ATLAS Offline Software
EgammaARTmonitoring_plotsMaker.py
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1 #!/usr/bin/env python
2 #
3 # Copyright (C) 2002-2021 CERN for the benefit of the ATLAS collaboration.
4 #
5 
6 import sys
7 from ROOT import (gDirectory, gROOT, gStyle, kTRUE,
8  TCanvas, TFile, TLegend, TPad, kBlack, kBlue,
9  kRed, kGreen, kOrange, kCyan, kPink, kGray)
10 
11 # gROOT.SetBatch(kTRUE)
12 gStyle.SetOptStat(0)
13 
14 cluster_list = [
15  {'name': 'clusterAll', 'title': 'Clusters - Inclusive'},
16  {'name': 'cluster10GeV', 'title': 'Clusters - 10 GeV'},
17  {'name': 'clusterPromptAll', 'title': 'Clusters from Prompt - Inclusive'},
18  {'name': 'clusterPrompt10GeV', 'title': 'Clusters from Prompt - 10 GeV'},
19 ]
20 
21 cluster_list_photon = [
22  {'name': 'clusterUnconvPhoton',
23  'title': 'Clusters Unconverted Photons'},
24  {'name': 'clusterConvPhoton',
25  'title': 'Clusters Converted Photons'},
26  {'name': 'clusterConvPhotonSi',
27  'title': 'Clusters Converted Photons - Si'},
28  {'name': 'clusterConvPhotonSiSi',
29  'title': 'Clusters Converted Photons - SiSi'},
30  {'name': 'clusterConvPhotonTRT',
31  'title': 'Clusters Converted Photons - TRT'},
32  {'name': 'clusterConvPhotonTRTTRT',
33  'title': 'Clusters Converted Photons - TRTTRT'},
34  {'name': 'clusterConvPhotonSiTRT',
35  'title': 'Clusters Converted Photons - SiTRT'},
36 ]
37 
38 photon_cluster_list = [
39  {'name': 'clusterUnconvPhoton',
40  'title': 'Clusters Unconverted Photons'},
41  {'name': 'clusterConvPhoton',
42  'title': 'Clusters Converted Photons'},
43  {'name': 'clusterConvPhotonSi',
44  'title': 'Clusters Converted Photons - Si'},
45  {'name': 'clusterConvPhotonSiSi',
46  'title': 'Clusters Converted Photons - SiSi'},
47  {'name': 'clusterConvPhotonTRT',
48  'title': 'Clusters Converted Photons - TRT'},
49  {'name': 'clusterConvPhotonTRTTRT',
50  'title': 'Clusters Converted Photons - TRTTRT'},
51  {'name': 'clusterConvPhotonSiTRT',
52  'title': 'Clusters Converted Photons - SiTRT'},
53 
54 ]
55 
56 
57 electron_comparison_list = [
58  {'name': 'showerShapesAll',
59  'title': 'Shower Shape - Inclusive'},
60  {'name': 'showerShapes10GeV',
61  'title': 'Shower Shape - 10 GeV'},
62  {'name': 'isolationAll',
63  'title': 'Isolation'},
64  {'name': 'recoElectronAll',
65  'title': 'Reconstructed Electron'},
66  {'name': 'truthRecoElectronLooseLH',
67  'title': 'Reconstructed Electron LooseLH'},
68  {'name': 'truthRecoElectronMediumLH',
69  'title': 'Reconstructed Electron MediumLH'},
70  {'name': 'truthRecoElectronTightLH',
71  'title': 'Reconstructed Electron TightLH'},
72  {'name': 'truthElectronAll',
73  'title': 'True Electron'},
74  {'name': 'truthPromptElectronAll',
75  'title': 'True Prompt Electron'},
76  {'name': 'truthElectronRecoElectronAll',
77  'title': 'True Electron Reconstructed as Electron'},
78  {'name': 'truthPromptElectronWithTrack',
79  'title': 'True Prompt Electron with Track'},
80  {'name': 'truthPromptElectronWithGSFTrack',
81  'title': 'True Prompt Electron with GSFTrack'},
82  {'name': 'truthPromptElectronWithReco',
83  'title': 'True Prompt Electron with Reco Electron'},
84  {'name': 'trackingEfficiency',
85  'title': 'Tracking Efficiency'},
