def setUp(self):
client = get_dask_Client(memory_limit=4 * 1024 * 1024 * 1024)
arlexecute.set_client(client)
from data_models.parameters import arl_path
self.dir = arl_path('test_results')
def setUp(self):
from data_models.parameters import arl_path
self.dir = arl_path('test_results')
arlexecute.set_client(use_dask=False)
numpy.random.seed(180555)
def test_useFunction(self):
def square(x):
return x**2
arlexecute.set_client(use_dask=False)
graph = arlexecute.execute(square)(numpy.arange(10))
assert (arlexecute.compute(graph) == numpy.array(
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81])).all()
arlexecute.close()
def setUp(self):
from data_models.parameters import arl_path
self.dir = arl_path('test_results')
self.frequency = numpy.linspace(1e8, 1.5e8, 3)
self.channel_bandwidth = numpy.array([2.5e7, 2.5e7, 2.5e7])
self.phasecentre = SkyCoord(ra=+15.0 * u.deg, dec=-60.0 * u.deg, frame='icrs', equinox='J2000')
self.times = numpy.linspace(-300.0, 300.0, 3) * numpy.pi / 43200.0
arlexecute.set_client(use_dask=False)
def test_useDaskSync(self):
def square(x):
return x**2
arlexecute.set_client(use_dask=True)
graph = arlexecute.execute(square)(numpy.arange(10))
result = arlexecute.compute(graph, sync=True)
assert (result == numpy.array([0, 1, 4, 9, 16, 25, 36, 49, 64,
81])).all()
arlexecute.close()
def setUp(self):
super(ARLExecuteTestCase, self).setUp()
import os
from wrappers.arlexecute.execution_support.arlexecute import arlexecute
use_dlg = os.environ.get('ARL_TESTS_USE_DLG', '0') == '1'
use_dask = os.environ.get('ARL_TESTS_USE_DASK', '1') == '1'
arlexecute.set_client(use_dask=use_dask, use_dlg=use_dlg)
# Start a daliuge node manager for these tests; make sure it can see
# the arl modules. The node manager will be shut down at tearDown
if use_dlg:
from dlg import tool
arl_root = os.path.normpath(os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')))
self.nm_proc = tool.start_process('nm', ['--dlg-path', arl_root])
def trial_case(results,
seed=180555,
context='wstack',
nworkers=8,
threads_per_worker=1,
memory=8,
processes=True,
order='frequency',
nfreqwin=7,
ntimes=3,
rmax=750.0,
facets=1,
wprojection_planes=1,
use_dask=True,
use_serial=False):
""" Single trial for performance-timings
Simulates visibilities from GLEAM including phase errors
Makes dirty image and PSF
Runs ICAL pipeline
The results are in a dictionary:
'context': input - a string describing concisely the purpose of the test
'time overall', overall execution time (s)
'time create gleam', time to create GLEAM prediction graph
'time predict', time to execute GLEAM prediction graph
'time corrupt', time to corrupt data_models
'time invert', time to make dirty image
'time psf invert', time to make PSF
'time ICAL graph', time to create ICAL graph
'time ICAL', time to execute ICAL graph
'context', type of imaging e.g. 'wstack'
'nworkers', number of workers to create
'threads_per_worker',
'nnodes', Number of nodes,
'processes', 'order', Ordering of data_models
'nfreqwin', Number of frequency windows in simulation
'ntimes', Number of hour angles in simulation
'rmax', Maximum radius of stations used in simulation (m)
'facets', Number of facets in deconvolution and imaging
'wprojection_planes', Number of wprojection planes
'vis_slices', Number of visibility slices (per Visibbility)
'npixel', Number of pixels in image
'cellsize', Cellsize in radians
'seed', Random number seed
'dirty_max', Maximum in dirty image
'dirty_min', Minimum in dirty image
'psf_max',
'psf_min',
'restored_max',
'restored_min',
'deconvolved_max',
'deconvolved_min',
'residual_max',
'residual_min',
'git_info', GIT hash (not definitive since local mods are possible)
:param results: Initial state
:param seed: Random number seed (used in gain simulations)
:param context: imaging context
:param context: Type of context: '2d'|'timeslice'|'wstack'
:param nworkers: Number of dask workers to use
:param threads_per_worker: Number of threads per worker
:param processes: Use processes instead of threads 'processes'|'threads'
:param order: See simulate_list_list_arlexecute_workflow_workflowkflow
:param nfreqwin: See simulate_list_list_arlexecute_workflow_workflowkflow
:param ntimes: See simulate_list_list_arlexecute_workflow_workflowkflow
:param rmax: See simulate_list_list_arlexecute_workflow_workflowkflow
:param facets: Number of facets to use
:param wprojection_planes: Number of wprojection planes to use
:param use_dask: Use dask or immediate evaluation
:param kwargs:
:return: results dictionary
"""
numpy.random.seed(seed)
results['seed'] = seed
start_all = time.time()
results['context'] = context
results['hostname'] = socket.gethostname()
results['git_hash'] = git_hash()
results['epoch'] = time.strftime("%Y-%m-%d %H:%M:%S")
zerow = False
print("Context is %s" % context)
results['nworkers'] = nworkers
results['threads_per_worker'] = threads_per_worker
results['processes'] = processes
results['memory'] = memory
results['order'] = order
results['nfreqwin'] = nfreqwin
results['ntimes'] = ntimes
results['rmax'] = rmax
results['facets'] = facets
results['wprojection_planes'] = wprojection_planes
results['use_dask'] = use_dask
print("At start, configuration is {0!r}".format(results))
# Parameters determining scale
frequency = numpy.linspace(0.8e8, 1.2e8, nfreqwin)
centre = nfreqwin // 2
if nfreqwin > 1:
channel_bandwidth = numpy.array(nfreqwin *
[frequency[1] - frequency[0]])
else:
channel_bandwidth = numpy.array([1e6])
times = numpy.linspace(-numpy.pi / 3.0, numpy.pi / 3.0, ntimes)
phasecentre = SkyCoord(ra=+30.0 * u.deg,
dec=-60.0 * u.deg,
frame='icrs',
equinox='J2000')
if use_dask:
client = get_dask_Client(threads_per_worker=threads_per_worker,
memory_limit=memory * 1024 * 1024 * 1024,
n_workers=nworkers)
arlexecute.set_client(client)
nodes = findNodes(arlexecute.client)
unodes = list(numpy.unique(nodes))
results['nnodes'] = len(unodes)
print("Defined %d workers on %d nodes" % (nworkers, results['nnodes']))
print("Workers are: %s" % str(nodes))
else:
arlexecute.set_client(use_dask=use_dask)
results['nnodes'] = 1
unodes = None
vis_list = simulate_list_arlexecute_workflow(
'LOWBD2',
frequency=frequency,
channel_bandwidth=channel_bandwidth,
times=times,
phasecentre=phasecentre,
order=order,
format='blockvis',
rmax=rmax)
print("****** Visibility creation ******")
vis_list = arlexecute.persist(vis_list)
# Find the best imaging parameters but don't bring the vis_list back here
def get_wf(bv):
v = convert_blockvisibility_to_visibility(bv)
return advise_wide_field(v,
guard_band_image=6.0,
delA=0.02,
facets=facets,
wprojection_planes=wprojection_planes,
oversampling_synthesised_beam=4.0)
wprojection_planes = 1
advice = arlexecute.compute(arlexecute.execute(get_wf)(vis_list[0]),
sync=True)
npixel = advice['npixels2']
cellsize = advice['cellsize']
if context == 'timeslice':
vis_slices = ntimes
print("Using timeslice with %d slices" % vis_slices)
elif context == '2d':
vis_slices = 1
else:
context = 'wstack'
vis_slices = 5 * advice['vis_slices']
print("Using wstack with %d slices" % vis_slices)
results['vis_slices'] = vis_slices
results['cellsize'] = cellsize
results['npixel'] = npixel
gleam_model_list = [
arlexecute.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=0.3,
applybeam=True) for f, freq in enumerate(frequency)
]
start = time.time()
print("****** Starting GLEAM model creation ******")
gleam_model_list = arlexecute.compute(gleam_model_list, sync=True)
cmodel = smooth_image(gleam_model_list[centre])
export_image_to_fits(cmodel,
"pipelines-timings-arlexecute-gleam_cmodel.fits")
end = time.time()
results['time create gleam'] = end - start
print("Creating GLEAM model took %.2f seconds" % (end - start))
gleam_model_list = arlexecute.scatter(gleam_model_list)
vis_list = predict_list_arlexecute_workflow(vis_list,
gleam_model_list,
vis_slices=vis_slices,
context=context)
start = time.time()
print("****** Starting GLEAM model visibility prediction ******")
vis_list = arlexecute.compute(vis_list, sync=True)
end = time.time()
results['time predict'] = end - start
print("GLEAM model Visibility prediction took %.2f seconds" %
(end - start))
# Corrupt the visibility for the GLEAM model
print("****** Visibility corruption ******")
vis_list = corrupt_list_arlexecute_workflow(vis_list, phase_error=1.0)
start = time.time()
vis_list = arlexecute.compute(vis_list, sync=True)
vis_list = arlexecute.scatter(vis_list)
end = time.time()
results['time corrupt'] = end - start
print("Visibility corruption took %.2f seconds" % (end - start))
# Create an empty model image
model_list = [
arlexecute.execute(create_image_from_visibility)(
vis_list[f],
npixel=npixel,
cellsize=cellsize,
frequency=[frequency[f]],
channel_bandwidth=[channel_bandwidth[f]],
polarisation_frame=PolarisationFrame("stokesI"))
for f, freq in enumerate(frequency)
]
model_list = arlexecute.compute(model_list, sync=True)
model_list = arlexecute.scatter(model_list)
psf_list = invert_list_arlexecute_workflow(vis_list,
model_list,
vis_slices=vis_slices,
context=context,
facets=facets,
dopsf=True)
start = time.time()
print("****** Starting PSF calculation ******")
psf, sumwt = arlexecute.compute(psf_list, sync=True)[centre]
end = time.time()
results['time psf invert'] = end - start
print("PSF invert took %.2f seconds" % (end - start))
results['psf_max'] = qa_image(psf).data['max']
results['psf_min'] = qa_image(psf).data['min']
dirty_list = invert_list_arlexecute_workflow(vis_list,
model_list,
vis_slices=vis_slices,
context=context,
facets=facets)
start = time.time()
print("****** Starting dirty image calculation ******")
dirty, sumwt = arlexecute.compute(dirty_list, sync=True)[centre]
end = time.time()
results['time invert'] = end - start
print("Dirty image invert took %.2f seconds" % (end - start))
print("Maximum in dirty image is ", numpy.max(numpy.abs(dirty.data)),
", sumwt is ", sumwt)
qa = qa_image(dirty)
results['dirty_max'] = qa.data['max']
results['dirty_min'] = qa.data['min']
# Create the ICAL pipeline to run 5 major cycles, starting selfcal at cycle 1. A global solution across all
# frequencies (i.e. Visibilities) is performed.
start = time.time()
print("****** Starting ICAL ******")
controls = create_calibration_controls()
controls['T']['first_selfcal'] = 1
controls['G']['first_selfcal'] = 3
controls['B']['first_selfcal'] = 4
controls['T']['timescale'] = 'auto'
controls['G']['timescale'] = 'auto'
controls['B']['timescale'] = 1e5
if nfreqwin > 6:
nmoment = 3
algorithm = 'mmclean'
elif nfreqwin > 2:
nmoment = 2
algorithm = 'mmclean'
else:
nmoment = 1
algorithm = 'msclean'
start = time.time()
ical_list = ical_list_arlexecute_workflow(vis_list,
model_imagelist=model_list,
context='wstack',
calibration_context='TG',
controls=controls,
scales=[0, 3, 10],
algorithm=algorithm,
nmoment=nmoment,
niter=1000,
fractional_threshold=0.1,
threshold=0.1,
nmajor=5,
gain=0.25,
vis_slices=vis_slices,
timeslice='auto',
global_solution=False,
psf_support=64,
do_selfcal=True)
end = time.time()
results['time ICAL graph'] = end - start
print("Construction of ICAL graph took %.2f seconds" % (end - start))
# Execute the graph
start = time.time()
result = arlexecute.compute(ical_list, sync=True)
deconvolved, residual, restored = result
end = time.time()
results['time ICAL'] = end - start
print("ICAL graph execution took %.2f seconds" % (end - start))
qa = qa_image(deconvolved[centre])
results['deconvolved_max'] = qa.data['max']
results['deconvolved_min'] = qa.data['min']
export_image_to_fits(deconvolved[centre],
"pipelines-timings-arlexecute-ical_deconvolved.fits")
qa = qa_image(residual[centre][0])
results['residual_max'] = qa.data['max']
results['residual_min'] = qa.data['min']
export_image_to_fits(residual[centre][0],
"pipelines-timings-arlexecute-ical_residual.fits")
qa = qa_image(restored[centre])
results['restored_max'] = qa.data['max']
results['restored_min'] = qa.data['min']
export_image_to_fits(restored[centre],
"pipelines-timings-arlexecute-ical_restored.fits")
#
arlexecute.close()
end_all = time.time()
results['time overall'] = end_all - start_all
print("At end, results are {0!r}".format(results))
return results
pp = pprint.PrettyPrinter()
def init_logging():
logging.basicConfig(filename='%s/gleam-pipeline.log' % results_dir,
filemode='a',
format='%(thread)s %(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s',
datefmt='%H:%M:%S',
level=logging.INFO)
if __name__ == '__main__':
log = logging.getLogger()
print("Starting gleam_simulate_list_arlexecute_pipeline")
arlexecute.set_client(use_dask=True)
arlexecute.run(init_logging)
# We create a graph to make the visibility. The parameter rmax determines the distance of the furthest antenna/stations used. All over parameters are determined from this number.
