def test_nested_files_to_copy(self):
"""Test that file copying works when tables have files."""
# Create random test file
testfile = "testfile.txt"
with open(testfile, "w") as f:
f.write("test")
self.addCleanup(os.remove, testfile)
# Output files
testdirectory = "./testout"
self.addCleanup(shutil.rmtree, testdirectory)
self.addCleanup(os.remove, "submission.tar.gz")
# Add resource to table, add table to Submission
sub = Submission()
tab = Table('test')
tab.add_additional_resource("a_resource", testfile, True)
sub.add_table(tab)
# Write outputs
sub.create_files(testdirectory)
# Check that test file is actually in the tar ball
with tarfile.open("submission.tar.gz", "r:gz") as tar:
try:
tar.getmember(testfile)
except KeyError:
self.fail(
"Submission.create_files failed to write all files to tar ball."
)
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 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_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 (expected)")
table.description = "Expected likelihood surface."
table.location = "Results from additional material"
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("Expected -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_g_vs_kappa_gam.pdf")
return table
def test_add_image(self):
"""Get test PDF"""
# Get test PDF
some_pdf = "%s/minimal.pdf" % os.path.dirname(__file__)
test_table = Table("Some Table")
# This should work fine
try:
try:
test_table.add_image(some_pdf)
except RuntimeError:
self.fail("Table.add_image raised an unexpected RuntimeError.")
except TypeError:
self.fail("Table.add_image raised an unexpected TypeError.")
# Try wrong argument types
wrong_type = [None, 5, {}, []]
for argument in wrong_type:
with self.assertRaises(TypeError):
test_table.add_image(argument)
# Try non-existing paths:
nonexisting = ["/a/b/c/d/e", "./daskjl/aksj/asdasd.pdf"]
for argument in nonexisting:
with self.assertRaises(RuntimeError):
test_table.add_image(argument)
def test_write_images(self):
"""Test the write_images function."""
test_table = Table("Some Table")
# Get test PDF
some_pdf = "%s/minimal.pdf" % os.path.dirname(__file__)
# This should work fine
test_table.add_image(some_pdf)
testdir = tmp_directory_name()
self.addCleanup(shutil.rmtree, testdir)
try:
test_table.write_images(testdir)
except TypeError:
self.fail("Table.write_images raised an unexpected TypeError.")
# Check that output file exists
expected_file = os.path.join(testdir, "minimal.png")
self.assertTrue(os.path.exists(expected_file))
# Try wrong type of input argument
bad_arguments = [None, 5, {}, []]
for argument in bad_arguments:
with self.assertRaises(TypeError):
test_table.write_images(argument)
self.doCleanups()
def test_create_files_with_removal(self):
"""Test the removal of old files in create_files()"""
testdir = tmp_directory_name()
# Step 1: Create test directory containing random file
os.makedirs(testdir)
self.addCleanup(shutil.rmtree, testdir)
testfile = os.path.join(testdir, "test.txt")
with open(testfile, "w") as f:
f.write("test")
self.assertTrue(os.path.isfile(testfile))
# Step 2: Create submission and write output to test directory
# Without overwriting of files
test_submission = Submission()
tab = Table("test")
test_submission.add_table(tab)
test_submission.create_files(testdir, remove_old=False)
# Test file should still exist
self.assertTrue(os.path.isfile(testfile))
# Step 3: Recreate submission files with removal
test_submission.create_files(testdir, remove_old=True)
# Test file should no longer exist
self.assertFalse(os.path.isfile(testfile))
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_create_files(self):
"""Test create_files() for Submission."""
testdir = tmp_directory_name()
test_submission = Submission()
tab = Table("test")
test_submission.add_table(tab)
test_submission.create_files(testdir)
self.doCleanups()
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_images_multiple_executions(self):
"""
write_images is supposed to only recreate output PNG
files if the output file does not yet exist or is outdated
relative to the input file.
"""
test_table = Table("Some Table")
some_pdf = "%s/minimal.pdf" % os.path.dirname(__file__)
test_table.add_image(some_pdf)
testdir = "test_output"
self.addCleanup(shutil.rmtree, testdir)
expected_main_file = os.path.join(testdir, "minimal.png")
expected_thumbnail_file = os.path.join(testdir, "thumb_minimal.png")
# Output files should not yet exist
self.assertTrue(not os.path.exists(expected_main_file))
self.assertTrue(not os.path.exists(expected_thumbnail_file))
# Run the function
test_table.write_images(testdir)
# Output files now exist
self.assertTrue(os.path.exists(expected_main_file))
self.assertTrue(os.path.exists(expected_thumbnail_file))
# Make sure that output is not recreated if input file is unchanged
modified_time_main = os.path.getmtime(expected_main_file)
modified_time_thumbnail = os.path.getmtime(expected_thumbnail_file)
test_table.write_images(testdir)
self.assertEqual(modified_time_main, os.path.getmtime(expected_main_file))
self.assertEqual(modified_time_thumbnail, os.path.getmtime(expected_thumbnail_file))
# Make sure that a change in input file triggers recreation
os.utime(some_pdf, None)
test_table.write_images(testdir)
self.assertTrue(modified_time_main < os.path.getmtime(expected_main_file))
self.assertTrue(modified_time_thumbnail < os.path.getmtime(expected_thumbnail_file))
def test_name_checks(self):
"""Test the table name checks."""
# This should work fine
good_name = "64 characters or fewer"
try:
good_table = Table(good_name)
except ValueError:
self.fail("Table initializer raised unexpected ValueError.")
self.assertEqual(good_name, good_table.name)
# Check name that is too long
too_long_name = "x"*100
# In the initializer
with self.assertRaises(ValueError):
_bad_table = Table(too_long_name)
# Using the setter
with self.assertRaises(ValueError):
good_table.name = too_long_name
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_copy_files(self):
"""Test the copy_files function."""
test_table = Table("Some Table")
some_pdf = "%s/minimal.pdf" % os.path.dirname(__file__)
testdir = tmp_directory_name()
self.addCleanup(shutil.rmtree, testdir)
os.makedirs(testdir)
test_table.add_additional_resource("a plot",some_pdf, copy_file=True)
test_table.copy_files(testdir)
def test_add_table_typechecks(self):
"""Test the type checks in the add_table function."""
# Verify that the type check works
test_submission = Submission()
test_table = Table("Some Table")
test_variable = Variable("Some Variable")
test_uncertainty = Uncertainty("Some Uncertainty")
try:
test_submission.add_table(test_table)
except TypeError:
self.fail("Submission.add_table raised an unexpected TypeError.")
with self.assertRaises(TypeError):
test_submission.add_table(5)
with self.assertRaises(TypeError):
test_submission.add_table([1, 3, 5])
with self.assertRaises(TypeError):
test_submission.add_table("a string")
with self.assertRaises(TypeError):
test_submission.add_table(test_variable)
with self.assertRaises(TypeError):
test_submission.add_table(test_uncertainty)
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
tableF4b.keywords["reactions"] = [
"P P --> TOP TOPBAR X", "P P --> TOP TOPBAR GAMMA"
]
tableF4b.keywords["cmenergies"] = [13000.0]
tableF4b.keywords["observables"] = ["N"]
tableF4b.keywords["phrases"] = [
"Top", "Quark", "Photon", "lepton+jets", "semileptonic", "Cross Section",
"Proton-Proton Scattering", "Inclusive", "Differential"
]
#tableF4b.keywords()
submission.add_table(tableF4b)
###
### SF table
###
tabSF = Table("Table 4")
tabSF.description = "Extracted scale factors for the contribution from misidentified electrons for the three data-taking periods, and the Z$\gamma$, W$\gamma$ simulations."
tabSF.location = "Table 4"
sfType = Variable("Scale factor",
is_independent=True,
is_binned=False,
units="")
sfType.values = [
"Misidentified electrons (2016)", "Misidentified electrons (2017)",
"Misidentified electrons (2018)", "Z$\gamma$ normalization",
"W$\gamma$ normalization"
]
value = Variable("Value", is_independent=False, is_binned=False, units="")
value.values = [2.25, 2.00, 1.52, 1.01, 1.13]
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 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
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
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 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 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 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
"HEPData/inputs/smp17010/folders_dressedleptons/output_root/matrix03__XSRatioSystPt.root"
)
# Read the histogram
data_covariance_mm_Pt = reader_covariance_mm_Pt.read_hist_2d(
"covariance_totsum_0")
# Create variable objects
x_covariance_mm_Pt = Variable("Bin X", is_independent=True, is_binned=True)
x_covariance_mm_Pt.values = data_covariance_mm_Pt["x_edges"]
y_covariance_mm_Pt = Variable("Bin Y", is_independent=True, is_binned=False)
y_covariance_mm_Pt.values = data_covariance_mm_Pt["y"]
z_covariance_mm_Pt = Variable("covariance Matrix",
is_independent=False,
is_binned=False)
z_covariance_mm_Pt.values = data_covariance_mm_Pt["z"]
table_covariance_XSRatio_mm_Pt = Table("cov matr norm xs aux 1a")
table_covariance_XSRatio_mm_Pt.description = "Covariance matrix for normalized cross sections using dressed level leptons for all bins used in bins of Z pt in the dimuon final state."
table_covariance_XSRatio_mm_Pt.location = "Supplementary material"
for var in [x_covariance_mm_Pt, y_covariance_mm_Pt, z_covariance_mm_Pt]:
table_covariance_XSRatio_mm_Pt.add_variable(var)
submission.add_table(table_covariance_XSRatio_mm_Pt)
# Create a reader for the input file
reader_covariance_mm_Rap = RootFileReader(
"HEPData/inputs/smp17010/folders_dressedleptons/output_root/matrix03__XSRatioSystRap.root"
)
# Read the histogram
data_covariance_mm_Rap = reader_covariance_mm_Rap.read_hist_2d(
"covariance_totsum_0")
# Create variable objects
x_covariance_mm_Rap = Variable("Bin X", is_independent=True, is_binned=True)
def make_table():
params = ['r_ggH', 'r_VBF', 'r_VH', 'r_top']
# Load results + xsbr data
inputMode = "mu"
translatePOIs = LoadTranslations("translate/pois_%s.json" % inputMode)
with open(
"/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/OtherScripts/HEPdata/hepdata_lib/hig-19-015/inputs/correlations_mu.json",
"r") as jf:
correlations = json.load(jf)
with open(
"/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/OtherScripts/HEPdata/hepdata_lib/hig-19-015/inputs/correlations_expected_mu.json",
"r") as jf:
correlations_exp = json.load(jf)
# Make table of results
table = Table("Correlations: production mode signal strength")
table.description = "Observed and expected correlations between the parameters in the production mode signal strength fit."
table.location = "Results from additional material"
table.keywords["reactions"] = ["P P --> H ( --> GAMMA GAMMA ) X"]
pois_x = Variable("Parameter (x)", is_independent=True, is_binned=False)
pois_y = Variable("Parameter (y)", is_independent=True, is_binned=False)
c = Variable("Observed correlation", is_independent=False, is_binned=False)
c.add_qualifier("SQRT(S)", 13, "TeV")
c.add_qualifier("MH", '125.38', "GeV")
c_exp = Variable("Expected correlation",
is_independent=False,
is_binned=False)
c_exp.add_qualifier("SQRT(S)", 13, "TeV")
c_exp.add_qualifier("MH", '125.38', "GeV")
poiNames_x = []
poiNames_y = []
corr = []
corr_exp = []
for ipoi in params:
for jpoi in params:
poiNames_x.append(str(Translate(ipoi, translatePOIs)))
poiNames_y.append(str(Translate(jpoi, translatePOIs)))
# Extract correlation coefficient
corr.append(correlations["%s__%s" % (ipoi, jpoi)])
corr_exp.append(correlations_exp["%s__%s" % (ipoi, jpoi)])
pois_x.values = poiNames_x
pois_y.values = poiNames_y
c.values = np.round(np.array(corr), 3)
c_exp.values = np.round(np.array(corr_exp), 3)
# Add variables to table
table.add_variable(pois_x)
table.add_variable(pois_y)
table.add_variable(c)
table.add_variable(c_exp)
# Add figure
table.add_image(
"/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/OtherScripts/HEPdata/hepdata_lib/hig-19-015/inputs/perproc_mu_corr.pdf"
)
return table
def make_table():
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
from hepdata_lib import Submission, Table, Variable, Uncertainty
#INITIALIZE
submission = Submission()
#ABSTRACT
submission.read_abstract("input/abstract.txt")
submission.add_link(
"Webpage with all figures and tables",
"https://cms-results.web.cern.ch/cms-results/public-results/publications/SMP-19-013/"
)
submission.add_link("arXiv", "http://arxiv.org/abs/arXiv:2105.12780")
submission.add_record_id(1865855, "inspire")
#FIGURE 2 UPPER LEFT
fig2_ul = Table("Figure 2 (upper left)")
fig2_ul.description = "Distribution of the transverse momentum of the diphoton system for the $\mathrm{W}\gamma\gamma$ electron channel. The predicted yields are shown with their pre-fit normalisations. The observed data, the expected signal contribution and the background estimates are presented with error bars showing the corresponding statistical uncertainties."
