fix signal count

Author: Luigi Dello Stritto

machine_learning_hep/analysis/analyzer_jets.py

@@ -401,7 +401,7 @@ def _roofit_mass(self, level, hist, ipt, pdfnames, param_names, fitcfg, roows=No
         if level == "data":
             mean_sgn = ws.var(self.p_param_names["gauss_mean"])
             sigma_sgn = ws.var(self.p_param_names["gauss_sigma"])
-            (sig, sig_err, bkg, bkg_err, signif, signif_err, s_over_b, s_over_b_err) = calc_signif(
+            (sig, sig_err, _, _, bkg, bkg_err, signif, signif_err, s_over_b, s_over_b_err) = calc_signif(
                 ws, res, pdfnames, param_names, mean_sgn, sigma_sgn
             )
             add_text_info_perf(textInfoLeft, sig, sig_err, bkg, bkg_err, s_over_b, s_over_b_err, signif, signif_err)

machine_learning_hep/analysis/analyzerdhadrons.py

@@ -183,7 +183,7 @@ def _roofit_mass(self, level, hist, ipt, pdfnames, param_names, fitcfg, roows=No
         if level == "data":
             mean_sgn = ws.var(self.p_param_names["gauss_mean"])
             sigma_sgn = ws.var(self.p_param_names["gauss_sigma"])
-            (sig, sig_err, bkg, bkg_err, signif, signif_err, s_over_b, s_over_b_err) = calc_signif(
+            (sig, sig_err, _, _, bkg, bkg_err, signif, signif_err, s_over_b, s_over_b_err) = calc_signif(
                 ws, res, pdfnames, param_names, mean_sgn, sigma_sgn
             )
 
@@ -372,7 +372,7 @@ def fit(self):
                         if level == "data":
                             mean_sgn = roo_ws.var(self.p_param_names["gauss_mean"])
                             sigma_sgn = roo_ws.var(self.p_param_names["gauss_sigma"])
-                            (sig, sig_err, _, _, signif, signif_err, s_over_b, s_over_b_err) = calc_signif(
+                            (sig, sig_err, _, _, _, _, signif, signif_err, s_over_b, s_over_b_err) = calc_signif(
                                 roo_ws, roo_res, self.p_pdfnames, self.p_param_names, mean_sgn, sigma_sgn
                             )
 

machine_learning_hep/analysis/analyzerdhadrons_mult.py

@@ -211,7 +211,7 @@ def _roofit_mass(self, level, hist, ipt, pdfnames, param_names, fitcfg, roows=No
         if level == "data":
             mean_sgn = ws.var(self.p_param_names["gauss_mean"])
             sigma_sgn = ws.var(self.p_param_names["gauss_sigma"])
-            (sig, sig_err, bkg, bkg_err, signif, signif_err, s_over_b, s_over_b_err) = calc_signif(
+            (sig, sig_err, _, _, bkg, bkg_err, signif, signif_err, s_over_b, s_over_b_err) = calc_signif(
                 ws, res, pdfnames, param_names, mean_sgn, sigma_sgn
             )
 
@@ -344,7 +344,9 @@ def fit(self):
                             )
                         h_invmass = rfile.Get("hmass_" + suffix)
                         # Rebin
-                        h_invmass.Rebin(self.p_rebin[ipt])
+                        #rebin = self.p_rebin[ibin2][ipt]
+                        rebin = self.p_rebin[ipt]
+                        h_invmass.Rebin(rebin)
                         if h_invmass.GetEntries() < 100:  # TODO: reconsider criterion
                             self.logger.error(
                                 "Not enough entries to fit for %s, pt bin %d, mult bin %d", level, ipt, ibin2
@@ -427,7 +429,7 @@ def fit(self):
                             if level == "data":
                                 mean_sgn = roo_ws.var(self.p_param_names["gauss_mean"])
                                 sigma_sgn = roo_ws.var(self.p_param_names["gauss_sigma"])
-                                (sig, sig_err, _, _, signif, signif_err, s_over_b, s_over_b_err) = calc_signif(
+                                (sig, sig_err, _, _, _, _, signif, signif_err, s_over_b, s_over_b_err) = calc_signif(
                                     roo_ws, roo_res, self.p_pdfnames, self.p_param_names, mean_sgn, sigma_sgn
                                 )
 
@@ -575,7 +577,7 @@ def efficiency(self):
                 h_sel_fd_sl.SetName("signal_loss_fd_mult%d" % imult)
                 h_sel_fd_sl.Write()
 
-                legslFD.AddEntry(h_sel_fd_sl, legeffstring, "LEP")
+                legslFD.AddEntry(h_sel_fd_sl, legeffFDstring, "LEP")
                 h_sel_fd_sl.GetXaxis().SetTitle("#it{p}_{T} (GeV/#it{c})")
                 h_sel_fd_sl.GetYaxis().SetTitle("Signal loss (feeddown) %s" % (self.p_latexnhadron))
                 h_sel_fd_sl.SetMinimum(0.7)

machine_learning_hep/fitting/roofitter.py

@@ -121,7 +121,24 @@ def fit_mass_new(
             # c.Update()
 
         if level == "data" and USE_EXTMODEL and frame is not None:
-            residuals = frame.residHist("data", "pdf_bkg")
+            tmp_frame = m.frame()
+            dh.plotOn(tmp_frame, ROOT.RooFit.Name("data"))
+
+            if (ws.pdf("refl") and ws.pdf("corr")):
+                model.plotOn(
+                    tmp_frame,
+                    ROOT.RooFit.Components("bkg,refl,corr"),
+                    ROOT.RooFit.Name("bkg_total")
+                )
+            else:
+                model.plotOn(
+                    tmp_frame,
+                    ROOT.RooFit.Components("bkg"),
+                    ROOT.RooFit.Name("bkg_total")
+                )
+
+            residuals = tmp_frame.residHist("data", "bkg_total")
+
             residual_frame = m.frame()
             residual_frame.addPlotable(residuals, "P")
 
@@ -176,6 +193,8 @@ def calc_signif(roows, res, pdfnames, param_names, mean_sgn, sigma_sgn):
     if not USE_EXTMODEL:
         return (0., 0., 0., 0., 0., 0, 0, 0.)
     f_sig = roows.pdf(pdfnames["pdf_sig"])
+
+    # total signal under the fit function
     n_signal = res.floatParsFinal().find("n_signal").getVal()
     sigma_n_signal = res.floatParsFinal().find("n_signal").getError()
 
@@ -197,6 +216,7 @@ def calc_signif(roows, res, pdfnames, param_names, mean_sgn, sigma_sgn):
     signal_integral = f_sig.createIntegral(massvar_set, norm_set, signal_range)
     bkg_integral = f_bkg.createIntegral(massvar_set, norm_set, signal_range)
 
+    # signal under the fit function in 3sigma
     n_signal_signal = signal_integral.getVal() * n_signal
     n_bkg_signal = bkg_integral.getVal() * n_bkg
 
@@ -224,6 +244,8 @@ def calc_signif(roows, res, pdfnames, param_names, mean_sgn, sigma_sgn):
     )
 
     return (
+        n_signal,
+        sigma_n_signal,
         n_signal_signal,
         sigma_n_signal_signal,
         n_bkg_signal,