def __init__(self,
cuts,
weight=lambda s: 1,
slices_raw=False,
slices_cor=False):
h1 = None
h2 = None
h3 = None
h4 = None
if slices_raw:
h1 = ROOT.TH1D(hID(), '', 10, 3.00, 3.20)
h1.Sumw2()
h2 = ROOT.TH1D(hID(), '', 9, 2.24, 2.33)
h2.Sumw2()
if slices_cor:
h3 = ROOT.TH1D(hID(), '', 10, 3.00, 3.20)
h3.Sumw2()
h4 = ROOT.TH1D(hID(), '', 9, 2.24, 2.33)
h4.Sumw2()
SpsiD.__init__(self,
lambda s: 3.0 <= s.m_psi <= 3.2 and 2.24 <= s.m_Lc <= 2.33 and cuts(
s),
('Lc', 2.24, 2.33),
weight,
'psi',
h1, h2, h3, h4)
def __init__(self,
cuts,
weight=lambda s: 1,
slices_raw=False,
slices_cor=False):
h1 = None
h2 = None
h3 = None
h4 = None
if slices_raw:
h1 = ROOT.TH1D(hID(), '', 20, 3.00, 3.20)
h1.Sumw2()
h2 = ROOT.TH1D(hID(), '', 20, 1.82, 1.92)
h2.Sumw2()
if slices_cor:
h3 = ROOT.TH1D(hID(), '', 10, 3.00, 3.20)
h3.Sumw2()
h4 = ROOT.TH1D(hID(), '', 10, 1.82, 1.92)
h4.Sumw2()
SpsiD.__init__(self,
lambda s: 3.0 <= s.m_psi <= 3.2 and 1.82 <= s.m_D0 <= 1.92 and cuts(
s),
('D0', 1.82, 1.92),
weight,
'psi',
h1, h2, h3, h4)
def __init__(self,
cuts,
weight=lambda s: 1,
slices_raw=False,
slices_cor=False):
h1 = None
h2 = None
h3 = None
h4 = None
if slices_raw:
h1 = ROOT.TH1D(hID(), '', 10, 3.00, 3.20)
h1.Sumw2()
h2 = ROOT.TH1D(hID(), '', 14, 1.90, 2.04)
h2.Sumw2()
if slices_cor:
h3 = ROOT.TH1D(hID(), '', 10, 3.00, 3.20)
h3.Sumw2()
h4 = ROOT.TH1D(hID(), '', 7, 1.90, 2.04)
h4.Sumw2()
SpsiD.__init__(self,
lambda s: 3.0 <= s.m_psi <= 3.2 and 1.9 <= s.m_Ds <= 2.04 and cuts(
s),
('Ds', 1.9, 2.04),
weight,
'psi',
h1, h2, h3, h4)
def __init__(self,
cuts,
weight=lambda s: 1,
slices_raw=False,
slices_cor=False):
h1 = None
h2 = None
h3 = None
h4 = None
if slices_raw:
h1 = ROOT.TH1D(hID(), '', 18, 1.82, 1.91)
h1.Sumw2()
h2 = ROOT.TH1D(hID(), '', 14, 1.90, 2.04)
h2.Sumw2()
if slices_cor:
h3 = ROOT.TH1D(hID(), '', 6, 1.82, 1.91)
h3.Sumw2()
h4 = ROOT.TH1D(hID(), '', 7, 1.90, 2.04)
h4.Sumw2()
DD.__init__(
self,
lambda s: 1.82 <= s.m_Dp <= 1.91 and 1.9 <= s.m_Ds <= 2.04 and cuts(
s),
('Dp', 1.82, 1.91),
('Ds', 1.9, 2.04),
weight,
h1, h2, h3, h4)
def __init__(self,
cuts,
weight=lambda s: 1,
slices_raw=False,
slices_cor=False):
h1 = None
h2 = None
h3 = None
h4 = None
if slices_raw:
h1 = ROOT.TH1D(hID(), '', 9, 2.24, 2.33)
h1.Sumw2()
h2 = ROOT.TH1D(hID(), '', 9, 2.24, 2.33)
h2.Sumw2()
if slices_cor:
h3 = ROOT.TH1D(hID(), '', 9, 2.24, 2.33)
h3.Sumw2()
h4 = ROOT.TH1D(hID(), '', 9, 2.24, 2.33)
h4.Sumw2()
DD.__init__(
self,
lambda s: 2.24 <= s.m_Lc1 <= 2.33 and 2.24 <= s.m_Lc2 <= 2.33 and cuts(
s),
('Lc1', 2.24, 2.33),
('Lc2', 2.24, 2.33),
weight,
h1, h2, h3, h4)
def __init__(self,
histo,
pt_axis=ROOT.TAxis(20, 2.0, 12.0),
y_axis=ROOT.TAxis(10, 2.0, 4.5),
cuts=lambda s: True):
Selector.__init__(self, None, self) # initialize the base
self._cuts = cuts
self._pt_axis = pt_axis
self. _y_axis = y_axis
self._histos = {}
self._events = 0L
#
for iPt in pt_axis:
for iY in y_axis:
hTotal = histo.Clone(hID())
hAccept = histo.Clone(hID())
hReject = histo.Clone(hID())
hTotal . SetTitle('Total')
hAccept . SetTitle('Accept')
hReject . SetTitle('Reject')
self._histos[(iPt, iY)] = [
hTotal,
hAccept,
hReject,
]
def __init__(self, h1, h2):
h = h1.Clone(hID())
h.Reset()
if not h.GetSumw2():
h.Sumw2()
self.h1_ = h
h = h2.Clone(hID())
h.Reset()
if not h.GetSumw2():
h.Sumw2()
self.h2_ = h
self.axis_1 = self.h1_.GetXaxis()
self.axis_2 = self.h2_.GetXaxis()
self.histos_1 = {}
self.histos_2 = {}
for ibin in self.axis_2:
h = self.h1_.Clone(hID())
self.histos_1[ibin] = h
for ibin in self.axis_1:
h = self.h2_.Clone(hID())
self.histos_2[ibin] = h
def __init__(self,
cuts,
weight=lambda s: 1,
slices_raw=False,
slices_cor=False):
h1 = None
h2 = None
h3 = None
h4 = None
if slices_raw:
h1 = ROOT.TH1D(hID(), '', 7, 1.90, 2.04)
h1.Sumw2()
h2 = ROOT.TH1D(hID(), '', 6, 2.24, 2.33)
h2.Sumw2()
if slices_cor:
h3 = ROOT.TH1D(hID(), '', 7, 1.90, 2.04)
h3.Sumw2()
h4 = ROOT.TH1D(hID(), '', 6, 2.24, 2.33)
h4.Sumw2()
DD.__init__(
self,
lambda s: 1.9 <= s.m_Ds <= 2.04 and 2.24 <= s.m_Lc <= 2.33 and cuts(
s),
('Ds', 1.9, 2.04),
('Lc', 2.24, 2.33),
weight,
h1, h2, h3, h4)
def __init__(self,
cuts,
weight=lambda s: 1,
slices_raw=False,
slices_cor=False):
h1 = None
h2 = None
h3 = None
h4 = None
if slices_raw:
h1 = ROOT.TH1D(hID(), '', 20, 1.82, 1.92)
h1.Sumw2()
h2 = ROOT.TH1D(hID(), '', 18, 1.82, 1.91)
h2.Sumw2()
if slices_cor:
h3 = ROOT.TH1D(hID(), '', 10, 1.82, 1.92)
h3.Sumw2()
h4 = ROOT.TH1D(hID(), '', 9, 1.82, 1.91)
h4.Sumw2()
DD.__init__(
self,
lambda s: 1.8 <= s.m_D0 <= 1.92 and 1.82 <= s.m_Dp <= 1.91 and cuts(
s),
('D0', 1.8, 1.92),
('Dp', 1.82, 1.91),
weight,
h1, h2, h3, h4)
def __init__(self,
cuts,
weight=lambda s: 1,
slices_raw=False,
slices_cor=False):
h1 = None
h2 = None
h3 = None
h4 = None
if slices_raw:
h1 = ROOT.TH1D(hID(), '', 10, 1.82, 1.92)
h1.Sumw2()
h2 = ROOT.TH1D(hID(), '', 9, 2.24, 2.33)
h2.Sumw2()
if slices_cor:
h3 = ROOT.TH1D(hID(), '', 8, 1.82, 1.92)
h3.Sumw2()
h4 = ROOT.TH1D(hID(), '', 9, 2.24, 2.33)
h4.Sumw2()
DD.__init__(
self,
lambda s: 1.8 <= s.m_D0 <= 1.92 and 2.24 <= s.m_Lc <= 2.33 and cuts(
s),
('D0', 1.8, 1.92),
('Lc', 2.24, 2.33),
weight,
h1, h2, h3, h4)
def __init__(self,
histo,
pv_axis=ROOT.TAxis(10, 0, 10),
spd_axis=ROOT.TAxis(100, 0, 1000),
ot_axis=ROOT.TAxis(150, 0, 15000),
tlong_axis=ROOT.TAxis(100, 0, 500),
tbest_axis=ROOT.TAxis(100, 0, 1000)):
Selector.__init__(self, None, self) # initialize the base
#
# PV
self.pv_axis = pv_axis
self.pv = {}
for i in pv_axis:
h = histo.Clone(hID())
h.Sumw2()
self.pv[i] = h
#
# SPD
self.spd_axis = spd_axis
self.spd = {}
for i in spd_axis:
h = histo.Clone(hID())
h.Sumw2()
self.spd[i] = h
# OT
self.ot_axis = ot_axis
self.ot = {}
for i in ot_axis:
h = histo.Clone(hID())
h.Sumw2()
self.ot[i] = h
# Long
self.tlong_axis = tlong_axis
self.tlong = {}
for i in tlong_axis:
h = histo.Clone(hID())
h.Sumw2()
self.tlong[i] = h
# Best
self.tbest_axis = tbest_axis
self.tbest = {}
for i in tbest_axis:
h = histo.Clone(hID())
h.Sumw2()
self.tbest[i] = h
self._events = 0
def fitHisto ( self , histo , draw = False , *args ) :
"""
Fit the histogram
"""
self.lst = ROOT.RooArgList ( self.mass )
self.imp = ROOT.RooFit.Import ( histo )
self.hset = ROOT.RooDataHist (
hID() ,
"Data set for histogram '%s'" % histo.GetTitle() ,
self.lst ,
self.imp
)
result = self.pdf.fitTo ( self.hset ,
ROOT.RooFit.Save () ,
*args )
if not draw : return result
frame = self.mass.frame()
self.hset.plotOn ( frame )
self.pdf .plotOn ( frame ,
ROOT.RooFit.Components ( self.background.pdf.GetName() ) ,
ROOT.RooFit.LineStyle ( ROOT.kDashed ) ,
ROOT.RooFit.LineColor ( ROOT.kBlue ) )
self.pdf .plotOn ( frame ,
ROOT.RooFit.LineColor ( ROOT.kRed ) )
frame.Draw()
return result, frame
def __init__(self,
pt_axis=pt_axis_psi,
y_axis=y_axis_psi,
cuts=lambda s: 3.0 <= s.mass <= 3.2):
MuPidSelector.__init__(self,
ROOT.TH1F(
hID(), 'Psi+-template', 200, 3.0, 3.2),
pt_axis,
y_axis,
cuts)
def __init__(self,
scale=VE(1, 0)):
Selector.__init__(self, None, self) # initialize the base
self.scale = scale
self.dz_0 = ROOT.TH1F(hID(), '', 500, -250, 250)
self.dz_1 = ROOT.TH1F(hID(), '', 100, -10, 10)
self.pid_0 = ROOT.TH1F(hID(), '', 204, -51, 51) # true pileup
## pileup with |dz|
self.pid_1 = ROOT.TH1F(hID(), '', 204, -51, 51)
self.pid_2 = ROOT.TH1F(hID(), '', 204, -51, 51) # B-feeddown
self.pid_3 = ROOT.TH1F(hID(), '', 204, -51, 51) # signal
self.dz_0 . Sumw2()
self.dz_1 . Sumw2()
self.pid_0 . Sumw2()
self.pid_1 . Sumw2()
self.pid_2 . Sumw2()
self.pid_3 . Sumw2()
self.se_0 = SE()
self.se_1 = SE()
self.se_2 = SE()
self.se_3 = SE()
self._events = 0
def sPlot ( self ,
dataset ,
*args ) :
splot = ROOT.RooStats.SPlot ( "sPlot_" + hID() ,
"sPlot" ,
dataset ,
self.pdf ,
self.alist2 )
self._splots += [ splot ]
return splot
def __init__(self,
mass, # mass-variable
selection, # Tree-selection
cuts=lambda s: True,
weight=lambda s: 1,
name='',
tmva_weights='',
short=False):
if not name:
name = hID()
SelectorWithCuts.__init__(self, selection) # initialize the base
self.num_re = re.compile(r"[(\d)]")
self._cuts = cuts
self._weight = weight
self._short = short
self.variables = {}
# meta info about vector-variables
# contain pairs (name without `[N]`, N as int)
self.meta = {}
self.mass = mass
self.varset = ROOT.RooArgSet(self.mass)
for v in varlist:
self.add_variable(v[0], v[4], v[2] - 0.1, v[3] + 0.1,)
self.data = ROOT.RooDataSet(
#
name,
"Bu -> J/psi KK pi: " + self.mass.GetName(),
#
self.varset
)
ROOT.SetOwnership(self.data, False)
self.mmin = self.mass.getMin()
self.mmax = self.mass.getMax()
self._events = 0
self._counter = SE()
self._progress = None
self._total = 1
self._tmva_weights = tmva_weights
def fit(self,
fun1,
fun2):
h1 = self.h1_.Clone(hID())
h2 = self.h2_.Clone(hID())
mnerr = 1.e+9
n1, r1 = fun1(self.h1_)
for k in self.histos_1:
h = self.histos_1[k]
n, r = fun1(h)
if r and 0 == r.Status() and 0 != n.value() and 0 < n.error():
mnerr = min(mnerr, n.error())
h2[k] = abs(n)
n2, r2 = fun2(self.h2_)
for k in self.histos_2:
h = self.histos_2[k]
n, r = fun2(h)
if r and 0 == r.Status() and 0 != n.value() and 0 < n.error():
mnerr = min(mnerr, n.error())
h1[k] = abs(n)
# adjust null entries (== empty histograms)
for h in (h1, h2):
for i in h:
n = h[i]
if n.error() < mnerr:
n.setError(mnerr)
h[i] = n
n1, r1 = fun1(h1)
n2, r2 = fun2(h2)
return (n1, r1, h1), (n2, r2, h2)
def __init__(self,
pt_axis=pt_axis_psip,
y_axis=y_axis_psip,
l0=True,
l1=True,
l2=True,
cuts=lambda s: 3.61 <= s.m_psip <= 3.77):
TrgSelector.__init__(
self,
'psip', # see TrgEff
ROOT.TH1F(hID(), "Psi'-template", 160, 3.61, 3.77),
pt_axis,
y_axis,
l0,
l1,
l2,
cuts)
def __init__(self,
pt_axis=pt_axis_ups,
y_axis=y_axis_ups,
l0=True,
l1=True,
l2=True,
cuts=lambda s: 8.5 <= s.m_ups <= 11):
TrgSelector.__init__(
self,
'ups', # see TrgEff
ROOT.TH1F(hID(), "Y-template", 250, 8.5, 11),
pt_axis,
y_axis,
l0,
l1,
l2,
cuts)
def __init__(self,
pt_axis=pt_axis_psi,
y_axis=y_axis_all,
l0=True,
l1=True,
l2=True,
cuts=lambda s: 3.0 <= s.m_psi <= 3.2):
TrgSelector.__init__(
self,
'psi', # see TrgEff
ROOT.TH1F(hID(), 'Psi+-template', 200, 3.0, 3.2),
pt_axis,
y_axis,
l0,
l1,
l2,
cuts)
def __init__(self,
pt_axis=pt_axis_ds,
y_axis=y_axis_all,
l0=True,
l1=True,
l2=True,
cuts=lambda s: 1.9 <= s.m_Ds <= 2.04):
TrgSelector.__init__(
self,
'Ds', # see TrgEff
ROOT.TH1F(hID(), 'Ds+-template', 140, 1.9, 2.04),
pt_axis,
y_axis,
l0,
l1,
l2,
cuts)
def __init__(self,
pt_axis=pt_axis_lc,
y_axis=y_axis_all,
l0=True,
l1=True,
l2=True,
cuts=lambda s: 2.24 <= s.m_Lc <= 2.33):
TrgSelector.__init__(
self,
'Lc', # see TrgEff
ROOT.TH1F(hID(), 'c+-template', 90, 2.24, 2.33),
pt_axis,
y_axis,
l0,
l1,
l2,
cuts)
def __init__(self,
pt_axis=pt_axis_had,
y_axis=y_axis_all,
l0=True,
l1=True,
l2=True,
cuts=lambda s: 1.82 <= s.m_Dp <= 1.91):
TrgSelector.__init__(
self,
'Dp', # see TrgEff
ROOT.TH1F(hID(), 'D+-template', 90, 1.82, 1.91),
pt_axis,
y_axis,
l0,
l1,
l2,
cuts)
def getLumi(tree):
"""
Get Lumi
"""
import ROOT
from AnalysisPython.PyRoUts import hID, VE
#
hL = ROOT.TH1F(hID(), '', 10, -1, 4)
hL.Sumw2()
tree.Project(hL.GetName(), '1', 'IntegratedLuminosity')
l0 = hL.accumulate()
#
tree.Project(hL.GetName(), '1',
'IntegratedLuminosity+IntegratedLuminosityErr')
l1 = hL.accumulate()
#
# tree.Project ( hL.GetName() , 'IntegratedLuminosity+IntegratedLuminosityErr')
# l2 = hL.accumulate()
#
return VE(l0.value(), (l1.value() - l0.value()) ** 2)
def cross_T(tree,
first,
second,
gec):
"""
calculate the cross-section in nanobarns
>>> tree = ...
>>> cross_section, syst = cross_T ( tree , 'D0' , 'D0' )
"""
if issubclass(tree .__class__, (ROOT.RooAbsData, ROOT.RooDataSet)):
tree = tree.store().tree()
h0 = ROOT.TH1F(hID(), '', 10, -1, 4)
h0.Sumw2()
tree.Project(h0.GetName(), '1', 'S1S2_sw')
n_raw = h0.accumulate()
tree.Project(h0.GetName(), '1', 'S1S2_sw*weight')
n_corr = h0.accumulate()
return n_raw, n_corr, cross_N(n_corr, first, second, gec)
def __init__(self,
var_,
histo,
pt_axis,
y_axis,
l0=True,
l1=True,
l2=True,
cuts=lambda s: True):
Selector.__init__(self, None, self) # initialize the base
self._l0 = l0
self._l1 = l1
self._l2 = l2
trg = self._l0 or self._l1 or self._l2
if not trg:
raise TypeError, 'Trigger is not specified '
self._cuts = cuts
self._pt_axis = pt_axis
self. _y_axis = y_axis
self._histos = {}
self._events = 0L
#
self.pt_ = 'pt_' + var_
self.y_ = 'y_' + var_
self.m_ = 'm_' + var_
#
self.l0_tos = var_ + '_l0tos_1'
self.l0_tis = var_ + '_l0tis_2'
#
self.l1_tos = var_ + '_l1tos_1'
self.l1_tis = var_ + '_l1tis_2'
#
self.l2_tos = var_ + '_l2tos_1'
self.l2_tis = var_ + '_l2tis_2'
self._var = var_
for iPt in pt_axis:
for iY in y_axis:
hTotal = histo.Clone(hID())
hExTos = histo.Clone(hID())
hTisTos = histo.Clone(hID())
hExTis = histo.Clone(hID())
hTob = histo.Clone(hID())
hTotal . SetTitle('Total')
hExTis . SetTitle('ExTis')
hExTos . SetTitle('ExTos')
hTisTos . SetTitle('Tis&Tos')
hTob . SetTitle('Tob')
self._histos[(iPt, iY)] = [
hTotal,
hExTos,
hTisTos,
hExTis,
hTob
]
#
## global bin
#
hTotal = histo.Clone(hID())
hExTos = histo.Clone(hID())
hTisTos = histo.Clone(hID())
hExTis = histo.Clone(hID())
hTob = histo.Clone(hID())
hTotal . SetTitle('Total')
hExTis . SetTitle('ExTis')
hExTos . SetTitle('ExTos')
hTisTos . SetTitle('Tis&Tos')
hTob . SetTitle('Tob')
self._histos0 = [
hTotal,
hExTos,
hTisTos,
hExTis,
hTob
]
#
# progress bar
#
self._progress = None
self._events = 0