86  {'name': 'GSFEfficiency',
87  'title': 'GSF Efficiency'},
88  {'name': 'matchingEfficiency',
89  'title': 'Matching Efficiency'},
90  {'name': 'reconstructionEfficiency',
91  'title': 'Reconstruction Efficiency'},
92  {'name': 'recoElectronLooseLHEfficiency',
93  'title': 'Reconstructed Electron LooseLH Efficiency'},
94  {'name': 'recoElectronMediumLHEfficiency',
95  'title': 'Reconstructed Electron MediumLH Efficiency'},
96  {'name': 'recoElectronTightLHEfficiency',
97  'title': 'Reconstructed Electron TightLH Efficiency'},
98 ]
99 
100 photon_comparison_list = [
101  {'name': 'recoPhotonAll',
102  'title': 'Reconstructed Photon'},
103  {'name': 'truthPhotonRecoPhoton',
104  'title': 'True photon reconstructed as photon'},
105  {'name': 'truthConvPhoton',
106  'title': 'True converted photon'},
107  {'name': 'truthConvRecoConv',
108  'title': 'True conversion reconstructed as converted photon'},
109  {'name': 'truthConvRecoConv1Si',
110  'title': 'True conversion reconstructed as 1 Si conv'},
111  {'name': 'truthConvRecoConv1TRT',
112  'title': 'True conversion reconstructed as 1 TRT conv'},
113  {'name': 'truthConvRecoConv2Si',
114  'title': 'True conversion reconstructed as Si-Si conv'},
115  {'name': 'truthConvRecoConv2TRT',
116  'title': 'True conversion reconstructed as TRT-TRT conv'},
117  {'name': 'truthConvRecoConv2SiTRT',
118  'title': 'True conversion reconstructed as Si-TRT conv'},
119  {'name': 'truthConvRecoUnconv',
120  'title': 'True conversion reconstructed as unconverted photon'},
121  {'name': 'truthUnconvPhoton', 'title': 'True unconverted photon'},
122  {'name': 'truthUnconvRecoConv',
123  'title': 'True unconverted reconstructed as conv photon'},
124  {'name': 'truthUnconvRecoUnconv',
125  'title': 'True unconverted reconstructed as unconverted photon'},
126  {'name': 'showerShapesAll',
127  'title': 'Shower Shape - Inclusive'},
128  {'name': 'showerShapes10GeV',
129  'title': 'Shower Shape - 10 GeV'},
130  {'name': 'isolationAll',
131  'title': 'Isolation'},
132  {'name': 'recoPhotonUnconvLooseLH',
133  'title': 'Unconverted Photon LooseLH'},
134  {'name': 'recoPhotonUnconvTightLH',
135  'title': 'Unconverted Photon TightLH'},
136  {'name': 'recoPhotonConvLooseLH',
137  'title': 'Converted Photon LooseLH'},
138  {'name': 'recoPhotonConvTightLH',
139  'title': 'Converted Photon TightLH'},
140  {'name': 'recoPhotonUnconvIsoFixedCutTight',
141  'title': 'FixedCutTight Unconverted Photon'},
142  {'name': 'recoPhotonUnconvIsoFixedCutTightCaloOnly',
143  'title': 'FixedCutTightCaloOnly Unconverted Photon'},
144  {'name': 'recoPhotonUnconvIsoFixedCutLoose',
145  'title': 'FixedCutLoose Unconverted Photon'},
146  {'name': 'recoPhotonConvIsoFixedCutTight',
147  'title': 'FixedCutTight Converted Photon'},
148  {'name': 'recoPhotonConvIsoFixedCutTightCaloOnly',
149  'title': 'FixedCutTightCaloOnly Converted Photon'},
150  {'name': 'recoPhotonConvIsoFixedCutLoose',
151  'title': 'FixedCutLoose Converted Photon'},
152  {'name': 'truthPhotonUnconvRecoUnconvEfficiency',
153  'title': 'True Conv #rightarrow Conv'},
154  {'name': 'truthPhotonRecoConvEfficiency',
155  'title': 'True Conv #rightarrow Conv'},
156  {'name': 'recoPhotonUnconvIsoFixedCutTightEfficiency',
