def draw_legend(self, dilepton, cumulative, log_x):
# Legend placement coordinates and sizes depend on factors set
# elsewhere, too, so this is fragile.
# if dilepton == 'MuonsPlusMuonsMinus' and cumulative:
if dilepton == '' and cumulative:
legend = ROOT.TLegend(0.60, 0.69, 0.86, 0.88)
elif log_x:
legend = ROOT.TLegend(0.60, 0.55, 0.86, 0.88)
else:
if self.join_dy_and_jets:
legend = ROOT.TLegend(0.60, 0.50, 0.86, 0.88)
else:
legend = ROOT.TLegend(0.60, 0.69, 0.86, 0.88)
#legend = ROOT.TLegend(0.60, 0.60, 0.86, 0.88)
legend.SetFillColor(0)
legend.SetBorderSize(0)
# Add an entry for the data points.
# entry = legend.AddEntry('data_marker', 'Data', 'EP')
# entry.SetMarkerStyle(20)
# entry.SetMarkerSize(0.8)
# entry.SetMarkerColor(ROOT.kBlack)
# Add entries for the MC samples to the legend, respecting
# join groups (i.e. don't add the same nice-name twice).
legend_already = set()
for sample in reversed(self.samples):
if sample.is_zprime and not self.should_draw_zprime(dilepton):
continue
nice_name = sample.nice_name
join_name = self.get_join_name(sample.name)
if join_name is not None:
if join_name in legend_already:
continue
else:
legend_already.add(join_name)
nice_name = join_name
legend.AddEntry(sample.histogram, nice_name, 'F') # Gets the color and fill style from the histogram.
legend.SetTextSize(0.03)
legend.Draw('same')
## "EP" in TLegend::AddEntry doesn't seem to work, so draw the error bar by hand ##for data !
# ll = ROOT.TLine()
# if log_x:
# ll.DrawLineNDC(0.632, 0.845, 0.632, 0.875)
# else:
# ll.DrawLineNDC(0.632, 0.835, 0.632, 0.875)
return legend
def doit(fn, mass_name, dir_name, tree_name, cut):
f = ROOT.TFile(fn)
t = f.Get(dir_name).Get(tree_name)
rlem = detree(t, 'run:lumi:event:' + mass_name, cut, lambda x:
(int(x[0]), int(x[1]), int(x[2]), float(x[3])))
me = defaultdict(list)
for r, l, e, m in rlem:
me[(r, l, e)].append(m)
return me
#!/usr/bin/env python
#####run with python GetPrescalesAnalyzer.py zp2mu_histos_prescale.root Mu24eta2p1
import os, sys
from collections import defaultdict
from pprint import pprint
from SUSYBSMAnalysis.Zprime2muAnalysis.roottools import ROOT, detree
print sys.argv
f = ROOT.TFile(sys.argv[1])
t = f.Get('%s/t' % sys.argv[2])
print t.GetEntriesFast(), 'entries in tree'
de = defaultdict(list)
dp = defaultdict(list)
for (run, lumi, event, l1, hlt) in detree(t, 'run:lumi:event:l1:hlt'):
de[(run, lumi)].append(event)
dp[(run, lumi)].append((l1, hlt))
for k, v in de.iteritems():
assert len(v) == len(set(v))
for k, v in dp.iteritems():
if len(set(v)) != 1:
print 'run/LS %s with %i events has multiple prescale values: %s' % (
k, len(de[k]), sorted(set(v)))
lscounts = defaultdict(int)
evcounts = defaultdict(int)
#!/usr/bin/env python
import os
from SUSYBSMAnalysis.Zprime2muAnalysis.roottools import ROOT
path = 'data/Run2012MuonsOnly/ana_datamc_data.root'
tmp_fn = 'micro_ntuple.temp.txt'
branch_spec = 'vertex_m'
cut = 'OurSel2012'
f = ROOT.TFile(path)
t = f.SimpleNtupler.Get('t')
t.GetPlayer().SetScanRedirect(True)
t.GetPlayer().SetScanFileName(tmp_fn)
t.Scan(branch_spec, cut)
t.GetPlayer().SetScanRedirect(False)
f.Close()
lines = [
line.split(' *')[1:] for line in open(tmp_fn).readlines()
if ' * ' in line and 'Row' not in line
]
lines = ['\t'.join(y.strip() for y in x) for x in lines]
open(tmp_fn, 'wt').write('\n'.join(lines))
f = ROOT.TFile(path.replace('.root', '.microtuple.root'), 'CREATE')
t = ROOT.TTree('t', '')
t.ReadFile(tmp_fn, branch_spec)
f.Write()
f.Close()
h = h.Rebin(len(bins) - 1, h.GetName() + '_rebin', bins)
if roundto is not None:
for i in xrange(1, h.GetNbinsX() + 1):
width = h.GetBinWidth(i)
if width > roundto:
h.SetBinContent(i, h.GetBinContent(i) * roundto / width)
return h
#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#
# First mu+mu-, differential and cumulative.
for cumulative in (False, True):
histos = {}
for sample in samples:
f = ROOT.TFile(mumu_mc_fn_base % sample.name)
h = f.Histosstartup.Get(mumu_histogram).Clone()
if mumu_rebin_variable and not cumulative:
h = variable_rebin_and_normalize(h, mumu_rebin_variable,
mumu_rebin_roundto)
else:
h.Rebin(mumu_rebin_factor)
h.Scale(sample.partial_weight * mumu_scale)
if cumulative:
h = cumulative_histogram(h)
h.SetName(h.GetName().replace('_cumulative_ge', ''))
histos[sample.name] = h
f = ROOT.TFile(mumu_data_fn)
dataHist = f.Histosstartup.Get(mumu_histogram).Clone('data')
if mumu_rebin_variable and not cumulative:
# cut='dil_chosen==0 && fabs(lep_eta[0])<2.4 && fabs(lep_eta[1])<2.4 && GoodDataRan && GoodVtx && lep_pt[0]>53. && lep_pt[1]>53. && lep_isTrackerMuon[0]==1 && lep_isTrackerMuon[1]==1 && lep_isGlobalMuon[0]==1 && lep_isGlobalMuon[1]==1 && fabs(lep_dB[0]) < 0.2 && fabs(lep_dB[1]) < 0.2 && ( lep_numberOfMatchedStations[0] > 1 || (lep_numberOfMatchedStations[0] == 1 && !(lep_stationMask[0] == 1 || lep_stationMask[0] == 16)) || (lep_numberOfMatchedStations[0] == 1 && (lep_stationMask[0] == 1 || lep_stationMask[0] == 16) && lep_numberOfMatchedRPCLayers[0] > 2)) && (lep_numberOfMatchedStations[1] > 1 || (lep_numberOfMatchedStations[1] == 1 && !(lep_stationMask[1] == 1 || lep_stationMask[1] == 16)) || (lep_numberOfMatchedStations[1] == 1 && (lep_stationMask[1] == 1 || lep_stationMask[1] == 16) && lep_numberOfMatchedRPCLayers[1] > 2)) && lep_glb_numberOfValidMuonHits[0]>0 && lep_glb_numberOfValidMuonHits[1]>0 && lep_glb_numberOfValidPixelHits[0]>=1 && lep_glb_numberOfValidPixelHits[1]>=1 && lep_glb_numberOfValidTrackerLayers[0]>5 && lep_glb_numberOfValidTrackerLayers[1]>5 && lep_sumPt[0]/lep_tk_pt[0]<0.10 && lep_sumPt[1]/lep_tk_pt[1]<0.10 && lep_pt_err[0]/lep_pt[0]<0.3 && lep_pt_err[1]/lep_pt[1]<0.3 && cos_angle>-0.9998 && lep_id[0]*lep_id[1]<0 && (lep_triggerMatchPt[0]>50 || lep_triggerMatchPt[1]>50) && vertex_chi2 < 20 && vertex_m > 120 && vertex_m < 400'
# cut='dil_chosen==0 && fabs(lep_eta[0])<2.4 && fabs(lep_eta[1])<2.4 && GoodDataRan && GoodVtx && lep_pt[0]>53. && lep_pt[1]>53. && lep_isTrackerMuon[0]==1 && lep_isTrackerMuon[1]==1 && lep_isGlobalMuon[0]==1 && lep_isGlobalMuon[1]==1 && fabs(lep_dB[0]) < 0.2 && fabs(lep_dB[1]) < 0.2 && ( lep_numberOfMatchedStations[0] > 1 || (lep_numberOfMatchedStations[0] == 1 && !(lep_stationMask[0] == 1 || lep_stationMask[0] == 16)) || (lep_numberOfMatchedStations[0] == 1 && (lep_stationMask[0] == 1 || lep_stationMask[0] == 16) && lep_numberOfMatchedRPCLayers[0] > 2)) && (lep_numberOfMatchedStations[1] > 1 || (lep_numberOfMatchedStations[1] == 1 && !(lep_stationMask[1] == 1 || lep_stationMask[1] == 16)) || (lep_numberOfMatchedStations[1] == 1 && (lep_stationMask[1] == 1 || lep_stationMask[1] == 16) && lep_numberOfMatchedRPCLayers[1] > 2)) && lep_glb_numberOfValidMuonHits[0]>0 && lep_glb_numberOfValidMuonHits[1]>0 && lep_glb_numberOfValidPixelHits[0]>=1 && lep_glb_numberOfValidPixelHits[1]>=1 && lep_glb_numberOfValidTrackerLayers[0]>5 && lep_glb_numberOfValidTrackerLayers[1]>5 && lep_sumPt[0]/lep_tk_pt[0]<0.10 && lep_sumPt[1]/lep_tk_pt[1]<0.10 && lep_pt_err[0]/lep_pt[0]<0.3 && lep_pt_err[1]/lep_pt[1]<0.3 && cos_angle>-0.9998 && lep_id[0]*lep_id[1]<0 && (lep_triggerMatchPt[0]>50 || lep_triggerMatchPt[1]>50) && vertex_chi2 < 20 && vertex_m > 400 && vertex_m < 600'
# cut='dil_chosen==0 && fabs(lep_eta[0])<2.4 && fabs(lep_eta[1])<2.4 && GoodDataRan && GoodVtx && lep_pt[0]>53. && lep_pt[1]>53. && lep_isTrackerMuon[0]==1 && lep_isTrackerMuon[1]==1 && lep_isGlobalMuon[0]==1 && lep_isGlobalMuon[1]==1 && fabs(lep_dB[0]) < 0.2 && fabs(lep_dB[1]) < 0.2 && ( lep_numberOfMatchedStations[0] > 1 || (lep_numberOfMatchedStations[0] == 1 && !