def test_calculate_image_frequency_moments(self):
frequency = numpy.linspace(0.9e8, 1.1e8, 9)
cube = create_low_test_image_from_gleam(npixel=512, cellsize=0.0001, frequency=frequency)
log.debug(export_image_to_fits(cube, fitsfile='%s/test_moments_cube.fits' % (self.dir)))
original_cube = copy_image(cube)
moment_cube = calculate_image_frequency_moments(cube, nmoments=3)
log.debug(export_image_to_fits(moment_cube, fitsfile='%s/test_moments_moment_cube.fits' % (self.dir)))
reconstructed_cube = calculate_image_from_frequency_moments(cube, moment_cube)
log.debug(export_image_to_fits(reconstructed_cube, fitsfile='%s/test_moments_reconstructed_cube.fits' % (
self.dir)))
error = numpy.std(reconstructed_cube.data - original_cube.data)
assert error < 0.2
def deconvolve_cube(dirty: Image, psf: Image, **kwargs):
""" Clean using a variety of algorithms
Functions that clean a dirty image using a point spread function. The algorithms available are:
hogbom: Hogbom CLEAN See: Hogbom CLEAN A&A Suppl, 15, 417, (1974)
msclean: MultiScale CLEAN See: Cornwell, T.J., Multiscale CLEAN (IEEE Journal of Selected Topics in Sig Proc,
2008 vol. 2 pp. 793-801)
mfsmsclean: MultiScale Multi-Frequency See: U. Rau and T. J. Cornwell, “A multi-scale multi-frequency
deconvolution algorithm for synthesis imaging in radio interferometry,” A&A 532, A71 (2011).
For example::
comp, residual = deconvolve_cube(dirty, psf, niter=1000, gain=0.7, algorithm='msclean',
scales=[0, 3, 10, 30], threshold=0.01)
:param dirty: Image dirty image
:param psf: Image Point Spread Function
:param window: Window image (Bool) - clean where True
:param algorithm: Cleaning algorithm: 'msclean'|'hogbom'|'mfsmsclean'
:param gain: loop gain (float) 0.7
:param threshold: Clean threshold (0.0)
:param fracthres: Fractional threshold (0.01)
:param scales: Scales (in pixels) for multiscale ([0, 3, 10, 30])
:param nmoments: Number of frequency moments (default 3)
:param findpeak: Method of finding peak in mfsclean: 'Algorithm1'|'ASKAPSoft'|'CASA'|'ARL', Default is ARL.
:returns: componentimage, residual
"""
assert type(dirty) == Image, "Type is %s" % (type(dirty))
assert type(psf) == Image, "Type is %s" % (type(psf))
window = get_parameter(kwargs, 'window', None)
if window == 'quarter':
qx = dirty.shape[3] // 4
qy = dirty.shape[2] // 4
window = numpy.zeros_like(dirty.data)
window[..., (qy + 1):3 * qy, (qx + 1):3 * qx] = 1.0
log.info('deconvolve_cube: Cleaning inner quarter of each sky plane')
else:
window = None
psf_support = get_parameter(kwargs, 'psf_support', None)
if isinstance(psf_support, int):
if (psf_support < psf.shape[2] // 2) and (
(psf_support < psf.shape[3] // 2)):
centre = [psf.shape[2] // 2, psf.shape[3] // 2]
psf.data = psf.data[..., (centre[0] - psf_support):(centre[0] +
psf_support),
(centre[1] - psf_support):(centre[1] +
psf_support)]
log.info('deconvolve_cube: PSF support = +/- %d pixels' %
(psf_support))
algorithm = get_parameter(kwargs, 'algorithm', 'msclean')
if algorithm == 'msclean':
log.info(
"deconvolve_cube: Multi-scale clean of each polarisation and channel separately"
)
gain = get_parameter(kwargs, 'gain', 0.7)
assert 0.0 < gain < 2.0, "Loop gain must be between 0 and 2"
thresh = get_parameter(kwargs, 'threshold', 0.0)
assert thresh >= 0.0
niter = get_parameter(kwargs, 'niter', 100)
assert niter > 0
scales = get_parameter(kwargs, 'scales', [0, 3, 10, 30])
fracthresh = get_parameter(kwargs, 'fractional_threshold', 0.01)
assert 0.0 < fracthresh < 1.0
comp_array = numpy.zeros_like(dirty.data)
residual_array = numpy.zeros_like(dirty.data)
for channel in range(dirty.data.shape[0]):
for pol in range(dirty.data.shape[1]):
if psf.data[channel, pol, :, :].max():
log.info("deconvolve_cube: Processing pol %d, channel %d" %
(pol, channel))
if window is None:
comp_array[channel, pol, :, :], residual_array[channel, pol, :, :] = \
msclean(dirty.data[channel, pol, :, :], psf.data[channel, pol, :, :],
None, gain, thresh, niter, scales, fracthresh)
else:
comp_array[channel, pol, :, :], residual_array[channel, pol, :, :] = \
msclean(dirty.data[channel, pol, :, :], psf.data[channel, pol, :, :],
window[channel, pol, :, :], gain, thresh, niter, scales, fracthresh)
else:
log.info("deconvolve_cube: Skipping pol %d, channel %d" %
(pol, channel))
comp_image = create_image_from_array(comp_array, dirty.wcs)
residual_image = create_image_from_array(residual_array, dirty.wcs)
elif algorithm == 'msmfsclean' or algorithm == 'mfsmsclean':
findpeak = get_parameter(kwargs, "findpeak", 'ARL')
log.info(
"deconvolve_cube: Multi-scale multi-frequency clean of each polarisation separately"
)
nmoments = get_parameter(kwargs, "nmoments", 3)
assert nmoments > 0, "Number of frequency moments must be greater than zero"
dirty_taylor = calculate_image_frequency_moments(dirty,
nmoments=nmoments)
psf_taylor = calculate_image_frequency_moments(psf,
nmoments=2 * nmoments)
gain = get_parameter(kwargs, 'gain', 0.7)
assert 0.0 < gain < 2.0, "Loop gain must be between 0 and 2"
thresh = get_parameter(kwargs, 'threshold', 0.0)
assert thresh >= 0.0
niter = get_parameter(kwargs, 'niter', 100)
assert niter > 0
scales = get_parameter(kwargs, 'scales', [0, 3, 10, 30])
fracthresh = get_parameter(kwargs, 'fractional_threshold', 0.1)
assert 0.0 < fracthresh < 1.0
comp_array = numpy.zeros(dirty_taylor.data.shape)
residual_array = numpy.zeros(dirty_taylor.data.shape)
for pol in range(dirty_taylor.data.shape[1]):
if psf_taylor.data[0, pol, :, :].max():
log.info("deconvolve_cube: Processing pol %d" % (pol))
if window is None:
comp_array[:, pol, :, :], residual_array[:, pol, :, :] = \
msmfsclean(dirty_taylor.data[:, pol, :, :], psf_taylor.data[:, pol, :, :],
None, gain, thresh, niter, scales, fracthresh, findpeak)
else:
comp_array[:, pol, :, :], residual_array[:, pol, :, :] = \
msmfsclean(dirty_taylor.data[:, pol, :, :], psf_taylor.data[:, pol, :, :],
window[:, pol, :, :], gain, thresh, niter, scales, fracthresh, findpeak)
else:
log.info("deconvolve_cube: Skipping pol %d" % (pol))
comp_image = create_image_from_array(comp_array, dirty_taylor.wcs)
residual_image = create_image_from_array(residual_array,
dirty_taylor.wcs)
return_moments = get_parameter(kwargs, "return_moments", False)
if not return_moments:
log.info("Deconvolve_cube: calculating spectral cubes")
comp_image = calculate_image_from_frequency_moments(
dirty, comp_image)
residual_image = calculate_image_from_frequency_moments(
dirty, residual_image)
else:
log.info("Deconvolve_cube: constructed moment cubes")
elif algorithm == 'hogbom':
log.info(
"deconvolve_cube: Hogbom clean of each polarisation and channel separately"
)
gain = get_parameter(kwargs, 'gain', 0.7)
assert 0.0 < gain < 2.0, "Loop gain must be between 0 and 2"
thresh = get_parameter(kwargs, 'threshold', 0.0)
assert thresh >= 0.0
niter = get_parameter(kwargs, 'niter', 100)
assert niter > 0
fracthresh = get_parameter(kwargs, 'fractional_threshold', 0.1)
assert 0.0 <= fracthresh < 1.0
comp_array = numpy.zeros(dirty.data.shape)
residual_array = numpy.zeros(dirty.data.shape)
for channel in range(dirty.data.shape[0]):
for pol in range(dirty.data.shape[1]):
if psf.data[channel, pol, :, :].max():
log.info("deconvolve_cube: Processing pol %d, channel %d" %
(pol, channel))
if window is None:
comp_array[channel, pol, :, :], residual_array[channel, pol, :, :] = \
hogbom(dirty.data[channel, pol, :, :], psf.data[channel, pol, :, :],
None, gain, thresh, niter, fracthresh)
else:
comp_array[channel, pol, :, :], residual_array[channel, pol, :, :] = \
hogbom(dirty.data[channel, pol, :, :], psf.data[channel, pol, :, :],
window[channel, pol, :, :], gain, thresh, niter, fracthresh)
else:
log.info("deconvolve_cube: Skipping pol %d, channel %d" %
(pol, channel))
comp_image = create_image_from_array(comp_array, dirty.wcs)
residual_image = create_image_from_array(residual_array, dirty.wcs)
else:
raise ValueError('deconvolve_cube: Unknown algorithm %s' % algorithm)
return comp_image, residual_image