From d4687d98dd318c102576156726f8e97135f95ebe Mon Sep 17 00:00:00 2001
From: Nikolai Hartmann <Nikolai.Hartmann@physik.uni-muenchen.de>
Date: Mon, 27 Aug 2018 15:35:04 +0200
Subject: [PATCH] earlier argparse in plot_NN_2D.py
---
scripts/plot_NN_2D.py | 53 ++++++++++++++++++++++---------------------
1 file changed, 27 insertions(+), 26 deletions(-)
diff --git a/scripts/plot_NN_2D.py b/scripts/plot_NN_2D.py
index f5f9fee..3ddb6ba 100755
--- a/scripts/plot_NN_2D.py
+++ b/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:
--
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