nfreqwin = 7
ntimes = 11
rmax = 300.0
frequency = numpy.linspace(0.9e8, 1.1e8, nfreqwin)
channel_bandwidth = numpy.array(nfreqwin * [frequency[1] - frequency[0]])
times = numpy.linspace(-numpy.pi / 3.0, numpy.pi / 3.0, ntimes)
phasecentre = SkyCoord(ra=+30.0 * u.deg, dec=-60.0 * u.deg, frame='icrs', equinox='J2000')
vis_list = simulate_list_arlexecute_workflow('LOWBD2',
rmax=rmax,
frequency=frequency,
logging.basicConfig(
filename='%s/gleam-pipeline.log' % results_dir,
filemode='a',
format=
'%(thread)s %(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s',
datefmt='%H:%M:%S',
level=logging.INFO)
if __name__ == '__main__':
pp = pprint.PrettyPrinter()
log = logging.getLogger()
logging.info("Starting ical_list_arlexecute_workflow-pipeline")
arlexecute.set_client(get_dask_Client())
arlexecute.run(init_logging)
# Load data from previous simulation
vislist = import_blockvisibility_from_hdf5('gleam_simulation_vislist.hdf')
print(vislist[0])
cellsize = 0.001
npixel = 1024
pol_frame = PolarisationFrame("stokesI")
model_list = [
arlexecute.execute(create_image_from_visibility)(
v, npixel=1024, cellsize=cellsize, polarisation_frame=pol_frame)
for v in vislist
def init_logging():
logging.basicConfig(
filename='%s/ska-pipeline.log' % results_dir,
filemode='a',
format='%%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s',
datefmt='%H:%M:%S',
level=logging.INFO)
if __name__ == '__main__':
log = logging.getLogger()
print("Starting ska-pipelines pipeline")
arlexecute.set_client(use_dask=True,
threads_per_worker=1,
memory_limit=4e9,
local_dir=dask_dir)
print(arlexecute.client)
arlexecute.run(init_logging)
# We create a graph to make the visibility. The parameter rmax determines the distance of the
# furthest antenna/stations used. All over parameters are determined from this number.
nfreqwin = 41
ntimes = 5
rmax = 750.0
centre = nfreqwin // 2
frequency = numpy.linspace(0.9e8, 1.1e8, nfreqwin)
channel_bandwidth = numpy.array(nfreqwin * [frequency[1] - frequency[0]])
times = numpy.linspace(-numpy.pi / 3.0, numpy.pi / 3.0, ntimes)
help='Context: 2d|timeslice|wstack')
parser.add_argument('--nchan',
type=int,
default=40,
help='Number of channels to process')
args = parser.parse_args()
print(args)
log = logging.getLogger()
logging.info("Starting Imaging pipeline")
use_dask = args.use_dask == 'True'
arlexecute.set_client(use_dask=use_dask,
threads_per_worker=args.threads,
memory_limit=args.memory * 1024 * 1024 * 1024,
n_workers=args.nworkers,
local_dir=dask_dir)
arlexecute.run(init_logging)
nchan = args.nchan
uvmax = 450.0
nfreqwin = 2
centre = 0
cellsize = 0.0004
npixel = args.npixel
psfwidth = (((8.0 / 2.35482004503) / 60.0) * numpy.pi / 180.0) / cellsize
context = args.context
if context == 'wstack':
vis_slices = 45
def actualSetUp(self, add_errors=False, freqwin=1, block=False, dospectral=True, dopol=False, zerow=False):
arlexecute.set_client(use_dask=False)
self.npixel = 256
self.low = create_named_configuration('LOWBD2', rmax=750.0)
self.freqwin = freqwin
self.vis_list = list()
self.ntimes = 5
self.times = numpy.linspace(-3.0, +3.0, self.ntimes) * numpy.pi / 12.0
if freqwin > 1:
self.frequency = numpy.linspace(0.8e8, 1.2e8, self.freqwin)
self.channelwidth = numpy.array(freqwin * [self.frequency[1] - self.frequency[0]])
else:
self.frequency = numpy.array([0.8e8])
self.channelwidth = numpy.array([1e6])
if dopol:
self.vis_pol = PolarisationFrame('linear')
self.image_pol = PolarisationFrame('stokesIQUV')
f = numpy.array([100.0, 20.0, -10.0, 1.0])
else:
self.vis_pol = PolarisationFrame('stokesI')
self.image_pol = PolarisationFrame('stokesI')
f = numpy.array([100.0])
if dospectral:
flux = numpy.array([f * numpy.power(freq / 1e8, -0.7) for freq in self.frequency])
else:
flux = numpy.array([f])
self.phasecentre = SkyCoord(ra=+180.0 * u.deg, dec=-60.0 * u.deg, frame='icrs', equinox='J2000')
self.vis_list = [arlexecute.execute(ingest_unittest_visibility)(self.low,
[self.frequency[freqwin]],
[self.channelwidth[freqwin]],
self.times,
self.vis_pol,
self.phasecentre, block=block,
zerow=zerow)
for freqwin, _ in enumerate(self.frequency)]
self.model_list = [arlexecute.execute(create_unittest_model, nout=freqwin)(self.vis_list[freqwin],
self.image_pol,
npixel=self.npixel)
for freqwin, _ in enumerate(self.frequency)]
self.components_list = [arlexecute.execute(create_unittest_components)(self.model_list[freqwin],
flux[freqwin, :][numpy.newaxis, :])
for freqwin, _ in enumerate(self.frequency)]
self.model_list = [arlexecute.execute(insert_skycomponent, nout=1)(self.model_list[freqwin],
self.components_list[freqwin])
for freqwin, _ in enumerate(self.frequency)]
self.vis_list = [arlexecute.execute(predict_skycomponent_visibility)(self.vis_list[freqwin],
self.components_list[freqwin])
for freqwin, _ in enumerate(self.frequency)]
# Calculate the model convolved with a Gaussian.
self.model = arlexecute.compute(self.model_list[0], sync=True)
self.cmodel = smooth_image(self.model)
export_image_to_fits(self.model, '%s/test_imaging_model.fits' % self.dir)
export_image_to_fits(self.cmodel, '%s/test_imaging_cmodel.fits' % self.dir)
if add_errors and block:
self.vis_list = [arlexecute.execute(insert_unittest_errors)(self.vis_list[i])
for i, _ in enumerate(self.frequency)]
self.vis = arlexecute.compute(self.vis_list[0], sync=True)
self.components = arlexecute.compute(self.components_list[0], sync=True)
def trial_case(results,
seed=180555,
context='wstack',
nworkers=8,
threads_per_worker=1,
memory=8,
processes=True,
order='frequency',
nfreqwin=7,
ntimes=3,
rmax=750.0,
facets=1,
wprojection_planes=1,
use_dask=True,
use_serial_imaging=True,
flux_limit=0.3,
nmajor=5,
dft_threshold=1.0,
use_serial_clean=True,
write_fits=False):
""" Single trial for performance-timings
Simulates visibilities from GLEAM including phase errors
Makes dirty image and PSF
Runs ICAL pipeline
The results are in a dictionary:
'context': input - a string describing concisely the purpose of the test
'time overall', overall execution time (s)
'time predict', time to execute GLEAM prediction graph
'time invert', time to make dirty image
'time invert graph', time to make dirty image graph
'time ICAL graph', time to create ICAL graph
'time ICAL', time to execute ICAL graph
'context', type of imaging e.g. 'wstack'
'nworkers', number of workers to create
'threads_per_worker',
'nnodes', Number of nodes,
'processes', 'order', Ordering of data_models
'nfreqwin', Number of frequency windows in simulation
'ntimes', Number of hour angles in simulation
'rmax', Maximum radius of stations used in simulation (m)
'facets', Number of facets in deconvolution and imaging
'wprojection_planes', Number of wprojection planes
'vis_slices', Number of visibility slices (per Visibbility)
'npixel', Number of pixels in image
'cellsize', Cellsize in radians
'seed', Random number seed
'dirty_max', Maximum in dirty image
'dirty_min', Minimum in dirty image
'restored_max',
'restored_min',
'deconvolved_max',
'deconvolved_min',
'residual_max',
'residual_min',
'git_info', GIT hash (not definitive since local mods are possible)
:param results: Initial state
:param seed: Random number seed (used in gain simulations)
:param context: imaging context
:param context: Type of context: '2d'|'timeslice'|'wstack'
:param nworkers: Number of dask workers to use
:param threads_per_worker: Number of threads per worker
:param processes: Use processes instead of threads 'processes'|'threads'
:param order: See simulate_list_list_arlexecute_workflow_workflowkflow
:param nfreqwin: See simulate_list_list_arlexecute_workflow_workflowkflow
:param ntimes: See simulate_list_list_arlexecute_workflow_workflowkflow
:param rmax: See simulate_list_list_arlexecute_workflow_workflowkflow
:param facets: Number of facets to use
:param wprojection_planes: Number of wprojection planes to use
:param use_dask: Use dask or immediate evaluation
:return: results dictionary
"""
if use_dask:
client = get_dask_Client(threads_per_worker=threads_per_worker,
processes=threads_per_worker == 1,
memory_limit=memory * 1024 * 1024 * 1024,
n_workers=nworkers)
arlexecute.set_client(client)
nodes = findNodes(arlexecute.client)
print("Defined %d workers on %d nodes" % (nworkers, len(nodes)))
print("Workers are: %s" % str(nodes))
else:
arlexecute.set_client(use_dask=use_dask)
results['nnodes'] = 1
def init_logging():
logging.basicConfig(
filename='pipelines_arlexecute_timings.log',
filemode='w',
format='%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s',
datefmt='%H:%M:%S',
level=logging.INFO)
init_logging()
log = logging.getLogger()
