def test_create_image_from_array(self):
m31model_by_array = create_image_from_array(self.m31image.data, wcs=None)
m31model_by_array = create_image_from_array(self.m31image.data, self.m31image.wcs)
add_image(self.m31image, m31model_by_array)
add_image(self.m31image, m31model_by_array, docheckwcs=True)
assert m31model_by_array.shape == self.m31image.shape
log.debug(export_image_to_fits(self.m31image, fitsfile='%s/test_model.fits' % (self.dir)))
log.debug(qa_image(m31model_by_array, context='test_create_from_image'))
def invert_2d_from_grid(uv_grid_vis,
sumwt,
im: Image,
normalize: bool = True,
**kwargs):
"""
Perform the 2d fourier transform on the gridded uv visibility
:param uv_grid_vis: the gridded uv visibility
:param sumwt: the weight for the uv visibility
:param im: image template (not changed)
:param return: resulting image[nchan, npol, ny, nx], sum of weights[nchan, npol]
"""
### Calculate gcf -> grid correction function ###
# 2D Prolate spheroidal angular function is separable
npixel = get_parameter(kwargs, "npixel", 512)
nx = npixel
ny = npixel
nu = numpy.abs(2.0 * (numpy.arange(nx) - nx // 2) / nx)
gcf1d, _ = grdsf(nu)
gcf = numpy.outer(gcf1d, gcf1d)
gcf[gcf > 0.0] = gcf.max() / gcf[gcf > 0.0]
result = numpy.real(ifft(uv_grid_vis)) * gcf
#Create image array
resultimage = create_image_from_array(result, im.wcs)
if normalize:
resultimage = normalize_sumwt(resultimage, sumwt)
return resultimage, sumwt
def test_w_kernel_list(self):
oversampling = 4
kernelwidth = 128
kernel_indices, kernels = w_kernel_list(self.vis,
self.model,
kernelwidth=kernelwidth,
wstep=50,
oversampling=oversampling)
assert numpy.max(numpy.abs(kernels[0].data)) > 0.0
assert len(kernel_indices) > 0
assert max(kernel_indices) == len(kernels) - 1
assert type(kernels[0]) == numpy.ndarray
assert len(kernels[0].shape) == 4
assert kernels[0].shape == (oversampling, oversampling, kernelwidth, kernelwidth), \
"Actual shape is %s" % str(kernels[0].shape)
kernel0 = create_image_from_array(kernels[0], self.model.wcs)
kernel0.data = kernel0.data.real
export_image_to_fits(kernel0,
"%s/test_w_kernel_list_kernel0.fits" % (self.dir))
with self.assertRaises(AssertionError):
kernel_indices, kernels = w_kernel_list(self.vis,
self.model,
kernelwidth=32,
wstep=50,
oversampling=3,
maxsupport=128)
def reppre_ifft_kernel(ix, data_extract_lsm, dep_image, insert_method,
applybeam):
frequency = dep_image["frequency"]
comps = []
for i in data_extract_lsm.value:
if i[0][2] == ix[2]:
comps = i[1]
wcs = dep_image["wcs"]
shape = dep_image["shape"]
polarisation_frame = dep_image["polarisation_frame"]
model = create_image_from_array(np.zeros(shape),
wcs,
polarisation_frame=polarisation_frame)
model = insert_skycomponent(model, comps, insert_method=insert_method)
if applybeam:
beam = create_low_test_beam(model)
model.data = model.data * beam
result = model
label = "Reprojection Predict + IFFT (14645.6 MB, 2.56 Tflop) "
# print(label + str(result))
return result
def setUp(self):
self.dir = './test_results'
os.makedirs(self.dir, exist_ok=True)
self.lowcore = create_named_configuration('LOWBD2-CORE')
self.times = (numpy.pi / (12.0)) * numpy.linspace(-3.0, 3.0, 7)
self.frequency = numpy.array([1e8])
self.channel_bandwidth = numpy.array([1e6])
self.phasecentre = SkyCoord(ra=+180.0 * u.deg, dec=-60.0 * u.deg, frame='icrs', equinox=2000.0)
self.vis = create_visibility(self.lowcore, self.times, self.frequency,
channel_bandwidth=self.channel_bandwidth,
phasecentre=self.phasecentre, weight=1.0,
polarisation_frame=PolarisationFrame('stokesI'))
self.vis.data['vis'] *= 0.0
# Create model
self.test_model = create_test_image(cellsize=0.001, phasecentre=self.vis.phasecentre,
frequency=self.frequency)
self.vis = predict_2d(self.vis, self.test_model)
assert numpy.max(numpy.abs(self.vis.vis)) > 0.0
self.model = create_image_from_visibility(self.vis, npixel=512, cellsize=0.001,
polarisation_frame=PolarisationFrame('stokesI'))
self.dirty, sumwt = invert_2d(self.vis, self.model)
self.psf, sumwt = invert_2d(self.vis, self.model, dopsf=True)
