def _day_loop(detection_streams, template, min_cc, interpolate=False,
cores=False, debug=0):
"""
Function to loop through multiple detections for one template.
Designed to run for the same day of data for I/O simplicity, but as you
are passing stream objects it could run for all the detections ever, as
long as you have the RAM!
:type detection_streams: list
:param detection_streams: List of all the detections for this template that
you want to compute the optimum pick for. Individual things in list
should be of obspy.core.stream.Stream type.
:type template: obspy.core.stream.Stream
:param template: The original template used to detect the detections passed
:type min_cc: float
:param min_cc: Minimum cross-correlation value to be allowed for a pick.
:type interpolate: bool
:param interpolate: Interpolate the correlation function to achieve \
sub-sample precision.
:returns: Catalog object containing Event objects for each detection
created by this template.
:rtype: obspy.core.event.Catalog
"""
from multiprocessing import Pool, cpu_count
# Used to run detections in parallel
from obspy.core.event import Catalog
if not cores:
num_cores = cpu_count()
else:
num_cores = cores
if num_cores > len(detection_streams):
num_cores = len(detection_streams)
pool = Pool(processes=num_cores)
# Parallelize generation of events for each detection:
# results is a list of (i, event class)
results = [pool.apply_async(_channel_loop, args=(detection_streams[i],
template, min_cc,
interpolate, i, debug))
for i in range(len(detection_streams))]
pool.close()
events_list = [p.get() for p in results]
pool.join()
events_list.sort(key=lambda tup: tup[0]) # Sort based on i.
temp_catalog = Catalog()
temp_catalog.events = [event_tup[1] for event_tup in events_list]
return temp_catalog
def _day_loop(detection_streams,
template,
min_cc,
detections,
horizontal_chans,
vertical_chans,
interpolate,
cores,
parallel,
debug=0):
"""
Function to loop through multiple detections for one template.
Designed to run for the same day of data for I/O simplicity, but as you
are passing stream objects it could run for all the detections ever, as
long as you have the RAM!
:type detection_streams: list
:param detection_streams:
List of all the detections for this template that you want to compute
the optimum pick for. Individual things in list should be of
:class:`obspy.core.stream.Stream` type.
:type template: obspy.core.stream.Stream
:param template: The original template used to detect the detections passed
:type min_cc: float
:param min_cc: Minimum cross-correlation value to be allowed for a pick.
:type detections: list
:param detections:
List of detections to associate events with an input detection.
:type horizontal_chans: list
:param horizontal_chans:
List of channel endings for horizontal-channels, on which S-picks will
be made.
:type vertical_chans: list
:param vertical_chans:
List of channel endings for vertical-channels, on which P-picks will
be made.
:type interpolate: bool
:param interpolate:
Interpolate the correlation function to achieve sub-sample precision.
:type debug: int
:param debug: debug output level 0-5.
:returns:
Catalog object containing Event objects for each detection created by
this template.
:rtype: :class:`obspy.core.event.Catalog`
"""
if len(detection_streams) == 0:
return Catalog()
if not cores:
num_cores = cpu_count()
else:
num_cores = cores
if num_cores > len(detection_streams):
num_cores = len(detection_streams)
if parallel:
pool = Pool(processes=num_cores)
debug_print('Made pool of %i workers' % num_cores, 4, debug)
# Parallel generation of events for each detection:
# results will be a list of (i, event class)
results = [
pool.apply_async(
_channel_loop, (detection_streams[i], ), {
'template': template,
'min_cc': min_cc,
'detection_id': detections[i].id,
'interpolate': interpolate,
'i': i,
'pre_lag_ccsum': detections[i].detect_val,
'detect_chans': detections[i].no_chans,
'horizontal_chans': horizontal_chans,
'vertical_chans': vertical_chans
}) for i in range(len(detection_streams))
]
pool.close()
events_list = [p.get() for p in results]
pool.join()
events_list.sort(key=lambda tup: tup[0]) # Sort based on index.
else:
events_list = []
for i in range(len(detection_streams)):
events_list.append(
_channel_loop(detection=detection_streams[i],
template=template,
min_cc=min_cc,
detection_id=detections[i].id,
interpolate=interpolate,
i=i,
pre_lag_ccsum=detections[i].detect_val,
detect_chans=detections[i].no_chans,
horizontal_chans=horizontal_chans,
vertical_chans=vertical_chans,
debug=debug))
temp_catalog = Catalog()
temp_catalog.events = [event_tup[1] for event_tup in events_list]
return temp_catalog