# Initialise logging on the workers. This appears to only work using the process scheduler.
arlexecute.run(init_logging)
def lprint(*args):
log.info(*args)
print(*args)
lprint("Starting pipelines_arlexecute_timings")
numpy.random.seed(seed)
results['seed'] = seed
start_all = time.time()
results['context'] = context
results['hostname'] = socket.gethostname()
results['git_hash'] = git_hash()
results['epoch'] = time.strftime("%Y-%m-%d %H:%M:%S")
lprint("Context is %s" % context)
results['nworkers'] = nworkers
results['threads_per_worker'] = threads_per_worker
results['processes'] = processes
results['memory'] = memory
results['order'] = order
results['nfreqwin'] = nfreqwin
results['ntimes'] = ntimes
results['rmax'] = rmax
results['facets'] = facets
results['wprojection_planes'] = wprojection_planes
results['dft threshold'] = dft_threshold
results['use_dask'] = use_dask
lprint("At start, configuration is:")
lprint(sort_dict(results))
# Parameters determining scale of simulation.
frequency = numpy.linspace(1.0e8, 1.2e8, nfreqwin)
centre = nfreqwin // 2
if nfreqwin > 1:
channel_bandwidth = numpy.array(nfreqwin *
[frequency[1] - frequency[0]])
else:
channel_bandwidth = numpy.array([1e6])
times = numpy.linspace(-numpy.pi / 4.0, numpy.pi / 4.0, ntimes)
phasecentre = SkyCoord(ra=+0.0 * u.deg,
dec=-40.0 * u.deg,
frame='icrs',
equinox='J2000')
lprint("****** Visibility creation ******")
# Create the empty BlockVisibility's and persist these on the cluster
tmp_bvis_list = simulate_list_arlexecute_workflow(
'LOWBD2',
frequency=frequency,
channel_bandwidth=channel_bandwidth,
times=times,
phasecentre=phasecentre,
order=order,
format='blockvis',
rmax=rmax)
tmp_vis_list = [
arlexecute.execute(convert_blockvisibility_to_visibility)(bv)
for bv in tmp_bvis_list
]
tmp_vis_list = arlexecute.client.compute(tmp_vis_list, sync=True)
vis_list = arlexecute.gather(tmp_vis_list)
import matplotlib.pyplot as plt
plt.clf()
plt.hist(vis_list[0].w, bins=100)
plt.title('Histogram of w samples: rms=%.1f (wavelengths)' %
numpy.std(vis_list[0].w))
plt.xlabel('W (wavelengths)')
plt.show()
plt.clf()
plt.hist(vis_list[0].uvdist, bins=100)
plt.title('Histogram of uvdistance samples')
plt.xlabel('UV Distance (wavelengths)')
plt.show()
arlexecute.client.cancel(tmp_vis_list)
future_vis_list = arlexecute.scatter(vis_list)
# Find the best imaging parameters but don't bring the vis_list back here
print("****** Finding wide field parameters ******")
future_advice = [
arlexecute.execute(advise_wide_field)(
v,
guard_band_image=6.0,
delA=0.1,
facets=facets,
wprojection_planes=wprojection_planes,
oversampling_synthesised_beam=4.0) for v in future_vis_list
]
future_advice = arlexecute.compute(future_advice)
advice = arlexecute.client.gather(future_advice)[-1]
arlexecute.client.cancel(future_advice)
# Deconvolution via sub-images requires 2^n
npixel = advice['npixels2']
results['npixel'] = npixel
cellsize = advice['cellsize']
results['cellsize'] = cellsize
lprint("Image will have %d by %d pixels, cellsize = %.6f rad" %
(npixel, npixel, cellsize))
# Create an empty model image
tmp_model_list = [
arlexecute.execute(create_image)(
npixel=npixel,
cellsize=cellsize,
frequency=[frequency[f]],
channel_bandwidth=[channel_bandwidth[f]],
phasecentre=phasecentre,
polarisation_frame=PolarisationFrame("stokesI"))
for f, freq in enumerate(frequency)
]
model_list = arlexecute.compute(tmp_model_list, sync=True)
future_model_list = arlexecute.scatter(model_list)
lprint("****** Setting up imaging parameters ******")
# Now set up the imaging parameters
template_model = create_image(
npixel=npixel,
cellsize=cellsize,
frequency=[frequency[centre]],
phasecentre=phasecentre,
channel_bandwidth=[channel_bandwidth[centre]],
polarisation_frame=PolarisationFrame("stokesI"))
gcfcf = [create_pswf_convolutionfunction(template_model)]
if context == 'timeslice':
vis_slices = ntimes
lprint("Using timeslice with %d slices" % vis_slices)
elif context == '2d':
vis_slices = 1
elif context == "wprojection":
wstep = advice['wstep']
nw = advice['wprojection_planes']
vis_slices = 1
support = advice['nwpixels']
results['wprojection_planes'] = nw
lprint("****** Starting W projection kernel creation ******")
lprint("Using wprojection with %d planes with wstep %.1f wavelengths" %
(nw, wstep))
lprint("Support of wprojection = %d pixels" % support)
gcfcf = [
create_awterm_convolutionfunction(template_model,
nw=nw,
wstep=wstep,
oversampling=4,
support=support,
use_aaf=True)
]
lprint("Size of W projection gcf, cf = %.2E bytes" % get_size(gcfcf))
else:
context = 'wstack'
vis_slices = advice['vis_slices']
lprint("Using wstack with %d slices" % vis_slices)
gcfcf = arlexecute.scatter(gcfcf, broadcast=True)
results['vis_slices'] = vis_slices
# Make a skymodel from gleam, with bright sources as components and weak sources in an image
lprint("****** Starting GLEAM skymodel creation ******")
future_skymodel_list = [
arlexecute.execute(create_low_test_skymodel_from_gleam)(
npixel=npixel,
cellsize=cellsize,
frequency=[frequency[f]],
phasecentre=phasecentre,
polarisation_frame=PolarisationFrame("stokesI"),
flux_limit=flux_limit,
flux_threshold=dft_threshold,
flux_max=5.0) for f, freq in enumerate(frequency)
]
# We use predict_skymodel so that we can use skycomponents as well as images
lprint("****** Starting GLEAM skymodel prediction ******")
predicted_vis_list = [
predict_skymodel_list_arlexecute_workflow(future_vis_list[f],
[future_skymodel_list[f]],
context=context,
vis_slices=vis_slices,
facets=facets,
gcfcf=gcfcf)[0]
for f, freq in enumerate(frequency)
]
# Corrupt the visibility for the GLEAM model
lprint("****** Visibility corruption ******")
tmp_corrupted_vis_list = corrupt_list_arlexecute_workflow(
predicted_vis_list, phase_error=1.0, seed=seed)
lprint("****** Weighting and tapering ******")
tmp_corrupted_vis_list = weight_list_arlexecute_workflow(
tmp_corrupted_vis_list, future_model_list)
tmp_corrupted_vis_list = taper_list_arlexecute_workflow(
tmp_corrupted_vis_list, 0.003 * 750.0 / rmax)
tmp_corrupted_vis_list = arlexecute.compute(tmp_corrupted_vis_list,
sync=True)
corrupted_vis_list = arlexecute.gather(tmp_corrupted_vis_list)
# arlexecute.client.cancel(predicted_vis_list)
arlexecute.client.cancel(tmp_corrupted_vis_list)
future_corrupted_vis_list = arlexecute.scatter(corrupted_vis_list)
# At this point the only futures are of scatter'ed data so no repeated calculations should be
# incurred.
lprint("****** Starting dirty image calculation ******")
start = time.time()
dirty_list = invert_list_arlexecute_workflow(
future_corrupted_vis_list,
future_model_list,
vis_slices=vis_slices,
context=context,
facets=facets,
use_serial_invert=use_serial_imaging,
gcfcf=gcfcf)
results['size invert graph'] = get_size(dirty_list)
lprint('Size of dirty graph is %.3E bytes' %
(results['size invert graph']))
end = time.time()
results['time invert graph'] = end - start
lprint("Construction of invert graph took %.3f seconds" % (end - start))
start = time.time()
dirty, sumwt = arlexecute.compute(dirty_list, sync=True)[centre]
end = time.time()
results['time invert'] = end - start
lprint("Dirty image invert took %.3f seconds" % (end - start))
lprint("Maximum in dirty image is %f, sumwt is %s" %
(numpy.max(numpy.abs(dirty.data)), str(sumwt)))
qa = qa_image(dirty)
results['dirty_max'] = qa.data['max']
results['dirty_min'] = qa.data['min']
if write_fits:
export_image_to_fits(
dirty, "pipelines_arlexecute_timings-%s-dirty.fits" % context)
lprint("****** Starting prediction ******")
start = time.time()
tmp_vis_list = predict_list_arlexecute_workflow(
future_corrupted_vis_list,
future_model_list,
vis_slices=vis_slices,
context=context,
facets=facets,
use_serial_predict=use_serial_imaging,
gcfcf=gcfcf)
result = arlexecute.compute(tmp_vis_list, sync=True)
# arlexecute.client.cancel(tmp_vis_list)
end = time.time()
results['time predict'] = end - start
lprint("Predict took %.3f seconds" % (end - start))
# Create the ICAL pipeline to run major cycles, starting selfcal at cycle 1. A global solution across all
# frequencies (i.e. Visibilities) is performed.
print("Using subimage clean")
deconvolve_facets = 8
deconvolve_overlap = 16
deconvolve_taper = 'tukey'
lprint("****** Starting ICAL graph creation ******")
controls = create_calibration_controls()
controls['T']['first_selfcal'] = 1
controls['T']['timescale'] = 'auto'
start = time.time()
ical_list = ical_list_arlexecute_workflow(
future_corrupted_vis_list,
model_imagelist=future_model_list,
context=context,
vis_slices=vis_slices,
scales=[0, 3, 10],
algorithm='mmclean',
nmoment=3,
niter=1000,
fractional_threshold=0.1,
threshold=0.01,
nmajor=nmajor,
gain=0.25,
psf_support=64,
deconvolve_facets=deconvolve_facets,
deconvolve_overlap=deconvolve_overlap,
deconvolve_taper=deconvolve_taper,
timeslice='auto',
global_solution=True,
do_selfcal=True,
calibration_context='T',
controls=controls,
use_serial_predict=use_serial_imaging,
use_serial_invert=use_serial_imaging,
use_serial_clean=use_serial_clean,
gcfcf=gcfcf)
results['size ICAL graph'] = get_size(ical_list)
lprint('Size of ICAL graph is %.3E bytes' % results['size ICAL graph'])
end = time.time()
results['time ICAL graph'] = end - start
lprint("Construction of ICAL graph took %.3f seconds" % (end - start))
print("Current objects on cluster: ")
pp.pprint(arlexecute.client.who_has())
#
# Execute the graph
lprint("****** Executing ICAL graph ******")
start = time.time()
deconvolved, residual, restored, gaintables = arlexecute.compute(ical_list,
sync=True)
end = time.time()
results['time ICAL'] = end - start
lprint("ICAL graph execution took %.3f seconds" % (end - start))
qa = qa_image(deconvolved[centre])
results['deconvolved_max'] = qa.data['max']
results['deconvolved_min'] = qa.data['min']
deconvolved_cube = image_gather_channels(deconvolved)
if write_fits:
export_image_to_fits(
deconvolved_cube,
"pipelines_arlexecute_timings-%s-ical_deconvolved.fits" % context)
qa = qa_image(residual[centre][0])
results['residual_max'] = qa.data['max']
results['residual_min'] = qa.data['min']
residual_cube = remove_sumwt(residual)
residual_cube = image_gather_channels(residual_cube)
if write_fits:
export_image_to_fits(
residual_cube,
"pipelines_arlexecute_timings-%s-ical_residual.fits" % context)
qa = qa_image(restored[centre])
results['restored_max'] = qa.data['max']
results['restored_min'] = qa.data['min']
restored_cube = image_gather_channels(restored)
if write_fits:
export_image_to_fits(
restored_cube,
"pipelines_arlexecute_timings-%s-ical_restored.fits" % context)
#
arlexecute.close()
end_all = time.time()
results['time overall'] = end_all - start_all
lprint("At end, results are:")
results = sort_dict(results)
lprint(results)
return results
def test_setup_errors(self):
with self.assertRaises(AssertionError):
arlexecute.set_client(client=1)
# We will use dask
# In[2]:
threads_per_worker = 16
memory = 384
nworkers = 1
filename = args.schedulerfile
client = get_dask_Client(threads_per_worker=threads_per_worker,
processes=threads_per_worker == 1,
memory_limit=memory * 1024 * 1024 * 1024,
n_workers=nworkers,
with_file=True,
scheduler_file=filename)
arlexecute.set_client(client)
nodes = findNodes(arlexecute.client)
print("Defined %d workers on %d nodes" % (nworkers, len(nodes)))
print("Workers are: %s" % str(nodes))
#arlexecute.set_client(use_dask=True)
init_logging()
log = logging.getLogger()
logging.info("Starting imaging-pipeline")
# Initialise logging on the workers. This appears to only work using
# the process scheduler.
arlexecute.run(init_logging)
# In[3]:
args = parser.parse_args()
print("Starting LOW low level RFI simulation")
pp.pprint(vars(args))
write_ms = args.write_ms == "True"
numpy.random.seed(args.seed)
if args.use_dask == "True":
client = get_dask_Client(threads_per_worker=1,
processes=True,
memory_limit=32 * 1024 * 1024 * 1024,
n_workers=8)
arlexecute.set_client(client=client)
print(arlexecute.client)
else:
print("Running in serial mode")
arlexecute.set_client(use_dask=False)
emitter_location = EarthLocation(lon=args.emitter_longitude,
lat=args.emitter_latitude,
height=0.0)
emitter_power = args.emitter_power
print("Emitter is %.1f kW at location %s" %
(1e-3 * emitter_power, emitter_location.geodetic))
if args.waterfall == "True":
waterfall = True
else:
def simulation(self,
args,
time_series='wind',
band='B2',
context='singlesource',
vp_directory=''):
ra = args.ra
declination = args.declination
use_radec = args.use_radec == "True"
integration_time = args.integration_time
time_range = args.time_range
time_chunk = args.time_chunk
offset_dir = args.offset_dir
pbtype = args.pbtype
pbradius = args.pbradius
rmax = args.rmax
flux_limit = args.flux_limit
npixel = args.npixel
shared_directory = args.shared_directory
vp_directory = args.vp_directory
# Simulation specific parameters
global_pe = numpy.array(args.global_pe)
static_pe = numpy.array(args.static_pe)
dynamic_pe = args.dynamic_pe
seed = args.seed
basename = os.path.basename(os.getcwd())
# client = get_dask_Client()
use_dask = False
arlexecute.set_client(use_dask=use_dask)
# Set up details of simulated observation
nfreqwin = 1
if band == 'B1':
frequency = [0.765e9]
elif band == 'B2':
frequency = [1.36e9]
elif band == 'Ku':
frequency = [12.179e9]
else:
raise ValueError("Unknown band %s" % band)
phasecentre = SkyCoord(ra=ra * u.deg,
dec=declination * u.deg,
frame='icrs',
equinox='J2000')
bvis_graph = create_standard_mid_simulation_arlexecute_workflow(
band, rmax, phasecentre, time_range, time_chunk, integration_time,
shared_directory)
future_bvis_list = arlexecute.persist(bvis_graph)
vis_graph = [
arlexecute.execute(convert_blockvisibility_to_visibility)(bv)
for bv in future_bvis_list
]
future_vis_list = arlexecute.persist(vis_graph, sync=True)
# We need the HWHM of the primary beam, and the location of the nulls
HWHM_deg, null_az_deg, null_el_deg = find_pb_width_null(
pbtype, frequency)
HWHM = HWHM_deg * numpy.pi / 180.0
FOV_deg = 8.0 * 1.36e9 / frequency[0]
advice_list = arlexecute.execute(advise_wide_field)(
future_vis_list[0], guard_band_image=1.0, delA=0.02, verbose=False)
advice = arlexecute.compute(advice_list, sync=True)
pb_npixel = 1024
d2r = numpy.pi / 180.0
pb_cellsize = d2r * FOV_deg / pb_npixel
cellsize = advice['cellsize']
# Now construct the components
original_components, offset_direction = create_simulation_components(
context, phasecentre, frequency, pbtype, offset_dir, flux_limit,
pbradius * HWHM, pb_npixel, pb_cellsize)
vp_list = [
arlexecute.execute(create_image_from_visibility)(
bv,
npixel=pb_npixel,
frequency=frequency,
nchan=nfreqwin,
cellsize=pb_cellsize,
phasecentre=phasecentre,
override_cellsize=False) for bv in future_bvis_list
]
vp_list = [
arlexecute.execute(create_vp)(vp,
pbtype,
pointingcentre=phasecentre,
use_local=not use_radec)
for vp in vp_list
]
future_vp_list = arlexecute.persist(vp_list)
# Make one image per component
future_model_list = [
arlexecute.execute(create_image_from_visibility)(
future_vis_list[0],
npixel=npixel,
frequency=frequency,
nchan=nfreqwin,
cellsize=cellsize,
phasecentre=offset_direction,
polarisation_frame=PolarisationFrame("stokesI"))
for i, _ in enumerate(original_components)
]
a2r = numpy.pi / (3600.0 * 1800)
no_error_gtl = None
error_gtl = None
if time_series == '':
global_pointing_error = global_pe
static_pointing_error = static_pe
pointing_error = dynamic_pe
no_error_gtl, error_gtl = \
create_pointing_errors_gaintable_arlexecute_workflow(future_bvis_list, original_components,
sub_vp_list=future_vp_list,
use_radec=use_radec,
pointing_error=a2r * pointing_error,
static_pointing_error=a2r * static_pointing_error,
global_pointing_error=a2r * global_pointing_error,
seed=seed,
show=False, basename=basename)
elif time_series == 'wind':
no_error_gtl, error_gtl = \
create_pointing_errors_gaintable_arlexecute_workflow(future_bvis_list, original_components,
sub_vp_list=future_vp_list,
use_radec=use_radec,
time_series=time_series,
time_series_type='precision',
seed=seed,
show=False, basename=basename)
elif time_series == 'gravity':
no_error_gtl, error_gtl = \
create_surface_errors_gaintable_arlexecute_workflow(band, future_bvis_list, original_components,
vp_directory=vp_directory, use_radec=use_radec,
show=False, basename=basename)
else:
raise ValueError("Unknown type of error %s" % time_series)
# Now make all the residual images
vis_comp_chunk_dirty_list = \
calculate_residual_from_gaintables_arlexecute_workflow(future_bvis_list, original_components,
future_model_list,
no_error_gtl, error_gtl)
# Add the resulting images
error_dirty_list = sum_invert_results_arlexecute(
vis_comp_chunk_dirty_list)
# Actually compute the graph assembled above
error_dirty, sumwt = arlexecute.compute(error_dirty_list, sync=True)
return error_dirty, sumwt
return lowcore
if __name__ == '__main__':
log = logging.getLogger()
log.setLevel(logging.DEBUG)
log.addHandler(logging.StreamHandler(sys.stdout))
pylab.rcParams['figure.figsize'] = (12.0, 12.0)
pylab.rcParams['image.cmap'] = 'rainbow'
lowcore = create_configuration('MUSER')
# lowcore = create_named_configuration('MUSER')
# arlexecute.set_client(use_dask=True)
arlexecute.set_client(use_dask=True, threads_per_worker=1, memory_limit=32 * 1024 * 1024 * 1024, n_workers=8,
local_dir=dask_dir)
times = numpy.array([-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0]) * (numpy.pi / 12.0)
frequency = numpy.array([4e8])
channel_bandwidth = numpy.array([25e6])
reffrequency = numpy.max(frequency)
phasecentre = SkyCoord(ra=+5 * u.deg, dec=20 * u.deg, frame='icrs', equinox='J2000')
vt = create_visibility(lowcore, times, frequency, channel_bandwidth=channel_bandwidth,
weight=1.0, phasecentre=phasecentre,
polarisation_frame=PolarisationFrame('stokesI'))
advice = advise_wide_field(vt, wprojection_planes=1)
vt.data['vis'] *= 0.0
npixel=256
model = create_image_from_visibility(vt, npixel=npixel, cellsize=6.8e-5, nchan=1,
def trial_case(results, seed=180555, context='wstack', nworkers=8, threads_per_worker=1, memory=8,
processes=True, order='frequency', nfreqwin=7, ntimes=3, rmax=750.0,
facets=1, wprojection_planes=1, use_dask=True, use_serial_imaging=False,
flux_limit=0.3, nmajor=5, dft_threshold=1.0):
