def makePercentReplacedPlot():
keylist = ['PAT-genLxy', 'DSA-genLxy']
HISTS = HG.getAddedSignalHistograms(FILE, fs, keylist)
for key, h in HISTS.iteritems():
pass
#h.Rebin(5)
g = R.TGraphAsymmErrors(HISTS[keylist[0]], HISTS[keylist[1]], 'cp')
p = Plotter.Plot(g, '', '', 'p')
p.setTitles(Y='% replaced selected DSA dimuons', X='gen L_{xy} [cm]')
#RT.addBinWidth(p)
canvas = Plotter.Canvas(lumi=fs)
#canvas = Plotter.Canvas(lumi=fs, logy=True)
canvas.addMainPlot(p)
canvas.firstPlot.SetMinimum(0.)
#canvas.firstPlot.SetMinimum(0.0001)
canvas.firstPlot.SetMaximum(1.)
canvas.cleanup('pdfs/percentReplacedVSgenLxy_{}.pdf'.format(fs))
def fillDistributionParameters(MUON, fs, sp, quantity):
# get histograms and define plots
h = HistogramGetter.getHistogram(f, (fs, sp), MUON+'_'+quantity+'_Matched').Clone()
mean = h.GetMean()
sigma = h.GetStdDev()
overflow = h.GetBinContent(h.GetNbinsX()+1)
if sp not in DATA[fs]: DATA[fs][sp] = {}
if MUON not in DATA[fs][sp]: DATA[fs][sp][MUON] = {}
if quantity not in DATA[fs][sp][MUON]: DATA[fs][sp][MUON][quantity] = {
'mean':0,
'sigma':0,
'overflow':0,
}
DATA[fs][sp][MUON][quantity]['mean' ] = mean
DATA[fs][sp][MUON][quantity]['sigma'] = sigma
DATA[fs][sp][MUON][quantity]['overflow'] = overflow
def makeLxyPlot():
hkeys = ['Lxy_{}'.format(tag) for tag in TAGS]
HISTS = HG.getAddedSignalHistograms(FILES['Signal'], '2Mu2J', hkeys)
for key in hkeys:
HISTS[key].Rebin(10)
for tag in TAGS:
zz = 'Lxy_{}'.format(tag)
print zz, HISTS[zz].Integral(
HISTS[zz].GetXaxis().FindBin(100.),
HISTS[zz].GetNbinsX() + 1), HISTS['Lxy_00'].Integral(
HISTS['Lxy_00'].GetXaxis().FindBin(100.),
HISTS['Lxy_00'].GetNbinsX() + 1)
g = {}
for tag in TAGS:
#g[tag] = R.TGraphAsymmErrors(HISTS['Lxy_{}'.format(tag)], HISTS['Lxy_00'], 'cp')
g[tag] = HISTS['Lxy_{}'.format(tag)].Clone()
g[tag].Divide(HISTS['Lxy_00'])
#p = {tag:Plotter.Plot(g[tag], TAGS[tag]['leg'], 'lp', 'p') for tag in TAGS}
p = {
tag: Plotter.Plot(g[tag], TAGS[tag]['leg'], 'lp', 'hist p')
for tag in TAGS
}
c = Plotter.Canvas()
for tag in ('00', '05', '10', '15'):
c.addMainPlot(p[tag])
p[tag].setColor(TAGS[tag]['col'])
c.makeLegend(lWidth=0.2, pos='bl')
c.firstPlot.setTitles(X='', Y='', copy=HISTS[hkeys[0]])
c.firstPlot.SetMinimum(0.8)
c.firstPlot.SetMaximum(1.01)
c.firstPlot.GetXaxis().SetRangeUser(0., 400.)
c.cleanup('Lxy_deltaR_PD.pdf')
def makeBinnedResPlotBinwise(MUONS, outputTag, quantity, q2, fs, sp):
defaultColorOrder = (R.kRed, R.kBlue, R.kGreen, R.kMagenta)
h = {}
for MUON in MUONS:
h[MUON] = HistogramGetter.getHistogram(f, (fs, sp), '{M}_{Q}ResVS{Q2}'.format(M=MUON, Q=quantity, Q2=q2)).Clone()
# leaving space for the bin number
fname = 'pdfs/SRR_{}_{}_{}-Binned-Bin-{{}}_{}HTo2XTo{}_{}.pdf'.format(outputTag, quantity+'Res', q2, 'Trig-' if TRIGGER else '', fs, SPStr(sp))
pretty, binranges, values, colors, colors2, legName = getBinningValues(q2)
for i, key in enumerate(binranges):
canvas = Plotter.Canvas(lumi=SPLumiStr(fs, *sp))
projections, plots = {}, {}
for j, MUON in enumerate(MUONS):
projections[MUON] = h[MUON].ProjectionY('_'+str(i)+'_'+str(j), key[0], key[1])
plots[MUON] = Plotter.Plot(projections[MUON], MUON, 'l', 'hist')
RT.addFlows(plots[MUON])
plots[MUON].Rebin(10)
if plots[MUON].Integral() != 0:
plots[MUON].Scale(1./plots[MUON].Integral())
plots[MUON].SetLineColor(defaultColorOrder[j])
canvas.addMainPlot(plots[MUON])
canvas.makeLegend(lWidth=.25, pos='tr')
canvas.legend.resizeHeight()
canvas.setMaximum(recompute=True)
canvas.drawText(legName.format(Q2=pretty, V1=values[key][0], V2=values[key][1]), (canvas.legend.GetX1NDC()+.01, canvas.legend.GetY1NDC()-.04))
for j, MUON in enumerate(MUONS):
plots[MUON].SetLineColor(defaultColorOrder[j])
plots[MUON].setTitles(X=plots[MUON].GetXaxis().GetTitle(), Y='Normalized Counts')
canvas.drawText('#color[{}]{{'.format(defaultColorOrder[j]) + 'RMS = {:.4f}'.format(plots[MUON].GetStdDev()) + '}',
(canvas.legend.GetX1NDC()+.01, canvas.legend.GetY1NDC()-(j*0.04)-.08)
)
RT.addBinWidth(canvas.firstPlot)
canvas.cleanup(fname.format(i+1))
def makeIntegratedSEQSignal(hkey='SEQ'):
HISTS = HG.getAddedSignalHistograms(FILES['2Mu2J'], '2Mu2J', hkey)
h = HISTS[hkey]
p = Plotter.Plot(h, '', '', 'hist')
canvas = Plotter.Canvas(lumi='2Mu2J', cWidth=1600)
canvas.addMainPlot(p)
p.SetLineColor(R.kBlue)
setBinLabels(canvas)
canvas.firstPlot.setTitles(X='Cut', Y='Normalized Counts')
canvas.firstPlot.SetMaximum(3.e5 if 'DSA' not in hkey else 1.e5)
canvas.firstPlot.SetMinimum(0.)
