Rename old PeakletClassification as PeakletClassificationVanilla (#…

…1487)

straxen/plugins/events/__init__.py

@@ -1,54 +1,63 @@
-from . import corrected_areas
-from .corrected_areas import *
+from . import events
+from .events import *
 
-from . import distinct_channels
-from .distinct_channels import *
+from . import event_basics_som
+from .event_basics_som import *
 
-from . import energy_estimates
-from .energy_estimates import *
+from . import event_basics_vanilla
+from .event_basics_vanilla import *
+
+from . import event_positions
+from .event_positions import *
 
 from . import event_ambience
 from .event_ambience import *
 
-from . import event_area_per_channel
-from .event_area_per_channel import *
+from . import event_shadow
+from .event_shadow import *
 
-from . import event_waveform
-from .event_waveform import *
+from . import event_se_score
+from .event_se_score import *
 
-from . import event_top_bottom_params
-from .event_top_bottom_params import *
+from . import veto_proximity
+from .veto_proximity import *
 
-from . import event_basics
-from .event_basics import *
+from . import distinct_channels
+from .distinct_channels import *
+
+from . import energy_estimates
+from .energy_estimates import *
+
+from . import corrected_areas
+from .corrected_areas import *
 
 from . import event_info
 from .event_info import *
 
-from . import event_info_double
-from .event_info_double import *
+from . import event_area_per_channel
+from .event_area_per_channel import *
 
 from . import event_pattern_fit
 from .event_pattern_fit import *
 
-from . import event_positions
-from .event_positions import *
+from . import event_info_double
+from .event_info_double import *
 
-from . import event_shadow
-from .event_shadow import *
+from . import event_nearest_triggering
+from .event_nearest_triggering import *
+
+from . import event_waveform
+from .event_waveform import *
+
+from . import event_top_bottom_params
+from .event_top_bottom_params import *
 
 from . import event_w_bayes_class
 from .event_w_bayes_class import *
 
-from . import events
-from .events import *
-
 from . import s2_recon_pos_diff
 from .s2_recon_pos_diff import *
 
-from . import veto_proximity
-from .veto_proximity import *
-
 from . import event_position_uncertainty
 from .event_position_uncertainty import *
 
@@ -63,12 +72,3 @@
 
 from . import multi_scatter
 from .multi_scatter import *
-
-from . import event_basics_som
-from .event_basics_som import *
-
-from . import event_nearest_triggering
-from .event_nearest_triggering import *
-
-from . import event_se_score
-from .event_se_score import *

straxen/plugins/events/event_basics_som.py

@@ -1,13 +1,13 @@
 import strax
 import numpy as np
 
-from straxen.plugins.events.event_basics import EventBasics
+from straxen.plugins.events.event_basics_vanilla import EventBasicsVanilla
 
 export, __all__ = strax.exporter()
 
 
 @export
-class EventBasicsSOM(EventBasics):
+class EventBasicsSOM(EventBasicsVanilla):
     """Adds SOM fields for S1 and S2 peaks to event basics."""
 
     __version__ = "0.0.1"

straxen/plugins/events/event_basics.py → straxen/plugins/events/event_basics_vanilla.py

@@ -8,7 +8,7 @@
 
 
 @export
-class EventBasics(strax.Plugin):
+class EventBasicsVanilla(strax.Plugin):
     """Computes the basic properties of the main/alternative S1/S2 within an event.
 
     The main S1 and alternative S1 are given by the largest two S1-Peaks within the event. The main

straxen/plugins/events/event_nearest_triggering.py

@@ -77,7 +77,7 @@ def compute(self, events, peaks):
         split_peaks = strax.split_by_containment(peaks, events)
         result = np.zeros(len(events), self.dtype)
 
-        straxen.EventBasics.set_nan_defaults(result)
+        straxen.EventBasicsVanilla.set_nan_defaults(result)
 
         # 1. Assign peaks features to main S1 and main S2 in the event
         for event_i, (event, sp) in enumerate(zip(events, split_peaks)):

straxen/plugins/events/event_shadow.py

@@ -108,7 +108,7 @@ def compute(self, events, peaks):
         split_peaks = strax.split_by_containment(peaks, events)
         result = np.zeros(len(events), self.dtype)
 
-        straxen.EventBasics.set_nan_defaults(result)
+        straxen.EventBasicsVanilla.set_nan_defaults(result)
 
