Add residual plots

Author: Luigi Dello Stritto

machine_learning_hep/analysis/analyzer_jets.py

@@ -348,7 +348,7 @@ def _correct_efficiency(self, hist, ipt):
     def _roofit_mass(self, level, hist, ipt, pdfnames, param_names, fitcfg, roows = None, filename = None):
         if fitcfg is None:
             return None, None
-        res, ws, frame = self.fitter.fit_mass_new(hist, pdfnames, fitcfg, level, roows, True)
+        res, ws, frame, residual_frame = self.fitter.fit_mass_new(hist, pdfnames, fitcfg, level, roows, True)
         frame.SetTitle(f'inv. mass for p_{{T}} {self.bins_candpt[ipt]} - {self.bins_candpt[ipt+1]} GeV/c')
         c = TCanvas()
 
@@ -372,6 +372,14 @@ def _roofit_mass(self, level, hist, ipt, pdfnames, param_names, fitcfg, roows =
             self.logger.warning('Invalid fit result for %s', hist.GetName())
             filename = filename.replace('.png', '_invalid.png')
         self._save_canvas(c, filename)
+
+        if level == "data":
+            residual_frame.SetTitle(f'inv. mass for p_{{T}} {self.bins_candpt[ipt]} - {self.bins_candpt[ipt+1]} GeV/c')
+            cres = TCanvas()
+            residual_frame.Draw()
+            filename = filename.replace('.png', '_residual.png')
+            self._save_canvas(cres, filename)
+
         return res, ws
 
 

machine_learning_hep/analysis/analyzerdhadrons.py

@@ -159,7 +159,7 @@ def _save_hist(self, hist, filename, option = ''):
     def _roofit_mass(self, level, hist, ipt, pdfnames, param_names, fitcfg, roows = None, filename = None):
         if fitcfg is None:
             return None, None
-        res, ws, frame = self.fitter.fit_mass_new(hist, pdfnames, fitcfg, level, roows, True)
+        res, ws, frame, residual_frame = self.fitter.fit_mass_new(hist, pdfnames, fitcfg, level, roows, True)
         frame.SetTitle(f'inv. mass for p_{{T}} {self.bins_candpt[ipt]} - {self.bins_candpt[ipt+1]} GeV/c')
         c = TCanvas()
 
@@ -187,6 +187,14 @@ def _roofit_mass(self, level, hist, ipt, pdfnames, param_names, fitcfg, roows =
             # func_tot.Print('v')
             filename = filename.replace('.png', '_invalid.png')
             self._save_canvas(c, filename)
+
+        if level == "data":
+            residual_frame.SetTitle(f'inv. mass for p_{{T}} {self.bins_candpt[ipt]} - {self.bins_candpt[ipt+1]} GeV/c')
+            cres = TCanvas()
+            residual_frame.Draw()
+            filename = filename.replace('.png', '_residual.png')
+            self._save_canvas(cres, filename)
+
         return res, ws
 
 
@@ -320,8 +328,6 @@ def fit(self):
                         roo_res, roo_ws = self._roofit_mass(
                             level, h_invmass, ipt, self.p_pdfnames, self.p_param_names, fitcfg, roows,
                             f'roofit/h_mass_fitted_pthf-{ptrange[0]}-{ptrange[1]}_{level}.png')
-                        # if level == 'mc':
-                        #     roo_ws.Print()
                         self.roo_ws[level][ipt] = roo_ws
                         self.roows[ipt] = roo_ws
                         if roo_res.status() == 0:

machine_learning_hep/analysis/analyzerdhadrons_mult.py

@@ -176,7 +176,7 @@ def _save_hist(self, hist, filename, option = ''):
     def _roofit_mass(self, level, hist, ipt, pdfnames, param_names, fitcfg, roows = None, filename = None):
         if fitcfg is None:
             return None, None
-        res, ws, frame = self.fitter.fit_mass_new(hist, pdfnames, fitcfg, level, roows, True)
+        res, ws, frame, residual_frame = self.fitter.fit_mass_new(hist, pdfnames, fitcfg, level, roows, True)
         frame.SetTitle(f'inv. mass for p_{{T}} {self.bins_candpt[ipt]} - {self.bins_candpt[ipt+1]} GeV/c')
         c = TCanvas()
 
@@ -204,6 +204,14 @@ def _roofit_mass(self, level, hist, ipt, pdfnames, param_names, fitcfg, roows =
             # func_tot.Print('v')
             filename = filename.replace('.png', '_invalid.png')
             self._save_canvas(c, filename)
+
+        if level == "data":
+            residual_frame.SetTitle(f'inv. mass for p_{{T}} {self.bins_candpt[ipt]} - {self.bins_candpt[ipt+1]} GeV/c')
+            cres = TCanvas()
+            residual_frame.Draw()
+            filename = filename.replace('.png', '_residual.png')
+            self._save_canvas(cres, filename)
+
         return res, ws
 
 

machine_learning_hep/fitting/roofitter.py

@@ -61,6 +61,7 @@ def fit_mass_new(self, hist, pdfnames, fit_spec, level, roows = None, plot = Fal
         else:
             res = model.fitTo(dh, Save=True, PrintLevel=-1)
         frame = None
+        residual_frame = None
         if plot:
             c = ROOT.TCanvas()
             c.SetLogy()
@@ -89,7 +90,24 @@ def fit_mass_new(self, hist, pdfnames, fit_spec, level, roows = None, plot = Fal
             #                      ROOT.RooFit.LineStyle(ROOT.ELineStyle.kDashed))
             # c.Modified()
             # c.Update()
-        return (res, ws, frame)
+
+        if level == "data":
+            residuals = frame.residHist()
+            residual_frame = m.frame()
+            residual_frame.addPlotable(residuals, "P")
+
+            n_signal_ext = ROOT.RooRealVar("n_signal_ext", "Expected signal events", n_signal.getVal(), 0, 1e6)
+            signal_pdf_ext = ROOT.RooExtendPdf("signal_pdf_ext", "Extended signal PDF", signal_pdf, n_signal_ext)
+
+            signal_pdf_ext.plotOn(
+                residual_frame,
+                ROOT.RooFit.LineColor(ROOT.kBlue),
+                ROOT.RooFit.Normalization(1.0, ROOT.RooAbsReal.RelativeExpected))
+
+            residual_frame.SetAxisRange(range_m[0], range_m[1], "X")
+            residual_frame.SetYTitle("Residuals")
+
+        return (res, ws, frame, residual_frame)
 
 
     def fit_mass(self, hist, fit_spec, plot = False):