def createNtupleInputMaker(filename,
treename="",
inputprefix="GUESS",
Nvar="",
momentumVars=(),
inputType=None,
masslessMode=False,
inputsuffix=""):
""" Create & configure a NtupleInputMaker class
The function will try to guess eveything from the given filename
TODO: Figure out input name in more cases.
"""
from ROOT import TFile, TTree
syncMessageLevel()
f = TFile(filename)
if not f.IsOpen():
m_log.error('Could not open ROOT file with name ', filename)
return
# ---------------------------------
# get tree
tree = None
if treename == "":
# use the first TTree found
keys = [k.GetName() for k in f.GetListOfKeys()]
for k in keys:
t = f.Get(k)
if isinstance(t, TTree):
tree = t
treename = k
break
else:
tree = f.Get(treename)
if not bool(tree):
m_log.error("Couldn't find tree in ", filename)
return
# ---------------------------------
branches = [b.GetName() for b in tree.GetListOfBranches()]
# ---------------------------------
# Guess input prefix if not set
if inputprefix == "GUESS":
# branches, lower case
for bn in branches:
# we'll look for vars containing 'input'
if 'input' in bn.lower() and '_' in bn:
inputprefix = bn[:bn.find('_')] # get the part before '_'
break
# we'll look for vars without prefix'
if bn.lower() == 'eta' or bn.lower() == 'px':
inputprefix = ''
break
if inputprefix == "GUESS":
m_log.error("Couldn't guess proper prefix for input variables")
return
else:
m_log.info("Found prefix input = " + inputprefix)
# ---------------------------------
if inputprefix == '':
# retrieve all variables starting with inputprefix
branches = [b for b in branches if '_' not in b]
# retrieve all vars from the branch name above : the XX part in bla_XX
vars = dict([(b.lower(), b) for b in branches])
else:
if not inputprefix.endswith('_'): inputprefix += '_'
# retrieve all variables starting with inputprefix
branches = [b for b in branches if b.startswith(inputprefix)]
# retrieve all vars from the branch name above : the XX part in bla_XX
vars = dict([(b[b.find('_') + 1:].lower(), b[b.find('_') + 1:])
for b in branches])
# ---------------------------------
# Guess kinematic variables
if momentumVars == ():
# try px,py,pz,e
vars_set = set(vars.keys())
if vars_set.issuperset(set(['px', 'py', 'pz', 'e'])):
momentumVars = tuple([vars[k] for k in ('px', 'py', 'pz', 'e')])
elif vars_set.issuperset(set(['eta', 'phi', 'pt', 'e'])):
momentumVars = tuple([vars[k] for k in ('eta', 'phi', 'pt', 'e')])
elif vars_set.issuperset(set(['eta', 'phi', 'p_t', 'e'])):
momentumVars = tuple([vars[k] for k in ('eta', 'phi', 'p_t', 'e')])
elif vars_set.issuperset(set(['eta', 'phi', 'pt', 'm'])):
momentumVars = tuple([vars[k] for k in ('eta', 'phi', 'pt', 'm')])
elif vars_set.issuperset(set(['eta', 'phi', 'p_t'])):
momentumVars = tuple([vars[k] for k in ('eta', 'phi', 'p_t')])
elif vars_set.issuperset(set(['eta', 'phi', 'pt'])):
momentumVars = tuple([vars[k] for k in ('eta', 'phi', 'pt')])
if momentumVars == ():
m_log.error("Couldn't guess kinematic input variables")
return
else:
m_log.info("Found kinematic input = " + str(momentumVars))
# ---------------------------------
# Guess input type
if inputType == None:
vtype = _branchType(tree.GetBranch(inputprefix + momentumVars[0]))
momkey = (momentumVars[0] + momentumVars[-1]).lower()
inputType = {
'pxevector_double': SJ.NtupleInputMaker.PxPyPzE_vector_double,
'pxevector_float': SJ.NtupleInputMaker.PxPyPzE_vector_float,
'pxearray_double': SJ.NtupleInputMaker.PxPyPzE_array_double,
'pxearray_float': SJ.NtupleInputMaker.PxPyPzE_array_float,
'etaevector_double': SJ.NtupleInputMaker.EtaPhiPtE_vector_double,
'etaevector_float': SJ.NtupleInputMaker.EtaPhiPtE_vector_float,
'etaearray_double': SJ.NtupleInputMaker.EtaPhiPtE_array_double,
'etaearray_float': SJ.NtupleInputMaker.EtaPhiPtE_array_float,
'etamvector_double': SJ.NtupleInputMaker.EtaPhiPtM_vector_double,
'etamvector_float': SJ.NtupleInputMaker.EtaPhiPtM_vector_float,
'etamarray_double': SJ.NtupleInputMaker.EtaPhiPtM_array_double,
'etamarray_float': SJ.NtupleInputMaker.EtaPhiPtM_array_float,
'etaptvector_double': SJ.NtupleInputMaker.EtaPhiPt_vector_double,
'etaptvector_float': SJ.NtupleInputMaker.EtaPhiPt_vector_float,
'etaptarray_double': SJ.NtupleInputMaker.EtaPhiPt_array_double,
'etaptarray_float': SJ.NtupleInputMaker.EtaPhiPt_array_float
}[momkey + vtype]
m_log.info("Input variables type = " + vtype)
# ---------------------------------
# Guess variable N
# only needed if array input
if Nvar == "":
for nName in ['n', 'num', 'nparticle']:
if nName in vars:
Nvar = vars[nName]
if Nvar == "":
if 'array' in vtype:
m_log.error(
"Couldn't guess proper input_n variable, please define manually."
)
return
else:
m_log.info(
"Couldn't guess proper input_n variable, input_n will be read from vector size."
)
else:
m_log.info("Found prefix input_n = " + inputprefix + Nvar)
# ---------------------------------
# guess if PDG Ids are stored
pdgName = None
for b in branches:
if 'pdg' in b.lower():
pdgName = b[b.find('_') + 1:]
break
input = SJ.NtupleInputMaker(inputType)
input.set_prefix(inputprefix)
input.set_n_name(Nvar)
input.set_variables(*momentumVars)
input.setFileTree(filename, treename)
input.set_name("InputJet")
input.set_masslessMode(masslessMode) #
if (pdgName is not None):
input.read_pdgId(True)
input.set_pdgId_name(pdgName)
return input
def readTree():
# open file
file = TFile(fileName, "READ")
if not file.IsOpen():
print "File", fileName, "does not exist. WILL ABORT!!!"
assert (False)
# open tree
tree = file.Get(treeName)
if tree == None:
print "tree", treeName, "doesn't exist in file", fileName, ". WILL ABORT!!!"
assert (False)
# determine how many entries to run on
nrEntries = tree.GetEntries()
if desiredNrEntries < 0 or desiredNrEntries > nrEntries:
actualNrEntries = nrEntries
else:
actualNrEntries = desiredNrEntries
if debug:
print "We will run over", actualNrEntries, "entries."
# we create a file to store histograms
outputfile = TFile(outputfileName, "RECREATE")
dict_scale_hist = get_dict_scale_hist(list_scale, debug)
# run over the entries of the tree
# unlike in C++, no need to define the branches in advance
for i, entry in enumerate(tree):
if i >= actualNrEntries:
continue
if debug or i % 1000 == 0:
print "******* new entry", i, " **********"
# we are looping over jets, each has the information of Pt, Eta, Phi, E
# they are available at different calibration stages:
# Nominal, OneMu, PtRecoBukin, PtRecoGauss, Regression
# we also know what is the correct simulated information: Parton
# we want to compare the different calibrations and see which one
# models better the Higgs boson mass
# all Pt, E, M are in GeV
# Higgs -> bb, meaning the b1 and b2 in this tree
# let's see how we get a variable from the tree
for scale in list_scale:
M = get_M(entry, scale, debug)
dict_scale_hist[scale].Fill(M)
#dict_scale_hist[scale].Fit("gaus")
# done loop over all the entries in the tree
#Gaussian = TF1 ("Gauss",Gauss(),48.5,168.5,3)
Bukins = TF1("Bukin", Bukin(), 48.5, 168.5, 6)
Bukins.SetLineColor(3)
#Stack = THStack ("Stack", "Stacked Histograms")
#Leg = TLegend (x1,y1,x2,y2)
for scale in list_scale:
#Gaussian.SetParameters(80,dict_scale_hist[scale].GetMean(),dict_scale_hist[scale].GetRMS())
#dict_scale_hist[scale].Fit(Gaussian,"+")
Bukins.SetParameters(80, dict_scale_hist[scale].GetMean(),
dict_scale_hist[scale].GetRMS(), 0, 0, 0)
dict_scale_hist[scale].Fit(Bukins, "+")
#Stack.Add(dict_scale_hist[scale])
#Stack.Draw("option")
#Leg.Draw()
outputfile.Write()
outputfile.Close()
def main():
gROOT.SetBatch(1)
sampleNames = [
#'Tag1_Top1',
#'Tag1_Top2',
'Tag1_Top1_lhood',
'Tag1_Top2_lhood',
#'Tag1_TopLepHad_lhood',
'Tag1_SystemPt',
'Tag1_SystemMass',
'Tag1_SystemRapidity',
]
channels = ['el', 'mu']
generators = ['Alpgen', 'McAtNlo']
toys = 'toy5000'
systematic = 'nominal'
method_forFile = 'svd'
method_forHist = 'SVD'
regValue = 'reg4'
baseRecoFileName = 'RecoClosure'
baseFilePath = '../data/March_4/Alpgen/MCClosure'
can = TCanvas("can", "can", 0, 0, 800, 600)
can.SetMargin(0.2, 0.05, 0.15, 0.03)
# loop over the sample names since each one needs its own plots
for sampleName in sampleNames:
print 'loop', sampleName
x_title = ''
y_title = "ratio of unfolded over truth #frac{dN}{d"
y_max = 1.2
y_min = 0.8
latex_label = ''
if sampleName is 'Tag1_SystemMass':
x_title = 'M_{t#bar{t}} [GeV]'
y_title += 'M_{t#bar{t}}}'
latex_label = 't\\bar{t} Mass: '
elif sampleName is 'Tag1_SystemPt':
x_title = 'p_{T}^{t#bar{t}} [GeV]'
y_title += 'p_{T}^{t#bar{t}}}'
latex_label = 't\\bar{t} p_{T}: '
y_max = 1.6
y_min = 0.6
elif sampleName is 'Tag1_SystemRapidity':
x_title = 'y_{t#bar{t}}'
y_title += 'y_{t#bar{t}}}'
latex_label = 't\\bar{t} Rapidity: '
elif sampleName is 'Tag1_Top1_lhood':
x_title = 'leptonic top p_{T} [GeV]'
y_title += 'p_{T}^{t}}'
latex_label = 'top p_{T}: '
elif sampleName is 'Tag1_Top2_lhood':
x_title = 'hadronic top p_{T} [GeV]'
y_title += 'p_{T}^{t}}'
latex_label = 'top p_{T}: '
# loop over the channels since they will also each get their own plots
for channel in channels:
print 'loop', channel
outFilename = baseFilePath + '/alpgenMcAtNloClosureTest_' + sampleName + '_' + channel + '.eps'
#loop over the generator files and load plots
genhistos = {}
for generator in generators:
# filename where plots are stored
inFilename = (baseFilePath + '/' + baseRecoFileName + '_' +
sampleName + '_' + channel + '_unfoldWith' +
generator + '_' + toys + '_' + method_forFile +
'_' + regValue + '.root')
inFile = TFile(inFilename)
if not inFile.IsOpen():
print 'ERROR opening input file:', inFilename
return
# build histogram names to retrieve
baseHistoPath = ('unfolding/toys/' + systematic + '/' +
channel + '/' + sampleName + '/' +
method_forHist + '/' + regValue + '/' + toys +
'/')
baseHistoName = ('H_' + channel + '_' + sampleName + '_' +
method_forHist + '_' + regValue + '_' + toys +
'_')
histos = {}
truth_name = baseHistoPath + baseHistoName + 'mc_truth'
histos['truth'] = inFile.Get(truth_name)
if not histos['truth']:
print 'ERROR could not load histogram', truth_name, 'from file', inFilename
return
histos['truth'].SetDirectory(0)
reco_name = baseHistoPath + baseHistoName + 'mc_reco_diffxs'
histos['reco'] = inFile.Get(reco_name)
if not histos['reco']:
print 'ERROR could not load histogram', reco_name, 'from file', inFilename
return
histos['reco'].SetDirectory(0)
measured_name = baseHistoPath + baseHistoName + 'data_measured'
histos['measured'] = inFile.Get(measured_name)
if not histos['measured']:
print 'ERROR could not load histogram', measured_name, 'from file', inFilename
return
histos['measured'].SetDirectory(0)
diffxs_name = baseHistoPath + baseHistoName + 'data_unfolded_diffxs'
histos['diffxs'] = inFile.Get(diffxs_name)
if not histos['diffxs']:
print 'ERROR could not load histogram', diffxs_name, 'from file', inFilename
return
histos['diffxs'].SetDirectory(0)
diffnx_name = baseHistoPath + baseHistoName + 'data_unfolded'
histos['diffnx'] = inFile.Get(diffnx_name)
if not histos['diffnx']:
print 'ERROR could not load histogram', diffnx_name, 'from file', inFilename
return
histos['diffnx'].SetDirectory(0)
genhistos[generator] = histos
inFile.Close()
# end generators
# create ratio plot for Alpgen[0] unfolding MC@NLO[1], divided by MC@NLO truth
ratio01 = TH1F(genhistos[generators[0]]['truth'])
ratio01.SetDirectory(0)
ratio01.SetName('ratio_' + generator[0] + 'Unfolding' +
generator[1] + '_over_' + generator[1] + 'Truth')
# divide the unfolded result by the truth from the generator that cooresponds
# to the distribution that was unfolded
ratio01.Divide(genhistos[generators[0]]['diffnx'],
genhistos[generators[1]]['truth'])
ratio01.GetYaxis().SetTitle(y_title)
ratio01.GetXaxis().SetTitle(x_title)
ratio01.SetMaximum(y_max)
ratio01.SetMinimum(y_min)
# create ratio plot for MC@NLO[1] unfolding Alpgen[0], divided by Alpgen truth
ratio10 = TH1F(genhistos[generators[0]]['truth'])
ratio10.SetDirectory(0)
ratio01.SetName('ratio_' + generator[1] + 'Unfolding' +
generator[0] + '_over_' + generator[0] + 'Truth')
ratio10.Divide(genhistos[generators[1]]['diffnx'],
genhistos[generators[0]]['truth'])
ratio10.GetYaxis().SetTitle(y_title)
ratio10.GetXaxis().SetTitle(x_title)
ratio10.SetMaximum(y_max)
ratio10.SetMinimum(y_min)
can.cd()
ratio01.SetMarkerColor(kBlack)
ratio01.SetMarkerStyle(20)
ratio01.SetLineWidth(2)
ratio01.Draw()
ratio10.SetMarkerColor(kRed)
ratio10.SetLineColor(kRed)
ratio10.SetLineWidth(1)
ratio10.SetMarkerStyle(23)
ratio10.Draw('same')
channel_label = latex_label
if channel is 'el':
channel_label += ' e+jets'
elif channel is 'mu':
channel_label += ' \\mu+jets'
legend = TLegend(0.22, 0.95, 0.7, 0.75, channel_label)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.AddEntry(ratio01,
generators[0] + ' unfolding ' + generators[1],
"lp")
legend.AddEntry(ratio10,
generators[1] + ' unfolding ' + generators[0],
"lp")
legend.Draw('same')
#tex = TLatex()
#tex.SetNDC()
#tex.SetTextFont(62)
#tex.DrawLatex(0.23,0.88,channel_label)
can.SaveAs(outFilename)
def ReweightFile(filepath,LumiData,rwOptions):
paraFile=rwOptions.get('parametersFile','para_config.txt')
entriesFromFile=rwOptions.get('entriesFromFile',True)
entriesFromHistogram=rwOptions.get('entriesFromHistogram',False)
if entriesFromHistogram:
entriesFromFile=False
#raw_input('going to make the call with '+str(filepath))
FileName,InDir,Sample,SubSample,Estimation,Tail,AbsPath = BreakDownInputPath(filepath)
#=====the output should be the same as
#=====the filename + '_RW2X', where X is the lumi
OutFileName=filepath
print ""
print "---------------------------------------------"
print "INSIDE ReweightFile"
print ' filepath= ',str(filepath)
print ' filename= ',OutFileName
#
#
OutFileName=OutFileName.replace('.root','_RWTo'+str(int(LumiData))+'.root')
#OutFileName=AbsPath.replace(FileName,OutFileName)
#
#
print ''
print " Going to reweight the file",filepath, "to the lumi",LumiData
print ""
print " the outFileName will be ",OutFileName
#INPUT FILE#
infile=TFile(filepath,"READ")
#except:
#print "Unexpected error:", sys.exc_info()[0]
#print 'infile is ',str(infile)
#return
infile.cd()
if not infile.IsOpen():
print " the file could not be opened. Bye"
return
#============
#
#
#
#
outfile=TFile(OutFileName,"RECREATE")
outfile.cd()
#
#
#
#=======PARAMETERS FOR THE REWEIGHTING
parameters_dict={}
config=open(paraFile,'r')
for line in config:
if line.find('#') == -1:
thisline=line.split()
#
if len(thisline)==0:
continue
try:
parameters_dict[thisline[0]]=float(thisline[2])
except IndexError:
print "this is not going to work"
print "thisline is", thisline
print 'and the index is ',thisline[0]
raise
else:
pass
#===========
filtereff_key='FE_'+Sample+'_'+SubSample
try:
FE=parameters_dict[filtereff_key]
except KeyError:
print filtereff_key, "does not exist in the dictionary"
return
#
xsec_key='xs_'+Sample+'_'+SubSample
try:
XS=parameters_dict[xsec_key]
except KeyError:
print xsec_key, "does not exist in the dictionary"
return
#
if entriesFromFile:
tnoe_key='TNoE_'+Sample+'_'+SubSample
try:
TNOE=parameters_dict[tnoe_key]
except KeyError:
print tnoe_key, "does not exist in the dictionary"
return
elif entriesFromHistogram:
entriesHistoPath=rwOptions['entriesHistoPath']
entriesHisto=infile.Get(entriesHistoPath)
if str(entriesHisto).find('nil') != -1:
print 'the entries histo ',entriesHisto.GetName(),' was not found in ',infile.GetPath()
#
if entriesHisto.GetNBinsX() > 1:
print 'the histo has more than one bin!! ',entriesHisto.GetNbinsX()
TNOE=entriesHisto.GetBinContent(1)
#COMPUTE THE WEIGHTS
#if LumiData==-1:
#LumiData=5097.
print "----Reweighting information----"
print ' XS =',XS
print ' FE =',FE
print ' TNOE =',TNOE
Weight=float(LumiData)*XS*FE/TNOE;
print " LumiData =",LumiData
print " cross section is",XS
print " FE is ", FE
print " number of entries ",TNOE
print " the weight is ",Weight
#raw_input("ready to continue?")
#
#
#
#REWEIGHT ALL THE HISTOS IN THE FILE:
LoopAndScale(infile,Weight)
#outfile.Write()
#
#
#
#
#MAKE THE ROOT FILE REMEMBER
outfile.cd()
histoname='RWto'+str(int(LumiData))
isrw=infile.Get(histoname)
#
#
if str(isrw).find('nil') != -1:
#create it
newh=TH1D(histoname,"",1,0.5,1.5)
newh.Fill(1.0)
newh.Write()
#
datehist=outfile.Get("date_of_reweighting_to_"+str(int(LumiData)))
date=commands.getoutput('date +%s')
if str(datehist).find('nil') != -1:
dateh=TH1D("date_of_reweighting_to_"+str(int(LumiData)),date,1,0.5,1.5)
dateh.Fill(1.0)
dateh.Write()
else:
datehist.SetTitle(date)
datehist.Write("",TObject.kOverwrite)
#
outfile.Write()
print "EXITING ReweightFile. Bye"
print "----------------------------------"
print ""
return OutFileName
from sys import argv
from glob import glob
gROOT.SetBatch(True)
files = argv[1:]
for word in files:
for f in glob(word):
if not path.exists(f):
print "could not file", f
continue
if not f.endswith(".root"):
print "input must be .root file!"
continue
dirname = path.dirname(path.abspath(f))
print "saving pdf in", dirname
infile = TFile(f)
if infile.IsOpen() and not infile.IsZombie() and not infile.TestBit(
TFile.kRecovered):
tree_fit_sb = infile.Get("tree_fit_sb")
gStyle.SetOptStat(221112211)
c = TCanvas()
tree_fit_sb.Draw("r")
outname = dirname + "/r_" + path.basename(f)
outname = outname.replace(".root", ".pdf")
c.SaveAs(outname)
infile.Close()
else:
print "file is broken: ", f
import sys
import ROOT
from ROOT import TFile, TTree, TH1D, TCanvas, gROOT, TPad, TGaxis, TColor, TLegend
#import matplotlib
file1_path = "/uboone/data/users/guzowski/numi_flux/fgd.root"
file1 = TFile(file1_path, 'READ')
if (file1.IsOpen()):
print 'File ', file1_path, ' is open'
if (file1.IsOpen() == False):
quit()
# these are the corrected gSimple flux histograms
flux_nue_000 = file1.Get("nueFluxHisto000")
flux_nuebar_000 = file1.Get("anueFluxHisto000")
flux_nue_555 = file1.Get("nueFluxHisto555")
flux_nuebar_555 = file1.Get("anueFluxHisto555")
flux_nue_999 = file1.Get("nueFluxHisto999")
flux_nuebar_999 = file1.Get("anueFluxHisto999")
c1 = TCanvas("c1", "c1", 800, 600)
c1.cd()
flux_nue_555.SetLineColor(46)
flux_nue_999.SetLineColor(32)
flux_nue_000.Draw("hist")
flux_nue_555.Draw("hist same")
flux_nue_999.Draw("hist same")
c1.Print("plots/nue_flux_inside_detector_position.pdf")
def main():
gROOT.SetBatch(1)
SetAtlasStyle()
channels = ["ejets", "mujets"]
#channels = ["mujets"]
variables = [
"Top1_lhood", "Top2_lhood", "SystemMass", "SystemPt", "SystemRapidity"
]
#variables = ["Top1_lhood"]
for j in range(len(variables)):
variable = variables[j]
print j, variable
can = TCanvas(variable, variable, 0, 0, 800, 600)
can.SetMargin(0.2, 0.05, 0.15, 0.03)
label = ''
regnumber = ''
if variable is 'Top1_lhood':
label = 'Leptonic top p^{t}_{T}'
regnumber = "reg4"
elif variable is 'Top2_lhood':
label = 'Hadronic top p^{t}_{T}'
regnumber = "reg4"
elif variable is 'SystemMass':
label = 't#bar{t} System mass'
regnumber = "reg3"
elif variable is 'SystemPt':
label = 't#bar{t} System p_{T}'
regnumber = "reg3"
elif variable is 'SystemRapidity':
label = 't#bar{t} System rapidity'
regnumber = "reg4"
legend = TLegend(0.6, 0.95, 0.95, 0.75, label)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
ratioPlots = []
for i in range(len(channels)):
channel = channels[i]
print i, channel
postfix = ''
labelChannel = ''
if channel is 'ejets':
postfix = 'el'
labelChannel = 'e + jets'
elif channel is 'mujets':
postfix = 'mu'
labelChannel = '#mu + jets'
elif channel is 'combined':
postfix = 'co'
filename = "../data/data_Unfolded_14_02_2013/Alpgen/nominal_Tag1_" + variable + "_" + postfix + "_toy5000_svd_" + regnumber + ".root"
fileDi = TFile(filename)
if not fileDi.IsOpen():
print 'ERROR opening ', filename
m_histo = fileDi.Get("unfolding/toys/nominal/" + postfix +
"/Tag1_" + variable + "/SVD/" + regnumber +
"/toy5000/H_" + postfix + "_Tag1_" +
variable + "_SVD_" + regnumber +
"_toy5000_Regularization")
m_histo.SetName("diFactor" + variable + postfix)
nbins = m_histo.GetNbinsX() - 1
diFactor = m_histo.Clone()
diFactor.SetDirectory(0)
diFactor.SetNdivisions(508)
diFactor.GetXaxis().SetRangeUser(1, nbins + 1)
for bin in range(nbins):
bindiFactor = m_histo.GetBinContent(bin + 1)
print bin, bindiFactor
diFactor.SetBinContent(bin + 2, bindiFactor)
diFactor.SetTitle("")
diFactor.SetMarkerColor(kBlack + i)
diFactor.SetMarkerStyle(20 + i)
diFactor.SetLineColor(kBlack + i)
diFactor.GetYaxis().SetRangeUser(0.005, 400)
diFactor.GetYaxis().SetTitle("|d_{i}|")
diFactor.GetXaxis().SetTitle("bin number")
ratioPlots.append(diFactor)
legend.AddEntry(diFactor, labelChannel, "p")
if i > 0:
diFactor.Draw('histosame')
else:
diFactor.Draw("histo")
can.cd()
horizontal = TF1("line", "pol1", 0, 2800)
horizontal.SetParameters(1, 0)
horizontal.SetLineColor(kBlack)
horizontal.SetLineStyle(2)
horizontal.Draw('same')
legend.Draw('same')
can.SetLogy()
can.SaveAs("regularizationValue_" + variable + ".eps")
def updatestatus(jobstatus, outdir, name):
from ROOT import TFile
print("Updating job status")
print("Total: " + str(len(jobstatus)))
# get the qstat job listing
proccommand = 'qstat | grep dx5412'
proc = subprocess.Popen(proccommand, stdout=subprocess.PIPE, shell=True)
qstat_result = proc.stdout.read()
for key in jobstatus:
index = jobstatus[key][0]
# if job is completed, we don't need to check again
if jobstatus[key][1] == 2:
continue
# check if the job is still underway
jobinprocess = qstat_result.find((name + str(index) + ' ').encode())
if jobinprocess >= 0:
jobstatus[key][1] = 1
continue
# if the job is not still underway,
# check to see if the job has completed properly
# if not, mark to resubmit
outDirMod = ''
if key[1] == 'mtrack':
outDirMod = 'tow_0_track_-1'
if key[1] == 'ptrack':
outDirMod = 'tow_0_track_1'
if key[1] == 'mtow':
outDirMod = 'tow_-1_track_0'
if key[1] == 'ptow':
outDirMod = 'tow_1_track_0'
if key[1] == 'nom':
outDirMod = 'tow_0_track_0'
if outdir.startswith('/'):
filename = outdir + '/' + outDirMod + \
'/' + name + str(index) + '.root'
else:
filename = os.getcwd() + '/' + outdir + '/' + outDirMod + \
'/' + name + str(index) + '.root'
if os.path.isfile(filename):
outputfile = TFile(filename, "READ")
if outputfile.IsZombie():
print("job " + str(index + 1) + " of " + str(len(jobstatus)) +
" complete: file is zombie, resubmit")
jobstatus[key][1] = 0
os.remove(filename)
elif outputfile.IsOpen():
print("job " + str(index + 1) + " of " + str(len(jobstatus)) +
" complete: ROOT file healthy")
print(filename)
jobstatus[key][1] = 2
outputfile.Close()
else:
print("job " + str(index + 1) + " of " + str(len(jobstatus)) +
" undefined file status, resubmit")
jobstatus[key][1] = 0
else:
print("undefined status: job " + str(index + 1) + " of " +
str(len(jobstatus)) + " marked for submission")
jobstatus[key][1] = 0
return jobstatus
def main():
gROOT.SetBatch(1)
SetAtlasStyle()
#channels = ["ejets","mujets"]
channels = ["ejets"]
variables = [
"Top1_lhood", "Top2_lhood", "SystemMass", "SystemPt", "SystemRapidity"
]
for j in range(len(variables)):
variable = variables[j]
print j, variable
can = TCanvas(variable, variable, 0, 0, 800, 600)
can.SetMargin(0.2, 0.05, 0.15, 0.03)
ratioPlots = []
label = ''
latexlabel = ""
if variable is 'Top1_lhood':
label = 'Leptonic top p^{t}_{T}'
latexlabel = "#frac{d#sigma}{dp^{t}_{T}}_{rew} #times #left[#frac{d#sigma}{dp^{t}_{T}}_{std}#right]^{-1}"
elif variable is 'Top2_lhood':
label = 'Hadronic top p^{t}_{T}'
latexlabel = "#frac{d#sigma}{dp_{T}^{t}}_{rew} #times #left[#frac{d#sigma}{dp_{T}^{t}}_{std}#right]^{-1}"
elif variable is 'SystemMass':
label = 't#bar{t} System mass'
latexlabel = "#frac{d#sigma}{dM_{t#bar{t}}}_{rew} #times #left[#frac{d#sigma}{dM_{t#bar{t}}}_{std}#right]^{-1}"
elif variable is 'SystemRapidity':
label = 't#bar{t} System rapidity'
latexlabel = "#frac{d#sigma}{dY_{t#bar{t}}}_{rew} #times #left[#frac{d#sigma}{dY_{t#bar{t}}}_{std}#right]^{-1}"
elif variable is 'SystemPt':
label = 't#bar{t} System p_{T}'
latexlabel = "#frac{d#sigma}{dp^{t#bar{t}}_{T}}_{rew} #times #left[#frac{d#sigma}{d}p^{t#bar{t}}_{T}_{std}#right]^{-1}"
legend = TLegend(0.25, 0.95, 0.5, 0.75, label)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
for i in range(len(channels)):
channel = channels[i]
print i, channel
postfix = ''
if channel is 'ejets':
postfix = '_el'
elif channel is 'mujets':
postfix = '_mu'
elif channel is 'combined':
postfix = ''
filename = "../data/data_Unfolded_14_02_2013/Alpgen/SVD/unfoldedResult_Tag1_" + variable + postfix + ".root"
file_std = TFile(filename)
if not file_std.IsOpen():
print 'ERROR opening ', filename
filename = "../data/test_Unfolded_08_03_2013/Alpgen/SVD/unfoldedResult_Tag1_" + variable + postfix + ".root"
file_rew = TFile(filename)
if not file_rew.IsOpen():
print 'ERROR opening ', filename
diffxs_std = file_std.Get("diffxs_stat")
diffxs_std.SetName("diffxs_stat_std" + postfix)
measured_std = file_std.Get("measured")
measured_std.SetName("measured_std" + postfix)
diffxs_rew = file_rew.Get("diffxs_stat")
measured_rew = file_rew.Get("measured")
measured_rew.SetName("measured_rew" + postfix)
diffxs_rew.SetName("diffxs_stat_rew" + postfix)
ratio = diffxs_rew.Clone("diffxs_stat_ratio" + postfix)
ratio.SetDirectory(0)
ratio.GetYaxis().SetTitle(latexlabel)
ratio.GetYaxis().SetTitleOffset(1.6)
ratio.Divide(diffxs_std)
ratio.GetYaxis().SetRangeUser(0.9, 1.2)
ratio.SetMarkerColor(kBlack + i)
ratio.SetMarkerStyle(20 + i)
ratio.SetLineColor(kBlack + i)
ratio1 = measured_rew.Clone("measured_ratio" + postfix)
ratio1.SetDirectory(0)
ratio1.GetYaxis().SetTitle(latexlabel)
ratio1.GetYaxis().SetTitleOffset(1.6)
ratio1.Divide(measured_std)
ratio1.GetYaxis().SetRangeUser(0.7, 1.2)
if variable is 'SystemRapidity':
ratio.GetYaxis().SetRangeUser(-0.07, 0.04)
ratio1.SetMarkerColor(kRed + i)
ratio1.SetMarkerStyle(20 + i)
ratio1.SetLineColor(kRed + i)
ratioPlots.append(ratio)
ratioPlots.append(ratio1)
legend.AddEntry(ratio, "unfolded", "p")
legend.AddEntry(ratio1, "measured", "p")
can.cd()
if i > 0:
ratio.Draw('same')
else:
ratio.Draw()
ratio1.Draw('same')
can.cd()
horizontal = TF1("line", "pol1", 200, 2800)
horizontal.SetParameters(1, 0)
horizontal.SetLineColor(kBlack)
horizontal.SetLineStyle(2)
horizontal.Draw('same')
legend.Draw('same')
can.SaveAs(variable + ".eps")
controlPlots = [
'InclusiveJetBinLeptonPt', 'Top1Pt', 'Top2Pt', 'SystemMass', 'SystemPt'
]
ratioPlots1 = []
for j in range(len(controlPlots)):
controlPlot = controlPlots[j]
can1 = TCanvas(controlPlot, controlPlot, 0, 0, 800, 600)
can1.SetMargin(0.2, 0.05, 0.15, 0.03)
legend1 = TLegend(0.45, 0.95, 0.90, 0.75, "")
legend1.SetFillStyle(0)
legend1.SetBorderSize(0)
labelx = ''
if controlPlot is 'InclusiveJetBinLeptonPt':
labelx = 'lepton p_{T}, all-jet bin[GeV/c]'
elif controlPlot is 'Top1Pt':
labelx = 'Leptonic top p^{t}_{T}[GeV/c]'
elif controlPlot is 'Top2Pt':
labelx = 'Hadronic top p^{t}_{T}[GeV/c]'
elif controlPlot is 'SystemMass':
labelx = 'M_{t#bar{t}}[GeV/c^{2}]'
elif controlPlot is 'SystemPt':
labelx = 'p^{t#bar{t}}_{T}[GeV/c]'
for i in range(len(channels)):
channel = channels[i]
print i, channel
postfix = ''
if channel is 'ejets':
postfix = '_el'
elif channel is 'mujets':
postfix = '_mu'
elif channel is 'combined':
postfix = ''
filename = "../data/data_14_02_2013/nominal/ToUnfold_nominal_AlpgenJimmy/tagged_" + channel + ".root"
cfile_std = TFile(filename)
if not cfile_std.IsOpen():
print 'ERROR opening ', filename
filename = "../data/test_08_03_2013/nominal/ToUnfold_nominal_AlpgenJimmy/tagged_" + channel + ".root"
cfile_rew = TFile(filename)
if not cfile_rew.IsOpen():
print 'ERROR opening ', filename
control_std = cfile_std.Get(controlPlot + "_Data")
control_std.SetName("control_std" + postfix)
control_rew = cfile_rew.Get(controlPlot + "_Data")
control_rew.SetName("control_rew" + postfix)
control_std.SetDirectory(0)
control_std.SetTitle("")
control_std.GetYaxis().SetTitle("Events")
control_std.GetXaxis().SetTitle(labelx)
control_std.GetYaxis().SetTitleOffset(1.6)
#control_std.GetYaxis().SetRangeUser(0.7,1.3)
control_std.SetMarkerColor(kBlack + i)
control_std.SetMarkerStyle(20 + i)
control_std.SetLineColor(kBlack + i)
control_rew.SetMarkerColor(kRed + i)
control_rew.SetMarkerStyle(20 + i)
control_rew.SetLineColor(kRed + i)
if i > 0:
control_std.Draw('same')
control_rew.Draw('same')
else:
control_std.Draw()
control_rew.Draw('same')
legend1.AddEntry(control_std, "nominal", "p")
legend1.AddEntry(control_rew, "Lepton p_{T} reweighting", "p")
legend1.Draw('same')
can1.SetLogy()
can1.SaveAs(controlPlot + "_log.eps")
def CombinedRootFiles(self, path='', VBF_cut='invMAk4sel_1p0'):
#pT_suffix = 'pT'
mjj_suffix = '_mjj'
VBF = (self.LastCut == VBF_cut)
if (VBF):
name_suffix = '_afterVBFsel'
else:
name_suffix = ''
for i in range(len(self.SHists)):
filename = self.getFileName(i)
if (VBF):
oFile = TFile(path + "/%s_mjj.root" % filename, "UPDATE")
#pT_file = TFile(path+"/%s.root"%filename,"UPDATE");
# file= TFile(path+"/%s_SignalInjection.root"%filename,"UPDATE");
# file= TFile(path+"/%s_SidebandData.root"%filename,"UPDATE");
else:
oFile = TFile(path + "/%s_mjj.root" % filename, "RECREATE")
# file= TFile(path+"/%s_SignalInjection.root"%filename,"RECREATE");
# file= TFile(path+"/%s_SidebandData.root"%filename,"RECREATE");
if (not oFile.IsOpen()):
print("Error: Could not open File No. %i" % i)
# r_newbinning=range(0,14000,100)
# d_newbinning=array('d')
# for b in r_newbinning:
# d_newbinning.append(b)
# radion=self.SHists[i].Rebin(len(d_newbinning)-1,"new binning",d_newbinning)
# qcd_data=self.BHist.Rebin(len(d_newbinning)-1,"new binning",d_newbinning)
# radion.Write('radion_invMass'+name_suffix)
# qcd_data.Write('qcd_invMass'+name_suffix)
# qcd_data.Write('data_invMass'+name_suffix)
#With SidebandData
# signalHist=self.SHists[i]
# backgroundHist=self.BHist
# sidebandDataHist=self.sidebandDataHist
# signalHist.Write('radion_invMass'+name_suffix)
# backgroundHist.Write('qcd_invMass'+name_suffix)
# sidebandDataHist.Write('data_invMass'+name_suffix)
import numpy as np
binning = np.linspace(0, 10000, 10001)
signalHist = self.SHists[i].Rebin(len(binning) - 1, '', binning)
backgroundHist = self.BHist.Rebin(len(binning) - 1, '', binning)
# #For SignalInjectionTest #try this -> sig+bg shapes to pseudo sig+bg points
#signalHist=self.SHists[i]
#backgroundHist=self.BHist
#fakedataHist=backgroundHist.Clone()
#fakedataHist.Add(signalHist)
#signalHist.Write('radion_invMass'+name_suffix)
#backgroundHist.Write('qcd_invMass'+name_suffix)
#fakedataHist.Write('data_invMass'+name_suffix)
signalHist.Scale(4.178272981)
backgroundHist.Scale(4.178272981)
#Standard (with Background as FakeData)-> sig +bg shape to bg(pseudodata)points
signalHist.Write('radion_invMass' + name_suffix)
backgroundHist.Write('qcd_invMass' + name_suffix)
backgroundHist.Write('data_invMass' + name_suffix)
update_progress(i + 1, len(self.SHists))
oFile.Close()
h=dict_scale_hist[scale]
if DrawBothHistAndFit:
h.Draw("same")
if DrawJustFit:
h.Draw("func same")
#Set Canvas Title
pave = TPaveText(0.00,0.9,0.3,1.0,"tblrNDC")
pave.SetTextColor(1)
pave.SetTextSize(0.05)
pave.AddText("Histogram Fits")
pave.Draw("same")
# Draw legend
mylegend.Draw("same")
c.Print("output/fitted.pdf")
####### Execute ########
# open file
file=TFile(fileName,"READ")
if not file.IsOpen():
print "File",fileName,"does not exist. WILL ABORT!!!"
assert(False)
scalestring = "Nominal,OneMu,OneMuNu,AllMu,AllMuNu,PtRecoBukin,PtRecoGauss,Regression" #Does not include Parton
Overlay(scalestring)
Fitting(scalestring,Fit_id)
class AnalysisSuiteGainMap:
__slots__ = [
'ADCPKPOS_SECTOR_AVG', 'ADCPKPOS_SECTOR_STDDEV', 'ANA_UNI_GRANULARITY',
'AVGCLUSTSIZE_SECTOR_AVG', 'AVGCLUSTSIZE_SECTOR_STDDEV', 'DEBUG',
'DETECTOR', 'DET_IMON_QC5_RESP_UNI', 'DET_IMON_POINTS', 'FILE_IN',
'FILE_OUT', 'GAIN_CALCULATOR', 'GAIN_LAMBDA', 'GAIN_LAMBDA_ERR',
'GAIN_AVG_POINTS', 'GAIN_STDDEV_POINTS', 'GAIN_MAX_POINTS',
'G2D_MAP_ABS_RESP_UNI', 'G2D_MAP_AVG_CLUST_SIZE_ORIG',
'G2D_MAP_AVG_CLUST_SIZE_NORM', 'G2D_MAP_GAIN_ORIG', 'PD_CALCULATOR',
'PD_AVG_POINTS', 'PD_STDDEV_POINTS', 'PD_MAX_POINTS', 'PD_MIN_POINTS'
]
def __init__(self,
file_out,
inputfilename="",
params_gain=PARAMS_GAIN(),
params_det=PARAMS_DET(),
params_discharge=PARAMS_PD(),
debug=False):
self.ADCPKPOS_SECTOR_AVG = 0. #Average of the fitted cluster ADC PkPos in defined (ieta,iphi) sector
self.ADCPKPOS_SECTOR_STDDEV = 0. #Std. Dev. of the fitted cluster ADC PkPos in defined (ieta,iphi) sector
self.ANA_UNI_GRANULARITY = 32
self.AVGCLUSTSIZE_SECTOR_AVG = 0. #Average of Average Cluster Size distributions in defined (ieta,iphi) sector
self.AVGCLUSTSIZE_SECTOR_STDDEV = 0. #Std. Dev. of Average Cluster Size distributions in defined (ieta,iphi) sector
self.DEBUG = debug
self.DETECTOR = params_det
self.DET_IMON_QC5_RESP_UNI = params_det.DET_IMON_QC5_RESP_UNI
self.DET_IMON_POINTS = []
self.FILE_IN = []
if len(inputfilename) > 0:
self.FILE_IN = TFile(str(inputfilename), "READ", "", 1)
self.FILE_OUT = file_out
self.GAIN_CALCULATOR = params_gain
self.GAIN_LAMBDA = 1.
self.GAIN_LAMBDA_ERR = 0.
self.GAIN_AVG_POINTS = [] #Average Gain over the entire detector
self.GAIN_STDDEV_POINTS = [
] #Std. Dev of Gain over the entire detector
self.GAIN_MAX_POINTS = [] #Max Gain over the entire detector
self.GAIN_MIN_POINTS = [] #Min Gain over the entire detector
self.G2D_MAP_ABS_RESP_UNI = TGraph2D(
) #Absolute Response Uniformity Map
self.G2D_MAP_AVG_CLUST_SIZE_ORIG = TGraph2D(
) #Absolute Avg Cluster Size Map
self.G2D_MAP_AVG_CLUST_SIZE_NORM = TGraph2D(
) #Normalized " "
self.G2D_MAP_GAIN_ORIG = TGraph2D() #Effective Gain Map
self.PD_CALCULATOR = params_discharge
self.PD_AVG_POINTS = [] #Avg P_D over entire detector
self.PD_STDDEV_POINTS = [] #Std. Dev of P_D over entire detector
self.PD_MAX_POINTS = [] #Max P_D over the entire detector
self.PD_MIN_POINTS = [] #Min P_D over the entire detector
return
def reset(self, debug=False):
#Close TFiles
self.closeTFiles(debug)
#Reset Variables
self.DEBUG = debug
self.ADCPKPOS_SECTOR_AVG = 0.
self.ADCPKPOS_SECTOR_STDDEV = 0.
self.ANA_UNI_GRANULARITY = 32
self.AVGCLUSTSIZE_SECTOR_AVG = 0.
self.AVGCLUSTSIZE_SECTOR_STDDEV = 0.
self.DET_IMON_QC5_RESP_UNI = 0.
self.GAIN_LAMBDA = 1.
self.GAIN_LAMBDA_ERR = 0.
#Reset classes
self.DETECTOR.reset()
#Clear Lists
del self.DET_IMON_POINTS[:]
del self.GAIN_AVG_POINTS[:]
del self.GAIN_STDDEV_POINTS[:]
del self.GAIN_MAX_POINTS[:]
del self.GAIN_MIN_POINTS[:]
del self.PD_AVG_POINTS[:]
del self.PD_STDDEV_POINTS[:]
del self.PD_MAX_POINTS[:]
del self.PD_MIN_POINTS[:]
#Clear TObjects?
#self.G2D_MAP_ABS_RESP_UNI
#self.G2D_MAP_AVG_CLUST_SIZE_ORIG
#self.G2D_MAP_AVG_CLUST_SIZE_NORM
#self.G2D_MAP_GAIN_ORIG
return
#Determines the Average & Std. Dev. ADC PkPos in the (DETPOS_IETA, DETPOS_IPHI) sector
def avgROSectorADCPkPos(self):
#Load the plot
strPlotName = "SectorEta{0}/g_iEta{0}_clustADC_Fit_PkPos".format(
self.DETECTOR.DETPOS_IETA)
gSector_clustADC_Fit_PkPos = self.FILE_IN.Get(strPlotName)
#Calculate the iphi sector boundaries
list_sectBoundary = self.DETECTOR.calcROSectorBoundariesByEta(
self.DETECTOR.DETPOS_IETA)
#Print to user - Section Boundaries
#if self.DEBUG == True:
#for i in range(0,len(list_sectBoundary)):
#print list_sectBoundary[i]
#Loop over points in the plot
list_clustADC_Fit_PkPos = []
for i in range(0, gSector_clustADC_Fit_PkPos.GetN()):
#Get the i^th point in this plot
fPx = Double(0.0)
fPy = Double(0.0)
gSector_clustADC_Fit_PkPos.GetPoint(i, fPx, fPy)
#Check if this point is within the defined (ieta,iphi) sector, if so store it for later use
if list_sectBoundary[self.DETECTOR.DETPOS_IPHI -
1] <= fPx and fPx <= list_sectBoundary[
self.DETECTOR.DETPOS_IPHI]:
#Print to user - selected data points
#if self.DEBUG == True:
#print "{0}\t{1}\t{2}".format(i, fPx, fPy)
#store data point
list_clustADC_Fit_PkPos.append(fPy)
#Store this list as a numpy array and then remove all outliers
array_clustADC_Fit_PkPos = np.array(list_clustADC_Fit_PkPos)
array_clustADC_Fit_PkPos = rejectOutliers(array_clustADC_Fit_PkPos)
if self.DEBUG:
print "np.mean(list_clustADC_Fit_PkPos) = {0}".format(
np.mean(list_clustADC_Fit_PkPos))
print "np.mean(array_clustADC_Fit_PkPos) = {0}\t No Outliers".format(
str(np.mean(array_clustADC_Fit_PkPos)))
#Calculate Average
self.ADCPKPOS_SECTOR_AVG = np.mean(
array_clustADC_Fit_PkPos
) #Average of the fitted cluster ADC PkPos in defined (ieta,iphi) sector
self.ADCPKPOS_SECTOR_STDDEV = np.std(
array_clustADC_Fit_PkPos
) #Std Dev of the fitted cluster ADC PkPos in defined (ieta,iphi) sector
print "Avg PkPos = {0}+/-{1}".format(self.ADCPKPOS_SECTOR_AVG,
self.ADCPKPOS_SECTOR_STDDEV)
return
#Determine the average of the average cluster sizes within a single readout sector
def avgROSectorAvgClustSize(self):
#Load the plot
strPlotName = "SectorEta{0}/h_iEta{0}_clustSize_v_clustPos".format(
self.DETECTOR.DETPOS_IETA)
hSector_clustSize_v_clustPos = self.FILE_IN.Get(strPlotName)
#Calculate the iphi sector boundaries
list_sectBoundary = self.DETECTOR.calcROSectorBoundariesByEta(
self.DETECTOR.DETPOS_IETA)
#Print to user - Section Boundaries
if self.DEBUG == True:
for i in range(0, len(list_sectBoundary)):
print list_sectBoundary[i]
#Loop over points in the plot
list_avgClustSize = []
for i in range(1, hSector_clustSize_v_clustPos.GetNbinsX() + 1):
fBinCenter = hSector_clustSize_v_clustPos.GetXaxis().GetBinCenter(
i)
#Check if this point is within the defined (ieta,iphi) sector, if so store it for later use
if list_sectBoundary[
self.DETECTOR.DETPOS_IPHI -
1] <= fBinCenter and fBinCenter <= list_sectBoundary[
self.DETECTOR.DETPOS_IPHI]:
#Project out cluster size distribution for *this* slice
strPlotName = "h_iEta{0}Slice{1}_clustSize".format(
self.DETECTOR.DETPOS_IETA, i)
h_clustSize = hSector_clustSize_v_clustPos.ProjectionY(
strPlotName, i, i, "")
fAvgClustSize = h_clustSize.GetMean()
#store data point
list_avgClustSize.append(fAvgClustSize)
#Print to user - selected data points
if self.DEBUG == True:
print "{0}\t{1}\t{2}".format(i, fBinCenter, fAvgClustSize)
#Store this list as a numpy array and then remove all outliers
array_avgClustSize = np.array(list_avgClustSize)
array_avgClustSize = rejectOutliers(array_avgClustSize)
if self.DEBUG:
print "np.mean(list_avgClustSize) = {0}".format(
np.mean(list_avgClustSize))
print "np.mean(array_avgClustSize) = {0}\t No Outliers".format(
np.mean(array_avgClustSize))
#Calculate Average
self.AVGCLUSTSIZE_SECTOR_AVG = np.mean(
array_avgClustSize
) #Average of the fitted cluster ADC PkPos in defined (ieta,iphi) sector
self.AVGCLUSTSIZE_SECTOR_STDDEV = np.std(
array_avgClustSize
) #Std. Dev. of the fitted cluster ADC PkPos in defined (ieta,iphi) sector
print "Avg of Avg Clust Size = {0}+/-{1}".format(
self.AVGCLUSTSIZE_SECTOR_AVG, self.AVGCLUSTSIZE_SECTOR_STDDEV)
return
#alpha(x) = exp([0]*(x-x0) ) where x is hvPt and x0 is self.DET_IMON_QC5_RESP_UNI
def calcAlpha(self, hvPt):
return np.exp(self.GAIN_CALCULATOR.GAIN_CURVE_P0 *
(hvPt - self.DETECTOR.DET_IMON_QC5_RESP_UNI))
#Determines the linear correlation factor lambda which relates Gain to ADC counts
def calcROSectorLambda(self):
gain = self.GAIN_CALCULATOR.calcGain(self.DET_IMON_QC5_RESP_UNI)
gain_err = self.GAIN_CALCULATOR.calcGainErr(self.DET_IMON_QC5_RESP_UNI)
self.GAIN_LAMBDA = gain / self.ADCPKPOS_SECTOR_AVG
self.GAIN_LAMBDA_ERR = (1. / self.ADCPKPOS_SECTOR_AVG) * np.sqrt(
np.square(gain_err) + np.square(self.ADCPKPOS_SECTOR_STDDEV *
gain / self.ADCPKPOS_SECTOR_AVG) -
2. * gain_err * self.ADCPKPOS_SECTOR_STDDEV * gain /
self.ADCPKPOS_SECTOR_AVG)
print "lambda = {0}+/-{1}".format(self.GAIN_LAMBDA,
self.GAIN_LAMBDA_ERR)
return
#Determines the gain map from the absolute response uniformity map
def calcGainMap(self, strDetName):
#Load the absolute response uniformity map
strPlotName = "Summary/g2D_{0}_ResponseFitPkPos_AllEta".format(
strDetName)
if self.DEBUG:
print "Attempted to Load:"
print strPlotName
self.G2D_MAP_ABS_RESP_UNI = self.FILE_IN.Get(strPlotName)
#Setup the gain map
self.G2D_MAP_GAIN_ORIG.Set(self.G2D_MAP_ABS_RESP_UNI.GetN())
self.G2D_MAP_GAIN_ORIG.SetName("g2D_{0}_EffGain_AllEta_{1}".format(
strDetName, int(self.DET_IMON_QC5_RESP_UNI)))
#Get the arrays that make the response uniformity map
array_fPx = self.G2D_MAP_ABS_RESP_UNI.GetX()
array_fPy = self.G2D_MAP_ABS_RESP_UNI.GetY()
array_fPz = self.G2D_MAP_ABS_RESP_UNI.GetZ()
#Loop Over all Points of self.G2D_MAP_ABS_RESP_UNI
array_Gain_Vals = np.zeros(self.G2D_MAP_ABS_RESP_UNI.GetN())
array_PD_Vals = np.zeros(self.G2D_MAP_ABS_RESP_UNI.GetN())
for i in range(0, self.G2D_MAP_ABS_RESP_UNI.GetN()):
#Set the i^th point in self.G2D_MAP_GAIN_ORIG
array_Gain_Vals[i] = array_fPz[i] * self.GAIN_LAMBDA
array_PD_Vals[i] = self.PD_CALCULATOR.calcPD(array_fPz[i] *
self.GAIN_LAMBDA)
self.G2D_MAP_GAIN_ORIG.SetPoint(i, array_fPx[i], array_fPy[i],
array_fPz[i] * self.GAIN_LAMBDA)
#Store Average, Std. Dev., Max, & Min Gain
array_Gain_Vals = rejectOutliers(array_Gain_Vals)
self.DET_IMON_POINTS.append(self.DET_IMON_QC5_RESP_UNI)
self.GAIN_AVG_POINTS.append(np.mean(array_Gain_Vals))
self.GAIN_STDDEV_POINTS.append(np.std(array_Gain_Vals))
self.GAIN_MAX_POINTS.append(np.max(array_Gain_Vals))
self.GAIN_MIN_POINTS.append(np.min(array_Gain_Vals))
#Store Average, Std. Dev., Max & Min P_D
array_PD_Vals = rejectOutliers(array_PD_Vals)
self.PD_AVG_POINTS.append(np.mean(array_PD_Vals))
self.PD_STDDEV_POINTS.append(np.std(array_PD_Vals))
self.PD_MAX_POINTS.append(np.max(array_PD_Vals))
self.PD_MIN_POINTS.append(np.min(array_PD_Vals))
#Draw the effective gain map
canv_Gain_Map_Orig = TCanvas(
"canv_{0}_EffGain_AllEta_{1}".format(
strDetName, int(self.DET_IMON_QC5_RESP_UNI)),
"Gain Map - Original {0}".format(self.DET_IMON_QC5_RESP_UNI), 600,
600)
canv_Gain_Map_Orig.cd()
canv_Gain_Map_Orig.cd().SetLogz(1)
self.G2D_MAP_GAIN_ORIG.Draw("TRI2Z")
#Write the effective gain map to the output file
dir_hvOrig = self.FILE_OUT.mkdir("GainMap_HVPt{0}".format(
int(self.DET_IMON_QC5_RESP_UNI)))
dir_hvOrig.cd()
canv_Gain_Map_Orig.Write()
self.G2D_MAP_GAIN_ORIG.Write()
return
#Determines the gain map from the absolute response uniformity map for an arbitrary voltage
def calcGainMapHV(self, strDetName, hvPt):
#Create the new TGraph2D - Gain
g2D_Map_Gain_hvPt = TGraph2D(self.G2D_MAP_GAIN_ORIG.GetN())
g2D_Map_Gain_hvPt.SetName("g2D_{0}_EffGain_AllEta_{1}".format(
strDetName, int(hvPt)))
#Create the new TGraph2D - Discharge Probability
g2D_Map_PD_hvPt = TGraph2D(self.G2D_MAP_GAIN_ORIG.GetN())
g2D_Map_PD_hvPt.SetName("g2D_{0}_PD_AllEta_{1}".format(
strDetName, int(hvPt)))
#Get the arrays that make the response uniformity map
array_fPx = self.G2D_MAP_GAIN_ORIG.GetX()
array_fPy = self.G2D_MAP_GAIN_ORIG.GetY()
array_fPz = self.G2D_MAP_GAIN_ORIG.GetZ()
#Calculate alpha
alpha = self.calcAlpha(hvPt)
#Loop Over all Points of self.G2D_MAP_ABS_RESP_UNI
array_Gain_Vals = np.zeros(self.G2D_MAP_ABS_RESP_UNI.GetN())
array_PD_Vals = np.zeros(self.G2D_MAP_ABS_RESP_UNI.GetN())
for i in range(0, self.G2D_MAP_ABS_RESP_UNI.GetN()):
#Set the i^th point in self.G2D_MAP_GAIN_ORIG
array_Gain_Vals[i] = array_fPz[i] * alpha
array_PD_Vals[i] = self.PD_CALCULATOR.calcPD(array_fPz[i] * alpha)
g2D_Map_Gain_hvPt.SetPoint(i, array_fPx[i], array_fPy[i],
array_fPz[i] * alpha)
g2D_Map_PD_hvPt.SetPoint(
i, array_fPx[i], array_fPy[i],
self.PD_CALCULATOR.calcPD(array_fPz[i] * alpha))
#Store Average, Std. Dev., Max, & Min Gain
array_Gain_Vals = rejectOutliers(array_Gain_Vals)
self.DET_IMON_POINTS.append(hvPt)
self.GAIN_AVG_POINTS.append(np.mean(array_Gain_Vals))
self.GAIN_STDDEV_POINTS.append(np.std(array_Gain_Vals))
self.GAIN_MAX_POINTS.append(np.max(array_Gain_Vals))
self.GAIN_MIN_POINTS.append(np.min(array_Gain_Vals))
#Store Average, Std. Dev., Max & Min P_D
array_PD_Vals = rejectOutliers(array_PD_Vals)
self.PD_AVG_POINTS.append(np.mean(array_PD_Vals))
self.PD_STDDEV_POINTS.append(np.std(array_PD_Vals))
self.PD_MAX_POINTS.append(np.max(array_PD_Vals))
self.PD_MIN_POINTS.append(np.min(array_PD_Vals))
#Draw the effective gain map
canv_Gain_Map_hvPt = TCanvas(
"canv_{0}_EffGain_AllEta_{1}".format(strDetName, int(hvPt)),
"Gain Map - hvPt = {0}".format(hvPt), 600, 600)
canv_Gain_Map_hvPt.cd()
canv_Gain_Map_hvPt.cd().SetLogz(1)
g2D_Map_Gain_hvPt.Draw("TRI2Z")
#Draw the discharge probability map
canv_PD_Map_hvPt = TCanvas(
"canv_{0}_PD_AllEta_{1}".format(strDetName, int(hvPt)),
"Discharge Probability Map - hvPt = {0}".format(hvPt), 600, 600)
canv_PD_Map_hvPt.cd()
canv_PD_Map_hvPt.cd().SetLogz(1)
g2D_Map_PD_hvPt.Draw("TRI2Z")
#Write the effective gain map to the output file
dir_hvPt = self.FILE_OUT.mkdir("GainMap_HVPt{0}".format(int(hvPt)))
dir_hvPt.cd()
canv_Gain_Map_hvPt.Write()
g2D_Map_Gain_hvPt.Write()
canv_PD_Map_hvPt.Write()
g2D_Map_PD_hvPt.Write()
return g2D_Map_Gain_hvPt
#Determines the average cluster size map for the entire detector
def calcClusterSizeMap(self, strDetName):
#Create the container which will store the clusterSize
iNEtaSectors = len(self.DETECTOR.LIST_DET_GEO_PARAMS)
iNBinNum = self.ANA_UNI_GRANULARITY * iNEtaSectors * self.DETECTOR.LIST_DET_GEO_PARAMS[
0].NBCONNECT
array_shape = (iNBinNum, 3)
array_clustSize = np.zeros(array_shape)
#Create the average cluster size map
strPlotName = "g2D_{0}_AvgClustSize_AllEta_{1}".format(
strDetName, int(self.DET_IMON_QC5_RESP_UNI))
self.G2D_MAP_AVG_CLUST_SIZE_ORIG.Set(
iNBinNum) #Set number of pts, see comments above
self.G2D_MAP_AVG_CLUST_SIZE_ORIG.SetName(strPlotName)
self.G2D_MAP_AVG_CLUST_SIZE_ORIG.SetTitle("")
#Create the average cluster size map
strPlotName = "g2D_{0}_AvgClustSizeNormalized_AllEta_{1}".format(
strDetName, int(self.DET_IMON_QC5_RESP_UNI))
self.G2D_MAP_AVG_CLUST_SIZE_NORM.Set(
iNBinNum) #Set number of pts, see comments above
self.G2D_MAP_AVG_CLUST_SIZE_NORM.SetName(strPlotName)
self.G2D_MAP_AVG_CLUST_SIZE_NORM.SetTitle("")
for iEta in range(1, iNEtaSectors + 1):
#Get the Eta Sector
etaSector = self.DETECTOR.LIST_DET_GEO_PARAMS[iEta - 1]
#Load the cluster size vs cluster position plot for this iEta value
strPlotName = "SectorEta{0}/h_iEta{0}_clustSize_v_clustPos".format(
iEta)
if self.DEBUG:
print "Attempted to Load:"
print strPlotName
h_clustSize_v_clustPos = self.FILE_IN.Get(strPlotName)
#Loop over the x-bins of this plot
for iSlice in range(1, h_clustSize_v_clustPos.GetNbinsX() + 1):
#Project out cluster size distribution for *this* slice
strPlotName = "h_iEta{0}Slice{1}_clustSize".format(
iEta, iSlice)
h_clustSize = h_clustSize_v_clustPos.ProjectionY(
strPlotName, iSlice, iSlice, "")
#Store average cluster size, y-position and x-position
array_clustSize[
(iEta - 1) * h_clustSize_v_clustPos.GetNbinsX() + iSlice -
1] = (
h_clustSize_v_clustPos.GetXaxis().GetBinCenter(iSlice),
etaSector.SECTPOS, h_clustSize.GetMean())
#Set this point in the plot - Absolute
self.G2D_MAP_AVG_CLUST_SIZE_ORIG.SetPoint(
(iEta - 1) * h_clustSize_v_clustPos.GetNbinsX() + iSlice -