window = numpy.zeros(shape=self.model.shape, dtype=numpy.bool)
window[..., 129:384, 129:384] = True
self.innerquarter = create_image_from_array(window, self.model.wcs)
def sum_invert_bag_results(invert_list, normalize=True):
"""Sum a list of invert results, optionally normalizing at the end
Can be used in bag.map()
:param invert_list: List of results from invert: Image, weight tuples
:param normalize: Normalize by the sum of weights
"""
assert isinstance(invert_list, collections.Iterable), invert_list
result = None
weight = None
for i, a in enumerate(invert_list):
assert isinstance(a[0], Image), "Item is not an image: %s" % str(a[0])
if i == 0:
result = create_image_from_array(a[0].data * a[1], a[0].wcs, a[0].polarisation_frame)
weight = a[1]
else:
result.data += a[0].data * a[1]
weight += a[1]
assert weight is not None and result is not None, "No valid images found"
if normalize:
result = normalize_sumwt(result, weight)
return result, weight
def image_gather_channels(image_list: List[Image],
im: Image = None,
subimages=1) -> Image:
"""Gather a list of subimages back into an image using the channel_iterator
If the template image is not given then ti will be formed assuming that the list has
been generated by image_scatter_channels with subimages = number of channels
:param image_list: List of subimages
:param im: Output image
:param facets: Number of image partitions on each axis (2)
:return: list of subimages
"""
if im is None:
nchan = len(image_list)
_, npol, ny, nx = image_list[0].shape
im_shape = nchan, npol, ny, ny
im = create_image_from_array(
numpy.zeros(im_shape, dtype=image_list[0].data.dtype),
image_list[0].wcs, image_list[0].polarisation_frame)
for i, slab in enumerate(image_channel_iter(im, subimages=subimages)):
slab.data[...] = image_list[i].data[...]
return im
def setUp(self):
self.dir = './test_results'
os.makedirs(self.dir, exist_ok=True)
self.niter = 1000
self.lowcore = create_named_configuration('LOWBD2-CORE')
self.nchan = 5
self.times = (numpy.pi / 12.0) * numpy.linspace(-3.0, 3.0, 7)
self.frequency = numpy.linspace(0.9e8, 1.1e8, self.nchan)
self.channel_bandwidth = numpy.array(self.nchan * [self.frequency[1] - self.frequency[0]])
self.phasecentre = SkyCoord(ra=+0.0 * u.deg, dec=-45.0 * u.deg, frame='icrs', equinox='J2000')
self.vis = create_visibility(self.lowcore, self.times, self.frequency, self.channel_bandwidth,
phasecentre=self.phasecentre, weight=1.0,
polarisation_frame=PolarisationFrame('stokesI'))
self.vis.data['vis'] *= 0.0
# Create model
self.test_model = create_low_test_image_from_gleam(npixel=512, cellsize=0.001,
phasecentre=self.vis.phasecentre,
frequency=self.frequency,
channel_bandwidth=self.channel_bandwidth)
beam = create_low_test_beam(self.test_model)
export_image_to_fits(beam, "%s/test_deconvolve_msmfsclean_beam.fits" % self.dir)
self.test_model.data *= beam.data
export_image_to_fits(self.test_model, "%s/test_deconvolve_msmfsclean_model.fits" % self.dir)
self.vis = predict_2d(self.vis, self.test_model)
assert numpy.max(numpy.abs(self.vis.vis)) > 0.0
self.model = create_image_from_visibility(self.vis, npixel=512, cellsize=0.001,
polarisation_frame=PolarisationFrame('stokesI'))
self.dirty, sumwt = invert_2d(self.vis, self.model)
self.psf, sumwt = invert_2d(self.vis, self.model, dopsf=True)
export_image_to_fits(self.dirty, "%s/test_deconvolve_msmfsclean_dirty.fits" % self.dir)
export_image_to_fits(self.psf, "%s/test_deconvolve_msmfsclean_psf.fits" % self.dir)
window = numpy.ones(shape=self.model.shape, dtype=numpy.bool)
window[..., 129:384, 129:384] = True
self.innerquarter = create_image_from_array(window, self.model.wcs)
def create_low_test_beam(model: Image) -> Image:
"""Create a test power beam for LOW using an image from OSKAR
:param model: Template image
:return: Image
"""
beam = import_image_from_fits(arl_path('data/models/SKA1_LOW_beam.fits'))
# Scale the image cellsize to account for the different in frequencies. Eventually we will want to
# use a frequency cube
log.info("create_low_test_beam: primary beam is defined at %.3f MHz" %
(beam.wcs.wcs.crval[2] * 1e-6))
