Use numpy and strax native dtypes, not "<i8" or "<f4" (#1502)

* Use numpy native dtypes, not `"<i8"` or `"<f4"`

* Use `np.bool_` inside numba decorated function

* Debug

straxen/analyses/bokeh_waveform_plot.py

@@ -101,7 +101,7 @@ def event_display_interactive(
         raise ValueError("Found an event without peaks this should not had have happened.")
 
     # Select main/alt S1/S2s based on time and endtime in event:
-    m_other_peaks = np.ones(len(peaks), dtype=np.bool_)  # To select non-event peaks
+    m_other_peaks = np.ones(len(peaks), dtype=bool)  # To select non-event peaks
     endtime = strax.endtime(peaks)
 
     signal = {}
@@ -570,7 +570,7 @@ def plot_pmt_array(
     # Plotting PMTs:
     pmts = straxen.pmt_positions()
     if plot_all_pmts:
-        mask_pmts = np.zeros(len(pmts), dtype=np.bool_)
+        mask_pmts = np.zeros(len(pmts), dtype=bool)
     else:
         mask_pmts = to_pe == 0
     pmts_on = pmts[~mask_pmts]

straxen/analyses/waveform_plot.py

@@ -191,7 +191,7 @@ def plot_records_matrix(
         # labels in the case of strings.
         # Make a dict that converts the label to an int
         int_labels = {h: i for i, h in enumerate(set(ylabs))}
-        mask = np.ones(len(ylabs), dtype=np.bool_)
+        mask = np.ones(len(ylabs), dtype=bool)
         # If the label (int) is different wrt. its neighbour, show it
         mask[1:] = np.abs(np.diff([int_labels[y] for y in ylabs])) > 0
         # Only label the selection

straxen/plugins/afterpulses/afterpulse_processing.py

@@ -412,45 +412,41 @@ def dtype_afterpulses():
     - The afterpulse datatype
 
     """
-    dtype_ap = [
-        (("Channel/PMT number", "channel"), "<i2"),
-        (("Time resolution in ns", "dt"), "<i2"),
-        (("Start time of the interval (ns since unix epoch)", "time"), "<i8"),
-        (("Length of the interval in samples", "length"), "<i4"),
-        (("Integral in ADC x samples", "area"), "<i4"),
-        (("Pulse area in PE", "area_pe"), "<f4"),
-        (("Sample index in which hit starts", "left"), "<i2"),
+    dtype_ap = strax.interval_dtype + [
+        (("Integral in ADC x samples", "area"), np.int32),
+        (("Pulse area in PE", "area_pe"), np.float32),
+        (("Sample index in which hit starts", "left"), np.int16),
         (
             (
                 "Sample index in which hit area succeeds 10% of total area",
                 "sample_10pc_area",
             ),
-            "<i2",
+            np.int16,
         ),
         (
             (
                 "Sample index in which hit area succeeds 50% of total area",
                 "sample_50pc_area",
             ),
-            "<i2",
+            np.int16,
         ),
-        (("Sample index of hit maximum", "max"), "<i2"),
+        (("Sample index of hit maximum", "max"), np.int16),
         (
             (
                 "Index of first sample in record just beyond hit (exclusive bound)",
                 "right",
             ),
-            "<i2",
+            np.int16,
         ),
-        (("Height of hit in ADC counts", "height"), "<i4"),
-        (("Height of hit in PE", "height_pe"), "<f4"),
-        (("Delay of hit w.r.t. LED hit in same WF, in samples", "tdelay"), "<i2"),
+        (("Height of hit in ADC counts", "height"), np.int32),
+        (("Height of hit in PE", "height_pe"), np.float32),
+        (("Delay of hit w.r.t. LED hit in same WF, in samples", "tdelay"), np.int16),
         (
             (
                 "Internal (temporary) index of fragment in which hit was found",
                 "record_i",
             ),
-            "<i4",
+            np.int32,
         ),
         (
             ("Index of sample in record where integration starts", "left_integration"),

straxen/plugins/events/event_basics_vanilla.py

@@ -66,9 +66,7 @@ def infer_dtype(self):
         # Basic event properties
         self._set_posrec_save()
         self._set_dtype_requirements()
-        dtype = []
-        dtype += strax.time_fields
-        dtype += [
+        dtype = strax.time_fields + [
             ("n_peaks", np.int32, "Number of peaks in the event"),
             ("drift_time", np.float32, "Drift time between main S1 and S2 in ns"),
             ("event_number", np.int64, "Event number in this dataset"),
@@ -128,8 +126,8 @@ def _set_dtype_requirements(self):
             ("range_90p_area", np.float32, "width, 90% area [ns]"),
             ("rise_time", np.float32, "time between 10% and 50% area quantiles [ns]"),
             ("area_fraction_top", np.float32, "fraction of area seen by the top PMT array"),
-            ("tight_coincidence", np.int16, "Channel within tight range of mean"),
-            ("n_saturated_channels", np.int16, "Total number of saturated channels"),
+            ("tight_coincidence", np.int16, "channel within tight range of mean"),
+            ("n_saturated_channels", np.int16, "total number of saturated channels"),
         )
 
     def setup(self):

straxen/plugins/led_cal/led_calibration.py

@@ -8,11 +8,11 @@
 """
 
 from immutabledict import immutabledict
-import strax
-import straxen
 import numba
 import numpy as np
 import scipy.stats as sps
+import strax
+import straxen
 