157  'title': 'True Conv #rightarrow Conv'},
158  {'name': 'recoPhotonUnconvIsoFixedCutTightCaloOnlyEfficiency',
159  'title': 'True Conv #rightarrow Conv'},
160  {'name': 'recoPhotonUnconvIsoFixedCutLooseEfficiency',
161  'title': 'True Conv #rightarrow Conv'},
162  {'name': 'recoPhotonConvIsoFixedCutTightEfficiency',
163  'title': 'True Conv #rightarrow Conv'},
164  {'name': 'recoPhotonConvIsoFixedCutTightCaloOnlyEfficiency',
165  'title': 'True Conv #rightarrow Conv'},
166  {'name': 'recoPhotonConvIsoFixedCutLooseEfficiency',
167  'title': 'True Conv #rightarrow Conv'},
168  {'name': 'recoPhotonUnconvLooseLHEfficiency',
169  'title': 'Unconverted Photon LooseLH Efficiency'},
170  {'name': 'recoPhotonUnconvTightLHEfficiency',
171  'title': 'Unconverted Photon TightLH Efficiency'},
172  {'name': 'recoPhotonConvLooseLHEfficiency',
173  'title': 'Converted Photon LooseLH Efficiency'},
174  {'name': 'recoPhotonConvTightLHEfficiency',
175  'title': 'Converted Photon TightLH Efficiency'},
176 ]
177 
178 photon_fraction_list = [
179  {'name': 'truthPhotonConvRecoConvEfficiency',
180  'color': kBlack, 'title': 'True Conv #rightarrow Conv'},
181  {'name': 'truthPhotonConvRecoConv1SiEfficiency', 'color': kBlue +
182  2, 'title': 'True Conv #rightarrow 1 Si Conv'},
183  {'name': 'truthPhotonConvRecoConv1TRTEfficiency', 'color': kRed +
184  2, 'title': 'True Conv #rightarrow 1 TRT Conv'},
185  {'name': 'truthPhotonConvRecoConv2SiEfficiency', 'color': kGreen +
186  2, 'title': 'True Conv #rightarrow Si-Si Conv'},
187  {'name': 'truthPhotonConvRecoConv2TRTEfficiency', 'color': kOrange + 2,
188  'title': 'True Conv #rightarrow TRT-TRT Conv'},
189  {'name': 'truthPhotonConvRecoConv2SiTRTEfficiency', 'color': kCyan + 2,
190  'title': 'True Conv #rightarrow Si-TRT Conv'},
191  {'name': 'truthPhotonConvRecoUnconvEfficiency',
192  'color': kPink + 2, 'title': 'True Conv #rightarrow Unconv'}
193 ]
194 
195 photonfake_fraction_list = [
196  {'name': 'truthPhotonUnconvRecoConvEfficiency',
197  'color': kBlack, 'title': 'True Unconv #rightarrow Conv'},
198  {'name': 'truthPhotonUnconvRecoConv1SiEfficiency', 'color': kBlue +
199  2, 'title': 'True Unconv #rightarrow 1 Si Conv'},
200  {'name': 'truthPhotonUnconvRecoConv1TRTEfficiency', 'color': kRed +
201  2, 'title': 'True Unconv #rightarrow 1 TRT Conv'},
202  {'name': 'truthPhotonUnconvRecoConv2SiEfficiency', 'color': kGreen +
203  2, 'title': 'True Unconv #rightarrow Si-Si Conv'},
204  {'name': 'truthPhotonUnconvRecoConv2TRTEfficiency', 'color': kOrange + 2,
205  'title': 'True Unconv #rightarrow TRT-TRT Conv'},
206  {'name': 'truthPhotonUnconvRecoConv2SiTRTEfficiency', 'color': kCyan + 2,
207  'title': 'True Unconv #rightarrow Si-TRT Conv'},
208 ]
209 
210 photon_efficiency_list = [
211  {'name': 'truthPhotonRecoPhotonEfficiency',
212  'color': kBlack, 'title': 'All photons'},
213  {'name': 'truthPhotonRecoPhotonOrElectronEfficiency', 'color': kGreen + 2,
214  'title': 'All photons + electrons'},
215  {'name': 'truthPhotonConvRecoEfficiency', 'color': kRed,
216  'title': 'True converted'},
217  {'name': 'truthPhotonUnconvRecoEfficiency', 'color': kBlue,
218  'title': 'True unconverted'}
219 ]
220 