nchan, npol, ny, nx = model.shape
# We need to interpolate each frequency channel separately. The beam is assumed to just scale with
# frequency.
reprojected_beam = create_empty_image_like(model)
for chan in range(nchan):
model2dwcs = model.wcs.sub(2).deepcopy()
model2dshape = [model.shape[2], model.shape[3]]
beam2dwcs = beam.wcs.sub(2).deepcopy()
# The frequency axis is the second to last in the beam
frequency = model.wcs.sub(['spectral']).wcs_pix2world([chan], 0)[0]
fscale = beam.wcs.wcs.crval[2] / frequency
beam2dwcs.wcs.cdelt = fscale * beam.wcs.sub(2).wcs.cdelt
beam2dwcs.wcs.crpix = beam.wcs.sub(2).wcs.crpix
beam2dwcs.wcs.crval = model.wcs.sub(2).wcs.crval
beam2dwcs.wcs.ctype = model.wcs.sub(2).wcs.ctype
model2dwcs.wcs.crpix = [
model.shape[2] // 2 + 1, model.shape[3] // 2 + 1
]
beam2d = create_image_from_array(beam.data[0, 0, :, :], beam2dwcs,
model.polarisation_frame)
reprojected_beam2d, footprint = reproject_image(beam2d,
model2dwcs,
shape=model2dshape)
assert numpy.max(
footprint.data) > 0.0, "No overlap between beam and model"
reprojected_beam2d.data *= reprojected_beam2d.data
reprojected_beam2d.data[footprint.data <= 0.0] = 0.0
for pol in range(npol):
reprojected_beam.data[chan,
pol, :, :] = reprojected_beam2d.data[:, :]
return reprojected_beam
def image_raster_iter(im: Image, **kwargs):
"""Create an image_raster_iter generator, returning images, optionally with overlaps
The WCS is adjusted appropriately for each raster element. Hence this is a coordinate-aware
way to iterate through an image.
Provided we don't break reference semantics, memory should be conserved
To update the image in place:
for r in raster(im, facets=2)::
r.data[...] = numpy.sqrt(r.data[...])
:param im: Image
:param facets: Number of image partitions on each axis (2)
:param overlap: overlap in pixels
:param kwargs: throw away unwanted parameters
"""
nchan, npol, ny, nx = im.shape
facets = get_parameter(kwargs, "facets", 1)
overlap = get_parameter(kwargs, 'overlap', 0)
log.debug("raster_overlap: predicting using %d x %d image partitions" %
(facets, facets))
assert facets <= ny, "Cannot have more raster elements than pixels"
assert facets <= nx, "Cannot have more raster elements than pixels"
sx = int((nx // facets))
sy = int((ny // facets))
dx = int((nx // facets) + 2 * overlap)
dy = int((ny // facets) + 2 * overlap)
log.debug('raster_overlap: spacing of raster (%d, %d)' % (dx, dy))
for fy in range(facets):
y = ny // 2 + sy * (fy - facets // 2) - overlap
for fx in range(facets):
x = nx // 2 + sx * (fx - facets // 2) - overlap
if (x >= 0) and (x + dx) <= nx and (y >= 0) and (y + dy) <= ny:
log.debug('raster_overlap: partition (%d, %d) of (%d, %d)' %
(fy, fx, facets, facets))
# Adjust WCS
wcs = im.wcs.deepcopy()
wcs.wcs.crpix[0] -= x
wcs.wcs.crpix[1] -= y
# yield image from slice (reference!)
yield create_image_from_array(im.data[..., y:y + dy, x:x + dx],
wcs, im.polarisation_frame)
def gather_image_kernel(data, facets, image_iter, **kwargs):
data_dict, image_metadata = data
wcs, polarisation_frame, shape = image_metadata
result = create_image_from_array(np.empty(shape),
wcs=wcs,
polarisation_frame=polarisation_frame)
i = 0
sumwt = np.zeros([shape[0], shape[1]])
for dpatch in image_iter(result, facets=facets, **kwargs):
assert i < len(
data_dict), "Too few results in gather_image_iteration_results"
if data_dict[i] is not None:
assert len(data_dict[i]) == 2, data_dict[i]
dpatch.data[...] = data_dict[i][0]
sumwt += data_dict[i][1]
i += 1
return result, sumwt
def create_deconvolve_graph(sc, dirty_graph, psf_graph, model_graph, **kwargs):
def make_cube(img_graph, nchan, has_sumwt=True, ret_idx=False):
imgs = img_graph.collect()
l = [Image() for i in range(nchan)]
idx = [() for i in range(nchan)]
for tup in imgs:
if has_sumwt:
l[tup[0][2]] = tup[1][0]
else:
l[tup[0][2]] = tup[1]
if ret_idx:
idx[tup[0][2]] = tup[0]
if ret_idx:
return l, idx
else:
return l
nchan = get_parameter(kwargs, "nchan", 1)
algorithm = get_parameter(kwargs, "algorithm", "mmclean")
if algorithm == "mmclean" and nchan > 1:
im, idx = make_cube(dirty_graph, nchan, ret_idx=True)
dirty_cube = image_gather_channels(im, subimages=nchan)
psf_cube = image_gather_channels(make_cube(psf_graph, nchan),
subimages=nchan)
model_cube = image_gather_channels(make_cube(model_graph,
nchan,
has_sumwt=False),
subimages=nchan)
result = deconvolve_cube(dirty_cube, psf_cube, **kwargs)
result[0].data += model_cube.data
ret_img = []
pl = result[0].polarisation_frame
nchan, npol, ny, nx = result[0].shape
for i in range(nchan):
wcs = result[0].wcs.deepcopy()
wcs.wcs.crpix[3] -= i
ret_img.append(
create_image_from_array(
result[0].data[i, ...].reshape(1, npol, ny, nx), wcs, pl))
return sc.parallelize([i for i in range(nchan)
]).map(lambda ix: (idx[ix], ret_img[ix]))
else:
return deconvolve_handle(dirty_graph, psf_graph, model_graph, **kwargs)
def gather_deconvolved_image_kernel(datas, facets, **kwargs):
data, image_metadata = datas
dic = {}
for im in data:
dic[im.facet_id] = im
wcs, polarisation_frame, shape = image_metadata
result = create_image_from_array(np.empty(shape),
wcs=wcs,
polarisation_frame=polarisation_frame)
i = 0
for dpatch in image_raster_iter(result, facets=facets, **kwargs):
assert i < len(
data), "Too few results in gather_image_iteration_results"
dpatch.data[...] = dic[i].data[...]
i += 1
return result
def image_raster_iter(im: Image, **kwargs) -> Image:
"""Create an image_raster_iter generator, returning images
The WCS is adjusted appropriately for each raster element. Hence this is a coordinate-aware
way to iterate through an image.
Provided we don't break reference semantics, memory should be conserved
To update the image in place:
for r in raster(im, facets=2)::
r.data[...] = numpy.sqrt(r.data[...])
:param im: Image
:param facets: Number of image partitions on each axis (2)
:param kwargs: throw away unwanted parameters
"""
facets = get_parameter(kwargs, "facets", 1)
log.debug("raster: predicting using %d x %d image partitions" %
(facets, facets))
assert facets <= im.nheight, "Cannot have more raster elements than pixels"
assert facets <= im.nwidth, "Cannot have more raster elements than pixels"
assert im.nheight % facets == 0, "The %d partitions must exactly fill the image %d" % (
facets, im.nheight)
assert im.nwidth % facets == 0, "The %d partitions must exactly fill the image %d" % (
facets, im.width)
dx = int(im.nwidth // facets)
dy = int(im.nheight // facets)
log.debug('raster: spacing of raster (%d, %d)' % (dx, dy))
for y in range(0, im.nheight, dy):
for x in range(0, im.nwidth, dx):
log.debug('raster: partition (%d, %d) of (%d, %d)' %
(x // dx, y // dy, facets, facets))
# Adjust WCS
wcs = im.wcs.deepcopy()
wcs.wcs.crpix[0] -= x
wcs.wcs.crpix[1] -= y
# Yield image from slice (reference!)
yield create_image_from_array(im.data[..., y:y + dy, x:x + dx],
wcs, im.polarisation_frame)
def image_channel_iter(im: Image, subimages=1) -> Image:
"""Create a image_channel_iter generator, returning images
The WCS is adjusted appropriately for each raster element. Hence this is a coordinate-aware
way to iterate through an image.
