def fitBCMax():
tree = TChain("skimTree")
tree.Add("~/project/cal-lat/latSkimDS3*")
cutFile = TFile("~/project/lat/latSkimDS3_0_0.root")
theCut = cutFile.Get("theCut").GetTitle()
# scatter plot
h0 = wl.H2D(tree,"h0",100,0,20,500,parLim[0],parLim[1],thisVar+":trapENFCal",theCut)
# fitModel10 = "[0] + [1]*x + [2]*x**2 + [3] * exp(-1.0 * ((x-[4])**2 / (2 * [5]**2)) )"
h1 = wl.H1D(tree,"h1",20,9.5,11.5,"trapENFCal",theCut)
h2 = wl.H1D(tree,"h2",20,9.5,11.5,"trapENFCal",theCut+checkCut)
f0 = TF1("f0","gaus",10,11)
h1.Fit("f0","Lq")
[norm,mu,sig] = wl.GetPars(f0)
f1 = TF1("f1", fitModel10, 9.5,11)
wl.SetPars(f1,[-209, 41, -2, norm, mu, sig])
f1.SetLineColor(4)
h1.Fit("f1","Lq")
[p0,p1,p2,norm1,mu,sig] = wl.GetPars(f1)
f2 = TF1("f2", fitModel10, 9.5,11)
wl.SetPars(f2,[-209, 41, -2, norm1, mu, sig])
f2.SetLineColor(2)
h2.Fit("f2","Lq")
[p0,p1,p2,norm2,mu,sig] = wl.GetPars(f2)
retention = 100*norm2/norm1
print "Retention:",retention," Cut used:",checkCut
def MakeCutPlot(c,cal,var,eb,elo,ehi,vb,vlo,vhi,d2Cut,d1Cut,outPlot,fastMode):
""" Creates a channel-specific energy calibration plot. """
# Calculate cut vals (assumes plot range is correct)
h1 = wl.H1D(cal,vb,vlo,vhi,var,d1Cut)
h1Sum = h1.Integral()
if h1Sum == 0:
print "Error: Failed %s, histogram sum is 0 so cannot normalize, setting to [0,0,0,0,0]"%(var)
return 0,0,0,0,0
h1.Scale(1/h1Sum)
try:
cut99,cut95,cut01,cut05,cut90 = wl.GetIntegralPoints(h1)
except:
print "Error: Failed %s using cut %s, setting to [0,0,0,0,0]"%(var,d1Cut)
return 0,0,0,0,0
if fastMode:
print "Returning fastMode output: ", cut99,cut95,cut01,cut05,cut90
return cut99,cut95,cut01,cut05,cut90
# Generate the plot for inspection.
c.cd(2)
gPad.SetLogy(0)
h1.GetXaxis().SetRangeUser(cut01-abs(0.25*cut01), cut99 + abs(0.25*cut99) )
h1.SetTitle("")
h1.GetXaxis().SetTitle(var)
h1.Draw("hist")
c.cd(1)
gPad.SetLogy(0)
cal.Draw("%s:trapENFCal>>b(%d,%d,%d,%d,%.3E,%.3E)"%(var,eb+10,elo-5,ehi+5,vb,cut01-abs(0.25*cut01),cut99+abs(0.25*cut99)) ,d2Cut)
l1, l2, l3 = TLine(), TLine(), TLine()
l1.SetLineColor(ROOT.kGreen)
l2.SetLineColor(ROOT.kRed)
l3.SetLineColor(ROOT.kMagenta)
l1.DrawLine(elo-5, cut99, ehi+5, cut99)
l2.DrawLine(elo-5, cut95, ehi+5, cut95)
l2.DrawLine(elo-5, cut05, ehi+5, cut05)
l1.DrawLine(elo-5, cut01, ehi+5, cut01)
c.cd(3)
x_h1, y_h1 = wl.npTH1D(h1)
int_h1 = wl.integFunc(y_h1)
g2 = TGraph(len(x_h1), x_h1, int_h1)
g2.GetXaxis().SetRangeUser(cut01-abs(0.3*cut01), cut99 + abs(0.3*cut99) )
g2.SetTitle("")
g2.GetXaxis().SetTitle(var)
g2.GetYaxis().SetTitle("Percentile")
g2.Draw("ACP")
l1.DrawLine(cut99, 0, cut99, 1)
l2.DrawLine(cut95, 0, cut95, 1)
l1.DrawLine(cut01, 0, cut01, 1)
l2.DrawLine(cut05, 0, cut05, 1)
c.Print(outPlot)
return cut99,cut95,cut01,cut05,cut90
def MakeCutPlot(c, cal, var, eb, elo, ehi, vb, vlo, vhi, d2Draw, d2Cut, d1Cut,
outPlot, fastMode):
""" Repeated code is the DEVIL. Even if you have to pass in 1,000,000 arguments. """
# Calculate cut vals (assumes plot range is correct)
h1 = wl.H1D(cal, vb, vlo, vhi, var, d1Cut)
h1Sum = h1.Integral()
if h1Sum == 0:
return 0, 0, 0, 0, 0
h1.Scale(1 / h1Sum)
cut99, cut95, cut01, cut05, cut90 = wl.GetIntegralPoints(h1)
if fastMode:
return cut99, cut95, cut01, cut05, cut90
# Generate the plot for inspection.
c.cd(2)
gPad.SetLogy(0)
h1.Draw("hist")
c.cd(1)
gPad.SetLogy(0)
cal.Draw(d2Draw, d2Cut)
l1, l2 = TLine(), TLine()
l1.SetLineColor(ROOT.kGreen)
l2.SetLineColor(ROOT.kRed)
l1.DrawLine(elo, cut99, ehi, cut99)
l2.DrawLine(elo, cut95, ehi, cut95)
l2.DrawLine(elo, cut05, ehi, cut05)
l1.DrawLine(elo, cut01, ehi, cut01)
c.cd(3)
x_h1, y_h1 = wl.npTH1D(h1)
int_h1 = wl.integFunc(y_h1)
g2 = TGraph(len(x_h1), x_h1, int_h1)
g2.Draw("ACP")
l1.DrawLine(cut99, 0, cut99, 1)
l2.DrawLine(cut95, 0, cut95, 1)
l1.DrawLine(cut01, 0, cut01, 1)
l2.DrawLine(cut05, 0, cut05, 1)
c.Print(outPlot)
return cut99, cut95, cut01, cut05, cut90
def main(argv):
inDir, cutDir, outDir = ".", ".", "./plots"
specMode = False
dsNum, modNum, chNum = -1, -1, -1
skimTree = ROOT.TChain("skimTree")
bins, lower, upper = {1250}, 0, 250
outFile = ROOT.TFile()
for i, opt in enumerate(argv):
if opt == "-spec":
specMode = True
print "Spectrum Mode"
if opt == "-d":
inDir, outDir = argv[i + 1], argv[i + 2]
if opt == "-ch":
chNum = int(argv[i + 1])
print("Drawing specific channel %d" % (chNum))
if opt == "-s":
dsNum, modNum = int(argv[i + 1]), int(argv[i + 2])
print("Drawing DS-%d Module-%d" % (dsNum, modNum))
if opt == "-cal":
calMode = True
print("Drawing Calibration runs")
cInfo = ds.CalInfo()
EnergyList = [[1., 5.], [2., 4.], [4., 9.], [9., 12.], [12., 40.],
[40., 50.], [50., 100.]]
# -- Load channel list --
if chNum == -1:
chList = ds.GetGoodChanList(dsNum)
if dsNum == 5 and modNum == 1: # remove 692 and 1232
chList = [
584, 592, 598, 608, 610, 614, 624, 626, 628, 632, 640, 648,
658, 660, 662, 672, 678, 680, 688, 690, 694
]
if dsNum == 5 and modNum == 2:
# chList = [1106, 1110, 1120, 1124, 1128, 1170, 1172, 1174, 1176, 1204, 1208, 1298, 1302, 1330, 1332]
# Removed ch 1110, 1208, and 1332
chList = [
1106, 1120, 1124, 1128, 1170, 1172, 1174, 1176, 1204, 1298,
1302, 1330
]
else:
chList = [chNum]
# -- Load calibration files --
if dsNum == -1 or modNum == -1:
print "DS, subDS, or module number not set properly, exiting"
return
else:
# Limit to 10 calibration runs because that's all Clint processed!
for subNum in cInfo.master["ds%d_m%d" % (dsNum, modNum)].keys():
calList = cInfo.GetCalList("ds%d_m%d" % (dsNum, modNum),
subNum,
runLimit=10)
for i in calList:
skimTree.Add("%s/latSkimDS%d_run%d_*" % (inDir, dsNum, i))
# List of histograms (for summing together) and Dictionary of histograms (for different cuts)
hList, hDict = [], {}
# Create a list of DataFrames for each channel to concatenate at the end
# cutNames = ["BasicCut", "+tailSlope", "+riseNoise", "+fitSlo"]
cutNames = ["BasicCut", "+riseNoise", "+fitSlo"]
# cutNames = ["BasicCut", "+tailSlope", "+bcMax", "+fitSlo"]
if specMode:
outFile = ROOT.TFile("%s/CalibHistograms_DS%d.root" % (outDir, dsNum),
"RECREATE")
for subNum in cInfo.master["ds%d_m%d" % (dsNum, modNum)].keys():
runCut = "&&run>=%d&&run<=%d" % (
cInfo.master["ds%d_m%d" % (dsNum, modNum)][subNum][1],
cInfo.master["ds%d_m%d" % (dsNum, modNum)][subNum][2])
# DB style
fsD = ds.getDBRecord("fitSlo_ds%d_idx%d_m%d_Peak" %
(dsNum, subNum, modNum))
rnD = ds.getDBRecord("riseNoise_ds%d_idx%d_m%d_Peak" %
(dsNum, subNum, modNum))
bcD = ds.getDBRecord("bcMax_ds%d_idx%d_m%d_Peak" %
(dsNum, subNum, modNum))
# Get threshold info
# goodRuns,badRuns,goodRunSigmas = ds.GetThreshDicts(dsNum)
# threshrunCut = "&&("
# for idx2,runRange in enumerate(goodRuns[ch]):
# threshrunCut += "(run>=%d&&run<=%d)||" % (runRange[0],runRange[1])
# # Strip all spaces to save space
# totalCut = megaCut.replace(" ", "") + threshrunCut[:-2] + ")"
# Create new key for dictionaries according to subNum
hDict[subNum] = []
for idx, ch in enumerate(chList):
# Append empty list for every subNum to store channel-based
hDict[subNum].append([])
# Set high gain only!
channelCut = "channel==%d&&gain==0" % (ch)
# Create new array for each subDS
riseNoiseCut, fitSloCut = "", ""
if rnD[ch][2] == 0 or fsD[ch][2] == 0:
continue
else:
riseNoiseCut = '&&riseNoise<%.2f' % (rnD[ch][2])
fitSloCut = '&&fitSlo<%.2f' % (fsD[ch][2])
# Set cuts here
PSA1 = channelCut + runCut + riseNoiseCut
PSA2 = channelCut + runCut + riseNoiseCut + fitSloCut
# Save all cuts into a list to iterate through
cutList = [channelCut + runCut, PSA1, PSA2]
for idx2, cuts in enumerate(cutList):
if specMode:
hDict[subNum][idx].append(ROOT.TH1D())
hDict[subNum][idx][idx2] = wl.H1D(skimTree,
bins,
lower,
upper,
"trapENFCal",
cuts,
Title="h0_%d_Ch%d_%d" %
(subNum, ch, idx2))
print("Drawn: h0_%d_Ch%d_%d" % (subNum, ch, idx2))
# Merge histograms into a list of histograms per cut
if specMode:
outFile.cd()
ROOT.gStyle.SetOptStat(0)
c1 = ROOT.TCanvas("c1", "c1", 1100, 800)
c1.SetLogy()
leg1 = ROOT.TLegend(0.35, 0.8, 0.65, 0.89)
leg1.SetBorderSize(0)
for idx2, cuts in enumerate(cutList):
hList.append(ROOT.TH1D())
hList[idx2] = hDict[0][0][idx2]
for subNum in cInfo.master["ds%d_m%d" % (dsNum, modNum)].keys():
for idx, ch in enumerate(chList[1:]):
hDict[subNum][idx][idx2].Write()
hList[idx2].Add(hDict[subNum][idx][idx2])
hList[idx2].SetTitle("")
hList[idx2].GetXaxis().SetTitle("Energy (keV)")
hList[idx2].GetYaxis().SetTitle("Counts/ %.1f keV" % (float(
(upper - lower) / bins)))
# hList[idx2].SetMinimum(0.1) # Arbitrary unit right now...
hList[idx2].SetLineColor(idx2 + 1)
hList[idx2].Draw("SAME")
leg1.AddEntry(hList[idx2], "%s" % cutNames[idx2], "l")
leg1.Draw()
c1.SaveAs("%s/Spec_ds%d_m%d.pdf" % (outDir, dsNum, modNum))
c1.SaveAs("%s/Spec_ds%d_m%d.C" % (outDir, dsNum, modNum))
outFile.Close()
def burstCut(dsNum):
""" ./tuneCuts.py [dsNum] -burst """
# rates = {0:(30,5), 1:(20,5), 3:(50,5), 4:(20,3), 5:(150.,5.)} # v1 - before fitSLo
rates = {
0: (20, 5),
1: (20, 5),
3: (20, 5),
4: (20, 5),
5: (40., 5)
} # v2 - after fitSlo
chList = ds.GetGoodChanList(dsNum)
nDets = len(chList)
maxRate = rates[dsNum][0]
maxChanRate = rates[dsNum][0] * rates[dsNum][1] / float(nDets)
print "maxRate %d nDets %d factor %d maxChanRate %.2f" % (
rates[dsNum][0], nDets, rates[dsNum][1], maxChanRate)
energyCut = "trapENFCal >= 1"
ignoreList = {0: [656], 3: [592, 692], 4: [1332], 5: [692, 1232, 1124]}
bkg = ROOT.TChain("skimTree")
for ch in chList:
if ch not in ignoreList[dsNum]:
f = "~/project/latskim/latSkimDS%d_ch%d.root" % (dsNum, ch)
print "Added", f
bkg.Add(f)
# append to the text file (input to ds_livetime.cc)
ds_livetimeList = open("burstCut_v1.txt", 'a')
for key in ignoreList:
killChs = ""
for val in ignoreList[key]:
killChs += " %d " % val
ds_livetimeList.write("%s %s \n" % (key, killChs))
c0 = ROOT.TCanvas("c0", "c0", 1000, 600)
rlo, rhi = ds.dsRanges[dsNum][0], ds.dsRanges[dsNum][1]
clo, chi = 570, 700
if dsNum == 4: clo, chi = 1100, 1400
if dsNum == 5: clo, chi = 570, 1400
h0 = wl.H2D(bkg, rhi - rlo, rlo, rhi, chi - clo, clo, chi, "channel:run",
energyCut, "Run Number", "Channel")
h0.Draw("colz")
c0.SetLogz(1)
c0.Print("./plots/burst/channelRateDS%d.pdf" % dsNum)
c1 = ROOT.TCanvas("c", "c", 1600, 600)
c1.Divide(2, 1, 0)
c1.cd(1)
ROOT.gPad.SetLogy(1)
h1 = wl.H1D(bkg, rhi - rlo, rlo, rhi, "run", energyCut, "Run Number",
"Counts")
h1.SetLineColor(ROOT.kRed)
h1.Draw()
# Run & channel-based burst cut.
runs, rates = wl.npTH1D(h1, "i")
idx = np.where(rates > maxRate)
print "Noisy runs:", runs[idx]
burstCut, invBurstCut = energyCut + " && ", energyCut + " && ("
print "maxChanRate:", maxChanRate
for i, run in enumerate(runs[idx]):
runCut = " && run==%d" % run
# h2.append(ROOT.TH1D())
h2 = wl.H1D(bkg, chi - clo, clo, chi, "channel", energyCut + runCut,
"channel", "Counts")
if h2.GetEntries() == 0:
continue
chans, chRates = wl.npTH1D(h2, "i")
idx2 = np.where(chRates > maxChanRate)
print "run", int(run), "noisy chans", chans[idx2], "rates", chRates[
idx2], "total entries", h2.GetEntries(
), "runCut:", energyCut + runCut
# Write run + channel groups to the file (input to ds_livetime.cc)
noisyChans = ""
for ch in chans[idx2]:
noisyChans += "%d " % ch
ds_livetimeList.write("%d %s \n" % (run, noisyChans))
# Make the TCut
runBurst = "&& !(run==%d && (" % run
invBurst = "|| (run==%d && (" % run
if i == 0:
runBurst = runBurst[3:]
invBurst = invBurst[3:]
for ch in chans[idx2]:
runBurst += "channel==%d|| " % ch
invBurst += "channel==%d|| " % ch
runBurst = runBurst[:-3] + ")) "
invBurst = invBurst[:-3] + ")) "
burstCut += runBurst
invBurstCut += invBurst
invBurstCut += ")"
ds_livetimeList.close()
if len(runs[idx]) == 0:
burstCut, invBurstCut = energyCut, energyCut
# add the dead channels back in
for ch in ignoreList[dsNum]:
burstCut += " && channel!=%d" % ch
print "\nBURST CUT:"
print burstCut
print "\nINVERSE BURST CUT:"
print invBurstCut
print ""
h1a = wl.H1D(bkg, rhi - rlo, rlo, rhi, "run", burstCut)
h1a.SetLineColor(ROOT.kBlue)
h1a.Draw("same")
c1.cd(2)
ROOT.gPad.SetLogy(1)
h1.Draw("hist")
h1b = wl.H1D(bkg, rhi - rlo, rlo, rhi, "run", invBurstCut)
h1b.SetLineColor(ROOT.kBlue)
h1b.Draw("hist same")
c1.Print("./plots/burst/burstCutDS%d.pdf" % dsNum)
c2 = TCanvas("c2", "c2", 1000, 600)
c2.SetLogy(1)
eb, elo, ehi = 150, 0, 30 # 5 bins/kev
h2 = wl.H1D(bkg, eb, elo, ehi, "trapENFCal", "", "Energy", "Counts")
h2.SetLineColor(ROOT.kRed)
h3 = wl.H1D(bkg, eb, elo, ehi, "trapENFCal", burstCut)
h3.SetLineColor(ROOT.kBlue)
h2.Draw("hist")
h3.Draw("hist same")
c2.Print("./plots/burst/energySpecDS%d.pdf" % dsNum)
def SaveHistogramsIDX(dType='Bkg', binsize=0.1, lower=0, upper=250):
"""
Saves background or calibration data before and after cuts into histograms
"""
outDir = '/projecta/projectdirs/majorana/users/bxyzhu/LATv2/plots/spectra'
calDir = '/projecta/projectdirs/majorana/users/wisecg/cal-lat'
bkgDir = '/projecta/projectdirs/majorana/users/wisecg/bg-lat'
bkgcutDir = '/projecta/projectdirs/majorana/users/wisecg/cuts'
calcutDir = '/projecta/projectdirs/majorana/users/bxyzhu/cuts'
cInfo = ds.CalInfo()
bins = int((upper - lower) / binsize)
# Basic cut
mNum = 1
cuts = "gain==0 && mHL=={} && isGood && !muVeto && !(C==1&&isLNFill1) && !(C==2&&isLNFill2) && C!=0&&P!=0&&D!=0" % (
mNum)
skimTreeCal = ROOT.TChain("skimTree")
skimTreeBkg = ROOT.TChain("skimTree")
skimCutCal = ROOT.TChain("skimTree")
skimCutBkg = ROOT.TChain("skimTree")
dsList = [0, 1, 2, 3, 4, 5]
outFile = ROOT.TFile(outDir + "/AThresh_mHL{}.root" % (mNum), "RECREATE")
outFile.cd()
for iDS, dsNum in enumerate(dsList):
nMods = [1]
if dsNum == 4: nMods = [2]
if dsNum == 5: nMods = [1, 2]
for modNum in nMods:
chList = ds.GetGoodChanList(dsNum)
if dsNum == 5 and modNum == 1: # remove 692 and 1232 (both beges, so who cares)
chList = [
584, 592, 598, 608, 610, 614, 624, 626, 628, 632, 640, 648,
658, 660, 662, 672, 678, 680, 688, 690, 694
]
elif dsNum == 5 and modNum == 2:
chList = [
1106, 1110, 1120, 1124, 1128, 1170, 1172, 1174, 1176, 1204,
1208, 1298, 1302, 1330, 1332
]
# Total channel histograms, split by dataset
nRangesCal = [
0, len(cInfo.master['ds{}_m{}'.format(dsNum, modNum)])
]
nRangesBkg = [0, ds.dsMap[dsNum]]
if dType == 'Cal':
for calidx in range(nRangesCal[0], nRangesCal[1]):
print("Drawing DS{} calidx{} mod{}".format(
dsNum, calidx, modNum))
hCutList, hFullList = [], []
skimTreeCal.Reset()
calList = cInfo.GetCalList("ds{}_m{}".format(
dsNum, modNum),
calidx,
runLimit=10)
for run in calList:
skimTreeCal.Add("{}/latSkimDS{}_run{}_*.root".format(
calDir, dsNum, run))
for idx, ch in enumerate(chList):
# Reset Tree every calidx + ch
skimCutCal.Reset()
if not os.path.exists(
"{}/calfs_rn/calfs_rn-DS{}-{}-ch{}.root".
format(calcutDir, dsNum, calidx, ch)):
hCutList.append(ROOT.TH1D())
hFullList.append(ROOT.TH1D())
print(
"Channel {}, calidx {} doesn't exist, skipping"
.format(ch, calidx))
continue
skimCutCal.Add(
"{}/calfs_rn/calfs_rn-DS{}-{}-ch{}.root".format(
calcutDir, dsNum, calidx, ch))
hCutList.append(ROOT.TH1D())
hFullList.append(ROOT.TH1D())
# Add additional cut here for channel
hCutList[idx] = wl.H1D(
skimCutCal,
bins,
lower,
upper,
"trapENFCal",
cuts + "&& channel=={}".format(ch),
Title="hCalDS{}_Ch{}_CalIdx{}".format(
dsNum, ch, calidx),
Name="hDS{}_Ch{}_CalIdx{}".format(
dsNum, ch, calidx))
hFullList[idx] = wl.H1D(
skimTreeCal,
bins,
lower,
upper,
"trapENFCal",
cuts + "&&channel=={}".format(ch),
Title="hFullDS_{}_Ch{}_CalIdx{}".format(
dsNum, ch, calidx),
Name="hCalFullDS{}_Ch{}_CalIdx{}".format(
dsNum, ch, calidx))
# Write all histograms (even if they're empty -- for debugging purposes)
hCutList[idx].Write()
hFullList[idx].Write()
if dType == 'Bkg':
for bkgidx in range(nRangesBkg[0], nRangesBkg[1] + 1):
print("Drawing DS{} bkgidx{} mod{}".format(
dsNum, bkgidx, modNum))
hCutList, hFullList = [], []
skimTreeBkg.Reset()
skimTreeBkg.Add("{}/latSkimDS{}_{}_*.root".format(
bkgDir, dsNum, bkgidx))
for idx, ch in enumerate(chList):
# Reset Tree every bkgidx + ch
skimCutBkg.Reset()
if not os.path.exists(
"{}/fs_rn/fs_rn-DS{}-{}-ch{}.root".format(
bkgcutDir, dsNum, bkgidx, ch)):
hCutList.append(ROOT.TH1D())
hFullList.append(ROOT.TH1D())
print(
"Channel {}, bkgidx {} has no entries, skipping"
.format(ch, bkgidx))
continue
skimCutBkg.Add(
"{}/fs_rn/fs_rn-DS{}-{}-ch{}.root".format(
bkgcutDir, dsNum, bkgidx, ch))
hCutList.append(ROOT.TH1D())
hFullList.append(ROOT.TH1D())
# Add additional cut here for channel
hCutList[idx] = wl.H1D(
skimCutBkg,
bins,
lower,
upper,
"trapENFCal",
cuts + "&& channel=={}".format(ch),
Title="hBkgDS{}_Ch{}_BkgIdx{}".format(
dsNum, ch, bkgidx),
Name="hDS{}_Ch{}_BkgIdx{}".format(
dsNum, ch, bkgidx))
hFullList[idx] = wl.H1D(
skimTreeBkg,
bins,
lower,
upper,
"trapENFCal",
cuts + "&&channel=={}".format(ch),
Title="hFullDS_{}_Ch{}_BkgIdx{}".format(
dsNum, ch, bkgidx),
Name="hBkgFullDS{}_Ch{}_BkgIdx{}".format(
dsNum, ch, bkgidx))
# Write all histograms -- for debugging
hCutList[idx].Write()
hFullList[idx].Write()
# Write total histogram and close
outFile.Close()
return 0
def GenerateCorrectedSpectra(dsNum=1,
dType='isNat',
binsize=0.1,
binsize2=0.001,
lower=0,
upper=250):
"""
Calculates analysis threshold and applies analysis threshold to exposure calculation
Saves histograms into ROOT file
"""
ROOT.gStyle.SetOptStat(0)
bgDir, outDir = '/Users/brianzhu/project/cuts/fs_rn', '/Users/brianzhu/macros/code/LAT/plots/AThresh'
bins = int((upper - lower) / binsize)
bins2 = int((upper - lower) / binsize2)
cuts = "{} && gain==0 && mHL==1 && isGood && !muVeto && !(C==1&&isLNFill1) && !(C==2&&isLNFill2) && C!=0&&P!=0&&D!=0".format(
dType)
chList = ds.GetGoodChanList(dsNum, dType[2:])
if dsNum == 5: # remove 692 and 1232 (both beges, so who cares)
if 692 in chList: chList.remove(692)
if 1232 in chList: chList.remove(1232)
nRanges = [0, ds.dsMap[dsNum]]
if dsNum == 5: nRanges[0] = 80 # exclude DS-5A
athresh = GetAnaylsisThreshold(dsNum, True)
threshDict = {}
threshDictSave = {}
specIDXDict = {}
specDict = {}
outFile = ROOT.TFile(
outDir + '/Bkg_{}_DS{}_Test.root'.format(dType, dsNum), "RECREATE")
cutTree = ROOT.TChain("skimTree")
TotalSpec = ROOT.TH1D('DS{}_{}_Corr'.format(dsNum, dType), '', bins, lower,
upper)
UncorrTotalSpec = ROOT.TH1D('DS{}_{}_UnCorr'.format(dsNum, dType), '',
bins, lower, upper)
for bkgidx in range(nRanges[0], nRanges[1] + 1):
# Get Threshold dictionary
tD = ds.getDBRecord("thresh_ds{}_bkgidx{}".format(dsNum, bkgidx))
# Get Analysis Threshold Dictionary and Exposure Dictionary here
for idx, ch in enumerate(chList):
if ch in tD:
# Reset Tree
cutTree.Reset()
# Check dictionaries to see if variables exist
if ch not in athresh[bkgidx]:
print(
"Warning: Analysis threshold doesn't exist for ch{} bkgidx{}"
.format(ch, bkgidx))
continue
if ch not in ex.Exposure[dsNum][bkgidx]:
print("Warning: Exposure doesn't exist for ch{} bkgidx{}".
format(ch, bkgidx))
continue
if not os.path.exists(
bgDir +
"/fs_rn-DS{}-{}-ch{}.root".format(dsNum, bkgidx, ch)):
print(
"Warning: Background data doesn't exist for ch{} bkgidx{}"
.format(ch, bkgidx))
continue
# Load Background Data with cuts applied
cutTree.Add(
bgDir +
"/fs_rn-DS{}-{}-ch{}.root".format(dsNum, bkgidx, ch))
# Create Trigger Efficiency function
mu, sigma = tD[ch][0], tD[ch][1]
threshFnc = ROOT.TF1(
"fEff_{}_{}_{}".format(dsNum, ch, bkgidx),
"0.5*(1+TMath::Erf((x-[0])/(TMath::Sqrt(2)*[1])))", 0, 250)
threshFnc.SetParameters(mu, abs(sigma))
# Create Analysis Threshold + Exposure function
dExp, dAThresh = ex.Exposure[dsNum][bkgidx][ch], athresh[
bkgidx][ch]
expFnc = ROOT.TF1("fExp_{}_{}_{}".format(dsNum, ch, bkgidx),
"[0]*(x>[1])", 0, 250)
expFnc.SetParameters(dExp, dAThresh)
# Print out info
print(
"DS{} Ch{} BkgIdx{} Exposure {} Thresh {} Analysis Threshold {} "
.format(dsNum, ch, bkgidx, dExp, mu, dAThresh))
specIDXDict.setdefault(bkgidx, {}).setdefault(ch, ROOT.TH1D())
specIDXDict[bkgidx][ch] = wl.H1D(
cutTree,
bins,
lower,
upper,
"trapENFCal",
cuts +
"&& trapENFCal>{:.2f} && channel=={}".format(dAThresh, ch),
Title="hBkg_Ch{}_Bkgidx{}".format(ch, bkgidx),
Name="hBkg_Ch{}_Bkgidx{}".format(ch, bkgidx))
# Exposure function for scaling
h3 = ROOT.TH1D("hCh{}_Bkgidx{}_Scale".format(ch, bkgidx), "",
bins, lower, upper)
for i in range(h3.GetNbinsX() + 1):
if i < dAThresh * 10:
continue # Round up to set analysis threshold
h3.SetBinContent(i, dExp)
# Scale here -- trying to make it legible instead of messy
h3.Multiply(threshFnc)
threshDict.setdefault(ch,
h3.Clone("Efficiency_ch{}".format(ch)))
threshDict[ch].Add(h3)
# Save exposure function to histogram
h4 = ROOT.TH1D("hCh{}_Bkgidx{}".format(ch, bkgidx), "", bins2,
lower, upper)
for i in range(h4.GetNbinsX() + 1):
if i < dAThresh * 1000:
continue # Round up to set analysis threshold
h4.SetBinContent(i, dExp)
# Scale here
h4.Multiply(threshFnc)
threshDictSave.setdefault(
ch, h4.Clone("hEff_DS{}_ch{}".format(dsNum, ch)))
threshDictSave[ch].Add(h4)
if specIDXDict[bkgidx][ch].Integral() == 0:
print("Ch {} has 0 counts, not saving".format(ch))
continue
specDict.setdefault(
ch, specIDXDict[bkgidx][ch].Clone('hBkg_Ch{}'.format(ch)))
specDict[ch].Add(specIDXDict[bkgidx][ch])
specIDXDict[bkgidx][ch].Write()
EffTot = ROOT.TH1D("DS{}_EffTot_Divide".format(dsNum),
"DS{} {}".format(dsNum, dType), bins, lower, upper)
EffTotSave = ROOT.TH1D("DS{}_{}_EffTot".format(dsNum, dType),
"DS{} {}".format(dsNum, dType), bins2, lower, upper)
for ch in specDict:
if ch not in threshDict:
print("Ch {} not in threshDict... you should've fixed this!".
format(ch))
continue
TotalSpec.Add(specDict[ch])
UncorrTotalSpec.Add(specDict[ch])
EffTot.Add(threshDict[ch])
EffTotSave.Add(threshDictSave[ch])
# Save Channel specific
h1 = specDict[ch].Clone('hDS{}_ChTot_Ch{}'.format(dsNum, ch))
h1.SetTitle('hDS{}_ChTot_Ch{}'.format(dsNum, ch))
threshDictSave[ch].Write()
h1.Write()
print('Channels in DS{} -- {}'.format(dsNum, dType), specDict.keys())
# Save all histograms with fancy names and axes
EffTotSave.GetYaxis().SetTitle('Exposure (kg-day)')
EffTotSave.GetXaxis().SetTitle('Energy (keV)')
EffTotSave.Write()
UncorrTotalSpec.GetXaxis().SetTitle('Energy (keV)')
UncorrTotalSpec.GetYaxis().SetTitle('Counts')
UncorrTotalSpec.Write()
TotalSpec.Divide(EffTot)
TotalSpec.GetXaxis().SetTitle('Energy (keV)')
TotalSpec.GetYaxis().SetTitle('Counts/(0.1 keV)/kg/day')
TotalSpec.Write()
outFile.Close()
def SaveCalHistograms():
outDir = '/projecta/projectdirs/majorana/users/bxyzhu/LATv2/plots/spectra'
calDir = '/projecta/projectdirs/majorana/users/wisecg/cal-lat'
cutDir = '/projecta/projectdirs/majorana/users/bxyzhu/cuts'
cInfo = ds.CalInfo()
bins,lower,upper = 250,0,250
# Basic cut
mNum = 2
cuts = "gain==0 && mHL==%d && isGood && !muVeto && !(C==1&&isLNFill1) && !(C==2&&isLNFill2) && C!=0&&P!=0&&D!=0"%(mNum)
skimTree = ROOT.TChain("skimTree")
skimCut = ROOT.TChain("skimTree")
dsList = [0, 1, 2, 3, 4, 5]
# dsList = [1]
outFile = ROOT.TFile(outDir + "/CalAcceptance_wfstd_mHL%d.root"%(mNum), "RECREATE")
outFile.cd()
# Total histogram (all datasets)
hCutTotal = ROOT.TH1D("hCutTotal", "", bins,lower,upper)
hFullTotal = ROOT.TH1D("hFullTotal", "", bins,lower,upper)
# Total histogram for Dataset
hDSTotal = []
hDSFullTotal = []
dMissingCh = [0, 1, 2, 3, 4, 5]
dThreshCutCh = [0, 1, 2, 3, 4, 5]
for iDS, dsNum in enumerate(dsList):
hDSTotal.append(ROOT.TH1D())
hDSTotal[iDS] = ROOT.TH1D("hDS%d"%(dsNum), "", bins,lower,upper)
hDSFullTotal.append(ROOT.TH1D())
hDSFullTotal[iDS] = ROOT.TH1D("hDS%d_Full"%(dsNum), "", bins,lower,upper)
nMods = [1]
if dsNum == 4: nMods = [2]
if dsNum == 5: nMods = [1, 2]
for modNum in nMods:
chList = ds.GetGoodChanList(dsNum)
if dsNum==5 and modNum==1: # remove 692 and 1232 (both beges, so who cares)
chList = [584, 592, 598, 608, 610, 614, 624, 626, 628, 632, 640, 648, 658, 660, 662, 672, 678, 680, 688, 690, 694]
elif dsNum==5 and modNum==2:
chList = [1106, 1110, 1120, 1124, 1128, 1170, 1172, 1174, 1176, 1204, 1208, 1298, 1302, 1330, 1332]
# Total channel histograms, split by dataset
hChTotalList = []
hChFullTotalList = []
for idx, ch in enumerate(chList):
hChTotalList.append(ROOT.TH1D())
hChTotalList[idx] = ROOT.TH1D("hDS%d_Ch%d"%(dsNum, ch), "", bins,lower,upper)
hChFullTotalList.append(ROOT.TH1D())
hChFullTotalList[idx] = ROOT.TH1D("hDS%d_Ch%d_Full"%(dsNum, ch), "", bins,lower,upper)
nRanges = [0, len(cInfo.master['ds%d_m%d'%(dsNum, modNum)])]
for calidx in range(nRanges[0], nRanges[1]):
print "Drawing DS%d calidx%d mod%d"%(dsNum, calidx, modNum)
hCutList, hFullList = [], []
skimTree.Reset()
calList = cInfo.GetCalList("ds%d_m%d" % (dsNum, modNum), calidx, runLimit=10)
for run in calList:
skimTree.Add("%s/latSkimDS%d_run%d_*.root"%(calDir, dsNum, run))
for idx, ch in enumerate(chList):
# Reset Tree every calidx + ch
skimCut.Reset()
skimCut.Add("%s/calwf/calwfstd-DS%d-%d-ch%d.root"%(cutDir, dsNum, calidx, ch))
if skimCut.GetEntries() == 0:
hCutList.append(ROOT.TH1D())
hFullList.append(ROOT.TH1D())
print "Channel %d, idx %d has no entries, skipping"
continue
hCutList.append(ROOT.TH1D())
hFullList.append(ROOT.TH1D())
# Add additional cut here for channel
hCutList[idx] = wl.H1D(skimCut,bins,lower,upper, "trapENFCal", cuts+"&& channel==%d"%(ch), Title="hDS%d_Ch%d_%d"%(dsNum,ch,calidx), Name="hDS%d_Ch%d_%d"%(dsNum, ch,calidx))
hFullList[idx] = wl.H1D(skimTree,bins,lower,upper, "trapENFCal", cuts+"&&channel==%d"%(ch),Title="hFullDS_%d_Ch%d_%d"%(dsNum,ch,calidx), Name="hFullDS%d_Ch%d_%d"%(dsNum,ch,calidx))
# Only write channel specific if there are counts
if hCutList[idx].Integral() > 0:
# Add ch+calidx histograms to ch total histogram if
hCutList[idx].Write()
hFullList[idx].Write()
hChTotalList[idx].Add(hCutList[idx])
hChFullTotalList[idx].Add(hFullList[idx])
# Add individual ch+calidx histograms to total histogram and total DS histogram
hCutTotal.Add(hCutList[idx])
hFullTotal.Add(hFullList[idx])
hDSTotal[iDS].Add(hCutList[idx])
hDSFullTotal[iDS].Add(hFullList[idx])
# Write Total channel histograms
for idx, ch in enumerate(chList):
if hChTotalList[idx].Integral() > 0:
hChTotalList[idx].Write()
hChFullTotalList[idx].Write()
else:
print "Channel %d has no entries!"%(ch)
# Write total DS histograms
hDSTotal[iDS].Write()
hDSFullTotal[iDS].Write()
# Write total histogram and close
hFullTotal.Write()
hCutTotal.Write()
outFile.Close()
return 0
def getEff():
""" ./job-panda.py -getEff
METHOD:
open up the latskim file for each channel.
loop over the good run ranges.
for each good range, make an energy histogram.
then calculate the efficiency curve based on the sigma value
and convolve it with the histogram points.
"""
import numpy as np
import waveLibs as wl
import scipy.special as spec
import matplotlib.pyplot as plt
from ROOT import TFile, TTree, TH1D, TF1, TCanvas, gROOT
import ROOT, random
gROOT.ProcessLine(".x ~/env/MJDClintPlotStyle.C")
# gROOT.ProcessLine("gErrorIgnoreLevel = 3001;") # suppress ROOT messages
bins, xlo, xhi = 50, 0, 15 # set it just high enough that the first bin center isn't negative
hSumCorr = TH1D("hSumCorr", "hSumCorr", bins, xlo, xhi)
hSumUncr = TH1D("hSumUncr", "hSumUncr", bins, xlo, xhi)
dsNum = 1
# ch = 578
for ch in ds.GetGoodChanList(dsNum):
inFile = TFile(homePath + "/project/latskim/latSkimDS%d_ch%d.root" %
(dsNum, ch))
tree = inFile.Get("skimTree")
fileCut = inFile.Get("theCut").GetTitle()
_, _, goodRunErfs = ds.GetThreshDicts(dsNum)
hUnc = wl.H1D(tree, bins, xlo, xhi, "trapENFCal", fileCut)
hSumUncr.Add(hUnc)
for erfs in goodRunErfs[ch]:
runCut = " && run >= %d && run <= %d" % (erfs[0], erfs[1])
theCut = fileCut + runCut
h1 = wl.H1D(tree, bins, xlo, xhi, "trapENFCal", theCut)
h1x, h1y = wl.npTH1D(h1)
# calculate efficiency curve
thisErf = TF1("thisErf",
"0.5*(1+TMath::Erf((x-[0])/(TMath::Sqrt(2)*[1]) ))")
thisErf.SetParameter(0, erfs[2]) # mu
thisErf.SetParameter(1, erfs[3]) # sigma
h1.Divide(thisErf)
# thisErf = 0.5 * (1 + spec.erf( (h1x - mu) / (np.sqrt(2) * sig) ))
# h1yScaled = h1y / thisErf
# nameStr = str(random.uniform(1.,2.))
# h2 = TH1D(nameStr,nameStr,bins,xlo,xhi)
# for i in range(bins):
# h2.SetBinContent(i,h1yScaled[i])
hSumCorr.Add(h1)
# eff-corrected spectrum.
c = TCanvas("c", "c", 800, 600)
c.SetLogy(1)
hSumCorr.SetLineColor(ROOT.kBlue)
hSumCorr.Draw("hist")
hSumUncr.SetLineColor(ROOT.kRed)
hSumUncr.Draw("hist same")
# l1 = TLegend
c.Print("./plots/effWeight/eff_DS%d.pdf" % dsNum)
def fitSlo238(tree,theCut):
# for calibration data, keep 99% of the fast events in the 238 peak.
# this is practice for doing this with the 10 kev peak in natural bg data.
# scatter plot
h1 = wl.H2D(tree,"h1",500,0,250,500,0,200,"fitSlo:trapENFCal",theCut)
cts1 = h1.Integral( *wl.Get2DBins(h1,0,250,0,200) )
# -- 238 peak --
h2 = wl.H1D(tree,"h2",50,237,240,"trapENFCal",theCut)
cts2 = h2.Integral( *wl.Get1DBins(h2,237,240) )
h2.SetLineColor(4)
f0 = TF1("f0","gaus",237,240)
h2.Fit("f0","Lq") # use to set initial guesses
f1 = TF1("f1", "[0] * x + [1] * exp(-1.0 * (TMath::Power((x-[2]),2) / (2 * TMath::Power([3],2)) ))", 237, 240)
wl.SetPars(f1,[1,418,238,0.381])
f1.SetLineColor(4)
h2.Fit("f1","q")
[flat, norm1, mu, sig] = wl.GetPars(f1)
h3 = wl.H1D(tree,"h3",50,237,240,"trapENFCal",theCut+" && fitSlo < 20")
cts3 = h3.Integral( *wl.Get1DBins(h3,237,240) )
f2 = TF1("f2", "[0] * x + [1] * exp(-1.0 * (TMath::Power((x-[2]),2) / (2 * TMath::Power([3],2)) ))", 237, 240)
wl.SetPars(f2,[1,418,238,0.381])
f2.SetLineColor(2)
h3.Fit("f2","q")
[flat, norm2, mu, sig] = wl.GetPars(f2)
retention = 100*norm2/norm1
print "norm1 %.2f norm2 %.2f retention: %.2f" % (norm1, norm2, retention)
# -- Plots --
c1 = TCanvas("c1","Bob Ross's Canvas",1200,600)
c1.Divide(2,1) # x, y
c1.cd(1)
gPad.SetLogz()
h1.Draw("COLZ")
c1.cd(2)
h2.Draw()
h3.SetLineColor(2) # red
h3.Draw("same")
l1 = TLegend(0.6,0.7,0.87,0.92)
l1.AddEntry(h2,"basic","l")
l1.AddEntry(f1,"basic fit:","l")
# l1.AddEntry(f1,"flat %.2f norm %.2f" % (flat,norm1) ,"")
# l1.AddEntry(f1,"mu %.2f sig %.2f" % (mu,sig) ,"")
l1.AddEntry(h3,"+fitSlo < 20","l")
l1.AddEntry(h3,"Retention: %.3f" % retention,"")
l1.Draw("same")
c1.Update()
c1.Print("./plots/cal-fitSlo.pdf")
def fitArb10(tree,theCut):
# fit an arbitrary low-energy cut
print theCut
theCut += " && !isEnr"
checkCut = ""
thisVar = "bandTime"
fileName = "./plots/lat-max.pdf"
parLim = [0,20000]
# scatter plot
h0 = wl.H2D(tree,"h0",100,0,20,500,parLim[0],parLim[1],thisVar+":trapENFCal",theCut)
# -- 10 peak --
fitModel10 = "[0] + [1]*x + [2]*x**2 + [3] * exp(-1.0 * ((x-[4])**2 / (2 * [5]**2)) )"