221 photon_conversion_list = [
222  {'name': 'truthConvRecoConv2Si', 'color': kGreen +
223  2, 'title': 'True Conv #rightarrow Si-Si Conv'},
224  {'name': 'truthConvRecoConv1Si', 'color': kBlue +
225  2, 'title': 'True Conv #rightarrow 1 Si Conv'},
226  {'name': 'truthConvRecoConv1TRT', 'color': kRed +
227  2, 'title': 'True Conv #rightarrow 1 TRT Conv'},
228  {'name': 'truthConvRecoConv2TRT', 'color': kOrange +
229  2, 'title': 'True Conv #rightarrow TRT-TRT Conv'},
230  {'name': 'truthConvRecoConv2SiTRT', 'color': kCyan +
231  2, 'title': 'True Conv #rightarrow Si-TRT Conv'},
232 ]
233 
234 photon_track_list = [
235  {'name': 'InDetTracks', 'color': kBlack,
236  'title': 'All tracks'},
237  {'name': 'InDetTracksMatchElectron', 'color': kOrange,
238  'title': 'Matched to true electrons'},
239  {'name': 'InDetTracksNotElectron', 'color': kBlue,
240  'title': 'Not matched to true electrons'},
241  {'name': 'InDetTracksMatchPion', 'color': kGreen +
242  2, 'title': 'Matched to true Pion'},
243  {'name': 'InDetTracksNotMatched', 'color': kCyan +
244  2, 'title': 'Not matched to truth'}
245 ]
246 
247 photon_trackTRT_list = [
248  {'name': 'InDetTracksTRT', 'color': kBlack,
249  'title': 'All tracks'},
250  {'name': 'InDetTracksTRTMatchElectron', 'color': kOrange,
251  'title': 'Matched to true electrons'},
252  {'name': 'InDetTracksTRTNotElectron', 'color': kBlue,
253  'title': 'Not matched to true electrons'},
254  {'name': 'InDetTracksTRTMatchPion', 'color': kGreen +
255  2, 'title': 'Matched to true Pion'},
256  {'name': 'InDetTracksTRTNotMatched', 'color': kCyan +
257  2, 'title': 'Not matched to truth'}
258 ]
259 
260 photon_trackhighpT_list = [
261  {'name': 'InDetTrackshighpT', 'color': kBlack,
262  'title': 'All tracks'},
263  {'name': 'InDetTracksMatchElectronhighpT', 'color': kOrange,
264  'title': 'Matched to true electrons'},
265  {'name': 'InDetTracksNotElectronhighpT', 'color': kBlue,
266  'title': 'Not matched to true electrons'},
267  {'name': 'InDetTracksMatchPionhighpT', 'color': kGreen +
268  2, 'title': 'Matched to true Pion'},
269  {'name': 'InDetTracksNotMatchedhighpT', 'color': kCyan +
270  2, 'title': 'Not matched to truth'}
271 ]
272 
273 photon_trackTRThighpT_list = [
274  {'name': 'InDetTracksTRThighpT', 'color': kBlack,
275  'title': 'All tracks'},
276  {'name': 'InDetTracksTRTMatchElectronhighpT', 'color': kOrange,
277  'title': 'Matched to true electrons'},
278  {'name': 'InDetTracksTRTNotElectronhighpT', 'color': kBlue,
279  'title': 'Not matched to true electrons'},
280  {'name': 'InDetTracksTRTMatchPionhighpT', 'color': kGreen +
281  2, 'title': 'Matched to true Pion'},
282  {'name': 'InDetTracksTRTNotMatchedhighpT', 'color': kCyan +
283  2, 'title': 'Not matched to truth'}
284 ]
285 
286 
287 def get_key_names(file, directory=""):
288  """
289  Function to get the key elements name from a given directory
290  :param file: TFile
291  :param directory: Directory
292  :return:
293  """
294  file.cd(directory)
295  return [key.GetName() for key in gDirectory.GetListOfKeys()]
296 
297 
298 def make_comparison_plots(type, f_base, f_nightly, result_file):
299  """
300 
301  :param type: electron or gamma
302  :param f_base: TFile with the baseline plots
