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straxen/plugins/events/event_nans.py

@@ -4,31 +4,32 @@
 
 export, __all__ = strax.exporter()
 
+
 @export
 class GiveNans(strax.Plugin):
-    """
-    A test plugin that just gives nans
-    """
-
-    __version__ = '0.0.2'
-
-    depends_on = ('events',)
-    provides = 'event_nans'
-    data_kind = 'events'
-
-    dtype = [(('Start time since unix epoch [ns]', 'time'), np.int64),
-            (('Exclusive end time since unix epoch [ns]', 'endtime'), np.int64),
-            (('Just nans for approximately half of the events', 'event_nans'), np.float64),
-            (('Just -1 for approximately half of the events', 'event_negs'), np.int64)]
-    
+    """A test plugin that just gives nans."""
+
+    __version__ = "0.0.2"
+
+    depends_on = ("events",)
+    provides = "event_nans"
+    data_kind = "events"
+
+    dtype = [
+        (("Start time since unix epoch [ns]", "time"), np.int64),
+        (("Exclusive end time since unix epoch [ns]", "endtime"), np.int64),
+        (("Just nans for approximately half of the events", "event_nans"), np.float64),
+        (("Just -1 for approximately half of the events", "event_negs"), np.int64),
+    ]
+
     def compute(self, events):
         result = np.zeros(len(events), self.dtype)
-        result['time'] = events['time']
-        result['endtime'] = events['endtime']
-        
-        if len(events)>0:
-            halflen = int(len(events)/2)
-            result['event_nans'][0] = np.nan
-            result['event_negs'][0] = -1
-            
+        result["time"] = events["time"]
+        result["endtime"] = events["endtime"]
+
+        if len(events) > 0:
+            halflen = int(len(events) / 2)
+            result["event_nans"][0] = np.nan
+            result["event_negs"][0] = -1
+
         return result

straxen/plugins/events/event_s2_fwhm.py

@@ -8,80 +8,88 @@
 
 @export
 class S2FWHM2(strax.Plugin):
+    """This is a default plugin, like used by many plugins in straxen. It finds the full-width half-
+    maximum of the main and alternate S2 peak for each event.
+
+    This is the exact same code as S2FWHM, except for the fact that I set the first FWHM of each
+    chunk to a NaN, and the 'random_stuff' field to -1.
+
     """
-    This is a default plugin, like used by many plugins in straxen. It 
-    finds the full-width half-maximum of the main and alternate S2
-    peak for each event.
-    
-    This is the exact same code as S2FWHM, except for the fact that I set
-    the first FWHM of each chunk to a NaN, and the 'random_stuff' field to -1.
-    """
-    __version__ = '0.0.1'
-
-    depends_on = ('event_basics', 'peaks')
-    provides = 's2_fwhm_2'
-    data_kind = 'events'
-
-    smoothing = straxen.URLConfig(default=False, type=bool, track = True,
-                                    help="Flag for whether or not the"
-                                         " waveform is smoothed or not.")
-    averaging_samples = straxen.URLConfig(default=3, type=int,  track=True,
-                                    help="Number of samples to average over.")
-
+
+    __version__ = "0.0.1"
+
+    depends_on = ("event_basics", "peaks")
+    provides = "s2_fwhm_2"
+    data_kind = "events"
+
+    smoothing = straxen.URLConfig(
+        default=False,
+        type=bool,
+        track=True,
+        help="Flag for whether or not the waveform is smoothed or not.",
+    )
+    averaging_samples = straxen.URLConfig(
+        default=3, type=int, track=True, help="Number of samples to average over."
+    )
+
     # We really could just type the dtype, but for demonstration purposes
     # we can use infer_dtype
     def infer_dtype(self):
-        dtype = [(('Start time since unix epoch [ns]', 'time'), np.int64),
-                 (('Exclusive end time since unix epoch [ns]', 'endtime'), np.int64),
-                 (('FWHM for s2 S2 in the event', 's2_fwhm'), np.float32),
-                 (('FWHM for alt_s2 S2 in the event', 'alt_s2_fwhm'), np.float32),
-                (('An extra field just to detect something', 'random_stuff'), np.int32)]
+        dtype = [
+            (("Start time since unix epoch [ns]", "time"), np.int64),
+            (("Exclusive end time since unix epoch [ns]", "endtime"), np.int64),
+            (("FWHM for s2 S2 in the event", "s2_fwhm"), np.float32),
+            (("FWHM for alt_s2 S2 in the event", "alt_s2_fwhm"), np.float32),
+            (("An extra field just to detect something", "random_stuff"), np.int32),
+        ]
 