(lep_stationMask[0] == 1 || lep_stationMask[0] == 16)) || (lep_numberOfMatchedStations[0] == 1 && (lep_stationMask[0] == 1 || lep_stationMask[0] == 16) && lep_numberOfMatchedRPCLayers[0] > 2)) && (lep_numberOfMatchedStations[1] > 1 || (lep_numberOfMatchedStations[1] == 1 && !(lep_stationMask[1] == 1 || lep_stationMask[1] == 16)) || (lep_numberOfMatchedStations[1] == 1 && (lep_stationMask[1] == 1 || lep_stationMask[1] == 16) && lep_numberOfMatchedRPCLayers[1] > 2)) && lep_glb_numberOfValidMuonHits[0]>0 && lep_glb_numberOfValidMuonHits[1]>0 && lep_glb_numberOfValidPixelHits[0]>=1 && lep_glb_numberOfValidPixelHits[1]>=1 && lep_glb_numberOfValidTrackerLayers[0]>5 && lep_glb_numberOfValidTrackerLayers[1]>5 && lep_sumPt[0]/lep_tk_pt[0]<0.10 && lep_sumPt[1]/lep_tk_pt[1]<0.10 && lep_pt_err[0]/lep_pt[0]<0.3 && lep_pt_err[1]/lep_pt[1]<0.3 && cos_angle>-0.9998 && lep_id[0]*lep_id[1]<0 && (lep_triggerMatchPt[0]>50 || lep_triggerMatchPt[1]>50) && vertex_chi2 < 20 && vertex_m > 600 && vertex_m < 900'
# cut='dil_chosen==0 && fabs(lep_eta[0])<2.4 && fabs(lep_eta[1])<2.4 && GoodDataRan && GoodVtx && lep_pt[0]>53. && lep_pt[1]>53. && lep_isTrackerMuon[0]==1 && lep_isTrackerMuon[1]==1 && lep_isGlobalMuon[0]==1 && lep_isGlobalMuon[1]==1 && fabs(lep_dB[0]) < 0.2 && fabs(lep_dB[1]) < 0.2 && ( lep_numberOfMatchedStations[0] > 1 || (lep_numberOfMatchedStations[0] == 1 && !(lep_stationMask[0] == 1 || lep_stationMask[0] == 16)) || (lep_numberOfMatchedStations[0] == 1 && (lep_stationMask[0] == 1 || lep_stationMask[0] == 16) && lep_numberOfMatchedRPCLayers[0] > 2)) && (lep_numberOfMatchedStations[1] > 1 || (lep_numberOfMatchedStations[1] == 1 && !(lep_stationMask[1] == 1 || lep_stationMask[1] == 16)) || (lep_numberOfMatchedStations[1] == 1 && (lep_stationMask[1] == 1 || lep_stationMask[1] == 16) && lep_numberOfMatchedRPCLayers[1] > 2)) && lep_glb_numberOfValidMuonHits[0]>0 && lep_glb_numberOfValidMuonHits[1]>0 && lep_glb_numberOfValidPixelHits[0]>=1 && lep_glb_numberOfValidPixelHits[1]>=1 && lep_glb_numberOfValidTrackerLayers[0]>5 && lep_glb_numberOfValidTrackerLayers[1]>5 && lep_sumPt[0]/lep_tk_pt[0]<0.10 && lep_sumPt[1]/lep_tk_pt[1]<0.10 && lep_pt_err[0]/lep_pt[0]<0.3 && lep_pt_err[1]/lep_pt[1]<0.3 && cos_angle>-0.9998 && lep_id[0]*lep_id[1]<0 && (lep_triggerMatchPt[0]>50 || lep_triggerMatchPt[1]>50) && vertex_chi2 < 20 && vertex_m > 900 && vertex_m < 1300'
# cut='dil_chosen==0 && fabs(lep_eta[0])<2.4 && fabs(lep_eta[1])<2.4 && GoodDataRan && GoodVtx && lep_pt[0]>53. && lep_pt[1]>53. && lep_isTrackerMuon[0]==1 && lep_isTrackerMuon[1]==1 && lep_isGlobalMuon[0]==1 && lep_isGlobalMuon[1]==1 && fabs(lep_dB[0]) < 0.2 && fabs(lep_dB[1]) < 0.2 && ( lep_numberOfMatchedStations[0] > 1 || (lep_numberOfMatchedStations[0] == 1 && !(lep_stationMask[0] == 1 || lep_stationMask[0] == 16)) || (lep_numberOfMatchedStations[0] == 1 && (lep_stationMask[0] == 1 || lep_stationMask[0] == 16) && lep_numberOfMatchedRPCLayers[0] > 2)) && (lep_numberOfMatchedStations[1] > 1 || (lep_numberOfMatchedStations[1] == 1 && !(lep_stationMask[1] == 1 || lep_stationMask[1] == 16)) || (lep_numberOfMatchedStations[1] == 1 && (lep_stationMask[1] == 1 || lep_stationMask[1] == 16) && lep_numberOfMatchedRPCLayers[1] > 2)) && lep_glb_numberOfValidMuonHits[0]>0 && lep_glb_numberOfValidMuonHits[1]>0 && lep_glb_numberOfValidPixelHits[0]>=1 && lep_glb_numberOfValidPixelHits[1]>=1 && lep_glb_numberOfValidTrackerLayers[0]>5 && lep_glb_numberOfValidTrackerLayers[1]>5 && lep_sumPt[0]/lep_tk_pt[0]<0.10 && lep_sumPt[1]/lep_tk_pt[1]<0.10 && lep_pt_err[0]/lep_pt[0]<0.3 && lep_pt_err[1]/lep_pt[1]<0.3 && cos_angle>-0.9998 && lep_id[0]*lep_id[1]<0 && (lep_triggerMatchPt[0]>50 || lep_triggerMatchPt[1]>50) && vertex_chi2 < 20 && vertex_m > 1300 && vertex_m < 1800'
# cut='dil_chosen==0 && fabs(lep_eta[0])<2.4 && fabs(lep_eta[1])<2.4 && GoodDataRan && GoodVtx && lep_pt[0]>53. && lep_pt[1]>53. && lep_isTrackerMuon[0]==1 && lep_isTrackerMuon[1]==1 && lep_isGlobalMuon[0]==1 && lep_isGlobalMuon[1]==1 && fabs(lep_dB[0]) < 0.2 && fabs(lep_dB[1]) < 0.2 && ( lep_numberOfMatchedStations[0] > 1 || (lep_numberOfMatchedStations[0] == 1 && !