def makePlot(trees,
cut,
variable,
combination,
name,
title=None,
datasetName="Data"):
from ROOT import TCanvas, TPad, TLegend, kBlue, kRed, TLatex
from src.ratios import RatioGraph
from src.histos import getHisto
from math import sqrt
from random import random
if title is None:
title = name
cut = cut.replace("inv", "p4.M()")
if "iso" in variable:
cut = cut.replace("id1 < 0.15", "id1 < 10")
cut = cut.replace("id2 < 0.15", "id2 < 10")
print cut
baseColor = kBlue
if "jzb" in name:
baseColor = kRed
rmue = 1.21
trigger = {"EE": 0.970, "EMu": 0.942, "MuMu": 0.964}
histos = {
"MuMu": getHisto(trees["MuMu"], cut, variable),
"EE": getHisto(trees["EE"], cut, variable),
"EMu": getHisto(trees["EMu"], cut, variable),
"EMu_MuLead": getHisto(trees["EMu"], "(%s) * (pt2 > pt1)" % cut,
variable),
}
rmueSys = 0
predictionSrc = "EMu"
if combination == "Both":
sameFlavour = histos["MuMu"].Clone("sameFlavourSum")
sameFlavour.Add(histos["EE"])
#~ nllPredictionScale = 0.5* sqrt(trigger["EE"]*trigger["MuMu"])*1./trigger["EMu"] *(rmue+1./(rmue))
#~ rmueSys = sqrt(sum(i**2 for i in [0.5*(1-(1./rmue**2))*0.1 + (1./trigger["EMu"] + 0.5*(1./trigger["EE"]+1./trigger["MuMu"]))*0.05]))
nllPredictionScale = 1.02
rmueSys = 0.07
#0.5*(1-1./rmue**2)*0.1
elif combination in ["EE", "MuMu"]:
rmueFactor = rmue
if combination == "EE":
rmueFactor = 1. / rmue
nllPredictionScale = 0.5 * sqrt(
trigger["EE"] * trigger["MuMu"]) * 1. / trigger["EMu"] * rmueFactor
rmueSys = sqrt(0.1**2 + (0.67 / 1.025)**2) * nllPredictionScale
sameFlavour = histos[combination].Clone("sameFlavourSum")
elif combination == "MuMuSF":
predictionSrc = "MuMu"
rmueFactor = 1. / rmue
nllPredictionScale = trigger["EE"] * 1. / trigger["MuMu"] * (rmueFactor
**2)
rmueSys = (2 * rmue) * 0.1
sameFlavour = histos["EE"].Clone("sameFlavourSum")
else:
raise StandardError, "unknown combination' %s'" % combination
prediction = histos[predictionSrc].Clone("ofPrediction")
predictionMuLead = histos["EMu_MuLead"].Clone("ofPrediction_MuLeading")
prediction.Scale(nllPredictionScale)
predictionMuLead.Scale(nllPredictionScale)
rooTex = {
"Both": "ee + #mu#mu",
"EE": "ee",
"MuMu": "#mu#mu",
"MuMuSF": "ee",
"EMu_Prediction": "OF-Prediciton",
"MuMu_Prediction": "SF(#mu#mu)-Prediciton"
}
canv = TCanvas("canv", "", 800, 800)
canv.Draw()
pad = TPad("main_%x" % (1e16 * random()), "main", 0.01, 0.25, 0.99, 0.99)
pad.SetNumber(1)
pad.Draw()
canv.cd()
resPad = TPad("residual_%x" % (1e16 * random()), "residual", 0.01, 0.01,
0.99, 0.25)
resPad.SetNumber(2)
resPad.Draw()
pad.cd()
#~ if "iso" in variable:
#~ canv.SetLogy(1)
#~ pad.SetLogy(1)
sameFlavour.SetMarkerStyle(20)
sameFlavour.SetLineColor(1)
sameFlavour.Draw("E P")
sameFlavour.SetTitle(title)
import ROOT
histos["MuMu"].SetLineColor(ROOT.kBlack)
histos["MuMu"].SetFillColor(ROOT.kWhite)
prediction.SetLineColor(1)
prediction.SetFillColor(baseColor - 7)
prediction.Draw("SAME Hist")
if predictionSrc == "EMu":
predictionMuLead.SetLineColor(1)
predictionMuLead.SetFillColor(baseColor - 9)
predictionMuLead.Draw("SAME Hist")
sameFlavour.Draw("E P SAME")
histos["MuMu"].Draw("SAME Hist")
pad.RedrawAxis()
canv.RedrawAxis()
leg = TLegend(0.75, 0.65, 1, 0.9)
leg.SetFillColor(0)
leg.AddEntry(sameFlavour, rooTex[combination], "P")
leg.AddEntry(histos["MuMu"], "#mu#mu events", "l")
leg.AddEntry(prediction, rooTex[predictionSrc + "_Prediction"], "F")
if predictionSrc == "EMu":
leg.AddEntry(predictionMuLead, "#mu leading", "F")
leg.Draw()
# print "_".join([name,variable]),
# print "EE",histos["EE"].GetEntries(), "MuMu:", histos["MuMu"].GetEntries(), "EMu:", histos["EMu"].GetEntries(),
# print " SF:", sameFlavour.GetEntries(), "OF:", prediction.GetEntries()
# tex = TLatex()
# tex.SetNDC()
# tex.SetTextSize()
# tex.DrawLatex(0.6, 0.7, title)
residuals = RatioGraph(sameFlavour, prediction)
residuals.addErrorBySize(rmueSys, rmueSys, add=False, color=kBlue - 9)
residuals.draw(resPad, yMin=0.5, yMax=1.5)
canv.Update()
variable = variable.replace("(", "").replace(")", "")
plotPath = "fig/%s.pdf" % ("_".join(
[datasetName, name, combination, variable]))
canv.Print(plotPath)
pad.Delete()
resPad.Delete()
canv.Delete()
# raw_input()
return plotPath
