earlier argparse in plot_NN_2D.py

scripts/plot_NN_2D.py

@@ -2,30 +2,6 @@
 
 import sys
 import argparse
-import logging
-logging.basicConfig()
-
-import numpy as np
-
-import ROOT
-ROOT.gROOT.SetBatch()
-ROOT.PyConfig.IgnoreCommandLineOptions = True
-
-from KerasROOTClassification import load_from_dir
-from KerasROOTClassification.plotting import (
-    get_mean_event,
-    plot_NN_vs_var_2D,
-    plot_profile_2D,
-    plot_hist_2D_events,
-    plot_cond_avg_actmax_2D,
-    plot_NN_vs_var_2D_all,
-)
-from KerasROOTClassification.utils import (
-    get_single_neuron_function,
-    get_max_activation_events,
-    weighted_quantile,
-    get_ranges
-)
 
 parser = argparse.ArgumentParser(description='Create various 2D plots for a single neuron')
 parser.add_argument("project_dir")
@@ -53,6 +29,31 @@ parser.add_argument("-s", "--step-size", help="step size for activation maximisa
 
 args = parser.parse_args()
 
+import logging
+logging.basicConfig()
+
+import numpy as np
+
+import ROOT
+ROOT.gROOT.SetBatch()
+ROOT.PyConfig.IgnoreCommandLineOptions = True
+
+from KerasROOTClassification import load_from_dir
+from KerasROOTClassification.plotting import (
+    get_mean_event,
+    plot_NN_vs_var_2D,
+    plot_profile_2D,
+    plot_hist_2D_events,
+    plot_cond_avg_actmax_2D,
+    plot_NN_vs_var_2D_all,
+)
+from KerasROOTClassification.utils import (
+    get_single_neuron_function,
+    get_max_activation_events,
+    weighted_quantile,
+    get_ranges
+)
+
 if args.all_neurons and (not args.mode.startswith("mean")):
     parser.error("--all-neurons currently only supported for mean_sig and mean_bkg")
 
@@ -94,8 +95,8 @@ except AttributeError:
 # percentilesx = weighted_quantile(varx_test[x_not_masked], [0.01, 0.99], sample_weight=total_weights[x_not_masked])
 # percentilesy = weighted_quantile(vary_test[y_not_masked], [0.01, 0.99], sample_weight=total_weights[y_not_masked])
 
-percentilesx = get_ranges(c.x_test, [0.01, 0.99], total_weights, mask_value=mask_value, filter_index=varx_index)[0]
-percentilesy = get_ranges(c.x_test, [0.01, 0.99], total_weights, mask_value=mask_value, filter_index=vary_index)[0]
+percentilesx = get_ranges(c.x_test, [0.01, 0.99], total_weights, mask_value=mask_value, filter_index=varx_index, max_evts=10000)[0][0]
+percentilesy = get_ranges(c.x_test, [0.01, 0.99], total_weights, mask_value=mask_value, filter_index=vary_index, max_evts=10000)[0][0]
 
 
 if args.xrange is not None: