def profile_plots(pt_array, ntrk_array, weight, gluon_pt_array,
gluon_ntrk_array, gluon_weight_array, quark_pt_array,
quark_ntrk_array, quark_weight_array, islogpt):
#gStyle.SetOptFit()
print 'total events: ' + str(len(pt_array))
print 'gluons: ' + str(len(gluon_pt_array))
print 'quarks: ' + str(len(quark_pt_array))
c1 = TCanvas('c1', '', 200, 10, 700, 500)
fittype = 'pol1'
if islogpt:
ptmin = 4
ptmax = 7
nbins = 10
else:
ptmin = 0
ptmax = 1300
nbins = 10
hquark = TProfile('hquark', 'Quark Profile of nTrk vs ln(pt)', nbins,
ptmin, ptmax, ntrkmin, ntrkmax)
weighted_hquark = TProfile('weighted_hquark',
'Quark Weighted Profile of nTrk vs ln(pt)',
nbins, ptmin, ptmax, ntrkmin, ntrkmax)
count = 0
for i, j, k in zip(quark_pt_array, quark_ntrk_array, quark_weight_array):
count += 1
if count % 100000 == 0:
print 'processed ' + str(count)
hquark.Fill(i, j)
weighted_hquark.Fill(i, j, k)
draw_hist_with_tag_eff(hquark, "quark", quark_pt_array, quark_ntrk_array,
gluon_pt_array, gluon_ntrk_array)
draw_hist_with_tag_eff(weighted_hquark, "weightedquark", quark_pt_array,
quark_ntrk_array, gluon_pt_array, gluon_ntrk_array)
hgluon = TProfile('hgluon', 'Gluon Profile of nTrk vs ln(pt)', nbins,
ptmin, ptmax, ntrkmin, ntrkmax)
weighted_hgluon = TProfile('weighted_hgluon',
'Gluon Weighted Profile of nTrk vs ln(pt)',
nbins, ptmin, ptmax, ntrkmin, ntrkmax)
for i, j, k in zip(gluon_pt_array, gluon_ntrk_array, gluon_weight_array):
hgluon.Fill(i, j)
weighted_hgluon.Fill(i, j, k)
draw_hist_with_tag_eff(hgluon, "gluon", quark_pt_array, quark_ntrk_array,
gluon_pt_array, gluon_ntrk_array)
draw_hist_with_tag_eff(weighted_hgluon, "weightedgluon", quark_pt_array,
quark_ntrk_array, gluon_pt_array, gluon_ntrk_array)
c3 = TCanvas('c3', '', 200, 10, 700, 500)
weighted_hquark.SetMaximum(30)
weighted_hquark.Draw("HIST")
weighted_hgluon.Draw("HIST SAME")
weighted_hquark.SetLineColor(2)
c3.SaveAs(settings.outputdir + '/overlay.pdf')
def staff():
FitParam = ROOT.FitParameters_t()
f = TFile("FitParam.root", "RECREATE")
tree = TTree('T', "Fitparameters from main program")
tree.Branch("FitParam", FitParam, 'Drift_Velocity:Fit_Gradient')
fname = "test.txt"
hdv = TH1F("hdv", "Drift Velocity Distribution", 100, 0.002615, 0.002645)
hfg = TH1F("hfg", "Track Angle Distribution", 100, 0, 0.3)
hprof = TProfile("hprof", "Profile of Drift Velocity vs Track Angle", 100,
0.002615, 0.002645, 0, 0.3)
histos = ['hdv', 'hfg', 'hprof']
for name in histos:
exec('%sFill = %s.Fill' % (name, name))
for line in open(fname).readlines():
t = list(filter(lambda x: x, re.split(',', line)))
FitParam.Drift_Velocity = float(t[0])
FitParam.Fit_Gradient = float(t[1])
hdv.Fill(float(t[0]))
hfg.Fill(float(t[1]))
hprof.Fill(float(t[0]), float(t[1]))
tree.Fill()
c1 = TCanvas("c1", "Drift Velocity Histogram", 200, 10, 700, 500)
c1.SetGridx()
c1.SetGridy()
hdv.Print()
hdv.Draw()
c1.Modified()
c1.Update()
c2 = TCanvas("c2", "Angular Distribution Histogram", 200, 10, 700, 500)
c2.SetGrid()
hfg.Print()
hfg.Draw()
c2.Modified()
c2.Update()
c3 = TCanvas("c3", "Profile", 200, 10, 700, 500)
hprof.Print()
hprof.Draw()
c3.Modified()
c3.Update()
tree.Print()
tree.Write()
for name in histos:
exec('del %sFill' % name)
del histos
x = input("Enter any key to continue")
def plotting_init(data, trainvar, histo_dict, masses, weights='totalWeight'):
""" Initializes the plotting
Parameters:
-----------
data : pandas DataFrame
Data to be used for creating the TProfiles
trainvar : str
Name of the training variable.
histo_dict : dict
Dictionary containing the info for plotting for a given trainvar
masses : list
List of masses to be used
[weights='totalWeight'] : str
What column to be used for weight in the data.
Returns:
--------
canvas : ROOT.TCanvas instance
canvas to be plotted on
profile : ROOT.TProfile instance
profile for the fitting
"""
canvas = TCanvas('canvas', 'TProfile plot', 200, 10, 700, 500)
canvas.GetFrame().SetBorderSize(6)
canvas.GetFrame().SetBorderMode(-1)
signal_data = data.loc[data['target'] == 1]
gen_mHH_values = np.array(signal_data['gen_mHH'].values, dtype=np.float)
trainvar_values = np.array(signal_data[trainvar].values, dtype=np.float)
weights = np.array(signal_data[weights].values, dtype=np.float)
sanity_check = len(gen_mHH_values) == len(trainvar_values) == len(weights)
assert sanity_check
title = 'Profile of ' + str(trainvar) + ' vs gen_mHH'
num_bins = (len(masses) - 1)
xlow = masses[0]
xhigh = (masses[(len(masses) - 1)] + 100.0)
ylow = histo_dict["min"]
yhigh = histo_dict["max"]
profile = TProfile('profile', title, num_bins, xlow, xhigh, ylow, yhigh)
mass_bins = np.array(masses, dtype=float)
profile.SetBins((len(mass_bins) - 1), mass_bins)
profile.GetXaxis().SetTitle("gen_mHH (GeV)")
profile.GetYaxis().SetTitle(str(trainvar))
for x, y, w in zip(gen_mHH_values, trainvar_values, weights):
profile.Fill(x, y, w)
profile.Draw()
canvas.Modified()
canvas.Update()
return canvas, profile
def plotPulls(optunf="Bayes",
ntest=10,
leff=True,
loufl=False,
optfun="exp",
opttfun="",
gmean=-1.0,
nrebin=4):
if opttfun == "":
opttfun = optfun
if optfun == "blobel":
gmean = 0.0
funttxt, funtxt = funtxts(opttfun, optfun, leff, loufl)
global histos, canv, canv2
histos = []
canv = TCanvas("canv", "thruth vs reco pulls", 600, 800)
canv.Divide(1, 3)
canv2 = TCanvas("canv2", "P(chi^2)", 600, 800)
canv2.Divide(2, 3)
if optunf == "BasisSplines":
bininfo = BinInfo(nrebin)
unfoldtester = UnfoldTester(optunf, nrebin)
else:
bininfo = BinInfo()
unfoldtester = UnfoldTester(optunf)
trainer = Trainer(bininfo, opttfun, optfun)
tester = Tester(bininfo, optfun)
hbininfo = bininfo.create(optfun)
dx = hbininfo["mhi"]
for sigma, ipad in [[0.01 * dx, 1], [0.03 * dx, 2], [0.1 * dx, 3]]:
txt = optunf + ", smear mu, s.d.= " + str(gmean) + ", " + str(
sigma) + ", train: " + funttxt + ", test: " + funtxt + ", " + str(