         # 1. Assign peaks features to main S1 and main S2 in the event
         for event_i, (event, sp) in enumerate(zip(events, split_peaks)):

straxen/plugins/events_nv/event_positions_nv.py

@@ -54,7 +54,7 @@ def compute(self, events_nv, hitlets_nv):
         angle = get_average_angle(hits_in_events, self.pmt_properties)
         event_angles["angle"] = angle
         compute_positions(event_angles, hits_in_events, self.pmt_properties)
-        strax.copy_to_buffer(events_nv, event_angles, f"_copy_events_nv")
+        strax.copy_to_buffer(events_nv, event_angles, "_copy_events_nv")
 
         return event_angles
 

straxen/plugins/peaklets/__init__.py

@@ -1,12 +1,12 @@
 from . import peaklets
 from .peaklets import *
 
-from . import peaklet_classification
-from .peaklet_classification import *
-
 from . import peaklet_classification_som
 from .peaklet_classification_som import *
 
+from . import peaklet_classification_vanilla
+from .peaklet_classification_vanilla import *
+
 from . import peaklet_positions_cnf
 from .peaklet_positions_cnf import *
 

straxen/plugins/peaklets/peaklet_classification_som.py

@@ -1,7 +1,7 @@
 import numpy as np
 import numpy.lib.recfunctions as rfn
 from scipy.spatial.distance import cdist
-from straxen.plugins.peaklets.peaklet_classification import PeakletClassification
+from straxen.plugins.peaklets.peaklet_classification_vanilla import PeakletClassificationVanilla
 import numba
 
 import strax
@@ -11,7 +11,7 @@
 
 
 @export
-class PeakletClassificationSOM(PeakletClassification):
+class PeakletClassificationSOM(PeakletClassificationVanilla):
     """
     Self-Organizing Maps (SOM)
     https://xe1t-wiki.lngs.infn.it/doku.php?id=xenon:xenonnt:lsanchez:unsupervised_neural_network_som_methods

straxen/plugins/peaklets/peaklet_classification.py → straxen/plugins/peaklets/peaklet_classification_vanilla.py

@@ -9,13 +9,13 @@
 
 
 @export
-class PeakletClassification(strax.Plugin):
+class PeakletClassificationVanilla(strax.Plugin):
     """Classify peaklets as unknown, S1, or S2."""
 
     __version__ = "3.0.3"
 
-    provides: Union[str, tuple] = "peaklet_classification"
     depends_on = "peaklets"
+    provides: Union[str, tuple] = "peaklet_classification"
     dtype = strax.peak_interval_dtype + [("type", np.int8, "Classification of the peak(let)")]
 
     s1_risetime_area_parameters = straxen.URLConfig(

straxen/plugins/peaklets_he/peaklet_classification_he.py

@@ -1,18 +1,19 @@
 import strax
 
-from straxen.plugins.peaklets.peaklet_classification import PeakletClassification
+from straxen.plugins.peaklets.peaklet_classification_vanilla import PeakletClassificationVanilla
 from straxen.plugins.defaults import HE_PREAMBLE
 
 export, __all__ = strax.exporter()
 
 
 @export
-class PeakletClassificationHighEnergy(PeakletClassification):
-    __doc__ = HE_PREAMBLE + (PeakletClassification.__doc__ or "")
-    provides = "peaklet_classification_he"
-    depends_on = "peaklets_he"
+class PeakletClassificationHighEnergy(PeakletClassificationVanilla):
+    __doc__ = HE_PREAMBLE + (PeakletClassificationVanilla.__doc__ or "")
+
     __version__ = "0.0.2"
     child_plugin = True
+    depends_on = "peaklets_he"
+    provides = "peaklet_classification_he"
 
     def compute(self, peaklets_he):
         return super().compute(peaklets_he)

straxen/plugins/peaks/__init__.py

@@ -1,17 +1,14 @@
-from . import peak_top_bottom_params
-from .peak_top_bottom_params import *
-
-from . import peak_ambience
-from .peak_ambience import *
+from . import peaks_som
+from .peaks_som import *
 
-from . import peak_basics
-from .peak_basics import *
+from . import peaks_vanilla
+from .peaks_vanilla import *
 
-from . import peak_classification_bayes
-from .peak_classification_bayes import *
+from . import peak_basics_som
+from .peak_basics_som import *
 
-from . import peak_s1_positions_cnn
-from .peak_s1_positions_cnn import *
+from . import peak_basics_vanilla
+from .peak_basics_vanilla import *
 
 from . import peak_positions_cnf
 from .peak_positions_cnf import *
@@ -22,29 +19,32 @@
 from . import peak_positions
 from .peak_positions import *
 