(lep_stationMask[0] == 1 || lep_stationMask[0] == 16)) || (lep_numberOfMatchedStations[0] == 1 && (lep_stationMask[0] == 1 || lep_stationMask[0] == 16) && lep_numberOfMatchedRPCLayers[0] > 2)) && (lep_numberOfMatchedStations[1] > 1 || (lep_numberOfMatchedStations[1] == 1 && !(lep_stationMask[1] == 1 || lep_stationMask[1] == 16)) || (lep_numberOfMatchedStations[1] == 1 && (lep_stationMask[1] == 1 || lep_stationMask[1] == 16) && lep_numberOfMatchedRPCLayers[1] > 2)) && lep_glb_numberOfValidMuonHits[0]>0 && lep_glb_numberOfValidMuonHits[1]>0 && lep_glb_numberOfValidPixelHits[0]>=1 && lep_glb_numberOfValidPixelHits[1]>=1 && lep_glb_numberOfValidTrackerLayers[0]>5 && lep_glb_numberOfValidTrackerLayers[1]>5 && lep_sumPt[0]/lep_tk_pt[0]<0.10 && lep_sumPt[1]/lep_tk_pt[1]<0.10 && lep_pt_err[0]/lep_pt[0]<0.3 && lep_pt_err[1]/lep_pt[1]<0.3 && cos_angle>-0.9998 && lep_id[0]*lep_id[1]<0 && (lep_triggerMatchPt[0]>50 || lep_triggerMatchPt[1]>50) && vertex_chi2 < 20 && vertex_m > 1800 && vertex_m < 8000'
# cut='dil_chosen==0 && fabs(lep_eta[0])<2.4 && fabs(lep_eta[1])<2.4 && GoodDataRan && GoodVtx && lep_pt[0]>53. && lep_pt[1]>53. && lep_isTrackerMuon[0]==1 && lep_isTrackerMuon[1]==1 && lep_isGlobalMuon[0]==1 && lep_isGlobalMuon[1]==1 && fabs(lep_dB[0]) < 0.2 && fabs(lep_dB[1]) < 0.2 && ( lep_numberOfMatchedStations[0] > 1 || (lep_numberOfMatchedStations[0] == 1 && !(lep_stationMask[0] == 1 || lep_stationMask[0] == 16)) || (lep_numberOfMatchedStations[0] == 1 && (lep_stationMask[0] == 1 || lep_stationMask[0] == 16) && lep_numberOfMatchedRPCLayers[0] > 2)) && (lep_numberOfMatchedStations[1] > 1 || (lep_numberOfMatchedStations[1] == 1 && !(lep_stationMask[1] == 1 || lep_stationMask[1] == 16)) || (lep_numberOfMatchedStations[1] == 1 && (lep_stationMask[1] == 1 || lep_stationMask[1] == 16) && lep_numberOfMatchedRPCLayers[1] > 2)) && lep_glb_numberOfValidMuonHits[0]>0 && lep_glb_numberOfValidMuonHits[1]>0 && lep_glb_numberOfValidPixelHits[0]>=1 && lep_glb_numberOfValidPixelHits[1]>=1 && lep_glb_numberOfValidTrackerLayers[0]>5 && lep_glb_numberOfValidTrackerLayers[1]>5 && lep_sumPt[0]/lep_tk_pt[0]<0.10 && lep_sumPt[1]/lep_tk_pt[1]<0.10 && lep_pt_err[0]/lep_pt[0]<0.3 && lep_pt_err[1]/lep_pt[1]<0.3 && cos_angle>-0.9998 && lep_id[0]*lep_id[1]<0 && (lep_triggerMatchPt[0]>50 || lep_triggerMatchPt[1]>50) && vertex_chi2 < 20 && vertex_m > 780 && vertex_m < 785'
cut = 'GoodVtx && fabs(lep_eta[0])<2.4 && fabs(lep_eta[1])<2.4 && lep_pt[0]>53. && lep_pt[1]>53. && lep_isTrackerMuon[0]==1 && lep_isTrackerMuon[1]==1 && lep_isGlobalMuon[0]==1 && lep_isGlobalMuon[1]==1 && fabs(lep_dB[0]) < 0.2 && fabs(lep_dB[1]) < 0.2 && ( lep_numberOfMatchedStations[0] > 1 || (lep_numberOfMatchedStations[0] == 1 && !(lep_stationMask[0] == 1 || lep_stationMask[0] == 16)) || (lep_numberOfMatchedStations[0] == 1 && (lep_stationMask[0] == 1 || lep_stationMask[0] == 16) && lep_numberOfMatchedRPCLayers[0] > 2)) && (lep_numberOfMatchedStations[1] > 1 || (lep_numberOfMatchedStations[1] == 1 && !(lep_stationMask[1] == 1 || lep_stationMask[1] == 16)) || (lep_numberOfMatchedStations[1] == 1 && (lep_stationMask[1] == 1 || lep_stationMask[1] == 16) && lep_numberOfMatchedRPCLayers[1] > 2)) && lep_glb_numberOfValidMuonHits[0]>0 && lep_glb_numberOfValidMuonHits[1]>0 && lep_glb_numberOfValidPixelHits[0]>=1 && lep_glb_numberOfValidPixelHits[1]>=1 && lep_glb_numberOfValidTrackerLayers[0]>5 && lep_glb_numberOfValidTrackerLayers[1]>5 && lep_sumPt[0]/lep_tk_pt[0]<0.10 && lep_sumPt[1]/lep_tk_pt[1]<0.10 && lep_pt_err[0]/lep_pt[0]<0.3 && lep_pt_err[1]/lep_pt[1]<0.3 && cos_angle>-0.9998 && lep_id[0]*lep_id[1]<0 && (lep_triggerMatchPt[0]>50 || lep_triggerMatchPt[1]>50) && vertex_chi2 < 20 && event == 317392 && lumi == 666' # && vertex_m > 400'# && vertex_m < 900'
