def test_jec_txt_scalefactors():
extractor = lookup_tools.extractor()
extractor.add_weight_sets([
"testJEC * tests/samples/Fall17_17Nov2017_V32_MC_L2Relative_AK4PFPuppi.jec.txt",
"* * tests/samples/Fall17_17Nov2017_V32_MC_Uncertainty_AK4PFPuppi.junc.txt",
"* * tests/samples/Autumn18_V8_MC_UncertaintySources_AK4PFchs.junc.txt",
])
extractor.finalize()
evaluator = extractor.make_evaluator()
counts, test_eta, test_pt = dummy_jagged_eta_pt()
jec_out = evaluator[
'testJECFall17_17Nov2017_V32_MC_L2Relative_AK4PFPuppi'](test_eta,
test_pt)
print(jec_out)
junc_out = evaluator['Fall17_17Nov2017_V32_MC_Uncertainty_AK4PFPuppi'](
test_eta, test_pt)
print(junc_out)
assert ('Autumn18_V8_MC_UncertaintySources_AK4PFchs_AbsoluteScale'
in evaluator.keys())
junc_out = evaluator[
'Autumn18_V8_MC_UncertaintySources_AK4PFchs_AbsoluteScale'](test_eta,
test_pt)
print(junc_out)
def test_btag_csv_scalefactors():
extractor = lookup_tools.extractor()
extractor.add_weight_sets(["testBTag * tests/samples/testBTagSF.btag.csv"])
extractor.finalize()
evaluator = extractor.make_evaluator()
counts, test_eta, test_pt = dummy_jagged_eta_pt()
# discriminant used for reshaping, zero otherwise
test_discr = np.zeros_like(test_eta)
sf_out = evaluator['testBTagCSVv2_1_comb_up_0'](test_eta, test_pt,
test_discr)
print(sf_out)
def test_jec_txt_scalefactors():
extractor = lookup_tools.extractor()
extractor.add_weight_sets([
"testJEC * tests/samples/Fall17_17Nov2017_V32_MC_L2Relative_AK4PFPuppi.jec.txt"
])
extractor.finalize()
evaluator = extractor.make_evaluator()
counts, test_eta, test_pt = dummy_jagged_eta_pt()
jec_out = evaluator[
'testJECFall17_17Nov2017_V32_MC_L2Relative_AK4PFPuppi'](test_eta,
test_pt)
print(jec_out)
def test_histo_json_scalefactors():
extractor = lookup_tools.extractor()
extractor.add_weight_sets(
["testJson * tests/samples/EIDISO_WH_out.histo.json"])
extractor.finalize()
evaluator = extractor.make_evaluator()
counts, test_eta, test_pt = dummy_jagged_eta_pt()
sf_out = evaluator['testJsonEIDISO_WH/eta_pt_ratio_value'](test_eta,
test_pt)
sf_err_out = evaluator['testJsonEIDISO_WH/eta_pt_ratio_error'](test_eta,
test_pt)
print(sf_out)
print(sf_err_out)
def test_root_scalefactors():
extractor = lookup_tools.extractor()
extractor.add_weight_sets([
"testSF2d scalefactors_Tight_Electron tests/samples/testSF2d.histo.root"
])
extractor.finalize()
evaluator = extractor.make_evaluator()
counts, test_eta, test_pt = dummy_jagged_eta_pt()
# test flat eval
test_out = evaluator["testSF2d"](test_eta, test_pt)
# test structured eval
test_eta_jagged = awkward.JaggedArray.fromcounts(counts, test_eta)
test_pt_jagged = awkward.JaggedArray.fromcounts(counts, test_pt)
test_out_jagged = evaluator["testSF2d"](test_eta_jagged, test_pt_jagged)
assert (test_out_jagged.counts == counts).all()
assert (test_out == test_out_jagged.flatten()).all()
# From make_expected_lookup.py
expected_output = np.array([
0.90780139, 0.82748538, 0.86332178, 0.86332178, 0.97981155, 0.79701495,
0.88245934, 0.82857144, 0.91884059, 0.97466666, 0.94072163, 1.00775194,
0.82748538, 1.00775194, 0.97203946, 0.98199672, 0.80655736, 0.90893763,
0.88245934, 0.79701495, 0.82748538, 0.82857144, 0.91884059, 0.90893763,
0.97520661, 0.97520661, 0.82748538, 0.91884059, 0.97203946, 0.88245934,
0.79701495, 0.9458763, 1.00775194, 0.80655736, 1.00775194, 1.00775194,
0.98976982, 0.98976982, 0.86332178, 0.94072163, 0.80655736, 0.98976982,
0.96638656, 0.9458763, 0.90893763, 0.9529984, 0.9458763, 0.9529984,
0.80655736, 0.80655736, 0.80655736, 0.98976982, 0.97466666, 0.98199672,
0.86332178, 1.03286386, 0.94072163, 1.03398061, 0.82857144, 0.80655736,
1.00775194, 0.80655736
])
diff = np.abs(test_out - expected_output)
print("Max diff: %.16f" % diff.max())
print("Median diff: %.16f" % np.median(diff))
print("Diff over threshold rate: %.1f %%" %
(100 * (diff >= 1.e-8).sum() / diff.size))
assert (diff < 1.e-8).all()
from zpeak_analysis import ZpeakAnalysis
executor = UprootExecutor("zpeak")
# executor = SparkExecutor("local", "ZPeak", 20)
config = Config(
executor = executor,
dataset_manager=InMemoryFilesDatasetManager(database_file="demo_datasets.csv")
)
app = App(config=config)
print(app.datasets.get_names())
print(app.datasets.get_file_list("ZJetsToNuNu_HT-600To800_13TeV-madgraph"))
# Create a broadcast variable for the non-event data
weightsext = lookup_tools.extractor()
correctionDescriptions = open("newCorrectionFiles.txt").readlines()
weightsext.add_weight_sets(correctionDescriptions)
weightsext.finalize()
weights_eval = weightsext.make_evaluator()
dataset = app.read_dataset("DY Jets")
print(dataset.columns)
print(dataset.count())
slim = dataset.select_columns(["nElectron",
"Electron_pt",
"Electron_eta",
"Electron_phi",
"Electron_mass",
#!/usr/bin/env python
import json
import gzip
import lz4.frame as lz4f
import cloudpickle
import pickle
import uproot
import numexpr
import numpy as np
from fnal_column_analysis_tools import hist, lookup_tools
from fnal_column_analysis_tools.hist import plot
corrections = {}
extractor = lookup_tools.extractor()
extractor.add_weight_sets(
["2017_n2ddt_ * correction_files/n2ddt_transform_2017MC.root"])
extractor.add_weight_sets([
"2017_mutrigger_ * correction_files/EfficienciesAndSF_RunBtoF_Nov17Nov2017.root"
])
extractor.add_weight_sets(
["2017_muid_ * correction_files/Muon2017_RunBCDEF_SF_ID.json"])
extractor.add_weight_sets(
["2017_muiso_ * correction_files/Muon2017_RunBCDEF_SF_ISO.json"])
extractor.finalize()
evaluator = extractor.make_evaluator()
corrections['2017_n2ddt_rho_pt'] = evaluator['2017_n2ddt_Rho2D']
corrections['2017_mutrigweight_pt_abseta'] = evaluator[
'2017_mutrigger_Mu50_PtEtaBins/efficienciesDATA/pt_abseta_DATA']
corrections['2017_mutrigweight_pt_abseta_mutrigweightShift'] = evaluator[
