forked from aboletti/selection_and_fits
-
Notifications
You must be signed in to change notification settings - Fork 0
/
perBin_massFit.py
388 lines (297 loc) · 15.8 KB
/
perBin_massFit.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
import argparse
parser = argparse.ArgumentParser(description="")
# parser.add_argument("inputfile" , help = "Path to the input ROOT file")
parser.add_argument("dimusel" , help = "Define if keep or remove dimuon resonances. You can choose: keepPsiP, keepJpsi, rejectPsi, keepPsi")
parser.add_argument("year" , help = "choose among:2016,2017,2018", default = '2018')
args = parser.parse_args()
'''
code to fit the B0 mass distribution:
- unbinned fit
- possibility to apply cuts on the dimuon mass [B0&Psi cut in RunI analysis] (e.g. to exclude the Jpsi mass region, or the psi) via the parameter dimusel
'''
import os, sys
from os import path
sys.path.insert(0, os.environ['HOME'] + '/.local/lib/python2.7/site-packages')
import ROOT
from ROOT import gSystem
ROOT.gROOT.SetBatch(True)
gSystem.Load('libRooFit')
gSystem.Load('utils/func_roofit/libRooDoubleCBFast')
from ROOT import RooFit, RooRealVar, RooDataSet, RooArgList, RooTreeData, RooArgSet, RooAddPdf, RooFormulaVar
from ROOT import RooGaussian, RooExponential, RooChebychev, RooProdPdf, RooCBShape, TFile, RooPolynomial
import sys, math
from uncertainties import ufloat
import random
ROOT.RooMsgService.instance().setGlobalKillBelow(4)
ROOT.Math.MinimizerOptions.SetDefaultMaxFunctionCalls(50000)
def _getFittedVar(varName, w=None):
if w is not None:
return ufloat (w.var(varName).getVal() , w.var(varName).getError())
else :
return ufloat (varName.getVal() , varName.getError())
def _goodFit(r):
return (r.status()==0 and r.covQual() == 3)
def _accFit(r):
return (r.status()==4 and r.covQual() == 3)
def _writeFitStatus(r):
str_status = "GOOD" if r.status()==0 else "NOT CONV"
txt = ROOT.TLatex(.16,.7, "fit status: " + str_status + ", covQ = %s" %r.covQual() )
txt . SetNDC() ;
txt . SetTextSize(0.033) ;
txt . SetTextFont(42)
return txt
def _writeChi2(chi2):
txt = ROOT.TLatex(.16,.6, "fit #chi^{2}: %.1f "%chi2 )
txt . SetNDC() ;
txt . SetTextSize(0.033) ;
txt . SetTextFont(42)
return txt
def _constrainVar(var):
constr = _getFittedVar(var.GetName(), w)
gauss_constr = RooGaussian( "c_%s" %var.GetName() ,
"c_%s" %var.GetName() ,
var ,
ROOT.RooFit.RooConst( constr.n ),
ROOT.RooFit.RooConst( constr.s )
)
print 'constraining var', var.GetName(), ': ', constr.n , ' with uncertainty ' , constr.s
return gauss_constr
from utils.utils import *
from utils.fit_functions import *
nSigma_psiRej = 3.
