| 1 |
< |
from samplesclass import sample |
| 2 |
< |
from printcolor import printc |
| 1 |
> |
import sys,os |
| 2 |
|
import pickle |
| 3 |
|
import ROOT |
| 5 |
– |
from ROOT import TFile, TTree |
| 6 |
– |
import ROOT |
| 4 |
|
from array import array |
| 5 |
+ |
from printcolor import printc |
| 6 |
|
from BetterConfigParser import BetterConfigParser |
| 7 |
< |
import sys,os |
| 7 |
> |
from TreeCache import TreeCache |
| 8 |
|
|
| 9 |
|
class HistoMaker: |
| 10 |
< |
def __init__(self, path, config, region, optionsList,rescale=1,which_weightF='weightF'): |
| 10 |
> |
def __init__(self, samples, path, config, optionsList): |
| 11 |
|
self.path = path |
| 12 |
|
self.config = config |
| 13 |
|
self.optionsList = optionsList |
| 16 |
– |
self.rescale = rescale |
| 17 |
– |
self.which_weightF=which_weightF |
| 18 |
– |
self.region = region |
| 14 |
|
self.lumi=0. |
| 15 |
+ |
self.cuts = [] |
| 16 |
+ |
for options in optionsList: |
| 17 |
+ |
self.cuts.append(options['cut']) |
| 18 |
+ |
self.tc = TreeCache(self.cuts,samples,path) |
| 19 |
|
|
| 21 |
– |
def getScale(self,job,subsample=-1,MC_rescale_factor=1): |
| 22 |
– |
anaTag=self.config.get('Analysis','tag') |
| 23 |
– |
input = TFile.Open(self.path+'/'+job.getpath()) |
| 24 |
– |
CountWithPU = input.Get("CountWithPU") |
| 25 |
– |
CountWithPU2011B = input.Get("CountWithPU2011B") |
| 26 |
– |
#print lumi*xsecs[i]/hist.GetBinContent(1) |
| 27 |
– |
if subsample>-1: |
| 28 |
– |
xsec=float(job.xsec[subsample]) |
| 29 |
– |
sf=float(job.sf[subsample]) |
| 30 |
– |
else: |
| 31 |
– |
xsec=float(job.xsec) |
| 32 |
– |
sf=float(job.sf) |
| 33 |
– |
theScale = 1. |
| 34 |
– |
if anaTag == '7TeV': |
| 35 |
– |
theScale = float(self.lumi)*xsec*sf/(0.46502*CountWithPU.GetBinContent(1)+0.53498*CountWithPU2011B.GetBinContent(1))*MC_rescale_factor/float(job.split) |
| 36 |
– |
elif anaTag == '8TeV': |
| 37 |
– |
theScale = float(self.lumi)*xsec*sf/(CountWithPU.GetBinContent(1))*MC_rescale_factor/float(job.split) |
| 38 |
– |
input.Close() |
| 39 |
– |
return theScale |
| 20 |
|
|
| 21 |
< |
|
| 22 |
< |
def getHistoFromTree(self,job,subsample=-1): |
| 23 |
< |
if self.lumi == 0: raise Exception("You're trying to plot with no lumi") |
| 21 |
> |
def get_histos_from_tree(self,job): |
| 22 |
> |
if self.lumi == 0: |
| 23 |
> |
raise Exception("You're trying to plot with no lumi") |
| 24 |
|
|
| 25 |
|
hTreeList=[] |
| 26 |
|
groupList=[] |
| 33 |
|
plot_path = self.config.get('Directories','plotpath') |
| 34 |
|
addOverFlow=eval(self.config.get('Plot_general','addOverFlow')) |
| 35 |
|
|
| 56 |
– |
scratchDir = os.environ["TMPDIR"] |
| 57 |
– |
#scratchDir = '/shome/peller/' |
| 58 |
– |
# define treeCut |
| 59 |
– |
if job.type != 'DATA': |
| 60 |
– |
if type(self.region)==str: |
| 61 |
– |
cutcut=self.config.get('Cuts',self.region) |
| 62 |
– |
elif type(self.region)==list: |
| 63 |
– |
#replace vars with other vars in the cutstring (used in DC writer) |
| 64 |
– |
cutcut=self.config.get('Cuts',self.region[0]) |
| 65 |
– |
cutcut=cutcut.replace(self.region[1],self.region[2]) |
| 66 |
– |
#print cutcut |
| 67 |
– |
if subsample>-1: |
| 68 |
– |
treeCut='%s & %s'%(cutcut,job.subcuts[subsample]) |
| 69 |
– |
else: |
| 70 |
– |
treeCut='%s'%(cutcut) |
| 71 |
– |
elif job.type == 'DATA': |
| 72 |
– |
cutcut=self.config.get('Cuts',self.region) |
| 73 |
– |
treeCut='%s'%(cutcut) |
| 74 |
– |
|
| 75 |
– |
# get and skim the Trees |
| 76 |
– |
output=TFile.Open(scratchDir+'/tmp_plotCache_%s_%s.root'%(self.region,job.identifier),'recreate') |
| 77 |
– |
input = TFile.Open(self.path+'/'+job.getpath(),'read') |
| 78 |
– |
Tree = input.Get(job.tree) |
| 79 |
– |
output.cd() |
| 80 |
– |
CuttedTree=Tree.CopyTree(treeCut) |
| 81 |
– |
input.Close() |
| 82 |
– |
del input |
| 36 |
|
# get all Histos at once |
| 84 |
– |
weightF=self.config.get('Weights',self.which_weightF) |
| 37 |
|
for options in self.optionsList: |
| 38 |
< |
if subsample>-1: |
| 39 |
< |
name=job.subnames[subsample] |
| 40 |
< |
group=job.group[subsample] |
| 41 |
< |
else: |
| 42 |
< |
name=job.name |
| 43 |
< |
group=job.group |
| 44 |
< |
treeVar=options[0] |
| 45 |
< |
name=options[1] |
| 46 |
< |
nBins=int(options[3]) |
| 47 |
< |
xMin=float(options[4]) |
| 96 |
< |
xMax=float(options[5]) |
| 38 |
> |
name=job.name |
| 39 |
> |
group=job.group |
| 40 |
> |
treeVar=options['var'] |
| 41 |
> |
name=options['name'] |
| 42 |
> |
nBins=int(options['nBins']) |
| 43 |
> |
xMin=float(options['xMin']) |
| 44 |
> |
xMax=float(options['xMax']) |
| 45 |
> |
weightF=options['weight'] |
| 46 |
> |
treeCut='%s'%(options['cut']) |
| 47 |
> |
CuttedTree = self.tc.get_tree(job,treeCut) |
| 48 |
|
|
| 49 |
|
#options |
| 50 |
|
|
| 51 |
|
if job.type != 'DATA': |
| 52 |
|
if CuttedTree.GetEntries(): |
| 53 |
|
|
| 54 |
< |
if 'RTight' in treeVar or 'RMed' in treeVar: drawoption = '(%s)*(%s)'%(weightF,BDT_add_cut) |
| 55 |
< |
else: drawoption = '%s'%(weightF) |
| 56 |
< |
output.cd() |
| 54 |
> |
if 'RTight' in treeVar or 'RMed' in treeVar: |
| 55 |
> |
drawoption = '(%s)*(%s)'%(weightF,BDT_add_cut) |
| 56 |
> |
else: |
| 57 |
> |
drawoption = '%s'%(weightF) |
| 58 |
|
CuttedTree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax), drawoption, "goff,e") |
| 59 |
|
full=True |
| 60 |
|
else: |
| 61 |
|
full=False |
| 62 |
|
elif job.type == 'DATA': |
| 63 |
< |
if options[11] == 'blind': |
| 63 |
> |
if options['blind'] == 'blind': |
| 64 |
|
output.cd() |
| 65 |
|
if treeVar == 'H.mass': |
| 66 |
|
CuttedTree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax),treeVar+'<90. || '+treeVar + '>150.' , "goff,e") |
| 68 |
|
CuttedTree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax),treeVar+'<0', "goff,e") |
| 69 |
|
|
| 70 |
|
else: |
| 119 |
– |
output.cd() |
| 71 |
|
CuttedTree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax),'1', "goff,e") |
| 72 |
|
full = True |
| 73 |
|
if full: |
| 74 |
|
hTree = ROOT.gDirectory.Get(name) |
| 75 |
|
else: |
| 125 |
– |
output.cd() |
| 76 |
|
hTree = ROOT.TH1F('%s'%name,'%s'%name,nBins,xMin,xMax) |
| 77 |
|
hTree.Sumw2() |
| 78 |
|
if job.type != 'DATA': |
| 80 |
|
if TrainFlag: |
| 81 |
|
MC_rescale_factor=2. |
| 82 |
|
print 'I RESCALE BY 2.0' |
| 83 |
< |
else: MC_rescale_factor = 1. |
| 84 |
< |
ScaleFactor = self.getScale(job,subsample,MC_rescale_factor) |
| 85 |
< |
else: ScaleFactor = self.getScale(job,subsample) |
| 83 |
> |
else: |
| 84 |
> |
MC_rescale_factor = 1. |
| 85 |
> |
ScaleFactor = self.tc.get_scale(job,self.config,self.lumi)*MC_rescale_factor |
| 86 |
> |
else: |
| 87 |
> |
ScaleFactor = self.tc.get_scale(job,self.config,self.lumi) |
| 88 |
|
if ScaleFactor != 0: |
| 89 |
|
hTree.Scale(ScaleFactor) |
| 90 |
|
#print '\t-->import %s\t Integral: %s'%(job.name,hTree.Integral()) |
| 101 |
|
hTreeList.append(hTree) |
| 102 |
|
groupList.append(group) |
| 103 |
|
|
| 152 |
– |
output.Close() |
| 153 |
– |
del output |
| 104 |
|
return hTreeList, groupList |
| 105 |
|
|
| 106 |
|
|
