From 0580fed6a2abd805b3d4804d987f929f51bd6559 Mon Sep 17 00:00:00 2001 From: Jonathon Langford <35494417+jonathon-langford@users.noreply.github.com> Date: Tue, 12 Mar 2024 16:36:54 +0000 Subject: [PATCH] Moving parametric exercise inputs into combine (#913) * Moving parametric exercise inputs into combine * Flake8 fixes * flake8 tryagain * Now fixing black --------- Co-authored-by: Jonathon Langford --- data/tutorials/parametric_exercise/config.py | 2 + .../construct_models_bias_study_part4.py | 142 +++ .../construct_models_multipdf_part5.py | 102 ++ .../construct_models_part1.py | 239 +++++ .../construct_models_part6.py | 305 ++++++ .../parametric_exercise/data_part1.root | Bin 0 -> 85616 bytes .../parametric_exercise/data_part6.root | Bin 0 -> 118294 bytes .../parametric_exercise/datacard_part1.txt | 20 + .../datacard_part1_with_norm.txt | 20 + .../parametric_exercise/datacard_part3.txt | 38 + .../datacard_part4_exp.txt | 38 + .../datacard_part4_poly.txt | 38 + .../datacard_part4_pow.txt | 38 + .../parametric_exercise/datacard_part5.txt | 40 + .../datacard_part6_Tag0.txt | 44 + .../datacard_part6_Tag1.txt | 44 + .../parametric_exercise/mc_part1.root | Bin 0 -> 406493 bytes .../parametric_exercise/mc_part3.root | Bin 0 -> 3625790 bytes .../parametric_exercise/mc_part6.root | Bin 0 -> 14478619 bytes .../tutorials/parametric_exercise/part1.ipynb | 615 ++++++++++++ .../tutorials/parametric_exercise/part2.ipynb | 338 +++++++ .../tutorials/parametric_exercise/part3.ipynb | 620 ++++++++++++ .../tutorials/parametric_exercise/part4.ipynb | 332 +++++++ .../tutorials/parametric_exercise/part5.ipynb | 316 +++++++ .../tutorials/parametric_exercise/part6.ipynb | 893 ++++++++++++++++++ .../parametric_exercise/plot_2D_scan.py | 140 +++ .../parametric_exercise/plot_bias_pull.py | 60 ++ .../parametric_exercise/plot_pdfindex.py | 32 + .../parametric_exercise/simple_fits.py | 41 + .../parametric_exercise/systematics.py | 156 +++ docs/tutorial2023/parametric_exercise.md | 9 +- 31 files changed, 4657 insertions(+), 5 deletions(-) create mode 100644 data/tutorials/parametric_exercise/config.py create mode 100644 data/tutorials/parametric_exercise/construct_models_bias_study_part4.py create mode 100644 data/tutorials/parametric_exercise/construct_models_multipdf_part5.py create mode 100644 data/tutorials/parametric_exercise/construct_models_part1.py create mode 100644 data/tutorials/parametric_exercise/construct_models_part6.py create mode 100644 data/tutorials/parametric_exercise/data_part1.root create mode 100644 data/tutorials/parametric_exercise/data_part6.root create mode 100644 data/tutorials/parametric_exercise/datacard_part1.txt create mode 100644 data/tutorials/parametric_exercise/datacard_part1_with_norm.txt create mode 100644 data/tutorials/parametric_exercise/datacard_part3.txt create mode 100644 data/tutorials/parametric_exercise/datacard_part4_exp.txt create mode 100644 data/tutorials/parametric_exercise/datacard_part4_poly.txt create mode 100644 data/tutorials/parametric_exercise/datacard_part4_pow.txt create mode 100644 data/tutorials/parametric_exercise/datacard_part5.txt create mode 100644 data/tutorials/parametric_exercise/datacard_part6_Tag0.txt create mode 100644 data/tutorials/parametric_exercise/datacard_part6_Tag1.txt create mode 100644 data/tutorials/parametric_exercise/mc_part1.root create mode 100644 data/tutorials/parametric_exercise/mc_part3.root create mode 100644 data/tutorials/parametric_exercise/mc_part6.root create mode 100644 data/tutorials/parametric_exercise/part1.ipynb create mode 100644 data/tutorials/parametric_exercise/part2.ipynb create mode 100644 data/tutorials/parametric_exercise/part3.ipynb create mode 100644 data/tutorials/parametric_exercise/part4.ipynb create mode 100644 data/tutorials/parametric_exercise/part5.ipynb create mode 100644 data/tutorials/parametric_exercise/part6.ipynb create mode 100644 data/tutorials/parametric_exercise/plot_2D_scan.py create mode 100644 data/tutorials/parametric_exercise/plot_bias_pull.py create mode 100644 data/tutorials/parametric_exercise/plot_pdfindex.py create mode 100644 data/tutorials/parametric_exercise/simple_fits.py create mode 100644 data/tutorials/parametric_exercise/systematics.py diff --git a/data/tutorials/parametric_exercise/config.py b/data/tutorials/parametric_exercise/config.py new file mode 100644 index 00000000000..6a7eb90bd83 --- /dev/null +++ b/data/tutorials/parametric_exercise/config.py @@ -0,0 +1,2 @@ +# Path to plot directory +plot_dir = "." diff --git a/data/tutorials/parametric_exercise/construct_models_bias_study_part4.py b/data/tutorials/parametric_exercise/construct_models_bias_study_part4.py new file mode 100644 index 00000000000..7e2353f72d8 --- /dev/null +++ b/data/tutorials/parametric_exercise/construct_models_bias_study_part4.py @@ -0,0 +1,142 @@ +import ROOT +from config import plot_dir + +ROOT.gROOT.SetBatch(True) +print("Plotting directory: %s" % plot_dir) + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# # Define mass and weight variables +# mass = ROOT.RooRealVar("CMS_hgg_mass", "CMS_hgg_mass", 125, 100, 180) +# weight = ROOT.RooRealVar("weight","weight",0,0,1) +# +# # Load the data +# f = ROOT.TFile("data_part1.root","r") +# t = f.Get("data_Tag0") +# +# # Convert to RooDataSet +# data = ROOT.RooDataSet("data_Tag0", "data_Tag0", +# t, ROOT.RooArgSet(mass), "", "weight" +# ) +# +# # Define ranges to fit +# n_bins = 80 +# binning = ROOT.RooFit.Binning(n_bins,100,180) +# mass.setRange("loSB", 100, 115 ) +# mass.setRange("hiSB", 135, 180 ) +# mass.setRange("full", 100, 180 ) +# fit_range = "loSB,hiSB" + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# # Define the different background model pdf choices +# # And fit to the data mass sidebands +# # RooExponential +# alpha = ROOT.RooRealVar("alpha", "alpha", -0.05, -0.2, 0 ) +# model_exp_bkg = ROOT.RooExponential("model_exp_bkg_Tag0", +# "model_exp_bkg_Tag0", mass, alpha +# ) +# # Fit model to data sidebands +# model_exp_bkg.fitTo( data, ROOT.RooFit.Range(fit_range), +# ROOT.RooFit.Minimizer("Minuit2","minimize"), +# ROOT.RooFit.SumW2Error(True), ROOT.RooFit.PrintLevel(-1) +# ) +# +# # RooChebychev polynomial: 4th order +# poly_1 = ROOT.RooRealVar("poly_1","T1 of chebychev polynomial", 0.01, -4, 4) +# poly_2 = ROOT.RooRealVar("poly_2","T2 of chebychev polynomial", 0.01, -4, 4) +# poly_3 = ROOT.RooRealVar("poly_3","T3 of chebychev polynomial", 0.01, -4, 4) +# poly_4 = ROOT.RooRealVar("poly_4","T4 of chebychev polynomial", 0.01, -4, 4) +# model_poly_bkg = ROOT.RooChebychev("model_poly_bkg_Tag0", +# "model_poly_bkg_Tag0", mass, +# ROOT.RooArgList(poly_1,poly_2,poly_3,poly_4) +# ) +# # Fit model to data sidebands +# model_poly_bkg.fitTo( data, ROOT.RooFit.Range(fit_range), +# ROOT.RooFit.Minimizer("Minuit2","minimize"), +# ROOT.RooFit.SumW2Error(True), ROOT.RooFit.PrintLevel(-1) +# ) +# +# # Power law function: using RooGenericPdf functionality +# pow_1 = ROOT.RooRealVar("pow_1","Exponent of power law", -3, -10, -0.0001) +# model_pow_bkg = ROOT.RooGenericPdf("model_pow_bkg_Tag0", +# "TMath::Power(@0,@1)", ROOT.RooArgList(mass,pow_1) +# ) +# # Fit model to data sidebands +# model_pow_bkg.fitTo( data, ROOT.RooFit.Range(fit_range), +# ROOT.RooFit.Minimizer("Minuit2","minimize"), +# ROOT.RooFit.SumW2Error(True), ROOT.RooFit.PrintLevel(-1) +# ) + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# # Lets plot the model fit to the data +# can = ROOT.TCanvas() +# plot = mass.frame() +# # We have to be careful with the normalisation as we only fit over sidebands +# data.plotOn( plot, binning, +# ROOT.RooFit.MarkerColor(0), ROOT.RooFit.LineColor(0) +# ) +# model_exp_bkg.plotOn(plot, ROOT.RooFit.NormRange(fit_range), +# ROOT.RooFit.Range("full"), ROOT.RooFit.LineColor(2), +# ROOT.RooFit.Name("Exponential") +# ) +# model_poly_bkg.plotOn(plot, ROOT.RooFit.NormRange(fit_range), +# ROOT.RooFit.Range("full"), ROOT.RooFit.LineColor(3), +# ROOT.RooFit.Name("Polynomial") +# ) +# model_pow_bkg.plotOn(plot, ROOT.RooFit.NormRange(fit_range), +# ROOT.RooFit.Range("full"), ROOT.RooFit.LineColor(4), +# ROOT.RooFit.Name("PowerLaw") +# ) +# data.plotOn( plot, ROOT.RooFit.CutRange(fit_range), binning ) +# plot.Draw() +# +# leg = ROOT.TLegend(0.55,0.6,0.85,0.85) +# leg.AddEntry("Exponential", "Exponential", "L") +# leg.AddEntry("Polynomial", "Chebychev polynomial (4th order)", "L") +# leg.AddEntry("PowerLaw", "Power law", "L") +# leg.Draw("Same") +# +# can.Update() +# can.SaveAs("%s/part4_data_sidebands.png"%plot_dir) + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# # Define normalisation objects +# # Data-driven fit: bkg model to have freely floating yield in final fit +# norm_exp = ROOT.RooRealVar("model_exp_bkg_Tag0_norm", +# "Number of background events in Tag0 (exponential)", +# data.numEntries(), 0, 3*data.numEntries() +# ) +# norm_poly = ROOT.RooRealVar("model_poly_bkg_Tag0_norm", +# "Number of background events in Tag0 (polynomial)", +# data.numEntries(), 0, 3*data.numEntries() +# ) +# norm_pow = ROOT.RooRealVar("model_pow_bkg_Tag0_norm", +# "Number of background events in Tag0 (power law)", +# data.numEntries(), 0, 3*data.numEntries() +# ) +# +# # Also we want parameters of models to be floating in final fit to data +# # Therefore no need to set the shape parameters of the model to be constant + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# # Lets save the data as a RooDataHist with 320 bins between 100 and 180 +# mass.setBins(320) +# data_hist = ROOT.RooDataHist("data_hist_Tag0", "data_hist_Tag0", mass, data ) +# +# # Save the background model and data set to a RooWorkspace +# w_bkg = {} +# for pdf in ['exp','poly','pow']: +# f_out = ROOT.TFile("workspace_bkg_%s.root"%pdf, "RECREATE") +# w_bkg[pdf] = ROOT.RooWorkspace("workspace_bkg","workspace_bkg") +# getattr(w_bkg[pdf], "import")(data_hist) +# if pdf == 'exp': +# getattr(w_bkg[pdf], "import")(norm_exp) +# getattr(w_bkg[pdf], "import")(model_exp_bkg) +# elif pdf == 'poly': +# getattr(w_bkg[pdf], "import")(norm_poly) +# getattr(w_bkg[pdf], "import")(model_poly_bkg) +# elif pdf == 'pow': +# getattr(w_bkg[pdf], "import")(norm_pow) +# getattr(w_bkg[pdf], "import")(model_pow_bkg) +# w_bkg[pdf].Print() +# w_bkg[pdf].Write() +# f_out.Close() diff --git a/data/tutorials/parametric_exercise/construct_models_multipdf_part5.py b/data/tutorials/parametric_exercise/construct_models_multipdf_part5.py new file mode 100644 index 00000000000..b2e0360d62a --- /dev/null +++ b/data/tutorials/parametric_exercise/construct_models_multipdf_part5.py @@ -0,0 +1,102 @@ +import ROOT + +ROOT.gROOT.SetBatch(True) + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# # Define mass and weight variables +# mass = ROOT.RooRealVar("CMS_hgg_mass", "CMS_hgg_mass", 125, 100, 180) +# weight = ROOT.RooRealVar("weight","weight",0,0,1) +# +# # Load the data +# f = ROOT.TFile("data_part1.root","r") +# t = f.Get("data_Tag0") +# +# data = ROOT.RooDataSet("data_Tag0", "data_Tag0", +# t, ROOT.RooArgSet(mass), "", "weight" +# ) +# +# # Define ranges to fit for initial parameter values +# mass.setRange("loSB", 100, 115 ) +# mass.setRange("hiSB", 135, 180 ) +# mass.setRange("full", 100, 180 ) +# fit_range = "loSB,hiSB" + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# # Define the different background model pdf choices +# # And fit to the data mass sidebands +# # RooExponential +# alpha = ROOT.RooRealVar("alpha", "alpha", -0.05, -0.2, 0 ) +# model_exp_bkg = ROOT.RooExponential("model_exp_bkg_Tag0", +# "model_exp_bkg_Tag0", mass, alpha +# ) +# # Fit model to data sidebands +# model_exp_bkg.fitTo(data, ROOT.RooFit.Range(fit_range), +# ROOT.RooFit.Minimizer("Minuit2","minimize"), +# ROOT.RooFit.SumW2Error(True), ROOT.RooFit.PrintLevel(-1) +# ) +# +# # RooChebychev polynomial: 4th order +# poly_1 = ROOT.RooRealVar("poly_1","T1 of chebychev polynomial", 0.01, -4, 4) +# poly_2 = ROOT.RooRealVar("poly_2","T2 of chebychev polynomial", 0.01, -4, 4) +# poly_3 = ROOT.RooRealVar("poly_3","T3 of chebychev polynomial", 0.01, -4, 4) +# poly_4 = ROOT.RooRealVar("poly_4","T4 of chebychev polynomial", 0.01, -4, 4) +# model_poly_bkg = ROOT.RooChebychev("model_poly_bkg_Tag0", +# "model_poly_bkg_Tag0", mass, +# ROOT.RooArgList(poly_1,poly_2,poly_3,poly_4) +# ) +# # Fit model to data sidebands +# model_poly_bkg.fitTo( data, ROOT.RooFit.Range(fit_range), +# ROOT.RooFit.Minimizer("Minuit2","minimize"), +# ROOT.RooFit.SumW2Error(True), ROOT.RooFit.PrintLevel(-1) +# ) +# +# # Power law function: using RooGenericPdf functionality +# pow_1 = ROOT.RooRealVar("pow_1","Exponent of power law", -3, -10, -0.0001) +# model_pow_bkg = ROOT.RooGenericPdf("model_pow_bkg_Tag0", +# "TMath::Power(@0,@1)", ROOT.RooArgList(mass,pow_1) +# ) +# # Fit model to data sidebands +# model_pow_bkg.fitTo( data, ROOT.RooFit.Range(fit_range), +# ROOT.RooFit.Minimizer("Minuit2","minimize"), +# ROOT.RooFit.SumW2Error(True), ROOT.RooFit.PrintLevel(-1) +# ) + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# # Make a RooCategory object: this will control which PDF is "active" +# cat = ROOT.RooCategory("pdfindex_Tag0", +# "Index of Pdf which is active for Tag0" +# ) +# +# # Make a RooArgList of the models +# models = ROOT.RooArgList() +# models.add(model_exp_bkg) +# models.add(model_poly_bkg) +# models.add(model_pow_bkg) +# +# # Build the RooMultiPdf object +# multipdf = ROOT.RooMultiPdf("multipdf_Tag0", +# "MultiPdf for Tag0", cat, models +# ) + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# # Define normalisation object +# # Data-driven fit, want the background model to have a freely floating yield +# norm = ROOT.RooRealVar("multipdf_Tag0_norm", +# "Number of background events in Tag0", +# data.numEntries(), 0, 3*data.numEntries() +# ) +# +# # Lets save the data as a RooDataHist with 320 bins between 100 and 180 +# mass.setBins(320) +# data_hist = ROOT.RooDataHist("data_hist_Tag0", "data_hist_Tag0", mass, data ) +# +# # Save the background model and data set to a RooWorkspace +# f_out = ROOT.TFile("workspace_bkg_multipdf.root", "RECREATE") +# w_bkg = ROOT.RooWorkspace("workspace_bkg","workspace_bkg") +# getattr(w_bkg, "import")(data_hist) +# getattr(w_bkg, "import")(cat) +# getattr(w_bkg, "import")(norm) +# getattr(w_bkg, "import")(multipdf) +# w_bkg.Print() +# w_bkg.Write() +# f_out.Close() diff --git a/data/tutorials/parametric_exercise/construct_models_part1.py b/data/tutorials/parametric_exercise/construct_models_part1.py new file mode 100644 index 00000000000..009388bf676 --- /dev/null +++ b/data/tutorials/parametric_exercise/construct_models_part1.py @@ -0,0 +1,239 @@ +import ROOT +from config import plot_dir + +ROOT.gROOT.SetBatch(True) +print("Plotting directory: %s" % plot_dir) + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# # Signal modelling +# f = ROOT.TFile("mc_part1.root","r") +# # Load TTree +# t = f.Get("ggH_Tag0") +# +# # Define mass and weight variables +# mass = ROOT.RooRealVar("CMS_hgg_mass", "CMS_hgg_mass", 125, 100, 180) +# weight = ROOT.RooRealVar("weight","weight",0,0,1) +# +# # Convert to RooDataSet +# mc = ROOT.RooDataSet("ggH_Tag0","ggH_Tag0", +# t, ROOT.RooArgSet(mass,weight), "", "weight" +# ) +# +# # Lets plot the signal mass distribution +# can = ROOT.TCanvas() +# plot = mass.frame() +# mc.plotOn(plot) +# plot.Draw() +# can.Update() +# can.SaveAs("%s/part1_signal_mass.png"%plot_dir) + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# # Introduce a RooRealVar into the workspace for the Higgs mass +# MH = ROOT.RooRealVar("MH", "MH", 125, 120, 130 ) +# MH.setConstant(True) +# +# # Signal peak width +# sigma = ROOT.RooRealVar("sigma_ggH_Tag0", "sigma_ggH_Tag0", 2, 1, 5) +# +# # Define the Gaussian with mean=MH and width=sigma +# model = ROOT.RooGaussian("model_ggH_Tag0", "model_ggH_Tag0", +# mass, MH, sigma +# ) +# +# # Fit Gaussian to MC events and plot +# model.fitTo(mc,ROOT.RooFit.SumW2Error(True)) +# +# can = ROOT.TCanvas() +# plot = mass.frame() +# mc.plotOn(plot) +# model.plotOn( plot, ROOT.RooFit.LineColor(2) ) +# plot.Draw() +# can.Update() +# can.Draw() +# can.SaveAs("%s/part1_signal_model_v0.png"%plot_dir) + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# # Define a model with a variable mean +# dMH = ROOT.RooRealVar("dMH_ggH_Tag0", "dMH_ggH_Tag0", 0, -1, 1 ) +# mean = ROOT.RooFormulaVar("mean_ggH_Tag0", "mean_ggH_Tag0", +# "(@0+@1)", ROOT.RooArgList(MH,dMH) +# ) +# model = ROOT.RooGaussian( "model_ggH_Tag0", "model_ggH_Tag0", +# mass, mean, sigma +# ) +# +# # Fit the new model with a variable mean +# model.fitTo(mc,ROOT.RooFit.SumW2Error(True),ROOT.RooFit.PrintLevel(-1)) +# +# # Show model for different values of MH +# can = ROOT.TCanvas() +# plot = mass.frame() +# MH.setVal(120) +# model.plotOn( plot, ROOT.RooFit.LineColor(2) ) +# MH.setVal(125) +# model.plotOn( plot, ROOT.RooFit.LineColor(3) ) +# MH.setVal(130) +# model.plotOn( plot, ROOT.RooFit.LineColor(4) ) +# plot.Draw() +# can.Update() +# can.Draw() +# can.SaveAs("%s/part1_signal_model_v1.png"%plot_dir) + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# # Save the model to a workspace +# MH.setVal(125) +# dMH.setConstant(True) +# sigma.setConstant(True) +# f_out = ROOT.TFile("workspace_sig.root", "RECREATE") +# w_sig = ROOT.RooWorkspace("workspace_sig","workspace_sig") +# getattr(w_sig, "import")(model) +# w_sig.Print() +# w_sig.Write() +# f_out.Close() + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# # Define SM cross section and branching fraction values +# xs_ggH = 48.58 #in [pb] +# br_gamgam = 2.7e-3 +# +# # Calculate the efficiency and print output +# sumw = mc.sumEntries() +# eff = sumw/(xs_ggH*br_gamgam) +# print("Efficiency of ggH events landing in Tag0 is: %.2f%%"%(eff*100)) +# +# # Calculate the total yield and print output +# lumi = 138000 +# N = xs_ggH*br_gamgam*eff*lumi +# print("For 138fb^-1, total normalisation of signal is: +# N = xs * br * eff * lumi = %.2f events"%N +# ) + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# # Background modelling +# f = ROOT.TFile("data_part1.root","r") +# t = f.Get("data_Tag0") +# +# # Convert TTree to a RooDataSet +# data = ROOT.RooDataSet("data_Tag0", "data_Tag0", +# t, ROOT.RooArgSet(mass), "", "weight" +# ) +# +# # Define mass sideband ranges on the mass variable +# n_bins = 80 +# binning = ROOT.RooFit.Binning(n_bins,100,180) +# mass.setRange("loSB", 100, 115 ) +# mass.setRange("hiSB", 135, 180 ) +# mass.setRange("full", 100, 180 ) +# fit_range = "loSB,hiSB" +# +# # Plot the data in the mass sidebands +# can = ROOT.TCanvas() +# plot = mass.frame() +# data.plotOn( plot, ROOT.RooFit.CutRange(fit_range), binning ) +# plot.Draw() +# can.Update() +# can.Draw() +# can.SaveAs("%s/part1_data_sidebands.png"%plot_dir) + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# # Define the background model slope parameter +# alpha = ROOT.RooRealVar("alpha", "alpha", -0.05, -0.2, 0 ) +# model_bkg = ROOT.RooExponential("model_bkg_Tag0", +# "model_bkg_Tag0", mass, alpha +# ) +# +# # Fit model to data sidebands +# model_bkg.fitTo( data, ROOT.RooFit.Range(fit_range), +# ROOT.RooFit.PrintLevel(-1) +# ) +# +# # Let's plot the model fit to the data +# can = ROOT.TCanvas() +# plot = mass.frame() +# # We have to be careful with the normalisation as we only fit over sidebands +# # First do an invisible plot of the full data set +# data.plotOn( plot, binning, +# ROOT.RooFit.MarkerColor(0), ROOT.RooFit.LineColor(0) +# ) +# model_bkg.plotOn( plot, ROOT.RooFit.NormRange(fit_range), +# ROOT.RooFit.Range("full"), ROOT.RooFit.LineColor(2) +# ) +# data.plotOn( plot, ROOT.RooFit.CutRange(fit_range), binning ) +# plot.Draw() +# can.Update() +# can.Draw() +# can.SaveAs("%s/part1_bkg_model.png"%plot_dir) + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# # Define background model normalisation term, which is freely floating +# norm = ROOT.RooRealVar("model_bkg_Tag0_norm", +# "Number of background events in Tag0", +# data.numEntries(), 0, 3*data.numEntries() +# ) +# alpha.setConstant(False) + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# # Save the background model and data set to a RooWorkspace +# f_out = ROOT.TFile("workspace_bkg.root", "RECREATE") +# w_bkg = ROOT.RooWorkspace("workspace_bkg","workspace_bkg") +# getattr(w_bkg, "import")(data) +# getattr(w_bkg, "import")(norm) +# getattr(w_bkg, "import")(model_bkg) +# w_bkg.Print() +# w_bkg.Write() +# f_out.Close() + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# # Extension: signal model normalisation object +# # Build signal model normalisation components +# xs_ggH = ROOT.RooRealVar("xs_ggH", "Cross section of ggH in [pb]", 48.58 ) +# br_gamgam = ROOT.RooRealVar("BR_gamgam", +# "Branching ratio of Higgs to gamma gamma", 0.0027 +# ) +# eff_ggH_Tag0 = ROOT.RooRealVar("eff_ggH_Tag0", +# "Efficiency for ggH events to land in Tag0", eff +# ) +# # Set values to be constant +# xs_ggH.setConstant(True) +# br_gamgam.setConstant(True) +# eff_ggH_Tag0.setConstant(True) +# # Define normalisation component as product of these three variables +# norm_sig = ROOT.RooProduct("model_ggH_Tag0_norm", +# "Normalisation term for ggH in Tag 0", +# ROOT.RooArgList(xs_ggH,br_gamgam,eff_ggH_Tag0) +# ) + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# # Build new signal model workspace with signal normalisation term. +# f_out = ROOT.TFile("workspace_sig_with_norm.root", "RECREATE") +# w_sig = ROOT.RooWorkspace("workspace_sig","workspace_sig") +# getattr(w_sig, "import")(model) +# getattr(w_sig, "import")(norm_sig) +# w_sig.Print() +# w_sig.Write() +# f_out.Close() + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# # Extension: binned likelihood model +# f = ROOT.TFile("data_part1.root","r") +# t = f.Get("data_Tag0") +# +# # Convert TTree to a RooDataSet +# data = ROOT.RooDataSet("data_Tag0", "data_Tag0", +# t, ROOT.RooArgSet(mass,weight), "", "weight" +# ) +# +# # Set bin number for mass variables +# mass.setBins(320) +# data_hist = ROOT.RooDataHist("data_hist_Tag0", "data_hist_Tag0", mass, data) +# +# # Save the background model with the RooDataHist instead +# f_out = ROOT.TFile("workspace_bkg_binned.root", "RECREATE") +# w_bkg = ROOT.RooWorkspace("workspace_bkg","workspace_bkg") +# getattr(w_bkg, "import")(data_hist) +# getattr(w_bkg, "import")(norm) +# getattr(w_bkg, "import")(model_bkg) +# w_bkg.Print() +# w_bkg.Write() +# f_out.Close() diff --git a/data/tutorials/parametric_exercise/construct_models_part6.py b/data/tutorials/parametric_exercise/construct_models_part6.py new file mode 100644 index 00000000000..1a818c10fa9 --- /dev/null +++ b/data/tutorials/parametric_exercise/construct_models_part6.py @@ -0,0 +1,305 @@ +import ROOT + +print("Using ROOT version: %s" % ROOT.