def dip_residual_method():
hh = TH2F("hh", "", 20, hodo_center - 4.0, hodo_center + 4.0, 200,
-2, 2)
cuts = 'n_tracks==1 && amp_max[{}]>1000 && amp_max[{}]>1000'.format(
self.xtal[0], self.xtal[1])
t_tree.Draw("(fit_time[{0}]-fit_time[{1}]):{2}>>hh".format(
self.xtal[0], self.xtal[1], axis[0]), cuts,
"COLZ") # Plot dt vs (X or Y)
hh.FitSlicesY() # Fit slices with gaussians
gr = TGraphErrors(t_file.Get(
"hh_1")) # hh_1 is the histo of means from FitSlicesY
## Sorry for the confusing names. We plot dt vs (X or Y), so dt is our y_var, and dx is our x_var, the distance term (ie X OR Y)
points = range(gr.GetN())
dx = array('d', gr.GetX())
dt = array('d', gr.GetY())
p1 = TF1("p1", "pol1")
TGraph(gr.GetN(), dx, dt).Fit("p1",
"WQ") # Fit dt_mean vs Y linearly
## Sum each 3 consecutive residuals, take the max from this value's abs(), and the middle index is where the "dip" is farthest from the fit, the "center"
res = [dt[i] - p1.Eval(dx[i])
for i in points] # The residual between the fit and data
sum_res = [abs(sum(res[i:i + 3])) for i in points[:-2]
] # Sum 3 residuals ([:-2] to avoid index out of range)
axis_center = dx[
sum_res.index(max(sum_res)) +
1] # +1 b/c we index the 1st out of 3 residuals, but we want the middle one
return axis_center
def _convert_tgrapherrors(root_obj: TGraphErrors) -> dict:
"""
Take the values from a TGraphErrors and add them to a series
of arrays
Parameters
----------
root_obj : TGraphError
ROOT TGraphError
Returns
-------
dict
Dictionary with x, y, xerr, yerr points
"""
xm, ym, x_errm, y_errm = (root_obj.GetX(),
root_obj.GetY(),
root_obj.GetEX(),
root_obj.GetEY())
x, y, x_err, y_err = [], [], [], []
for n in range(0, root_obj.GetN()):
x.append(xm[n])
y.append(ym[n])
x_err.append(x_errm[n])
y_err.append(y_errm[n])
return {'x': np.array(x), 'y': np.array(y),
'xerr': np.array(x_err), 'yerr': np.array(y_err)}
def sub2pull(dataHist, pdfwErrs):
from ROOT import TGraphErrors
from math import sqrt
ThePull = TGraphErrors(dataHist.GetN())
pdfi = 0
for i in range(0, ThePull.GetN()):
while (dataHist.GetX()[i] > pdfwErrs.GetX()[pdfi]):
pdfi += 1
#print 'pull point:',i,'pdfi:',pdfi
diff = dataHist.GetY()[i] - pdfwErrs.GetY()[pdfi]
if (diff < 0):
err2 = pdfwErrs.GetErrorY(pdfi)**2 + dataHist.GetErrorYhigh(i)**2
else:
err2 = pdfwErrs.GetErrorY(pdfi)**2 + dataHist.GetErrorYlow(i)**2
if (err2>0):
pull = diff/(sqrt(err2)*1.2)
else:
pull = 0
ThePull.SetPoint(i, dataHist.GetX()[i], pull)
ThePull.SetPointError(i, 0., 1.)
ThePull.SetName(dataHist.GetName() + "_pull")
ThePull.SetTitle("data")
return ThePull
def testIthr():
lines = get_lines('DAC_scan_ithr_0x40to0xf0.dat')
gr1 = TGraphErrors()
gr2 = TGraphErrors()
fUnit = 1000. / 0.7
yUnit = 'e^{-}'
for line in lines:
if len(line) == 0: continue
if line[0] in ['#', '\n']: continue
fs = line.rstrip().split()
ix = int(fs[0])
gr1.SetPoint(ix, float(fs[1]), float(fs[2]) * fUnit)
gr1.SetPointError(ix, 0, float(fs[3]) * fUnit)
gr2.SetPoint(ix, float(fs[1]), float(fs[4]) * fUnit)
gr2.SetPointError(ix, 0, float(fs[5]) * fUnit)
useAtlasStyle()
gStyle.SetMarkerStyle(20)
gr1.SetMarkerStyle(20)
gr1.Draw('AP')
h1 = gr1.GetHistogram()
h1.GetYaxis().SetTitle("Threshold [" + yUnit + "]")
h1.GetXaxis().SetTitle("I_{Thre} code")
# h1.GetYaxis().SetRangeUser(0,0.2)
gPad.SetTicks(1, 0)
gPad.SetRightMargin(0.16)
y1b = 0
y2b = 15
x1 = h1.GetXaxis().GetXmax()
y1 = h1.GetYaxis().GetXmin()
y2 = h1.GetYaxis().GetXmax()
raxis = TGaxis(x1, y1, x1, y2, y1b, y2b, 506, "+L")
raxis.SetLineColor(2)
raxis.SetLabelColor(2)
raxis.SetTitleColor(2)
raxis.SetTitle("ENC [" + yUnit + "]")
raxis.Draw()
nP = gr2.GetN()
Ys = gr2.GetY()
EYs = gr2.GetEY()
Y = array(
'd', [y1 + (y2 - y1) / (y2b - y1b) * (Ys[i] - y1b) for i in range(nP)])
EY = array('d', [(y2 - y1) / (y2b - y1b) * EYs[i] for i in range(nP)])
gr2x = TGraphErrors(nP, gr2.GetX(), Y, gr2.GetEX(), EY)
gr2x.SetMarkerStyle(24)
gr2x.SetLineColor(2)
gr2x.SetMarkerColor(2)
gr2x.Draw('Psame')
waitRootCmdX()
def main():
from optparse import OptionParser
parser = OptionParser()
parser.add_option("-i", "--inputfile", dest="inputfile")
parser.add_option("",
"--spline3",
action="store_true",
dest="spline3",
default=False)
(options, args) = parser.parse_args()
from ROOT import TFile, TGraph, TGraphErrors, NULL
import csv
with open(options.inputfile) as f:
reader = csv.reader(f, delimiter=" ", skipinitialspace=True)
rows = list(reader)
outfile = TFile("%s.root" % options.inputfile, "RECREATE")
g = TGraphErrors(len(rows))
for i, row in enumerate(rows):
g.SetPoint(i, float(row[1]) / 1000., float(row[2]))
g.SetPointError(i, 0.0, float(row[3]))
if options.spline3:
g_spline3 = TGraph(10 * (g.GetN() - 1))
x = g.GetX()
y = g.GetY()
for i in range(g.GetN() - 1):
step = (x[i + 1] - x[i]) / 10
for j in range(10):
index = 10 * i + j
x_ = x[i] + j * step
y_ = g.Eval(x_, NULL, "S")
g_spline3.SetPoint(index, x_, y_)
g_spline3.SetName(options.inputfile)
g_spline3.Write()
else:
g.SetName(options.inputfile)
g.Write()
outfile.Close()
def addErrors(err1, err2):
from ROOT import TGraphErrors
from math import sqrt
newErrs = TGraphErrors(err1)
for i in range(0, newErrs.GetN()):
newErrs.SetPoint(i, newErrs.GetX()[i], err1.GetY()[i] + err2.GetY()[i])
newErrs.SetPointError(i, err1.GetErrorX(i),
sqrt(err1.GetErrorY(i)**2 + \
err2.GetErrorY(i)**2)
)
return newErrs
def ApplyBinShiftCorrectionGeneral(self, hist, fit):
"""
Alternative method for bin shift correction:
- Apply user-default model for bin-shift correction
- don't multiply by pt
@param hist: Input spectrum for the bin shift correction
@param fit: Model for the bin-shift correction
@return: The bin-shift corrected spectrum as graph
"""
h = deepcopy(hist)
hist.SetName("htemp")
result = TGraphErrors(h)
for i in range(0, result.GetN()):
result.GetEX()[i] = 0.
y = 0
#for now 10 iterations fixes
for k in range(0, 10):
for i in range(1, h.GetNbinsX() + 1):
y = fit.Integral(h.GetBinLowEdge(i),
h.GetBinUpEdge(i)) / h.GetBinWidth(i)
x = self.FindX(y, fit, h.GetBinLowEdge(i), h.GetBinUpEdge(i))
result.GetX()[i - 1] = x
# remove points that are 0
while result.GetY()[0] < 1.e-99:
result.RemovePoint(0)
mybin = 0
for biniter in range(0, result.GetN()):
if result.GetY()[biniter] < 1.e-99:
mybin = biniter
break
while result.RemovePoint(mybin) > 0:
continue
return result
def GetData(file, scale=1., sed=True, title='SED', barUL=True):
GetData.Ng += 1
g = TGraphErrors(file)
gUL = TGraphErrors()
if sed:
for i in range(g.GetN()):
g.GetY()[i] = pow(g.GetX()[i], 2) * g.GetY()[i] * 1e-6 / scale
g.GetEY()[i] = pow(g.GetX()[i], 2) * g.GetEY()[i] * 1e-6 / scale
g.GetX()[i] *= 1e-3
idel = 0
nUL = 0
while idel < g.GetN():
if g.GetEY()[idel] < 0:
gUL.SetPoint(nUL, g.GetX()[idel], g.GetY()[idel])
if barUL:
gUL.SetPointError(nUL, 0, g.GetY()[idel] * 1e-5)
nUL += 1
g.RemovePoint(idel)
else:
idel += 1
if sed:
g.SetTitle(title + ";Energy [GeV];E^{2}dN/dE [TeV cm^{-2} s^{-1}]")
else:
g.SetTitle(title + ";Energy [MeV];dN/dE [cm^{-2} s^{-1} MeV^{-1}]")
g.SetLineColor(kRed)
g.SetMarkerColor(kRed)
g.SetMarkerStyle(20)
g.SetName("g%d" % GetData.Ng)
gUL.SetLineColor(g.GetLineColor())
gUL.SetName("gUL%d" % GetData.Ng)
return g, gUL
def getGraph(Ts, filename):
lines = None
with open(filename) as f1:
lines = f1.readlines()
Tx = None
for line in lines:
### find the associated tamperature
line = line.rstrip()
if len(line) == 0:
Tx = None
continue
if line[0] == '#':
fs = line.split()
data = fs[1] + ' ' + fs[2]
b = parse(data)
if b is None: continue
for x in Ts:
for y in x.Times:
if b > y[0] and b < y[1]:
Tx = x
print b, Tx.T
break
if Tx is not None: break
continue
if Tx is None: continue
### Add the samples to it
fs = line.split(',')
if len(fs) < 3:
continue
Tx.data.append(float(fs[2]))
gr1 = TGraphErrors()
for t in Ts:
if len(t.data) == 0: continue
### calculate the mean and err
### Fill in the tgraph
print t.T, len(t.data), nm.mean(t.data), nm.std(t.data)
i = gr1.GetN()
gr1.SetPoint(i, t.T, nm.mean(t.data))
gr1.SetPointError(i, 0, nm.std(t.data))
return gr1
def scanKK():
'''Used to test the scan of one or more parameters'''
nChips = 19 # the magic number
# nChan = len(chans)
iP = 1
cd = CommonData()
cd.setupConnection()
sc1 = SensorConfig(cd)
### get list of chains to be updated
# chains = set([sc1.tms1mmX19chainSensors[sc1.tms1mmX19sensorInChain[c]][0] for c in chans])
chan = 5
# cd.inputVs = [3., 0., 3., 0., 0., 0.]
# cd.inputVs = [3., 0., 0.732, 1.68, 0., 0.]
# cd.inputVs = [0.75, 2.15, 0.7, 0., 0., 0.]
show = True
g1 = None
if show:
g1 = TGraphErrors()
xj = open('scan_KK_6.ttl','w')
### point insert scan
ipar = 2
pts = [(0.,None),(3.,None)]
while True:
print pts
pt, xy, r1, k = insertPoint(pts)
print '-'*30
print pt,xy,r1, k
print '-'*30
if xy<3 and (r1<0.01 or xy/r1<0.1 or k<0.007): break
cd.inputVs[ipar] = pt
cd.updatePars(chan, None, False)
sc1.update_sensor(chan)
time.sleep(3)
cd.fetch()
m,v = getMeanVar(cd.adcData[chan])
print ' '.join([str(x) for x in [chan, m, v]+cd.inputVs])
xj.write(' '.join([str(x) for x in [chan, m, v]+cd.inputVs])+'\n')
if g1:
n1 = g1.GetN()
g1.SetPoint(n1, pt, m)
g1.SetPointError(n1, 0, v)
if xy == 999:
pts[r1] = (pt,m)
else:
pts.append((pt,m))
### simple scan
# while cd.inputVs[0]>0.001:
# cd.updatePars(chan, None, False)
# sc1.update_sensor(chan)
# time.sleep(2)
# cd.fetch()
#
# m,v = getMeanVar(cd.adcData[chan])
# print ' '.join([str(x) for x in [chan, m, v]+cd.inputVs])
# xj.write(' '.join([str(x) for x in [chan, m, v]+cd.inputVs])+'\n')
#
# if g1:
# g1.SetPoint(g1.GetN(), cd.inputVs[0], m)
# cd.inputVs[0] *= 2./3
if g1:
g1.SetMarkerStyle(4)
g1.SetMarkerColor(2)
g1.SetLineColor(2)
g1.Draw("AP")
h1 = g1.GetHistogram()
h1.GetXaxis().SetTitle(cd.voltsNames[ipar]+' [V]')
h1.GetYaxis().SetTitle("V_{out} [V]")
lt = TLatex()
lt.DrawLatexNDC(0.2,0.92,"Chip %d"%chan)
waitRootCmdX()
def analyzeData(country, total, active, recovered, deaths, tStart, tStop,
totalPopulation, symptomaticFraction, transmissionProbability,
recoveryRate, doSmearing, doFit):
ntuple = [
tStart, tStop, totalPopulation, symptomaticFraction,
transmissionProbability, recoveryRate
]
farFromMax = 0.95
growthRate = 0.13
carryingCapacity = 4e5
################
# Active cases #
################
myCanvActive = TCanvas('myCanvActive_' + country,
'Active cases ' + country)
xValues = [
i for i in range(len(active.keys())) if i >= tStart and i <= tStop
]
yValues = [
active[k] for i, k in enumerate(sorted(active.keys()))
if i >= tStart and i <= tStop
]
erryValues = assignErrors(yValues)
myGraphActive = TGraphErrors()
myGraphActive.SetMarkerStyle(20)
for i in range(len(xValues)):
myGraphActive.SetPoint(myGraphActive.GetN(), xValues[i], yValues[i])
myGraphActive.SetPointError(myGraphActive.GetN() - 1, 0, erryValues[i])
myGraphActive.Draw('APE1')
myGraphActive.GetHistogram().GetXaxis().SetTitle('Time (days)')
myGraphActive.GetHistogram().GetYaxis().SetTitle(
'Active cases affected by CoViD-19')
historyActive = 0.
for i, k in enumerate(sorted(active.keys())):
if i < tStart:
historyActive += active[k]
ntuple.extend([historyActive, xValues, yValues, erryValues])
print('==> History active cases:', historyActive)
###################
# Build the model #
###################
evActive = evolution([yValues[0], carryingCapacity, growthRate], tStart,
tStop, totalPopulation, recoveryRate,
symptomaticFraction, transmissionProbability,
historyActive)
if doFit == True:
evActive.runOptimization(xValues, yValues, erryValues, [], doSmearing)
evActive.evolve(evActive.tStop, evActive.parValues, True)
if doSmearing == True:
evActive.smearing()
evActiveGraphN = evActive.getGraphN()
evActiveGraphN.Draw('PL same')
statActive = evActive.addStats(evActive.parNames, evActive.parValues)
print(
'==> Active cases, history active cases, p-infected, Carrying capacity, total population alive',
evActive.evolve(tStop, evActive.parValues), 'at day', tStop)
print('==> Percentage population with antibodies',
round(100. * evActive.totalInfected(tStop) / totalPopulation),
'% at day', tStop, '(herd immunity at', evActive.herdImmunity(),
'%)')
print('==> Doubling time:', round(log(2.) / evActive.parValues[2], 1),
'days')
now = TLine(
len(active) - 1, 0,
len(active) - 1, evActive.fitFun.Eval(len(active) - 1))
now = TLine(len(active) - 1, 0, len(active) - 1, 1)
now.SetLineColor(4)
now.SetLineWidth(2)
now.Draw('same')
willbe = TLine(evActive.fitFun.GetMaximumX(), 0,
evActive.fitFun.GetMaximumX(),
evActive.fitFun.GetMaximum())
willbe.SetLineColor(6)
willbe.SetLineWidth(2)
willbe.Draw('same')
myCanvActiveR0 = TCanvas('myCanvActiveR0_' + country, 'R0 ' + country)
evActiveGraphR0 = evActive.getGraphR0(evActiveGraphN)
evActiveGraphR0.Draw('APL')
evActiveGraphR0.GetHistogram().GetXaxis().SetTitle('Time (days)')
evActiveGraphR0.GetHistogram().GetYaxis().SetTitle('R')
myCanvActiveR0.SetGrid()
myCanvActiveR0.Modified()
myCanvActiveR0.Update()
myCanvActive.SetGrid()
myCanvActive.Modified()
myCanvActive.Update()
#################
# CONTROL PLOTS #
#################
###############
# Total cases #
###############
myCanvTotal = TCanvas('myCanvTotal_' + country, 'Total cases ' + country)
myGraphTotal = TGraphErrors()
myGraphTotal.SetMarkerStyle(20)
for k in sorted(total.keys()):
myGraphTotal.SetPoint(myGraphTotal.GetN(), myGraphTotal.GetN(),
total[k])
myGraphTotal.SetPointError(myGraphTotal.GetN() - 1, 0, sqrt(total[k]))
myGraphTotal.Draw('APE1')
myGraphTotal.GetHistogram().GetXaxis().SetTitle('Time (days)')
myGraphTotal.GetHistogram().GetYaxis().SetTitle(
'Total cases affected by CoViD-19')
myCanvTotal.SetGrid()
myCanvTotal.Modified()
myCanvTotal.Update()
##########
# Deaths #
##########
myCanvDeaths = TCanvas('myCanvDeaths_' + country, 'Deaths ' + country)
myGraphDeaths = TGraphErrors()
myGraphDeaths.SetMarkerStyle(20)
for k in sorted(deaths.keys()):
myGraphDeaths.SetPoint(myGraphDeaths.GetN(), myGraphDeaths.GetN(),
deaths[k])
myGraphDeaths.SetPointError(myGraphDeaths.GetN() - 1, 0,
sqrt(deaths[k]))
myGraphDeaths.Draw('APE1')
myGraphDeaths.GetHistogram().GetXaxis().SetTitle('Time (days)')
myGraphDeaths.GetHistogram().GetYaxis().SetTitle('Total deaths')
myCanvDeaths.SetGrid()
myCanvDeaths.Modified()
myCanvDeaths.Update()
########################
# Ratio deaths / total #
########################
myCanvRatio01 = TCanvas('myCanvRatio01_' + country, 'Ratio ' + country)
myGraphRatio01 = TGraphErrors()
myGraphRatio01.SetMarkerStyle(20)
for k in sorted(deaths.keys()):
myGraphRatio01.SetPoint(myGraphRatio01.GetN(), myGraphRatio01.GetN(),
deaths[k] / total[k])
myGraphRatio01.SetPointError(
myGraphRatio01.GetN() - 1, 0,
myGraphRatio01.GetY()[myGraphRatio01.GetN() - 1] *
sqrt(deaths[k] / (deaths[k] * deaths[k]) + total[k] /
(total[k] * total[k])))
myGraphRatio01.Draw('APE1')
myGraphRatio01.GetHistogram().GetXaxis().SetTitle('Time (days)')
myGraphRatio01.GetHistogram().GetYaxis().SetTitle(
'Total deaths / Total cases')
myCanvRatio01.SetGrid()
myCanvRatio01.Modified()
myCanvRatio01.Update()
############################################
# Ratio delta(deaths + recovered) / active #
############################################
myCanvRatio02 = TCanvas('myCanvRatio02_' + country, 'Ratio ' + country)
myGraphRatio02 = TGraphErrors()
myGraphRatio02.SetMarkerStyle(20)
sortedKeys = sorted(deaths.keys())
for i, k in enumerate(sortedKeys[1:]):
numerator = abs(deaths[k] - deaths[sortedKeys[i]] + recovered[k] -
recovered[sortedKeys[i]])
denominator = active[k]
myGraphRatio02.SetPoint(
myGraphRatio02.GetN(),
myGraphRatio02.GetN() + 1,
numerator / denominator if denominator != 0 else 0)
myGraphRatio02.SetPointError(
myGraphRatio02.GetN() - 1, 0,
(myGraphRatio02.GetY()[myGraphRatio02.GetN() - 1] *
sqrt(numerator / pow(numerator, 2) +
denominator / pow(denominator, 2)))
if denominator != 0 else 0)
myGraphRatio02.Draw('AP')
myGraphRatio02.GetHistogram().GetXaxis().SetTitle('Time (days)')
myGraphRatio02.GetHistogram().GetYaxis().SetTitle(
'#Delta Recovered (alive + dead) / Active cases')
myCanvRatio02.SetGrid()
myCanvRatio02.Modified()
myCanvRatio02.Update()
######################################
# Ratio delta(deaths) / delta(total) #
######################################
myCanvRatio03 = TCanvas('myCanvRatio03_' + country, 'Ratio ' + country)
myGraphRatio03 = TGraphErrors()
myGraphRatio03.SetMarkerStyle(20)
sortedKeys = sorted(deaths.keys())
for i, k in enumerate(sortedKeys[1:]):
numerator = abs(deaths[k] - deaths[sortedKeys[i]])
denominator = total[k] - total[sortedKeys[i]]
myGraphRatio03.SetPoint(
myGraphRatio03.GetN(),
myGraphRatio03.GetN() + 1,
numerator / denominator if denominator != 0 else 0)
myGraphRatio03.SetPointError(
myGraphRatio03.GetN() - 1, 0,
(myGraphRatio03.GetY()[myGraphRatio03.GetN() - 1] *
sqrt(numerator / pow(numerator, 2) +
denominator / pow(denominator, 2)))
if denominator != 0 else 0)
myGraphRatio03.Draw('AP')
myGraphRatio03.GetHistogram().GetXaxis().SetTitle('Time (days)')
myGraphRatio03.GetHistogram().GetYaxis().SetTitle(
'#Delta deaths / #Delta total')
myCanvRatio03.SetGrid()
myCanvRatio03.Modified()
myCanvRatio03.Update()
if doFit == False:
return [
ntuple, myCanvTotal, myGraphTotal, myCanvActive, myGraphActive,
myCanvDeaths, myGraphDeaths, myCanvRatio01, myGraphRatio01,
myCanvRatio02, myGraphRatio02, myCanvRatio03, myGraphRatio03
]
return [
ntuple, myCanvTotal, myGraphTotal, myCanvActive, myGraphActive,
evActive, evActiveGraphN, statActive, now, willbe, myCanvActiveR0,
evActiveGraphR0, myCanvDeaths, myGraphDeaths, myCanvRatio01,
myGraphRatio01, myCanvRatio02, myGraphRatio02, myCanvRatio03,
myGraphRatio03
]
def plot2BodyDist(theFitter, pars, chi2, ndf,
Err = -1, NP = False, Prefix = "Mjj", Left = False):
from ROOT import gPad, TLatex, TCanvas, kRed, kCyan, kBlue, \
RooFit, RooPlot, RooCurve, RooAbsReal, TGraphErrors, TLine, \
RooWjjMjjFitter
if pars.includeMuons and pars.includeElectrons:
modeString = ''
elif pars.includeMuons:
modeString = 'Muon'
elif pars.includeElectrons:
modeString = 'Electron'
else:
modeString = ''
mf = theFitter.stackedPlot(False, RooWjjMjjFitter.mjj, Left)
mf.SetName("%s_Stacked" % (Prefix));
sf = theFitter.residualPlot(mf, "h_background", "dibosonPdf", False)
sf.SetName("%s_Subtracted" % (Prefix));
pf = theFitter.residualPlot(mf, "h_total", "", True)
pf.SetName("%s_Pull" % (Prefix))
pf2 = pf.emptyClone("%s_Pull_Corrected" % (Prefix))
pf2.SetMinimum(-5.)
