def restore_list_arlexecute_workflow(model_imagelist,
psf_imagelist,
residual_imagelist=None,
**kwargs):
""" Create a graph to calculate the restored image
:param model_imagelist: Model list
:param psf_imagelist: PSF list
:param residual_imagelist: Residual list
:param kwargs: Parameters for functions in components
:return:
"""
if residual_imagelist is None:
residual_imagelist = []
psf_list = arlexecute.execute(remove_sumwt,
nout=len(psf_imagelist))(psf_imagelist)
if len(residual_imagelist) > 0:
residual_list = arlexecute.execute(
remove_sumwt, nout=len(residual_imagelist))(residual_imagelist)
result = [
arlexecute.execute(restore_cube)(model_imagelist[i], psf_list[i],
residual_list[i], **kwargs)
for i, _ in enumerate(model_imagelist)
]
else:
result = [
arlexecute.execute(restore_cube)(model_imagelist[i], psf_list[i],
**kwargs)
for i, _ in enumerate(model_imagelist)
]
return arlexecute.optimize(result)
def deconvolve_list_channel_arlexecute_workflow(dirty_list, psf_list, model_imagelist, subimages, **kwargs):
"""Create a graph for deconvolution by channels, adding to the model
Does deconvolution channel by channel.
:param subimages:
:param dirty_list:
:param psf_list: Must be the size of a facet
:param model_imagelist: Current model
:param kwargs: Parameters for functions in components
:return:
"""
def deconvolve_subimage(dirty, psf):
assert isinstance(dirty, Image)
assert isinstance(psf, Image)
comp = deconvolve_cube(dirty, psf, **kwargs)
return comp[0]
def add_model(sum_model, model):
assert isinstance(output, Image)
assert isinstance(model, Image)
sum_model.data += model.data
return sum_model
output = arlexecute.execute(create_empty_image_like, nout=1, pure=True)(model_imagelist)
dirty_lists = arlexecute.execute(image_scatter_channels, nout=subimages, pure=True)(dirty_list[0],
subimages=subimages)
results = [arlexecute.execute(deconvolve_subimage)(dirty_list, psf_list[0])
for dirty_list in dirty_lists]
result = arlexecute.execute(image_gather_channels, nout=1, pure=True)(results, output, subimages=subimages)
result = arlexecute.execute(add_model, nout=1, pure=True)(result, model_imagelist)
return arlexecute.optimize(result)
def residual_list_arlexecute_workflow(vis,
model_imagelist,
context='2d',
gcfcf=None,
**kwargs):
""" Create a graph to calculate residual image
:param vis:
:param model_imagelist: Model used to determine image parameters
:param context:
:param gcfcg: tuple containing grid correction and convolution function
:param kwargs: Parameters for functions in components
:return:
"""
model_vis = zero_list_arlexecute_workflow(vis)
model_vis = predict_list_arlexecute_workflow(model_vis,
model_imagelist,
context=context,
gcfcf=gcfcf,
**kwargs)
residual_vis = subtract_list_arlexecute_workflow(vis, model_vis)
result = invert_list_arlexecute_workflow(residual_vis,
model_imagelist,
dopsf=False,
normalize=True,
context=context,
gcfcf=gcfcf,
**kwargs)
return arlexecute.optimize(result)
def taper_list_arlexecute_workflow(vis_list, size_required):
"""Taper to desired size
:param vis_list:
:param size_required:
:return:
"""
result = [arlexecute.execute(taper_visibility_gaussian, nout=1)(v, beam=size_required) for v in vis_list]
return arlexecute.optimize(result)
def restore_list_arlexecute_workflow(model_imagelist, psf_imagelist, residual_imagelist=None, restore_facets=1,
restore_overlap=0, restore_taper='tukey', **kwargs):
""" Create a graph to calculate the restored image
:param model_imagelist: Model list
:param psf_imagelist: PSF list
:param residual_imagelist: Residual list
:param kwargs: Parameters for functions in components
:param restore_facets: Number of facets used per axis (used to distribute)
:param restore_overlap: Overlap in pixels (0 is best)
:param restore_taper: Type of taper.