__version__) + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# # Signal modelling +# # Open ROOT file containing trees for both ggH and VBF +# f = ROOT.TFile("mc_part6.root") +# +# # Define mass and weight variables +# mass = ROOT.RooRealVar("CMS_hgg_mass", "CMS_hgg_mass", 125, 100, 180) +# weight = ROOT.RooRealVar("weight","weight",0,0,1) +# +# # Load the MC as RooDataSets +# mc = {} +# +# procs = ['ggH','VBF'] +# cats = ['Tag0','Tag1'] +# +# systs = ['scale','smear','JEC','photonID'] +# +# for proc in procs: +# for cat in cats: +# key = "%s_%s"%(proc,cat) +# t = f.Get(key) +# mc[key] = ROOT.RooDataSet(key, key, t, +# ROOT.RooArgSet(mass,weight), "", "weight" +# ) +# for syst in systs: +# for direction in ['Up','Down']: +# key_syst = "%s_%s%s01Sigma"%(key,syst,direction) +# t = f.Get(key_syst) +# mc[key_syst] = ROOT.RooDataSet(key_syst, key_syst, +# t, ROOT.RooArgSet(mass,weight), "", "weight" +# ) +# +# # Calculate the variations in mean/sigma for the systematic-varied MC +# # Let's first build the model to fit +# mean = ROOT.RooRealVar("mean", "mean", 125, 124, 126) +# sigma = ROOT.RooRealVar("sigma", "sigma", 2, 1, 3) +# gaus = ROOT.RooGaussian("model", "model", mass, mean, sigma) +# +# mean_values, sigma_values = {}, {} +# for proc in procs: +# for cat in cats: +# key = "%s_%s"%(proc,cat) +# gaus.fitTo(mc[key], ROOT.RooFit.SumW2Error(True) ) +# gaus.fitTo(mc[key], ROOT.RooFit.SumW2Error(True) ) +# mean_values[key] = [mean.getVal(), mean.getError()] +# sigma_values[key] = [sigma.getVal(), sigma.getError()] +# for syst in ['scale','smear']: +# key_up = "%s_%sUp01Sigma"%(key,syst) +# key_down = "%s_%sDown01Sigma"%(key,syst) +# gaus.fitTo(mc[key_up], ROOT.RooFit.SumW2Error(True) ) +# gaus.fitTo(mc[key_up], ROOT.RooFit.SumW2Error(True) ) +# mean_values[key_up] = [mean.getVal(), mean.getError()] +# sigma_values[key_up] = [sigma.getVal(), sigma.getError()] +# gaus.fitTo(mc[key_down], ROOT.RooFit.SumW2Error(True) ) +# gaus.fitTo(mc[key_down], ROOT.RooFit.SumW2Error(True) ) +# mean_values[key_down] = [mean.getVal(), mean.getError()] +# sigma_values[key_down] = [sigma.getVal(), sigma.getError()] +# +# # Store variations to bake into model +# scale_variations, smear_variations = {}, {} +# for proc in procs: +# for cat in cats: +# key = "%s_%s"%(proc,cat) +# # Scale +# syst = "scale" +# key_up = "%s_%sUp01Sigma"%(key,syst) +# key_down = "%s_%sDown01Sigma"%(key,syst) +# scale_variations[key] = 0.5*( +# abs(mean_values[key_up][0]/mean_values[key][0]-1) + +# abs(mean_values[key_down][0]/mean_values[key][0]-1) +# ) +# # Smear +# syst = "smear" +# key_up = "%s_%sUp01Sigma"%(key,syst) +# key_down = "%s_%sDown01Sigma"%(key,syst) +# smear_variations[key] = 0.5*( +# abs(sigma_values[key_up][0]/sigma_values[key][0]-1) + +# abs(sigma_values[key_down][0]/sigma_values[key][0]-1) +# ) +# +# # Now lets construct the signal models to save in the workspace +# MH = ROOT.RooRealVar("MH", "MH", 125, 120, 130 ) +# MH.setConstant(True) +# +# # Dicts to store the shape parameters +# dMH, mean_formula, sigma, sigma_formula = {}, {}, {}, {} +# models = {} +# +# # Also build nuisance parameters for scale and smearing effects +# eta = ROOT.RooRealVar("nuisance_scale", "nuisance_scale", 0, -5, 5) +# eta.setConstant(True) +# chi = ROOT.RooRealVar("nuisance_smear", "nuisance_smear", 0, -5, 5) +# chi.setConstant(True) +# +# for proc in procs: +# for cat in cats: +# key = "%s_%s"%(proc,cat) +# dMH[key] = ROOT.RooRealVar("dMH_%s"%key, "dMH_%s"%key, 0, -2, 2 ) +# mean_formula[key] = ROOT.RooFormulaVar("mean_%s"%key, "mean_%s"%key, +# "(@0+@1)*(1+%.4f*@2)"%scale_variations[key], +# ROOT.RooArgList(MH,dMH[key],eta) +# ) +# +# sigma[key] = ROOT.RooRealVar("sigma_%s_nominal"%key, +# "sigma_%s_nominal"%key, 2, 1, 5 +# ) +# sigma_formula[key] = ROOT.RooFormulaVar("sigma_%s"%key, +# "sigma_%s"%key, "@0*(1+%.4f*@1)"%smear_variations[key], +# ROOT.RooArgList(sigma[key],chi) +# ) +# +# models[key] = ROOT.RooGaussian("model_%s"%key, "model_%s"%key, mass, +# mean_formula[key], sigma_formula[key] +# ) +# +# # Fit model to MC +# models[key].fitTo( mc[key], ROOT.RooFit.SumW2Error(True) ) +# +# # Set shape parameters of model to be constant +# # i.e. fixed in fit to data +# dMH[key].setConstant(True) +# sigma[key].setConstant(True) +# +# # Now let's build the normalisation objects +# +# # From LHCHWG twiki: +# # https://twiki.cern.ch/twiki/bin/view/LHCPhysics/CERNYellowReportPageAt13TeV +# xs = {} +# xs['ggH'] = ROOT.RooRealVar("xs_ggH", "Cross section of ggH in [pb]", 48.58 ) +# xs['ggH'].setConstant(True) +# xs['VBF'] = ROOT.RooRealVar("xs_VBF", "Cross section of VBF in [pb]", 3.782 ) +# xs['VBF'].setConstant(True) +# +# br_gamgam = ROOT.RooRealVar("BR_gamgam", +# "Branching ratio of Higgs to gamma gamma", 2.7e-3 +# ) +# br_gamgam.setConstant(True) +# +# # Calculate selection efficiency for the different proc/cat combinations +# # And define norm objects +# eff, norms = {}, {} +# for proc in procs: +# for cat in cats: +# key = "%s_%s"%(proc,cat) +# sumw = mc[key].sumEntries() +# e = sumw/(xs[proc].getVal()*br_gamgam.getVal()) +# eff[key] = ROOT.RooRealVar("eff_%s"%key, +# "Efficiency for %s events to land in %s"%(proc,cat), e +# ) +# # Set constant +# eff[key].setConstant(True) +# # Define normalisation objects +# norms[key] = ROOT.RooProduct("model_%s_norm"%key, +# "Normalisation term for %s in %s"%(proc,cat), +# ROOT.RooArgList(xs[proc],br_gamgam,eff[key]) +# ) +# +# # Build new signal model workspace with all models +# f_out = ROOT.TFile("workspace_sig_part6.root", "RECREATE") +# w_sig = ROOT.RooWorkspace("workspace_sig","workspace_sig") +# for model in models.values(): getattr(w_sig, "import")(model) +# for norm in norms.values(): getattr(w_sig, "import")(norm) +# w_sig.Print() +# w_sig.Write() +# f_out.Close() + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# # Background model construction +# # Build a RooMultiPdf to fit the background distribution in each category +# mass = ROOT.RooRealVar("CMS_hgg_mass", "CMS_hgg_mass", 125, 100, 180) +# weight = ROOT.RooRealVar("weight","weight",0,0,1) +# +# # Define mass ranges to find initial background model parameter values +# mass.setRange("loSB", 100, 115 ) +# mass.setRange("hiSB", 135, 180 ) +# mass.setRange("full", 100, 180 ) +# fit_range = "loSB,hiSB" +# +# # Define dicts to store the background model objects +# alpha, poly_1, poly_2, poly_3, poly_4, pow_1 = {}, {}, {}, {}, {}, {} +# pdfs = {} +# index = {} +# models_bkg = {} +# multipdfs = {} +# norms_bkg = {} +# +# # Open the file +# f = ROOT.TFile("data_part6.root","r") +# +# data = {} +# for cat in cats: +# +# # Build a RooDataSet for the category +# t = f.Get("data_%s"%cat) +# data[cat] = ROOT.RooDataSet("data_%s"%cat, "data_%s"%cat, +# t, ROOT.RooArgSet(mass), "", "weight" +# ) +# +# alpha[cat] = ROOT.RooRealVar("alpha_%s"%cat, "alpha_%s"%cat, +# -0.05, -0.2, 0 +# ) +# pdfs["%s_exp"%cat] = ROOT.RooExponential("model_bkg_exp_%s"%cat, +# "model_bkg_exp_%s"%cat, mass, alpha[cat] +# ) +# pdfs["%s_exp"%cat].fitTo( data[cat], ROOT.RooFit.Range(fit_range), +# ROOT.RooFit.Minimizer("Minuit2","minimize"), +# ROOT.RooFit.SumW2Error(True) +# ) +# +# poly_1[cat] = ROOT.RooRealVar("poly_1_%s"%cat,"poly_1_%s"%cat, +# 0.01, -4, 4 +# ) +# poly_2[cat] = ROOT.RooRealVar("poly_2_%s"%cat,"poly_2_%s"%cat, +# 0.01, -4, 4 +# ) +# poly_3[cat] = ROOT.RooRealVar("poly_3_%s"%cat,"poly_3_%s"%cat, +# 0.01, -4, 4 +# ) +# poly_4[cat] = ROOT.RooRealVar("poly_4_%s"%cat,"poly_4_%s"%cat, +# 0.01, -4, 4 +# ) +# pdfs["%s_poly"%cat] = ROOT.RooChebychev("model_bkg_poly_%s"%cat, +# "model_bkg_poly_%s"%cat, mass, +# ROOT.RooArgList(poly_1[cat],poly_2[cat],poly_3[cat],poly_4[cat]) +# ) +# pdfs["%s_poly"%cat].fitTo(data[cat], ROOT.RooFit.Range(fit_range), +# ROOT.RooFit.Minimizer("Minuit2","minimize"), +# ROOT.RooFit.SumW2Error(True) +# ) +# +# pow_1[cat] = ROOT.RooRealVar("pow_1_%s"%cat,"pow_1_%s"%cat, +# -3, -10, -0.0001 +# ) +# pdfs["%s_pow"%cat] = ROOT.RooGenericPdf("model_bkg_pow_%s"%cat, +# "TMath::Power(@0,@1)", ROOT.RooArgList(mass,pow_1[cat]) +# ) +# pdfs["%s_pow"%cat].fitTo( data[cat], ROOT.RooFit.Range(fit_range), +# ROOT.RooFit.Minimizer("Minuit2","minimize"), +# ROOT.RooFit.SumW2Error(True) +# ) +# +# # Make RooCategory index object to control which pdf is acitve +# index[cat] = ROOT.RooCategory("pdfindex_%s"%cat, +# "Index of Pdf which is active for %s"%cat +# ) +# +# # Make ArgList of models +# models_bkg[cat] = ROOT.RooArgList() +# models_bkg[cat].add( pdfs["%s_exp"%cat] ) +# models_bkg[cat].add( pdfs["%s_poly"%cat] ) +# models_bkg[cat].add( pdfs["%s_pow"%cat] ) +# +# # Build the RooMultiPdf object +# multipdfs[cat] = ROOT.RooMultiPdf("multipdf_%s"%cat, +# "Multipdf for %s"%cat, index[cat], models_bkg[cat] +# ) +# +# # As usual for the data driven fit +# # We want the background to have a freely floating yield +# norms_bkg[cat] = ROOT.RooRealVar("multipdf_%s_norm"%cat, +# "Number of background events in %s"%cat, +# data[cat].numEntries(), 0, 3*data[cat].numEntries() +# ) +# +# # Lets again save the data as RooDataHist with 320 bins +# mass.setBins(320) +# data_hist = {} +# for cat in cats: +# data_hist[cat] = ROOT.RooDataHist("data_hist_%s"%cat, +# "data_hist_%s"%cat, mass, data[cat] +# ) +# +# # Now save the background models and data sets to a workspace +# f_out = ROOT.TFile("workspace_bkg_part6.root", "RECREATE") +# w_bkg = ROOT.RooWorkspace("workspace_bkg","workspace_bkg") +# for cat in cats: +# getattr(w_bkg, "import")(data_hist[cat]) +# getattr(w_bkg, "import")(index[cat]) +# getattr(w_bkg, "import")(norms_bkg[cat]) +# getattr(w_bkg, "import")(multipdfs[cat]) +# w_bkg.Print() +# w_bkg.Write() +# f_out.Close() + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# # Finally lets calculate the systematic yield variations +# # And add to the datacard +# print(" --> Yield variations to add to the datacard...") +# for proc in procs: +# for cat in cats: +# key = "%s_%s"%(proc,cat) +# for syst in ['JEC','photonID']: +# key_up = "%s_%sUp01Sigma"%(key,syst) +# key_down = "%s_%sDown01Sigma"%(key,syst) +# yield_variation_up = mc[key_up].sumEntries() / +# mc[key].sumEntries() +# yield_variation_down = mc[key_down].sumEntries() / +# 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+observation -1 +--------------------------------------------- +bin Tag0 Tag0 +process ggH bkg_mass +process 0 1 +rate 181.01 1.0 +--------------------------------------------- diff --git a/data/tutorials/parametric_exercise/datacard_part1_with_norm.txt b/data/tutorials/parametric_exercise/datacard_part1_with_norm.txt new file mode 100644 index 00000000000..22deba1055b --- /dev/null +++ b/data/tutorials/parametric_exercise/datacard_part1_with_norm.txt @@ -0,0 +1,20 @@ +# Datacard example for combine tutorial 2023 (part 1) +--------------------------------------------- +imax 1 +jmax 1 +kmax * +--------------------------------------------- + +shapes ggH Tag0 workspace_sig_with_norm.root workspace_sig:model_ggH_Tag0 +shapes bkg_mass Tag0 workspace_bkg.root workspace_bkg:model_bkg_Tag0 +shapes data_obs Tag0 workspace_bkg.root workspace_bkg:data_Tag0 + +--------------------------------------------- +bin Tag0 +observation -1 +--------------------------------------------- +bin Tag0 Tag0 +process ggH bkg_mass +process 0 1 +rate 138000 1.0 +--------------------------------------------- diff --git a/data/tutorials/parametric_exercise/datacard_part3.txt b/data/tutorials/parametric_exercise/datacard_part3.txt new file mode 100644 index 00000000000..83da3559f84 --- /dev/null +++ b/data/tutorials/parametric_exercise/datacard_part3.txt @@ -0,0 +1,38 @@ +# Datacard example for combine tutorial 2023 (part 1) +--------------------------------------------- +imax 1 +jmax 1 +kmax * +--------------------------------------------- + +shapes ggH Tag0 workspace_sig_with_syst.root workspace_sig:model_ggH_Tag0 +shapes bkg_mass Tag0 workspace_bkg.root workspace_bkg:model_bkg_Tag0 +shapes data_obs Tag0 workspace_bkg.root workspace_bkg:data_Tag0 + +--------------------------------------------- +bin Tag0 +observation -1 +--------------------------------------------- +bin Tag0 Tag0 +process ggH bkg_mass +process 0 1 +rate 138000 1.0 +--------------------------------------------- +lumi_13TeV lnN 1.016 - +CMS_hgg_trigger lnN 1.002 - +CMS_hgg_presel lnN 1.007 - +CMS_hgg_phoIdMva lnN 1.05 - +CMS_scale_j lnN 0.951/1.056 - +CMS_res_j lnN 1.01 - +--------------------------------------------- +BR_hgg lnN 0.980/1.021 - +QCDscale_ggH lnN 1.039 - +pdf_Higgs_ggH lnN 1.019 - +alphaS_ggH lnN 1.026 - +UnderlyingEvent lnN 0.989/1.011 - +PartonShower lnN 0.971/1.029 - +--------------------------------------------- +nuisance_scale param 0.0 1.0 +nuisance_smear param 0.0 1.0 +--------------------------------------------- +theory group = BR_hgg QCDscale_ggH pdf_Higgs_ggH alphaS_ggH UnderlyingEvent PartonShower diff --git a/data/tutorials/parametric_exercise/datacard_part4_exp.txt b/data/tutorials/parametric_exercise/datacard_part4_exp.txt new file mode 100644 index 00000000000..33a4e351595 --- /dev/null +++ b/data/tutorials/parametric_exercise/datacard_part4_exp.txt @@ -0,0 +1,38 @@ +# Datacard example for combine tutorial 2023 (part 1) +--------------------------------------------- +imax 1 +jmax 1 +kmax * +--------------------------------------------- + +shapes ggH Tag0 workspace_sig_with_syst.root workspace_sig:model_ggH_Tag0 +shapes bkg_mass Tag0 workspace_bkg_exp.root workspace_bkg:model_exp_bkg_Tag0 +shapes data_obs Tag0 workspace_bkg_exp.root workspace_bkg:data_hist_Tag0 + +--------------------------------------------- +bin Tag0 +observation -1 +--------------------------------------------- +bin Tag0 Tag0 +process ggH bkg_mass +process 0 1 +rate 138000 1.0 +--------------------------------------------- +lumi_13TeV lnN 1.016 - +CMS_hgg_trigger lnN 1.002 - +CMS_hgg_presel lnN 1.007 - +CMS_hgg_phoIdMva lnN 1.05 - +CMS_scale_j lnN 0.956/1.077 - +CMS_res_j lnN 1.01 - +--------------------------------------------- +BR_hgg lnN 0.980/1.021 - +QCDscale_ggH lnN 1.039 - +pdf_Higgs_ggH lnN 1.019 - +alphaS_ggH lnN 1.026 - +UnderlyingEvent lnN 0.989/1.011 - +PartonShower lnN 0.971/1.029 - +--------------------------------------------- +nuisance_scale param 0.0 1.0 +nuisance_smear param 0.0 1.0 +--------------------------------------------- +theory group = BR_hgg QCDscale_ggH pdf_Higgs_ggH alphaS_ggH UnderlyingEvent PartonShower diff --git a/data/tutorials/parametric_exercise/datacard_part4_poly.txt b/data/tutorials/parametric_exercise/datacard_part4_poly.txt new file mode 100644 index 00000000000..52c03f77027 --- /dev/null +++ b/data/tutorials/parametric_exercise/datacard_part4_poly.txt @@ -0,0 +1,38 @@ +# Datacard example for combine tutorial 2023 (part 1) +--------------------------------------------- +imax 1 +jmax 1 +kmax * +--------------------------------------------- + +shapes ggH Tag0 workspace_sig_with_syst.root workspace_sig:model_ggH_Tag0 +shapes bkg_mass Tag0 workspace_bkg_poly.root workspace_bkg:model_poly_bkg_Tag0 +shapes data_obs Tag0 workspace_bkg_poly.root workspace_bkg:data_hist_Tag0 + +--------------------------------------------- +bin Tag0 +observation -1 +--------------------------------------------- +bin Tag0 Tag0 +process ggH bkg_mass +process 0 1 +rate 138000 1.0 +--------------------------------------------- +lumi_13TeV lnN 1.016 - +CMS_hgg_trigger lnN 1.002 - +CMS_hgg_presel lnN 1.007 - +CMS_hgg_phoIdMva lnN 1.05 - +CMS_scale_j lnN 0.956/1.077 - +CMS_res_j lnN 1.01 - +--------------------------------------------- +BR_hgg lnN 0.980/1.021 - +QCDscale_ggH lnN 1.039 - +pdf_Higgs_ggH lnN 1.019 - +alphaS_ggH lnN 1.026 - +UnderlyingEvent lnN 0.989/1.011 - +PartonShower lnN 0.971/1.029 - +--------------------------------------------- +nuisance_scale param 0.0 1.0 +nuisance_smear param 0.0 1.0 +--------------------------------------------- +theory group = BR_hgg QCDscale_ggH pdf_Higgs_ggH alphaS_ggH UnderlyingEvent PartonShower diff --git a/data/tutorials/parametric_exercise/datacard_part4_pow.txt b/data/tutorials/parametric_exercise/datacard_part4_pow.txt new file mode 100644 index 00000000000..824e259c29a --- /dev/null +++ b/data/tutorials/parametric_exercise/datacard_part4_pow.txt @@ -0,0 +1,38 @@ +# Datacard example for combine tutorial 2023 (part 1) +--------------------------------------------- +imax 1 +jmax 1 +kmax * +--------------------------------------------- + +shapes ggH Tag0 workspace_sig_with_syst.root workspace_sig:model_ggH_Tag0 +shapes bkg_mass Tag0 workspace_bkg_pow.root workspace_bkg:model_pow_bkg_Tag0 +shapes data_obs Tag0 workspace_bkg_pow.root workspace_bkg:data_hist_Tag0 + +--------------------------------------------- +bin Tag0 +observation -1 +--------------------------------------------- +bin Tag0 Tag0 +process ggH bkg_mass +process 0 1 +rate 138000 1.0 +--------------------------------------------- +lumi_13TeV lnN 1.016 - +CMS_hgg_trigger lnN 1.002 - +CMS_hgg_presel lnN 1.007 - +CMS_hgg_phoIdMva lnN 1.05 - +CMS_scale_j lnN 0.956/1.077 - +CMS_res_j lnN 1.01 - +--------------------------------------------- +BR_hgg lnN 0.980/1.021 - +QCDscale_ggH lnN 1.039 - +pdf_Higgs_ggH lnN 1.019 - +alphaS_ggH lnN 1.026 - +UnderlyingEvent lnN 0.989/1.011 - +PartonShower lnN 0.971/1.029 - +--------------------------------------------- +nuisance_scale param 0.0 1.0 +nuisance_smear param 0.0 1.0 +--------------------------------------------- +theory group = BR_hgg QCDscale_ggH pdf_Higgs_ggH alphaS_ggH UnderlyingEvent PartonShower diff --git a/data/tutorials/parametric_exercise/datacard_part5.txt b/data/tutorials/parametric_exercise/datacard_part5.txt new file mode 100644 index 00000000000..c5de0a2ec98 --- /dev/null +++ b/data/tutorials/parametric_exercise/datacard_part5.txt @@ -0,0 +1,40 @@ +# Datacard example for combine tutorial 2023 (part 1) +--------------------------------------------- +imax 1 +jmax 1 +kmax * +--------------------------------------------- + +shapes ggH Tag0 workspace_sig_with_syst.root workspace_sig:model_ggH_Tag0 +shapes bkg_mass Tag0 workspace_bkg_multipdf.root workspace_bkg:multipdf_Tag0 +shapes data_obs Tag0 workspace_bkg_multipdf.root workspace_bkg:data_hist_Tag0 + +--------------------------------------------- +bin Tag0 +observation -1 +--------------------------------------------- +bin Tag0 Tag0 +process ggH bkg_mass +process 0 1 +rate 138000 1.0 +--------------------------------------------- +lumi_13TeV lnN 1.016 - +CMS_hgg_trigger lnN 1.002 - +CMS_hgg_presel lnN 1.007 - +CMS_hgg_phoIdMva lnN 1.05 - +CMS_scale_j lnN 0.951/1.056 - +CMS_res_j lnN 1.01 - +--------------------------------------------- +BR_hgg lnN 0.980/1.021 - +QCDscale_ggH lnN 1.039 - +pdf_Higgs_ggH lnN 1.019 - +alphaS_ggH lnN 1.026 - +UnderlyingEvent lnN 0.989/1.011 - +PartonShower lnN 0.971/1.029 - +--------------------------------------------- +nuisance_scale param 0.0 1.0 +nuisance_smear param 0.0 1.0 +--------------------------------------------- +theory group = BR_hgg QCDscale_ggH pdf_Higgs_ggH alphaS_ggH UnderlyingEvent PartonShower +--------------------------------------------- +pdfindex_Tag0 discrete diff --git a/data/tutorials/parametric_exercise/datacard_part6_Tag0.txt b/data/tutorials/parametric_exercise/datacard_part6_Tag0.txt new file mode 100644 index 00000000000..271dec9247a --- /dev/null +++ b/data/tutorials/parametric_exercise/datacard_part6_Tag0.txt @@ -0,0 +1,44 @@ +# Datacard example for combine tutorial 2023 (part 6) +--------------------------------------------- +imax * +jmax * +kmax * +--------------------------------------------- + +shapes ggH Tag0 workspace_sig_part6.root workspace_sig:model_ggH_Tag0 +shapes VBF Tag0 workspace_sig_part6.root workspace_sig:model_VBF_Tag0 +shapes bkg_mass Tag0 workspace_bkg_part6.root workspace_bkg:multipdf_Tag0 +shapes data_obs Tag0 workspace_bkg_part6.root workspace_bkg:data_hist_Tag0 + +--------------------------------------------- +bin Tag0 +observation -1 +--------------------------------------------- +bin Tag0 Tag0 Tag0 +process ggH VBF bkg_mass +process 0 -1 1 +rate 138000 138000 1.0 +--------------------------------------------- +lumi_13TeV lnN 1.016 1.016 - +CMS_hgg_trigger lnN 1.002 1.022 - +CMS_hgg_presel lnN 1.007 1.006 - +CMS_hgg_phoIdMva lnN 1.05 1.05 - +CMS_scale_j lnN 0.951/1.056 0.851/1.144 - +CMS_res_j lnN 1.01 1.03 - +--------------------------------------------- +BR_hgg lnN 0.980/1.021 0.980/1.021 - +QCDscale_ggH lnN 1.039 - - +pdf_Higgs_ggH lnN 1.019 - - +alphaS_ggH lnN 1.026 - - +QCDscale_qqH lnN - 0.997/1.004 - +pdf_Higgs_qqH lnN - 1.021 - +alphaS_qqH lnN - 1.005 - +UnderlyingEvent lnN 0.989/1.011 0.995/1.008 - +PartonShower lnN 0.971/1.029 0.955/1.034 - +--------------------------------------------- +nuisance_scale param 0.0 1.0 +nuisance_smear param 0.0 1.0 +--------------------------------------------- +theory group = BR_hgg QCDscale_ggH pdf_Higgs_ggH alphaS_ggH QCDscale_qqH pdf_Higgs_qqH alphaS_qqH UnderlyingEvent PartonShower +--------------------------------------------- +pdfindex_Tag0 discrete diff --git a/data/tutorials/parametric_exercise/datacard_part6_Tag1.txt b/data/tutorials/parametric_exercise/datacard_part6_Tag1.txt new file mode 100644 index 00000000000..caec229b9e0 --- /dev/null +++ b/data/tutorials/parametric_exercise/datacard_part6_Tag1.txt @@ -0,0 +1,44 @@ +# Datacard example for combine tutorial 2023 (part 6) +--------------------------------------------- +imax * +jmax * +kmax * +--------------------------------------------- + +shapes ggH Tag1 workspace_sig_part6.root workspace_sig:model_ggH_Tag1 +shapes VBF Tag1 workspace_sig_part6.root workspace_sig:model_VBF_Tag1 +shapes bkg_mass Tag1 workspace_bkg_part6.root workspace_bkg:multipdf_Tag1 +shapes data_obs Tag1 workspace_bkg_part6.root workspace_bkg:data_hist_Tag1 + +--------------------------------------------- +bin Tag1 +observation -1 +--------------------------------------------- +bin Tag1 Tag1 Tag1 +process ggH VBF bkg_mass +process 0 -1 1 +rate 138000 138000 1.0 +--------------------------------------------- +lumi_13TeV lnN 1.016 1.016 - +CMS_hgg_trigger lnN 1.002 1.022 - +CMS_hgg_presel lnN 1.007 1.006 - +CMS_hgg_phoIdMva lnN 1.05 1.05 - +CMS_scale_j lnN 0.951/1.048 0.842/1.158 - +CMS_res_j lnN 1.01 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