""" Single trial for performance-timings
Simulates visibilities from GLEAM including phase errors
Makes dirty image and PSF
Runs ICAL pipeline
The results are in a dictionary:
'context': input - a string describing concisely the purpose of the test
'time overall', overall execution time (s)
'time create gleam', time to create GLEAM prediction graph
'time predict', time to execute GLEAM prediction graph
'time corrupt', time to corrupt data_models
'time invert', time to make dirty image
'time psf invert', time to make PSF
'time ICAL graph', time to create ICAL graph
'time ICAL', time to execute ICAL graph
'context', type of imaging e.g. 'wstack'
'nworkers', number of workers to create
'threads_per_worker',
'nnodes', Number of nodes,
'processes', 'order', Ordering of data_models
'nfreqwin', Number of frequency windows in simulation
'ntimes', Number of hour angles in simulation
'rmax', Maximum radius of stations used in simulation (m)
'facets', Number of facets in deconvolution and imaging
'wprojection_planes', Number of wprojection planes
'vis_slices', Number of visibility slices (per Visibbility)
'npixel', Number of pixels in image
'cellsize', Cellsize in radians
'seed', Random number seed
'dirty_max', Maximum in dirty image
'dirty_min', Minimum in dirty image
'psf_max',
'psf_min',
'restored_max',
'restored_min',
'deconvolved_max',
'deconvolved_min',
'residual_max',
'residual_min',
'git_info', GIT hash (not definitive since local mods are possible)
:param results: Initial state
:param seed: Random number seed (used in gain simulations)
:param context: imaging context
:param context: Type of context: '2d'|'timeslice'|'wstack'
:param nworkers: Number of dask workers to use
:param threads_per_worker: Number of threads per worker
:param processes: Use processes instead of threads 'processes'|'threads'
:param order: See simulate_list_list_arlexecute_workflow_workflowkflow
:param nfreqwin: See simulate_list_list_arlexecute_workflow_workflowkflow
:param ntimes: See simulate_list_list_arlexecute_workflow_workflowkflow
:param rmax: See simulate_list_list_arlexecute_workflow_workflowkflow
:param facets: Number of facets to use
:param wprojection_planes: Number of wprojection planes to use
:param use_dask: Use dask or immediate evaluation
:return: results dictionary
"""
if use_dask:
client = get_dask_Client(threads_per_worker=threads_per_worker,
processes = threads_per_worker == 1,
memory_limit=memory * 1024 * 1024 * 1024,
n_workers=nworkers)
arlexecute.set_client(client)
nodes = findNodes(arlexecute.client)
print("Defined %d workers on %d nodes" % (nworkers, len(nodes)))
print("Workers are: %s" % str(nodes))
else:
arlexecute.set_client(use_dask=use_dask)
results['nnodes'] = 1
def init_logging():
logging.basicConfig(filename='pipelines-arlexecute-timings.log',
filemode='a',
format='%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s',
datefmt='%H:%M:%S',
level=logging.INFO)
init_logging()
log = logging.getLogger()
# Initialise logging on the workers. This appears to only work using the process scheduler.
arlexecute.run(init_logging)
def lprint(s):
log.info(s)
print(s)
lprint("Starting pipelines-arlexecute-timings")
numpy.random.seed(seed)
results['seed'] = seed
start_all = time.time()
results['context'] = context
results['hostname'] = socket.gethostname()
results['git_hash'] = git_hash()
results['epoch'] = time.strftime("%Y-%m-%d %H:%M:%S")
lprint("Context is %s" % context)
results['nworkers'] = nworkers
results['threads_per_worker'] = threads_per_worker
results['processes'] = processes
results['memory'] = memory
results['order'] = order
results['nfreqwin'] = nfreqwin
results['ntimes'] = ntimes
results['rmax'] = rmax
results['facets'] = facets
results['wprojection_planes'] = wprojection_planes
results['dft threshold'] = dft_threshold
results['use_dask'] = use_dask
lprint("At start, configuration is:")
lprint(results)
# Parameters determining scale
frequency = numpy.linspace(1.0e8, 1.2e8, nfreqwin)
centre = nfreqwin // 2
if nfreqwin > 1:
channel_bandwidth = numpy.array(nfreqwin * [frequency[1] - frequency[0]])
else:
channel_bandwidth = numpy.array([1e6])
times = numpy.linspace(-numpy.pi / 4.0, numpy.pi / 4.0, ntimes)
phasecentre = SkyCoord(ra=+30.0 * u.deg, dec=-60.0 * u.deg, frame='icrs', equinox='J2000')
bvis_list = simulate_list_arlexecute_workflow('LOWBD2',
frequency=frequency,
channel_bandwidth=channel_bandwidth,
times=times,
phasecentre=phasecentre,
order=order,
format='blockvis',
rmax=rmax)
lprint("****** Visibility creation ******")
bvis_list = arlexecute.compute(bvis_list, sync=True)
vis_list = [arlexecute.execute(convert_blockvisibility_to_visibility(bv)) for bv in bvis_list]
vis_list = arlexecute.compute(vis_list, sync=True)
# Find the best imaging parameters but don't bring the vis_list back here
def get_wf(v):
return advise_wide_field(v, guard_band_image=6.0,
delA=0.1,
facets=facets,
wprojection_planes=wprojection_planes,
oversampling_synthesised_beam=4.0)
advice = arlexecute.compute(arlexecute.execute(get_wf)(vis_list[-1]), sync=True)
# Deconvolution via sub-images requires 2^n
npixel = advice['npixels2']
results['npixel'] = npixel
cellsize = advice['cellsize']
results['cellsize'] = cellsize
lprint("Image will have %d by %d pixels, cellsize = %.6f rad" % (npixel, npixel, cellsize))
# Create an empty model image
model_list = [arlexecute.execute(create_image_from_visibility)
(vis_list[f],
npixel=npixel, cellsize=cellsize,
frequency=[frequency[f]],
channel_bandwidth=[channel_bandwidth[f]],
polarisation_frame=PolarisationFrame("stokesI"))
for f, freq in enumerate(frequency)]
model_list = arlexecute.compute(model_list, sync=True)
model_list = arlexecute.scatter(model_list)
start = time.time()
vis_list = weight_list_arlexecute_workflow(vis_list, model_list)
vis_list = taper_list_arlexecute_workflow(vis_list, 0.003 * 750.0 / rmax)
print("****** Starting weighting and tapering ******")
vis_list = arlexecute.compute(vis_list, sync=True)
end = time.time()
results['time weight'] = end - start
print("Weighting took %.3f seconds" % (end - start))
vis_list = arlexecute.scatter(vis_list)
# Now set up the imaging parameters
gcfcf_list = [None for i in range(nfreqwin)]
if context == 'timeslice':
vis_slices = ntimes
lprint("Using timeslice with %d slices" % vis_slices)
elif context == '2d':
vis_slices = 1
elif context == "wprojection":
wstep = advice['wstep']
nw = advice['wprojection_planes']
vis_slices = 1
support = advice['nwpixels']
results['wprojection_planes'] = nw
lprint("Using wprojection with %d planes with wstep %.1f wavelengths" % (nw, wstep))
start = time.time()
lprint("****** Starting W projection kernel creation ******")
gcfcf_list = [arlexecute.execute(create_awterm_convolutionfunction, nout=1)
