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_write_yaml(self):
"""Test write_yaml() for Table."""
test_table = Table("Some Table")
test_variable = Variable("Some Variable")
test_table.add_variable(test_variable)
try:
test_table.write_yaml("test_output")
except TypeError:
self.fail("Table.write_yaml raised an unexpected TypeError.")
with self.assertRaises(TypeError):
test_table.write_yaml(None)
def test_write_yaml(self):
"""Test write_yaml() for Table."""
test_table = Table("Some Table")
test_variable = Variable("Some Variable")
test_table.add_variable(test_variable)
testdir = tmp_directory_name()
self.addCleanup(shutil.rmtree, testdir)
try:
test_table.write_yaml(testdir)
except TypeError:
self.fail("Table.write_yaml raised an unexpected TypeError.")
with self.assertRaises(TypeError):
test_table.write_yaml(None)
self.doCleanups()
def test_yaml_output(self):
"""Test yaml dump"""
tmp_dir = tmp_directory_name()
# Create test dictionary
testlist = [("x", 1.2), ("x", 2.2), ("y", 0.12), ("y", 0.22)]
testdict = defaultdict(list)
for key, value in testlist:
testdict[key].append(value)
# Create test submission
test_submission = Submission()
test_table = Table("TestTable")
x_variable = Variable("X", is_independent=True, is_binned=False)
x_variable.values = testdict['x']
y_variable = Variable("Y", is_independent=False, is_binned=False)
y_variable.values = testdict['y']
test_table.add_variable(x_variable)
test_table.add_variable(y_variable)
test_submission.add_table(test_table)
test_submission.create_files(tmp_dir)
# Test read yaml file
table_file = os.path.join(tmp_dir, "testtable.yaml")
try:
with open(table_file, 'r') as testfile:
testyaml = yaml.safe_load(testfile)
except yaml.YAMLError as exc:
print(exc)
# Test compare yaml file to string
testtxt = (
"dependent_variables:\n- header:\n name: Y\n values:\n" +
" - value: 0.12\n - value: 0.22\nindependent_variables:\n" +
"- header:\n name: X\n values:\n - value: 1.2\n - value: 2.2\n"
)
with open(table_file, 'r') as testfile:
testyaml = testfile.read()
self.assertEqual(str(testyaml), testtxt)
self.addCleanup(os.remove, "submission.tar.gz")
self.addCleanup(shutil.rmtree, tmp_dir)
self.doCleanups()
def test_add_variable(self):
"""Test the add_variable function."""
# Verify that the type check works
test_table = Table("Some variable")
test_variable = Variable("Some variable")
test_uncertainty = Uncertainty("Some Uncertainty")
try:
test_table.add_variable(test_variable)
except TypeError:
self.fail("Table.add_variable raised an unexpected TypeError.")
with self.assertRaises(TypeError):
test_table.add_variable(5)
with self.assertRaises(TypeError):
test_table.add_variable([1, 3, 5])
with self.assertRaises(TypeError):
test_table.add_variable("a string")
with self.assertRaises(TypeError):
test_table.add_variable(test_uncertainty)
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 convertCorrMatrixToYaml( rootfile, label, variablex, variabley, unit, object="output_corr_matrix_syst", var="Syst. correlation" ):
tab = Table(label)
reader = RootFileReader(rootfile)
cov = reader.read_hist_2d(object)
xbins = Variable( variablex, is_independent=True, is_binned=True, units=unit)
xbins.values = cov["x_edges"]
ybins = Variable( variabley, is_independent=True, is_binned=True, units=unit)
ybins.values = cov["y_edges"]
data = Variable( var, is_independent=False, is_binned=False, units="%")
data.values = cov["z"]
data.add_qualifier("SQRT(S)","13","TeV")
data.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
tab.add_variable(xbins)
tab.add_variable(ybins)
tab.add_variable(data)
return tab
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 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 convertSRHistToYaml( rootfile, label, variable, unit ):
tab = Table(label)
reader = RootFileReader(rootfile)
data = reader.read_hist_1d("dataAR")
gen = reader.read_hist_1d("TTG_centralnoChgIsoNoSieiephotoncat0")
had = reader.read_hist_1d("TTG_centralnoChgIsoNoSieiephotoncat134")
misID = reader.read_hist_1d("TTG_centralnoChgIsoNoSieiephotoncat2")
qcd = reader.read_hist_1d("QCD")
uncUp = reader.read_hist_1d("totalUncertainty_up")
uncDown = reader.read_hist_1d("totalUncertainty_down")
rootfile = ROOT.TFile(rootfile,"READ")
statHist = rootfile.Get("dataAR")
unc = []
statunc = []
relunc = []
tot = []
for i, i_up in enumerate(uncUp["y"]):
stat = statHist.GetBinError(i+1)
u = abs(i_up-uncDown["y"][i])*0.5
sim = sum([ gen["y"][i], had["y"][i], misID["y"][i], qcd["y"][i] ])
unc.append(u)
statunc.append(stat)
tot.append(sim)
relunc.append(u*100./sim)
xbins = Variable( variable, is_independent=True, is_binned=True, units=unit)
xbins.values = data["x_edges"]
ydata = Variable( "Observed", is_independent=False, is_binned=False)
ydata.values = data["y"]
ydata.add_qualifier("CHANNEL","l+jets")
ydata.add_qualifier("SQRT(S)","13","TeV")
ydata.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
ytot = Variable( "Total simulation", is_independent=False, is_binned=False)
ytot.values = tot
ytot.add_qualifier("CHANNEL","l+jets")
ytot.add_qualifier("SQRT(S)","13","TeV")
ytot.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
ygen = Variable( "Genuine $\gamma$", is_independent=False, is_binned=False)
ygen.values = gen["y"]
ygen.add_qualifier("CHANNEL","l+jets")
ygen.add_qualifier("SQRT(S)","13","TeV")
ygen.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
yhad = Variable( "Hadronic $\gamma$", is_independent=False, is_binned=False)
yhad.values = had["y"]
yhad.add_qualifier("CHANNEL","l+jets")
yhad.add_qualifier("SQRT(S)","13","TeV")
yhad.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
ymisID = Variable( "Misid. e", is_independent=False, is_binned=False)
ymisID.values = misID["y"]
ymisID.add_qualifier("CHANNEL","l+jets")
ymisID.add_qualifier("SQRT(S)","13","TeV")
ymisID.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
yqcd = Variable( "Multijet", is_independent=False, is_binned=False)
yqcd.values = qcd["y"]
yqcd.add_qualifier("CHANNEL","l+jets")
yqcd.add_qualifier("SQRT(S)","13","TeV")
yqcd.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
yunc = Uncertainty( "syst" )
yunc.is_symmetric = True
yunc.values = unc
ystatunc = Uncertainty( "stat" )
ystatunc.is_symmetric = True
ystatunc.values = statunc
ydata.uncertainties.append(ystatunc)
ytot.uncertainties.append(yunc)
tab.add_variable(xbins)
tab.add_variable(ydata)
tab.add_variable(ytot)
tab.add_variable(ygen)
tab.add_variable(yhad)
tab.add_variable(ymisID)
tab.add_variable(yqcd)
return tab
def convertSRPlotToYaml():
tab = Table("Figure 6")
reader = RootFileReader("../regionPlots/SR_incl.root")
data = reader.read_hist_1d("0dc_2016data")
tot = reader.read_hist_1d("0dc_2016total")
totbkg = reader.read_hist_1d("0dc_2016total_background")
ttg = reader.read_hist_1d("0dc_2016signal")
misID = reader.read_hist_1d("0dc_2016misID")
had = reader.read_hist_1d("0dc_2016fakes")
other = reader.read_hist_1d("0dc_2016other")
wg = reader.read_hist_1d("0dc_2016WG")
qcd = reader.read_hist_1d("0dc_2016QCD")
zg = reader.read_hist_1d("0dc_2016ZG")
rootfile = ROOT.TFile("../regionPlots/SR_incl.root","READ")
totHist = rootfile.Get("0dc_2016total")
datHist = rootfile.Get("0dc_2016data")
unc = []
statunc = []
relunc = []
for i, i_tot in enumerate(tot["y"]):
u = totHist.GetBinError(i+1)
ustat = datHist.GetBinError(i+1)
unc.append(u)
statunc.append(ustat)
relunc.append(u*100./i_tot)
crBinLabel = [
"SR3, e, $M_{3}$ $<$ 280 GeV",
"SR3, e, 280 $\leq$ $M_{3}$ $<$ 420 GeV",
"SR3, e, $M_{3}$ $\geq$ 420 GeV",
"SR3, $\mu$, $M_{3}$ $<$ 280 GeV",
"SR3, $\mu$, 280 $\leq$ $M_{3}$ $<$ 420 GeV",
"SR3, $\mu$, $M_{3}$ $\geq$ 420 GeV",
"SR4p, e, $M_{3}$ $<$ 280 GeV",
"SR4p, e, 280 $\leq$ $M_{3}$ $<$ 420 GeV",
"SR4p, e, $M_{3}$ $\geq$ 420 GeV",
"SR4p, $\mu$, $M_{3}$ $<$ 280 GeV",
"SR4p, $\mu$, 280 $\leq$ $M_{3}$ $<$ 420 GeV",
"SR4p, $\mu$, $M_{3}$ $\geq$ 420 GeV",
]
xbins = Variable( "Bin", is_independent=True, is_binned=False)
xbins.values = crBinLabel
ydata = Variable( "Observed", is_independent=False, is_binned=False)
ydata.values = data["y"]
ydata.add_qualifier("SQRT(S)","13","TeV")
ydata.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
ytot = Variable( "Total simulation", is_independent=False, is_binned=False)
ytot.values = tot["y"]
ytot.add_qualifier("SQRT(S)","13","TeV")
ytot.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
ytotbkg = Variable( "Total background", is_independent=False, is_binned=False)
ytotbkg.values = totbkg["y"]
ytotbkg.add_qualifier("SQRT(S)","13","TeV")
ytotbkg.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
ywg = Variable( "$W\gamma$", is_independent=False, is_binned=False)
ywg.values = wg["y"]
ywg.add_qualifier("SQRT(S)","13","TeV")
ywg.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
yzg = Variable( "$Z\gamma$", is_independent=False, is_binned=False)
yzg.values = zg["y"]
yzg.add_qualifier("SQRT(S)","13","TeV")
yzg.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
ymisID = Variable( "Misid. e", is_independent=False, is_binned=False)
ymisID.values = misID["y"]
ymisID.add_qualifier("SQRT(S)","13","TeV")
ymisID.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
yhad = Variable( "Hadronic $\gamma$", is_independent=False, is_binned=False)
yhad.values = had["y"]
yhad.add_qualifier("SQRT(S)","13","TeV")
yhad.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
yqcd = Variable( "Multijet", is_independent=False, is_binned=False)
yqcd.values = qcd["y"]
yqcd.add_qualifier("SQRT(S)","13","TeV")
yqcd.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
yttg = Variable( "tt$\gamma$", is_independent=False, is_binned=False)
yttg.values = ttg["y"]
yttg.add_qualifier("SQRT(S)","13","TeV")
yttg.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
yother = Variable( "Other", is_independent=False, is_binned=False)
yother.values = other["y"]
yother.add_qualifier("SQRT(S)","13","TeV")
yother.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
# yunc = Variable( "Total uncertainty", is_independent=False, is_binned=False)
# yunc.values = unc
# yunc.add_qualifier("SQRT(S)","13","TeV")
# yunc.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
#yrelunc = Variable( "Rel. uncertainty (%)", is_independent=False, is_binned=False)
#yrelunc.values = relunc
yunc = Uncertainty( "syst" )
yunc.is_symmetric = True
yunc.values = unc
ystatunc = Uncertainty( "stat" )
ystatunc.is_symmetric = True
ystatunc.values = statunc
ydata.uncertainties.append(ystatunc)
ytot.uncertainties.append(yunc)
tab.add_variable(xbins)
tab.add_variable(ydata)
tab.add_variable(ytot)
tab.add_variable(ytotbkg)
tab.add_variable(yttg)
tab.add_variable(ymisID)
tab.add_variable(yhad)
tab.add_variable(yother)
tab.add_variable(ywg)
tab.add_variable(yqcd)
tab.add_variable(yzg)
# tab.add_variable(yunc)
return tab
def convertMlgHistToYaml( rootfile, label, variable, channel ):
tab = Table(label)
reader = RootFileReader(rootfile)
data = reader.read_hist_1d("dataAR")
misID = reader.read_hist_1d("TTG_centralnoChgIsoNoSieiephotoncat2")
wg = reader.read_hist_1d("WG_centralnoChgIsoNoSieiephotoncat0")
zg = reader.read_hist_1d("ZG_centralnoChgIsoNoSieiephotoncat0")
other = reader.read_hist_1d("TTG_centralnoChgIsoNoSieiephotoncat0")
had = reader.read_hist_1d("TTG_centralnoChgIsoNoSieiephotoncat134")
qcd = reader.read_hist_1d("QCD")
uncUp = reader.read_hist_1d("totalUncertainty_up")
uncDown = reader.read_hist_1d("totalUncertainty_down")
rootfile = ROOT.TFile(rootfile,"READ")
statHist = rootfile.Get("dataAR")
unc = []
statunc = []
relunc = []
tot = []
for i, i_up in enumerate(uncUp["y"]):
stat = statHist.GetBinError(i+1)
u = abs(i_up-uncDown["y"][i])*0.5
all = [ misID["y"][i], wg["y"][i], zg["y"][i], other["y"][i], had["y"][i], qcd["y"][i] ]
sim = sum(all)
unc.append(u)
statunc.append(stat)
tot.append(sim)
relunc.append(u*100./sim)
xbins = Variable( variable, is_independent=True, is_binned=True, units="GeV")
xbins.values = data["x_edges"]
ydata = Variable( "Observed", is_independent=False, is_binned=False)
ydata.values = data["y"]
ydata.add_qualifier("CHANNEL",channel)
ydata.add_qualifier("SQRT(S)","13","TeV")
ydata.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
ytot = Variable( "Total simulation", is_independent=False, is_binned=False)
ytot.values = tot
ytot.add_qualifier("CHANNEL",channel)
ytot.add_qualifier("SQRT(S)","13","TeV")
ytot.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
ymisID = Variable( "Misid. e", is_independent=False, is_binned=False)
ymisID.values = misID["y"]
ymisID.add_qualifier("CHANNEL",channel)
ymisID.add_qualifier("SQRT(S)","13","TeV")
ymisID.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
yhad = Variable( "Hadronic $\gamma$", is_independent=False, is_binned=False)
yhad.values = had["y"]
yhad.add_qualifier("CHANNEL",channel)
yhad.add_qualifier("SQRT(S)","13","TeV")
yhad.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
ywg = Variable( "W$\gamma$", is_independent=False, is_binned=False)
ywg.values = wg["y"]
ywg.add_qualifier("CHANNEL",channel)
ywg.add_qualifier("SQRT(S)","13","TeV")
ywg.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
yzg = Variable( "Z$\gamma$", is_independent=False, is_binned=False)
yzg.values = zg["y"]
yzg.add_qualifier("CHANNEL",channel)
yzg.add_qualifier("SQRT(S)","13","TeV")
yzg.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
yother = Variable( "Other", is_independent=False, is_binned=False)
yother.values = other["y"]
yother.add_qualifier("CHANNEL",channel)
yother.add_qualifier("SQRT(S)","13","TeV")
yother.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
yqcd = Variable( "Multijet", is_independent=False, is_binned=False)
yqcd.values = qcd["y"]
yqcd.add_qualifier("CHANNEL",channel)
yqcd.add_qualifier("SQRT(S)","13","TeV")
yqcd.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
# yunc = Variable( "Total systematic uncertainty", is_independent=False, is_binned=False)
# yunc.values = unc
# yunc.add_qualifier("SQRT(S)","13","TeV")
# yunc.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
#yrelunc = Variable( "Rel. uncertainty (%)", is_independent=False, is_binned=False)
#yrelunc.values = relunc
yunc = Uncertainty( "syst" )
yunc.is_symmetric = True
yunc.values = unc
ystatunc = Uncertainty( "stat" )
ystatunc.is_symmetric = True
ystatunc.values = statunc
ydata.uncertainties.append(ystatunc)
ytot.uncertainties.append(yunc)
tab.add_variable(xbins)
tab.add_variable(ydata)
tab.add_variable(ytot)
tab.add_variable(ymisID)
tab.add_variable(ywg)
tab.add_variable(yzg)
tab.add_variable(yother)
tab.add_variable(yhad)
tab.add_variable(yqcd)
# tab.add_variable(yunc)
return tab
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
is_independent=False,
is_binned=False,
units="")
figure2_yields2.digits = 0
figure2_yields2.values = [int(x) for x in figure2_load[:, 2]]
figure2_yields2.add_qualifier("", "Cross Section (fb)")
figure2_yields3 = Variable("Negative uncertainty",
is_independent=False,
is_binned=False,
units="")
figure2_yields3.digits = 0
figure2_yields3.values = [int(x) for x in figure2_load[:, 3]]
figure2_yields3.add_qualifier("", "Cross Section (fb)")
figure2.add_variable(figure2_data)
figure2.add_variable(figure2_yields1)
figure2.add_variable(figure2_yields2)
figure2.add_variable(figure2_yields3)
submission.add_table(figure2)
for figure2 in submission.tables:
figure2.keywords["cmenergies"] = [13000]
### End Figure 2
### Begin Table 2
table2 = Table("Table 2")
table2.description = "Experimental uncertainties affecting transfer factors in the analysis that is used to estimate the W background in the signal region (SR). The number of W boson events are denoted as $W_{SR}$ for the SR and in analogy as $W_{\mu\\nu}$ ($W_{e\\nu}$) for the single-muon (single-electron) control region (CR)."
table2.location = "Data from Table 2"
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 convertEFTPtHistToYaml( rootfile, label, channel ):
tab = Table(label)
reader = RootFileReader(rootfile)
data = reader.read_hist_1d("data")
tot = reader.read_hist_1d("total")
totbkg = reader.read_hist_1d("total_background")
ttg = reader.read_hist_1d("signal")
misID = reader.read_hist_1d("misID")
had = reader.read_hist_1d("fakes")
other = reader.read_hist_1d("other")
wg = reader.read_hist_1d("WG")
qcd = reader.read_hist_1d("QCD")
zg = reader.read_hist_1d("ZG")
eftbf = reader.read_hist_1d("bestFit")
eftctZ045 = reader.read_hist_1d("ctZ0.45")
eftctZI045 = reader.read_hist_1d("ctZI0.45")
eftctZm045 = reader.read_hist_1d("ctZ-0.45")
rootfile = ROOT.TFile(rootfile,"READ")
totHist = rootfile.Get("total")
datHist = rootfile.Get("data")
unc = []
statunc = []
relunc = []
for i, i_tot in enumerate(tot["y"]):
u = totHist.GetBinError(i+1)
ustat = datHist.GetBinError(i+1)
unc.append(u)
statunc.append(ustat)
relunc.append(u*100./i_tot)
xbins = Variable( "$p_{T}(\gamma)$", is_independent=True, is_binned=True, units="GeV")
xbins.values = data["x_edges"]
ydata = Variable( "Observed", is_independent=False, is_binned=False)
ydata.values = data["y"]
ydata.add_qualifier("CHANNEL",channel)
ydata.add_qualifier("SQRT(S)","13","TeV")
ydata.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
ytot = Variable( "Total simulation", is_independent=False, is_binned=False)
ytot.values = tot["y"]
ytot.add_qualifier("CHANNEL",channel)
ytot.add_qualifier("SQRT(S)","13","TeV")
ytot.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
ytotbkg = Variable( "Total background", is_independent=False, is_binned=False)
ytotbkg.values = totbkg["y"]
ytotbkg.add_qualifier("CHANNEL",channel)
ytotbkg.add_qualifier("SQRT(S)","13","TeV")
ytotbkg.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
ywg = Variable( "$W\gamma$", is_independent=False, is_binned=False)
ywg.values = wg["y"]
ywg.add_qualifier("CHANNEL",channel)
ywg.add_qualifier("SQRT(S)","13","TeV")
ywg.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
yzg = Variable( "$Z\gamma$", is_independent=False, is_binned=False)
yzg.values = zg["y"]
yzg.add_qualifier("CHANNEL",channel)
yzg.add_qualifier("SQRT(S)","13","TeV")
yzg.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
ymisID = Variable( "Misid. e", is_independent=False, is_binned=False)
ymisID.values = misID["y"]
ymisID.add_qualifier("CHANNEL",channel)
ymisID.add_qualifier("SQRT(S)","13","TeV")
ymisID.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
yhad = Variable( "Hadronic $\gamma$", is_independent=False, is_binned=False)
yhad.values = had["y"]
yhad.add_qualifier("CHANNEL",channel)
yhad.add_qualifier("SQRT(S)","13","TeV")
yhad.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
yqcd = Variable( "Multijet", is_independent=False, is_binned=False)
yqcd.values = qcd["y"]
yqcd.add_qualifier("CHANNEL",channel)
yqcd.add_qualifier("SQRT(S)","13","TeV")
yqcd.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
yttg = Variable( "tt$\gamma$", is_independent=False, is_binned=False)
yttg.values = ttg["y"]
yttg.add_qualifier("CHANNEL",channel)
yttg.add_qualifier("SQRT(S)","13","TeV")
yttg.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
yother = Variable( "Other", is_independent=False, is_binned=False)
yother.values = other["y"]
yother.add_qualifier("CHANNEL",channel)
yother.add_qualifier("SQRT(S)","13","TeV")
yother.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
yeftbf = Variable( "SM-EFT best fit", is_independent=False, is_binned=False)
yeftbf.values = eftbf["y"]
yeftbf.add_qualifier("CHANNEL",channel)
yeftbf.add_qualifier("SQRT(S)","13","TeV")
yeftbf.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
yeftctZ045 = Variable( "$c_{tZ} = 0.45$", is_independent=False, is_binned=False, units="($\Lambda$/TeV)$^2$")
yeftctZ045.values = eftctZ045["y"]
yeftctZ045.add_qualifier("CHANNEL",channel)
yeftctZ045.add_qualifier("SQRT(S)","13","TeV")
yeftctZ045.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
yeftctZI045 = Variable( "$c^I_{tZ} = 0.45$", is_independent=False, is_binned=False, units="($\Lambda$/TeV)$^2$")
yeftctZI045.values = eftctZI045["y"]
yeftctZI045.add_qualifier("CHANNEL",channel)
yeftctZI045.add_qualifier("SQRT(S)","13","TeV")
yeftctZI045.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
yeftctZm045 = Variable( "$c_{tZ} = -0.45$", is_independent=False, is_binned=False, units="($\Lambda$/TeV)$^2$")
yeftctZm045.values = eftctZm045["y"]
yeftctZm045.add_qualifier("CHANNEL",channel)
yeftctZm045.add_qualifier("SQRT(S)","13","TeV")
yeftctZm045.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
yunc = Uncertainty( "syst" )
yunc.is_symmetric = True
yunc.values = unc
ystatunc = Uncertainty( "stat" )
ystatunc.is_symmetric = True
ystatunc.values = statunc
ydata.uncertainties.append(ystatunc)
ytot.uncertainties.append(yunc)
tab.add_variable(xbins)
tab.add_variable(ydata)
tab.add_variable(ytot)
tab.add_variable(yeftbf)
tab.add_variable(yeftctZ045)
tab.add_variable(yeftctZI045)
tab.add_variable(yeftctZm045)
tab.add_variable(ytotbkg)
tab.add_variable(yttg)
tab.add_variable(yhad)
tab.add_variable(ymisID)
tab.add_variable(yother)
tab.add_variable(ywg)
tab.add_variable(yqcd)
tab.add_variable(yzg)
return tab
if fig["type_stat"].lower() == "tgraphasymmerrors":
y_stat = Uncertainty("stat. uncertainty", is_symmetric=False)
y_stat.values = stat["dy"]
y.add_uncertainty(y_stat)
elif fig["type_stat"].lower() in ["tgrapherrors", "th1"]:
y_stat = Uncertainty("stat. uncertainty", is_symmetric=True)
y_stat.values = stat["dy"]
y.add_uncertainty(y_stat)
if fig["type_syst"].lower() == "tgraphasymmerrors":
y_syst = Uncertainty("syst. uncertainty", is_symmetric=False)
y_syst.values = syst["dy"]
y.add_uncertainty(y_syst)
elif fig["type_syst"].lower() in ["tgrapherrors", "th1"]:
y_syst = Uncertainty("syst. uncertainty", is_symmetric=True)
y_syst.values = syst["dy"]
y.add_uncertainty(y_syst)
# write table
if fig["type_stat"].lower() == "th2":
table.add_variable(x1)
table.add_variable(x2)
table.add_variable(y)
else:
table.add_variable(x1)
table.add_variable(y)
submission.add_table(table)
submission.create_files("output")
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)
table2_yields2 = Variable("ZV events",
is_independent=False,
is_binned=False,
units="")
table2_yields2.values = [float(x) for x in data2[:, 3]]
table2_yields2.add_qualifier("Expected events", "ZV selection")
table2_yields2.add_qualifier("SQRT(S)", 13, "TeV")
table2_unc2 = Uncertainty("stat,syst", is_symmetric=True)
table2_unc2.values = [float(x) for x in data2[:, 4]]
table2_yields1.add_uncertainty(table2_unc1)
table2_yields2.add_uncertainty(table2_unc2)
table2.add_variable(table2_data)
table2.add_variable(table2_yields1)
table2.add_variable(table2_yields2)
submission.add_table(table2)
for table2 in submission.tables:
table2.keywords["cmenergies"] = [13000]
### End Table 2
### Begin Table 3
table3 = Table("Table 3")
table3.description = "Observed and expected lower and upper 95\% CL limits on the parameters of the quartic operators S0, S1, M0, M1, M6, M7, T0, T1, and T2 in $\mathrm{WV}$ and $\mathrm{ZV}$ channels. The last two columns show the observed and expected limits for the combination of the $\mathrm{WV}$ and $\mathrm{ZV}$ channels."
table3.location = "Data from Table 3"
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
table4_yields1.add_qualifier("SQRT(S)", 13, "TeV")
table4_yields1.add_qualifier("L$_{\mathrm{int}}$", 137, "fb$^{-1}$")
table4_yields2 = Variable("Uncertainty", is_independent=False, is_binned=False, units="")
table4_yields2.values = [float(x) for x in data4[:,3]]
table4_yields2.add_qualifier("Source of uncertainty", "$\mathrm{W}^\pm_{\mathrm{L}}\mathrm{W}^\pm_{\mathrm{X}}$")
table4_yields2.add_qualifier("SQRT(S)", 13, "TeV")
table4_yields2.add_qualifier("L$_{\mathrm{int}}$", 137, "fb$^{-1}$")
table4_yields3 = Variable("Uncertainty", is_independent=False, is_binned=False, units="")
table4_yields3.values = [float(x) for x in data4[:,4]]
table4_yields3.add_qualifier("Source of uncertainty", "$\mathrm{W}^\pm_{\mathrm{T}}\mathrm{W}^\pm_{\mathrm{T}}$")
table4_yields3.add_qualifier("SQRT(S)", 13, "TeV")
table4_yields3.add_qualifier("L$_{\mathrm{int}}$", 137, "fb$^{-1}$")
table4.add_variable(table4_data)
table4.add_variable(table4_yields0)
table4.add_variable(table4_yields1)
table4.add_variable(table4_yields2)
table4.add_variable(table4_yields3)
submission.add_table(table4)
for table4 in submission.tables:
table4.keywords["cmenergies"] = [13000]
### End Table 4
### Begin Table 5
table5 = Table("Table 5")
table5.description = "Expected yields from various SM processes and observed data events in WW SR. The combination of the statistical and systematic uncertainties is shown. The expected yields are shown with their best fit normalizations from the simultaneous fit for the $\mathrm{W}^\pm_{\mathrm{L}}\mathrm{W}^\pm_{\mathrm{L}}$ and $\mathrm{W}^\pm_{\mathrm{X}}\mathrm{W}^\pm_{\mathrm{T}}$ cross sections. The $\mathrm{W}^\pm_{\mathrm{L}}\mathrm{W}^\pm_{\mathrm{T}}$ and $\mathrm{W}^\pm_{\mathrm{T}}\mathrm{W}^\pm_{\mathrm{T}}$ yields are obtained from the $\mathrm{W}^\pm_{\mathrm{X}}\mathrm{W}^\pm_{\mathrm{T}}$ yield assuming the SM prediction for the ratio of the yields. The tVx background yield includes the contributions from tt$\mathrm{V}$ and tZq processes."
table2_yields0.add_qualifier("SQRT(S)", 13, "TeV")
table2_yields0.add_qualifier("L$_{\mathrm{int}}$", 137, "fb$^{-1}$")
table2_yields1 = Variable("Uncertainty",
is_independent=False,
is_binned=False,
units="")
table2_yields1.values = [float(x) for x in data2[:, 2]]
table2_yields1.add_qualifier(
"Source of uncertainty",
"$\Delta \mu$ for $s_{\mathrm{H}}=1.0$ and $m_{\mathrm{H}_{5}}=500~\mathrm{GeV}$"
)
table2_yields1.add_qualifier("SQRT(S)", 13, "TeV")
table2_yields1.add_qualifier("L$_{\mathrm{int}}$", 137, "fb$^{-1}$")
table2.add_variable(table2_data)
table2.add_variable(table2_yields0)
table2.add_variable(table2_yields1)
submission.add_table(table2)
for table2 in submission.tables:
table2.keywords["cmenergies"] = [13000]
### End Table 2
### Begin Table 3
table3 = Table("Table 3")
table3.description = "Expected signal and background yields from various SM processes and observed data events in all regions used in the analysis. The expected background yields are shown with their normalizations from the simultaneous fit for the background-only hypothesis, i.e., assuming no contributions from the $\mathrm{H}^{\pm}$ and $\mathrm{H}^{\pm\pm}$ processes. The expected signal yields are shown for $s_{\mathrm{H}}=1.0$ in the GM model. The combination of the statistical and systematic uncertainties is shown."
table3.location = "Data from Table 3"
is_independent=False,
is_binned=False,
units="")
BulkG.values = data[:, 1]
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."
fig2_ul_Others.values = fig2_ul_in[:, 7]
fig2_ul_Others_stat = Uncertainty("stat", is_symmetric=True)
fig2_ul_Others_stat.values = fig2_ul_in[:, 8]
fig2_ul_Others.add_uncertainty(fig2_ul_Others_stat)
#Misid. jets
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"] = [
def convertUnfoldingHistToYaml( rootfile, label, variable, unit ):
tab = Table(label)
reader = RootFileReader(rootfile)
data = reader.read_hist_1d("unfoled_spectrum")
simP8 = reader.read_hist_1d("fiducial_spectrum")
simHpp = reader.read_hist_1d("fiducial_spectrum_Hpp")
simH7 = reader.read_hist_1d("fiducial_spectrum_H7")
totalUncUp = reader.read_hist_1d("totalUncertainty_up")
mcstatUncUp = reader.read_hist_1d("mcStat_up")
matrixUncUp = reader.read_hist_1d("totalMatrixVariationUnc_up")
statUncUp = reader.read_hist_1d("stat_up")
totunc = []
reltotunc = []
statunc = []
relstatunc = []
for i, i_up in enumerate(totalUncUp["y"]):
tot = data["y"][i]
utot = (i_up - tot)**2
utot += (mcstatUncUp["y"][i] - tot)**2
utot += matrixUncUp["y"][i]**2
utot = sqrt(utot)
ustat = statUncUp["y"][i] - tot
totunc.append(utot)
statunc.append(ustat)
reltotunc.append(utot*100./tot)
relstatunc.append(ustat*100./tot)
xbins = Variable( variable, is_independent=True, is_binned=True, units=unit)
xbins.values = data["x_edges"]
ydata = Variable( "Observed", is_independent=False, is_binned=False)
ydata.values = data["y"]
ydata.add_qualifier("SQRT(S)","13","TeV")
ydata.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
yunc = Uncertainty( "total" )
yunc.is_symmetric = True
yunc.values = totunc
ystatunc = Uncertainty( "stat" )
ystatunc.is_symmetric = True
ystatunc.values = statunc
# ydata.uncertainties.append(ystatunc)
# ydata.uncertainties.append(yunc)
ysimP8 = Variable( "Simulation MG5_aMC + Pythia8", is_independent=False, is_binned=False)
ysimP8.values = simP8["y"]
ysimP8.add_qualifier("SQRT(S)","13","TeV")
ysimP8.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
ysimH7 = Variable( "Simulation MG5_aMC + Herwig7", is_independent=False, is_binned=False)
ysimH7.values = simH7["y"]
ysimH7.add_qualifier("SQRT(S)","13","TeV")
ysimH7.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
ysimHpp = Variable( "Simulation MG5_aMC + Herwig++", is_independent=False, is_binned=False)
ysimHpp.values = simHpp["y"]
ysimHpp.add_qualifier("SQRT(S)","13","TeV")
ysimHpp.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
tab.add_variable(xbins)
tab.add_variable(ydata)
tab.add_variable(ysimP8)
tab.add_variable(ysimH7)
tab.add_variable(ysimHpp)
return tab
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
def convertWJetsHistToYaml( rootfile, label, channel ):
tab = Table(label)
reader = RootFileReader(rootfile)
data = reader.read_hist_1d("dataAR")
# ttg = reader.read_hist_1d("TTG_centralall")
top = reader.read_hist_1d("Top_centralall")
dy = reader.read_hist_1d("DY_LO_centralall")
wjets = reader.read_hist_1d("WJets_centralall")
# wg = reader.read_hist_1d("WG_centralall")
# zg = reader.read_hist_1d("ZG_centralall")
other = reader.read_hist_1d("other_centralall")
qcd = reader.read_hist_1d("QCD")
uncUp = reader.read_hist_1d("totalUncertainty_up")
uncDown = reader.read_hist_1d("totalUncertainty_down")
rootfile = ROOT.TFile(rootfile,"READ")
statHist = rootfile.Get("dataAR")
unc = []
statunc = []
relunc = []
tot = []
for i, i_up in enumerate(uncUp["y"]):
stat = statHist.GetBinError(i+1)
u = abs(i_up-uncDown["y"][i])*0.5
# all = [ ttg["y"][i], top["y"][i], dy["y"][i], wjets["y"][i], wg["y"][i], zg["y"][i], other["y"][i], qcd["y"][i] ]
all = [ top["y"][i], dy["y"][i], wjets["y"][i], other["y"][i], qcd["y"][i] ]
sim = sum(all)
unc.append(u)
statunc.append(stat)
tot.append(sim)
relunc.append(u*100./sim)
xbins = Variable( "$m_{T}(W)$", is_independent=True, is_binned=True, units="GeV")
xbins.values = data["x_edges"]
ydata = Variable( "Observed", is_independent=False, is_binned=False)