Provided we don't break reference semantics, memory should be conserved
To update the image in place:
for r in raster(im, facets=2)::
r.data[...] = numpy.sqrt(r.data[...])
:param im: Image
:param channel_width: Number of image partitions on each axis (2)
"""
nchan, npol, ny, nx = im.shape
assert subimages <= nchan, "More subimages %d than channels %d" % (
subimages, nchan)
step = nchan // subimages
channels = numpy.array(range(0, nchan, step), dtype='int')
assert len(
channels
) == subimages, "subimages %d does not match length of channels %d" % (
subimages, len(channels))
for i, channel in enumerate(channels):
if i + 1 < len(channels):
channel_max = channels[i + 1]
else:
channel_max = nchan
# Adjust WCS
wcs = im.wcs.deepcopy()
wcs.wcs.crpix[3] -= channel
# Yield image from slice (reference!)
yield create_image_from_array(im.data[channel:channel_max, ...], wcs,
im.polarisation_frame)
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 create_low_test_image_from_gleam(npixel=512,
polarisation_frame=PolarisationFrame(
"stokesI"),
cellsize=0.000015,
frequency=numpy.array([1e8]),
channel_bandwidth=numpy.array([1e6]),
phasecentre=None,
kind='cubic',
applybeam=False,
flux_limit=0.1,
radius=None,
insert_method='Nearest',
nchan=16) -> Image:
"""Create LOW test image from the GLEAM survey
Stokes I is estimated from a cubic spline fit to the measured fluxes. The polarised flux is always zero.
See http://www.mwatelescope.org/science/gleam-survey The catalog is available from Vizier.
VIII/100 GaLactic and Extragalactic All-sky MWA survey (Hurley-Walker+, 2016)
GaLactic and Extragalactic All-sky Murchison Wide Field Array (GLEAM) survey. I: A low-frequency extragalactic
catalogue. Hurley-Walker N., et al., Mon. Not. R. Astron. Soc., 464, 1146-1167 (2017), 2017MNRAS.464.1146H
:param npixel: Number of pixels
:param polarisation_frame: Polarisation frame (default PolarisationFrame("stokesI"))
:param cellsize: cellsize in radians
:param frequency:
:param channel_bandwidth: Channel width (Hz)
:param phasecentre: phasecentre (SkyCoord)
:param kind: Kind of interpolation (see scipy.interpolate.interp1d) Default: linear
:return: Image
"""
if phasecentre is None:
phasecentre = SkyCoord(ra=+15.0 * u.deg,
dec=-35.0 * u.deg,
frame='icrs',
equinox='J2000')
if radius is None:
radius = npixel * cellsize / numpy.sqrt(2.0)
sc = create_low_test_skycomponents_from_gleam(
flux_limit=flux_limit,
polarisation_frame=polarisation_frame,
frequency=frequency,
phasecentre=phasecentre,
kind=kind,
radius=radius,
nchan=nchan)
if polarisation_frame is None:
polarisation_frame = PolarisationFrame("stokesI")
npol = polarisation_frame.npol
nchan = len(frequency)
shape = [nchan, npol, npixel, npixel]
w = WCS(naxis=4)
# The negation in the longitude is needed by definition of RA, DEC
w.wcs.cdelt = [
-cellsize * 180.0 / numpy.pi, cellsize * 180.0 / numpy.pi, 1.0,
channel_bandwidth[0]
]
w.wcs.crpix = [npixel // 2 + 1, npixel // 2 + 1, 1.0, 1.0]
w.wcs.ctype = ["RA---SIN", "DEC--SIN", 'STOKES', 'FREQ']
w.wcs.crval = [phasecentre.ra.deg, phasecentre.dec.deg, 1.0, frequency[0]]
w.naxis = 4
w.wcs.radesys = 'ICRS'
w.wcs.equinox = 2000.0
model = create_image_from_array(numpy.zeros(shape),
w,
polarisation_frame=polarisation_frame)
model = insert_skycomponent(model, sc, insert_method=insert_method)
if applybeam:
beam = create_low_test_beam(model)
model.data[...] *= beam.data[...]
log.info(qa_image(model, context='create_low_test_image_from_gleam'))
return model
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 create_low_test_image_from_gleam(npixel=512, polarisation_frame=PolarisationFrame("stokesI"), cellsize=0.000015,
frequency=numpy.array([1e8]), channel_bandwidth=numpy.array([1e6]),
phasecentre=None, kind='cubic'):
"""Create LOW test image from the GLEAM survey
Stokes I is estimated from a cubic spline fit to the measured fluxes. The polarised flux is always zero.
See http://www.mwatelescope.org/science/gleam-survey The catalog is available from Vizier.
VIII/100 GaLactic and Extragalactic All-sky MWA survey (Hurley-Walker+, 2016)
GaLactic and Extragalactic All-sky Murchison Wide Field Array (GLEAM) survey. I: A low-frequency extragalactic
catalogue. Hurley-Walker N., et al., Mon. Not. R. Astron. Soc., 464, 1146-1167 (2017), 2017MNRAS.464.1146H
:param npixel: Number of pixels
:param polarisation_frame: Polarisation frame (default PolarisationFrame("stokesI"))
:param cellsize: cellsize in radians
:param frequency:
:param channel_bandwidth: Channel width (Hz)
:param phasecentre: phasecentre (SkyCoord)
:param kind: Kind of interpolation (see scipy.interpolate.interp1d) Default: linear
:returns: Image
"""
fitsfile = arl_path("data/models/GLEAM_EGC.fits")
hdulist = fits.open(fitsfile, lazy_load_hdus=False)
recs = hdulist[1].data[0].array
ras = recs['RAJ2000']
decs = recs['DEJ2000']
if phasecentre is None:
phasecentre = SkyCoord(ra=+15.0 * u.deg, dec=-35.0 * u.deg, frame='icrs', equinox=2000.0)
if polarisation_frame is None:
polarisation_frame = PolarisationFrame("stokesI")
npol = polarisation_frame.npol
nchan = len(frequency)
shape = [nchan, npol, npixel, npixel]
w = WCS(naxis=4)
# The negation in the longitude is needed by definition of RA, DEC
w.wcs.cdelt = [-cellsize * 180.0 / numpy.pi, cellsize * 180.0 / numpy.pi, 1.0, channel_bandwidth[0]]
w.wcs.crpix = [npixel // 2, npixel // 2, 1.0, 1.0]
w.wcs.ctype = ["RA---SIN", "DEC--SIN", 'STOKES', 'FREQ']
w.wcs.crval = [phasecentre.ra.deg, phasecentre.dec.deg, 1.0, frequency[0]]
w.naxis = 4
w.wcs.radesys = 'ICRS'
w.wcs.equinox = 2000.0
model = create_image_from_array(numpy.zeros(shape), w, polarisation_frame=polarisation_frame)
p = w.sub(2).wcs_world2pix(numpy.array(ras), numpy.array(decs), 1)
p = numpy.array(p)
mask = numpy.isfinite(p[0, :])
p = [p[0, mask], p[1, mask]]
ip = numpy.round(p).astype('int')
ok = numpy.where((0 <= ip[0, :]) & (npixel > ip[0, :]) & (0 <= ip[1, :]) & (npixel > ip[1, :]))[0]
ps = ip[:, ok]
# For every source, we read all measured fluxes and interpolate to the
# required frequencies
fluxes = []
gleam_freqs = numpy.array([76, 84, 92, 99, 107, 115, 122, 130, 143, 151, 158, 166, 174, 181, 189, 197, 204,
212, 220, 227])
for source in ok:
this_source_fluxes = numpy.zeros(len(gleam_freqs))
for i, f in enumerate(gleam_freqs):
this_source_fluxes[i] = recs['int_flux_%03d' % (f)][source]
fint = interpolate.interp1d(gleam_freqs * 1.0e6, this_source_fluxes, kind=kind)
fluxes.append(fint(frequency))
fluxes = numpy.array(fluxes)
actual_flux = numpy.sum(fluxes)
log.info('create_low_test_image_from_gleam: %d sources inside the image' % (ps.shape[1]))
log.info('create_low_test_image_from_gleam: Average flux per channel in image = %.3f' % (actual_flux/float(nchan)))
for iflux, flux in enumerate(fluxes):
if not numpy.isnan(flux).any() and flux.all() > 0.0:
model.data[:, 0, ps[1, iflux], ps[0, iflux]] = flux[:]
hdulist.close()
return model
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
def create_low_test_image_from_s3(npixel=16384,
polarisation_frame=PolarisationFrame(
"stokesI"),
cellsize=0.000015,
frequency=numpy.array([1e8]),
channel_bandwidth=numpy.array([1e6]),
phasecentre=None,
fov=20) -> Image:
"""Create LOW test image from S3
The input catalog was generated at http://s-cubed.physics.ox.ac.uk/s3_sex using the following query::
Database: s3_sex
SQL: select * from Galaxies where (pow(10,itot_151)*1000 > 1.0) and (right_ascension between -5 and 5) and (declination between -5 and 5);;
Number of rows returned: 29966
There are three possible tables to use::
data/models/S3_151MHz_10deg.csv, use fov=10
data/models/S3_151MHz_20deg.csv, use fov=20
data/models/S3_151MHz_40deg.csv, use fov=40
The component spectral index is calculated from the 610MHz and 151MHz, and then calculated for the specified
frequencies.
