def import_data(self, altData=None):
w = self.w
if altData:
altData.SetName('data')
roo.wimport(w, altData)
return
obs_ = w.argSet(','.join(self.observables))
obs = r.RooArgList(obs_)
dataHists = dict([(L,
unqueue(chan.samples['data'].datas[0],
self.asymmetry))
for L, chan in self.channels.items()])
datas = [(L, r.RooDataHist('data_' + L, 'N_{obs}^{%s}' % L, obs, data))
for L, data in dataHists.items()]
if not self.quiet:
print self.observables[0]
print '\n'.join('%s: %.6f (%.6f)' %
((L, ) + lib.asymmetry(hist.ProjectionX()))
for L, hist in dataHists.items())
print self.observables[1]
print '\n'.join('%s: %.6f (%.6f)' %
((L, ) + lib.asymmetry(hist.ProjectionY()))
for L, hist in dataHists.items())
print
[roo.wimport(w, dat) for _, dat in datas]
args = [r.RooFit.Import(*dat) for dat in datas]
roo.wimport(
w,
r.RooDataHist('data', 'N_{obs}', obs,
r.RooFit.Index(w.arg('channel')), *args))
def import_asymmetry(self, w):
if not self.asymmetry: return
roo.factory(w, "falphaT[0.18, -0.8, 0.8]")
roo.factory(w, "expr::alphaT('@0/@1',{falphaT,f_qg})")
roo.factory(w, "expr::alphaL('@0/@1',{falphaL,f_qq})")
[(roo.factory(w, "SUM::%(n)s( alphaT * %(n)s_both, %(n)s_symm )" % {'n': L + '_ttag'}),
roo.factory(w, "SUM::%(n)s( alphaT * %(n)s_both, %(n)s_symm )" % {'n': L + '_ttqg'}),
roo.factory(w, "SUM::%(n)s( alphaL * %(n)s_both, %(n)s_symm )" % {'n': L + '_ttqq'}))
for L in self.channels.keys()]
[(roo.factory(w, "SUM::%(n)s( 0 * %(n)s_both, %(n)s_symm )" % {'n': L + '_ttag'}),
roo.factory(w, "SUM::%(n)s( 0 * %(n)s_both, %(n)s_symm )" % {'n': L + '_ttqg'}),
roo.factory(w, "SUM::%(n)s( 0 * %(n)s_both, %(n)s_symm )" % {'n': L + '_ttqq'}))
for L in self.channels_qcd.keys()]
assert self.gen.samples['tt'].datas[0].GetXaxis().GetTitle() == genNameX
assert self.gen.samples['tt'].datas[0].GetYaxis().GetTitle() == genNameY
for n, d in self.gen.samples.items():
y,ey = lib.asymmetry(d.datasX[0])
p,ep = lib.asymmetry(d.datasY[0])
roo.wimport_const(w, 'Ac_y_' + n, y)
roo.wimport_const(w, 'Ac_phi_' + n, p)
roo.wimport_const(w, 'err_Ac_y_' + n, ey)
roo.wimport_const(w, 'err_Ac_phi_' + n, ep)
def asymmStr(self):
unqueued = lib.unQueuedBins(self.datas[0], 5, [-1, 1], [-1, 1])
unqx = unqueued.ProjectionX()
unqy = unqueued.ProjectionY()
ay = tuple([100 * f for f in lib.asymmetry(unqx)])
ap = tuple([100 * f for f in lib.asymmetry(unqy)])
del unqueued, unqx, unqy
return '; '.join([('ay: % .2f(%.2f)' % ay).rjust(8),
('ap: % .2f(%.2f)' % ap).rjust(8)])
def asymmStr(self):
unqueued = lib.unQueuedBins(self.datas[0],5,[-1,1],[-1,1])
unqx = unqueued.ProjectionX()
unqy = unqueued.ProjectionY()
ay = tuple([100*f for f in lib.asymmetry(unqx)])
ap = tuple([100*f for f in lib.asymmetry(unqy)])
del unqueued, unqx, unqy
return '; '.join([ ('ay: % .2f(%.2f)'%ay).rjust(8),
('ap: % .2f(%.2f)'%ap).rjust(8)
])
def calibrations(self, pars, which='mn', calSlice=(None,None), N=1000, label='', **kwargs):
sampleList = [c['sample'] for c in calibration_specs() if c['which']==which]
prePre = pars['dirIncrement'] in [0,4,5]
args = {
'signal':pars['signal'],
'sigPrefix':pars['sigPre'],
'dirPrefix':"R%02d" % (pars['R0_'] + pars['dirIncrement']),
'genDirPre':pars['genDirPre'],
'prePre':prePre,
'templateID':None,
'hackZeroBins':pars['hackZeroBins'] and 'QCD'==part,
'sampleList': sampleList
}
alt_channels = dict( [ ((lep,part), inputs.channel_data(lep, part, **args))
for lep in ['el','mu'] for part in ['top','QCD']] )
# get Ac_phi_ttalt and Ac_y_ttalt
filePattern = 'data/stats_top_mu_%s.root'
tag = 'ph_sn_jn_20'
tfile = r.TFile.Open(filePattern%tag)
h = lib.get(tfile,'genTopTanhDeltaAbsY_genTopDPtDPhi/'+ sampleList[0])
Ac_y_ttalt = lib.asymmetry(h.ProjectionX())[0]
Ac_phi_ttalt = lib.asymmetry(h.ProjectionY())[0]
tfile.Close()
model = self.central.model
model.import_alt_model(alt_channels)
wGen = model.w
# bring xs_ttalt to the most consistant value possible
wGen.arg('d_xs_ttalt').setVal((wGen.arg('expect_mu_tt').getVal() + wGen.arg('expect_el_tt').getVal()) /
(wGen.arg('expect_mu_ttalt').getVal() + wGen.arg('expect_el_ttalt').getVal()) - 1)
# not clear how to do the same for factor_*_qcd (equivalent bg representations)
truth = dict([(s,eval(s)) for s in ['Ac_y_ttalt','Ac_phi_ttalt']])
altItems = ['expect_%s_ttalt'%s for s in ['el','mu']]
for item in (set(fit.modelItems()+altItems)-set(fit.altmodelNonItems())): truth[item] = wGen.arg(item).getVal()
mcstudy = r.RooMCStudy(wGen.pdf('altmodel'),
wGen.argSet(','.join(model.observables+['channel'])),
r.RooFit.Binned(True),
r.RooFit.Extended(True)
)
mcstudy.generate(N,0,True)
for i in range(N)[slice(*calSlice)]:
alt = mcstudy.genData(i)
pars['label'] = '%s_cal%s%03d'%(label,which,i)
with open(self.outNameBase + pars['label'] + '.log', 'w') as log:
pars['log']=log
f = fit(altData=alt, **pars)
f.ttreeWrite(self.outNameBase + pars['label'] + '.root', truth)
def import_data(self, data, name=""):
roo.wimport_const(self.w,"Ac_data"+name, lib.asymmetry(data)[0])
roo.wimport(self.w,
r.RooDataHist('data'+name,
'N_{obs}',
r.RooArgList(self.w.argSet('x')),
data))
def import_asymmetry(self, w):
alpha_max = 0.99 * lib.alphaMax(self.symm, self.anti)
roo.factory(w, "alpha[0, -%f, %f]"%(alpha_max,alpha_max))
roo.factory(w, 'SUM::model( alpha * pdf_template_both, pdf_template_symm)')
ac, eac = lib.asymmetry(self.template)
roo.wimport_const(w, "Ac_template", ac)
roo.factory(w, "prod::Ac(Ac_template, alpha)")
def import_asymmetry(self, w):
if not self.asymmetry: return
roo.factory(w, "falphaT[0.18, -0.8, 0.8]")
roo.factory(w, "expr::alphaT('@0/@1',{falphaT,f_qg})")
roo.factory(w, "expr::alphaL('@0/@1',{falphaL,f_qq})")
[(roo.factory(
w, "SUM::%(n)s( alphaT * %(n)s_both, %(n)s_symm )" %
{'n': L + '_ttag'}),
roo.factory(
w, "SUM::%(n)s( alphaT * %(n)s_both, %(n)s_symm )" %
{'n': L + '_ttqg'}),
roo.factory(
w, "SUM::%(n)s( alphaL * %(n)s_both, %(n)s_symm )" %
{'n': L + '_ttqq'})) for L in self.channels.keys()]
[(roo.factory(
w,
"SUM::%(n)s( 0 * %(n)s_both, %(n)s_symm )" % {'n': L + '_ttag'}),
roo.factory(
w,
"SUM::%(n)s( 0 * %(n)s_both, %(n)s_symm )" % {'n': L + '_ttqg'}),
roo.factory(
w,
"SUM::%(n)s( 0 * %(n)s_both, %(n)s_symm )" % {'n': L + '_ttqq'}))
for L in self.channels_qcd.keys()]
assert self.gen.samples['tt'].datas[0].GetXaxis().GetTitle(
) == genNameX
assert self.gen.samples['tt'].datas[0].GetYaxis().GetTitle(
) == genNameY
for n, d in self.gen.samples.items():
y, ey = lib.asymmetry(d.datasX[0])
p, ep = lib.asymmetry(d.datasY[0])
roo.wimport_const(w, 'Ac_y_' + n, y)
roo.wimport_const(w, 'Ac_phi_' + n, p)
roo.wimport_const(w, 'err_Ac_y_' + n, ey)
roo.wimport_const(w, 'err_Ac_phi_' + n, ep)
def import_data(self, altData=None):
w = self.w
if altData:
altData.SetName('data')
roo.wimport(w, altData)
return
obs_ = w.argSet(','.join(self.observables))
obs = r.RooArgList(obs_)
dataHists = dict([(L,unqueue(chan.samples['data'].datas[0], self.asymmetry)) for L,chan in self.channels.items()])
datas = [(L, r.RooDataHist('data_' + L, 'N_{obs}^{%s}' % L, obs, data)) for L, data in dataHists.items()]
if not self.quiet:
print self.observables[0]
print '\n'.join('%s: %.6f (%.6f)'%((L,)+lib.asymmetry(hist.ProjectionX())) for L,hist in dataHists.items())
print self.observables[1]
print '\n'.join('%s: %.6f (%.6f)'%((L,)+lib.asymmetry(hist.ProjectionY())) for L,hist in dataHists.items())
print
[roo.wimport(w, dat) for _, dat in datas]
args = [r.RooFit.Import(*dat) for dat in datas]
roo.wimport(w, r.RooDataHist('data', 'N_{obs}', obs,
r.RooFit.Index(w.arg('channel')), *args))
def __init__(self, props, outName, fName = 'data/stats_top_mu_ph_sn_jn_20.root', dName = 'genTopTanhDeltaAbsY', propOrder = []):
tFile = r.TFile.Open(fName)
hists = {}
smax = 0
amax = 0
leg = r.TLegend(0.7,0.2,0.9,0.45)
leg.SetBorderSize(0)
leg.SetFillColor(r.kWhite)
leg.SetTextFont(42)
for key in (propOrder if propOrder else props):
h = tFile.Get(dName + '/' + key)
N = h.GetEntries()
asymm = lib.asymmetry(h)
print key, N, asymm
h.UseCurrentStyle()
h.Rebin(2)
#h.SetTitle(';%s;(1/#sigma)(d#sigma/d%s)'%(Xl,Xl))
h.SetTitle(';%s;Probability / %.2f'%(Xl,h.GetBinWidth(1)))
h.GetYaxis().SetTitleOffset(1)
#h.Scale(1./h.Integral(),'width')
h.Scale(1./h.Integral())
col,width,style = props[key]
h.SetLineColor(col)
h.SetLineWidth(width)
h.SetLineStyle(style)
h.GetXaxis().SetNdivisions(5,4,0,False)
leg.AddEntry(h, labels[key] if key in labels else key, 'L')
hists[key] = lib.symmAnti(h)
smax = max(smax, hists[key][0].GetMaximum())
amax = max(amax, hists[key][1].GetMaximum())
c = r.TCanvas("canvas","", 800,800)
c.Print(outName + '[')
for i,(n,(h,_)) in enumerate(hists.items()):
h.SetMaximum(1.15*smax)
h.SetMinimum(0)
h.Draw('hist' + ('' if not i else 'same'))
leg.Draw()
text.DrawTextNDC(0.45,0.96,"Symmetric")
dostamp()
c.Print(outName)
for i,(n,(_,h)) in enumerate(hists.items()):
h.SetMaximum(1.1*amax)
h.SetMinimum(-1.1*amax)
h.Draw('hist' + ('' if not i else 'same'))
leg.Draw()
text.DrawTextNDC(0.45,0.96,"Antisymmetric")
dostamp()
c.Print(outName)
c.Print(outName + ']')
tFile.Close()
def import_asymmetry(self, w):
alpha_max = 0.99 * min(chan.samples['tt'].alphaMax for chan in
self.channels.values() +
self.channels_qcd.values())
roo.factory(w, "alpha[1, -%f, %f]"%(alpha_max,alpha_max))