ntest) + " tests"
hPulls = TProfile("pulls", txt, hbininfo["tbins"], hbininfo["tlo"],
hbininfo["thi"])
hPulls.SetErrorOption("s")
hPulls.SetYTitle("Thruth reco pull")
histos.append(hPulls)
hChisq = TH1D("chisq", "P(chi^2) rec " + txt, 10, 0.0, 1.0)
hChisqm = TH1D("chisqm", "P(chi^2) mea " + txt, 10, 0.0, 1.0)
histos.append(hChisq)
histos.append(hChisqm)
measurement = createMeasurement(gmean, sigma, leff, optfun)
response = trainer.train(measurement, loufl=loufl)
for itest in range(ntest):
print "Test", itest
unfold, hTrue, hMeas = unfoldtester.rununfoldtest(
tester, measurement, response)
unfold.PrintTable(cout, hTrue, 2)
hReco = unfold.Hreco(2)
nbin = hReco.GetNbinsX()
if hbininfo["nrebin"] > 1:
hTrue = hTrue.Rebin(nrebin)
for ibin in range(nbin + 1):
truevalue = hTrue.GetBinContent(ibin)
recvalue = hReco.GetBinContent(ibin)
error = hReco.GetBinError(ibin)
if error > 0.0:
pull = (recvalue - truevalue) / error
hPulls.Fill(hReco.GetBinCenter(ibin), pull)
chisq = unfold.Chi2(hTrue, 2)
hChisq.Fill(TMath.Prob(chisq, hTrue.GetNbinsX()))
chisqm = unfold.Chi2measured()
pchisqm = TMath.Prob(chisqm, hMeas.GetNbinsX() - hReco.GetNbinsX())
print "Chisq measured=", chisqm, "P(chi^2)=", pchisqm
hChisqm.Fill(pchisqm)
canv.cd(ipad)
gStyle.SetErrorX(0)
hPulls.SetMinimum(-3.0)
hPulls.SetMaximum(3.0)
hPulls.SetMarkerSize(1.0)
hPulls.SetMarkerStyle(20)
hPulls.SetStats(False)
hPulls.Draw()
canv2.cd(ipad * 2 - 1)
hChisq.Draw()
canv2.cd(ipad * 2)
hChisqm.Draw()
fname = "RooUnfoldTestPulls_" + optunf + "_" + opttfun + "_" + optfun
if loufl:
fname += "_oufl"
canv.Print(fname + ".pdf")
fname = "RooUnfoldTestChisq_" + optunf + "_" + opttfun + "_" + optfun
if loufl:
fname += "_oufl"
canv2.Print(fname + ".pdf")
return
# In[12]:
hpxpy.Draw('COLZ')
c2.Modified()
c2.Update()
c2.Draw()
# In[13]:
hpxpy.Draw('LEGO')
c2.Modified()
c2.Update()
c2.Draw()
# In[14]:
c3 = gROOT.FindObject('c3')
if c3:
c3 = 0
c3 = TCanvas('c3', 'c3', 200, 10, 700, 500)
hprof.Draw()
c3.Modified()
c3.Update()
c3.Draw()
# In[15]:
c4 = gROOT.FindObject('c4')
if c4:
c4 = 0
c4 = TCanvas('c4', 'c4', 200, 10, 700, 500)
c4.Divide(2, 2)
c4.cd(1)
ntuple.Draw("pz:px")
c4.cd(2)
ntuple.Draw("pz:py")
c4.cd(3)
ntuple.Draw("pz:px*px + py*py")
def main():
parser = ArgumentParser(description='Scan over ROOT files to find the '+ \
'lumisections belonging to a specific run.')