cut_base = applyB0PsiCut(args.dimusel, nSigma_psiRej)
q2binning = [
1,
2,
4.3,
6,
8.68,
10.09,
12.86,
14.18,
16,
19,
]
def fitMC(fulldata, correctTag, ibin):
print 'now fitting: ', ibin, ' for ', correctTag*'correctTag ', (1-correctTag)*'wrongTag'
cut = cut_base + '&& (mumuMass*mumuMass > %s && mumuMass*mumuMass < %s)'%(q2binning[ibin], q2binning[ibin+1])
data = fulldata.reduce(RooArgSet(thevarsMC), cut)
pol_c1 = RooRealVar ("p1" , "coeff x^0 term" , -0.5, -10, 10);
bkg_pol = RooChebychev("bkg_pol" , "bkg_pol" , tagged_mass, RooArgList(pol_c1));
signalFunction = bkg_pol ### just a placeholder
nsig = RooRealVar("Yield" , "nsig" , 10000, 0, 1000000)
nbkg = RooRealVar("nbkg" , "nbkg" , 10, 0, 100000 )
doextended = False
fitrange = "mcrange"
nbins = 70
if correctTag:
doubleG( B0Mass_ , initial_sigma1 , initial_sigma2, 0.8, tagged_mass, w, "RT%s"%ibin) ## (mean_ , sigma1_, sigma2_, f1_)
signalFunction = w.pdf("doublegaus_RT%s"%ibin)
fitFunction = RooAddPdf ("fitfunction" , "fit function" , RooArgList(signalFunction, bkg_pol), RooArgList(nsig, nbkg))
doextended = True
fitrange = "full"
nbins = 60
else:
mean = RooRealVar ("mean^{WT%s}"%ibin, "massWT" , B0Mass_, 5, 6, "GeV")
sigmaCB = RooRealVar ("#sigma_{CB}^{WT%s}"%ibin, "sigmaCB" , 0.03 , 0, 1 )
alpha1 = RooRealVar ("#alpha_{1}^{WT%s}"%ibin, "#alpha_{1}" , 0.5 , 0, 10 )
alpha2 = RooRealVar ("#alpha_{2}^{WT%s}"%ibin, "#alpha_{2}" , 0.5 , 0, 10 )
n1 = RooRealVar ("n_{1}^{WT%s}"%ibin, "n_1" , 2 , 0, 90 )
n2 = RooRealVar ("n_{2}^{WT%s}"%ibin, "n_2" , 1 , 0, 90 )
doublecb = ROOT.RooDoubleCBFast("doublecb_%s"%ibin, "doublecb", tagged_mass, mean, sigmaCB, alpha1, n1, alpha2, n2)
# getattr(w, 'import')(doublecb)
signalFunction = doublecb
fitFunction = doublecb
getattr(w,"import")(signalFunction)
r = fitFunction.fitTo(data, RooFit.Extended(doextended), RooFit.Save(), RooFit.Range(fitrange))
print 'fit status: ', r.status(), r.covQual()
if not _goodFit(r):
r = fitFunction.fitTo(data, RooFit.Extended(doextended), RooFit.Save(), RooFit.Range(fitrange))
print 'fit status (redo): ', r.status(), r.covQual()
if not _goodFit(r) and correctTag:
r = fitFunction.fitTo(data, RooFit.Extended(doextended), RooFit.Save(), RooFit.Range(fitrange))
print 'fit status (redo2): ', r.status(), r.covQual()
params = signalFunction.getParameters(RooArgSet(tagged_mass))
w.saveSnapshot("reference_fit_%s_%s"%('RT'*correctTag + 'WT'*(1-correctTag), ibin),params,ROOT.kTRUE)
frame = tagged_mass.frame(RooFit.Range(fitrange))
data.plotOn(frame, RooFit.Binning(nbins), RooFit.MarkerSize(.7))
drawPdfComponents(fitFunction, frame, ROOT.kGreen if correctTag else ROOT.kViolet, RooFit.NormRange(fitrange), RooFit.Range(fitrange), isData=False)
fitFunction.plotOn(frame, RooFit.NormRange(fitrange), RooFit.Range(fitrange) )
fitFunction.paramOn(frame, RooFit.Layout(0.62,0.86,0.88))
frame.Draw()