# cut='fabs(lep_eta[0])<1.2 && fabs(lep_eta[1])<1.2 && fabs(lep_eta[0])<2.4 && fabs(lep_eta[1])<2.4 && GoodDataRan && GoodVtx && lep_pt[0]>53. && lep_pt[1]>53. && lep_isTrackerMuon[0]==1 && lep_isTrackerMuon[1]==1 && lep_isGlobalMuon[0]==1 && lep_isGlobalMuon[1]==1 && fabs(lep_dB[0]) < 0.2 && fabs(lep_dB[1]) < 0.2 && ( lep_numberOfMatchedStations[0] > 1 || (lep_numberOfMatchedStations[0] == 1 && !(lep_stationMask[0] == 1 || lep_stationMask[0] == 16)) || (lep_numberOfMatchedStations[0] == 1 && (lep_stationMask[0] == 1 || lep_stationMask[0] == 16) && lep_numberOfMatchedRPCLayers[0] > 2)) && (lep_numberOfMatchedStations[1] > 1 || (lep_numberOfMatchedStations[1] == 1 && !(lep_stationMask[1] == 1 || lep_stationMask[1] == 16)) || (lep_numberOfMatchedStations[1] == 1 && (lep_stationMask[1] == 1 || lep_stationMask[1] == 16) && lep_numberOfMatchedRPCLayers[1] > 2)) && lep_glb_numberOfValidMuonHits[0]>0 && lep_glb_numberOfValidMuonHits[1]>0 && lep_glb_numberOfValidPixelHits[0]>=1 && lep_glb_numberOfValidPixelHits[1]>=1 && lep_glb_numberOfValidTrackerLayers[0]>5 && lep_glb_numberOfValidTrackerLayers[1]>5 && lep_sumPt[0]/lep_tk_pt[0]<0.10 && lep_sumPt[1]/lep_tk_pt[1]<0.10 && lep_pt_err[0]/lep_pt[0]<0.3 && lep_pt_err[1]/lep_pt[1]<0.3 && cos_angle>-0.9998 && lep_id[0]*lep_id[1]<0 && (lep_triggerMatchPt[0]>50 || lep_triggerMatchPt[1]>50) && vertex_chi2 < 20'
#(fabs(lep_eta[0])>1.2 || fabs(lep_eta[1])>1.2)
#cut='lep_pt[0]>53. && lep_pt[1]>53. && lep_isTrackerMuon[0]==1 && lep_isTrackerMuon[1]==1 && lep_isGlobalMuon[0]==1 && lep_isGlobalMuon[1]==1 && fabs(lep_dB[0]) < 0.2 && fabs(lep_dB[1]) < 0.2 && lep_numberOfMatchedStations[0] > 1 && lep_numberOfMatchedStations[1] > 1 && lep_glb_numberOfValidMuonHits[0]>0 && lep_glb_numberOfValidMuonHits[1]>0 && lep_glb_numberOfValidPixelHits[0]>=1 && lep_glb_numberOfValidPixelHits[1]>=1 && lep_glb_numberOfValidTrackerLayers[0]>5 && lep_glb_numberOfValidTrackerLayers[1]>5 && lep_sumPt[0]/lep_tk_pt[0]<0.10 && lep_sumPt[1]/lep_tk_pt[1]<0.10 && lep_pt_err[0]/lep_pt[0]<0.3 && lep_pt_err[1]/lep_pt[1]<0.3 && cos_angle>-0.9998 && lep_id[0]*lep_id[1]<0 && (lep_triggerMatchPt[0]>50 || lep_triggerMatchPt[1]>50) && vertex_chi2 < 20 && vertex_m>900'
f = ROOT.TFile(path)
t = f.SimpleNtupler.Get('t')
t.GetPlayer().SetScanRedirect(True)
t.GetPlayer().SetScanFileName(tmp_fn)
#- colsize=10 is needed in order not to currupt the event number
t.Scan(branch_spec, cut, "colsize=15 precision=10")
#t.Scan('*', cut)
t.GetPlayer().SetScanRedirect(False)
f.Close()
lines = [
line.split(' *')[1:] for line in open(tmp_fn).readlines()
if ' * ' in line and 'Row' not in line
]
#- This loop is to keep only the first (highest-rank) dimuon in an event
prev_run = prev_lumi = prev_event = sum_pt = 0
# tt_sample = 'NoBinned'
tt_sample = ''
rebin = 1
for mumu_histogram in histogram:
if 'Pt' in mumu_histogram:
rebin = 20
if '_bb' in mumu_histogram:
Zpeak = 1.0328 #0.9638
elif '_be' in mumu_histogram:
Zpeak = 1.0420 #0.9688
else:
Zpeak = 1 #1.0386#0.9610
MC_Stack = ROOT.THStack('MC', '')
MC_Stack_cum = ROOT.THStack('MC_cum', '')
dati = ROOT.TH1F()
h = ROOT.TH1F()
h_cum = ROOT.TH1F()
for sample in samples:
file_dati = ROOT.TFile(mumu_Dati)
dati = file_dati.Our2016MuonsPlusMuonsMinusHistos.Get(
mumu_histogram).Clone()
dati.Rebin(rebin)
dati.SetMarkerColor(1)
dati.SetLineColor(1)
dati.SetLineWidth(2)
def save_histos(self, cutset, dilepton, quantity_to_compare, cumulative):
if cutset != 'Our2012':
return
if dilepton != 'MuonsPlusMuonsMinus':
return
# if quantity_to_compare != 'DimuonMassVtxConstrainedLog':
if quantity_to_compare != 'DimuonMassVertexConstrained':
return
zdy_ifois = 0
prompt_ifois = 0
for sample in self.samples:
if sample.is_zprime:
continue
if 'dy' in sample.name or sample.name == 'zmumu':
print 'dy: ',sample.name
if zdy_ifois == 0:
zdy = sample.histogram.Clone('zdy')
zdy_ifois = 1
else:
zdy.Add(sample.histogram, 1.)
if sample.name in ('ttbar', 'ttbar_powheg', 'tW', 'tbarW', 'ztautau', 'ww', 'wz', 'zz'):
print 'ttbar and others', sample.name
if prompt_ifois == 0:
prompt = sample.histogram.Clone('prompt')
prompt_ifois = 1
else:
prompt.Add(sample.histogram, 1.)
