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 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 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