def __init__(self, data=[], *args): # title='hist', description='hist', nbins=100, xmin=0.0, xmax=100.0):
"""Creates a root TH1F of data (an iterable) with title, description, nbins, xmax and xmin.
The number of bins defaults to 100, if the range values aren't set they are
based from the data."""
if data:
xmin = float(min(data))
xmax = float(max(data))
# args = (title, description, nbins, xmin, xmax)
TH1F.__init__(self, *args) # title, description, nbins, xmin, xmax)
self.addList(data)
else:
# args = (tite, description, nbins, xmin, xmax)
TH1F.__init__(self, *args) # title, description, nbins, xmin, xmax)
def closePDF(outfile,canvas):
canvas.Print(outfile+".pdf]")
gStyle.SetOptStat(0)
c = TCanvas("c","c",800,600)
outfile = remainder[0]
outfileroot = TFile(remainder[0]+".root","RECREATE")
infile = TFile(remainder[1])
histo = infile.Get("myana/myana_trigNLay")
nevents = histo.GetEntries()
#h = TH1F("h", "h", histo.GetNbinsX(), histo.GetXaxis().GetBinLowEdge(1), histo.GetXaxis().GetBinUpEdge(histo.GetNbinsX()))
h = TH1F("h", "h", 20, 0, 20)
pulse = 37 #MHz
mu = 1. #average electons / bunch
for i in range(2, h.GetNbinsX()):
n = histo.Integral(i,histo.GetNbinsX())
rate = n * pulse * mu / 1000 #kHz
h.SetBinContent(i, rate)
error = 0
if(n != 0):
error = np.sqrt(1/n) * rate
else:
error = np.sqrt(1/2.) * pulse * mu / 1000.
h.SetBinError(i, error)
if "plus" in filename: tree = tree + "_JESUp"
# tree = tree+weight
print "Current Tree Path : ", tree
rootfile = basepath + f
if not os.path.exists(rootfile):
print 'File Does Not exist'
break
else:
print 'Adding File : ', rootfile
rtfile = TFile(rootfile)
if "PUWeight" in weight:
tmp = TH1F('tmp', weight + ' Unfolding; SF; Number of Events',
50, 0, 5)
else:
tmp = TH1F('tmp', weight + ' Unfolding; SF; Number of Events',
50, 0, 2)
hist = rtfile.Get(tree)
canvas = TCanvas("CanvasRef", "CanvasTitle", 800, 600)
canvas.SetGridy()
drawparams = weight + ">>tmp"
hist.Draw(drawparams)
canvas.Update()
canvas.SaveAs(outpath + filename + '/' + weight + ' ' +
hist.GetTitle() + '.pdf')
canvas.Close()
rtfile.Close()
def plot_clusterdb(dbfilename=None, histofilename="MyHistos.root"):
if dbfilename == None:
return None
dbfile = gROOT.FindObject(dbfilename)
if dbfile:
dbfile.Close()
dbfile = TFile(dbfilename, 'READ')
h_Weight = gROOT.FindObject("hDB_Weight")
h_Weight.SetDirectory(0)
h_U = gROOT.FindObject("hDB_U")
h_U.SetDirectory(0)
h_V = gROOT.FindObject("hDB_V")
h_V.SetDirectory(0)
h_Var_U = gROOT.FindObject("hDB_Sigma2_U")
h_Var_U.SetDirectory(0)
h_Var_V = gROOT.FindObject("hDB_Sigma2_V")
h_Var_V.SetDirectory(0)
dbfile.Close()
# First, we want to compute a list of all different cluster
# types found in the db
typeset = []
for bin in range(1, h_Weight.GetNbinsX() + 1):
# The bin label decodes the cluster shape. The
# label contains tokens seperated by "D". The
# first token is the cluster size, all other
# are digits. Each digits contains three tokens
# seperated by "."; nameley iu, iv and signal.
# These can be converted to integers.
label = h_Weight.GetXaxis().GetBinLabel(bin)
current_type = ""
for tok in re.split('D', label):
addr = re.split('\.', tok)
if len(addr) == 1:
current_type += tok
else:
current_type += "D" + addr[0] + '.' + addr[1]
if not current_type in typeset:
typeset.append(current_type)
print("Number of labels in clusterDB is ", h_Weight.GetNbinsX())
print("Number of types in clusterDB is ", len(typeset))
histofile = gROOT.FindObject(histofilename)
if histofile:
histofile.Close()
histofile = TFile(histofilename, 'RECREATE',
'Resolution plots created from ' + dbfilename)
histomap_weight = {}
histomap_u = {}
histomap_v = {}
histomap_sigu = {}
histomap_sigv = {}
for currenttype in typeset:
map_weight = {}
map_u = {}
map_v = {}
map_sigu = {}
map_sigv = {}
for bin in range(1, h_Weight.GetNbinsX() + 1):
# The bin label decodes the cluster shape. The
# label contains tokens seperated by "D". The
# first token is the cluster size, all other
# are digits. Each digits contains three tokens
# seperated by "."; nameley iu, iv and signal.
# These can be converted to integers.
label = h_Weight.GetXaxis().GetBinLabel(bin)
# We compute the type string by stripping all
# signal information from the label
current_type = ""
for tok in re.split('D', label):
addr = re.split('\.', tok)
if len(addr) == 1:
current_type += tok
else:
current_type += "D" + addr[0] + '.' + addr[1]
if current_type == currenttype:
map_weight[label] = h_Weight.GetBinContent(bin)
map_u[label] = h_U.GetBinContent(bin)
map_v[label] = h_V.GetBinContent(bin)
map_sigu[label] = TMath.Sqrt(h_Var_U.GetBinContent(bin))
map_sigv[label] = TMath.Sqrt(h_Var_V.GetBinContent(bin))
histofile.cd("")
histofile.mkdir(currenttype)
histofile.cd(currenttype)
# these are the reprocessed histos for viewing
LABELS = len(map_weight)
histomap_weight[currenttype] = TH1F("hweight_" + currenttype, "",
LABELS, 0, LABELS)
histomap_weight[currenttype].SetStats(0)
histomap_weight[currenttype].SetFillColor(38)
histomap_weight[currenttype].SetYTitle("weight")
histomap_u[currenttype] = TH1F("hu_" + currenttype, "", LABELS, 0,
LABELS)
histomap_u[currenttype].SetStats(0)
histomap_u[currenttype].SetFillColor(38)
histomap_u[currenttype].SetYTitle("offset u [mm]")
histomap_v[currenttype] = TH1F("hv_" + currenttype, "", LABELS, 0,
LABELS)
histomap_v[currenttype].SetStats(0)
histomap_v[currenttype].SetFillColor(38)
histomap_v[currenttype].SetYTitle("offset v [mm]")
histomap_sigu[currenttype] = TH1F("hsigu_" + currenttype, "", LABELS,
0, LABELS)
histomap_sigu[currenttype].SetStats(0)
histomap_sigu[currenttype].SetFillColor(38)
histomap_sigu[currenttype].SetYTitle("cluster sigma u [mm]")
histomap_sigv[currenttype] = TH1F("hsigv_" + currenttype, "", LABELS,
0, LABELS)
histomap_sigv[currenttype].SetStats(0)
histomap_sigv[currenttype].SetFillColor(38)
histomap_sigv[currenttype].SetYTitle("cluster sigma v [mm]")
for i, label in enumerate(map_weight.keys()):
histomap_weight[currenttype].SetBinContent(i + 1,
map_weight[label])
histomap_u[currenttype].SetBinContent(i + 1, map_u[label])
histomap_v[currenttype].SetBinContent(i + 1, map_v[label])
histomap_sigu[currenttype].SetBinContent(i + 1, map_sigu[label])
histomap_sigv[currenttype].SetBinContent(i + 1, map_sigv[label])
histomap_weight[currenttype].GetXaxis().SetBinLabel(i + 1, label)
histomap_u[currenttype].GetXaxis().SetBinLabel(i + 1, label)
histomap_v[currenttype].GetXaxis().SetBinLabel(i + 1, label)
histomap_sigu[currenttype].GetXaxis().SetBinLabel(i + 1, label)
histomap_sigv[currenttype].GetXaxis().SetBinLabel(i + 1, label)
# summary histograms on type resolution
histofile.cd("")
TYPES = len(typeset)
htypes_sigu = TH1F("htypes_sigu", "", TYPES, 0, TYPES)
htypes_sigu.SetStats(0)
htypes_sigu.SetFillColor(38)
htypes_sigu.SetYTitle("weighted cluster sigma u [mm]")
htypes_sigv = TH1F("htypes_sigv", "", TYPES, 0, TYPES)
htypes_sigv.SetStats(0)
htypes_sigv.SetFillColor(38)
htypes_sigv.SetYTitle("weighted cluster sigma v [mm]")
htypes_weight = TH1F("htypes_weight", "", TYPES, 0, TYPES)
htypes_weight.SetStats(0)
htypes_weight.SetFillColor(38)
htypes_weight.SetYTitle("weight")
for j, currenttype in enumerate(typeset):
htypes_sigu.GetXaxis().SetBinLabel(j + 1, currenttype)
htypes_sigv.GetXaxis().SetBinLabel(j + 1, currenttype)
htypes_weight.GetXaxis().SetBinLabel(j + 1, currenttype)
weightedTypeVarU = 0.0
weightedTypeVarV = 0.0
typeNorm = 0.0
for bin in range(1, histomap_weight[currenttype].GetNbinsX() + 1):
w = histomap_weight[currenttype].GetBinContent(bin)
typeNorm += w
weightedTypeVarU += w * TMath.Power(
histomap_sigu[currenttype].GetBinContent(bin), 2)
weightedTypeVarV += w * TMath.Power(
histomap_sigv[currenttype].GetBinContent(bin), 2)
htypes_weight.SetBinContent(j + 1, typeNorm)
if typeNorm > 0:
weightedTypeVarU /= typeNorm
weightedTypeVarV /= typeNorm
htypes_sigu.SetBinContent(j + 1, TMath.Sqrt(weightedTypeVarU))
htypes_sigv.SetBinContent(j + 1, TMath.Sqrt(weightedTypeVarV))
else:
htypes_sigu.SetBinContent(j + 1, 0) # invalid
htypes_sigv.SetBinContent(j + 1, 0) # invalid
# Standardized plots
SDTYPES = len(standard_types)
hsdtypes_sigu = TH1F("hsdtypes_sigu", "", SDTYPES, 0, SDTYPES)
hsdtypes_sigu.SetStats(0)
hsdtypes_sigu.SetFillColor(38)
hsdtypes_sigu.SetYTitle("weighted cluster sigma u [mm]")
hsdtypes_sigv = TH1F("hsdtypes_sigv", "", SDTYPES, 0, SDTYPES)
hsdtypes_sigv.SetStats(0)
hsdtypes_sigv.SetFillColor(38)
hsdtypes_sigv.SetYTitle("weighted cluster sigma v [mm]")
hsdtypes_weight = TH1F("hsdtypes_weight", "", SDTYPES, 0, SDTYPES)
hsdtypes_weight.SetStats(0)
hsdtypes_weight.SetFillColor(38)
hsdtypes_weight.SetYTitle("weight")
for j, currenttype in enumerate(standard_types):
hsdtypes_sigu.GetXaxis().SetBinLabel(j + 1, currenttype)
hsdtypes_sigv.GetXaxis().SetBinLabel(j + 1, currenttype)
hsdtypes_weight.GetXaxis().SetBinLabel(j + 1, currenttype)
weightedTypeVarU = 0.0
weightedTypeVarV = 0.0
typeNorm = 0.0
if currenttype in histomap_weight:
for bin in range(1, histomap_weight[currenttype].GetNbinsX() + 1):
w = histomap_weight[currenttype].GetBinContent(bin)
typeNorm += w
weightedTypeVarU += w * TMath.Power(
histomap_sigu[currenttype].GetBinContent(bin), 2)
weightedTypeVarV += w * TMath.Power(
histomap_sigv[currenttype].GetBinContent(bin), 2)
hsdtypes_weight.SetBinContent(j + 1, typeNorm)
if typeNorm > 0:
weightedTypeVarU /= typeNorm
weightedTypeVarV /= typeNorm
hsdtypes_sigu.SetBinContent(j + 1, TMath.Sqrt(weightedTypeVarU))
hsdtypes_sigv.SetBinContent(j + 1, TMath.Sqrt(weightedTypeVarV))
else:
hsdtypes_sigu.SetBinContent(j + 1, 0) # invalid
hsdtypes_sigv.SetBinContent(j + 1, 0) # invalid
# summary histograms on overall resolution
hweighted_sigma_sensor = TH1F("hweighted_sigma_sensor", "", 2, 0, 2)
hweighted_sigma_sensor.SetStats(0)
hweighted_sigma_sensor.SetFillColor(38)
hweighted_sigma_sensor.SetYTitle("cluster sigma [mm]")
hweighted_sigma_sensor.GetXaxis().SetBinLabel(1, "sigma u")
hweighted_sigma_sensor.GetXaxis().SetBinLabel(2, "sigma v")
weightedVarU = 0.0
weightedVarV = 0.0
norm = 0.0
for bin in range(1, h_Weight.GetNbinsX() + 1):
w = h_Weight.GetBinContent(bin)
norm += w
weightedVarU += w * h_Var_U.GetBinContent(bin)
weightedVarV += w * h_Var_V.GetBinContent(bin)
print("Number of tracks used for calibration is ", norm)
if norm > 0:
weightedVarU /= norm
weightedVarV /= norm
hweighted_sigma_sensor.SetBinContent(1, TMath.Sqrt(weightedVarU))
hweighted_sigma_sensor.SetBinContent(2, TMath.Sqrt(weightedVarV))
print("Weighted clusterDB sigmaU [mm]: ", TMath.Sqrt(weightedVarU))
print("Weighted clusterDB sigmaV [mm]: ", TMath.Sqrt(weightedVarV))
else:
hweighted_sigma_sensor.SetBinContent(1, 0) # invalid
hweighted_sigma_sensor.SetBinContent(2, 0) # invalid
histofile.Write()
histofile.Close()
fName = sys.argv[1]
f = TFile(fName, "UPDATE")
f.mkdir("MuMuChannel")
f.mkdir("EEChannel")
#data=TH1F("data_obs125","data_obs125",28,-0.2,1.2)
#dataprod=TH1F("proddata_obs125","proddata_obs125",28,-0.2,1.2)
data = None
for c in channels:
for l in list:
h_ = {}
p_ = {}
f.cd()
if "DY" in l and True:
tmp = f.Get(c[:2] + "/" + l)
h = TH1F("DY125", "DY125", tmp.GetNbinsX(),
tmp.GetXaxis().GetXmin(),
tmp.GetXaxis().GetXmax())
p = TH1F("prodDY125", "prodDY125", tmp.GetNbinsX(),
tmp.GetXaxis().GetXmin(),
tmp.GetXaxis().GetXmax())
for bin in DYbins:
h_[bin] = f.Get(c[:2] + "/DY" + bin + "125")
p_[bin] = f.Get(c[:2] + "/prodDY" + bin + "125")
binScale = 1.
if bin != "0to50":
binScale = float(DYrew[c + sDYs[DYbins.index(bin) - 1] +
"Extra_norm"])
for sdy in sDYs:
h_[bin].Add(f.Get(c[:2] + "/" + sdy + bin + "125"))
p_[bin].Add(f.Get(c[:2] + "/prod" + sdy + bin + "125"))
h_[bin].Scale(binScale)
bins = [float(i) for i in cfg.get(section,'obsBins').split(',')]
# print bins
# break;
outfile_newF = TFile.Open('signal_proc_'+section+'.root','RECREATE')
# print ('outfile_newF = signal_proc'+section+'.root');
for i in range(1,len(bins)):
if (model=="par1_TH1" or model=="par1_TF1"):
theBaseData = TH1F('theBaseData_'+section+'_'+str(i),'Base Histogram for RooDataHist',
nGridPar1Bins,par1GridMin,par1GridMax)
newFormatInput = TH1D('bin_content_par1_'+str(i),'bincontent',
nGridPointsForNewF,par1GridMin,par1GridMax)
elif (model=="par1par2_TH2" or model=="par1par2_TF2"):
theBaseData = TH2F('theBaseData_'+section+'_'+str(i),'Base Histogram for RooDataHist',
nGridPar1Bins,par1GridMin,par1GridMax,
nGridPar2Bins,par2GridMin,par2GridMax)
newFormatInput = TH2D('bin_content_par1_par2_'+str(i),'bincontent',
nGridPointsForNewF,par1GridMin,par1GridMax,
nGridPointsForNewF,par2GridMin,par2GridMax)
elif (model=="par1par2par3_TH3" or model=="par1par2par3_TF3"):
theBaseData = TH3F('theBaseData_'+section+'_'+str(i),'Base Histogram for RooDataHist',
nGridPar1Bins,par1GridMin,par1GridMax,
nGridPar2Bins,par2GridMin,par2GridMax,
nGridPar3Bins,par3GridMin,par3GridMax)
newFormatInput = TH3D('bin_content_par1_par2_par3_'+str(i),'bincontent',
def main():
gStyle.SetOptStat(0)
c1 = TCanvas('c1', 'c1', 3)
gPad.SetTickx()
gPad.SetTicky()
c1.SetLeftMargin(0.12)
h_frame = TH1F('frame', '', 50, 0, 1000)
h_frame.SetXTitle('P^{miss}_{T} (GeV)')
h_frame.SetYTitle('Arbitrary units')
h_frame.SetMaximum(0.4)
h_higgsPt = TH1F('h_higgsPt', 'h_higgsPt', 50, 0, 1000)
h_higgsPtList = []
for i in range(6):
h_higgsPtList.append(TH1F(histName_zp2HDM[i], '', 50, 0, 1000))
h_higgsPtList[i].SetLineWidth(2)
h_higgsPt_BarList = []
for i in range(6):
h_higgsPt_BarList.append(TH1F(histName_bar[i], '', 40, 0, 800))
h_higgsPt_BarList[i].SetLineWidth(2)
## test code
'''
ivVectList = getPtList(hApath)
for fourV in ivVectList:
h_higgsPt.Fill(fourV.pt)
'''
## loop all combination
leg = TLegend(0.32, 0.57, 0.87, 0.87)
leg.SetBorderSize(0)
for i in range(3):
ivVectList = getPtList(hApathList[i])
h_higgsPtList[i].SetLineColor(87 + 4 * i)
leg.AddEntry(h_higgsPtList[i], legtext[i])
for fourV in ivVectList:
h_higgsPtList[i].Fill(fourV.pt)
for i in range(3):
ivVectList = getPtList(hApathList_5[i])
h_higgsPtList[i + 3].SetLineColor(61 + 4 * i)
leg.AddEntry(h_higgsPtList[i + 3], legtext_5[i])
for fourV in ivVectList:
h_higgsPtList[i + 3].Fill(fourV.pt)
h_frame.Draw('hist')
#h_higgsPt.Draw('histsame')
for i in range(6):
h_higgsPtList[i].DrawNormalized('histsame')
leg.Draw()
c1.Print('Zp2HDM_higgsPt.pdf')
colorList = [95, 91, 87, 61, 65, 69]
#colorList = [61,95,65,91,69,87]
## Baryonic
info_bar = TLatex(0, 0.255, 'CMS')
info_bar_2 = TLatex(750, 0.255, '35.9 fb^{-1} (13 TeV)')
info_bar.SetTextSize(0.03)
info_bar_2.SetTextSize(0.03)
info_bar_2.SetTextAlign(31)
h_frame.SetMaximum(0.25)
h_frame.SetAxisRange(0., 750., "X")
leg.Clear()
for i in range(6):
ivVectList = getPtList('BaryonicFile/' + hApath_barList[i])
h_higgsPt_BarList[i].SetLineColor(colorList[i])
#h_higgsPt_BarList[i].SetLineColor(90-6*i)
leg.AddEntry(h_higgsPt_BarList[i], legtext_bar[i])
for fourV in ivVectList:
h_higgsPt_BarList[i].Fill(fourV.pt)
h_frame.Draw('hist')
for i in range(5, -1, -1):
h_higgsPt_BarList[i].DrawNormalized('histsame')
leg.Draw()
info_bar.Draw()
info_bar_2.Draw()
c1.Print('Baryonic_higgsPt.pdf')
f = TFile('rootFile/Zp2HDM_missPt.root', 'recreate')
for i in range(6):
h_higgsPtList[i].SetLineColor(1)
h_higgsPtList[i].Write()
f.Close()
f = TFile('rootFile/BaryonicZp_missPt.root', 'recreate')
for i in range(6):
h_higgsPt_BarList[i].SetLineColor(1)
h_higgsPt_BarList[i].Write()
f.Close()
gStyle.SetPadTopMargin(0.06)
gStyle.SetPadRightMargin(0.04)
gStyle.SetPadLeftMargin(0.15)
f = ROOT.TFile.Open("/afs/cern.ch/work/n/nchernya/VBFZll/plotter/output_txt14/EWK_LLJJ_%s_QCDScalenom_JESnom_v25_bdt_alldata4_qglweightsnorm_vetoeff_reminiaod.root"%channel)
path='/afs/cern.ch/user/n/nchernya/eos_mount/0/cms/store/group/phys_higgs/vbfHbb/V25_passall/EWK_LLJJ_MLL-50_MJJ-120_13TeV-madgraph-pythia8/VHBB_HEPPY_V25/170401_193326/0000/tree*.root'
hist_sel = f.Get("hPVs")
hist_sel.Scale(1./hist_sel.Integral())
chain = TChain("tree")
chain.Add(path)
hnPVs = TH1F("hPVs_all","",50,0,50)
hnPVs.GetXaxis().SetTitle("nPVs")
chain.Draw("nPVs>>hPVs_all","puWeight*genWeight/TMath::Abs(genWeight)")
hnPVs.Scale(1./hnPVs.Integral())
hnPVs.SetLineColor(2)
hnPVs.SetLineWidth(2)
hist_sel.SetLineColor(ROOT.kBlue)
hist_sel.SetLineWidth(2)
hist_sel.SetLineStyle(7)
hratio = hist_sel.Clone("new")
hratio.Divide(hnPVs)
from ROOT import TFile, TH1F, TCanvas
from papas_analysis_gael.tools.style import papas_style, cms_style, cms_style2, papas_style2
from papas_analysis_gael.tools.HistComparator import HistComparator
file = TFile('./rootfiles/charged_hadron_tree4.root')
tree = file.Get('events')
Erec_Egen = HistComparator(tree, style1 = cms_style, style2 = papas_style,
nbin = 500, xmin = 0., xmax = 3.,
xvar = 'E_{rec}/E_{gen}',
var1 = 'cmsjet_e/gen_jet_e',
cut = 'gen_jet_e>20 && simtrack_len==1 && abs(gen_jet_eta)<1.3 && gen_jet_e<100',
var2 = 'papasjet_e/gen_jet_e')
Erec_Egen.canva.SetLogy()
no_region = TH1F("no_region","no region",300,0,2)
tree.Project("no_region","cmsjet_e/gen_jet_e","gen_jet_e>20 && simtrack_len==1 && abs(gen_jet_eta)<1.3 && gen_jet_e<100 && (cmsjet_e/gen_jet_e<1.03 || cmsjet_e/gen_jet_e>1.3)")
region_no130 = TH1F("region_no130","region_no130",300,0,2)
tree.Project("region_no130","cmsjet_e/gen_jet_e","gen_jet_e>20 && simtrack_len==1 && abs(gen_jet_eta)<1.3 && gen_jet_e<100 && cmsjet_e/gen_jet_e>1.03 && cmsjet_e/gen_jet_e<1.3 && tagged_pdgid!=130")
region_corrected = TH1F("region_corrected","region_corrected",300,0,2)
tree.Project("region_corrected","(cmsjet_e-tagged_e)/gen_jet_e","gen_jet_e>20 && simtrack_len==1 && abs(gen_jet_eta)<1.3 && gen_jet_e<100 && cmsjet_e/gen_jet_e>1.03 && cmsjet_e/gen_jet_e<1.3 && tagged_pdgid==130")
total = TH1F("total","total",300,0,2)
total.Add(no_region, region_no130)
total.Add(region_corrected)
total.SetLineColor(1)
can0 = TCanvas()
previous = TH1F("previous","previous",300,0,2)
tree.Project("previous",'cmsjet_e/gen_jet_e','gen_jet_e>20 && simtrack_len==1 && abs(gen_jet_eta)<1.3 && gen_jet_e<100')
previous.SetLineColor(4)
def main(argv):
#Usage controls from OptionParser
parser_usage = "outputfile.root filename1.root ... filenameN.root"
parser = OptionParser(usage = parser_usage)
(options, args) = parser.parse_args(argv)
if(len(args) == 0):
parser.print_help()
return
if(SCAN_OVER_ENERGY and SCAN_OVER_THETA):
print "ERROR: both scan flags turned on! Please select either energy or theta to scan over..."
return
if(not SCAN_OVER_ENERGY and not SCAN_OVER_THETA):
print "ERROR: neither scan flags turned on! Please select either energy or theta to scan over..."
return
c1 = TCanvas("c1","c1",1600,900)
file_list = []
curr_P_val = 0.1
for i in range(1,len(args)): file_list.append(TFile.Open(argv[i],'read'))
scan_val_arr = array('d',[])
scan_val_arr_arr_err = array('d',[])
effic_gauscore_arr = array('d',[])
effic_gauscore_err_arr = array('d',[])
effic_anyquality_arr = array('d',[])
effic_anyquality_err_arr = array('d',[])
effic_gauscore_1show_arr = array('d',[])
effic_gauscore_1show_err_arr = array('d',[])
effic_anyquality_1show_arr = array('d',[])
effic_anyquality_1show_err_arr = array('d',[])
# curr_P_val = 0.1
for i in range(0,len(file_list)):
print "Current file: " + argv[i+1]
#For normalization
# h_curr_showers = file_list[i].Get("pi_pm_gun_hists/h_NFCALShowers")
h_ThrownPionP_curr = TH1F()
h_ThrownPhotonTheta_curr = TH1F()
h_ThrownPionP_curr = file_list[i].Get("pi_pm_gun_hists/h_ThrownPionP")
h_ThrownPhotonTheta_curr = file_list[i].Get("pi_pm_gun_hists/h_ThrownPionTheta")
norm_count = h_ThrownPionP_curr.GetEntries()
if(norm_count < MIN_EVENTS): continue
# atleast_one_shower = norm_count-h_curr_showers.GetBinContent(1) #Bin 1 corresponds to 0 showers
# curr_P_val = my_gaus_fit.GetParameter(1)
curr_P_val = h_ThrownPionP_curr.GetBinLowEdge( h_ThrownPionP_curr.GetMaximumBin()+1 )
curr_theta_val = h_ThrownPhotonTheta_curr.GetBinLowEdge( h_ThrownPhotonTheta_curr.GetMaximumBin()+1 )
print "Current P: " + str(curr_P_val)
print "Current theta: " + str(curr_theta_val) + "\n"
BELOW_MEAN_TO_FIT = E_BELOWTHROWN_TOFIT*curr_P_val/2
ABOVE_MEAN_TO_FIT = E_BELOWTHROWN_TOFIT*curr_P_val/2
h_curr = file_list[i].Get("pi_pm_gun_hists/h_ChargedTrackHypothesis_P")
my_gaus_fit = TF1("my_gaus_fit","gausn",0.001,12.)
my_gaus_fit.SetParLimits(0,0,100000)
my_gaus_fit.SetParLimits(1,curr_P_val-0.2,curr_P_val+0.1)
my_gaus_fit.SetParLimits(2,0.005,0.4)
my_gaus_fit.SetNpx(1000);
h_curr.GetXaxis().SetRangeUser(curr_P_val-3,curr_P_val+3.0)
h_curr.Fit(my_gaus_fit,"Q","",curr_P_val-BELOW_MEAN_TO_FIT,curr_P_val+ABOVE_MEAN_TO_FIT)
h_curr.Fit(my_gaus_fit,"QL","",curr_P_val-BELOW_MEAN_TO_FIT,curr_P_val+ABOVE_MEAN_TO_FIT)
if(POLY_ORDER>=1):
gaus_fit_amplitude = my_gaus_fit.GetParameter(0)
gaus_fit_mean = my_gaus_fit.GetParameter(1)
gaus_fit_sigma = my_gaus_fit.GetParameter(2)
gaus_plus_poly_fit = TF1("gaus_plus_poly_fit","gausn+pol"+str(POLY_ORDER)+"(3)",0.001,12.)
gaus_plus_poly_fit.SetParameter(0,gaus_fit_amplitude)
gaus_plus_poly_fit.SetParameter(1,gaus_fit_mean)
gaus_plus_poly_fit.SetParameter(2,gaus_fit_sigma)
gaus_plus_poly_fit.SetParLimits(0,0,100000)
gaus_plus_poly_fit.SetParLimits(1,curr_P_val-0.2,curr_P_val+0.1)
gaus_plus_poly_fit.SetParLimits(2,0.005,0.4)
h_curr.Fit(gaus_plus_poly_fit,"Q","",curr_P_val-BELOW_MEAN_TO_FIT,curr_P_val+ABOVE_MEAN_TO_FIT)
h_curr.Fit(gaus_plus_poly_fit,"QL","",curr_P_val-BELOW_MEAN_TO_FIT,curr_P_val+ABOVE_MEAN_TO_FIT)
twogaus_plus_poly_fit = TF1("twogaus_plus_poly_fit","gausn+gausn(3)+pol"+str(POLY_ORDER)+"(6)",0.001,12.)
twogaus_plus_poly_fit.SetParameter(0,gaus_plus_poly_fit.GetParameter(0))
twogaus_plus_poly_fit.SetParameter(1,gaus_plus_poly_fit.GetParameter(1))
twogaus_plus_poly_fit.SetParameter(2,gaus_plus_poly_fit.GetParameter(2))
twogaus_plus_poly_fit.SetParameter(6,gaus_plus_poly_fit.GetParameter(3))
twogaus_plus_poly_fit.SetParameter(7,gaus_plus_poly_fit.GetParameter(4))
twogaus_plus_poly_fit.SetParameter(8,gaus_plus_poly_fit.GetParameter(5))
twogaus_plus_poly_fit.SetParLimits(0,0,100000)
twogaus_plus_poly_fit.SetParLimits(1,curr_P_val-0.2,curr_P_val+0.1)
twogaus_plus_poly_fit.SetParLimits(2,0.005,0.4)
twogaus_plus_poly_fit.SetParLimits(3,0,100000)
twogaus_plus_poly_fit.SetParLimits(4,curr_P_val-0.2,curr_P_val+0.1)
twogaus_plus_poly_fit.SetParLimits(5,0.005,0.4)
h_curr.Fit(twogaus_plus_poly_fit,"Q","",curr_P_val-BELOW_MEAN_TO_FIT,curr_P_val+ABOVE_MEAN_TO_FIT)
h_curr.Fit(twogaus_plus_poly_fit,"QL","",curr_P_val-BELOW_MEAN_TO_FIT,curr_P_val+ABOVE_MEAN_TO_FIT)
c1.SaveAs(".plots/FitE_"+str(curr_P_val)+".png")
effic_gauscore_arr.append((my_gaus_fit.GetParameter(0)/h_curr.GetBinWidth(0))/norm_count)
effic_gauscore_err_arr.append(my_gaus_fit.GetParError(0)/h_curr.GetBinWidth(0)/norm_count)
# effic_anyquality_arr.append(atleast_one_shower/norm_count)
# effic_anyquality_err_arr.append(0)
if(SCAN_OVER_ENERGY): scan_val_arr.append(curr_P_val)
if(SCAN_OVER_THETA): scan_val_arr.append(curr_theta_val)
scan_val_arr_arr_err.append(0)
# curr_P_val+=0.05
gr_gauscore_effic = TGraphErrors( len(file_list), scan_val_arr, effic_gauscore_arr, scan_val_arr_arr_err, effic_gauscore_err_arr)
gr_gauscore_effic.SetMarkerStyle(15)
gr_gauscore_effic.SetMarkerSize(1.2)
gr_gauscore_effic.SetMarkerColor(kBlue)
gr_gauscore_effic.SetName("gr_gauscore_effic")
gr_gauscore_effic.SetTitle("Efficiency at 1 GeV")
gr_gauscore_effic.GetXaxis().SetTitle("Photon #theta (degrees)")
gr_gauscore_effic.GetYaxis().SetTitle("Efficiency")
# gr_gauscore_effic.GetXaxis().SetRangeUser(0,12.)
gr_gauscore_effic.Draw("AP")
c1.SaveAs("GaussianCoreEfficiency.png")
# gr_anyquality_effic.Draw("AP")
# c1.SaveAs("AnyQualityEfficiency.png")
f_out = TFile(argv[0],"RECREATE")
f_out.cd()
gr_gauscore_effic.Write()
# gr_anyquality_effic.Write()
# gr_gauscore_1show_effic.Write()
# gr_anyquality_1show_effic.Write()
f_out.Close()
print("Done ")
parser.add_option("-b", "--batch", action="store_true", default=False, dest="batch")
(options, args) = parser.parse_args()
if options.batch: gROOT.SetBatch(True)
#scp lxplus.cern.ch:/afs/cern.ch/cms/CAF/CMSCOMM/COMM_DQM/certification/Collisions16/13TeV/PileUp/pileup_latest.txt data/JSON/
#pileupCalc.py -i data/JSON/Cert_271036-276811_13TeV_PromptReco_Collisions16_JSON_NoL1T.txt --inputLumiJSON data/JSON/pileup_latest.txt --calcMode true --minBiasXsec 71300 --maxPileupBin 60 --numPileupBins 60 data/PU_71300.root
# https://raw.githubusercontent.com/cms-sw/cmssw/CMSSW_7_4_X/SimGeneral/MixingModule/python/mix_2015_25ns_Startup_PoissonOOTPU_cfi.py
#probValue = [4.8551E-07, 1.74806E-06, 3.30868E-06, 1.62972E-05, 4.95667E-05, 0.000606966, 0.003307249, 0.010340741, 0.022852296, 0.041948781, 0.058609363, 0.067475755, 0.072817826, 0.075931405, 0.076782504, 0.076202319, 0.074502547, 0.072355135, 0.069642102, 0.064920999, 0.05725576, 0.047289348, 0.036528446, 0.026376131, 0.017806872, 0.011249422, 0.006643385, 0.003662904, 0.001899681, 0.00095614, 0.00050028, 0.000297353, 0.000208717, 0.000165856, 0.000139974, 0.000120481, 0.000103826, 8.88868E-05, 7.53323E-05, 6.30863E-05, 5.21356E-05, 4.24754E-05, 3.40876E-05, 2.69282E-05, 2.09267E-05, 1.5989E-05, 4.8551E-06, 2.42755E-06, 4.8551E-07, 2.42755E-07, 1.21378E-07, 4.8551E-08]
# https://github.com/cms-sw/cmssw/blob/CMSSW_8_1_X/SimGeneral/MixingModule/python/mix_2016_25ns_SpringMC_PUScenarioV1_PoissonOOTPU_cfi.py
probValue = [0.000829312873542, 0.00124276120498, 0.00339329181587, 0.00408224735376, 0.00383036590008, 0.00659159288946, 0.00816022734493, 0.00943640833116, 0.0137777376066, 0.017059392038, 0.0213193035468, 0.0247343174676, 0.0280848773878, 0.0323308476564, 0.0370394341409, 0.0456917721191, 0.0558762890594, 0.0576956187107, 0.0625325287017, 0.0591603758776, 0.0656650815128, 0.0678329011676, 0.0625142146389, 0.0548068448797, 0.0503893295063, 0.040209818868, 0.0374446988111, 0.0299661572042, 0.0272024759921, 0.0219328403791, 0.0179586571619, 0.0142926728247, 0.00839941654725, 0.00522366397213, 0.00224457976761, 0.000779274977993, 0.000197066585944, 7.16031761328e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
mc = TH1F("2016_25ns_SpringMC_PUScenarioV1", "True nPV distribution", 60, 0, 60)
mc.Sumw2()
for i in range(60): mc.SetBinContent(i+1, probValue[i])
mc.SetLineWidth(3)
mc.SetLineColor(1)
mc.SetLineStyle(2)
mc.Scale(1./mc.Integral())
if options.save:
outFile = TFile("../data/PU_MC.root", "RECREATE")
outFile.cd()
mc.Write()
outFile.Close()
print "Histograms written to ../data/PU_MC.root file"
exit()
m_H2 = []
m_H3 = []
b_pT = []
b_eta = []
b_phi = []
bbar_pT = []
bbar_eta = []
bbar_phi = []
a_pT = []
a_eta = []
a_phi = []
h_m_H1 = TH1F('run_' + i, "H3(1MeV)", 100, 100, 150)
h_m_H2 = TH1F('run_' + i, "H3(1MeV)", 1000, 0, 1000)
h_b_pT = TH1F('run_' + i, "H3(1MeV)", 100, 0, 600)
h_b_eta = TH1F('run_' + i, "H3(1MeV)", 100, -5, 5)
h_b_phi = TH1F('run_' + i, "H3(1MeV)", 10, -4, 4)
h_bbar_pT = TH1F('run_' + i, "H3(1MeV)", 100, 0, 600)
h_bbar_eta = TH1F('run_' + i, "H3(1MeV)", 100, -5, 5)
h_bbar_phi = TH1F('run_' + i, "H3(1MeV)", 10, -4, 4)
h_a_pT = TH1F('run_' + i, "H3(1MeV)", 100, 0, 600)
h_a_eta = TH1F('run_' + i, "H3(1MeV)", 100, -5, 5)
h_a_phi = TH1F('run_' + i, "H3(1MeV)", 10, -4, 4)
files = glob.glob(path + 'run_' + i + '/*.lhe')
def main(debug=True, real_data=False, plots=False):
start = time.time()
if (debug):
dframe = pickle.load(
openfile(
"/data/Derived/D0kINT7HighMultwithJets/vAN-20191003_ROOT6-1/pp_2018_data/260_20191004-0008/skpkldecmerged/AnalysisResultsReco4_6_0.65.pkl.lz4",
"rb"))
else:
if (real_data):
dfreco0 = pickle.load(
openfile(
"/data/Derived/D0kINT7HighMultwithJets/vAN-20191003_ROOT6-1/pp_2018_data/260_20191004-0008/skpkldecmerged/AnalysisResultsReco1_2_0.75.pkl.lz4",
"rb"))
dfreco1 = pickle.load(
openfile(
"/data/Derived/D0kINT7HighMultwithJets/vAN-20191003_ROOT6-1/pp_2018_data/260_20191004-0008/skpkldecmerged/AnalysisResultsReco2_4_0.75.pkl.lz4",
"rb"))
dfreco2 = pickle.load(
openfile(
"/data/Derived/D0kINT7HighMultwithJets/vAN-20191003_ROOT6-1/pp_2018_data/260_20191004-0008/skpkldecmerged/AnalysisResultsReco4_6_0.65.pkl.lz4",
"rb"))
dfreco3 = pickle.load(
openfile(
"/data/Derived/D0kINT7HighMultwithJets/vAN-20191003_ROOT6-1/pp_2018_data/260_20191004-0008/skpkldecmerged/AnalysisResultsReco6_8_0.65.pkl.lz4",
"rb"))
dfreco4 = pickle.load(
openfile(
"/data/Derived/D0kINT7HighMultwithJets/vAN-20191003_ROOT6-1/pp_2018_data/260_20191004-0008/skpkldecmerged/AnalysisResultsReco8_24_0.45.pkl.lz4",
"rb"))
frames = [dfreco0, dfreco1, dfreco2, dfreco3, dfreco4]
dframe = pd.concat(frames)
else:
dfreco0 = pickle.load(
openfile(
"/data/Derived/D0kINT7HighMultwithJets/vAN-20191003_ROOT6-1/pp_2018_mc_prodD2H/261_20191004-0007/skpkldecmerged/AnalysisResultsReco1_2_0.75.pkl.lz4",
"rb"))
dfreco1 = pickle.load(
openfile(
"/data/Derived/D0kINT7HighMultwithJets/vAN-20191003_ROOT6-1/pp_2018_mc_prodD2H/261_20191004-0007/skpkldecmerged/AnalysisResultsReco2_4_0.75.pkl.lz4",
"rb"))
dfreco2 = pickle.load(
openfile(
"/data/Derived/D0kINT7HighMultwithJets/vAN-20191003_ROOT6-1/pp_2018_mc_prodD2H/261_20191004-0007/skpkldecmerged/AnalysisResultsReco4_6_0.65.pkl.lz4",
"rb"))
dfreco3 = pickle.load(
openfile(
"/data/Derived/D0kINT7HighMultwithJets/vAN-20191003_ROOT6-1/pp_2018_mc_prodD2H/261_20191004-0007/skpkldecmerged/AnalysisResultsReco6_8_0.65.pkl.lz4",
"rb"))
dfreco4 = pickle.load(
openfile(
"/data/Derived/D0kINT7HighMultwithJets/vAN-20191003_ROOT6-1/pp_2018_mc_prodD2H/261_20191004-0007/skpkldecmerged/AnalysisResultsReco8_24_0.45.pkl.lz4",
"rb"))
frames = [dfreco0, dfreco1, dfreco2, dfreco3, dfreco4]
dframe = pd.concat(frames)
#dframe = dframe.query("y_test_probxgboost>0.5")
#dframe = dframe.query("pt_cand > 10")
#dframe = dframe.query("pt_cand < 10")
dfreco = dframe.reset_index(drop=True)
end = time.time()
print("Data loaded in", end - start, "sec")
if (debug):
print("Debug mode: reduced data")
dfreco = dfreco[:1000000]
print("Size of data", dfreco.shape)
print(dfreco.columns)
binning = 200
hfile = TFile('pre_selection_histos.root', 'RECREATE',
'ROOT file with histograms')
cYields = TCanvas('cYields', 'The Fit Canvas')
fit_fun1 = TF1("fit_fun1", "expo", 1.64, 1.82)
fit_fun2 = TF1("fit_fun2", "gaus", 1.82, 1.92)
fit_total = TF1("fit_total", "expo(0) + gaus(2) + expo(5)", 1.64, 2.1)
h_invmass = TH1F("invariant mass", "", binning, dfreco.inv_mass.min(),
dfreco.inv_mass.max())
fill_hist(h_invmass, dfreco.inv_mass)
h_invmass.Fit(fit_fun1, "R")
par1 = fit_fun1.GetParameters()
h_invmass.Fit(fit_fun2, "R+")
par2 = fit_fun2.GetParameters()
fit_total.SetParameters(par1[0], par1[1], par2[0], par2[1], par2[2],
par1[0], par1[1])
h_invmass.Fit(fit_total, "R+")
par = fit_total.GetParameters()
h_invmass.Draw()
cYields.SaveAs("h_invmass.png")
if (plots):
cYields.SetLogy(True)
h_d_len = TH1F("d_len", "", 200, dfreco.d_len.min(),
dfreco.d_len.max())
fill_hist(h_d_len, dfreco.d_len)
h_d_len.Draw()
cYields.SaveAs("h_d_len.png")
h_norm_dl = TH1F("norm dl", "", 200, dfreco.norm_dl.min(),
dfreco.norm_dl.max())
fill_hist(h_norm_dl, dfreco.norm_dl)
h_norm_dl.Draw()
cYields.SaveAs("h_norm_dl.png")
cYields.SetLogy(False)
h_cos_p = TH1F("cos_p", "", 200, dfreco.cos_p.min(),
dfreco.cos_p.max())
fill_hist(h_cos_p, dfreco.cos_p)
h_cos_p.Draw()
cYields.SaveAs("h_cos_p.png")
cYields.SetLogy(True)
h_nsigTPC_K_0 = TH1F("nsigma TPC K_0", "",
200, dfreco.nsigTPC_K_0.min(),
dfreco.nsigTPC_K_0.max())
fill_hist(h_nsigTPC_K_0, dfreco.nsigTPC_K_0)
h_nsigTPC_K_0.Draw()
cYields.SaveAs("nsigTPC_K_0.png")
h_nsigTPC_K_1 = TH1F("nsigTPC_K_1 ", "", 200, dfreco.nsigTPC_K_1.min(),
dfreco.nsigTPC_K_1.max())
fill_hist(h_nsigTPC_K_1, dfreco.nsigTPC_K_1)
h_nsigTPC_K_1.Draw()
cYields.SaveAs("h_nsigTPC_K_1.png")
h_nsigTOF_K_0 = TH1F("nsigma TOF K_0", "",
200, dfreco.nsigTOF_K_0.min(),
dfreco.nsigTOF_K_0.max())
fill_hist(h_nsigTOF_K_0, dfreco.nsigTOF_K_0)
h_nsigTOF_K_0.Draw()
cYields.SaveAs("nsigTOF_K_0.png")
h_nsigTOF_K_1 = TH1F("nsigTOF_K_1 ", "", 200, dfreco.nsigTOF_K_1.min(),
dfreco.nsigTOF_K_1.max())
fill_hist(h_nsigTOF_K_1, dfreco.nsigTOF_K_1)
h_nsigTOF_K_1.Draw()
cYields.SaveAs("h_nsigTOF_K_1.png")
cYields.SetLogy(False)
h_pt_prong0 = TH1F("pt prong_0", "", 200, dfreco.pt_prong0.min(),
dfreco.pt_prong0.max())
fill_hist(h_pt_prong0, dfreco.pt_prong0)
h_pt_prong0.Draw()
cYields.SaveAs("h_pt_prong0.png")
h_pt_prong1 = TH1F("pt prong_1", "", 200, dfreco.pt_prong1.min(),
dfreco.pt_prong1.max())
fill_hist(h_pt_prong1, dfreco.pt_prong1)
h_pt_prong1.Draw()
cYields.SaveAs("h_pt_prong1.png")
h_eta_prong0 = TH1F("eta prong_0", "", 200, dfreco.eta_prong0.min(),
dfreco.eta_prong0.max())
fill_hist(h_eta_prong0, dfreco.eta_prong0)
h_eta_prong0.Draw()
cYields.SaveAs("h_eta_prong0.png")
h_eta_prong1 = TH1F("eta prong_1", "", 200, dfreco.eta_prong1.max(),
dfreco.eta_prong1.max())
fill_hist(h_eta_prong1, dfreco.eta_prong1)
h_eta_prong1.Draw()
cYields.SaveAs("h_eta_prong1.png")
h_eta_cand = TH1F("eta cand", "", 200, dfreco.eta_cand.min(),
dfreco.eta_cand.max())
fill_hist(h_eta_cand, dfreco.eta_cand)
h_eta_cand.Draw()
cYields.SaveAs("h_eta_cand.png")
h_phi_cand = TH1F("phi cand", "", 200, dfreco.eta_cand.min(),
dfreco.eta_cand.max())
fill_hist(h_phi_cand, dfreco.phi_cand)
h_phi_cand.Draw()
cYields.SaveAs("h_phi_cand.png")
h_pt_cand = TH1F("pt cand", "", 200, dfreco.pt_cand.min(),
dfreco.pt_cand.max())
fill_hist(h_pt_cand, dfreco.pt_cand)
h_pt_cand.Draw()
cYields.SaveAs("h_pt_cand.png")
grouped = dfreco.groupby(["run_number", "ev_id"])
grouplen = pd.array(grouped.size())
gmin = grouplen.min()
gmax = grouplen.max()
g_bins = gmax - gmin
print("creating grouplen array", end - start, "sec")
h_grouplen = TH1F("group_length", "", int(g_bins), gmin, gmax)
fill_hist(h_grouplen, grouplen)
cYields.SetLogy(True)
h_grouplen.Draw()
cYields.SaveAs("h_grouplen.png")
hfile.Write()
return dfreco
def dqm_getSingleHist_json(server, run, dataset, hist, rootContent=False):
postfix = "?rootcontent=1" if rootContent else ""
datareq = urllib2.Request(('%s/jsonfairy/archive/%s/%s/%s%s') %
(server, run, dataset, hist, postfix))
datareq.add_header('User-agent', ident)
# Get data
data = eval(
re.sub(r"\bnan\b", "0",
urllib2.build_opener(X509CertOpen()).open(datareq).read()),
{"__builtins__": None}, {})
histo = data['hist']
# Now convert into real ROOT histogram object
if 'TH1' in histo['type']:
# The following assumes a TH1F object
contents = histo['bins']['content']
nbins = len(contents)
xmin = histo['xaxis']['first']['value']
xmax = histo['xaxis']['last']['value']
roothist = TH1F(histo['stats']['name'], histo['title'], nbins, xmin,
xmax)
for xx in range(1, nbins + 1):
roothist.SetBinContent(xx, contents[xx - 1])
roothist.SetBinError(xx, histo['bins']['error'][xx - 1])
roothist.SetEntries(histo['stats']['entries'])
stats = array('d')
stats.append(histo['stats']['entries'])
stats.append(histo['stats']['entries'])
stats.append(histo['stats']['entries'] *
histo['stats']['mean']['X']['value'])
stats.append(
(histo['stats']['rms']['X']['value'] *
histo['stats']['rms']['X']['value'] +
histo['stats']['mean']['X']['value'] *
histo['stats']['mean']['X']['value']) * histo['stats']['entries'])
roothist.PutStats(stats)
elif (histo['type'] == 'TProfile'):
contents = histo['bins']['content']
nbins = len(contents)
xmin = histo['xaxis']['first']['value']
xmax = histo['xaxis']['last']['value']
roothist = TProfile(histo['stats']['name'], histo['title'], nbins,
xmin, xmax)
roothist.SetErrorOption("g")
for xx in range(0, nbins):
if (histo['bins']['error'][xx] != 0):
ww = 1. / (histo['bins']['error'][xx] *
histo['bins']['error'][xx])
else:
ww = 0.
roothist.Fill(
xmin + (2 * xx + 1) * ((xmax - xmin) / (nbins * 2.0)),
contents[xx], ww)
# roothist.SetBinContent(xx, contents[xx-1])
# roothist.SetBinError(xx, histo['bins']['error'][xx-1])
roothist.SetEntries(histo['stats']['entries'])
stats = array('d')
for i in range(0, 6):
stats.append(i)
roothist.GetStats(stats)
stats[0] = (histo['stats']['entries'])
stats[1] = (histo['stats']['entries'])
stats[2] = (histo['stats']['entries'] *
histo['stats']['mean']['X']['value'])
stats[3] = ((histo['stats']['rms']['X']['value'] *
histo['stats']['rms']['X']['value'] +
histo['stats']['mean']['X']['value'] *
histo['stats']['mean']['X']['value']) *
histo['stats']['entries'])
roothist.PutStats(stats)
elif 'TH2' in histo['type']:
contents = histo['bins']['content']
nbinsx = histo['xaxis']['last']['id']
xmin = histo['xaxis']['first']['value']
xmax = histo['xaxis']['last']['value']
nbinsy = histo['yaxis']['last']['id']
ymin = histo['yaxis']['first']['value']
ymax = histo['yaxis']['last']['value']
roothist = TH2F(histo['stats']['name'], histo['title'], nbinsx, xmin,
xmax, nbinsy, ymin, ymax)
for xx in range(1, nbinsx + 1):
for yy in range(1, nbinsy + 1):
roothist.SetBinContent(xx, yy, contents[yy - 1][xx - 1])
roothist.SetEntries(histo['stats']['entries'])
stats = array('d')
stats.append(histo['stats']['entries'])
stats.append(histo['stats']['entries'])
stats.append(histo['stats']['entries'] *
histo['stats']['mean']['X']['value'])
stats.append(
(histo['stats']['rms']['X']['value'] *
histo['stats']['rms']['X']['value'] +
histo['stats']['mean']['X']['value'] *
histo['stats']['mean']['X']['value']) * histo['stats']['entries'])
stats.append(histo['stats']['entries'] *
histo['stats']['mean']['Y']['value'])
stats.append(
(histo['stats']['rms']['Y']['value'] *
histo['stats']['rms']['Y']['value'] +
histo['stats']['mean']['Y']['value'] *
histo['stats']['mean']['Y']['value']) * histo['stats']['entries'])
roothist.PutStats(stats)
elif (histo['type'] == 'TProfile2D'):
contents = histo['bins']['content']
nbinsx = histo['xaxis']['last']['id']
xmin = histo['xaxis']['first']['value']
xmax = histo['xaxis']['last']['value']
nbinsy = histo['yaxis']['last']['id']
ymin = histo['yaxis']['first']['value']
ymax = histo['yaxis']['last']['value']
roothist = TProfile2D(histo['stats']['name'], histo['title'], nbinsx,
xmin, xmax, nbinsy, ymin, ymax)
for xx in range(0, nbinsx):
for yy in range(0, nbinsy):
roothist.Fill(
xmin + (2 * xx + 1) * ((xmax - xmin) / (nbinsx * 2.0)),
ymin + (2 * yy + 1) * ((ymax - ymin) / (nbinsy * 2.0)), 0,
1)
for xx in range(1, nbinsx + 1):
for yy in range(1, nbinsy + 1):
roothist.SetBinContent(xx, yy, contents[yy - 1][xx - 1])
roothist.SetEntries(histo['stats']['entries'])
return roothist
from ROOT import TCanvas, TFile, TProfile, TNtuple, TH1F, TH2F from ROOT import gROOT, gBenchmark, gRandom, gSystem, Double
# Create a new canvas, and customize it.
c1 = TCanvas( 'c1', 'Dynamic Filling Example', 200, 10, 700, 500 ) c1.SetFillColor( 42 ) c1.GetFrame().SetFillColor( 21 )
c1.GetFrame().SetBorderSize( 6 ) c1.GetFrame().SetBorderMode( -1 )
# Create a new ROOT binary machine independent file. Note that this file may contain any kind of ROOT objects, histograms,
# pictures, graphics objects, detector geometries, tracks, events, etc.. This file is now becoming the current directory.
hfile = gROOT.FindObject( 'py-hsimple.root' ) if hfile:
hfile.Close() hfile = TFile( 'py-hsimple.root', 'RECREATE', 'Demo ROOT file with histograms' )
# Create some histograms, a profile histogram and an ntuple
hpx = TH1F( 'hpx', 'This is the px distribution', 100, -4, 4 ) hpxpy = TH2F( 'hpxpy', 'py vs px', 40, -4, 4, 40, -4, 4 ) hprof =
TProfile( 'hprof', 'Profile of pz versus px', 100, -4, 4, 0, 20 ) ntuple = TNtuple( 'ntuple', 'Demo ntuple', 'px:py:pz:random:i' )
# Set canvas/frame attributes.
hpx.SetFillColor( 48 ) gBenchmark.Start( 'hsimple' )
# Initialize random number generator.
gRandom.SetSeed() rannor, rndm = gRandom.Rannor, gRandom.Rndm
# For speed, bind and cache the Fill member functions,
histos = [ 'hpx', 'hpxpy', 'hprof', 'ntuple' ] for name in histos:
exec('%sFill = %s.Fill' % (name,name))
# Fill histograms randomly.
px, py = Double(), Double() kUPDATE = 1000 for i in range( 25000 ):
# Generate random values.
rannor( px, py )
pz = px*px + py*py
random = rndm(1)
# Fill histograms.
hpx.Fill( px )
hpxpy.Fill( px, py )
hprof.Fill( px, pz )
ntuple.Fill( px, py, pz, random, i )
# Update display every kUPDATE events.
if i and i%kUPDATE == 0:
# Simple example of generating a histogram from a gaussian function
# and fitting it
#
# Author: Izaak Neutelings (August 2017)
# https://root.cern.ch/root/htmldoc/guides/users-guide/FittingHistograms.html
# https://root.cern.ch/doc/master/classTFormula.html
# https://root.cern.ch/doc/master/classTF1.html
import ROOT
from ROOT import TF1, TH1F, TCanvas, TLegend, kBlack, kBlue, kRed, kViolet, kGreen
from math import exp
print ">>> generating signal..."
function1 = TF1("f1", "gaus", 0, 10)
function1.SetParameters(1, 2, 0.5)
hist = TH1F("signal", "signal", 50, 0, 5)
hist.FillRandom("f1", 1000)
print ">>> make fit functions..."
# pol1 = [0]+[1]*x+[2]*x**2
# gaus = [0]*exp(-0.5*((x-[1])/[2])**2)
norm = hist.GetMaximum()
function2 = TF1("f2", "gaus", 0, 10)
function3 = TF1("f3", "pol2", 0, 10)
function2.SetParNames("N", "mu", "sigma")
function3.SetParNames("C", "B", "A")
function2.SetParameters(1.3 * norm, 1, 0.4) # start values to help fit
function3.SetParameters(-3 * norm, 4 * norm, -norm) # start values to help fit
function2.SetParLimits(0, 0.2 * norm, 1.5 * norm) # set limits on parameter 1
function3.SetParLimits(1, 0.5 * norm, 10.0 * norm) # set limits on parameter 1
function3.SetParLimits(2, -1.4 * norm,
def calc_punzi_FOM_vs_ctau(cutlist,
labellist=[],
mass_point=40,
additional_string="",
alpha=2,
CL=5,
FOM='punzi',
header=""):
file = {}
nevt = {}
tree = {}
effs = {}
chain = {}
hist = {}
eff_dict = {k: {} for k in cutlist}
back_int = {k: {} for k in cutlist}
back_int_weight = {k: {} for k in cutlist}
back_eff = {k: {} for k in cutlist}
punzi_dict = {k: {} for k in cutlist}
graph = {}
back_graph = {}
ncuts = len(cutlist)
if labellist == []:
labellist = cutlist
print NTUPLEDIR
print "............."
#prepare ctau ordered array for 1D plot
mass_array = []
ctau_array = []
#for signal we have the normal efficiency
for i, s in enumerate(sign):
file[s] = TFile(NTUPLEDIR + samples[s]['files'][0] + ".root",
"READ") # Read TFile
tree[s] = file[s].Get("ntuple/tree") # Read TTree
nevt[s] = (file[s].Get('counter/c_nEvents')).GetBinContent(
1) # all gen events before cuts!
#tree[s] = file[s].Get("skim") # Read TTree
#nevt[s] = tree[s].GetEntries("")#if the tree is skimmed, this becomes a relative denominator
#nevt[s] = (file[s].Get('c_nEvents')).GetBinContent(1)# all gen events before cuts!
filename = TFile(NTUPLEDIR + samples[s]['files'][0] + ".root", "READ")
if verbose_add: print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
if verbose_add: print filename
if verbose_add: print "x-check: n gen events in counter, first bin:"
if verbose_add: print(filename.Get('c_nEvents')).GetBinContent(1)
if verbose_add: print "x-check: n entries in counter:"
if verbose_add: print(filename.Get('c_nEvents')).GetEntries()
effs[s] = [0] * (ncuts + 1)
effs[s] = [0] * (ncuts + 1)
weight = "1" #"EventWeight"
var = "isMC"
if samples[s]['mass'] not in mass_array:
mass_array.append(samples[s]['mass'])
if samples[s]['ctau'] not in ctau_array:
ctau_array.append(samples[s]['ctau'])
for j, c in enumerate(cutlist):
tot_gen = nevt[s]
n = tree[s].GetEntries("(" + cutlist[j] + ")")
#wat?#test_op = cutlist[j] + " && number_of_matched_Jets>=1"
#wat?#n = tree[s].GetEntries("(" + test_op + ")")
###BUGFIX: efficiency should be computed w.r.t. histo integral
#hist[s+"_cut"+str(j)] = TH1F(s+"_cut"+str(j), ";"+variable[var]['title'], variable[var]['nbins'], variable[var]['min'], variable[var]['max'])
#hist[s+"_cut"+str(j)].Sumw2()
#cutstring = "("+weight+")" + ("*("+cutlist[j]+")" if len(cutlist[j])>0 else "")
#tree[s].Project(s+"_cut"+str(j), var, cutstring)
#hist[s+"_cut"+str(j)].SetOption("%s" % tree[s].GetTree().GetEntriesFast())
if verbose_add: print '\n'
if verbose_add:
print '**********************************************'
if verbose_add: print "cut: ", c
if verbose_add: print 'over signal ', s
if verbose_add: print '\n'
if verbose_add: print "signal num: ", n
if verbose_add: print "signal den: ", tot_gen
#if verbose_add: print "BUGFIX!!!!!!!!!!!"
#if verbose: print "BUGFIX!!!!!!!!!!!"
#if verbose_add: print "signal num from integral: ", hist[s+"_cut"+str(j)].Integral()
#if verbose_add: print "signal den from generator: ", tot_gen
#if verbose: print "BUGFIX!!!!!!!!!!!"
if verbose_add:
print("signal eff %.2f") % (float(n) / (tot_gen) * 100)
if tot_gen == 0:
effs[s][j] = float(0.)
else:
effs[s][j] = (float(n) / (tot_gen))
eff_dict[c][s] = {
'mass': samples[s]['mass'],
'ctau': samples[s]['ctau'],
'eff': effs[s][j],
'nevents': n
}
#sort mass array
masses = np.array(mass_array)
masses.sort()
ctaus = np.array(ctau_array)
ctaus.sort()
#define multigraph
mg = TMultiGraph()
#leg = TLegend(0.78, 0.7, 0.98, 0.98)
#leg2 = TLegend(0., 0.4, 0.98, 0.98)
#leg2 = TLegend(0.3, 0.11, 0.65, 0.45)#DCMS,gen matching
leg2 = TLegend(0.4, 0.11, 0.85, 0.45) #DCMS,summary plot
leg2 = TLegend(0.4 - 0.3, 0.11 + 0.43, 0.85 + 0.05 - 0.3,
0.45 + 0.43) #EXO,summary plot
leg2 = TLegend(0.4, 0.11, 0.85 + 0.05, 0.45) #EXO,summary plot
leg3 = TLegend(0., 0.5, 0.5, 1.) #2 plots
leg = TLegend(0., 0.4, 0.98, 0.98)
leg.SetTextSize(0.03)
leg2.SetTextSize(0.03)
leg2.SetTextSize(0.025)
leg.SetBorderSize(0)
leg2.SetBorderSize(0)
leg.SetHeader("Signal: m_{#pi}=" + str(mass_point) + " GeV")
leg2.SetHeader("Signal: m_{#pi}=" + str(mass_point) + " GeV")
leg3.SetTextSize(0.03)
leg3.SetTextSize(0.025)
leg3.SetBorderSize(0)
leg3.SetHeader("Signal: m_{#pi}=" + str(mass_point) + " GeV")
#for background let's first consider the cut
for j, c in enumerate(cutlist):
print '\n'
print "cut: ", c
print 'over background'
print '\n'
#then loop over background
integral = 0
weighted_integral = 0
back_tot_events = 0
for i, s in enumerate(back):
chain[s] = TChain("ntuple/tree")
#chain[s] = TChain("skim")
#print "back: ", s
back_file = {}
for p, ss in enumerate(samples[s]['files']):
back_file[ss] = TFile(NTUPLEDIR + ss + ".root",
"READ") # Read TFile
#?#if verbose: print "file: ", ss
#?#if verbose: print "gen events: ", (back_file[ss].Get('counter/c_nEvents')).GetBinContent(1)
#?#if verbose: print "tree events: ", (back_file[ss].Get('ntuple/tree')).GetEntries()
back_tot_events += (
back_file[ss].Get('counter/c_nEvents')).GetBinContent(1)
#back_tot_events += (back_file[ss].Get('c_nEvents')).GetBinContent(1)
chain[s].Add(NTUPLEDIR + ss + ".root")
#print "MODIFIED WEIGHT!!!!!!"
#weight = ("EventWeight*%s/5000." % str(back_tot_events))
weight = "EventWeight"
#var = "nCHSJets"
var = "isMC"
hist[s] = TH1F(s, ";" + variable[var]['title'],
variable[var]['nbins'], variable[var]['min'],
variable[var]['max'])
hist[s].Sumw2()
cutstring = "(" + weight + ")" + ("*(" + cutlist[j] + ")"
if len(cutlist[j]) > 0 else "")
chain[s].Project(s, var, "") #"1*"+"("+weight+")")
hist[s].SetOption("%s" % chain[s].GetTree().GetEntriesFast())
#if verbose: print "Hist content, no cut:"
#if verbose: print hist[s].Print()
#?#if verbose: print "events in the histo with get entries with empty project: ", hist[s].GetEntries()
#?#if verbose: print "area under histo with empty project: ", hist[s].Integral()
chain[s].Project(s, var, cutstring) #"1*"+"("+weight+")")
hist[s].SetOption("%s" % chain[s].GetTree().GetEntriesFast())
hist[s].Scale(
samples[s]['weight'] if hist[s].Integral() >= 0 else 0)
#?#if verbose: print "events in the histo with get entries after project: ", hist[s].GetEntries()
#?#if verbose: print "area under histo after project: ", hist[s].Integral()
if verbose: print "Hist content, with cut:"
if verbose: print hist[s].Print()
integral += hist[s].GetEntries()
weighted_integral += hist[s].Integral()
back_int[c] = integral
back_int_weight[c] = weighted_integral
if back_tot_events == 0:
back_eff[c] = float(0.)
else:
back_eff[c] = float(integral) / float(back_tot_events)
if verbose: print "cut: ", c
if verbose: print "back tot events (unweighted):", back_tot_events
if verbose: print "back integral (unweighted): ", back_int[c]
if verbose: print "back integral (weighted): ", back_int_weight[c]
if verbose: print "back eff (unweighted): ", back_eff[c] * 100
if FOM == "signaleff":
punzi_dict[c]['back'] = {'back': back_eff[c] * 100}
for i, s in enumerate(sign):
if verbose:
print "signal efficiency: ", eff_dict[c][s]['eff'] * 100
if FOM == "punzi":
punzi_dict[c][s] = {
'sign':
eff_dict[c][s]['eff'] /
(CL**2 / 2. + alpha * math.sqrt(back_int_weight[c]) +
(CL / 2.) * math.sqrt(CL**2 + 4 * alpha * math.sqrt(
back_int_weight[c]) + 4 * back_int_weight[c]))
}
elif FOM == "signaleff":
punzi_dict[c][s] = {'sign': eff_dict[c][s]['eff'] * 100}
elif FOM == "entries":
punzi_dict[c][s] = {'sign': eff_dict[c][s]['nevents']}
else:
print "not punzi FOM, aborting!"
exit()
if FOM == "signaleff":
dummy = TGraph() #len(ct),ct, np.array(ct))
dummy.SetMarkerStyle(0)
dummy.SetLineWidth(2)
dummy.SetMarkerSize(1.)
dummy.SetLineColor(15)
dummy.SetLineStyle(2)
if header != "":
leg2.AddEntry(dummy, header, '')
leg3.AddEntry(dummy, header, '')
#for each cut, we need a graph
for j, c in enumerate(cutlist):
#first let's build the ordered punzi vector w.r.t. masses, for a chosen ctau
punzi_array = []
back_array = []
for la in ctaus:
#la = str(a)
if la == 0.001:
st = CHANNEL + "_M" + str(mass_point) + "_ctau0"
elif la == 0.05 or la == 0.1:
st = CHANNEL + "_M" + str(mass_point) + "_ctau" + str(
str(la).replace("0.", "0p"))
else:
st = CHANNEL + "_M" + str(mass_point) + "_ctau" + str(int(la))
#st = "VBFH_M"+str(mass_point)+"_ctau"+str(a)
punzi_array.append(punzi_dict[c][st]['sign'])
mass = array('d', masses)
ct = array('d', ctaus)
p_array = array('d', punzi_array)
#graph[c] = TGraph(len(mass),mass, np.array(p_array))
graph[c] = TGraph(len(ct), ct, np.array(p_array))
graph[c].SetMarkerStyle(markers[j]) #21
graph[c].SetLineWidth(3)
graph[c].SetMarkerSize(1.2)
graph[c].SetMarkerColor(colors[j])
graph[c].SetLineColor(colors[j])
graph[c].SetFillColor(colors[j])
#graph[c].SetLogx()
leg.AddEntry(graph[c], labellist[j], 'PL')
leg2.AddEntry(graph[c], labellist[j], 'PL')
leg3.AddEntry(graph[c], labellist[j], 'PL')
mg.Add(graph[c])
if FOM == "signaleff":
#add plot for background
for a in ctaus:
back_array.append(punzi_dict[c]['back']['back'])
mass = array('d', masses)
ct = array('d', ctaus)
e_array = array('d', back_array)
#back_graph[c] = TGraph(len(mass),mass, np.array(e_array))
back_graph[c] = TGraph(len(ct), ct, np.array(e_array))
back_graph[c].SetMarkerStyle(0)
back_graph[c].SetLineWidth(2)
back_graph[c].SetMarkerSize(1.)
back_graph[c].SetMarkerColor(colors[j])
back_graph[c].SetLineColor(colors[j])
back_graph[c].SetLineStyle(2)
back_graph[c].SetFillColor(colors[j])
#back_graph[c].SetLogx()
#leg.AddEntry(back_graph[c],labellist[j]+" bkg.",'PL')
#w#leg2.AddEntry(back_graph[c],labellist[j]+" bkg.",'PL')
#w#mg.Add(back_graph[c])
if FOM == "signaleff":
dummy = TGraph(len(ct), ct, np.array(e_array))
dummy.SetMarkerStyle(0)
dummy.SetLineWidth(2)
dummy.SetMarkerSize(1.)
dummy.SetLineColor(15)
dummy.SetLineStyle(2)
#w#leg2.AddEntry(dummy, 'cuts on bkg.','PL')
#cmg = TCanvas("cmg", "cmg", 2000, 1400)
#cmg = TCanvas("cmg", "cmg", 2000, 800)#best
#cmg = TCanvas("cmg", "cmg", 1200, 1000)
cmg = TCanvas("cmg", "cmg", 1300, 800) #DCMS
cmg.cd()
cmg.SetGrid()
cmg.SetLogx()
#if FOM=="signaleff":
# cmg.SetLogx()
#pad1 = TPad("pad1", "pad1", 0, 0., 0.85, 1.0)
#pad1 = TPad("pad1", "pad1", 0, 0., 0.7, 1.0)
#pad1.SetGrid()
#pad1.SetLogx()
if FOM == "signaleff":
print "LOL"
#pad1.SetLogy()
#pad1.SetLogy()
#pad1.Draw()
#pad1.cd()
#W#if FOM=="signaleff":
#w#mg.SetMaximum(101)
#mg.SetMinimum(1.e-50)
mg.SetMinimum(0.) #!!
mg.Draw("APL")
mg.GetXaxis().SetTitleSize(0.05)
mg.GetYaxis().SetTitleSize(0.05)
mg.GetXaxis().SetTitle('c#tau_{#pi} (mm)')
mg.GetYaxis().SetTitleOffset(0.9)
if FOM == "punzi":
mg.GetYaxis().SetTitle('Punzi significance @ ' + str(alpha) +
' #sigma, ' + CHANNEL + ' cuts')
#mg.GetYaxis().SetTitleOffset(1.5)
elif FOM == "signaleff":
#mg.GetYaxis().SetTitle('Signal efficiency, '+CHANNEL+' cuts (%)')
mg.GetYaxis().SetTitle('Signal gen-matching efficiency, ' + CHANNEL +
' (%)')
elif FOM == "entries":
mg.GetYaxis().SetTitle('Signal entries surviving cuts')
else:
print "not punzi FOM, aborting"
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.05)
latex.SetTextColor(1)
latex.SetTextFont(42)
latex.SetTextAlign(33)
latex.SetTextFont(62)
latex.DrawLatex(0.25, 0.96, "CMS")
latex.SetTextFont(52)
latex.DrawLatex(0.66, 0.96, "Simulation Preliminary")
cmg.Update()
cmg.cd()
leg2.SetTextSize(0.04)
#leg.Clear()#?????????
#w#leg2.Draw()
cmgL = TCanvas("cmgL", "cmgL", 2000, 800) #DCMS
cmgL.cd()
#pad2 = TPad("pad2", "pad2", 0.85, 0., 1, 1.0)
#pad2 = TPad("pad2", "pad2", 0.7, 0., 1, 1.0)
#pad2.SetGrid()
#pad2.SetLogx()macro/VBF_punzi_LLP_AOD.py
#pad2.Draw()
#pad2.cd()
leg3.SetTextSize(0.04)
#leg.Clear()#?????????
leg3.Draw()
cmgL.Update()
if FOM == "punzi":
cmg.Print(OUTPUTDIR + "Punzi_correct_" + CHANNEL + "_m" +
str(mass_point) + "_" + str(alpha) + "sigma" +
additional_string + ".pdf")
cmg.Print(OUTPUTDIR + "Punzi_correct_" + CHANNEL + "_m" +
str(mass_point) + "_" + str(alpha) + "sigma" +
additional_string + ".png")
cmgL.Print(OUTPUTDIR + "Punzi_correct_" + CHANNEL + "_m" +
str(mass_point) + "_" + str(alpha) + "sigma" +
additional_string + "_L.pdf")
cmgL.Print(OUTPUTDIR + "Punzi_correct_" + CHANNEL + "_m" +
str(mass_point) + "_" + str(alpha) + "sigma" +
additional_string + "_L.png")
elif FOM == "signaleff":
cmg.Print(OUTPUTDIR + "SignalEff_" + CHANNEL + "_m" + str(mass_point) +
additional_string + ".pdf")
cmg.Print(OUTPUTDIR + "SignalEff_" + CHANNEL + "_m" + str(mass_point) +
additional_string + ".png")
cmgL.Print(OUTPUTDIR + "SignalEff_" + CHANNEL + "_m" +
str(mass_point) + additional_string + "_L.pdf")
cmgL.Print(OUTPUTDIR + "SignalEff_" + CHANNEL + "_m" +
str(mass_point) + additional_string + "_L.png")
elif FOM == "entries":
cmg.Print(OUTPUTDIR + "SignalEntries_" + CHANNEL + "_m" +
str(mass_point) + additional_string + ".pdf")
cmg.Print(OUTPUTDIR + "SignalEntries_" + CHANNEL + "_m" +
str(mass_point) + additional_string + ".png")
cmgL.Print(OUTPUTDIR + "SignalEntries_" + CHANNEL + "_m" +
str(mass_point) + additional_string + "_L.pdf")
cmgL.Print(OUTPUTDIR + "SignalEntries_" + CHANNEL + "_m" +
str(mass_point) + additional_string + "_L.png")
else:
print "not punzi FOM, aborting"
if not options.bash: raw_input("Press Enter to continue...")
cmg.Close()
# ROOT imports
import ROOT
from ROOT import TChain, TH1F, TFile, vector, gROOT
# custom ROOT classes
from ROOT import alp, ComposableSelector, CounterOperator, TriggerOperator, JetFilterOperator, BTagFilterOperator, JetPairingOperator, DiJetPlotterOperator
from ROOT import BaseOperator, EventWriterOperator, IsoMuFilterOperator, MetFilterOperator, JetPlotterOperator, FolderOperator, MiscellPlotterOperator
from ROOT import ThrustFinderOperator, HemisphereProducerOperator, HemisphereWriterOperator, DiHiggsFilterOperator
# imports from ../python
from Analysis.alp_analysis.alpSamples import samples
from Analysis.alp_analysis.samplelists import samlists
from Analysis.alp_analysis.triggerlists import triggerlists
from Analysis.alp_analysis.workingpoints import wps
TH1F.AddDirectory(0)
# parsing parameters
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("-e",
"--numEvts",
help="number of events",
type=int,
default='-1')
parser.add_argument("-s", "--samList", help="sample list", default="")
parser.add_argument("--btag", help="which btag algo", default='cmva')
parser.add_argument("-i",
"--iDir",
help="input directory",
def pullsVertical(fileName):
content = filterPullFile(fileName)
nbins, off = len(content), 0.10
b_pulls = TH1F("b_pulls", ";;Pulls", nbins, 0.-off, nbins-off)
s_pulls = TH1F("s_pulls", ";;Pulls", nbins, 0.+off, nbins+off) #
for i, s in enumerate(content):
l = s.split()
b_pulls.GetXaxis().SetBinLabel(i+1, l[0])
s_pulls.GetXaxis().SetBinLabel(i+1, l[0])
b_pulls.SetBinContent(i+1, float(l[1]))
b_pulls.SetBinError(i+1, float(l[2]))
s_pulls.SetBinContent(i+1, float(l[3]))
s_pulls.SetBinError(i+1, float(l[4]))
b_pulls.SetFillStyle(3005)
b_pulls.SetFillColor(923)
b_pulls.SetLineColor(923)
b_pulls.SetLineWidth(1)
b_pulls.SetMarkerStyle(20)
b_pulls.SetMarkerSize(1.25)
s_pulls.SetLineColor(602)
s_pulls.SetMarkerColor(602)
s_pulls.SetMarkerStyle(24) #24
s_pulls.SetLineWidth(1)
b_pulls.GetYaxis().SetRangeUser(-2.5, 2.5)
# Graphs
h_pulls = TH2F("pulls", "", 6, -3., 3., nbins, 0, nbins)
B_pulls = TGraphAsymmErrors(nbins)
S_pulls = TGraphAsymmErrors(nbins)
boxes = []
canvas = TCanvas("canvas", "Pulls", 600, 150+nbins*10)#nbins*20)
canvas.cd()
canvas.SetGrid(0, 1)
canvas.GetPad(0).SetTopMargin(0.01)
canvas.GetPad(0).SetRightMargin(0.01)
canvas.GetPad(0).SetBottomMargin(0.05)
canvas.GetPad(0).SetLeftMargin(0.25)#(0.25)#(0.065)
canvas.GetPad(0).SetTicks(1, 1)
for i, s in enumerate(content):
l = s.split()
if "1034h" in l[0]: l[0]="CMS_PDF_13TeV"
h_pulls.GetYaxis().SetBinLabel(i+1, l[0].replace('CMS2016_', ''))#C
#y1 = gStyle.GetPadBottomMargin()
#y2 = 1. - gStyle.GetPadTopMargin()
#h = (y2 - y1) / float(nbins)
#y1 = y1 + float(i) * h
#y2 = y1 + h
#box = TPaveText(0, y1, 1, y2, 'NDC')
#box.SetFillColor(0)
#box.SetTextSize(0.02)
#box.SetBorderSize(0)
#box.SetTextAlign(12)
#box.SetMargin(0.005)
#if i % 2 == 0:
# box.SetFillColor(18)
#box.Draw()
#boxes.append(box)
B_pulls.SetPoint(i+1,float(l[1]),float(i+1)-0.3)#C
B_pulls.SetPointError(i+1,float(l[2]),float(l[2]),0.,0.)#C
for i, s in enumerate(content):
l = s.split()
S_pulls.SetPoint(i+1,float(l[3]),float(i+1)-0.7)#C
S_pulls.SetPointError(i+1,float(l[4]),float(l[4]),0.,0.)#C
h_pulls.GetXaxis().SetTitle("(#hat{#theta} - #theta_{0}) / #Delta#theta")
h_pulls.GetXaxis().SetLabelOffset(-0.01)
h_pulls.GetXaxis().SetTitleOffset(.6)
h_pulls.GetYaxis().SetNdivisions(nbins, 0, 0)
B_pulls.SetFillColor(1)
B_pulls.SetLineColor(1)
B_pulls.SetLineStyle(1)
B_pulls.SetLineWidth(2)
B_pulls.SetMarkerColor(1)
B_pulls.SetMarkerStyle(20)
B_pulls.SetMarkerSize(1)#(0.75)
S_pulls.SetFillColor(629)
S_pulls.SetLineColor(629)
S_pulls.SetMarkerColor(629)
S_pulls.SetLineWidth(2)
S_pulls.SetMarkerStyle(20)
S_pulls.SetMarkerSize(1)
box1 = TBox(-1., 0., 1., nbins)
box1.SetFillStyle(3001)
#box1.SetFillStyle(0)
box1.SetFillColor(417)
box1.SetLineWidth(2)
box1.SetLineStyle(2)
box1.SetLineColor(417)
box2 = TBox(-2., 0., 2., nbins)
box2.SetFillStyle(3001)
#box2.SetFillStyle(0)
box2.SetFillColor(800)
box2.SetLineWidth(2)
box2.SetLineStyle(2)
box2.SetLineColor(800)
leg = TLegend(0.1, -0.05, 0.7, 0.08)
leg.SetBorderSize(0)
leg.SetFillStyle(0)
leg.SetFillColor(0)
leg.SetNColumns(2)
leg.AddEntry(B_pulls, "B-only fit", "lp")
leg.AddEntry(S_pulls, "S+B fit", "lp")
if text: leg.AddEntry(0, text, "")
h_pulls.Draw("")
box2.Draw()
box1.Draw()
B_pulls.Draw("P6SAME")
S_pulls.Draw("P6SAME")
leg.Draw()
# drawCMS(35867, "Preliminary")
# drawAnalysis("VH")
# drawRegion(outName)
canvas.Print(outName+".png")
canvas.Print(outName+".pdf")
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
px1=float (chi[0].split()[6])
py1=float (chi[0].split()[7])
pz1=float (chi[0].split()[8])
e1=float (chi[0].split()[9])
p1=TLorentzVector(px1,py1,pz1,e1)
if chi and b:
pi=[]
pi=p+p1
m_A0.append(pi.M())
phi_A0.append(pi.Phi())
eta_A0.append(pi.Eta())
pt_A0.append(pi.Pt())
h_mass1=TH1F("Inv.Mass of A0","",200,200,600)
for i in m_A0:
h_mass1.Fill(i)
h_pt1=TH1F("pT of A0","",100,-100,1000)
for i in pt_A0:
h_pt1.Fill(i)
h_phi1=TH1F("Phi of A0","",10,-5,5)
for i in phi_A0:
h_phi1.Fill(i)
h_eta1=TH1F("Eta of A0","",10,-8,8)
for i in eta_A0:
h_eta1.Fill(i)
def pulls(fileName):
content = filterPullFile(fileName)
nbins, off = len(content), 0.10
b_pulls = TH1F("b_pulls", ";;Pulls", nbins, 0.-off, nbins-off)
s_pulls = TH1F("s_pulls", ";;Pulls", nbins, 0.+off, nbins+off) #
for i, s in enumerate(content):
l = s.split()
b_pulls.GetXaxis().SetBinLabel(i+1, l[0])
s_pulls.GetXaxis().SetBinLabel(i+1, l[0])
b_pulls.SetBinContent(i+1, float(l[1]))
b_pulls.SetBinError(i+1, float(l[2]))
s_pulls.SetBinContent(i+1, float(l[3]))
s_pulls.SetBinError(i+1, float(l[4]))
b_pulls.SetFillStyle(3005)
b_pulls.SetFillColor(923)
b_pulls.SetLineColor(923)
b_pulls.SetLineWidth(2)
b_pulls.SetMarkerStyle(20)
b_pulls.SetMarkerSize(1.25)
s_pulls.SetLineColor(602)
s_pulls.SetMarkerColor(602)
s_pulls.SetMarkerStyle(24) #24
s_pulls.SetLineWidth(2)
b_pulls.GetYaxis().SetRangeUser(-2.5, 2.5)
canvas = TCanvas("canvas", "Pulls", 1600, 800)
canvas.cd()
canvas.GetPad(0).SetTopMargin(0.06)
canvas.GetPad(0).SetRightMargin(0.05)
canvas.GetPad(0).SetBottomMargin(0.15)
canvas.GetPad(0).SetTicks(1, 1)
# box = TBox(950., 105., 2000., 200.)
# box.SetFillStyle(3354)
# #box.SetFillStyle(0)
# box.SetFillColor(1)
# box.SetLineWidth(2)
# box.SetLineStyle(2)
# box.SetLineColor(1)
# box.Draw()
# Draw
b_pulls.Draw("PE1")
#b_pulls.Draw("B")
s_pulls.Draw("SAME, PE1")
leg = TLegend(0.25, 0.95, 0.75, 0.995)
leg.SetBorderSize(0)
leg.SetFillStyle(0)
leg.SetFillColor(0)
leg.SetNColumns(2)
leg.AddEntry(b_pulls, "background-only fit", "flp")
leg.AddEntry(s_pulls, "signal+background fit", "lp")
if text: leg.AddEntry(0, text, "")
line = TLine()
line.DrawLine(0., 0., nbins, 0.)
line.SetLineStyle(7)
line.SetLineWidth(2)
line.SetLineColor(417)
line.DrawLine(0., 1., nbins, 1.)
line.DrawLine(0., -1., nbins, -1.)
line.SetLineColor(800)
line.DrawLine(0., 2., nbins, 2.)
line.DrawLine(0., -2., nbins, -2.)
leg.Draw()
# drawCMS(LUMI, "Simulation")
# drawAnalysis("DM")
# drawRegion(channel)
# canvas.Print(outName+".jpg")
canvas.Print(outName+".png")
canvas.Print(outName+".pdf")
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
def createH2():
h2 = TH1F("h2", "h2", 100, -5, 5)
h2.FillRandom("gaus")
h2.SetLineColor(kRed)
h2.SetLineWidth(2)
return h2
list_PT_T = ["T", "PT"]
energy = ["5", "10", "20", "40"]
for E in range(4):
f1 = ROOT.TFile.Open(
"/Users/ms08962476/singularity/TIming_Studies/tev" + str(energy[E]) +
"mm_pythia6_zprime" + str(energy[E]) +
"tev_qq_with_Eta_cut_for_component_check_1_reco.root", 'r')
f2 = ROOT.TFile.Open(
"/Users/ms08962476/singularity/TIming_Studies/tev" + str(energy[E]) +
"mm_pythia6_zprime" + str(energy[E]) +
"tev_ww_with_Eta_cut_for_component_check_1_reco.root", 'r')
myTree_QQ = f1.Get("BDT_variables_Reco")
myTree_WW = f2.Get("BDT_variables_Reco")
for j in range(2):
h1 = TH1F("QQ_plot0", "QQ_plot0", 20, 0, 0.1)
h2 = TH1F("WW_plot0", "WW_plot0", 20, 0, 0.1)
h3 = TH1F("QQ_plot1", "QQ_plot1", 20, 0, 0.1)
h4 = TH1F("WW_plot1", "WW_plot1", 20, 0, 0.1)
h5 = TH1F("QQ_plot2", "QQ_plot2", 20, 0, 0.1)
h6 = TH1F("WW_plot2", "WW_plot2", 20, 0, 0.1)
h7 = TH1F("QQ_plot3", "QQ_plot3", 20, 0, 0.1)
h8 = TH1F("WW_plot3", "WW_plot3", 20, 0, 0.1)
h9 = TH1F("QQ_plot4", "QQ_plot4", 20, 0, 0.1)
h10 = TH1F("WW_plot4", "WW_plot4", 20, 0, 0.1)
for iii in range(int(myTree_QQ.GetEntriesFast())):
myTree_QQ.GetEntry(iii)
A = myTree_QQ.dR_Tr0T_HPt_Reco
B = myTree_QQ.dR_Tr1T_HPt_Reco
C = myTree_QQ.dR_Tr2T_HPt_Reco
histo = []
for iMPA in range(0, numMPA):
c1.cd(iMPA + 1)
memory = open('data/asynchronous_data_noise_MPA' +
str(iMPA + 1)).read().splitlines()
threshold = int(memory[1])
events = [int(event) for event in memory[3:]
] # Skip first 3 lines, here the threshold is stored
histData = [[hits, events.count(hits)] for hits in set(events)
] # Calculate how often each number of hits occurs
print histData
histo.append(
TH1F(
"h1", "Common noise analysis for MPA" + str(iMPA + 1) +
" at Threshold " + str(threshold), max(events), 0, max(events)))
for hits in histData:
for i in range(0, hits[1]):
histo[iMPA].Fill(hits[0])
XRange = int(histo[iMPA].GetMean() * 2)
histo[iMPA].SetBins(XRange, 0, XRange)
histo[iMPA].GetYaxis().SetTitle("Number of events")
histo[iMPA].GetXaxis().SetTitle("Number of hits")
# Gauss fit
fit = histo[iMPA].Fit("gaus")
histo[iMPA].Draw()
gPad.Update()
### dataframe for output
df_out = DataFrame(columns=[
'run', 'lumi', 'event', 'MET', 'MT', 'Njets_PassID', 'Nbjets_PassID',
'ElePt', 'EleEta', 'ElePhi', 'Jet1Pt', 'Jet1Eta', 'Jet1Phi', 'Jet2Pt',
'Jet2Eta', 'Jet2Phi', 'Jet3Pt', 'Jet3Eta', 'Jet3Phi', 'Jet1Idx', 'Jet2Idx',
'Jet3Idx', 'weight'
])
df_out_wmunu_cr = DataFrame(columns=[
'run', 'lumi', 'event', 'MET', 'MT', 'Njets_PassID', 'Nbjets_PassID',
'Jet1Pt', 'Jet1Eta', 'Jet1Phi', 'Jet2Pt', 'Jet2Eta', 'Jet2Phi', 'Jet3Pt',
'Jet3Eta', 'Jet3Phi', 'Jet1Idx', 'Jet2Idx', 'Jet3Idx', 'MuPt', 'MuEta',
'MuPhi', 'weight'
])
recoil_den = TH1F("recoil_den", "recoil_den", 100, 0.0, 1000.)
recoil_num = TH1F("recoil_num", "recoil_num", 100, 0.0, 1000.)
def WriteHisto():
outputfilename = infile.split("/")[-1]
#fout = TFile(outputfilename, "RECREATE")
fout = TFile("tempout.root", "RECREATE")
fout.cd()
recoil_den.Write()
recoil_num.Write()
jetvariables = [
'st_THINnJet', 'st_THINjetPx', 'st_THINjetPy', 'st_THINjetPz',
'st_THINjetEnergy', 'st_THINjetCISVV2', 'st_THINjetHadronFlavor',
def compare_clusterdb(dblist=[],
histofilename="MyHistos.root",
compare_type="2D0.0D0.1"):
if dblist == []:
return None
dict_sigu = {}
dict_sigv = {}
dict_nlabels = {}
dict_ntypes = {}
dict_coverage = {}
dict_trafo = {}
for dbfilename in dblist:
# open cluster db
dbfile = gROOT.FindObject(dbfilename)
if dbfile:
dbfile.Close()
dbfile = TFile(dbfilename, 'READ')
# read data for comparison plots
swADCSteps = dbfile.Get("DB_swADCSteps")
adclabel = ''
if swADCSteps:
for index in range(0, swADCSteps.GetNrows()):
adclabel = adclabel + 'S' + str(int(swADCSteps[index]))
dict_trafo[dbfilename] = adclabel
print("db sw adc ", dict_trafo[dbfilename])
coverageobj = dbfile.Get("hDB_Coverage")
if coverageobj:
dict_coverage[dbfilename] = coverageobj.GetBinContent(1)
else:
dict_coverage[dbfilename] = -1
print("db coverage ", dict_coverage[dbfilename])
weights = dbfile.Get("hDB_Weight")
dict_nlabels[dbfilename] = weights.GetNbinsX()
print("db labels ", dict_nlabels[dbfilename])
typeset = []
for bin in range(1, weights.GetNbinsX() + 1):
current_type = get_labeltype(
label=weights.GetXaxis().GetBinLabel(bin))
if not current_type in typeset:
typeset.append(current_type)
dict_ntypes[dbfilename] = len(typeset)
print("db types ", dict_ntypes[dbfilename])
histo_sigma2U = dbfile.Get("hDB_Sigma2_U")
histo_sigma2V = dbfile.Get("hDB_Sigma2_V")
weightedSigma2U = 0.0
weightedSigma2V = 0.0
labelNorm = 0.0
for bin in range(1, weights.GetNbinsX() + 1):
current_type = get_labeltype(
label=weights.GetXaxis().GetBinLabel(bin))
if current_type == compare_type:
w = weights.GetBinContent(bin)
labelNorm += w
weightedSigma2U += w * histo_sigma2U.GetBinContent(bin)
weightedSigma2V += w * histo_sigma2V.GetBinContent(bin)
if labelNorm > 0:
weightedSigma2U /= labelNorm
weightedSigma2V /= labelNorm
dict_sigu[dbfilename] = TMath.Sqrt(weightedSigma2U)
dict_sigv[dbfilename] = TMath.Sqrt(weightedSigma2V)
else:
dict_sigu[dbfilename] = 0
dict_sigv[dbfilename] = 0
print("db sigma2 u ", dict_sigu[dbfilename])
print("db sigma2 v ", dict_sigv[dbfilename])
dbfile.Close()
histofile = gROOT.FindObject(histofilename)
if histofile:
histofile.Close()
histofile = TFile(histofilename, 'RECREATE',
'Resolution plots created from ' + dbfilename)
# summary histograms on type resolution
histofile.cd("")
NDB = len(dblist)
hcoverage = TH1F("hcoverage", "", NDB, 0, NDB)
hcoverage.SetStats(0)
hcoverage.SetFillColor(38)
hcoverage.SetYTitle("cluster coverage [%]")
hcoverage.SetXTitle("cluster db")
hntypes = TH1F("hntypes", "", NDB, 0, NDB)
hntypes.SetStats(0)
hntypes.SetFillColor(38)
hntypes.SetYTitle("number of cluster types")
hntypes.SetXTitle("cluster db")
hnlabels = TH1F("hnlabels", "", NDB, 0, NDB)
hnlabels.SetStats(0)
hnlabels.SetFillColor(38)
hnlabels.SetYTitle("number of cluster labels")
hnlabels.SetXTitle("cluster db")
hsigmau = TH1F("hsigmau", "", NDB, 0, NDB)
hsigmau.SetStats(0)
hsigmau.SetFillColor(38)
hsigmau.SetYTitle("cluster sigma u [mm]")
hsigmau.SetXTitle("cluster db")
hsigmav = TH1F("hsigmav", "", NDB, 0, NDB)
hsigmav.SetStats(0)
hsigmav.SetFillColor(38)
hsigmav.SetYTitle("cluster sigma v [mm]")
hsigmav.SetXTitle("cluster db")
for j, dbfilename in enumerate(dblist):
hcoverage.GetXaxis().SetBinLabel(j + 1, str(dict_trafo[dbfilename]))
hntypes.GetXaxis().SetBinLabel(j + 1, str(dict_trafo[dbfilename]))
hnlabels.GetXaxis().SetBinLabel(j + 1, str(dict_trafo[dbfilename]))
hsigmau.GetXaxis().SetBinLabel(j + 1, str(dict_trafo[dbfilename]))
hsigmav.GetXaxis().SetBinLabel(j + 1, str(dict_trafo[dbfilename]))
hcoverage.SetBinContent(j + 1, dict_coverage[dbfilename])
hntypes.SetBinContent(j + 1, dict_ntypes[dbfilename])
hnlabels.SetBinContent(j + 1, dict_nlabels[dbfilename])
hsigmau.SetBinContent(j + 1, dict_sigu[dbfilename])
hsigmav.SetBinContent(j + 1, dict_sigv[dbfilename])
histofile.Write()
histofile.Close()
def plot(path, ecms, xmin, xmax, num_charm):
try:
f_data = TFile(path[0])
t_data = f_data.Get('save')
entries_data = t_data.GetEntries()
logging.info('data entries :' + str(entries_data))
except:
logging.error(path[0] + 'is invalid!')
sys.exit()
try:
f_side1 = TFile(path[1])
t_side1 = f_side1.Get('save')
entries_side1 = t_side1.GetEntries()
logging.info('data(side1) entries :' + str(entries_side1))
except:
logging.error(path[1] + ' is invalid!')
sys.exit()
try:
f_side2 = TFile(path[2])
t_side2 = f_side2.Get('save')
entries_side2 = t_side2.GetEntries()
logging.info('data(side2) entries :' + str(entries_side2))
except:
logging.error(path[2] + ' is invalid!')
sys.exit()
try:
f_side3 = TFile(path[3])
t_side3 = f_side3.Get('save')
entries_side3 = t_side3.GetEntries()
logging.info('data(side3) entries :' + str(entries_side3))
except:
logging.error(path[3] + ' is invalid!')
sys.exit()
try:
f_side4 = TFile(path[4])
t_side4 = f_side4.Get('save')
entries_side4 = t_side4.GetEntries()
logging.info('data(side4) entries :' + str(entries_side4))
except:
logging.error(path[4] + ' is invalid!')
sys.exit()
mbc = TCanvas('mbc', 'mbc', 800, 600)
set_canvas_style(mbc)
xbins = 50
ytitle = 'Eentries'
xtitle = 'Cos(OtherShws)'
h_data = TH1F('data', 'data', xbins, xmin, xmax)
set_histo_style(h_data, xtitle, ytitle, 1, -1)
cos_othershws_fill(t_data, h_data, num_charm)
h_side1 = TH1F('side1', 'side1', xbins, xmin, xmax)
set_histo_style(h_side1, xtitle, ytitle, 3, 3004)
cos_othershws_fill(t_side1, h_side1, num_charm)
h_side2 = TH1F('side2', 'side2', xbins, xmin, xmax)
set_histo_style(h_side2, xtitle, ytitle, 3, 3004)
cos_othershws_fill(t_side2, h_side2, num_charm)
h_side3 = TH1F('side3', 'side3', xbins, xmin, xmax)
set_histo_style(h_side3, xtitle, ytitle, 3, 3004)
cos_othershws_fill(t_side3, h_side3, num_charm)
h_side4 = TH1F('side4', 'side4', xbins, xmin, xmax)
set_histo_style(h_side4, xtitle, ytitle, 3, 3004)
cos_othershws_fill(t_side4, h_side4, num_charm)
h_side1.Add(h_side2)
h_side1.Scale(0.5)
h_side3.Add(h_side4)
h_side3.Scale(0.25)
h_side1.Add(h_side3, -1)
h_data.Draw('E1')
hs = THStack('hs', 'Stacked')
hs.Add(h_side1)
hs.Draw('same')
h_data.Draw('sameE1')
legend = TLegend(0.5, 0.6, 0.8, 0.85)
leg_title = str(ecms) + ' MeV'
set_legend(legend, h_data, h_side1, leg_title)
legend.Draw()
if not os.path.exists('./figs/'):
os.makedirs('./figs/')
mbc.SaveAs('./figs/cos_othershws_' + str(ecms) + '_' + str(num_charm) +
'.pdf')
raw_input('Enter anything to end...')
def findElectrons(opts):
### Load Python modules
import os
import math
import numpy as np
from array import array
from os.path import isdir, abspath
### Load ROOT modules
from ROOT import TClonesArray, TFile, TTree, gSystem, gROOT, AddressOf
from ROOT import TH2F, TH1F, TMath, TGraphAsymmErrors
###Load DAMPE libs
gSystem.Load("libDmpEvent.so")
gSystem.Load("libDmpEventFilter.so")
gSystem.Load("libDmpKernel.so")
gSystem.Load("libDmpService.so")
###Load DAMPE modules
from ROOT import DmpChain, DmpEvent, DmpFilterOrbit, DmpPsdBase, DmpCore
from ROOT import DmpSvcPsdEposCor, DmpVSvc #DmpRecPsdManager
import DMPSW
gROOT.SetBatch(True)
############################# Searching for electrons
####### Reading input files
#Creating DAMPE chain for input files
dmpch = DmpChain("CollectionTree")
#Reading input files
if not opts.input:
files = [f.replace("\n","") for f in open(opts.list,'r').readlines()]
for ifile, f in enumerate(files):
DMPSW.IOSvc.Set("InData/Read" if ifile == 0 else "InData/ReadMore",f)
if os.path.isfile(f):
dmpch.Add(f)
if opts.verbose:
print('\nInput file read: {} -> {}'.format(ifile,f))
else:
DMPSW.IOSvc.Set("InData/Read",opts.input)
if os.path.isfile(opts.input):
dmpch.Add(opts.input)
if opts.verbose:
print('\nInput file read: {}'.format(opts.input))
#Defining the total number of events
nevents = dmpch.GetEntries()
if opts.verbose:
print('\nTotal number of events: {}'.format(nevents))
print("\nPrinting the chain...\n")
dmpch.Print()
####### Setting the output directory to the chain
dmpch.SetOutputDir(abspath(opts.outputDir),"electrons")
####### Processing input files
###Histos
#Defining log binning
#np.logspace binning
nBins=1000
eMax=6
eMin=0
eBinning = np.logspace(eMin, eMax, num=(nBins+1))
#custom binning
'''
nBins = 1000
eMin=0.1
eMax=1000000
EDmax = []
EDEdge = []
EDstepX=np.log10(eMax/eMin)/nBins
for iedge in range(0, nBins):
EDEdge.append(eMin*pow(10,iedge*EDstepX))
EDmax.append(eMin*pow(10,(iedge+1)*EDstepX))
EDEdge.append(EDmax[-1])
Edges= array('d',EDEdge) # this makes a bound array for TH1F
'''
#Pointing
h_terrestrial_lat_vs_long = TH2F("h_terrestrial_lat_vs_long","latitude vs longitude",360,0,360,180,-90,90)
## Energy
h_energy_all = TH1F("h_energy_all","all particle energy",nBins,eBinning)
h_energyCut = TH1F("h_energyCut","all particle energy - 20 GeV cut",nBins,eBinning)
h_energyCut_SAAcut = TH1F("h_energyCut_SAAcut","all particle energy - 20 GeV cut (no SAA)",nBins,eBinning)
h_energyCut_noTrack = TH1F("h_energyCut_noTrack","all particle energy - 20 GeV cut (NO TRACK)",nBins,eBinning)
h_energyCut_Track = TH1F("h_energyCut_Track","all particle energy - 20 GeV cut (TRACK)",nBins,eBinning)
h_energyCut_TrackMatch = TH1F("h_energyCut_TrackMatch","all particle energy - 20 GeV cut (TRACK match)",nBins,eBinning)
##BGO
h_energyBGOl=[] #energy of BGO vertical layer (single vertical plane)
for BGO_idxl in range(14):
histoName = "h_energyBGOl_" + str(BGO_idxl)
histoTitle = "BGO energy deposit layer " + str(BGO_idxl)
tmpHisto = TH1F(histoName,histoTitle,1000,0,1e+6)
h_energyBGOl.append(tmpHisto)
h_energyBGOb = [] #energy of BGO lateral layer (single bars of a plane)
h_BGOb_maxEnergyFraction = [] #fraction of the maximum released energy for each bar on each layer of the BGO calorimeter
for BGO_idxl in range(14):
tmp_eLayer = []
for BGO_idxb in range(23):
histoName = "h_energyBGOl_" + str(BGO_idxl) + "_BGOb_" + str(BGO_idxb)
histoTitle = "BGO energy deposit layer " + str(BGO_idxl) + " bar " + str(BGO_idxb)
tmpHisto = TH1F(histoName,histoTitle,1000,0,1e+6)
tmp_eLayer.append(tmpHisto)
maxhistoName = "h_BGO_maxEnergyFraction_l_" + str(BGO_idxl)
maxhistoTitle = "fraction of the maximum released energy layer " + str(BGO_idxl)
tmpMaxHisto = TH1F(maxhistoName,maxhistoTitle,100,0,1)
h_BGOb_maxEnergyFraction.append(tmpMaxHisto)
h_energyBGOb.append(tmp_eLayer)
h_BGOl_maxEnergyFraction = TH1F("h_BGOl_maxEnergyFraction","Fraction of the maximum released energy",100,0,1)
h_thetaBGO = TH1F("h_thetaBGO","theta BGO",100,0,90)
##STK
h_STK_nTracks = TH1F("h_STK_nTracks","number of tracks",1000,0,1000)
h_STK_trackChi2norm = TH1F("h_STK_trackChi2norm","\chi^2/n track",100,0,200)
h_STK_nTracksChi2Cut = TH1F("h_STK_nTracksChi2Cut","number of tracks (\chi^2 cut)",1000,0,1000)
h_stk_cluster_XvsY = []
for iLayer in range(6):
hName = 'h_stkCluster_XvsY_l_'+str(iLayer)
hTitle = 'cluster X vs Y - plane '+str(iLayer)
tmpHisto = TH2F(hName,hTitle,1000,-500,500,1000,-500,500)
h_stk_cluster_XvsY.append(tmpHisto)
h_ThetaSTK = TH1F("h_ThetaSTK","theta STK",100,0,90)
h_deltaTheta = TH1F("h_deltaTheta","\Delta theta",500,-100,100)
h_resX_STK_BGO = TH1F("h_resX_STK_BGO","BGO/STK residue layer X",200,-1000,1000)
h_resY_STK_BGO = TH1F("h_resY_STK_BGO","BGO/STK residue layer Y",200,-1000,1000)
h_imapctPointSTK = TH2F("h_imapctPointSTK","STK impact point",1000,-500,500,1000,-500,500)
h_stk_chargeClusterX = TH1F("h_stk_chargeClusterX","STK charge on cluster X",10000,0,10000)
h_stk_chargeClusterY = TH1F("h_stk_chargeClusterY","STK charge on cluster Y",10000,0,10000)
##PSD
h_psd_ChargeX = []
for lidx in range (2):
histoName = "h_psd_ChargeX_l" + str(lidx)
histoTitle = "PSD X charge layer " + str(lidx)
tmpHisto = TH1F(histoName,histoTitle,10000,0,10000)
h_psd_ChargeX.append(tmpHisto)
h_psd_ChargeY = []
for lidx in range (2):
histoName = "h_psd_ChargeY_l" + str(lidx)
histoTitle = "PSD Y charge layer " + str(lidx)
tmpHisto = TH1F(histoName,histoTitle,10000,0,10000)
h_psd_ChargeY.append(tmpHisto)
###
### Analysis cuts
eCut = 50 #Energy cut in GeV
### DAMPE geometry
BGOzTop = 46.
BGOzBot = 448.
#Filtering for SAA
if not opts.mc:
DMPSW.IOSvc.Set("OutData/NoOutput", "True")
DMPSW.IOSvc.Initialize()
pFilter = DmpFilterOrbit("EventHeader")
pFilter.ActiveMe()
#Starting loop on files
if opts.debug:
if opts.verbose:
print('\nDebug mode activated... the number of chain events is limited to 1000')
nevents = 1000
for iev in xrange(nevents):
if opts.mc:
DmpVSvc.gPsdECor.SetMCflag(1)
pev=dmpch.GetDmpEvent(iev)
#Get latitude and longitude
longitude = pev.pEvtAttitude().lon_geo
latitude = pev.pEvtAttitude().lat_geo
#Get particle total energy
etot=pev.pEvtBgoRec().GetTotalEnergy()/1000.
h_energy_all.Fill(etot)
if etot < eCut:
continue
h_energyCut.Fill(etot)
#Get BGO energy deposit for each layer (vertical BGO shower profile)
v_bgolayer = np.array([pev.pEvtBgoRec().GetELayer(ibgo) for ibgo in range(14)])
for BGO_idxl in range(14):
h_energyBGOl[BGO_idxl].Fill(v_bgolayer[BGO_idxl])
#Get BGO energy deposit for each bar (lateral BGO shower profile) of each layer
for ilay in xrange(0,14):
v_bgolayer_bars = np.array([pev.pEvtBgoRec().GetEdepPos(ilay,ibar) for ibar in xrange(0,23)])
#Fraction of the maximum energy deposit of the particle crossing the BGO on a certain layer (single bars)
h_BGOb_maxEnergyFraction[ilay].Fill(np.max(v_bgolayer_bars)/1000./etot)
for idx_BGOb in range (23):
h_energyBGOb[ilay][idx_BGOb].Fill(v_bgolayer_bars[idx_BGOb])
#Fraction of the maximum energy deposit of the particle crossing the BGO
h_BGOl_maxEnergyFraction.Fill(np.max(v_bgolayer)/1000./etot)
#BGO acceptance projection
projectionX_BGO_BGOTop = pev.pEvtBgoRec().GetInterceptXZ() +BGOzTop * pev.pEvtBgoRec().GetSlopeXZ()
projectionY_BGO_BGOTop = pev.pEvtBgoRec().GetInterceptYZ() +BGOzTop * pev.pEvtBgoRec().GetSlopeYZ()
#SAA filter
if not opts.mc:
inSAA = pFilter.IsInSAA(pev.pEvtHeader().GetSecond())
#inSAA = False
if (inSAA):
continue
h_energyCut_SAAcut.Fill(etot)
h_terrestrial_lat_vs_long.Fill(longitude,latitude)
tgZ = math.atan(np.sqrt( (pev.pEvtBgoRec().GetSlopeXZ()*pev.pEvtBgoRec().GetSlopeXZ()) + (pev.pEvtBgoRec().GetSlopeYZ()*pev.pEvtBgoRec().GetSlopeYZ()) ) );
theta_bgo = tgZ*180./math.pi
h_thetaBGO.Fill(theta_bgo)
#Tracks
ntracks = pev.NStkKalmanTrack()
if ntracks < 0:
print "\nTRACK ERROR: number of tracks < 0 - ABORTING\n"
break
if ntracks == 0:
h_energyCut_noTrack.Fill(etot)
h_STK_nTracks.Fill(ntracks)
h_energyCut_Track.Fill(etot)
res_X_min = 1000
res_Y_min = 1000
trackID_X = -9
trackID_Y = -9
lTrackIDX = []
lTrackIDY = []
residueXmin = []
residueYmin = []
#Loop on STK tracks to get the STK charge measurement
for iTrack in range(ntracks):
tmpTrack = pev.pStkKalmanTrack(iTrack)
chi2_norm = tmpTrack.getChi2()/(tmpTrack.getNhitX()+tmpTrack.getNhitY()-4)
h_STK_trackChi2norm.Fill(chi2_norm)
if chi2_norm > 25:
continue
h_STK_nTracksChi2Cut.Fill(ntracks)
l0ClusterX = l0ClusterY = False
for iCluster in range(tmpTrack.GetNPoints()):
clux = tmpTrack.pClusterX(iCluster)
cluy = tmpTrack.pClusterY(iCluster)
if clux and clux.getPlane() == 0:
l0ClusterX = True
if cluy and cluy.getPlane() == 0:
l0ClusterY = True
# check plot for the dead region of STK
if(clux and cluy):
h_stk_cluster_XvsY[clux.getPlane()].Fill(clux.GetX(),cluy.GetY())
if l0ClusterX == False and l0ClusterY == False:
continue
#### Tracks characteristics
theta_stk =math.acos(tmpTrack.getDirection().CosTheta())*180./math.pi;
delta_theta_STK_BGO = theta_stk - theta_bgo
#STK impact point
trackImpactPointX = tmpTrack.getImpactPoint().x()
trackImpactPointY = tmpTrack.getImpactPoint().y()
#Track projections
trackProjX = tmpTrack.getDirection().x()*(BGOzTop - tmpTrack.getImpactPoint().z()) + tmpTrack.getImpactPoint().x()
trackProjY = tmpTrack.getDirection().y()*(BGOzTop - tmpTrack.getImpactPoint().z()) + tmpTrack.getImpactPoint().y()
#Track residues
resX_STK_BGO = projectionX_BGO_BGOTop - trackProjX
resY_STK_BGO = projectionY_BGO_BGOTop - trackProjY
resX_STK_BGO_top = trackImpactPointX - (pev.pEvtBgoRec().GetInterceptXZ() + tmpTrack.getImpactPoint().z() * pev.pEvtBgoRec().GetSlopeXZ())
resY_STK_BGO_top = trackImpactPointY - (pev.pEvtBgoRec().GetInterceptYZ() + tmpTrack.getImpactPoint().z() * pev.pEvtBgoRec().GetSlopeYZ())
####
h_ThetaSTK.Fill(theta_stk)
h_deltaTheta.Fill(delta_theta_STK_BGO)
h_imapctPointSTK.Fill(trackImpactPointX,trackImpactPointY)
h_resX_STK_BGO.Fill(tmpTrack.getImpactPoint().x() - (pev.pEvtBgoRec().GetInterceptXZ() + tmpTrack.getImpactPoint().z() * pev.pEvtBgoRec().GetSlopeXZ()))
h_resY_STK_BGO.Fill(tmpTrack.getImpactPoint().y() - (pev.pEvtBgoRec().GetInterceptYZ() + tmpTrack.getImpactPoint().z() * pev.pEvtBgoRec().GetSlopeYZ()))
if abs(theta_stk - theta_bgo) > 25:
continue
#Selecting good tracks for charge measurement
if abs(resX_STK_BGO_top) < 200 and abs(resX_STK_BGO) < 60:
lTrackIDX.append(tmpTrack)
residueXmin.append(res_X_min)
if res_X_min > abs(resX_STK_BGO_top):
res_X_min = abs(resX_STK_BGO_top)
trackID_X = iTrack
if abs(resY_STK_BGO_top) < 200 and abs(resY_STK_BGO) < 60:
lTrackIDY.append(tmpTrack)
residueYmin.append(res_Y_min)
if res_Y_min > abs(resY_STK_BGO_top):
res_Y_min = abs(resY_STK_BGO_top)
trackID_Y = iTrack
if(trackID_X == -9):
continue
if(trackID_Y == -9):
continue
track_ID = -9
#print trackID_X
if(trackID_X == trackID_Y):
track_ID = trackID_X
else:
trackX = pev.pStkKalmanTrack(trackID_X)
trackY = pev.pStkKalmanTrack(trackID_Y)
chi2X = trackX.getChi2() /(trackX.getNhitX()+trackX.getNhitY()-4);
chi2Y = trackY.getChi2() /(trackY.getNhitX()+trackY.getNhitY()-4);
npointX = trackX.GetNPoints()
npointY = trackY.GetNPoints()
if(npointX == npointY or abs(npointX - npointY) == 1):
if(chi2X < chi2Y):
if trackID_X in lTrackIDY:
track_ID = trackID_X
elif trackID_Y in lTrackIDX:
track_ID = trackID_Y
else:
common_id = list(set(lTrackIDX).intersection(lTrackIDY))
searchForTrack(
common_id,
lTrackIDX,
lTrackIDY,
residueXmin,
residueYmin,
track_ID
)
else:
if trackID_Y in lTrackIDX:
track_ID = trackID_Y
elif trackID_X in lTrackIDY:
track_ID = trackID_X
else:
common_id = list(set(lTrackIDX).intersection(lTrackIDY))
searchForTrack(
common_id,
lTrackIDX,
lTrackIDY,
residueXmin,
residueYmin,
track_ID
)
else:
if(npointX > npointY):
if trackID_X in lTrackIDY:
track_ID = trackID_X
elif trackID_Y in lTrackIDX:
track_ID = trackID_Y
else:
common_id = list(set(lTrackIDX).intersection(lTrackIDY))
searchForTrack(
common_id,
lTrackIDX,
lTrackIDY,
residueXmin,
residueYmin,
track_ID
)
else:
if trackID_Y in lTrackIDX:
track_ID = trackID_Y
elif trackID_X in lTrackIDY:
track_ID = trackID_X
else:
common_id = list(set(lTrackIDX).intersection(lTrackIDY))
searchForTrack(
common_id,
lTrackIDX,
lTrackIDY,
residueXmin,
residueYmin,
track_ID
)
if(track_ID == -9):
continue
h_energyCut_TrackMatch.Fill(etot)
#Select the matched track
track_sel = pev.pStkKalmanTrack(track_ID)
theta_track_sel =math.acos(track_sel.getDirection().CosTheta())*180./math.pi;
deltaTheta_rec_sel = theta_bgo - theta_track_sel
track_correction = track_sel.getDirection().CosTheta();
cluChargeX = -1000
cluChargeY = -1000
for iclu in xrange(0,track_sel.GetNPoints()):
clux = track_sel.pClusterX(iclu)
cluy = track_sel.pClusterY(iclu)
if (clux and clux.getPlane() == 0):
cluChargeX = clux.getEnergy()*track_correction
if (cluy and cluy.getPlane() == 0):
cluChargeY = cluy.getEnergy()*track_correction
h_stk_chargeClusterX.Fill(cluChargeX)
h_stk_chargeClusterY.Fill(cluChargeY)
#Loop on PSD hits to get PSD charge measurement
'''
#PSD fiducial volume cut
psd_YZ_top = -324.7
psd_XZ_top = -298.5
stk_to_psd_topY = (track_sel.getDirection().y()*(psd_YZ_top - track_sel.getImpactPoint().z()) + track_sel.getImpactPoint().y())
stk_to_psd_topX = (track_sel.getDirection().x()*(psd_XZ_top - track_sel.getImpactPoint().z()) + track_sel.getImpactPoint().x())
if(abs(stk_to_psd_topX) > 400.):
continue
if(abs(stk_to_psd_topY) > 400.):
continue
'''
PSDXlayer0 = -298.5
PSDXlayer1 = -284.5
PSDYlayer0 = -324.7
PSDYlayer1 = -310.7
psdChargeX = [[]for _ in range(2)]
psdGIDX = [[]for _ in range(2)]
psdPathlengthX = [[]for _ in range(2)]
psdPositionX = [[]for _ in range(2)]
psdChargeY = [[]for _ in range(2)]
psdGIDY = [[]for _ in range(2)]
psdPathlengthY = [[]for _ in range(2)]
psdPositionY = [[]for _ in range(2)]
for lPSD in xrange(0,pev.NEvtPsdHits()):
if pev.pEvtPsdHits().IsHitMeasuringX(lPSD):
crossingX = False
lenghtX = [-99999.,-99999.]
array_lenghtX = array('d',lenghtX)
if(pev.pEvtPsdHits().GetHitZ(lPSD) == PSDXlayer0):
npsdX = 0
if(pev.pEvtPsdHits().GetHitZ(lPSD)== PSDXlayer1):
npsdX = 1
if not opts.mc:
crossingX = DmpVSvc.gPsdECor.GetPathLengthPosition(pev.pEvtPsdHits().fGlobalBarID[lPSD],track_sel.getDirection(),track_sel.getImpactPoint(), array_lenghtX)
if crossingX:
psdChargeX[npsdX].append(pev.pEvtPsdHits().fEnergy[lPSD])
psdGIDX[npsdX].append(pev.pEvtPsdHits().fGlobalBarID[lPSD])
psdPathlengthX[npsdX].append(array_lenghtX[1])
psdPositionX[npsdX].append(pev.pEvtPsdHits().GetHitX(lPSD))
elif pev.pEvtPsdHits().IsHitMeasuringY(lPSD):
crossingY = False
lenghtY = [-99999.,-99999.]
array_lenghtY = array('d',lenghtY)
if(pev.pEvtPsdHits().GetHitZ(lPSD) == PSDYlayer0):
npsdY = 0
if(pev.pEvtPsdHits().GetHitZ(lPSD)== PSDYlayer1):
npsdY = 1
if not opts.mc:
crossingY = DmpVSvc.gPsdECor.GetPathLengthPosition(pev.pEvtPsdHits().fGlobalBarID[lPSD],track_sel.getDirection(),track_sel.getImpactPoint(), array_lenghtY)
if crossingY:
psdChargeY[npsdY].append(pev.pEvtPsdHits().fEnergy[lPSD])
psdGIDY[npsdY].append(pev.pEvtPsdHits().fGlobalBarID[lPSD])
psdPathlengthY[npsdY].append(array_lenghtY[1])
psdPositionY[npsdY].append(pev.pEvtPsdHits().GetHitY(lPSD))
'''
print psdChargeX
print psdGIDX
print psdPathlengthX
print psdPositionX
print psdChargeY
print psdGIDY
print psdPathlengthY
print psdPositionY
'''
psdFinalChargeX = [-999,-999]
psdFinalChargeY = [-999,-999]
#psdFinalChargeX_corr = [-999,-999]
#psdFinalChargeY_corr = [-999,-999]
psdFinalChargeX_proj = [-999,-999]
psdFinalChargeY_proj = [-999,-999]
psdX_pathlength = [-999,-999]
psdY_pathlength = [-999,-999]
psdX_position = [-999,-999]
psdY_position = [-999,-999]
PsdEC_tmpX = 0.
PsdEC_tmpY = 0.
for ipsd in xrange(0,2):
if(len(psdChargeY[ipsd]) > 0):
pos_max_len = np.argmax(psdPathlengthY[ipsd])
lenghtY = [-99999.,-99999.]
array_lenghtY = array('d',lenghtY)
test_pos = False
if not opts.mc:
test_pos = DmpVSvc.gPsdECor.GetPathLengthPosition(psdGIDY[ipsd][pos_max_len],track_sel.getDirection(),track_sel.getImpactPoint(), array_lenghtY)
'''
PsdEC_tmpY = -1.
if test_pos:
PsdEC_tmpY = DmpVSvc.gPsdECor.GetPsdECorSp3(psdGIDY[ipsd][pos_max_len], array_lenghtY[0])
'''
psdFinalChargeY[ipsd] = psdChargeY[ipsd][pos_max_len]
h_psd_ChargeY[ipsd].Fill(psdFinalChargeY[ipsd])
#psdFinalChargeY_corr[ipsd] = psdChargeY[ipsd][pos_max_len]*PsdEC_tmpY
psdFinalChargeY_proj[ipsd] = array_lenghtY[0]
psdY_pathlength[ipsd] = array_lenghtY[1]
psdY_position[ipsd] = psdPositionY[ipsd][pos_max_len]
if(len(psdChargeX[ipsd]) > 0):
pos_max_len = np.argmax(psdPathlengthX[ipsd])
lenghtX = [-99999.,-99999.]
array_lenghtX = array('d',lenghtX)
test_pos = False
if not opts.mc:
test_pos = DmpVSvc.gPsdECor.GetPathLengthPosition(psdGIDX[ipsd][pos_max_len],track_sel.getDirection(),track_sel.getImpactPoint(), array_lenghtY)
'''
PsdEC_tmpY = -1.
if test_pos:
PsdEC_tmpX = DmpVSvc.gPsdECor.GetPsdECorSp3(psdGIDX[ipsd][pos_max_len], array_lenghtX[0])
'''
psdFinalChargeX[ipsd] = psdChargeX[ipsd][pos_max_len]
h_psd_ChargeX[ipsd].Fill(psdFinalChargeX[ipsd])
#psdFinalChargeX_corr[ipsd] = psdChargeX[ipsd][pos_max_len]*PsdEC_tmpX
psdFinalChargeX_proj[ipsd] = array_lenghtX[0]
psdX_pathlength[ipsd] = array_lenghtX[1]
psdX_position[ipsd] = psdPositionX[ipsd][pos_max_len]
### Writing output files to file
if opts.data:
tf_skim = TFile(opts.outputFile,"RECREATE")
h_energy_all.Write()
h_energyCut.Write()
h_energyCut_SAAcut.Write()
h_energyCut_noTrack.Write()
h_energyCut_Track.Write()
h_energyCut_TrackMatch.Write()
for BGO_idxl in range(14):
h_energyBGOl[BGO_idxl].Write()
h_BGOb_maxEnergyFraction[BGO_idxl].Write()
for BGO_idxb in range(23):
h_energyBGOb[BGO_idxl][BGO_idxb].Write()
h_thetaBGO.Write()
h_BGOl_maxEnergyFraction.Write()
h_terrestrial_lat_vs_long.Write()
h_STK_nTracks.Write()
h_STK_trackChi2norm.Write()
h_STK_nTracksChi2Cut.Write()
for iLayer in range(6):
h_stk_cluster_XvsY[iLayer].Write()
h_ThetaSTK.Write()
h_deltaTheta.Write()
h_imapctPointSTK.Write()
h_resX_STK_BGO.Write()
h_resY_STK_BGO.Write()
h_stk_chargeClusterX.Write()
h_stk_chargeClusterY.Write()
h_psd_ChargeX[0].Write()
h_psd_ChargeX[1].Write()
h_psd_ChargeY[0].Write()
h_psd_ChargeY[1].Write()
tf_skim.Close()
def get_result(result, f, model):
try:
if match('^overlapIntegral$', result):
val = f.get('overlap_true').GetVal()
err = f.get('overlap_rms').GetVal()
if val < 0.0:
hist = TH1F('thisoverlap', '', 1000, 0.0, 1.0)
f.Get('corrTree').Draw('overlapTrue>>thisoverlap', '', 'goff')
val = hist.GetMean()
err = hist.GetRMS()
return '{:.3e}<br />±{:.3e}'.format(val, err)
elif match('^overlapDiff$', result):
try:
hist = f.get('overlap_diff')
except NameError:
f.Get('corrTree').Draw('overlapDiff>>hnew', '', 'goff')
val = gDirectory.Get('hnew').GetMean()
mini, maxi = val-0.5, val+0.5
hist = TH1F('myoverlap', '', 1000, mini, maxi)
f.Get('corrTree').Draw('overlapDiff>>myoverlap', '', 'goff')
val = hist.GetMean()*100.0
err = hist.GetRMS()*100.0
return '{:.3f}%<br />±{:.3f}%'.format(val, err)
elif match('^randomized ovDiff$', result):
hist = TH1F('myoverlap', '', 1000, -0.5, 0.5)
f.Get('rndmzd_corrTree').Draw('overlapDiff>>myoverlap', '', 'goff')
val = hist.GetMean()*100.0
err = hist.GetRMS()*100.0
return '{:.3f}%<br />±{:.3f}%'.format(val, err)
elif match('^chiSq$', result):
val = (
f.get('chisqX1').GetVal() + f.get('chisqX2').GetVal()
+ f.get('chisqY1').GetVal() + f.get('chisqY2').GetVal()
)
return '{:.0f}'.format(val)
elif match('^d.o.f.$', result):
val = (
f.get('dofX1').GetVal() + f.get('dofX2').GetVal()
+ f.get('dofY1').GetVal() + f.get('dofY2').GetVal()
)
return '{:.0f}'.format(val)
elif match('chiSq/dof', result):
val = (
f.get('chisqX1').GetVal() + f.get('chisqX2').GetVal()
+ f.get('chisqY1').GetVal() + f.get('chisqY2').GetVal()
) / (
f.get('dofX1').GetVal() + f.get('dofX2').GetVal()
+ f.get('dofY1').GetVal() + f.get('dofY2').GetVal()
- model.dof()
)
return '{:.4f}'.format(val)
elif match('^time of fit$', result):
return f.get('timestamp').GetTitle()
elif match('^time of simulation$', result):
return f.get('corrTimestamp').GetTitle()
elif match('^time of residuals$', result):
return f.get('resTimestamp').GetTitle()
elif match('^time of randomization$', result):
return f.get('rndmzdTimestamp').GetTitle()
elif match('^neg.log.lik.$', result):
val = f.get('fitResult').minNll()
return '{:e}'.format(val)
elif match('^scaling$', result):
val = f.get('scaling').GetVal()*1.0e4
return '1={:.2f}µm'.format(val)
else:
raise NameError()
except NameError:
return ''
except TypeError:
return ''
def plot( sample, n, v, sel, hbins, hmin, hmax, hlog, xlabel, ylabel, dim ):
#hlog=False;
global output
file = {}
tree = {}
hist = {}
leaf = {}
xsec = {}
max = 0
min = 1e99
ROOT.gStyle.SetOptStat(1111)
ROOT.gROOT.SetBatch(True)
for i, s in enumerate(sample):
file[s] = TFile( input + s + ".root", "READ")
tree[s] = file[s].Get("Physics")
if dim==1:
hist[s] = TH1F(s, ";"+v, hbins, hmin , hmax)
tree[s].Project(s, v, "%s"%sel)
leaf[s] = tree[s].GetLeaf("xsec1")
leaf[s].GetBranch().GetEntry(1)
xsec[s] = leaf[s].GetValue()
hist[s].SetLineColor(colour[i])
hist[s].SetLineWidth(2)#3
hist[s].SetFillColorAlpha(colour[i],0.35)
hist[s].SetFillStyle(3005)
if hist[s].GetMaximum() > max: max = hist[s].GetMaximum()*6
if hist[s].GetMinimum() < min: min = hist[s].GetMinimum()
#leg = TLegend(0.4, 0.9-0.035*len(sample), 0.68, 0.89)
c1 = TCanvas("c1", "Gen", 1600, 1200)
c1.cd()
hist[sample[0]].SetMaximum(max*1.2)
hist[sample[0]].SetMinimum(min+1.e6)
hist[sample[0]].GetXaxis().SetTitle("%s" %xlabel)
hist[sample[0]].GetYaxis().SetTitle("%s" %ylabel)
hist[sample[0]].SetTitle("%s" %n)
if len(sample)>1:
for i, s in enumerate(sample):
hist[s].Draw("HIST" if i==0 else "HIST, SAME")
else:
hist[s].Draw("HIST")
if hlog:
c1.GetPad(0).SetLogy()
elif dim==2:
if len(hbins)!=2 or len(hmin)!=2 or len(hmax)!=2:
print "dimension of hbins, hmin, hmax does not correspond to\
2 dimensional histogram parameters."; exit;
else:
#X axis parameters follow by Y axis parameters
hist[s] = TH2F( s, ";"+v, hbins[0], hmin[0] , hmax[0], hbins[1], hmin[1] , hmax[1] )
# v in the form of x:y
tree[s].Project(s, v, "%s"%sel,"colz")
c1 = TCanvas("c1", "Gen", 1600, 1200)
c1.cd()
hist[s].Draw("COLZ")
#if hlog:
# c1.GetPad(0).SetLogy()
# c1.GetPad(0).SetLogx()
else:
print "Unkown dimension"
exit;
#leg.Draw()
c1.Update()
drawlabel( 0.37 , 0.934 , "CMS Simulation" )
output+="VH/"+sample[0]+"/"
if not hlog:
output+="Lin/"
elif hlog:
output+="Log/"
if not os.path.exists(output):
os.makedirs(output)
c1.Print( output + n + ".pdf")
c1.Print( output + n + ".png")
def defineHisto(self):
self.h_total.append(TH1F('h_total', 'h_total', 2, 0, 2))
self.h_total_weight.append(
TH1F('h_total_weight', 'h_total_weight', 2, 0, 2))
self.h_npass.append(TH1F('h_npass', 'h_nass', 2, 0, 2))
# self.h_cutflow=TH1F('h_cutflow_','h_cutflow_',7, 0, 7) # Cutflow
self.h_met.append(TH1F('h_met_', 'h_met_', 1000, 0., 1000.))
#metbins_ = [200,350,500,1000]
#self.h_met_rebin.append(TH1F('h_met_rebin_'+postname, 'h_met_rebin'+postname, 3, array(('d'),metbins_)))
#self.h_mass.append(TH1F('h_mass_'+postname, 'h_mass_'+postname, 400,0.,400.))
self.h_met_vs_mass.append(
TH2F('h_met_vs_mass_', 'h_met_vs_mass_', 1000, 0., 1000., 250, 0,
250.))
# self.h_csv1.append(TH1F('h_csv1_', 'h_csv1_', 20,0.,1.))
# self.h_csv2.append(TH1F('h_csv2_', 'h_csv2_', 20,0.,1.))
#self.h_mt.append(TH1F('h_mt_'+postname,'h_mt_'+postname,100,400.,1400.))
#self.h_dPhi.append(TH1F('h_dPhi_'+postname,'h_dPhi_'+postname,70, -3.5, 3.5 ))
self.h_N_e.append(TH1F('h_N_e_', 'h_N_e_', 5, 0, 5))
self.h_N_mu.append(TH1F('h_N_mu_', 'h_N_mu_', 5, 0, 5))
self.h_N_tau.append(TH1F('h_N_tau_', 'h_N_tau_', 5, 0, 5))
self.h_N_Pho.append(TH1F('h_N_Pho_', 'h_N_Pho_', 5, 0, 5))
self.h_N_b.append(TH1F('h_N_b_', 'h_N_b_', 10, 0, 10))
self.h_N_j.append(TH1F('h_N_j_', 'h_N_j_', 10, 0, 10))
allquantlist = AllQuantList.getAll()
preselquantlist = AllQuantList.getPresel()
regquants = AllQuantList.getRegionQuants()
def getBins(quant):
if 'eta' in quant:
bins = '30'
low = '-3'
high = '3'
elif 'dPhi' in quant:
bins = '32'
low = '0'
high = '3.2'
elif 'phi' in quant:
bins = '64'
low = '-3.2'
high = '3.2'
elif 'csv' in quant:
bins = '50'
low = '0.'
high = '1.'
elif 'iso' in quant:
bins = '50'
low = '0.'
high = '0.25'
elif 'Zmass' in quant:
bins = '60'
low = '70.'
high = '110.'
elif 'Wmass' in quant:
bins = '80'
low = '0.'
high = '400.'
elif 'met' in quant:
bins = '40'
low = '0.'
high = '2000.'
elif 'nca15jet' in quant:
bins = '5'
low = '0'
high = '5'
elif 'nak8jet' in quant:
bins = '5'
low = '0'
high = '5'
elif 'bb_Mass' in quant:
bins = '25'
low = '0.0'
high = '250.'
elif 'chf' in quant or 'nhf' in quant or 'EF' in quant:
bins = '40'
low = '0.'
high = '1.'
elif 'njet' in quant:
bins = '12'
low = '0'
high = '12'
elif 'ntau' in quant or 'npho' in quant or 'nele' in quant or 'nmu' in quant or 'nUnclean' in quant:
bins = '6'
low = '0'
high = '6'
elif 'recoil' in quant:
bins = '40'
low = '0.'
high = '2000.'
elif '_dR_' in quant:
bins = '60'
low = '0.'
high = '6.'
elif 'lep1_pT' in quant or 'jet2_pT' in quant:
bins = '100'
low = '0.'
high = '1000.'
elif 'lep2_pT' in quant:
bins = '200'
low = '0.'
high = '1000.'
elif 'dr_jet_sr2' in quant or 'dr_jet_sr1' in quant:
bins = '400'
low = '0.'
high = '4.'
elif 'PV' in quant:
bins = '100'
low = '0.'
high = '100.'
elif 'syst' in quant:
bins = '40'
low = '0.'
high = '2000.'
else: # for pT, mass, etc.
bins = '50'
low = '0.'
high = '1000.'
return bins, low, high
for quant in allquantlist:
bins, low, high = getBins(quant)
exec("self.h_" + quant + ".append(TH1F('h_" + quant + "_','h_" +
quant + "_'," + bins + "," + low + "," + high + "))")
for quant in preselquantlist:
bins, low, high = getBins(quant)
exec("self.h_" + quant + ".append(TH1F('h_" + quant + "_','h_" +
quant + "_'," + bins + "," + low + "," + high + "))")
for quant in regquants:
bins, low, high = getBins(quant)
exec("self.h_" + quant + ".append(TH1F('h_" + quant + "_','h_" +
quant + "_'," + bins + "," + low + "," + high + "))")
def getBins2D(quant):
ZpTbins = '50'
ZpTlow = '0.'
ZpThigh = '500.'
Rbins = '15'
Rlow = '200.'
Rhigh = '500.'
Mbins = '20'
Mlow = '0.'
Mhigh = '500.'
if 'ZpT_Recoil' in quant:
return ZpTbins, ZpTlow, ZpThigh, Rbins, Rlow, Rhigh
elif 'ZpT_MET' in quant:
return ZpTbins, ZpTlow, ZpThigh, Mbins, Mlow, Mhigh
elif 'MET_Recoil' in quant:
return Mbins, Mlow, Mhigh, Rbins, Rlow, Rhigh
Histos2D = AllQuantList.getHistos2D()
for quant in Histos2D:
xbins, xlow, xhigh, ybins, ylow, yhigh = getBins2D(quant)
exec("self.h_" + quant + ".append(TH2F('h_" + quant + "_','h_" +
quant + "_'," + xbins + "," + xlow + "," + xhigh + "," +
ybins + "," + ylow + "," + yhigh + "))")
h_met_pdf_tmp = []
for ipdf in range(2):
midname = str(ipdf)
h_met_pdf_tmp.append(
TH1F('h_met_pdf' + '_' + midname + '_', 'h_met_pdf', 1000, 0.,
1000.))
self.h_met_pdf.append(h_met_pdf_tmp)
h_met_muR_tmp = []
for imuR in range(2):
midname = str(imuR)
h_met_muR_tmp.append(
TH1F('h_met_muR' + '_' + midname + '_', 'h_met_muR', 1000, 0.,
1000.))
self.h_met_muR.append(h_met_muR_tmp)
h_met_muF_tmp = []
for imuF in range(2):
midname = str(imuF)
h_met_muF_tmp.append(
TH1F('h_met_muF' + '_' + midname + '_', 'h_met_muF', 1000, 0.,
1000.))
self.h_met_muF.append(h_met_muF_tmp)
print "Histograms defined"
