Make bad PMT list read-in work

hax/treemakers/posrec.py

@@ -1,5 +1,6 @@
 import hax
 import numpy as np
+import json
 from hax.minitrees import TreeMaker
 from pax.PatternFitter import PatternFitter
 from pax.configuration import load_configuration
@@ -98,12 +99,13 @@ def load_nn(self):
 
         json_file_nn = open(utils.data_file_name(self.tfnn_model), 'r')
         loaded_model_json = json_file_nn.read()
-        json_file_nn.close()
         self.loaded_nn = model_from_json(loaded_model_json)
+        json_file_nn.close()
 
-        #Get bad PMT List in Json file: 
+        # Get bad PMT List in JSON file:
         json_file_nn = open(utils.data_file_name(self.tfnn_model), 'r')
         loaded_model_json_dict = json.load(json_file_nn)
+        self.list_bad_pmts = loaded_model_json_dict['badPMTList']
         json_file_nn.close()
 
         weights_file = utils.data_file_name(self.tfnn_weights)
@@ -165,17 +167,11 @@ def extract_data(self, event):
         s2 = event.peaks[interaction.s2]
 
         # Position reconstruction based on NN from TensorFlow
-
-        # Hardcode warning, this should come with NN model eventually
-        # xenon:xenon1t:analysis:sciencerun1:pmt#list_of_pmts_excluded_from_sr1_analysis_shingo
-        tensorflow, badpmtlist = self.loaded_nn()
-        list_bad_pmts = badpmtlist['badPMTList']
-
         s2apc = np.array(list(s2.area_per_channel))
         s2apc_clean = []
 
         for ipmt, s2_t in enumerate(s2apc):
-            if ipmt not in list_bad_pmts and ipmt < self.ntop_pmts:
+            if ipmt not in self.list_bad_pmts and ipmt < self.ntop_pmts:
                 s2apc_clean.append(s2_t)
 
         s2apc_clean = np.asarray(s2apc_clean)