Fix minor issues

Author: qgp

machine_learning_hep/analysis/analyzer_jets.py

@@ -589,9 +589,6 @@ def fit(self):
                         if iptjet is None:
                             if not fitcfg.get("per_ptjet"):
                                 for jptjet in range(get_nbins(h, 1)):
-                                    self.logger.info(
-                                        "Overwriting roows_ptjet for %s iptjet %s ipt %d", level, jptjet, ipt
-                                    )
                                     self.roows[(jptjet, ipt)] = roo_ws.Clone()
                                     self.roo_ws[(level, jptjet, ipt)] = roo_ws.Clone()
                             if level in ("data", "mc"):
@@ -705,10 +702,7 @@ def _subtract_sideband(self, hist, var, mcordata, ipt):
             self.logger.info("Scaling sidebands in ptjet-%s bins: %s using %s", label, bins_ptjet, fh_sideband)
             hx = project_hist(fh_sideband, (0,), {}) if get_dim(fh_sideband) > 1 else fh_sideband
             for iptjet in bins_ptjet:
-                if iptjet:
-                    n = hx.GetBinContent(iptjet)
-                    self.logger.info("Need to scale in ptjet %i: %g", iptjet, n)
-                    if n <= 0:
+                if iptjet and hx.GetBinContent(iptjet) <= 0:
                         continue
                 rws = self.roo_ws.get((mcordata, iptjet, ipt))
                 if not rws:
@@ -1071,6 +1065,7 @@ def estimate_feeddown(self):
         """Estimate feeddown from legacy Run 2 trees or gen-only simulation"""
         match self.cfg("fd_input", "tree"):
             case "tree":
+                self.logger.info("Reading feeddown information from trees")
                 with TFile(self.cfg("fd_root")) as rfile:
                     powheg_xsection = rfile.Get("fHistXsection")
                     powheg_xsection_scale_factor = powheg_xsection.GetBinContent(1) / powheg_xsection.GetEntries()
@@ -1126,6 +1121,8 @@ def estimate_feeddown(self):
                         if fh := rfile.Get(f"h_mass-ptjet-pthf{label}"):
                             h3_fd_gen_orig[var] = project_hist(fh, list(range(1, get_dim(fh))), {})
                             ensure_sumw2(h3_fd_gen_orig[var])
+                            self._save_hist(project_hist(h3_fd_gen_orig[var], [0, 2], {}), f"fd/h_ptjet-{var}_feeddown_genonly_noeffscaling.png")
+                powheg_xsection_scale_factor = 1. # FIXME: retrieve cross section
 
             case fd_input:
                 self.logger.critical("Invalid feeddown input %s", fd_input)

machine_learning_hep/data/data_run3/database_ml_parameters_D0Jet_pp.yml

@@ -53,7 +53,7 @@ D0Jet_pp:
     read:
       evtorig_mc:
         level: mc
-        index: fIndexHfD0CollBases
+        index: fIndexHfD0McCollBases
         trees:
           O2hfd0collbase: [fNumContrib, fPosZ]
         filter: "abs(fPosZ) < 10."
@@ -227,11 +227,11 @@ D0Jet_pp:
 
       evtorig_data:
         level: data
-        source: evtorig
+        source: colldata
         file: AnalysisResultsEvtOrig.parquet
       evt_data:
         level: data
-        source: evtorig
+        source: colldata
         file: AnalysisResultsEvt.parquet
 
       evtorig_fd:
@@ -446,7 +446,6 @@ D0Jet_pp:
   #region analysis
   analysis:
     anahptspectrum: "D0Kpi" #D0Kpi, DplusKpipi, DstarD0pi, DsKKpi, LctopKpi, LcK0Sp # used in analysis/analyzerdhadrons_mult.py
-    fd_input: "sim"
     fd_method: "Nb" #fc, Nb
     cctype: "pp"
     inputfonllpred: data/fonll/D0DplusDstarPredictions_13TeV_y05_all_300416_BDShapeCorrected.root # used in machine_learning_hep/hf_pt_spectrum.py
@@ -808,6 +807,7 @@ D0Jet_pp:
         use_matched: true
 
       frac_mcana: .2 # fraction of MC sample for the closure
+      fd_input: "sim"
       fd_root: "/data2/vkucera/powheg/trees_powheg_fd_central.root" # systematics
       fd_parquet: "/data2/jklein/powheg/trees_powheg_fd_central.parquet" # systematics