(svis.uvw, l, m))
# Need to put correct mapping here
_, frequency = get_frequency_map(svis, None)
frequency = list(frequency)
nchan = max(frequency) + 1
npol = svis.polarisation_frame.npol
flux = numpy.zeros([nchan, npol])
weight = numpy.zeros([nchan, npol])
coords = svis.vis, svis.weight, phasor, list(frequency)
for v, wt, p, ic in zip(*coords):
for pol in range(npol):
flux[ic, pol] += numpy.real(wt[pol] * v[pol] * p)
weight[ic, pol] += wt[pol]
flux[weight > 0.0] = flux[weight > 0.0] / weight[weight > 0.0]
flux[weight <= 0.0] = 0.0
return flux, weight
def predict_skycomponent_visibility_old(vis: Visibility, sc: Union[Skycomponent, List[Skycomponent]]) -> Visibility:
"""Predict the visibility from a Skycomponent, add to existing visibility, for Visibility
:param vis: Visibility
:param sc: Skycomponent or list of SkyComponents
:return: Visibility
"""
assert isinstance(vis, Visibility), "vis is not a Visibility: %r" % vis
if not isinstance(sc, collections.Iterable):
sc = [sc]
_, im_nchan = list(get_frequency_map(vis, None))
npol = vis.polarisation_frame.npol
for comp in sc:
assert_same_chan_pol(vis, comp)
l, m, n = skycoord_to_lmn(comp.direction, vis.phasecentre)
phasor = simulate_point(vis.uvw, l, m)
for ivis in range(vis.nvis):
for pol in range(npol):
vis.data['vis'][ivis, pol] += comp.flux[im_nchan[ivis], pol] * phasor[ivis]
# coords = phasor, ichan
# for pol in range(npol):
# vis.data['vis'][:,pol] += [comp.flux[ic, pol] * p for p, ic in zip(*coords)]
return vis
def test_get_frequency_map_different_channel(self):
self.model = create_image_from_visibility(self.vis, npixel=512, cellsize=0.001,
frequency=self.startfrequency, nchan=3,
channel_bandwidth=2e7)
spectral_mode, vfrequency_map = get_frequency_map(self.vis, self.model)
assert numpy.max(vfrequency_map) == self.model.nchan - 1
assert spectral_mode == 'channel'
def weight_visibility(vis: Visibility, im: Image, **kwargs) -> Visibility:
""" Reweight the visibility data using a selected algorithm
Imaging uses the column "imaging_weight" when imaging. This function sets that column using a
variety of algorithms
Options are:
- Natural: by visibility weight (optimum for noise in final image)
- Uniform: weight of sample divided by sum of weights in cell (optimum for sidelobes)
- Super-uniform: As uniform, by sum of weights is over extended box region
- Briggs: Compromise between natural and uniform
- Super-briggs: As Briggs, by sum of weights is over extended box region
:param vis:
:param im:
:return: visibility with imaging_weights column added and filled
"""
assert isinstance(vis, Visibility), "vis is not a Visibility: %r" % vis
assert get_parameter(kwargs, "padding", False) is False
spectral_mode, vfrequencymap = get_frequency_map(vis, im)
polarisation_mode, vpolarisationmap = get_polarisation_map(vis, im)
uvw_mode, shape, padding, vuvwmap = get_uvw_map(vis, im)
density = None
densitygrid = None
weighting = get_parameter(kwargs, "weighting", "uniform")
vis.data['imaging_weight'], density, densitygrid = weight_gridding(
im.data.shape, vis.data['weight'], vuvwmap, vfrequencymap,
vpolarisationmap, weighting)
return vis, density, densitygrid
def test_get_frequency_map_channel(self):
self.model = create_image_from_visibility(self.vis, npixel=512, cellsize=0.001,
nchan=self.vnchan,
frequency=self.startfrequency)
spectral_mode, vfrequency_map = get_frequency_map(self.vis, self.model)
assert numpy.max(vfrequency_map) == self.model.nchan - 1
assert numpy.min(vfrequency_map) == 0
assert spectral_mode == 'channel'
def test_get_frequency_map_s3(self):
self.model = create_low_test_image_from_s3(
npixel=64,