[roo.factory(w, "SUM::%(n)s( alpha * %(n)s_both, %(n)s_symm )" % {'n': L+'_tt'})
for L in (self.channels.keys() + self.channels_qcd.keys())]
assert self.gen.samples['tt'].datas[0].GetXaxis().GetTitle() == genNameX
assert self.gen.samples['tt'].datas[0].GetYaxis().GetTitle() == genNameY
for n, d in self.gen.samples.items():
hists = {'XL':d.datasX[0], 'XT':d.datasY[0]}
ac, eac = lib.asymmetry(hists[self.observables[0]])
roo.wimport_const(w, 'Ac_'+n, ac)
roo.wimport_const(w, 'err_Ac_'+n, eac)
keep.append(s)
keep.append(a)
can.Print(fname)
if __name__=="__main__":
print '\t'.join(['', "m(%)",'c(%)','ain(%)','aout(%)','','Ac(%)', 'Ac_measure(%)', '', 'slope'])
for lep in ['el','mu']:
print lep
lep_hists = hists(lep)
display(lep+'_recohists.pdf', lep_hists)
m = simpleModel(lep_hists['ph'])
for k,v in sorted(lep_hists.items()):
m.import_data(v, k)
m.minimize(k)
Ac = lib.asymmetry(v)[0]
Ac_meas = m.val('Ac')
print k.rjust(6), (4*"% 0.2f ") % tuple(100*c for c in classify5(v)),
print '|\t\t', '% .2f'%(100*Ac),
print '\t', "% .2f" % (100*Ac_meas),
print 3*'\t', "% .2f" % (Ac_meas / Ac)
pars = dict(zip(['middle','central','ain','aout'], classify5(lep_hists['ph'])))
pars['N'] = 10000
h = r.TH2D('ph_symm_mods','log (A_{meas} / A_{true});log(c/c_{0});log[(m/c) / (m_{0}/c_{0})]', 11, -0.2, 0.2, 11, -0.2, 0.2)
for iC in range(h.GetNbinsX()):
for iM in range(h.GetNbinsY()):
local_pars = dict(pars)
cfactor = math.exp(h.GetXaxis().GetBinCenter(1+iC))
mfactor = math.exp(h.GetYaxis().GetBinCenter(1+iM))
def unqueue(h):
return lib.unQueuedBins(h,5,[-1,1],[-1,1])
threeD = False
comps = ['ttag','ttqg','ttqq']
colors = [r.kBlack, r.kGreen, r.kBlue, r.kRed]
projections = {}
book = autoBook("stuff")
for extra in [True,False,'only']:
for template in [None]+range(1000):
print template
#sys.stdout.flush()
channels = dict([(lep, channel_data(lep, 'top', signal='fitTopQueuedBin5_TridiscriminantWTopQCD', threeD=threeD, extra=extra, templateID=template,getTT=True)) for lep in ['el', 'mu']])
for lep,ch in channels.items():
for comp,color in zip(comps,colors):
name = '_'.join(['extra' if extra=='only' else 'total' if extra else 'orig' ,lep, comp])
if template==None: name += '_actual'
tot = unqueue(ch.samples[comp].datas[0].ProjectionX())
v = (100*lib.asymmetry(tot.ProjectionX())[0],
100*lib.asymmetry(tot.ProjectionY())[0])
book.fill( v, name, (100,100), (-3,-3), (3,3))
tfile = r.TFile.Open("jiggled_asymmetries.root","RECREATE")
for key,hist in book.items():
hist.Write()
tfile.Close()
def old_calibrations(self, pars, which='mn', calSlice=(None,None), N=1000, label='', **kwargs):
sampleList = [c['sample'] for c in calibration_specs() if c['which']==which]
prePre = pars['dirIncrement'] in [0,4,5]
args = {
'signal':pars['signal'],
'sigPrefix':pars['sigPre'],
'dirPrefix':"R%02d" % (pars['R0_'] + pars['dirIncrement']),
'genDirPre':pars['genDirPre'],
'prePre':prePre,
'templateID':None,
'sampleList': sampleList,
'rebin':pars['rebin'],
'no3D':pars['no3D']
}
alt_channels = dict( [ ((lep,part), inputs.channel_data(lep, part, **args))
for lep in ['el','mu'] for part in ['top','QCD']] )
if 'xsfactor' in kwargs:
alt_channels[('el','top')].samples['ttalt'].xs *= kwargs['xsfactor']
alt_channels[('mu','top')].samples['ttalt'].xs *= kwargs['xsfactor']
# get Ac_phi_ttalt and Ac_y_ttalt
filePattern = 'data/stats_top_mu_%s.root'
tag = 'ph_sn_jn_20'
tfile = r.TFile.Open(filePattern%tag)
h = lib.get(tfile,'genTopTanhDeltaAbsY_genTopDPtDPhi/'+ sampleList[0])
Ac_y_ttalt = lib.asymmetry(h.ProjectionX())[0]
Ac_phi_ttalt = lib.asymmetry(h.ProjectionY())[0]
tfile.Close()
model = self.central.model
model.import_alt_model(alt_channels, pars['nobg'])
wGen = model.w
# bring xs_ttalt to the most consistant value possible
wGen.arg('d_xs_ttalt').setVal((wGen.arg('expect_mu_tt').getVal() + wGen.arg('expect_el_tt').getVal()) /
(wGen.arg('expect_mu_ttalt').getVal() + wGen.arg('expect_el_ttalt').getVal()) - 1)
if not (-0.5 < wGen.arg('d_xs_ttalt').getVal() < 1.5):
print 'ttalt xs invalid! Adjust calibration_specs!'
exit()
# not clear how to do the same for factor_*_qcd (equivalent bg representations)
truth = {'Ac': Ac_y_ttalt if genNames['XL'][3:] in pars['signal'] else Ac_phi_ttalt}
altItems = ['expect_%s_ttalt'%s for s in ['el','mu']]
for item in (set(fit.modelItems()+altItems)-set()): truth[item] = wGen.arg(item).getVal()
truth.update({'Ac_raw_el_model':model.Ac_raw('el','alt%smodel'%pars['nobg']),
'Ac_raw_mu_model':model.Ac_raw('mu','alt%smodel'%pars['nobg'])})
mcstudy = r.RooMCStudy(wGen.pdf('alt%smodel'%pars['nobg']),
wGen.argSet(','.join(model.observables+['channel'])),
r.RooFit.Binned(True),
r.RooFit.Extended(True)
)
mcstudy.generate(N,0,True)
for i in range(*calSlice):
alt = mcstudy.genData(i)
pars['label'] = '%s_cal%s%03d'%(label,which,i)
pars['quiet'] = True
with open(self.outNameBase + pars['label'] + '.log', 'w') as log:
pars['log']=log
f = fit(altData=alt, **pars)
f.ttreeWrite(self.outNameBase + pars['label'] + '.root', truth)
if self.doVis: f.model.visualize(self.outNameBase + pars['label'] + '.pdf', nobg=pars['nobg'])
def Ac_raw(self, channel, model=None):
hist = (self.data_hist(channel) if not model
else self.expected_histogram(model + '_'+channel))
return [lib.asymmetry(h)[0] for h in self.proj(hist)]
for extra in [True, False, 'only']:
for template in [None] + range(1000):
print template
#sys.stdout.flush()
channels = dict([
(lep,
channel_data(lep,
'top',
signal='fitTopQueuedBin5_TridiscriminantWTopQCD',
threeD=threeD,
extra=extra,
templateID=template,
getTT=True)) for lep in ['el', 'mu']
])
for lep, ch in channels.items():
for comp, color in zip(comps, colors):
name = '_'.join([
'extra' if extra == 'only' else
'total' if extra else 'orig', lep, comp
])
if template == None: name += '_actual'
tot = unqueue(ch.samples[comp].datas[0].ProjectionX())
v = (100 * lib.asymmetry(tot.ProjectionX())[0],
100 * lib.asymmetry(tot.ProjectionY())[0])
book.fill(v, name, (100, 100), (-3, -3), (3, 3))
tfile = r.TFile.Open("jiggled_asymmetries.root", "RECREATE")
for key, hist in book.items():
hist.Write()
tfile.Close()
def asymmStr(self):
Ac = tuple([100*f for f in lib.asymmetry(self.datas[0])])
return ("%. 2f(%.2f)" % Ac).rjust(8)