parser.add_argument('-b', action='store_true', help='enable batch mode')
parser.add_argument('--dataset', required=True, choices=['PromptReco2015', \
'ReRecoOct2015', 'ReRecoDec2015', 'PromptReco2016', \
'2015ReRecoJan2017', '2016ReRecoJan2017'], \
help='specify data-taking period and reconstruction')
parser.add_argument('-n', nargs=1, default=1, type=int, help='Specify '+ \
'the number of ZeroBias datasets to be included')
parser.add_argument('-run', nargs=1, required=True, type=int, \
help='Specify the run number for the selection of '+ \
'the lumisections')
parser.add_argument('-range', nargs=2, type=int, help='Specify a range '+ \
'in lumisections for the histogram')
parser.add_argument('-X', dest='coords', action='append_const', \
const='vtx_x', help='look for vtx_x')
parser.add_argument('-Y', dest='coords', action='append_const', \
const='vtx_y', help='look for vtx_y')
parser.add_argument('-noscan', action='store_const', const=True, \
default=False, help='don\'t repeat the scan of the '+ \
'ROOT files, just do the plotting')
parser.add_argument('-title', nargs=1, help='Specify a title for the '+ \
'histogram')
args = parser.parse_args()
from importlib import import_module
from lsctools import config
from lsctools.config import options as O, EOSPATH as eos
from lsctools.tools import openRootFileU, closeRootFile, writeFiles, \
plotName, plotTitle, loadFiles, plotPath, \
drawSignature
from lsctools.prepare import loopOverRootFiles
from ROOT import TChain, TObject, TProfile, TCanvas, gStyle, gPad
getattr(config, 'PCC' + args.dataset)()
O['fulltrees'] = O['fulltrees'][:args.n]
run = args.run[0]
files = []
if args.noscan:
files = loadFiles('fulltrees_' + str(run))
else:
def action(tree, filename):
condition = 'run == ' + str(run)
if tree.GetEntries(condition) > 0:
files.append(filename)
print '<<< Found file:', filename
loopOverRootFiles(action, 'fulltrees')
writeFiles(files, 'fulltrees_' + str(run))
chain = TChain(O['treename']['fulltrees'])
for filename in files:
chain.Add(eos + filename)
name = 'vtxPos_perLS'
title1 = 'run' + str(run) + '_perLS'
title2 = 'Run ' + str(run)
if (args.title):
title2 = args.title[0] + ' (' + title2 + ')'
f = openRootFileU(name)
if args.range:
mini = args.range[0]
maxi = args.range[1]
title1 += '_from' + str(mini) + 'to' + str(maxi)
else:
print '<<< Get minimum lumisection'
mini = int(chain.GetMinimum('LS'))
print '<<< Get maximum lumisection'
maxi = int(chain.GetMaximum('LS'))
for coord in args.coords:
print '<<< Analyze coordinate', coord
histname = plotName(coord + '_' + title1, timestamp=False)
histtitl = plotTitle('- ' + title2)
histfile = plotName(coord + '_' + title1)
histpath = plotPath(coord + '_' + title1)
hist = TProfile(histname, histtitl, maxi - mini + 1, mini - 0.5,
maxi + 0.5)
chain.Draw(coord + '*1e4:LS>>' + histname, 'run == ' + str(run),
'goff')
hist.Write('', TObject.kOverwrite)
print '<<< Save plot:', histpath
canvas = TCanvas()
gStyle.SetOptStat(0)
hist.Draw()
hist.GetXaxis().SetTitle('LS')
hist.GetYaxis().SetTitle(coord + ' [#mum]')
hist.GetYaxis().SetTitleOffset(1.2)
for axis in [hist.GetXaxis(), hist.GetYaxis()]:
axis.SetTitleFont(133)
axis.SetTitleSize(16)
axis.SetLabelFont(133)
axis.SetLabelSize(12)
axis.CenterTitle()
drawSignature(histfile)
gPad.Modified()
gPad.Update()
canvas.Print(histpath)
canvas.Close()
closeRootFile(f, name)
def showENC():
fname1 = '/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan1.dat'
tree1 = TTree()
tree1.ReadFile(
'/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan1.dat',
'idX/i:vL/F:vH:A:D/i:R:W')
tree1.ReadFile(
'/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan2_mod.dat'
)
tree1.Show(500)
p1 = TProfile('p1', 'p1;#DeltaU [V];Prob', 50, 0.12, 0.2)
tree1.Draw("D:(vH-vL)>>p1", "", "profE")
### change it to tgraph
g1 = TGraphErrors()
for i in range(p1.GetNbinsX() + 2):
N = p1.GetBinEntries(i)
if N > 0:
print i, N, p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(
i), p1.GetBinError(i)
n = g1.GetN()
g1.SetPoint(n, p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(i))
g1.SetPointError(n, 0, p1.GetBinError(i))
# g1.SetMarkerColor(3)
# g1.SetLineColor(3)
p1.Draw("axis")
g1.Draw('Psame')
fun1 = TF1('fun1', '0.5*(1+TMath::Erf((x-[0])/(TMath::Sqrt(2)*[1])))',
0.05, 0.3)
fun1.SetParameter(0, 0.155)
fun1.SetParameter(1, 0.005)
g1.Fit(fun1)
fun1a = g1.GetFunction('fun1')
# p1.Fit(fun1)
# fun1a = p1.GetFunction('fun1')
fun1a.SetLineColor(2)
# p1.Draw("Esame")
v0 = fun1a.GetParameter(0)
e0 = fun1a.GetParError(0)
v1 = fun1a.GetParameter(1)
e1 = fun1a.GetParError(1)
print v0, v1
fUnit = 1000.
