metPhiCorrectionExample Namespace Reference

Variables
	rng = np.random.default_rng()
list	correction_labels = ["metphicorr_pfmet_mc", "metphicorr_puppimet_mc", "metphicorr_pfmet_data", "metphicorr_puppimet_data"]
list	eras = ["2018_UL", "2017_UL", "2016postVFP_UL", "2016preVFP_UL"]
list	run_ranges = [[315252, 325274], [297020, 306463], [278769, 284045], [272007, 278771]]
string	infile = "met.json.gz"
	is_data = None
	ceval = correctionlib.CorrectionSet.from_file("../POG/JME/{}/{}".format(era,infile))
tuple	pts = (rng.pareto(1.5,size=1000000))*100
	phis = rng.uniform(low=-3.14,high=3.14,size=1000000)
	npvs = rng.integers(low=0,high=200,size=1000000)
	runs = None
	corrected_pts = ceval["pt_{}".format(correction_label)].evaluate(pts,phis,npvs,runs)
	corrected_phis = ceval["phi_{}".format(correction_label)].evaluate(pts,phis,npvs,runs)
	fig
	axs
	sharey
	True
	tight_layout
	xlabel
	bins
	range
	ylabel

Detailed Description

In this test script, the different MET Phi Corrections are applied to uniform MET pt,phi distributions to check whether the corrections have an effect.
The number of primary vertices are also drawn from a uniform distribution. The run numbers come from a uniform distribution as well but the run ranges (for data)
are fitting the different eras to not cause crashes.
In the end two plots are created. The first one shows the effect of the corrections on the uniform MET phi distribution and the second one shows the same effect
but as a function of the primary vertices.
This is only a technical test, the resulting plots should not be taken too seriously.

Variable Documentation

axs

axs

bins

bins

ceval

ceval = correctionlib.CorrectionSet.from_file("../POG/JME/{}/{}".format(era,infile))

corrected_phis

corrected_phis = ceval["phi_{}".format(correction_label)].evaluate(pts,phis,npvs,runs)

corrected_pts

corrected_pts = ceval["pt_{}".format(correction_label)].evaluate(pts,phis,npvs,runs)

correction_labels

list correction_labels = ["metphicorr_pfmet_mc", "metphicorr_puppimet_mc", "metphicorr_pfmet_data", "metphicorr_puppimet_data"]

eras

list eras = ["2018_UL", "2017_UL", "2016postVFP_UL", "2016preVFP_UL"]

fig

fig

infile

string infile = "met.json.gz"

is_data

bool is_data = None

npvs

npvs = rng.integers(low=0,high=200,size=1000000)

phis

phis = rng.uniform(low=-3.14,high=3.14,size=1000000)

pts

pts = (rng.pareto(1.5,size=1000000))*100

range

range

rng

rng = np.random.default_rng()

run_ranges

list run_ranges = [[315252, 325274], [297020, 306463], [278769, 284045], [272007, 278771]]

runs

runs = None

sharey

sharey

tight_layout

tight_layout

True

True

xlabel

xlabel

ylabel

ylabel