def TextFileToRootGraphs(self, limit_text_file,med_idx=0):#, limit_text_filename):
filename = limit_text_file #limit_text_filename
limit_root_file = limit_text_file.replace(".txt",".root")
f = open(filename,"r")
med=array('f')
mchi=array('f')
expm2=array('f')
expm1=array('f')
expmed=array('f')
expp1=array('f')
expp2=array('f')
obs=array('f')
errx=array('f')
for line in f:
if len(line.rsplit())<7: continue
med.append(float(line.rstrip().split()[1]))
mchi.append(float(line.rstrip().split()[0]))
expm2.append(float(line.rstrip().split()[4]) - float(line.rstrip().split()[2]) )
expm1.append(float(line.rstrip().split()[4]) - float(line.rstrip().split()[3]) )
expmed.append(float(line.rstrip().split()[4]))
expp1.append(float(line.rstrip().split()[5]) - float(line.rstrip().split()[4]) )
expp2.append(float(line.rstrip().split()[6]) - float(line.rstrip().split()[4]) )
obs.append(float(line.rstrip().split()[7]))
errx.append(0.0)
print ('expm2: ', expm2)
print ('expm1: ', expm1)
print ('expmed: ', expmed)
print ('expp1: ', expp1)
print ('expp2: ', expp2)
g_exp2 = TGraphAsymmErrors(int(len(med)), med, expmed, errx, errx, expm2, expp2 ) ; g_exp2.SetName("exp2")
g_exp1 = TGraphAsymmErrors(int(len(med)), med, expmed, errx, errx, expm1, expp1 ) ; g_exp1.SetName("exp1")
g_expmed = TGraphAsymmErrors(int(len(med)), med, expmed) ; g_expmed.SetName("expmed")
g_obs = TGraphAsymmErrors(int(len(med)), med, obs ) ; g_obs.SetName("obs")
f1 = TFile(limit_root_file,'RECREATE')
g_exp2.Write()
g_exp1.Write()
g_expmed.Write()
g_obs.Write()
f1.Write()
f1.Close()
return limit_root_file
def HistToAsymmErrs(name,hist) :
X = []
EXlow = []
EXhigh = []
Y = []
EYlow = []
EYhigh = []
for i in range(hist.GetNbinsX()) :
X .append(hist.GetBinCenter (i+1))
EXlow .append(hist.GetBinWidth (i+1)/2.)
EXhigh.append(hist.GetBinWidth (i+1)/2.)
Y .append(hist.GetBinContent(i+1))
EYlow .append(hist.GetBinError (i+1))
EYhigh.append(hist.GetBinError (i+1))
a_X = array('d',X )
a_EXlow = array('d',EXlow )
a_EXhigh = array('d',EXhigh )
a_Y = array('d',Y )
a_EYlow = array('d',EYlow )
a_EYhigh = array('d',EYhigh )
asymm = TGraphAsymmErrors(len(a_X),a_X,a_Y,a_EXlow,a_EXhigh,a_EYlow,a_EYhigh)
asymm.GetXaxis().SetTitle(hist.GetXaxis().GetTitle())
asymm.GetYaxis().SetTitle(hist.GetYaxis().GetTitle())
asymm.SetLineWidth(2)
asymm.SetName(name)
return asymm
def makeResidHist(data, bkg):
pulls = TGraphAsymmErrors(data.GetN())
pulls.SetName("Pulls")
pulls.SetLineWidth(data.GetLineWidth())
pulls.SetLineStyle(data.GetLineStyle())
pulls.SetLineColor(data.GetLineColor())
pulls.SetMarkerSize(data.GetMarkerSize())
pulls.SetMarkerStyle(data.GetMarkerStyle())
pulls.SetMarkerColor(data.GetMarkerColor())
pulls.SetFillStyle(data.GetFillStyle())
pulls.SetFillColor(data.GetFillColor())
# Add histograms, calculate Poisson confidence interval on sum value
for i in range(data.GetN()):
x = data.GetX()[i]
dyl = data.GetErrorYlow(i)
dyh = data.GetErrorYhigh(i)
yy = data.GetY()[i] - bkg.Interpolate(x) #bkg.GetBinContent(i+1)
norm = dyl if yy > 0. else dyh
if norm == 0.:
yy, dyh, dyl = 0., 0., 0.
else:
yy /= norm
dyh /= norm
dyl /= norm
pulls.SetPoint(i, x, yy)
pulls.SetPointEYhigh(i, dyh)
pulls.SetPointEYlow(i, dyl)
return pulls
def efficiency(total, passed):
name = uuid4().hex
if total.GetDimension() == 1:
# Reset bin content for bins with 0 in the total to total = 1, passed
# = 0. Due to negative weights and low stats some bins can have
# passed > total; set the content for such bins to passed = total.
_make_consistent(passed, total)
# Divide(pass,total,"cl=0.683 b(1,1) mode")
rep = TGraphAsymmErrors(passed, total, "cl=0.683 b(1,1) mode")
#rep = TGraphAsymmErrors(passed, total)
#passed_bins = get_bins(passed, True)
#total_bins = get_bins(total, True)
#rep_bins = get_bins(rep)
#if all([x == 0.0 for x,y in rep_bins]):
#print('{0} {1}'.format(process._label, filter))
#print(['{0:7.2f}'.format(y) for x,y in passed_bins])
#print(['{0:7.2f}'.format(y) for x,y in total_bins])
#print(['{0:7.2f}'.format(y) for x,y in rep_bins])
#print(['({0:.0f}, {1:.2f})'.format(x, y) for x,y in rep_bins])
else:
rep = passed.Clone(name)
rep.Divide(total)
rep.SetName(name)
return rep
def QuadratureUpDown(asymlist,
hists=[],
doFlatUnc=False,
FlatUp=0.0,
FlatDown=0.0): # These are TGraphAsymmErrors
total_unc_up_sq = []
total_unc_dn_sq = []
for i in range(asymlist[0].GetN()):
total_unc_up_sq.append(0)
total_unc_dn_sq.append(0)
#
# asym err hists
#
for i in range(asymlist[0].GetN()):
for j in range(len(asymlist)):
E_up = asymlist[j].GetEYhigh()[i]
E_dn = asymlist[j].GetEYlow()[i]
total_unc_up_sq[i] += math.pow(E_up, 2)
total_unc_dn_sq[i] += math.pow(E_dn, 2)
#
# hists
#
for i in range(len(total_unc_up_sq)):
for j in range(len(hists)):
E_up = hists[j].GetBinError(i + 1)
E_dn = hists[j].GetBinError(i + 1)
total_unc_up_sq[i] += math.pow(E_up, 2)
total_unc_dn_sq[i] += math.pow(E_dn, 2)
#
# flat uncertainty
#
if doFlatUnc:
for i in range(len(total_unc_up_sq)):
total_unc_up_sq[i] += math.pow(FlatUp * asymlist[0].GetY()[i], 2)
total_unc_dn_sq[i] += math.pow(FlatDown * asymlist[0].GetY()[i], 2)
#
# sqrt
#
for i in range(len(total_unc_up_sq)):
total_unc_up_sq[i] = math.sqrt(total_unc_up_sq[i])
total_unc_dn_sq[i] = math.sqrt(total_unc_dn_sq[i])
#print 'Setting up unc to ',total_unc_up_sq[i]
#print 'Setting dn unc to ',total_unc_dn_sq[i]
#print total_unc_up_sq
#print total_unc_dn_sq
yup = array('d', total_unc_up_sq)
ydn = array('d', total_unc_dn_sq)
#print yup
#print ydn
result = TGraphAsymmErrors(asymlist[0].GetN(), asymlist[0].GetX(),
asymlist[0].GetY(), asymlist[0].GetEXlow(),
asymlist[0].GetEXhigh(), ydn, yup)
result.GetXaxis().SetTitle(asymlist[0].GetXaxis().GetTitle())
result.GetYaxis().SetTitle(asymlist[0].GetYaxis().GetTitle())
result.SetLineWidth(2)
result.SetName(asymlist[0].GetName() + ' Total Error')
return result
def envelope(thedict, thelist, nominal):
#
# Takes the nominal and makes an envelope around it
#
diff_up = nominal.Clone()
key = 'diff_up'
diff_up.SetNameTitle(key, key)
diff_dn = nominal.Clone()
key = 'diff_dn'
diff_dn.SetNameTitle(key, key)
for i in range(diff_up.GetNbinsX()):
diff_up.SetBinContent(i + 1, 0)
diff_dn.SetBinContent(i + 1, 0)
#
for var in thelist:
if var == 'dummy': continue
for i in range(nominal.GetNbinsX()):
curr_diff_up = diff_up.GetBinContent(i + 1)
curr_diff_dn = diff_dn.GetBinContent(i + 1)
nominal_bc = nominal.GetBinContent(i + 1)
thisvar_bc = thedict[var].GetBinContent(i + 1)
diff_up.SetBinContent(i + 1,
max(curr_diff_up, thisvar_bc - nominal_bc))
diff_dn.SetBinContent(i + 1,
min(curr_diff_dn, thisvar_bc - nominal_bc))
nbins = nominal.GetNbinsX()
x = array(
'd',
list(nominal.GetBinCenter(a + 1) for a in range(nominal.GetNbinsX())))
xup = array(
'd',
list(
nominal.GetBinWidth(a + 1) / 2.
for a in range(nominal.GetNbinsX())))
xdn = array(
'd',
list(
nominal.GetBinWidth(a + 1) / 2.
for a in range(nominal.GetNbinsX())))
y = array(
'd',
list(nominal.GetBinContent(a + 1) for a in range(nominal.GetNbinsX())))
yup = array(
'd',
list(diff_up.GetBinContent(a + 1) for a in range(nominal.GetNbinsX())))
ydn = array(
'd',
list(
diff_dn.GetBinContent(a + 1) * -1.
for a in range(nominal.GetNbinsX())))
result = TGraphAsymmErrors(nbins, x, y, xdn, xup, ydn, yup)
result.GetXaxis().SetTitle(nominal.GetXaxis().GetTitle())
result.GetYaxis().SetTitle(nominal.GetYaxis().GetTitle())
result.SetLineWidth(2)
result.SetName(nominal.GetName() + ' Envelope')
return result
def geterrorband(*hists, **kwargs):
"""Make an error band histogram for a list of histograms, or stack.
Returns an TGraphAsymmErrors object."""
verbosity = LOG.getverbosity(kwargs)
hists = unwraplistargs(hists)
hists = [
h.GetStack().Last() if isinstance(h, THStack) else h for h in hists
]
sysvars = kwargs.get(
'sysvars', []) # list of tuples with up/cent/down variation histograms
name = kwargs.get('name', None) or "error_" + hists[0].GetName()
title = kwargs.get(
'title', None) or ("Sys. + stat. unc." if sysvars else "Stat. unc.")
color = kwargs.get('color', kBlack)
hist0 = hists[0]
nbins = hist0.GetNbinsX()
if sysvars and isinstance(sysvars, dict):
sysvars = [v for k, v in sysvars.iteritems()]
error = TGraphAsymmErrors()
error.SetName(name)
error.SetTitle(title)
LOG.verb("geterrorband: Making error band for %s" % (hists), verbosity, 2)
TAB = LOG.table(
"%5s %7s %6s %10s %11s %-20s %-20s %-20s",
"%5d %7.6g %6.6g %10.2f %11.2f +%8.2f -%8.2f +%8.2f -%8.2f +%8.2f -%8.2f",
verb=verbosity,
level=3)
TAB.printheader("ibin", "xval", "xerr", "nevts", "sqrt(nevts)",
"statistical unc.", "systematical unc.", "total unc.")
ip = 0
for ibin in range(1, nbins + 1):
xval = hist0.GetXaxis().GetBinCenter(ibin)
xerr = 0 if ibin in [0, nbins +
1] else hist0.GetXaxis().GetBinWidth(ibin) / 2
yval = 0
statlow2, statupp2 = 0, 0
syslow2, sysupp2 = 0, 0
for hist in hists: # STATISTICS
yval += hist.GetBinContent(ibin)
statlow2 += hist.GetBinErrorLow(ibin)**2
statupp2 += hist.GetBinErrorUp(ibin)**2
for histup, hist, histdown in sysvars: # SYSTEMATIC VARIATIONS
syslow2 += (hist.GetBinContent(ibin) -
histdown.GetBinContent(ibin))**2
sysupp2 += (hist.GetBinContent(ibin) -
histup.GetBinContent(ibin))**2
ylow2, yupp2 = statlow2 + syslow2, statupp2 + sysupp2,
error.SetPoint(ip, xval, yval)
error.SetPointError(ip, xerr, xerr, sqrt(ylow2), sqrt(yupp2))
TAB.printrow(ibin, xval, xerr, yval, sqrt(abs(yval)), sqrt(statupp2),
sqrt(statlow2), sqrt(sysupp2), sqrt(syslow2), sqrt(yupp2),
sqrt(ylow2))
ip += 1
seterrorbandstyle(error, color=color)
#error.SetLineColor(hist0.GetLineColor())
error.SetLineWidth(hist0.GetLineWidth()) # use draw option 'E2 SAME'
return error
def makeEffPlotsVars(tree,
varx,
vary,
sel,
nbinx,
xmin,
xmax,
nbiny,
ymin,
ymax,
xtitle,
ytitle,
leglabel=None,
header='',
addon='',
option='pt',
marker=20):
binning = [20, 30, 40, 50, 60, 70, 80, 100, 150, 200]
c = TCanvas()
if option == 'pt':
_hist_ = TH1F('h_effp_' + addon, 'h_effp' + addon,
len(binning) - 1, array('d', binning))
_ahist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon,
len(binning) - 1, array('d', binning))
elif option == 'eta':
_hist_ = TH1F('h_effp_' + addon, 'h_effp' + addon, nbinx, xmin, xmax)
_ahist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon, nbinx, xmin,
xmax)
elif option == 'nvtx':
_hist_ = TH1F('h_effp_' + addon, 'h_effp' + addon,
len(vbinning) - 1, array('d', vbinning))
_ahist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon,
len(vbinning) - 1, array('d', vbinning))
tree.Draw(varx + ' >> ' + _hist_.GetName(), sel)
tree.Draw(varx + ' >> ' + _ahist_.GetName(), sel + ' && ' + vary)
g_efficiency = TGraphAsymmErrors()
g_efficiency.BayesDivide(_ahist_, _hist_)
g_efficiency.GetXaxis().SetTitle(xtitle)
g_efficiency.GetYaxis().SetTitle('efficiency')
g_efficiency.GetYaxis().SetNdivisions(507)
g_efficiency.SetLineWidth(3)
g_efficiency.SetName(header)
g_efficiency.SetMinimum(0.)
g_efficiency.GetYaxis().SetTitleOffset(1.3)
g_efficiency.SetMarkerStyle(marker)
g_efficiency.SetMarkerSize(1)
g_efficiency.Draw('ap')
# save(c, 'plots/' + addon)
return copy.deepcopy(g_efficiency)
def GetTGraphAsymmErrors(histo, offset):
graph_in = TGraphAsymmErrors(histo)
graph_in.SetName(histo.GetName() + '_tmp')
graph_out = TGraphAsymmErrors(histo)
graph_out.SetName(histo.GetName() + '_offset')
for i in range(graph_in.GetN()):
graph_out.GetX()[i] = graph_in.GetX()[i] + offset
graph_out.GetEXlow()[i] = graph_in.GetEXlow()[i] + offset
graph_out.GetEXhigh()[i] = graph_in.GetEXhigh()[i] - offset
x_min = graph_out.GetX()[0] - graph_out.GetEXlow()[0]
if (x_min > 0): x_min = 0
x_max = graph_out.GetX()[graph_out.GetN() -
1] + graph_out.GetEXhigh()[graph_out.GetN() - 1]
graph_out.GetXaxis().SetLimits(x_min, x_max)
return graph_out
def QuadratureUpDown(name,asymlist=[],hists=[],AddHists=False) : # These are TGraphAsymmErrors
asymlist1 = []
for i in asymlist :
asymlist1.append(i)
for i in range(len(hists)) :
asymlist1.append(HistToAsymmErrs(hists[i].GetName()+' QuadratureUpDown Asymm',hists[i]))
nbins = asymlist1[0].GetN()
total_unc_up_sq = []
total_unc_dn_sq = []
for i in range(nbins) :
total_unc_up_sq.append(0)
total_unc_dn_sq.append(0)
#
# asym err hists
#
for i in range(nbins) :
for j in range(len(asymlist1)) :
E_up = asymlist1[j].GetEYhigh()[i]
E_dn = asymlist1[j].GetEYlow()[i]
total_unc_up_sq[i] += math.pow(E_up,2)
total_unc_dn_sq[i] += math.pow(E_dn,2)
# #
# # hists
# #
# for i in range(nbins) :
# for j in range(len(hists)) :
# E_up = hists[j].GetBinError(i+1)
# E_dn = hists[j].GetBinError(i+1)
# total_unc_up_sq[i] += math.pow(E_up,2)
# total_unc_dn_sq[i] += math.pow(E_dn,2)
#
# sqrt
#
for i in range(nbins) :
total_unc_up_sq[i] = math.sqrt(total_unc_up_sq[i])
total_unc_dn_sq[i] = math.sqrt(total_unc_dn_sq[i])
y = []
for i in range(nbins) :
y.append(asymlist1[0].GetY()[i])
if AddHists :
for j in range(1,len(asymlist1)) :
y[-1] += asymlist1[j].GetY()[i]
ybincontent = array('d',y)
yup = array('d',total_unc_up_sq)
ydn = array('d',total_unc_dn_sq)
result = TGraphAsymmErrors(asymlist1[0].GetN(),asymlist1[0].GetX(),ybincontent
,asymlist1[0].GetEXlow(),asymlist1[0].GetEXhigh()
,ydn,yup)
result.GetXaxis().SetTitle(asymlist1[0].GetXaxis().GetTitle())
result.GetYaxis().SetTitle(asymlist1[0].GetYaxis().GetTitle())
result.SetLineWidth(2)
result.SetName(name)
return result
def getDataPoissonErrors(hist,
kPoisson=False,
drawZeroBins=False,
drawXbars=False,
centerBin=True):
'''Make data poisson errors for a histogram with two different methods:
- TH1.kPoisson
- chi-squared quantile
'''
# https://github.com/DESY-CMS-SUS/cmgtools-lite/blob/8_0_25/TTHAnalysis/python/plotter/mcPlots.py#L70-L102
# https://github.com/DESY-CMS-SUS/cmgtools-lite/blob/8_0_25/TTHAnalysis/python/plotter/susy-1lep/RcsDevel/plotDataPredictWithSyst.py#L12-L21
if kPoisson: hist.SetBinErrorOption(TH1D.kPoisson)
Nbins = hist.GetNbinsX()
xaxis = hist.GetXaxis()
alpha = (1 - 0.6827) / 2.
graph = TGraphAsymmErrors(Nbins)
graph.SetName(hist.GetName() + "_graph")
graph.SetTitle(hist.GetTitle())
for i in xrange(1, Nbins + 1):
N = hist.GetBinContent(i)
if N <= 0 and not drawZeroBins: continue
dN = hist.GetBinError(i)
yscale = 1
if centerBin:
x = xaxis.GetBinCenter(i)
else:
x = xaxis.GetBinLowEdge(i)
if N > 0 and dN > 0 and abs(dN**2 / N -
1) > 1e-4: # check is error is Poisson
yscale = (dN**2 / N)
N = (N / dN)**2
if kPoisson:
EYlow = hist.GetBinErrorLow(i)
EYup = hist.GetBinErrorUp(i)
else:
EYlow = (N - Math.chisquared_quantile_c(1 - alpha, 2 * N) /
2.) if N > 0 else 0
EYup = Math.chisquared_quantile_c(alpha, 2 * (N + 1)) / 2. - N
y = yscale * N
EXup = xaxis.GetBinUpEdge(i) - x if drawXbars else 0
EXlow = x - xaxis.GetBinLowEdge(i) if drawXbars else 0
graph.SetPoint(i - 1, x, y)
graph.SetPointError(i - 1, EXlow, EXup, EYlow, EYup)
#print ">>> getDataPoissonErrors - bin %2d: (x,y) = ( %3.1f - %4.2f + %4.2f, %4.2f - %4.2f + %4.2f )"%(i,x,EXlow,EXup,y,EYlow,EYup)
graph.SetLineWidth(hist.GetLineWidth())
graph.SetLineColor(hist.GetLineColor())
graph.SetLineStyle(hist.GetLineStyle())
graph.SetMarkerSize(hist.GetMarkerSize())
graph.SetMarkerColor(hist.GetMarkerColor())
graph.SetMarkerStyle(hist.GetMarkerStyle())
return graph
def dividebybinsize(hist, **kwargs):
"""Divide each bin by its bin width. If a histogram has assymmetric errors (e.g. data with Poisson),
return a TGraphAsymmErrors instead."""