ydata.values = data["y"]
ydata.add_qualifier("CHANNEL",channel)
ydata.add_qualifier("SQRT(S)","13","TeV")
ydata.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
ytot = Variable( "Total simulation", is_independent=False, is_binned=False)
ytot.values = tot
ytot.add_qualifier("CHANNEL",channel)
ytot.add_qualifier("SQRT(S)","13","TeV")
ytot.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
# yttg = Variable( "tt$\gamma$", is_independent=False, is_binned=False)
# yttg.values = ttg["y"]
# yttg.add_qualifier("CHANNEL",channel)
# yttg.add_qualifier("SQRT(S)","13","TeV")
# yttg.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
ytop = Variable( "t/tt", is_independent=False, is_binned=False)
ytop.values = top["y"]
ytop.add_qualifier("CHANNEL",channel)
ytop.add_qualifier("SQRT(S)","13","TeV")
ytop.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
ydy = Variable( "Drell-Yan", is_independent=False, is_binned=False)
ydy.values = dy["y"]
ydy.add_qualifier("CHANNEL",channel)
ydy.add_qualifier("SQRT(S)","13","TeV")
ydy.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
ywjets = Variable( "W+jets", is_independent=False, is_binned=False)
ywjets.values = wjets["y"]
ywjets.add_qualifier("CHANNEL",channel)
ywjets.add_qualifier("SQRT(S)","13","TeV")
ywjets.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
# ywg = Variable( "W$\gamma$", is_independent=False, is_binned=False)
# ywg.values = wg["y"]
# ywg.add_qualifier("CHANNEL",channel)
# ywg.add_qualifier("SQRT(S)","13","TeV")
# ywg.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
# yzg = Variable( "Z$\gamma$", is_independent=False, is_binned=False)
# yzg.values = zg["y"]
# yzg.add_qualifier("CHANNEL",channel)
# yzg.add_qualifier("SQRT(S)","13","TeV")
# yzg.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
yother = Variable( "Other", is_independent=False, is_binned=False)
yother.values = other["y"]
yother.add_qualifier("CHANNEL",channel)
yother.add_qualifier("SQRT(S)","13","TeV")
yother.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
yqcd = Variable( "Multijet", is_independent=False, is_binned=False)
yqcd.values = qcd["y"]
yqcd.add_qualifier("CHANNEL",channel)
yqcd.add_qualifier("SQRT(S)","13","TeV")
yqcd.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
# yunc = Variable( "Total systematic uncertainty", is_independent=False, is_binned=False)
# yunc.values = unc
# yunc.add_qualifier("SQRT(S)","13","TeV")
# yunc.add_qualifier("LUMINOSITY","137","fb$^{-1}$")
#yrelunc = Variable( "Rel. uncertainty (%)", is_independent=False, is_binned=False)
#yrelunc.values = relunc
yunc = Uncertainty( "syst" )
yunc.is_symmetric = True
yunc.values = unc
ystatunc = Uncertainty( "stat" )
ystatunc.is_symmetric = True
ystatunc.values = statunc
ydata.uncertainties.append(ystatunc)
ytot.uncertainties.append(yunc)
tab.add_variable(xbins)
tab.add_variable(ydata)
tab.add_variable(ytot)
tab.add_variable(ywjets)
tab.add_variable(yqcd)
tab.add_variable(ydy)
tab.add_variable(ytop)
tab.add_variable(yother)
# tab.add_variable(ywg)
# tab.add_variable(yzg)
# tab.add_variable(yttg)
# tab.add_variable(yunc)
return tab
"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"]
y_covariance_mm_Rap = Variable("Bin Y", is_independent=True, is_binned=False)
y_covariance_mm_Rap.values = data_covariance_mm_Rap["y"]
z_covariance_mm_Rap = Variable("covariance Matrix",
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$^{-1}$")
unc = Uncertainty("total")
unc.is_symmetric = True
unc.values = [0.29, 0.27, 0.17, 0.10, 0.08]
value.uncertainties.append(unc)
tabSF.add_variable(sfType)
tabSF.add_variable(value)
tabSF.keywords["reactions"] = [
"P P --> TOP TOPBAR X", "P P --> TOP TOPBAR GAMMA"
]
tabSF.keywords["cmenergies"] = [13000.0]
tabSF.keywords["phrases"] = [
"Top", "Quark", "Photon", "lepton+jets", "semileptonic", "Cross Section",
"Proton-Proton Scattering", "Inclusive", "Differential"
]
submission.add_table(tabSF)
###
### Fig 6
###
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)
submission.create_files("example_output")
