formatting fix

Luigi Dello Stritto · Luigi Dello Stritto · commit 8094ccb59b19 · 2025-03-21T15:20:30.000+01:00
diff --git a/machine_learning_hep/data/data_run3/database_ml_parameters_LcJet_pp.yml b/machine_learning_hep/data/data_run3/database_ml_parameters_LcJet_pp.yml
@@ -55,7 +55,6 @@ LcJet_pp:
           O2hflccollbase: [fNumContrib, fCentFT0M, fMultZeqNTracksPV]
         extra:
           fIsEventReject: 0
-
       collcnt:
         trees:
           O2collcount: [fReadCounts, fReadCountsWithTVX, fReadCountsWithTVXAndZVertexAndSel8, fReadCountsWithTVXAndZVertexAndSelMC]
@@ -75,7 +74,7 @@ LcJet_pp:
           O2hflcpbase: [fPt, fY, fEta, fPhi, fFlagMcMatchGen, fOriginMcGen]
           O2lccmcpjeto: [fIndexLCCMCPJETCOS, fIndexHFLCPBASES_0, fJetPt, fJetPhi, fJetEta, fJetNConstituents, fJetR]
           O2lccmcpjetmo: [fIndexArrayLCCMCDJETOS_hf, fIndexArrayLCCMCDJETOS_geo, fIndexArrayLCCMCDJETOS_pt]
-          O2lccmcpjetsso: [fEnergyMother, fPtLeading, fPtSubLeading, fTheta, fNSub2DR, fNSub1, fNSub2]
+          O2lccmcpjetsso: [fEnergyMother, fPtLeading, fPtSubLeading, fTheta, fNSub2DR, fNSub1, fNSub2, fPairTheta, fPairPt, fPairEnergy]
         tags:
           isstd: {var: fFlagMcMatchGen, req: [[1], []]}
           ismcsignal: {var: fFlagMcMatchGen, req: [[1], []], abs: true}
@@ -101,15 +100,15 @@ LcJet_pp:
           O2hflcml: [fMlScores]
           O2lccmcdjeto: [fIndexLCCMCDJETCOS, fIndexHFLCBASES_0, fJetPt, fJetPhi, fJetEta, fJetNConstituents, fJetR]
           O2lccmcdjetmo: [fIndexArrayLCCMCPJETOS_hf, fIndexArrayLCCMCPJETOS_geo, fIndexArrayLCCMCPJETOS_pt]
-          O2lccmcdjetsso: [fEnergyMother, fPtLeading, fPtSubLeading, fTheta, fNSub2DR, fNSub1, fNSub2]
+          O2lccmcdjetsso: [fEnergyMother, fPtLeading, fPtSubLeading, fTheta, fNSub2DR, fNSub1, fNSub2, fPairTheta, fPairPt, fPairEnergy]
         tags:
           isstd: {var: fFlagMcMatchRec, req: [[1], []]}
           ismcsignal: {var: fFlagMcMatchRec, req: [[1], []], abs: true}
           ismcbkg: {var: fFlagMcMatchRec, req: [[], [1]], abs: true}
           ismcprompt: {var: fOriginMcRec, req: [[0], []]}
           ismcfd: {var: fOriginMcRec, req: [[1], []]}
         extract_component:
-          #- { var: fMlScores, newvar: mlPromptScore, component: 1 }
+          - {var: fMlScores, newvar: mlPromptScore, component: 1}
           - {var: fMlScores, newvar: mlBkgScore, component: 0}
         filter: "fPt >= 1. and abs(fY) <= 0.8 and abs(fJetEta) < (.9 - (fJetR / 100.))" # TODO: check jet eta cut
         # swap: {cand: fCandidateSelFlag, var_swap: fIsCandidateSwapped, vars: [ismcsignal, ismcprompt, icmcfd]}
@@ -129,9 +128,9 @@ LcJet_pp:
           O2hflcsel: [fCandidateSelFlag]
           O2hflcml: [fMlScores]
           O2lccjeto: [fIndexLCCJETCOS, fIndexHFLCBASES_0, fJetPt, fJetPhi, fJetEta, fJetNConstituents, fJetR]
-          O2lccjetsso: [fIndexLCCJETOS, fEnergyMother, fPtLeading, fPtSubLeading, fTheta, fNSub2DR, fNSub1, fNSub2]
