Source code for straxen.plugins.events.event_waveform
import numpy as np
import strax
import straxen
export, __all__ = strax.exporter()
[docs]@export
class EventWaveform(strax.Plugin):
"""Simple plugin that provides total (data) and top (data_top) waveforms for main and
alternative S1/S2 in the event."""
depends_on = ("event_basics", "peaks")
provides = "event_waveform"
__version__ = "0.0.1"
compressor = "zstd"
save_when = strax.SaveWhen.EXPLICIT
n_top_pmts = straxen.URLConfig(default=straxen.n_top_pmts, type=int, help="Number of top PMTs")
[docs] def infer_dtype(self):
# setting data type from peak dtype
pfields_ = self.deps["peaks"].dtype_for("peaks").fields
# populating data type
infoline = {
"s1": "main S1",
"s2": "main S2",
"alt_s1": "alternative S1",
"alt_s2": "alternative S2",
}
dtype = []
# populating waveform samples
ptypes = ["s1", "s2", "alt_s1", "alt_s2"]
for type_ in ptypes:
dtype += [
(
(f"Waveform for {infoline[type_]} [ PE / sample ]", f"{type_}_data"),
pfields_["data"][0],
)
]
dtype += [
(
(f"Top waveform for {infoline[type_]} [ PE / sample ]", f"{type_}_data_top"),
pfields_["data_top"][0],
)
]
dtype += [
(
(f"Length of the interval in samples for {infoline[type_]}", f"{type_}_length"),
pfields_["length"][0],
)
]
dtype += [
(
(f"Width of one sample for {infoline[type_]} [ns]", f"{type_}_dt"),
pfields_["dt"][0],
)
]
# populating S1 n channel properties
dtype += [
(("Main S1 count of contributing PMTs", "s1_n_channels"), np.int16),
(("Main S1 top count of contributing PMTs", "s1_top_n_channels"), np.int16),
]
dtype += strax.time_fields
return dtype
[docs] def compute(self, events, peaks):
result = np.zeros(len(events), self.dtype)
result["time"] = events["time"]
result["endtime"] = strax.endtime(events)
split_peaks = strax.split_by_containment(peaks, events)
for event_i, (event, sp) in enumerate(zip(events, split_peaks)):
for type_ in ["s1", "s2", "alt_s1", "alt_s2"]:
type_index = event[f"{type_}_index"]
if type_index != -1:
type_area_per_channel = sp["area_per_channel"][type_index]
result[f"{type_}_length"][event_i] = sp["length"][type_index]
result[f"{type_}_data"][event_i] = sp["data"][type_index]
result[f"{type_}_data_top"][event_i] = sp["data_top"][type_index]
result[f"{type_}_dt"][event_i] = sp["dt"][type_index]
if type_ == "s1":
result["s1_n_channels"][event_i] = (type_area_per_channel > 0).sum()
result["s1_top_n_channels"][event_i] = (
type_area_per_channel[: self.config["n_top_pmts"]] > 0
).sum()
return result