make kin plot working

validphys2/src/validphys/commondataparser.py

@@ -761,13 +761,15 @@ def folder(self):
     def cm_energy(self):
         """Return the center of mass energy as GeV if it can be understood from the name
         otherwise return None"""
-        energy_string = self.setname.split("_")[2]
-        if energy_string == "NOTFIXED":
+
+        if "NOTFIXED" in self.setname:
             return None
-        if energy_string.endswith("GEV"):
-            factor = 1.0
-        elif energy_string.endswith("TEV"):
+        if "GEV" in self.setname:
+            factor = 1
+            energy_string = [a for a in self.setname.split("_") if "GEV" in a][0]
+        elif "TEV" in self.setname:
             factor = 1000
+            energy_string = [a for a in self.setname.split("_") if "TEV" in a][0]
         else:
             return None
         return float(energy_string[:-3].replace("P", ".")) * factor

validphys2/src/validphys/process_options.py

@@ -146,20 +146,22 @@ def _jets_xq2map(kin_info):
     # Then compute x, Q2
     pT = kin_info[_Vars.pT]
     ratio = pT / kin_info[_Vars.sqrts]
-    x1 = 2 * ratio * np.exp(kin_info[_Vars.y])
-    x2 = 2 * ratio * np.exp(-kin_info[_Vars.y])
+    rap = kin_info.get_one_of(_Vars.y, _Vars.eta, _Vars.abs_eta)
+    x1 = 2 * ratio * np.exp(rap)
+    x2 = 2 * ratio * np.exp(-rap)
     q2 = pT * pT
     x = np.concatenate((x1, x2))
     return np.clip(x, a_min=None, a_max=1, out=x), np.concatenate((q2, q2))
 
 
 def _dijets_xq2map(kin_info):
     # Here we can have either ystar or ydiff, but in either case we need to do the same
-    ylab = kin_info.get_one_of(_Vars.ystar, _Vars.ydiff)
+    ylab_1 = kin_info.get_one_of(_Vars.ystar, _Vars.ydiff, _Vars.eta_1, _Vars.abs_eta_1)
+    ylab_2 = kin_info.get_one_of(_Vars.ystar, _Vars.ydiff, _Vars.eta_2, _Vars.abs_eta_2)
     # Then compute x, Q2
     ratio = kin_info[_Vars.m_jj] / kin_info[_Vars.sqrts]
-    x1 = ratio * np.exp(ylab)
-    x2 = ratio * np.exp(-ylab)
+    x1 = ratio * np.exp(ylab_1)
+    x2 = ratio * np.exp(-ylab_2)
     q2 = kin_info[_Vars.m_jj] * kin_info[_Vars.m_jj]
     x = np.concatenate((x1, x2))
     return np.clip(x, a_min=None, a_max=1, out=x), np.concatenate((q2, q2))