Belle II Software light-2406-ragdoll
printReporting.py
1#!/usr/bin/env python
2
3
10
11"""
12 Call this "python3 fei/printReporting.py"
13 in a directory containing the monitoring output of the FEI
14 It will print out a summary and create some plots
15"""
16
17
18import fei.monitoring as monitoring
19
20
21def bold(text):
22 """Use ANSI sequence to show string in bold"""
23 return '\x1b[1m' + text + '\x1b[0m'
24
25
26def print_summary(p):
27 try:
28 monitoring.MonitorROCPlot(p, monitoring.removeJPsiSlash(p.particle.identifier + '_ROC'))
29 monitoring.MonitorDiagPlot(p, monitoring.removeJPsiSlash(p.particle.identifier + '_Diag'))
30 if p.particle.identifier in ['B+:generic', 'B0:generic']:
31 monitoring.MonitorMbcPlot(p, monitoring.removeJPsiSlash(p.particle.identifier + '_Money'))
32 if p.particle.identifier in ['B+:semileptonic', 'B0:semileptonic']:
33 monitoring.MonitorCosBDLPlot(p, monitoring.removeJPsiSlash(p.particle.identifier + '_Money'))
34 except BaseException:
35 pass
36 print(bold(p.particle.identifier))
37 print('Total cpu time spent reconstructing this particle: ',
38 p.module_statistic.particle_time + sum(p.module_statistic.channel_time.values()))
39 print('Total amount of Monte Carlo particles in training sample: ', p.mc_count)
40 print('Number of decay channels: ', p.reconstructed_number_of_channels, '/', p.total_number_of_channels)
41 print(bold('PreCut'))
42 print('UserCut', p.particle.preCutConfig.userCut)
43 print('BestCandidateCut', p.particle.preCutConfig.bestCandidateVariable,
44 p.particle.preCutConfig.bestCandidateCut, p.particle.preCutConfig.bestCandidateMode)
45 print('VertexCut', p.particle.preCutConfig.vertexCut)
46 print('Stats before ranking')
47 print(sum(p.before_ranking.values()))
48 print('Stats after ranking')
49 print(sum(p.after_ranking.values()))
50 print('Stats after vertex')
51 print(sum(p.after_vertex.values()))
52 print('Stats after classifier')
53 print(sum(p.after_classifier.values()))
54 print(bold('PostCut'))
55 print('Absolute', p.particle.postCutConfig.value)
56 print('Ranking', p.particle.postCutConfig.bestCandidateCut)
57 print('Stats before postcut')
58 print(p.before_postcut)
59 print('Stats before ranking postcut')
60 print(p.before_ranking_postcut)
61 print('Stats after ranking postcut')
62 print(p.after_ranking_postcut)
63 print(bold('Tag unique signal'))
64 print('Stats before tag')
65 print(p.before_tag)
66 print('Stats after tag')
67 print(p.after_tag)
68 print(bold('Multivariate classifier'))
69 print('Method', p.particle.mvaConfig.method)
70 print('Target', p.particle.mvaConfig.target)
71 print(bold('Individual channels'))
72 for channel in p.particle.channels:
73 print(bold(channel.label))
74 print('Stats before ranking')
75 print(p.before_ranking[channel.label])
76 print('Stats after ranking')
77 print(p.after_ranking[channel.label])
78 print('Stats after vertex')
79 print(p.after_vertex[channel.label])
80 print('Stats after classifier')
81 print(p.after_classifier[channel.label])
82
83
84if __name__ == '__main__':
85 particles, configuration = monitoring.load_config()
86 for particle in particles:
87 monitoringParticle = monitoring.MonitoringParticle(particle)
88 print_summary(monitoringParticle)