def analysis(**kwargs):
"""Performs event analysis/picking over a set of seismic signals.
Performs event detection if parameter 'threshold' is not None, otherwise
performs event picking.
"""
# Get file list
file_list = kwargs.pop('FILEIN', [])
# Get debug level
debug = kwargs.get('verbosity', 1)
if debug:
print_settings(**kwargs)
if kwargs.get('no_multiprocessing', False):
analysis_chunk_task((file_list, kwargs))
else:
processes = kwargs.get('processes', multiprocessing.cpu_count())
p = multiprocessing.Pool(processes=processes)
p.map(
analysis_chunk_task,
itertools.izip(
collections.chunkify(file_list,
len(file_list) / processes),
itertools.repeat(kwargs)))
p.close()
p.join()
def detect(self, alg, trace_list=None, allow_multiprocessing=True, **kwargs):
"""
"""
trace_list = self.traces if trace_list is None else trace_list[:]
n_traces = len(trace_list)
if allow_multiprocessing and n_traces > 1:
processes = min(mp.cpu_count(), n_traces)
p = mp.Pool(processes=processes)
processed_traces = p.map(_detect, itertools.izip(itertools.repeat(alg),
collections.chunkify(trace_list, n_traces / processes),
itertools.repeat(kwargs)))
processed_traces = collections.flatten_list(processed_traces)
# Update existing traces w. new events and cf from processed events
for trace, processed_trace in zip(trace_list, processed_traces):
new_events = [event for event in processed_trace.events if event not in trace.events]
for event in new_events:
trace.add_event_from_copy(event)
trace.cf = processed_trace.cf[:]
# Cleanup
del processed_traces
del trace_list
p.close()
p.join()
gc.collect(2)
else:
_detect((alg, trace_list, kwargs))
def analysis(**kwargs):
"""Performs event analysis/picking over a set of seismic signals.
Performs event detection if parameter 'threshold' is not None, otherwise
performs event picking.
"""
# Get file list
file_list = kwargs.pop('FILEIN', [])
# Get debug level
debug = kwargs.get('verbosity', 1)
if debug:
print_settings(**kwargs)
if kwargs.get('no_multiprocessing', False):
analysis_chunk_task((file_list, kwargs))
else:
processes = kwargs.get('processes', multiprocessing.cpu_count())
p = multiprocessing.Pool(processes=processes)
p.map(analysis_chunk_task, itertools.izip(collections.chunkify(file_list, len(file_list) / processes),
itertools.repeat(kwargs)))
p.close()
p.join()