verbosity = LOG.getverbosity(kwargs)
LOG.verbose('dividebybinsize: "%s"' % (hist.GetName()), verbosity, 2)
zero = kwargs.get('zero', True) # include bins that are zero in TGraph
zeroerrs = kwargs.get('zeroerrs', True) # include errors for zero bins
errorX = kwargs.get('errorX', gStyle.GetErrorX()) # horizontal error bars
nbins = hist.GetXaxis().GetNbins()
TAB = LOG.table("%5s %8.6g %8.6g %10.3f %9.4f %8.4f %8.4f %10.4f",
verb=verbosity,
level=3)
TAB.printheader("ibin", "xval", "width", "yval", "yerr", "yupp", "ylow",
"yerr/width")
if hist.GetBinErrorOption(
) == TH1.kPoisson: # make asymmetric Poisson errors (like for data)
graph = TGraphAsymmErrors()
graph.SetName(hist.GetName() + "_graph")
graph.SetTitle(hist.GetTitle())
copystyle(graph, hist)
ip = 0 # skip zero bins if not zero
for ibin in xrange(1, nbins + 1):
xval = hist.GetXaxis().GetBinCenter(ibin)
width = hist.GetXaxis().GetBinWidth(ibin)
xerr = width / 2 if errorX else 0
yval = hist.GetBinContent(ibin)
yerr = hist.GetBinError(ibin)
yupp = hist.GetBinErrorUp(ibin)
ylow = hist.GetBinErrorLow(ibin)
TAB.printrow(ibin, xval, width, yval, yerr, yupp, ylow,
yval / width)
hist.SetBinContent(ibin, yval / width)
hist.SetBinError(ibin, yerr / width)
if yval != 0 or zero:
graph.SetPoint(ip, xval, yval / width)
if yval != 0 or zeroerrs:
graph.SetPointError(ip, xerr, xerr, ylow / width,
yupp / width)
else:
graph.SetPointError(ip, xerr, xerr, 0, 0)
ip += 1
return graph
else:
for ibin in xrange(0, nbins + 2):
xval = hist.GetXaxis().GetBinCenter(ibin)
width = hist.GetXaxis().GetBinWidth(ibin)
yval = hist.GetBinContent(ibin)
yerr = hist.GetBinError(ibin)
hist.SetBinContent(ibin, yval / width)
hist.SetBinError(ibin, yerr / width)
TAB.printrow(ibin, xval, width, yval,
yerr, hist.GetBinErrorUp(ibin),
hist.GetBinErrorLow(ibin), yval / width)
return hist
def makeEffPlotsVars(tree,
varx,
vary,
sel,
nbinx,
xmin,
xmax,
nbiny,
ymin,
ymax,
xtitle,
ytitle,
leglabel=None,
header='',
addon='',
option='pt',
marker=20,
col=1):
binning = [20, 200] if args.onebin else [
20, 30, 40, 50, 60, 70, 80, 100, 150, 200
]
if option == 'pt':
_hist_ = TH1F('h_effp_' + addon, 'h_effp' + addon,
len(binning) - 1, array('d', binning))
_ahist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon,
len(binning) - 1, array('d', binning))
elif option == 'eta':
_hist_ = TH1F('h_effp_' + addon, 'h_effp' + addon, nbinx, xmin, xmax)
_ahist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon, nbinx, xmin,
xmax)
tree.Draw(varx + ' >> ' + _hist_.GetName(), sel)
tree.Draw(varx + ' >> ' + _ahist_.GetName(), sel + ' && ' + vary)
g_efficiency = TGraphAsymmErrors()
g_efficiency.Divide(_ahist_, _hist_, "cl=0.683 b(1,1) mode")
g_efficiency.GetXaxis().SetTitle(xtitle)
g_efficiency.GetYaxis().SetTitle('efficiency')
g_efficiency.GetYaxis().SetNdivisions(507)
g_efficiency.SetLineWidth(3)
g_efficiency.SetName(header)
g_efficiency.SetMinimum(0.)
g_efficiency.GetYaxis().SetTitleOffset(1.3)
g_efficiency.SetMarkerStyle(marker)
g_efficiency.SetMarkerSize(1)
g_efficiency.SetMarkerColor(col)
g_efficiency.SetLineColor(col)
g_efficiency.Draw('ap')
# save(c, 'plots/' + addon)
return g_efficiency
def rescaleEff(fin):
var = "pt"
num = fin.Get("%s_rv_cat0_tot" % var).Clone()
den = fin.Get("%s_cat0_tot" % var).Clone()
xbins = numpy.array([0., 10., 20., 30., 40., 50., 70., 110., 250.])
num_rebin = num.Rebin(len(xbins) - 1, "num_rebin", xbins)
den_rebin = den.Rebin(len(xbins) - 1, "den_rebin", xbins)
eff = TGraphAsymmErrors(num_rebin, den_rebin)
eff.SetName("efficiency")
return eff
def getGraph(result, label):
masses = result["mass"]
massErr = result["massErr"]
sigma = result["sigma"]
sigmaErr = result["sigmaErr"]
res = TGraphAsymmErrors(len(masses))
res.SetName(label)
for i, mass in enumerate(masses):
res.SetPoint(i, mass, sigma[i])
res.SetPointError(i, massErr[i], massErr[i], sigmaErr[i], sigmaErr[i])
return res
def FlatErrorToAsymmErrs(name,nomhist,up_frac=0.,dn_frac=0.) :
nom_2_asymm = HistToAsymmErrs(nomhist.GetName()+' FlatErrorToAsymmErrs Asymm',nomhist)
EYlow = []
EYhigh = []
for i in range(nom_2_asymm.GetN()) :
EYlow.append(nom_2_asymm.GetY()[i]*dn_frac)
EYhigh.append(nom_2_asymm.GetY()[i]*up_frac)
a_EYlow = array('d',EYlow )
a_EYhigh = array('d',EYhigh )
result = TGraphAsymmErrors(nom_2_asymm.GetN(),nom_2_asymm.GetX(),nom_2_asymm.GetY()
,nom_2_asymm.GetEXlow(),nom_2_asymm.GetEXhigh()
,a_EYlow,a_EYhigh)
result.GetXaxis().SetTitle(nom_2_asymm.GetXaxis().GetTitle())
result.GetYaxis().SetTitle(nom_2_asymm.GetYaxis().GetTitle())
result.SetLineWidth(2)
result.SetName(name)
return result
def getGraph(result, label, Data=False):
ptda = result["ptda"]
if Data:
sigma = result["da_nChi2"]
else:
sigma = result["mc_nChi2"]
sigmaErr = 0
res = TGraphAsymmErrors(len(ptda))
res.SetName(label)
for i, pt in enumerate(ptda):
res.SetPoint(i, pt, sigma[i])
res.SetPointError(i, ptda[i] - ptbins[i], ptbins[i + 1] - ptda[i], 0,
0)
return res
def diffGraph(self, g, h, opt):
gdiff = TGraphAsymmErrors(g)
gdiff.SetName(g.GetName() + h.GetName() + '_diff')
for i in range(0, gdiff.GetN()):
x = Double(0.)
