def test_pad_image(self):
m31image = create_test_image(cellsize=0.001, frequency=[1e8], canonical=True)
padded = pad_image(m31image, [1, 1, 1024, 1024])
assert padded.shape == (1, 1, 1024, 1024)
padded = pad_image(m31image, [3, 4, 2048, 2048])
assert padded.shape == (3, 4, 2048, 2048)
with self.assertRaises(ValueError):
padded = pad_image(m31image, [1, 1, 100, 100])
with self.assertRaises(IndexError):
padded = pad_image(m31image, [1, 1])
def test_fftim_factors(self):
for i in [3, 5, 7]:
npixel = 256 * i
m31image = create_test_image(cellsize=0.001, frequency=[1e8], canonical=True)
padded = pad_image(m31image, [1, 1, npixel, npixel])
assert padded.shape == (1, 1, npixel, npixel)
padded_fft = fft_image(padded)
padded_fft_ifft = fft_image(padded_fft, m31image)
numpy.testing.assert_array_almost_equal(padded.data, padded_fft_ifft.data.real, 12)
padded_fft.data = numpy.abs(padded_fft.data)
export_image_to_fits(padded_fft, fitsfile='%s/test_m31_fft_%d.fits' % (self.dir, npixel))
def get_kernel_list(vis: Visibility, im: Image, **kwargs):
"""Get the list of kernels, one per visibility
"""
shape = im.data.shape
npixel = shape[3]
cellsize = numpy.pi * im.wcs.wcs.cdelt[1] / 180.0
kernelname = get_parameter(kwargs, "kernel", "2d")
oversampling = get_parameter(kwargs, "oversampling", 8)
padding = get_parameter(kwargs, "padding", 2)
gcf, _ = anti_aliasing_calculate((padding * npixel, padding * npixel), oversampling)
wabsmax = numpy.max(numpy.abs(vis.w))
if kernelname == 'wprojection' and wabsmax > 0.0:
# wprojection needs a lot of commentary!
log.debug("get_kernel_list: Using wprojection kernel")
# The field of view must be as padded! R_F is for reporting only so that
# need not be padded.
fov = cellsize * npixel * padding
r_f = (cellsize * npixel / 2) ** 2 / abs(cellsize)
log.debug("get_kernel_list: Fresnel number = %f" % (r_f))
delA = get_parameter(kwargs, 'wloss', 0.02)
advice = advise_wide_field(vis, delA)
wstep = get_parameter(kwargs, "wstep", advice['w_sampling_primary_beam'])
log.debug("get_kernel_list: Using w projection with wstep = %f" % (wstep))
# Now calculate the maximum support for the w kernel
kernelwidth = get_parameter(kwargs, "kernelwidth",
(2 * int(round(numpy.sin(0.5 * fov) * npixel * wabsmax * cellsize))))
kernelwidth = max(kernelwidth, 8)
assert kernelwidth % 2 == 0
log.debug("get_kernel_list: Maximum w kernel full width = %d pixels" % (kernelwidth))
padded_shape=[im.shape[0], im.shape[1], im.shape[2] * padding, im.shape[3] * padding]
remove_shift = get_parameter(kwargs, "remove_shift", True)
padded_image = pad_image(im, padded_shape)
kernel_list = w_kernel_list(vis, padded_image, oversampling=oversampling, wstep=wstep,
kernelwidth=kernelwidth, remove_shift=remove_shift)
else:
kernelname = '2d'
kernel_list = standard_kernel_list(vis, (padding * npixel, padding * npixel),
oversampling=oversampling)
return kernelname, gcf, kernel_list
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