303  :param f_nightly: TFile with the nightly plots
304  :param result_file: TFile with the resulting comparison
305  """
306  comparison_list = (
307  photon_comparison_list if type == 'gamma'
308  else electron_comparison_list)
309  for folder in comparison_list:
310  for histo in get_key_names(f_nightly, folder['name']):
311  h_base = f_base.Get(folder['name'] + '/' + histo)
312  h_nightly = f_nightly.Get(folder['name'] + '/' + histo)
313  if not h_base or not h_nightly:
314  print(histo,' is missing in one of the files ',h_base,h_nightly)
315  continue
316  if h_base.GetEntries() == 0 or h_nightly.GetEntries() == 0:
317  continue
318  make_ratio_plot(h_base, h_nightly, folder['title'], result_file)
319 
320 
321 def makeIQEPlots(inHist, name):
322  outHist = inHist.QuantilesX(0.75, "EResolution_IQE_mu")
323  outHist.GetXaxis().SetTitle("<#mu>")
324  outHist.GetYaxis().SetTitle("IQE")
325  outHist25 = inHist.QuantilesX(0.25, "EResolutio_IQE_mu_25")
326  outHist.Add(outHist25, -1)
327  outHist.Scale(1/1.349)
328 
329  return outHist.Clone(inHist.GetName() + "_" + name)
330 
331 
332 def make_profile_plots(f_base, f_nightly, result_file, particle_type):
333 
334  cluster_list_to_loop = cluster_list
335 
336  if particle_type == "gamma":
337  cluster_list_to_loop = cluster_list + cluster_list_photon
338 
339  for i, folder in enumerate(cluster_list_to_loop):
340  for histo in get_key_names(f_nightly, folder['name']):
341  if '2D' not in histo and not 'profile' in histo:
342  continue
343  h_base = f_base.Get(folder['name'] + '/' + histo)
344  h_nightly = f_nightly.Get(folder['name'] + '/' + histo)
345  if not h_base or not h_nightly:
346  print(histo,' is missing in one of the files ',h_base,h_nightly)
347  continue
348  if h_base.GetEntries() == 0 or h_nightly.GetEntries() == 0:
349  continue
350  if 'mu' in histo:
351  h_base = makeIQEPlots(h_base, 'IQE')
352  h_nightly = makeIQEPlots(h_nightly, 'IQE')
353  y_axis_label = 'IQE'
354  else:
355  h_base.SetDirectory(0)
356  h_nightly.SetDirectory(0)
357  y_axis_label = "Mean %s" % (h_base.GetTitle())
358  h_base.SetTitle("")
359  make_ratio_plot(h_base, h_nightly,
360  folder['title'], result_file, y_axis_label)
361 
362 
363 def make_conversion_plot(f_base, f_nightly, result_file):
364  """
365  This function creates conversion plots to study reco vs true
366  converion radius for the various conversion categoried
367  """
368  for histo in get_key_names(f_nightly, 'truthConvRecoConv2Si'):
369  variable_name = histo.split("_", 1)[1]
370 
371  if variable_name != "convRadiusTrueVsReco":
372  continue
373 
374  c1 = TCanvas()
375 
376  leg = TLegend(0.1, 0.75, 0.9, 0.9)
377  leg.SetNColumns(2)
378 
379  leg2 = TLegend(0.5, 0.7, 0.9, 0.75)
380  leg2.SetNColumns(2)
381 
382  for i, folder in enumerate(photon_conversion_list):
383 
384  baseline = f_base.Get(
385  folder['name'] + '/' + folder['name'] + "_" + variable_name)
386  baseline.SetDirectory(0)
387  nightly = f_nightly.Get(
388  folder['name'] + '/' + folder['name'] + "_" + variable_name)
389  nightly.SetDirectory(0)
390 
391  if baseline.Integral() != 0:
392  baseline.Scale(1/baseline.Integral())
393  if nightly.Integral() != 0:
394  nightly.Scale(1/nightly.Integral())
395 
396  baseline.SetMinimum(
397  min(baseline.GetMinimum(), baseline.GetMinimum()) * 0.7)
398  baseline.SetMaximum(
399  max(baseline.GetMaximum(), baseline.GetMaximum()) * 1.4)
400 
401  baseline.GetXaxis().SetTitle(
402  "R^{reco}_{conv. vtx} - R^{true}_{conv. vtx} [mm]")
403  baseline.GetYaxis().SetTitle("normalized to unity")
404 
405  baseline.SetLineColor(folder['color'])
406  nightly.SetLineColor(folder['color'])
407  baseline.SetMarkerColor(folder['color'])
408  nightly.SetMarkerColor(folder['color'])
409 
410  baseline.SetMarkerStyle(1)
411  nightly.SetMarkerStyle(20)
412 
413  leg.AddEntry(nightly, folder['title'], "p")
414 
415  if i == 0:
416  baseline.Draw("hist ")
417 
418  baselineDummy = baseline.Clone()
419  baselineDummy.SetLineColor(kGray+3)
420  baselineDummy.SetMarkerColor(kGray+3)
421  nightlyDummy = nightly.Clone()
422  nightlyDummy.SetLineColor(kGray+3)
423  nightlyDummy.SetMarkerColor(kGray+3)
424  leg2.AddEntry(baselineDummy, "Baseline", "l")
425  leg2.AddEntry(nightlyDummy, "Nightly", "p")
426  else:
427  baseline.Draw("same hist")
428 
429  nightly.Draw("p same")
430 
431  leg.Draw()
432  leg2.Draw()
433 
434  c1.Update()
435 
436  result_file.cd()
437 
438  c1.SaveAs("ConversionRadiusTrueVsReco.png")
439 
440  c1.Write("ConversionRadiusTrueVsReco")
441 
442 
444  f_base, f_nightly, result_file,
445  example_folder, folder_list, plot_name,
446  axis_title, ymin, ymax, normalize=False):
447  """
448  This functions created a photon validation plot with efficiencies
449  and fractions
450 
451  :param f_base TFile with the baseline histograms:
452  :param f_nightly TFile with the nightly histograms:
453  """
454  for histo in get_key_names(f_nightly, example_folder):
455 
456  variable_name = histo.split("_", 1)[1]
457 
458  c1 = TCanvas()
459 
460  leg = TLegend(0.1, 0.75, 0.9, 0.9)
461  leg.SetNColumns(2)
462 
463  leg2 = TLegend(0.5, 0.7, 0.9, 0.75)
464  leg2.SetNColumns(2)
465 
466  for i, folder in enumerate(folder_list):
467 
468  baseline = f_base.Get(
469  folder['name'] + '/' + folder['name'] + "_" + variable_name)
470  baseline.SetDirectory(0)
471  nightly = f_nightly.Get(
472  folder['name'] + '/' + folder['name'] + "_" + variable_name)
473  nightly.SetDirectory(0)
474 
475  if normalize and 'vs' not in variable_name:
476  if baseline.Integral() != 0:
477  baseline.Scale(1/baseline.Integral())
478  if nightly.Integral() != 0:
479  nightly.Scale(1/nightly.Integral())
480 
481  baseline.SetMinimum(ymin)
482  baseline.SetMaximum(ymax)
483 
484  baseline.GetYaxis().SetTitle(axis_title)
485 
486  baseline.SetLineColor(folder['color'])
487  nightly.SetLineColor(folder['color'])
488  baseline.SetMarkerColor(folder['color'])
489  nightly.SetMarkerColor(folder['color'])
490 
491  baseline.SetMarkerStyle(1)
492  nightly.SetMarkerStyle(20)
493 
494  leg.AddEntry(nightly, folder['title'], "p")
495 
496  if i == 0:
497  baseline.Draw("hist ")
498 
499  baselineDummy = baseline.Clone()
500  baselineDummy.SetLineColor(kGray+3)
501  baselineDummy.SetMarkerColor(kGray+3)
502  nightlyDummy = nightly.Clone()
503  nightlyDummy.SetLineColor(kGray+3)
504  nightlyDummy.SetMarkerColor(kGray+3)
