Add plugin to get data of multiple peaks at event level

straxen/plugins/events/peaks_of_event.py

@@ -0,0 +1,137 @@
+import strax
+import numpy as np
+
+MAX_NUMBER_OF_S1_PEAKS_PER_EVENT = 5
+MAX_NUMBER_OF_S2_PEAKS_PER_EVENT = 5
+PEAK_WAVEFORM_LENGTH = 200
+
+
+class MultiPeakData(strax.Plugin):
+    """Plugin that extracts information of multiple peaks in an event."""
+
+    __version__ = "0.0.2"
+
+    depends_on = ("peaks", "peak_per_event", "events", "peak_positions", "peak_basics")
+    provides = "multi_peak_data"
+    data_kind = "events"
+    save_when = strax.SaveWhen.TARGET
+
+    dtype = [
+        (
+            ("time difference of S1 peaks to event start time", "s1_delta_time_i"),
+            np.int64,
+            MAX_NUMBER_OF_S1_PEAKS_PER_EVENT,
+        ),
+        (
+            ("time difference of S2 peaks to event start time", "s2_delta_time_i"),
+            np.int64,
+            MAX_NUMBER_OF_S2_PEAKS_PER_EVENT,
+        ),
+        (
+            ("sample width in ns of the S1 waveform", "s1_peak_dt_i"),
+            np.int32,
+            MAX_NUMBER_OF_S1_PEAKS_PER_EVENT,
+        ),
+        (
+            ("sample width in ns of the S2 waveform", "s2_peak_dt_i"),
+            np.int32,
+            MAX_NUMBER_OF_S2_PEAKS_PER_EVENT,
+        ),
+        (("x position of S2 i", "s2_x_position_i"), np.float32, MAX_NUMBER_OF_S2_PEAKS_PER_EVENT),
+        (("y position of S2 i", "s2_y_position_i"), np.float32, MAX_NUMBER_OF_S2_PEAKS_PER_EVENT),
+        (("area fraction top of S2 i", "s2_aft_i"), np.float32, MAX_NUMBER_OF_S2_PEAKS_PER_EVENT),
+        (("area fraction top of S1 i", "s1_aft_i"), np.float32, MAX_NUMBER_OF_S1_PEAKS_PER_EVENT),
+        (
+            ("Sum Waveform of S1 peaks", "s1_waveform_i"),
+            np.float32,
+            (MAX_NUMBER_OF_S1_PEAKS_PER_EVENT, PEAK_WAVEFORM_LENGTH),
+        ),
+        (
+            ("Sum Waveform of S2 peaks", "s2_waveform_i"),
+            np.float32,
+            (MAX_NUMBER_OF_S2_PEAKS_PER_EVENT, PEAK_WAVEFORM_LENGTH),
+        ),
+        (
+            ("Start of S1 waverforms", "s1_waveform_start_i"),
+            np.float32,
+            (MAX_NUMBER_OF_S1_PEAKS_PER_EVENT, PEAK_WAVEFORM_LENGTH),
+        ),
+        *strax.time_fields,
+    ]
+
+    def compute(self, peaks, events):
+
+        # Just interested in S1 or S2 peaks.
+        # Here would be the place to apply further peak quality cuts.
+        peaks = peaks[peaks["type"] != 0]
+
+        peaks_per_event = strax.split_by_containment(peaks, events)
+
+        result = np.zeros(len(events), dtype=self.dtype)
+
+        for i, peaks_in_event in enumerate(peaks_per_event):
+
+            n_peaks_in_event = len(peaks_in_event)
+
+            if n_peaks_in_event == 0:
+                continue
+            else:
+
+                # Get the largest S1s and S2s
+                s1_peaks = peaks_in_event[peaks_in_event["type"] == 1]
+                s2_peaks = peaks_in_event[peaks_in_event["type"] == 2]
+
+                s1_peaks = s1_peaks[np.argsort(s1_peaks["area"])[::-1]]
+                s2_peaks = s2_peaks[np.argsort(s2_peaks["area"])[::-1]]
+
+                n_s1_peaks_in_event = len(s1_peaks)
+                n_s2_peaks_in_event = len(s2_peaks)
+
+                # Save the data of the n largest peaks
+                result[i]["s1_delta_time_i"][0:n_s1_peaks_in_event] = (
+                    s1_peaks["time"][0:MAX_NUMBER_OF_S1_PEAKS_PER_EVENT] - peaks_in_event["time"][0]
+                )
+                result[i]["s2_delta_time_i"][0:n_s2_peaks_in_event] = (
+                    s2_peaks["time"][0:MAX_NUMBER_OF_S2_PEAKS_PER_EVENT] - peaks_in_event["time"][0]
+                )
+
+                result[i]["s1_peak_dt_i"][0:n_s1_peaks_in_event] = s1_peaks["dt"][
+                    0:MAX_NUMBER_OF_S1_PEAKS_PER_EVENT
+                ]
+                result[i]["s2_peak_dt_i"][0:n_s2_peaks_in_event] = s2_peaks["dt"][
+                    0:MAX_NUMBER_OF_S2_PEAKS_PER_EVENT
+                ]
+
+                result[i]["s2_x_position_i"][0:n_s2_peaks_in_event] = s2_peaks["x"][
+                    0:MAX_NUMBER_OF_S2_PEAKS_PER_EVENT
+                ]
+                result[i]["s2_y_position_i"][0:n_s2_peaks_in_event] = s2_peaks["y"][
+                    0:MAX_NUMBER_OF_S2_PEAKS_PER_EVENT
+                ]
+                result[i]["s2_aft_i"][0:n_s2_peaks_in_event] = s2_peaks["area_fraction_top"][
+                    0:MAX_NUMBER_OF_S2_PEAKS_PER_EVENT
+                ]
+
+                result[i]["s1_aft_i"][0:n_s1_peaks_in_event] = s1_peaks["area_fraction_top"][
+                    0:MAX_NUMBER_OF_S1_PEAKS_PER_EVENT
+                ]
+
+                result[i]["s1_waveform_i"][0:n_s1_peaks_in_event] = s1_peaks["data"][
+                    0:MAX_NUMBER_OF_S1_PEAKS_PER_EVENT
+                ]
+                result[i]["s2_waveform_i"][0:n_s2_peaks_in_event] = s2_peaks["data"][
+                    0:MAX_NUMBER_OF_S2_PEAKS_PER_EVENT
+                ]
+
+                result[i]["s1_waveform_start_i"][0:n_s1_peaks_in_event] = s1_peaks["data_start"][
+                    0:MAX_NUMBER_OF_S1_PEAKS_PER_EVENT
+                ]
+
+        # If waveform_start was not explicitly saved, we can take it from the regular waveform
+        mask = result["s1_peak_dt_i"] == 10
+        result["s1_waveform_start_i"][mask] = result["s1_waveform_i"][mask]
+
+        result["time"] = events["time"]
+        result["endtime"] = events["endtime"]
+
+        return result