 # This makes sure shorthands for only the necessary functions
 # are made available under straxen.[...]
@@ -162,15 +162,11 @@ class LEDCalibration(strax.Plugin):
         help=("Minimum hit amplitude in numbers of baseline_rms above baseline. "),
     )
 
-    dtype = [
+    dtype = strax.interval_dtype + [
         (("Area averaged in integration windows", "area"), np.float32),
         (("Area averaged in noise integration windows", "area_noise"), np.float32),
         (("Amplitude in LED window", "amplitude_led"), np.float32),
         (("Amplitude in off LED window", "amplitude_noise"), np.float32),
-        (("Channel", "channel"), np.int16),
-        (("Start time of the interval (ns since unix epoch)", "time"), np.int64),
-        (("Time resolution in ns", "dt"), np.int16),
-        (("Length of the interval in samples", "length"), np.int32),
         (("Whether there was a hit found in the record", "triggered"), bool),
         (("Sample index of the hit that defines the window position", "hit_position"), np.uint8),
         (("Window used for integration", "integration_window"), np.uint8, (2,)),
@@ -281,28 +277,28 @@ def get_records(raw_records, baseline_window, led_cal_record_length):
 
     record_length_padded = np.shape(raw_records.dtype["data"])[0]
 
-    _dtype = [
-        (("Start time since unix epoch [ns]", "time"), "<i8"),
-        (("Length of the interval in samples", "length"), "<i4"),
-        (("Width of one sample [ns]", "dt"), "<i2"),
-        (("Channel/PMT number", "channel"), "<i2"),
+    _dtype = strax.interval_dtype + [
         (
             (
                 "Length of pulse to which the record belongs (without zero-padding)",
                 "pulse_length",
             ),
-            "<i4",
+            np.int32,
         ),
-        (("Fragment number in the pulse", "record_i"), "<i2"),
+        (("Fragment number in the pulse", "record_i"), np.int16),
         (
             ("Baseline in ADC counts. data = int(baseline) - data_orig", "baseline"),
-            "f4",
+            np.float32,
         ),
         (
             ("Baseline RMS in ADC counts. data = baseline - data_orig", "baseline_rms"),
-            "f4",
+            np.float32,
+        ),
+        (
+            ("Waveform data in raw ADC counts with 0 padding", "data"),
+            np.float32,
+            (record_length_padded,),
         ),
-        (("Waveform data in raw ADC counts with 0 padding", "data"), "f4", (record_length_padded,)),
     ]
 
     records = np.zeros(len(raw_records), dtype=_dtype)

straxen/plugins/peaks/peak_basics_vanilla.py

@@ -35,10 +35,8 @@ class PeakBasicsVanilla(strax.Plugin):
     )
 
     def infer_dtype(self):
-        dtype = [
-            (("Start time of the peak (ns since unix epoch)", "time"), np.int64),
-            (("End time of the peak (ns since unix epoch)", "endtime"), np.int64),
-            (("Weighted center time of the peak (ns since unix epoch)", "center_time"), np.int64),
+        dtype = strax.time_fields + [
+            (("Weighted center time of the peak [ns]", "center_time"), np.int64),
             (("Peak integral in PE", "area"), np.float32),
             (("Number of hits contributing at least one sample to the peak", "n_hits"), np.int32),
             (("Number of PMTs contributing to the peak", "n_channels"), np.int16),

straxen/plugins/raw_records_coin_nv/nveto_recorder.py

@@ -325,7 +325,7 @@ def pulse_in_interval(raw_records, record_links, start_times, end_times):
 
     """
     nrr = len(raw_records)
-    result = np.zeros(nrr, np.bool_)
+    result = np.zeros(nrr, bool)
 
     last_interval_seen = 0
     for ind, rr in enumerate(raw_records):
@@ -430,7 +430,7 @@ def _coincidence(rr, nfold=4, resolving_time=300):
     """
     # 1. estimate time difference between fragments:
     start_times = rr["time"]
-    mask = np.zeros(len(start_times), dtype=np.bool_)
+    mask = np.zeros(len(start_times), dtype=bool)
     t_diff = np.diff(start_times, prepend=start_times[0])
 
     # 2. Now we have to check if n-events are within resolving time: