def update(self, v=True):
# Update branches:
if hasattr(self.ana, "branches"):
self.branches = self.ana.branches[self.key]
else:
self.branches = False
# Apply cuts:
if hasattr(self.ana, "cut"):
gROOT.cd()
self.tt_in = self.tt_in.CopyTree(self.ana.cut)
if v: print "Fetching the number of events in {} ...".format(self.key)
# self.n_total = self.tt_in.GetEntries() # The total number of events in the input dataset
if self.ana.count:
self.ns = root.tc_nevents(
self.tt_in
) # The total number of events in each file of the input TChain
self.n = sum(self.ns)
else:
if not self.ns:
print "This isn't implemented, yet!"
print "I have to use the key to look in the DB for tuple nevents"
sys.exit()
# samples = dataset.fetch_samples(process=self.key)
# tuples = []
# for sample in samples:
# tuples.extend([tup for tup in sample.tuples if tup.generation == "spring15" and tup.suffix == "pt400"])
# self.n_total_list = [N for tup in tuples for N in tup.ns]
if not self.n:
print "[!!] The TChain you want to run over has no events."
sys.exit()
if v: print "\tThere are {} events.".format(self.n)
def __init__(self, sample ,File, WS , PDF , eff_file , thqDomBinName , low,high):
self.Sample = sample
self.File = File
self.WS = WS
self.PDF = PDF
self.file = TFile.Open( self.File )
#self.file.ls()
self.ws = self.file.Get(self.WS)
#print sample
#self.file.ls()
#self.ws.Print()
#print self.ws, self.File, self.WS
self.pdf = self.ws.pdf( self.PDF )
self.norm = self.ws.function( self.PDF + "_norm" )
self.CT = self.ws.var("CtOverCv")
self.CV = self.ws.var("CV")
self.Low = low
self.High = high
self.EfficiencyFile = TFile.Open( eff_file )
gROOT.cd()
self.EffHist = self.EfficiencyFile.Get("%(sample)s/%(bin)s/h%(sample)s_%(bin)s_eff_CtCv" % {"sample":sample , "bin":thqDomBinName}).Clone("Eff_%s" % sample )
self.EfficiencyFile.Close()
self.EffHist.Print()
def __init__(
self,
ana, # The analyzer it's in
key, # Key used to identify the loop
n_default=-1, # The default number of events to run over (use "-1" for "all") (see "run" to see why)
progress=True, # Turn on progress bar.
):
self.ana = ana
self.key = key
self.n_default = n_default
self.ns = self.ana.tt_info[self.key]["ns"]
self.n = sum(self.ns)
self.n_run = 0 # The number of events to loop over in the current "run".
# self.rand = True # True: select the events randomly, False: select events from the beginning
# self.v = True # Verbose mode
tt_in = ana.tt_in[key]
self.progress = progress
# Apply cuts:
if hasattr(self.ana, "cut"):
gROOT.cd()
tt_in = tt_in.CopyTree(self.ana.cut)
self.tt_in = tt_in
self.tt_out = None
if ana.save:
self.tt_out = ana.tt_out[key]
def make_root_plot_from_results_file(rid,
is_parallel,
draw_string,
cut_string="",
option_string=""):
"""Makes a ROOT plot from results file based on a draw and cut string."""
# gStyle.SetOptStat("iMRKS")
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkglobal.results_dir + "Results_RID" + str(
rid) + "_" + letter + ".root"
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return
root_file = TFile(file_path, "READ")
hit_tree = gDirectory.Get('hitTree')
gROOT.cd()
c1 = rootnotes.canvas("Canvas", (1200, 800))
hit_tree.Draw(draw_string, cut_string, option_string)
root_file.Close()
return c1
def make_txt_hist_from_root_file(file_path_txt, rid, is_parallel, draw_string, hist_dim, cut_string="",
option_string="", titles=("", "", "")):
"""Given a draw/cut string, take an SRK ROOT results file and create a histogram from it."""
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(rid) + "_" + letter + ".root"
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return
root_file = TFile(file_path, "READ")
hit_tree = gDirectory.Get('hitTree')
gROOT.cd()
histogram = TH1D("tempHist", "tempHist", hist_dim[0], hist_dim[1], hist_dim[2])
total_draw_string = draw_string + " >> tempHist"
hit_tree.Draw(total_draw_string, cut_string, option_string)
make_txt_from_hist(file_path_txt, histogram, titles)
root_file.Close()
return
def make_txt_hist_from_root_file(file_path_txt,
rid,
is_parallel,
draw_string,
hist_dim,
cut_string="",
option_string="",
titles=("", "", "")):
"""Given a draw/cut string, take an SRK ROOT results file and create a histogram from it."""
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(
rid) + "_" + letter + ".root"
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return
root_file = TFile(file_path, "READ")
hit_tree = gDirectory.Get('hitTree')
gROOT.cd()
histogram = TH1D("tempHist", "tempHist", hist_dim[0], hist_dim[1],
hist_dim[2])
total_draw_string = draw_string + " >> tempHist"
hit_tree.Draw(total_draw_string, cut_string, option_string)
make_txt_from_hist(file_path_txt, histogram, titles)
root_file.Close()
return
def calc_delta_stats_same_tracks(run_id, use_wrapping=True):
"""Calculates differences in orientation of fields when tracks are the same"""
hit_trees = []
root_files = []
for is_parallel in [True, False]:
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkglobal.results_dir + "Results_RID" + str(run_id) + "_" + letter + ".root"
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return {}
root_files.append(TFile(file_path, "READ"))
hit_trees.append(gDirectory.Get('hitTree'))
gROOT.cd()
delta_phi_list = []
for i in xrange(hit_trees[0].GetEntries()):
hit_trees[0].GetEntry(i)
hit_trees[1].GetEntry(i)
delta_phi = hit_trees[0].phi - hit_trees[1].phi
delta_phi_list.append(delta_phi)
if use_wrapping:
delta_phi_mean = srkmisc.reduce_periodics(delta_phi_list)
else:
delta_phi_mean = srkmisc.careful_mean(delta_phi_list)
delta_phi_std = srkmisc.careful_std(delta_phi_list)
root_files[0].Close()
root_files[1].Close()
return [delta_phi_mean, delta_phi_std]
def make_alpha_vs_phi_plot(run_id, is_parallel):
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(run_id) + "_" + letter + ".root"
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return {}
root_file = TFile(file_path, "READ")
hit_tree = gDirectory.Get('hitTree')
gROOT.cd()
num_events = hit_tree.GetEntries()
phi_list = []
alpha_list = []
radius = srkdata.get_data_for_rids_from_database([run_id], "ChamberRadius")[0][0]
print radius
for i in xrange(num_events):
hit_tree.GetEntry(i)
phi_list.append(hit_tree.phi)
alpha = get_alpha_angle_2d(radius, hit_tree.pos0, hit_tree.vel0)
alpha_list.append(alpha)
root_file.Close()
phi_mean = srkmisc.reduce_periodics(phi_list)
delta_phi_list = map(lambda x: x - phi_mean, phi_list)
plt.scatter(alpha_list, delta_phi_list)
def make_alpha_vs_phi_plot(run_id, is_parallel):
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(
run_id) + "_" + letter + ".root"
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return {}
root_file = TFile(file_path, "READ")
hit_tree = gDirectory.Get('hitTree')
gROOT.cd()
num_events = hit_tree.GetEntries()
phi_list = []
alpha_list = []
radius = srkdata.get_data_for_rids_from_database([run_id],
"ChamberRadius")[0][0]
print radius
for i in xrange(num_events):
hit_tree.GetEntry(i)
phi_list.append(hit_tree.phi)
alpha = get_alpha_angle_2d(radius, hit_tree.pos0, hit_tree.vel0)
alpha_list.append(alpha)
root_file.Close()
phi_mean = srkmisc.reduce_periodics(phi_list)
delta_phi_list = map(lambda x: x - phi_mean, phi_list)
plt.scatter(alpha_list, delta_phi_list)
def get_result_data(leaf_names, run_id, is_parallel):
"""Get result data directly from root files based on leaf names"""
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(
run_id) + "_" + letter + ".root"
print "Opening ", file_path
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return {}
root_file = TFile(file_path, "READ")
hit_tree = gDirectory.Get('hitTree')
gROOT.cd()
num_events = hit_tree.GetEntries()
data = []
for leaf_name in leaf_names:
temp_data = []
for i in xrange(num_events):
hit_tree.GetEntry(i)
temp_data.append(
hit_tree.GetBranch(leaf_name).GetListOfLeaves()[0].GetValue())
data.append(temp_data)
root_file.Close()
return data
def calc_delta_stats_same_tracks(run_id, use_wrapping=True):
"""Calculates differences in orientation of fields when tracks are the same"""
hit_trees = []
root_files = []
for is_parallel in [True, False]:
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkglobal.results_dir + "Results_RID" + str(
run_id) + "_" + letter + ".root"
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return {}
root_files.append(TFile(file_path, "READ"))
hit_trees.append(gDirectory.Get('hitTree'))
gROOT.cd()
delta_phi_list = []
for i in xrange(hit_trees[0].GetEntries()):
hit_trees[0].GetEntry(i)
hit_trees[1].GetEntry(i)
delta_phi = hit_trees[0].phi - hit_trees[1].phi
delta_phi_list.append(delta_phi)
if use_wrapping:
delta_phi_mean = srkmisc.reduce_periodics(delta_phi_list)
else:
delta_phi_mean = srkmisc.careful_mean(delta_phi_list)
delta_phi_std = srkmisc.careful_std(delta_phi_list)
root_files[0].Close()
root_files[1].Close()
return [delta_phi_mean, delta_phi_std]
def GetTriggerScalers(filename):
inputfile = TFile.Open(filename)
inputfile.cd("PtEMCalTriggerTask")
gDirectory.ls()
tasklist = gDirectory.Get("results")
histlist = tasklist.FindObject("histosPtEMCalTriggerHistograms")
gROOT.cd()
triggerhist = histlist.FindObject("hEventTriggers")
inputfile.Close()
# Get number of Min. Bias counts
mbcounts = GetCounts(triggerhist, "MinBias")
print "MinBias counts: %d" %(mbcounts)
triggerhist.GetAxis(0).SetRange(2,2)
triggercounts = {}
for trigger in triggerlookup.keys():
if trigger == "MinBias":
continue
triggercounts[trigger] = GetCounts(triggerhist, trigger)
print "Number of events for trigger %s: %d" %(trigger, triggercounts[trigger])
hScalers = TH1D("triggerScalers", "trigger scalers", len(triggercounts), -0.5, len(triggercounts) - 0.5)
counter = 1
for trigger in triggercounts.keys():
scaler = float(mbcounts)/float(triggercounts[trigger])
print "Scaler for trigger %s: %f" %(trigger, scaler)
hScalers.GetXaxis().SetBinLabel(counter, trigger)
hScalers.SetBinContent(counter, scaler)
counter += 1
outputfile = TFile("TriggerScalers.root", "RECREATE")
outputfile.cd()
hScalers.Write()
outputfile.Close()
def get_result_data(leaf_names, run_id, is_parallel):
"""Get result data directly from root files based on leaf names"""
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(run_id) + "_" + letter + ".root"
print "Opening ", file_path
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return {}
root_file = TFile(file_path, "READ")
hit_tree = gDirectory.Get('hitTree')
gROOT.cd()
num_events = hit_tree.GetEntries()
data = []
for leaf_name in leaf_names:
temp_data = []
for i in xrange(num_events):
hit_tree.GetEntry(i)
temp_data.append(hit_tree.GetBranch(leaf_name).GetListOfLeaves()[0].GetValue())
data.append(temp_data)
root_file.Close()
return data
def __ReadFile(self, filename):
inputfile = TFile.Open(filename)
gROOT.cd()
graphlist = inputfile.Get("Results%d" %(self.__limit))
ptreachgraph = graphlist.FindObject("ptreach")
inputfile.Close()
return ptreachgraph
def __init__(self, sample ,File, WS , PDF , eff_file , thqDomBinName , low,high , binCut):
self.Cut = binCut
self.Sample = sample
self.File = File
self.WS = WS
self.PDF = PDF
self.file = TFile.Open( self.File )
self.ws = self.file.Get(self.WS)
self.DS = self.ws.data( "%s_125_13TeV_THQLeptonicTag_50" % (self.Sample) )
#self.file.ls()
#print sample
#self.file.ls()
#self.ws.Print()
#print self.ws, self.File, self.WS
self.pdf = self.ws.pdf( self.PDF )
self.norm = self.ws.function( self.PDF + "_norm" )
self.CT = self.ws.var("CtOverCv")
self.CT_Default = self.CT.getVal()
self.CV = self.ws.var("CV")
self.CV_Default = self.CV.getVal()
self.Low = low
self.High = high
self.EfficiencyFile = TFile.Open( eff_file )
gROOT.cd()
self.EffHist = self.EfficiencyFile.Get("%(sample)s/%(bin)s/h%(sample)s_%(bin)s_eff_CtCv" % {"sample":sample , "bin":thqDomBinName}).Clone("Eff_%s" % sample )
self.EfficiencyFile.Close()
#self.EffHist.Print()
self.Eff_CtCvInfo = CtCvCpInfo( "Eff_CtCvInfo_%s_%s" % (sample , thqDomBinName) )
self.Eff_CtCvInfo.FillFrom2DHisto( self.EffHist )
def __call__(self , x , p=0):
#x[0] = f_Neg
#x[1] = f_0
#x[2] = rec_gen factor
#no parameter is needed
LL = 0.0
for nBin in self.CosThetaRange():
N_BKG = self.hSum.GetBinContent(nBin)
CosTheta = self.hSum.GetBinCenter(nBin)
self.WF1.SetParameters(x[0] , x[1])
N_TT = 0.0
if self.Tree:
# treeName = self.TreeNameFormat % {'bin':nBin}
# tree = self.TreeFile.Get(treeName)
# genCosTheta = array.array('d' , [Double(0.0)])
# eventWeight = array.array('d' , [Double(0.0)])
# lumiWeight = array.array('d' , [Double(0.0)])
# tree.SetBranchAddress("GenCosTheta" , genCosTheta)
# tree.SetBranchAddress("EventWeight" , eventWeight)
# tree.SetBranchAddress("LumiWeight" , lumiWeight)
# for entry in range(0 , tree.GetEntries() ):
# tree.GetEntry(entry)
# evtweight = eventWeight[0]*lumiWeight[0]
# WeightVal = self.WF1.Eval(genCosTheta[0])
for entry in self.TreeInfo[nBin]:
evtweight = entry[1]
WeightVal = self.WF1.Eval(entry[0])
N_TT += (evtweight*WeightVal)
elif not self.TwoD :
WeightVal = self.WF1.Eval(CosTheta)
N_TT = WeightVal*self.TTBar.GetBinContent(nBin)
else:
gROOT.cd()
hGenInfo = self.TTBar.ProjectionY( '_py' , nBin , nBin , 'o')
hGenInfo.Multiply( self.WF1 )
N_TT = hGenInfo.Integral()
N_MC = N_BKG + x[2]*N_TT
N_Data = self.hData.GetBinContent(nBin)
if not len(self.ErrorVariations) == 0:
variation = self.ErrorVariations[nBin]
N_Data += variation*sqrt(N_Data)
LL += self.DistributionFunction( N_MC , N_Data )
return LL
def get_hist_from_files(directory, ):
"""
Return a histogram energy spectrum constructed from root files in directory.
"""
root_file_names = glob.glob('%s/*.root' % directory)
root_file_names.sort()
gROOT.cd() # deal with TH1D/TFile/python scope issues!!
name = directory.split('/')[0]
hist = TH1D('%s' % name, '', 3000, 0, 3000)
hist.Sumw2()
hist.SetLineWidth(2)
for root_file_name in root_file_names:
root_file = TFile(root_file_name)
tree = root_file.Get('fTree')
hist.GetDirectory().cd()
n_entries = tree.Draw('fTotalEnergy*1e3 >> +%s' % hist.GetName(),
'fTotalEnergy>0', 'goff')
tree.GetEntry(0)
a_max = tree.fMCRun.GetAmax()
peak_energy = 2614.5
if a_max == 214:
#peak_energy = 1764.49
peak_energy = 609.320
peak_counts = hist.GetBinContent(hist.FindBin(peak_energy))
roi_counts = hist.Integral(
hist.FindBin(2039),
hist.FindBin(2041) - 1,
)
ratio = roi_counts / peak_counts
ratio_err = ratio * math.sqrt(1.0 / peak_counts + 1.0 / roi_counts)
print '%s | %.1f-keV peak: %i +/- %.1f | roi: %i +/- %.1f | ratio: (%.2f +/- %.2f) x 10^-3' % (
name,
peak_energy,
peak_counts,
math.sqrt(peak_counts),
roi_counts,
math.sqrt(roi_counts),
ratio * 1e3,
ratio_err * 1e3,
)
hist.Rebin(5)
hist.SetXTitle('Energy [keV]')
hist.SetYTitle('Counts / %.1f keV' % hist.GetBinWidth(1))
hist.GetYaxis().SetTitleOffset(1.2)
return hist
def ProjectDraw(var, cut, Lumi, samplelist, pd):
plt.cfg.register(samplelist)
groupList = plt.cfg.getGroupPlot()
histList = OrderedDict()
VAR = var
# remove extra space
CUT = ' '.join(filter(None, selection[cut].split(" ")))
# group tag
for igroup in groupList:
CUT_ = CUT
if igroup == 'BkgSum': continue
#if igroup in [ 'Fake', 'DATA' ] : CUT_ = CUT.replace("isbVeto && ","")
print col.CYAN + "drawLambda::GroupTag : ", igroup + col.ENDC
histList[igroup] = {}
##sample tag
for isample in groupList[igroup]['samples']:
print("isample : ", isample)
histList[igroup][isample] = []
if 'weights' in samples[isample].keys():
for jsample in samples[isample]['weights'].keys():
WEIGHTS = '(%s)*(%s)' % (
expressAliases(samples[isample]['weight']),
samples[isample]['weights'][jsample])
#WEIGHTS = '(%s)*(%s)' %( samples[isample]['weight'] , samples[isample]['weights'][jsample] )
if igroup not in ['Fake', 'DATA']:
WEIGHTS = "%s*(%s)" % (str(
float(Lumi) / 1000.), WEIGHTS)
filelist = [
x for x in samples[isample]['name']
if os.path.basename(x).split('_', 1)[-1].replace(
'.root', '').split('__part')[0] == jsample
]
files = makeVectorList(filelist)
df = ROOT.RDataFrame("Events", files)
gROOT.cd()
histList[igroup][isample].append(
makeHisto(df, VAR, CUT_, WEIGHTS, jsample))
else:
WEIGHTS = expressAliases(samples[isample]['weight'])
#WEIGHTS = samples[isample]['weight']
if igroup not in ['Fake', 'DATA']:
WEIGHTS = "%s*(%s)" % (str(float(Lumi) / 1000.), WEIGHTS)
filelist = samples[isample]['name']
files = makeVectorList(filelist)
df = ROOT.RDataFrame("Events", files)
gROOT.cd()
histList[igroup][isample].append(
makeHisto(df, VAR, CUT_, WEIGHTS, isample))
pass
# apply action
return applyAction(histList, groupList)
def mybook(name, n, lo, hi):
# This will make a AIDA histogram.
# I don't use this because i want to use all root methods
# on the histograms, and i also want to be able to use root_pickle
# with them.
#return book ("/stat/" + name, name, n, lo, hi)
# This will make a native root histogram.
gROOT.cd() # otherwise, we can get hists created in input files...
return TH1F(name, name, n, lo, hi)
def readTree(self, file_name, tree_name='tree'):
if file_name in self.__class__._t_keeper:
ttree = self.__class__._t_keeper[file_name]
else:
tfile = self.__class__._f_keeper[file_name] = TFile.Open(file_name)
ttree = self.__class__._t_keeper[file_name] = tfile.Get(tree_name)
gROOT.cd()
return ttree
def unpickle(self, fileName = '', quiet=False):
"""
Unpickle (load) a Sample class from disk into memory.
fileName : pickled file (path + file name)
quite : print debug messages ?
"""
if fileName == '':
fileName = './'+self.name+'.pickled.root'
fileName = fileName.strip()
self.pickleFileName = fileName
theFile = TFile( fileName )
if not theFile:
print " ERROR loading root file"
return
res = root_pickle.load( theFile, 0)
theList = self.listPickleVars
# add variables and histograms
for v in theList:
if v.find("resultTObjects") != -1 and res.has_key("resultTObjects"):
for obj in res["resultTObjects"]:
try:
res["resultTObjects"][obj]
except NameError:
print "can't access object: %s in resultTObjects dict ?!?" % obj
continue
gROOT.cd("/")
# print "hist = %s" % hist
self.resultTObjects[obj] = res["resultTObjects"][obj].Clone()
else:
try:
exec("self."+v+" = deepcopy(res[v])")
except AttributeError:
if not quiet:
print "warning : this class has no variables with the name: %s --> go on w/o setting this variable" % (v)
except KeyError:
if not quiet:
print "warning : the variable %s was not found in the pickled result --> go on w/o setting this variable" % (v)
pass
pass
pass
pass
del res
theFile.Close()
del theFile
pass
def do_cd(self, args=''):
"""Change directory to specified directory. See also: pathspec."""
if args.strip() == '-':
success = self.oldpwd.cd()
else:
success = self.pwd.cd(args)
if not success:
print('cd: {}: No such file or directory'.format(args))
else:
if not args.strip():
gROOT.cd()
def do_cd(self, args=''):
"""Change directory to specified directory. See also: pathspec."""
if args.strip() == '-':
success = self.oldpwd.cd()
else:
success = self.pwd.cd(args)
if not success:
print('cd: {}: No such file or directory'.format(args))
else:
if not args.strip():
gROOT.cd()
def read_back_profile_file(profile_file):
from ROOT import TFile, TIter, gROOT
root_file = TFile(profile_file)
list_of_keys = root_file.GetListOfKeys()
hists = {}
gROOT.cd() # clone into memory
for key_itr in TIter(list_of_keys):
key_name = key_itr.GetName()
hist = key_itr.ReadObj().Clone(str(random.random()))
hists[key_name] = hist
return hists
def make_sz_prob_dist(run_id, is_parallel, use_wrapping=True):
"""Make a spin direction (after flip) detection probability histogram"""
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(
run_id) + "_" + letter + ".root"
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return []
root_file = TFile(file_path, "READ")
hit_tree = gDirectory.Get('hitTree')
gROOT.cd()
num_events = hit_tree.GetEntries()
phi_list = []
theta_list = []
for i in xrange(num_events):
hit_tree.GetEntry(i)
phi_list.append(hit_tree.phi)
theta_list.append(hit_tree.theta)
root_file.Close()
if use_wrapping:
phi_mean = srkmisc.reduce_periodics(phi_list)
else:
phi_mean = srkmisc.careful_mean(phi_list)
phi_std = srkmisc.careful_std(phi_list)
theta_std = srkmisc.careful_std(theta_list)
comb_std = np.sqrt(phi_std * phi_std + theta_std * theta_std)
print "Combined Standard deviation: %e" % (comb_std)
x = np.linspace(-30, 30, num=60)
y = []
for num_std_dev in x:
sz_det_prob = 0
for phi, theta in zip(phi_list, theta_list):
sz_det_prob += calc_opposite_spin_prob(
phi - phi_mean + comb_std * num_std_dev, theta)
sz_det_prob /= num_events
y.append(1 - sz_det_prob)
return x, y
def get_hist(
name,
bin_width=5.0,
max_bin=2000,
):
gROOT.cd() # deal with TH1D/TFile/python scope issues!!
n_bins = int(1.0*max_bin/bin_width)
hist = TH1D('%s' % name, '', n_bins, 0, max_bin)
hist.Sumw2()
#print '----> making hist: %s' % hist.GetName()
return hist
def __init__(self , file0):
gROOT.cd()
self.PullF0 = file0.Get("hPullF0").Rebin(10 , "PullF0")
self.PullFNeg = file0.Get("hPullFNeg").Rebin( 10 , "PullFNeg")
self.PullFPos = file0.Get("hPullFPos").Rebin( 10 , "PullFPos")
self.ResF0 = file0.Get("hResF0").Clone("ResF0")
self.ResFNeg = file0.Get("hResFNeg").Clone("ResFNeg")
self.ResFPos = file0.Get("hResFPos").Clone("ResFPos")
self.NEntries = self.PullFPos.GetEntries()
self.AllResutls = {}
def createRatio(h1, h2):
Nbins = h1.GetNbinsX()
gROOT.cd()
Ratio = h1.Clone("Ratio")
hStackLast = h2.Clone("hStackLast")
for i in range(Nbins):
stackcontent = hStackLast.GetBinContent(i)
stackerror = hStackLast.GetBinError(i)
datacontent = h1.GetBinContent(i)
dataerror = h1.GetBinError(i)
print "stackcontent: ", stackcontent, " and data content: ", datacontent
ratiocontent = 0
if (datacontent != 0):
ratiocontent = datacontent / stackcontent
if (datacontent != 0):
error = ratiocontent * (math.sqrt(
math.pow((dataerror / datacontent), 2) +
math.pow((stackerror / stackcontent), 2)))
else:
error = 2.07
print "ratio content: ", ratiocontent, " and error: ", error
Ratio.SetBinContent(i, ratiocontent)
Ratio.SetBinError(i, error)
Ratio.GetYaxis().SetRangeUser(0.0, 2.2)
Ratio.SetStats(0)
Ratio.GetYaxis().CenterTitle()
Ratio.SetMarkerStyle(20)
Ratio.SetMarkerSize(0.7)
line = ROOT.TLine(40, 1., 120, 1.)
line.SetLineStyle(8)
Ratio.GetYaxis().SetLabelSize(0.14)
Ratio.GetYaxis().SetTitleSize(0.12)
Ratio.GetYaxis().SetLabelFont(42)
Ratio.GetYaxis().SetTitleFont(42)
Ratio.GetYaxis().SetTitleOffset(0.25)
Ratio.GetYaxis().SetNdivisions(100)
Ratio.GetYaxis().SetTickLength(0.05)
Ratio.GetXaxis().SetLabelSize(0.15)
Ratio.GetXaxis().SetTitleSize(0.12)
Ratio.GetXaxis().SetLabelFont(42)
Ratio.GetXaxis().SetTitleFont(42)
Ratio.GetXaxis().SetTitleOffset(0.90)
Ratio.GetXaxis().SetTickLength(0.05)
#Ratio.Draw("pex0")
#line.SetLineColor(kBlack)
#line.Draw("same");
return Ratio, line
def GetFunction(name ,F3 , VarToFix , Value ):
functor = F32F2(F3 , VarToFix , Value)
gROOT.cd()
ret = None
if VarToFix == 1:
ret = TF2( name , functor , F3.GetYmin() , F3.GetYmax() , F3.GetZmin() , F3.GetZmax() , F3.GetNpar() )
elif VarToFix == 2:
ret = TF2( name , functor , F3.GetXmin() , F3.GetXmax() , F3.GetZmin() , F3.GetZmax() , F3.GetNpar() )
elif VarToFix == 3:
ret = TF2( name , functor , F3.GetXmin() , F3.GetXmax() , F3.GetYmin() , F3.GetYmax() , F3.GetNpar() )
return [ret , functor]
def make_sz_prob_dist(run_id, is_parallel, use_wrapping=True):
"""Make a spin direction (after flip) detection probability histogram"""
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(run_id) + "_" + letter + ".root"
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return []
root_file = TFile(file_path, "READ")
hit_tree = gDirectory.Get('hitTree')
gROOT.cd()
num_events = hit_tree.GetEntries()
phi_list = []
theta_list = []
for i in xrange(num_events):
hit_tree.GetEntry(i)
phi_list.append(hit_tree.phi)
theta_list.append(hit_tree.theta)
root_file.Close()
if use_wrapping:
phi_mean = srkmisc.reduce_periodics(phi_list)
else:
phi_mean = srkmisc.careful_mean(phi_list)
phi_std = srkmisc.careful_std(phi_list)
theta_std = srkmisc.careful_std(theta_list)
comb_std = np.sqrt(phi_std * phi_std + theta_std * theta_std)
print "Combined Standard deviation: %e" % (comb_std)
x = np.linspace(-30, 30, num=60)
y = []
for num_std_dev in x:
sz_det_prob = 0
for phi, theta in zip(phi_list, theta_list):
sz_det_prob += calc_opposite_spin_prob(phi - phi_mean + comb_std * num_std_dev, theta)
sz_det_prob /= num_events
y.append(1 - sz_det_prob)
return x, y
def createDataH1(ch, channels):
mc2017Files = glob(
"/data/uhussain/SkimmedZZNtuples_mc_2018-03-06_hadd/2017ZZTo4L/2018-03-06-ZZTo4L-powheg_ext1-ZZ4l2018-preselectionToQCDVeto-v1/*.root"
)
tree = ROOT.TChain(ch + "/ntuple")
meta_tree = ROOT.TChain("metaInfo/metaInfo")
for f in mc2017Files:
tree.Add(f)
meta_tree.Add(f)
data_hist = "ZMass_2017_" + mcSamples[0] + "_" + ch
#dataFile = ROOT.TFile("/data/uhussain/SkimmedZZNtuples_mc_2018-03-06_hadd/results_full/ZZTo4L-powheg_ext1.root")
##print dataFiles
#tree = dataFile.Get(ch+"/ntuple")
#meta_tree = dataFile.Get("metaInfo/metaInfo")
data_hist = "ZMass2017_" + ch
h1 = ROOT.TH1F(data_hist, data_hist, nbins, 40, 120)
if (ch == "eeee" or ch == "mmmm"):
tree.Draw(channels[ch] + ">>" + data_hist,
"genWeight*(nZZTightIsoElec+nZZTightIsoMu==4)")
#Function to retrieve mZ1 in ch=eemm
else:
tree.Draw(
"(( abs(e1_e2_Mass-91.1876)<abs(m1_m2_Mass-91.1876) ) ? e1_e2_Mass : m1_m2_Mass)>>"
+ data_hist, "genWeight*(nZZTightIsoElec+nZZTightIsoMu==4)")
sumweights_hist = ROOT.TH1D("sumweights", "sumweights", 1, 0, 100)
meta_tree.Draw("1>>sumweights", "summedWeights")
sumweights = sumweights_hist.Integral()
print "2017sumweights: ", sumweights
nevents_hist = ROOT.TH1D("nevents", "nevents", 1, 0, 100)
meta_tree.Draw("1>>nevents", "nevents")
nevents = nevents_hist.Integral()
print "2017nevents: ", nevents
xsec = 1.256 * 1000
h1.Scale((xsec * lumi) / sumweights)
h1.SetLineColor(ROOT.kRed)
h1.SetLineWidth(2)
h1.SetStats(0)
h1.SetTitle("")
h1.GetXaxis().SetTitle("")
h1.GetXaxis().SetTickLength(0)
h1.GetXaxis().SetLabelOffset(999)
h1.GetYaxis().SetTitle("")
h1.GetYaxis().SetTickLength(0)
h1.GetYaxis().SetLabelOffset(999)
print h1.Integral()
gROOT.cd()
h2017 = h1.Clone()
return h2017
def readTree(self, file_name, tree_name='tree', verbose=False):
'''Cache files/trees'''
if file_name in self.__class__._t_keeper:
ttree = self.__class__._t_keeper[file_name]
if verbose:
print 'got cached tree', ttree
else:
tfile = self.__class__._f_keeper[file_name] = TFile.Open(file_name)
ttree = self.__class__._t_keeper[file_name] = tfile.Get(tree_name)
if verbose:
print 'read tree', ttree, 'from file', file_name
gROOT.cd()
return ttree
def GetFunction(name , justOne = -2):
functor = dGammaFunctin(justOne)
gROOT.cd()
ret = TF1(name , functor , -1.0 , 1.0 , 3)
ret.SetParName(0 , "FNeg" );
ret.SetParName(1 , "F0" );
ret.SetParName(2 , "NormaFactor" );
ret.SetParLimits(0 , 0.0 , 1.0);
ret.SetParLimits(1 , 0.0 , 1.0);
ret.SetParameters( 0.3 , 0.7 , 1.0 );
return ret;
def BuildFromRootFile(filename, name):
result = ResultData(name)
inputfile = TFile.Open(filename)
gROOT.cd()
keyIter = TIter(inputfile.GetListOfKeys())
key = keyIter.Next()
while key:
if key.GetName() == "MCTruth":
result.SetMCTruth(ParticleTHnSparse(key.ReadObj()))
else:
result.SetData(key.GetName(), DataSet.BuildFromRootPrimitive(key.ReadObj()))
key = keyIter.Next()
inputfile.Close()
print "Results successfully reconstructed from file %s" %(filename)
#result.Print()
return result
def BuildFromRootFile(filename, name):
result = ResultData(name)
inputfile = TFile.Open(filename)
gROOT.cd()
keyIter = TIter(inputfile.GetListOfKeys())
key = keyIter.Next()
while key:
if key.GetName() == "MCTruth":
result.SetMCTruth(ParticleTHnSparse(key.ReadObj()))
else:
result.SetData(key.GetName(), DataSet.BuildFromRootPrimitive(key.ReadObj()))
key = keyIter.Next()
inputfile.Close()
print "Results successfully reconstructed from file %s" %(filename)
#result.Print()
return result
def createMCStack(ch,channels):
mcSamples = {"DYJetsToLL_M10to50":18610,"DYJetsToLLM-50_ext1":6104,"ggHZZ_ext1":0.01218,"GluGluZZTo2e2mu":0.00319,
"GluGluZZTo2e2tau":0.00319,"GluGluZZTo2mu2tau": 0.00319,"GluGluZZTo4e":0.00159,"GluGluZZTo4mu":0.00159,
"ttH_HToZZ_4L_ext1":0.000393,"TTJets-amcatnlo":815.96,"TTTo2L2Nu-powheg":87.31,"WminusHToZZ_ext1":0.000147,
"WplusHToZZ_ext1":0.000232,"WZTo3LNu":4.430,"ZHToZZ_4L":0.000668,"ZZTo4L-powheg_ext1":1.256}
#channels = ["eeee/ntuple","eemm/ntuple", "eeee/ntuple"]
MCStack = ROOT.THStack("stack", "stack")
ROOT.SetOwnership(MCStack, False)
for mc_Sample in mcSamples.keys():
#print "mcSample is: ", mc_Sample
mcPath = Date+'-'+mc_Sample+'-'+analysis+'-'+selection
mcDir = os.path.join(Dir,mcPath)
print 'mcDir: ',mcDir
mcFiles = glob(mcDir+"/*.root")
MCchain = ROOT.TChain(ch+"/ntuple")
MetaChain = ROOT.TChain("metaInfo/metaInfo")
for g in mcFiles:
MCchain.Add(g)
MetaChain.Add(g)
hist_name = "ZMass"+mc_Sample+ch
hist = ROOT.TH1F(hist_name, hist_name, nbins, 40, 120)
#ROOT.SetOwnership(hist, False)
if (ch=="eeee" or ch=="mmmm"):
MCchain.Draw(channels[ch]+">>"+hist_name,"nZZTightIsoElec+nZZTightIsoMu==4")
#Function to retrieve mZ1 in ch=eemm
else:
MCchain.Draw("(( abs(e1_e2_Mass-91.1876)<abs(m1_m2_Mass-91.1876) ) ? e1_e2_Mass : m1_m2_Mass)>>"+hist_name, "nZZTightIsoElec+nZZTightIsoMu==4")
sumweights_hist = ROOT.TH1D("sumweights", "sumweights", 1,0,100)
MetaChain.Draw("1>>sumweights","summedWeights")
sumweights = sumweights_hist.Integral()
xsec = mcSamples[mc_Sample]*1000
print "sumweights: ",sumweights
hist.Scale((xsec*lumi)/sumweights)
#hist.Scale(xsec*lumi)
hist.SetFillColor(ROOT.kBlue)
gROOT.cd()
hnew = hist.Clone()
#if MCStack.GetHists():
# print "Before length is", len(MCStack.GetHists())
MCStack.Add(hnew)
#print "length is", len(MCStack.GetHists())
#print hist.Integral()
#ROOT.SetOwnership(MCStack, False)
for i in MCStack.GetHists(): print "Name is", i.GetName(), "Integral is", i.Integral()
return MCStack
def get_dir(self, path=None):
"""Return directory from path.
Doesn't check for non-directory. It's the caller's
responsibility.
"""
if not path:
return gDirectory.GetDirectory('')
else:
path = pathspec(path)
with savepwd():
if path.rfile: # need to change to correct file first
if path.rfile not in [f.GetName() for f in self.files]:
# opening a file changes dir to the new file
self.files += [ROOT.TFile.Open(path.rfile, 'read')]
else:
gROOT.cd('{}:'.format(path.rfile))
return gDirectory.GetDirectory(path.rpath)
def get_dir(self, path=None):
"""Return directory from path.
Doesn't check for non-directory. It's the caller's
responsibility.
"""
if not path:
return gDirectory.GetDirectory('')
else:
path = pathspec(path)
with savepwd():
if path.rfile: # need to change to correct file first
if path.rfile not in [f.GetName() for f in self.files]:
# opening a file changes dir to the new file
self.files += [ROOT.TFile.Open(path.rfile, 'read')]
else:
gROOT.cd('{}:'.format(path.rfile))
return gDirectory.GetDirectory(path.rpath)
def makeTrajPlot():
gStyle.SetCanvasPreferGL(kTRUE)
gROOT.cd()
if not h.has_key("world"):
h["world"] = TH3F("world", "world", 100, -5.0, 5.0, 100, -5.0, 5.0, 100, -50.0, 51.0)
h["world2d"] = TH2F("world2d", "world2d", 100, -5.0, 5.0, 100, -50.0, 51.0)
h["world"].GetXaxis().SetTitle("x-axis")
h["world"].GetYaxis().SetTitle("y-axis")
h["world"].GetZaxis().SetTitle("beam")
h["world"].SetStats(0)
h["trajectories"].cd(1)
h["world"].Draw("gl")
h["trajectories"].cd(2)
h["world2d"].Draw()
h["world_leg"] = TLegend(0.38, 0.62, 0.62, 0.89)
for E in range(25, 425, 50):
makeEntryLists(E - 25, E + 25, 3)
h["trajectories"].cd(2)
h["world_leg"].Draw()
def main():
''' please make sure the script is running on ROOT>=6 , otherwise you need to add SumW2 when you declare a histograms '''
for region in Regions:
for POI in POIs:
dirOfRootplas = DirOfRootplas + POI + "/"
for channel in channels:
# select which samples to loop over for each channel
if "mm" in channel: samplesToLoop = samplesMM
elif "ee" in channel: samplesToLoop = samplesEE
else: samplesToLoop = samples
samplesToUse = samplesToLoop[:]
# create a log file to save the output
SystLogName = dirOfRootplas + Prefix + region + "_" + channel + "_TableOfSystematics_" + Nuisance + ".txt"
SystLogFile = file(SystLogName, "w")
# open and read root file
filename = Prefix + region + "_" + channel + Postfix
inputfile = read_rootfile(filename, dirOfRootplas)
gROOT.cd()
print samplesToLoop
for sample in samplesToLoop:
hist = inputfile.Get(sample)
hist_error = ROOT.Double(0)
hist_integral = hist.IntegralAndError(
0, hist.GetNbinsX(), hist_error)
Rates[sample] = round(hist_integral, 3)
Uncertainties[sample] = round(hist_error, 3)
# write begin parts of datacards
print >> SystLogFile, "In Channel " + channel + ":"
begin = "Samples Nominal Stats"
for np in Nuisances[Nuisance]:
begin += " " + np
print >> SystLogFile, begin
# write each systematics
writecard(samplesToUse, Nuisance, channel, inputfile,
SystLogFile)
def calc_step_tree(file_path, inp_periodic_stop_time):
"""Calculate time and spin detection probabilities from step tree."""
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return []
print "Calculating step tree for {}".format(file_path)
root_file = TFile(file_path, "READ")
step_tree = gDirectory.Get('stepTree')
hit_tree = gDirectory.Get('hitTree')
gROOT.cd()
user_info_list = hit_tree.GetUserInfo()
periodic_stop_time = inp_periodic_stop_time
for i in xrange(user_info_list.GetEntries()):
par = user_info_list.At(i)
if par.GetName() == 'PeriodicStopTime':
periodic_stop_time = float(par.GetTitle())
break
num_events = hit_tree.GetEntries()
num_steps_per_event = step_tree.GetEntries() / num_events
time_data = np.zeros(num_steps_per_event)
sx_prob_data = np.zeros(num_steps_per_event)
# Set times
for j in xrange(num_steps_per_event):
time_data[j] = ((j + 1) * periodic_stop_time)
# Add data
for i in xrange(num_events):
if i % 100 == 0:
print "Stepping for Event {} in {}".format(i, file_path)
for j in xrange(num_steps_per_event):
step_tree.GetEntry(i * num_steps_per_event + j)
sx_prob_data[j] += step_tree.sxProb
# Normalize
sx_prob_data /= float(num_events)
root_file.Close()
return zip(time_data, sx_prob_data)
def calc_step_tree(file_path, inp_periodic_stop_time):
"""Calculate time and spin detection probabilities from step tree."""
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return []
print "Calculating step tree for {}".format(file_path)
root_file = TFile(file_path, "READ")
step_tree = gDirectory.Get('stepTree')
hit_tree = gDirectory.Get('hitTree')
gROOT.cd()
user_info_list = hit_tree.GetUserInfo()
periodic_stop_time = inp_periodic_stop_time
for i in xrange(user_info_list.GetEntries()):
par = user_info_list.At(i)
if par.GetName() == 'PeriodicStopTime':
periodic_stop_time = float(par.GetTitle())
break
num_events = hit_tree.GetEntries()
num_steps_per_event = step_tree.GetEntries() / num_events
time_data = np.zeros(num_steps_per_event)
sx_prob_data = np.zeros(num_steps_per_event)
# Set times
for j in xrange(num_steps_per_event):
time_data[j] = ((j + 1) * periodic_stop_time)
# Add data
for i in xrange(num_events):
if i % 100 == 0:
print "Stepping for Event {} in {}".format(i, file_path)
for j in xrange(num_steps_per_event):
step_tree.GetEntry(i * num_steps_per_event + j)
sx_prob_data[j] += step_tree.sxProb
# Normalize
sx_prob_data /= float(num_events)
root_file.Close()
return zip(time_data, sx_prob_data)
def __init__(self, Samples , directory):
self.PropName = directory.GetName()
dircontents = directory.GetListOfKeys()
firsthisto = directory.Get( dircontents.At(0).GetName() )
#firsthisto.Print("ALL")
self.ForLegend = {}
self.XSections = {}
self.AllSampleHistos = {}
for sample in Samples:
if sample.IsData:
self.DataSName = sample.HistoCat
# h_in_dir = directory.Get( "%s_%s" % ( self.PropName , sample.Name ) )
# if h_in_dir :
# h_in_dir.reset()
# setattr( self , sample.Name , h_in_dir )
# else:
gROOT.cd()
hnew = firsthisto.Clone("%s_%s" % ( self.PropName , sample.Name ) )
hnew.Reset()
setattr( self , sample.Name , hnew )
hhh = getattr( self , sample.Name )
hhh.SetLineColor( 1 )
hhh.SetLineWidth( 2 )
if not sample.IsData :
hhh.SetFillColor( sample.Color )
hhh.SetFillStyle( 1001 )
else:
hhh.SetStats(0)
self.AllSampleHistos[sample.Name] = hhh
if( self.ForLegend.get(sample.HistoCat) ):
self.ForLegend[sample.HistoCat].append( sample.Name )
else:
self.ForLegend[sample.HistoCat] = [ sample.Name ]
self.XSections[sample.Name] = sample.XSection
def makeTrajPlot():
gStyle.SetCanvasPreferGL(kTRUE)
bookCanvas(h,key='trajectories',title='trajectories of surviving muons',nx=1600,ny=800,cx=2,cy=1)
gROOT.cd()
if not h.has_key('world'):
h['world'] = TH3F('world','world',100,-5.,5.,100,-5.,5.,100,-50.,51.)
h['world2d'] = TH2F('world2d','world2d',100,-5.,5.,100,-50.,51.)
h['world'].GetXaxis().SetTitle('x-axis')
h['world'].GetYaxis().SetTitle('y-axis')
h['world'].GetZaxis().SetTitle('beam')
h['world'].SetStats(0)
h['trajectories'].cd(1)
h['world'].Draw('gl')
h['trajectories'].cd(2)
h['world2d'].Draw()
h['world_leg'] = TLegend(0.38,0.62,0.62,0.89)
for E in range(50,450,50):
makeEntryLists(E,3)
h['trajectories'].cd(2)
h['world_leg'].Draw()
def ReduceFile(inputRootFile):
from ROOT import TTree, TFile, gROOT
print 'Processing ', file
outRootFile = '' + inputRootFile
strreplace = ['RD_', '_CMSSW532', '_private']
for str in strreplace:
outRootFile = outRootFile.replace(str, '')
fout = TFile(outRootFile,"recreate")
f = TFile(dir + inputRootFile,"read")
tree = f.Get("WJet")
tree.SetBranchStatus('*', 0)
for name in Branches: tree.SetBranchStatus(name, 1)
gROOT.cd()
tree2 = tree.CopyTree(cuts)
fout.cd()
tree2.Write()
fout.Close()
def readTree(self, file_name, tree_name='tree', verbose=False, friend_func=None):
'''Cache files/trees'''
if file_name in self.__class__._t_keeper:
ttree = self.__class__._t_keeper[file_name]
if verbose:
print 'got cached tree', ttree
else:
tfile = self.__class__._f_keeper[file_name] = TFile.Open(file_name)
ttree = self.__class__._t_keeper[file_name] = tfile.Get(tree_name)
if verbose:
print 'read tree', ttree, 'from file', file_name
if friend_func:
file_name = friend_func(file_name)
friend_tree = self.readTree(file_name, tree_name, verbose)
ttree.AddFriend(friend_tree)
gROOT.cd()
return ttree
def makeCovariancePlot(self, varList1, varList2, cut=""):
fIn = TFile(self.sigFilePath)
t = fIn.Get(self.treeName)
gROOT.cd()
c = Covariator(t)
nVars1 = len(varList1)
isSymmetric = False
if varList2 == None:
varList2 = varList1
isSymmetric = True
nVars2 = len(varList2)
hplot = TH2D('cov' + str(Plotter.counter),
'cov' + str(Plotter.counter), nVars1, 0, nVars1, nVars2,
0, nVars2)
Plotter.counter += 1
xaxis = hplot.GetXaxis()
yaxis = hplot.GetYaxis()
matrix = empty([nVars1, nVars2])
if isSymmetric:
for iV in xrange(nVars1):
xaxis.SetBinLabel(iV + 1, varList1[iV])
yaxis.SetBinLabel(iV + 1, varList1[iV])
for jV in xrange(iV + 1):
varA = varList1[iV]
varB = varList1[jV]
val = c.covSuperFast(varA, varB, cut)
hplot.SetBinContent(jV + 1, iV + 1, val)
hplot.SetBinContent(iV + 1, jV + 1, val)
matrix[iV, jV] = val
matrix[jV, iV] = val
else:
for iV in xrange(nVars1):
xaxis.SetBinLabel(iV + 1, varList1[iV])
for jV in xrange(nVars2):
yaxis.SetBinLabel(jV + 1, varList2[jV])
varA = varList1[iV]
varB = varList2[jV]
val = c.covSuperFast(varA, varB, cut)
hplot.SetBinContent(iV + 1, jV + 1, val)
matrix[iV, jV] = val
return hplot, matrix
def __init__(self, Samples, directory):
self.PropName = directory.GetName()
dircontents = directory.GetListOfKeys()
firsthisto = directory.Get(dircontents.At(0).GetName())
#firsthisto.Print("ALL")
self.ForLegend = {}
self.XSections = {}
self.AllSampleHistos = {}
for sample in Samples:
if sample.IsData:
self.DataSName = sample.HistoCat
# h_in_dir = directory.Get( "%s_%s" % ( self.PropName , sample.Name ) )
# if h_in_dir :
# h_in_dir.reset()
# setattr( self , sample.Name , h_in_dir )
# else:
gROOT.cd()
hnew = firsthisto.Clone("%s_%s" % (self.PropName, sample.Name))
hnew.Reset()
setattr(self, sample.Name, hnew)
hhh = getattr(self, sample.Name)
hhh.SetLineColor(1)
hhh.SetLineWidth(2)
if not sample.IsData:
hhh.SetFillColor(sample.Color)
hhh.SetFillStyle(1001)
else:
hhh.SetStats(0)
self.AllSampleHistos[sample.Name] = hhh
if (self.ForLegend.get(sample.HistoCat)):
self.ForLegend[sample.HistoCat].append(sample.Name)
else:
self.ForLegend[sample.HistoCat] = [sample.Name]
self.XSections[sample.Name] = sample.XSection
def processSpectrum(doc_list, input_directory='..', type='pulser',\
hist_name='hist', \
energy_scale='*1', cuts='', use_baseline_cut=False, opposite_baseline_cut=False):
import re
from array import array
from ROOT import TFile, TH1D, TTree, TObject, TEventList, gROOT
from soudan_database import SoudanServer
number_of_sigma = 3
hist = TH1D(hist_name, hist_name, 8192, 0, eval("8192%s" % energy_scale))
hist.GetXaxis().SetTitle("Energy (keV)")
hist.GetYaxis().SetTitle("Counts")
server = SoudanServer()
for run_number in doc_list:
doc = server.get_run(run_number.id)
try:
string_of_file_name = eval('doc.output_data_file_tier_3.%s.lfn' % type)
except:
print "Incorrect type"
continue
print doc._get_id()
open_file = TFile("%s/%s" % (input_directory,string_of_file_name))
main_tree = open_file.Get("wf_analysis")
coinc_tree = open_file.Get("event_coincidence")
main_tree.AddFriend(coinc_tree)
real_cuts = cuts
if use_baseline_cut:
not_string = ""
if opposite_baseline_cut:
not_string = "!"
additional_cuts = " && %s(abs(fitConstant-%f) <= %f*%f) " % \
(not_string, doc.baseline_dict.average_fit_constant,\
number_of_sigma, doc.baseline_dict.average_fit_rms)
real_cuts += additional_cuts
gROOT.cd()
main_tree.Draw("-energy%s>> +%s" % (energy_scale, hist.GetName()),real_cuts, "goff")
open_file.Close()
return hist
def makeRootDataFrameFromTree(self, tree_file_name, tree_name='tree', verbose=False, friend_name='ML', friend_file_name=None):
'''Cache files/trees'''
ttree = self.readTree(tree_file_name, tree_name, verbose)
if verbose:
print ('read dataframe', dataframe, 'from file', tree_file_name)
if friend_file_name:
ttree.AddFriend(friend_name + '=tree',friend_file_name)
#VALIDATE#
validate1 = ttree.GetEntries('l2_pt - %s.l2_pt'%friend_name)
validate2 = ttree.GetEntries('event - %s.event'%friend_name)
if not validate1+validate2 == 0: print ('\n\tERROR: FRIEND TREE NOT ALIGNED, FAKERATE USELESS', m, n)
gROOT.cd()
# dataframe = RDataFrame(tree_name,tree_file_name)
dataframe = RDataFrame(ttree)
return dataframe
def createMCStack(ch,channels):
mcSamples = {"ggHZZ":0.01218,"GluGluZZTo2e2mu":0.00319,
"GluGluZZTo2e2tau":0.00319,"GluGluZZTo2mu2tau": 0.00319,"GluGluZZTo4e":0.00159,"GluGluZZTo4mu":0.00159,
"TTTo2L2Nu-powheg":87.31,"ZZTo4L-powheg_ext1":1.256}
#channels = ["eeee/ntuple","eemm/ntuple", "eeee/ntuple"]
MCStack = ROOT.THStack("stack", "stack")
ROOT.SetOwnership(MCStack, False)
for mc_Sample in mcSamples.keys():
#print "mcSample is: ", mc_Sample
mcFile = os.path.join(mcDir,mc_Sample)
mcFile = mcFile+'.root'
f1 = ROOT.TFile(mcFile)
tree = f1.Get(ch+"/ntuple")
meta_tree = f1.Get("metaInfo/metaInfo")
hist_name = "ZMass"+mc_Sample+ch
hist = ROOT.TH1F(hist_name, hist_name, nbins, 40, 120)
#ROOT.SetOwnership(hist, False)
if (ch=="eeee" or ch=="mmmm"):
tree.Draw(channels[ch]+">>"+hist_name,"nZZTightIsoElec+nZZTightIsoMu==4")
#Function to retrieve mZ1 in ch=eemm
else:
tree.Draw("(( abs(e1_e2_Mass-91.1876)<abs(m1_m2_Mass-91.1876) ) ? e1_e2_Mass : m1_m2_Mass)>>"+hist_name, "nZZTightIsoElec+nZZTightIsoMu==4")
sumweights_hist = ROOT.TH1D("sumweights", "sumweights", 1,0,100)
meta_tree.Draw("1>>sumweights","summedWeights")
sumweights = sumweights_hist.Integral()
xsec = mcSamples[mc_Sample]*1000
hist.Scale(xsec*lumi/sumweights)
#hist.Scale(xsec*lumi)
hist.SetFillColor(ROOT.kBlue)
gROOT.cd()
hnew = hist.Clone()
#if MCStack.GetHists():
# print "Before length is", len(MCStack.GetHists())
MCStack.Add(hnew)
#print "length is", len(MCStack.GetHists())
#print hist.Integral()
#ROOT.SetOwnership(MCStack, False)
for i in MCStack.GetHists(): print "Name is", i.GetName(), "Integral is", i.Integral()
return MCStack
def create2DHistograms(hist_cfg):
histos = HistoStack(hist_cfg.name)
for cfg in hist_cfg.cfgs:
# First check whether it's a sub-histo or not
if isinstance(cfg, HistogramCfg):
histos.Add(cfg.name, createHistograms(cfg).Sum())
elif isinstance(cfg, BasicHistogramCfg):
print "Opening file", cfg.histo_file_name
histo_file = TFile.Open(cfg.histo_file_name)
print "Retrieving histo", cfg.histo_name
hist = histo_file.Get(cfg.histo_name)
hist.__class__ = TH2F
hist.SetDirectory(0)
hist.SetName(cfg.name + '_' + hist.GetName())
histo_file.Close()
gROOT.cd()
#
hist.Scale(cfg.scale)
if not cfg.is_data:
hist.Scale(hist_cfg.lumi * cfg.xsec / cfg.sumweights)
histos.Add(cfg.name, hist)
return histos
def morphAllLambda(basisFile, lambdaZ, dKappaGamma, morphedHists, debug=False):
fbasis = TFile(basisFile)
gROOT.cd()
for i in range(1, 4):
h0Name = 'signal_lambdaZ_%.2f_deltaKappaGamma_%.1f' % (lambdaZ,
dKappaGamma)
h1Name = 'signal_lambdaZ_%.2f_deltaKappaGamma_%.1f' % (lambdaZ + 0.04,
dKappaGamma)
morphName = 'signal_lambdaZ_%.2f_deltaKappaGamma_%.2f' % (
lambdaZ + 0.01 * i, dKappaGamma)
hist0 = fbasis.Get(h0Name)
hist1 = fbasis.Get(h1Name)
if i == lambdaZ:
morphedHists.append(hist0)
morphedHists.append(hist1)
gROOT.cd()
morphedhist = morph(hist0, hist1, lambdaZ, lambdaZ + 0.04,
lambdaZ + 0.01 * i, debug)
morphedhist.SetName(morphName)
morphedHists.append(morphedhist)
if debug:
hist0.SetLineColor(kBlue)
hist1.SetLineColor(kRed)
if mHbasis < mHmorph:
hist0.Draw('hist')
hist1.Draw('histsame')
else:
hist1.Draw('hist')
hist0.Draw('histsame')
morphedHists[-1].SetLineStyle(9)
morphedHists[-1].Draw('histsame')
gPad.Update()
gPad.WaitPrimitive()
def morphAllLambda(basisFile, lambdaZ, dKappaGamma, morphedHists, debug = False):
fbasis = TFile(basisFile)
gROOT.cd()
for i in range(1,4):
h0Name = 'signal_lambdaZ_%.2f_deltaKappaGamma_%.1f' % (lambdaZ, dKappaGamma)
h1Name = 'signal_lambdaZ_%.2f_deltaKappaGamma_%.1f' % (lambdaZ+0.04, dKappaGamma)
morphName = 'signal_lambdaZ_%.2f_deltaKappaGamma_%.2f' % (lambdaZ+0.01*i, dKappaGamma)
hist0 = fbasis.Get(h0Name)
hist1 = fbasis.Get(h1Name)
if i==lambdaZ:
morphedHists.append(hist0)
morphedHists.append(hist1)
gROOT.cd()
morphedhist = morph(hist0, hist1, lambdaZ, lambdaZ+0.04,
lambdaZ+0.01*i, debug)
morphedhist.SetName(morphName)
morphedHists.append(morphedhist)
if debug:
hist0.SetLineColor(kBlue)
hist1.SetLineColor(kRed)
if mHbasis < mHmorph:
hist0.Draw('hist')
hist1.Draw('histsame')
else:
hist1.Draw('hist')
hist0.Draw('histsame')
morphedHists[-1].SetLineStyle(9)
morphedHists[-1].Draw('histsame')
gPad.Update()
gPad.WaitPrimitive()
def DrawHiggsMass(inputRootFile, scale = 1.0):
f = TFile('data/' + inputRootFile,"read")
tree = f.Get("WJet")
gROOT.cd()
histName = inputRootFile.replace('.root','')
hist = TH1D(histName, '', 20, 100., 900.)
