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:
: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), 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, vis_list 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 = visibility_scatter(vis_list, 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 = predict_ignore_none(sub_vis_list, model_imagelist[ivis], g)
# Sum all sub-visibilities
image_vis_lists.append(image_vis_list)
image_results_list.append(visibility_gather(image_vis_lists, vis_list, vis_iter))
return image_results_list
else:
image_results_list_list = list()
for ivis, 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(vis_list, vis_iter, vis_slices)
# 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 = predict_ignore_none(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_list.append(visibility_gather(facet_vis_lists, vis_list, vis_iter))
return image_results_list_list
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:
: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
"""
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']
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, g):
if vis is not None:
return invert(vis, model, context=context, dopsf=dopsf, normalize=normalize,
gcfcf=g, **kwargs)
else:
return create_empty_image_like(model), 0.0
# 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, 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_vis_lists = visibility_scatter(vis_list, vis_iter, vis_slices=vis_slices)
# Iterate within each vis_list
vis_results = list()
for sub_vis_list in sub_vis_lists:
vis_results.append(invert_ignore_none(sub_vis_list, template_model_imagelist[ivis],
g))
results_vislist.append(sum_invert_results(vis_results))
return results_vislist
else:
for ivis, 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_vis_lists = visibility_scatter(vis_list, vis_iter, vis_slices=vis_slices)
# Iterate within each vis_list
vis_results = list()
for sub_vis_list in sub_vis_lists:
facet_vis_results = list()
for facet_list in facet_lists:
facet_vis_results.append(invert_ignore_none(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