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",