lt = TLatex()
lt.DrawLatexNDC(
0.185, 0.89,
'#mu = {0:.1f} #pm {1:.1f} mV'.format(v0 * fUnit, e0 * fUnit))
lt.DrawLatexNDC(
0.185, 0.84,
'#sigma = {0:.1f} #pm {1:.1f} mV'.format(v1 * fUnit, e1 * fUnit))
print 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1)
fun2 = TF1('gaus1', 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1))
fun2.SetLineColor(4)
fun2.SetLineStyle(2)
fun2.Draw('same')
lg = TLegend(0.7, 0.4, 0.95, 0.5)
lg.SetFillStyle(0)
lg.AddEntry(p1, 'Measurement', 'p')
lg.AddEntry(fun1a, 'Fit', 'l')
lg.AddEntry(fun2, 'Gaus', 'l')
lg.Draw()
waitRootCmdX()
def showENC(self):
tree1 = TTree()
header = 'idX/i:vL/F:vH:A:D/i:R:W'
first = True
for f in self.dataFiles:
if first: tree1.ReadFile(f, header)
else: tree1.ReadFile(f)
p1 = TProfile('p1', 'p1;#DeltaU [V];Prob', self.bins[0],
tree1.GetMinimum('vH-vL') * 0.8,
tree1.GetMaximum('vH-vL') * 1.2)
tree1.Draw("D:(vH-vL)>>p1", "", "profE")
### change it to tgraph
g1 = TGraphErrors()
for i in range(p1.GetNbinsX() + 2):
N = p1.GetBinEntries(i)
if N > 0:
print i, N, p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(
i), p1.GetBinError(i)
n = g1.GetN()
g1.SetPoint(n,
p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(i))
g1.SetPointError(n, 0, p1.GetBinError(i))
p1.Draw("axis")
g1.Draw('Psame')
fun1 = TF1('fun1', '0.5*(1+TMath::Erf((x-[0])/(TMath::Sqrt(2)*[1])))',
0.05, 0.3)
fun1.SetParameter(0, 0.155)
fun1.SetParameter(1, 0.005)
g1.Fit(fun1)
fun1a = g1.GetFunction('fun1')
fun1a.SetLineColor(2)
v0 = fun1a.GetParameter(0)
e0 = fun1a.GetParError(0)
v1 = fun1a.GetParameter(1)
e1 = fun1a.GetParError(1)
print v0, v1
fUnit = 1000.