fig2_ul.location = "Data from Figure 2 on Page 6 of the preprint"
fig2_ul.keywords["observables"] = ["Diphoton pT"]
fig2_ul.keywords["reactions"] = [
"P P --> W GAMMA GAMMA --> ELECTRON NU GAMMA GAMMA"
]
fig2_ul_in = np.loadtxt("input/fig2_ul.txt", skiprows=1)
#diphoton pT
fig2_ul_pt = Variable("$p_T^{\gamma\gamma}$",
is_independent=True,
is_binned=False,
units="GeV")
fig2_ul_pt.values = fig2_ul_in[:, 0]
import pandas as pd
from hepdata_lib import Submission
from hepdata_lib import Table
from hepdata_lib import RootFileReader
from hepdata_lib import Variable
from hepdata_lib import Uncertainty
submission = Submission()
df = pd.read_csv("input.csv")
df = df.fillna("")
for index, fig in df.iterrows():
print(fig["figure_name"])
# create a table
table = Table(fig["figure_name"])
table.description = fig["description"]
table.location = fig["paper_location"]
# read figures
reader = RootFileReader(fig["file_location"])
if fig["type_stat"].lower() in ["tgraph", "tgrapherrors", "tgraphasymmerrors"]:
stat = reader.read_graph(fig["name_stat"])
elif fig["type_stat"].lower() == "th1":
stat = reader.read_hist_1d(fig["name_stat"])
elif fig["type_stat"].lower() == "th2":
stat = reader.read_hist_2d(fig["name_stat"])
else:
print("ERROR: {}, type not recognized!".format(fig["figure_name"]))
if fig["type_syst"].lower() in ["tgraph", "tgrapherrors", "tgraphasymmerrors"]:
from hepdata_lib import Submission
import numpy as np
submission = Submission()
submission.read_abstract("hepdata_lib/examples/example_inputs/abstract.txt")
submission.add_link(
"Webpage with all figures and tables",
"https://cms-results.web.cern.ch/cms-results/public-results/publications/B2G-16-029/"
)
submission.add_link("arXiv", "http://arxiv.org/abs/arXiv:1802.09407")
submission.add_record_id(1657397, "inspire")
### Table
from hepdata_lib import Table
table = Table("Additional Figure 1")
table.description = "Signal selection efficiency times acceptance as a function of resonance mass for a spin-2 bulk graviton decaying to WW and a spin-1 W' decaying to WZ."
table.location = "Data from additional Figure 1"
table.keywords["observables"] = ["ACC", "EFF"]
table.keywords["reactions"] = [
"P P --> GRAVITON --> W+ W-", "P P --> WPRIME --> W+/W- Z0"
]
data = np.loadtxt("hepdata_lib/examples/example_inputs/effacc_signal.txt",
skiprows=2)
print(data)
### Variable
from hepdata_lib import Variable
import numpy as np
submission = Submission()
sig_digits = 3
submission.read_abstract("HEPData/inputs/hig20017/abstract.txt")
submission.add_link(
"Webpage with all figures and tables",
"http://cms-results.web.cern.ch/cms-results/public-results/publications/HIG-20-017/index.html"
)
#submission.add_link("arXiv", "http://arxiv.org/abs/arXiv:xxxx.xxxxx")
#submission.add_record_id(1818160, "inspire")
### Begin Table 2
table2 = Table("Table 2")
table2.description = "Summary of the impact of the systematic uncertainties on the extracted signal strength; for the case of a background-only simulated data set, i.e., assuming no contributions from the $\mathrm{H}^{\pm}$ and $\mathrm{H}^{\pm\pm}$ processes, and including a charged Higgs boson signal for values of $s_{\mathrm{H}}=1.0$ and $m_{\mathrm{H}_{5}}=500$ GeV in the GM model."
table2.location = "Data from Table 2"
table2.keywords["observables"] = ["Uncertainty"]
table2.keywords["reactions"] = ["P P --> W W j j", "P P --> W Z j j"]
table2.keywords["phrases"] = [
"Same-sign WW", "WZ", "Georgi-Machacek", "Charged Higgs", "VBF"
]
data2 = np.loadtxt("HEPData/inputs/hig20017/systematics.txt",
dtype='string',
skiprows=2)
print(data2)