cellsize=0.001,
frequency=self.frequency,
channel_bandwidth=self.channel_bandwidth)
spectral_mode, vfrequency_map = get_frequency_map(self.vis, self.model)
assert numpy.max(vfrequency_map) == self.model.nchan - 1
assert spectral_mode == 'channel'
def predict_skycomponent_visibility(
vis: Union[Visibility, BlockVisibility],
sc: Union[Skycomponent, List[Skycomponent]]) -> Visibility:
"""Predict the visibility from a Skycomponent, add to existing visibility, for Visibility or BlockVisibility
:param vis: Visibility or BlockVisibility
:param sc: Skycomponent or list of SkyComponents
:return: Visibility or BlockVisibility
"""
if not isinstance(sc, collections.Iterable):
sc = [sc]
if isinstance(vis, Visibility):
_, im_nchan = list(get_frequency_map(vis, None))
npol = vis.polarisation_frame.npol
for comp in sc:
assert_same_chan_pol(vis, comp)
l, m, n = skycoord_to_lmn(comp.direction, vis.phasecentre)
phasor = simulate_point(vis.uvw, l, m)
for ivis in range(vis.nvis):
for pol in range(npol):
vis.data['vis'][ivis, pol] += comp.flux[im_nchan[ivis],
pol] * phasor[ivis]
elif isinstance(vis, BlockVisibility):
nchan = vis.nchan
npol = vis.npol
k = numpy.array(vis.frequency) / constants.c.to('m/s').value
for comp in sc:
assert_same_chan_pol(vis, comp)
flux = comp.flux
if comp.polarisation_frame != vis.polarisation_frame:
flux = convert_pol_frame(flux, comp.polarisation_frame,
vis.polarisation_frame)
l, m, n = skycoord_to_lmn(comp.direction, vis.phasecentre)
for chan in range(nchan):
phasor = simulate_point(vis.uvw * k[chan], l, m)
for pol in range(npol):
vis.data['vis'][..., chan,
pol] += flux[chan, pol] * phasor[...]
return vis
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 invert_2d_base_timing(vis: Visibility, im: Image, dopsf: bool = False, normalize: bool = True, **kwargs) \
-> (Image, numpy.ndarray, tuple):
""" Invert using 2D convolution function, including w projection optionally
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)
:return: resulting image
"""
opt = get_parameter(kwargs, 'opt', False)
if not opt:
log.debug('Using original algorithm')
else:
log.debug('Using optimized algorithm')
if not isinstance(vis, Visibility):
svis = coalesce_visibility(vis, **kwargs)
else:
svis = copy_visibility(vis)
if dopsf:
svis.data['vis'] = numpy.ones_like(svis.data['vis'])
svis = shift_vis_to_image(svis, im, tangent=True, inverse=False)
nchan, npol, ny, nx = im.data.shape
padding = {}
if get_parameter(kwargs, "padding", False):
padding = {'padding': get_parameter(kwargs, "padding", False)}
spectral_mode, vfrequencymap = get_frequency_map(svis, im, opt)
polarisation_mode, vpolarisationmap = get_polarisation_map(svis, im)
uvw_mode, shape, padding, vuvwmap = get_uvw_map(svis, im, **padding)
kernel_name, gcf, vkernellist = get_kernel_list(svis, im, **kwargs)
# Optionally pad to control aliasing
imgridpad = numpy.zeros(
[nchan, npol,
int(round(padding * ny)),
int(round(padding * nx))],
dtype='complex')
# Use original algorithm
if not opt:
time_grid = -time.time()
imgridpad, sumwt = convolutional_grid(vkernellist, imgridpad,
svis.data['vis'],
svis.data['imaging_weight'],
vuvwmap, vfrequencymap,
vpolarisationmap)
time_grid += time.time()
# Use optimized algorithm
else:
time_grid = -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)
sumwt = numpy.zeros((imgridpad.shape[0], imgridpad.shape[1]),
dtype=numpy.float64)
convolutional_grid_c(imgridpad, sumwt, native_order(svis.data['vis']),
native_order(svis.data['imaging_weight']),
native_order(kernels_c),
native_order(kernel_indices),
native_order(vuvwmap),
native_order(vfrequencymap_c))
time_grid += time.time()
