def test_scatter_gather_facet(self):
m31original = create_test_image(
polarisation_frame=PolarisationFrame('stokesI'))
assert numpy.max(numpy.abs(m31original.data)), "Original is empty"
for nraster in [1, 4, 8]:
m31model = create_test_image(
polarisation_frame=PolarisationFrame('stokesI'))
image_list = image_scatter_facets(m31model, facets=nraster)
for patch in image_list:
assert patch.data.shape[3] == (m31model.data.shape[3] // nraster), \
"Number of pixels in each patch: %d not as expected: %d" % (patch.data.shape[3],
(m31model.data.shape[3] // nraster))
assert patch.data.shape[2] == (m31model.data.shape[2] // nraster), \
"Number of pixels in each patch: %d not as expected: %d" % (patch.data.shape[2],
(m31model.data.shape[2] // nraster))
patch.data[...] = 1.0
m31reconstructed = create_empty_image_like(m31model)
m31reconstructed = image_gather_facets(image_list,
m31reconstructed,
facets=nraster)
flat = image_gather_facets(image_list,
m31reconstructed,
facets=nraster,
return_flat=True)
assert numpy.max(numpy.abs(
flat.data)), "Flat is empty for %d" % nraster
assert numpy.max(numpy.abs(
m31reconstructed.data)), "Raster is empty for %d" % nraster
def test_scatter_gather_facet_overlap_taper(self):
m31original = create_test_image(polarisation_frame=PolarisationFrame('stokesI'))
assert numpy.max(numpy.abs(m31original.data)), "Original is empty"
for taper in ['linear', None]:
for nraster, overlap in [(1, 0), (4, 8), (8, 8), (8, 16)]:
m31model = create_test_image(polarisation_frame=PolarisationFrame('stokesI'))
image_list = image_scatter_facets(m31model, facets=nraster, overlap=overlap, taper=taper)
for patch in image_list:
assert patch.data.shape[3] == (2 * overlap + m31model.data.shape[3] // nraster), \
"Number of pixels in each patch: %d not as expected: %d" % (patch.data.shape[3],
(2 * overlap + m31model.data.shape[3] //
nraster))
assert patch.data.shape[2] == (2 * overlap + m31model.data.shape[2] // nraster), \
"Number of pixels in each patch: %d not as expected: %d" % (patch.data.shape[2],
(2 * overlap + m31model.data.shape[2] //
nraster))
m31reconstructed = create_empty_image_like(m31model)
m31reconstructed = image_gather_facets(image_list, m31reconstructed, facets=nraster, overlap=overlap,
taper=taper)
flat = image_gather_facets(image_list, m31reconstructed, facets=nraster, overlap=overlap,
taper=taper, return_flat=True)
export_image_to_fits(m31reconstructed,
"%s/test_image_gather_scatter_%dnraster_%doverlap_%s_reconstructed.fits" %
(self.dir, nraster, overlap, taper))
export_image_to_fits(flat,
"%s/test_image_gather_scatter_%dnraster_%doverlap_%s_flat.fits" %
(self.dir, nraster, overlap, taper))
assert numpy.max(numpy.abs(flat.data)), "Flat is empty for %d" % nraster
assert numpy.max(numpy.abs(m31reconstructed.data)), "Raster is empty for %d" % nraster
# Create test image
frequency = numpy.array([1e8])
phasecentre = SkyCoord(ra=+15.0 * u.deg,
dec=-35.0 * u.deg,
frame='icrs',
equinox='J2000')
model = create_test_image(frequency=frequency,
phasecentre=phasecentre,
cellsize=0.001,
polarisation_frame=PolarisationFrame('stokesI'))
# Rank 0 scatters the test image
if rank == 0:
subimages = image_scatter_facets(model, facets=facets)
subimages = numpy.array_split(subimages, size)
else:
subimages = list()
sublist = comm.scatter(subimages, root=0)
root_images = imagerooter(sublist)
roots = comm.gather(root_images, root=0)
if rank == 0:
results = sum(roots, [])
root_model = create_empty_image_like(model)
result = image_gather_facets(results, root_model, facets=facets)
numpy.testing.assert_array_almost_equal_nulp(result.data**2,
numpy.abs(model.data), 7)
def deconvolve_list_serial_workflow(dirty_list,
psf_list,
model_imagelist,
prefix='',
**kwargs):
"""Create a graph for deconvolution, adding to the model
:param dirty_list:
:param psf_list:
:param model_imagelist:
:param kwargs: Parameters for functions in components
:return: (graph for the deconvolution, graph for the flat)
"""
nchan = len(dirty_list)
def deconvolve(dirty, psf, model, facet, gthreshold):
import time
starttime = time.time()
if prefix == '':
lprefix = "facet %d" % facet
else:
lprefix = "%s, facet %d" % (prefix, facet)
nmoments = get_parameter(kwargs, "nmoments", 0)
if nmoments > 0:
moment0 = calculate_image_frequency_moments(dirty)
this_peak = numpy.max(numpy.abs(
moment0.data[0, ...])) / dirty.data.shape[0]
else:
this_peak = numpy.max(numpy.abs(dirty.data[0, ...]))