505  leg2.AddEntry(baselineDummy, "Baseline", "l")
506  leg2.AddEntry(nightlyDummy, "Nightly", "p")
507  else:
508  baseline.Draw("same hist")
509 
510  nightly.Draw("p same")
511 
512  leg.Draw()
513  leg2.Draw()
514 
515  c1.Update()
516 
517  result_file.cd()
518 
519  c1.SaveAs(plot_name + "_" + variable_name + ".png")
520 
521  c1.Write(plot_name + "_" + variable_name)
522 
523 
524 def make_ratio_plot(h_base, h_nightly, name, result_file, y_axis_label=None):
525  """
526 
527  :param h_base: Baseline histogram
528  :param h_nightly: Nightly histogram
529  :param name: Human-readable name of the histogram
530  :param result_file: TFile where the output is saved
531  :param y_axis_label: Y axis label is case is needed
532  (fraction vs efficiency)
533  """
534  histogram_name = h_nightly.GetName()
535 
536  type_name = histogram_name.split("_", 1)[0]
537  variable_name = histogram_name.split("_", 1)[1]
538 
539  c1 = TCanvas()
540 
541  main_pad = TPad("main_pad", "top", 0.00, 0.25, 1.00, 1.00)
542  main_pad.SetLeftMargin(0.12)
543  main_pad.SetRightMargin(0.04)
544  main_pad.SetTopMargin(0.02)
545  main_pad.SetBottomMargin(0.02)
546  main_pad.SetTicky(0)
547  main_pad.SetTickx(0)
548  main_pad.Draw()
549 
550  ratio_pad = TPad("ratio_pad", "bottom", 0.00, 0.00, 1.00, 0.25)
551  ratio_pad.SetLeftMargin(0.12)
552  ratio_pad.SetRightMargin(0.04)
553  ratio_pad.SetTopMargin(0.03)
554  ratio_pad.SetTickx(0)
555  ratio_pad.SetBottomMargin(0.36)
556  ratio_pad.Draw()
557 
558  h_base.SetLineColor(4)
559  h_base.SetLineWidth(2)
560 
561  h_nightly.SetMarkerStyle(8)
562  h_nightly.SetMarkerSize(0.5)
563 
564  main_pad.cd()
565 
566  if y_axis_label is not None:
567  h_base.GetYaxis().SetTitle(y_axis_label)
568  h_base.GetYaxis().SetTitle(y_axis_label)
569 
570  if '2D' not in variable_name or 'profile' in variable_name:
571  h_base.Draw()
572 
573  h_nightly.Draw(
574  "same p" if '2D' not in variable_name or 'profile' in variable_name
575  else 'colz')
576 
577  c1.Update()
578 
579  h_base.GetXaxis().SetLabelSize(0)
580  h_base.GetXaxis().SetLabelOffset(999)
581 
582  h_base.SetMinimum(min(h_base.GetMinimum(), h_nightly.GetMinimum()) * 0.7)
583  h_base.SetMaximum(max(h_base.GetMaximum(), h_nightly.GetMaximum()) * 1.3)
584 
585  leg = TLegend(0.4, 0.88, 0.9, 0.95)
586  leg.SetHeader(name, "C")
587  leg.SetNColumns(2)
588  leg.SetFillStyle(0)
589  leg.SetBorderSize(0)
590  leg.AddEntry(h_base, "Baseline", "l")
591  leg.AddEntry(h_nightly, "Nightly", "p")
592  leg.Draw()
593 
594  c1.Update()
595 
596  ratio_pad.cd()
597 
598  h1clone = h_nightly.Clone()
599  h1clone.Sumw2()
600  h1clone.SetStats(0)
601  h1clone.Divide(h_base)
602  h1clone.SetMarkerColor(1)
603  h1clone.SetMarkerStyle(20)
604  h1clone.GetYaxis().SetRangeUser(0.95, 1.05)
605  gStyle.SetOptStat(0)
606  h1clone.GetXaxis().SetLabelSize(0.10)
607  h1clone.GetXaxis().SetTitleSize(0.17)
608  h1clone.GetYaxis().SetLabelSize(0.10)
609  h1clone.GetYaxis().SetTitle("Ratio")
610  h1clone.GetYaxis().CenterTitle(1)
611  h1clone.GetYaxis().SetTitleSize(0.15)
612  h1clone.GetYaxis().SetTitleOffset(0.3)
613  h1clone.GetYaxis().SetNdivisions(505)
614 
615  h1clone.Draw("hist")
616 
617  c1.Update()
618 
619  result_file.cd()
620 
