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_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 test_create_files(self):
"""Test create_files() for Submission."""
test_submission = Submission()
try:
test_submission.create_files("test_output")
except TypeError:
self.fail(
"Submission.create_files raised an unexpected TypeError.")
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_create_files(self):
"""Test create_files() for Submission."""
testdir = "test_output"
test_submission = Submission()
self.addCleanup(os.remove, "submission.tar.gz")
self.addCleanup(shutil.rmtree, testdir)
test_submission.create_files(testdir)
self.doCleanups()
def test_yaml_output(self):
"""Test yaml dump"""
tmp_dir = tmp_directory_name()
# Create test dictionary
testlist = [("x", 1.2), ("x", 2.2), ("y", 0.12), ("y", 0.22)]
testdict = defaultdict(list)
for key, value in testlist:
testdict[key].append(value)
# Create test submission
test_submission = Submission()
test_table = Table("TestTable")
x_variable = Variable("X", is_independent=True, is_binned=False)
x_variable.values = testdict['x']
y_variable = Variable("Y", is_independent=False, is_binned=False)
y_variable.values = testdict['y']
test_table.add_variable(x_variable)
test_table.add_variable(y_variable)
test_submission.add_table(test_table)
test_submission.create_files(tmp_dir)
# Test read yaml file
table_file = os.path.join(tmp_dir, "testtable.yaml")
try:
with open(table_file, 'r') as testfile:
testyaml = yaml.safe_load(testfile)
except yaml.YAMLError as exc:
print(exc)
# Test compare yaml file to string
testtxt = (
"dependent_variables:\n- header:\n name: Y\n values:\n" +
" - value: 0.12\n - value: 0.22\nindependent_variables:\n" +
"- header:\n name: X\n values:\n - value: 1.2\n - value: 2.2\n"
)
with open(table_file, 'r') as testfile:
testyaml = testfile.read()
self.assertEqual(str(testyaml), testtxt)
self.addCleanup(os.remove, "submission.tar.gz")
self.addCleanup(shutil.rmtree, tmp_dir)
self.doCleanups()
reader_covariance_ee_PtRap4 = RootFileReader(
"HEPData/inputs/smp17010/folders_bornleptons/output_root/matrix13__XSRatioSystPtRap4.root"
)
# Read the histogram
data_covariance_ee_PtRap4 = reader_covariance_ee_PtRap4.read_hist_2d(
"covariance_totsum_1")
# Create variable objects
x_covariance_ee_PtRap4 = Variable("Bin X", is_independent=True, is_binned=True)
x_covariance_ee_PtRap4.values = data_covariance_ee_PtRap4["x_edges"]
y_covariance_ee_PtRap4 = Variable("Bin Y",
is_independent=True,
is_binned=False)
y_covariance_ee_PtRap4.values = data_covariance_ee_PtRap4["y"]
z_covariance_ee_PtRap4 = Variable("covariance Matrix",
is_independent=False,
is_binned=False)
z_covariance_ee_PtRap4.values = data_covariance_ee_PtRap4["z"]
table_covariance_XSRatio_ee_PtRap4 = Table("cov matr norm xs aux 6e")
table_covariance_XSRatio_ee_PtRap4.description = "Covariance matrix for normalized cross sections using born level leptons for all bins used in bins of Z pt for the 1.6 < |y(Z)| < 2.4 bin in the dielectron final state."