self.lt.DrawLatexNDC(
0.185, 0.89,
'#mu = {0:.1f} #pm {1:.1f} mV'.format(v0 * fUnit, e0 * fUnit))
self.lt.DrawLatexNDC(
0.185, 0.84,
'#sigma = {0:.1f} #pm {1:.1f} mV'.format(v1 * fUnit, e1 * fUnit))
if self.Info:
self.lt.DrawLatexNDC(0.185, 0.6, self.Info)
print 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1)
fun2 = TF1('gaus1', 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1))
fun2.SetLineColor(4)
fun2.SetLineStyle(2)
fun2.Draw('same')
lg = TLegend(0.7, 0.4, 0.95, 0.5)
lg.SetFillStyle(0)
lg.AddEntry(p1, 'Measurement', 'p')
lg.AddEntry(fun1a, 'Fit', 'l')
lg.AddEntry(fun2, 'Gaus', 'l')
lg.Draw()
waitRootCmdX()
effsSignal = computeEff(namesSignal)
for name in namesBackground:
print "running on background sample", name
os.system("./Run.sh \"" + name + "\"" + " \"" +
str(startingParallelDiffCut) + "\"")
effsBackground = computeEff(namesBackground)
# Draw the histogram
from ROOT import gROOT, TCanvas, TH1F, TProfile
gROOT.Reset()
c1 = TCanvas('c1', '<eff(signal)>/sqrt(eff(background))')
#
# Create a one dimensional function and draw it
#
histo = TProfile('effSoverSqrtEffB', '<eff(signal)>/sqrt(eff(background))',
int(len(effsSignal)), float(sorted(effsSignal.iterkeys())[0]),
float(sorted(effsSignal.iterkeys())[len(effsSignal) - 1]))
for key in sorted(effsSignal.iterkeys()):
print "eff(signal)/sqrt(eff(background))[" + key + "] =", effsSignal[
key] / math.sqrt(effsBackground[key])
histo.Fill(float(key), effsSignal[key] / math.sqrt(effsBackground[key]))
histo.Draw()
histo.SetXTitle("parallelism cut")
c1.SaveAs("canvas.pdf")
print("Processed %d of %d events..." % (ievent, NEntries))
ntuple.GetEntry(ievent)
n_weight = ntuple.__getattr__('weight')
n_ptPruned = ntuple.__getattr__('ptPruned')
if (170 < n_ptPruned < 1000):
x.append(n_ptPruned)
y.append(n_weight)
print(n_weight)
hprof2d.Fill(n_ptPruned, n_weight)
# hprof2d.LabelsOption("h","pT")
hprof2d.SetMinimum(-0.2)
hprof2d.SetMaximum(0.3)
hprof2d.Draw()
c.SaveAs("old2dhist.png")
# # H = verification
# nbins = 100
# # xedges = 101
# # yedges =4
# H, xedges, yedges = np.histogram2d(x,y,bins=nbins)
#
# # print (xedges)
# print (yedges)
# # H= H.T
# # print (H)
# # verification = H
# # print (verification)
def loop(fname):
genPars = Handle("vector<reco::GenParticle>")
genParsLabel = "prunedGenParticles"
gPar = [Handle("vector<pat::PackedGenParticle>"), "packedGenParticles"]
vertices = [
Handle("vector<reco::Vertex>"), "offlineSlimmedPrimaryVertices"
]
mus = [Handle("vector<pat::Muon>"), "slimmedMuons"]
sip2d = TH1F("SIP2D", "SIP2D", 40, -10., 10.)
sip3d = TH1F("SIP3D", "SIP3D", 40, -10., 10.)
sipxy = TH1F("tk2d", "TK SIPXY", 40, -10., 10.)
sipz = TH1F("tk3z", "TK SIPZ", 40, -10., 10.)
sip3d_l = TH1F("SIP2D l", "SIP2D l", 40, -10., 10.)
sip3d_h = TH1F("SIP2D h", "SIP2D h", 40, -10., 10.)
sip3d_best = TH1F("SIP2D best", "SIP2D best", 40, -10., 10.)
vert = TH1F("zpv", "zpv", 100, -10., 10.)
sip_v = TProfile("SIP2D vs zpv", "SIP2D best vs zpv", 50, 0., 5., 0., 10.)