data = self.hdata.Clone('data')
data_tgraph = poisson_intervalize(self.hdata, True)
data_tgraph.SetName("data_tgraph")
file_name = 'dimuon_histos_differential.root'
if cumulative:
file_name = 'dimuon_histos_cumulative.root'
histFile = ROOT.TFile.Open(file_name, 'recreate')
if not histFile:
raise RuntimeError, \
"Could not open '%s' as an output root file" % output
data.Write()
prompt.Write()
zdy.Write()
if not cumulative:
nbins = data.GetNbinsX()
data_tgraph_per_GeV = ROOT.TGraphAsymmErrors(data_tgraph)
data_tgraph_per_GeV.SetName("data_tgraph_per_GeV")
zdy_per_GeV = zdy.Clone('zdy_per_GeV')
prompt_per_GeV = prompt.Clone('prompt_per_GeV')
for i in xrange(1, nbins):
c_data = data.GetBinContent(i)/data.GetBinWidth(i)
e_data = data.GetBinError(i)/data.GetBinWidth(i)
print i, 'nevents = ', data.GetBinContent(i), ' low edge = ', data.GetBinLowEdge(i), ' width = ', data.GetBinWidth(i), 'events/GeV = ', c_data, '+/-', e_data
x = data.GetBinLowEdge(i) + data.GetBinWidth(i)/2.
err_low = data_tgraph.GetErrorYlow(i-1)/data.GetBinWidth(i)
err_high = data_tgraph.GetErrorYhigh(i-1)/data.GetBinWidth(i)
print ' x = ', x, ' err_low = ', data_tgraph.GetErrorYlow(i-1), err_low, ' err_high = ', data_tgraph.GetErrorYhigh(i-1), err_high
data_tgraph_per_GeV.SetPoint(i-1, x, c_data)
data_tgraph_per_GeV.SetPointEYlow(i-1, err_low)
data_tgraph_per_GeV.SetPointEYhigh(i-1, err_high)
c_zdy = zdy.GetBinContent(i)/zdy.GetBinWidth(i)
e_zdy = zdy.GetBinError(i)/zdy.GetBinWidth(i)
zdy_per_GeV.SetBinContent(i, c_zdy)
zdy_per_GeV.SetBinError(i, e_zdy)
c_prompt = prompt.GetBinContent(i)/prompt.GetBinWidth(i)
e_prompt = prompt.GetBinError(i)/prompt.GetBinWidth(i)
prompt_per_GeV.SetBinContent(i, c_prompt)
prompt_per_GeV.SetBinError(i, e_prompt)
data_tgraph.Write()
data_tgraph_per_GeV.Write()
zdy_per_GeV.Write()
prompt_per_GeV.Write()
def __init__(self, options):
self.histo_dir = options.histo_dir
self.print_table = options.print_table
self.save_plots = options.save_plots
self.rescale_lumi = options.rescale_lumi
self.lumi_syst_frac = options.lumi_syst_frac
self.draw_zprime = options.draw_zprime
self.use_poisson_intervals = options.use_poisson_intervals
self.put_overflow_in_last_bin = options.put_overflow_in_last_bin
self.do_joins = options.do_joins
self.join_ttbar_and_other_prompt_leptons = options.join_ttbar_and_other_prompt_leptons
self.join_dy_and_jets = options.join_dy_and_jets
self.qcd_from_data = options.qcd_from_data
self.guess_yrange = options.guess_yrange
if not os.path.isdir(self.histo_dir):
raise ValueError('histo_dir %s is not a directory' % self.histo_dir)
self.data_fn = os.path.join(self.histo_dir, 'ana_datamc_data.root')
if not os.path.isfile(self.data_fn):
raise ValueError('data_fn %s is not a file' % self.data_fn)
self.data_f = ROOT.TFile(self.data_fn)
if not self.data_f.IsOpen() or self.data_f.IsZombie():
raise ValueError('data_fn %s is not a ROOT file' % self.data_fn)
# We look for a separate mc dir in our histo_dir first, for
# the case where we want to use a specific set of MC files for
# a given histo_dir. If none there, check if there is one
# specified in options. Else, use the general mc dir from the
# current directory.
self.mc_dir = os.path.join(self.histo_dir, 'mc')
if not os.path.isdir(self.mc_dir):
if hasattr(options, 'mc_dir'):
self.mc_dir = options.mc_dir
else:
self.mc_dir = 'mc'
if not os.path.isdir(self.mc_dir):
raise ValueError('mc_dir %s is not a directory' % self.mc_dir)
# If the options override the int_lumi, take that; otherwise
# try to get the int_lumi from the file corresponding to
# data_fn.
if type(options.override_int_lumi) == float:
self.int_lumi = options.override_int_lumi
print self.int_lumi
else:
lumi_fn = self.data_fn.replace('.root', '.lumi')
self.int_lumi = self.parse_lumi_from_log(lumi_fn)
if self.int_lumi is None:
raise ValueError('int_lumi could not be parsed from file %s' % lumi_fn)
if self.int_lumi < 0:
raise ValueError('int_lumi %f makes no sense' % self.int_lumi)
# Use all samples specified in MCSamples that are not
# requested to be dropped in the options. The sample objects
# already-set values (like color, partial_weight) we won't
# change, but the objects themselves may be modified. In
# particular, we will use them as storage containers for
# things like ROOT histograms and calculated things like scale
# factors and event counts.
self.samples = [s for s in MCSamples.samples if options.exclude_samples is None or s.name not in options.exclude_samples]
