def test_scale_values(self):
'''Test behavior of Uncertainty.scale_values function'''
values = list(range(0, 300, 1))
uncertainty = [x + random.uniform(0, 2) for x in values]
testvar = Variable("testvar")
testvar.is_binned = False
testvar.units = "GeV"
testvar.values = values
testunc = Uncertainty("testunc")
testunc.is_symmetric = True
testunc.values = uncertainty
testvar.uncertainties.append(testunc)
assert testvar.values == values
self.assertTrue(testunc.values == uncertainty)
for factor in [random.uniform(0, 10000) for x in range(100)]:
# Check that scaling works
testvar.scale_values(factor)
scaled_values = [factor * x for x in values]
scaled_uncertainty = [factor * x for x in uncertainty]
self.assertTrue(all(test_utilities.float_compare(x, y)
for x, y in zip(testvar.values, scaled_values)))
self.assertTrue(all(test_utilities.float_compare(x, y)
for x, y in zip(testunc.values, scaled_uncertainty)))
# Check that inverse also works
testvar.scale_values(1. / factor)
self.assertTrue(all(test_utilities.float_compare(x, y)
for x, y in zip(testvar.values, values)))
self.assertTrue(all(test_utilities.float_compare(x, y)
for x, y in zip(testunc.values, uncertainty)))
def test_set_values_from_intervals(self):
'''Test behavior of Uncertainy.test_set_values_from_intervals function'''
# Dummy central values and variatons relative to central value
npoints = 100
values = list(range(0, npoints, 1))
uncertainty = [(-random.uniform(0, 1), random.uniform(0, 1)) for _ in range(100)]
# Convert +- error to interval bounds
intervals = [(val + unc_minus, val + unc_plus)
for val, (unc_minus, unc_plus) in zip(values, uncertainty)]
# Reference uses "normal" assignment
refunc = Uncertainty("reference_unc")
refunc.is_symmetric = False
refunc.values = uncertainty
# Test uses new function
testunc = Uncertainty("test_unc")
testunc.is_symmetric = False
testunc.set_values_from_intervals(intervals, nominal=values)
# Check that both agree
self.assertTrue(all((test_utilities.tuple_compare(tup1, tup2) \
for tup1, tup2 in zip(testunc.values, refunc.values))))
def test_add_uncertainty(self):
'''Test behavior of Variable.add_uncertainty function'''
var = Variable("testvar")
var.is_binned = False
var.values = range(5)
# Normal behavior
unc = Uncertainty("testunc")
unc.is_symmetric = True
unc.values = [x * 0.1 for x in var.values]
var.add_uncertainty(unc)
self.assertTrue(len(var.uncertainties) == 1)
self.assertTrue(var.uncertainties[0] == unc)
# Reset variable but leave uncertainty as is
var.uncertainties = []
var.values = []
var.add_uncertainty(unc)
self.assertTrue(len(var.uncertainties) == 1)
self.assertTrue(var.uncertainties[0] == unc)
# Exception testing
var.values = range(5)
def wrong_input_type():
'''Call add_uncertainty with invalid input type.'''
var.add_uncertainty("this is not a proper input argument")
self.assertRaises(TypeError, wrong_input_type)
def wrong_input_properties():
'''Call add_uncertainty with invalid input properties.'''
unc2 = Uncertainty("testunc2")
unc2.is_symmetric = True
unc2.values = unc.values + [3]
var.add_uncertainty(unc2)
self.assertRaises(ValueError, wrong_input_properties)
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 test_make_dict(self):
"""Test the make_dict function."""