pf2.SetMaximum(5.)
corrPull = False
lf = theFitter.stackedPlot(True, RooWjjMjjFitter.mjj, Left)
lf.SetName("%s_Stacked_Log" % (Prefix));
if Err > 0:
totalPdf = theFitter.getWorkSpace().pdf('totalPdf')
## Ntotal = totalPdf.expectedEvents(iset)
## print 'Ntotal:',Ntotal
h_dibosonPdf = sf.getCurve('h_dibosonPdf')
totalPdf.plotOn(sf,
RooFit.ProjWData(theFitter.getWorkSpace().data('data')),
RooFit.Normalization(Err, RooAbsReal.Raw),
#RooFit.AddTo('h_dibosonPdf', 1., 1.),
#RooFit.Invisible(),
RooFit.Name('h_ErrUp'),
RooFit.Range('RangeForPlot'),
RooFit.NormRange('RangeForPlot'),
RooFit.LineColor(kRed), RooFit.LineStyle(3))
h_ErrUp = sf.getCurve('h_ErrUp')
sf.remove('h_ErrUp', False)
ErrBand = TGraphErrors(h_dibosonPdf.GetN(), h_dibosonPdf.GetX(),
h_dibosonPdf.GetY())
for pt in range(1, ErrBand.GetN()):
ErrBand.SetPointError(pt, 0,
h_ErrUp.interpolate(ErrBand.GetX()[pt]))
ErrBand.SetName("ErrBand")
ErrBand.SetTitle("Uncertainty")
ErrBand.SetLineColor(kRed)
## ErrBand.SetLineWidth(0)
## ErrBand.SetLineStyle(0)
ErrBand.SetFillColor(kRed)
ErrBand.SetFillStyle(3353)
#ErrBand.Draw('ap3')
#h_ErrUp.Draw('lp')
#gPad.Update()
#gPad.WaitPrimitive()
## h_ErrUp.Draw("al")
## h_ErrUp.GetXaxis().Set(36, 40., 400.)
## gPad.Update()
## gPad.WaitPrimitive()
## h_UpBand = RooCurve("h_UpBand", "Uncertainty", h_dibosonPdf, h_ErrUp,
## 1., 1.)
## h_UpBand.SetLineStyle(3)
## h_UpBand.SetLineColor(kBlue+1)
## h_DownBand = RooCurve("h_DownBand", "Uncertainty", h_dibosonPdf, h_ErrUp,
## 1., -1.)
## h_DownBand.SetLineStyle(3)
## h_DownBand.SetLineColor(kBlue+1)
## sf.addPlotable(h_UpBand, "L")
## sf.addPlotable(h_DownBand, "L")
sf.addObject(ErrBand, "3")
#sf.Print("v")
sf.drawAfter('h_dibosonPdf', 'ErrBand')
#sf.Print("v")
sf.drawAfter('ErrBand', 'theData')
#sf.Print("v")
sf.findObject('theLegend').AddEntry(ErrBand, 'Uncertainty', 'f')
sf.findObject('theLegend').SetY1NDC(sf.findObject('theLegend').GetY1NDC() - 0.057)
sf.findObject('theLegend').SetY1(sf.findObject('theLegend').GetY1NDC())
corrPull = True
pf2.addObject(sub2pull(sf.getHist('theData'),
sf.findObject('ErrBand')),
'p0')
for item in range(0, int(pf.numItems())):
firstItem = pf.getObject(item)
if (type(firstItem) == TLine):
newLine = TLine(firstItem)
newLine.SetY1(4.)
newLine.SetY2(-4.)
pf2.addObject(newLine, 'l')
#SetOwnership(newLine, False)
if NP:
NPPdf = theFitter.makeNPPdf();
NPNorm = 4.*0.11*46.8/12.*pars.intLumi
if (modeString == 'Electron'):
if pars.njets == 2:
NPNorm *= 0.0381
elif pars.njets == 3:
NPNorm *= 0.0123
else:
if pars.njets == 2:
NPNorm *= 0.0550
elif pars.njets == 3:
NPNorm *= 0.0176
print '**** N_NP:', NPNorm,'****'
NPPdf.plotOn(sf, RooFit.ProjWData(theFitter.getWorkSpace().data('data')),
RooFit.Normalization(NPNorm, RooAbsReal.Raw),
RooFit.AddTo('h_dibosonPdf', 1., 1.),
RooFit.Name('h_NP'),
RooFit.Range('RangeForPlot'),
RooFit.NormRange('RangeForPlot'),
RooFit.LineColor(kBlue), RooFit.LineStyle(2))
h_NP = sf.getCurve('h_NP')
sf.drawBefore('h_dibosonPdf', 'h_NP')
#sf.Print("v")
sf.findObject('theLegend').AddEntry(h_NP, "CDF-like Signal", "L")
sf.findObject('theLegend').SetY1NDC(sf.findObject('theLegend').GetY1NDC() - 0.057)
sf.findObject('theLegend').SetY1(sf.findObject('theLegend').GetY1NDC())
l = TLatex()
l.SetNDC()
l.SetTextSize(0.045)
l.SetTextFont(42)
cstacked = TCanvas("cstacked", "stacked")
mf.Draw()
if (chi2 > 0):
l.DrawLatex(0.55, 0.49,
'#chi^{2}/dof = %0.3f/%d' % (chi2, ndf)
)
pyroot_logon.cmsLabel(cstacked, pars.intLumi/1000, prelim = True)
cstacked.Print('Wjj_%s_%s_%ijets_Stacked.pdf' % (Prefix, modeString,
pars.njets))
cstacked.Print('Wjj_%s_%s_%ijets_Stacked.png' % (Prefix, modeString,
pars.njets))
c2 = TCanvas("c2", "stacked_log")
c2.SetLogy()
lf.Draw()
pyroot_logon.cmsPrelim(c2, pars.intLumi/1000)
c2.Print('Wjj_%s_%s_%ijets_Stacked_log.pdf' % (Prefix, modeString,
pars.njets))
c2.Print('Wjj_%s_%s_%ijets_Stacked_log.png' % (Prefix, modeString,
pars.njets))
c3 = TCanvas("c3", "subtracted")
sf.Draw()
pyroot_logon.cmsLabel(c3, pars.intLumi/1000, prelim = True)
c3.Print('Wjj_%s_%s_%ijets_Subtracted.pdf' % (Prefix, modeString,
pars.njets))
c3.Print('Wjj_%s_%s_%ijets_Subtracted.png' % (Prefix, modeString,
pars.njets))
c4 = TCanvas("c4", "pull")
pf.Draw()
c4.SetGridy()
pyroot_logon.cmsPrelim(c4, pars.intLumi/1000)
c4.Print('Wjj_%s_%s_%ijets_Pull.pdf' % (Prefix, modeString, pars.njets))
c4.Print('Wjj_%s_%s_%ijets_Pull.png' % (Prefix, modeString, pars.njets))
c5 = None
if corrPull:
c5 = TCanvas("c5", "corrected pull")
pf2.Draw()
c5.SetGridy()
pyroot_logon.cmsPrelim(c5, pars.intLumi/1000)
c5.Print('Wjj_%s_%s_%ijets_Pull_Corrected.pdf' % (Prefix, modeString,
pars.njets))
c5.Print('Wjj_%s_%s_%ijets_Pull_Corrected.png' % (Prefix, modeString,
pars.njets))
return ([mf,sf,pf2,lf],[cstacked,c2,c3,c5])
def runGlobalFit(country, active, totalPopulation, symptomaticFraction,
transmissionProbability, recoveryRate, doSmearing, doFit):
tStart = 0
tStop = 9 + 6 + 12 + 42 + 61 + 40 + 50 + 200
timeList = [
9, 9 + 6, 9 + 6 + 12, 9 + 6 + 12 + 42, 9 + 6 + 12 + 42 + 61,
9 + 6 + 12 + 42 + 61 + 40, 9 + 6 + 12 + 42 + 61 + 40 + 50, tStop
]
ntuple = [
tStart, tStop, totalPopulation, symptomaticFraction,
transmissionProbability, recoveryRate
]
################
# Active cases #
################
myCanvActive = TCanvas('myCanvActive_' + country,
'Active cases ' + country)
xValues = [
i for i in range(len(active.keys())) if i >= tStart and i <= tStop
]
yValues = [
active[k] for i, k in enumerate(sorted(active.keys()))
if i >= tStart and i <= tStop
]
erryValues = assignErrors(yValues)
myGraphActive = TGraphErrors()
myGraphActive.SetMarkerStyle(20)
for i in range(len(xValues)):
myGraphActive.SetPoint(myGraphActive.GetN(), xValues[i], yValues[i])
myGraphActive.SetPointError(myGraphActive.GetN() - 1, 0, erryValues[i])
myGraphActive.Draw('APE1')
myGraphActive.GetHistogram().GetXaxis().SetTitle('Time (days)')
myGraphActive.GetHistogram().GetYaxis().SetTitle(
'Active cases affected by CoViD-19')
ntuple.extend([
0, xValues, yValues, erryValues, timeList, 1894, 16052, 0.435, 0.438,
0.308, 0.216, 0.054, 0.435 / 2.5, 0.435 / 1.5, 0.435
]) # 0.0133, 0.175, 0.217, 0.393
if doFit == True:
###################
# Build the model #
###################
evActive = evolution([ntuple[11], ntuple[12], ntuple[13]], tStart,
timeList[0], totalPopulation, recoveryRate,
symptomaticFraction, transmissionProbability)
evolutions = [
evolution([0, 0, ntuple[14 + i]], timeList[i], timeList[i + 1], 0,
recoveryRate, symptomaticFraction,
transmissionProbability) for i in range(3)
]
evolutions.extend([
evolution([0, 0, ntuple[14 + 3]], timeList[3], timeList[4], 0,
0.035, symptomaticFraction, transmissionProbability / 8)
])
evolutions.extend([
evolution([0, 0, ntuple[14 + 4]], timeList[4], timeList[5], 0,
recoveryRate, symptomaticFraction,
transmissionProbability / 2.5)
])
evolutions.extend([
evolution([0, 0, ntuple[14 + 5]], timeList[5], timeList[6], 0,
recoveryRate, symptomaticFraction,
transmissionProbability / 1.5)
])
evolutions.extend([
evolution([0, 0, ntuple[14 + 6]], timeList[6], timeList[7], 0,
recoveryRate, symptomaticFraction,
transmissionProbability)
])
istat, parValues, parNames = evActive.runGlobalOptimization(
evolutions, xValues, yValues, erryValues, [0, 1, 2, 3, 4, 5],
doSmearing)
evActive.evolveGlobal(evolutions, evolutions[-1].tStop, parValues,
True)
if doSmearing == True:
evActive.smearing()
evActive.setFitFun(evActive.evolveLookUpWrapper, tStart, tStop,
parNames, parValues)
evActiveGraphN = evActive.getGraphN()
evActiveGraphN.Draw('PL same')
statActive = evActive.addStats(parNames, parValues)
print(
'==> Active cases, history active cases * dt, p-infected, Carrying capacity, total population alive',
evActive.evolveGlobal(evolutions, tStop, parValues), 'at day',
tStop)
print(
'==> Percentage population with antibodies',
round(100. *
evActive.totalInfectedGlobal(evolutions, tStop, parValues) /
totalPopulation), '% at day', tStop)
now = TLine(
len(active) - 1, 0,
len(active) - 1, evActive.fitFun.Eval(len(active) - 1))
now.SetLineColor(4)
now.SetLineWidth(2)
now.Draw('same')
willbe = TLine(evActive.fitFun.GetMaximumX(), 0,
evActive.fitFun.GetMaximumX(),
evActive.fitFun.GetMaximum())
willbe.SetLineColor(6)
willbe.SetLineWidth(2)
willbe.Draw('same')
myCanvActiveR0 = TCanvas('myCanvActiveR0_' + country, 'R0 ' + country)
evActiveGraphR0 = evActive.getGraphR0(evActiveGraphN)
evActiveGraphR0.Draw('APL')
evActiveGraphR0.GetHistogram().GetXaxis().SetTitle('Time (days)')
evActiveGraphR0.GetHistogram().GetYaxis().SetTitle('R')
myCanvActiveR0.SetGrid()
myCanvActiveR0.Modified()
myCanvActiveR0.Update()
myCanvActive.SetGrid()
myCanvActive.Modified()
myCanvActive.Update()
if doFit == False:
return [ntuple, myCanvActive, myGraphActive]
return [
ntuple, myCanvActive, myGraphActive, evActive, evActiveGraphN,
statActive, now, willbe, myCanvActiveR0, evActiveGraphR0
]
def main():
gStyle.SetOptStat(0)
BIAS_DIR = global_paths.BIASDIR+args.btagging+"/"
if args.year == 'run2c':
BIAS_DIR += "combined_run2/"
#BIAS_DIR += "combined_run2_signal{}/"
## individual plots stored in run2c_masspoints
## extract pulls
pulls = TGraphErrors()
for m in range(1600,8001,100):
#try:
pull0=int(PULL0[m])
#pull0=10.
#tfile = TFile.Open(BIAS_DIR+"fitDiagnostics_M{mass}.root".format(mass=m), "READ")
#tfile = TFile.Open(BIAS_DIR.format(pull0)+"fitDiagnostics_M{mass}.root".format(mass=m), "READ")
#tree = tfile.Get("tree_fit_sb")
tree = TChain("tree_fit_sb")
for seed in ['123456', '234567', '345678', '456789', '567891', '678912', '789123', '891234', '912345', '123459']:
tree.Add(BIAS_DIR+"fitDiagnostics_M{mass}_{seed}.root".format(mass=m, seed=seed))
## the method proposed in the documemtation
#hist = TH1D("hist", "hist", 20, -5, 5)
#tree.Project("hist", "(r-1)/(0.5*(rHiErr+rLoErr))")
#fit_func = TF1("gaussfit","gaus" , -5., 5.)
#hist.Fit(fit_func, "E")
#pulls.SetPoint(pulls.GetN(), m, fit_func.GetParameter(1)) ## get mean of gaussian fit
## Alberto's method
#hist = TH1D("s_pulls", ";%s/#sigma_{r};Number of toys" % ("(r - "+str(pull0)+")"), 25, -5, +5) #
hist = TH1D("s_pulls", ";%s/#sigma_{r};Number of toys" % ("#Deltar"), 25, -5, +5) #
for i in range(tree.GetEntries()):
if hist.GetEntries() >= 1000: continue
tree.GetEntry(i)
#print "r = {} (+{}, -{})".format(tree.r, tree.rHiErr, tree.rLoErr)
##if tree.rLoErr < 0.: continue
if abs(tree.r+1.) < 0.001: continue
if abs(tree.r-1.) < 0.001: continue
if abs(tree.r-0.) < 0.001: continue
#if abs(tree.rLoErr)>8.: continue # trying to skip these values FIXME
if tree.rHiErr==0. or tree.rLoErr==0.: continue
#print "r = {} (+{}, -{})".format(tree.r, tree.rHiErr, tree.rLoErr)
#pull = (tree.r-pull0)/(0.5*(abs(tree.rHiErr)+abs(tree.rLoErr))) ## documentation approach
#pull = (tree.r-pull0)/abs(tree.rHiErr) if tree.r-pull0 < 0. else (tree.r-pull0)/abs(tree.rLoErr) ## Alberto's sign convention
pull = (tree.r-pull0)/abs(tree.rHiErr) if tree.r-pull0 > 0. else (tree.r-pull0)/abs(tree.rLoErr) ## my own approach
#pull = (tree.r-pull0)/abs(tree.rHiErr) if tree.r < 0. else (tree.r-pull0)/abs(tree.rLoErr) ## Alberto's sign convention but depending directly on the sign of r
#pull = (tree.r-pull0)/abs(tree.rHiErr) if tree.r > 0. else (tree.r-pull0)/abs(tree.rLoErr) ## my own approach with an rErr dependence on r, not r-1
hist.Fill(pull)
## individual plots for checking the fit quality
c1 = TCanvas("c1", "Pulls", 600, 600)
c1.cd()
#c1.GetPad(0).SetTopMargin(0.06)
#c1.GetPad(0).SetRightMargin(0.05)
#c1.GetPad(0).SetBottomMargin(0.15)
#c1.GetPad(0).SetTicks(1, 1)
hist.GetXaxis().SetTitleSize(0.045)
hist.GetYaxis().SetTitleSize(0.045)
hist.GetYaxis().SetTitleOffset(1.1)
hist.GetXaxis().SetTitleOffset(1.05)
hist.GetXaxis().SetLimits(-5, 5.)
hist.GetYaxis().SetLimits(0, 200.)
hist.SetMinimum(0.)
hist.SetMaximum(190.)
c1.SetTopMargin(0.05)
##print "@ m= {}: \t mean = {}".format(m, hist.GetMean())
#pulls.SetPoint(pulls.GetN(), m, hist.GetMean()) ## get actual mean of histogram
fit_func = TF1("gaussfit","gaus" , -3., 3.)
###fit_func.SetParameter(1, hist.GetMean())
#fit_func.SetParameter(1, 0.)
###fit_func.SetParLimits(1, -0.8, 0.8)
#fit_func.SetParameter(2, 1.)
###fit_func.SetParameter(0, 45.)
