def plotSpectrum():
useMalbek = False
eName = "energy_keV"
eName = "trapENFCal"
# load workspace
f = TFile("./data/fitWorkspace.root")
fitWorkspace = f.Get("fitWorkspace")
fData = fitWorkspace.allData().front()
fitResult = fitWorkspace.allGenericObjects().front()
nPars = fitResult.floatParsFinal().getSize()
fEnergy = fitWorkspace.var(eName)
modelPDF = fitWorkspace.pdf("model")
# fitWorkspace.Print()
# get a list of pdf names
pdfNames = []
pdfList = fitWorkspace.allPdfs() # goddamn RooArgSet
itr = pdfList.createIterator()
var = itr.Next()
while var:
name = var.GetName()
if "ext" in name:
pdfNames.append(name)
var = itr.Next()
pdfNames = sorted(pdfNames)
# -- create spectrum rooplot --
nCol = float(gStyle.GetNumberOfColors())
binSize = 0.2
nBins = int((eHi - eLo) / binSize + 0.5)
fSpec = fEnergy.frame(RF.Range(eLo, eHi), RF.Bins(nBins))
fData.plotOn(fSpec)
modelPDF.plotOn(fSpec, RF.LineColor(ROOT.kRed), RF.Name("FullModel"))
chiSquare = fSpec.chiSquare(nPars)
# draw components
leg = TLegend(0.83, 0.1, 0.97, 0.9)
leg.AddEntry(fSpec.findObject("FullModel"),
"model #chi^{2}=%.3f" % chiSquare, "l")
for idx in range(len(pdfNames)):
name = pdfNames[idx]
lineCol = gStyle.GetColorPalette(int(nCol / len(pdfNames) * idx))
modelPDF.plotOn(fSpec, RF.Components(name), RF.LineColor(lineCol),
RF.Name(name))
plotName = name
if "ext-" in name:
plotName = plotName[4:]
leg.AddEntry(fSpec.findObject(name), plotName, "l")
# create normalized residual ("pull") rooplot
# (draw full model again so residuals are calculated against it)
modelPDF.plotOn(fSpec, RF.LineColor(ROOT.kRed), RF.Name("FullModel"))
res = fSpec.pullHist()
fRes = fEnergy.frame(RF.Title(" "))
fRes.addPlotable(res, "P")
# -- print fit results --
print "-- SPECFIT RESULTS -- "
print "%-10s = %.3f" % ("chiSq", chiSquare)
fitValsFinal = getRooArgDict(fitResult.floatParsFinal())
for name in sorted(fitValsFinal):
fitVal = fitValsFinal[name]
if "amp-" in name:
error = fitWorkspace.var(name).getError()
print "%-10s = best %-7.3f error %.3f (w/o profile)" % (
name, fitVal, error)
elif "mu-" in name:
# compare the energy offset
pkName = name[3:]
pct = 100 * (1 - fitVal / pkList[pkName])
print "%-10s : fit %-6.3f lit %-6.3f (%.3f%%)" % (
name, fitVal, pkList[pkName], pct)
elif "sig-" in name:
# compare the sigma difference
pkName = name[4:]
pct = 100 * (1 - fitVal / getSigma(pkList[pkName]))
print "%-10s : fit %-6.3f func %-6.3f (%.3f%%)" % (
name, fitVal, getSigma(pkList[pkName]), pct)
else:
print "%s = %.4f" % (name, fitVal)
continue
# -- make spectrum plot w/ residual, w/ all the formatting crap --
gStyle.SetOptStat(0)
gStyle.SetPalette(
ROOT.kRainBow) # https://root.cern.ch/doc/master/classTColor.html#C06
c = TCanvas("c", "Bob Ross's Canvas", 1400, 1000)
p1 = TPad("p1", "spectrum", 0., 0.3, 1., 1.)
p2 = TPad("p2", "residual", 0., 0., 1., 0.3)
p1.Draw()
p2.Draw()
p1.SetRightMargin(0.2)
p1.SetBottomMargin(0.)
p2.SetRightMargin(0.2)
p2.SetTopMargin(0.)
p2.SetBottomMargin(0.4)
p1.cd()
# p1.SetGrid()
fSpec.SetTitle("")
fSpec.GetXaxis().SetLabelSize(0.)
fSpec.GetYaxis().SetLabelSize(0.05)
fSpec.GetYaxis().SetTitleSize(0.06)
fSpec.GetYaxis().SetTitleOffset(0.6)
fSpec.GetYaxis().SetTitle("Events / (%.1f keV)" % binSize)
fSpec.Draw()
leg.Draw("same")
p2.cd()
fRes.GetXaxis().SetLabelSize(0.1)
fRes.GetYaxis().SetLabelSize(0.1)
fRes.GetXaxis().SetTitleSize(0.15)
fRes.GetYaxis().SetTitle("Normalized Resid.")
fRes.GetYaxis().SetTitleOffset(0.3)
fRes.GetYaxis().SetTitleSize(0.1)
fRes.Draw()
zeroLine = ROOT.TLine(eLo, 0., eHi, 0.)
zeroLine.SetLineColor(ROOT.kBlack)
zeroLine.Draw("same")
c.Print("./plots/spectrum.pdf")
# -- correlation matrix --
c2 = TCanvas("c2", "Bob Ross's Canvas", 1100, 800)
c2.SetLeftMargin(0.15)
c2.SetRightMargin(0.12)
gStyle.SetPalette(ROOT.kDarkBodyRadiator)
corrMat = fitResult.correlationHist("Correlation Matrix")
corrMat.SetTitle("")
corrMat.GetXaxis().SetLabelSize(0.02)
corrMat.Draw("colz")
c2.Print("./plots/corrMatrix.pdf")
def fitModel(makePlots=False):
from ROOT import TFile, TH1D, TCanvas, TLegend, gStyle
# === load data into workspace ===
tf = TFile("%s/data/latDS%s.root" %
(dsi.latSWDir, ''.join([str(d) for d in dsList])))
tt = tf.Get("skimTree")
tCut = "isEnr==1" if enr is True else "isEnr==0"
hitE = ROOT.RooRealVar("trapENFCal", "Energy", eLo, eHi, "keV")
hEnr = ROOT.RooRealVar("isEnr", "isEnr", 0, 1, "")
# hitW = ROOT.RooRealVar("weight", "weight", 1, 1000, "")
fData = ROOT.RooDataSet("data", "data", tt, ROOT.RooArgSet(hitE, hEnr),
tCut)
# fData = ROOT.RooDataSet("data", "data", tt, ROOT.RooArgSet(hitE, hEnr, hitW), "", "weight")
fitWorkspace = ROOT.RooWorkspace("fitWorkspace", "Fit Workspace")
getattr(fitWorkspace, 'import')(hitE)
getattr(fitWorkspace, 'import')(fData)
# getattr(fitWorkspace,'import')(fWeight)
tf2 = TFile("%s/data/specPDFs.root" % dsi.latSWDir)
pdfList = ROOT.RooArgList("shapes")
# === background model ===
hList = getHistList()
bkgList = []
for h in hList:
hB, name = h[0], h[1]
pars = bkgVals[name]
bkN = ROOT.RooRealVar("amp-" + name, "amp-" + name, pars[1], pars[2],
pars[3])
bkDH = ROOT.RooDataHist("dh-" + name, "dh-" + name,
ROOT.RooArgList(hitE), RF.Import(hB))
bkPDF = ROOT.RooHistPdf("pdf-" + name, "pdf-" + name,
ROOT.RooArgSet(hitE), bkDH, 2)
bkExt = ROOT.RooExtendPdf("ext-" + name, "ext-" + name, bkPDF, bkN)
hitE.setRange(eLo, eHi)
bkgList.append([bkExt, name, bkN, bkDH, bkPDF])
# this is separate b/c all the RooVars have to remain in memory
for bkg in bkgList:
pdfList.add(bkg[0])
model = ROOT.RooAddPdf("model", "total PDF", pdfList)
if makePlots:
# === make a rooplot of the initial guess parameters, don't let roofit normalize automatically
leg = TLegend(0.83, 0.5, 0.97, 0.9)
gStyle.SetPalette(ROOT.kRainBow)
nCol = float(gStyle.GetNumberOfColors())
fSpec = hitE.frame(RF.Range(eLo, eHi), RF.Bins(nB))
fData.plotOn(fSpec)
# wouter's note: DON'T DELETE
# "the default behavior is when you plot a p.d.f. on an empty frame it is
# plotted with unit normalization. When you plot it on a frame with data in
# it, it will normalize to the number of events in that dataset."
# (then after you do a fit, the pdf normalization changes again ...)
nData = fData.numEntries()
nTot = 0
for i, ext in enumerate(bkgList):
extPDF, name = ext[0], ext[1]
col = gStyle.GetColorPalette(int(nCol / len(bkgList) * i))
extPDF.plotOn(
fSpec, RF.LineColor(col),
RF.Normalization(bkgVals[name][1], ROOT.RooAbsReal.Raw),
RF.Name(name))
leg.AddEntry(fSpec.findObject(name), name, "l")
nTot += bkgVals[name][1]
model.plotOn(fSpec, RF.LineColor(ROOT.kRed), RF.Name("fmodel"),
RF.Normalization(nTot, ROOT.RooAbsReal.Raw))
leg.AddEntry(fSpec.findObject("fmodel"), "Full Model", "l")
c = TCanvas("c", "c", 1400, 1000)
fSpec.SetTitle("")
fSpec.Draw()
leg.Draw("same")
c.Print("%s/plots/sf-before.pdf" % dsi.latSWDir)
c.Clear()
# === make a pyplot of the same thing
tCut = "isEnr" if enr else "!isEnr"
tCut += " && trapENFCal >= %.1f && trapENFCal <= %.1f" % (eLo, eHi)
n = tt.Draw("trapENFCal", tCut, "goff")
trapE = tt.GetV1()
trapE = [trapE[i] for i in range(n)]
x, hData = wl.GetHisto(trapE, eLo, eHi, epb)
hErr = np.asarray([np.sqrt(h) for h in hData])
plt.errorbar(x,
hData,
yerr=hErr,
c='k',
ms=5,
linewidth=0.5,
fmt='.',
capsize=1,
zorder=1) # pretty convincing rooplot fake
cmap = plt.cm.get_cmap('jet', len(hList) + 2)
pdfs = []
for i, h in enumerate(hList):
name = h[1]
x, y, xpb = wl.npTH1D(h[0])
x, y = normPDF(x, y, eLo, eHi)
nCts = bkgVals[h[1]][1] # the initial guess
if abs(nCts - np.sum(y * nCts)) > 2:
print("norm error, %s nCts %d y*nCts %d" %
(name, nCts, np.sum(y * nCts)))
# plt.step(x, y * nCts / xpb, c=cmap(i), lw=2, label="%s init cts: %d" % (name, nCts)) # plot the histo
xS = np.arange(eLo, eHi, 0.001) # plot a smoothed version
yS = spline(x - xpb / 2, y, xS)
plt.plot(xS,
yS * nCts / xpb,
c=cmap(i),
lw=2,
label="%s init cts: %d" % (name, nCts))
pdfs.append([x, y, xpb, nCts])
xT, yT = getTotalModel(pdfs, eLo, eHi, epb, smooth=True)
plt.step(xT,
yT / epb,
c='r',
lw=2,
label="Raw (no eff. corr): %d cts" % nTot)
plt.xlabel("Energy (keV)", ha='right', x=1)
plt.ylabel("Counts / %.1f keV" % epb, ha='right', y=1)
plt.legend(loc=1, fontsize=12)
plt.xlim(eLo, eHi)
plt.ylim(ymin=0)
plt.tight_layout()
# plt.show()
plt.savefig("%s/plots/sf-before-mpl.pdf" % dsi.latSWDir)
# === alright, now run the fit and output to the workspace
minimizer = ROOT.RooMinimizer(
model.createNLL(fData, RF.NumCPU(2, 0), RF.Extended(True)))
minimizer.setPrintLevel(-1)
minimizer.setStrategy(2)
minimizer.migrad()
fitResult = minimizer.save()
# according to the internet, covQual==3 is a good indicator that it converged
print("Fitter is done. Fit Cov Qual:", fitResult.covQual())
# save workspace to a TFile
getattr(fitWorkspace, 'import')(fitResult)
getattr(fitWorkspace, 'import')(model)
tf3 = TFile("%s/data/fitWorkspace.root" % dsi.latSWDir, "RECREATE")
fitWorkspace.Write()
tf3.Close()
def plotFit(plotRate=False):
from ROOT import TFile, TCanvas, TH1D, TLegend, gStyle
f = TFile("%s/data/fitWorkspace.root" % dsi.latSWDir)
fitWorkspace = f.Get("fitWorkspace")
fData = fitWorkspace.allData().front()
fitResult = fitWorkspace.allGenericObjects().front()
nPars = fitResult.floatParsFinal().getSize()
hitE = fitWorkspace.var("trapENFCal")
model = fitWorkspace.pdf("model")
nData = fData.numEntries()
fCov = fitResult.covQual()
# fitWorkspace.Print()
# === get fit results: {name : [nCts, err]} ===
fitVals = {}
for i in range(nPars):
fp = fitResult.floatParsFinal()
name = fp.at(i).GetName()
fitVal, fitErr = fp.at(i).getValV(), fp.at(i).getError()
if "amp" in name:
fitVals[name.split('-')[1]] = [fitVal, fitErr]
# for f in fitVals:
# print(f, fitVals[f])
# === make a rooplot of the fit ===
leg = TLegend(0.83, 0.5, 0.97, 0.9)
gStyle.SetPalette(ROOT.kRainBow)
nCol = float(gStyle.GetNumberOfColors())
fSpec = hitE.frame(RF.Range(eLo, eHi), RF.Bins(nB))
fData.plotOn(fSpec)
for i, name in enumerate(bkgModel):
pdfName = "ext-" + name
col = gStyle.GetColorPalette(int(nCol / len(bkgModel) * i))
model.plotOn(fSpec, RF.Components(pdfName), RF.LineColor(col),
RF.LineStyle(ROOT.kDashed), RF.Name(name))
leg.AddEntry(fSpec.findObject(name), name, "l")
chiSquare = fSpec.chiSquare(nPars)
model.plotOn(fSpec, RF.LineColor(ROOT.kRed), RF.Name("fmodel"))
leg.AddEntry(fSpec.findObject("fmodel"),
"Full Model, #chi^{2}/NDF = %.3f" % chiSquare, "l")
c = TCanvas("c", "c", 1400, 1000)
fSpec.SetTitle("")
fSpec.Draw()
leg.Draw("same")
c.Print("%s/plots/sf-after.pdf" % dsi.latSWDir)
c.Clear()
# === duplicate the rooplot in matplotlib ===
plt.close()
tf = TFile("%s/data/latDS%s.root" %
(dsi.latSWDir, ''.join([str(d) for d in dsList])))
tt = tf.Get("skimTree")
tCut = "isEnr==1" if enr is True else "isEnr==0"
tCut = "isEnr" if enr else "!isEnr"
tCut += " && trapENFCal >= %.1f && trapENFCal <= %.1f" % (eLo, eHi)
n = tt.Draw("trapENFCal", tCut, "goff")
trapE = tt.GetV1()
trapE = [trapE[i] for i in range(n)]
x, hData = wl.GetHisto(trapE, eLo, eHi, epb)
if plotRate:
hErr = np.asarray([np.sqrt(h) / detExp
for h in hData]) # statistical error
plt.errorbar(x,
hData / detExp,
yerr=hErr,
c='k',
ms=5,
linewidth=0.5,
fmt='.',
capsize=1,
zorder=1)
else:
hErr = np.asarray([np.sqrt(h) for h in hData]) # statistical error
plt.errorbar(x,
hData,
yerr=hErr,
c='k',
ms=5,
linewidth=0.5,
fmt='.',
capsize=1,
zorder=1)
# get the list of histograms and plot the components
hList = getHistList()
cmap = plt.cm.get_cmap('jet', len(hList) + 2)
pdfs, pdfsCorr, nTot, nTotC = [], [], 0, 0
for i, h in enumerate(hList):
name = h[1]
x, y, xpb = wl.npTH1D(h[0])
x, y = normPDF(x, y, eLo, eHi)
nCts, nErr = fitVals[name] # final result
yc = nCts * getEffCorr(x, y, inv=True)
if abs(nCts - np.sum(y * nCts)) > 2:
print("norm error, %s nCts %d y*nCts %d" %
(name, nCts, np.sum(y * nCts)))
# plt.step(x, y * nCts * (epb/xpb), c=cmap(i), lw=2, label="%s cts: %.2f±%.2f" % (name, nCts, nErr)) # plot the histo
xS = np.arange(eLo, eHi, 0.001) # plot a smoothed version
yS = spline(x - xpb / 2, y, xS)
if plotRate:
plt.plot(xS,
yS * nCts * (epb / xpb) / detExp,
"--",
c=cmap(i),
lw=2,
label="%s %.2f ± %.2f" %
(name, nCts / detExp, nErr / detExp))
else:
plt.plot(xS,
yS * nCts * (epb / xpb),
c=cmap(i),
lw=2,
label="%s cts: %d" % (name, nCts))
pdfs.append([x, y, xpb, nCts])
pdfsCorr.append([x, yc, xpb, nCts])
nTot += nCts
nTotC += np.sum(
yc
) # reverse the efficiency correction to get the "true" number of counts
# get the fit model, and the efficiency-corrected final model
xT, yT = getTotalModel(pdfs, eLo, eHi, epb, smooth=True)
xTc, yTc = getTotalModel(pdfsCorr, eLo, eHi, epb, smooth=True, amp=False)
if plotRate:
plt.plot(xT,
yT / detExp,
'r',
lw=2,
alpha=0.7,
label="Rate: %.2f" % (nTot / detExp))
plt.plot(xTc,
yTc / detExp,
c='m',
lw=3,
alpha=0.7,
label="Eff.corr: %.2f " % (nTotC / detExp))
plt.ylabel("Counts / keV / kg-d", ha='right', y=1)
else:
plt.plot(xT,
yT,
'r',
lw=2,
alpha=0.7,
label="Raw (no eff. corr): %d cts" % nTot)
plt.plot(xTc,
yTc,
c='m',
lw=3,
alpha=0.7,
label="Efficiency corrected: %d cts" % nTotC)
plt.ylabel("Counts / %.1f keV" % epb, ha='right', y=1)
plt.xlabel("Energy (keV)", ha='right', x=1)
plt.legend(loc=1, fontsize=12)
plt.xticks(np.arange(int(eLo) - 1, eHi + 1, 5))
plt.xlim(eLo, eHi)
plt.ylim(ymin=0)
plt.tight_layout()
# plt.show()
plt.savefig("%s/plots/sf-after-mpl.pdf" % dsi.latSWDir)
def plotFit():
axPeak = 2.464
# load workspace
f = TFile("./data/splitWorkspace.root")
fitWorkspace = f.Get("fitWorkspace")
fData = fitWorkspace.allData().front()
fitResult = fitWorkspace.allGenericObjects().front()
nPars = fitResult.floatParsFinal().getSize()
fEnergy = fitWorkspace.var("energy_keV")
modelPDF = fitWorkspace.pdf("model")
# fitWorkspace.Print()
pdfNames = ["ext-axion"]
# -- create spectrum rooplot --
nCol = float(gStyle.GetNumberOfColors())
binSize = 0.04
nBins = int((eHi - eLo) / binSize + 0.5)
fSpec = fEnergy.frame(RF.Range(eLo, eHi), RF.Bins(nBins))
fData.plotOn(fSpec)
modelPDF.plotOn(fSpec, RF.LineColor(ROOT.kRed), RF.Name("FullModel"))
chiSquare = fSpec.chiSquare(nPars)
modelPDF.plotOn(fSpec, RF.Components(pdfNames[0]),
RF.LineColor(ROOT.kBlue), RF.Name(pdfNames[0]))
# -- make a fake Gaussian --
# gaus = ROOT.TF1("g","gaus",-3, eHi)
# gaus.SetParameters(10., axPeak, getSigma(axPeak))
# gaus.SetParameter(0,10.)
# gaus.SetParameter(1, axPeak)
# gaus.SetParameter(2, getSigma(axPeak))
# erf
# rrvGaus = ROOT.RooRealVar("ax","ax",-3.,3.)
# rarGaus = RF.bindFunction("gaus", ROOT.TMath.Erf, rrvGaus)
# rarGaus.Print()
# frame2 = rrvGaus.frame(RF.Title("mygaus"))
# rarGaus.plotOn(frame2, RF.LineColor(ROOT.kGreen), RF.LineStyle(ROOT.kDashed), RF.Name("axGaus"))
# c0 = TCanvas("c0","",800,600)
# frame2.Draw()
# c0.Print("./plots/testGaus.pdf")
# -- print fit results --
print "-- SHIFTFIT RESULTS -- "
print "%-10s = %.3f" % ("chiSq", chiSquare)
fitValsFinal = getRooArgDict(fitResult.floatParsFinal())
bkgVal = 0.
for name in sorted(fitValsFinal):
fitVal = fitValsFinal[name]
if "amp-" in name:
error = fitWorkspace.var(name).getError()
print "%-10s = best %-7.3f error %.3f (w/o profile)" % (
name, fitVal, error)
elif "mu-" in name:
# compare the energy offset
pkName = name[3:]
pct = 100 * (1 - fitVal / axPeak)
print "%-10s : fit %-6.3f lit %-6.3f (%.3f%%)" % (name, fitVal,
axPeak, pct)
elif "sig-" in name:
# compare the sigma difference
pkName = name[4:]
pct = 100 * (1 - fitVal / getSigma(axPeak))
print "%-10s : fit %-6.3f func %-6.3f (%.3f%%)" % (
name, fitVal, getSigma(axPeak), pct)
else:
print "%s = %.4f (%.4f)" % (name, fitVal, fitVal / nBins)
bkgVal = fitVal / nBins
continue
# -- make spectrum plot --
c = TCanvas("c", "Bob Ross's Canvas", 1400, 1000)
c.SetRightMargin(0.2)
fSpec.SetTitle(" ")
fSpec.Draw()
ymax = fSpec.GetMaximum()
l1 = ROOT.TLine(axPeak, 0., axPeak, ymax)
l1.SetLineColor(ROOT.kBlue)
l1.SetLineWidth(2)
l1.Draw("same")
leg = TLegend(0.83, 0.6, 0.97, 0.9)
leg.AddEntry(fSpec.findObject("FullModel"),
"model #chi^{2}=%.3f" % chiSquare, "l")
leg.AddEntry(fSpec.findObject("FullModel"), "cts/bin=%.2f" % bkgVal, "")
leg.AddEntry(fSpec.findObject(pdfNames[0]), "axion gaussian", "l")
leg.AddEntry(l1, "axion-%.2f" % axPeak, "l")
leg.Draw("same")
c.Print("./plots/shiftFit.pdf")
# -- get FC Limit --
# Calculate the confidence interval for the axion peak, which is too low to use profile likelihood.
# TFeldmanCousins version:
# https://root.cern.ch/root/html/tutorials/math/FeldmanCousins.C.html
# RooStats version:
# https://root.cern.ch/root/html/tutorials/roostats/StandardFeldmanCousinsDemo.C.html
# FC Paper: https://arxiv.org/pdf/physics/9711021.pdf
f = TFeldmanCousins(0.95)
N_obs = 1.
N_bkg = bkgVal / 3.
ul = f.CalculateUpperLimit(N_obs, N_bkg)
ll = f.GetLowerLimit()
print "For %.2f events observed, and %.2f background events," % (N_obs,
N_bkg)
print "F-C method gives UL: %.2f and LL %.2f (90%% CL)" % (ul, ll)