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Fix timing of peaks when ordering in center_time #1208

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Jun 24, 2023
Merged

Fix timing of peaks when ordering in center_time #1208

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14 changes: 12 additions & 2 deletions straxen/plugins/peaks/peak_ambience.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@ class PeakAmbience(strax.OverlapWindowPlugin):
References:
* v0.0.7 reference: xenon:xenonnt:ac:prediction:shadow_ambience
"""
__version__ = '0.0.7'
__version__ = '0.0.8'
depends_on = ('lone_hits', 'peak_basics', 'peak_positions')
provides = 'peak_ambience'
data_kind = 'peaks'
Expand Down Expand Up @@ -75,7 +75,11 @@ def infer_dtype(self):
return dtype

def compute(self, lone_hits, peaks):
return self.compute_ambience(lone_hits, peaks, peaks)
argsort = np.argsort(peaks['center_time'], kind='mergesort')
_peaks = np.sort(peaks, order='center_time')
result = np.zeros(len(peaks), self.dtype)
_quick_assign(argsort, result, self.compute_ambience(lone_hits, peaks, _peaks))
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return result

def compute_ambience(self, lone_hits, peaks, current_peak):
# 1. Initialization
Expand Down Expand Up @@ -186,3 +190,9 @@ def distance_in_xy(peak_a, peak_b):
"""Distance between S2s in (x,y)"""
return np.sqrt((peak_a['x'] - peak_b['x']) ** 2 +
(peak_a['y'] - peak_b['y']) ** 2)


@numba.njit
def _quick_assign(indices, results, inputs):
for i, r in zip(indices, inputs):
results[i] = r
10 changes: 7 additions & 3 deletions straxen/plugins/peaks/peak_shadow.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
import numpy as np
import numba
from scipy.stats import halfcauchy
from .peak_ambience import distance_in_xy
from .peak_ambience import distance_in_xy, _quick_assign
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import strax
import straxen

Expand All @@ -18,7 +18,7 @@ class PeakShadow(strax.OverlapWindowPlugin):
* v0.1.5 reference: xenon:xenonnt:ac:prediction:shadow_ambience
"""

__version__ = '0.1.5'
__version__ = '0.1.6'

depends_on = ('peak_basics', 'peak_positions')
provides = 'peak_shadow'
Expand Down Expand Up @@ -98,7 +98,11 @@ def shadowdtype(self):
return dtype

def compute(self, peaks):
return self.compute_shadow(peaks, peaks)
argsort = np.argsort(peaks['center_time'], kind='mergesort')
_peaks = np.sort(peaks, order='center_time')
result = np.zeros(len(peaks), self.dtype)
_quick_assign(argsort, result, self.compute_shadow(peaks, _peaks))
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return result

def compute_shadow(self, peaks, current_peak):
# 1. Define time window for each peak, we will find previous peaks within these time windows
Expand Down