+          O2lccjetsso: [fIndexLCCJETOS, fEnergyMother, fPtLeading, fPtSubLeading, fTheta, fNSub2DR, fNSub1, fNSub2, fPairTheta, fPairPt, fPairEnergy]
         extract_component:
-          #- { var: fMlScores, newvar: mlPromptScore, component: 1 }
+          - {var: fMlScores, newvar: mlPromptScore, component: 1}
           - {var: fMlScores, newvar: mlBkgScore, component: 0}
         filter: "fPt >= 1. and abs(fY) <= 0.8 and abs(fJetEta) < (.9 - (fJetR / 100.))" # TODO: check jet eta cut
 
@@ -358,46 +357,49 @@ LcJet_pp:
       branching_ratio: 6.24e-2 # used
 
       observables:
-        zg:
-          bins_gen_fix: [6, -.1, .5]
-          bins_det_fix: [6, -.1, .5]
-          label: "#it{z}_{g}"
-          label_y: "(1/#it{N}_{jet ch}) d#it{N}/d#it{z}_{g}"
-        nsd:
-          bins_gen_fix: [10, -.5, 9.5]
-          bins_det_fix: [10, -.5, 9.5]
-          label: "#it{n}_{SD}"
-          label_y: "(1/#it{N}_{jet ch}) d#it{N}/d#it{n}_{SD}"
-        rg:
-          bins_gen_fix: [11, -.1, 1.]
-          bins_det_fix: [11, -.1, 1.]
-          label: "#it{R}_{g}"
-          label_y: "(1/#it{N}_{jet ch}) d#it{N}/d#it{R}_{g}"
+        #zg:
+        #  bins_gen_fix: [6, -.1, .5]
+        #  bins_det_fix: [6, -.1, .5]
+        #  label: "#it{z}_{g}"
+        #  label_y: "(1/#it{N}_{jet ch}) d#it{N}/d#it{z}_{g}"
+        #nsd:
+        #  bins_gen_fix: [10, -.5, 9.5]
+        #  bins_det_fix: [10, -.5, 9.5]
+        #  label: "#it{n}_{SD}"
+        #  label_y: "(1/#it{N}_{jet ch}) d#it{N}/d#it{n}_{SD}"
+        #rg:
+        #  bins_gen_fix: [11, -.1, 1.]
+        #  bins_det_fix: [11, -.1, 1.]
+        #  label: "#it{R}_{g}"
+        #  label_y: "(1/#it{N}_{jet ch}) d#it{N}/d#it{R}_{g}"
         zpar:
           # bins_gen_fix: [10, 0., 1.]
           # bins_det_fix: [10, 0., 1.]
           bins_gen_var: [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.]
           bins_det_var: [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.]
           label: "#it{z}_{#parallel}"
           label_y: "(1/#it{N}_{jet ch}) d#it{N}/d#it{z}_{#parallel}"
-        dr:
-          bins_gen_fix: [10, 0., 1.]
-          bins_det_fix: [10, 0., 1.]
-          label: "#Delta#it{r}"
-        lntheta:
-          bins_gen_fix: [10, 0., 5.]
-          bins_det_fix: [10, 0., 5.]
-          label: "#minusln(#it{#theta})"
-          arraycols: [3]
-        lnkt:
-          bins_gen_fix: [10, -8., 2.]
-          bins_det_fix: [10, -8., 2.]
-          label: "ln(#it{k}_{T}/(GeV/#it{c}))"
-          arraycols: [3]
-        lntheta-lnkt:
-          arraycols: [3, 4]
-
-      #n_rebin: [4,4,5,5,6,6,7,7,9,9]
+          #dr:
+          #  bins_gen_fix: [10, 0., 1.]
+          #  bins_det_fix: [10, 0., 1.]
+          #  label: "#Delta#it{r}"
+          #lntheta:
+          #  bins_gen_fix: [10, 0., 5.]
+          #  bins_det_fix: [10, 0., 5.]
+          #  label: "#minusln(#it{#theta})"
+          #  arraycols: [3]
+          #lnkt:
+          #  bins_gen_fix: [10, -8., 2.]
+          #  bins_det_fix: [10, -8., 2.]