#
eventsRef = Events(fname)
nw = 0
nehpt = 0
nwhpt = 0
nech = 0
nwch = 0
#
for i in range(0, eventsRef.size()):
a = eventsRef.to(i)
print "Event", i
a = eventsRef.getByLabel(genParsLabel, genPars)
zpv = 0
gpvp = genPars.product()[0].vertex()
for part in genPars.product():
if (part.vz() != 0):
zpv = part.vz()
gpvp = part.vertex()
break
print "zpv ", zpv
#
nmu = 0
nel = 0
nch1 = 0
nch2 = 0
gmu = []
for part in genPars.product():
if (part.status() != 1): continue
if (abs(part.pdgId()) == 13 and part.pt() > 5
and abs(part.eta()) < 2.4):
gmu.append((part.phi(), part.eta(), part.charge() * part.pt()))
if (abs(part.pdgId()) == 13 and part.pt() > 5
and abs(part.eta()) < 2.4):
nmu += 1
if (abs(part.pdgId()) == 11 and part.pt() > 7
and abs(part.eta()) < 2.4):
nel += 1
if (abs(part.pdgId()) == 13 and part.pt() > 8
and abs(part.eta()) < 2.4):
nch1 += 1
if (abs(part.pdgId()) == 11 and part.pt() > 10
and abs(part.eta()) < 2.4):
nch1 += 1
if (abs(part.pdgId()) == 13 and part.pt() > 20
and abs(part.eta()) < 2.4):
nch2 += 1
if (abs(part.pdgId()) == 11 and part.pt() > 20
and abs(part.eta()) < 2.4):
nch2 += 1
# if (abs(part.pdgId())==13):
# print "part", part.phi(),part.eta(), part.pt(), part.vz(), part.vx(), part.vy(), part.mass(), part.pdgId(), part.status()
# print "nmu ", nmu,nel
# print gmu
a = eventsRef.getByLabel(vertices[1], vertices[0])
minz = 99999.
iv = 0
ii = 0
pv = vertices[0].product()[0]
pvp = vertices[0].product()[0].position()
nv = vertices[0].product().size()
for v in vertices[0].product():
if (abs(v.z() - zpv) < minz):
minz = abs(v.z() - zpv)
iv = ii
ii += 1
print "pv ", iv, minz
if (iv != 0): nw += 1
# if (nmu+nel>3) :
if (nmu > 1):
nehpt += 1
if (iv != 0): nwhpt += 1
# if (nch1>0 and nch2>0) :
if (nch1 < 1): continue
#
nech += 1
if (iv != 0): nwch += 1
a = eventsRef.getByLabel(mus[1], mus[0])
pmus = []
for mu in mus[0].product():
if (mu.pt() < 5): continue
# if ( mu.isTrackerMuon() or mu.isGlobalMuon()) :
if (mu.isGlobalMuon()):
pmus.append(
(mu.phi(), mu.eta(), mu.pt() * mu.charge(),
mu.dB(2) / mu.edB(2), mu.dB(1) / mu.edB(1),
mu.track().dxy(gpvp) / mu.track().dxyError(),
mu.track().dz(gpvp) / mu.track().dzError(),
mu.track().hitPattern().pixelLayersWithMeasurement()))
# print 'mu', iv, mu.phi(), mu.eta(), mu.pt(), mu.dB(2)/mu.edB(2), mu.dB(1)/mu.edB(1), mu.isTrackerMuon(), mu.isGlobalMuon()
# print pmus
matches = []
i = 0
for g in gmu:
j = 0
for mu in pmus:
j += 1
if (g[2] / mu[2] < 0.5 or g[2] / mu[2] > 2.0): continue
dr = dR2(g[0], g[1], mu[0], mu[1])
if (dr > 0.04): continue
matches.append((i, j - 1, dr, abs(1. - g[2] / mu[2])))
#print "matched mu", mu
i += 1
if (len(matches) < 1): continue
vert.Fill((pv.z() - zpv) / pv.zError())
k = matches[0][0]
best = 99999
dr = 999999
for m in matches:
# if (abs(pv.z()-zpv)<3*pv.zError()) :
if (pmus[m[1]][7] > 2):
# if (nv<21) :
sip3d_l.Fill(pmus[m[1]][3])
else:
sip3d_h.Fill(pmus[m[1]][3])
sip2d.Fill(pmus[m[1]][4])
sip3d.Fill(pmus[m[1]][3])
sipxy.Fill(pmus[m[1]][5])
sipz.Fill(pmus[m[1]][6])
sip_v.Fill(abs(pv.z() - zpv) / pv.zError(), abs(pmus[m[1]][5]))
if (m[0] != k):
sip3d_best.Fill(best)
k = m[0]
best = pmus[m[1]][4]
dr = m[3]
else:
if (m[3] < dr):
dr = m[3]
best = pmus[m[1]][4]
if (dr < 9999): sip3d_best.Fill(best)
print "wrong pv", nw, nehpt, nwhpt, nech, nwch
c1 = TCanvas('c1', fname, 200, 10, 1000, 1400)
gStyle.SetOptStat(111111)
gStyle.SetHistLineWidth(2)
c1.Divide(2, 4)
c1.cd(1).SetLogy()
sip2d.DrawNormalized()
e = TF1("q", "0.5*exp(-0.5*x*x)/sqrt(6.28)", -10., 10.)
