fake1est = compute_errors(fr1)
fake2est = compute_errors(fr2)
double_est = 0
if fr12en:
double_est = fr12s*get_rel_counting_err(fr12en)
total_yield = fake1est + fake2est - double_est
return total_yield
# Make these importable
int_lumi = analysis_cfg.INT_LUMI
skips = ['DoubleEl', 'EM']
samples, plotter = data_tool.build_data(
'VH', analysis_cfg.JOBID, 'scratch_results',
int_lumi, skips, count='emt/skimCounter')
if __name__ == "__main__":
# Load the MC-DATA correction functions
log.info("Loading MC-DATA corrections")
ROOT.gROOT.ProcessLine('.L corrections.C++')
log.info("Loading fake rate functions")
ROOT.gROOT.ProcessLine('.L fake_rates.C++')
log.info("Beginning analysis at %s", _START)
############################################################################
### Load the data ##########################################################
############################################################################
varname = match.group('var')
assert(varname)
barrel_info_clone['denom'].append('%sAbsEta <= 1.48' % varname)
endcap_info_clone['denom'].append('%sAbsEta > 1.48' % varname)
fakerates[fr + '_endcap'] = endcap_info_clone
fakerates[fr + '_barrel'] = barrel_info_clone
output_file = ROOT.TFile("results_singleMuFakeRates.root", "RECREATE")
for data_set, skips, int_lumi, puTag in [
('2011A', ['2011B', 'EM',], 2170, 'pu2011A'),
#('2011B', ['2011A', 'EM'], 2170),
('2011AB', ['EM',], 4684, 'pu2011AB') ]:
log.info("Plotting dataset: %s", data_set)
samples, plotter = data_tool.build_data(
'Mu', '2012-04-14-v1-MuonTP', 'scratch_results', int_lumi, skips,
count = '/mm/skimCounter', unweighted = False)
legend = plotter.build_legend(
'/em/skimCounter',
include = ['*'],
exclude = ['*data*','*VH*'],
drawopt='lf')
def saveplot(filename):
# Save the current canvas
filetype = '.pdf'
canvas.SetLogy(False)
canvas.Update()
canvas.Print(os.path.join(
"plots", 'singleMuFakeRates', data_set, filename + filetype))
#!/usr/bin/env python
import ROOT
from rootpy.utils import asrootpy
from rootpy.plotting import Canvas, Legend, HistStack
from FinalStateAnalysis.Utilities.AnalysisPlotter import styling,samplestyles
import rootpy.io as io
import FinalStateAnalysis.StatTools.poisson as poisson
# This stuff is just so we can get matching styles as analysis.py
import FinalStateAnalysis.PatTools.data as data_tool
int_lumi = 5000
skips = ['DoubleEl', 'EM']
samples, plotter = data_tool.build_data(
'VH', '2012-04-14-v1-WHAnalyze', 'scratch_results',
int_lumi, skips, count='emt/skimCounter')
# Get stupid templates to build the styles automatically
signal = asrootpy(plotter.get_histogram(
'VH120',
'emt/skimCounter',
).th1)
wz = asrootpy(plotter.get_histogram(
'WZ',
'emt/skimCounter',
).th1)
zz = asrootpy(plotter.get_histogram(
'ZZ',
'emt/skimCounter',
'Electron_EBtag < 3.3',
'abs(Muon2DZ) < 0.2',
'abs(ElectronDZ) < 0.2',
'Electron_MissingHits < 0.5',
'Electron_hasConversion < 0.5',
'Muon2AbsEta < 2.1',
'Muon2_MtToMET < 40',
'NIsoMuonsPt5_Nmuons < 0.5',
'vtxNDOF > 0',
'vtxChi2/vtxNDOF < 10',
]
samples, plotter = data_tool.build_data(
'Mu', '2012-04-14-v1-MuonTP', 'scratch_results',
4684, ['MuEG', 'DoubleMu', 'EM'], count='em/skimCounter')
canvas = ROOT.TCanvas("basdf", "aasdf", 1200, 600)
canvas.Divide(2)
def saveplot(filename):
# Save the current canvas
filetype = '.pdf'
canvas.SetLogy(False)
canvas.Update()
canvas.Print(os.path.join(
"plots", 'muEGTrigger', filename + filetype))
canvas.SetLogy(True)
canvas.Update()
canvas.Print(os.path.join(
from json import encoder
encoder.FLOAT_REPR = lambda o: format(o, '.4f')
# Setup logging
logging.basicConfig(
filename='estimateScaleSystematics.log',level=logging.DEBUG, filemode='w')
log = logging.getLogger("analysis")
stderr_log = logging.StreamHandler()
log.addHandler(stderr_log)
int_lumi = analysis_cfg.INT_LUMI
jobid = '2012-02-01-v1-WHAnalyze'
skips = ['DoubleEl', 'EM']
samples, plotter = data_tool.build_data(
'VH', jobid, 'scratch_results',
int_lumi, skips, count='emt/skimCounter')
results = {}
for channel, channel_cfg in analysis_cfg.cfg.iteritems():
log.info("Analyzing channel: %s", channel)
if channel in analysis_cfg.skip:
log.warning("Skipping channel %s!!", channel)
continue
channel_results = {}
results[channel] = channel_results
# The baseline selection
baseline = channel_cfg['baseline']
# Get samples to exclude
exclude = channel_cfg['exclude'] + ['data*']
ntuple = channel_cfg['ntuple']
filetype = '.pdf'
#legend.Draw()
canvas.SetLogy(False)
canvas.Update()
canvas.Print(os.path.join(
"plots", 'controlPlots', filename + filetype))
canvas.SetLogy(True)
canvas.Update()
canvas.Print(os.path.join(
"plots", 'controlPlots', filename + '_log' + filetype))
skips = ['EM', 'DoubleEl']
int_lumi = 4700
samples, plotter = data_tool.build_data(
'VH', '2012-02-19-v1-WHAnalyze', 'scratch_results', int_lumi, skips,
count = '/mmt/skimCounter', unweighted = False)
base_dimuon_selection = [
#'(run < 5 && Muon1Pt > 13.5 || Muon1Pt > 13.365)',
#'(run < 5 && Muon2Pt > 9 || Muon2Pt > 8.91)',
'Muon1Pt > 20',
'Muon2Pt > 10',
'Muon1AbsEta < 1.44',
'Muon2AbsEta < 1.44',
'Muon1_MuRelIso < 0.1',
'Muon2_MuRelIso < 0.1',
'Muon1_MuID_WWID > 0.5',
'Muon2_MuID_WWID > 0.5',
'NIsoMuonsPt5_Nmuons < 0.5',
import analysis_cfg
import ROOT
import os
import math
import uncertainties
import FinalStateAnalysis.PatTools.data as data_tool
if __name__ == "__main__":
log.info("Beginning analysis")
############################################################################
### Load the data ##########################################################
############################################################################
int_lumi = analysis_cfg.INT_LUMI
skips = ['DoubleEl', 'EM', 'MuEG']
samples, plotter = data_tool.build_data(
'VH', analysis_cfg.JOBID, 'scratch_results',
int_lumi, skips, count='emt/skimCounter', unweighted=True)
mumusamples, mumuplotter = data_tool.build_data(
'VH', '2012-01-28-v1-MuonTP', 'scratch_results',
int_lumi, skips + ['WW', 'VH', 'TTW', 'TTZ', 'WWW', 'WZ', 'ZZ'],
count='emt/skimCounter', unweighted=True)
canvas = ROOT.TCanvas("basdf", "aasdf", 800, 600)
channel = analysis_cfg.cfg['mmt']
charge_cat_cfg = channel['charge_categories']['mumu']
object1_cfg = charge_cat_cfg['object1']
object2_cfg = charge_cat_cfg['object2']
object3_cfg = charge_cat_cfg['object3']
assert varname
barrel_info_clone["denom"].append("%sAbsEta <= 1.48" % varname)
endcap_info_clone["denom"].append("%sAbsEta > 1.48" % varname)
fakerates[fr + "_endcap"] = endcap_info_clone
fakerates[fr + "_barrel"] = barrel_info_clone
output_file = ROOT.TFile("results_singleMuFakeRates.root", "RECREATE")
for data_set, skips, int_lumi, puTag in [
("2011A", ["2011B", "EM"], 2170, "pu2011A"),
# ('2011B', ['2011A', 'EM'], 2170),
("2011AB", ["EM"], 4684, "pu2011AB"),
]:
log.info("Plotting dataset: %s", data_set)
samples, plotter = data_tool.build_data(
"Mu", "2012-04-14-v1-MuonTP", "scratch_results", int_lumi, skips, count="/mm/skimCounter", unweighted=False
)
legend = plotter.build_legend("/em/skimCounter", include=["*"], exclude=["*data*", "*VH*"], drawopt="lf")
def saveplot(filename):
# Save the current canvas
filetype = ".pdf"
canvas.SetLogy(False)
canvas.Update()
canvas.Print(os.path.join("plots", "singleMuFakeRates", data_set, filename + filetype))
canvas.SetLogy(True)
canvas.Update()
canvas.Print(os.path.join("plots", "singleMuFakeRates", data_set, filename + "_log" + filetype))
for fr_type, fr_info in fakerates.iteritems():
