def create_image(ny,
nx,
frequency,
phasecentre,
cellsize=0.001,
polarisation_frame=PolarisationFrame('linear')):
'''
创建和上个函数等效的非并行的image
:param ny:
:param nx:
:param frequency:
:param phasecentre:
:param cellsize:
:param polarisation_frame:
:return:
'''
wcs4 = WCS(naxis=4)
wcs4.wcs.crpix = [ny // 2, nx // 2 + 1.0, 1.0, 1.0]
wcs4.wcs.cdelt = [
-180.0 * cellsize / np.pi, +180.0 * cellsize / np.pi, 1.0,
frequency[1] - frequency[0]
]
wcs4.wcs.crval = [
phasecentre.ra.deg, phasecentre.dec.deg, 1.0, frequency[0]
]
wcs4.wcs.ctype = ["RA---SIN", "DEC--SIN", 'STOKES', 'FREQ']
wcs4.wcs.radesys = 'ICRS'
wcs4.wcs.equinox = 2000.00
nchan = frequency.shape[0]
npol = polarisation_frame.npol
image = Image()
image.wcs = wcs4
image.data = np.zeros([nchan, npol, ny, nx])
image.polarisation_frame = polarisation_frame
return image
def import_image_from_fits(fitsfile: str, mute_warnings=True) -> Image:
""" Read an Image from fits
:param fitsfile:
:return: Image
"""
fim = Image()
with warnings.catch_warnings():
warnings.simplefilter('ignore')
hdulist = fits.open(arl_path(fitsfile))
fim.data = hdulist[0].data
fim.wcs = WCS(arl_path(fitsfile))
hdulist.close()
if len(fim.data) == 2:
fim.polarisation_frame = PolarisationFrame('stokesI')
else:
try:
fim.polarisation_frame = polarisation_frame_from_wcs(
fim.wcs, fim.data.shape)
except:
fim.polarisation_frame = PolarisationFrame('stokesI')
log.debug(
"import_image_from_fits: created %s image of shape %s, size %.3f (GB)"
% (fim.data.dtype, str(fim.shape), image_sizeof(fim)))
log.debug("import_image_from_fits: Max, min in %s = %.6f, %.6f" %
(fitsfile, fim.data.max(), fim.data.min()))
assert isinstance(fim, Image)
return fim
def image_para_to_image(ims: List[image_for_para],
image_share: image_share) -> Image:
'''
将并行image_for_para还原为原本的Image类，验证算法正确性用
:param ims: 只有一个facet的并行Image list 类型：list[image_para]
:param image_share: 并行Image的共享消息
:return: 还原后的image
'''
image = Image()
datatype = None
if type(ims[0]) == tuple:
datatype = ims[0][1].data.dtype
else:
datatype = ims[0].data.dtype
data = np.zeros(
[image_share.nchan, image_share.npol, image_share.ny, image_share.nx],
dtype=datatype)
dy = 0
dx = 0
if type(ims[0]) == tuple:
dy = ims[0][1].ny
dx = ims[0][1].nx
else:
dy = ims[0].ny
dx = ims[0].nx
assert image_share.ny // dy == image_share.nx // dx
facet = image_share.ny // dy
for im in ims:
if type(im) == tuple:
im = im[1]
nchan = im.channel
npol = im.polarisation
y = im.facet // facet
x = im.facet % facet
data[nchan, npol, y * dy:(y + 1) * dy, x * dx:(x + 1) * dx] = im.data
image.data = data
image.wcs = image_share.wcs
image.polarisation_frame = image_share.polarisation_frame
return image
def create_image_from_array(
data: numpy.array,
wcs: WCS = None,
polarisation_frame=PolarisationFrame('stokesI')) -> Image:
""" Create an image from an array and optional wcs
:param data: Numpy.array
:param wcs: World coordinate system
:param polarisation_frame: Polarisation Frame
:return: Image
"""
fim = Image()
fim.polarisation_frame = polarisation_frame
fim.data = data
if wcs is None:
fim.wcs = None
else:
fim.wcs = wcs.deepcopy()
if image_sizeof(fim) >= 1.0:
log.debug(
"create_image_from_array: created %s image of shape %s, size %.3f (GB)"
% (fim.data.dtype, str(fim.shape), image_sizeof(fim)))
assert isinstance(fim, Image), "Type is %s" % type(fim)
return fim
def normalize_sumwt(im: Image, sumwt) -> Image:
"""Normalize out the sum of weights
:param im: Image, im.data has shape [nchan, npol, ny, nx]
:param sumwt: Sum of weights [nchan, npol]
"""
nchan, npol, _, _ = im.data.shape
assert nchan == sumwt.shape[0]
assert npol == sumwt.shape[1]
for chan in range(nchan):
for pol in range(npol):
if sumwt[chan, pol] > 0.0:
im.data[chan, pol, :, :] /= sumwt[chan, pol]
else:
im.data[chan, pol, :, :] = 0.0
return im
def replicate_image(im: Image, polarisation_frame=PolarisationFrame('stokesI'), frequency=numpy.array([1e8])) \
-> Image:
""" Make a new canonical shape Image, extended along third and fourth axes by replication.
The order of the data is [chan, pol, dec, ra]
:param frequency:
:param im:
:param polarisation_frame: Polarisation_frame
:param nchan: Number of spectral channels
:return: Image
"""
if len(im.data.shape) == 2:
fim = Image()
newwcs = WCS(naxis=4)
newwcs.wcs.crpix = [
im.wcs.wcs.crpix[0] + 1.0, im.wcs.wcs.crpix[1] + 1.0, 1.0, 1.0
]
newwcs.wcs.cdelt = [im.wcs.wcs.cdelt[0], im.wcs.wcs.cdelt[1], 1.0, 1.0]
newwcs.wcs.crval = [
im.wcs.wcs.crval[0], im.wcs.wcs.crval[1], 1.0, frequency[0]
]
newwcs.wcs.ctype = [
im.wcs.wcs.ctype[0], im.wcs.wcs.ctype[1], 'STOKES', 'FREQ'
]
nchan = len(frequency)
npol = polarisation_frame.npol
fim.polarisation_frame = polarisation_frame
fim.wcs = newwcs
fshape = [nchan, npol, im.data.shape[1], im.data.shape[0]]
fim.data = numpy.zeros(fshape)
log.info("replicate_image: replicating shape %s to %s" %
(im.data.shape, fim.data.shape))
for i3 in range(nchan):
fim.data[i3, 0, :, :] = im.data[:, :]
return fim
else:
return im
def create_empty_image_like(im: Image) -> Image:
""" Create an empty image like another in shape and wcs
:param im:
:return: Image
"""
assert type(im) == Image, "Type is %s" % type(im)
fim = Image()
fim.polarisation_frame = im.polarisation_frame
fim.data = numpy.zeros_like(im.data)
if im.wcs is None:
fim.wcs = None
else:
fim.wcs = copy.deepcopy(im.wcs)
if image_sizeof(im) >= 1.0:
log.debug("create_empty_image_like: created %s image of shape %s, size %.3f (GB)" %
(fim.data.dtype, str(fim.shape), image_sizeof(fim)))
assert type(fim) == Image, "Type is %s" % type(fim)
return fim
def copy_image(im: Image) -> Image:
""" Create an image from an array
Performs deepcopy of data, breaking reference semantics
:param im:
:return: Image
"""
assert isinstance(im, Image), "Type is %s" % type(im)
fim = Image()
fim.polarisation_frame = im.polarisation_frame
fim.data = copy.deepcopy(im.data)
if im.wcs is None:
fim.wcs = None
else:
fim.wcs = copy.deepcopy(im.wcs)
if image_sizeof(fim) >= 1.0:
log.debug("copy_image: copied %s image of shape %s, size %.3f (GB)" %
(fim.data.dtype, str(fim.shape), image_sizeof(fim)))
assert type(fim) == Image
return fim
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
def image_sizeof(im: Image):
""" Return size in GB
"""
return im.size()
def deconvolve_cube_complex(dirty: Image, psf: Image, **kwargs) -> (Image, Image):
""" Clean using the complex Hogbom algorithm for polarised data (2016MNRAS.462.3483P)
The algorithm available is:
hogbom-complex: See: Pratley L. & Johnston-Hollitt M., (2016), MNRAS, 462, 3483.
This code is based upon the deconvolve_cube code for standard Hogbom clean available in ARL.
Args:
dirty (numpy array): The dirty image, i.e., the image to be deconvolved.
psf (numpy array): The point spread-function.
window (float): Regions where clean components are allowed. If True, entire dirty Image is allowed.
algorithm (str): Cleaning algorithm: 'hogbom-complex' only.
gain (float): The "loop gain", i.e., the fraction of the brightest pixel that is removed in each iteration.
threshold (float): Cleaning stops when the maximum of the absolute deviation of the residual is less than this value.
niter (int): Maximum number of components to make if the threshold `thresh` is not hit.
fractional_threshold (float): The predefined fractional threshold at which to stop cleaning.
Returns:
comp_image: clean component image.
residual_image: residual image.
"""
assert isinstance(dirty, Image), "Type is %s" % (type(dirty))
assert isinstance(psf, Image), "Type is %s" % (type(psf))
window_shape = get_parameter(kwargs, 'window_shape', None)
if window_shape == 'quarter':
qx = dirty.shape[3] // 4
qy = dirty.shape[2] // 4
window = np.zeros_like(dirty.data)
window[..., (qy + 1):3 * qy, (qx + 1):3 * qx] = 1.0
log.info('deconvolve_cube_complex: 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_complex: PSF support = +/- %d pixels' % (psf_support))
algorithm = get_parameter(kwargs, 'algorithm', 'msclean')
if algorithm == 'hogbom-complex':
log.info("deconvolve_cube_complex: Hogbom-complex 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 = np.zeros(dirty.data.shape)
residual_array = np.zeros(dirty.data.shape)
for channel in range(dirty.data.shape[0]):
for pol in range(dirty.data.shape[1]):
if pol == 0 or pol == 3:
if psf.data[channel, pol, :, :].max():
log.info("deconvolve_cube_complex: 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_complex: Skipping pol %d, channel %d" % (pol, channel))
if pol == 1:
if psf.data[channel, 1:2, :, :].max():
log.info("deconvolve_cube_complex: Processing pol 1 and 2, channel %d" % (channel))
if window is None:
comp_array[channel, 1, :, :], comp_array[channel, 2, :, :], residual_array[channel, 1, :, :], residual_array[channel, 2, :, :] = hogbom_complex(dirty.data[channel, 1, :, :], dirty.data[channel, 2, :, :], psf.data[channel, 1, :, :], psf.data[channel, 2, :, :], None, gain, thresh, niter, fracthresh)
else:
comp_array[channel, 1, :, :], comp_array[channel, 2, :, :], residual_array[channel, 1, :, :], residual_array[channel, 2, :, :] = hogbom_complex(dirty.data[channel, 1, :, :], dirty.data[channel, 2, :, :], psf.data[channel, 1, :, :], psf.data[channel, 2, :, :], window[channel, pol, :, :], gain, thresh, niter, fracthresh)
else:
log.info("deconvolve_cube_complex: Skipping pol 1 and 2, channel %d" % (channel))
if pol == 2:
continue
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_complex: Unknown algorithm %s' % algorithm)
return comp_image, residual_image