canvas.scaleMargins(.5, 'L')
canvas.firstPlot.scaleTitleOffsets(.5, 'Y')
R.TGaxis.SetExponentOffset(0., 0.02, "y")
canvas.cleanup('pdfs/NM1_{}{}{}_2Mu2J.pdf'.format(hkey, BUMPSTRING,
IDPHISTRING))
def makeIntegratedNM1Signal(hkey='NM1'):
#HISTS, INDIV = HG.getAddedSignalHistograms(FILES['2Mu2J'], '2Mu2J', 'NM1', getIndividuals=True)
#totalBin1 = sum([INDIV['NM1'][ref].GetBinContent(1) for ref in SIGNALPOINTS])
#for ibin in range(1, len(CUTS)+2):
# totalBin = sum([INDIV['NM1'][ref].GetBinContent(ibin) for ref in SIGNALPOINTS])
# scaleFactor = totalBin1/totalBin
# for ref in SIGNALPOINTS:
# INDIV['NM1'][ref].SetBinContent(ibin, INDIV['NM1'][ref].GetBinContent(ibin)/totalBin*scaleFactor)
HISTS = HG.getAddedSignalHistograms(FILES['2Mu2J'], '2Mu2J', hkey)
totalBin1 = HISTS[hkey].GetBinContent(1)
for ibin in range(1, len(CUTS) + 2):
totalBin = HISTS[hkey].GetBinContent(ibin)
scaleFactor = totalBin1 / totalBin
HISTS[hkey].SetBinContent(ibin, scaleFactor)
h = HISTS[hkey]
p = Plotter.Plot(h, '', '', 'hist')
canvas = Plotter.Canvas(lumi='2Mu2J', cWidth=1600)
canvas.addMainPlot(p)
p.SetLineColor(R.kBlue)
setBinLabels(canvas)
canvas.firstPlot.setTitles(X='Cut', Y='n#minus1 Eff.')
canvas.firstPlot.SetMaximum(1.2)
canvas.firstPlot.SetMinimum(0.)
canvas.scaleMargins(.5, 'L')
canvas.firstPlot.scaleTitleOffsets(.5, 'Y')
canvas.cleanup('pdfs/NM1_{}{}{}_2Mu2J.pdf'.format(hkey, BUMPSTRING,
IDPHISTRING))
def makeRepEffectPlots(hkey):
HISTS, PConfig = HG.getBackgroundHistograms(FILES['MC'], hkey)
HISTS = HISTS[hkey]
PConfig = PConfig[hkey]
PLOTS = {}
for key in HG.BGORDER + ('stack', ):
PLOTS[key] = Plotter.Plot(HISTS[key], *PConfig[key])
canvas = Plotter.Canvas(
lumi='MC Background, {} PAT association (36.3 fb^{{-1}})'.format(
hkey.replace('LxySig-', '').replace('Lxy-', '')),
logy=True,
cWidth=600 if ARGS.SQUARE else 800)
for key in HG.BGORDER:
PLOTS[key].setColor(HG.PLOTCONFIG[key]['COLOR'], which='LF')
canvas.addMainPlot(PLOTS['stack'])
canvas.firstPlot.setTitles(X='', copy=PLOTS[HG.BGORDER[0]])
canvas.firstPlot.setTitles(Y='Event Yield')
canvas.makeLegend(lWidth=.3, pos='tr', autoOrder=False, fontscale=0.8)
for ref in reversed(HG.BGORDER):
canvas.addLegendEntry(PLOTS[ref])
canvas.legend.resizeHeight()
RT.addBinWidth(canvas.firstPlot)
canvas.firstPlot.SetMaximum(10.**7.)
canvas.firstPlot.SetMinimum(10.**0.)
if ARGS.SQUARE:
canvas.firstPlot.scaleTitleOffsets(1.1, 'X')
z = PLOTS['stack'].plot.GetStack().Last()
print 'MC', hkey, z.Integral(0, z.GetNbinsX() + 1)
canvas.cleanup('pdfs/REPEFF_MC_{}.pdf'.format(hkey), mode='LUMI')
def importHistograms(DISTRIBUTION_SPECS):
"""
Read and add specified histograms from one or many files.
Extracts histograms from one or many ROOT files (defined via the argument
'DISTRIBUTION_SPECS') and adds them to the given HistSpecs objects.
Parameters
----------
DISTRIBUTION_SPECS : HistSpecs object
An instance of the HistSpecs class, which defines all the relevant histogram
properties
"""
# perform some basic sanity checks
for el in DISTRIBUTION_SPECS:
filepath = el.getAttribute("filepath")
histname_identifier = el.getAttribute("histname_identifier")
histname_exclude = el.getAttribute("histname_exclude")
types = ((filepath, str), (histname_identifier, str),
(histname_exclude, list))
for o, t in types:
if not isinstance(o, t):
raise Exception(
"Invalid type of {}: should be {}, but is {}".format(
o, t, type(o)))
if not os.path.exists(filepath):
raise Exception("No such file: {}".format(filepath))
# dictionary structure for collecting the relevant distributions
hists = {}
cnt_files = 1
for el in DISTRIBUTION_SPECS:
filepath = el.getAttribute("filepath")
histname_identifier = el.getAttribute("histname_identifier")
histname_exclude = el.getAttribute("histname_exclude")
print("[{}/{}] Reading file {}...".format(cnt_files,
len(DISTRIBUTION_SPECS),
filepath))
f_allhists = HistogramGetter.getHistograms(filepath)
histogram = None
for dataset in f_allhists:
selected_hists_names = getHistNames(f_allhists,
dataset,
histname_identifier,
exclude=histname_exclude)
print("hist names: {}".format(selected_hists_names))
if len(selected_hists_names) > 1:
raise Exception(
"Ambiguous histogram identifier: {} would select the following {} histograms: {}"
.format(histname_identifier, len(selected_hists_names),
'\n'.join(selected_hists_names)))
for key in selected_hists_names:
if histogram is None:
histogram = f_allhists[dataset][key].Clone()
else:
histogram.Add(f_allhists[dataset][key])
if histogram:
print("\tImported histogram {}".format(histogram.GetName()))
else:
print("\tNo histograms found.")
# prepare the histogram
RT.addFlows(histogram)
if el.getAttribute("histogram") is not None:
print(
"Warning: Histogram already exists in the HistSpecs object and will be overwritten"
)
# add the actual histrogram to the corresponding HistSpecs object
el.setAttribute("histogram", histogram)
cnt_files += 1
del f_allhists
import DisplacedDimuons.Analysis.HistogramGetter as HG
import DisplacedDimuons.Analysis.AnalysisTools as AT
import numpy as np
from OptimizerTools import SignalInfo, ScaleFactor, calculateFOM, PARSER
ARGS = PARSER.parse_args()
FIGURE_OF_MERIT = ARGS.FOM
PRETTY_LEG = {'ZBi': 'Z_{Bi}', 'ZPL': 'Z_{PL}'}
FILE = R.TFile.Open(
'../nMinusOne/roots/NM1Distributions_Trig_HTo2XTo2Mu2J.root')
DFILE = R.TFile.Open('../nMinusOne/roots/NM1Distributions_IDPHI_DATA.root')
HISTS = {
sp: HG.getHistogram(FILE, ('2Mu2J', sp), 'mass')
for sp in SIGNALPOINTS
}
SCALED = {sp: HISTS[sp].Clone() for sp in SIGNALPOINTS}
for sp in SIGNALPOINTS:
integral = SCALED[sp].Integral(0, SCALED[sp].GetNbinsX() + 1)
SCALED[sp].Scale(1. / (integral if integral != 0. else 1.))
CUM = {sp: SCALED[sp].GetCumulative() for sp in SIGNALPOINTS}
DISTS = {
sp: Plotter.Plot(HISTS[sp], 'm_{{X}} = {} GeV'.format(sp[1]), 'l', 'hist')
for sp in SIGNALPOINTS
}
PLOTS = {
sp: Plotter.Plot(CUM[sp], 'm_{{X}} = {} GeV'.format(sp[1]), 'l', 'hist')
for sp in SIGNALPOINTS
}
def makeKinkFOMPlot(quantity, masses, sigmaBMode='DEFAULT'):
points = sorted(
[sp for sp in SignalInfo if sp[0] == masses[0] and sp[1] == masses[1]],
key=lambda x: x[2])
s = {
points[0]:
HG.getHistogram(FILES['Signal'], (fs, points[0]),
'{}_1'.format(quantity)).Clone(),
points[1]:
HG.getHistogram(FILES['Signal'], (fs, points[1]),
'{}_10'.format(quantity)).Clone(),
}
if sigmaBMode == 'DEFAULT':
s[points[0]].Scale(ScaleFactor(points[0], 1))
s[points[1]].Scale(ScaleFactor(points[1], 10))
extra = ''
elif sigmaBMode == 'SAME':
s[points[0]].Scale(ScaleFactor(points[0], 1))
s[points[1]].Scale(ScaleFactor(points[0], 1))
extra = '_SAME'
elif sigmaBMode == 'GLOBAL':
s[points[0]].Scale(ScaleFactor(points[0], 1, 1.e-2))
s[points[1]].Scale(ScaleFactor(points[1], 1, 1.e-2))
extra = '_GLOBAL'
DHists, DPConfig = HG.getDataHistograms(FILES['Data'],
'{}_1'.format(quantity),
addFlows=False)
b = DHists['{}_1'.format(quantity)]['data']
# get cumulatives
bCum = b.GetCumulative(CONFIG[quantity]['forward'])
sCum = {p: s[p].GetCumulative(CONFIG[quantity]['forward']) for p in points}
fom = {p: sCum[p].Clone() for p in points}
nBins = sCum[points[0]].GetNbinsX()
xAxis = sCum[points[0]].GetXaxis()
# dictionaries instead of numbers because we have 2 sets of ZBi curves
fom_max = {p: 0. for p in points}
opt_cut = {p: 0. for p in points}
opt_s = {p: 0. for p in points}
opt_b = {p: 0. for p in points}
for ibin in range(1, nBins + 1):
for p in points:
S, B, cutVal, FOMs = calculateFOM(s[p], b, sCum[p], bCum, nBins,
ibin, xAxis,
CONFIG[quantity]['forward'])
if FOMs[FIGURE_OF_MERIT] > fom_max[p]:
fom_max[p] = FOMs[FIGURE_OF_MERIT]
opt_cut[p] = cutVal
opt_s[p] = S
opt_b[p] = B
fom[p].SetBinContent(ibin, FOMs[FIGURE_OF_MERIT])
pl = {
points[0]:
Plotter.Plot(fom[points[0]],
'c#tau = {} mm, nominal'.format(points[0][2]), 'l',
'hist p'),
points[1]:
Plotter.Plot(fom[points[1]],
'c#tau = {} mm, reweighted'.format(points[0][2]), 'l',
'hist p'),
}
colors = {
points[0]: R.kBlue,
points[1]: R.kRed,
}
c = Plotter.Canvas(lumi='({} GeV, {} GeV)'.format(*masses))
c.addMainPlot(pl[points[0]])
c.addMainPlot(pl[points[1]])
for p in points:
pl[p].setColor(colors[p], which='LM')
c.makeLegend(lWidth=0.3, pos='tr')
c.legend.SetMargin(0.1)
c.legend.resizeHeight()
lines = {
p: R.TLine(opt_cut[p], 0., opt_cut[p],
max(fom_max.values()) * 1.1)
for p in points
}
for p in points:
lines[p].SetLineStyle(2)
lines[p].SetLineColor(colors[p])
lines[p].Draw()
for i, p in enumerate(points):
c.drawText(text='#color[{:d}]{{opt = {:.1f}}}'.format(
colors[p], opt_cut[p]),
pos=(c.legend.GetX2(), c.legend.GetY1() - .04 - i * .04),
align='br')
c.firstPlot.SetMinimum(0.)
c.firstPlot.SetMaximum(max(fom_max.values()) * 1.1)
c.firstPlot.setTitles(Y=PRETTY_LEG)
RT.addBinWidth(c.firstPlot)
c.cleanup('pdfs/KINK_FOM{}_{}_{}_{}_{}.pdf'.format(extra, quantity,
masses[0], masses[1],
FIGURE_OF_MERIT))
def compareFOMPlot(quantity, sp, sigmaBMode='GLOBAL'):
keys = FACTORS
for FACTOR in FACTORS:
s = HG.getHistogram(FILES['Signal'], (fs, sp),
'{}_{}'.format(quantity, FACTOR)).Clone()
if sigmaBMode == 'GLOBAL':
s.Scale(ScaleFactor(sp, 1, 1.e-2))
extra = 'GLOBAL'
DHists, DPConfig = HG.getDataHistograms(FILES['Data'],
'{}_1'.format(quantity),
addFlows=False)
b = DHists['{}_1'.format(quantity)]['data']
# get cumulatives
bCum = b.GetCumulative(CONFIG[quantity]['forward'])
sCum = s.GetCumulative(CONFIG[quantity]['forward'])
fom = {z: sCum.Clone() for z in ('ZBi', 'ZPL')}
nBins = sCum.GetNbinsX()
xAxis = sCum.GetXaxis()
# dictionaries instead of numbers because we have 2 sets of ZBi curves
fom_max = {z: 0. for z in ('ZBi', 'ZPL')}
opt_cut = {z: 0. for z in ('ZBi', 'ZPL')}
opt_s = {z: 0. for z in ('ZBi', 'ZPL')}
opt_b = {z: 0. for z in ('ZBi', 'ZPL')}
for ibin in range(1, nBins + 1):
S, B, cutVal, FOMs = calculateFOM(s, b, sCum, bCum, nBins, ibin,
xAxis,
CONFIG[quantity]['forward'])
for z in ('ZBi', 'ZPL'):
if FOMs[z] > fom_max[z]:
fom_max[z] = FOMs[z]
opt_cut[z] = cutVal
opt_s[z] = S
opt_b[z] = B
fom[z].SetBinContent(ibin, FOMs[z])
p = {
z: Plotter.Plot(fom[z], PRETTY_LEG[z], 'l', 'hist p')
for z in fom
}
colors = {'ZBi': R.kRed, 'ZPL': R.kBlue}
c = Plotter.Canvas(lumi='({} GeV, {} GeV, {} mm){}'.format(
sp[0], sp[1], sp[2], '' if FACTOR ==
1 else ' #rightarrow {} mm'.format(sp[2] / FACTOR)))
for z in fom:
c.addMainPlot(p[z])
p[z].setColor(colors[z], which='LM')
c.makeLegend(lWidth=0.1, pos='tr')
#c.legend.SetMargin(0.1)
c.legend.resizeHeight()
lines = {
z: R.TLine(opt_cut[z], 0., opt_cut[z],
max(fom_max.values()) * 1.1)
for z in fom
}
for z in fom:
lines[z].SetLineStyle(2)
lines[z].SetLineColor(colors[z])
lines[z].Draw()
for i, z in enumerate(fom.keys()):
c.drawText(text='#color[{:d}]{{opt = {:.1f}}}'.format(
colors[z], opt_cut[z]),
pos=(c.legend.GetX2(),
c.legend.GetY1() - .04 - i * .04),
align='br')
c.firstPlot.SetMinimum(0.)
c.firstPlot.SetMaximum(max(fom_max.values()) * 1.1)
c.firstPlot.setTitles(Y='Figure of Merit')
RT.addBinWidth(c.firstPlot)
c.cleanup('pdfs/COMPARE_FOM-{}_{}_{}_{}_{}_{}.pdf'.format(
extra, quantity, sp[0], sp[1], sp[2], FACTOR))
keyList = {
'Prompt': [
'hLessLxySig',
'hMoreLxySig',
'hLessDeltaPhi',
'hMoreDeltaPhi',
],
'Full': ['LxySig_IDPHI', 'LxySig_DPHI'],
}
DHists = {'Prompt': {}, 'Full': {}}
for which in keyList:
if which == 'Prompt':
for key in keyList[which]:
DHists[which][key] = HG.getHistogram(FILES[which], 'Data',
key).Clone()
else:
D, P = HG.getDataHistograms(FILES[which],
keyList[which],
addFlows=False)
DHists[which] = {key: D[key]['data'] for key in D}
#### Correction factors from "inverted PAT" CR and SR ####
# these are 0.5 bins
#DHists['Prompt']['hLessLxySig'].GetXaxis().SetRange(1, 12)
#DHists['Prompt']['hMoreLxySig'].GetXaxis().SetRange(1, 12)
# now they're 1 bins
DHists['Prompt']['hLessLxySig'].Rebin(2)
else:
FILE = R.TFile.Open(ARGS.FILE)
if not FILE:
raise Exception('{}: No such file'.format(ARGS.FILE))
MCONLY = ARGS.MCONLY
HKEY = ARGS.HKEY
TOTAL = ARGS.TOTAL
# some constants
BGSAMPLES = ['WJets', 'WW', 'WZ', 'ZZ', 'tW', 'tbarW', 'ttbar', 'QCD20toInf-ME', 'DY10to50', 'DY50toInf']
DATASAMPLES = ['DoubleMuonRun2016{}-07Aug17{}'.format(era, '' if era != 'B' else '-v2') for era in ('B', 'C', 'D', 'E', 'F', 'G', 'H')]
# get the stacked MC
h = {}
MCStack = HistogramGetter.getHistogram(FILE, BGSAMPLES[0], HKEY)
if not MCStack:
raise Exception('{}: No such MC key'.format(HKEY))
MCStack = MCStack.Clone()
MCStack.Scale(HistogramGetter.PLOTCONFIG[BGSAMPLES[0]]['WEIGHT'])
for SAMPLE in BGSAMPLES:
h[SAMPLE] = HistogramGetter.getHistogram(FILE, SAMPLE, HKEY).Clone()
h[SAMPLE].Scale(HistogramGetter.PLOTCONFIG[SAMPLE]['WEIGHT'])
for SAMPLE in BGSAMPLES[1:]:
MCStack.Add(h[SAMPLE])
# get the added data
if not MCONLY:
DataStack = HistogramGetter.getHistogram(FILE, DATASAMPLES[0], HKEY)
if not DataStack:
raise Exception('{}: No such data key'.format(HKEY))
def makeResPlots(metric, quantity, sp=None):
PRETTY = {
'pTRes_hits_less': {
'mnice': 'p_{T} res.',
'qnice': 'N(hits)',
'leg': 'N(hits) #leq 12',
'col': R.kRed
},
'pTRes_hits_more': {
'mnice': 'p_{T} res.',
'qnice': 'N(hits)',
'leg': 'N(hits) > 12',
'col': R.kBlue
},
'pTRes_fpte_less': {
'mnice': 'p_{T} res.',
'qnice': '#sigma_{p_{T}}/p_{T}',
'leg': '#sigma_{p_{T}}/p_{T} < 1',
'col': R.kBlue
},
'pTRes_fpte_more': {
'mnice': 'p_{T} res.',
'qnice': '#sigma_{p_{T}}/p_{T}',
'leg': '#sigma_{p_{T}}/p_{T} #geq 1',
'col': R.kRed
},
'qdiff_hits_less': {
'mnice': 'charge diff.',
'qnice': 'N(hits)',
'leg': 'N(hits) #leq 12',
'col': R.kRed
},
'qdiff_hits_more': {
'mnice': 'charge diff.',
'qnice': 'N(hits)',
'leg': 'N(hits) > 12',
'col': R.kBlue
},
'qdiff_fpte_less': {
'mnice': 'charge diff.',
'qnice': '#sigma_{p_{T}}/p_{T}',
'leg': '#sigma_{p_{T}}/p_{T} < 1',
'col': R.kBlue
},
'qdiff_fpte_more': {
'mnice': 'charge diff.',
'qnice': '#sigma_{p_{T}}/p_{T}',
'leg': '#sigma_{p_{T}}/p_{T} #geq 1',
'col': R.kRed
},
}
witches = ('less', 'more') if quantity == 'fpte' else ('more', 'less')
hkeys = ['{}_{}_{}'.format(metric, quantity, which) for which in witches]
if sp is None:
HISTS = HG.getAddedSignalHistograms(FILES['Signal'], '2Mu2J', hkeys)
else:
HISTS = {}
for key in hkeys:
HISTS[key] = HG.getHistogram(FILES['Signal'], ('2Mu2J', sp),
key).Clone()
PLOTS = {}
for key in hkeys:
HISTS[key].Scale(1. /
HISTS[key].Integral(0, HISTS[key].GetNbinsX() + 1))
#RT.addFlows(HISTS[key])
PLOTS[key] = Plotter.Plot(HISTS[key], PRETTY[key]['leg'], 'l', 'hist')
#canvas = Plotter.Canvas(lumi='{} by {}'.format(PRETTY[hkeys[0]]['mnice'], PRETTY[hkeys[0]]['qnice']))
lumi = 'H#rightarrow2X#rightarrow2#mu'
if sp is None:
lumi += ', all samples combined'
else:
lumi += ' ({} GeV, {} GeV, {} mm)'.format(*sp)
canvas = Plotter.Canvas(lumi=lumi, cWidth=600 if ARGS.SQUARE else 800)
for key in hkeys:
canvas.addMainPlot(PLOTS[key])
PLOTS[key].setColor(PRETTY[key]['col'], which='L')
canvas.firstPlot.SetMinimum(0.)
canvas.setMaximum()
if metric == 'qdiff':
canvas.firstPlot.SetMaximum(1.)
canvas.firstPlot.setTitles(Y='Density')
if 'qdiff' in hkeys[0]:
canvas.firstPlot.SetNdivisions(3)
canvas.makeLegend(lWidth=0.27, pos='tr')
canvas.legend.resizeHeight()
RT.addBinWidth(canvas.firstPlot)
if ARGS.SQUARE:
canvas.firstPlot.scaleTitleOffsets(1.1, 'X')
if metric == 'pTRes':
canvas.firstPlot.scaleTitleOffsets(1.3, 'Y')
canvas.cleanup('pdfs/QCUTRES_Sig_{}_{}_{}.pdf'.format(
metric, quantity, 'Global' if sp is None else SPStr(sp)),
mode='LUMI')
import ROOT as R
import DisplacedDimuons.Analysis.HistogramGetter as HG
from DisplacedDimuons.Common.Constants import SIGNALPOINTS
# see commit from Mar 5 2019 for the patch required to run studyPTCut.py
f = R.TFile.Open('roots/PTCutPlots_2Mu2J.root')
for sp in SIGNALPOINTS:
h = HG.getHistogram(f, ('2Mu2J', sp), 'nMatches')
maxMatched = max(
[h.GetBinContent(i) for i in xrange(1,
h.GetNbinsX() + 1)])
ibin = h.GetMaximumBin()
print '{:4d} {:3d} {:4d} : '.format(*sp),
print 'the maximum {:5d} occurs @ pT > {:2d}'.format(
int(maxMatched), int(ibin - 1)),
if ibin != 11:
if int(maxMatched - h.GetBinContent(11)) != 0:
print ', but 10 GeV would only cause a loss of {:2d} events ({:.2%})'.format(
int(maxMatched - h.GetBinContent(11)),
1. - (h.GetBinContent(11) / maxMatched))
else:
print ' -- but 10 GeV is an equivalently optimal cut'
else:
print ' -- this is the optimal cut'
DATA = initializeData()
with open('text/replaceSignal.txt') as f:
for line in f:
if '%' in line or '---' in line: continue
cols = line.strip('\n').split()
fs = cols[0]
sp = tuple(map(int, cols[1:4]))
#DATA[fs][sp]['den'] = int(cols[4])
#DATA[fs][sp]['num'] = int(cols[5])
DATA[fs][sp]['repPct'] = float(cols[6])
f = R.TFile.Open('roots/PATMuonStudyPlots_Trig_Combined_BS8_2Mu2J.root')
fs = '2Mu2J'
for sp in SIGNALPOINTS:
h = HG.getHistogram(f, (fs, sp), 'DSA-LxySig')
DATA[fs][sp]['DSA-LxySig'] = h.GetMean()
DATA[fs][sp]['DSA-LxySig-Overflow'] = h.GetBinContent(h.GetNbinsX() + 1)
h = HG.getHistogram(f, (fs, sp), 'PAT-LxySig')
DATA[fs][sp]['PAT-LxySig'] = h.GetMean()
DATA[fs][sp]['PAT-LxySig-Overflow'] = h.GetBinContent(h.GetNbinsX() + 1)
DATA[fs][sp]['Frac-LxySig-Overflow'] = h.GetBinContent(
h.GetNbinsX() + 1) / h.Integral(0,
h.GetNbinsX() + 1) * 100.
h = HG.getHistogram(f, (fs, sp), 'PAT-vtxChi2')
DATA[fs][sp]['PAT-vtxChi2'] = h.GetMean()
DATA[fs][sp]['PAT-vtxChi2-Overflow'] = h.GetBinContent(h.GetNbinsX() + 1)
"pTdiff": (-1.1, 10.0),
"d0": (0.0, 200),
"L1pTres": (-1.0, 8.0),
"L2pTres": (-1.0, 8.0),
"dimVtxChi2": (0.0, 60.0),
"dimLxySig": (0.0, 230.0),
"chi2": (0.0, 30.0),
"pTSig": (0.0, 2.0),
"nStations": (0.0, 10.0),
"nCSCDTHits": (10.0, 60.0),
"chargeprod": (-1.0, 2.0),
"charge": (-1.0, 2.0),
}
HISTS = HistogramGetter.getHistograms(
"/afs/cern.ch/work/s/stempl/private/DDM/cosmics-studies/simulation-validation/Cosmics2016_UGMT-bottomOnly_HLT-ppSeed.root"
)
def makePerSamplePlots(selection=None):
rebinning_exceptions = ("pTdiff", "nCSCDTHits")
h = {}
p = {}
for dataset in HISTS:
if selection is not None:
selected_hists_names = getHistNames(dataset, *selection)
else:
selected_hists_names = HISTS[dataset]
for key in selected_hists_names:
PP.PARSER.add_argument('--thesis', dest='THESIS', action='store_true')
ARGS = PP.PARSER.parse_args()
##################################
#### Get histograms and scale ####
##################################
FILES = {
'DataNom' : R.TFile.Open('roots/MyDataPileupHistogram.root'),
'DataLow' : R.TFile.Open('roots/MyDataPileupHistogram_Low.root'),
'DataHigh': R.TFile.Open('roots/MyDataPileupHistogram_High.root'),
'Signal' : R.TFile.Open('roots/pileup_HTo2XTo2Mu2J.root'),
}
HISTS, INDIV = HG.getAddedSignalHistograms(FILES['Signal'], '2Mu2J', 'pileup', True)
HISTS, INDIV = HISTS['pileup'], INDIV['pileup']
KEYS = ('Nom', 'Low', 'High')
DATA = {key:FILES['Data'+key].Get('pileup').Clone() for key in KEYS}
for key in DATA:
DATA[key].Scale(1./DATA[key].Integral())
for sp in INDIV:
INDIV[sp].Scale(1./INDIV[sp].Integral())
HISTS.Scale(1./HISTS.Integral())
# saves the weight for a printing pass afterwards
import ROOT as R
import DisplacedDimuons.Analysis.HistogramGetter as HG
import DisplacedDimuons.Analysis.Plotter as Plotter
FILES = {'_PATOnly':0, '_NoRep':0, '':0}
HISTS = {}
for key in FILES:
FILES[key] = R.TFile.Open('roots/ZephyrPlots_Trig_Combined_BS9{}_2Mu2J.root'.format(key))
HISTS[key] = HG.getAddedSignalHistograms(FILES[key], '2Mu2J', ('GEN-Lxy', 'GEN-Lxy-PAT', 'GEN-Lxy-HYB', 'GEN-Lxy-DSA'))
# relative signal match eff
h = {}
h['PAT'] = HISTS['_PATOnly']['GEN-Lxy'].Clone()
h['HYB'] = HISTS['' ]['GEN-Lxy'].Clone()
h['DSA'] = HISTS['_NoRep' ]['GEN-Lxy'].Clone()
for key in h:
h[key].Rebin(5)
h['PAT'].Divide(h['DSA'])
h['HYB'].Divide(h['DSA'])
h.pop('DSA')
p = {}
config = {'PAT' : ('DSA-DSA or PAT-PAT only', R.kRed), 'HYB' : ('DSA-DSA, PAT-PAT, or DSA-PAT', R.kBlue)}
for key in h:
p[key] = Plotter.Plot(h[key], config[key][0], 'lp', 'p')
canvas = Plotter.Canvas(lumi='2Mu2J')
for key in ('PAT', 'HYB'):
canvas.addMainPlot(p[key])
def makeSignalPlot(key):
h = HG.getAddedSignalHistograms(FILES['2Mu2J'], '2Mu2J', key)
h = h[key]
p = Plotter.Plot(h, '', '', 'hist')
RT.addBinWidth(p)
c = Plotter.Canvas(lumi='H#rightarrow2X#rightarrow2#mu, {}'.format(
CUTS[key]['lumi']),
cWidth=600 if key != 'deltaPhi' else 800,
logy=CUTS[key]['logy'])
c.addMainPlot(p)
c.firstPlot.setColor(R.kBlue)
c.firstPlot.scaleTitleOffsets(1.1, 'Y')
if key == 'deltaPhi':
axis = c.firstPlot.GetXaxis()
labeldict = {
1: '0',
25: '#pi/4',
50: '#pi/2',
75: '3#pi/4',
100: '#pi'
}
for i in xrange(1, 101):
val = labeldict.get(i)
if val is None: val = ''
axis.SetBinLabel(i, val)
axis.SetNdivisions(216, False)
axis.SetLabelSize(.06)
axis.SetTickLength(0.03)
axis.SetTickSize(0.03)
axis.LabelsOption('h')
c.mainPad.Update()
ymin = c.mainPad.GetUymin()
ymax = c.mainPad.GetUymax()
if c.logy:
ymax = 10.**ymax
xB = R.TGaxis(0., ymin,
R.TMath.Pi() - .07, ymin, 0., R.TMath.Pi(), 216, "+B",
.03)
xB.SetLabelSize(0)
xB.Draw('same')
xT = R.TGaxis(0., ymax,
R.TMath.Pi() - .07, ymax, 0., R.TMath.Pi(), 216, "-B",
.03)
xT.SetLabelSize(0)
xT.Draw('same')
if key == 'pT':
c.firstPlot.GetXaxis().SetRangeUser(0., 600.)
eff = getEff(p, key)
c.drawText('{:.1%} efficient'.format(eff),
align='tr',
pos=(1. - c.margins['r'] - .03, 1. - c.margins['t'] - .03))
c.mainPad.Update()
ymax = c.mainPad.GetUymax()
line = R.TLine(CUTS[key]['val'], 0., CUTS[key]['val'],
ymax if not c.logy else 10.**ymax)
line.SetLineStyle(2)
line.Draw()
if key == 'trkChi2': c.firstPlot.GetXaxis().SetTitle('trk. #chi^{2}/dof')
c.cleanup('pdfs/NM1_2Mu2J_{}.pdf'.format(key), mode='LUMI')
#fOld = R.TFile.Open('roots/ZephyrPlots_Trig_Combined_NS_NH_FPTE_HLT_REP_PQ1_PT_PC_LXYE_MASS_CHI2_DSAPROXMATCH_HTo2XTo2Mu2J.root')
#fNew = R.TFile.Open('roots/ZephyrPlots_Trig_Combined_NS_NH_FPTE_HLT_REP_PQ1_PT_PC_LXYE_MASS_CHI2_DPT_HTo2XTo2Mu2J.root')
fOld = R.TFile.Open(
'roots/ZephyrPlots_Trig_Combined_NS_NH_FPTE_HLT_REP_PT_PC_LXYE_MASS_CHI2_VTX_COSA_SFPTE_HTo2XTo2Mu2J.root'
)
fNew = R.TFile.Open(
'roots/ZephyrPlots_Trig_Combined_NS_NH_FPTE_HLT_REP_PT_PC_LXYE_MASS_CHI2_VTX_COSA_SFPTE_PROXTHRESH_HTo2XTo2Mu2J.root'
)
def deleteHistogram(HIST):
R.gROOT.ProcessLine('delete gROOT->FindObject("' + HIST.GetName() + '")')
hOld = HG.getAddedSignalHistograms(fOld, '2Mu2J', 'GEN-Lxy-DSA')
g = hOld['GEN-Lxy-DSA'].Clone()
g.SetName('new')
deleteHistogram(hOld['GEN-Lxy-DSA'])
HybOld = HG.getAddedSignalHistograms(fOld, '2Mu2J', 'GEN-Lxy-HYB')
g.Add(HybOld['GEN-Lxy-HYB'])
deleteHistogram(HybOld['GEN-Lxy-HYB'])
hNew = HG.getAddedSignalHistograms(fNew, '2Mu2J', 'GEN-Lxy-DSA')
h = hNew['GEN-Lxy-DSA'].Clone()
h.SetName('old')
deleteHistogram(hNew['GEN-Lxy-DSA'])
HybNew = HG.getAddedSignalHistograms(fNew, '2Mu2J', 'GEN-Lxy-HYB')
h.Add(HybNew['GEN-Lxy-HYB'])
PARSER.add_argument('--quantity' , dest='QUANTITY' , default='vtxChi2')
PARSER.add_argument('--cutstring', dest='CUTSTRING', default='Combined_NS_NH_FPTE_HLT_REP_PQ1_PT_PC_LXYE_MASS')
PARSER.add_argument('--cut' , dest='CUT' , default=50., type=float)
PARSER.add_argument('--rtypes' , dest='RTYPES' , default='DPH')
ARGS = PARSER.parse_args()
f = R.TFile.Open('roots/ZephyrPlots_Trig_{}_HTo2XTo2Mu2J.root'.format(ARGS.CUTSTRING))
RTYPES = []
if 'D' in ARGS.RTYPES: RTYPES.append('DSA')
if 'P' in ARGS.RTYPES: RTYPES.append('PAT')
if 'H' in ARGS.RTYPES: RTYPES.append('HYB')
print '{:4s} {:3s} {:4s} {:>10s} {:>6s} {:>6s} {:>6s}'.format('mH', 'mX', 'cTau', 'Mean', 'OFlow', 'OFlow%', '%>{}'.format(ARGS.CUT))
for sp in SIGNALPOINTS:
for i, key in enumerate(RTYPES):
if i == 0:
h = HG.getHistogram(f, ('2Mu2J', sp), key+'-'+ARGS.QUANTITY)
else:
h.Add(HG.getHistogram(f, ('2Mu2J', sp), key+'-'+ARGS.QUANTITY))
c = h.GetCumulative()
ibin = c.FindBin(float(ARGS.CUT))
print '{:4d} {:3d} {:4d} {:10.2f} {:6.0f} {:6.2f} {:6.2f}'.format(
sp[0], sp[1], sp[2],
h.GetMean(),
h.GetBinContent(h.GetNbinsX()+1),
h.GetBinContent(h.GetNbinsX()+1)/h.Integral(0, h.GetNbinsX()+1)*100.,
100.-c.GetBinContent(ibin) /h.Integral(0, h.GetNbinsX()+1)*100.
)
def makeOverlaidResPlot(MUONS, fs, sp, quantity, outputTag=None):
# whether the plot is dif or res makes a difference wrt binning, fit range, and stats box positions
# only pT is a res type; the others are all dif types
ISDIF = quantity != 'pT'
# what to name the plot. if MUONS is length 1, no sense to pass it twice, so get it automatically
if outputTag is None:
outputTag = MUONS[0]
# colors, in order of MUONS, and also hashed
defaultColorOrder = (R.kBlue, R.kRed, R.kGreen)
colorDict = dict(zip(MUONS, defaultColorOrder))
# get histograms and define plots
h = {}
for MUON in MUONS:
h[MUON] = HistogramGetter.getHistogram(f, (fs, sp), MUON+'_'+quantity+'Res').Clone()
p = {}
for MUON in MUONS:
RT.addFlows(h[MUON])
if not ISDIF:
h[MUON].Rebin(5)
else:
h[MUON].Rebin(10)
p[MUON] = Plotter.Plot(h[MUON], 'Signal MC ({})'.format(MUON[:3]), 'l', 'hist')
# define and fit gaussians to everything. Set FITRANGE to be something useful.
funcs = {}
fplots = {}
for MUON in MUONS:
if quantity == 'pT':
FITRANGE = (-0.4, 0.3)
elif quantity == 'eta':
FITRANGE = (-0.1, 0.1)
else:
FITRANGE = (-20., 20.)
funcs[MUON] = R.TF1('f'+MUON, 'gaus', *FITRANGE)
h[MUON].Fit('f'+MUON, 'R')
fplots[MUON] = Plotter.Plot(funcs[MUON], 'Gaussian fit ({})'.format(MUON[:3]), 'l', '')
# define canvas, add plots. addS is so that statsbox will be drawn later.
# these all should be pretty obvious until...
canvas = Plotter.Canvas(lumi=SPLumiStr(fs, *sp))
for i, MUON in enumerate(MUONS):
canvas.addMainPlot(p[MUON], addS=True if i!=0 else False)
canvas.addMainPlot(fplots[MUON])
if len(MUONS) > 1:
canvas.firstPlot.setTitles(X=canvas.firstPlot.GetXaxis().GetTitle().replace(MUONS[0],'Reco'))
canvas.makeLegend(lWidth=.25, pos='tl')
canvas.legend.resizeHeight()
for MUON in MUONS:
p [MUON].SetLineColor(colorDict[MUON] )
fplots[MUON].SetLineColor(colorDict[MUON]+1)
RT.addBinWidth(canvas.firstPlot)
# dif type: fit boxes go down the left side
# res type: fit boxes go down the middle
# stats boxes are on the right
paves = []
for i, MUON in enumerate(MUONS):
paves.append(canvas.makeStatsBox(p[MUON], color=colorDict[MUON]))
Plotter.MOVE_OBJECT(paves[-1], Y=-.2*i)
if not ISDIF:
canvas.setFitBoxStyle(h[MUON], lWidth=0.275, pos='tr')
else:
canvas.setFitBoxStyle(h[MUON], lWidth=0.275, pos='tl')
sbox = p[MUON].FindObject('stats')
sbox.SetTextColor(colorDict[MUON]+1)
if not ISDIF:
Plotter.MOVE_OBJECT(sbox, Y=-.15*i, X=-.18, NDC=True)
else:
Plotter.MOVE_OBJECT(sbox, Y=-.15*i-0.04*4.1, NDC=True)
fname = 'pdfs/SRR_{}_{}_{}HTo2XTo{}_{}.pdf'.format(outputTag, quantity+'Res', 'Trig-' if TRIGGER else '', fs, SPStr(sp))
canvas.cleanup(fname)
def optimizeCut(fs, sp, quantity):
# get histograms
s = HG.getHistogram(FILES['Signal'], (fs, sp), quantity)
DHists, DPConfig = HG.getDataHistograms(FILES['Data'], quantity)
b = DHists[quantity]['data']
n = s.Integral(0, s.GetNbinsX()+1)
s.Scale(ScaleFactor(sp, sigmaB=1.e-2))
# get cumulatives
sCum = s.GetCumulative(CONFIG[quantity]['forward'])
bCum = b.GetCumulative(CONFIG[quantity]['forward'])
fom = sCum.Clone()
print '**** Data (|DeltaPhi| > Pi/2) vs. HTo2XTo{} ({} GeV, {} GeV, {} mm) (|DeltaPhi| < Pi/2)'.format(fs, *sp)
print '**** Quantity = {:s} ::: Passing (N-1) = {:.0f} ::: N_gen = {:.0f} ::: Sigma*B = {:.3f} pb ::: 2016 ILumi = 35922 /pb'.format(
CONFIG[quantity]['pretty'], n, SignalInfo[sp]['sumWeights'][1], SignalInfo[sp]['sigmaBLimit'])
print '{:>8s} {:>7s} {:>7s} {:>7s} {:>6s} {:>6s}'.format('CutVal', 'S', 'B=nOff', 'S+B=nOn', 'ZBi', 'ZPL')
# fill f.o.m. histogram, and keep track of max f.o.m. and cut value
nBins = sCum.GetNbinsX()
xAxis = sCum.GetXaxis()
fom_max = 0.
opt_cut = 0.
opt_s = 0.
opt_b = 0.
for ibin in range(1,nBins+1):
S, B, cutVal, FOMs = calculateFOM(s, b, sCum, bCum, nBins, ibin, xAxis, CONFIG[quantity]['forward'])
print '{:8.3f} {:7.3f} {:7.3f} {:7.3f} {:6.3f} {:6.3f}'.format(cutVal, S, B, S+B, FOMs['ZBi'], FOMs['ZPL'])
if FOMs[FIGURE_OF_MERIT] > fom_max:
fom_max = FOMs[FIGURE_OF_MERIT]
opt_cut = cutVal
opt_s = S
opt_b = B
fom.SetBinContent(ibin, FOMs[FIGURE_OF_MERIT])
# make plots
p = {}
p['sig'] = Plotter.Plot(sCum, 'signal' , 'l', 'hist')
p['bg' ] = Plotter.Plot(bCum, 'data' , 'l', 'hist')
p['fom'] = Plotter.Plot(fom , PRETTY_LEG , 'l', 'hist')
# make canvas, colors, maximum
canvas = Plotter.Canvas(lumi=SPLumiStr(fs, *sp))
canvas.addMainPlot(p['sig'])
canvas.addMainPlot(p['bg' ])
canvas.addMainPlot(p['fom'])
p['sig'].SetLineColor(R.kBlue )
p['bg' ].SetLineColor(R.kRed )
p['fom'].SetLineColor(R.kGreen)
canvas.setMaximum()
canvas.firstPlot.SetMinimum(0.)
