def test_coalesce_decoalesce_with_iter(self):
for rows in vis_timeslice_iter(self.blockvis):
visslice = create_visibility_from_rows(self.blockvis, rows)
cvisslice = convert_blockvisibility_to_visibility(visslice)
assert numpy.min(cvisslice.frequency) == numpy.min(self.frequency)
assert numpy.min(cvisslice.frequency) > 0.0
dvisslice = decoalesce_visibility(cvisslice)
assert dvisslice.nvis == visslice.nvis
def test_vis_timeslice_iterator_single(self):
self.actualSetUp(times=numpy.zeros([1]))
nchunks = len(list(vis_timeslice_iter(self.vis)))
log.debug('Found %d chunks' % (nchunks))
for chunk, rows in enumerate(vis_timeslice_iter(self.vis)):
visslice = create_visibility_from_rows(self.vis, rows)
assert visslice.vis[0].real == visslice.time[0]
assert len(rows)
def test_vis_wstack_iterator(self):
self.actualSetUp()
nchunks = len(list(vis_wstack_iter(self.vis, wstack=10.0)))
log.debug('Found %d chunks' % (nchunks))
assert nchunks > 1
for chunk, rows in enumerate(vis_wstack_iter(self.vis, wstack=10.0)):
assert len(rows)
visslice = create_visibility_from_rows(self.vis, rows)
assert numpy.sum(visslice.nvis) < self.vis.nvis
def test_vis_timeslice_iterator(self):
self.actualSetUp()
nchunks = len(list(vis_timeslice_iter(self.vis)))
log.debug('Found %d chunks' % (nchunks))
assert nchunks > 1
for chunk, rows in enumerate(vis_timeslice_iter(self.vis)):
visslice = create_visibility_from_rows(self.vis, rows)
assert visslice.vis[0].real == visslice.time[0]
assert len(rows)
assert numpy.sum(rows) < self.vis.nvis
def visibility_scatter(vis, vis_iter, **kwargs):
"""Scatter an visibility into a list of subvisibilities
:param vis: Visibility
:param vis_iter: visibility iterator
:returns: list of subvisibilitys
"""
visibility_list = list()
for i, rows in enumerate(vis_iter(vis, **kwargs)):
if rows is not None:
subvis = create_visibility_from_rows(vis, rows)
visibility_list.append(subvis)
else:
visibility_list.append(None)
return visibility_list
def predict_with_vis_iterator(vis,
model,
vis_iter=vis_slice_iter,
predict=predict_2d,
**kwargs):
"""Iterate through prediction in chunks
This knows about the structure of predict in different execution frameworks but not
anything about the actual processing.
"""
log.debug("predict_with_vis_iterator: Processing chunks")
# Do each chunk in turn
for rows in vis_iter(vis, **kwargs):
if rows is not None:
visslice = create_visibility_from_rows(vis, rows)
visslice = predict(visslice, model, **kwargs)
vis.data['vis'][rows] += visslice.data['vis']
return vis
def invert_with_vis_iterator(vis,
im,
dopsf=False,
normalize=True,
vis_iter=vis_slice_iter,
invert=invert_2d,
**kwargs):
""" Invert using a specified iterator and invert
This knows about the structure of invert in different execution frameworks but not
anything about the actual processing.
:param vis:
:param im:
:param dopsf: Make the psf instead of the dirty image
:param normalize: Normalize by the sum of weights (True)
:param kwargs:
:return:
"""
resultimage = create_empty_image_like(im)
i = 0
for rows in vis_iter(vis, **kwargs):
if rows is not None:
visslice = create_visibility_from_rows(vis, rows)
workimage, sumwt = invert(visslice,
im,
dopsf,
normalize=False,
**kwargs)
resultimage.data += workimage.data
if i == 0:
totalwt = sumwt
else:
totalwt += sumwt
i += 1
if normalize:
resultimage = normalize_sumwt(resultimage, totalwt)
return resultimage, totalwt
def test_vis_slice_iterator_vis_slices(self):
self.actualSetUp()
for chunk, rows in enumerate(vis_slice_iter(self.vis, vis_slices=11)):
visslice = create_visibility_from_rows(self.vis, rows)
assert len(rows)
assert visslice.vis[0].real == visslice.time[0]