:return:
"""
assert len(model_imagelist) == len(psf_imagelist)
if residual_imagelist is not None:
assert len(model_imagelist) == len(residual_imagelist)
if restore_facets % 2 == 0 or restore_facets == 1:
actual_number_facets = restore_facets
else:
actual_number_facets = max(1, (restore_facets - 1))
psf_list = arlexecute.execute(remove_sumwt, nout=len(psf_imagelist))(psf_imagelist)
# Scatter each list element into a list. We will then run restore_cube on each
facet_model_list = [arlexecute.execute(image_scatter_facets, nout=actual_number_facets * actual_number_facets)
(model, facets=restore_facets, overlap=restore_overlap, taper=restore_taper)
for model in model_imagelist]
facet_psf_list = [arlexecute.execute(image_scatter_facets, nout=actual_number_facets * actual_number_facets)
(psf, facets=restore_facets, overlap=restore_overlap, taper=restore_taper)
for psf in psf_list]
if residual_imagelist is not None:
residual_list = arlexecute.execute(remove_sumwt, nout=len(residual_imagelist))(residual_imagelist)
facet_residual_list = [
arlexecute.execute(image_scatter_facets, nout=actual_number_facets * actual_number_facets)
(residual, facets=restore_facets, overlap=restore_overlap, taper=restore_taper)
for residual in residual_list]
facet_restored_list = [[arlexecute.execute(restore_cube, nout=actual_number_facets * actual_number_facets)
(model=facet_model_list[i][im], psf=facet_psf_list[i][im],
residual=facet_residual_list[i][im],
**kwargs)
for im, _ in enumerate(facet_model_list[i])] for i, _ in enumerate(model_imagelist)]
else:
facet_restored_list = [[arlexecute.execute(restore_cube, nout=actual_number_facets * actual_number_facets)
(model=facet_model_list[i][im], psf=facet_psf_list[i][im],
**kwargs)
for im, _ in enumerate(facet_model_list[i])] for i, _ in enumerate(model_imagelist)]
# Now we run restore_cube on each and gather the results across all facets
restored_imagelist = [arlexecute.execute(image_gather_facets)
(facet_restored_list[i], model_imagelist[i], facets=restore_facets,
overlap=restore_overlap, taper=restore_taper)
for i, _ in enumerate(model_imagelist)]
return arlexecute.optimize(restored_imagelist)
def weight_list_arlexecute_workflow(vis_list, model_imagelist, gcfcf=None, weighting='uniform', **kwargs):
""" Weight the visibility data
This is done collectively so the weights are summed over all vis_lists and then
corrected
:param vis_list:
:param model_imagelist: Model required to determine weighting parameters
:param weighting: Type of weighting
:param kwargs: Parameters for functions in graphs
:return: List of vis_graphs
"""
centre = len(model_imagelist) // 2
if gcfcf is None:
gcfcf = [arlexecute.execute(create_pswf_convolutionfunction)(model_imagelist[centre])]
def grid_wt(vis, model, g):
if vis is not None:
if model is not None:
griddata = create_griddata_from_image(model)
griddata = grid_weight_to_griddata(vis, griddata, g[0][1])
return griddata
else:
return None
else:
return None
weight_list = [arlexecute.execute(grid_wt, pure=True, nout=1)(vis_list[i], model_imagelist[i],
gcfcf)
for i in range(len(vis_list))]
merged_weight_grid = arlexecute.execute(griddata_merge_weights, nout=1)(weight_list)
merged_weight_grid = arlexecute.persist(merged_weight_grid, broadcast=True)
def re_weight(vis, model, gd, g):
if gd is not None:
if vis is not None:
# Ensure that the griddata has the right axes so that the convolution
# function mapping works
agd = create_griddata_from_image(model)
agd.data = gd[0].data
vis = griddata_reweight(vis, agd, g[0][1])
return vis
else:
return None
else:
return vis
result = [arlexecute.execute(re_weight, nout=1)(v, model_imagelist[i], merged_weight_grid, gcfcf)
for i, v in enumerate(vis_list)]
return arlexecute.optimize(result)
def zero_list_arlexecute_workflow(vis_list):
""" Initialise vis to zero: creates new data holders
:param vis_list:
:return: List of vis_lists
"""
def zero(vis):
if vis is not None:
zerovis = copy_visibility(vis)
zerovis.data['vis'][...] = 0.0
return zerovis
else:
return None
result = [arlexecute.execute(zero, pure=True, nout=1)(v) for v in vis_list]
return arlexecute.optimize(result)
def subtract_list_arlexecute_workflow(vis_list, model_vislist):
""" Initialise vis to zero
:param vis_list:
:param model_vislist: Model to be subtracted
:return: List of vis_lists
"""
def subtract_vis(vis, model_vis):
if vis is not None and model_vis is not None:
assert vis.vis.shape == model_vis.vis.shape
subvis = copy_visibility(vis)
subvis.data['vis'][...] -= model_vis.data['vis'][...]