-from . import peak_proximity
-from .peak_proximity import *
+from . import peak_ambience
+from .peak_ambience import *
 
 from . import peak_shadow
 from .peak_shadow import *
 
-from . import peaks
-from .peaks import *
+from . import peak_se_score
+from .peak_se_score import *
+
+from . import peak_proximity
+from .peak_proximity import *
+
+from . import peak_nearest_triggering
+from .peak_nearest_triggering import *
+
+from . import peak_top_bottom_params
+from .peak_top_bottom_params import *
+
+from . import peak_s1_positions_cnn
+from .peak_s1_positions_cnn import *
 
 from . import peak_per_event
 from .peak_per_event import *
 
 from . import peak_corrections
 from .peak_corrections import *
 
-from . import peaks_som
-from .peaks_som import *
-
-from . import peak_basics_som
-from .peak_basics_som import *
-
-from . import peak_nearest_triggering
-from .peak_nearest_triggering import *
-
-from . import peak_se_score
-from .peak_se_score import *
+from . import peak_classification_bayes
+from .peak_classification_bayes import *

straxen/plugins/peaks/_peak_positions_base.py

@@ -1,11 +1,11 @@
 import strax
-from .peaks import Peaks
+from .peaks_vanilla import PeaksVanilla
 
 export, __all__ = strax.exporter()
 
 
 @export
-class PeakPositionsBase(Peaks):
+class PeakPositionsBase(PeaksVanilla):
 
     __version__ = "0.0.0"
     child_plugin = True

straxen/plugins/peaks/peak_basics_som.py

@@ -1,12 +1,12 @@
 import numpy as np
 import strax
-from straxen.plugins.peaks.peak_basics import PeakBasics
+from straxen.plugins.peaks.peak_basics_vanilla import PeakBasicsVanilla
 
 export, __all__ = strax.exporter()
 
 
 @export
-class PeakBasicsSOM(PeakBasics):
+class PeakBasicsSOM(PeakBasicsVanilla):
     """Adds SOM fields to peak basics to be propgated to event basics."""
 
     __version__ = "0.0.1"

straxen/plugins/peaks/peak_basics.py → straxen/plugins/peaks/peak_basics_vanilla.py

@@ -8,7 +8,7 @@
 
 
 @export
-class PeakBasics(strax.Plugin):
+class PeakBasicsVanilla(strax.Plugin):
     """Compute the basic peak-properties, thereby dropping structured arrays.
 
     NB: This plugin can therefore be loaded as a pandas DataFrame.

straxen/plugins/peaks/peak_nearest_triggering.py

@@ -79,7 +79,7 @@ def compute_triggering(self, peaks, current_peak):
         _peaks = _peaks[_is_triggering]
         # init result
         result = np.zeros(len(current_peak), self.dtype)
-        straxen.EventBasics.set_nan_defaults(result)
+        straxen.EventBasicsVanilla.set_nan_defaults(result)
 
         # use center_time as the anchor of things
         things = np.zeros(len(_peaks), dtype=strax.time_fields)

straxen/plugins/peaks/peak_per_event.py

@@ -30,7 +30,7 @@ def compute(self, events, peaks):
         split_peaks = strax.split_by_containment(peaks, events)
         split_peaks_ind = strax.fully_contained_in(peaks, events)
         result = np.zeros(len(peaks), self.dtype)
-        straxen.EventBasics.set_nan_defaults(result)
+        straxen.EventBasicsVanilla.set_nan_defaults(result)
 
         # Assign peaks features to main S1 and main S2 in the event
         for event_i, (event, sp) in enumerate(zip(events, split_peaks)):

straxen/plugins/peaks/peaks_som.py

@@ -1,12 +1,12 @@
 import strax
 import numpy as np
-from straxen.plugins.peaks.peaks import Peaks
+from straxen.plugins.peaks.peaks_vanilla import PeaksVanilla
 
 export, __all__ = strax.exporter()
 
 
 @export
-class PeaksSOM(Peaks):
+class PeaksSOM(PeaksVanilla):
     """Same as Peaks but include in addition SOM type field to be propagated to event_basics.
 
     Thus, only change dtype.
@@ -30,7 +30,7 @@ def infer_dtype(self):
     def compute(self, peaklets, merged_s2s):
         result = super().compute(peaklets, merged_s2s)
 
-        # For merged S2s SOM and straxen type are undefined:
+        # For merged_s2s SOM and straxen type are undefined:
         _is_merged_s2 = np.isin(result["time"], merged_s2s["time"]) & np.isin(
             strax.endtime(result), strax.endtime(merged_s2s)
         )

straxen/plugins/peaks/peaks.py → straxen/plugins/peaks/peaks_vanilla.py

@@ -9,7 +9,7 @@
 
 
 @export
-class Peaks(strax.Plugin):
+class PeaksVanilla(strax.Plugin):
     """Merge peaklets and merged S2s such that we obtain our peaks (replacing all peaklets that were
     later re-merged as S2s).