def drawTemplateFitter(tf, canvas = None, trueVal = None) :
if not canvas : canvas = r.TCanvas()
else: canvas.Clear()
canvas.cd(0)
canvas.Divide(2,2)
#------1
LL = r.TGraph(len(tf.pars), np.array(tf.pars), tf.templatesN2LL)
fit = r.TF1("fit","pol3",min(tf.pars),max(tf.pars))
for i,c in enumerate(tf.coefficients) : fit.SetParameter(i,c)
xMaxLL = [r.TGraph(2,np.array([val,val]),np.array([min(tf.templatesN2LL),max(tf.templatesN2LL)])) for val in [tf.value,tf.value+tf.error,tf.value-tf.error]]
for h in xMaxLL : h.SetLineColor(r.kBlue)
if trueVal != None :
xMaxLL.append(r.TGraph(2,np.array([trueVal,trueVal]),np.array([min(tf.templatesN2LL),max(tf.templatesN2LL)])))
xMaxLL[-1].SetLineColor(r.kRed)
xMaxLL[-1].SetLineWidth(2)
xMaxLL[1].SetLineStyle(r.kDashed)
xMaxLL[2].SetLineStyle(r.kDashed)
LL.SetTitle("best fit: %s | corrected: %s"%(utils.roundString(tf.value,tf.error, noSci=True),
utils.roundString(tf.value-tf.bias, tf.error*tf.pull,noSci=True)) +
(" | %0.3f"%trueVal if trueVal is not None else ""))
canvas.cd(1)
LL.Draw('A*')
for h in reversed(xMaxLL) : h.Draw('')
fit.Draw('same')
#------2
n2lls = utils.rHist("-2logLs", *np.histogram([toy.n2LL for toy in tf.ensemble], 100) )
n2ll = n2lls.Clone('-2logL') ; n2ll.Reset(); n2ll.SetBinContent(n2ll.FindFixBin(tf.n2LL), n2lls.GetMaximum());
n2ll.SetFillColor(r.kBlue); n2lls.SetTitle("p-value: %0.4f"%tf.p_value)
canvas.cd(2)
n2lls.Draw()
n2ll.Draw('same')
#------3
edges = range(len(tf.observed)+1)
observed = utils.rHist("observed", tf.observed, edges, True)
spars,stemplates = zip(*sorted(zip(tf.pars,tf.templates)))
templates = [utils.rHist("template%d"%i, templ, edges) for i,templ in enumerate(stemplates) if i in [0,len(spars)-1,len(spars)/2]]
observed.SetTitle("observed")
observed.SetMarkerStyle(20)
for i,templ in enumerate(templates) : templ.SetLineColor([r.kRed,r.kGreen,r.kBlue][i])
maxHeight = max(h.GetMaximum() for h in [observed]+templates)
for h in [observed]+templates : h.SetMaximum(1.1*maxHeight)
canvas.cd(3)
observed.Draw()
for t in templates : t.Draw("same")
#------4
relMean = np.mean(tf.relResiduals)
pull = utils.rHist("relative residuals", *np.histogram(tf.relResiduals,100,(relMean-5,relMean+5)))
canvas.cd(4)
pull.Draw()
canvas.Update()
return canvas,observed,templates,LL,fit,xMaxLL,n2lls,n2ll,pull
def drawComponentSolver(cs, canvas = None) :
if not canvas : canvas = r.TCanvas()
canvas.cd(0)
canvas.Divide(2,2)
rTemplates = [utils.rHist("template%d"%i,d,range(len(d)+1)) for i,d in enumerate(cs.components)]
rObs = utils.rHist("observed",cs.observed,range(len(d)+1),True)
rObs.SetTitle("ML fractions: "+", ".join(utils.roundString(f,e,noSci=True) for f,e in zip(cs.fractions,cs.errors) ))
nlls = utils.rHist("-logLs", *np.histogram([-toy.logL for toy in cs.ensemble], 100) )
nll = nlls.Clone('-logL') ; nll.Reset(); nll.SetBinContent(nll.FindFixBin(-cs.logL), nlls.GetMaximum());
nll.SetFillColor(r.kBlue); nll.SetTitle("p-value: %0.4f"%cs.p_value)
pulls = [ utils.rHist("relative residuals %d"%i, *np.histogram(pull,100,(-5,5))) for i,pull in enumerate(cs.relResiduals.transpose())]
corr = cs.correlation
corrH = r.TH2D("correlations","correlations", len(corr), -0.5, len(corr)-0.5, len(corr), -0.5, len(corr)-0.5)
for i,j in itertools.product(range(len(corr)), repeat = 2) : corrH.SetBinContent(i+1,j+1,round(corr[i][j],3))
corrH.SetMaximum(1)
corrH.SetMinimum(-1)
canvas.cd(3)
corrH.Draw("colztext")
stats = []
for i,a,t in zip(range(len(cs.fractions)),pulls,rTemplates) :
t.SetFillColor(i+2)
t.SetLineColor(i+2)
a.SetLineColor(i+2)
a.SetMaximum(1.1*max(h.GetMaximum() for h in pulls))
canvas.cd(4); a.Draw("sames" if i else "")
canvas.Update()
st = a.GetListOfFunctions().FindObject("stats")
st.SetOptStat(1101)
st.SetLineColor(i+2)
st.SetY1NDC(0.85-0.16*i)
st.SetY2NDC(1.0-0.16*i)
stats.append( st.Clone("stats%d"%i) )
for s in stats : s.Draw()
canvas.cd(2) ; nll.Draw(); nlls.Draw("histsame")
rObs.SetMarkerStyle(20)
def draw(i,logY = False) :
canvas.cd(i).SetLogy(logY)
rObs.Draw("e")
for t in rTemplates : t.Draw("histsame")
rObs.Draw("esame")
base = utils.rHist("base",cs.base,range(len(cs.base)+1)) ; base.SetFillColor(r.kGray)
rTemplates = sorted(rTemplates,key=lambda t: -t.Integral()) + [base]
for t,h in reversed(zip(rTemplates[:-1],rTemplates[1:])) : t.Add(h)
draw(1, logY = False)
#draw(3, logY = True)
canvas.Update()
return [canvas,rObs,rTemplates,stats,pulls,nlls,nll,corrH]
def drawTemplateEnsembles(ens, canvas = None) :
if not canvas : canvas = r.TCanvas()
else : canvas.Clear()
canvas.cd(0)
canvas.Divide(2,2)
bias = r.TGraph(len(ens.pars)-2, np.array(ens.pars[1:-1]), np.array(ens.biases[1:-1]))
pull = r.TGraph(len(ens.pars)-2, np.array(ens.pars[1:-1]), np.array(ens.pulls[1:-1]))
err = r.TGraph(len(ens.pars)-2, np.array(ens.pars[1:-1]), np.array(ens.meanErrors[1:-1]))
rrs = [utils.rHist("relRes%.3f"%par,*np.histogram(rr,100,(-5,5))) for par,rr in zip(ens.pars,ens.relativeResiduals)[1:-1]]
for item in [bias,pull,err] : item.SetMarkerStyle(20)
height = 1.1 * max(rr.GetMaximum() for rr in rrs)
for rr in rrs : rr.SetMaximum(height)
bias.Fit("pol1","Q"); b0 = bias.GetFunction("pol1").GetParameter(0); b1 = bias.GetFunction("pol1").GetParameter(1)
pull.Fit("pol0","Q"); p0 = pull.GetFunction("pol0").GetParameter(0)
canvas.cd(1) ; bias.SetTitle("Bias : %.3f + %.3f x"%(b0,b1)); bias.Draw("AP")
canvas.cd(2) ; pull.SetTitle("Pull : %.3f"%p0); pull.Draw("AP");
canvas.cd(3) ; err.SetTitle("Expected Uncertainty : %.3f"%ens.sensitivity) ; err.SetMinimum(0); err.SetMaximum(2*max(ens.meanErrors)); err.Draw("AP")
canvas.cd(4)
for i,rr in enumerate(rrs) :
rr.SetLineColor(i)
rr.Draw("same" if i else "")
return canvas,bias,pull,err,rrs