niceFrame(frame, '')
frame. addObject(_writeFitStatus(r))
## evaluate sort of chi2 and save number of RT/WT events
observables = RooArgSet(tagged_mass)
flparams = fitFunction.getParameters(observables)
nparam = int(flparams.selectByAttrib("Constant",ROOT.kFALSE).getSize())
if correctTag:
frame. addObject(_writeChi2( frame.chiSquare("fitfunction_Norm[tagged_mass]_Range[full]_NormRange[full]", "h_fullmc", nparam) ))
dict_s_rt[ibin] = _getFittedVar(nsig)
nRT = RooRealVar ("nRT_%s"%ibin, "yield of RT signal",0,1.E6)
nRT.setVal( dict_s_rt[ibin].n)
nRT.setError(dict_s_rt[ibin].s)
getattr(w,"import")(nRT)
else:
frame. addObject(_writeChi2( frame.chiSquare("doublecb_%s_Norm[tagged_mass]_Comp[doublecb_%s]_Range[mcrange]_NormRange[mcrange]"%(ibin,ibin), "h_fullmc", nparam) ))
dict_s_wt[ibin] = ufloat(data.sumEntries(), math.sqrt(data.sumEntries()))
nWT = RooRealVar ("nWT_%s"%ibin, "yield of WT signal",0,1.E6)
nWT.setVal( dict_s_wt[ibin].n)
nWT.setError(dict_s_wt[ibin].s)
getattr(w,"import")(nWT)
# chi2 = frame.chiSquare("doublecb_%s_Norm[tagged_mass]_Comp[doublecb_%s]_Range[mcrange]_NormRange[mcrange]"%(ibin,ibin), "h_fullmc", nparam)
# if chi2 == -1:
# chi2 = frame.chiSquare("gauscb_%s_Norm[tagged_mass]_Comp[gauscb_%s]_Range[mcrange]_NormRange[mcrange]"%(ibin,ibin), "h_fullmc", nparam)
# frame. addObject(_writeChi2( chi2 ) )
frame.Draw()
frame.SetTitle('correctly'*correctTag + 'wrongly'*(1-correctTag) + ' tagged events')
# c1.SetLogy()
c1.SaveAs('fit_results_mass/save_fit_mc_%s_%s_%sT_newCB.pdf'%(ibin, args.year, "R"*correctTag + "W"*(1-correctTag)))
out_f.cd()
r.Write('results_%s_%s'%(correctTag*'RT' + (1-correctTag)*'WT', ibin))
def fitData(fulldata, ibin):
cut = cut_base + '&& (mumuMass*mumuMass > %s && mumuMass*mumuMass < %s)'%(q2binning[ibin], q2binning[ibin+1])
data = fulldata.reduce(RooArgSet(tagged_mass,mumuMass,mumuMassE), cut)
fraction = dict_s_rt[ibin] / (dict_s_rt[ibin] + dict_s_wt[ibin])
print 'mistag fraction on MC for bin ', ibin , ' : ' , fraction.n , '+/-', fraction.s
### creating RT component
w.loadSnapshot("reference_fit_RT_%s"%ibin)
sigmart1 = w.var("#sigma_{1}^{RT%s}"%ibin )
sigmart2 = w.var("#sigma_{2}^{RT%s}"%ibin )
massrt = w.var("mean^{RT%s}"%ibin )
f1rt = w.var("f^{RT%s}"%ibin)
theRTgauss = w.pdf("doublegaus_RT%s"%ibin)
c_sigma_rt1 = _constrainVar(sigmart1)
c_sigma_rt2 = _constrainVar(sigmart2)
c_mean_rt = _constrainVar(massrt)
c_f1rt = _constrainVar(f1rt)
### creating WT component
w.loadSnapshot("reference_fit_WT_%s"%ibin)
meanwt = w.var("mean^{WT%s}"%ibin)
sigmawt = w.var("#sigma_{CB}^{WT%s}"%ibin)
alphawt1 = w.var("#alpha_{1}^{WT%s}"%ibin)
alphawt2 = w.var("#alpha_{2}^{WT%s}"%ibin)
nwt1 = w.var("n_{1}^{WT%s}"%ibin)
nwt2 = w.var("n_{2}^{WT%s}"%ibin)
theWTgauss = w.pdf("doublecb_%s"%ibin)
c_mean_wt = _constrainVar(meanwt)
c_sigma_wt = _constrainVar(sigmawt)
c_alpha_wt1 = _constrainVar(alphawt1)
c_alpha_wt2 = _constrainVar(alphawt2)
c_n_wt1 = _constrainVar(nwt1)
c_n_wt2 = _constrainVar(nwt2)
### creating constraints for the RT component
c_RTgauss = RooProdPdf ("c_RTgauss" , "c_RTgauss" , RooArgList(theRTgauss, c_sigma_rt1, c_sigma_rt2, c_mean_rt, c_f1rt ) )
c_vars = RooArgSet(c_sigma_rt1, c_sigma_rt2, c_f1rt, c_mean_rt)
c_vars.add(c_sigma_wt)
c_vars.add(c_mean_wt)
c_vars.add(c_alpha_wt1)
c_vars.add(c_alpha_wt2)
c_vars.add(c_n_wt1)
c_vars.add(c_n_wt2)
### creating constraints for the WT component
c_WTgauss = RooProdPdf ("c_WTgauss" , "c_WTgauss" , RooArgList(theWTgauss, c_alpha_wt1, c_n_wt1, c_sigma_wt, c_mean_wt, c_alpha_wt2, c_n_wt2 ) )
frt = RooRealVar ("F_{RT}" , "frt" , fraction.n , 0, 1)
signalFunction = RooAddPdf ("sumgaus" , "rt+wt" , RooArgList(c_RTgauss,c_WTgauss), RooArgList(frt))
c_frt = RooGaussian("c_frt" , "c_frt" , frt, ROOT.RooFit.RooConst(fraction.n) , ROOT.RooFit.RooConst(fraction.s) )
c_signalFunction = RooProdPdf ("c_signalFunction", "c_signalFunction", RooArgList(signalFunction, c_frt))
c_vars.add(frt)
### now create background parametrization
slope = RooRealVar ("slope" , "slope" , 0.5, -10, 10);
bkg_exp = RooExponential("bkg_exp" , "exponential" , slope, tagged_mass );
pol_c1 = RooRealVar ("p1" , "coeff x^0 term" , 0.5, -10, 10);
pol_c2 = RooRealVar ("p2" , "coeff x^1 term" , 0.5, -10, 10);
bkg_pol = RooChebychev ("bkg_pol" , "2nd order pol" , tagged_mass, RooArgList(pol_c1,pol_c2));
nsig = RooRealVar("Yield" , "signal frac" , 4000, 0, 1000000);
nbkg = RooRealVar("nbkg" , "bkg fraction" , 1000, 0, 550000);
# fitFunction = RooAddPdf ("fitfunction" , "fit function" , RooArgList(c_signalFunction, bkg_pol), RooArgList(nsig, nbkg))
fitFunction = RooAddPdf ("fitfunction" , "fit function" , RooArgList(c_signalFunction, bkg_exp), RooArgList(nsig, nbkg))
r = fitFunction.fitTo(data,
RooFit.Extended(True),
RooFit.Save(),
RooFit.Range("full"),
RooFit.Verbose(False),
ROOT.RooFit.Constrain(c_vars)
)
frame = tagged_mass.frame( RooFit.Range("full") )
data.plotOn(frame, RooFit.Binning(35), RooFit.MarkerSize(.7))
fitFunction.plotOn(frame);
drawPdfComponents(fitFunction, frame, ROOT.kAzure, RooFit.NormRange("full"), RooFit.Range("full"), isData = True)
parList = RooArgSet (nsig, massrt, sigmart1, sigmart2, f1rt, meanwt, sigmawt, alphawt1)
parList.add(alphawt2)
parList.add(nwt1)
parList.add(nwt2)
parList.add(frt)
fitFunction.paramOn(frame, RooFit.Parameters(parList), RooFit.Layout(0.62,0.86,0.89))
frame.Draw()
niceFrame(frame, '')
frame. addObject(_writeFitStatus(r))
if not args.year=='test': writeCMS(frame, args.year, [ q2binning[ibin], q2binning[ibin+1] ])
frame.Draw()
c1.SaveAs('fit_results_mass/save_fit_data_%s_%s_LMNR.pdf'%(ibin, args.year))
tData = ROOT.TChain('ntuple')
tMC = ROOT.TChain('ntuple')
if args.year == 'test':