+          #  label: "ln(#it{k}_{T}/(GeV/#it{c}))"
+          #  arraycols: [3]
+          #lntheta-lnkt:
+          #  arraycols: [3, 4]
+
+      #n_rebin: [2, 2, 2, 2, 2, 2, 4, 5, 5, 5]
+      n_rebin: [3]
+      mass_fit_lim: [1.9, 2.62] # histogram range of the invariant mass distribution [GeV/c^2]
+      bin_width: 0.001 # bin width of the invariant mass histogram
       pdf_names:
         pdf_sig: "sig"
         pdf_bkg: "bkg"
@@ -410,59 +412,204 @@ LcJet_pp:
 
       mass_roofit:
         - level: mc
-          # per_ptjet: true
-          ptrange: [1., 5.]
+          ptrange: [0., 2.]
+          range: [2.18, 2.39]
+          components:
+            sig:
+              fn: "Gaussian::peak(m[1., 10], mean[2.286, 2.283,2.289], sigma_g1[.007,.007,.012])"
+            wide:
+              fn: 'Gaussian::wide(m, mean, expr("n*sigma_g1", n[1.,5.], sigma_g1))'
+            model:
+              fn: "SUM::sig(f_peak[0.,1.]*peak, wide)"
+        - level: mc
+          ptrange: [2., 3.]
+          range: [2.18, 2.39]
+          components:
+            sig:
+              fn: "Gaussian::peak(m[1., 10], mean[2.286, 2.283,2.289], sigma_g1[.007,.007,.012])"
+            wide:
+              fn: 'Gaussian::wide(m, mean, expr("n*sigma_g1", n[1.,5.], sigma_g1))'
+            model:
+              fn: "SUM::sig(f_peak[0.,1.]*peak, wide)"
+        - level: mc
+          ptrange: [3., 4.]
+          range: [2.18, 2.39]
+          components:
+            sig:
+              fn: "Gaussian::peak(m[1., 10], mean[2.286, 2.283,2.289], sigma_g1[.007,.007,.012])"
+            wide:
+              fn: 'Gaussian::wide(m, mean, expr("n*sigma_g1", n[1.,5.], sigma_g1))'
+            model:
+              fn: "SUM::sig(f_peak[0.,1.]*peak, wide)"
+        - level: mc
+          ptrange: [4., 5.]
+          range: [2.18, 2.39]
+          components:
+            sig:
+              fn: "Gaussian::peak(m[1., 10], mean[2.286, 2.283,2.289], sigma_g1[.007,.007,.012])"
+            wide:
+              fn: 'Gaussian::wide(m, mean, expr("n*sigma_g1", n[1.,5.], sigma_g1))'
+            model:
+              fn: "SUM::sig(f_peak[0.,1.]*peak, wide)"
+        - level: mc
+          ptrange: [5., 6.]
+          range: [2.18, 2.39]
+          components:
+            sig:
+              fn: "Gaussian::peak(m[1., 10], mean[2.286, 2.283,2.289], sigma_g1[.012,.01,.016])"
+            wide:
+              fn: 'Gaussian::wide(m, mean, expr("n*sigma_g1", n[1.,5.], sigma_g1))'
+            model:
+              fn: "SUM::sig(f_peak[0.,1.]*peak, wide)"
+        - level: mc
+          ptrange: [6., 7.]
+          range: [2.17, 2.40]
+          components:
+            sig:
+              fn: "Gaussian::peak(m[1., 10], mean[2.286, 2.283,2.289], sigma_g1[.012,.01,.016])"
+            wide:
+              fn: 'Gaussian::wide(m, mean, expr("n*sigma_g1", n[1.,5.], sigma_g1))'
+            model:
+              fn: "SUM::sig(f_peak[0.,1.]*peak, wide)"
+        - level: mc
+          ptrange: [7., 8.]
           range: [2.10, 2.45]
           components:
             sig:
-              fn: "Gaussian::peak(m[1., 5.], mean[2.282,2.29], sigma_g1[.007,.006,.015])"
+              fn: "Gaussian::peak(m[1., 10], mean[2.2865, 2.283,2.289], sigma_g1[.013,.013,.020])"
             wide:
               fn: 'Gaussian::wide(m, mean, expr("n*sigma_g1", n[1.,5.], sigma_g1))'
             model:
               fn: "SUM::sig(f_peak[0.,1.]*peak, wide)"
         - level: mc
-          # per_ptjet: true
-          ptrange: [5., 30.]
