def get_dataset(varargset, ftree, cut="", wt="", scale=1):
"""Return a dataset.
Return a dataset from the ntuple `ftree'. Apply a selection cut
using the `cutVar' variable and the selection `cut'.
"""
from rplot.fixes import ROOT
from rplot.tselect import Tsplice
splice = Tsplice(ftree)
splice.make_splice("sel", cut)
from ROOT import RooDataSet, RooFit, RooFormulaVar, RooArgList
tmpdst = RooDataSet("tmpdataset", "", varargset, RooFit.Import(ftree))
if wt:
wtvar = RooFormulaVar("wt", "{}*@0".format(scale), RooArgList(varargset[wt]))
wtvar = tmpdst.addColumn(wtvar)
varargset.remove(varargset[wt])
varargset.add(wtvar)
dst = RooDataSet("dataset", "Dataset", varargset, RooFit.Import(tmpdst), RooFit.WeightVar(wtvar))
varargset.remove(wtvar)
dst = dst.reduce(varargset)
return dst
def get_dataset(varargset, ftree, cut='', wt='', scale=1):
"""Return a dataset.
Return a dataset from the ntuple `ftree'. Apply a selection cut
using the `cutVar' variable and the selection `cut'.
"""
from rplot.fixes import ROOT
from rplot.tselect import Tsplice
splice = Tsplice(ftree)
splice.make_splice('sel', cut)
from ROOT import RooDataSet, RooFit, RooFormulaVar, RooArgList
tmpdst = RooDataSet('tmpdataset', '', varargset, RooFit.Import(ftree))
if wt:
wtvar = RooFormulaVar('wt', '{}*@0'.format(scale),
RooArgList(varargset[wt]))
wtvar = tmpdst.addColumn(wtvar)
varargset.remove(varargset[wt])
varargset.add(wtvar)
dst = RooDataSet('dataset', 'Dataset', varargset,
RooFit.Import(tmpdst), RooFit.WeightVar(wtvar))
varargset.remove(wtvar)
dst = dst.reduce(varargset)
return dst
def fill_dataset(varargset, ftree, wt, wtvar, cut=""):
"""Return a dataset (slow, get_dataset is more efficient).
Return a dataset from the ntuple `ftree', also apply `cut'. Use
`wt' as the weight expression in the tree. `wtvar' is the
corresponding RooRealVar weight. Note, varargset should contain
wtvar.
The dataset is filled by iterating over the tree. This is needed
when you want to ensure different datasets have the same weight
variable names, so that they can be combined later on. This is
needed even if they are combined as different categories.
"""
from rplot.fixes import ROOT
from ROOT import RooDataSet, RooFit, TTreeFormula
from helpers import suppress_warnings
suppress_warnings()
from rplot.tselect import Tsplice
splice = Tsplice(ftree)
splice.make_splice("sel", cut)
formulae = {}
wtname = wtvar.GetName()
for var in varargset:
name = var.GetName()
expr = wt if name == wtname else name
formulae[name] = TTreeFormula(name, expr, ftree)
dataset = RooDataSet("dataset", "Dataset", varargset, RooFit.WeightVar(wtvar))
for i in xrange(ftree.GetEntries()):
ftree.GetEntry(i)
for var, expr in formulae.iteritems():
realvar = varargset.find(var)
realvar.setVal(expr.EvalInstance())
dataset.add(varargset, varargset[wtname].getVal())
return dataset
def fill_dataset(varargset, ftree, wt, wtvar, cut=''):
"""Return a dataset (slow, get_dataset is more efficient).
Return a dataset from the ntuple `ftree', also apply `cut'. Use
`wt' as the weight expression in the tree. `wtvar' is the
corresponding RooRealVar weight. Note, varargset should contain
wtvar.
The dataset is filled by iterating over the tree. This is needed
when you want to ensure different datasets have the same weight
variable names, so that they can be combined later on. This is
needed even if they are combined as different categories.
"""
from rplot.fixes import ROOT
from ROOT import RooDataSet, RooFit, TTreeFormula
from helpers import suppress_warnings
suppress_warnings()
from rplot.tselect import Tsplice
splice = Tsplice(ftree)
splice.make_splice('sel', cut)
formulae = {}
wtname = wtvar.GetName()
for var in varargset:
name = var.GetName()
expr = wt if name == wtname else name
formulae[name] = TTreeFormula(name, expr, ftree)
dataset = RooDataSet('dataset', 'Dataset', varargset,
RooFit.WeightVar(wtvar))
for i in xrange(ftree.GetEntries()):
ftree.GetEntry(i)
for var, expr in formulae.iteritems():
realvar = varargset.find(var)
realvar.setVal(expr.EvalInstance())
dataset.add(varargset, varargset[wtname].getVal())
return dataset
infile = session.bkg_file
tree = ROOT.TChain('MyChain')
map(lambda f: tree.Add(f), infile)
if not tree:
sys.exit('Unable to read input trees.')
from rplot.tselect import Tsplice
splice = Tsplice(tree)
if options.ntuple.find('data') >= 0:
cut = ROOT.TCut(session.cut_both)
elif 'sig' == options.ntuple:
cut = ROOT.TCut(session.cut_sig)
elif 'bkg' == options.ntuple:
cut = ROOT.TCut(session.cut_bkg)
splice.make_splice(options.ntuple, cut)
# disable fluff
ROOT.gStyle.SetOptTitle(0)
ROOT.gStyle.SetOptStat(0)
variables = options.vars
marks = options.marks if options.marks else []
nvars, nmarks = len(variables), len(marks) if marks else 0
if nvars > nmarks:
marks += [None] * (nvars - nmarks)
hists = {}
# check
if options.arrow and nvars > 1:
print '{}: annotating with arrows supported only for single plots, ' \
'ignoring option'.format(__file__)
infile = session.bkg_file
tree = ROOT.TChain('MyChain')
map(lambda f: tree.Add(f), infile)
if not tree:
sys.exit('Unable to read input trees.')
from rplot.tselect import Tsplice
splice = Tsplice(tree)
if options.ntuple.find('data') >= 0:
cut = ROOT.TCut(session.cut_both)
elif 'sig' == options.ntuple:
cut = ROOT.TCut(session.cut_sig)
elif 'bkg' == options.ntuple:
cut = ROOT.TCut(session.cut_bkg)
splice.make_splice(options.ntuple, cut)
# disable fluff
ROOT.gStyle.SetOptTitle(0)
ROOT.gStyle.SetOptStat(0)
variables = options.vars
marks = options.marks if options.marks else []
nvars, nmarks = len(variables), len(marks) if marks else 0
if nvars > nmarks:
marks += [None] * (nvars - nmarks)
hists = {}
# check
if options.arrow and nvars > 1:
print '{}: annotating with arrows supported only for single plots, ' \
'ignoring option'.format(__file__)