621  c1.SaveAs(type_name + '_' + variable_name + ".png")
622 
623  c1.Write(type_name + '_' + variable_name)
624 
625 
626 if __name__ == '__main__':
627 
628  gROOT.SetBatch(kTRUE)
629  gStyle.SetOptStat(0)
630 
631  baseline_file = TFile(sys.argv[1])
632  nightly_file = TFile(sys.argv[2])
633  particle_type = sys.argv[3] # it can be 'electron' or 'gamma'
634 
635  output_file = TFile("BN_ComparisonPlots_" +
636  particle_type + ".hist.root", "RECREATE")
637 
638  if particle_type == 'gamma':
639 
641  baseline_file,
642  nightly_file,
643  output_file, 'truthPhotonConvRecoConvEfficiency',
644  photon_fraction_list, 'ConvertionEff_TrueConv',
645  "Efficiency and fraction", 0., 1.3)
647  baseline_file, nightly_file,
648  output_file, 'truthPhotonUnconvRecoConvEfficiency',
649  photonfake_fraction_list, 'ConvertionEff_TrueUnconv',
650  "Efficiency and fraction", 0., 0.2)
652  baseline_file, nightly_file,
653  output_file, 'truthPhotonRecoPhotonEfficiency',
654  photon_efficiency_list, 'PhotonEff',
655  "Efficiency", 0.8, 1.15)
657  baseline_file, nightly_file, output_file,
658  'InDetTracks', photon_track_list, 'Track',
659  "Tracks", 0., 1.3, True)
661  baseline_file, nightly_file, output_file,
662  'InDetTracksTRT',
663  photon_trackTRT_list, 'TrackTRT',
664  "Tracks", 0., 1.3, True)
666  baseline_file, nightly_file,
667  output_file, 'InDetTrackshighpT',
668  photon_trackhighpT_list, 'TrackhighpT',
669  "Tracks", 0., 1.3, True)
671  baseline_file, nightly_file,
672  output_file, 'InDetTracksTRThighpT',
673  photon_trackTRThighpT_list, 'TrackTRThighpT',
674  "Tracks", 0., 1.3, True)
675  make_conversion_plot(baseline_file, nightly_file, output_file)
676 
677  make_comparison_plots(particle_type, baseline_file,
678  nightly_file, output_file)
679 
680  make_profile_plots(baseline_file, nightly_file, output_file, particle_type)
EgammaARTmonitoring_plotsMaker.make_conversion_plot
def make_conversion_plot(f_base, f_nightly, result_file)
Definition: EgammaARTmonitoring_plotsMaker.py:363
max
#define max(a, b)
Definition: cfImp.cxx:41
EgammaARTmonitoring_plotsMaker.make_profile_plots
def make_profile_plots(f_base, f_nightly, result_file, particle_type)
Definition: EgammaARTmonitoring_plotsMaker.py:332
EgammaARTmonitoring_plotsMaker.make_comparison_plots
def make_comparison_plots(type, f_base, f_nightly, result_file)
Definition: EgammaARTmonitoring_plotsMaker.py:298
EgammaARTmonitoring_plotsMaker.makeIQEPlots
def makeIQEPlots(inHist, name)
Definition: EgammaARTmonitoring_plotsMaker.py:321
min
#define min(a, b)
Definition: cfImp.cxx:40
EgammaARTmonitoring_plotsMaker.make_ratio_plot
def make_ratio_plot(h_base, h_nightly, name, result_file, y_axis_label=None)
Definition: EgammaARTmonitoring_plotsMaker.py:524
Muon::print
std::string print(const MuPatSegment &)
Definition: MuonTrackSteering.cxx:28
EgammaARTmonitoring_plotsMaker.make_photon_fraction_plot
def make_photon_fraction_plot(f_base, f_nightly, result_file, example_folder, folder_list, plot_name, axis_title, ymin, ymax, normalize=False)
Definition: EgammaARTmonitoring_plotsMaker.py:443
EgammaARTmonitoring_plotsMaker.get_key_names
def get_key_names(file, directory="")
Definition: EgammaARTmonitoring_plotsMaker.py:287