self.hdata = None
# Defaults for the dileptons, cutsets, and mass ranges for the
# ASCII table.
self.quantities_to_compare = ['DileptonMass', 'DimuonMassVertexConstrained','DileptonPt', 'LeptonPt','LeptonPhi', 'LeptonEta','NVertices']
self.dileptons = ['MuonsPlusMuonsMinus', 'MuonsSameSign', 'MuonsAllSigns', 'MuonsElectronsOppSign', 'MuonsElectronsSameSign', 'MuonsElectronsAllSigns']
# self.dileptons = ['MuonsPlusMuonsMinus', 'MuonsSameSign', 'MuonsAllSigns', 'MuonsElectronsOppSign', 'MuonsElectronsSameSign', 'MuonsElectronsAllSigns'] ########
self.dileptons = ['']
# self.cutsets = ['Our2012', 'Simple', 'OurMuPrescaled'] #################
self.cutsets = ['Histosinner', 'Histostunepnew', 'Histosstartup']
self.mass_ranges_for_table = [(50,),(60,120)]
#self.mass_ranges_for_table = [(60,120), (120,200), (200,400), (400,600), (120,), (200,), (400,), (600,)]
# self.mass_ranges_for_table = [(60,120), (120,200), (120,400), (400,600), (600,900,), (900,1300), (1300,1800), (1800,), (120,), (200,), (400,), (600,)]
if options.include_quantities is not None:
self.quantities_to_compare = options.include_quantities
if options.include_dileptons is not None:
self.dileptons = options.include_dileptons
if options.include_cutsets is not None:
self.cutsets = options.include_cutsets
if options.include_mass_ranges_for_table is not None:
self.mass_ranges_for_table = options.include_mass_ranges_for_table
if options.for_rescale_factors:
self.save_plots = False
self.rescale_lumi = False
self.mass_ranges_for_table = [(60,120)]
# self.dileptons = ['MuonsPlusMuonsMinus']
self.dileptons = ['']
self.setup_root()
base = 'plots/mc'
if options.plot_dir_tag is not None:
base += '_' + options.plot_dir_tag
self.plot_dir_base = os.path.join(base, os.path.basename(self.histo_dir))
os.system('mkdir -p %s' % self.plot_dir_base)
width,height = options.plot_size.split(',')
self.ps = plot_saver(self.plot_dir_base, size=(int(width), int(height)), pdf_log=True, pdf=True)
self.table_sections = []
self.table_rows = []
def draw_data_on_mc(self, cutset, dilepton, quantity_to_compare, cumulative):
# Make a Stack for the MC histograms. We draw it first so the
# data points will be drawn on top later. We assume that in
# the list of samples, the join groups are contiguous already
# so that like-colored histograms will be next to each other.
s = ROOT.THStack('s', '')
for sample in self.samples:
# Don't stack the Z' samples.
if sample.is_zprime:
continue
s.Add(sample.histogram) # Assumes they've already been prepared.
# Figure out its titles.
xtitle = self.titleize(quantity_to_compare) % (self.subtitleize(dilepton), self.unitize(quantity_to_compare))
if cumulative:
# E.g. Events >= m(mu+mu-).
ytitle = 'Events #geq %s' % (self.titleize(quantity_to_compare) % (self.subtitleize(dilepton), ''))
else:
# E.g. Events/5 GeV.
ytitle = 'Events / %i%s' % (self.get_rebin_factor(dilepton, quantity_to_compare), self.unitize(quantity_to_compare).translate(None, '()[]')) # Events/5 GeV. JMTBAD assumes original histogram had 1 GeV bins and was rebinned simply -- ignores rebin_histogram ability to have arb. bins
if quantity_to_compare == 'DileptonRap':
ytitle = 'Events / 0.5'
elif quantity_to_compare == 'RelIsoSumPt':
ytitle = 'Events / 0.02'
elif quantity_to_compare == 'RelCombIso':
ytitle = 'Events / 0.05'
elif quantity_to_compare == 'DimuonMassVtxConstrainedLog':
ytitle = 'Events / bin'
s.SetTitle(';%s;%s' % (xtitle, ytitle))
s.Draw('hist')
# Must call Draw first or the THStack doesn't have a histogram/axes.
s.GetXaxis().SetTitleOffset(0.9)
s.GetXaxis().SetTitleSize(0.047)
if 'MuonsSameSign' in dilepton:
s.GetYaxis().SetTitleOffset(0.9)
else:
s.GetYaxis().SetTitleOffset(1.0)
s.GetYaxis().SetTitleSize(0.047)
# Set the x-axis range as specified. Then determine what
# the real extrema of the data histogram and the MC stack are
# over the xrange specified.
xrange = self.get_x_axis_range(cutset, dilepton, quantity_to_compare)
mymin, mymax = None, None
if xrange is not None:
s.GetXaxis().SetRangeUser(*xrange)
# self.hdata.GetXaxis().SetRangeUser(*xrange)
mymin = real_hist_min(s.GetStack().Last(), user_range=xrange) * 0.7
mymax = real_hist_max(s.GetStack().Last(), user_range=xrange, use_error_bars=False) * 1.05
# if self.hdata.GetEntries() > 0:
# rhm = real_hist_max(self.hdata, user_range=xrange)
# mymax = max(mymax, rhm)
#
# if self.guess_yrange:
# mymin = 0
# mymax = real_hist_max(self.hdata, user_range=xrange)
# Can override the above fussing.
# yrange = self.get_y_axis_range(dilepton, cumulative)
# if yrange is not None:
# if yrange[0] is not None:
# mymin = yrange[0]
# if yrange[1] is not None:
# mymax = yrange[1]
mymin = 0.005
if mymin is not None: s.SetMinimum(mymin)
if mymax is not None: s.SetMaximum(mymax)
