def test_no_reported_statistics():
'Look at the log file and report if it contains a statistics line'
with LogCapture() as l_cap:
as_pandas(f_single
.Select(lambda e: e.Jets("AntiKt4EMTopoJets")
.First()
.pt() / 1000.0)
)
assert str(l_cap).find('TFileAccessTracer INFO Sending') == -1
def test_flatten_array():
# A very simple flattening of arrays
training_df = as_pandas(f_single
.SelectMany('lambda e: e.Jets("AntiKt4EMTopoJets")')
.Select('lambda j: j.pt()/1000.0'))
assert abs(training_df.iloc[0]['col1'] - 52.02462890625) < 0.001 # type: ignore ()
assert int(training_df.iloc[0]['col1']) != int(training_df.iloc[1]['col1']) # type: ignore
def test_event_info_includes():
'Make sure event info is pulling in the correct includes'
training_df = as_pandas(f_single
.Select(lambda e: e.EventInfo("EventInfo"))
.Select(lambda e: e.runNumber()))
print(training_df)
assert len(training_df) == 10
def test_first_object_in_event():
# Make sure First puts it if statement in the right place.
training_df = as_pandas(f_single
.Select(lambda e: e.Jets("AntiKt4EMTopoJets")
.First()
.pt() / 1000.0)
)
assert int(training_df.iloc[0]['col1']) == 52 # type: ignore
def test_single_column_output():
# And a power operator
training_df = as_pandas(f_single
.SelectMany(lambda e: e.Jets("AntiKt4EMTopoJets"))
.Select(lambda j: j.pt() ** 2))
print(training_df)
assert int(training_df.iloc[0]['col1']) == 2706562012 # type: ignore
assert int(training_df.iloc[0]['col1']) != int(training_df.iloc[1]['col1']) # type: ignore
def test_simple_dict_output():
# A very simple flattening of arrays, and the binary division operator
training_df = as_pandas(f_single
.SelectMany(lambda e: e.Jets("AntiKt4EMTopoJets"))
.Select(lambda j: {
'JetPt': j.pt() / 1000.0
}))
print(training_df)
assert abs(training_df.iloc[0]['JetPt'] - 52.02462890625) < 0.001 # type: ignore ()
assert int(training_df.iloc[0]['JetPt']) != int(training_df.iloc[1]['JetPt']) # type: ignore
def test_first_object_in_event_with_where():
# Make sure First puts it's if statement in the right place.
training_df = as_pandas(f_single
.Select(lambda e: e.Jets("AntiKt4EMTopoJets")
.Select(lambda j: j.pt() / 1000.0)
.Where(lambda jpt: jpt > 10.0)
.First())
)
assert int(training_df.iloc[0]['col1']) == 52 # type: ignore
assert len(training_df) == 10
def test_select_first_of_array():
# The hard part is that First() here does not return a single item, but, rather, an array that
# has to be aggregated over.
training_df = as_pandas(f_single
.Select(lambda e: e.Jets("AntiKt4EMTopoJets")
.Select(lambda _: e.Tracks("InDetTrackParticles"))
.First()
.Count())
)
assert training_df.iloc[0]['col1'] == 394
assert training_df.iloc[1]['col1'] == 387
assert training_df.iloc[-1]['col1'] == 381
def test_not_in_where():
# THis is a little tricky because the First there is actually running over one jet in the event. Further, the Where
# on the number of tracks puts us another level down. So it is easy to produce code that compiles, but the First's if statement
# is very much in the wrong place.
training_df = as_pandas(f_single
.Select(lambda e: e.Jets("AntiKt4EMTopoJets")
.Select(lambda j: e.Tracks("InDetTrackParticles")
.Where(lambda t: not (t.pt() > 1000.0)))
.First()
.Count())
)
assert training_df.iloc[0]['col1'] == 258
def test_md_job_options():
'''Run object corrections as we go
Based on the following code:
'''
training_df = as_pandas(f_single
.MetaData({
'metadata_type': 'add_job_script',
'name': 'sys_error_tool',
'script': [
"# Set up the systematics loader/handler service:",
"from AnaAlgorithm.DualUseConfig import createService",
"from AnaAlgorithm.AlgSequence import AlgSequence",
"calibrationAlgSeq = AlgSequence()",
"sysService = createService( 'CP::SystematicsSvc', 'SystematicsSvc', sequence = calibrationAlgSeq )",
"sysService.sigmaRecommended = 1",
"# Add sequence to job",
],
})
.MetaData({
'metadata_type': 'add_job_script',
'name': 'pileup_tool',
'script': [
"from AsgAnalysisAlgorithms.PileupAnalysisSequence import makePileupAnalysisSequence",
"pileupSequence = makePileupAnalysisSequence( 'mc' )",
"pileupSequence.configure( inputName = 'EventInfo', outputName = 'EventInfo_%SYS%' )",
"# print( pileupSequence ) # For debugging",
"calibrationAlgSeq += pileupSequence",
],
'depends_on': ['sys_error_tool']
})
.MetaData({
'metadata_type': 'add_job_script',
'name': 'jet_corrections',
'script': [
"jetContainer = 'AntiKt4EMTopoJets'",
"from JetAnalysisAlgorithms.JetAnalysisSequence import makeJetAnalysisSequence",
"jetSequence = makeJetAnalysisSequence( 'mc', jetContainer, enableCutflow=True, enableKinematicHistograms=True )",
"jetSequence.configure( inputName = jetContainer, outputName = 'AnalysisJetsBase_%SYS%' )",
"calibrationAlgSeq += jetSequence",
"# print( jetSequence ) # For debugging",
"",
"# Include, and then set up the jet analysis algorithm sequence:",
"from JetAnalysisAlgorithms.JetJvtAnalysisSequence import makeJetJvtAnalysisSequence",
"jvtSequence = makeJetJvtAnalysisSequence( 'mc', jetContainer, enableCutflow=True )",
"jvtSequence.configure( inputName = { 'eventInfo' : 'EventInfo_%SYS%',",
" 'jets' : 'AnalysisJetsBase_%SYS%' },",
" outputName = { 'jets' : 'AnalysisJets_%SYS%' },",
" affectingSystematics = { 'jets' : jetSequence.affectingSystematics() } )",
"calibrationAlgSeq += jvtSequence",
"# print( jvtSequence ) # For debugging",
"calibrationAlgSeq.addSelfToJob( job )", # TODO: Can I do this earlier?
"print(job) # for debugging",
],
'depends_on': ['pileup_tool']
})
# TODO: get the sequence above running (add to the job)
# TODO: Make sure we correct the proper container, and see if the jet energy changes as expected.
.SelectMany(lambda e: e.Jets("AnalysisJets_NOSYS"))
.Select(lambda j: {
'JetPt': j.pt() / 1000.0
}))
print(training_df)
assert abs(training_df.iloc[0]['JetPt'] - 50.10308984375) < 0.001 # type: ignore ()
assert int(training_df.iloc[0]['JetPt']) != int(training_df.iloc[1]['JetPt']) # type: ignore
def test_truth_particles():
training_df = as_pandas(f_single
.Select(lambda e: e.TruthParticles('TruthParticles').Count()))
assert training_df.iloc[0]['col1'] == 1450