+          ptrange: [8., 10.]
           range: [2.10, 2.45]
           components:
             sig:
-              fn: "Gaussian::peak(m[1., 5.], mean[2.282,2.29], sigma_g1[.01,.008,.030])"
+              fn: "Gaussian::peak(m[1., 10], mean[2.2865, 2.283,2.289], sigma_g1[.013,.013,.020])"
             wide:
               fn: 'Gaussian::wide(m, mean, expr("n*sigma_g1", n[1.,5.], sigma_g1))'
             model:
               fn: "SUM::sig(f_peak[0.,1.]*peak, wide)"
-        - ptrange: [1., 5.]
-          range: [2.16, 2.40]
-          fix_params: ["n", "f_peak"]
-          # per_ptjet: true
+        - level: mc
+          ptrange: [10., 12.]
+          range: [2.10, 2.45]
+          components:
+            sig:
+              fn: "Gaussian::peak(m[1., 10], mean[2.2865, 2.283,2.289], sigma_g1[.013,.013,.020])"
+            wide:
+              fn: 'Gaussian::wide(m, mean, expr("n*sigma_g1", n[1.,5.], sigma_g1))'
+            model:
+              fn: "SUM::sig(f_peak[0.,1.]*peak, wide)"
+        - level: mc
+          ptrange: [12., 16.]
+          range: [2.10, 2.45]
+          components:
+            sig:
+              fn: "Gaussian::peak(m[1., 10], mean[2.2865, 2.283,2.289], sigma_g1[.020,.020,.029])"
+            wide:
+              fn: 'Gaussian::wide(m, mean, expr("n*sigma_g1", n[1.,5.], sigma_g1))'
+            model:
+              fn: "SUM::sig(f_peak[0.,1.]*peak, wide)"
+        - level: mc
+          ptrange: [16., 30.]
+          range: [2.10, 2.47]
+          components:
+            sig:
+              fn: "Gaussian::peak(m[1., 10], mean[2.2865, 2.283,2.289], sigma_g1[.020,.020,.029])"
+            wide:
+              fn: 'Gaussian::wide(m, mean, expr("n*sigma_g1", n[1.,5.], sigma_g1))'
+            model:
+              fn: "SUM::sig(f_peak[0.,1.]*peak, wide)"
+        - ptrange: [0., 2.]
+          range: [2.21, 2.36] #2.216, 2.36
+          fix_params: ['n', 'f_peak']
+          components:
+            #sig:
+            #fn: 'Gaussian::sig(m, mean[2.28,2.29], sigma_g1[.005,.005,.015])'
+            bkg:
+              fn: 'Polynomial::bkg(m, {a1[-0.15, -3, 3], a2[0.1, -3., 3]})'
+            model:
+              fn: 'SUM::sum(f_sig[0.,1.]*sig, bkg)'
+        - ptrange: [2., 3.]
+          range: [2.20, 2.37]
+          fix_params: ['n', 'f_peak']
+          components:
+            #sig:
+            #   fn: 'Gaussian::sig(m, mean[2.28,2.29], sigma_g1[.005,.005,.015])'
+            bkg:
+              fn: 'Polynomial::bkg(m, {a1[-0.15, -3, 3], a2[0.1, -3., 3]})'
+            model:
+              fn: 'SUM::sum(f_sig[0.,1.]*sig, bkg)'
+        - ptrange: [3., 4.]
+          range: [2.18, 2.38]
+          fix_params: ['n', 'f_peak']
+          components:
+            #sig:
+            #fn: 'Gaussian::sig(m, mean[2.28,2.29], sigma_g1[.005,.005,.015])'
+            bkg:
+              fn: 'Polynomial::bkg(m, {a1[-0.15, -3, 3], a2[0.1, -3., 3]})'
+            model:
+              fn: 'SUM::sum(f_sig[0., 1.]*sig, bkg)'
+        - ptrange: [4., 5.]