###fit_func.SetParLimits(0, 30., 100.)
hist.Fit(fit_func, "E")
hist.Draw()
drawCMS(-1, "Simulation Preliminary", year='run2')
drawMass("m_{Z'} = "+str(m)+" GeV")
c1.Print("plots/bias/run2c_masspoints/bias_fit_"+str(m)+"_"+args.year+".pdf")
c1.Print("plots/bias/run2c_masspoints/bias_fit_"+str(m)+"_"+args.year+".png")
n = pulls.GetN()
pulls.SetPoint(n, m, fit_func.GetParameter(1)) ## get fitted gaussian mean
pulls.SetPointError(n, 0., fit_func.GetParError(1)) ## set gaussian width as error
hist.Delete()
c1.Delete()
#tfile.Close()
#except:
# print "something went wrong in m =", m
## draw pulls
c = TCanvas("canvas", "canvas", 800, 600)
pulls.SetTitle(";m_{Z'} (GeV);mean #Deltar/#sigma_{r}")
pulls.SetMarkerStyle(2)
pulls.SetMarkerColor(2)
pulls.SetLineColor(2)
pulls.SetLineWidth(2)
#pulls.GetYaxis().SetNdivisions(1020)
pulls.SetMinimum(-0.5)
pulls.SetMaximum(0.5)
pulls.Draw("APL")
zeroline = TGraph()
zeroline.SetPoint(zeroline.GetN(), 1000, 0)
zeroline.SetPoint(zeroline.GetN(), 8600, 0)
zeroline.SetMarkerStyle(7)
zeroline.SetMarkerSize(0)
zeroline.SetLineStyle(15)
zeroline.SetLineColor(1)
zeroline.Draw("PL")
c.SetGrid()
pulls.GetXaxis().SetTitleSize(0.045)
pulls.GetYaxis().SetTitleSize(0.045)
pulls.GetYaxis().SetTitleOffset(1.1)
pulls.GetXaxis().SetTitleOffset(1.05)
pulls.GetXaxis().SetLimits(1350., 8150.)
c.SetTopMargin(0.05)
drawCMS(-1, "Simulation Preliminary", year='run2')
c.Print("plots/bias/bias_study_"+args.year+".png")
c.Print("plots/bias/bias_study_"+args.year+".pdf")
jmean = gmean.Eval(m, 0, "S")
jsigma = gsigma.Eval(m, 0, "S")
jalpha1 = galpha1.Eval(m, 0, "S")
jslope1 = gslope1.Eval(m, 0, "S")
jalpha2 = galpha2.Eval(m, 0, "S")
jslope2 = gslope2.Eval(m, 0, "S")
else:
jmean = fmean.GetParameter(0) + fmean.GetParameter(1)*m + fmean.GetParameter(2)*m*m
jsigma = fsigma.GetParameter(0) + fsigma.GetParameter(1)*m + fsigma.GetParameter(2)*m*m
jalpha1 = falpha1.GetParameter(0) + falpha1.GetParameter(1)*m + falpha1.GetParameter(2)*m*m
jslope1 = fslope1.GetParameter(0) + fslope1.GetParameter(1)*m + fslope1.GetParameter(2)*m*m
jalpha2 = falpha2.GetParameter(0) + falpha2.GetParameter(1)*m + falpha2.GetParameter(2)*m*m
jslope2 = fslope2.GetParameter(0) + fslope2.GetParameter(1)*m + fslope2.GetParameter(2)*m*m
inorm.SetPoint(inorm.GetN(), m, syield)
signalNorm[m].setVal(max(0., syield))
imean.SetPoint(imean.GetN(), m, jmean)
if jmean > 0: vmean[m].setVal(jmean)
isigma.SetPoint(isigma.GetN(), m, jsigma)
if jsigma > 0: vsigma[m].setVal(jsigma)
ialpha1.SetPoint(ialpha1.GetN(), m, jalpha1)
if not jalpha1==0: valpha1[m].setVal(jalpha1)
islope1.SetPoint(islope1.GetN(), m, jslope1)
if jslope1 > 0: vslope1[m].setVal(jslope1)
ialpha2.SetPoint(ialpha2.GetN(), m, jalpha2)
def showENC(self):
tree1 = TTree()
header = 'idX/i:vL/F:vH:A:D/i:R:W'
first = True
for f in self.dataFiles:
if first: tree1.ReadFile(f, header)
else: tree1.ReadFile(f)
p1 = TProfile('p1', 'p1;#DeltaU [V];Prob', self.bins[0],
tree1.GetMinimum('vH-vL') * 0.8,
tree1.GetMaximum('vH-vL') * 1.2)
tree1.Draw("D:(vH-vL)>>p1", "", "profE")
### change it to tgraph
g1 = TGraphErrors()
for i in range(p1.GetNbinsX() + 2):
N = p1.GetBinEntries(i)
if N > 0:
print i, N, p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(
i), p1.GetBinError(i)
n = g1.GetN()
g1.SetPoint(n,
p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(i))
g1.SetPointError(n, 0, p1.GetBinError(i))
p1.Draw("axis")
g1.Draw('Psame')
fun1 = TF1('fun1', '0.5*(1+TMath::Erf((x-[0])/(TMath::Sqrt(2)*[1])))',
0.05, 0.3)
fun1.SetParameter(0, 0.155)
fun1.SetParameter(1, 0.005)
g1.Fit(fun1)
fun1a = g1.GetFunction('fun1')
fun1a.SetLineColor(2)
v0 = fun1a.GetParameter(0)
e0 = fun1a.GetParError(0)
v1 = fun1a.GetParameter(1)
e1 = fun1a.GetParError(1)
print v0, v1
fUnit = 1000.
self.lt.DrawLatexNDC(
0.185, 0.89,
'#mu = {0:.1f} #pm {1:.1f} mV'.format(v0 * fUnit, e0 * fUnit))
self.lt.DrawLatexNDC(
0.185, 0.84,
'#sigma = {0:.1f} #pm {1:.1f} mV'.format(v1 * fUnit, e1 * fUnit))
if self.Info:
self.lt.DrawLatexNDC(0.185, 0.6, self.Info)
print 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1)
fun2 = TF1('gaus1', 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1))
fun2.SetLineColor(4)
fun2.SetLineStyle(2)
fun2.Draw('same')
lg = TLegend(0.7, 0.4, 0.95, 0.5)
lg.SetFillStyle(0)
lg.AddEntry(p1, 'Measurement', 'p')
lg.AddEntry(fun1a, 'Fit', 'l')
lg.AddEntry(fun2, 'Gaus', 'l')
lg.Draw()
waitRootCmdX()
def showENC():
fname1 = '/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan1.dat'
tree1 = TTree()
tree1.ReadFile(
'/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan1.dat',
'idX/i:vL/F:vH:A:D/i:R:W')
tree1.ReadFile(
'/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan2_mod.dat'
)
tree1.Show(500)
p1 = TProfile('p1', 'p1;#DeltaU [V];Prob', 50, 0.12, 0.2)
tree1.Draw("D:(vH-vL)>>p1", "", "profE")
### change it to tgraph
g1 = TGraphErrors()
for i in range(p1.GetNbinsX() + 2):
N = p1.GetBinEntries(i)
if N > 0:
print i, N, p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(
i), p1.GetBinError(i)
n = g1.GetN()
g1.SetPoint(n, p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(i))
g1.SetPointError(n, 0, p1.GetBinError(i))
# g1.SetMarkerColor(3)
# g1.SetLineColor(3)
p1.Draw("axis")
g1.Draw('Psame')
fun1 = TF1('fun1', '0.5*(1+TMath::Erf((x-[0])/(TMath::Sqrt(2)*[1])))',
0.05, 0.3)
fun1.SetParameter(0, 0.155)
fun1.SetParameter(1, 0.005)
g1.Fit(fun1)
fun1a = g1.GetFunction('fun1')
# p1.Fit(fun1)
# fun1a = p1.GetFunction('fun1')
fun1a.SetLineColor(2)
# p1.Draw("Esame")
v0 = fun1a.GetParameter(0)
e0 = fun1a.GetParError(0)
v1 = fun1a.GetParameter(1)
e1 = fun1a.GetParError(1)
print v0, v1
fUnit = 1000.
lt = TLatex()
lt.DrawLatexNDC(
0.185, 0.89,
'#mu = {0:.1f} #pm {1:.1f} mV'.format(v0 * fUnit, e0 * fUnit))
lt.DrawLatexNDC(
0.185, 0.84,
'#sigma = {0:.1f} #pm {1:.1f} mV'.format(v1 * fUnit, e1 * fUnit))
print 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1)
fun2 = TF1('gaus1', 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1))
fun2.SetLineColor(4)
fun2.SetLineStyle(2)
fun2.Draw('same')
lg = TLegend(0.7, 0.4, 0.95, 0.5)
lg.SetFillStyle(0)
lg.AddEntry(p1, 'Measurement', 'p')
lg.AddEntry(fun1a, 'Fit', 'l')
lg.AddEntry(fun2, 'Gaus', 'l')
lg.Draw()
waitRootCmdX()
def ApplyBinShiftCorrection(self, hist):
"""
Apply bin-shift correction to the input spectrum using an iterative procedure
@param hist: Input spectrum
@return: Bin-shift corrected spectrum
"""
h = deepcopy(hist)
h.SetName("htemp")
# Bin shift correction performed in model specturm * pt
for i in range(1, h.GetNbinsX() + 1):
pt = h.GetBinCenter(i)
h.SetBinContent(i, h.GetBinContent(i) * pt)
h.SetBinError(i, h.GetBinError(i) * pt)
result = TGraphErrors(h)
for i in range(0, result.GetN()):
result.GetEX()[i] = 0.
fitfun = TF1("fitfun", "([0]*(1.+x/[1])^(-[2])*x)-[3]", 0.15, 100.0)
fitfun.SetParameter(0, 1000)
fitfun.SetParameter(1, 1)
fitfun.SetParameter(2, 5)
fitfun.FixParameter(3, 0)
h.Fit(fitfun, "")
self.__StableFit(h, fitfun, True)
# Iterative approach:
# - Use model to get the mean of the function inside the bin
# - Get the X where the mean is found
# - Use the new coordinate (x,y) for the next iteration of the fit
# for now 10 iterations fixed
for k in range(1, 11):
for i in range(1, h.GetNbinsX() + 1):
y = fitfun.Integral(h.GetBinLowEdge(i),
h.GetBinUpEdge(i)) / h.GetBinWidth(i)
result.GetX()[i - 1] = self.FindX(y, fitfun,
h.GetBinLowEdge(i),
h.GetBinUpEdge(i))
self.__StableFit(result, fitfun, False)
# Undo multiplication with pt
for i in range(0, result.GetN()):
pt = result.GetX()[i]
result.GetY()[i] /= pt
result.GetEY()[i] /= pt
#remove points that are 0
while result.GetY()[0] < 1.e-99:
result.RemovePoint(0)
bval = 0
for mybin in range(0, result.GetN() + 1):
if result.GetY()[bin] < 1.e-99:
bval = mybin
break
while result.RemovePoint(bval) > 0:
continue
return result
def showDAC(fname, Infox=None, saveName='temp_figs/test', mode=1):
lines = None
with open(fname, 'r') as f1:
lines = f1.readlines()
gr1 = TGraphErrors()
gr2 = TGraph()
gr3 = TGraph()
gr4 = TGraph()
largeError = -1
largeErrorI = None
largeErrorMS = None
for line in lines:
line = line.rstrip()
if len(line) == 0:
continue
elif line[0] == '#':
fs = line.split()
code = int(fs[3][5:], 16)
print code
else:
ms = [float(x) for x in line.split(',')][1::2]
if mode == 0: ms = [float(line)]
mean = nm.mean(ms)
error = nm.std(ms)
if error > largeError:
largeError = error
largeErrorI = code
largeErrorMS = ms
gr1.SetPoint(code, code, mean)
gr1.SetPointError(code, 0, error)
print largeErrorI, largeErrorMS, nm.mean(largeErrorMS), nm.mean(ms)
for i in range(len(largeErrorMS)):
gr4.SetPoint(i, i, largeErrorMS[i])
N = gr1.GetN()
Ys = gr1.GetY()
print Ys[0], Ys[N - 1], Ys[largeErrorI]
LSB = (Ys[N - 1] - Ys[0]) / (N - 1)
print LSB
for i in range(N):
gr2.SetPoint(i, i, Ys[i] - (Ys[0] + i * LSB))
gr3.SetPoint(i, i, Ys[i] - Ys[i - 1] - LSB if i > 0 else 0)
line = TLine()
lt = TLatex()
cav1 = TCanvas('cav1', 'cav1', 1000, 800)
cav1.Divide(2, 2)
cav1.cd(1)
gr1.Draw('AP')
h1 = gr1.GetHistogram()
h1.GetXaxis().SetTitle('Code')
h1.GetYaxis().SetTitle('U [V]')
ln1 = line.DrawLine(0, Ys[0], N - 1, Ys[N - 1])
ln1.SetLineColor(2)
rgInfo = '[{0:.2g},{1:.2g}] V'.format(Ys[0], Ys[N - 1])
if Infox:
rgInfo = Infox + ': ' + rgInfo
lt.DrawLatexNDC(0.2, 0.85, rgInfo)
cav1.cd(2)
gr4.SetMarkerStyle(20)
gr4.Draw('AP')
h4 = gr4.GetHistogram()
h4.GetXaxis().SetTitle('#it{i}th')
h4.GetYaxis().SetTitle('U [V]')
lt.DrawLatexNDC(
0.2, 0.85,
"{0:d} measurements for code={1:d}".format(len(largeErrorMS),
largeErrorI))
cav1.cd(3)
gr2.SetFillColor(2)
gr2.Draw('APB')
h2 = gr2.GetHistogram()
h2.GetXaxis().SetTitle('Code')
h2.GetYaxis().SetTitle('INL [V]')
lY = LSB if gr2.GetMean(2) > 0 else -LSB
ln2 = line.DrawLine(0, lY, N, lY)
ln2.SetLineStyle(2)
lt.DrawLatexNDC(
0.6, 0.85,
"Max INL={0:.1f} LSB".format(max([abs(x) for x in gr2.GetY()]) / LSB))
cav1.cd(4)
gr3.SetFillColor(4)
gr3.Draw('APB')
h3 = gr3.GetHistogram()
h3.GetXaxis().SetTitle('Code')
h3.GetYaxis().SetTitle('DNL [V]')
lY = LSB if gr3.GetMean(2) > 0 else -LSB
ln3 = line.DrawLine(0, lY, N, lY)
ln3.SetLineStyle(2)
lt.DrawLatexNDC(
0.6, 0.85,
"Max DNL={0:.1f} LSB".format(max([abs(x) for x in gr3.GetY()]) / LSB))
cav1.cd()
waitRootCmdX(saveName)
def studyVqqResolution(rootFile):
#get all from file
histos = {}
inF = TFile.Open(rootFile)
keys = inF.GetListOfKeys()
for k in keys:
obj = inF.Get(k.GetName())
obj.SetDirectory(0)
histos[k.GetName()] = obj
inF.Close()
#plot
gROOT.SetBatch()
gROOT.SetStyle('Plain')
gStyle.SetOptStat(0)
gStyle.SetOptFit(1111)
gStyle.SetOptTitle(0)
gStyle.SetStatFont(42)
kin = ['', '30to40', '40to50', '50to75', '75to100', '100toInf']
for k in kin:
c = TCanvas('c', 'c', 600, 600)
c.cd()
c.SetCanvasSize(1000, 500)
c.SetWindowSize(1000, 500)
c.Divide(2, 1)
c.cd(1)
histos['deta' + k + 'barrel'].SetLineWidth(2)
histos['deta' + k + 'barrel'].SetTitle('barrel')
histos['deta' + k + 'barrel'].Draw('hist')
histos['deta' + k + 'endcap'].SetLineWidth(2)
histos['deta' + k + 'endcap'].SetLineStyle(7)
histos['deta' + k + 'endcap'].SetTitle('endcap')
histos['deta' + k + 'endcap'].Draw('histsame')
leg = TLegend(0.6, 0.92, 0.9, 0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(histos['deta' + k + 'barrel'], 'barrel', 'f')
leg.AddEntry(histos['deta' + k + 'endcap'], 'endcap', 'f')
leg.SetNColumns(2)
leg.Draw()
drawHeader()
c.cd(2)
histos['dphi' + k + 'barrel'].SetLineWidth(2)
histos['dphi' + k + 'barrel'].SetTitle('barrel')
histos['dphi' + k + 'barrel'].Draw('hist')
histos['dphi' + k + 'endcap'].SetLineWidth(2)
histos['dphi' + k + 'endcap'].SetLineStyle(7)
histos['dphi' + k + 'endcap'].SetTitle('endcap')
histos['dphi' + k + 'endcap'].Draw('histsame')
c.Modified()
c.Update()
c.SaveAs('dr_%s.png' % k)
labels = []
responseVars = ['dpt', 'den', 'dphi', 'deta', 'dr']
for r in responseVars:
barrelResponse = TGraphErrors()
barrelResponse.SetName(r + 'barrelresponse')
barrelResponse.SetLineWidth(2)
barrelResponse.SetFillStyle(0)
barrelResponse.SetMarkerStyle(20)
barrelCoreResponse = barrelResponse.Clone(r + 'barrelcoreresponse')
endcapResponse = TGraphErrors()
endcapResponse.SetName(r + 'endcapresponse')
endcapResponse.SetLineWidth(2)
endcapResponse.SetFillStyle(0)
endcapResponse.SetMarkerStyle(24)
endcapCoreResponse = endcapResponse.Clone(r + 'endcapresponse')
for k in kin:
c.cd()
c.Clear()
c.SetWindowSize(1000, 500)
c.Divide(2, 1)
for i in [1, 2]:
c.cd(i)
reg = 'barrel'
if i == 2: reg = 'endcap'
h = histos[r + k + reg]
x = RooRealVar("x",
h.GetXaxis().GetTitle(),
h.GetXaxis().GetXmin(),
h.GetXaxis().GetXmax())
data = RooDataHist("data", "dataset with x", RooArgList(x), h)
frame = x.frame()
RooAbsData.plotOn(data, frame,
RooFit.DataError(RooAbsData.SumW2))
mean1 = RooRealVar("mean1", "mean1", 0, -0.5, 0.5)
sigma1 = RooRealVar("sigma1", "sigma1", 0.1, 0.01, 1.0)
gauss1 = RooGaussian("g1", "g", x, mean1, sigma1)
if r == 'dpt' or r == 'den':
mean2 = RooRealVar("mean2", "mean2", 0, -0.5, 0.5)
sigma2 = RooRealVar("sigma2", "sigma2", 0.1, 0.01, 1.0)
alphacb = RooRealVar("alphacb", "alphacb", 1, 0.1, 3)
ncb = RooRealVar("ncb", "ncb", 4, 1, 100)
gauss2 = RooCBShape("cb2", "cb", x, mean2, sigma2, alphacb,
ncb)
else:
mean1.setRange(0, 0.5)
mean2 = RooRealVar("mean2", "mean", 0, 0, 1)
sigma2 = RooRealVar("sigma2", "sigma", 0.1, 0.01, 1.0)
gauss2 = RooGaussian("g2", "g", x, mean2, sigma2)
frac = RooRealVar("frac", "fraction", 0.9, 0.0, 1.0)
if data.sumEntries() < 100:
frac.setVal(1.0)
frac.setConstant(True)
model = RooAddPdf("sum", "g1+g2", RooArgList(gauss1, gauss2),
RooArgList(frac))
status = model.fitTo(data, RooFit.Save()).status()
if status != 0: continue
model_cdf = model.createCdf(RooArgSet(x))
cl = 0.90
ul = 0.5 * (1.0 + cl)
closestToCL = 1.0
closestToUL = -1
closestToMedianCL = 1.0
closestToMedian = -1
for ibin in xrange(1, h.GetXaxis().GetNbins() * 10):
xval = h.GetXaxis().GetXmin() + (
ibin - 1) * h.GetXaxis().GetBinWidth(ibin) / 10.
x.setVal(xval)
cdfValToCL = math.fabs(model_cdf.getVal() - ul)
if cdfValToCL < closestToCL:
closestToCL = cdfValToCL
closestToUL = xval
cdfValToCL = math.fabs(model_cdf.getVal() - 0.5)
if cdfValToCL < closestToMedianCL:
closestToMedianCL = cdfValToCL
closestToMedian = xval
RooAbsPdf.plotOn(model, frame)
frame.Draw()
if i == 1: drawHeader()
labels.append(TPaveText(0.6, 0.92, 0.9, 0.98, 'brNDC'))
ilab = len(labels) - 1
labels[ilab].SetName(r + k + 'txt')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
kinReg = k.replace('to', '-')
kinReg = kinReg.replace('Inf', '#infty')
labels[ilab].AddText('[' + reg + '] ' + kinReg)
labels[ilab].Draw()
resolutionVal = math.fabs(closestToUL - closestToMedian)
responseGr = barrelResponse
responseCoreGr = barrelCoreResponse
coreResolutionVal = sigma1.getVal()
coreResolutionErr = sigma1.getError()
if frac.getVal() < 0.7 and (sigma2.getVal() < sigma1.getVal()):
coreResolutionVal = sigma2.getVal()
coreResolutionErr = sigma2.getError()
if i == 2:
responseGr = endcapResponse
responseCoreGr = endcapCoreResponse
if k != '':
nrespPts = responseGr.GetN()
kinAvg = 150
kinWidth = 50
if k == '30to40':
kinAvg = 35
kinWidth = 5
if k == '40to50':
kinAvg = 45
kinWidth = 5
if k == '50to75':
kinAvg = 62.5
kinWidth = 12.5
elif k == '75to100':
kinAvg = 87.5
kinWidth = 12.5
responseGr.SetPoint(nrespPts, kinAvg, resolutionVal)
responseCoreGr.SetPoint(nrespPts, kinAvg,
coreResolutionVal)
responseCoreGr.SetPointError(nrespPts, kinWidth,
coreResolutionErr)
labels.append(TPaveText(0.15, 0.7, 0.4, 0.9, 'brNDC'))
ilab = len(labels) - 1
labels[ilab].SetName(r + k + 'fitrestxt')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
labels[ilab].AddText('Gaussian #1 (f=%3.3f)' % frac.getVal())
labels[ilab].AddText('#mu=%3.3f#pm%3.3f' %
(mean1.getVal(), mean1.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f' %
(sigma1.getVal(), sigma1.getError()))
labels[ilab].AddText('Gaussian #2 (f=%3.3f)' %
(1 - frac.getVal()))
labels[ilab].AddText('#mu=%3.3f#pm%3.3f' %
(mean2.getVal(), mean2.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f' %
(sigma2.getVal(), sigma2.getError()))
labels[ilab].Draw()
c.Modified()
c.Update()
c.SaveAs(r + 'res_' + k + '.png')
frame = TGraphErrors()
frame.SetPoint(0, 0, 0)
frame.SetPoint(1, 200, 0.3)
frame.SetMarkerStyle(1)
frame.SetFillStyle(0)
frame.SetName('frame')
cresp = TCanvas('cresp', 'cresp', 500, 500)
cresp.cd()
frame.Draw('ap')
barrelResponse.Draw('pl')
endcapResponse.Draw('pl')
frame.GetXaxis().SetTitle("Quark transverse momentum [GeV]")
frame.GetYaxis().SetTitle("Resolution %3.2f C.L." % cl)
frame.GetYaxis().SetTitleOffset(1.4)
frame.GetYaxis().SetNdivisions(10)
drawHeader()
leg = TLegend(0.6, 0.92, 0.9, 0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(barrelResponse, 'barrel', 'fp')
leg.AddEntry(endcapResponse, 'endcap', 'fp')
leg.SetNColumns(2)
leg.Draw()
cresp.Modified()
cresp.Update()
cresp.SaveAs(r + 'res_evol.png')
frameCore = frame.Clone('framecore')
cresp.Clear()
frameCore.Draw('ap')
barrelCoreResponse.Draw('pl')
endcapCoreResponse.Draw('pl')
frameCore.GetXaxis().SetTitle("Quark transverse momentum [GeV]")
frameCore.GetYaxis().SetTitle("Core resolution")
frameCore.GetYaxis().SetTitleOffset(1.4)
frameCore.GetYaxis().SetNdivisions(10)
frameCore.GetYaxis().SetRangeUser(0, 0.2)
drawHeader()
leg = TLegend(0.6, 0.92, 0.9, 0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(barrelCoreResponse, 'barrel', 'fp')
leg.AddEntry(endcapCoreResponse, 'endcap', 'fp')
leg.SetNColumns(2)
leg.Draw()
cresp.Modified()
cresp.Update()
cresp.SaveAs(r + 'rescore_evol.png')
bosons = ['h', 'z', 'w']
kin = ['', '50', '100']
region = ['', 'bb', 'eb', 'ee']
for k in kin:
for r in region:
c = TCanvas('c', 'c', 600, 600)
c.cd()
histos['mjj' + k + r].Rebin()
histos['mjj' + k + r].Draw()
ic = 1
leg = TLegend(0.6, 0.92, 0.9, 0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(histos['mjj' + k + r], 'inclusive', 'f')
for b in bosons:
if histos[b + 'mjj' + k + r].Integral() <= 0: continue
ic = ic + 1
histos[b + 'mjj' + k + r].Rebin()
histos[b + 'mjj' + k + r].SetLineColor(ic)
histos[b + 'mjj' + k + r].SetLineWidth(2)
histos[b + 'mjj' + k + r].SetMarkerColor(ic)
histos[b + 'mjj' + k + r].SetMarkerStyle(1)
histos[b + 'mjj' + k + r].SetFillStyle(3000 + ic)
histos[b + 'mjj' + k + r].SetFillColor(ic)
histos[b + 'mjj' + k + r].Draw('histsame')
leg.AddEntry(histos[b + 'mjj' + k + r], b, "f")
leg.SetNColumns(ic)
leg.Draw()
drawHeader()
labels.append(TPaveText(0.65, 0.8, 0.9, 0.9, 'brNDC'))
ilab = len(labels) - 1
labels[ilab].SetName(k + r + 'mjj')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
regionTitle = "inclusive"
if r == 'bb': regionTitle = 'barrel-barrel'
if r == 'eb': regionTitle = 'endcap-barrel'
if r == 'ee': regionTitle = 'endcap-endcap'
labels[ilab].AddText(regionTitle)
ptthreshold = 30
if k != '': ptthreshold = float(k)
labels[ilab].AddText('p_{T}>%3.0f GeV' % ptthreshold)
labels[ilab].Draw()
c.Modified()
c.Update()
c.SaveAs('mjj' + k + r + '.png')
massResolutionGrs = []
for r in region:
massResolution = TGraphErrors()
massResolution.SetName(r + 'dm')
massResolution.SetLineWidth(2)
massResolution.SetFillStyle(0)
massResolution.SetMarkerStyle(20 + len(massResolutionGrs))
massResolution.SetMarkerColor(1 + len(massResolutionGrs))
massResolution.SetLineColor(1 + len(massResolutionGrs))
massResolution.SetFillColor(1 + len(massResolutionGrs))
massResolutionGrs.append(massResolution)
for k in kin:
c = TCanvas('c', 'c', 600, 600)
c.cd()
h = histos['dmjj' + k + r]
x = RooRealVar("x",
h.GetXaxis().GetTitle(),
h.GetXaxis().GetXmin(),
h.GetXaxis().GetXmax())
data = RooDataHist("data", "dataset with x", RooArgList(x), h)
frame = x.frame()
RooAbsData.plotOn(data, frame, RooFit.DataError(RooAbsData.SumW2))
mean1 = RooRealVar("mean1", "mean1", 0, -0.5, 0.5)
sigma1 = RooRealVar("sigma1", "sigma1", 0.1, 0.01, 1.0)
gauss1 = RooGaussian("g1", "g", x, mean1, sigma1)
mean2 = RooRealVar("mean2", "mean2", 0, -0.5, 0.5)
sigma2 = RooRealVar("sigma2", "sigma2", 0.1, 0.01, 1.0)
alphacb = RooRealVar("alphacb", "alphacb", 1, 0.1, 3)
ncb = RooRealVar("ncb", "ncb", 4, 1, 100)
gauss2 = RooCBShape("cb2", "cb", x, mean2, sigma2, alphacb, ncb)
frac = RooRealVar("frac", "fraction", 0.9, 0.0, 1.0)
model = RooAddPdf("sum", "g1+g2", RooArgList(gauss1, gauss2),
RooArgList(frac))
status = model.fitTo(data, RooFit.Save()).status()
if status != 0: continue
RooAbsPdf.plotOn(model, frame)
frame.Draw()
labels.append(TPaveText(0.6, 0.65, 0.85, 0.9, 'brNDC'))
ilab = len(labels) - 1
labels[ilab].SetName(r + k + 'dmfitrestxt')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
labels[ilab].AddText('Gaussian #1 (f=%3.3f)' % frac.getVal())
labels[ilab].AddText('#mu=%3.3f#pm%3.3f' %
(mean1.getVal(), mean1.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f' %
(sigma1.getVal(), sigma1.getError()))
labels[ilab].AddText('Gaussian #2 (f=%3.3f)' % (1 - frac.getVal()))
labels[ilab].AddText('#mu=%3.3f#pm%3.3f' %
(mean2.getVal(), mean2.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f' %
(sigma2.getVal(), sigma2.getError()))
labels[ilab].Draw()
drawHeader()
labels.append(TPaveText(0.15, 0.8, 0.4, 0.9, 'brNDC'))
ilab = len(labels) - 1
labels[ilab].SetName(k + r + 'dmjj')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
regionTitle = "inclusive"
if r == 'bb': regionTitle = 'barrel-barrel'
if r == 'eb': regionTitle = 'endcap-barrel'
if r == 'ee': regionTitle = 'endcap-endcap'
labels[ilab].AddText(regionTitle)
ptthreshold = 30
if k != '': ptthreshold = float(k)
labels[ilab].AddText('p_{T}>%3.0f GeV' % ptthreshold)
labels[ilab].Draw()
c.Modified()
c.Update()
c.SaveAs('dmjj' + k + r + '.png')
massResolution.SetTitle(regionTitle)
ip = massResolution.GetN()
x = 40
xerr = 10
if k == '50':
x = 75
xerr = 25
elif k == '100':
x = 150
xerr = 50
y = sigma1.getVal()
yerr = sigma1.getError()
if frac.getVal() < 0.8:
if sigma2.getVal() < sigma1.getVal():
y = sigma2.getVal()
ey = sigma2.getError()
massResolution.SetPoint(ip, x, y)
massResolution.SetPointError(ip, xerr, yerr)
frame = TGraphErrors()
frame.SetPoint(0, 0, 0)
frame.SetPoint(1, 200, 0.2)
frame.SetMarkerStyle(1)
frame.SetFillStyle(0)
frame.SetName('dmframe')
cdmevol = TCanvas('cdmevol', 'cdmevol', 500, 500)
cdmevol.cd()
frame.Draw('ap')
leg = TLegend(0.6, 0.92, 0.9, 0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
for dmGr in massResolutionGrs:
dmGr.Draw('pl')
leg.AddEntry(dmGr, dmGr.GetTitle(), 'fp')
frame.GetXaxis().SetTitle("Leading quark transverse momentum [GeV]")
frame.GetYaxis().SetTitle("Core resolution")
frame.GetYaxis().SetTitleOffset(1.4)
frame.GetYaxis().SetNdivisions(10)
drawHeader()
leg.SetNColumns(2)
leg.Draw()
cdmevol.Modified()
cdmevol.Update()
cdmevol.SaveAs('dm_evol.png')
c = TCanvas('c', 'c', 600, 600)
c.cd()
histos['sel'].Draw('histtext')
drawHeader()
c.Modified()
c.Update()
c.SaveAs('selection.png')
return
def acceptance(year):
genPoints = [
1800, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 7000, 8000
]
treeSign = {}
ngenSign = {}
nevtSign = {}
nevtSign_eta = {}
nevtSign_dEta = {}
eff = TGraphErrors()
eff_eta = TGraphErrors()
eff_dEta = TGraphErrors()
for i, m in enumerate(genPoints):
ngenSign[m] = 0.
nevtSign[m] = 0.
nevtSign_eta[m] = 0.
nevtSign_dEta[m] = 0.
if year == "run2":
years = ['2016', '2017', '2018']
else:
years = [year]
for yr in years:
signName = "MC_signal_{}_M{}".format(yr, m)
sfile = TFile(ACCEPTANCEDIR + signName + "_acceptanceHist.root",
"READ")
ngenSign[m] += sample["ZpBB_M" + str(m)]['genEvents'][yr]
#all_events_hist = sfile.Get('all_events')
#nEvents = all_events_hist.GetBinContent(1)
#ngenSign[m] += nEvents
passing_events_hist = sfile.Get('passing')
eta_flag_hist = sfile.Get('eta_flag')
dEta_flag_hist = sfile.Get('dEta_flag')
nEvents = passing_events_hist.GetBinContent(1)
nEvents_eta = eta_flag_hist.GetBinContent(1)
nEvents_dEta = dEta_flag_hist.GetBinContent(1)
nevtSign[m] += nEvents
nevtSign_eta[m] += nEvents_eta
nevtSign_dEta[m] += nEvents_dEta
sfile.Close()
print m, ":", nevtSign[m], "/", ngenSign[
m], "=", nevtSign[m] / ngenSign[m]
if nevtSign[m] == 0 or ngenSign[m] < 0: continue
n = eff.GetN()
eff.SetPoint(n, m, nevtSign[m] / ngenSign[m])
eff.SetPointError(n, 0, math.sqrt(nevtSign[m]) / ngenSign[m])
eff_eta.SetPoint(n, m, nevtSign_eta[m] / ngenSign[m])
eff_eta.SetPointError(n, 0, math.sqrt(nevtSign_eta[m]) / ngenSign[m])
eff_dEta.SetPoint(n, m, nevtSign_dEta[m] / ngenSign[m])
eff_dEta.SetPointError(n, 0, math.sqrt(nevtSign_dEta[m]) / ngenSign[m])
eff.SetMarkerColor(4)
eff.SetMarkerStyle(24)
eff.SetMarkerSize(2)
eff.SetLineColor(4)
eff.SetLineWidth(3)
eff_eta.SetMarkerColor(2)
eff_eta.SetMarkerStyle(23)
eff_eta.SetMarkerSize(2)
eff_eta.SetLineColor(2)
eff_eta.SetLineWidth(2)
eff_eta.SetLineStyle(2)
eff_dEta.SetMarkerColor(418)
eff_dEta.SetMarkerStyle(23)
eff_dEta.SetMarkerSize(2)
eff_dEta.SetLineColor(418)
eff_dEta.SetLineWidth(2)
eff_dEta.SetLineStyle(2)
n = eff.GetN()
maxEff = 0.
leg = TLegend(0.15, 0.7, 0.95, 0.8)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
#leg.SetY1(leg.GetY2()-len([x for x in channels if eff[x].GetN() > 0])/2.*0.045)
leg.AddEntry(eff, "total")
leg.AddEntry(eff_eta, "|#eta|<2.5")
leg.AddEntry(eff_dEta, "#Delta#eta<1.1")
#legS = TLegend(0.5, 0.85-0.045, 0.9, 0.85)
#legS.SetBorderSize(0)
#legS.SetFillStyle(0) #1001
#legS.SetFillColor(0)
#legS.AddEntry(eff['sum'], "Total efficiency (1 b tag + 2 b tag)", "pl")
c1 = TCanvas("c1", "Signal Acceptance", 1200, 800)
c1.cd(1)
eff.Draw("APL")
eff_eta.Draw("SAME, PL")
eff_dEta.Draw("SAME, PL")
leg.Draw()
#legS.Draw()
#setHistStyle(eff["sum"], 1.1)
eff.SetTitle(";m_{Z'} (GeV);Acceptance")
eff.SetMinimum(0.)
eff.SetMaximum(max(1.5, maxEff * 1.5)) #0.65
eff.GetXaxis().SetTitleSize(0.045)
eff.GetYaxis().SetTitleSize(0.045)
eff.GetYaxis().SetTitleOffset(1.1)
eff.GetXaxis().SetTitleOffset(1.05)
eff.GetXaxis().SetRangeUser(1500, 8000)
c1.SetTopMargin(0.05)
#drawCMS(-1, "Simulation Preliminary", year=year) #Preliminary
#drawCMS(-1, "Work in Progress", year=year, suppressCMS=True)
drawCMS(-1, "", year=year, suppressCMS=True)
drawAnalysis("")
c1.Print("plots/Efficiency/" + year + "_Acceptance.pdf")
c1.Print("plots/Efficiency/" + year + "_Acceptance.png")
x[0] - steps[0] - steps[1] - steps[2])
return tax / x[0] * 100.
data = []
data.append([10, [0, 8025]])
data.append([15, [8025, 32550]])
data.append([25, [32550, 78850]])
data.append([28, [78850, 164550]])
data.append([33, [164550, 357700]])
data.append([35, [357700, 1000000]])
gr = TGraphErrors(0)
for dd in data:
yVal = dd[0]
xVal = dd[1]
xAve = (xVal[0] + xVal[1]) / 2.
xErr = (xVal[1] - xVal[0]) / 2.
gr.SetPoint(gr.GetN(), xAve, yVal)
gr.SetPointError(gr.GetN() - 1, xErr, 0.)
gr.Draw("alp")
canTax = TF1("canTax", canadianTax, 0., 1000000., 0)
canTax.Draw("same")
yy = raw_input("wanna quit?")
if yy == "n":
print "ok"
def signal(category):
interPar = True
n = len(genPoints)
cColor = color[category] if category in color else 4
nBtag = category.count('b')
isAH = False #relict from using Alberto's more complex script
if not os.path.exists(PLOTDIR + "MC_signal_" + YEAR):
os.makedirs(PLOTDIR + "MC_signal_" + YEAR)
#*******************************************************#
# #
# Variables and selections #
# #
#*******************************************************#
X_mass = RooRealVar("jj_mass_widejet", "m_{jj}", X_min, X_max, "GeV")
j1_pt = RooRealVar("jpt_1", "jet1 pt", 0., 13000., "GeV")
jj_deltaEta = RooRealVar("jj_deltaEta_widejet", "", 0., 5.)
jbtag_WP_1 = RooRealVar("jbtag_WP_1", "", -1., 4.)
jbtag_WP_2 = RooRealVar("jbtag_WP_2", "", -1., 4.)
fatjetmass_1 = RooRealVar("fatjetmass_1", "", -1., 2500.)
fatjetmass_2 = RooRealVar("fatjetmass_2", "", -1., 2500.)
jid_1 = RooRealVar("jid_1", "j1 ID", -1., 8.)
jid_2 = RooRealVar("jid_2", "j2 ID", -1., 8.)
jnmuons_1 = RooRealVar("jnmuons_1", "j1 n_{#mu}", -1., 8.)
jnmuons_2 = RooRealVar("jnmuons_2", "j2 n_{#mu}", -1., 8.)
jnmuons_loose_1 = RooRealVar("jnmuons_loose_1", "jnmuons_loose_1", -1., 8.)
jnmuons_loose_2 = RooRealVar("jnmuons_loose_2", "jnmuons_loose_2", -1., 8.)
nmuons = RooRealVar("nmuons", "n_{#mu}", -1., 10.)
nelectrons = RooRealVar("nelectrons", "n_{e}", -1., 10.)
HLT_AK8PFJet500 = RooRealVar("HLT_AK8PFJet500", "", -1., 1.)
HLT_PFJet500 = RooRealVar("HLT_PFJet500", "", -1., 1.)
HLT_CaloJet500_NoJetID = RooRealVar("HLT_CaloJet500_NoJetID", "", -1., 1.)
HLT_PFHT900 = RooRealVar("HLT_PFHT900", "", -1., 1.)
HLT_AK8PFJet550 = RooRealVar("HLT_AK8PFJet550", "", -1., 1.)
HLT_PFJet550 = RooRealVar("HLT_PFJet550", "", -1., 1.)
HLT_CaloJet550_NoJetID = RooRealVar("HLT_CaloJet550_NoJetID", "", -1., 1.)
HLT_PFHT1050 = RooRealVar("HLT_PFHT1050", "", -1., 1.)
#HLT_DoublePFJets100_CaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets100_CaloBTagDeepCSV_p71" , "", -1., 1. )
#HLT_DoublePFJets116MaxDeta1p6_DoubleCaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets116MaxDeta1p6_DoubleCaloBTagDeepCSV_p71", "", -1., 1. )
#HLT_DoublePFJets128MaxDeta1p6_DoubleCaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets128MaxDeta1p6_DoubleCaloBTagDeepCSV_p71", "", -1., 1. )
#HLT_DoublePFJets200_CaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets200_CaloBTagDeepCSV_p71" , "", -1., 1. )
#HLT_DoublePFJets350_CaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets350_CaloBTagDeepCSV_p71" , "", -1., 1. )
#HLT_DoublePFJets40_CaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets40_CaloBTagDeepCSV_p71" , "", -1., 1. )
weight = RooRealVar("eventWeightLumi", "", -1.e9, 1.e9)
# Define the RooArgSet which will include all the variables defined before
# there is a maximum of 9 variables in the declaration, so the others need to be added with 'add'
variables = RooArgSet(X_mass)
variables.add(
RooArgSet(j1_pt, jj_deltaEta, jbtag_WP_1, jbtag_WP_2, fatjetmass_1,
fatjetmass_2, jnmuons_1, jnmuons_2, weight))
variables.add(
RooArgSet(nmuons, nelectrons, jid_1, jid_2, jnmuons_loose_1,
jnmuons_loose_2))
variables.add(
RooArgSet(HLT_AK8PFJet500, HLT_PFJet500, HLT_CaloJet500_NoJetID,
HLT_PFHT900, HLT_AK8PFJet550, HLT_PFJet550,
HLT_CaloJet550_NoJetID, HLT_PFHT1050))
#variables.add(RooArgSet(HLT_DoublePFJets100_CaloBTagDeepCSV_p71, HLT_DoublePFJets116MaxDeta1p6_DoubleCaloBTagDeepCSV_p71, HLT_DoublePFJets128MaxDeta1p6_DoubleCaloBTagDeepCSV_p71, HLT_DoublePFJets200_CaloBTagDeepCSV_p71, HLT_DoublePFJets350_CaloBTagDeepCSV_p71, HLT_DoublePFJets40_CaloBTagDeepCSV_p71))
X_mass.setRange("X_reasonable_range", X_mass.getMin(), X_mass.getMax())
X_mass.setRange("X_integration_range", X_mass.getMin(), X_mass.getMax())
if VARBINS:
binsXmass = RooBinning(len(abins) - 1, abins)
X_mass.setBinning(binsXmass)
plot_binning = RooBinning(
int((X_mass.getMax() - X_mass.getMin()) / 100.), X_mass.getMin(),
X_mass.getMax())
else:
X_mass.setBins(int((X_mass.getMax() - X_mass.getMin()) / 10))
binsXmass = RooBinning(int((X_mass.getMax() - X_mass.getMin()) / 100.),
X_mass.getMin(), X_mass.getMax())
plot_binning = binsXmass
X_mass.setBinning(plot_binning, "PLOT")
#X_mass.setBins(int((X_mass.getMax() - X_mass.getMin())/10))
#binsXmass = RooBinning(int((X_mass.getMax() - X_mass.getMin())/100), X_mass.getMin(), X_mass.getMax())
#X_mass.setBinning(binsXmass, "PLOT")
massArg = RooArgSet(X_mass)
# Cuts
if BTAGGING == 'semimedium':
SRcut = aliasSM[category]
#SRcut = aliasSM[category+"_vetoAK8"]
else:
SRcut = alias[category].format(WP=working_points[BTAGGING])
#SRcut = alias[category+"_vetoAK8"].format(WP=working_points[BTAGGING])
if ADDSELECTION: SRcut += SELECTIONS[options.selection]
print " Cut:\t", SRcut
#*******************************************************#
# #
# Signal fits #
# #
#*******************************************************#
treeSign = {}
setSignal = {}
vmean = {}
vsigma = {}
valpha1 = {}
vslope1 = {}
valpha2 = {}
vslope2 = {}
smean = {}
ssigma = {}
salpha1 = {}
sslope1 = {}
salpha2 = {}
sslope2 = {}
sbrwig = {}
signal = {}
signalExt = {}
signalYield = {}
signalIntegral = {}
signalNorm = {}
signalXS = {}
frSignal = {}
frSignal1 = {}
frSignal2 = {}
frSignal3 = {}
# Signal shape uncertainties (common amongst all mass points)
xmean_jes = RooRealVar(
"CMS" + YEAR + "_sig_" + category + "_p1_scale_jes",
"Variation of the resonance position with the jet energy scale", 0.02,
-1., 1.) #0.001
smean_jes = RooRealVar(
"CMS" + YEAR + "_sig_" + category + "_p1_jes",
"Change of the resonance position with the jet energy scale", 0., -10,
10)
xsigma_jer = RooRealVar(
"CMS" + YEAR + "_sig_" + category + "_p2_scale_jer",
"Variation of the resonance width with the jet energy resolution",
0.10, -1., 1.)
ssigma_jer = RooRealVar(
"CMS" + YEAR + "_sig_" + category + "_p2_jer",
"Change of the resonance width with the jet energy resolution", 0.,
-10, 10)
xmean_jes.setConstant(True)
smean_jes.setConstant(True)
xsigma_jer.setConstant(True)
ssigma_jer.setConstant(True)
for m in massPoints:
signalMass = "%s_M%d" % (stype, m)
signalName = "ZpBB_{}_{}_M{}".format(YEAR, category, m)
sampleName = "ZpBB_M{}".format(m)
signalColor = sample[sampleName][
'linecolor'] if signalName in sample else 1
# define the signal PDF
vmean[m] = RooRealVar(signalName + "_vmean", "Crystal Ball mean", m,
m * 0.96, m * 1.05)
smean[m] = RooFormulaVar(signalName + "_mean", "@0*(1+@1*@2)",
RooArgList(vmean[m], xmean_jes, smean_jes))
vsigma[m] = RooRealVar(signalName + "_vsigma", "Crystal Ball sigma",
m * 0.0233, m * 0.019, m * 0.025)
ssigma[m] = RooFormulaVar(
signalName + "_sigma", "@0*(1+@1*@2)",
RooArgList(vsigma[m], xsigma_jer, ssigma_jer))
valpha1[m] = RooRealVar(
signalName + "_valpha1", "Crystal Ball alpha 1", 0.2, 0.05, 0.28
) # number of sigmas where the exp is attached to the gaussian core. >0 left, <0 right
salpha1[m] = RooFormulaVar(signalName + "_alpha1", "@0",
RooArgList(valpha1[m]))
#vslope1[m] = RooRealVar(signalName + "_vslope1", "Crystal Ball slope 1", 10., 0.1, 20.) # slope of the power tail
vslope1[m] = RooRealVar(signalName + "_vslope1",
"Crystal Ball slope 1", 13., 10.,
20.) # slope of the power tail
sslope1[m] = RooFormulaVar(signalName + "_slope1", "@0",
RooArgList(vslope1[m]))
valpha2[m] = RooRealVar(signalName + "_valpha2",
"Crystal Ball alpha 2", 1.)
valpha2[m].setConstant(True)
salpha2[m] = RooFormulaVar(signalName + "_alpha2", "@0",
RooArgList(valpha2[m]))
#vslope2[m] = RooRealVar(signalName + "_vslope2", "Crystal Ball slope 2", 6., 2.5, 15.) # slope of the higher power tail
## FIXME test FIXME
vslope2_estimation = -5.88111436852 + m * 0.00728809389442 + m * m * (
-1.65059568762e-06) + m * m * m * (1.25128996309e-10)
vslope2[m] = RooRealVar(signalName + "_vslope2",
"Crystal Ball slope 2", vslope2_estimation,
vslope2_estimation * 0.9, vslope2_estimation *
1.1) # slope of the higher power tail
## FIXME end FIXME
sslope2[m] = RooFormulaVar(
signalName + "_slope2", "@0",
RooArgList(vslope2[m])) # slope of the higher power tail
signal[m] = RooDoubleCrystalBall(signalName,
"m_{%s'} = %d GeV" % ('X', m), X_mass,
smean[m], ssigma[m], salpha1[m],
sslope1[m], salpha2[m], sslope2[m])
# extend the PDF with the yield to perform an extended likelihood fit
signalYield[m] = RooRealVar(signalName + "_yield", "signalYield", 50,
0., 1.e15)
signalNorm[m] = RooRealVar(signalName + "_norm", "signalNorm", 1., 0.,
1.e15)
signalXS[m] = RooRealVar(signalName + "_xs", "signalXS", 1., 0., 1.e15)
signalExt[m] = RooExtendPdf(signalName + "_ext", "extended p.d.f",
signal[m], signalYield[m])
# ---------- if there is no simulated signal, skip this mass point ----------
if m in genPoints:
if VERBOSE: print " - Mass point", m
# define the dataset for the signal applying the SR cuts
treeSign[m] = TChain("tree")
if YEAR == 'run2':
pd = sample[sampleName]['files']
if len(pd) > 3:
print "multiple files given than years for a single masspoint:", pd
sys.exit()
for ss in pd:
if not '2016' in ss and not '2017' in ss and not '2018' in ss:
print "unknown year given in:", ss
sys.exit()
else:
pd = [x for x in sample[sampleName]['files'] if YEAR in x]
if len(pd) > 1:
print "multiple files given for a single masspoint/year:", pd
sys.exit()
for ss in pd:
if options.unskimmed:
j = 0
while True:
if os.path.exists(NTUPLEDIR + ss + "/" + ss +
"_flatTuple_{}.root".format(j)):
treeSign[m].Add(NTUPLEDIR + ss + "/" + ss +
"_flatTuple_{}.root".format(j))
j += 1
else:
print "found {} files for sample:".format(j), ss
break
else:
if os.path.exists(NTUPLEDIR + ss + ".root"):
treeSign[m].Add(NTUPLEDIR + ss + ".root")
else:
print "found no file for sample:", ss
if treeSign[m].GetEntries() <= 0.:
if VERBOSE:
print " - 0 events available for mass", m, "skipping mass point..."
signalNorm[m].setVal(-1)
vmean[m].setConstant(True)
vsigma[m].setConstant(True)
salpha1[m].setConstant(True)
sslope1[m].setConstant(True)
salpha2[m].setConstant(True)
sslope2[m].setConstant(True)
signalNorm[m].setConstant(True)
signalXS[m].setConstant(True)
continue
#setSignal[m] = RooDataSet("setSignal_"+signalName, "setSignal", variables, RooFit.Cut(SRcut), RooFit.WeightVar("eventWeightLumi*BTagAK4Weight_deepJet"), RooFit.Import(treeSign[m]))
setSignal[m] = RooDataSet("setSignal_" + signalName, "setSignal",
variables, RooFit.Cut(SRcut),
RooFit.WeightVar(weight),
RooFit.Import(treeSign[m]))
if VERBOSE:
print " - Dataset with", setSignal[m].sumEntries(
), "events loaded"
# FIT
entries = setSignal[m].sumEntries()
if entries < 0. or entries != entries: entries = 0
signalYield[m].setVal(entries)
# Instead of eventWeightLumi
#signalYield[m].setVal(entries * LUMI / (300000 if YEAR=='run2' else 100000) )
if treeSign[m].GetEntries(SRcut) > 5:
if VERBOSE: print " - Running fit"
frSignal[m] = signalExt[m].fitTo(setSignal[m], RooFit.Save(1),
RooFit.Extended(True),
RooFit.SumW2Error(True),
RooFit.PrintLevel(-1))
if VERBOSE:
print "********** Fit result [", m, "] **", category, "*" * 40, "\n", frSignal[
m].Print(), "\n", "*" * 80
if VERBOSE: frSignal[m].correlationMatrix().Print()
drawPlot(signalMass + "_" + category, stype + category, X_mass,
signal[m], setSignal[m], frSignal[m])
else:
print " WARNING: signal", stype, "and mass point", m, "in category", category, "has 0 entries or does not exist"
# Remove HVT cross sections
#xs = getCrossSection(stype, channel, m)
xs = 1.
signalXS[m].setVal(xs * 1000.)
signalIntegral[m] = signalExt[m].createIntegral(
massArg, RooFit.NormSet(massArg),
RooFit.Range("X_integration_range"))
boundaryFactor = signalIntegral[m].getVal()
if boundaryFactor < 0. or boundaryFactor != boundaryFactor:
boundaryFactor = 0
if VERBOSE:
print " - Fit normalization vs integral:", signalYield[
m].getVal(), "/", boundaryFactor, "events"
signalNorm[m].setVal(boundaryFactor * signalYield[m].getVal() /
signalXS[m].getVal()
) # here normalize to sigma(X) x Br = 1 [fb]
vmean[m].setConstant(True)
vsigma[m].setConstant(True)
valpha1[m].setConstant(True)
vslope1[m].setConstant(True)
valpha2[m].setConstant(True)
vslope2[m].setConstant(True)
signalNorm[m].setConstant(True)
signalXS[m].setConstant(True)
#*******************************************************#
# #
# Signal interpolation #
# #
#*******************************************************#
### FIXME FIXME just for a test FIXME FIXME
#print
#print
#print "slope2 fit results:"
#print
#y_vals = []
#for m in genPoints:
# y_vals.append(vslope2[m].getVal())
#print "m =", genPoints
#print "y =", y_vals
#sys.exit()
### FIXME FIXME test end FIXME FIXME
# ====== CONTROL PLOT ======
color_scheme = [
636, 635, 634, 633, 632, 633, 636, 635, 634, 633, 632, 633, 636, 635,
634, 633, 632, 633, 636, 635, 634, 633, 632, 633, 636, 635, 634, 633,
632, 633, 636, 635, 634, 633, 632, 633, 636, 635, 634, 633, 632, 633
]
c_signal = TCanvas("c_signal", "c_signal", 800, 600)
c_signal.cd()
frame_signal = X_mass.frame()
for j, m in enumerate(genPoints):
if m in signalExt.keys():
#print "color:",(j%9)+1
#print "signalNorm[m].getVal() =", signalNorm[m].getVal()
#print "RooAbsReal.NumEvent =", RooAbsReal.NumEvent
signal[m].plotOn(
frame_signal, RooFit.LineColor(color_scheme[j]),
RooFit.Normalization(signalNorm[m].getVal(),
RooAbsReal.NumEvent),
RooFit.Range("X_reasonable_range"))
frame_signal.GetXaxis().SetRangeUser(0, 10000)
frame_signal.Draw()
drawCMS(-1, "Simulation Preliminary", year=YEAR)
#drawCMS(-1, "Work in Progress", year=YEAR, suppressCMS=True)
#drawCMS(-1, "", year=YEAR, suppressCMS=True)
drawAnalysis(category)
drawRegion(category)
c_signal.SaveAs(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" +
category + "_Signal.pdf")
c_signal.SaveAs(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" +
category + "_Signal.png")
#if VERBOSE: raw_input("Press Enter to continue...")
# ====== CONTROL PLOT ======
# Normalization
gnorm = TGraphErrors()
gnorm.SetTitle(";m_{X} (GeV);integral (GeV)")
gnorm.SetMarkerStyle(20)
gnorm.SetMarkerColor(1)
gnorm.SetMaximum(0)
inorm = TGraphErrors()
inorm.SetMarkerStyle(24)
fnorm = TF1("fnorm", "pol9", 700, 3000)
fnorm.SetLineColor(920)
fnorm.SetLineStyle(7)
fnorm.SetFillColor(2)
fnorm.SetLineColor(cColor)
# Mean
gmean = TGraphErrors()
gmean.SetTitle(";m_{X} (GeV);gaussian mean (GeV)")
gmean.SetMarkerStyle(20)
gmean.SetMarkerColor(cColor)
gmean.SetLineColor(cColor)
imean = TGraphErrors()
imean.SetMarkerStyle(24)
fmean = TF1("fmean", "pol1", 0, 10000)
fmean.SetLineColor(2)
fmean.SetFillColor(2)
# Width
gsigma = TGraphErrors()
gsigma.SetTitle(";m_{X} (GeV);gaussian width (GeV)")
gsigma.SetMarkerStyle(20)
gsigma.SetMarkerColor(cColor)
gsigma.SetLineColor(cColor)
isigma = TGraphErrors()
isigma.SetMarkerStyle(24)
fsigma = TF1("fsigma", "pol1", 0, 10000)
fsigma.SetLineColor(2)
fsigma.SetFillColor(2)
# Alpha1
galpha1 = TGraphErrors()
galpha1.SetTitle(";m_{X} (GeV);crystal ball lower alpha")
galpha1.SetMarkerStyle(20)
galpha1.SetMarkerColor(cColor)
galpha1.SetLineColor(cColor)
ialpha1 = TGraphErrors()
ialpha1.SetMarkerStyle(24)
falpha1 = TF1("falpha", "pol1", 0, 10000) #pol0
falpha1.SetLineColor(2)
falpha1.SetFillColor(2)
# Slope1
gslope1 = TGraphErrors()
gslope1.SetTitle(";m_{X} (GeV);exponential lower slope (1/Gev)")
gslope1.SetMarkerStyle(20)
gslope1.SetMarkerColor(cColor)
gslope1.SetLineColor(cColor)
islope1 = TGraphErrors()
islope1.SetMarkerStyle(24)
fslope1 = TF1("fslope", "pol1", 0, 10000) #pol0
fslope1.SetLineColor(2)
fslope1.SetFillColor(2)
# Alpha2
galpha2 = TGraphErrors()
galpha2.SetTitle(";m_{X} (GeV);crystal ball upper alpha")
galpha2.SetMarkerStyle(20)
galpha2.SetMarkerColor(cColor)
galpha2.SetLineColor(cColor)
ialpha2 = TGraphErrors()
ialpha2.SetMarkerStyle(24)
falpha2 = TF1("falpha", "pol1", 0, 10000) #pol0
falpha2.SetLineColor(2)
falpha2.SetFillColor(2)
# Slope2
gslope2 = TGraphErrors()
gslope2.SetTitle(";m_{X} (GeV);exponential upper slope (1/Gev)")
gslope2.SetMarkerStyle(20)
gslope2.SetMarkerColor(cColor)
gslope2.SetLineColor(cColor)
islope2 = TGraphErrors()
islope2.SetMarkerStyle(24)
fslope2 = TF1("fslope", "pol1", 0, 10000) #pol0
fslope2.SetLineColor(2)
fslope2.SetFillColor(2)
n = 0
for i, m in enumerate(genPoints):
if not m in signalNorm.keys(): continue
if signalNorm[m].getVal() < 1.e-6: continue
if gnorm.GetMaximum() < signalNorm[m].getVal():
gnorm.SetMaximum(signalNorm[m].getVal())
gnorm.SetPoint(n, m, signalNorm[m].getVal())
#gnorm.SetPointError(i, 0, signalNorm[m].getVal()/math.sqrt(treeSign[m].GetEntriesFast()))
gmean.SetPoint(n, m, vmean[m].getVal())
gmean.SetPointError(n, 0,
min(vmean[m].getError(), vmean[m].getVal() * 0.02))
gsigma.SetPoint(n, m, vsigma[m].getVal())
gsigma.SetPointError(
n, 0, min(vsigma[m].getError(), vsigma[m].getVal() * 0.05))
galpha1.SetPoint(n, m, valpha1[m].getVal())
galpha1.SetPointError(
n, 0, min(valpha1[m].getError(), valpha1[m].getVal() * 0.10))
gslope1.SetPoint(n, m, vslope1[m].getVal())
gslope1.SetPointError(
n, 0, min(vslope1[m].getError(), vslope1[m].getVal() * 0.10))
galpha2.SetPoint(n, m, salpha2[m].getVal())
galpha2.SetPointError(
n, 0, min(valpha2[m].getError(), valpha2[m].getVal() * 0.10))
gslope2.SetPoint(n, m, sslope2[m].getVal())
gslope2.SetPointError(
n, 0, min(vslope2[m].getError(), vslope2[m].getVal() * 0.10))
#tmpVar = w.var(var+"_"+signalString)
#print m, tmpVar.getVal(), tmpVar.getError()
n = n + 1
gmean.Fit(fmean, "Q0", "SAME")
gsigma.Fit(fsigma, "Q0", "SAME")
galpha1.Fit(falpha1, "Q0", "SAME")
gslope1.Fit(fslope1, "Q0", "SAME")
galpha2.Fit(falpha2, "Q0", "SAME")
gslope2.Fit(fslope2, "Q0", "SAME")
# gnorm.Fit(fnorm, "Q0", "", 700, 5000)
#for m in [5000, 5500]: gnorm.SetPoint(gnorm.GetN(), m, gnorm.Eval(m, 0, "S"))
#gnorm.Fit(fnorm, "Q", "SAME", 700, 6000)
gnorm.Fit(fnorm, "Q", "SAME", 1800, 8000) ## adjusted recently
for m in massPoints:
if vsigma[m].getVal() < 10.: vsigma[m].setVal(10.)
# Interpolation method
syield = gnorm.Eval(m)
spline = gnorm.Eval(m, 0, "S")
sfunct = fnorm.Eval(m)
#delta = min(abs(1.-spline/sfunct), abs(1.-spline/syield))
delta = abs(1. - spline / sfunct) if sfunct > 0 else 0
syield = spline
if interPar:
#jmean = gmean.Eval(m)
#jsigma = gsigma.Eval(m)
#jalpha1 = galpha1.Eval(m)
#jslope1 = gslope1.Eval(m)
#jalpha2 = galpha2.Eval(m)
#jslope2 = gslope2.Eval(m)
jmean = gmean.Eval(m, 0, "S")
jsigma = gsigma.Eval(m, 0, "S")
jalpha1 = galpha1.Eval(m, 0, "S")
jslope1 = gslope1.Eval(m, 0, "S")
jalpha2 = galpha2.Eval(m, 0, "S")
jslope2 = gslope2.Eval(m, 0, "S")
else:
jmean = fmean.GetParameter(
0) + fmean.GetParameter(1) * m + fmean.GetParameter(2) * m * m
jsigma = fsigma.GetParameter(0) + fsigma.GetParameter(
1) * m + fsigma.GetParameter(2) * m * m
jalpha1 = falpha1.GetParameter(0) + falpha1.GetParameter(
1) * m + falpha1.GetParameter(2) * m * m
jslope1 = fslope1.GetParameter(0) + fslope1.GetParameter(
1) * m + fslope1.GetParameter(2) * m * m
jalpha2 = falpha2.GetParameter(0) + falpha2.GetParameter(
1) * m + falpha2.GetParameter(2) * m * m
jslope2 = fslope2.GetParameter(0) + fslope2.GetParameter(
1) * m + fslope2.GetParameter(2) * m * m
inorm.SetPoint(inorm.GetN(), m, syield)
signalNorm[m].setVal(max(0., syield))
imean.SetPoint(imean.GetN(), m, jmean)
if jmean > 0: vmean[m].setVal(jmean)
isigma.SetPoint(isigma.GetN(), m, jsigma)
if jsigma > 0: vsigma[m].setVal(jsigma)
ialpha1.SetPoint(ialpha1.GetN(), m, jalpha1)
if not jalpha1 == 0: valpha1[m].setVal(jalpha1)
islope1.SetPoint(islope1.GetN(), m, jslope1)
if jslope1 > 0: vslope1[m].setVal(jslope1)
ialpha2.SetPoint(ialpha2.GetN(), m, jalpha2)
if not jalpha2 == 0: valpha2[m].setVal(jalpha2)
islope2.SetPoint(islope2.GetN(), m, jslope2)
if jslope2 > 0: vslope2[m].setVal(jslope2)
#### newly introduced, not yet sure if helpful:
vmean[m].removeError()
vsigma[m].removeError()
valpha1[m].removeError()
valpha2[m].removeError()
vslope1[m].removeError()
vslope2[m].removeError()
#signalNorm[m].setConstant(False) ## newly put here to ensure it's freely floating in the combine fit
#c1 = TCanvas("c1", "Crystal Ball", 1200, 1200) #if not isAH else 1200
#c1.Divide(2, 3)
c1 = TCanvas("c1", "Crystal Ball", 1800, 800)
c1.Divide(3, 2)
c1.cd(1)
gmean.SetMinimum(0.)
gmean.Draw("APL")
imean.Draw("P, SAME")
drawRegion(category)
drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME
c1.cd(2)
gsigma.SetMinimum(0.)
gsigma.Draw("APL")
isigma.Draw("P, SAME")
drawRegion(category)
drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME
c1.cd(3)
galpha1.Draw("APL")
ialpha1.Draw("P, SAME")
drawRegion(category)
drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME
galpha1.GetYaxis().SetRangeUser(0., 1.1) #adjusted upper limit from 5 to 2
c1.cd(4)
gslope1.Draw("APL")
islope1.Draw("P, SAME")
drawRegion(category)
drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME
gslope1.GetYaxis().SetRangeUser(0.,
150.) #adjusted upper limit from 125 to 60
if True: #isAH:
c1.cd(5)
galpha2.Draw("APL")
ialpha2.Draw("P, SAME")
drawRegion(category)
drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME
galpha2.GetYaxis().SetRangeUser(0., 2.)
c1.cd(6)
gslope2.Draw("APL")
islope2.Draw("P, SAME")
drawRegion(category)
drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME
gslope2.GetYaxis().SetRangeUser(0., 20.)
c1.Print(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" + category +
"_SignalShape.pdf")
c1.Print(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" + category +
"_SignalShape.png")
c2 = TCanvas("c2", "Signal Efficiency", 800, 600)
c2.cd(1)
gnorm.SetMarkerColor(cColor)
gnorm.SetMarkerStyle(20)
gnorm.SetLineColor(cColor)
gnorm.SetLineWidth(2)
gnorm.Draw("APL")
inorm.Draw("P, SAME")
gnorm.GetXaxis().SetRangeUser(genPoints[0] - 100, genPoints[-1] + 100)
gnorm.GetYaxis().SetRangeUser(0., gnorm.GetMaximum() * 1.25)
drawCMS(-1, "Simulation Preliminary", year=YEAR)
#drawCMS(-1, "Work in Progress", year=YEAR, suppressCMS=True)
#drawCMS(-1, "", year=YEAR, suppressCMS=True)
drawAnalysis(category)
drawRegion(category)
c2.Print(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" + category +
"_SignalNorm.pdf")
c2.Print(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" + category +
"_SignalNorm.png")
#*******************************************************#
# #
# Generate workspace #
# #
#*******************************************************#
# create workspace
w = RooWorkspace("Zprime_" + YEAR, "workspace")
for m in massPoints:
getattr(w, "import")(signal[m], RooFit.Rename(signal[m].GetName()))
getattr(w, "import")(signalNorm[m],
RooFit.Rename(signalNorm[m].GetName()))
getattr(w, "import")(signalXS[m], RooFit.Rename(signalXS[m].GetName()))
w.writeToFile(WORKDIR + "MC_signal_%s_%s.root" % (YEAR, category), True)
print "Workspace", WORKDIR + "MC_signal_%s_%s.root" % (
YEAR, category), "saved successfully"
def plotDistributionComparisonPlot(cfg):
multiGraph = TMultiGraph()
multiGraph.SetName("triggerRateMultiGraph")
tfiles = []
histograms = []
canvas = TCanvas("canvas", "canvas", 800, 800)
'''Contains the legend'''
legend = TLegend(0.3, 0.7, 0.90, 0.9)
'''Maximum value container, used to scale histograms'''
maximumY = float("-inf")
pad1 = TPad("pad1", "pad1", 0, 0.3, 1, 1.0)
pad1.SetBottomMargin(0.05) # Upper and lower plot are joined
#pad1.SetBottomMargin(0) # Upper and lower plot are joined
pad1.SetGridx() # Vertical grid
pad1.Draw() # Draw the upper pad: pad1
pad1.cd() # pad1 becomes the current pad
for histogramFileNameAndTitle in cfg.plots:
tfile = TFile(histogramFileNameAndTitle[0])
tfiles.append(tfile)
histogram = tfile.Get(histogramFileNameAndTitle[1])
histograms.append(histogram)
if histogram.ClassName() == "TH1F":
histogram.SetStats(0) # No statistics on upper plot
maximumY = histogram.GetMaximum(
) if histogram.GetMaximum() > maximumY else maximumY
legend.AddEntry(histogram, histogramFileNameAndTitle[2], "l")
# histograms[0] settings
histograms[0].SetMarkerColor(4)
histograms[0].SetLineColor(4)
histograms[0].SetLineWidth(1)
# Y axis histograms[0] plot settings
histograms[0].GetYaxis().SetTitleSize(20)
histograms[0].GetYaxis().SetTitleFont(43)
histograms[0].GetYaxis().SetTitleOffset(1.55)
#histograms[0].Scale(1./histograms[0].GetEntries())
if histograms[0].ClassName() == "TH1F":
histograms[0].Draw(
"SAME HIST") # Draw histograms[1] on top of histograms[0]
else:
histograms[0].Draw(
"SAME APE") # Draw histograms[1] on top of histograms[0]
#multiGraph.Add(histograms[0])
if getattr(cfg, "xRange", None) is not None:
histograms[0].GetXaxis().SetRangeUser(cfg.xRange[0], cfg.xRange[1])
gPad.RedrawAxis()
if getattr(cfg, "xAxisLabel", None) is not None:
histograms[0].GetXaxis().SetTitle(cfg.xAxisLabel)
gPad.RedrawAxis()
if getattr(cfg, "yAxisLabel", None) is not None:
histograms[0].GetYaxis().SetTitle(cfg.yAxisLabel)
gPad.RedrawAxis()
if getattr(cfg, "yRange", None) is not None:
histograms[0].GetYaxis().SetRangeUser(cfg.yRange[0], cfg.yRange[1])
gPad.RedrawAxis()
else:
maximumY *= 1.1
histograms[0].GetYaxis().SetRangeUser(1e-6, maximumY)
if getattr(cfg, "logY", False):
canvas.SetLogy()
# histograms[1] settings
histograms[1].SetMarkerColor(2)
histograms[1].SetLineColor(2)
histograms[1].SetLineWidth(1)
#histograms[1].Scale(1./histograms[1].GetEntries())
if histograms[1].ClassName() == "TH1F":
histograms[1].Draw(
"SAME HIST") # Draw histograms[1] on top of histograms[0]
else:
histograms[1].Draw(
"SAME PE") # Draw histograms[1] on top of histograms[0]
#multiGraph.Add(histograms[1])
#if multiGraph.GetListOfGraphs() != None:
# multiGraph.Draw("SAME PE")
# Do not draw the Y axis label on the upper plot and redraw a small
# axis instead, in order to avoid the first label (0) to be clipped.
#histograms[0].GetYaxis().SetLabelSize(0.)
#axis = TGaxis( 0, 20, 0, maximumY, 20, maximumY, 510,"")
#axis.SetLabelFont(43) # Absolute font size in pixel (precision 3)
#axis.SetLabelSize(15)
#axis.Draw()
# Adding a small text with the chi-squared
chiSquared = 0
if (histograms[0].ClassName() == "TGraph") or (histograms[0].ClassName()
== "TGraphErrors"):
numberOfBins = histograms[0].GetN()
numberOfDegreesOfFreedom = numberOfBins
else:
numberOfBins = histograms[0].GetNbinsX()
numberOfDegreesOfFreedom = numberOfBins
for x in xrange(
1, numberOfBins + 1
): # numberOfBins contains last bin, numberOfBins+1 contains the overflow (latter excluded), underflow also excluded
if (histograms[0].ClassName()
== "TGraph") or (histograms[0].ClassName() == "TGraphErrors"):
binContent0 = histograms[0].GetY()[x - 1]
else:
binContent0 = histograms[0].GetBinContent(x)
if (histograms[1].ClassName()
== "TGraph") or (histograms[1].ClassName() == "TGraphErrors"):
binContent1 = histograms[1].GetY()[x - 1]
else:
binContent1 = histograms[1].GetBinContent(x)
bin0ErrorSquared = binContent0
bin1ErrorSquared = binContent1
#bin1ErrorSquared = 0
if (binContent0 == 0) and (binContent1 == 0):
numberOfDegreesOfFreedom -= 1 #No data means one less degree of freedom
else:
binDifferenceSquared = (binContent0 - binContent1)**2
chiSquaredTerm = binDifferenceSquared / (bin0ErrorSquared +
bin1ErrorSquared)
chiSquared += chiSquaredTerm
if chiSquaredTerm > chiSquaredWarningThreshold:
if (histograms[0].ClassName()
== "TGraph") or (histograms[0].ClassName()
== "TGraphErrors"):
print "Bin", x, "-", histograms[0].GetX()[
x - 1], "has a CS=", chiSquaredTerm
else:
print "Bin", x, "-", histograms[0].GetBinCenter(
x), "has a CS=", chiSquaredTerm
chiSquareLabel = TPaveText(0.7, 0.6, 0.9, 0.4)
chiSquareLabel.AddText("#chi^{2}/ndf = " + str(chiSquared) + "/" +
str(numberOfDegreesOfFreedom) + " = " +
str(chiSquared / numberOfDegreesOfFreedom))
chiSquareLabel.Draw()
print "FINAL CS IS", format(
chiSquared,
".2f") + "/" + str(numberOfDegreesOfFreedom) + " = " + format(
chiSquared / numberOfDegreesOfFreedom, ".2f")
legend.SetHeader(
"#chi^{2}/ndf = " + format(chiSquared, ".2f") + "/" +
str(numberOfDegreesOfFreedom) + " = " +
format(chiSquared / numberOfDegreesOfFreedom, ".2f"), "C")
legend.Draw()
# lower plot will be in pad
canvas.cd() # Go back to the main canvas before defining pad2
pad2 = TPad("pad2", "pad2", 0, 0.05, 1, 0.3)
pad2.SetTopMargin(0)
pad2.SetBottomMargin(0.2)
pad2.SetGridx() # vertical grid
pad2.Draw()
pad2.cd() # pad2 becomes the current pad
pad2.SetGridy()
# Define the ratio plot
ratioPlot = TGraphErrors(histograms[0])
ratioPlot.SetName("ratioPlot")
graph_histo0 = TGraphErrors(histograms[0])
graph_histo1 = TGraphErrors(histograms[1])
ratioPlot.SetLineColor(1)
ratioPlot.SetMinimum(0.6) # Define Y ..
ratioPlot.SetMaximum(1.5) # .. range
#ratioPlot.Sumw2()
#ratioPlot.SetStats(0) # No statistics on lower plot
#Dividing point by point
for index in xrange(0, ratioPlot.GetN()):
if graph_histo1.GetY()[index] == 0:
ratioPlot.GetY()[index] = 0
ratioPlot.GetEY()[index] = 0
else:
ratioPlot.GetY()[index] /= graph_histo1.GetY()[index]
ratioPlot.GetEY()[index] = sqrt(
((graph_histo1.GetY()[index])**2 *
(graph_histo0.GetEY()[index])**2 +
(graph_histo0.GetY()[index])**2 *
(graph_histo1.GetEY()[index])**2) /
(graph_histo1.GetY()[index])**4)
ratioPlot.SetMarkerStyle(21)
if getattr(cfg, "xRange", None) is not None:
ratioPlot.GetXaxis().SetRangeUser(cfg.xRange[0], cfg.xRange[1])
gPad.RedrawAxis()
if getattr(cfg, "yRangeRatio", None) is not None:
ratioPlot.GetYaxis().SetRangeUser(cfg.yRangeRatio[0],
cfg.yRangeRatio[1])
gPad.RedrawAxis()
ratioPlot.Draw("APE") # Draw the ratio plot
line0 = TLine(ratioPlot.GetXaxis().GetXmin(), 1,
ratioPlot.GetXaxis().GetXmax(), 1)
line0.SetLineColor(2)
line0.SetLineWidth(2)
line0.SetLineStyle(2)
line0.Draw()
# Ratio plot (ratioPlot) settings
ratioPlot.SetTitle("") # Remove the ratio title
# Y axis ratio plot settings
ratioPlot.GetYaxis().SetTitle("Ratio #frac{blue}{red}")
ratioPlot.GetYaxis().SetNdivisions(505)
ratioPlot.GetYaxis().SetTitleSize(20)
ratioPlot.GetYaxis().SetTitleFont(43)
ratioPlot.GetYaxis().SetTitleOffset(1.55)
ratioPlot.GetYaxis().SetLabelFont(
43) # Absolute font size in pixel (precision 3)
ratioPlot.GetYaxis().SetLabelSize(15)
# X axis ratio plot settings
ratioPlot.GetXaxis().SetTitleSize(20)
ratioPlot.GetXaxis().SetTitleFont(43)
ratioPlot.GetXaxis().SetTitleOffset(4.)
ratioPlot.GetXaxis().SetLabelFont(
43) # Absolute font size in pixel (precision 3)
ratioPlot.GetXaxis().SetLabelSize(15)
xRangeBinning = getattr(cfg, "simplifiedRatioPlotXRangeBinning", None)
if xRangeBinning is not None:
simplifiedRatioPlot = TGraphErrors(len(xRangeBinning) - 1)
simplifiedRatioPlot.SetName("simplifiedRatioPlot")
ratioPlotIndex = 0
for idx in xrange(0, simplifiedRatioPlot.GetN()):
yAverage = 0.
yMax = float("-inf")
yMin = float("+inf")
nPoints = 0.
simplifiedRatioPlot.GetX()[idx] = (xRangeBinning[idx] +
xRangeBinning[idx + 1]) / 2.
simplifiedRatioPlot.GetEX()[idx] = (xRangeBinning[idx + 1] -
xRangeBinning[idx]) / 2.
while (ratioPlot.GetX()[ratioPlotIndex] < xRangeBinning[idx]):
ratioPlotIndex += 1
while ((ratioPlotIndex < ratioPlot.GetN()) and
(ratioPlot.GetX()[ratioPlotIndex] < xRangeBinning[idx + 1])
and
(ratioPlot.GetX()[ratioPlotIndex] >= xRangeBinning[idx])):
yAverage += ratioPlot.GetY()[ratioPlotIndex]
if (yMax < ratioPlot.GetY()[ratioPlotIndex] +
ratioPlot.GetEY()[ratioPlotIndex]):
yMax = ratioPlot.GetY()[ratioPlotIndex] + ratioPlot.GetEY(
)[ratioPlotIndex]
if (yMin > ratioPlot.GetY()[ratioPlotIndex] -
ratioPlot.GetEY()[ratioPlotIndex]):
yMin = ratioPlot.GetY()[ratioPlotIndex] - ratioPlot.GetEY(
)[ratioPlotIndex]
nPoints += 1.
ratioPlotIndex += 1
simplifiedRatioPlot.GetY()[idx] = yAverage / nPoints
simplifiedRatioPlot.GetEY()[idx] = (yMax - yMin) / 2.
saveFile = TFile(cfg.saveFileName, "RECREATE")
saveFile.cd()
canvas.Write()
histograms[0].Write()
histograms[1].Write()
if multiGraph.GetListOfGraphs() != None:
multiGraph.Write()
ratioPlot.Write()
if xRangeBinning is not None:
simplifiedRatioPlot.Write()
saveFile.Close()
for tfile in tfiles:
tfile.Close()
def signal(channel, stype):
if 'VBF' in channel:
stype = 'XZHVBF'
else:
stype = 'XZH'
# HVT model
if stype.startswith('X'):
signalType = 'HVT'
genPoints = [800, 1000, 1200, 1400, 1600, 1800, 2000, 2500, 3000, 3500, 4000, 4500, 5000]
massPoints = [x for x in range(800, 5000+1, 100)]
interPar = True
else:
print "Signal type", stype, "not recognized"
return
n = len(genPoints)
category = channel
cColor = color[category] if category in color else 1
nElec = channel.count('e')
nMuon = channel.count('m')
nLept = nElec + nMuon
nBtag = channel.count('b')
if '0b' in channel:
nBtag = 0
X_name = "VH_mass"
if not os.path.exists(PLOTDIR+stype+category): os.makedirs(PLOTDIR+stype+category)
#*******************************************************#
# #
# Variables and selections #
# #
#*******************************************************#
X_mass = RooRealVar( "X_mass", "m_{ZH}", XBINMIN, XBINMAX, "GeV")
J_mass = RooRealVar( "H_mass", "jet mass", LOWMIN, HIGMAX, "GeV")
V_mass = RooRealVar( "V_mass", "V jet mass", -9., 1.e6, "GeV")
CSV1 = RooRealVar( "H_csv1", "", -999., 2. )
CSV2 = RooRealVar( "H_csv2", "", -999., 2. )
DeepCSV1= RooRealVar( "H_deepcsv1", "", -999., 2. )
DeepCSV2= RooRealVar( "H_deepcsv2", "", -999., 2. )
H_ntag = RooRealVar( "H_ntag", "", -9., 9. )
H_dbt = RooRealVar( "H_dbt", "", -2., 2. )
H_tau21 = RooRealVar( "H_tau21", "", -9., 2. )
H_eta = RooRealVar( "H_eta", "", -9., 9. )
H_tau21_ddt = RooRealVar( "H_ddt", "", -9., 2. )
MaxBTag = RooRealVar( "MaxBTag", "", -10., 2. )
H_chf = RooRealVar( "H_chf", "", -1., 2. )
MinDPhi = RooRealVar( "MinDPhi", "", -1., 99. )
DPhi = RooRealVar( "DPhi", "", -1., 99. )
DEta = RooRealVar( "DEta", "", -1., 99. )
Mu1_relIso = RooRealVar( "Mu1_relIso", "", -1., 99. )
Mu2_relIso = RooRealVar( "Mu2_relIso", "", -1., 99. )
nTaus = RooRealVar( "nTaus", "", -1., 99. )
Vpt = RooRealVar( "V.Pt()", "", -1., 1.e6 )
V_pt = RooRealVar( "V_pt", "", -1., 1.e6 )
H_pt = RooRealVar( "H_pt", "", -1., 1.e6 )
VH_deltaR=RooRealVar( "VH_deltaR", "", -1., 99. )
isZtoNN = RooRealVar( "isZtoNN", "", 0., 2. )
isZtoEE = RooRealVar( "isZtoEE", "", 0., 2. )
isZtoMM = RooRealVar( "isZtoMM", "", 0., 2. )
isHtobb = RooRealVar( "isHtobb", "", 0., 2. )
isVBF = RooRealVar( "isVBF", "", 0., 2. )
isMaxBTag_loose = RooRealVar( "isMaxBTag_loose", "", 0., 2. )
weight = RooRealVar( "eventWeightLumi", "", -1.e9, 1.e9 )
Xmin = XBINMIN
Xmax = XBINMAX
# Define the RooArgSet which will include all the variables defined before
# there is a maximum of 9 variables in the declaration, so the others need to be added with 'add'
variables = RooArgSet(X_mass, J_mass, V_mass, CSV1, CSV2, H_ntag, H_dbt, H_tau21)
variables.add(RooArgSet(DEta, DPhi, MaxBTag, MinDPhi, nTaus, Vpt))
variables.add(RooArgSet(DeepCSV1, DeepCSV2,VH_deltaR, H_tau21_ddt))
variables.add(RooArgSet(isZtoNN, isZtoEE, isZtoMM, isHtobb, isMaxBTag_loose, weight))
variables.add(RooArgSet(isVBF, Mu1_relIso, Mu2_relIso, H_chf, H_pt, V_pt,H_eta))
#X_mass.setRange("X_extended_range", X_mass.getMin(), X_mass.getMax())
X_mass.setRange("X_reasonable_range", X_mass.getMin(), X_mass.getMax())
X_mass.setRange("X_integration_range", Xmin, Xmax)
X_mass.setBins(int((X_mass.getMax() - X_mass.getMin())/100))
binsXmass = RooBinning(int((X_mass.getMax() - X_mass.getMin())/100), X_mass.getMin(), X_mass.getMax())
X_mass.setBinning(binsXmass, "PLOT")
massArg = RooArgSet(X_mass)
# Cuts
SRcut = selection[category]+selection['SR']
print " Cut:\t", SRcut
#*******************************************************#
# #
# Signal fits #
# #
#*******************************************************#
treeSign = {}
setSignal = {}
vmean = {}
vsigma = {}
valpha1 = {}
vslope1 = {}
smean = {}
ssigma = {}
salpha1 = {}
sslope1 = {}
salpha2 = {}
sslope2 = {}
a1 = {}
a2 = {}
sbrwig = {}
signal = {}
signalExt = {}
signalYield = {}
signalIntegral = {}
signalNorm = {}
signalXS = {}
frSignal = {}
frSignal1 = {}
frSignal2 = {}
frSignal3 = {}
# Signal shape uncertainties (common amongst all mass points)
xmean_fit = RooRealVar("sig_p1_fit", "Variation of the resonance position with the fit uncertainty", 0.005, -1., 1.)
smean_fit = RooRealVar("CMSRunII_sig_p1_fit", "Change of the resonance position with the fit uncertainty", 0., -10, 10)
xmean_jes = RooRealVar("sig_p1_scale_jes", "Variation of the resonance position with the jet energy scale", 0.010, -1., 1.) #0.001
smean_jes = RooRealVar("CMSRunII_sig_p1_jes", "Change of the resonance position with the jet energy scale", 0., -10, 10)
xmean_e = RooRealVar("sig_p1_scale_e", "Variation of the resonance position with the electron energy scale", 0.001, -1., 1.)
smean_e = RooRealVar("CMSRunII_sig_p1_scale_e", "Change of the resonance position with the electron energy scale", 0., -10, 10)
xmean_m = RooRealVar("sig_p1_scale_m", "Variation of the resonance position with the muon energy scale", 0.001, -1., 1.)
smean_m = RooRealVar("CMSRunII_sig_p1_scale_m", "Change of the resonance position with the muon energy scale", 0., -10, 10)
xsigma_fit = RooRealVar("sig_p2_fit", "Variation of the resonance width with the fit uncertainty", 0.02, -1., 1.)
ssigma_fit = RooRealVar("CMSRunII_sig_p2_fit", "Change of the resonance width with the fit uncertainty", 0., -10, 10)
xsigma_jes = RooRealVar("sig_p2_scale_jes", "Variation of the resonance width with the jet energy scale", 0.010, -1., 1.) #0.001
ssigma_jes = RooRealVar("CMSRunII_sig_p2_jes", "Change of the resonance width with the jet energy scale", 0., -10, 10)
xsigma_jer = RooRealVar("sig_p2_scale_jer", "Variation of the resonance width with the jet energy resolution", 0.020, -1., 1.)
ssigma_jer = RooRealVar("CMSRunII_sig_p2_jer", "Change of the resonance width with the jet energy resolution", 0., -10, 10)
xsigma_e = RooRealVar("sig_p2_scale_e", "Variation of the resonance width with the electron energy scale", 0.001, -1., 1.)
ssigma_e = RooRealVar("CMSRunII_sig_p2_scale_e", "Change of the resonance width with the electron energy scale", 0., -10, 10)
xsigma_m = RooRealVar("sig_p2_scale_m", "Variation of the resonance width with the muon energy scale", 0.040, -1., 1.)
ssigma_m = RooRealVar("CMSRunII_sig_p2_scale_m", "Change of the resonance width with the muon energy scale", 0., -10, 10)
xalpha1_fit = RooRealVar("sig_p3_fit", "Variation of the resonance alpha with the fit uncertainty", 0.03, -1., 1.)
salpha1_fit = RooRealVar("CMSRunII_sig_p3_fit", "Change of the resonance alpha with the fit uncertainty", 0., -10, 10)
xslope1_fit = RooRealVar("sig_p4_fit", "Variation of the resonance slope with the fit uncertainty", 0.10, -1., 1.)
sslope1_fit = RooRealVar("CMSRunII_sig_p4_fit", "Change of the resonance slope with the fit uncertainty", 0., -10, 10)
xmean_fit.setConstant(True)
smean_fit.setConstant(True)
xmean_jes.setConstant(True)
smean_jes.setConstant(True)
xmean_e.setConstant(True)
smean_e.setConstant(True)
xmean_m.setConstant(True)
smean_m.setConstant(True)
xsigma_fit.setConstant(True)
ssigma_fit.setConstant(True)
xsigma_jes.setConstant(True)
ssigma_jes.setConstant(True)
xsigma_jer.setConstant(True)
ssigma_jer.setConstant(True)
xsigma_e.setConstant(True)
ssigma_e.setConstant(True)
xsigma_m.setConstant(True)
ssigma_m.setConstant(True)
xalpha1_fit.setConstant(True)
salpha1_fit.setConstant(True)
xslope1_fit.setConstant(True)
sslope1_fit.setConstant(True)
# the alpha method is now done.
for m in massPoints:
signalString = "M%d" % m
signalMass = "%s_M%d" % (stype, m)
signalName = "%s%s_M%d" % (stype, category, m)
signalColor = sample[signalMass]['linecolor'] if signalName in sample else 1
# define the signal PDF
vmean[m] = RooRealVar(signalName + "_vmean", "Crystal Ball mean", m, m*0.5, m*1.25)
smean[m] = RooFormulaVar(signalName + "_mean", "@0*(1+@1*@2)*(1+@3*@4)*(1+@5*@6)*(1+@7*@8)", RooArgList(vmean[m], xmean_e, smean_e, xmean_m, smean_m, xmean_jes, smean_jes, xmean_fit, smean_fit))
vsigma[m] = RooRealVar(signalName + "_vsigma", "Crystal Ball sigma", m*0.035, m*0.01, m*0.4)
sigmaList = RooArgList(vsigma[m], xsigma_e, ssigma_e, xsigma_m, ssigma_m, xsigma_jes, ssigma_jes, xsigma_jer, ssigma_jer)
sigmaList.add(RooArgList(xsigma_fit, ssigma_fit))
ssigma[m] = RooFormulaVar(signalName + "_sigma", "@0*(1+@1*@2)*(1+@3*@4)*(1+@5*@6)*(1+@7*@8)*(1+@9*@10)", sigmaList)
valpha1[m] = RooRealVar(signalName + "_valpha1", "Crystal Ball alpha", 1., 0., 5.) # number of sigmas where the exp is attached to the gaussian core. >0 left, <0 right
salpha1[m] = RooFormulaVar(signalName + "_alpha1", "@0*(1+@1*@2)", RooArgList(valpha1[m], xalpha1_fit, salpha1_fit))
vslope1[m] = RooRealVar(signalName + "_vslope1", "Crystal Ball slope", 10., 1., 60.) # slope of the power tail #10 1 60
sslope1[m] = RooFormulaVar(signalName + "_slope1", "@0*(1+@1*@2)", RooArgList(vslope1[m], xslope1_fit, sslope1_fit))
salpha2[m] = RooRealVar(signalName + "_alpha2", "Crystal Ball alpha", 2, 1., 5.) # number of sigmas where the exp is attached to the gaussian core. >0 left, <0 right
sslope2[m] = RooRealVar(signalName + "_slope2", "Crystal Ball slope", 10, 1.e-1, 115.) # slope of the power tail
#define polynomial
#a1[m] = RooRealVar(signalName + "_a1", "par 1 for polynomial", m, 0.5*m, 2*m)
a1[m] = RooRealVar(signalName + "_a1", "par 1 for polynomial", 0.001*m, 0.0005*m, 0.01*m)
a2[m] = RooRealVar(signalName + "_a2", "par 2 for polynomial", 0.05, -1.,1.)
#if channel=='nnbbVBF' or channel=='nn0bVBF':
# signal[m] = RooPolynomial(signalName,"m_{%s'} = %d GeV" % (stype[1], m) , X_mass, RooArgList(a1[m],a2[m]))
#else:
# signal[m] = RooCBShape(signalName, "m_{%s'} = %d GeV" % (stype[1], m), X_mass, smean[m], ssigma[m], salpha1[m], sslope1[m]) # Signal name does not have the channel
signal[m] = RooCBShape(signalName, "m_{%s'} = %d GeV" % (stype[1], m), X_mass, smean[m], ssigma[m], salpha1[m], sslope1[m]) # Signal name does not have the channel
# extend the PDF with the yield to perform an extended likelihood fit
signalYield[m] = RooRealVar(signalName+"_yield", "signalYield", 100, 0., 1.e6)
signalNorm[m] = RooRealVar(signalName+"_norm", "signalNorm", 1., 0., 1.e6)
signalXS[m] = RooRealVar(signalName+"_xs", "signalXS", 1., 0., 1.e6)
signalExt[m] = RooExtendPdf(signalName+"_ext", "extended p.d.f", signal[m], signalYield[m])
vslope1[m].setMax(50.)
vslope1[m].setVal(20.)
#valpha1[m].setVal(1.0)
#valpha1[m].setConstant(True)
if 'bb' in channel and 'VBF' not in channel:
if 'nn' in channel:
valpha1[m].setVal(0.5)
elif '0b' in channel and 'VBF' not in channel:
if 'nn' in channel:
if m==800:
valpha1[m].setVal(2.)
vsigma[m].setVal(m*0.04)
elif 'ee' in channel:
valpha1[m].setVal(0.8)
if m==800:
#valpha1[m].setVal(1.2)
valpha1[m].setVal(2.5)
vslope1[m].setVal(50.)
elif 'mm' in channel:
if m==800:
valpha1[m].setVal(2.)
vsigma[m].setVal(m*0.03)
else:
vmean[m].setVal(m*0.9)
vsigma[m].setVal(m*0.08)
elif 'bb' in channel and 'VBF' in channel:
if 'nn' in channel:
if m!=1800:
vmean[m].setVal(m*0.8)
vsigma[m].setVal(m*0.08)
valpha1[m].setMin(1.)
elif 'ee' in channel:
valpha1[m].setVal(0.7)
elif 'mm' in channel:
if m==800:
vslope1[m].setVal(50.)
valpha1[m].setVal(0.7)
elif '0b' in channel and 'VBF' in channel:
if 'nn' in channel:
valpha1[m].setVal(3.)
vmean[m].setVal(m*0.8)
vsigma[m].setVal(m*0.08)
valpha1[m].setMin(1.)
elif 'ee' in channel:
if m<2500:
valpha1[m].setVal(2.)
if m==800:
vsigma[m].setVal(m*0.05)
elif m==1000:
vsigma[m].setVal(m*0.03)
elif m>1000 and m<1800:
vsigma[m].setVal(m*0.04)
elif 'mm' in channel:
if m<2000:
valpha1[m].setVal(2.)
if m==1000 or m==1800:
vsigma[m].setVal(m*0.03)
elif m==1200 or m==1600:
vsigma[m].setVal(m*0.04)
#if m < 1000: vsigma[m].setVal(m*0.06)
# If it's not the proper channel, make it a gaussian
#if nLept==0 and 'VBF' in channel:
# valpha1[m].setVal(5)
# valpha1[m].setConstant(True)
# vslope1[m].setConstant(True)
# salpha2[m].setConstant(True)
# sslope2[m].setConstant(True)
# ---------- if there is no simulated signal, skip this mass point ----------
if m in genPoints:
if VERBOSE: print " - Mass point", m
# define the dataset for the signal applying the SR cuts
treeSign[m] = TChain("tree")
for j, ss in enumerate(sample[signalMass]['files']):
treeSign[m].Add(NTUPLEDIR + ss + ".root")
if treeSign[m].GetEntries() <= 0.:
if VERBOSE: print " - 0 events available for mass", m, "skipping mass point..."
signalNorm[m].setVal(-1)
vmean[m].setConstant(True)
vsigma[m].setConstant(True)
salpha1[m].setConstant(True)
sslope1[m].setConstant(True)
salpha2[m].setConstant(True)
sslope2[m].setConstant(True)
signalNorm[m].setConstant(True)
signalXS[m].setConstant(True)
continue
setSignal[m] = RooDataSet("setSignal_"+signalName, "setSignal", variables, RooFit.Cut(SRcut), RooFit.WeightVar(weight), RooFit.Import(treeSign[m]))
if VERBOSE: print " - Dataset with", setSignal[m].sumEntries(), "events loaded"
# FIT
signalYield[m].setVal(setSignal[m].sumEntries())
if treeSign[m].GetEntries(SRcut) > 5:
if VERBOSE: print " - Running fit"
frSignal[m] = signalExt[m].fitTo(setSignal[m], RooFit.Save(1), RooFit.Extended(True), RooFit.SumW2Error(True), RooFit.PrintLevel(-1))
if VERBOSE: print "********** Fit result [", m, "] **", category, "*"*40, "\n", frSignal[m].Print(), "\n", "*"*80
if VERBOSE: frSignal[m].correlationMatrix().Print()
drawPlot(signalMass, stype+channel, X_mass, signal[m], setSignal[m], frSignal[m])
else:
print " WARNING: signal", stype, "and mass point", m, "in channel", channel, "has 0 entries or does not exist"
# Remove HVT cross section (which is the same for Zlep and Zinv)
if stype == "XZHVBF":
sample_name = 'Zprime_VBF_Zh_Zlephinc_narrow_M-%d' % m
else:
sample_name = 'ZprimeToZHToZlepHinc_narrow_M%d' % m
xs = xsection[sample_name]['xsec']
signalXS[m].setVal(xs * 1000.)
signalIntegral[m] = signalExt[m].createIntegral(massArg, RooFit.NormSet(massArg), RooFit.Range("X_integration_range"))
boundaryFactor = signalIntegral[m].getVal()
if VERBOSE:
print " - Fit normalization vs integral:", signalYield[m].getVal(), "/", boundaryFactor, "events"
if channel=='nnbb' and m==5000:
signalNorm[m].setVal(2.5)
elif channel=='nn0b' and m==5000:
signalNorm[m].setVal(6.7)
else:
signalNorm[m].setVal( boundaryFactor * signalYield[m].getVal() / signalXS[m].getVal()) # here normalize to sigma(X) x Br(X->VH) = 1 [fb]
a1[m].setConstant(True)
a2[m].setConstant(True)
vmean[m].setConstant(True)
vsigma[m].setConstant(True)
valpha1[m].setConstant(True)
vslope1[m].setConstant(True)
salpha2[m].setConstant(True)
sslope2[m].setConstant(True)
signalNorm[m].setConstant(True)
signalXS[m].setConstant(True)
#*******************************************************#
# #
# Signal interpolation #
# #
#*******************************************************#
# ====== CONTROL PLOT ======
c_signal = TCanvas("c_signal", "c_signal", 800, 600)
c_signal.cd()
frame_signal = X_mass.frame()
for m in genPoints[:-2]:
if m in signalExt.keys():
signal[m].plotOn(frame_signal, RooFit.LineColor(sample["%s_M%d" % (stype, m)]['linecolor']), RooFit.Normalization(signalNorm[m].getVal(), RooAbsReal.NumEvent), RooFit.Range("X_reasonable_range"))
frame_signal.GetXaxis().SetRangeUser(0, 6500)
frame_signal.Draw()
drawCMS(-1, YEAR, "Simulation")
drawAnalysis(channel)
drawRegion(channel)
c_signal.SaveAs(PLOTDIR+"/"+stype+category+"/"+stype+"_Signal.pdf")
c_signal.SaveAs(PLOTDIR+"/"+stype+category+"/"+stype+"_Signal.png")
#if VERBOSE: raw_input("Press Enter to continue...")
# ====== CONTROL PLOT ======
# Normalization
gnorm = TGraphErrors()
gnorm.SetTitle(";m_{X} (GeV);integral (GeV)")
gnorm.SetMarkerStyle(20)
gnorm.SetMarkerColor(1)
gnorm.SetMaximum(0)
inorm = TGraphErrors()
inorm.SetMarkerStyle(24)
fnorm = TF1("fnorm", "pol9", 800, 5000) #"pol5" if not channel=="XZHnnbb" else "pol6" #pol5*TMath::Floor(x-1800) + ([5]*x + [6]*x*x)*(1-TMath::Floor(x-1800))
fnorm.SetLineColor(920)
fnorm.SetLineStyle(7)
fnorm.SetFillColor(2)
fnorm.SetLineColor(cColor)
# Mean
gmean = TGraphErrors()
gmean.SetTitle(";m_{X} (GeV);gaussian mean (GeV)")
gmean.SetMarkerStyle(20)
gmean.SetMarkerColor(cColor)
gmean.SetLineColor(cColor)
imean = TGraphErrors()
imean.SetMarkerStyle(24)
fmean = TF1("fmean", "pol1", 0, 5000)
fmean.SetLineColor(2)
fmean.SetFillColor(2)
# Width
gsigma = TGraphErrors()
gsigma.SetTitle(";m_{X} (GeV);gaussian width (GeV)")
gsigma.SetMarkerStyle(20)
gsigma.SetMarkerColor(cColor)
gsigma.SetLineColor(cColor)
isigma = TGraphErrors()
isigma.SetMarkerStyle(24)
fsigma = TF1("fsigma", "pol1", 0, 5000)
fsigma.SetLineColor(2)
fsigma.SetFillColor(2)
# Alpha1
galpha1 = TGraphErrors()
galpha1.SetTitle(";m_{X} (GeV);crystal ball lower alpha")
galpha1.SetMarkerStyle(20)
galpha1.SetMarkerColor(cColor)
galpha1.SetLineColor(cColor)
ialpha1 = TGraphErrors()
ialpha1.SetMarkerStyle(24)
falpha1 = TF1("falpha", "pol0", 0, 5000)
falpha1.SetLineColor(2)
falpha1.SetFillColor(2)
# Slope1
gslope1 = TGraphErrors()
gslope1.SetTitle(";m_{X} (GeV);exponential lower slope (1/Gev)")
gslope1.SetMarkerStyle(20)
gslope1.SetMarkerColor(cColor)
gslope1.SetLineColor(cColor)
islope1 = TGraphErrors()
islope1.SetMarkerStyle(24)
fslope1 = TF1("fslope", "pol0", 0, 5000)
fslope1.SetLineColor(2)
fslope1.SetFillColor(2)
# Alpha2
galpha2 = TGraphErrors()
galpha2.SetTitle(";m_{X} (GeV);crystal ball upper alpha")
galpha2.SetMarkerStyle(20)
galpha2.SetMarkerColor(cColor)
galpha2.SetLineColor(cColor)
ialpha2 = TGraphErrors()
ialpha2.SetMarkerStyle(24)
falpha2 = TF1("falpha", "pol0", 0, 5000)
falpha2.SetLineColor(2)
falpha2.SetFillColor(2)
# Slope2
gslope2 = TGraphErrors()
gslope2.SetTitle(";m_{X} (GeV);exponential upper slope (1/Gev)")
gslope2.SetMarkerStyle(20)
gslope2.SetMarkerColor(cColor)
gslope2.SetLineColor(cColor)
islope2 = TGraphErrors()
islope2.SetMarkerStyle(24)
fslope2 = TF1("fslope", "pol0", 0, 5000)
fslope2.SetLineColor(2)
fslope2.SetFillColor(2)
n = 0
for i, m in enumerate(genPoints):
if not m in signalNorm.keys(): continue
if signalNorm[m].getVal() < 1.e-6: continue
signalString = "M%d" % m
signalName = "%s_M%d" % (stype, m)
if gnorm.GetMaximum() < signalNorm[m].getVal(): gnorm.SetMaximum(signalNorm[m].getVal())
gnorm.SetPoint(n, m, signalNorm[m].getVal())
gmean.SetPoint(n, m, vmean[m].getVal())
gmean.SetPointError(n, 0, min(vmean[m].getError(), vmean[m].getVal()*0.02))
gsigma.SetPoint(n, m, vsigma[m].getVal())
gsigma.SetPointError(n, 0, min(vsigma[m].getError(), vsigma[m].getVal()*0.05))
galpha1.SetPoint(n, m, valpha1[m].getVal())
galpha1.SetPointError(n, 0, min(valpha1[m].getError(), valpha1[m].getVal()*0.10))
gslope1.SetPoint(n, m, vslope1[m].getVal())
gslope1.SetPointError(n, 0, min(vslope1[m].getError(), vslope1[m].getVal()*0.10))
galpha2.SetPoint(n, m, salpha2[m].getVal())
galpha2.SetPointError(n, 0, min(salpha2[m].getError(), salpha2[m].getVal()*0.10))
gslope2.SetPoint(n, m, sslope2[m].getVal())
gslope2.SetPointError(n, 0, min(sslope2[m].getError(), sslope2[m].getVal()*0.10))
n = n + 1
print "fit on gmean:"
gmean.Fit(fmean, "Q0", "SAME")
print "fit on gsigma:"
gsigma.Fit(fsigma, "Q0", "SAME")
print "fit on galpha:"
galpha1.Fit(falpha1, "Q0", "SAME")
print "fit on gslope:"
gslope1.Fit(fslope1, "Q0", "SAME")
galpha2.Fit(falpha2, "Q0", "SAME")
gslope2.Fit(fslope2, "Q0", "SAME")
#for m in [5000, 5500]: gnorm.SetPoint(gnorm.GetN(), m, gnorm.Eval(m, 0, "S"))
gnorm.Fit(fnorm, "Q", "SAME", 700, 5000)
for m in massPoints:
signalName = "%s_M%d" % (stype, m)
if vsigma[m].getVal() < 10.: vsigma[m].setVal(10.)
# Interpolation method
syield = gnorm.Eval(m)
spline = gnorm.Eval(m, 0, "S")
sfunct = fnorm.Eval(m)
#delta = min(abs(1.-spline/sfunct), abs(1.-spline/syield))
delta = abs(1.-spline/sfunct) if sfunct > 0 else 0
syield = spline
if interPar:
jmean = gmean.Eval(m)
jsigma = gsigma.Eval(m)
jalpha1 = galpha1.Eval(m)
jslope1 = gslope1.Eval(m)
else:
jmean = fmean.GetParameter(0) + fmean.GetParameter(1)*m + fmean.GetParameter(2)*m*m
jsigma = fsigma.GetParameter(0) + fsigma.GetParameter(1)*m + fsigma.GetParameter(2)*m*m
jalpha1 = falpha1.GetParameter(0) + falpha1.GetParameter(1)*m + falpha1.GetParameter(2)*m*m
jslope1 = fslope1.GetParameter(0) + fslope1.GetParameter(1)*m + fslope1.GetParameter(2)*m*m
inorm.SetPoint(inorm.GetN(), m, syield)
signalNorm[m].setVal(syield)
imean.SetPoint(imean.GetN(), m, jmean)
if jmean > 0: vmean[m].setVal(jmean)
isigma.SetPoint(isigma.GetN(), m, jsigma)
if jsigma > 0: vsigma[m].setVal(jsigma)
ialpha1.SetPoint(ialpha1.GetN(), m, jalpha1)
if not jalpha1==0: valpha1[m].setVal(jalpha1)
islope1.SetPoint(islope1.GetN(), m, jslope1)
if jslope1 > 0: vslope1[m].setVal(jslope1)
c1 = TCanvas("c1", "Crystal Ball", 1200, 800)
c1.Divide(2, 2)
c1.cd(1)
gmean.SetMinimum(0.)
gmean.Draw("APL")
imean.Draw("P, SAME")
drawRegion(channel)
c1.cd(2)
gsigma.SetMinimum(0.)
gsigma.Draw("APL")
isigma.Draw("P, SAME")
drawRegion(channel)
c1.cd(3)
galpha1.Draw("APL")
ialpha1.Draw("P, SAME")
drawRegion(channel)
galpha1.GetYaxis().SetRangeUser(0., 5.)
c1.cd(4)
gslope1.Draw("APL")
islope1.Draw("P, SAME")
drawRegion(channel)
gslope1.GetYaxis().SetRangeUser(0., 125.)
if False:
c1.cd(5)
galpha2.Draw("APL")
ialpha2.Draw("P, SAME")
drawRegion(channel)
c1.cd(6)
gslope2.Draw("APL")
islope2.Draw("P, SAME")
drawRegion(channel)
gslope2.GetYaxis().SetRangeUser(0., 10.)
c1.Print(PLOTDIR+stype+category+"/"+stype+"_SignalShape.pdf")
c1.Print(PLOTDIR+stype+category+"/"+stype+"_SignalShape.png")
c2 = TCanvas("c2", "Signal Efficiency", 800, 600)
c2.cd(1)
gnorm.SetMarkerColor(cColor)
gnorm.SetMarkerStyle(20)
gnorm.SetLineColor(cColor)
gnorm.SetLineWidth(2)
gnorm.Draw("APL")
inorm.Draw("P, SAME")
gnorm.GetXaxis().SetRangeUser(genPoints[0]-100, genPoints[-1]+100)
gnorm.GetYaxis().SetRangeUser(0., gnorm.GetMaximum()*1.25)
drawCMS(-1,YEAR , "Simulation")
drawAnalysis(channel)
drawRegion(channel)
c2.Print(PLOTDIR+stype+category+"/"+stype+"_SignalNorm.pdf")
c2.Print(PLOTDIR+stype+category+"/"+stype+"_SignalNorm.png")
#*******************************************************#
# #
# Generate workspace #
# #
#*******************************************************#
# create workspace
w = RooWorkspace("ZH_RunII", "workspace")
for m in massPoints:
getattr(w, "import")(signal[m], RooFit.Rename(signal[m].GetName()))
getattr(w, "import")(signalNorm[m], RooFit.Rename(signalNorm[m].GetName()))
getattr(w, "import")(signalXS[m], RooFit.Rename(signalXS[m].GetName()))
w.writeToFile("%s%s.root" % (WORKDIR, stype+channel), True)
print "Workspace", "%s%s.root" % (WORKDIR, stype+channel), "saved successfully"
sys.exit()
class RatePlotProducerPileUp(Analyzer):
'''Analyzer creating a rate plot with pile up events and saving it to ROOT and SVG format.
Example::
rate = cfg.Analyzer(
RatePlotProducerPileUp,
file_label = 'tfile1',
plot_name = 'rate',
plot_title = 'A rate plot',
zerobias_rate = 1/25e-9,
input_objects = 'jets',
bins = [30, 40, 50, 60],
yscale = 1e6,
scale_factors = [3, 5, 4]
)
* file_label: (Facultative) Name of a TFileService. If specified, the histogram will be saved in that root file, otherwise it will be saved in a <plot_name>.png and <plot_name>.root file
* plot_name: Name of the plot (Key in the output root file).
* plot_title: Title of the plot.
* bins: Array containing the bins to be tested
* zerobias_rate: zero bias trigger rate, 40 MHz @ 25 ns bunch spacing
* input_objects: name of the particle collection
* yscale: y level of the reference line
* scale_factors: custom factors to be applied to the bin, 1 is assumed in case of missing parameter or if less factors than bins are provided
'''
'''Generates a threshold function'''
def thresholdTriggerGenerator(self, threshold):
def thresholdTrigger(ptc):
return ptc.pt() > threshold
return thresholdTrigger
def beginLoop(self, setup):
super(RatePlotProducerPileUp, self).beginLoop(setup)
self.hasTFileService = hasattr(self.cfg_ana, "file_label")
if self.hasTFileService:
servname = '_'.join([
'heppy.framework.services.tfile.TFileService',
self.cfg_ana.file_label
])
tfileservice = setup.services[servname]
self.rootfile = tfileservice.file
else:
self.rootfile = TFile(
'/'.join([self.dirName, self.cfg_ana.plot_name + '.root']),
'recreate')
bins = array("f", self.cfg_ana.bins)
self.histogram = TH1F(self.cfg_ana.plot_name, self.cfg_ana.plot_title,
len(bins) - 1, bins)
self.numberOfEvents = self.cfg_comp.nGenEvents
def process(self, event):
'''Process the event.
event must contain
* self.cfg_ana.input_objects: collection of objects to be selected
These objects must be usable by the filtering function
self.cfg_ana.trigger_func.
'''
input_collection = getattr(event, self.cfg_ana.input_objects)
#We want accept events without objects if the threshold is 0 or less
startIdx = 0
#Adding events for that thresholds
for x in range(0, len(self.cfg_ana.bins) - 1):
if self.cfg_ana.bins[x] <= 0:
self.histogram.AddBinContent(x + 1)
startIdx = x + 1
#startIdx keeps track of where the positive thresholds start
# MET is not iterable, it is a single object
# We treat here single objects
if not isinstance(input_collection, collections.Iterable):
for x in range(startIdx, len(self.cfg_ana.bins) - 1):
# Preparing the check function
trigger_func = self.thresholdTriggerGenerator(
self.cfg_ana.bins[x])
# Checking if the object passes the trigger
if trigger_func(input_collection):
self.histogram.AddBinContent(x + 1)
else:
#If no item passes the threshold I can stop
break
elif isinstance(input_collection, collections.Mapping):
# Iterating through all the objects
for x in range(startIdx, len(self.cfg_ana.bins) - 1):
# Checking what thresholds are satisfied
isPassed = False
for key, val in input_collection.iteritems():
# Preparing the check function
trigger_func = self.thresholdTriggerGenerator(
self.cfg_ana.bins[x])
# Checking if the object passes the trigger
if trigger_func(val):
self.histogram.AddBinContent(x + 1)
isPassed = True
# We don't need to check for other objects
break
if not isPassed:
#If no objects passes the threshold I can stop
break
else:
for x in range(startIdx, len(self.cfg_ana.bins) - 1):
# Checking what thresholds are satisfied
isPassed = False
for obj in input_collection:
# Preparing the check function
trigger_func = self.thresholdTriggerGenerator(
self.cfg_ana.bins[x])
# Checking if the object passes the trigger
if trigger_func(obj):
self.histogram.AddBinContent(x + 1)
isPassed = True
# We don't need to check for other objects
break
if not isPassed:
#If no objects passes the threshold I can stop
break
def write(self, setup):
self.rootfile.cd()
#Rescaling to corresponding rate
if self.cfg_ana.normalise:
normalisation = self.cfg_ana.zerobias_rate / self.numberOfEvents
self.graphErrors = TGraphErrors(self.histogram)
for x in xrange(0, self.graphErrors.GetN()):
#Rescaling everything to have rates
self.graphErrors.GetEY()[x] *= normalisation
self.graphErrors.GetY()[x] *= normalisation
self.graphErrors.SetName(self.cfg_ana.plot_name)
self.histogram = self.graphErrors
if hasattr(self.cfg_ana, "scale_factors"):
for x in xrange(0, len(self.cfg_ana.scale_factors)):
self.histogram.SetBinContent(
x + 1,
self.histogram.GetBinContent(x + 1) *
self.cfg_ana.scale_factors[x])
self.histogram.Write()
xMax = self.histogram.GetXaxis().GetXmax()
xMin = self.histogram.GetXaxis().GetXmin()
yMin = self.histogram.GetMinimum()
yMax = self.histogram.GetMaximum()
self.histogram.SetMarkerStyle(20)
self.histogram.SetMarkerColor(4)
self.histogram.SetLineColor(1)
c1 = TCanvas("canvas_" + self.cfg_ana.plot_name,
self.cfg_ana.plot_title, 600, 600)
c1.SetGridx()
c1.SetGridy()
self.histogram.Draw("PE")
c1.SetLogy(True)
c1.Update()
c1.Print(self.cfg_ana.plot_name + ".svg", "svg")
NSMassPoint.GetXaxis().SetLabelSize(0.15)
NSMassPoint.GetYaxis().SetLabelSize(0.05)
NSMassPoint.GetYaxis().SetTitleOffset(0.42) # 1.2 = 20% larger
NSMassPoint.SetTitleOffset(0.42) # 1.2 = 20% larger
NSMassPoint.SetTitle("Bkg Estimtae in each signal injected mass point: Trijet Inclusive Window: "+str(windowWidth))
NSMassPoint.SetMarkerStyle(1)
NSMassPoint.GetYaxis().SetRange(1, 1000000)
NSMassPoint.SetMarkerColor(0)
NSMassPoint.SetLineColor(0)
NSMassPoint.Draw() #this is to make sure the x axis of pad1 mataches that of pad2
NSErrorBar.SetFillColor(29)
NSErrorBar.SetMarkerColor(1)
NSErrorBar.Draw("LE3 same")
NSErrorBar.Draw("P same")
#NSMassPoint.Draw("E same")
print("N : ", NSErrorBar.GetN())
#NSErrorBar.Draw("P")
#JBMassPoint.SetMarkerStyle(2)
#JBMassPoint.SetMarkerColor(2)
#JBMassPoint.SetLineColor(2)
#JBMassPoint.Draw("P same")
JBGraph.SetMarkerStyle(5)
JBGraph.SetMarkerColor(2)
JBGraph.SetLineColor(2)
JBGraph.Draw("P same")
r.gStyle.SetOptStat(0);
#leg = ROOT.TLegend(0.5,0.34,0.81,0.51)
leg = ROOT.TLegend(0.6,0.7,0.99,0.90)
def main():
args = parser()
filename = args.filename
os.system('mkdir -p rootfiles')
os.system('mkdir -p plots')
f = tables.open_file(filename)
print(f"Successfully opened file: {filename}.")
data = f.get_node('/data')
print("Read /data")
event_ids = getDataCol('event_id', data)
waveforms = getDataCol('waveform', data)
times = getDataCol('time', data)
timestamps = getDataCol('timestamp', data)
thresFlags = getDataCol('thresholdFlags', data)
i_1V1 = getDataCol('i_1V1', data)
i_1V35 = getDataCol('i_1V35', data)
i_1V8 = getDataCol('i_1V8', data)
i_2V5 = getDataCol('i_2V5', data)
i_3V3 = getDataCol('i_3V3', data)
v_1V1 = getDataCol('v_1V1', data)
v_1V35 = getDataCol('v_1V35', data)
v_1V8 = getDataCol('v_1V8', data)
v_2V5 = getDataCol('v_2V5', data)
v_3V3 = getDataCol('v_3V3', data)
nsamples = len(waveforms[0])
fq = np.linspace(0, 240, nsamples)
gmean = np.mean(waveforms[0])
globalmean = np.mean(waveforms)
print(np.mean(waveforms[0]))
c = TCanvas("c", "c", 800, 600)
c.Draw()
c.SetGrid()
of = TFile('rootfiles/{0}.root'.format(filename.split('.h', 1)[0]),
"RECREATE")
h = TH1D('qdist', 'qdist;ADC count;Entry', 500, -100, 900)
hpk = TH1D('peak', 'peak;ADC count;Entry', 500, -100, 900)
havg = TH1D('avgwf', 'Averaged Waveform;Sampling Bin;ADC count', nsamples,
0, nsamples)
havgfft = TH1D('avgfft',
'FFT Averaged Waveform;Frequency [MHz];Amplitude [LSB]',
int(len(fq) / 2) + 1, 0, 120 + 240 / (nsamples - 1))
hifft = TH1D('Subtwf', 'Averaged Waveform;Sampling Bin;ADC count',
nsamples, 0, nsamples)
hsfft = TH1D('Subtfft',
'FFT Averaged Waveform;Frequency [MHz];Amplitude [LSB]',
int(len(fq) / 2) + 1, 0, 120 + 240 / (nsamples - 1))
hsspe = TH1D('qdist_subt', 'qdist_subt;ADC count;Entry', 500, -100, 900)
hmax = TH1D('hmax', 'hmax;ADC count;Entry', 500, -100, 900)
bsfluc = TH1D('bsfluc', 'bsfluc;ADC count;Entry', 100,
int(globalmean) - 50,
int(globalmean) + 50)
bsshift = TGraphErrors()
bsshift.SetName('bsshift')
bsshift.SetTitle('bsshift;Event Number;Baseline [LSB]')
nrshift = TGraphErrors()
nrshift.SetName('nrshift')
nrshift.SetTitle('NoiseRateShift;Event Number;Noise Rate [Hz]')
rmsshift = TGraphErrors()
rmsshift.SetName('rmsshift')
rmsshift.SetTitle('RMSNoiseShift;Event Number;RMS Noise [LSB]')
rmstime = TGraphErrors()
rmstime.SetName('rmstime')
rmstime.SetTitle('rmstime;Unix time [s];RMS Noise [LSB]')
powerc = TGraphErrors()
powerc.SetName('powerc')
powerc.SetTitle('powerc;Event Number;Power Consumption [W]')
h_powerc = TH1D('h_powerc', 'h_powerc;Power Consumption [W];Entry', 100, 0,
5)
winmin = args.minimum
winmax = args.minimum + args.window
bsstart = args.baselineEst
print(f'Total #Events: {len(waveforms)}')
print(
f'Setting... Window [{winmin}:{winmax}] and Pedestal start from {bsstart}.\n'
)
topdir = gDirectory.GetDirectory(gDirectory.GetPath())
subdir = topdir.mkdir("Waveforms")
subdir2 = topdir.mkdir("FFT")
subdir3 = topdir.mkdir("proj")
thrdir = topdir.mkdir("thresFlags")
fltwfs = []
starttimestamp = timestamps[0]
procN = len(waveforms)
if args.nevents > 0:
procN = args.nevents
for i in tqdm(range(procN)):
if i < args.nskip:
continue
waveform = waveforms[i]
timestamp = timestamps[i]
bsfluc.Fill(np.mean(waveform))
n = bsshift.GetN()
bsshift.Set(n + 1)
bsshift.SetPoint(n, i, np.mean(waveform))
bsshift.SetPointError(n, 0, np.std(waveform))
n = rmsshift.GetN()
rmsshift.Set(n + 1)
rmsshift.SetPoint(n, i, np.std(waveform))
rmsshift.SetPointError(n, 0, 0)
hmax.Fill(np.max(waveform) - np.mean(waveform))
timeinterval = ((timestamp - starttimestamp - i * nsamples) / 240.e6 -
i * 0.501)
nr = 0
nrerr = 0
if timeinterval > 0:
nr = (i + 1) / timeinterval
nrerr = np.sqrt(i + 1) / timeinterval
n = nrshift.GetN()
nrshift.Set(n + 1)
nrshift.SetPoint(n, i, nr)
nrshift.SetPointError(n, 0, nrerr)
# FFT & IFFT ###
F_abs_amp = doFFT(waveform)
F = np.fft.fft(waveform)
F2 = np.copy(F)
fc = 60
df = 2
F2[(fq > fc - df) & (fq < fc + df)] = 0
F2[(fq > 2 * fc - df) & (fq < 2 * fc + df)] = 0
F2[(fq > 240 - fc - df) & (fq < 240 - fc + df)] = 0
F2_abs = np.abs(F2)
F2_abs_amp = F2_abs / len(waveform) * 2
F2_abs_amp[0] = F_abs_amp[0] / 2
F2_ifft = np.fft.ifft(F2)
F2_ifft_real = F2_ifft.real
######
baseline_mean = np.mean(waveform[bsstart:nsamples])
if args.fixbs:
baseline_mean = globalmean
center = int(baseline_mean)
proj = TH1D(f'proj{i}', f'Projection waveform{i};ADC count;Entry', 300,
center - 150, center + 150)
selected = waveform[waveform < np.mean(waveform) + 10]
for j in range(len(selected)):
proj.Fill(selected[j])
#proj.Fit("gaus")
#if len(selected) > 0:
# f = proj.GetFunction('gaus')
# norm = f.GetParameter(0)
# mean = f.GetParameter(1)
# sigma = f.GetParameter(2)
reduced_waveform = waveform - baseline_mean
scale = (nsamples - bsstart) / (winmax - winmin)
#h.Fill(sum(waveform[winmin:winmax])-sum(waveform[bsstart:nsamples])/scale)
h.Fill(sum(reduced_waveform[winmin:winmax]))
hsspe.Fill(
np.sum(F2_ifft_real[winmin:winmax]) - baseline_mean *
(winmax - winmin))
hpk.Fill(
max(waveform[winmin:winmax]) - np.mean(waveform[bsstart:nsamples]))
hfft = TH1D(f'FFT{i}', 'FFT{i};Frequency [MHz];Amplitude [LSB]',
int(len(fq) / 2) + 1, 0, 120 + 240 / (nsamples - 1))
hfft2 = TH1D(f'FFT2{i}', 'FFT2{i};Frequency [MHz];Amplitude [LSB]',
int(len(fq) / 2) + 1, 0, 120 + 240 / (nsamples - 1))
for j in range(int(len(F_abs_amp) / 2. + 1)):
hfft.Fill(fq[j], F_abs_amp[j])
hfft2.Fill(fq[j], F2_abs_amp[j])
h2 = TH1D(f'w{i}', 'Waveform{i};Sampling Bin;ADC count', nsamples, 0,
nsamples)
for j in range(len(waveform)):
h2.Fill(j, waveform[j])
#h2.Draw("hist")
#if np.max(F2_abs_amp[1:int(len(F2_abs_amp)/2.+1)]) > 0.5:
# c.SetLogy(0)
# h2.SetLineColor(4)
# h2.Draw("hist")
# c.Print(f"plots/w{i}.pdf(")
# c.SetLogy(1)
# hfft.SetLineColor(4)
# hfft.Draw("hist")
# c.Print(f"plots/w{i}.pdf)")
htot = TH1D(f'thresflags{i}',
'ThresholdFlags{i};Sampling Bin;Threshold Flag', nsamples,
0, nsamples)
for j in range(len(thresFlags[i])):
htot.Fill(j, thresFlags[i][j])
if max(waveform) - baseline_mean < args.threshold:
continue
fltwfs.append(waveform)
if args.silent:
del hfft
del hfft2
del h2
del htot
del proj
del waveform
del timestamp
del F
del F2
gc.collect()
continue
subdir2.cd()
hfft.Write()
subdir.cd()
h2.Write()
subdir3.cd()
proj.Write()
thrdir.cd()
htot.Write()
print('')
for i in range(len(i_1V1)):
internal_power = v_1V1[i] * i_1V1[i] + v_1V35[i] * i_1V35[i] + v_1V8[
i] * i_1V8[i] + v_2V5[i] * i_2V5[i] + v_3V3[i] * i_3V3[i]
n = powerc.GetN()
powerc.Set(n + 1)
powerc.SetPoint(n, i, internal_power * 1e-3)
powerc.SetPointError(n, 0, 0)
h_powerc.Fill(internal_power * 1e-3)
avgfltwfs = np.mean(fltwfs, axis=0)
for i in range(len(avgfltwfs)):
havg.Fill(i, avgfltwfs[i])
Favg_abs_amp = doFFT(avgfltwfs)
Favg = np.fft.fft(avgfltwfs)
Favg2 = np.copy(Favg)
Favg2[(fq > 59) & (fq < 61)] = 0
Favg2[(fq > 119) & (fq < 121)] = 0
Favg2[(fq > 179) & (fq < 181)] = 0
Favg2_abs = np.abs(Favg2)
Favg2_abs_amp = Favg2_abs / len(avgfltwfs) * 2
Favg2_abs_amp[0] = Favg_abs_amp[0] / 2
Favg2_ifft = np.fft.ifft(Favg2)
Favg2_ifft_real = Favg2_ifft.real
for i in range(int(len(Favg_abs_amp) / 2. + 1)):
havgfft.Fill(fq[i], Favg_abs_amp[i])
for i in range(int(len(Favg2_abs_amp) / 2. + 1)):
hsfft.Fill(fq[i], Favg2_abs_amp[i])
for i in range(len(Favg2_ifft_real)):
hifft.Fill(i, Favg2_ifft_real[i])
topdir.cd()
h.Write()
hpk.Write()
havg.Write()
havgfft.Write()
hsfft.Write()
hifft.Write()
hsspe.Write()
hmax.Write()
bsfluc.Write()
bsshift.Write()
nrshift.Write()
rmsshift.Write()
powerc.Write()
h_powerc.Write()
of.Close()
f.close()