# Calculate (data-bckg)/bckg. Do it before TH1 gets converted
# to TGraphAsymmErrors by poisson_intervalize.
# if not cumulative:
# ifois = 0
# for sample in self.samples:
# # Don't add the Z' samples.
# if sample.is_zprime:
# continue
# if ifois == 0:
# mc_sum = sample.histogram.Clone()
# ifois = 1
# else:
# mc_sum.Add(sample.histogram, 1.)
#
# data_mc_diff = self.hdata.Clone()
# data_mc_diff.Divide(mc_sum)
# nbins = data_mc_diff.GetNbinsX()
# for ibin in range(1, nbins):
# f_bin = data_mc_diff.GetBinContent(ibin)
# data_mc_diff.SetBinContent(ibin, f_bin-1.)
# Now draw the data on top of the stack.
# self.hdata.SetStats(0)
# data_draw_cmd = 'same p e'
# if self.use_poisson_intervals:
# self.hdata = poisson_intervalize(self.hdata, True)
# data_draw_cmd += ' z'
# if mymin is not None: self.hdata.SetMinimum(mymin)
# if mymax is not None: self.hdata.SetMaximum(mymax)
# self.hdata.SetMarkerStyle(20)
# self.hdata.SetMarkerSize(0.8)
# self.hdata.Draw(data_draw_cmd)
# Draw the Z' curve separately. It is overlaid, not stacked
# with the rest of the MC expectation.
zp = self.get_zprime_histogram()
if self.should_draw_zprime(dilepton) and zp:
# JMTBAD Extend to the rest of the Z' samples when there are any.
zp.SetTitle('')
zp.SetLineWidth(2)
if xrange is not None:
zp.GetXaxis().SetRangeUser(*xrange)
zp.SetMinimum(mymin)
zp.SetMaximum(mymax)
zp.SetStats(0)
zp.Draw('hist same')
# Use log(x) whenever needed
log_x = self.get_log_x(cutset, dilepton, quantity_to_compare)
if log_x:
self.ps.c.SetLogx()
# Adorn the plot with legend and labels.
l = self.draw_legend(dilepton, cumulative, log_x)
# t = ROOT.TPaveLabel(0.20, 0.89, 0.86, 0.99, 'CMS Preliminary #sqrt{s} = 8 TeV #int L dt = %.f pb^{-1}' % round(self.int_lumi), 'brNDC')
t = ROOT.TPaveLabel(0.30, 0.89, 0.96, 0.99, 'CMS Preliminary #sqrt{s} = 13 TeV #int L dt = 5 fb^{-1}', 'brNDC')
t.SetTextSize(0.35)
t.SetBorderSize(0)
t.SetFillColor(0)
t.SetFillStyle(0)
t.Draw()
# Done; save it!
plot_fn = dilepton + 'plot'
if cumulative:
plot_fn += '_cumulative'
self.ps.save(plot_fn)
if log_x:
self.ps.c.SetLogx(0)
def prepare_mc_histograms(self, cutset, dilepton, quantity_to_compare, cumulative):
# For each sample in the list of MC samples, we get the
# specified histogram from the appropriate input file, clone
# it, rebin/scale/otherwise manipulate it as necessary, and
# store the result as The Histogram in the sample object.
for sample in self.samples:
if (self.qcd_from_data and 'MuPrescaled' not in cutset and 'Electron' not in dilepton and sample.name == 'inclmu15' and quantity_to_compare in ['DileptonMass', 'DimuonMassVertexConstrained', 'DimuonMassVtxConstrainedLog']):
# Replace QCD MC prediction by that obtained from data.
mc_fn = os.path.join('./', 'qcd_from_data.root')
f = ROOT.TFile(mc_fn)
if 'MuonsSameSign' in dilepton:
sample.histogram = f.Get('ss').Clone()
elif 'MuonsPlusMuonsMinus' in dilepton:
sample.histogram = f.Get('os').Clone()
elif 'MuonsAllSigns' in dilepton:
sample.histogram = f.Get('ss').Clone()
sample.histogram.Add(f.Get('os'), 1.)
else:
print "+++ Unknown dilepton type! +++"
if (quantity_to_compare == 'DimuonMassVtxConstrainedLog'):
# Re-pack fixed-bin-width histogram into a variable-bin-width one
mc_fn = os.path.join(self.mc_dir, 'ana_datamc_%s.root' % sample.name)
f = ROOT.TFile(mc_fn)
varbin_histogram = getattr(f, self.get_dir_name(cutset, dilepton)).Get(quantity_to_compare).Clone()
nbins_var = varbin_histogram.GetNbinsX()
nbins_fix = sample.histogram.GetNbinsX()
for ibin_var in range(1, nbins_var+1):
xlow_var = varbin_histogram.GetBinLowEdge(ibin_var)
xupp_var = xlow_var + varbin_histogram.GetBinWidth(ibin_var)
# print ibin_var, xlow_var, xupp_var
cbin = 0
for ibin_fix in range(1, nbins_fix+1):
bin_center = sample.histogram.GetBinCenter(ibin_fix)
if (bin_center > xlow_var and bin_center < xupp_var):
# print " ", ibin_fix, bin_center, sample.histogram.GetBinContent(ibin_fix)
cbin = cbin + sample.histogram.GetBinContent(ibin_fix)
# print " ", ibin_var, cbin
varbin_histogram.SetBinContent(ibin_var, cbin)
sample.histogram = varbin_histogram.Clone()
sample.scaled_by = 1. # Assume that the histogram is appropriately normalized.
else:
mc_fn = os.path.join(self.mc_dir, 'ana_datamc_%s.root' % sample.name)
f = ROOT.TFile(mc_fn)
sample.histogram = getattr(f, self.get_dir_name(cutset, dilepton)).Get(quantity_to_compare).Clone()
sample.scaled_by = self.int_lumi * self.get_lumi_rescale_factor(cutset, dilepton) * sample.partial_weight
#print sample.name,' ',self.int_lumi,' ',self.get_lumi_rescale_factor(cutset, dilepton),' ',sample.partial_weight #NEW TO CHECK#
if 'MuPrescaled' in cutset:
sample.scaled_by = sample.scaled_by / overall_prescale
# Assume that the QCD histogram includes W+jets as well.
if (self.qcd_from_data and 'MuPrescaled' not in cutset and 'Electron' not in dilepton and sample.name in ('wmunu', 'wjets') and quantity_to_compare in ['DileptonMass', 'DimuonMassVertexConstrained', 'DimuonMassVtxConstrainedLog']):
nbins = sample.histogram.GetNbinsX()
for ibin in range(1, nbins+1):
sample.histogram.SetBinContent(ibin, 0.)
sample.histogram.SetBinError(ibin, 0.)
self.rebin_histogram(sample.histogram, cutset, dilepton, quantity_to_compare)
sample.histogram.Scale(sample.scaled_by)
if cumulative:
sample.histogram = cumulative_histogram(sample.histogram)
else:
self.handle_overflows(sample.histogram, self.get_x_axis_range(cutset, dilepton, quantity_to_compare))
sample.histogram.SetMarkerStyle(0)
color = self.get_color(sample)
sample.histogram.SetLineColor(color)
if not sample.is_zprime:
sample.histogram.SetFillColor(color)
sample.histogram.SetLineColor(1) #NEW#