(m, nw=nw, wstep=wstep, oversampling=8, support=support, use_aaf=True)
for m in model_list]
gcfcf_list = arlexecute.compute(gcfcf_list, sync=True)
end = time.time()
results['time create wprojection'] = end - start
lprint("Creating W projection kernel took %.3f seconds" % (end - start))
cf_image = convert_convolutionfunction_to_image(gcfcf_list[centre][1])
cf_image.data = numpy.real(cf_image.data)
export_image_to_fits(cf_image, "pipelines-arlexecute-timings-wterm-cf.fits")
gcfcf_list = arlexecute.scatter(gcfcf_list)
else:
context = 'wstack'
vis_slices = advice['vis_slices']
lprint("Using wstack with %d slices" % vis_slices)
results['vis_slices'] = vis_slices
# Make a skymodel from gleam, with bright sources as components and weak sources in an image
lprint("****** Starting GLEAM skymodel creation ******")
start = time.time()
skymodel_list = [arlexecute.execute(create_low_test_skymodel_from_gleam)
(npixel=npixel, cellsize=cellsize, frequency=[frequency[f]],
phasecentre=phasecentre,
polarisation_frame=PolarisationFrame("stokesI"),
flux_limit=flux_limit,
flux_threshold=dft_threshold,
flux_max=5.0) for f, freq in enumerate(frequency)]
skymodel_list = arlexecute.compute(skymodel_list, sync=True)
end = time.time()
lprint("GLEAM skymodel creation took %.3f seconds" % (end - start))
results['time create gleam'] = end - start
lprint("****** Starting GLEAM skymodel prediction ******")
start = time.time()
predicted_vis_list = [predict_skymodel_list_arlexecute_workflow(vis_list[f], [skymodel_list[f]], context=context,
vis_slices=vis_slices, facets=facets,
gcfcf=[gcfcf_list[f]])[0]
for f, freq in enumerate(frequency)]
predicted_vis_list = arlexecute.compute(predicted_vis_list, sync=True)
end = time.time()
lprint("GLEAM skymodel prediction took %.3f seconds" % (end - start))
results['time predict gleam'] = end - start
lprint("****** Starting psf image calculation ******")
start = time.time()
predicted_vis_list = arlexecute.scatter(predicted_vis_list)
psf_list = invert_list_arlexecute_workflow(predicted_vis_list, model_list, vis_slices=vis_slices,
dopsf=True, context=context, facets=facets,
use_serial_invert=use_serial_imaging, gcfcf=gcfcf_list)
psf, sumwt = arlexecute.compute(psf_list, sync=True)[centre]
end = time.time()
results['time psf invert'] = end - start
lprint("PSF invert took %.3f seconds" % (end - start))
lprint("Maximum in psf image is %f, sumwt is %s" % (numpy.max(numpy.abs(psf.data)), str(sumwt)))
qa = qa_image(psf)
results['psf_max'] = qa.data['max']
results['psf_min'] = qa.data['min']
export_image_to_fits(psf, "pipelines-arlexecute-timings-%s-psf.fits" % context)
# Make a smoothed model image for comparison
# smoothed_model_list = restore_list_arlexecute_workflow(gleam_model_list, psf_list)
# smoothed_model_list = arlexecute.compute(smoothed_model_list, sync=True)
# smoothed_cube = image_gather_channels(smoothed_model_list)
# export_image_to_fits(smoothed_cube, "pipelines-arlexecute-timings-cmodel.fits")
# Create an empty model image
model_list = [arlexecute.execute(create_image_from_visibility)
(predicted_vis_list[f],
npixel=npixel, cellsize=cellsize,
frequency=[frequency[f]],
channel_bandwidth=[channel_bandwidth[f]],
polarisation_frame=PolarisationFrame("stokesI"))
for f, freq in enumerate(frequency)]
model_list = arlexecute.compute(model_list, sync=True)
model_list = arlexecute.scatter(model_list)
lprint("****** Starting dirty image calculation ******")
start = time.time()
dirty_list = invert_list_arlexecute_workflow(predicted_vis_list, model_list, vis_slices=vis_slices,
context=context, facets=facets,
use_serial_invert=use_serial_imaging, gcfcf=gcfcf_list)
dirty, sumwt = arlexecute.compute(dirty_list, sync=True)[centre]
end = time.time()
results['time invert'] = end - start
lprint("Dirty image invert took %.3f seconds" % (end - start))
lprint("Maximum in dirty image is %f, sumwt is %s" % (numpy.max(numpy.abs(dirty.data)), str(sumwt)))
qa = qa_image(dirty)
results['dirty_max'] = qa.data['max']
results['dirty_min'] = qa.data['min']
export_image_to_fits(dirty, "pipelines-arlexecute-timings-%s-dirty.fits" % context)
# Corrupt the visibility for the GLEAM model
lprint("****** Visibility corruption ******")
start = time.time()
corrupted_vis_list = corrupt_list_arlexecute_workflow(predicted_vis_list, phase_error=1.0, seed=seed)
corrupted_vis_list = arlexecute.compute(corrupted_vis_list, sync=True)
end = time.time()
results['time corrupt'] = end - start
lprint("Visibility corruption took %.3f seconds" % (end - start))
# Create the ICAL pipeline to run major cycles, starting selfcal at cycle 1. A global solution across all
# frequencies (i.e. Visibilities) is performed.
lprint("****** Starting ICAL ******")
controls = create_calibration_controls()
controls['T']['first_selfcal'] = 1
controls['T']['timescale'] = 'auto'
start = time.time()
ical_list = ical_list_arlexecute_workflow(corrupted_vis_list,
model_imagelist=model_list,
context=context,
vis_slices=vis_slices,
scales=[0, 3, 10],
algorithm='mmclean',
nmoment=3, niter=1000,
fractional_threshold=0.1,
threshold=0.01, nmajor=nmajor,
gain=0.25,
psf_support=64,
deconvolve_facets=8,
deconvolve_overlap=32,
deconvolve_taper='tukey',
timeslice='auto',
global_solution=True,
do_selfcal=True,
calibration_context='T',
controls=controls,
use_serial_predict=use_serial_imaging,
use_serial_invert=use_serial_imaging,
gcfcf=gcfcf_list)
end = time.time()
results['time ICAL graph'] = end - start
lprint("Construction of ICAL graph took %.3f seconds" % (end - start))
# Execute the graph
start = time.time()
result = arlexecute.compute(ical_list, sync=True)
deconvolved, residual, restored, gaintables = result
end = time.time()
results['time ICAL'] = end - start
lprint("ICAL graph execution took %.3f seconds" % (end - start))
qa = qa_image(deconvolved[centre])
results['deconvolved_max'] = qa.data['max']
results['deconvolved_min'] = qa.data['min']
deconvolved_cube = image_gather_channels(deconvolved)
export_image_to_fits(deconvolved_cube, "pipelines-arlexecute-timings-%s-ical_deconvolved.fits" % context)
qa = qa_image(residual[centre][0])
results['residual_max'] = qa.data['max']
results['residual_min'] = qa.data['min']
residual_cube = remove_sumwt(residual)
residual_cube = image_gather_channels(residual_cube)
export_image_to_fits(residual_cube, "pipelines-arlexecute-timings-%s-ical_residual.fits" % context)
qa = qa_image(restored[centre])
results['restored_max'] = qa.data['max']
results['restored_min'] = qa.data['min']
restored_cube = image_gather_channels(restored)
export_image_to_fits(restored_cube, "pipelines-arlexecute-timings-%s-ical_restored.fits" % context)
#
arlexecute.close()
end_all = time.time()
results['time overall'] = end_all - start_all
lprint("At end, results are:")
lprint(results)
return results