xaxis = hist.GetXaxis()
yaxis = hist.GetYaxis()
xaxis.SetTitle('Higgs mass [GeV]')
yaxis.SetTitle('Events / 40 GeV')
xaxis.SetLabelSize(0.05)
xaxis.SetTitleSize(0.05)
xaxis.SetNdivisions(505)
yaxis.SetLabelSize(0.05)
yaxis.SetTitleSize(0.05)
yaxis.SetTitleOffset(1.6)
yaxis.SetNoExponent()
tree.Draw("fit_mlvjj>>"+histName, cuts, "goff")
hist.Scale(scale)
return hist
def sobWeightedPlot(fileName, datasetName, channel, cat, log, mass, tanb):
c = TCanvas(fileName, '', 600, 600)
c.cd()
if log: c.SetLogy(1)
f = TFile('Plot_'+fileName+'.root')
gROOT.cd()
samples = ['ggH', 'Ztt', 'signal', 'data_obs', 'ttbar', 'EWK', 'Fakes', 'ggH_hww']
if fileName.find('emu') != -1 and isSM:
samples.append('ggH_hww')
histDict = {}
for sample in samples:
histDict[sample] = tfileGet(f, sample)
if not histDict[sample]:
print 'Missing histogram', sample, 'in file', 'Plot_'+fileName+'.root'
xminInset = 60 # 0
xmaxInset = 180 # 340 (for full range)
if tanb > 0:
xminInset = mass - 100
xmaxInset = mass + 100
ztt = histDict['Ztt']
ggH = histDict['ggH']
data = histDict['data_obs']
signal = histDict['signal']
ggH_hww = histDict['ggH_hww']
tt = histDict['ttbar']
ewk = histDict['EWK']
fakes = histDict['Fakes']
ztt.GetYaxis().SetRangeUser(0., 1.3*findMaxY(data, 0))
if log:
ztt.GetYaxis().SetRangeUser(0.001, 50.*findMaxY(data, 0))
ztt.GetXaxis().SetTitle('#bf{m_{#tau#tau} [GeV]}')
ztt.GetYaxis().SetTitle('#bf{S/B Weighted dN/dm_{#tau#tau} [1/GeV]}')
if tanb > 0. and not log:
ztt.GetXaxis().SetRangeUser(0., mass+200.)
ztt.SetTitleOffset(1.3, 'Y')
ztt.SetTitleOffset(1., 'X')
ztt.SetNdivisions(505)
for b in range(0, signal.GetNbinsX()+2):
if signal.GetBinCenter(b) < xminInset or xmaxInset < signal.GetBinCenter(b):
signal.SetBinContent(b, 0.)
signal.SetBinError(b, 0.)
signal.SetName('sig')
signal.SetFillStyle(3353) # 1001=solid , 3004,3005=diagonal
signal.SetFillColor(2)
signal.SetLineColor(2)
signal.SetLineStyle(1)
signal.SetLineWidth(0)
ggH.SetBinContent(0,0) # remove red line on top of y axis in plot
ggH.SetBinContent(ggH.GetNbinsX()+1,0)
ggH.SetBinError(0,0)
ggH.SetBinError(ggH.GetNbinsX()+1,0)
ggH.SetName('ggH')
ggH.SetFillStyle(3353) # 1001=solid , 3004,3005=diagonal
ggH.SetFillColor(2)
ggH.SetLineColor(2)
ggH.SetLineStyle(1)
ggH.SetLineWidth(0)
if ggH_hww:
errorBand = ggH_hww.Clone("errorBand")
else:
errorBand = ztt.Clone("errorBand")
errorBand.SetMarkerSize(0)
errorBand.SetFillColor(1)
errorBand.SetFillStyle(3013)
errorBand.SetLineWidth(1)
legend = TLegend()
mssmLabel = ''
if tanb > 0:
mssmLabel = "tan#beta={tanb}".format(tanb=tanb)
higgsLabel = "H(125 GeV)#rightarrow#tau#tau"
if tanb > 0:
higgsLabel="H(125 GeV)#rightarrow#tau#tau"
legend.SetFillStyle(0)
legend.SetFillColor(0)
legend.SetBorderSize(0)
legend.AddEntry(ggH,higgsLabel,"F")
if tanb>0:
legend.AddEntry(TObject(0), mssmLabel, "")
legend.AddEntry(data,"observed", "LP")
if ggH_hww:
legend.AddEntry(ggH_hww, "H(125 GeV)#rightarrowWW", "F")
legend.AddEntry(ztt, "Z#rightarrow#tau#tau","F")
legend.AddEntry(tt, "t#bar{t}","F")
legend.AddEntry(ewk, "electroweak","F")
legend.AddEntry(fakes, "QCD","F")
legend.SetX1NDC(0.63)
legend.SetX2NDC(1.05)
legend.SetY1NDC(0.27)
legend.SetY2NDC(0.48)
if log:
legend.SetX1NDC(0.18)
legend.SetX2NDC(0.60)
legend.SetY1NDC(0.17)
legend.SetY2NDC(0.38)
legend.SetTextSize(.028)
legend.SetTextAlign(12)
if ggH_hww:
dataDiff = diffPlot(data, ggH_hww, 1)
errBand=getErrorBand(ggH_hww)
else:
dataDiff = diffPlot(data, ztt, 1)
errBand=getErrorBand(ztt)
errBand.SetFillStyle(3013) # 1001=solid , 3004,3005=diagonal, 3013=hatched official for H.tau tau
errBand.SetFillColor(1)
errBand.SetLineStyle(1)
errBand.SetLineColor(1)
errBand.SetLineWidth(1)
errBandFrame = TH1F('errBandFrame', '', int((xmaxInset-xminInset)/dataDiff.GetBinWidth(1)),xminInset,xmaxInset)
errBandFrame.GetYaxis().SetTitle("")
errBandFrame.GetYaxis().SetRangeUser(-1.1*findMinY(dataDiff,0,xminInset,xmaxInset),2.0*findMaxY(dataDiff,0,xminInset,xmaxInset))
errBandFrame.GetYaxis().SetNdivisions(5)
errBandFrame.GetYaxis().SetLabelSize(0.06)
errBandFrame.GetXaxis().SetTitle("#bf{m_{#tau#tau} [GeV]} ")
errBandFrame.GetXaxis().SetTitleColor(kBlack)
errBandFrame.GetXaxis().SetTitleSize(0.07)
errBandFrame.GetXaxis().SetTitleOffset(0.95)
errBandFrame.GetXaxis().SetLabelSize(0.06)
errBandFrame.SetNdivisions(505)
legendDiff = TLegend()
legendDiff.SetFillStyle(0)
legendDiff.SetFillColor(0)
legendDiff.SetBorderSize(0)
legendDiff.AddEntry(signal,higgsLabel,"F")
if tanb>0:
legendDiff.AddEntry(TObject(0), mssmLabel, '') # That might not work in python
legendDiff.AddEntry(dataDiff,"Data - Background","LP")
legendDiff.AddEntry(errBand,"Bkg. Uncertainty","F")
legendDiff.SetX1NDC(0.45)
legendDiff.SetX2NDC(0.88)
legendDiff.SetY1NDC(0.67)
legendDiff.SetY2NDC(0.88)
if dataDiff.GetBinContent(dataDiff.FindBin(mass)) < 0.:
legendDiff.SetX1NDC(0.45)
legendDiff.SetX2NDC(0.88)
legendDiff.SetY1NDC(0.24)
legendDiff.SetY2NDC(0.45)
legendDiff.SetTextSize(.045)
legendDiff.SetTextAlign(12)
padBack = TPad("padBack","padBack",0.57,0.58,0.975,0.956) # TPad must be created after TCanvas otherwise ROOT crashes
padBack.SetFillColor(0)
pad = TPad("diff","diff",0.45,0.5,0.9765,0.957) # TPad must be created after TCanvas otherwise ROOT crashes
pad.cd()
pad.SetFillColor(0)
pad.SetFillStyle(0)
errBandFrame.Draw()
errBand.Draw("e2lsame")
signal.Draw("histsame")
line = TLine()
line.DrawLine(xminInset, 0, xmaxInset, 0)
dataDiff.Draw("pe same")
legendDiff.Draw()
pad.RedrawAxis()
c.cd()
ztt.Draw("hist")
ggH.Draw("histsame")
if ggH_hww:
ggH_hww.Draw("hist same")
ztt.Draw("hist same")
errorBand.Draw("e2 same")
tt.Draw("hist same")
ewk.Draw("hist same")
fakes.Draw("hist same")
data.Draw("pe same")
legend.Draw()
c.RedrawAxis()
padBack.Draw() # clear the background axe
pad.Draw()
CMSPrelim(datasetName, channel, cat)
c.Print('Plot_'+fileName+".eps")
c.Print('Plot_'+fileName+".png")
c.Print('Plot_'+fileName+".pdf")