# pylint: disable=no-self-use
var = Variable("testvar")
# With or without units
for units in ["", "GeV"]:
var.units = units
# Binned
var.is_binned = False
var.values = [1, 2, 3]
var.make_dict()
# Unbinned
var.is_binned = True
var.values = [(0, 1), (1, 2), (2, 3)]
var.make_dict()
# With symmetric uncertainty
unc1 = Uncertainty("unc1")
unc1.is_symmetric = True
unc1.values = [random.random() for _ in range(len(var.values))]
var.add_uncertainty(unc1)
var.make_dict()
# With asymmetric uncertainty
unc2 = Uncertainty("unc2")
unc2.is_symmetric = False
unc2.values = [(-random.random(), random.random())
for _ in range(len(var.values))]
var.add_uncertainty(unc2)
var.make_dict()
# With qualifiers (which only apply to dependent variables)
var.is_independent = False
var.add_qualifier("testqualifier1", 1, units="GeV")
var.add_qualifier("testqualifier2", 1, units="")
var.make_dict()
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 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_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_in = np.loadtxt("input/fig2_ul.txt", skiprows=1)
#diphoton pT
fig2_ul_pt = Variable("$p_T^{\gamma\gamma}$",
is_independent=True,
is_binned=False,
units="GeV")
fig2_ul_pt.values = fig2_ul_in[:, 0]
#Data
fig2_ul_Data = Variable("Data",
is_independent=False,
is_binned=False,
units="Events per bin")
fig2_ul_Data.values = fig2_ul_in[:, 1]
fig2_ul_Data_stat = Uncertainty("stat", is_symmetric=True)
fig2_ul_Data_stat.values = fig2_ul_in[:, 2]
fig2_ul_Data.add_uncertainty(fig2_ul_Data_stat)
#Wgg
fig2_ul_Wgg = Variable("$\mathrm{W}\gamma\gamma$",
is_independent=False,
is_binned=False,
units="Events per bin")
fig2_ul_Wgg.values = fig2_ul_in[:, 3]
fig2_ul_Wgg_stat = Uncertainty("stat", is_symmetric=True)
fig2_ul_Wgg_stat.values = fig2_ul_in[:, 4]
fig2_ul_Wgg.add_uncertainty(fig2_ul_Wgg_stat)
#Misid. electrons
fig2_ul_Mele = Variable("Misid. electrons",
table5.keywords["observables"] = ["Events"]
table5.keywords["reactions"] = ["P P --> W W j j"]
table5.keywords["phrases"] = ["VBS", "Polarized", "Same-sign WW"]
data5 = np.loadtxt("HEPData/inputs/smp20006/total_yields_llfit.txt", dtype='string')
print(data5)
table5_data = Variable("Process", is_independent=True, is_binned=False, units="")
table5_data.values = [str(x) for x in data5[:,0]]
table5_yields0 = Variable("Yields in WW signal region", is_independent=False, is_binned=False, units="")
table5_yields0.values = [float(x) for x in data5[:,1]]
table5_yields0.add_qualifier("SQRT(S)", 13, "TeV")
table5_unc0 = Uncertainty("total uncertainty", is_symmetric=True)
table5_unc0.values = [float(x) for x in data5[:,2]]
table5_yields0.add_uncertainty(table5_unc0)
table5_yields0.add_qualifier("L$_{\mathrm{int}}$", 137, "fb$^{-1}$")
table5.add_variable(table5_data)
table5.add_variable(table5_yields0)
submission.add_table(table5)
for table5 in submission.tables:
table5.keywords["cmenergies"] = [13000]
###End Table 5
### Begin Table 6
table6 = Table("Table 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")
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
# read points
if fig["type_stat"].lower() == "th2":
x1 = Variable(fig["x1_name"], is_independent=True, is_binned=False, units=fig["x1_units"])
x1.values = stat["x"]
x2 = Variable(fig["x2_name"], is_independent=True, is_binned=False, units=fig["x2_units"])
x2.values = stat["y"]
y = Variable(fig["y_name"], is_independent=False, is_binned=False, units=fig["y_units"])
y.values = stat["z"]
else:
x1 = Variable(fig["x1_name"], is_independent=True, is_binned=False, units=fig["x1_units"])
x1.values = stat["x"]
y = Variable(fig["y_name"], is_independent=False, is_binned=False, units=fig["y_units"])
y.values = stat["y"]
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)
tabSF.location = "Table 4"
sfType = Variable("Scale factor",
is_independent=True,
is_binned=False,
units="")
sfType.values = [
"Misidentified electrons (2016)", "Misidentified electrons (2017)",
"Misidentified electrons (2018)", "Z$\gamma$ normalization",
"W$\gamma$ normalization"
]
value = Variable("Value", is_independent=False, is_binned=False, units="")
value.values = [2.25, 2.00, 1.52, 1.01, 1.13]
value.add_qualifier("SQRT(S)", "13", "TeV")
value.add_qualifier("LUMINOSITY", "137", "fb$^{-1}$")
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"
]
dtype='string',
skiprows=2)
print(data3)
table3_data = Variable("Process",
is_independent=True,
is_binned=False,
units="")
table3_data.values = [str(x) for x in data3[:, 0]]
table3_yields0 = Variable("$\mathrm{W}^{\pm}\mathrm{W}^{\pm}$ SR",
is_independent=False,
is_binned=False,
units="")
table3_unc0 = Uncertainty("total uncertainty", is_symmetric=True)
table3_yields0.values = [float(x) for x in data3[:, 1]]
table3_unc0.values = [float(x) for x in data3[:, 2]]
table3_yields0.add_uncertainty(table3_unc0)
table3_yields0.add_qualifier("SQRT(S)", 13, "TeV")
table3_yields0.add_qualifier("L$_{\mathrm{int}}$", 137, "fb$^{-1}$")
table3_yields1 = Variable("WZ SR",
is_independent=False,
is_binned=False,
units="")
table3_unc1 = Uncertainty("total uncertainty", is_symmetric=True)
table3_yields1.values = [float(x) for x in data3[:, 3]]
table3_unc1.values = [float(x) for x in data3[:, 4]]
table3_yields1.add_uncertainty(table3_unc1)
table3_yields1.add_qualifier("SQRT(S)", 13, "TeV")
units="")
wjets.values = WJets["y"]
zjets = Variable("Number of zjets events",
is_independent=False,
is_binned=False,
units="")
zjets.values = ZJets["y"]
data = Variable("Number of data events",
is_independent=False,
is_binned=False,
units="")
data.values = Data["y"]
unc_totalbackground = Uncertainty("total uncertainty", is_symmetric=True)
unc_totalbackground.values = TotalBackground["dy"]
unc_data = Uncertainty("Poisson errors", is_symmetric=True)
unc_data.values = Data["dy"]
totalbackground.add_uncertainty(unc_totalbackground)
data.add_uncertainty(unc_data)
table2.add_variable(mmed)
table2.add_variable(sig)
table2.add_variable(totalbackground)
table2.add_variable(tt)
table2.add_variable(qcd)
table2.add_variable(zjets)
table2.add_variable(wjets)
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', '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 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
histoResultXS_0.keys()
mmed_FigAux2a = Variable("$p_{T}$",
is_independent=True,
is_binned=True,
units="GeV")
mmed_FigAux2a.values = histoResultXS_0["x_edges"]
# y-axis: N events
unfoldFigAux2a = Variable("Z to nn/ll cross section (pb)",
is_independent=False,
is_binned=False,
units="")
unfoldFigAux2a.values = histoResultXS_0["y"]
unc_unfoldFigAux2a = Uncertainty("", is_symmetric=True)
unc_unfoldFigAux2a.values = histoResultXS_0["dy"]
unfoldFigAux2a.add_uncertainty(unc_unfoldFigAux2a)
tableFigAux2a.add_variable(mmed_FigAux2a)
tableFigAux2a.add_variable(unfoldFigAux2a)
submission.add_table(tableFigAux2a)
### End FigAux2a
### Begin FigAux2b
reader_FigAux2b = RootFileReader("HEPData/inputs/smp18003/ZnnSystHist.root")
tableFigAux2b = Table("Figure Aux2b")
tableFigAux2b.description = "Cross section measurements in bins of Z $p_{T}$ on neutrinos and charged leptons."
tableFigAux2b.location = "Data from Figure Aux2b"
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 wrong_input_properties():
'''Call add_uncertainty with invalid input properties.'''
unc2 = Uncertainty("testunc2")
unc2.is_symmetric = True
unc2.values = unc.values + [3]
var.add_uncertainty(unc2)
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_data = Variable("Process",
is_independent=True,
is_binned=False,
units="none")
table2_data.values = [str(x) for x in data2[:, 0]]
table2_yields1 = Variable("WV events",
is_independent=False,
is_binned=False,
units="")
table2_yields1.values = [float(x) for x in data2[:, 1]]
table2_yields1.add_qualifier("Expected events", "WV selection")
table2_yields1.add_qualifier("SQRT(S)", 13, "TeV")
table2_yields1.add_qualifier("L$_{\mathrm{int}}$", 36.9, "fb$^{-1}$")
table2_unc1 = Uncertainty("stat,syst", is_symmetric=True)
table2_unc1.values = [float(x) for x in data2[:, 2]]
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)