y = Double(0.)
g.GetPoint(i, x, y)
gdiff.SetPoint(i, x, y - h.GetBinContent(i + 1))
if opt == 1: # keep bin errors
pass
elif opt == 2:
gdiff.SetPointEYhigh(
i,
math.sqrt(g.GetErrorYhigh(i)**2 + h.GetBinError(i + 1)**2))
return gdiff
def PseudoTextToGraphTimeScan(textfilename):
print "parsing: ", textfilename
dirname = "timescanOutput/"
filename = textfilename
f = open(filename, "r").readlines()
firstLine = f.pop(0) #removes the first line
adc_ = array('f')
adc_1 = array('f')
adc_2 = array('f')
adc_3 = array('f')
adc_4 = array('f')
prob = array('f')
prob_e = array('f')
errx = array('f')
for line in f:
adc_.append(float(line.rstrip().split()[0]))
adc_1.append(float(line.rstrip().split()[0]) + 0.1)
adc_2.append(float(line.rstrip().split()[0]) + 0.2)
adc_3.append(float(line.rstrip().split()[0]) + 0.3)
adc_4.append(float(line.rstrip().split()[0]) + 0.4)
prob.append(float(line.rstrip().split()[1]))
prob_e.append(float(line.rstrip().split()[2]))
errx.append(0.0)
g_prob_ = TGraphAsymmErrors(int(len(adc_)), adc_, prob, errx, errx, prob_e,
prob_e)
g_prob_.SetName("prob_")
f1 = TFile(filename.replace('.txt', '.root'), 'RECREATE')
g_prob_.Write()
f1.Close()
def LinearUpDown(name,asymlist=[],hists=[],AddHists=False) :
for i in range(len(hists)) :
asymlist.append(HistToAsymmErrs(hists[i].GetName()+' LinearUpDown Asymm',hists[i]))
nbins = asymlist[0].GetN()
total_unc_up = []
total_unc_dn = []
for i in range(nbins) :
total_unc_up.append(0)
total_unc_dn.append(0)
#
# asym err hists
#
for i in range(nbins) :
for j in range(len(asymlist)) :
total_unc_up[i] += asymlist[j].GetEYhigh()[i]
total_unc_dn[i] += asymlist[j].GetEYlow()[i]
y = []
for i in range(nbins) :
y.append(asymlist[0].GetY()[i])
if AddHists :
for j in range(1,len(asymlist)) :
y[-1] += asymlist[j].GetY()[i]
ybincontent = array('d',y)
yup = array('d',total_unc_up)
ydn = array('d',total_unc_dn)
result = TGraphAsymmErrors(asymlist[0].GetN(),asymlist[0].GetX(),ybincontent
,asymlist[0].GetEXlow(),asymlist[0].GetEXhigh()
,ydn,yup)
result.GetXaxis().SetTitle(asymlist[0].GetXaxis().GetTitle())
result.GetYaxis().SetTitle(asymlist[0].GetYaxis().GetTitle())
result.SetLineWidth(2)
result.SetName(name)
return result
return
def getRatio(result, result2, label, Data=False):
ptda = result["ptda"]
ptda2 = result2["ptda"]
if Data:
sigma = result["da_nChi2"]
sigma2 = result2["da_nChi2"]
else:
sigma = result["mc_nChi2"]
sigma2 = result2["mc_nChi2"]
print(sigma)
print(sigma2)
ratio = TGraphAsymmErrors(len(ptda))
ratio.SetName(label)
for i, pt in enumerate(ptda):
ratio.SetPoint(i, pt, sigma[i] / sigma2[i])
ratio.SetPointError(i, ptda[i] - ptbins[i], ptbins[i + 1] - ptda[i], 0,
0)
return ratio
def fixPlots_withErrors(middleGraph):
thisBandsGraph = TGraphAsymmErrors()
thisBandsGraph.SetName(middleGraph.GetName())
thisBandsGraph.SetMarkerStyle(middleGraph.GetMarkerStyle())
thisBandsGraph.SetMarkerSize(middleGraph.GetMarkerSize())
thisBandsGraph.SetMarkerColor(middleGraph.GetMarkerColor())
thisBandsGraph.SetLineColor(middleGraph.GetLineColor())
for iPoint in xrange(0, middleGraph.GetN()):
# Retrieve middle graph for (x,y) coordinates
dataPointX = Double(0)
dataPointY = Double(0)
dataErrorX = Double(0)
middleGraph.GetPoint(iPoint, dataPointX, dataPointY)
dataErrorX = middleGraph.GetErrorX(iPoint)
dataErrorY = middleGraph.GetErrorY(iPoint)
dataPointY = fabs(dataPointY - 1)
if (iPoint < middleGraph.GetN()):
#if(dataPointY != 0 and iPoint < middleGraph.GetN()):
print "fixPlots:", dataPointX
thisBandsGraph.SetPoint(iPoint, dataPointX, dataPointY)
thisBandsGraph.SetPointError(iPoint, dataErrorX, dataErrorX,
dataErrorY, dataErrorY)
#if (dataPointY == 0) :
#thisBandsGraph.SetPoint(iPoint, dataPointX, dataPointY)
#thisBandsGraph.SetPointError(iPoint, dataErrorX, dataErrorX, dataErrorY, dataErrorY)
return thisBandsGraph
def makeEffPlotsVars(tree,
varx,
numeratorAddSelection,
baseSelection,
binning,
xtitle='',
header='',
addon='',
marker=20,
col=1):
_denomHist_ = TH1F('h_effp_' + addon, 'h_effp' + addon,
len(binning) - 1, binning)
_nominatorHist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon,
len(binning) - 1, binning)
tree.Draw(varx + ' >> ' + _denomHist_.GetName(), baseSelection)
tree.Draw(varx + ' >> ' + _nominatorHist_.GetName(),
baseSelection + ' && ' + numeratorAddSelection)
g_eff = TGraphAsymmErrors()
g_eff.Divide(_nominatorHist_, _denomHist_, "cl=0.683 b(1,1) mode")
g_eff.GetXaxis().SetTitle(xtitle)
g_eff.GetYaxis().SetTitle('efficiency')
g_eff.GetYaxis().SetNdivisions(507)
g_eff.SetLineWidth(3)
g_eff.SetName(header)
g_eff.SetMinimum(0.)
g_eff.GetYaxis().SetTitleOffset(1.3)
g_eff.SetMarkerStyle(marker)
g_eff.SetMarkerSize(1)
g_eff.SetMarkerColor(col)
g_eff.SetLineColor(col)
g_eff.Draw('ap')
return g_eff
expm1.append(expmed_ - expm1_)
expmed.append(expmed_)
expp1.append(expp1_ - expmed_)
expp2.append(expp2_ - expmed_)
obs.append(obs_)
errx.append(0.0)
log += str(ival_) + " " + str(expm2_) + " " + str(
expm1_) + " " + str(expmed_) + " " + str(expp1_) + " " + str(
expp2_) + " " + str(obs_) + "\n"
# print ival, float(line.rstrip().split()[4]), xsec_, float(line.rstrip().split()[4])/xsec_
log += "\n"
g_exp2 = TGraphAsymmErrors(int(len(xvals)), xvals, expmed, errx, errx,
expm2, expp2)
g_exp2.SetName("exp2")
g_exp1 = TGraphAsymmErrors(int(len(xvals)), xvals, expmed, errx, errx,
expm1, expp1)
g_exp1.SetName("exp1")
g_expmed = TGraphAsymmErrors(int(len(xvals)), xvals, expmed)
g_expmed.SetName("expmed")
g_obs = TGraphAsymmErrors(int(len(xvals)), xvals, obs)
g_obs.SetName("obs")
f1 = TFile(scanname + '/' + scanname + '_MH4-' + str(M) + '.root',
'RECREATE')
g_exp2.Write()
g_exp1.Write()
g_expmed.Write()
g_obs.Write()
latex.SetTextColor(1)
latex.SetTextFont(42)
latex.SetTextAlign(33)
latex.SetTextSize(0.04)
latex.SetTextFont(62)
latex.DrawLatex(0.30, 0.96, "GIF++")
etichetta = TLatex()
etichetta.SetNDC()
etichetta.SetTextSize(0.04)
etichetta.SetTextColor(4)
etichetta.SetTextFont(102)
etichetta.DrawLatex(0.45, 0.8, "#splitline{SL1_L1 threshold scan}{Runs: "+str(run_interval)+"}")
can_scan_SL1_L1.Update()
can_scan_SL1_L1.Print(outpath + "ThresholdScan_SL1_L1_"+run_interval+".png")
can_scan_SL1_L1.Print(outpath + "ThresholdScan_SL1_L1_"+run_interval+".pdf")
graph.SetName("ThresholdScan_SL1_L1_"+run_interval)
new_file_SL1_L1 = TFile(outpath + "Graph_thresholdScan_SL1_L1_"+run_interval+ ".root",'RECREATE')
graph.Write()
graph.Set(0)
#SL1_L4
can_scan_SL1_L4 = TCanvas("can_scan_SL1_L4","can_scan_SL1_L4", 1000, 800)
can_scan_SL1_L4.SetGrid()
can_scan_SL1_L4.cd()
graph = TGraphAsymmErrors()
n=0
for a in sorted(scan_SL1_L4):
#print scan_SL1_L4[a]
graph.SetPoint(n,a,scan_SL1_L4[a])
n = n+1
graph.SetMarkerSize(1.)
def makeBand(middleGraph, lowerGraph, upperGraph, noorder):
bandsGraph = TGraphAsymmErrors()
bandsGraph.SetName(middleGraph.GetName())
bandsGraph.SetMarkerStyle(middleGraph.GetMarkerStyle())
bandsGraph.SetMarkerColor(middleGraph.GetMarkerColor())
bandsGraph.SetLineColor(middleGraph.GetLineColor())
x1 = Double(0)
x2 = Double(0)
x3 = Double(0)
y = []
y.append(Double(0))
y.append(Double(0))
y.append(Double(0))
tmp = Double(0)
if lowerGraph.GetN() != upperGraph.GetN() or lowerGraph.GetN(
) != middleGraph.GetN():
print "Plots don't have the same number of points!"
print "Lower: ", lowerGraph.GetN()
print "Upper: ", upperGraph.GetN()
print "Middle: ", middleGraph.GetN()
return 0
#again a hack to forget about the 1st point
for iPoint in xrange(1, lowerGraph.GetN()):
middleGraph.GetPoint(iPoint, x1, y[0])
lowerGraph.GetPoint(iPoint, x1, y[1])
upperGraph.GetPoint(iPoint, x1, y[2])
if (iPoint == lowerGraph.GetN() - 1): x2 = x1
else: upperGraph.GetPoint(iPoint + 1, x2, buf)
if (iPoint == 0): x3 = x1
else: upperGraph.GetPoint(iPoint - 1, x3, buf)
tmp = Double(0)
yup = Double(0)
yce = Double(0)
ydn = Double(0)
if noorder == 0:
yce = y[0]
ydn = y[1]
yup = y[2]
elif noorder == 1:
tmp = y[2]
if y[1] < y[2]:
y[2] = y[1]
y[1] = tmp
yce = y[0]
ydn = y[1]
yup = y[2]
else:
for p in [0, 1, 2]:
for q in [0, 1, 2]:
if y[q] > y[q + 1]:
tmp = y[q + 1]
y[q + 1] = y[q]
y[q] = tmp
ydn = y[0]
yce = y[1]
yup = y[2]
bandsGraph.SetPoint(iPoint, x1, yce)
ex0 = Double(lowerGraph.GetErrorX(iPoint))
binwl = Double(0)
binwh = Double(0)
if (ex0 == 0):
binwl = (x1 - x3) / 2.
binwh = (x2 - x1) / 2.
if binwl == 0.: binwl = binwh
if binwh == 0.: binwh = binwl
else:
binwl = ex0
binwh = ex0
dxl = Double(yce - ydn)
dxh = Double(yup - yce)
if noorder == 0:
if dxl < 0:
tmp = -dxl
dxl = dxh
dxh = tmp
bandsGraph.SetPointError(iPoint, binwl, binwh, dxl, dxh)
return bandsGraph
latex.SetTextSize(0.04)
latex.SetTextFont(62)
latex.DrawLatex(0.30, 0.96, "GIF++")
etichetta = TLatex()
etichetta.SetNDC()
etichetta.SetTextSize(0.04)
etichetta.SetTextColor(418)
etichetta.SetTextFont(102)
etichetta.DrawLatex(
0.2, 0.8, "#splitline{SL1_L1 HV scan}{Runs: " + str(run_interval) + "}")
can_HV_scan_SL1_L1.Update()
can_HV_scan_SL1_L1.Print(outpath + "HVScan_SL1_L1_" + str(run_interval) +
options.optionalstring + ".png")
can_HV_scan_SL1_L1.Print(outpath + "HVScan_SL1_L1_" + str(run_interval) +
options.optionalstring + ".pdf")
graph_HV_L1.SetName("HV_scan_SL1_L1_" + run_interval)
new_file_HV_scan_SL1_L1 = TFile(
outpath + "Graph_HV_scan_SL1_L1_" + run_interval + options.optionalstring +
".root", 'RECREATE')
graph_HV_L1.Write()
graph_HV_L1.Set(0)
print "Graph_HV_scan_SL1_L1_" + run_interval + options.optionalstring + ".root written in " + outpath
##Prepare the canvas to plot the HV scan for SL1_L4
can_HV_scan_SL1_L4 = TCanvas("can_HV_scan_SL1_L4", "can_HV_scan_SL1_L4", 1000,
800)
can_HV_scan_SL1_L4.SetGrid()
can_HV_scan_SL1_L4.cd()
##Prepare summary TGraph
graph_HV_L4 = TGraphAsymmErrors()
n = 0
def RootifyCrossBR(self):
for processname in self.processnames:
outfilename_cross = self.crosssecfolder+'/Crosssections_%s%s.root' % (processname, self.tag)
cross_module = importlib.import_module('crosssections.ChiPsi.Crosssections_%s' % (processname))
crosssections = cross_module.crosssection
if self.submit:
outfile = TFile(outfilename_cross, 'RECREATE')
else:
print yellow('--> Would have created outfile %s' % (outfilename_cross))
print green('--> Now at sample %s' % (processname))
for lamb in self.lambdas:
if not lamb in crosssections: continue
print green(' --> Now at lambda: %s' % (get_lambdastring(lamb)))
xsecs_per_mref = crosssections[lamb]
graph2d = TGraph2D()
npoints2d=0
set_points ={}
all_combinations = get_all_combinations(preferred_configurations=preferred_configurations)
for mlq in all_combinations:
set_points[mlq] = {}
for mch in all_combinations[mlq]:
set_points[mlq][mch] = False
for mref in xsecs_per_mref:
xsecs = xsecs_per_mref[mref]
final_xsecs = []
mdeps = array('d')
sigmas = array('d')
tot_los = array('d')
tot_his = array('d')
mdeps_lo = array('d')
mdeps_hi = array('d')
for tuple in xsecs:
mdep, sigma, q_lo, q_hi, pdf = tuple
tot_lo = XsecTotErr(sigma, q_lo, pdf)
tot_hi = XsecTotErr(sigma, q_hi, pdf)
final_xsecs.append((mdep, sigma, tot_lo, tot_hi))
mdeps.append(mdep)
sigmas.append(sigma)
tot_los.append(tot_lo)
tot_his.append(tot_hi)
mdeps_lo.append(0.)
mdeps_hi.append(0.)
if 'LQLQ' in processname or 'LQTChannel' in processname:
graph2d.SetPoint(npoints2d, mdep, mref, sigma)
set_points[mdep][mref] = True
elif 'PsiPsi' in processname:
graph2d.SetPoint(npoints2d, mref, mdep, sigma)
set_points[mref][mdep] = True
else:
raise ValueError('processname does not contain \'LQLQ\' or \'PsiPsi\', what kind of process are we looking at here?')
npoints2d += 1
# make TGraph out of it
graph = TGraphAsymmErrors(len(mdeps), mdeps, sigmas, mdeps_lo, mdeps_hi, tot_los, tot_his)
xaxistitle = 'M_{LQ} [GeV]' if ('LQLQ' in processname or 'LQTChannel' in processname) else 'M_{#chi_{1}} [GeV]'
graph.GetXaxis().SetTitle('M_{LQ} [GeV]')
graph.GetYaxis().SetTitle('#sigma [pb]')
graphname = processname
if 'LQLQ' in processname or 'LQTChannel' in processname:
graphname += '_MC1%i' % (mref)
elif 'PsiPsi' in processname:
graphname += '_MLQ%i' % (mref)
else:
raise ValueError('processname does not contain \'LQLQ\' or \'PsiPsi\', what kind of process are we looking at here?')
graphname += '_L%s' % (get_lambdastring(lamb))
graph.SetName(graphname)
# print 'graphname: %s' % (graphname)
graphtitle = processname
if 'LQLQ' in processname or 'LQTChannel' in processname:
graphtitle += ', M_{#chi_{1}} = %i GeV' % (mref)
elif 'PsiPsi' in processname:
graphtitle += ', M_{LQ} = %i GeV' % (mref)
graphtitle += ', #lambda = %s' % (get_lambdastring(lamb).replace('p', '.'))
# print 'graphtitle: %s' % (graphtitle)
graph.SetTitle(graphtitle)
if self.submit:
outfile.cd()
graph.Write()
else:
print yellow(' --> Would have written graph %s to outfile' % (graphname))
# fill remaining points in 2d graph with zeros
for mlq in set_points:
for mch in set_points[mlq]:
if not set_points[mlq][mch]:
graph2d.SetPoint(npoints2d, mlq, mch, 0.)
npoints2d += 1
graph2d.SetName(processname + '_L%s' % (get_lambdastring(lamb)))
graph2d.GetXaxis().SetTitle('M_{LQ} [GeV]')
graph2d.GetYaxis().SetTitle('M_{#chi_{1}} = %i [GeV]')
graph2d.GetZaxis().SetTitle('#sigma [pb]')
graph2d.SetTitle(processname + ', #lambda = %s' % (get_lambdastring(lamb).replace('p', '.')))
if self.submit:
graph2d.Write()
else:
print yellow(' --> Would have written 2d-graph to outfile')
if self.submit:
outfile.Close()
# also rootify BRs if we are looking at the LQLQ process without decays (just for fun, could also be any other LQLQ process)
if processname == 'LQLQ':
outfilename_br = self.crosssecfolder+'/Branchingratios_%s%s.root' % (processname, self.tag)
if self.submit:
outfile = TFile(outfilename_br, 'RECREATE')
else:
print yellow('--> Would have created outfile %s' % (outfilename_br))
br_module = importlib.import_module('crosssections.ChiPsi.Branchingratios_%s' % (processname))
allbrs = br_module.branchingratio
for lamb in allbrs.keys():
brs = allbrs[lamb]
brs2d = {}
npoints2d = {}
set_points ={}
for mlq in all_combinations:
set_points[mlq] = {}
for mch in all_combinations[mlq]:
set_points[mlq][mch] = {}
for decaymode in decaymode_dict.keys():
set_points[mlq][mch][decaymode] = False
decaymodes_present = []
for mlq in sorted(brs):
mchs_per_decaymode = {}
brs_per_decaymode = {}
for mch in sorted(brs[mlq]):
for decaymode in brs[mlq][mch]:
if not decaymode in decaymodes_present:
decaymodes_present.append(decaymode)
if not decaymode in mchs_per_decaymode.keys(): mchs_per_decaymode[decaymode] = array('d')
if not decaymode in brs_per_decaymode.keys(): brs_per_decaymode[decaymode] = array('d')
# if not decaymode in set_points[mlq][mch].keys():
# # print mlq, mch, decaymode
# set_points[mlq][mch][decaymode] = False
if not decaymode in brs2d.keys():
graphname2d = processname + ('_L%s_%i_%i' % (get_lambdastring(lamb), abs(decaymode[0]), abs(decaymode[1])))
# print graphname2d
npoints2d[decaymode] = 0
brs2d[decaymode] = TGraph2D()
brs2d[decaymode].SetName(graphname2d)
brs2d[decaymode].GetXaxis().SetTitle('M_{LQ} [GeV]')
brs2d[decaymode].GetYaxis().SetTitle('M_{#chi_{1}} = %i [GeV]')
brs2d[decaymode].GetZaxis().SetTitle('BR (LQLQ#rightarrow%s)' % (decaymode_dict[decaymode]))
brs2d[decaymode].SetTitle(processname + ', %s' % (decaymode_dict[decaymode]))
mchs_per_decaymode[decaymode].append(mch)
brs_per_decaymode[decaymode].append(brs[mlq][mch][decaymode][0])
brs2d[decaymode].SetPoint(npoints2d[decaymode], mlq, mch, brs[mlq][mch][decaymode][0])
set_points[mlq][mch][decaymode] = True
npoints2d[decaymode] += 1
for decaymode in mchs_per_decaymode.keys():
graph = TGraph(len(mchs_per_decaymode[decaymode]), mchs_per_decaymode[decaymode], brs_per_decaymode[decaymode])
graphname = processname + ('_MLQ%i_L%s_%i_%i' % (mlq, get_lambdastring(lamb), abs(decaymode[0]), abs(decaymode[1])))
graph.SetName(graphname)
graph.GetXaxis().SetTitle('M_{#chi_{1}} [GeV]')
graph.GetYaxis().SetTitle('BR (LQLQ#rightarrow%s)' % (decaymode_dict[decaymode]))
graph.SetTitle(processname + ', %s' % (decaymode_dict[decaymode]))
if self.submit:
graph.Write()
else:
print yellow(' --> Would have written graph %s to outfile' % (graphname))
# fill remaining points in 2d graph with zeros
for mlq in set_points:
for mch in set_points[mlq]:
for decaymode in set_points[mlq][mch]:
if not set_points[mlq][mch][decaymode] and decaymode in decaymodes_present:
# print decaymode
# print brs2d.keys()
# print npoints2d.keys()
# print 'Setting BR for MLQ=%i, MCH=%i, decay=(%i, %i) to 0' % (mlq, mch, decaymode[0], decaymode[1])
brs2d[decaymode].SetPoint(npoints2d[decaymode], mlq, mch, 0)
npoints2d[decaymode] += 1
if self.submit:
for decaymode in brs2d:
brs2d[decaymode].Write()
else:
print yellow(' --> Would have written 2d-graphs to outfile')
outfile.Close()
prob_29.append(float(line.rstrip().split()[9]))
prob_e_29.append(float(line.rstrip().split()[10]))
errx.append(0.0)
#adc_1 = [i+0.1 for i in adc_]
#adc_2 = [i+0.2 for i in adc_]
#adc_3 = [i+0.3 for i in adc_]
#adc_4 = [i+0.4 for i in adc_]
print adc_1
g_prob_ = TGraphAsymmErrors(int(len(adc_)), adc_, prob, errx, errx, prob_e,
prob_e)
g_prob_.SetName("prob_")
g_prob_26_ = TGraphAsymmErrors(int(len(adc_1)), adc_1, prob_26, errx, errx,
prob_e_26, prob_e_26)
g_prob_26_.SetName("prob_26_")
g_prob_27_ = TGraphAsymmErrors(int(len(adc_)), adc_2, prob_27, errx, errx,
prob_e_27, prob_e_27)
g_prob_27_.SetName("prob_27_")
g_prob_28_ = TGraphAsymmErrors(int(len(adc_)), adc_3, prob_28, errx, errx,
prob_e_28, prob_e_28)
g_prob_28_.SetName("prob_28_")
g_prob_29_ = TGraphAsymmErrors(int(len(adc_)), adc_4, prob_29, errx, errx,
prob_e_29, prob_e_29)
g_prob_29_.SetName("prob_29_")
f1 = TFile(filename.replace('.txt', '.root'), 'RECREATE')
def TextToGraphTimeScan(textfilename):
dirname="timescanOutput/"
#filenames=['timescanOutput/Rate_2017data_FrontTrain_cutval_2.txt','timescanOutput/Rate_2017data_FrontTrain_cutval_4.txt','timescanOutput/Rate_2017data_FrontTrain_cutval_6.txt','timescanOutput/Rate_2017data_FrontTrain_cutval_8.txt','timescanOutput/Rate_2017data_FrontTrain_cutval_10.txt']
filename = textfilename
f = open(filename,"r").readlines()
firstLine = f.pop(0) #removes the first line
adc_ = array('f')
adc_1 = array('f')
adc_2 = array('f')
adc_3 = array('f')
adc_4 = array('f')
prob = array('f')
prob_e = array('f')
prob_26 = array('f')
prob_e_26 = array('f')
prob_27 = array('f')
prob_e_27 = array('f')
prob_28 = array('f')
prob_e_28 = array('f')
prob_29 = array('f')
prob_e_29 = array('f')
errx = array('f')
for line in f:
adc_.append( float( line.rstrip().split()[0]))
adc_1.append( float( line.rstrip().split()[0]) + 0.1)
adc_2.append( float( line.rstrip().split()[0]) + 0.2)
adc_3.append( float( line.rstrip().split()[0]) + 0.3)
adc_4.append( float( line.rstrip().split()[0]) + 0.4)
prob.append( float( line.rstrip().split()[1]) )
prob_e.append( float( line.rstrip().split()[2]) )
prob_26.append( float( line.rstrip().split()[3]) )
prob_e_26.append( float( line.rstrip().split()[4]) )
prob_27.append( float( line.rstrip().split()[5]) )
prob_e_27.append( float( line.rstrip().split()[6]) )
prob_28.append( float( line.rstrip().split()[7]) )
prob_e_28.append( float( line.rstrip().split()[8]) )
prob_29.append( float( line.rstrip().split()[9]) )
prob_e_29.append( float( line.rstrip().split()[10]) )
errx.append(0.0)
#adc_1 = [i+0.1 for i in adc_]
#adc_2 = [i+0.2 for i in adc_]
#adc_3 = [i+0.3 for i in adc_]
#adc_4 = [i+0.4 for i in adc_]
print adc_1
g_prob_ = TGraphAsymmErrors(int(len(adc_)), adc_, prob, errx, errx, prob_e, prob_e ) ; g_prob_.SetName("prob_")
g_prob_26_ = TGraphAsymmErrors(int(len(adc_1)), adc_1, prob_26, errx, errx, prob_e_26, prob_e_26 ) ; g_prob_26_.SetName("prob_26_")
g_prob_27_ = TGraphAsymmErrors(int(len(adc_)), adc_2, prob_27, errx, errx, prob_e_27, prob_e_27 ) ; g_prob_27_.SetName("prob_27_")
g_prob_28_ = TGraphAsymmErrors(int(len(adc_)), adc_3, prob_28, errx, errx, prob_e_28, prob_e_28 ) ; g_prob_28_.SetName("prob_28_")
g_prob_29_ = TGraphAsymmErrors(int(len(adc_)), adc_4, prob_29, errx, errx, prob_e_29, prob_e_29 ) ; g_prob_29_.SetName("prob_29_")
f1 = TFile(filename.replace('.txt','.root'), 'RECREATE')
g_prob_.Write()
g_prob_26_.Write()
g_prob_27_.Write()
g_prob_28_.Write()
g_prob_29_.Write()
f1.Close()
