def test_crosssubtract_datamodel(self):
self.actualSetUp(zerow=True)
future_vis = arlexecute.scatter(self.vis_list[0])
future_skymodel_list = arlexecute.scatter(self.skymodel_list)
skymodel_vislist = predict_skymodel_list_arlexecute_workflow(
future_vis, future_skymodel_list, context='2d', docal=True)
skymodel_vislist = arlexecute.compute(skymodel_vislist, sync=True)
vobs = sum_predict_results(skymodel_vislist)
future_vobs = arlexecute.scatter(vobs)
skymodel_vislist = crosssubtract_datamodels_skymodel_list_arlexecute_workflow(
future_vobs, skymodel_vislist)
skymodel_vislist = arlexecute.compute(skymodel_vislist, sync=True)
result_skymodel = [
SkyModel(components=None, image=self.skymodel_list[-1].image)
for v in skymodel_vislist
]
self.vis_list = arlexecute.scatter(self.vis_list)
result_skymodel = invert_skymodel_list_arlexecute_workflow(
skymodel_vislist, result_skymodel, context='2d', docal=True)
results = arlexecute.compute(result_skymodel, sync=True)
assert numpy.max(numpy.abs(results[0][0].data)) > 0.0
assert numpy.max(numpy.abs(results[0][1])) > 0.0
if self.plot:
import matplotlib.pyplot as plt
from wrappers.arlexecute.image.operations import show_image
show_image(results[0][0],
title='Dirty image after cross-subtraction',
vmax=0.1,
vmin=-0.01)
plt.show()
def test_predictcal(self):
self.actualSetUp(zerow=True)
skymodel_vislist = predict_skymodel_list_serial_workflow(
self.vis_list[0], self.skymodel_list, context='2d', docal=True)
vobs = sum_predict_results(skymodel_vislist)
if self.plot:
def plotvis(i, v):
import matplotlib.pyplot as plt
uvr = numpy.hypot(v.u, v.v)
amp = numpy.abs(v.vis[:, 0])
plt.plot(uvr, amp, '.')
plt.title(str(i))
plt.show()
plotvis(0, vobs)
def test_predictcal(self):
self.actualSetUp(zerow=True)
future_vis = arlexecute.scatter(self.vis_list[0])
future_skymodel = arlexecute.scatter(self.skymodel_list)
skymodel_vislist = predict_skymodel_list_arlexecute_workflow(future_vis, future_skymodel,
context='2d', docal=True)
skymodel_vislist = arlexecute.compute(skymodel_vislist, sync=True)
vobs = sum_predict_results(skymodel_vislist)
if self.plot:
def plotvis(i, v):
import matplotlib.pyplot as plt
uvr = numpy.hypot(v.u, v.v)
amp = numpy.abs(v.vis[:, 0])
plt.plot(uvr, amp, '.')
plt.title(str(i))
plt.show()
plotvis(0, vobs)
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 predict_list_serial_workflow(vis_list,
model_imagelist,
vis_slices=1,
facets=1,
context='2d',
**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:
: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"
c = imaging_context(context)
vis_iter = c['vis_iterator']
predict = c['predict']
def predict_ignore_none(vis, model):
if vis is not None:
return predict(vis,
model,
context=context,
facets=facets,
vis_slices=vis_slices,
**kwargs)
else:
return None
image_results_list_list = list()
# Loop over all frequency windows
for freqwin, 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[freqwin],
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)
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 = predict_ignore_none(sub_vis_list, facet_list)
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
facets=facets)
# Create the graph to divide the visibility into slices. This is by copy.
sub_vis_lists = visibility_scatter(vis_lst, vis_iter, vis_slices)
facet_vis_lists = list()
#pdb.set_trace()
# 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)
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_lst, vis_iter))
predicted_vislist = image_results_list_list
#log.info('About to run corrupt to get corrupted visibility')
#corrupted_vislist = corrupt_list_serial_workflow(predicted_vislist, phase_error=1.0)
# Get the LSM. This is currently blank.
# In[ ]:
model_list = [
create_image_from_visibility(