Separation update

Adding more separation plots which provide in-depth information about inter-sample separation power.

In addition a full table with all information is created.

scripts/HelperModules/Plotting.py

@@ -85,11 +85,13 @@ def Separation2D(df_X, output_path, basename, ALabel="", CMLabel="", MyLabel="")
     df_X = df_X[df_X["Sample_Type"]!="Data"]
     N = np.unique(df_X["Sample_Name"].values)
     norm = {Sample_Name: np.sum(df_X[df_X["Sample_Name"]==Sample_Name]["Weight"].values) for Sample_Name in N}
+    Separation_dict = {}
     for var in df_X.columns:
-        ConverterMessage("Plotting Separation for "+var)
-        Canvas = TCanvas("c1"+var,"c1",800,600)
         if(var in ["eventNumber","Weight","Label","Sample_Name","Sample_Type"]):
             continue
+        Separation_dict[var]={}
+        Canvas = TCanvas("c1"+var,"c1",800,600)
+        ConverterMessage("Plotting Separation for "+var)
         binning = Hists.get_Binning(var)
         if(len(binning)==2):
             Hist2D = TH2D(var+"_hist","",binning[0],binning[1],len(N),0,len(N))
@@ -108,15 +110,54 @@ def Separation2D(df_X, output_path, basename, ALabel="", CMLabel="", MyLabel="")
         Hist2D.GetZaxis().SetTitle("Percentage of Events")
         if(ALabel.lower()!="none"):
             ATLASLabel(0.2,0.85, ALabel)
-        CustomLabel(0.2,0.8, CMLabel)
+        CustomLabel(0.2,0.80, CMLabel)
         if(MyLabel!=""):
             CustomLabel(0.2,0.75, MyLabel)
         Canvas.SaveAs(ensure_trailing_slash(output_path)+basename+"_"+var+".pdf")
         Canvas.SaveAs(ensure_trailing_slash(output_path)+basename+"_"+var+".png")
         Canvas.SaveAs(ensure_trailing_slash(output_path)+basename+"_"+var+".eps")
+        Canvas.Clear()
+
+        ConverterMessage("Plotting separation summary for "+var)
+        gStyle.SetPaintTextFormat(".2f")
+        Separation_Hist = TH2D("Separation_Hist","",len(N),0,len(N),len(N),0,len(N))
+        for i, Sample_Name_i in enumerate(N):
+            for j, Sample_Name_j in enumerate(N):
+                if(i<=j):
+                    Separation_Hist.SetBinContent(i+1,j+1,0)
+                else:
+                    hist_a = Hist2D.ProjectionX("hist_a",i,i+1)
+                    hist_b = Hist2D.ProjectionX("hist_b",j,j+1)
+                    s = Separation(hist_a, hist_b)*100
+                    Separation_Hist.SetBinContent(i+1,j+1,s)
+                    Separation_dict[var][Sample_Name_i+"_vs_"+Sample_Name_j]=s
+                    gROOT.FindObject("hist_a").Delete()
+                    gROOT.FindObject("hist_b").Delete()
+        Yaxis = Separation_Hist.GetYaxis()
+        Xaxis = Separation_Hist.GetXaxis()
+        for i,Sample_Name in enumerate(N):
+            Yaxis.SetBinLabel(i+1,Sample_Name)
+            Xaxis.SetBinLabel(i+1,Sample_Name)
+        Separation_Hist.Draw("colz text")
+        Separation_Hist.SetMaximum(50)
+        Separation_Hist.SetMinimum(0)
+        if(ALabel.lower()!="none"):
+            ATLASLabel(0.2,0.85, ALabel)
+        CustomLabel(0.2,0.80, CMLabel)
+        if(MyLabel!=""):
+            CustomLabel(0.2,0.75, MyLabel)
+        CustomLabel(0.2,0.70, Hists.get_Label(var).replace("[GeV]",""))
+        Separation_Hist.GetZaxis().SetTitle("S [%]")
+        Canvas.SaveAs(ensure_trailing_slash(output_path)+basename+"_Summary_"+var+".pdf")
+        Canvas.SaveAs(ensure_trailing_slash(output_path)+basename+"_Summary_"+var+".png")
+        Canvas.SaveAs(ensure_trailing_slash(output_path)+basename+"_Summary_"+var+".eps")
+        gROOT.FindObject("Separation_Hist").Delete()
         Canvas.Close()
         del Canvas
 
+    df = pd.DataFrame(data=Separation_dict)
+    df.to_latex(buf=ensure_trailing_slash(output_path)+basename+"_Summary.tex",index=False)
+
 def Stackplot_Binary(Samples, Predictions, N, T, W, output_path, basename, ALabel="", CMLabel="", MyLabel="", LogInt=0, DoSeparation=False):
     """Generate a simple stacked Signal/Background histogram