1,
h_clustSize_v_clustPos.GetXaxis().GetBinCenter(iSlice),
etaSector.SECTPOS, h_clustSize.GetMean())
#Set this point in the plot - Normalized
self.G2D_MAP_AVG_CLUST_SIZE_NORM.SetPoint(
(iEta - 1) * h_clustSize_v_clustPos.GetNbinsX() + iSlice -
1,
h_clustSize_v_clustPos.GetXaxis().GetBinCenter(iSlice),
etaSector.SECTPOS,
h_clustSize.GetMean() / self.AVGCLUSTSIZE_SECTOR_AVG)
#Print the cluster map to the user if requested
if self.DEBUG:
print "Average Cluster Size Map:"
print array_clustSize
#Draw the average cluster size map - Absolute
canv_AvgClustSize_Map_Orig = TCanvas(
"canv_{0}_AvgClustSize_AllEta_{1}".format(
strDetName, int(self.DET_IMON_QC5_RESP_UNI)),
"Average Cluster Size Map - Original {0}".format(
self.DET_IMON_QC5_RESP_UNI), 600, 600)
canv_AvgClustSize_Map_Orig.cd()
self.G2D_MAP_AVG_CLUST_SIZE_ORIG.Draw("TRI2Z")
#Draw the average cluster size map - Normalized
canv_AvgClustSize_Map_Norm = TCanvas(
"canv_{0}_AvgClustSizeNormalized_AllEta_{1}".format(
strDetName, int(self.DETECTOR.DET_IMON_QC5_RESP_UNI)),
"Average Cluster Size Map - Normalized {0}".format(
self.DETECTOR.DET_IMON_QC5_RESP_UNI), 600, 600)
canv_AvgClustSize_Map_Norm.cd()
self.G2D_MAP_AVG_CLUST_SIZE_NORM.Draw("TRI2Z")
#Write the average cluster size map to the output file
dir_hvOrig = self.FILE_OUT.GetDirectory(
"GainMap_HVPt{0}".format(int(self.DETECTOR.DET_IMON_QC5_RESP_UNI)),
False, "GetDirectory")
dir_hvOrig.cd()
canv_AvgClustSize_Map_Orig.Write()
self.G2D_MAP_AVG_CLUST_SIZE_ORIG.Write()
canv_AvgClustSize_Map_Norm.Write()
self.G2D_MAP_AVG_CLUST_SIZE_NORM.Write()
return
#Closes TFiles
def closeTFiles(self, debug=False):
if self.FILE_IN.IsOpen():
self.FILE_IN.Close()
if self.FILE_OUT.IsOpen():
self.FILE_OUT.Close()
return
#Plot Average Gain Over Entire Detector Area
def plotGainSummary(self, strDetName):
#Create the Plot - Average
gDet_AvgEffGain = TGraphErrors(len(self.GAIN_AVG_POINTS))
gDet_AvgEffGain.SetName("g_{0}_EffGainAvg".format(strDetName))
#Create the Plot - Max Gain
gDet_MaxEffGain = TGraphErrors(len(self.GAIN_MAX_POINTS))
gDet_MaxEffGain.SetName("g_{0}_EffGainMax".format(strDetName))
#Create the Plot - Min Gain
gDet_MinEffGain = TGraphErrors(len(self.GAIN_MIN_POINTS))
gDet_MinEffGain.SetName("g_{0}_EffGainMin".format(strDetName))
#Set and print the points
#print "===============Printing Gain Data==============="
#print "[BEGIN_DATA]"
#print "\tVAR_INDEP,VAR_DEP,VAR_DEP_ERR"
for i in range(0, len(self.GAIN_AVG_POINTS)):
#Average
gDet_AvgEffGain.SetPoint(i, self.DET_IMON_POINTS[i],
self.GAIN_AVG_POINTS[i])
gDet_AvgEffGain.SetPointError(i, 0, self.GAIN_STDDEV_POINTS[i])
#print "\t%f,%f,%f"%(self.DET_IMON_POINTS[i],self.GAIN_AVG_POINTS[i],self.GAIN_STDDEV_POINTS[i])
#Max
gDet_MaxEffGain.SetPoint(i, self.DET_IMON_POINTS[i],
self.GAIN_MAX_POINTS[i])
#Min
gDet_MinEffGain.SetPoint(i, self.DET_IMON_POINTS[i],
self.GAIN_MIN_POINTS[i])
pass
#print "[END_DATA]"
#print ""
#Draw
canv_AvgEffGain = TCanvas(
"canv_{0}_EffGainAvg".format(strDetName),
"{0} Average Effective Gain".format(strDetName), 600, 600)
canv_AvgEffGain.cd()
canv_AvgEffGain.cd().SetLogy()
gDet_AvgEffGain.GetXaxis().SetTitle("HV")
gDet_AvgEffGain.GetYaxis().SetTitle("#LT Effective Gain #GT")
gDet_AvgEffGain.GetYaxis().SetRangeUser(1e2, 1e6)
gDet_AvgEffGain.SetMarkerStyle(21)
gDet_AvgEffGain.Draw("AP")
gDet_MaxEffGain.Draw("sameL")
gDet_MinEffGain.Draw("sameL")
#Write
dir_Summary = self.FILE_OUT.mkdir("Summary")
dir_Summary.cd()
canv_AvgEffGain.Write()
gDet_AvgEffGain.Write()
gDet_MaxEffGain.Write()
gDet_MinEffGain.Write()
return
#Plot Average Gain Over Entire Detector Area
def plotPDSummary(self, strDetName):
#Create the Plot - Average
gDet_AvgPD = TGraphErrors(len(self.PD_AVG_POINTS))
gDet_AvgPD.SetName("g_{0}_PDAvg".format(strDetName))
#Create the Plot - Max Gain
gDet_MaxPD = TGraphErrors(len(self.PD_MAX_POINTS))
gDet_MaxPD.SetName("g_{0}_PDMax".format(strDetName))
#Create the Plot - Min Gain
gDet_MinPD = TGraphErrors(len(self.PD_MIN_POINTS))
gDet_MinPD.SetName("g_" + strDetName + "_PDMin")
gDet_MinPD.SetName("g_{0}_PDMin".format(strDetName))
#Set the points
for i in range(0, len(self.PD_AVG_POINTS)):
#Average
gDet_AvgPD.SetPoint(i, self.GAIN_AVG_POINTS[i],
self.PD_AVG_POINTS[i])
gDet_AvgPD.SetPointError(i, self.GAIN_STDDEV_POINTS[i],
self.PD_STDDEV_POINTS[i])
#Max
gDet_MaxPD.SetPoint(i, self.GAIN_AVG_POINTS[i],
self.PD_MAX_POINTS[i])
#Min
gDet_MinPD.SetPoint(i, self.GAIN_AVG_POINTS[i],
self.PD_MIN_POINTS[i])
#Draw
canv_AvgPD = TCanvas("canv_{0}_PDAvg".format(strDetName),
"{0} Discharge Probability".format(strDetName),
600, 600)
canv_AvgPD.cd()
canv_AvgPD.cd().SetLogx()
canv_AvgPD.cd().SetLogy()
gDet_AvgPD.GetXaxis().SetTitle("#LT Effective Gain #GT")
gDet_AvgPD.GetYaxis().SetTitle("Discharge Probability P_{D}")
gDet_AvgPD.GetYaxis().SetRangeUser(1e-11, 1e-6)
gDet_AvgPD.SetMarkerStyle(21)
gDet_AvgPD.Draw("AP")
gDet_MaxPD.Draw("sameL")
gDet_MinPD.Draw("sameL")
#Write
dir_Summary = self.FILE_OUT.GetDirectory("Summary")
dir_Summary.cd()
canv_AvgPD.Write()
gDet_AvgPD.Write()
gDet_MaxPD.Write()
gDet_MinPD.Write()
return
#Open Input File
def openInputFile(self, inputfilename):
self.FILE_IN = TFile(str(inputfilename), "READ", "", 1)
return
#Set the detector
def setDetector(self, params_det=PARAMS_DET()):
self.DETECTOR = params_det
return
#can't make NewPage for display
numplots = len(parsedPlots)
if (options.outputfile == "DISPLAY") and (numplots > maxperlist) :
printfunc ("ERROR: too many hists to print to display")
sys.exit(1)
from ROOT import TFile
#opening root files
for rootopt in parsedRoots :
if not isfile(rootopt.filename) :
printfunc ("ERROR: unexistent file:",rootopt.filename)
sys.exit(1)
root = TFile(rootopt.filename,"read")
if root.IsOpen() == 0 :
printfunc ("ERROR: can't open the file:",rootopt.filename)
sys.exit(1)
rootopt.rootfile = root
rootopt.tree = root.Get("COL/1")
printfunc ("Creating plots...")
plots = createPlots(parsedPlots,parsedRoots)
printfunc ("Filling plots...")
fillPlots(plots,parsedPlots,parsedRoots,eventext)
if (options.divide) :
printfunc ("Calculating ratio")
rootopt1 = parsedRoots.pop(0)
dividePlots(plots,rootopt1)
