Update

README.md

@@ -1,67 +1,3 @@
-1. Searching additional two b jets using DNN
-
-First, you need to make pandas arrays to train and evaluate. You can do it using runAna.py in deepAna directory.
-
-In runAna.py, there are several options.
-~~~
-array # If you turn on this option and run, runAna.py make arrays.
-array_test # For test. If you turn on this, runAna.py make only test set.
-array_train # If you turn on this option with array, runAna.py make training input.
-array_syst # If you turn on this option with array, runAna.py make systematic array sets.
-~~~
-
-Run as follows,
-
-~~~
-$ python runAna.py
-~~~
-
-If you make pandas arrays successfully, then you can make model file using model.py
-
-Run as follows,
-
-~~~
-$ python model.py
-~~~
-
-Then, you can make histogram using runAna.py with analysis option.
-~~~
-analysis # If you turn on this option, runAna.py make histogram root files.
-ana_test # For test.
-ana_syst # For systematics
-~~~
-
-Run as follows,
-
-~~~
-$ python runAna.py
-~~~
-
-Then, final root files are made.
-
-2. Differential cross section measurement.
-
-All the codes for measurement are in ttbbDiffXsec directory.
-
-You need to made acceptance distribution first.
-
-~~~
-$ root -l -b -q makeCriteria.C
-~~~
-
-Then, stability, purity and acceptance plots are made in output/post directory.
-And then you can run runUnfold.py. If you run this, you can get unfolding results.
-
-~~~
-$ python runUnfold.py
-~~~
-
-you can control unfolding option in ttbbDiffXsec.C
-
-3. Control plots
-
-If you want to make control plots, you can get this by running runAnalysis.py
-
-
-
-
+1. runAnalysis.py
+2. runPostProcess.py
+3. runUnfold.py

func/cutflow.py

@@ -0,0 +1,121 @@
+import os
+import sys
+
+import ROOT
+
+import saveAndLoadSamples as sls
+
+strTableTemplate = """
+\\begin{table}[htb]
+  \\rotatebox{90}{
+  \\footnotesize
+  \\begin{center}
+	\\caption{%(year)s yields table}
+	\\begin{tabular}{%(nRows)s}
+      \\hline\\hline
+      Sample & Ch0S0 & Ch0S1 & Ch0S2 & Ch1S0 & Ch1S1 & Ch1S2 & Ch2S0 & Ch2S1 & Ch2S2 \\\\
+      \\hline
+%(mcNevts)s
+      \\hline
+%(dataNevts)s
+%(totalMC)s
+%(ratio)s
+      \\hline\\hline
+    \\end{tabular}
+  \\end{center}
+  }
+\\end{table}
+"""
+
+class DrawYieldsTable(sls.LoadSamples):
+    def __init__(self, year, lumi, root_path, output_path, hist_name='h_nJets'):
+        self.year = year
+        self.lumi = float(lumi)
+        self.root_path = root_path
+        self.output_path = output_path
+        self.hist_name = hist_name
+
+        self.data = {}
+        self.samples = {}
+        self.xsecs = {}
+        self.genevt = {}
+
+        if not os.path.exists(self.output_path):
+            os.makedirs(self.output_path)
+
+        self._get_root_files()
+
+    def _get_root_files(self):
+        self.get_sample_info()
+
+        self.data['Muon'] = ROOT.TFile(os.path.join(self.root_path, 'hist_DataSingleMu.root'))
+        self.data['Electron'] = ROOT.TFile(os.path.join(self.root_path, 'hist_DataSingleEG.root'))
+        for sample in self.xsecs.values():
+            self.samples[sample[0]] = ROOT.TFile(os.path.join(self.root_path, 'hist_'+sample[0]+'.root'))
+
+    def draw_yields(self):
+        for ich in range(0,3):
+            steps = [1,3,4,6,7,8,9]
+
+            data_nevts = []
+            data_str = '     Data ' 
+            for istep in steps:
+                if ich == 0:
+                    TH1 = self.data['Muon'].Get(self.hist_name+'_Ch0_S'+str(istep))
+                elif ich == 1:
+                    TH1 = self.data['Electron'].Get(self.hist_name+'_Ch1_S'+str(istep))
+                else:
+                    TH1 = self.data['Muon'].Get(self.hist_name+'_Ch0_S'+str(istep))
+                    TH1temp = self.data['Electron'].Get(self.hist_name+'_Ch1_S'+str(istep))
+                    TH1.Add(TH1temp)
+
+                error = ROOT.Double()
+                nevts = TH1.IntegralAndError(1, TH1.GetNbinsX()+1, error)
+                data_str += '& $%.1f $ ' % (nevts)
+                data_nevts.append(nevts)
+            data_str += '\\\\\n'
+
+            mc_nevts = [0.0 for i in range(len(steps))]
+            mc_error = [0.0 for i in range(len(steps))]
+            mc_dicts = {}
+            for item in self.xsecs.items():
+                sample_name = item[1][0]
+                xsec = item[1][1]
+                sample = self.samples[sample_name]
+                scale = (self.lumi*xsec/self.genevt[sample_name])
+
+                temp = '      %s ' % (sample_name)
+                for idx, istep in enumerate(steps):
+                    TH1 = sample.Get(self.hist_name+'_Ch'+str(ich)+'_S'+str(istep))
+                    TH1.Scale(scale)
+
+                    #nevts = hist_tmp.Integral(0, hist_tmp.GetNbinsX()+1)
+                    error = ROOT.Double()
+                    nevts = TH1.IntegralAndError(1, TH1.GetNbinsX()+1, error)
+
+                    temp += '& $%.1f {\scriptstyle\ \pm\ %.1f}$ ' % (nevts, error)
+                    mc_nevts[idx] += nevts
+                    import math
+                    mc_error[idx] = math.sqrt(pow(mc_error[idx],2) + pow(error,2))
+                temp += '\\\\\n'
+                mc_dicts[item[0]] = temp
+
+            totalMC = '      TotalMC '
+            ratio = '      Ratio '
+            for idx, value in enumerate(mc_nevts):
+                temp = float(data_nevts[idx])/float(value)
+                totalMC += '& $%.1f {\scriptstyle\ \pm\ %.1f}$ ' % (value, mc_error[idx])
+                ratio += ' & $%.2f$ ' % temp
+            totalMC += '\\\\'
+            ratio += '\\\\'
+
+            str_dict = {}
+            str_dict['year'] = str(self.year)
+            str_dict['nRows'] = '|'+'c|'*len(steps)
+            str_dict['mcNevts'] = '\n'.join(value for value in mc_dicts.values())
+            str_dict['dataNevts'] = data_str
+            str_dict['totalMC'] = totalMC
+            str_dict['ratio'] = ratio
+
+            strOut = strTableTemplate % str_dict
+            with open(self.output_path+"/yields"+str(self.year)+'_ch'+str(ich)+".tex", 'w') as f: f.write(strOut)