# Fourier transform the padded grid to image, multiply by the gridding correction
# function, and extract the unpadded inner part.
# Normalise weights for consistency with transform
sumwt /= float(padding * int(round(padding * nx)) * ny)
imaginary = get_parameter(kwargs, "imaginary", False)
if imaginary:
log.debug("invert_2d_base: retaining imaginary part of dirty image")
result = extract_mid(ifft(imgridpad) * gcf, npixel=nx)
resultreal = create_image_from_array(result.real, im.wcs)
resultimag = create_image_from_array(result.imag, im.wcs)
if normalize:
resultreal = normalize_sumwt(resultreal, sumwt)
resultimag = normalize_sumwt(resultimag, sumwt)
return resultreal, sumwt, resultimag
else:
# Use original algorithm
if not opt:
time_ifft = -time.time()
inarr = ifft(imgridpad)
time_ifft += time.time()
# Use optimized algorithm
else:
time_ifft = -time.time()
inarr = numpy.zeros(imgridpad.shape, dtype=imgridpad.dtype)
ifft_c(inarr, imgridpad)
time_ifft += time.time()
result = extract_mid(numpy.real(inarr) * gcf, npixel=nx)
resultimage = create_image_from_array(result, im.wcs)
if normalize:
resultimage = normalize_sumwt(resultimage, sumwt)
return resultimage, sumwt, (time_grid, time_ifft)
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)
def invert_2d_base(vis: Visibility, im: Image, dopsf: bool = False, normalize: bool = True, **kwargs) \
-> (Image, numpy.ndarray):
""" Invert using 2D convolution function, including w projection optionally
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)
:return: resulting image
"""
if type(vis) is not Visibility:
svis = coalesce_visibility(vis, **kwargs)
else:
svis = copy_visibility(vis)
if dopsf:
svis.data['vis'] = numpy.ones_like(svis.data['vis'])
svis = shift_vis_to_image(svis, im, tangent=True, inverse=False)
nchan, npol, ny, nx = im.data.shape
spectral_mode, vfrequencymap = get_frequency_map(svis, im)
polarisation_mode, vpolarisationmap = get_polarisation_map(
svis, im, **kwargs)
uvw_mode, shape, padding, vuvwmap = get_uvw_map(svis, im, **kwargs)
kernel_name, gcf, vkernellist = get_kernel_list(svis, im, **kwargs)
# Optionally pad to control aliasing
imgridpad = numpy.zeros(
[nchan, npol,
int(round(padding * ny)),
int(round(padding * nx))],
dtype='complex')
imgridpad, sumwt = convolutional_grid(vkernellist, imgridpad,
svis.data['vis'],
svis.data['imaging_weight'], vuvwmap,
vfrequencymap, vpolarisationmap)
# Fourier transform the padded grid to image, multiply by the gridding correction
# function, and extract the unpadded inner part.
# Normalise weights for consistency with transform
sumwt /= float(padding * int(round(padding * nx)) * ny)
imaginary = get_parameter(kwargs, "imaginary", False)
if imaginary:
log.debug("invert_2d_base: retaining imaginary part of dirty image")
result = extract_mid(ifft(imgridpad) * gcf, npixel=nx)
resultreal = create_image_from_array(result.real, im.wcs)
resultimag = create_image_from_array(result.imag, im.wcs)
if normalize:
resultreal = normalize_sumwt(resultreal, sumwt)
resultimag = normalize_sumwt(resultimag, sumwt)
return resultreal, sumwt, resultimag
else:
result = extract_mid(numpy.real(ifft(imgridpad)) * gcf, npixel=nx)
resultimage = create_image_from_array(result, im.wcs)
if normalize:
resultimage = normalize_sumwt(resultimage, sumwt)
return resultimage, sumwt
