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Be compatible to https://github.com/AxFoundation/strax/pull/990 #1584

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Be compatible to https://github.com/AxFoundation/strax/pull/990 #1584

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2 changes: 1 addition & 1 deletion straxen/plugins/peaks/peak_ambience.py
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Original file line number Diff line number Diff line change
Expand Up @@ -76,7 +76,7 @@ def infer_dtype(self):
return dtype

def compute(self, lone_hits, peaks):
argsort = strax.stable_argsort(peaks["center_time"])
argsort = strax.stable_argsort(peaks, order="center_time")
_peaks = strax.stable_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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2 changes: 1 addition & 1 deletion straxen/plugins/peaks/peak_nearest_triggering.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,7 +61,7 @@ def get_window_size(self):
return 10 * self.shadow_time_window_backward

def compute(self, peaks):
argsort = strax.stable_argsort(peaks["center_time"])
argsort = strax.stable_argsort(peaks, order="center_time")
_peaks = strax.stable_sort(peaks, order="center_time")
result = np.zeros(len(peaks), self.dtype)
_quick_assign(argsort, result, self.compute_triggering(peaks, _peaks))
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2 changes: 1 addition & 1 deletion straxen/plugins/peaks/peak_se_score.py
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Expand Up @@ -147,7 +147,7 @@ def compute_se_score(self, peaks, _peaks):

def compute(self, peaks):
# sort peaks by center_time
argsort = strax.stable_argsort(peaks["center_time"])
argsort = strax.stable_argsort(peaks, order="center_time")
_peaks = strax.stable_sort(peaks, order="center_time")
mask_nan = np.isnan(_peaks["x"]) | np.isnan(_peaks["y"])
# prepare output
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2 changes: 1 addition & 1 deletion straxen/plugins/peaks/peak_shadow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -145,7 +145,7 @@ def shadowdtype(self):
return dtype

def compute(self, peaks):
argsort = strax.stable_argsort(peaks["center_time"])
argsort = strax.stable_argsort(peaks, order="center_time")
_peaks = strax.stable_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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