def numericalInvertPlot(graph):
#pick up point by point pl
#loop on all data points
nPoints = graph.GetN()
newGraph = TGraphAsymmErrors(nPoints)
#x=array('f')
#y=array('f')
#ey=array('f')
for iPoint in xrange(0, nPoints):
#get info on data point
dataPointX = Double(0)
dataPointY = Double(0)
graph.GetPoint(iPoint, dataPointX, dataPointY)
dataErrorX = graph.GetErrorX(iPoint)
dataErrorY = graph.GetErrorY(iPoint)
#numerical-invert the X
newDataPointX = dataPointX * dataPointY
newErrorXLeft = fabs(newDataPointX - (dataPointX - dataErrorX))
newErrorXRight = fabs(newDataPointX - (dataPointX + dataErrorX))
#setting the new graph (with asymm errors this time)
newGraph.SetPoint(iPoint, newDataPointX, dataPointY)
newGraph.SetPointEXhigh(iPoint, newErrorXRight)
newGraph.SetPointEXlow(iPoint, newErrorXLeft)
newGraph.SetPointEYhigh(iPoint, dataErrorY)
newGraph.SetPointEYlow(iPoint, dataErrorY)
print dataPointY, newDataPointX
#check that the numerical-inverted X is still in the previous bin - otherwise, ERROR!
binRangeLow = dataPointX - dataErrorX
binRangeHigh = dataPointX + dataErrorX
if newDataPointX < binRangeLow or newDataPointX > binRangeHigh:
print "Warning! Data point should not be here!"
print "Old data point: ", dataPointX
print "New NI data point:", newDataPointX
print "XLow: ", binRangeLow
print "XHigh: ", binRangeHigh
newGraph.SetPoint(iPoint, dataPointX, 0.0000000000001)
newGraph.SetPointEXhigh(iPoint, dataErrorX)
newGraph.SetPointEXlow(iPoint, dataErrorX)
newGraph.SetPointEYhigh(iPoint, dataErrorY)
newGraph.SetPointEYlow(iPoint, dataErrorY)
return newGraph
def convertToPoisson(h):
graph = TGraphAsymmErrors()
q = (1-0.6827)/2.
for i in range(1,h.GetNbinsX()+1):
x=h.GetXaxis().GetBinCenter(i)
xLow =h.GetXaxis().GetBinLowEdge(i)
xHigh =h.GetXaxis().GetBinUpEdge(i)
y=h.GetBinContent(i)
yLow=0
yHigh=0
if y !=0.0:
yLow = y-Math.chisquared_quantile_c(1-q,2*y)/2.
yHigh = Math.chisquared_quantile_c(q,2*(y+1))/2.-y
graph.SetPoint(i-1,x,y)
graph.SetPointEYlow(i-1,yLow)
graph.SetPointEYhigh(i-1,yHigh)
graph.SetPointEXlow(i-1,0.0)
graph.SetPointEXhigh(i-1,0.0)
graph.SetMarkerStyle(20)
graph.SetLineWidth(2)
graph.SetMarkerSize(1.)
graph.SetMarkerColor(kBlack)
return graph
def apply_centers(hPt, hCen):
#bin center points according to the data
cen = get_centers_from_toyMC(hCen)
gSig = TGraphAsymmErrors(hPt.GetNbinsX())
for i in xrange(hPt.GetNbinsX()):
#center point
#xcen = hPt.GetBinCenter(i+1)
xcen = cen[i]["val"]
#cross section value
gSig.SetPoint(i, xcen, hPt.GetBinContent(i + 1))
#vertical error
gSig.SetPointEYlow(i, hPt.GetBinErrorLow(i + 1))
gSig.SetPointEYhigh(i, hPt.GetBinErrorUp(i + 1))
#horizontal error
gSig.SetPointEXlow(i, cen[i]["err"])
gSig.SetPointEXhigh(i, cen[i]["err"])
return gSig
def fixData(hist, useGarwood=True, cutGrass=False, maxPoisson=False):
if hist == None: return
varBins = False
data = TGraphAsymmErrors()
alpha = 1 - 0.6827
for i in list(reversed(range(0, hist.GetNbinsX()))):
#print "bin", i, "x:", hist.GetX()[i], "y:", hist.GetY()[i]
# X error bars to 0 - do not move this, otherwise the first bin will disappear, thanks Wouter and Rene!
N = max(hist.GetBinContent(i + 1), 0.) # Avoid unphysical bins
data.SetPoint(i, hist.GetXaxis().GetBinCenter(i + 1), N)
if not varBins:
data.SetPointEXlow(i, 0)
data.SetPointEXhigh(i, 0)
# Garwood confidence intervals
if (useGarwood):
L = ROOT.Math.gamma_quantile(alpha / 2, N, 1.) if N > 0 else 0.
U = ROOT.Math.gamma_quantile_c(alpha / 2, N + 1, 1)
# maximum between Poisson and Sumw2 error bars
EL = N - L if not maxPoisson else max(N -
L, hist.GetBinErrorLow(i))
EU = U - N if not maxPoisson else max(U -
N, hist.GetBinErrorHigh(i))
data.SetPointEYlow(i, EL)
data.SetPointEYhigh(i, EU)
else:
data.SetPointEYlow(i, math.sqrt(N))
data.SetPointEYhigh(i, math.sqrt(N))
# Cut grass
if cutGrass and data.GetY()[i] > 0.: cutGrass = False
# Treatment for 0 bins
# if abs(hist.GetY()[i])<=1.e-6:
# if cutGrass: hist.SetPointError(i, hist.GetErrorXlow(i), hist.GetErrorXhigh(i), 1.e-6, 1.e-6, )
# if (hist.GetX()[i]>65 and hist.GetX()[i]<135 and hist.GetY()[i]==0): hist.SetPointError(i, hist.GetErrorXlow(i), hist.GetErrorXhigh(i), 1.e-6, 1.e-6, )
# hist.SetPoint(i, hist.GetX()[i], -1.e-4)
# X error bars
#if hist.GetErrorXlow(i)<1.e-4:
# binwidth = hist.GetX()[1]-hist.GetX()[0]
# hist.SetPointEXlow(i, binwidth/2.)
# hist.SetPointEXhigh(i, binwidth/2.)
data.SetMarkerColor(hist.GetMarkerColor())
data.SetMarkerStyle(hist.GetMarkerStyle())
data.SetMarkerSize(hist.GetMarkerSize())
#data.SetLineSize(hist.GetLineSize())
return data
def fixed_centers(hPt):
#fixed bin centers
gSig = TGraphAsymmErrors(hPt.GetNbinsX())
for i in xrange(hPt.GetNbinsX()):
#cross section value
gSig.SetPoint(i, hPt.GetBinCenter(i + 1), hPt.GetBinContent(i + 1))
#vertical error
gSig.SetPointEYlow(i, hPt.GetBinErrorLow(i + 1))
gSig.SetPointEYhigh(i, hPt.GetBinErrorUp(i + 1))
#horizontal error
gSig.SetPointEXlow(i, hPt.GetBinWidth(i + 1) / 2.)
gSig.SetPointEXhigh(i, hPt.GetBinWidth(i + 1) / 2.)
return gSig
def histToGraph(hist, name='', keepErrors=True, poissonErrors=True):
## Helper method to convert a histogram to a corresponding graph
# @hist TH1 object
# @name name of the graph (default is name of histogram)
# @keepErrors decide if the y-errors should be propagated to the graph
# @poissonErrors decide if the y-errors should be calculated as Poisson errors
# @return graph
if not name:
name = 'g%s' % (hist.GetName())
from ROOT import TGraphAsymmErrors
nBins = hist.GetNbinsX()
graph = TGraphAsymmErrors(nBins)
graph.SetNameTitle(name, hist.GetTitle())
xAxis = hist.GetXaxis()
for i in xrange(nBins):
xVal = xAxis.GetBinCenter(i + 1)
yVal = hist.GetBinContent(i + 1)
graph.SetPoint(i, xVal, yVal)
graph.SetPointEXlow(i, abs(xVal - xAxis.GetBinLowEdge(i + 1)))
graph.SetPointEXhigh(i, abs(xVal - xAxis.GetBinUpEdge(i + 1)))
if keepErrors:
if poissonErrors:
lo, hi = calculatePoissonErrors(yVal)
graph.SetPointEYlow(i, lo)
graph.SetPointEYhigh(i, hi)
else:
graph.SetPointEYlow(i, hist.GetBinErrorLow(i + 1))
graph.SetPointEYhigh(i, hist.GetBinErrorUp(i + 1))
# copy the style
graph.SetMarkerStyle(hist.GetMarkerStyle())
graph.SetMarkerColor(hist.GetMarkerColor())
graph.SetMarkerSize(hist.GetMarkerSize())
graph.SetLineStyle(hist.GetLineStyle())
graph.SetLineColor(hist.GetLineColor())
graph.SetLineWidth(hist.GetLineWidth())
graph.SetFillColor(hist.GetFillColor())
graph.SetFillStyle(hist.GetFillStyle())
return graph
ey_lo_ele = grEle.GetErrorYlow(i)
if y_mu < 1.e-9 or y_ele < 1.e-9:
ey_lo_ratio = 0.0
else:
ey_lo_ratio = y_ratio * math.hypot(ey_lo_mu / y_mu, ey_lo_ele / y_ele)
ey_hi_mu = grMu.GetErrorYhigh(i)
ey_hi_ele = grEle.GetErrorYhigh(i)
if y_mu < 1.e-9 or y_ele < 1.e-9:
ey_hi_ratio = 0.0
else:
ey_hi_ratio = y_ratio * math.hypot(ey_hi_mu / y_mu, ey_hi_ele / y_ele)
grRatio.SetPoint(i, x, y_ratio)
grRatio.SetPointEXlow(i, ex_lo)
grRatio.SetPointEXhigh(i, ex_hi)
grRatio.SetPointEYlow(i, ey_lo_ratio)
grRatio.SetPointEYhigh(i, ey_hi_ratio)
if True:
grRatio.SetMarkerStyle(20)
grRatio.SetMarkerSize(1.5)
grRatio.SetMarkerColor(1)
grRatio.SetLineStyle(1)
grRatio.SetLineColor(1)
grRatio.SetLineWidth(1)
grRatio.GetXaxis().SetLabelSize(0.04)
grRatio.GetXaxis().SetTitleSize(0.04)
grRatio.GetXaxis().SetTitleOffset(1.25)
def nuisance_plot(input_file_, orien_, sticker_, oname_):
pars = get_parameters_from_file(input_file_)
#adjust_parameters(pars)
N = len(pars)
g68 = TGraph(2 * N + 7)
g95 = TGraph(2 * N + 7)
gPR = TGraphAsymmErrors(N)
for i in range(0, N):
x = pars[i].value
y = i + 1.5
xerr = pars[i].error
yerr = 0.
if orien_ == 'h':
x, y = y, x
xerr, yerr = yerr, xerr
gPR.SetPoint(i, x, y)
gPR.SetPointEXlow(i, xerr)
gPR.SetPointEYlow(i, yerr)
gPR.SetPointEXhigh(i, xerr)
gPR.SetPointEYhigh(i, yerr)
for a in range(0, N + 3):
if orien_ == 'h':
g68.SetPoint(a, a, -1)
g95.SetPoint(a, a, -2)
g68.SetPoint(a + 1 + N + 2, N + 2 - a, 1)
g95.SetPoint(a + 1 + N + 2, N + 2 - a, 2)
else:
g68.SetPoint(a, -1, a)
g95.SetPoint(a, -2, a)
g68.SetPoint(a + 1 + N + 2, 1, N + 2 - a)
g95.SetPoint(a + 1 + N + 2, 2, N + 2 - a)
gPR.SetLineStyle(1)
gPR.SetLineWidth(1)
gPR.SetLineColor(1)
gPR.SetMarkerStyle(21)
gPR.SetMarkerSize(1.25)
g68.SetFillColor(kGreen)
g95.SetFillColor(kYellow)
if orien_ == 'h':
text_TL = TPaveText(0.100, 0.925, 0.440, 0.995, 'NDC')
text_TR = TPaveText(0.587, 0.925, 0.999, 0.993, 'NDC')
else:
text_TL = TPaveText(0.370, 0.945, 0.590, 0.995, 'NDC')
text_TR = TPaveText(0.600, 0.945, 0.995, 0.993, 'NDC')
text_TL.AddText(label_TL)
text_TL.SetFillColor(0)
text_TL.SetTextAlign(12)
text_TL.SetTextSize(0.06)
text_TL.SetTextFont(42)
text_TR.AddText(sticker_)
text_TR.SetFillColor(0)
text_TR.SetTextAlign(32)
text_TR.SetTextSize(0.05)
text_TR.SetTextFont(42)
if orien_ == 'h':
c = TCanvas('c', 'c', 1000, 700)
c.SetTopMargin(0.08)
c.SetRightMargin(0.02)
c.SetBottomMargin(0.16)
c.SetLeftMargin(0.11)
else:
c = TCanvas('c', 'c', 700, 1000)
c.SetTopMargin(0.06)
c.SetRightMargin(0.02)
c.SetBottomMargin(0.12)
c.SetLeftMargin(0.35 * 700 / 650)
c.SetTickx()
c.SetTicky()
c.Update()
g95.Draw('AF')
g68.Draw('F')
gPR.Draw('P')
text_TL.Draw('same')
text_TR.Draw('same')
ax_1 = g95.GetHistogram().GetYaxis()
ax_2 = g95.GetHistogram().GetXaxis()
if orien_ == 'h': ax_1, ax_2 = ax_2, ax_1
ax_1.Set(N + 2, 0, N + 2)
ax_1.SetNdivisions(-414)
for i in range(0, N):
ax_1.SetBinLabel(i + 2, pars[i].name)
g95.SetTitle('')
ax_2.SetTitle('post-fit nuisance parameters values')
#ax_2.SetTitle('deviation in units of #sigma')
ax_1.SetTitleSize(0.050)
ax_2.SetTitleSize(0.050)
ax_1.SetTitleOffset(1.4)
ax_2.SetTitleOffset(1.0)
ax_1.SetLabelSize(0.05)
#ax_2.SetLabelSize(0.05)
ax_1.SetRangeUser(0, N + 2)
ax_2.SetRangeUser(-2.4, 2.4)
g95.GetHistogram().Draw('axis,same')
c.SaveAs(oname_ + '.pdf')
c.Close()
