def predict_list_rsexecute_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.
Note that this call can be converted to a set of rsexecute calls to the serial
version, using argument use_serial_predict=True
:param vis_list: list of vis (or graph)
:param model_imagelist: list of models (or graph)
: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
For example::
dprepb_model = [rsexecute.execute(create_low_test_image_from_gleam)
(npixel=npixel, frequency=[frequency[f]], channel_bandwidth=[channel_bandwidth[f]],
cellsize=cellsize, phasecentre=phasecentre, polarisation_frame=PolarisationFrame("stokesI"),
flux_limit=3.0, applybeam=True)
for f, freq in enumerate(frequency)]
dprepb_model_list = rsexecute.persist(dprepb_model_list)
predicted_vis_list = predict_list_rsexecute_workflow(vis_list, model_imagelist=dprepb_model_list,
context='wstack', vis_slices=51)
predicted_vis_list = rsexecute.compute(predicted_vis_list , sync=True)
"""
if get_parameter(kwargs, "use_serial_predict", False):
from rascil.workflows.serial.imaging.imaging_serial import predict_list_serial_workflow
return [rsexecute.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_rsexecute_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) or isinstance(vis, BlockVisibility), vis
assert isinstance(model, Image), model
return predict(vis, model, context=context, gcfcf=g, **kwargs)
else:
return None
if gcfcf is None:
gcfcf = [rsexecute.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 the visibility into slices. This is by copy.
sub_vis_lists = rsexecute.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 = rsexecute.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(rsexecute.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 = rsexecute.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 = rsexecute.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 = rsexecute.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(rsexecute.execute(sum_predict_results)(facet_vis_results))
# Sum all sub-visibilities
image_results_list_list.append(
rsexecute.execute(visibility_gather, nout=1)(facet_vis_lists, subvis, vis_iter))
result = image_results_list_list
return rsexecute.optimize(result)
def predict_list_serial_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: list of vis
: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
"""
assert len(vis_list) == len(
model_imagelist), "Model must be the same length as the vis_list"
# Predict_2d does not clear the vis so we have to do it here.
vis_list = zero_list_serial_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) or isinstance(
vis, BlockVisibility), vis
assert isinstance(model, Image), model
return predict(vis, model, context=context, gcfcf=g, **kwargs)
else:
return None
if gcfcf is None:
gcfcf = [create_pswf_convolutionfunction(m) for m in model_imagelist]
# Loop over all frequency windows
if facets == 1:
image_results_list = list()
for ivis, sub_vis_list in enumerate(vis_list):
if len(gcfcf) > 1:
g = gcfcf[ivis]
else:
g = gcfcf[0]
# Loop over sub visibility
vis_predicted = copy_visibility(sub_vis_list, zero=True)
for rows in vis_iter(sub_vis_list, vis_slices):
row_vis = create_visibility_from_rows(sub_vis_list, rows)
row_vis_predicted = predict_ignore_none(
row_vis, model_imagelist[ivis], g)
if row_vis_predicted is not None:
vis_predicted.data['vis'][
rows, ...] = row_vis_predicted.data['vis']
image_results_list.append(vis_predicted)
return image_results_list
else:
image_results_list = list()
for ivis, sub_vis_list in enumerate(vis_list):
# Create the graph to divide an image into facets. This is by reference.
facet_lists = image_scatter_facets(model_imagelist[ivis],
facets=facets)
facet_vis_lists = list()
sub_vis_lists = visibility_scatter(sub_vis_list, vis_iter,
vis_slices)
# Loop over sub visibility
for sub_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 = predict_ignore_none(
sub_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(sum_predict_results(facet_vis_results))
# Sum all sub-visibilties
image_results_list.append(
visibility_gather(facet_vis_lists, sub_vis_list, vis_iter))
return image_results_list
def invert_list_rsexecute_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
Note that this call can be converted to a set of rsexecute calls to the serial
version, using argument use_serial_invert=True
:param vis_list: list of vis (or graph)
:param template_model_imagelist: list of template models (or graph)
: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) tuples, one per vis in vis_list
For example::
model_list = [rsexecute.execute(create_image_from_visibility)
(v, npixel=npixel, cellsize=cellsize, polarisation_frame=pol_frame)
for v in vis_list]
model_list = rsexecute.persist(model_list)
dirty_list = invert_list_rsexecute_workflow(vis_list, template_model_imagelist=model_list, context='wstack',
vis_slices=51)
dirty_sumwt_list = rsexecute.compute(dirty_list, sync=True)
dirty, sumwt = dirty_sumwt_list[centre]
"""
# 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 rascil.workflows.serial.imaging.imaging_serial import invert_list_serial_workflow
return [rsexecute.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 = [rsexecute.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 = rsexecute.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(rsexecute.execute(invert_ignore_none, pure=True)
(sub_sub_vis_list, template_model_imagelist[ivis], g))
results_vislist.append(sum_invert_results_rsexecute(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 = rsexecute.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 = rsexecute.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(
rsexecute.execute(invert_ignore_none, pure=True)(sub_sub_vis_list, facet_list, None))
vis_results.append(rsexecute.execute(gather_image_iteration_results, nout=1)
(facet_vis_results, template_model_imagelist[ivis]))
results_vislist.append(sum_invert_results_rsexecute(vis_results))
result = results_vislist
return rsexecute.optimize(result)
def invert_list_serial_workflow(vis_list,
template_model_imagelist,
dopsf=False,
normalize=True,
facets=1,
vis_slices=1,
context='2d',
gcfcf=None,
**kwargs):
""" Sum results from invert, iterating over the scattered image and vis_list
:param vis_list: list of vis
:param template_model_imagelist: list of template models
: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) tuples, one per vis in vis_list
For example::
model_list = [create_image_from_visibility
(v, npixel=npixel, cellsize=cellsize, polarisation_frame=pol_frame)
for v in vis_list]
dirty_list = invert_list_serial_workflow(vis_list, template_model_imagelist=model_list, context='wstack',
vis_slices=51)
dirty, sumwt = dirty_list[centre]
"""
if not isinstance(template_model_imagelist, collections.abc.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 = [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]
# Iterate within each vis_list
result_image = create_empty_image_like(
template_model_imagelist[ivis])
result_sumwt = numpy.zeros([
template_model_imagelist[ivis].nchan,
template_model_imagelist[ivis].npol
])
for rows in vis_iter(sub_vis_list, vis_slices):
row_vis = create_visibility_from_rows(sub_vis_list, rows)
result = invert_ignore_none(row_vis,
template_model_imagelist[ivis], g)
if result is not None:
result_image.data += result[1][:, :, numpy.newaxis, numpy.
newaxis] * result[0].data
result_sumwt += result[1]
result_image = normalize_sumwt(result_image, result_sumwt)
results_vislist.append((result_image, result_sumwt))
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 = image_scatter_facets(template_model_imagelist[ivis],
facets=facets)
# Create the graph to divide the visibility into slices. This is by copy.
sub_sub_vis_lists = visibility_scatter(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(
invert_ignore_none(sub_sub_vis_list, facet_list, None))
vis_results.append(
gather_image_iteration_results(
facet_vis_results, template_model_imagelist[ivis]))
results_vislist.append(sum_invert_results(vis_results))
return results_vislist
