def plot(file, var, region):
signalRegion = False
stack = StackMaker(config, var, region, signalRegion)
histosL = []
overlayL = []
print file
datas = []
datasL = []
for th1 in get_th1(file):
print th1.GetBinLowEdge(0)
if 'VH' in th1.GetName():
overlayL.append(th1)
th1.SetLineWidth(1)
if 'data_obs' in th1.GetName():
datasL.append(th1)
else:
histosL.append(th1)
print 'histoL'
print histosL
typs = []
typsL = []
overlay_typs = []
#append the name just once
for histo in histosL:
typsL.append(histo.GetName())
print histo.GetName()
if 'VH' in histo.GetName():
overlay_typs.append(histo.GetName())
print typsL
print 'Overlay list'
print overlayL
overlay_histo_dict = HistoMaker.orderandadd([{
overlay_typs[i]: overlayL[i]
} for i in range(len(overlayL))], ['VH', 'VV'])
overlayL2 = []
stack.histos = histosL
stack.typs = typsL
stack.datas = datasL
stack.datanames = region
for key in overlay_histo_dict:
overlayL2.append(overlay_histo_dict[key])
appendix = ''
stack.options['pdfName'] = stack.options['pdfName'].replace(
'.pdf', '_unweighted.' + opts.format)
stack.lumi = 18940
stack.doPlot()
print 'i am done!\n'
def plot(file,var,region):
signalRegion = False
stack = StackMaker(config,var,region,signalRegion)
histosL = []
overlayL = []
print file
datas = []
datasL = []
for th1 in get_th1(file):
print th1.GetBinLowEdge(0)
if 'VH' in th1.GetName():
overlayL.append(th1)
th1.SetLineWidth(1)
if 'data_obs' in th1.GetName():
datasL.append(th1)
else:
histosL.append(th1)
print 'histoL'
print histosL
typs = []
typsL = []
overlay_typs=[]
#append the name just once
for histo in histosL:
typsL.append(histo.GetName())
print histo.GetName()
if 'VH' in histo.GetName():
overlay_typs.append(histo.GetName())
print typsL
print 'Overlay list'
print overlayL
overlay_histo_dict = HistoMaker.orderandadd([{overlay_typs[i]:overlayL[i]} for i in range(len(overlayL))],['VH','VV'])
overlayL2=[]
stack.histos = histosL
stack.typs = typsL
stack.datas = datasL
stack.datanames=region
for key in overlay_histo_dict:
overlayL2.append(overlay_histo_dict[key])
appendix = ''
stack.options['pdfName'] = stack.options['pdfName'].replace('.pdf','_unweighted.'+opts.format)
stack.lumi = 18940
stack.doPlot()
print 'i am done!\n'
def doPlot():
#print "Read Ploting Region information"
#print "===============================\n"
if var_to_plot_:
vars = [var_to_plot_]
else:
vars = (config.get(section, 'vars')).split(',')#get the variables to be ploted in each region
print "The variables are", vars, "\n"
data = eval(config.get(section,'Datas'))# read the data corresponding to each CR (section)
mc = eval(config.get('Plot_general','samples'))# read the list of mc samples
total_lumi = eval(config.get('Plot_general','lumi'))
#print 'total lumi is', total_lumi
print "The list of mc samples is", mc
remove_sys_ = eval(config.get('Plot_general','remove_sys'))
#print "Check if is Signal Region"
#print "=========================\n"
SignalRegion = False
if config.has_option(section,'Signal'):
mc.append(config.get(section,'Signal'))
SignalRegion = True
#print "After addind the signal, the mc is", mc
#print "Getting information on data and mc samples"
#print "==========================================\n"
#print "Getting data sample"
datasamples = info.get_samples(data)
#print "datasamples is\n", datasamples
#print "Getting mc sample"
mcsamples = info.get_samples(mc)
#print "mc sample is\n"
#for sample in mcsamples:
#print "sample name", sample.name
GroupDict = eval(config.get('Plot_general','Group'))#Contained in plots. Listed in general, under Group [Samples] group. This is a dictionnary that descriebes what samples should share the same color under the stack plot.
#GETALL AT ONCE
options = []
Stacks = []
#print "Start Loop over the list of variables(to fill the StackMaker )" print "==============================================================\n"
for i in range(len(vars)):# loop over the list of variables to be ploted in each reagion
#print "The variable is ", vars[i], "\n"
Stacks.append(StackMaker(config,vars[i],region,SignalRegion, None, '_'+subcut_plotname))# defined in myutils DoubleStackMaker. The StackMaker merge together all the informations necessary to perform the plot (plot region, variables, samples and signal region ). "options" contains the variables information, including the cuts.
options.append(Stacks[i].options)
#print "Finished Loop over the list of variables(to fill the StackMaker )"
#print "================================================================\n"
#Prepare cached files in the temporary (tmpSamples) folder.
#def __init__(self, samples, path, config, optionsList, GroupDict=None, filelist=None, mergeplot=False, sample_to_merge=None, mergecaching=False):
Plotter=HistoMaker(samples=mcsamples+datasamples, path=path, config=config, optionsList=options, GroupDict=GroupDict, filelist=filelist, mergeplot=opts.mergeplot, sample_to_merge=sample_to_merge_, mergeCachingPart=mergeCachingPart, plotMergeCached = opts.mergecachingplot, branch_to_keep=None, dccut=None,remove_sys=remove_sys_)
if len(filelist)>0 or opts.mergeplot:
print('ONLY CACHING PERFORMED, EXITING');
sys.exit(1)
#print '\nProducing Plot of %s\n'%vars[v]
Lhistos = [[] for _ in range(0,len(vars))]
Ltyps = [[] for _ in range(0,len(vars))]
Ldatas = [[] for _ in range(0,len(vars))]
Ldatatyps = [[] for _ in range(0,len(vars))]
Ldatanames = [[] for _ in range(0,len(vars))]
Ljobnames = [[] for _ in range(0,len(vars))]
#print "Summing up the Luminosity"
#print "=========================\n"
#! Sums up the luminosity of the data:
lumicounter=0.
lumi=0.
if datasamples == []:
lumi = total_lumi
else:
print "Will run over datasamples to sum up the lumi"
for job in datasamples:
print "Datasample is", job
lumi+=float(job.lumi)
lumicounter+=1.
if lumicounter > 0:
lumi=lumi/lumicounter
print "The lumi is", lumi, "\n"
Plotter.lumi=lumi
mass = Stacks[0].mass
#print 'mcsamples',mcsamples
inputs=[]
for job in mcsamples:
# print 'job.name'
# cutOverWrite = None
# if addBlindingCut:
# cutOverWrite = config.get('Cuts',region)+' & ' + addBlindingCut
inputs.append((Plotter,"get_histos_from_tree",(job,True, subcut_, '1')))
#print 'inputs are', inputs
# if('pisa' in config.get('Configuration','whereToLaunch')):
multiprocess=int(config.get('Configuration','nprocesses'))
# multiprocess=0
start_time = time.time()
outputs = []
if multiprocess>1:
from multiprocessing import Pool
from myutils import GlobalFunction
p = Pool(multiprocess)
#print 'launching get_histos_from_tree with ',multiprocess,' processes'
outputs = p.map(GlobalFunction, inputs)
else:
#print 'launching get_histos_from_tree with ',multiprocess,' processes'
for input_ in inputs:
outputs.append(getattr(input_[0],input_[1])(*input_[2])) #ie. Plotter.get_histos_from_tree(job)
#print 'get_histos_from_tree DONE'
Overlaylist = []
print "All histograms retrived. DONE in ", str(time.time() - start_time)," s."
start_time = time.time()
for i,job in enumerate(mcsamples):
hDictList = outputs[i]
if job.name in mass:
histoList = []
for v in range(0,len(vars)):
histoCopy = deepcopy(hDictList[v].values()[0])
histoCopy.SetTitle(job.name)
histoList.append(histoCopy)
Overlaylist.append(histoList)
for v in range(0,len(vars)):
Lhistos[v].append(hDictList[v].values()[0])
Ltyps[v].append(hDictList[v].keys()[0])
Ljobnames[v].append(job.name)
#print "len(vars)=",len(vars)
#print "Ltyps is", Ltyps
##invert Overlaylist[variable][job] -> Overlaylist[job][variable]
#print "len(Overlaylist) before: ",len(Overlaylist)
#print "Overlaylist",Overlaylist
# newOverlaylist = [[None]*len(Overlaylist)]*len(vars)
# for i,OverlaySameSample in enumerate(Overlaylist):
# for j,Overlay in enumerate(OverlaySameSample):
# newOverlaylist[j][i] = Overlay
# Overlaylist = newOverlaylist
Overlaylist = [list(a) for a in zip(*Overlaylist)]
#print "len(Overlaylist) after: ",len(Overlaylist)
#print "Overlaylist",Overlaylist
##merge overlays in groups
for i in range(len(Overlaylist)):
newhistos = {}
#print "len(Overlaylist[i]):",Overlaylist[i]
for histo in Overlaylist[i]:
#print "histo.GetName()",histo.GetName(),
#print "histo.GetTitle()",histo.GetTitle(),
group = GroupDict[histo.GetTitle()]
if not group in newhistos.keys():
histo.SetTitle(group)
newhistos[group]=histo
else:
#print "Before newhistos[group].Integral()",newhistos[group].Integral(),
newhistos[group].Add(histo)
#print "After newhistos[group].Integral()",newhistos[group].Integral()
Overlaylist[i] = newhistos.values()
# ### ORIGINAL ###
# print 'mcsamples',mcsamples
# for job in mcsamples:
# print 'job.name',job.name
# #hTempList, typList = Plotter.get_histos_from_tree(job)
# if addBlindingCut:
# hDictList = Plotter.get_histos_from_tree(job,config.get('Cuts',region)+' & ' + addBlindingCut)
# else:
# print 'going to get_histos_from_tree'
# hDictList = Plotter.get_histos_from_tree(job)
# if job.name == mass:
# print 'job.name', job.name
# Overlaylist= deepcopy([hDictList[v].values()[0] for v in range(0,len(vars))])
# for v in range(0,len(vars)):
# Lhistos[v].append(hDictList[v].values()[0])
# Ltyps[v].append(hDictList[v].keys()[0])
# Ljobnames[v].append(job.name)
#print "DATA samples\n"
#! Get the data histograms
for job in datasamples:
if addBlindingCut:
if subcut_: dDictList = Plotter.get_histos_from_tree(job,True, config.get('Cuts',region)+' & ' + addBlindingCut +' & ' + subcut_)
else: dDictList = Plotter.get_histos_from_tree(job, True, config.get('Cuts',region)+' & ' + addBlindingCut, '1')
else:
if subcut_: dDictList = Plotter.get_histos_from_tree(job, True, config.get('Cuts',region)+' & ' + subcut_)
else: dDictList = Plotter.get_histos_from_tree(job, True, None, '1')
#! add the variables list for each job (Samples)
for v in range(0,len(vars)):
Ldatas[v].append(dDictList[v].values()[0])
Ldatatyps[v].append(dDictList[v].keys()[0])
Ldatanames[v].append(job.name)
for v in range(0,len(vars)):
#print "Ltyps[v]:",Ltyps[v]
#print "Lhistos[v]:",Lhistos[v]
#print "Ldatas[v]:",Ldatas[v]
#print "Ldatatyps[v]:",Ldatatyps[v]
#print "Ldatanames[v]:",Ldatanames[v]
#print "lumi:",lumi
histos = Lhistos[v]
typs = Ltyps[v]
Stacks[v].histos = Lhistos[v]
Stacks[v].typs = Ltyps[v]
Stacks[v].datas = Ldatas[v]
Stacks[v].datatyps = Ldatatyps[v]
Stacks[v].datanames= Ldatanames[v]
try:
if SignalRegion:
Stacks[v].overlay = Overlaylist[v] ## from
except Exception as e:
print "NO OVERLAY LIST:", e
Stacks[v].lumi = lumi
Stacks[v].jobnames= Ljobnames[v]
Stacks[v].normalize = eval(config.get(section,'Normalize'))
#print 'Stack[v].subcut is', Stacks[v].subcut
#if subcut_plotname: Stacks[v].subcut = '_'+subcut_plotname
#print 'again, Stack[v].subcut is', Stacks[v].subcut
Stacks[v].doPlot()
##FIXME##
# Stacks[v].histos = Lhistos[v]
# Stacks[v].typs = Ltyps[v]
# Stacks[v].datas = Ldatas[v]
# Stacks[v].datatyps = Ldatatyps[v]
# Stacks[v].datanames= Ldatanames[v]
# Stacks[v].normalize = True
# Stacks[v].options['pdfName'] = Stacks[v].options['pdfName'].replace('.pdf','_norm.pdf')
# Stacks[v].doPlot()
print 'i am done!\n'
print "Last part DONE in ", str(time.time() - start_time)," s."
print "tree_stack done in ", str(time.time() - begin_time)," s."
print 'DOPLOT END'
def doPlot():
#print "Read Ploting Region information"
#print "===============================\n"
vars = (config.get(section, 'vars')).split(
',') #get the variables to be ploted in each region
#print "The variables are", vars, "\n"
data = eval(config.get(
section, 'Datas')) # read the data corresponding to each CR (section)
mc = eval(config.get('Plot_general',
'samples')) # read the list of mc samples
total_lumi = eval(config.get('Plot_general', 'lumi'))
#print 'total lumi is', total_lumi
print "The list of mc samples is", mc
#print "Check if is Signal Region"
#print "=========================\n"
SignalRegion = False
if config.has_option(section, 'Signal'):
mc.append(config.get(section, 'Signal'))
SignalRegion = True
#print "After addind the signal, the mc is", mc
#print "Getting information on data and mc samples"
#print "==========================================\n"
#print "Getting data sample"
datasamples = info.get_samples(data)
#print "datasamples is\n", datasamples
#print "Getting mc sample"
mcsamples = info.get_samples(mc)
#print "mc sample is\n"
#for sample in mcsamples:
#print "sample name", sample.name
GroupDict = eval(
config.get('Plot_general', 'Group')
) #Contained in plots. Listed in general, under Group [Samples] group. This is a dictionnary that descriebes what samples should share the same color under the stack plot.
#GETALL AT ONCE
options = []
Stacks = []
#print "Start Loop over the list of variables(to fill the StackMaker )" print "==============================================================\n"
for i in range(
len(vars)
): # loop over the list of variables to be ploted in each reagion
#print "The variable is ", vars[i], "\n"
Stacks.append(
StackMaker(config, vars[i], region, SignalRegion)
) # defined in myutils DoubleStackMaker. The StackMaker merge together all the informations necessary to perform the plot (plot region, variables, samples and signal region ). "options" contains the variables information, including the cuts.
options.append(Stacks[i].options)
#print "Finished Loop over the list of variables(to fill the StackMaker )"
#print "================================================================\n"
#print 'loop options',options
# print 'options',options
#Prepare cached files in the temporary (tmpSamples) folder.
Plotter = HistoMaker(mcsamples + datasamples, path, config, options,
GroupDict, filelist, opts.mergeplot)
if len(filelist) > 0 or opts.mergeplot:
print('ONLY CACHING PERFORMED, EXITING')
sys.exit(1)
#print '\nProducing Plot of %s\n'%vars[v]
Lhistos = [[] for _ in range(0, len(vars))]
Ltyps = [[] for _ in range(0, len(vars))]
Ldatas = [[] for _ in range(0, len(vars))]
Ldatatyps = [[] for _ in range(0, len(vars))]
Ldatanames = [[] for _ in range(0, len(vars))]
Ljobnames = [[] for _ in range(0, len(vars))]
#print "Summing up the Luminosity"
#print "=========================\n"
#! Sums up the luminosity of the data:
lumicounter = 0.
lumi = 0.
if datasamples == []:
lumi = total_lumi
else:
print "Will run over datasamples to sum up the lumi"
for job in datasamples:
print "Datasample is", job
lumi += float(job.lumi)
lumicounter += 1.
if lumicounter > 0:
lumi = lumi / lumicounter
print "The lumi is", lumi, "\n"
Plotter.lumi = lumi
mass = Stacks[0].mass
#print 'mcsamples',mcsamples
inputs = []
for job in mcsamples:
# print 'job.name'
# cutOverWrite = None
# if addBlindingCut:
# cutOverWrite = config.get('Cuts',region)+' & ' + addBlindingCut
inputs.append((Plotter, "get_histos_from_tree", (job, True)))
#print 'inputs are', inputs
# if('pisa' in config.get('Configuration','whereToLaunch')):
multiprocess = int(config.get('Configuration', 'nprocesses'))
# multiprocess=0
outputs = []
if multiprocess > 1:
from multiprocessing import Pool
from myutils import GlobalFunction
p = Pool(multiprocess)
#print 'launching get_histos_from_tree with ',multiprocess,' processes'
outputs = p.map(GlobalFunction, inputs)
else:
#print 'launching get_histos_from_tree with ',multiprocess,' processes'
for input_ in inputs:
outputs.append(getattr(
input_[0],
input_[1])(*input_[2])) #ie. Plotter.get_histos_from_tree(job)
#print 'get_histos_from_tree DONE'
Overlaylist = []
for i, job in enumerate(mcsamples):
#print 'job.name',job.name,"mass==",mass
#hTempList, typList = Plotter.get_histos_from_tree(job)
hDictList = outputs[i]
if job.name in mass:
#print 'job.name == mass'
histoList = []
for v in range(0, len(vars)):
histoCopy = deepcopy(hDictList[v].values()[0])
histoCopy.SetTitle(job.name)
histoList.append(histoCopy)
Overlaylist.append(histoList)
# Overlaylist.append(deepcopy([hDictList[v].values()[0] for v in range(0,len(vars))]))
# >>>>>>> silviodonato/master
for v in range(0, len(vars)):
Lhistos[v].append(hDictList[v].values()[0])
Ltyps[v].append(hDictList[v].keys()[0])
Ljobnames[v].append(job.name)
#print "len(vars)=",len(vars)
#print "Ltyps is", Ltyps
##invert Overlaylist[variable][job] -> Overlaylist[job][variable]
#print "len(Overlaylist) before: ",len(Overlaylist)
#print "Overlaylist",Overlaylist
# newOverlaylist = [[None]*len(Overlaylist)]*len(vars)
# for i,OverlaySameSample in enumerate(Overlaylist):
# for j,Overlay in enumerate(OverlaySameSample):
# newOverlaylist[j][i] = Overlay
# Overlaylist = newOverlaylist
Overlaylist = [list(a) for a in zip(*Overlaylist)]
#print "len(Overlaylist) after: ",len(Overlaylist)
#print "Overlaylist",Overlaylist
##merge overlays in groups
for i in range(len(Overlaylist)):
newhistos = {}
#print "len(Overlaylist[i]):",Overlaylist[i]
for histo in Overlaylist[i]:
#print "histo.GetName()",histo.GetName(),
#print "histo.GetTitle()",histo.GetTitle(),
group = GroupDict[histo.GetTitle()]
if not group in newhistos.keys():
histo.SetTitle(group)
newhistos[group] = histo
else:
#print "Before newhistos[group].Integral()",newhistos[group].Integral(),
newhistos[group].Add(histo)
#print "After newhistos[group].Integral()",newhistos[group].Integral()
Overlaylist[i] = newhistos.values()
# ### ORIGINAL ###
# print 'mcsamples',mcsamples
# for job in mcsamples:
# print 'job.name',job.name
# #hTempList, typList = Plotter.get_histos_from_tree(job)
# if addBlindingCut:
# hDictList = Plotter.get_histos_from_tree(job,config.get('Cuts',region)+' & ' + addBlindingCut)
# else:
# print 'going to get_histos_from_tree'
# hDictList = Plotter.get_histos_from_tree(job)
# if job.name == mass:
# print 'job.name', job.name
# Overlaylist= deepcopy([hDictList[v].values()[0] for v in range(0,len(vars))])
# for v in range(0,len(vars)):
# Lhistos[v].append(hDictList[v].values()[0])
# Ltyps[v].append(hDictList[v].keys()[0])
# Ljobnames[v].append(job.name)
#print "DATA samples\n"
#! Get the data histograms
for job in datasamples:
if addBlindingCut:
dDictList = Plotter.get_histos_from_tree(
job,
config.get('Cuts', region) + ' & ' + addBlindingCut)
else:
dDictList = Plotter.get_histos_from_tree(job)
#! add the variables list for each job (Samples)
for v in range(0, len(vars)):
Ldatas[v].append(dDictList[v].values()[0])
Ldatatyps[v].append(dDictList[v].keys()[0])
Ldatanames[v].append(job.name)
for v in range(0, len(vars)):
#print "Ltyps[v]:",Ltyps[v]
#print "Lhistos[v]:",Lhistos[v]
#print "Ldatas[v]:",Ldatas[v]
#print "Ldatatyps[v]:",Ldatatyps[v]
#print "Ldatanames[v]:",Ldatanames[v]
#print "lumi:",lumi
histos = Lhistos[v]
typs = Ltyps[v]
Stacks[v].histos = Lhistos[v]
Stacks[v].typs = Ltyps[v]
Stacks[v].datas = Ldatas[v]
Stacks[v].datatyps = Ldatatyps[v]
Stacks[v].datanames = Ldatanames[v]
if SignalRegion:
Stacks[v].overlay = Overlaylist[v] ## from
Stacks[v].lumi = lumi
Stacks[v].jobnames = Ljobnames[v]
Stacks[v].normalize = eval(config.get(section, 'Normalize'))
Stacks[v].doPlot()
##FIXME##
# Stacks[v].histos = Lhistos[v]
# Stacks[v].typs = Ltyps[v]
# Stacks[v].datas = Ldatas[v]
# Stacks[v].datatyps = Ldatatyps[v]
# Stacks[v].datanames= Ldatanames[v]
# Stacks[v].normalize = True
# Stacks[v].options['pdfName'] = Stacks[v].options['pdfName'].replace('.pdf','_norm.pdf')
# Stacks[v].doPlot()
print 'i am done!\n'
print 'DOPLOT END'
def plot(fileList):
signalRegion = True
region = 'plot'
var = 'Hmass'
stack = StackMaker(config,var,region,signalRegion)
histosL = []
overlayL = []
#7-9 for the higgs
#5-6 for the VV
binmin=7
binmax=9
max_sb = 0
max_ssb = 0
for file in fileList:
if max_sb < get_s_over_b(file,binmin,binmax):
max_sb = get_s_over_b(file,binmin,binmax)
if max_ssb < get_s_over_sb(file,binmin,binmax):
max_ssb = get_s_over_sb(file,binmin,binmax)
print max_ssb
print max_sb
for file in fileList:
print file
print get_s_over_b(file,binmin,binmax)
if eval(opts.rescale) == False:
max_sb = 1.
max_ssb = 1.
for th1 in get_th1(file):
#th1.Sumw2()
if 's/b' in opts.fom:
th1.Scale(get_s_over_b(file,binmin,binmax)/max_sb)
if 's/s+b' in opts.fom:
th1.Scale(get_s_over_sb(file,binmin,binmax)/max_ssb)
if 'VV' in th1.GetName():
th1.SetName('VV')
if 'Zj1b' in th1.GetName():
th1.SetName('Zj2b')
if 'Wj1b' in th1.GetName():
th1.SetName('Wj2b')
# new stack for the overlay plot
if 'VH' in th1.GetName() or 'VV' in th1.GetName():
overlayL.append(th1)
histosL.append(th1)
print 'histoL'
print histosL
typs = []
typsL = []
datas = []
datasL = []
overlay_typs=[]
#append the name just once
for histo in histosL:
typsL.append(histo.GetName())
if 'data' in histo.GetName():
datasL.append(histo)
if 'VH' in histo.GetName() or 'VV' in histo.GetName():
overlay_typs.append(histo.GetName())
#datasL.append(datas)
#typsL.append(typs)
print typsL
print 'Overlay list'
print overlayL
overlay_histo_dict = HistoMaker.orderandadd([{overlay_typs[i]:overlayL[i]} for i in range(len(overlayL))],['VH','VV'])
overlayL2=[]
stack.histos = histosL
stack.typs = typsL
stack.datas = datasL
# stack.datatyps = Ldatatyps[v]
stack.datanames='data_obs'
for key in overlay_histo_dict:
overlayL2.append(overlay_histo_dict[key])
mjj_sub = eval(opts.sub)
if not mjj_sub:
stack.overlay = overlayL2
appendix = ''
if(eval(opts.rescale) == True):
appendix = '_rescaled_'
if 's/s+b' in opts.fom:
stack.options['pdfName'] = stack.options['pdfName'].replace('.pdf','_combined78tev_postFit_s_over_sb'+appendix+'.'+opts.format)
elif 's/b' in opts.fom:
stack.options['pdfName'] = stack.options['pdfName'].replace('.pdf','_combined78tev_postFit_s_over_b'+appendix+'.'+opts.format)
else:
stack.options['pdfName'] = stack.options['pdfName'].replace('.pdf','_unweighted.'+opts.format)
# stack.options['pdfName'] = stack.options['pdfName'].replace('.pdf','_highPt_7tev.pdf')
# stack.options['pdfName'] = stack.options['pdfName'].replace('.pdf','_combined_postFit_s_over_b_Hpt_weight_1.pdf'
stack.lumi = 18940
if mjj_sub == False:
stack.doPlot()
elif mjj_sub == True:
stack.options['pdfName'] = stack.options['pdfName'].replace('.'+opts.format,'_subtracted.'+opts.format)
stack.doSubPlot(['VH','VV'])
print 'i am done!\n'
## This is now incorporated in main SYS loop(line 243)
#UEPS
#for weightF_sys in weightF_systematics:
#for _weight in [config.get('Weights','%s_UP' %(weightF_sys)),config.get('Weights','%s_DOWN' %(weightF_sys))]:
# _cut = treecut
# _treevar = treevar
# _name = title
# appendList()
print '\n-----> Run MC HistoMaker...'
mc_hMaker = HistoMaker(all_samples,path,config,optionsList,GroupDict)
print '\n-----> Run Data HistoMaker...'
data_hMaker = HistoMaker(data_samples,path,config,[optionsList[0]])
#Calculate lumi
lumi = 0.
nData = 0
for job in data_samples:
nData += 1
lumi += float(job.lumi)
if nData > 1:
lumi = lumi/float(nData)
mc_hMaker.lumi = lumi
def plot(fileList):
signalRegion = True
#region = 'plot'
region = opts.region
var = 'HCSVmass'
stack = StackMaker(config, var, region, signalRegion)
histosL = []
overlayL = []
#7-9 for the higgs
#5-6 for the VV
binmin = 7
binmax = 9
max_sb = 0
max_ssb = 0
for file in fileList:
if max_sb < get_s_over_b(file, binmin, binmax):
max_sb = get_s_over_b(file, binmin, binmax)
if max_ssb < get_s_over_sb(file, binmin, binmax):
max_ssb = get_s_over_sb(file, binmin, binmax)
print max_ssb
print max_sb
for file in fileList:
print file
print get_s_over_b(file, binmin, binmax)
if eval(opts.rescale) == False:
max_sb = 1.
max_ssb = 1.
for th1 in get_th1(file):
#th1.Sumw2()
if 's/b' in opts.fom:
th1.Scale(get_s_over_b(file, binmin, binmax) / max_sb)
if 's/s+b' in opts.fom:
th1.Scale(get_s_over_sb(file, binmin, binmax) / max_ssb)
if 'VV' in th1.GetName():
th1.SetName('VV')
if 'Zj1b' in th1.GetName():
th1.SetName('Zj2b')
if 'Wj1b' in th1.GetName():
th1.SetName('Wj2b')
# new stack for the overlay plot
if 'VH' in th1.GetName() or 'VV' in th1.GetName():
overlayL.append(th1)
histosL.append(th1)
print 'histoL'
print histosL
typs = []
typsL = []
datas = []
datasL = []
overlay_typs = []
#append the name just once
for histo in histosL:
typsL.append(histo.GetName())
if 'data' in histo.GetName():
datasL.append(histo)
if 'VH' in histo.GetName() or 'VV' in histo.GetName():
overlay_typs.append(histo.GetName())
#datasL.append(datas)
#typsL.append(typs)
print typsL
print 'Overlay list'
print overlayL
overlay_histo_dict = HistoMaker.orderandadd([{
overlay_typs[i]: overlayL[i]
} for i in range(len(overlayL))], ['VH', 'VV'])
overlayL2 = []
stack.histos = histosL
stack.typs = typsL
stack.datas = datasL
# stack.datatyps = Ldatatyps[v]
stack.datanames = 'data_obs'
for key in overlay_histo_dict:
overlayL2.append(overlay_histo_dict[key])
mjj_sub = eval(opts.sub)
if not mjj_sub:
stack.overlay = overlayL2
appendix = ''
if (eval(opts.rescale) == True):
appendix = '_rescaled_'
if 's/s+b' in opts.fom:
stack.options['pdfName'] = stack.options['pdfName'].replace(
'.pdf',
'_combined78tev_postFit_s_over_sb' + appendix + '.' + opts.format)
elif 's/b' in opts.fom:
stack.options['pdfName'] = stack.options['pdfName'].replace(
'.pdf',
'_combined78tev_postFit_s_over_b' + appendix + '.' + opts.format)
else:
stack.options['pdfName'] = stack.options['pdfName'].replace(
'.pdf', '_unweighted.' + opts.format)
# stack.options['pdfName'] = stack.options['pdfName'].replace('.pdf','_highPt_7tev.pdf')
# stack.options['pdfName'] = stack.options['pdfName'].replace('.pdf','_combined_postFit_s_over_b_Hpt_weight_1.pdf'
stack.lumi = 18940
if mjj_sub == False:
stack.doPlot()
elif mjj_sub == True:
stack.options['pdfName'] = stack.options['pdfName'].replace(
'.' + opts.format, '_subtracted.' + opts.format)
stack.doSubPlot(['VH', 'VV'])
print 'i am done!\n'
_treevar = treevar
_name = title
_countHisto = "CountWeightedLHEWeightScale"
_countbin = lhe_num
appendList()
_countHisto = "CountWeighted"
_countbin = 0
#print '===================\n'
#print 'The option list is', optionsList
print 'Preparations for Histograms (HistoMakeri)'
print '=========================================\n'
mc_hMaker = HistoMaker(all_samples, path, config, optionsList, GroupDict)
data_hMaker = HistoMaker(data_samples, path, config, [optionsList[0]])
print 'Calculate luminosity'
print '====================\n'
#Calculate lumi
lumi = 0.
nData = 0
for job in data_samples:
nData += 1
lumi += float(job.lumi)
if nData > 1:
lumi = lumi / float(nData)
mc_hMaker.lumi = lumi
def doPlot():
vars = (config.get(section, 'vars')).split(',')
data = config.get(section, 'Datas')
mc = eval(config.get('Plot_general', 'samples'))
print ' with Vars:', vars
print ' Using MC samples: ', mc
print ' and Data samples: ', data
# Is set in plots. Can be the mass of ZH/ggZH.
SignalRegion = False
if config.has_option(section, 'Signal'):
mc.append(config.get(section, 'Signal'))
SignalRegion = True
#print '\n\n\t\t=====SignalRegion:', SignalRegion
datasamples = info.get_samples(data)
mcsamples = info.get_samples(mc)
GroupDict = eval(config.get('Plot_general', 'Group'))
#GETALL AT ONCE
options = []
Stacks = []
for i in range(len(vars)):
Stacks.append(StackMaker(config, vars[i], region, SignalRegion))
options.append(Stacks[i].options)
# Init the HistoMaker class
Plotter = HistoMaker(mcsamples + datasamples, path, config, options,
GroupDict)
#print '\nProducing Plot of %s\n'%vars[v]
Lhistos = [[] for _ in range(0, len(vars))]
Ltyps = [[] for _ in range(0, len(vars))]
Ldatas = [[] for _ in range(0, len(vars))]
Ldatatyps = [[] for _ in range(0, len(vars))]
Ldatanames = [[] for _ in range(0, len(vars))]
#Find out Lumi:
lumicounter = 0.
lumi = 0.
for job in datasamples:
lumi += float(job.lumi)
lumicounter += 1.
if lumicounter > 0:
lumi = lumi / lumicounter
print '\n\t\t Luminosity Auto Calculation: ', lumi
# temp hack
#lumi = 3000.
Plotter.lumi = lumi
mass = Stacks[0].mass
# Define the counters for statistics counting
mass_count = Counter()
bdt_count = Counter()
for job in mcsamples:
# hDictList returns list of TH1Fs of the specified vars to plot
if addBlindingCut:
hDictList = Plotter.get_histos_from_tree(
job,
config.get('Cuts', region) + ' & ' + addBlindingCut)
else:
hDictList = Plotter.get_histos_from_tree(job)
#print '\n\n\t\tJob name: ', job.name
if job.name == 'ZH125':
#or job.name == 'ggZH125':
Overlaylist = deepcopy(
[hDictList[v].values()[0] for v in range(0, len(vars))])
for v in range(0, len(vars)):
Lhistos[v].append(hDictList[v].values()[0])
Ltyps[v].append(hDictList[v].keys()[0])
hist = hDictList[v].values()[0]
# end variable loop
# end MC sample loop
# Loop over data
for job in datasamples:
# temp hack to turn off data
#break
#hTemp, typ = Plotter.get_histos_from_tree(job)
if addBlindingCut:
dDictList = Plotter.get_histos_from_tree(
job,
config.get('Cuts', region) + ' & ' + addBlindingCut)
else:
dDictList = Plotter.get_histos_from_tree(job)
for v in range(0, len(vars)):
Ldatas[v].append(dDictList[v].values()[0])
Ldatatyps[v].append(dDictList[v].keys()[0])
Ldatanames[v].append(job.name)
# produce the final plots and save
for v in range(0, len(vars)):
histos = Lhistos[v]
typs = Ltyps[v]
Stacks[v].histos = Lhistos[v]
Stacks[v].typs = Ltyps[v]
Stacks[v].datas = Ldatas[v]
Stacks[v].datatyps = Ldatatyps[v]
Stacks[v].datanames = Ldatanames[v]
if SignalRegion:
Stacks[v].overlay = Overlaylist[v]
Stacks[v].lumi = lumi
Stacks[v].doPlot()
Stacks[v].overlay = []
Stacks[v].histos = Lhistos[v]
Stacks[v].typs = Ltyps[v]
Stacks[v].datas = Ldatas[v]
Stacks[v].datatyps = Ldatatyps[v]
Stacks[v].datanames = Ldatanames[v]
Stacks[v].normalize = True
Stacks[v].options['pdfName'] = Stacks[v].options['pdfName'].replace(
'.pdf', '_norm.pdf')
#Stacks[v].doPlot()
print 'i am done!\n'
def doPlot():
vars = (config.get(section, 'vars')).split(',')
data = config.get(section,'Datas')
mc = eval(config.get('Plot_general','samples'))
SignalRegion = False
if config.has_option(section,'Signal'):
mc.append(config.get(section,'Signal'))
SignalRegion = True
datasamples = info.get_samples(data)
mcsamples = info.get_samples(mc)
GroupDict = eval(config.get('Plot_general','Group'))
#GETALL AT ONCE
options = []
Stacks = []
for i in range(len(vars)):
Stacks.append(StackMaker(config,vars[i],region,SignalRegion))
options.append(Stacks[i].options)
print options
Plotter=HistoMaker(mcsamples+datasamples,path,config,options,GroupDict)
#print '\nProducing Plot of %s\n'%vars[v]
Lhistos = [[] for _ in range(0,len(vars))]
Ltyps = [[] for _ in range(0,len(vars))]
Ldatas = [[] for _ in range(0,len(vars))]
Ldatatyps = [[] for _ in range(0,len(vars))]
Ldatanames = [[] for _ in range(0,len(vars))]
#Find out Lumi:
lumicounter=0.
lumi=0.
for job in datasamples:
lumi+=float(job.lumi)
lumicounter+=1.
if lumicounter > 0:
lumi=lumi/lumicounter
Plotter.lumi=lumi
mass = Stacks[0].mass
for job in mcsamples:
#hTempList, typList = Plotter.get_histos_from_tree(job)
if addBlindingCut:
hDictList = Plotter.get_histos_from_tree(job,config.get('Cuts',region)+' & ' + addBlindingCut)
else:
hDictList = Plotter.get_histos_from_tree(job)
if job.name == mass:
print job.name
Overlaylist= deepcopy([hDictList[v].values()[0] for v in range(0,len(vars))])
for v in range(0,len(vars)):
Lhistos[v].append(hDictList[v].values()[0])
Ltyps[v].append(hDictList[v].keys()[0])
for job in datasamples:
#hTemp, typ = Plotter.get_histos_from_tree(job)
if addBlindingCut:
dDictList = Plotter.get_histos_from_tree(job,config.get('Cuts',region)+' & ' + addBlindingCut)
else:
dDictList = Plotter.get_histos_from_tree(job)
for v in range(0,len(vars)):
Ldatas[v].append(dDictList[v].values()[0])
Ldatatyps[v].append(dDictList[v].keys()[0])
Ldatanames[v].append(job.name)
for v in range(0,len(vars)):
histos = Lhistos[v]
typs = Ltyps[v]
Stacks[v].histos = Lhistos[v]
Stacks[v].typs = Ltyps[v]
Stacks[v].datas = Ldatas[v]
Stacks[v].datatyps = Ldatatyps[v]
Stacks[v].datanames= Ldatanames[v]
#if SignalRegion:
# Stacks[v].overlay = Overlaylist[v]
Stacks[v].lumi = lumi
Stacks[v].doPlot()
Stacks[v].histos = Lhistos[v]
Stacks[v].typs = Ltyps[v]
Stacks[v].datas = Ldatas[v]
Stacks[v].datatyps = Ldatatyps[v]
Stacks[v].datanames= Ldatanames[v]
Stacks[v].normalize = True
Stacks[v].options['pdfName'] = Stacks[v].options['pdfName'].replace('.pdf','_norm.pdf')
Stacks[v].doPlot()
print 'i am done!\n'
def doPlot():
vars = (config.get(section, "vars")).split(",")
data = config.get(section, "Datas")
mc = eval(config.get("Plot_general", "samples"))
print " with Vars:", vars
print " Using MC samples: ", mc
print " and Data samples: ", data
# Is set in plots. Can be the mass of ZH/ggZH.
SignalRegion = False
if config.has_option(section, "Signal"):
mc.append(config.get(section, "Signal"))
SignalRegion = True
# print '\n\n\t\t=====SignalRegion:', SignalRegion
datasamples = info.get_samples(data)
mcsamples = info.get_samples(mc)
GroupDict = eval(config.get("Plot_general", "Group"))
# GETALL AT ONCE
options = []
Stacks = []
for i in range(len(vars)):
Stacks.append(StackMaker(config, vars[i], region, SignalRegion))
options.append(Stacks[i].options)
# Init the HistoMaker class
Plotter = HistoMaker(mcsamples + datasamples, path, config, options, GroupDict)
# print '\nProducing Plot of %s\n'%vars[v]
Lhistos = [[] for _ in range(0, len(vars))]
Ltyps = [[] for _ in range(0, len(vars))]
Ldatas = [[] for _ in range(0, len(vars))]
Ldatatyps = [[] for _ in range(0, len(vars))]
Ldatanames = [[] for _ in range(0, len(vars))]
# Find out Lumi:
lumicounter = 0.0
lumi = 0.0
for job in datasamples:
lumi += float(job.lumi)
lumicounter += 1.0
if lumicounter > 0:
lumi = lumi / lumicounter
print "\n\t\t Luminosity Auto Calculation: ", lumi
# temp hack
# lumi = 3000.
Plotter.lumi = lumi
mass = Stacks[0].mass
# Define the counters for statistics counting
mass_count = Counter()
bdt_count = Counter()
for job in mcsamples:
# hDictList returns list of TH1Fs of the specified vars to plot
if addBlindingCut:
hDictList = Plotter.get_histos_from_tree(job, config.get("Cuts", region) + " & " + addBlindingCut)
else:
hDictList = Plotter.get_histos_from_tree(job)
# print '\n\n\t\tJob name: ', job.name
if job.name == "ZH125":
# or job.name == 'ggZH125':
Overlaylist = deepcopy([hDictList[v].values()[0] for v in range(0, len(vars))])
for v in range(0, len(vars)):
Lhistos[v].append(hDictList[v].values()[0])
Ltyps[v].append(hDictList[v].keys()[0])
hist = hDictList[v].values()[0]
# end variable loop
# end MC sample loop
# Loop over data
for job in datasamples:
# temp hack to turn off data
# break
# hTemp, typ = Plotter.get_histos_from_tree(job)
if addBlindingCut:
dDictList = Plotter.get_histos_from_tree(job, config.get("Cuts", region) + " & " + addBlindingCut)
else:
dDictList = Plotter.get_histos_from_tree(job)
for v in range(0, len(vars)):
Ldatas[v].append(dDictList[v].values()[0])
Ldatatyps[v].append(dDictList[v].keys()[0])
Ldatanames[v].append(job.name)
# produce the final plots and save
for v in range(0, len(vars)):
histos = Lhistos[v]
typs = Ltyps[v]
Stacks[v].histos = Lhistos[v]
Stacks[v].typs = Ltyps[v]
Stacks[v].datas = Ldatas[v]
Stacks[v].datatyps = Ldatatyps[v]
Stacks[v].datanames = Ldatanames[v]
if SignalRegion:
Stacks[v].overlay = Overlaylist[v]
Stacks[v].lumi = lumi
Stacks[v].doPlot()
Stacks[v].overlay = []
Stacks[v].histos = Lhistos[v]
Stacks[v].typs = Ltyps[v]
Stacks[v].datas = Ldatas[v]
Stacks[v].datatyps = Ldatatyps[v]
Stacks[v].datanames = Ldatanames[v]
Stacks[v].normalize = True
Stacks[v].options["pdfName"] = Stacks[v].options["pdfName"].replace(".pdf", "_norm.pdf")
Stacks[v].doPlot()
print "i am done!\n"
def doPlot():
vars = (config.get(section, 'vars')).split(',')
# print 'vars',vars
data = config.get(section,'Datas')
mc = eval(config.get('Plot_general','samples'))
SignalRegion = False
if config.has_option(section,'Signal'):
mc.append(config.get(section,'Signal'))
SignalRegion = True
datasamples = info.get_samples(data)
mcsamples = info.get_samples(mc)
GroupDict = eval(config.get('Plot_general','Group'))
#GETALL AT ONCE
options = []
Stacks = []
for i in range(len(vars)):
Stacks.append(StackMaker(config,vars[i],region,SignalRegion))
options.append(Stacks[i].options)
print 'loop options',options
# print 'options',options
Plotter=HistoMaker(mcsamples+datasamples,path,config,options,GroupDict)
#print '\nProducing Plot of %s\n'%vars[v]
Lhistos = [[] for _ in range(0,len(vars))]
Ltyps = [[] for _ in range(0,len(vars))]
Ldatas = [[] for _ in range(0,len(vars))]
Ldatatyps = [[] for _ in range(0,len(vars))]
Ldatanames = [[] for _ in range(0,len(vars))]
Ljobnames = [[] for _ in range(0,len(vars))]
#Find out Lumi:
lumicounter=0.
lumi=0.
for job in datasamples:
lumi+=float(job.lumi)
lumicounter+=1.
if lumicounter > 0:
lumi=lumi/lumicounter
Plotter.lumi=lumi
mass = Stacks[0].mass
# multiprocess=16
# if multiprocess>0:
# from multiprocessing import Pool
# p = Pool(multiprocess)
## import pathos.multiprocessing as mp
## p = mp.ProcessingPool(multiprocess)
# myinputs = []
# for job in self.__sampleList:
# myoptions = self.putOptions()
# myinputs.append((myoptions,job))
#
# outputs = p.map(trim_treeMT, myinputs)
print 'mcsamples',mcsamples
inputs=[]
for job in mcsamples:
# print 'job.name'
cutOverWrite = None
if addBlindingCut:
cutOverWrite = config.get('Cuts',region)+' & ' + addBlindingCut
inputs.append((Plotter,"get_histos_from_tree",(job,cutOverWrite)))
multiprocess=64
outputs = []
if multiprocess>0:
from multiprocessing import Pool
from myutils import GlobalFunction
p = Pool(multiprocess)
print 'launching get_histos_from_tree with ',multiprocess,' processes'
outputs = p.map(GlobalFunction, inputs)
else:
print 'launching get_histos_from_tree with ',multiprocess,' processes'
for input_ in inputs:
outputs.append(getattr(input_[0],input_[1])(*input_[2])) #ie. Plotter.get_histos_from_tree(job,cutOverWrite)
print 'get_histos_from_tree DONE'
Overlaylist = []
for i,job in enumerate(mcsamples):
print 'job.name',job.name,"mass==",mass
#hTempList, typList = Plotter.get_histos_from_tree(job)
hDictList = outputs[i]
if job.name in mass:
print 'job.name == mass'
histoList = []
for v in range(0,len(vars)):
histoCopy = deepcopy(hDictList[v].values()[0])
histoCopy.SetTitle(job.name)
histoList.append(histoCopy)
Overlaylist.append(histoList)
# Overlaylist.append(deepcopy([hDictList[v].values()[0] for v in range(0,len(vars))]))
for v in range(0,len(vars)):
Lhistos[v].append(hDictList[v].values()[0])
Ltyps[v].append(hDictList[v].keys()[0])
Ljobnames[v].append(job.name)
print "len(vars)=",len(vars)
##invert Overlaylist[variable][job] -> Overlaylist[job][variable]
print "len(Overlaylist) before: ",len(Overlaylist)
print "Overlaylist",Overlaylist
# newOverlaylist = [[None]*len(Overlaylist)]*len(vars)
# for i,OverlaySameSample in enumerate(Overlaylist):
# for j,Overlay in enumerate(OverlaySameSample):
# newOverlaylist[j][i] = Overlay
# Overlaylist = newOverlaylist
Overlaylist = [list(a) for a in zip(*Overlaylist)]
print "len(Overlaylist) after: ",len(Overlaylist)
print "Overlaylist",Overlaylist
##merge overlays in groups
for i in range(len(Overlaylist)):
newhistos = {}
print "len(Overlaylist[i]):",Overlaylist[i]
for histo in Overlaylist[i]:
print "histo.GetName()",histo.GetName(),
print "histo.GetTitle()",histo.GetTitle(),
group = GroupDict[histo.GetTitle()]
if not group in newhistos.keys():
histo.SetTitle(group)
newhistos[group]=histo
else:
print "Before newhistos[group].Integral()",newhistos[group].Integral(),
newhistos[group].Add(histo)
print "After newhistos[group].Integral()",newhistos[group].Integral()
Overlaylist[i] = newhistos.values()
# ### ORIGINAL ###
# print 'mcsamples',mcsamples
# for job in mcsamples:
# print 'job.name',job.name
# #hTempList, typList = Plotter.get_histos_from_tree(job)
# if addBlindingCut:
# hDictList = Plotter.get_histos_from_tree(job,config.get('Cuts',region)+' & ' + addBlindingCut)
# else:
# print 'going to get_histos_from_tree'
# hDictList = Plotter.get_histos_from_tree(job)
# if job.name == mass:
# print 'job.name', job.name
# Overlaylist= deepcopy([hDictList[v].values()[0] for v in range(0,len(vars))])
# for v in range(0,len(vars)):
# Lhistos[v].append(hDictList[v].values()[0])
# Ltyps[v].append(hDictList[v].keys()[0])
# Ljobnames[v].append(job.name)
print 'datasamples',datasamples
for job in datasamples:
#hTemp, typ = Plotter.get_histos_from_tree(job)
if addBlindingCut:
dDictList = Plotter.get_histos_from_tree(job,config.get('Cuts',region)+' & ' + addBlindingCut)
else:
dDictList = Plotter.get_histos_from_tree(job)
for v in range(0,len(vars)):
Ldatas[v].append(dDictList[v].values()[0])
Ldatatyps[v].append(dDictList[v].keys()[0])
Ldatanames[v].append(job.name)
for v in range(0,len(vars)):
print "Ltyps[v]:",Ltyps[v]
print "Lhistos[v]:",Lhistos[v]
print "Ldatas[v]:",Ldatas[v]
print "Ldatatyps[v]:",Ldatatyps[v]
print "Ldatanames[v]:",Ldatanames[v]
print "lumi:",lumi
histos = Lhistos[v]
typs = Ltyps[v]
Stacks[v].histos = Lhistos[v]
Stacks[v].typs = Ltyps[v]
Stacks[v].datas = Ldatas[v]
Stacks[v].datatyps = Ldatatyps[v]
Stacks[v].datanames= Ldatanames[v]
#if SignalRegion:
Stacks[v].overlay = Overlaylist[v] ## from
Stacks[v].lumi = lumi
Stacks[v].jobnames= Ljobnames[v]
Stacks[v].doPlot()
##FIXME##
# Stacks[v].histos = Lhistos[v]
# Stacks[v].typs = Ltyps[v]
# Stacks[v].datas = Ldatas[v]
# Stacks[v].datatyps = Ldatatyps[v]
# Stacks[v].datanames= Ldatanames[v]
# Stacks[v].normalize = True
# Stacks[v].options['pdfName'] = Stacks[v].options['pdfName'].replace('.pdf','_norm.pdf')
# Stacks[v].doPlot()
print 'i am done!\n'
def doPlot():
vars = (config.get(section, 'vars')).split(',')
data = config.get(section,'Datas')
mc = eval(config.get('Plot_general','samples'))
SignalRegion = False
if config.has_option(section,'Signal'):
mc.append(config.get(section,'Signal'))
SignalRegion = True
datasamples = info.get_samples(data)
mcsamples = info.get_samples(mc)
GroupDict = eval(config.get('Plot_general','Group'))
#GETALL AT ONCE
options = []
Stacks = []
for i in range(len(vars)):
Stacks.append(StackMaker(config,vars[i],region,SignalRegion))
options.append(Stacks[i].options)
print options
Plotter=HistoMaker(mcsamples+datasamples,path,config,options,GroupDict)
#print '\nProducing Plot of %s\n'%vars[v]
Lhistos = [[] for _ in range(0,len(vars))]
Ltyps = [[] for _ in range(0,len(vars))]
Ldatas = [[] for _ in range(0,len(vars))]
Ldatatyps = [[] for _ in range(0,len(vars))]
Ldatanames = [[] for _ in range(0,len(vars))]
#Find out Lumi:
lumicounter=0.
lumi=0.
for job in datasamples:
lumi+=float(job.lumi)
lumicounter+=1.
if lumicounter > 0:
lumi=lumi/lumicounter
Plotter.lumi=lumi
mass = Stacks[0].mass
for job in mcsamples:
#hTempList, typList = Plotter.get_histos_from_tree(job)
if addBlindingCut:
hDictList = Plotter.get_histos_from_tree(job,config.get('Cuts',region)+' & ' + addBlindingCut)
else:
hDictList = Plotter.get_histos_from_tree(job)
if job.name == mass:
print job.name
Overlaylist= deepcopy([hDictList[v].values()[0] for v in range(0,len(vars))])
for v in range(0,len(vars)):
Lhistos[v].append(hDictList[v].values()[0])
Ltyps[v].append(hDictList[v].keys()[0])
for job in datasamples:
#hTemp, typ = Plotter.get_histos_from_tree(job)
if addBlindingCut:
dDictList = Plotter.get_histos_from_tree(job,config.get('Cuts',region)+' & ' + addBlindingCut)
else:
dDictList = Plotter.get_histos_from_tree(job)
for v in range(0,len(vars)):
Ldatas[v].append(dDictList[v].values()[0])
Ldatatyps[v].append(dDictList[v].keys()[0])
Ldatanames[v].append(job.name)
for v in range(0,len(vars)):
histos = Lhistos[v]
typs = Ltyps[v]
Stacks[v].histos = Lhistos[v]
Stacks[v].typs = Ltyps[v]
Stacks[v].datas = Ldatas[v]
Stacks[v].datatyps = Ldatatyps[v]
Stacks[v].datanames= Ldatanames[v]
#if SignalRegion:
# Stacks[v].overlay = Overlaylist[v]
Stacks[v].lumi = lumi
Stacks[v].doPlot()
Stacks[v].histos = Lhistos[v]
Stacks[v].typs = Ltyps[v]
Stacks[v].datas = Ldatas[v]
Stacks[v].datatyps = Ldatatyps[v]
Stacks[v].datanames= Ldatanames[v]
Stacks[v].normalize = True
Stacks[v].options['pdfName'] = Stacks[v].options['pdfName'].replace('.pdf','_norm.pdf')
Stacks[v].doPlot()
print 'i am done!\n'
_treevar = treevar
# append
appendList()
# UEPS
for weightF_sys in weightF_systematics:
for _weight in [config.get("Weights", "%s_UP" % (weightF_sys)), config.get("Weights", "%s_DOWN" % (weightF_sys))]:
_cut = treecut
_treevar = treevar
_name = title
appendList()
# for option in optionsList:
# print option['cut']
mc_hMaker = HistoMaker(all_samples, path, config, optionsList, GroupDict)
data_hMaker = HistoMaker(data_samples, path, config, [optionsList[0]])
# Calculate lumi
lumi = 0.0
nData = 0
for job in data_samples:
nData += 1
lumi += float(job.lumi)
if nData > 1:
lumi = lumi / float(nData)
mc_hMaker.lumi = lumi
data_hMaker.lumi = lumi
if addBlindingCut:
