Update for D0 jets

machine_learning_hep/analysis/analyzer_jets.py

@@ -1,5 +1,5 @@
 #############################################################################
-##  © Copyright CERN 0238. All rights not expressly granted are reserved.  ##
+##  © Copyright CERN 2023. All rights not expressly granted are reserved.  ##
 ##                                                                         ##
 ## This program is free software: you can redistribute it and/or modify it ##
 ##  under the terms of the GNU General Public License as published by the  ##
@@ -14,11 +14,11 @@
 
 import os
 import munch # pylint: disable=import-error, no-name-in-module
-from ROOT import TFile # pylint: disable=import-error, no-name-in-module
+from ROOT import TFile, TCanvas # pylint: disable=import-error, no-name-in-module
 
 from machine_learning_hep.analysis.analyzer import Analyzer
 
-class AnalyzerJets(Analyzer):
+class AnalyzerJets(Analyzer): # pylint: disable=too-many-instance-attributes
     species = "analyzer"
 
     def __init__(self, datap, case, typean, period):
@@ -46,12 +46,38 @@ def __init__(self, datap, case, typean, period):
         self.n_fileresp = datap["files_names"]["respfilename"]
         self.n_fileresp = os.path.join(self.d_resultsallpmc_proc, self.n_fileresp)
 
-    def qa(self): # pylint: disable=too-many-branches, too-many-locals
+    def qa(self): # pylint: disable=too-many-branches, too-many-locals, invalid-name
         self.logger.info("Running D0 jet qa")
 
+        print(self.n_filemass)
         with TFile(self.n_filemass) as rfile:
             histonorm = rfile.Get("histonorm")
             if not histonorm:
                 self.logger.critical('histonorm not found')
             p_nevents = histonorm.GetBinContent(1)
-            self.logger.debug(f'Number of selected event: {p_nevents}')
+            self.logger.debug('Number of selected event: %d', p_nevents)
+
+            for ipt in range(7):
+                c = TCanvas("Candidate mass")
+                h_invmass = rfile.Get(f'hmass_{ipt}')
+                if not h_invmass:
+                    self.logger.critical('hmass not found')
+                h_invmass.Print()
+                h_invmass.Draw()
+                c.SaveAs(f'hmass_{ipt}.png')
+
+                c = TCanvas("Candidate pt")
+                h_candpt = rfile.Get(f'hcandpt_{ipt}')
+                if not h_candpt:
+                    self.logger.critical('hcandpt not found')
+                h_candpt.Print()
+                h_candpt.Draw()
+                c.SaveAs(f'hcandpt_{ipt}.png')
+
+                c = TCanvas("Jet pt")
+                h_jetpt = rfile.Get(f'hjetpt_{ipt}')
+                if not h_jetpt:
+                    self.logger.critical('hjetpt not found')
+                h_jetpt.Print()
+                h_jetpt.Draw()
+                c.SaveAs(f'hjetpt_{ipt}.png')

machine_learning_hep/processer_jet.py

@@ -70,3 +70,15 @@ def process_histomass_single(self, index):
                     self.p_num_bins, self.p_mass_fit_lim[0], self.p_mass_fit_lim[1])
                 fill_hist(h_invmass_all, df.fM)
                 h_invmass_all.Write()
+
+                h_candpt_all = TH1F(
+                    f'hcandpt_{ipt}', "",
+                    self.p_num_bins, 0., 50.)
+                fill_hist(h_candpt_all, df.fPt)
+                h_candpt_all.Write()
+
+                h_jetpt_all = TH1F(
+                    f'hjetpt_{ipt}', "",
+                    self.p_num_bins, 0., 50.)
+                fill_hist(h_jetpt_all, df.fJetPt)
+                h_jetpt_all.Write()