return subvis
else:
return None
result = [arlexecute.execute(subtract_vis, pure=True, nout=1)(vis=vis_list[i],
model_vis=model_vislist[i])
for i in range(len(vis_list))]
return arlexecute.optimize(result)
def predict_list_arlexecute_workflow(vis_list, model_imagelist, context, vis_slices=1, facets=1,
gcfcf=None, **kwargs):
"""Predict, iterating over both the scattered vis_list and image
The visibility and image are scattered, the visibility is predicted on each part, and then the
parts are assembled.
:param vis_list:
:param model_imagelist: Model used to determine image parameters
:param vis_slices: Number of vis slices (w stack or timeslice)
:param facets: Number of facets (per axis)
:param context: Type of processing e.g. 2d, wstack, timeslice or facets
:param gcfcg: tuple containing grid correction and convolution function
:param kwargs: Parameters for functions in components
:return: List of vis_lists
"""
if get_parameter(kwargs, "use_serial_predict", False):
from workflows.serial.imaging.imaging_serial import predict_list_serial_workflow
return [arlexecute.execute(predict_list_serial_workflow, nout=1) \
(vis_list=[vis_list[i]],
model_imagelist=[model_imagelist[i]], vis_slices=vis_slices,
facets=facets, context=context, gcfcf=gcfcf, **kwargs)[0]
for i, _ in enumerate(vis_list)]
# Predict_2d does not clear the vis so we have to do it here.
vis_list = zero_list_arlexecute_workflow(vis_list)
c = imaging_context(context)
vis_iter = c['vis_iterator']
predict = c['predict']
if facets % 2 == 0 or facets == 1:
actual_number_facets = facets
else:
actual_number_facets = facets - 1
def predict_ignore_none(vis, model, g):
if vis is not None:
assert isinstance(vis, Visibility), vis
assert isinstance(model, Image), model
return predict(vis, model, context=context, gcfcf=g, **kwargs)
else:
return None
if gcfcf is None:
gcfcf = [arlexecute.execute(create_pswf_convolutionfunction)(m) for m in model_imagelist]
# Loop over all frequency windows
if facets == 1:
image_results_list = list()
for ivis, subvis in enumerate(vis_list):
if len(gcfcf) > 1:
g = gcfcf[ivis]
else:
g = gcfcf[0]
# Create the graph to divide an image into facets. This is by reference.
# Create the graph to divide the visibility into slices. This is by copy.
sub_vis_lists = arlexecute.execute(visibility_scatter, nout=vis_slices)(subvis,
vis_iter, vis_slices)
image_vis_lists = list()
# Loop over sub visibility
for sub_vis_list in sub_vis_lists:
# Predict visibility for this sub-visibility from this image
image_vis_list = arlexecute.execute(predict_ignore_none, pure=True, nout=1) \
(sub_vis_list, model_imagelist[ivis], g)
# Sum all sub-visibilities
image_vis_lists.append(image_vis_list)
image_results_list.append(arlexecute.execute(visibility_gather, nout=1)
(image_vis_lists, subvis, vis_iter))
result = image_results_list
else:
image_results_list_list = list()
for ivis, subvis in enumerate(vis_list):
# Create the graph to divide an image into facets. This is by reference.
facet_lists = arlexecute.execute(image_scatter_facets, nout=actual_number_facets ** 2)(
model_imagelist[ivis],
facets=facets)
# Create the graph to divide the visibility into slices. This is by copy.
sub_vis_lists = arlexecute.execute(visibility_scatter, nout=vis_slices)\
(subvis, vis_iter, vis_slices)
facet_vis_lists = list()
# Loop over sub visibility
for sub_vis_list in sub_vis_lists:
facet_vis_results = list()
# Loop over facets
for facet_list in facet_lists:
# Predict visibility for this subvisibility from this facet
facet_vis_list = arlexecute.execute(predict_ignore_none, pure=True, nout=1)\
(sub_vis_list, facet_list, None)
facet_vis_results.append(facet_vis_list)
# Sum the current sub-visibility over all facets
facet_vis_lists.append(arlexecute.execute(sum_predict_results)(facet_vis_results))
# Sum all sub-visibilities
image_results_list_list.append(
arlexecute.execute(visibility_gather, nout=1)(facet_vis_lists, subvis, vis_iter))
result = image_results_list_list
return arlexecute.optimize(result)
def deconvolve_list_arlexecute_workflow(dirty_list, psf_list, model_imagelist, prefix='', mask=None, **kwargs):
"""Create a graph for deconvolution, adding to the model
:param dirty_list:
:param psf_list:
:param model_imagelist:
:param prefix: Informative prefix to log messages
:param mask: Mask for deconvolution
:param kwargs: Parameters for functions in components
:return: graph for the deconvolution
"""
nchan = len(dirty_list)
# Number of moments. 1 is the sum.
nmoment = get_parameter(kwargs, "nmoment", 1)
if get_parameter(kwargs, "use_serial_clean", False):
from workflows.serial.imaging.imaging_serial import deconvolve_list_serial_workflow
return arlexecute.execute(deconvolve_list_serial_workflow, nout=nchan) \
(dirty_list, psf_list, model_imagelist, prefix=prefix, mask=mask, **kwargs)
def deconvolve(dirty, psf, model, facet, gthreshold, msk=None):
if prefix == '':
lprefix = "facet %d" % facet
else:
lprefix = "%s, facet %d" % (prefix, facet)
if nmoment > 0:
moment0 = calculate_image_frequency_moments(dirty)
this_peak = numpy.max(numpy.abs(moment0.data[0, ...])) / dirty.data.shape[0]
else:
ref_chan = dirty.data.shape[0] // 2
this_peak = numpy.max(numpy.abs(dirty.data[ref_chan, ...]))
if this_peak > 1.1 * gthreshold:
kwargs['threshold'] = gthreshold
result, _ = deconvolve_cube(dirty, psf, prefix=lprefix, mask=msk, **kwargs)
if result.data.shape[0] == model.data.shape[0]:
result.data += model.data
return result
else:
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_facets > 1 and deconvolve_overlap > 0:
deconvolve_number_facets = (deconvolve_facets - 2) ** 2
else:
deconvolve_number_facets = deconvolve_facets ** 2
deconvolve_model_imagelist = arlexecute.execute(image_gather_channels, nout=1)(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_trimmed = arlexecute.execute(remove_sumwt, nout=nchan)(dirty_list)
scattered_channels_facets_dirty_list = \
[arlexecute.execute(image_scatter_facets, nout=deconvolve_number_facets)(d, facets=deconvolve_facets,
overlap=deconvolve_overlap,
taper=deconvolve_taper)
for d in dirty_list_trimmed]
# Now we do a transpose and gather
scattered_facets_list = [
arlexecute.execute(image_gather_channels, nout=1)([scattered_channels_facets_dirty_list[chan][facet]
for chan in range(nchan)])
for facet in range(deconvolve_number_facets)]
psf_list_trimmed = arlexecute.execute(remove_sumwt, nout=nchan)(psf_list)
psf_list_trimmed = arlexecute.execute(image_gather_channels, nout=1)(psf_list_trimmed)