table_covariance_XSRatio_ee_PtRap4.location = "Supplementary material"
for var in [
x_covariance_ee_PtRap4, y_covariance_ee_PtRap4, z_covariance_ee_PtRap4
]:
table_covariance_XSRatio_ee_PtRap4.add_variable(var)
submission.add_table(table_covariance_XSRatio_ee_PtRap4)
### End covariance ee
outdir = "example_output"
submission.create_files(outdir)
if fig["type_stat"].lower() == "tgraphasymmerrors":
y_stat = Uncertainty("stat. uncertainty", is_symmetric=False)
y_stat.values = stat["dy"]
y.add_uncertainty(y_stat)
elif fig["type_stat"].lower() in ["tgrapherrors", "th1"]:
y_stat = Uncertainty("stat. uncertainty", is_symmetric=True)
y_stat.values = stat["dy"]
y.add_uncertainty(y_stat)
if fig["type_syst"].lower() == "tgraphasymmerrors":
y_syst = Uncertainty("syst. uncertainty", is_symmetric=False)
y_syst.values = syst["dy"]
y.add_uncertainty(y_syst)
elif fig["type_syst"].lower() in ["tgrapherrors", "th1"]:
y_syst = Uncertainty("syst. uncertainty", is_symmetric=True)
y_syst.values = syst["dy"]
y.add_uncertainty(y_syst)
# write table
if fig["type_stat"].lower() == "th2":
table.add_variable(x1)
table.add_variable(x2)
table.add_variable(y)
else:
table.add_variable(x1)
table.add_variable(y)
submission.add_table(table)
submission.create_files("output")
wcctZI.values = datactZctZIobs[:, 1]
nllctZctZIobs = Variable("-2$\Delta$ L (observed)",
is_independent=False,
is_binned=False)
nllctZctZIobs.values = datactZctZIobs[:, 2]
nllctZctZIobs.add_qualifier("SQRT(S)", "13", "TeV")
nllctZctZIobs.add_qualifier("LUMINOSITY", "137", "fb$^{-1}$")
tabctZctZI.add_variable(wcctZ)
tabctZctZI.add_variable(wcctZI)
tabctZctZI.add_variable(nllctZctZIobs)
tabctZctZI.add_image("../figures/ctZ_ctZI_wBkg.png")
#tabctZctZI.keywords()
tabctZctZI.keywords["reactions"] = [
"P P --> TOP TOPBAR X", "P P --> TOP TOPBAR GAMMA"
]
tabctZctZI.keywords["cmenergies"] = [13000.0]
tabctZctZI.keywords["observables"] = ["CLS"]
tabctZctZI.keywords["phrases"] = [
"Top", "Quark", "Photon", "lepton+jets", "semileptonic", "Cross Section",
"Proton-Proton Scattering", "Inclusive", "Differential"
]
submission.add_table(tabctZctZI)
###
### write files
###
submission.create_files()
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")
h = files[year].read_hist_1d(
"%s_%s_%s" % (proc, year.replace("A", "pre").replace(
"B", "post"), nnBin))
varArr += h["y"]
# For the fit and data graphs, there is an associated uncertainty
# So add that uncertainty to the Fit and Nobs variables
if proc == "Fit" or proc == "Nobs":
uncArr += h["dy"]
v.values = varArr
if proc == "Fit" or proc == "Nobs":
uncVals, isSymm = makeUncArray(uncArr)
unc = U("unc.", is_symmetric=isSymm)
unc.values = uncVals
v.add_uncertainty(unc)
# Add each sig1, sig2, fit, data variable to the corresponding table
tmap[year].add_variable(v)
for year, t in sorted(tmap.items()):
sub.add_table(t)
if __name__ == "__main__":
sub = Submission()
makeFitPlotHEPData(sub)
sub.create_files("output")
up1 = Variable(c.y_var, is_independent=False, is_binned=False, units=c.y_unit)
up1.values = graphs["Graph1"]['y']
up1.add_qualifier("Limit", "+1sigma")
down1 = Variable(c.y_var, is_independent=False, is_binned=False, units=c.y_unit)
down1.values = graphs["Graph4"]['y']
down1.add_qualifier("Limit", "-1sigma")
down2 = Variable(c.y_var, is_independent=False, is_binned=False, units=c.y_unit)
down2.values = graphs["Graph5"]['y']
down2.add_qualifier("Limit", "-2sigma")
table.add_variable(d)
table.add_variable(up2)
table.add_variable(up1)
table.add_variable(obs)
table.add_variable(exp)
table.add_variable(down1)
table.add_variable(down2)
submission.add_table(table)
if __name__ == "__main__":
submission = Submission()
for c in config:
outdict = read_limit_from_pickle(c.picklePath)
draw_limit_with_dict(c,outdict)
add_limit_to_submission(c,submission)
submission.create_files(out_name)