         return dtype
-    
-    def compute(self, events, peaks):    
+
+    def compute(self, events, peaks):
         result = np.zeros(len(events), self.dtype)
-        result['time'] = events['time']
-        result['endtime'] = events['endtime']
+        result["time"] = events["time"]
+        result["endtime"] = events["endtime"]
 
-        if len(events)>0:
+        if len(events) > 0:
             pks_per_ev = strax.split_by_containment(peaks, events)
-            pk_buffer = {t: -999*np.ones((len(events),len(peaks['data'][0]))) for t in ['s2', 'alt_s2']}
-            pk_dt = {t: 10*np.ones(len(events)) for t in ['s2', 'alt_s2']}
-
-            for t in ['s2', 'alt_s2']:
+            pk_buffer = {
+                t: -999 * np.ones((len(events), len(peaks["data"][0]))) for t in ["s2", "alt_s2"]
+            }
+            pk_dt = {t: 10 * np.ones(len(events)) for t in ["s2", "alt_s2"]}
+
+            for t in ["s2", "alt_s2"]:
                 for i, (ev, pk_ev) in enumerate(zip(events, pks_per_ev)):
-                    pk_buffer[t][i] = pk_ev[ev[f'{t}_index']]['data']
-                    pk_dt[t][i] = pk_ev[ev[f'{t}_index']]['dt']
-
-
-            for t in ['s2', 'alt_s2']:
+                    pk_buffer[t][i] = pk_ev[ev[f"{t}_index"]]["data"]
+                    pk_dt[t][i] = pk_ev[ev[f"{t}_index"]]["dt"]
+
+            for t in ["s2", "alt_s2"]:
                 if self.smoothing:
                     pk_buffer[t] = smooth(pk_buffer[t], self.averaging_samples)
-                result[f'{t}_fwhm'] = fwhm(pk_buffer[t], pk_dt[t])
-                
-            result['s2_fwhm'][0] = np.nan
-            result['random_stuff'][0] = -1
-        
+                result[f"{t}_fwhm"] = fwhm(pk_buffer[t], pk_dt[t])
+
+            result["s2_fwhm"][0] = np.nan
+            result["random_stuff"][0] = -1
+
         return result
-
+
+
 @numba.njit()
 def fwhm(wfs, dts):
     result = -np.ones(len(wfs))
     for i, (w, dt) in enumerate(zip(wfs, dts)):
         if w[0] == -999:
             result[i] = -1
         else:
-
-            above_half = np.where(w>=0.5*np.max(w))[0]
-            result[i] = (above_half[-1]-above_half[0])*dt
+            above_half = np.where(w >= 0.5 * np.max(w))[0]
+            result[i] = (above_half[-1] - above_half[0]) * dt
     return result
-
+
+
 @numba.njit()
-def smooth(wfs, smooth_samps ):
-    smooth_wfs = -999*np.ones((len(wfs),len(wfs[0])+smooth_samps-1))
+def smooth(wfs, smooth_samps):
+    smooth_wfs = -999 * np.ones((len(wfs), len(wfs[0]) + smooth_samps - 1))
     for wf_i, wf in enumerate(wfs):
-        if wf[0]!=-999:
-            smooth_wfs[wf_i] = np.convolve(wf, np.ones(smooth_samps)/smooth_samps)
-                
+        if wf[0] != -999:
+            smooth_wfs[wf_i] = np.convolve(wf, np.ones(smooth_samps) / smooth_samps)
+
     return smooth_wfs