If polarisation_frame is not stokesI then the image will a polarised axis but the values will be zero.
:param npixel: Number of pixels
:param polarisation_frame: Polarisation frame (default PolarisationFrame("stokesI"))
:param cellsize: cellsize in radians
:param frequency:
:param channel_bandwidth: Channel width (Hz)
:param phasecentre: phasecentre (SkyCoord)
:param fov: fov table to use
:return: Image
"""
ras = []
decs = []
fluxes = []
if phasecentre is None:
phasecentre = SkyCoord(ra=+180.0 * u.deg,
dec=-60.0 * u.deg,
frame='icrs',
equinox='J2000')
if polarisation_frame is None:
polarisation_frame = PolarisationFrame("I")
npol = polarisation_frame.npol
nchan = len(frequency)
shape = [nchan, npol, npixel, npixel]
w = WCS(naxis=4)
# The negation in the longitude is needed by definition of RA, DEC
w.wcs.cdelt = [
-cellsize * 180.0 / numpy.pi, cellsize * 180.0 / numpy.pi, 1.0,
channel_bandwidth[0]
]
w.wcs.crpix = [npixel // 2 + 1, npixel // 2 + 1, 1.0, 1.0]
w.wcs.ctype = ["RA---SIN", "DEC--SIN", 'STOKES', 'FREQ']
w.wcs.crval = [phasecentre.ra.deg, phasecentre.dec.deg, 1.0, frequency[0]]
w.naxis = 4
w.wcs.radesys = 'ICRS'
w.wcs.equinox = 2000.0
model = create_image_from_array(numpy.zeros(shape),
w,
polarisation_frame=polarisation_frame)
assert fov in [10, 20,
40], "Field of view invalid: use one of %s" % ([10, 20, 40])
with open(arl_path('data/models/S3_151MHz_%ddeg.csv' % (fov))) as csvfile:
readCSV = csv.reader(csvfile, delimiter=',')
r = 0
for row in readCSV:
# Skip first row
if r > 0:
ra = float(row[4]) + phasecentre.ra.deg
dec = float(row[5]) + phasecentre.dec.deg
alpha = (float(row[10]) - float(row[9])) / numpy.log10(
610.0 / 151.0)
flux = numpy.power(10, float(row[9])) * numpy.power(
frequency / 1.51e8, alpha)
ras.append(ra)
decs.append(dec)
fluxes.append(flux)
r += 1
csvfile.close()
p = w.sub(2).wcs_world2pix(numpy.array(ras), numpy.array(decs), 1)
total_flux = numpy.sum(fluxes)
fluxes = numpy.array(fluxes)
ip = numpy.round(p).astype('int')
ok = numpy.where((0 <= ip[0, :]) & (npixel > ip[0, :]) & (0 <= ip[1, :])
& (npixel > ip[1, :]))[0]
ps = ip[:, ok]
fluxes = fluxes[ok]
actual_flux = numpy.sum(fluxes)
log.info('create_low_test_image_from_s3: %d sources inside the image' %
(ps.shape[1]))
log.info(
'create_low_test_image_from_s3: average channel flux in S3 model = %.3f, actual average channel flux in '
'image = %.3f' %
(total_flux / float(nchan), actual_flux / float(nchan)))
for chan in range(nchan):
for iflux, flux in enumerate(fluxes):
model.data[chan, 0, ps[1, iflux], ps[0, iflux]] = flux[chan]
return model
def create_image_from_visibility(vis, **kwargs) -> Image:
"""Make an empty image from params and Visibility
:param vis:
:param phasecentre: Phasecentre (Skycoord)
:param channel_bandwidth: Channel width (Hz)
:param cellsize: Cellsize (radians)
:param npixel: Number of pixels on each axis (512)
:param frame: Coordinate frame for WCS (ICRS)
:param equinox: Equinox for WCS (2000.0)
:param nchan: Number of image channels (Default is 1 -> MFS)
:return: image
"""
assert isinstance(vis, Visibility) or isinstance(vis, BlockVisibility), \
"vis is not a Visibility or a BlockVisibility: %r" % (vis)
log.info(
"create_image_from_visibility: Parsing parameters to get definition of WCS"
)
imagecentre = get_parameter(kwargs, "imagecentre", vis.phasecentre)
phasecentre = get_parameter(kwargs, "phasecentre", vis.phasecentre)
# Spectral processing options
ufrequency = numpy.unique(vis.frequency)
vnchan = len(ufrequency)
frequency = get_parameter(kwargs, "frequency", vis.frequency)