tData.Add('/gwteray/users/fiorendi/final_ntuples_p5prime_allyears/2016Data_100k.root')
tMC.Add('/gwteray/users/fiorendi/final_ntuples_p5prime_allyears/2016MC_LMNR_100k.root')
else:
tData.Add('/gwteray/users/fiorendi/final_ntuples_p5prime_allyears/%sData_All_finalSelection.root'%args.year)
tMC.Add('/gwteray/users/fiorendi/final_ntuples_p5prime_allyears/%sMC_LMNR.root'%args.year)
# tMC.Add('/gwpool/users/fiorendi/p5prime/CMSSW_8_0_24/src/B0KstarMM/B0KstMuMu/bdt/feb5_ntuples_fixPUW/final_ntuples/%sMC_LMNR_NoL1Selection.root'%args.year)
tagged_mass = RooRealVar("tagged_mass" , "#mu^{+}#mu^{-}K#pi mass", 4.9, 5.6, "GeV")
mumuMass = RooRealVar("mumuMass" , "mumuMass" , 0, 6);
mumuMassE = RooRealVar("mumuMassE" , "mumuMassE", 0, 10000);
tagB0 = RooRealVar("tagB0" , "tagB0" , 0, 2);
tagged_mass.setRange("full", 5. ,5.6) ;
tagged_mass.setRange("mcrange",4.9,5.6) ;
thevars = RooArgSet()
thevars.add(tagged_mass)
thevars.add(mumuMass)
thevars.add(mumuMassE)
thevars.add(tagB0)
fulldata = RooDataSet('fulldata', 'fulldataset', tData, RooArgSet(thevars))
## add to the input tree the combination of the variables, to be used for the cuts on the dimuon mass
deltaB0Mfunc = RooFormulaVar("deltaB0M", "deltaB0M", "@0 - @1", RooArgList(tagged_mass,B0Mass) )
deltaJMfunc = RooFormulaVar("deltaJpsiM" , "deltaJpsiM" , "@0 - @1", RooArgList(mumuMass,JPsiMass) )
deltaPMfunc = RooFormulaVar("deltaPsiPM" , "deltaPsiPM" , "@0 - @1", RooArgList(mumuMass,PsiPMass) )
deltaB0M = fulldata.addColumn(deltaB0Mfunc) ;
deltaJpsiM = fulldata.addColumn(deltaJMfunc) ;
deltaPsiPM = fulldata.addColumn(deltaPMfunc) ;
genSignal = RooRealVar("genSignal" , "genSignal" , 0, 10);
thevarsMC = thevars;
thevarsMC.add(genSignal)
fullmc = RooDataSet('fullmc', 'fullmc', tMC, RooArgSet(thevarsMC))
deltaB0M = fullmc.addColumn(deltaB0Mfunc)
deltaJpsiM = fullmc.addColumn(deltaJMfunc)
deltaPsiPM = fullmc.addColumn(deltaPMfunc)
thevars.add(deltaB0M)
thevars.add(deltaJpsiM)
thevars.add(deltaPsiPM)
thevarsMC.add(deltaB0M)
thevarsMC.add(deltaJpsiM)
thevarsMC.add(deltaPsiPM)
### define correct and wrong tag samples
rt_mc = fullmc.reduce(RooArgSet(thevarsMC), '((tagB0==1 && genSignal==1) || (tagB0==0 && genSignal==2))')
wt_mc = fullmc.reduce(RooArgSet(thevarsMC), '((tagB0==0 && genSignal==1) || (tagB0==1 && genSignal==2))')
c1 = ROOT.TCanvas()
dict_s_rt = {}
dict_s_wt = {}
out_f = TFile ("results_fits_%s_newCB.root"%args.year,"RECREATE")
w = ROOT.RooWorkspace("w")
initial_n_1 = 3.
initial_n_2 = 1.
initial_a_1 = 1.
initial_a_2 = -1.
initial_sigma1 = 0.028
initial_sigma2 = 0.048
initial_sigmaCB = 0.048
for ibin in range(len(q2binning)-1):
print 'dimuon selection: ', args.dimusel
if args.dimusel == 'rejectPsi' and \
(q2binning[ibin] == 8.68 or q2binning[ibin] == 12.86):
continue
fitMC(rt_mc, True, ibin)
fitMC(wt_mc, False, ibin)
fitData(fulldata, ibin)
out_f.Close()
w.writeToFile(out_f.GetName(), False)