Minor bug fixes (post muon analysis)

analyzer/modules/baseline.py

@@ -42,13 +42,13 @@ def createSelection(events, analyzer):
     passes_b_dr = med_dr > 1
 
     if "HLT" in events.fields:
-        #hlt = functools.reduce(op.or_, [events.HLT[x] for x in hlt_names])
+        hlt = functools.reduce(op.or_, [events.HLT[x] for x in hlt_names])
+        selection.add('hlt',hlt)
         #passes_hlt = functools.reduce(op.or_, [events.HLT[x] for x in hlt_names])
         #selection.add("hlt", hlt)
-        for n in hlt_names:
-        #hlt = functools.reduce(op.or_, [events.HLT[x] for x in hlt_names])
-            selection.add(f"hlt_{n}", events.HLT[n])
-
+        #for n in hlt_names:
+        #   hlt = functools.reduce(op.or_, [events.HLT[x] for x in hlt_names])
+        #selection.add(f"hlt_{n}", events.HLT[n])
     selection.add("highptjet", passes_highptjet)
     selection.add("jets", passes_jets)
     selection.add("0Lep", passes_0Lep)
@@ -189,9 +189,10 @@ def createCRSelection(events, analyzer):
     # top_two_dr = ak.fill_none(filled_jets[:, 0].delta_r(filled_jets[:, 1]), False)
     hlt_names = analyzer.profile.hlt
     if "HLT" in events.fields:
-        for n in hlt_names:
-        #hlt = functools.reduce(op.or_, [events.HLT[x] for x in hlt_names])
-            selection.add(f"hlt_{n}", events.HLT[n])
+        hlt = functools.reduce(op.or_, [events.HLT[x] for x in hlt_names])
+        # for n in hlt_names:
+        #     selection.add(f"hlt_{n}", events.HLT[n])
+        selection.add('hlt', hlt)
     selection.add("ht1200", (events.HT >= 1200))
     selection.add("highptjet", (ak.fill_none(filled_jets[:, 0].pt > 300, False)))
     selection.add("jets", ((ak.num(good_jets) >= 4) & (ak.num(good_jets) <= 6)))

analyzer/modules/jets.py

@@ -102,13 +102,14 @@ def createJetHistograms(events, analyzer):
             name=f"$\phi$ of jet {i+1}",
             description=f"$\phi$ of jet {i+1}",
         )
-    analyzer.H(f"phi_vs_eta",
-                [makeAxis(50,-5,5,f"$\eta$"),
-                    makeAxis(50,-5,5,f"$\phi$")],
-                [gj.eta, gj.phi],
-                name=f"$\eta$ vs $\phi$ of jet ",
-                description=rf"$\eta$ vs $\phi$ of jet "
-                )
+        analyzer.H(f"phi_{i+1}_vs_eta_{i+1}",
+                    [makeAxis(50,-5,5,f"$\eta_{{{i+1}}}$"),
+                        makeAxis(50,-5,5,f"$\phi_{{{i+1}}}$")],
+                    [gj[:,i].eta,
+                        gj[:,i].phi],
+                    name=f"$\eta$ vs $\phi$ of jet {i+1}",
+                    description=rf"$\eta$ vs $\phi$ of jet {i+1}"
+                   )
 
     masks = {}
     for i, j in list(x for x in it.combinations(range(0, 4), 2) if x[0] != x[1]):
@@ -144,12 +145,12 @@ def createJetHistograms(events, analyzer):
         mask = ak.num(gj, axis=1) > i
         masked_jets = gj[mask]
         htratio = masked_jets[:, i].pt / events.HT[mask]
-        analyzer.H(f"pt_ht_ratio_{i}", 
+        analyzer.H(f"pt_ht_ratio_{i+1}", 
             hist.axis.Regular(50, 0, 1, name="pt_o_ht", label=r"$\frac{p_{T}}{HT}$"),
             htratio,
             mask=mask,
-            name=rf"Ratio of jet {i} $p_T$ to event HT",
-            description=rf"Ratio of jet {i} $p_T$ to event HT",
+            name=rf"Ratio of jet {i+1} $p_T$ to event HT",
+            description=rf"Ratio of jet {i+1} $p_T$ to event HT",
         )
 
     return events, analyzer

analyzer/plotting/high_level_plots.py

@@ -241,5 +241,8 @@ def plot2D(
     if zscorename and hasattr(ax, "cax"):
         cax = ax.cax
         cax.set_ylabel(zscorename)
-
+
+    #uncomment to place box around where the "hole" is in phi v eta for 2018 data.
+    # from matplotlib.patches import Rectangle
+    # ax.add_patch(Rectangle((-3.2,-1.57),1.9,0.7,fill=False,edgecolor='black'))
     return fig

analyzer/plotting/simple_plot.py

@@ -65,22 +65,24 @@ def __init__(
         #self.cut_list_for_plot and self.cut_table_dict are actually what get used in plotting
         #self.cut_list_for_plot is a string to be put on the plot itself and contains shortened names for the cuts.
         #Whereas self.cut_table_dict is a dictionary with keys=dataset names (Ex: Data2018) and values of each cut in plain words.
-        #self.cut_table_dict is plotted below the plots as a table.
+        #self.cut_table_dict is plotted below the plots as a table or individually.
 
         self.cut_list_dict = {}
         for i in results:
             for j in i.results.keys():
                 #list(dict.fromkeys(...)) gets rid of duplicates
                 self.cut_list_dict[j] =list(dict.fromkeys(i.results[j].cut_list))
 
-        cut_map = {"hlt": "", "ht1200": "HT ≥ 1200", "highptjet": "Jet-PT ≥ 300",
+        cut_map = {"hlt": "", "ht1200": "HT ≥ 1200", "highptjet": "Leading Jet-PT ≥ 300",
          "jets": "4 ≤ N-Jets ≤ 6", "0Lep": "0e, 0μ", "0looseb": "0b",
          "2bjet": "Med-b Jets ≥ 2", "1tightbjet": "Tight-b Jets ≥ 1",
          "b_dr": "b-jet ΔR > 1", "bbpt": "b-jet 1+2 > 200"}
 
         self.cut_table_dict = {}
         for dataset in self.cut_list_dict:
             cut_map['hlt'] = ' | '.join(self.sample_manager[dataset].profile.hlt)
+            for i in self.sample_manager[dataset].profile.hlt:
+                cut_map[f'hlt_{i}'] = i
             self.cut_table_dict[dataset] = [cut_map[i] for i in self.cut_list_dict[dataset]]
 
         self.cut_list_for_plot = []