inchan = get_parameter(kwargs, "nchan", vnchan)
reffrequency = frequency[0] * units.Hz
channel_bandwidth = get_parameter(kwargs, "channel_bandwidth",
vis.channel_bandwidth[0]) * units.Hz
if (inchan == vnchan) and vnchan > 1:
log.info(
"create_image_from_visibility: Defining %d channel Image at %s, starting frequency %s, and bandwidth %s"
% (inchan, imagecentre, reffrequency, channel_bandwidth))
elif (inchan == 1) and vnchan > 1:
assert numpy.abs(channel_bandwidth.value
) > 0.0, "Channel width must be non-zero for mfs mode"
log.info(
"create_image_from_visibility: Defining single channel MFS Image at %s, starting frequency %s, "
"and bandwidth %s" %
(imagecentre, reffrequency, channel_bandwidth))
elif inchan > 1 and vnchan > 1:
assert numpy.abs(channel_bandwidth.value
) > 0.0, "Channel width must be non-zero for mfs mode"
log.info(
"create_image_from_visibility: Defining multi-channel MFS Image at %s, starting frequency %s, "
"and bandwidth %s" %
(imagecentre, reffrequency, channel_bandwidth))
elif (inchan == 1) and (vnchan == 1):
assert numpy.abs(channel_bandwidth.value
) > 0.0, "Channel width must be non-zero for mfs mode"
log.info(
"create_image_from_visibility: Defining single channel Image at %s, starting frequency %s, "
"and bandwidth %s" %
(imagecentre, reffrequency, channel_bandwidth))
else:
raise ValueError(
"create_image_from_visibility: unknown spectral mode ")
# Image sampling options
npixel = get_parameter(kwargs, "npixel", 512)
uvmax = numpy.max((numpy.abs(vis.data['uvw'][:, 0:1])))
if isinstance(vis, BlockVisibility):
uvmax *= numpy.max(frequency) / constants.c.to('m/s').value
log.info("create_image_from_visibility: uvmax = %f wavelengths" % uvmax)
criticalcellsize = 1.0 / (uvmax * 2.0)
log.info(
"create_image_from_visibility: Critical cellsize = %f radians, %f degrees"
% (criticalcellsize, criticalcellsize * 180.0 / numpy.pi))
cellsize = get_parameter(kwargs, "cellsize", 0.5 * criticalcellsize)
log.info(
"create_image_from_visibility: Cellsize = %f radians, %f degrees"
% (cellsize, cellsize * 180.0 / numpy.pi))
override_cellsize = get_parameter(kwargs, "override_cellsize", True)
if override_cellsize and cellsize > criticalcellsize:
log.info(
"create_image_from_visibility: Resetting cellsize %f radians to criticalcellsize %f radians"
% (cellsize, criticalcellsize))
cellsize = criticalcellsize
pol_frame = get_parameter(kwargs, "polarisation_frame",
PolarisationFrame("stokesI"))
inpol = pol_frame.npol
# Now we can define the WCS, which is a convenient place to hold the info above
# Beware of python indexing order! wcs and the array have opposite ordering
shape = [inchan, inpol, npixel, npixel]
w = wcs.WCS(naxis=4)
# The negation in the longitude is needed by definition of RA, DEC
w.wcs.cdelt = [
-cellsize * 180.0 / numpy.pi, cellsize * 180.0 / numpy.pi, 1.0,
channel_bandwidth.to(units.Hz).value
]
# The numpy definition of the phase centre of an FFT is n // 2 (0 - rel) so that's what we use for
# the reference pixel. We have to use 0 rel everywhere.
w.wcs.crpix = [npixel // 2, npixel // 2, 1.0, 1.0]
w.wcs.ctype = ["RA---SIN", "DEC--SIN", 'STOKES', 'FREQ']
w.wcs.crval = [
phasecentre.ra.deg, phasecentre.dec.deg, 1.0,
reffrequency.to(units.Hz).value
]
w.naxis = 4
w.wcs.radesys = get_parameter(kwargs, 'frame', 'ICRS')
w.wcs.equinox = get_parameter(kwargs, 'equinox', 2000.0)
return create_image_from_array(numpy.zeros(shape),
wcs=w,
polarisation_frame=pol_frame)
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