def make_table(): params = ['r_ggH', 'r_VBF', 'r_VH', 'r_top'] # Load results + xsbr data inputMode = "mu" translatePOIs = LoadTranslations("translate/pois_%s.json" % inputMode) with open( "/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/OtherScripts/HEPdata/hepdata_lib/hig-19-015/inputs/correlations_mu.json", "r") as jf: correlations = json.load(jf) with open( "/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/OtherScripts/HEPdata/hepdata_lib/hig-19-015/inputs/correlations_expected_mu.json", "r") as jf: correlations_exp = json.load(jf) # Make table of results table = Table("Correlations: production mode signal strength") table.description = "Observed and expected correlations between the parameters in the production mode signal strength fit." table.location = "Results from additional material" table.keywords["reactions"] = ["P P --> H ( --> GAMMA GAMMA ) X"] pois_x = Variable("Parameter (x)", is_independent=True, is_binned=False) pois_y = Variable("Parameter (y)", is_independent=True, is_binned=False) c = Variable("Observed correlation", is_independent=False, is_binned=False) c.add_qualifier("SQRT(S)", 13, "TeV") c.add_qualifier("MH", '125.38', "GeV") c_exp = Variable("Expected correlation", is_independent=False, is_binned=False) c_exp.add_qualifier("SQRT(S)", 13, "TeV") c_exp.add_qualifier("MH", '125.38', "GeV") poiNames_x = [] poiNames_y = [] corr = [] corr_exp = [] for ipoi in params: for jpoi in params: poiNames_x.append(str(Translate(ipoi, translatePOIs))) poiNames_y.append(str(Translate(jpoi, translatePOIs))) # Extract correlation coefficient corr.append(correlations["%s__%s" % (ipoi, jpoi)]) corr_exp.append(correlations_exp["%s__%s" % (ipoi, jpoi)]) pois_x.values = poiNames_x pois_y.values = poiNames_y c.values = np.round(np.array(corr), 3) c_exp.values = np.round(np.array(corr_exp), 3) # Add variables to table table.add_variable(pois_x) table.add_variable(pois_y) table.add_variable(c) table.add_variable(c_exp) # Add figure table.add_image( "/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/OtherScripts/HEPdata/hepdata_lib/hig-19-015/inputs/perproc_mu_corr.pdf" ) return table
def make_table(): xparam = 'kappa_V' yparam = 'kappa_F' # Load results + xsbr data inputMode = "kappas" translatePOIs = LoadTranslations("translate/pois_%s.json" % inputMode) # Extract observed results fobs = "/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/flashggFinalFit/Combine/runFits_UL_redo_kVkF/output_scan2D_syst_fixedMH_v2_obs_kappa_V_vs_kappa_F.root" f_in = ROOT.TFile(fobs) t_in = f_in.Get("limit") xvals, yvals, deltaNLL = [], [], [] ev_idx = 0 for ev in t_in: xvals.append(getattr(ev, xparam)) yvals.append(getattr(ev, yparam)) deltaNLL.append(getattr(ev, "deltaNLL")) # Convert to numpy arrays as required for interpolation x = np.asarray(xvals) y = np.asarray(yvals) dnll = np.asarray(deltaNLL) v = 2 * (deltaNLL - np.min(deltaNLL)) # Make table of results table = Table("Kappas 2D: vector boson and fermion") table.description = "Observed likelihood surface." table.location = "Results from Figure 22" table.keywords["reactions"] = ["P P --> H ( --> GAMMA GAMMA ) X"] pois_x = Variable(str(Translate(xparam, translatePOIs)), is_independent=True, is_binned=False) pois_y = Variable(str(Translate(yparam, translatePOIs)), is_independent=True, is_binned=False) q = Variable("Observed -2$\\Delta$NLL", is_independent=False, is_binned=False) q.add_qualifier("SQRT(S)", 13, "TeV") q.add_qualifier("MH", '125.38', "GeV") pois_x.values = x pois_y.values = y q.values = np.round(np.array(v), 2) # Add variables to table table.add_variable(pois_x) table.add_variable(pois_y) table.add_variable(q) # Add figure table.add_image( "/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/OtherScripts/HEPdata/hepdata_lib/hig-19-015/inputs/scan2D_syst_obs_kappa_V_vs_kappa_F.pdf" ) return table
def makeCutFlow(submission, config): table = Table(config["name"]) table.description = config["description"] table.location = config["location"] #table.keywords["observables"] = ["SIG"] #table.keywords["reactions"] = ["P P --> TOP --> tt + 6j"] data1 = config["data1"] error1 = config["error1"] data2 = config["data2"] error2 = config["error2"] ##################################################################################### d = Variable("Step", is_independent=True, is_binned=False, units="") d.values = np.array(list(i for i in range(0, data1.size))) cuts = Variable("Selection requirement", is_independent=False, is_binned=False, units="") cuts.values = config["cutnames"] cuts.add_qualifier("SQRT(S)", 13, "TeV") cuts.add_qualifier("LUMINOSITY", config["lumi"], "fb$^{-1}$") obs1 = Variable("RPV $m_{\\tilde{t}}$ = 450 GeV", is_independent=False, is_binned=False, units="") obs1.values = data1 obs1.add_qualifier("SQRT(S)", 13, "TeV") obs1.add_qualifier("LUMINOSITY", config["lumi"], "fb$^{-1}$") unc_obs1 = Uncertainty("1 s.d.", is_symmetric=True) unc_obs1.values = error1 obs1.add_uncertainty(unc_obs1) obs2 = Variable("SYY $m_{\\tilde{t}}$ = 850 GeV", is_independent=False, is_binned=False, units="") obs2.values = data2 obs2.add_qualifier("SQRT(S)", 13, "TeV") obs2.add_qualifier("LUMINOSITY", config["lumi"], "fb$^{-1}$") unc_obs2 = Uncertainty("1 s.d.", is_symmetric=True) unc_obs2.values = error2 obs2.add_uncertainty(unc_obs2) table.add_variable(d) table.add_variable(cuts) table.add_variable(obs1) table.add_variable(obs2) submission.add_table(table)
def add_limit_to_submission(c,submission): from hepdata_lib import Table from hepdata_lib.c_file_reader import CFileReader from hepdata_lib import Variable, Uncertainty table = Table(c.y_var) table.description = 'Exclusion limit for '+c.y_var reader = CFileReader(c.outputPath) graphs = reader.get_graphs() d = Variable(c.x_var, is_independent=True, is_binned=False, units=c.x_unit) d.values = graphs["Graph2"]['x'] obs = Variable(c.y_var, is_independent=False, is_binned=False, units=c.y_unit) obs.values = graphs["Graph3"]['y'] obs.add_qualifier("Limit", "Observed") exp = Variable(c.y_var, is_independent=False, is_binned=False, units=c.y_unit) exp.values = graphs["Graph2"]['y'] exp.add_qualifier("Limit", "Expected") up2 = Variable(c.y_var, is_independent=False, is_binned=False, units=c.y_unit) up2.values = graphs["Graph0"]['y'] up2.add_qualifier("Limit", "+2sigma") up1 = Variable(c.y_var, is_independent=False, is_binned=False, units=c.y_unit) up1.values = graphs["Graph1"]['y'] up1.add_qualifier("Limit", "+1sigma") down1 = Variable(c.y_var, is_independent=False, is_binned=False, units=c.y_unit) down1.values = graphs["Graph4"]['y'] down1.add_qualifier("Limit", "-1sigma") down2 = Variable(c.y_var, is_independent=False, is_binned=False, units=c.y_unit) down2.values = graphs["Graph5"]['y'] down2.add_qualifier("Limit", "-2sigma") table.add_variable(d) table.add_variable(up2) table.add_variable(up1) table.add_variable(obs) table.add_variable(exp) table.add_variable(down1) table.add_variable(down2) submission.add_table(table)
def Plot_pp_pPb_Avg_FF_and_Ratio(Comb_Dict): label_size=22 axis_size=34 plot_power = False Colors = ["red","blue"] Markers = ["s","o"] fig = plt.figure(figsize=(8,8)) pp_sys_Error = 0 p_Pb_sys_Error = 0 fig.add_axes((0.1,0.3,0.88,0.6)) for SYS,sys_col,marker in zip(reversed(Systems),reversed(Colors),reversed(Markers)): #Systematics Efficiency_Uncertainty = 0.056*Comb_Dict["%s_Combined_FF"%(SYS)] Eta_Cor = Eta_Correction #see default_value.py for value Eta_Cor_Uncertainty = Eta_Correction_Uncertainty*Comb_Dict["%s_Combined_FF"%(SYS)] if not(Apply_Eta_Correction and SYS=="p-Pb"): Eta_Cor_Uncertainty = 0 #2% otherwise FF_Central = Comb_Dict["%s_Combined_FF"%(SYS)] #Eta Correction is applied when creating Dictionary! Sys_Uncertainty = np.sqrt(Efficiency_Uncertainty**2 + Comb_Dict["%s_purity_Uncertainty"%(SYS)]**2 + Eta_Cor_Uncertainty**2) if (SYS=="pp"): pp_sys_Error = Sys_Uncertainty elif (SYS=="p-Pb"): p_Pb_sys_Error=Sys_Uncertainty #Plots if (SYS=="pp"): leg_string = SYS if (SYS=="p-Pb"): leg_string = "p$-$Pb" plt.errorbar(zT_centers[:NzT-ZT_OFF_PLOT], Comb_Dict["%s_Combined_FF"%(SYS)][:NzT-ZT_OFF_PLOT],xerr=zT_widths[:NzT-ZT_OFF_PLOT]*0, yerr=Comb_Dict["%s_Combined_FF_Errors"%(SYS)][:NzT-ZT_OFF_PLOT],linewidth=1, fmt=marker,color=sys_col,capsize=0)#for lines plt.plot(zT_centers[:NzT-ZT_OFF_PLOT], Comb_Dict["%s_Combined_FF"%(SYS)][:NzT-ZT_OFF_PLOT],marker,linewidth=0,color=sys_col, label=leg_string)#for legend without lines if (SYS == "pp"): Sys_Plot_pp = plt.bar(zT_centers[:NzT-ZT_OFF_PLOT], Sys_Uncertainty[:NzT-ZT_OFF_PLOT]+Sys_Uncertainty[:NzT-ZT_OFF_PLOT], bottom=Comb_Dict["%s_Combined_FF"%(SYS)][:NzT-ZT_OFF_PLOT]-Sys_Uncertainty[:NzT-ZT_OFF_PLOT],width=zT_widths[:NzT-ZT_OFF_PLOT]*2, align='center',color=sys_col,alpha=0.3,edgecolor=sys_col) else: Sys_Plot_pp = plt.bar(zT_centers[:NzT-ZT_OFF_PLOT], Sys_Uncertainty[:NzT-ZT_OFF_PLOT]+Sys_Uncertainty[:NzT-ZT_OFF_PLOT], bottom=Comb_Dict["%s_Combined_FF"%(SYS)][:NzT-ZT_OFF_PLOT]-Sys_Uncertainty[:NzT-ZT_OFF_PLOT],width=zT_widths[:NzT-ZT_OFF_PLOT]*2,align='center',color=sys_col,fill=False,edgecolor="blue") if (plot_power): model,p,chi2dof = Fit_FF_PowerLaw(Comb_Dict,SYS) plt.plot(zT_centers[:NzT-ZT_OFF_PLOT], model, sys_col,label=r"%s $\alpha = %1.2f\pm 0.1 \chi^2 = %1.2f$"%(SYS,p,chi2dof)) if (Use_MC): plt.plot(zT_centers[:NzT-ZT_OFF_PLOT],pythia_FF,'--',color="forestgreen",label="PYTHIA 8.2 Monash") plt.errorbar(zT_centers[:NzT-ZT_OFF_PLOT],pythia_FF,yerr=pythia_FF_Errors,fmt='--',color="forestgreen",capsize=0) plt.yscale('log') plt.ylabel(r"$\frac{1}{N_{\mathrm{\gamma}}}\frac{\mathrm{d}^3N}{\mathrm{d}z_{\mathrm{T}}\mathrm{d}|\Delta\varphi|\mathrm{d}\Delta\eta}$",fontsize=axis_size,y=0.76) plt.ylim(0.037,15) plt.yticks(fontsize=20) plt.xticks(fontsize=0) plt.xlim(0,0.65) plt.tick_params(which='both',direction='in',right=True,top=True,bottom=False,length=10) plt.tick_params(which='minor',length=5) #pp_sys_Error = (Comb_Dict["pp_Combined_FF"][:NzT-ZT_OFF_PLOT])*math.sqrt(Rel_pUncert["pp"]**2+0.056**2) #p_Pb_sys_Error = (Comb_Dict["p-Pb_Combined_FF"][:NzT-ZT_OFF_PLOT])*math.sqrt(Rel_pUncert["p-Pb"]**2+0.056**2+Eta_Cor**2) Chi2,NDF,Pval = Get_pp_pPb_List_Chi2(Comb_Dict["pp_Combined_FF"][:NzT-ZT_OFF_PLOT], Comb_Dict["pp_Combined_FF_Errors"][:NzT-ZT_OFF_PLOT], pp_sys_Error, Comb_Dict["p-Pb_Combined_FF"][:NzT-ZT_OFF_PLOT], Comb_Dict["p-Pb_Combined_FF_Errors"][:NzT-ZT_OFF_PLOT], p_Pb_sys_Error) leg = plt.legend(numpoints=1,frameon=True,edgecolor='white', framealpha=0.0, fontsize=label_size,handlelength=1,labelspacing=0.2,loc='lower left',bbox_to_anchor=(0.001, 0.05)) plt.annotate(r"ALICE, $\sqrt{s_{\mathrm{_{NN}}}}=5.02$ TeV",xy=(0.115,0.008),xycoords='axes fraction', ha='left',va='bottom',fontsize=label_size) plt.annotate(r"%1.0f < $p_\mathrm{T}^{\gamma}$ < %1.0f GeV/$c$"%(pTbins[0],pTbins[N_pT_Bins]),xy=(0.97, 0.81), xycoords='axes fraction', ha='right', va='top', fontsize=label_size) plt.annotate(r"%1.1f < $p_\mathrm{T}^\mathrm{h}$ < %1.1f GeV/$c$"%(Min_Hadron_pT,Max_Hadron_pT),xy=(0.97, 0.89), xycoords='axes fraction', ha='right', va='top', fontsize=label_size) plt.annotate("$\chi^2/\mathrm{ndf}$ = %1.1f/%i, $p$ = %1.2f"%(Chi2*NDF,NDF,Pval), xy=(0.97, 0.97), xycoords='axes fraction', ha='right', va='top', fontsize=label_size) #HEP FF Fig5 = Table("Figure 5 Top Panel") Fig5.description = "$\gamma^\mathrm{iso}$-tagged fragmentation function for pp (red) and p$-$Pb data (blue) at $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV as measured by the ALICE detector. The boxes represent the systematic uncertainties while the vertical bars indicate the statistical uncertainties. The dashed green line corresponds to PYTHIA 8.2 Monash Tune. The $\chi^2$ test for the comparison of pp and p$-$Pb data incorporates correlations among different $z_\mathrm{T}$ intervals. A constant that was fit to the ratio is shown as grey band, with the width indicating the uncertainty on the fit." Fig5.location = "Data from Figure 5 Top Panel, Page 15" Fig5.keywords["observables"] = ["$\frac{1}{N_{\mathrm{\gamma}}}\frac{\mathrm{d}^3N}{\mathrm{d}z_{\mathrm{T}}\mathrm{d}\Delta\varphi\mathrm{d}\Delta\eta}$"] Fig5.add_image("./pics/LO/zT_Rebin_8_006zT06zT13fnew/Final_FFunction_and_Ratio.pdf") # x-axis: zT zt = Variable(r"$z_\mathrm{T}$", is_independent=True, is_binned=True, units="") zt.values = zT_edges Fig5.add_variable(zt) # y-axis: p-Pb Yields pPb_data = Variable("p$-$Pb conditional yield of associated hadrons", is_independent=False, is_binned=False, units="") pPb_data.values = Comb_Dict["p-Pb_Combined_FF"] pPb_sys = Uncertainty("p-Pb Systematic", is_symmetric=True) pPb_sys.values = p_Pb_sys_Error pPb_stat = Uncertainty("p-Pb Statistical", is_symmetric=True) pPb_stat.values = Comb_Dict["p-Pb_Combined_FF_Errors"] pPb_data.add_uncertainty(pPb_sys) pPb_data.add_uncertainty(pPb_stat) # y-axis: pp Yields pp_data = Variable("pp conditional yield of associated hadrons", is_independent=False, is_binned=False, units="") pp_data.values = Comb_Dict["pp_Combined_FF"] pp_sys = Uncertainty("pp Systematic", is_symmetric=True) pp_sys.values = pp_sys_Error pp_stat = Uncertainty("pp Statistical", is_symmetric=True) pp_stat.values = Comb_Dict["pp_Combined_FF_Errors"] pp_data.add_uncertainty(pp_sys) pp_data.add_uncertainty(pp_stat) # y-axis: PYTHIA Yields pythia_data = Variable("PYTHIA conditional yield of associated hadrons", is_independent=False, is_binned=False, units="") pythia_data.values = pythia_FF pythia_stat = Uncertainty("PYTHIA Statistical", is_symmetric=True) pythia_stat.values = pythia_FF_Errors pythia_data.add_uncertainty(pythia_stat) #Add everything to the HEP Table Fig5.add_variable(pPb_data) Fig5.add_variable(pp_data) Fig5.add_variable(pythia_data) submission.add_table(Fig5) #RATIO SECOND Y_AXIS fig.add_axes((0.1,0.1,0.88,0.2)) pPb_Combined = Comb_Dict["p-Pb_Combined_FF"] pPb_Combined_Errors = Comb_Dict["p-Pb_Combined_FF_Errors"] pPb_purity_Uncertainty = Comb_Dict["p-Pb_purity_Uncertainty"] pp_Combined = Comb_Dict["pp_Combined_FF"] pp_Combined_Errors = Comb_Dict["pp_Combined_FF_Errors"] pp_purity_Uncertainty = Comb_Dict["pp_purity_Uncertainty"] Ratio = pPb_Combined/pp_Combined Ratio_Error = np.sqrt((pPb_Combined_Errors/pPb_Combined)**2 + (pp_Combined_Errors/pp_Combined)**2)*Ratio Ratio_Plot = plt.errorbar(zT_centers[:NzT-ZT_OFF_PLOT], Ratio[:NzT-ZT_OFF_PLOT], yerr=Ratio_Error[:NzT-ZT_OFF_PLOT],xerr=zT_widths[:NzT-ZT_OFF_PLOT]*0, fmt='ko',capsize=0, ms=6,lw=1) #Save np.save("npy_files/%s_Averaged_FF_Ratio_%s.npy"%(Shower,description_string),Ratio) np.save("npy_files/%s_Averaged_FF_Ratio_Errors_%s.npy"%(Shower,description_string),Ratio_Error) Purity_Uncertainty = np.sqrt((pp_purity_Uncertainty/pp_Combined)**2 + (pPb_purity_Uncertainty/pPb_Combined)**2)*Ratio Efficiency_Uncertainty = np.ones(len(pPb_Combined))*0.056*math.sqrt(2)*Ratio Eta_Cor_Uncertainty = Eta_Correction_Uncertainty/Comb_Dict["p-Pb_Combined_FF"]*Ratio if (CorrectedP): Ratio_Systematic = np.sqrt(Purity_Uncertainty**2 + Efficiency_Uncertainty**2 + Eta_Cor_Uncertainty**2) Sys_Plot = plt.bar(zT_centers[:NzT-ZT_OFF_PLOT], Ratio_Systematic[:NzT-ZT_OFF_PLOT]+Ratio_Systematic[:NzT-ZT_OFF_PLOT], bottom=Ratio[:NzT-ZT_OFF_PLOT]-Ratio_Systematic[:NzT-ZT_OFF_PLOT], width=zT_widths[:NzT-ZT_OFF_PLOT]*2, align='center',color='black',alpha=0.25) ### ROOT LINEAR and CONSTANT FITS ### Ratio_TGraph = TGraphErrors() for izt in range (len(Ratio)-ZT_OFF_PLOT): Ratio_TGraph.SetPoint(izt,zT_centers[izt],Ratio[izt]) Ratio_TGraph.SetPointError(izt,0,Ratio_Error[izt]) Ratio_TGraph.Fit("pol0","S") f = Ratio_TGraph.GetFunction("pol0") chi2_red = f.GetChisquare()/f.GetNDF() pval = f.GetProb() p0 = f.GetParameter(0) p0e = f.GetParError(0) p0col = "grey" Show_Fits = True if (Show_Fits): sys_const = 0.19 #23% relative from purity + tracking #sys_const = 0.504245 #IRC plt.annotate("$c = {0:.2f} \pm {1:.2f} \pm {2:.2f}$".format(p0,p0e,sys_const), xy=(0.98, 0.9), xycoords='axes fraction', ha='right', va='top', color="black",fontsize=label_size,alpha=.9) plt.annotate(r"$p = %1.2f$"%(pval), xy=(0.98, 0.75), xycoords='axes fraction', ha='right', va='top', color="black",fontsize=label_size,alpha=.9) c_error = math.sqrt(p0e**2 + sys_const**2) plt.fill_between(np.arange(0,1.1,0.1), p0+c_error, p0-c_error,color=p0col,alpha=.3) ###LABELS/AXES### plt.axhline(y=1, color='k', linestyle='--') plt.xlabel("${z_\mathrm{T}} = p_\mathrm{T}^{\mathrm{h}}/p_\mathrm{T}^\gamma$",fontsize=axis_size-8,x=0.9) plt.ylabel(r"$\frac{\mathrm{p-Pb}}{\mathrm{pp}}$",fontsize=axis_size,y=0.5) plt.ylim((-0.0, 2.8)) plt.xticks(fontsize=20) plt.yticks([0.5,1.0,1.5,2.0,2.5],fontsize=20) plt.xlim(0,0.65) plt.tick_params(which='both',direction='in',right=True,bottom=True,top=True,length=10) plt.tick_params(which='both',direction='in',top=True,length=5) plt.savefig("pics/%s/%s/Final_FFunction_and_Ratio.pdf"%(Shower,description_string), bbox_inches = "tight") plt.show() #RATIO HEP FigRatio = Table("Figure 5 Bottom Panel") FigRatio.description = r"$\gamma^\mathrm{iso}$-tagged fragmentation function for pp (red) and p$-$Pb data (blue) at $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV as measured by the ALICE detector. The boxes represent the systematic uncertainties while the vertical bars indicate the statistical uncertainties. The dashed green line corresponds to PYTHIA 8.2 Monash Tune. The $\chi^2$ test for the comparison of pp and p$-$Pb data incorporates correlations among different $z_\mathrm{T}$ intervals. A constant that was fit to the ratio is shown as grey band, with the width indicating the uncertainty on the fit." FigRatio.location = "Data from Figure 5, Bottom Panel, Page 15" FigRatio.keywords["observables"] = [r"$\frac{1}{N_{\mathrm{\gamma}}}\frac{\mathrm{d}^3N}{\mathrm{d}z_{\mathrm{T}}\mathrm{d}\Delta\varphi\mathrm{d}\Delta\eta}$"] FigRatio.add_image("./pics/LO/zT_Rebin_8_006zT06zT13fnew/Final_FFunction_and_Ratio.pdf") # x-axis: zT zt_ratio = Variable(r"$z_\mathrm{T}$", is_independent=True, is_binned=True, units="") zt_ratio.values = zT_edges FigRatio.add_variable(zt_ratio) # y-axis: p-Pb Yields Ratio_HEP = Variable("Ratio conditional yield of associated hadrons in pp and p$-$Pb", is_independent=False, is_binned=False, units="") Ratio_HEP.values = Ratio Ratio_sys = Uncertainty("Ratio Systematic", is_symmetric=True) Ratio_sys.values = Ratio_Systematic Ratio_stat = Uncertainty("Ratio Statistical", is_symmetric=True) Ratio_stat.values = Ratio_Error Ratio_HEP.add_uncertainty(Ratio_stat) Ratio_HEP.add_uncertainty(Ratio_sys) FigRatio.add_variable(Ratio_HEP) submission.add_table(FigRatio)
import numpy as np submission = Submission() submission.read_abstract("hepdata_lib/examples/example_inputs/abstract.txt") submission.add_link( "Webpage with all figures and tables", "https://cms-results.web.cern.ch/cms-results/public-results/publications/B2G-16-029/" ) submission.add_link("arXiv", "http://arxiv.org/abs/arXiv:1802.09407") submission.add_record_id(1657397, "inspire") ### Table from hepdata_lib import Table table = Table("Additional Figure 1") table.description = "Signal selection efficiency times acceptance as a function of resonance mass for a spin-2 bulk graviton decaying to WW and a spin-1 W' decaying to WZ." table.location = "Data from additional Figure 1" table.keywords["observables"] = ["ACC", "EFF"] table.keywords["reactions"] = [ "P P --> GRAVITON --> W+ W-", "P P --> WPRIME --> W+/W- Z0" ] data = np.loadtxt("hepdata_lib/examples/example_inputs/effacc_signal.txt", skiprows=2) print(data) ### Variable from hepdata_lib import Variable d = Variable("Resonance mass",
) # Read the histogram data_covariance_mm_Pt = reader_covariance_mm_Pt.read_hist_2d( "covariance_totsum_0") # Create variable objects x_covariance_mm_Pt = Variable("Bin X", is_independent=True, is_binned=True) x_covariance_mm_Pt.values = data_covariance_mm_Pt["x_edges"] y_covariance_mm_Pt = Variable("Bin Y", is_independent=True, is_binned=False) y_covariance_mm_Pt.values = data_covariance_mm_Pt["y"] z_covariance_mm_Pt = Variable("covariance Matrix", is_independent=False, is_binned=False) z_covariance_mm_Pt.values = data_covariance_mm_Pt["z"] table_covariance_XSRatio_mm_Pt = Table("cov matr norm xs aux 1a") table_covariance_XSRatio_mm_Pt.description = "Covariance matrix for normalized cross sections using dressed level leptons for all bins used in bins of Z pt in the dimuon final state." table_covariance_XSRatio_mm_Pt.location = "Supplementary material" for var in [x_covariance_mm_Pt, y_covariance_mm_Pt, z_covariance_mm_Pt]: table_covariance_XSRatio_mm_Pt.add_variable(var) submission.add_table(table_covariance_XSRatio_mm_Pt) # Create a reader for the input file reader_covariance_mm_Rap = RootFileReader( "HEPData/inputs/smp17010/folders_dressedleptons/output_root/matrix03__XSRatioSystRap.root" ) # Read the histogram data_covariance_mm_Rap = reader_covariance_mm_Rap.read_hist_2d( "covariance_totsum_0") # Create variable objects x_covariance_mm_Rap = Variable("Bin X", is_independent=True, is_binned=True) x_covariance_mm_Rap.values = data_covariance_mm_Rap["x_edges"]
def make_table(): params = ['r_ggH', 'r_VBF', 'r_VH', 'r_top', 'r_inclusive'] # Load results + xsbr data inputExpResultsJson = '/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/flashggFinalFit/Plots/expected_UL_redo.json' inputObsResultsJson = '/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/flashggFinalFit/Plots/observed_UL_redo.json' inputMode = "mu" translatePOIs = LoadTranslations("translate/pois_%s.json" % inputMode) observed = CopyDataFromJsonFile(inputObsResultsJson, inputMode, params) expected = CopyDataFromJsonFile(inputExpResultsJson, inputMode, params) # Make table of results table = Table("Signal strengths") table.description = "Best-fit values and 68% confidence intervals for the signal strength modifiers. The uncertainty is decomposed ino the theoretical systematic, experimental systematic and statistical components. Additionally, the expected uncertainties derived using an asimov dataset are provided." table.location = "Results from Figure 16" table.keywords["reactions"] = ["P P --> H ( --> GAMMA GAMMA ) X"] pois = Variable("Parameter", is_independent=True, is_binned=False) poiNames = [] for poi in params: poiNames.append(str(Translate(poi, translatePOIs))) pois.values = poiNames # Dependent variables # Observed values obs = Variable("Observed", is_independent=False, is_binned=False, units='') obs.add_qualifier("SQRT(S)", 13, "TeV") obs.add_qualifier("MH", '125.38', "GeV") # Add uncertainties tot = Uncertainty("Total", is_symmetric=False) th = Uncertainty("Th. syst", is_symmetric=False) exp = Uncertainty("Exp. syst", is_symmetric=False) stat = Uncertainty("Stat only", is_symmetric=False) vals = [] hi_tot, lo_tot = [], [] hi_th, lo_th = [], [] hi_exp, lo_exp = [], [] hi_stat, lo_stat = [], [] for poi in params: vals.append(observed[poi]['Val']) hi_tot.append(abs(observed[poi]['ErrorHi'])) lo_tot.append(-1 * abs(observed[poi]['ErrorLo'])) hi_th.append(abs(observed[poi]['TheoryHi'])) lo_th.append(-1 * abs(observed[poi]['TheoryLo'])) hi_exp.append(abs(observed[poi]['SystHi'])) lo_exp.append(-1 * abs(observed[poi]['SystLo'])) hi_stat.append(abs(observed[poi]['StatHi'])) lo_stat.append(-1 * abs(observed[poi]['StatLo'])) tot.values = zip(np.round(np.array(lo_tot), 3), np.round(np.array(hi_tot), 3)) th.values = zip(np.round(np.array(lo_th), 3), np.round(np.array(hi_th), 3)) exp.values = zip(np.round(np.array(lo_exp), 3), np.round(np.array(hi_exp), 3)) stat.values = zip(np.round(np.array(lo_stat), 3), np.round(np.array(hi_stat), 3)) obs.values = np.round(np.array(vals), 3) obs.add_uncertainty(tot) obs.add_uncertainty(th) obs.add_uncertainty(exp) obs.add_uncertainty(stat) # Expected values ex = Variable("Expected", is_independent=False, is_binned=False, units='') ex.add_qualifier("SQRT(S)", 13, "TeV") ex.add_qualifier("MH", '125.38', "GeV") # Add uncertainties etot = Uncertainty("Total", is_symmetric=False) eth = Uncertainty("Th. syst", is_symmetric=False) eexp = Uncertainty("Exp. syst", is_symmetric=False) estat = Uncertainty("Stat only", is_symmetric=False) vals = [] hi_tot, lo_tot = [], [] hi_th, lo_th = [], [] hi_exp, lo_exp = [], [] hi_stat, lo_stat = [], [] for poi in params: vals.append(1.00) hi_tot.append(abs(expected[poi]['ErrorHi'])) lo_tot.append(-1 * abs(expected[poi]['ErrorLo'])) hi_th.append(abs(expected[poi]['TheoryHi'])) lo_th.append(-1 * abs(expected[poi]['TheoryLo'])) hi_exp.append(abs(expected[poi]['SystHi'])) lo_exp.append(-1 * abs(expected[poi]['SystLo'])) hi_stat.append(abs(expected[poi]['StatHi'])) lo_stat.append(-1 * abs(expected[poi]['StatLo'])) etot.values = zip(np.round(np.array(lo_tot), 3), np.round(np.array(hi_tot), 3)) eth.values = zip(np.round(np.array(lo_th), 3), np.round(np.array(hi_th), 3)) eexp.values = zip(np.round(np.array(lo_exp), 3), np.round(np.array(hi_exp), 3)) estat.values = zip(np.round(np.array(lo_stat), 3), np.round(np.array(hi_stat), 3)) ex.values = np.round(np.array(vals), 3) ex.add_uncertainty(etot) ex.add_uncertainty(eth) ex.add_uncertainty(eexp) ex.add_uncertainty(estat) # Add variables to table table.add_variable(pois) table.add_variable(obs) table.add_variable(ex) # Add figure table.add_image( "/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/OtherScripts/HEPdata/hepdata_lib/hig-19-015/inputs/perproc_mu_coloured.pdf" ) return table
#INITIALIZE submission = Submission() #ABSTRACT submission.read_abstract("input/abstract.txt") submission.add_link( "Webpage with all figures and tables", "https://cms-results.web.cern.ch/cms-results/public-results/publications/SMP-19-013/" ) submission.add_link("arXiv", "http://arxiv.org/abs/arXiv:2105.12780") submission.add_record_id(1865855, "inspire") #FIGURE 2 UPPER LEFT fig2_ul = Table("Figure 2 (upper left)") fig2_ul.description = "Distribution of the transverse momentum of the diphoton system for the $\mathrm{W}\gamma\gamma$ electron channel. The predicted yields are shown with their pre-fit normalisations. The observed data, the expected signal contribution and the background estimates are presented with error bars showing the corresponding statistical uncertainties." fig2_ul.location = "Data from Figure 2 on Page 6 of the preprint" fig2_ul.keywords["observables"] = ["Diphoton pT"] fig2_ul.keywords["reactions"] = [ "P P --> W GAMMA GAMMA --> ELECTRON NU GAMMA GAMMA" ] fig2_ul_in = np.loadtxt("input/fig2_ul.txt", skiprows=1) #diphoton pT fig2_ul_pt = Variable("$p_T^{\gamma\gamma}$", is_independent=True, is_binned=False, units="GeV") fig2_ul_pt.values = fig2_ul_in[:, 0]
def make_table(): params = [ 'r_ggH_0J_low', 'r_ggH_0J_high', 'r_ggH_1J_low', 'r_ggH_1J_med', 'r_ggH_1J_high', 'r_ggH_2J_low', 'r_ggH_2J_med', 'r_ggH_2J_high', 'r_ggH_BSM_low', 'r_ggH_BSM_med', 'r_ggH_BSM_high', 'r_qqH_low_mjj_low_pthjj', 'r_qqH_low_mjj_high_pthjj', 'r_qqH_high_mjj_low_pthjj', 'r_qqH_high_mjj_high_pthjj', 'r_qqH_VHhad', 'r_qqH_BSM', 'r_WH_lep_low', 'r_WH_lep_med', 'r_WH_lep_high', 'r_ZH_lep', 'r_ttH_low', 'r_ttH_medlow', 'r_ttH_medhigh', 'r_ttH_high', 'r_ttH_veryhigh', 'r_tH' ] # Load results + xsbr data inputXSBRjson = "/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/flashggFinalFit/Plots/jsons/xsbr_theory_stage1p2_extended_125p38.json" inputExpResultsJson = '/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/flashggFinalFit/Plots/expected_UL_redo.json' inputObsResultsJson = '/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/flashggFinalFit/Plots/observed_UL_redo.json' inputMode = "stage1p2_extended" translatePOIs = LoadTranslations("translate/pois_%s.json" % inputMode) with open(inputXSBRjson, "r") as jsonfile: xsbr_theory = json.load(jsonfile) observed = CopyDataFromJsonFile(inputObsResultsJson, inputMode, params) expected = CopyDataFromJsonFile(inputExpResultsJson, inputMode, params) mh = float(re.sub("p", ".", inputXSBRjson.split("_")[-1].split(".json")[0])) # Make table of results table = Table("STXS stage 1.2 minimal merging scheme") table.description = "Results of the minimal merging scheme STXS fit. The best fit cross sections are shown together with the respective 68% C.L. intervals. The uncertainty is decomposed into the systematic and statistical components. The expected uncertainties on the fitted parameters are given in brackets. Also listed are the SM predictions for the cross sections and the theoretical uncertainty in those predictions." table.location = "Results from Figure 20 and Table 13" table.keywords["reactions"] = ["P P --> H ( --> GAMMA GAMMA ) X"] pois = Variable("STXS region", is_independent=True, is_binned=False) poiNames = [] for poi in params: poiNames.append(str(Translate(poi, translatePOIs))) pois.values = poiNames # Dependent variables # SM predict xsbr_sm = Variable("SM predicted cross section times branching ratio", is_independent=False, is_binned=False, units='fb') xsbr_sm.add_qualifier("SQRT(S)", 13, "TeV") xsbr_sm.add_qualifier("ABS(YRAP(HIGGS))", '<2.5') xsbr_sm.add_qualifier("MH", '125.38', "GeV") theory = Uncertainty("Theory", is_symmetric=False) xsbr_vals = [] xsbr_hi_th, xsbr_lo_th = [], [] for poi in params: xsbr_vals.append(xsbr_theory[poi]['nominal']) xsbr_hi_th.append(xsbr_theory[poi]['High01Sigma']) xsbr_lo_th.append(-1 * abs(xsbr_theory[poi]['Low01Sigma'])) xsbr_sm.values = np.round(np.array(xsbr_vals), 3) theory.values = zip(np.round(np.array(xsbr_lo_th), 3), np.round(np.array(xsbr_hi_th), 3)) xsbr_sm.add_uncertainty(theory) # Observed cross section xsbr = Variable("Observed cross section times branching ratio", is_independent=False, is_binned=False, units='fb') xsbr.add_qualifier("SQRT(S)", 13, "TeV") xsbr.add_qualifier("ABS(YRAP(HIGGS))", '<2.5') xsbr.add_qualifier("MH", '125.38', "GeV") # Add uncertainties tot = Uncertainty("Total", is_symmetric=False) stat = Uncertainty("Stat only", is_symmetric=False) syst = Uncertainty("Syst", is_symmetric=False) xsbr_vals = [] xsbr_hi_tot, xsbr_lo_tot = [], [] xsbr_hi_stat, xsbr_lo_stat = [], [] xsbr_hi_syst, xsbr_lo_syst = [], [] for poi in params: xsbr_vals.append(xsbr_theory[poi]['nominal'] * observed[poi]['Val']) xsbr_hi_tot.append( abs(xsbr_theory[poi]['nominal'] * observed[poi]['ErrorHi'])) xsbr_lo_tot.append( -1 * abs(xsbr_theory[poi]['nominal'] * observed[poi]['ErrorLo'])) xsbr_hi_stat.append( abs(xsbr_theory[poi]['nominal'] * observed[poi]['StatHi'])) xsbr_lo_stat.append( -1 * abs(xsbr_theory[poi]['nominal'] * observed[poi]['StatLo'])) xsbr_hi_syst.append( abs(xsbr_theory[poi]['nominal'] * observed[poi]['SystHi'])) xsbr_lo_syst.append( -1 * abs(xsbr_theory[poi]['nominal'] * observed[poi]['SystLo'])) tot.values = zip(np.round(np.array(xsbr_lo_tot), 3), np.round(np.array(xsbr_hi_tot), 3)) stat.values = zip(np.round(np.array(xsbr_lo_stat), 3), np.round(np.array(xsbr_hi_stat), 3)) syst.values = zip(np.round(np.array(xsbr_lo_syst), 3), np.round(np.array(xsbr_hi_syst), 3)) xsbr.values = np.round(np.array(xsbr_vals), 3) xsbr.add_uncertainty(tot) xsbr.add_uncertainty(stat) xsbr.add_uncertainty(syst) # Observed ratio to SM xsbrr = Variable("Observed ratio to SM", is_independent=False, is_binned=False, units='') xsbrr.add_qualifier("SQRT(S)", 13, "TeV") xsbrr.add_qualifier("ABS(YRAP(HIGGS))", '<2.5') xsbrr.add_qualifier("MH", '125.38', "GeV") # Add uncertainties totr = Uncertainty("Total", is_symmetric=False) statr = Uncertainty("Stat only", is_symmetric=False) systr = Uncertainty("Syst", is_symmetric=False) xsbr_vals = [] xsbr_hi_tot, xsbr_lo_tot = [], [] xsbr_hi_stat, xsbr_lo_stat = [], [] xsbr_hi_syst, xsbr_lo_syst = [], [] for poi in params: xsbr_vals.append(observed[poi]['Val']) xsbr_hi_tot.append(abs(observed[poi]['ErrorHi'])) xsbr_lo_tot.append(-1 * abs(observed[poi]['ErrorLo'])) xsbr_hi_stat.append(abs(observed[poi]['StatHi'])) xsbr_lo_stat.append(-1 * abs(observed[poi]['StatLo'])) xsbr_hi_syst.append(abs(observed[poi]['SystHi'])) xsbr_lo_syst.append(-1 * abs(observed[poi]['SystLo'])) totr.values = zip(np.round(np.array(xsbr_lo_tot), 3), np.round(np.array(xsbr_hi_tot), 3)) statr.values = zip(np.round(np.array(xsbr_lo_stat), 3), np.round(np.array(xsbr_hi_stat), 3)) systr.values = zip(np.round(np.array(xsbr_lo_syst), 3), np.round(np.array(xsbr_hi_syst), 3)) xsbrr.values = np.round(np.array(xsbr_vals), 3) xsbrr.add_uncertainty(totr) xsbrr.add_uncertainty(statr) xsbrr.add_uncertainty(systr) # Expected cross section xsbr_exp = Variable("Expected cross section times branching ratio", is_independent=False, is_binned=False, units='fb') xsbr_exp.add_qualifier("SQRT(S)", 13, "TeV") xsbr_exp.add_qualifier("ABS(YRAP(HIGGS))", '<2.5') xsbr_exp.add_qualifier("MH", '125.38', "GeV") # Add uncertainties tot_exp = Uncertainty("Total", is_symmetric=False) stat_exp = Uncertainty("Stat only", is_symmetric=False) syst_exp = Uncertainty("Syst", is_symmetric=False) xsbr_vals = [] xsbr_hi_tot, xsbr_lo_tot = [], [] xsbr_hi_stat, xsbr_lo_stat = [], [] xsbr_hi_syst, xsbr_lo_syst = [], [] for poi in params: xsbr_vals.append(xsbr_theory[poi]['nominal']) xsbr_hi_tot.append( abs(xsbr_theory[poi]['nominal'] * expected[poi]['ErrorHi'])) xsbr_lo_tot.append( -1 * abs(xsbr_theory[poi]['nominal'] * expected[poi]['ErrorLo'])) xsbr_hi_stat.append( abs(xsbr_theory[poi]['nominal'] * expected[poi]['StatHi'])) xsbr_lo_stat.append( -1 * abs(xsbr_theory[poi]['nominal'] * expected[poi]['StatLo'])) xsbr_hi_syst.append( abs(xsbr_theory[poi]['nominal'] * expected[poi]['SystHi'])) xsbr_lo_syst.append( -1 * abs(xsbr_theory[poi]['nominal'] * expected[poi]['SystLo'])) tot_exp.values = zip(np.round(np.array(xsbr_lo_tot), 3), np.round(np.array(xsbr_hi_tot), 3)) stat_exp.values = zip(np.round(np.array(xsbr_lo_stat), 3), np.round(np.array(xsbr_hi_stat), 3)) syst_exp.values = zip(np.round(np.array(xsbr_lo_syst), 3), np.round(np.array(xsbr_hi_syst), 3)) xsbr_exp.values = np.round(np.array(xsbr_vals), 3) xsbr_exp.add_uncertainty(tot_exp) xsbr_exp.add_uncertainty(stat_exp) xsbr_exp.add_uncertainty(syst_exp) # Expected ratio to SM xsbrr_exp = Variable("Expected ratio to SM", is_independent=False, is_binned=False, units='') xsbrr_exp.add_qualifier("SQRT(S)", 13, "TeV") xsbrr_exp.add_qualifier("ABS(YRAP(HIGGS))", '<2.5') xsbrr_exp.add_qualifier("MH", '125.38', "GeV") # Add uncertainties totr_exp = Uncertainty("Total", is_symmetric=False) statr_exp = Uncertainty("Stat only", is_symmetric=False) systr_exp = Uncertainty("Syst", is_symmetric=False) xsbr_vals = [] xsbr_hi_tot, xsbr_lo_tot = [], [] xsbr_hi_stat, xsbr_lo_stat = [], [] xsbr_hi_syst, xsbr_lo_syst = [], [] for poi in params: xsbr_vals.append(1.00) xsbr_hi_tot.append(abs(expected[poi]['ErrorHi'])) xsbr_lo_tot.append(-1 * abs(expected[poi]['ErrorLo'])) xsbr_hi_stat.append(abs(expected[poi]['StatHi'])) xsbr_lo_stat.append(-1 * abs(expected[poi]['StatLo'])) xsbr_hi_syst.append(abs(expected[poi]['SystHi'])) xsbr_lo_syst.append(-1 * abs(expected[poi]['SystLo'])) totr_exp.values = zip(np.round(np.array(xsbr_lo_tot), 3), np.round(np.array(xsbr_hi_tot), 3)) statr_exp.values = zip(np.round(np.array(xsbr_lo_stat), 3), np.round(np.array(xsbr_hi_stat), 3)) systr_exp.values = zip(np.round(np.array(xsbr_lo_syst), 3), np.round(np.array(xsbr_hi_syst), 3)) xsbrr_exp.values = np.round(np.array(xsbr_vals), 3) xsbrr_exp.add_uncertainty(totr_exp) xsbrr_exp.add_uncertainty(statr_exp) xsbrr_exp.add_uncertainty(systr_exp) # Add variables to table table.add_variable(pois) table.add_variable(xsbr_sm) table.add_variable(xsbr) table.add_variable(xsbrr) table.add_variable(xsbr_exp) table.add_variable(xsbrr_exp) # Add figure table.add_image( "/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/OtherScripts/HEPdata/hepdata_lib/hig-19-015/inputs/stxs_dist_stage1p2_minimal.pdf" ) return table
] tableF4b.keywords["cmenergies"] = [13000.0] tableF4b.keywords["observables"] = ["N"] tableF4b.keywords["phrases"] = [ "Top", "Quark", "Photon", "lepton+jets", "semileptonic", "Cross Section", "Proton-Proton Scattering", "Inclusive", "Differential" ] #tableF4b.keywords() submission.add_table(tableF4b) ### ### SF table ### tabSF = Table("Table 4") tabSF.description = "Extracted scale factors for the contribution from misidentified electrons for the three data-taking periods, and the Z$\gamma$, W$\gamma$ simulations." tabSF.location = "Table 4" sfType = Variable("Scale factor", is_independent=True, is_binned=False, units="") sfType.values = [ "Misidentified electrons (2016)", "Misidentified electrons (2017)", "Misidentified electrons (2018)", "Z$\gamma$ normalization", "W$\gamma$ normalization" ] value = Variable("Value", is_independent=False, is_binned=False, units="") value.values = [2.25, 2.00, 1.52, 1.01, 1.13] value.add_qualifier("SQRT(S)", "13", "TeV") value.add_qualifier("LUMINOSITY", "137", "fb$^{-1}$")
from hepdata_lib import Submission from hepdata_lib import Table from hepdata_lib import RootFileReader from hepdata_lib import Variable from hepdata_lib import Uncertainty submission = Submission() df = pd.read_csv("input.csv") df = df.fillna("") for index, fig in df.iterrows(): print(fig["figure_name"]) # create a table table = Table(fig["figure_name"]) table.description = fig["description"] table.location = fig["paper_location"] # read figures reader = RootFileReader(fig["file_location"]) if fig["type_stat"].lower() in ["tgraph", "tgrapherrors", "tgraphasymmerrors"]: stat = reader.read_graph(fig["name_stat"]) elif fig["type_stat"].lower() == "th1": stat = reader.read_hist_1d(fig["name_stat"]) elif fig["type_stat"].lower() == "th2": stat = reader.read_hist_2d(fig["name_stat"]) else: print("ERROR: {}, type not recognized!".format(fig["figure_name"])) if fig["type_syst"].lower() in ["tgraph", "tgrapherrors", "tgraphasymmerrors"]: syst = reader.read_graph(fig["name_syst"])
def addLimitPlot(submission, config): table = Table(config["name"]) table.description = config["description"] table.location = config["location"] table.keywords["observables"] = ["SIG"] table.keywords["reactions"] = ["P P --> TOP --> tt + 6j"] table.add_image(config["image"]) reader = RootFileReader(config["inputData"]) data = reader.read_limit_tree() stop_pair_Br = np.array([ 10.00, 4.43, 2.15, 1.11, 0.609, 0.347, 0.205, 0.125, 0.0783, 0.0500, 0.0326, 0.0216, 0.0145, 0.00991, 0.00683, 0.00476, 0.00335, 0.00238, 0.00170, 0.00122, 0.000887, 0.000646, 0.000473 ]) stop_pair_Br1SPpercent = np.array([ 6.65, 6.79, 6.99, 7.25, 7.530, 7.810, 8.120, 8.450, 8.8000, 9.1600, 9.5300, 9.9300, 10.3300, 10.76, 11.2, 11.65, 12.12, 12.62, 13.13, 13.66, 14.21, 14.78, 15.37 ]) stop_pair_unc = stop_pair_Br * stop_pair_Br1SPpercent / 100.0 stop_pair_up = stop_pair_Br + stop_pair_unc stop_pair_down = stop_pair_Br - stop_pair_unc nData = len(data) for mass_id in range(0, nData): data[mass_id][1:] = stop_pair_Br[mass_id] * data[mass_id][1:] ##################################################################################### d = Variable("Top squark mass", is_independent=True, is_binned=False, units="GeV") d.values = data[:, 0] sig = Variable("Top squark cross section", is_independent=False, is_binned=False, units="pb") sig.values = np.array(stop_pair_Br[:nData]) sig.add_qualifier("Limit", "") sig.add_qualifier("SQRT(S)", 13, "TeV") sig.add_qualifier("LUMINOSITY", 137, "fb$^{-1}$") obs = Variable("Observed cross section upper limit at 95% CL", is_independent=False, is_binned=False, units="pb") obs.values = data[:, 6] obs.add_qualifier("Limit", "Observed") obs.add_qualifier("SQRT(S)", 13, "TeV") obs.add_qualifier("LUMINOSITY", 137, "fb$^{-1}$") exp = Variable("Expected cross section upper limit at 95% CL", is_independent=False, is_binned=False, units="pb") exp.values = data[:, 3] exp.add_qualifier("Limit", "Expected") exp.add_qualifier("SQRT(S)", 13, "TeV") exp.add_qualifier("LUMINOSITY", 137, "fb$^{-1}$") unc_sig = Uncertainty("1 s.d.", is_symmetric=False) unc_sig.set_values_from_intervals(zip(stop_pair_up[:nData], stop_pair_down[:nData]), nominal=sig.values) sig.add_uncertainty(unc_sig) # +/- 1 sigma unc_1s = Uncertainty("1 s.d.", is_symmetric=False) unc_1s.set_values_from_intervals(zip(data[:, 2], data[:, 4]), nominal=exp.values) exp.add_uncertainty(unc_1s) # +/- 2 sigma unc_2s = Uncertainty("2 s.d.", is_symmetric=False) unc_2s.set_values_from_intervals(zip(data[:, 1], data[:, 5]), nominal=exp.values) exp.add_uncertainty(unc_2s) table.add_variable(d) table.add_variable(sig) table.add_variable(obs) table.add_variable(exp) submission.add_table(table)
submission = Submission() sig_digits = 3 submission.read_abstract("HEPData/inputs/hig20017/abstract.txt") submission.add_link( "Webpage with all figures and tables", "http://cms-results.web.cern.ch/cms-results/public-results/publications/HIG-20-017/index.html" ) #submission.add_link("arXiv", "http://arxiv.org/abs/arXiv:xxxx.xxxxx") #submission.add_record_id(1818160, "inspire") ### Begin Table 2 table2 = Table("Table 2") table2.description = "Summary of the impact of the systematic uncertainties on the extracted signal strength; for the case of a background-only simulated data set, i.e., assuming no contributions from the $\mathrm{H}^{\pm}$ and $\mathrm{H}^{\pm\pm}$ processes, and including a charged Higgs boson signal for values of $s_{\mathrm{H}}=1.0$ and $m_{\mathrm{H}_{5}}=500$ GeV in the GM model." table2.location = "Data from Table 2" table2.keywords["observables"] = ["Uncertainty"] table2.keywords["reactions"] = ["P P --> W W j j", "P P --> W Z j j"] table2.keywords["phrases"] = [ "Same-sign WW", "WZ", "Georgi-Machacek", "Charged Higgs", "VBF" ] data2 = np.loadtxt("HEPData/inputs/hig20017/systematics.txt", dtype='string', skiprows=2) print(data2) table2_data = Variable("Source of uncertainty",
from hepdata_lib import Submission submission = Submission() from hepdata_lib import Table table = Table("pa all") table.description = "description." table.location = "upper left." table.keywords["observables"] = ["pa"] from hepdata_lib import RootFileReader reader = RootFileReader("root://eosuser.cern.ch//eos/user/v/vveckaln/analysis_MC13TeV_TTJets/plots/plotter.root") Data = reader.read_hist_1d("L_pull_angle_allconst_reco_leading_jet_scnd_leading_jet_DeltaRgt1p0/L_pull_angle_allconst_reco_leading_jet_scnd_leading_jet_DeltaRgt1p0") Unc = reader.read_hist_1d("L_pull_angle_allconst_reco_leading_jet_scnd_leading_jet_DeltaRgt1p0/L_pull_angle_allconst_reco_leading_jet_scnd_leading_jet_DeltaRgt1p0_totalMCUncShape") from hepdata_lib import Variable, Uncertainty mmed = Variable("pa", is_independent=True, is_binned=False, units="rad") mmed.values = signal["x"] data = Variable("N", is_independent=False, is_binned=False, units="") data.values = Data["y"] unc = Uncertainty("Total", is_symmetric=True) unc.values = Unc["dy"] data.add_uncertainty(unc) table.add_variable(mmed) table.add_variable(data) submission.add_table(table)
from hepdata_lib import Variable import numpy as np submission = Submission() submission.read_abstract("HEPData/inputs/smp18003/abstract.txt") submission.add_link( "Webpage with all figures and tables", "https://cms-results.web.cern.ch/cms-results/public-results/publications/SMP-18-003/" ) submission.add_link("arXiv", "https://arxiv.org/abs/2012.09254") submission.add_record_id(999999999, "inspire") ### Begin Figure 2 figure2 = Table("Figure 2") figure2.description = "The measured and predicted inclusive fiducial cross sections in fb. The experimental measurement includes both statistical and systematics uncertainties. The theoretical prediction includes both the QCD scale and PDF uncertainties." figure2.location = "Data from Figure 2" figure2.keywords["observables"] = ["SIG"] figure2.keywords["phrases"] = [ "Electroweak", "Cross Section", "Proton-Proton", "Z boson production" ] figure2.keywords["reactions"] = ["PP -> Z"] figure2_load = np.loadtxt("HEPData/inputs/smp18003/cross_section_results.txt", dtype='string', skiprows=2) print(figure2_load) figure2_data = Variable("", is_independent=True, is_binned=False, units="")
from hepdata_lib import Table from hepdata_lib import Variable import numpy as np submission = Submission() submission.read_abstract("HEPData/inputs/smp20006/abstract.txt") submission.add_link("Webpage with all figures and tables", "http://cms-results.web.cern.ch/cms-results/public-results/publications/SMP-20-006/index.html") submission.add_link("arXiv", "http://arxiv.org/abs/arXiv:2009.09429") submission.add_record_id(1818160, "inspire") ### Begin Table 4 table4 = Table("Table 4") table4.description = "Systematic uncertainties of the $\mathrm{W}^\pm_{\mathrm{L}}\mathrm{W}^\pm_{\mathrm{L}}$ and $\mathrm{W}^\pm_{\mathrm{X}}\mathrm{W}^\pm_{\mathrm{T}}$, and $\mathrm{W}^\pm_{\mathrm{L}}\mathrm{W}^\pm_{\mathrm{X}}$ and $\mathrm{W}^\pm_{\mathrm{T}}\mathrm{W}^\pm_{\mathrm{T}}$ cross section measurements in units of percent." table4.location = "Data from Table 4" table4.keywords["observables"] = ["Uncertainty"] table4.keywords["reactions"] = ["P P --> W W j j"] table4.keywords["phrases"] = ["VBS", "Polarized", "Same-sign WW"] data4 = np.loadtxt("HEPData/inputs/smp20006/systematics.txt", dtype='string', skiprows=2) print(data4) table4_data = Variable("Source of uncertainty", is_independent=True, is_binned=False, units="") table4_data.values = [str(x) for x in data4[:,0]] table4_yields0 = Variable("Uncertainty", is_independent=False, is_binned=False, units="") table4_yields0.values = [float(x) for x in data4[:,1]]
def make_table(): params = [ 'r_ggH_0J_low', 'r_ggH_0J_high', 'r_ggH_1J_low', 'r_ggH_1J_med', 'r_ggH_1J_high', 'r_ggH_2J_low', 'r_ggH_2J_med', 'r_ggH_2J_high', 'r_ggH_VBFlike', 'r_ggH_BSM', 'r_qqH_VBFlike', 'r_qqH_VHhad', 'r_qqH_BSM', 'r_WH_lep', 'r_ZH_lep', 'r_ttH', 'r_tH' ] # Load results + xsbr data inputMode = "stage1p2_maximal" translatePOIs = LoadTranslations("translate/pois_%s.json" % inputMode) with open( "/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/OtherScripts/HEPdata/hepdata_lib/hig-19-015/inputs/correlations_stage1p2_maximal.json", "r") as jf: correlations = json.load(jf) with open( "/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/OtherScripts/HEPdata/hepdata_lib/hig-19-015/inputs/correlations_expected_stage1p2_maximal.json", "r") as jf: correlations_exp = json.load(jf) # Make table of results table = Table("Correlations: STXS stage 1.2 maximal merging scheme") table.description = "Observed and expected correlations between the parameters in the STXS stage 1.2 maximal merging fit." table.location = "Results from Figure 19" table.keywords["reactions"] = ["P P --> H ( --> GAMMA GAMMA ) X"] pois_x = Variable("STXS region (x)", is_independent=True, is_binned=False) pois_y = Variable("STXS region (y)", is_independent=True, is_binned=False) c = Variable("Observed correlation", is_independent=False, is_binned=False) c.add_qualifier("SQRT(S)", 13, "TeV") c.add_qualifier("ABS(YRAP(HIGGS))", '<2.5') c.add_qualifier("MH", '125.38', "GeV") c_exp = Variable("Expected correlation", is_independent=False, is_binned=False) c_exp.add_qualifier("SQRT(S)", 13, "TeV") c_exp.add_qualifier("ABS(YRAP(HIGGS))", '<2.5') c_exp.add_qualifier("MH", '125.38', "GeV") poiNames_x = [] poiNames_y = [] corr = [] corr_exp = [] for ipoi in params: for jpoi in params: poiNames_x.append(str(Translate(ipoi, translatePOIs))) poiNames_y.append(str(Translate(jpoi, translatePOIs))) # Extract correlation coefficient corr.append(correlations["%s__%s" % (ipoi, jpoi)]) corr_exp.append(correlations_exp["%s__%s" % (ipoi, jpoi)]) pois_x.values = poiNames_x pois_y.values = poiNames_y c.values = np.round(np.array(corr), 3) c_exp.values = np.round(np.array(corr_exp), 3) # Add variables to table table.add_variable(pois_x) table.add_variable(pois_y) table.add_variable(c) table.add_variable(c_exp) # Add figure table.add_image( "/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/OtherScripts/HEPdata/hepdata_lib/hig-19-015/inputs/corrMatrix_stage1p2_maximal.pdf" ) return table
submission = Submission() sig_digits = 3 sig_digits2 = 2 submission.read_abstract("HEPData/inputs/smp18006/abstract.txt") submission.add_link( "Webpage with all figures and tables", "http://cms-results.web.cern.ch/cms-results/public-results/publications/SMP-18-006/index.html" ) submission.add_link("arXiv", "http://arxiv.org/abs/arXiv:1905.07445") submission.add_record_id(1735737, "inspire") ### Begin Table 2 table2 = Table("Table 2") table2.description = "Expected yields from various background processes in $\mathrm{WV}$ and $\mathrm{ZV}$ final states. The combination of the statistical and systematic uncertainties are shown. The predicted yields are shown with their best-fit normalizations from the background-only fit. The aQGC signal yields are shown for two aQGC scenarios with $f_{T2}/ \Lambda^{4} = -0.5$ TeV$^{-4}$ and $f_{T2}/ \Lambda^{4} = -2.5$ TeV$^{-4}$ for the $\mathrm{WV}$ and $\mathrm{ZV}$ channels, respectively. The charged Higgs boson signal yields are also shown for values of $s_{\mathrm{H}}=0.5$ and $m_{\mathrm{H}_{5}}=500$ GeV in the GM model. The statistical uncertainties are shown for the expected signal yields." table2.location = "Data from Table 2" table2.keywords["observables"] = ["Events"] table2.keywords["reactions"] = ["P P --> W V j j", "P P --> Z V j j"] table2.keywords["phrases"] = [ "aQGC", "Charged Higgs", "Georgi-Machacek", "VBS" ] data2 = np.loadtxt("HEPData/inputs/smp18006/total_yields.txt", dtype='string', skiprows=2) print(data2) table2_data = Variable("Process",