scattered_model_imagelist = \
arlexecute.execute(image_scatter_facets, nout=deconvolve_number_facets)(deconvolve_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)
nmoment = get_parameter(kwargs, "nmoment", 1)
use_moment0 = nmoment > 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 = arlexecute.execute(threshold_list, nout=1)(scattered_facets_list, threshold,
fractional_threshold,
use_moment0=use_moment0, prefix=prefix)
facet_list = numpy.arange(deconvolve_number_facets).astype('int')
if mask is None:
scattered_results_list = [
arlexecute.execute(deconvolve, nout=1)(d, psf_list_trimmed, m, facet, global_threshold)
for d, m, facet in zip(scattered_facets_list, scattered_model_imagelist, facet_list)]
else:
mask_list = \
arlexecute.execute(image_scatter_facets, nout=deconvolve_number_facets)(mask,
facets=deconvolve_facets,
overlap=deconvolve_overlap)
scattered_results_list = [
arlexecute.execute(deconvolve, nout=1)(d, psf_list_trimmed, m, facet, global_threshold, msk)
for d, m, facet, msk in zip(scattered_facets_list, scattered_model_imagelist, facet_list, mask_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 = arlexecute.execute(image_gather_facets, nout=1)(scattered_results_list,
deconvolve_model_imagelist,
facets=deconvolve_facets,
overlap=deconvolve_overlap,
taper=deconvolve_taper)
result_list = arlexecute.execute(image_scatter_channels, nout=nchan)(gathered_results_list, subimages=nchan)
return arlexecute.optimize(result_list)
def invert_list_arlexecute_workflow(vis_list, template_model_imagelist, context, dopsf=False, normalize=True,
facets=1, vis_slices=1, gcfcf=None, **kwargs):
""" Sum results from invert, iterating over the scattered image and vis_list
:param vis_list:
:param template_model_imagelist: Model used to determine image parameters
:param dopsf: Make the PSF instead of the dirty image
:param facets: Number of facets
:param normalize: Normalize by sumwt
:param vis_slices: Number of slices
:param context: Imaging context
:param gcfcg: tuple containing grid correction and convolution function
:param kwargs: Parameters for functions in components
:return: List of (image, sumwt) tuple
"""
# Use serial invert for each element of the visibility list. This means that e.g. iteration
# through w-planes or timeslices is done sequentially thus not incurring the memory cost
# of doing all at once.
if get_parameter(kwargs, "use_serial_invert", False):
from workflows.serial.imaging.imaging_serial import invert_list_serial_workflow
return [arlexecute.execute(invert_list_serial_workflow, nout=1) \
(vis_list=[vis_list[i]], template_model_imagelist=[template_model_imagelist[i]],
context=context, dopsf=dopsf, normalize=normalize, vis_slices=vis_slices,
facets=facets, gcfcf=gcfcf, **kwargs)[0]
for i, _ in enumerate(vis_list)]
if not isinstance(template_model_imagelist, collections.Iterable):
template_model_imagelist = [template_model_imagelist]
c = imaging_context(context)
vis_iter = c['vis_iterator']
invert = c['invert']
if facets % 2 == 0 or facets == 1:
actual_number_facets = facets
else:
actual_number_facets = max(1, (facets - 1))
def gather_image_iteration_results(results, template_model):
result = create_empty_image_like(template_model)
i = 0
sumwt = numpy.zeros([template_model.nchan, template_model.npol])
for dpatch in image_scatter_facets(result, facets=facets):
assert i < len(results), "Too few results in gather_image_iteration_results"
if results[i] is not None:
assert len(results[i]) == 2, results[i]
dpatch.data[...] = results[i][0].data[...]
sumwt += results[i][1]
i += 1
return result, sumwt
def invert_ignore_none(vis, model, gg):
if vis is not None:
return invert(vis, model, context=context, dopsf=dopsf, normalize=normalize,
gcfcf=gg, **kwargs)
else:
return create_empty_image_like(model), numpy.zeros([model.nchan, model.npol])
# If we are doing facets, we need to create the gcf for each image
if gcfcf is None and facets == 1:
gcfcf = [arlexecute.execute(create_pswf_convolutionfunction)(template_model_imagelist[0])]
# Loop over all vis_lists independently
results_vislist = list()
if facets == 1:
for ivis, sub_vis_list in enumerate(vis_list):
if len(gcfcf) > 1:
g = gcfcf[ivis]
else:
g = gcfcf[0]
# Create the graph to divide the visibility into slices. This is by copy.
sub_sub_vis_lists = arlexecute.execute(visibility_scatter, nout=vis_slices)\
(sub_vis_list, vis_iter, vis_slices=vis_slices)
# Iterate within each sub_sub_vis_list
vis_results = list()
for sub_sub_vis_list in sub_sub_vis_lists:
vis_results.append(arlexecute.execute(invert_ignore_none, pure=True)
(sub_sub_vis_list, template_model_imagelist[ivis], g))
results_vislist.append(sum_invert_results_arlexecute(vis_results))
result = results_vislist
else:
for ivis, sub_vis_list in enumerate(vis_list):
# Create the graph to divide an image into facets. This is by reference.
facet_lists = arlexecute.execute(image_scatter_facets, nout=actual_number_facets ** 2)(
template_model_imagelist[
ivis],
facets=facets)
# Create the graph to divide the visibility into slices. This is by copy.
sub_sub_vis_lists = arlexecute.execute(visibility_scatter, nout=vis_slices)\
(sub_vis_list, vis_iter, vis_slices=vis_slices)
# Iterate within each vis_list
vis_results = list()
for sub_sub_vis_list in sub_sub_vis_lists:
facet_vis_results = list()
for facet_list in facet_lists:
facet_vis_results.append(
arlexecute.execute(invert_ignore_none, pure=True)(sub_sub_vis_list, facet_list, None))
vis_results.append(arlexecute.execute(gather_image_iteration_results, nout=1)
(facet_vis_results, template_model_imagelist[ivis]))
results_vislist.append(sum_invert_results_arlexecute(vis_results))
result = results_vislist
return arlexecute.optimize(result)
def deconvolve_list_arlexecute_workflow(dirty_list, psf_list, model_imagelist, prefix='', mask=None, **kwargs):
"""Create a graph for deconvolution, adding to the model
:param dirty_list:
:param psf_list:
:param model_imagelist:
:param prefix: Informative prefix to log messages
:param mask: Mask for deconvolution
:param kwargs: Parameters for functions in components
:return: graph for the deconvolution
"""
nchan = len(dirty_list)
# Number of moments. 1 is the sum.
nmoment = get_parameter(kwargs, "nmoment", 1)
if get_parameter(kwargs, "use_serial_clean", False):
from workflows.serial.imaging.imaging_serial import deconvolve_list_serial_workflow
return arlexecute.execute(deconvolve_list_serial_workflow, nout=nchan) \
(dirty_list, psf_list, model_imagelist, prefix=prefix, mask=mask, **kwargs)
def deconvolve(dirty, psf, model, facet, gthreshold, msk=None):
if prefix == '':
lprefix = "facet %d" % facet
else:
lprefix = "%s, facet %d" % (prefix, facet)
if nmoment > 0:
moment0 = calculate_image_frequency_moments(dirty)
this_peak = numpy.max(numpy.abs(moment0.data[0, ...])) / dirty.data.shape[0]
else:
ref_chan = dirty.data.shape[0] // 2
this_peak = numpy.max(numpy.abs(dirty.data[ref_chan, ...]))
if this_peak > 1.1 * gthreshold:
kwargs['threshold'] = gthreshold
result, _ = deconvolve_cube(dirty, psf, prefix=lprefix, mask=msk, **kwargs)
if result.data.shape[0] == model.data.shape[0]:
result.data += model.data
return result
else:
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_facets > 1 and deconvolve_overlap > 0:
deconvolve_number_facets = (deconvolve_facets - 2) ** 2
else:
deconvolve_number_facets = deconvolve_facets ** 2
scattered_channels_facets_model_list = \
[arlexecute.execute(image_scatter_facets, nout=deconvolve_number_facets)(m, facets=deconvolve_facets,
overlap=deconvolve_overlap,
taper=deconvolve_taper)
for m in model_imagelist]
scattered_facets_model_list = [
arlexecute.execute(image_gather_channels, nout=1)([scattered_channels_facets_model_list[chan][facet]
for chan in range(nchan)])
for facet in range(deconvolve_number_facets)]
# Scatter the separate channel images into deconvolve facets and then gather channels for each facet.
# This avoids constructing the entire spectral cube.
# i.e. SCATTER BY FACET then SCATTER BY CHANNEL
dirty_list_trimmed = arlexecute.execute(remove_sumwt, nout=nchan)(dirty_list)
scattered_channels_facets_dirty_list = \
[arlexecute.execute(image_scatter_facets, nout=deconvolve_number_facets)(d, facets=deconvolve_facets,
overlap=deconvolve_overlap,
taper=deconvolve_taper)
for d in dirty_list_trimmed]
scattered_facets_dirty_list = [
arlexecute.execute(image_gather_channels, nout=1)([scattered_channels_facets_dirty_list[chan][facet]
for chan in range(nchan)])
for facet in range(deconvolve_number_facets)]
psf_list_trimmed = arlexecute.execute(remove_sumwt, nout=nchan)(psf_list)
def extract_psf(psf, facets):
spsf = create_empty_image_like(psf)
cx = spsf.shape[3] // 2
cy = spsf.shape[2] // 2
wx = spsf.shape[3] // facets
wy = spsf.shape[2] // facets
xbeg = cx - wx // 2
xend = cx + wx // 2
ybeg = cy - wy // 2
yend = cy + wy // 2
spsf.data = psf.data[..., ybeg:yend, xbeg:xend]
spsf.wcs.wcs.crpix[0] -= xbeg
spsf.wcs.wcs.crpix[1] -= ybeg
return spsf
psf_list_trimmed = [arlexecute.execute(extract_psf)(p, deconvolve_facets) for p in psf_list_trimmed]
psf_centre = arlexecute.execute(image_gather_channels, nout=1)([psf_list_trimmed[chan]
for chan in range(nchan)])
# 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)
nmoment = get_parameter(kwargs, "nmoment", 1)
use_moment0 = nmoment > 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 = arlexecute.execute(threshold_list, nout=1)(scattered_facets_dirty_list, threshold,
fractional_threshold,
use_moment0=use_moment0, prefix=prefix)
facet_list = numpy.arange(deconvolve_number_facets).astype('int')
if mask is None:
scattered_results_list = [
arlexecute.execute(deconvolve, nout=1)(d, psf_centre, m, facet,
global_threshold)
for d, m, facet in zip(scattered_facets_dirty_list, scattered_facets_model_list, facet_list)]
else:
mask_list = \
arlexecute.execute(image_scatter_facets, nout=deconvolve_number_facets)(mask,
facets=deconvolve_facets,
overlap=deconvolve_overlap)
scattered_results_list = [
arlexecute.execute(deconvolve, nout=1)(d, psf_centre, m, facet,
global_threshold, msk)
for d, m, facet, msk in
zip(scattered_facets_dirty_list, scattered_facets_model_list, facet_list, mask_list)]
# We want to avoid constructing the entire cube so we do the inverse of how we got here:
# i.e. SCATTER BY CHANNEL then GATHER BY FACET
# 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 = arlexecute.execute(image_gather_facets, nout=1)(scattered_results_list,
# deconvolve_model_imagelist,
# facets=deconvolve_facets,
# overlap=deconvolve_overlap,
# taper=deconvolve_taper)
# result_list = arlexecute.execute(image_scatter_channels, nout=nchan)(gathered_results_list, subimages=nchan)
scattered_channel_results_list = [arlexecute.execute(image_scatter_channels, nout=nchan)(scat, subimages=nchan)
for scat in scattered_results_list]
# The structure is now [[channels] for facets]. We do the reverse transpose to the one above.
result_list = [arlexecute.execute(image_gather_facets, nout=1)([scattered_channel_results_list[facet][chan]
for facet in range(deconvolve_number_facets)],
model_imagelist[chan], facets=deconvolve_facets,
overlap=deconvolve_overlap)
for chan in range(nchan)]
return arlexecute.optimize(result_list)