e.Draw("same")
c1.cd(2).SetLogy()
sip3d.DrawNormalized()
e.Draw("same")
c1.cd(3).SetLogy()
sipxy.DrawNormalized()
e.Draw("same")
c1.cd(4).SetLogy()
sipz.DrawNormalized()
e.Draw("same")
c1.cd(5).SetLogy()
sip3d_l.DrawNormalized()
e.Draw("same")
c1.cd(6).SetLogy()
sip3d_h.DrawNormalized()
e.Draw("same")
# sip3d_best.DrawNormalized()
c1.cd(7).SetLogy()
vert.DrawNormalized()
# ev = TF1("qv","0.2*exp(-0.5*x*x)/sqrt(6.28)",-10.,10.)
# ev.Draw("same")
c1.cd(8)
sip_v.Draw()
c1.Print("sipall" + fname + ".png")
nameY = "pT-pT(true)/pT(true)"
h = gPad.DrawFrame(Xmin, Ymin, Xmax, Ymax)
ax = h.GetXaxis()
ax.SetTitleOffset(0.8)
ax.SetTitle(nameX)
ay = h.GetYaxis()
ay.SetTitleOffset(0.8)
ay.SetTitle(nameY)
ax.SetTitleOffset(1.1)
ay.SetTitleOffset(1.4)
ax.Draw("same")
ay.Draw("same")
prof_eta.SetMarkerSize(1.1)
prof_eta.SetMarkerColor(1)
prof_eta.Draw("pe same")
prof_eta_nn.SetMarkerSize(1)
prof_eta_nn.SetMarkerColor(2)
prof_eta_nn.Draw("pe same")
out = "histo_" + outfile
hfile = TFile(out, "RECREATE", "DijetsMC")
c1.Write()
prof_eta.Write()
prof_pt.Write()
prof_eta_nn.Write()
prof_pt_nn.Write()
hfile.ls()
hfile.Close()
print "Write with cross sections written=", out
print "Event", i
a = eventsRef.getByLabel(label, tracksRef)
for track in tracksRef.product():
if (not track.quality(track.qualityByName(quality))): continue
dp = abs(track.outerPosition().phi() - track.outerMomentum().phi())
res = track.residuals()
hitP = track.hitPattern()
bin = lostHit.Fill(
dp,
hitP.numberOfLostHits(),
)
j = 0
for ih in range(0, hitP.numberOfHits()):
p = hitP.getHitPattern(ih)
if hitP.validHitFilter(p):
if hitP.stripHitFilter(p):
bin = pulls.Fill(res.residualX(j))
j += 1
c1 = TCanvas('c1', 'vi', 200, 10, 1000, 1000)
gStyle.SetOptStat(111111)
c1.Divide(2)
c1.cd(1)
#c1.SetLogy()
pulls.Draw()
c1.cd(2)
lostHit.Draw()
c1.Print("pullsttbar.png")
os.system("mv pullsttbar.png ~/www/histos/.")