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 run(self, FILEIN, csv=None, cf=False, **kwargs):
"""Event/picking detection on a given set of seismic signals.
Reads a list of command-line input arguments, performs event analysis
over a given list of seismic data inputs and generates a summary of
results.
Analysis can be performed in two ways: supervised or unsupervised mode.
In supervised mode the function graphs each of the candidate events
found and asks the user whether to accept them or not, whereas in
unsupervised mode the function just computes results without receiving
any feedback from users.
Args:
FILEIN: A list of binary or text file objects containing seismic
data.
csv: Determines whether to save a summary of results to a CSV
file or not. Default value is None, meaning no CSV summary
will be saved.
cf: Determines whether to save generated characteristic function
to a file (binary or text format) or not. Default value is
False.
"""
# Extract method name from kwargs
method = kwargs.get('method', 'ampa')
takanami = kwargs.get('takanami', False)
# Create a list of records from input files
factory = rc.RecordFactory(notif=clt.print_msg, **kwargs)
factory.on_notify = clt.print_msg
records = []
for f in FILEIN:
records.append(factory.create_record(f, method=method))
records = collections.flatten_list(records)
# Configure method
self.on_notify('Configuring %s method... ' % method.upper())
if method == 'stalta':
alg = stalta.StaLta(**kwargs)
else:
alg = ampa.Ampa(**kwargs)
self.on_notify("Done\n")
# Run analysis
self._do_analysis(records, alg, **kwargs)
# Show results
draw_results(records, method=method)
# Generate reports
if csv:
self.on_notify("Generating CSV report in %s... " % csv.name)
rc.generate_csv(records, csv)
self.on_notify("Done\n")
# Save cf
if cf:
for record in records:
# Create a new directory to store CFs
cf_path = os.path.abspath(self._cf_dir)
if not os.path.exists(cf_path):
os.makedirs(cf_path)
# Generate cf filenames
bname, rname = os.path.splitext(os.path.basename(os.path.abspath(record.filename)))
fname = os.path.join(cf_path, "%s.cf%s" % (bname, rname))
self.on_notify("Saving CF for input file %s in %s... " %
(record.filename, fname))
record.save_cf(fname, fmt=kwargs.get('cff', 'binary'),
dtype=kwargs.get('cfd', 'float64'),
byteorder=kwargs.get('cfb', 'native'))
self.on_notify("Done\n")
def run(self, FILEIN, csv=None, cf=False, **kwargs):
"""Event/picking detection on a given set of seismic signals.
Reads a list of command-line input arguments, performs event analysis
over a given list of seismic data inputs and generates a summary of
results.
Analysis can be performed in two ways: supervised or unsupervised mode.
In supervised mode the function graphs each of the candidate events
found and asks the user whether to accept them or not, whereas in
unsupervised mode the function just computes results without receiving
any feedback from users.
Args:
FILEIN: A list of binary or text file objects containing seismic
data.
csv: Determines whether to save a summary of results to a CSV
file or not. Default value is None, meaning no CSV summary
will be saved.
cf: Determines whether to save generated characteristic function
to a file (binary or text format) or not. Default value is
False.
"""
# Extract method name from kwargs
method = kwargs.get('method', 'ampa')
takanami = kwargs.get('takanami', False)
# Create a list of records from input files
factory = rc.RecordFactory(notif=clt.print_msg, **kwargs)
factory.on_notify = clt.print_msg
records = []
for f in FILEIN:
records.append(factory.create_record(f, method=method))
records = collections.flatten_list(records)
# Configure method
self.on_notify('Configuring %s method... ' % method.upper())
if method == 'stalta':
alg = stalta.StaLta(**kwargs)
else:
alg = ampa.Ampa(**kwargs)
self.on_notify("Done\n")
# Run analysis
self._do_analysis(records, alg, **kwargs)
# Show results
draw_results(records, method=method)
# Generate reports
if csv:
self.on_notify("Generating CSV report in %s... " % csv.name)
rc.generate_csv(records, csv)
self.on_notify("Done\n")
# Save cf
if cf:
for record in records:
# Create a new directory to store CFs
cf_path = os.path.abspath(self._cf_dir)
if not os.path.exists(cf_path):
os.makedirs(cf_path)
# Generate cf filenames
bname, rname = os.path.splitext(
os.path.basename(os.path.abspath(record.filename)))
fname = os.path.join(cf_path, "%s.cf%s" % (bname, rname))
self.on_notify("Saving CF for input file %s in %s... " %
(record.filename, fname))
record.save_cf(fname,
fmt=kwargs.get('cff', 'binary'),
dtype=kwargs.get('cfd', 'float64'),
byteorder=kwargs.get('cfb', 'native'))
self.on_notify("Done\n")