+          range: [2.19, 2.38]
+          fix_params: ['n', 'f_peak']
+          components:
+            # sig:
+            #   fn: 'Gaussian::sig(m, mean[2.28,2.29], sigma_g1[.005,.005,.015])'
+            bkg:
+              fn: 'Polynomial::bkg(m, {a1[-0.15, -3, 3], a2[0.1, -3., 3]})'
+            model:
+              fn: 'SUM::sum(f_sig[0.,1.]*sig, bkg)'
+        - ptrange: [5., 6.]
+          range: [2.18, 2.39]
+          fix_params: ['n', 'f_peak']
           components:
             # sig:
             #   fn: 'Gaussian::sig(m, mean[2.28,2.29], sigma_g1[.005,.005,.015])'
             bkg:
-              fn: "Polynomial::bkg(m, {a0[0.2, -3, 3], a1[-0.1, -3, 3], a2[0.1, 0.01, 3]})"
+              fn: 'Polynomial::bkg(m, {a1[-0.2 , -3, 3], a2[0., -3, 3.]})'
+            model:
+              fn: 'SUM::sum(f_sig[0.,1.]*sig, bkg)'
+        - ptrange: [6., 7.]
+          range: [2.16, 2.41]
+          fix_params: ['n', 'f_peak']
+          components:
+            # sig:
+            #   fn: 'Gaussian::sig(m, mean[2.28,2.29], sigma_g1[.005,.03])'
+            bkg:
+              fn: 'Polynomial::bkg(m, {a1[-0.2 , -3, 3], a2[0., -3, 3.]})'
+            model:
+              fn: 'SUM::sum(f_sig[0.,1.]*sig, bkg)'
+        - ptrange: [7., 8.]
+          range: [2.16, 2.41]
+          fix_params: ['n', 'f_peak']
+          components:
+            # sig:
+            #   fn: 'Gaussian::sig(m, mean[2.28,2.29], sigma_g1[.005,.03])'
+            bkg:
+              fn: 'Polynomial::bkg(m, {a1[-0.2 , -3, 3], a2[0., -3, 3.]})'
             model:
-              fn: "SUM::sum(f_sig[0.,1.]*sig, bkg)"
-        - ptrange: [5., 8.]
-          range: [2.1, 2.48]
-          fix_params: ["n", "f_peak"]
-          # per_ptjet: true
+              fn: 'SUM::sum(f_sig[0.,1.]*sig, bkg)'
+        - ptrange: [8., 10.]
+          range: [2.1, 2.46]
+          fix_params: ['n', 'f_peak']
           components:
             # sig:
             #   fn: 'Gaussian::sig(m, mean[2.28,2.29], sigma_g1[.005,.03])'
             bkg:
-              fn: "Polynomial::bkg(m, {a0[0.2, -3, 3], a1[0.2 , -3, 3], a2[0.2, -3, 3]})"
+              fn: 'Polynomial::bkg(m, {a1[-0.2 , -3, 3], a2[-0.2, -3, 3.]})'
             model:
-              fn: "SUM::sum(f_sig[0.,1.]*sig, bkg)"
-        - range: [2.05, 2.5]
-          fix_params: ["n", "f_peak"]
-          # per_ptjet: true
+              fn: 'SUM::sum(f_sig[0.,1.]*sig, bkg)'
+        - range: [2.1, 2.47]
+          fix_params: ['n', 'f_peak']
           components:
             # sig:
             #   fn: 'Gaussian::sig(m, mean[2.28,2.29], sigma_g1[.005,.03])'
             bkg:
-              fn: "Polynomial::bkg(m, {a0[0.2, -3, 3], a1[0.2 , -3, 3], a2[0.2, -3, 3]})"
+              fn: 'Polynomial::bkg(m, {a1[-0.2 , -3, 3], a2[-0.2, -3, 3.]})'
             model:
-              fn: "SUM::sum(f_sig[0.,1.]*sig, bkg)"
+              fn: 'SUM::sum(f_sig[0.,1.]*sig, bkg)'
 
       #sidesub_per_ptjet: true
       sidesub:
@@ -478,20 +625,17 @@ LcJet_pp:
         # par_fix: {1: 2.286}
         par_constrain: {1: [2.28, 2.29], 2: [.005, .03]}
         range: [2.08, 2.48]
-      mass_fit_lim: [1.9, 2.62] # histogram range of the invariant mass distribution [GeV/c^2]
-      bin_width: 0.001 # bin width of the invariant mass histogram
-      n_rebin: 3 # number of mass bins to merge
+
       efficiency:
         index_match: fIndexArrayLCCMCPJETOS_hf
         #extra_cols: ["mlPromptScore"]
-        extra_cols: ["mlBkgScore"]
+        extra_cols: ["mlBkgScore", "mlPromptScore"]
         correction_method: run3
 
         #reweight: [[0.86,0.89], [0.89,0.91], [0.90,0.93], [0.92,0.93], [0.93,0.93], [0.94,0.93], [0.95,0.93], [0.95,0.94], [0.95,0.94], [0.95,0.94]] #70-100
         #reweight: [[1.03,1.05], [1.02,1.04], [1.02,1.04], [1.02,1.04], [1.01,1.03], [1.01,1.03], [1.01,1.03], [1.01,1.03], [1.00,1.03], [1.01,1.02]] #20-70
         #reweight: [[1.15,1.16], [1.11,1.13], [1.10,1.10], [1.08,1.10], [1.07,1.10], [1.06,1.09], [1.06,1.08], [1.05,1.08], [1.05,1.08], [1.05,1.08]] #0-20
 
-
       unfolding_iterations: 8 # used, maximum iteration
       unfolding_iterations_sel: 5 # used, selected iteration # systematics
       unfolding_prior_flatness: 0. # ranges from 0. (no flatness) to 1. (flat)
@@ -600,7 +744,9 @@ LcJet_pp:
       # Additional cuts applied before mass histogram is filled
       use_cuts: True
       cuts: ["mlBkgScore < 0.03", "mlBkgScore < 0.04", "mlBkgScore < 0.07", "mlBkgScore < 0.09", "mlBkgScore < 0.11", "mlBkgScore < 0.15", "mlBkgScore < 0.18", "mlBkgScore < 0.25", "mlBkgScore < 0.35", "mlBkgScore < 0.35"] # (sel_an_binmin bins)
+      #mult_cuts: ["fCentFT0M >= 70 and fCentFT0M <= 100", "fCentFT0M >= 70 and fCentFT0M <= 100", "fCentFT0M >= 70 and fCentFT0M <= 100", "fCentFT0M >= 70 and fCentFT0M <= 100", "fCentFT0M >= 70 and fCentFT0M <= 100", "fCentFT0M >= 70 and fCentFT0M <= 100", "fCentFT0M >= 70 and fCentFT0M <= 100", "fCentFT0M >= 70 and fCentFT0M <= 100", "fCentFT0M >= 70 and fCentFT0M <= 100", "fCentFT0M >= 70 and fCentFT0M <= 100"] # (sel_an_binmin bins)
       #mult_cuts: ["fCentFT0M >= 20 and fCentFT0M <= 70", "fCentFT0M >= 20 and fCentFT0M <= 70", "fCentFT0M >= 20 and fCentFT0M <= 70", "fCentFT0M >= 20 and fCentFT0M <= 70", "fCentFT0M >= 20 and fCentFT0M <= 70", "fCentFT0M >= 20 and fCentFT0M <= 70", "fCentFT0M >= 20 and fCentFT0M <= 70", "fCentFT0M >= 20 and fCentFT0M <= 70", "fCentFT0M >= 20 and fCentFT0M <= 70", "fCentFT0M >= 20 and fCentFT0M <= 70"] # (sel_an_binmin bins)
+      #mult_cuts: ["fCentFT0M >= 0 and fCentFT0M <= 20", "fCentFT0M >= 0 and fCentFT0M <= 20", "fCentFT0M >= 0 and fCentFT0M <= 20", "fCentFT0M >= 0 and fCentFT0M <= 20", "fCentFT0M >= 0 and fCentFT0M <= 20", "fCentFT0M >= 0 and fCentFT0M <= 20", "fCentFT0M >= 0 and fCentFT0M <= 20", "fCentFT0M >= 0 and fCentFT0M <= 20", "fCentFT0M >= 0 and fCentFT0M <= 20", "fCentFT0M >= 0 and fCentFT0M <= 20"] # (sel_an_binmin bins)
 
   systematics: # used in machine_learning_hep/analysis/systematics.py
     probvariation:
