start SOM plugin

amstrax/contexts.py

@@ -40,6 +40,8 @@
         ax.PulseProcessingEXT,
         ax.PeaksEXT,
         ax.PeakBasicsEXT,
+        #ax.PeakWaveforms,
+        #ax.PeaksSOM
         # LED plugins not default
         # ax.RecordsLED,
         # ax.LEDCalibration,
@@ -100,6 +102,22 @@ def xams(output_folder='./strax_data',
 
     return st
 
+def xams_som(**kwargs):
+    """XENONnT context for the SOM."""
+
+    st = ax.contexts.xams(**kwargs)
+    #del st._plugin_class_registry["peaks"]
+    st.register(
+        (
+            ax.PeaksSOM,
+            ax.MergedPeaks,
+            #straxen.EventBasicsSOM,
+        )
+    )
+
+    return st
+
+
 def context_for_daq_reader(st: strax.Context,
                            run_id: str,
                            detector: str = 'xams',

amstrax/plugins/events/event_area_per_channel.py

@@ -18,7 +18,7 @@
 class EventAreaPerChannel(strax.Plugin):
     """Simple plugin that provides area per channel for main and alternative S1/S2 in the event."""
 
-    depends_on = ("event_basics", "peaks")
+    depends_on = ("event_basics", "merged_peaks")
     provides = ("event_area_per_channel", "event_n_channel")
     data_kind = immutabledict(zip(provides, ("events", "events")))
     __version__ = "0.1.1"

amstrax/plugins/events/event_waveform.py

@@ -10,7 +10,7 @@ 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")
+    depends_on = ("event_basics", "merged_peaks")
     provides = "event_waveform"
     __version__ = "0.0.1"
 

amstrax/plugins/events/events.py

@@ -16,7 +16,7 @@
                       'triggering peak'),
 )
 class Events(strax.OverlapWindowPlugin):
-    depends_on = ['peaks', 
+    depends_on = ['merged_peaks',
                   'peak_basics',
                   ]
     rechunk_on_save = False

amstrax/plugins/peaks/__init__.py

@@ -7,4 +7,8 @@
 from . import peak_positions
 from .peak_positions import *
 
+from . import peaks_som
+from .peaks_som import *
 
+from . import merged_peaks
+from .merged_peaks import *

amstrax/plugins/peaks/merged_peaks.py

@@ -0,0 +1,32 @@
+import numpy as np
+import numpy.lib.recfunctions as rfn
+from scipy.spatial.distance import cdist
+import os
+import numba
+
+import strax
+#from amstrax import Peaks
+
+export, __all__ = strax.exporter()
+
+@export
+class MergedPeaks(strax.MergeOnlyPlugin):
+    """
+    Self-Organizing Maps (SOM)
+    https://xe1t-wiki.lngs.infn.it/doku.php?id=xenon:xenonnt:lsanchez:unsupervised_neural_network_som_methods
+    For peaklet classification. We this pluggin will provide 2 data types, the 'type' we are
+    already familiar with, classifying peaklets as s1, s2 (using the new classification) or
+    unknown (from the previous classification). As well as a new data type, SOM type, which
+    will be assigned numbers based on the cluster in the SOM in which they are found. For
+    each version I will make some documentation in the corrections repository explaining
+    what I believe each cluster represents.
+
+    This correction/plugin is currently on the testing phase, feel free to use it if you are
+    curious or just want to test it or try it out but note this is note ready to be used in
+    analysis.
+    """
+    depends_on = ('peaks', 'peaks_som')
+    #data_kind = 'peaks'
+    # parallel = 'process'
+    provides = ('merged_peaks')
+

amstrax/plugins/peaks/peak_basics.py

@@ -70,7 +70,7 @@
 )
 class PeakBasics(strax.Plugin):
     provides = ("peak_basics",)
-    depends_on = ("peaks", )
+    depends_on = ("merged_peaks", )
     data_kind = "peaks"
 
     parallel = "False"
@@ -158,7 +158,8 @@
         # 0 = unknown
         # 1 = s1
         # 2 = s2
-
+        """        
+        # Remove previous classification from peaks
         is_s1 = p['area'] >= self.config['s1_min_area']
         is_s1 &= r['range_50p_area'] > self.config['s1_min_width']
         is_s1 &= r['range_50p_area'] < self.config['s1_max_width']
@@ -176,7 +177,7 @@
 
         r['type'][is_s1] = 1
         r['type'][is_s2] = 2
-
+        """
         return r
 
 

amstrax/plugins/peaks/peaks.py

@@ -37,7 +37,7 @@
     strax.Option('diagnose_sorting', track=False, default=False,
                  help="Enable runtime checks for sorting and disjointness"),
     strax.Option('n_tpc_pmts', track=False, default=False,
-                 help="Number of channels"), 
+                 help="Number of channels"),
     strax.Option('gain_to_pe_array', default=None,
                  help="Gain to pe array"),
 )
@@ -51,13 +51,13 @@
     __version__ = '0.1.50'
 
     def infer_dtype(self):
-    
+
         return strax.peak_dtype(n_channels=self.config['n_tpc_pmts'])
 
     def compute(self, records, start, end):
 
         r = records
-  
+
         if self.config['gain_to_pe_array'] is None:
             self.to_pe = np.ones(self.config['n_tpc_pmts'])
         else:
@@ -90,4 +90,4 @@
 
         strax.compute_widths(peaks)
 
-        return peaks
+        return peaks