if this_peak > 1.1 * gthreshold:
log.info(
"deconvolve_list_serial_workflow %s: cleaning - peak %.6f > 1.1 * threshold %.6f"
% (lprefix, this_peak, gthreshold))
kwargs['threshold'] = gthreshold
result, _ = deconvolve_cube(dirty, psf, prefix=lprefix, **kwargs)
if result.data.shape[0] == model.data.shape[0]:
result.data += model.data
else:
log.warning(
"deconvolve_list_serial_workflow %s: Initial model %s and clean result %s do not have the same shape"
% (lprefix, str(
model.data.shape[0]), str(result.data.shape[0])))
flux = numpy.sum(result.data[0, 0, ...])
log.info(
'### %s, %.6f, %.6f, True, %.3f # cycle, facet, peak, cleaned flux, clean, time?'
% (lprefix, this_peak, flux, time.time() - starttime))
return result
else:
log.info(
"deconvolve_list_serial_workflow %s: Not cleaning - peak %.6f <= 1.1 * threshold %.6f"
% (lprefix, this_peak, gthreshold))
log.info(
'### %s, %.6f, %.6f, False, %.3f # cycle, facet, peak, cleaned flux, clean, time?'
% (lprefix, this_peak, 0.0, time.time() - starttime))
return copy_image(model)
deconvolve_facets = get_parameter(kwargs, 'deconvolve_facets', 1)
deconvolve_overlap = get_parameter(kwargs, 'deconvolve_overlap', 0)
deconvolve_taper = get_parameter(kwargs, 'deconvolve_taper', None)
if deconvolve_overlap > 0:
deconvolve_number_facets = (deconvolve_facets - 2)**2
else:
deconvolve_number_facets = deconvolve_facets**2
model_imagelist = image_gather_channels(model_imagelist)
# Scatter the separate channel images into deconvolve facets and then gather channels for each facet.
# This avoids constructing the entire spectral cube.
# dirty_list = remove_sumwt, nout=nchan)(dirty_list)
scattered_channels_facets_dirty_list = \
[image_scatter_facets(d[0], facets=deconvolve_facets,
overlap=deconvolve_overlap,
taper=deconvolve_taper)
for d in dirty_list]
# Now we do a transpose and gather
scattered_facets_list = [
image_gather_channels([
scattered_channels_facets_dirty_list[chan][facet]
for chan in range(nchan)
]) for facet in range(deconvolve_number_facets)
]
psf_list = remove_sumwt(psf_list)
psf_list = image_gather_channels(psf_list)
scattered_model_imagelist = \
image_scatter_facets(model_imagelist,
facets=deconvolve_facets,
overlap=deconvolve_overlap)
# Work out the threshold. Need to find global peak over all dirty_list images
threshold = get_parameter(kwargs, "threshold", 0.0)
fractional_threshold = get_parameter(kwargs, "fractional_threshold", 0.1)
nmoments = get_parameter(kwargs, "nmoments", 0)
use_moment0 = nmoments > 0
# Find the global threshold. This uses the peak in the average on the frequency axis since we
# want to use it in a stopping criterion in a moment clean
global_threshold = threshold_list(scattered_facets_list,
threshold,
fractional_threshold,
use_moment0=use_moment0,
prefix=prefix)
facet_list = numpy.arange(deconvolve_number_facets).astype('int')
scattered_results_list = [
deconvolve(d, psf_list, m, facet, global_threshold) for d, m, facet in
zip(scattered_facets_list, scattered_model_imagelist, facet_list)
]
# Gather the results back into one image, correcting for overlaps as necessary. The taper function is is used to
# feather the facets together
gathered_results_list = image_gather_facets(scattered_results_list,
model_imagelist,
facets=deconvolve_facets,
overlap=deconvolve_overlap,
taper=deconvolve_taper)
flat_list = image_gather_facets(scattered_results_list,
model_imagelist,
facets=deconvolve_facets,
overlap=deconvolve_overlap,
taper=deconvolve_taper,
return_flat=True)
return image_scatter_channels(gathered_results_list,
subimages=nchan), flat_list