def test_write_images(self):
"""Test the write_images function."""
test_table = Table("Some Table")
# Get test PDF
some_pdf = "%s/minimal.pdf" % os.path.dirname(__file__)
# This should work fine
test_table.add_image(some_pdf)
testdir = tmp_directory_name()
self.addCleanup(shutil.rmtree, testdir)
try:
test_table.write_images(testdir)
except TypeError:
self.fail("Table.write_images raised an unexpected TypeError.")
# Check that output file exists
expected_file = os.path.join(testdir, "minimal.png")
self.assertTrue(os.path.exists(expected_file))
# Try wrong type of input argument
bad_arguments = [None, 5, {}, []]
for argument in bad_arguments:
with self.assertRaises(TypeError):
test_table.write_images(argument)
self.doCleanups()
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 test_add_image(self):
"""Get test PDF"""
# Get test PDF
some_pdf = "%s/minimal.pdf" % os.path.dirname(__file__)
test_table = Table("Some Table")
# This should work fine
try:
try:
test_table.add_image(some_pdf)
except RuntimeError:
self.fail("Table.add_image raised an unexpected RuntimeError.")
except TypeError:
self.fail("Table.add_image raised an unexpected TypeError.")
# Try wrong argument types
wrong_type = [None, 5, {}, []]
for argument in wrong_type:
with self.assertRaises(TypeError):
test_table.add_image(argument)
# Try non-existing paths:
nonexisting = ["/a/b/c/d/e", "./daskjl/aksj/asdasd.pdf"]
for argument in nonexisting:
with self.assertRaises(RuntimeError):
test_table.add_image(argument)
def test_write_images_multiple_executions(self):
"""
write_images is supposed to only recreate output PNG
files if the output file does not yet exist or is outdated
relative to the input file.
"""
test_table = Table("Some Table")
some_pdf = "%s/minimal.pdf" % os.path.dirname(__file__)
test_table.add_image(some_pdf)
testdir = "test_output"
self.addCleanup(shutil.rmtree, testdir)
expected_main_file = os.path.join(testdir, "minimal.png")
expected_thumbnail_file = os.path.join(testdir, "thumb_minimal.png")
# Output files should not yet exist
self.assertTrue(not os.path.exists(expected_main_file))
self.assertTrue(not os.path.exists(expected_thumbnail_file))
# Run the function
test_table.write_images(testdir)
# Output files now exist
self.assertTrue(os.path.exists(expected_main_file))
self.assertTrue(os.path.exists(expected_thumbnail_file))
# Make sure that output is not recreated if input file is unchanged
modified_time_main = os.path.getmtime(expected_main_file)
modified_time_thumbnail = os.path.getmtime(expected_thumbnail_file)
test_table.write_images(testdir)
self.assertEqual(modified_time_main, os.path.getmtime(expected_main_file))
self.assertEqual(modified_time_thumbnail, os.path.getmtime(expected_thumbnail_file))
# Make sure that a change in input file triggers recreation
os.utime(some_pdf, None)
test_table.write_images(testdir)
self.assertTrue(modified_time_main < os.path.getmtime(expected_main_file))
self.assertTrue(modified_time_thumbnail < os.path.getmtime(expected_thumbnail_file))
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)
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
fig2_ul_Mjets = Variable("Misid. jets",
is_independent=False,
is_binned=False,
units="Events per bin")
fig2_ul_Mjets.values = fig2_ul_in[:, 9]
fig2_ul_Mjets_stat = Uncertainty("stat", is_symmetric=True)
fig2_ul_Mjets_stat.values = fig2_ul_in[:, 10]
fig2_ul_Mjets.add_uncertainty(fig2_ul_Mjets_stat)
fig2_ul.add_variable(fig2_ul_pt)
fig2_ul.add_variable(fig2_ul_Data)
fig2_ul.add_variable(fig2_ul_Wgg)
fig2_ul.add_variable(fig2_ul_Mele)
fig2_ul.add_variable(fig2_ul_Others)
fig2_ul.add_variable(fig2_ul_Mjets)
fig2_ul.add_image("input/Figure_002-a.pdf")
submission.add_table(fig2_ul)
#FIGURE 2 UPPER RIGHT
fig2_ur = Table("Figure 2 (upper right)")
fig2_ur.description = "Distribution of the transverse momentum of the diphoton system for the $\mathrm{W}\gamma\gamma$ muon 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_ur.location = "Data from Figure 2 on Page 6 of the preprint"
fig2_ur.keywords["observables"] = ["Diphoton pT"]
fig2_ur.keywords["reactions"] = [
"P P --> W GAMMA GAMMA --> MUON NU GAMMA GAMMA"
]
fig2_ur_in = np.loadtxt("input/fig2_ur.txt", skiprows=1)
#diphoton pT
"$\pm$%.3f" % (datassm[4][3]),
"$\pm$%.3f" % (datassm[5][3]),
"$\pm$%.3f" % (datassm[6][3]),
"$\pm$%.3f" % (datassm[7][3]),
"$\pm$%.3f" % (datassm[8][3])
]
syst.add_qualifier("SQRT(S)", "13", "TeV")
syst.add_qualifier("LUMINOSITY", "137", "fb$^{-1}$")
tabssm.add_variable(ssmType)
tabssm.add_variable(ssm)
tabssm.add_variable(tot)
tabssm.add_variable(stat)
tabssm.add_variable(syst)
tabssm.add_image("../figures/summaryResult.png")
#tablessm.keywords()
tabssm.keywords["reactions"] = [
"P P --> TOP TOPBAR X", "P P --> TOP TOPBAR GAMMA"
]
tabssm.keywords["cmenergies"] = [13000.0]
tabssm.keywords["observables"] = ["SIG/SIG"]
tabssm.keywords["phrases"] = [
"Top", "Quark", "Photon", "lepton+jets", "semileptonic", "Cross Section",
"Proton-Proton Scattering", "Inclusive", "Differential"
]
submission.add_table(tabssm)
###
### Fig 9a
BulkG.add_qualifier("Efficiency times acceptance", "Bulk graviton --> WW")
BulkG.add_qualifier("SQRT(S)", 13, "TeV")
Wprime = Variable("Efficiency times acceptance",
is_independent=False,
is_binned=False,
units="")
Wprime.values = data[:, 2]
Wprime.add_qualifier("Efficiency times acceptance", "Wprime --> WZ")
Wprime.add_qualifier("SQRT(S)", 13, "TeV")
table.add_variable(d)
table.add_variable(BulkG)
table.add_variable(Wprime)
table.add_image("hepdata_lib/examples/example_inputs/signalEffVsMass.pdf")
submission.add_table(table)
for table in submission.tables:
table.keywords["cmenergies"] = [13000]
### Histogram
from hepdata_lib import Table
table2 = Table("Figure 4a")
table2.description = "Distribution in the reconstructed B quark mass, after applying all selections to events with no forward jet, compared to the background distributions estimated before fitting. The plot refers to the low-mass mB analysis. The expectations for signal MC events are given by the blue histogram lines. Different contributions to background are indicated by the colour-filled histograms. The grey-hatched error band shows total uncertainties in the background expectation. The ratio of observations to background expectations is given in the lower panel, together with the total uncertainties prior to fitting, indicated by the grey-hatched band."
table2.location = "Data from Figure 4 (upper left), located on page 12."
table2.keywords["observables"] = ["N"]
table2.add_image(
"hepdata_lib/examples/example_inputs/CMS-B2G-17-009_Figure_004-a.pdf")
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
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
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)