def compute(self, peaklets):
if not len(peaklets):
return peaklets[:0]
if self.config['s2_merge_max_gap'] < 0:
# Do not merge at all
merged_s2s = np.zeros(0, dtype=peaklets.dtype)
else:
# Find all groups of peaklets separated by < the gap
cluster_starts, cluster_stops = strax.find_peak_groups(
peaklets, self.config['s2_merge_max_gap'])
start_merge_at, end_merge_at = self.get_merge_instructions(
peaklets['time'],
strax.endtime(peaklets),
areas=peaklets['area'],
types=peaklets['type'],
cluster_starts=cluster_starts,
cluster_stops=cluster_stops,
max_duration=self.config['s2_merge_max_duration'],
max_area=self.config['s2_merge_max_area'])
merged_s2s = strax.merge_peaks(
peaklets,
start_merge_at,
end_merge_at,
max_buffer=int(self.config['s2_merge_max_duration'] //
peaklets['dt'].min()))
merged_s2s['type'] = 2
strax.compute_widths(merged_s2s)
return merged_s2s
def compute(self, peaklets, lone_hits):
if self.config['merge_without_s1']:
peaklets = peaklets[peaklets['type'] != 1]
if len(peaklets) <= 1:
return np.zeros(0, dtype=peaklets.dtype)
gap_thresholds = self.config['s2_merge_gap_thresholds']
max_gap = gap_thresholds[0][1]
max_area = 10**gap_thresholds[-1][0]
if max_gap < 0:
# Do not merge at all
return np.zeros(0, dtype=peaklets.dtype)
else:
# Max gap and area should be set by the gap thresholds
# to avoid contradictions
start_merge_at, end_merge_at = self.get_merge_instructions(
peaklets['time'],
strax.endtime(peaklets),
areas=peaklets['area'],
types=peaklets['type'],
gap_thresholds=gap_thresholds,
max_duration=self.config['s2_merge_max_duration'],
max_gap=max_gap,
max_area=max_area,
)
merged_s2s = strax.merge_peaks(
peaklets,
start_merge_at,
end_merge_at,
max_buffer=int(self.config['s2_merge_max_duration'] //
np.gcd.reduce(peaklets['dt'])),
)
merged_s2s['type'] = 2
# Updated time and length of lone_hits and sort again:
lh = np.copy(lone_hits)
del lone_hits
lh_time_shift = (lh['left'] - lh['left_integration']) * lh['dt']
lh['time'] = lh['time'] - lh_time_shift
lh['length'] = (lh['right_integration'] - lh['left_integration'])
lh = strax.sort_by_time(lh)
strax.add_lone_hits(merged_s2s, lh, self.to_pe)
strax.compute_widths(merged_s2s)
return merged_s2s
def compute(self, peaklets):
if not len(peaklets):
return peaklets[:0]
if self.config['s2_merge_max_gap'] < 0:
# Do not merge at all
merged_s2s = np.zeros(0, dtype=peaklets.dtype)
else:
# Find all groups of peaklets separated by < the gap
cluster_starts, cluster_stops = strax.find_peak_groups(
peaklets, self.config['s2_merge_max_gap'])
start_merge_at, end_merge_at = self.get_merge_instructions(
peaklets['time'],
strax.endtime(peaklets),
areas=peaklets['area'],
types=peaklets['type'],
cluster_starts=cluster_starts,
cluster_stops=cluster_stops,
max_duration=self.config['s2_merge_max_duration'],
max_area=self.config['s2_merge_max_area'])
merged_s2s = strax.merge_peaks(
peaklets,
start_merge_at,
end_merge_at,
max_buffer=int(self.config['s2_merge_max_duration'] //
peaklets['dt'].min()))
merged_s2s['type'] = 2
strax.compute_widths(merged_s2s)
if len(merged_s2s) == 0:
# Strax does not handle the case of no merged S2s well
# If there are none in the entire dataset, it will just keep
# waiting in Peaks forever.
# Thus, this ugly hack of passing a single fake merged S2
# in the middle of the chunk, which is removed later
merged_s2s = np.zeros(1, merged_s2s.dtype)
q = merged_s2s[0]
q['type'] = FAKE_MERGED_S2_TYPE
q['time'] = (peaklets[0]['time'] + strax.endtime(peaklets[0])) / 2
q['dt'] = 1
return merged_s2s
