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 w_kernel_list(vis: Visibility,
im: Image,
oversampling=1,
wstep=50.0,
kernelwidth=16,
**kwargs):
""" Calculate w convolution kernels
Uses create_w_term_like to calculate the w screen. This is exactly as wstacking does.
Returns (indices to the w kernel for each row, kernels)
Each kernel has axes [centre_v, centre_u, offset_v, offset_u]. We currently use the same
convolution function for all channels and polarisations. Changing that behaviour would
require modest changes here and to the gridding/degridding routines.
:param vis: visibility
:param image: Template image (padding, if any, occurs before this)
:param oversampling: Oversampling factor
:param wstep: Step in w between cached functions
:return: (indices to the w kernel for each row, kernels)
"""
nchan, npol, ny, nx = im.shape
gcf, _ = anti_aliasing_calculate((ny, nx))
assert oversampling % 2 == 0 or oversampling == 1, "oversampling must be unity or even"
assert kernelwidth % 2 == 0, "kernelwidth must be even"
wmaxabs = numpy.max(numpy.abs(vis.w))
log.debug(
"w_kernel_list: Maximum absolute w = %.1f, step is %.1f wavelengths" %
(wmaxabs, wstep))
def digitise(w, wstep):
return numpy.ceil((w + wmaxabs) / wstep).astype('int')
# Find all the unique indices for which we need a kernel
nwsteps = digitise(wmaxabs, wstep) + 1
w_list = numpy.linspace(-wmaxabs, +wmaxabs, nwsteps)
print('====', nwsteps, wstep, len(w_list))
wtemplate = copy_image(im)
wtemplate.data = numpy.zeros(wtemplate.shape, dtype=im.data.dtype)
padded_shape = list(wtemplate.shape)
padded_shape[3] *= oversampling
padded_shape[2] *= oversampling
# For all the unique indices, calculate the corresponding w kernel
kernels = list()
for w in w_list:
# Make a w screen
wscreen = create_w_term_like(wtemplate, w, vis.phasecentre, **kwargs)
wscreen.data /= gcf
assert numpy.max(numpy.abs(wscreen.data)) > 0.0, 'w screen is empty'
wscreen_padded = pad_image(wscreen, padded_shape)
wconv = fft_image(wscreen_padded)
wconv.data *= float(oversampling)**2
# For the moment, ignore the polarisation and channel axes
kernels.append(
convert_image_to_kernel(wconv, oversampling,
kernelwidth).data[0, 0, ...])
# Now make a lookup table from row number of vis to the kernel
kernel_indices = digitise(vis.w, wstep)
assert numpy.max(kernel_indices) < len(kernels), "wabsmax %f wstep %f" % (
wmaxabs, wstep)
assert numpy.min(kernel_indices) >= 0, "wabsmax %f wstep %f" % (wmaxabs,
wstep)
return kernel_indices, kernels