# scale f.o.m. and make new axis
fom.Scale(canvas.firstPlot.GetMaximum()/fom_max/1.05)
axis = R.TGaxis(xAxis.GetXmax(), 0., xAxis.GetXmax(), canvas.firstPlot.GetMaximum(), 0., fom_max*1.05, 510, '+L')
for attr in ('LabelFont', 'LabelOffset', 'TitleFont', 'TitleOffset', 'TitleSize'):
getattr(axis, 'Set'+attr)(getattr(xAxis, 'Get'+attr)())
axis.SetTitle('Figure of Merit')
axis.CenterTitle()
axis.Draw()
canvas.scaleMargins(1.1, edges='R')
# make the legend after
canvas.makeLegend(lWidth=.2, pos='br')
canvas.legend.resizeHeight()
canvas.legend.moveLegend(X=-.2, Y=.1)
# draw optimum text and line
x, y = canvas.legend.GetX1(), canvas.legend.GetY2()
canvas.drawText(text='#color[{:d}]{{opt. cut = {:.2f}}}'.format(R.kBlack, opt_cut), pos=(x, y+0.05), align='bl')
line = R.TLine(opt_cut, 0., opt_cut, canvas.firstPlot.GetMaximum())
line.SetLineStyle(2)
line.Draw()
# save
canvas.cleanup('pdfs/OPT_{}_{}_HTo2XTo{}_{}.pdf'.format(quantity, FIGURE_OF_MERIT, fs, SPStr(sp)))
import re
import ROOT as R
import DisplacedDimuons.Analysis.Plotter as Plotter
import DisplacedDimuons.Analysis.RootTools as RT
from DisplacedDimuons.Common.Utilities import SPStr, SPLumiStr
import DisplacedDimuons.Analysis.HistogramGetter as HistogramGetter
import DisplacedDimuons.Analysis.PlotterParser as PlotterParser
ARGS = PlotterParser.PARSER.parse_args()
TRIGGER = ARGS.TRIGGER
CUTSTRING = ARGS.CUTSTRING
MCONLY = ARGS.MCONLY
# get histograms
HISTS = HistogramGetter.getHistograms(
'../analyzers/roots/Main/RecoMuonPlots.root')
f = R.TFile.Open('../analyzers/roots/Main/RecoMuonPlots.root')
# make plots that are per sample
def makePerSamplePlots():
for ref in HISTS:
if not type(ref) == tuple: continue
for key in HISTS[ref]:
if 'deltaRGR' in key: continue
if 'VS' in key: continue
if type(ref) == tuple:
if ref[0] == '4Mu':
name = 'HTo2XTo4Mu_'
latexFS = '4#mu'
elif ref[0] == '2Mu2J':
def makeKinkDistPlot(quantity, masses):
points = sorted(
[sp for sp in SignalInfo if sp[0] == masses[0] and sp[1] == masses[1]],
key=lambda x: x[2])
s = {
points[0]:
HG.getHistogram(FILES['Signal'], (fs, points[0]),
'{}_1'.format(quantity)).Clone(),
points[1]:
HG.getHistogram(FILES['Signal'], (fs, points[1]),
'{}_10'.format(quantity)).Clone(),
}
s[points[0]].Scale(1. /
s[points[0]].Integral(0, s[points[0]].GetNbinsX() + 1))
s[points[1]].Scale(1. /
s[points[1]].Integral(0, s[points[1]].GetNbinsX() + 1))
#RT.addFlows(s[points[0]])
#RT.addFlows(s[points[1]])
s[points[0]].Rebin(2)
s[points[1]].Rebin(2)
pl = {
points[0]:
Plotter.Plot(s[points[0]],
'c#tau = {} mm, nominal'.format(points[0][2]), 'l',
'hist'),
points[1]:
Plotter.Plot(s[points[1]],
'c#tau = {} mm, reweighted'.format(points[0][2]), 'l',
'hist'),
}
colors = {
points[0]: R.kBlue,
points[1]: R.kRed,
}
c = Plotter.Canvas(lumi='({} GeV, {} GeV)'.format(*masses))
c.addMainPlot(pl[points[0]])
c.addMainPlot(pl[points[1]])
for p in points:
pl[p].setColor(colors[p], which='LM')
c.makeLegend(lWidth=0.3, pos='tr')
c.legend.SetMargin(0.1)
c.legend.resizeHeight()
c.drawText(text='lol',
pos=(c.legend.GetX1(), c.legend.GetY1() + .4),
NDC=False)
c.setMaximum()
RT.addBinWidth(c.firstPlot)
c.cleanup('pdfs/KINK_Dist_{}_{}_{}_{}.pdf'.format(quantity, masses[0],
masses[1],
FIGURE_OF_MERIT))
(125, 20, 1300): (R.kGreen + 0, 10),
}
for SPLIT, DO2MU, DO4MU in OPTIONS:
for quantity, isGreater, RESIZE in CONFIG:
LOGY = quantity in ('DSA_d0Sig', 'REF_d0Sig', 'LxySig')
if RESIZE:
LOGY = False
if TAG == 'Dimuon':
qkeyname = 'Dim_{}'.format(quantity)
elif TAG == 'RecoMuon':
qkeyname = quantity
h = {}
h['MC'] = HG.getHistogram(FILES['MC_Prompt'], BGORDER[0], qkeyname)
for key in BGORDER:
for fkey in ('MC_Prompt', 'MC_NoPrompt'):
if key == BGORDER[0] and fkey == 'MC_Prompt':
h['MC'].Scale(PC[key]['WEIGHT'])
else:
hTemp = HG.getHistogram(FILES[fkey], key, qkeyname)
hTemp.Scale(PC[key]['WEIGHT'])
h['MC'].Add(hTemp)
if not SPLIT:
h['2Mu'] = HG.getHistogram(FILES['Signal_Prompt'],
('2Mu2J', SPLIST[0]), qkeyname)
h['4Mu'] = HG.getHistogram(FILES['Signal_Prompt'],
('4Mu', SPLIST[0]), qkeyname)
import ROOT as R
import DisplacedDimuons.Analysis.HistogramGetter as HG
import DisplacedDimuons.Analysis.Plotter as Plotter
# the input histogram here is just two histograms with LxySig < 6 and DeltaPhi < pi/4 and > 3pi/4 separately
# the script to make it is validateBackground which is in oldscripts now
# this script makes a little less sense now, also it is superseded by smallLxySigConsistency,
# but I'm leaving it here in case you want to see how to to the KS probability
f = R.TFile.Open('roots/ValidationPlots_DATA.root')
D, P = HG.getDataHistograms(f, ('LxySig_DPHI', 'LxySig_IDPHI'), addFlows=False)
h = {k: D[k]['data'] for k in D}
c = {k: h[k].GetCumulative(False) for k in h}
cumRatio = c['LxySig_IDPHI'].Clone()
cumRatio.Divide(c['LxySig_DPHI'])
for k in h:
h[k].Rebin(5)
distRatio = h['LxySig_IDPHI'].Clone()
distRatio.Divide(h['LxySig_DPHI'])
pDist = {
k: Plotter.Plot(h[k],
'#Delta#Phi {} #pi/2'.format('<' if '_DPHI' in k else '>'),
'lp', 'hist e')
for k in h
import re
import ROOT as R
import DisplacedDimuons.Analysis.Plotter as Plotter
import DisplacedDimuons.Analysis.RootTools as RT
import DisplacedDimuons.Analysis.Selections as Selections
from DisplacedDimuons.Common.Utilities import SPStr, SPLumiStr
import DisplacedDimuons.Analysis.HistogramGetter as HistogramGetter
TRIGGER = False
# get histograms
HISTS = HistogramGetter.getHistograms(
'../analyzers/roots/Main/nMinusOnePlots.root')
f = R.TFile.Open('../analyzers/roots/Main/nMinusOnePlots.root')
# make per sample plots
def makePerSamplePlots():
for ref in HISTS:
for key in HISTS[ref]:
if type(ref) == tuple:
if ref[0] == '4Mu': name = 'HTo2XTo4Mu_'
elif ref[0] == '2Mu2J': name = 'HTo2XTo2Mu2J_'
name += SPStr(ref[1])
if TRIGGER:
name = 'Trig-' + name
lumi = SPLumiStr(ref[0], *ref[1])
legName = HistogramGetter.PLOTCONFIG['HTo2XTo' +
ref[0]]['LATEX']
else:
name = ref
import re
import ROOT as R
import DisplacedDimuons.Analysis.Plotter as Plotter
import DisplacedDimuons.Analysis.RootTools as RT
from DisplacedDimuons.Common.Constants import SIGNALPOINTS
from DisplacedDimuons.Common.Utilities import SPStr, SPLumiStr
import DisplacedDimuons.Analysis.HistogramGetter as HistogramGetter
import DisplacedDimuons.Analysis.PlotterParser as PlotterParser
ARGS = PlotterParser.PARSER.parse_args()
TRIGGER = ARGS.TRIGGER
CUTSTRING = ARGS.CUTSTRING
# get histograms
HISTS = HistogramGetter.getHistograms(
'../analyzers/roots/Main/SignalVertexFitEffPlots.root')
f = R.TFile.Open('../analyzers/roots/Main/SignalVertexFitEffPlots.root')
# make overlaid plots that combine all signal points
def makeEffPlots(quantity, fs, SP=None):
HKeys = {
'Eff': '{}Eff',
'Den': '{}Den',
}
for key in HKeys:
HKeys[key] = HKeys[key].format(quantity)
h = {}
p = {}
g = {}