h1 = wl.H1D(tree,"h1",20,9.5,11.5,"trapENFCal",theCut)
h2 = wl.H1D(tree,"h2",20,9.5,11.5,"trapENFCal",theCut+checkCut)
f0 = TF1("f0","gaus",10,11)
h1.Fit("f0","Lq")
[norm,mu,sig] = wl.GetPars(f0)
f1 = TF1("f1", fitModel10, 9.5,11)
wl.SetPars(f1,[-209, 41, -2, norm, mu, sig])
f1.SetLineColor(4)
h1.Fit("f1","Lq")
[p0,p1,p2,norm1,mu,sig] = wl.GetPars(f1)
f2 = TF1("f2", fitModel10, 9.5,11)
wl.SetPars(f2,[-209, 41, -2, norm1, mu, sig])
f2.SetLineColor(2)
h2.Fit("f2","Lq")
[p0,p1,p2,norm2,mu,sig] = wl.GetPars(f2)
retention = 100*norm2/norm1
print "Retention:",retention," Cut used:",checkCut
# -- Energy Spectrum --
h3 = wl.H1D(tree,"h3",250,0,50,"trapENFCal",theCut)
h4 = wl.H1D(tree,"h4",250,0,50,"trapENFCal",theCut+checkCut)
cts3 = h3.Integral( *wl.Get1DBins(h3,0,20))
cts4 = h4.Integral( *wl.Get1DBins(h4,0,20))
cts5 = h3.Integral( *wl.Get1DBins(h3,20,40))
cts6 = h4.Integral( *wl.Get1DBins(h4,20,40))
print "Cts. 0-20 %d before %d after. 20-40 %d before %d after" % (cts3,cts4,cts5,cts6)
h3.GetXaxis().SetRangeUser(0,20)
h4.GetXaxis().SetRangeUser(0,20)
# -- Plots --
c1 = TCanvas("c1","Bob Ross's Canvas",1800,600)
c1.Divide(3,1,0.00001) # TPad::Divide
c1.cd(1)
gPad.SetLogz()
h0.SetMinimum(1)
h0.Draw("COLZ")
c1.cd(2)
gPad.SetLogy()
h3.SetLineColor(4)
h3.Draw()
h4.SetLineColor(2)
h4.Draw("same")
l1 = TLegend(0.4,0.7,0.87,0.92)
l1.AddEntry(h1,"basic","l")
l1.AddEntry(h2,thisVar,"l")
l1.Draw("same")
c1.cd(3)
h1.SetLineColor(4) # blue
h1.Draw()
h2.SetLineColor(2) # red
h2.Draw("same")
l2 = TLegend(0.6,0.6,0.87,0.92)
l2.AddEntry(h1,"basic","l")
l2.AddEntry(h2,thisVar,"l")
l2.AddEntry(h2,"Retention: %.3f" % retention,"")
l2.Draw("same")
c1.Print(fileName)
def fitSlo10(tree,theCut):
theCut += " && !isEnr"
# scatter plot
h0 = wl.H2D(tree,"h0",100,0,20,500,0,200,"fitSlo:trapENFCal",theCut)
# -- 10 peak --
fitModel10 = "[0] + [1]*x + [2]*x**2 + [3] * exp(-1.0 * ((x-[4])**2 / (2 * [5]**2)) )"
h1 = wl.H1D(tree,"h1",20,9.5,11.5,"trapENFCal",theCut)
h1.SetLineColor(4)
# f1 = TF1("f1","gaus",10,11)
# h1.Fit("f1","L") # use to set initial guesses
f1 = TF1("f1", fitModel10, 9.5,11)
wl.SetPars(f1,[-209, 41, -2, 28, 10.3, 0.135])
f1.SetLineColor(4)
h1.Fit("f1","Lq")
[p0,p1,p2,norm1,mu,sig] = wl.GetPars(f1)
# print wl.GetPars(f1)
h2 = wl.H1D(tree,"h2",20,9.5,11.5,"trapENFCal",theCut+" && fitSlo < 18")
f2 = TF1("f2", fitModel10, 9.5,11)
wl.SetPars(f2,[-209, 41, -2, 28, 10.3, 0.135])
f2.SetLineColor(2)
h2.Fit("f2","Lq")
[p0,p1,p2,norm2,mu,sig] = wl.GetPars(f2)
retention1 = 100*norm2/norm1
# -- 6.54 (Fe55) peak --
# TODO: This doesn't work well. wait till you're using RooFit.
fitModel6 = "[0] * x + [1] * exp( -1.0 * ((x - [2])**2 / (2 * [3]**2)) )"
# fitModel6 = "[0] * exp( -1.0 * ((x - [1])**2 / (2 * [2]**2)) )"
h3 = wl.H1D(tree,"h3",20,5.5,7.5,"trapENFCal",theCut)
# f3 = TF1("f3","gaus",6,7)
# h3.Fit("f3","L") # use to set initial guesses
# f3 = TF1("f3", fitModel6, 6,7)
# wl.SetPars(f3,[-1, 19.4, 6.47, 0.631])
# f3.SetLineColor(4)
# h3.Fit("f3","Lq")
# [lin,norm3,mu,sig] = wl.GetPars(f3)
#
# h4 = wl.H1D(tree,"h4",20,5.5,7.5,"trapENFCal",theCut+" && fitSlo < 18")
# f4 = TF1("f4", fitModel6, 6,7)
# wl.SetPars(f4,[-1, 19.4, 6.47, 0.631])
# f4.SetLineColor(2)
# h4.Fit("f4","Lq")
# [lin,norm4,mu,sig] = wl.GetPars(f4)
# retention2 = 100*norm4/norm3
# print "norm3 %.2f norm4 %.2f retention2: %.2f" % (norm3, norm4, retention2)
# -- Plots --
c1 = TCanvas("c1","Bob Ross's Canvas",1200,600)
# c1.Divide(3,1,0.00001) # TPad::Divide
c1.Divide(2,1,0.00001)
c1.cd(1)
gPad.SetLogz()
h0.SetMinimum(1)
h0.Draw("COLZ")
c1.cd(2)
h1.SetLineColor(4) # blue
h1.Draw()
h2.SetLineColor(2) # red
h2.Draw("same")
l1 = TLegend(0.6,0.6,0.87,0.92)
l1.AddEntry(h1,"basic","l")
l1.AddEntry(h2,"+fitSlo < 20","l")
l1.AddEntry(h2,"Retention: %.3f" % retention1,"")
l1.Draw("same")
# c1.cd(3)
# h3.Draw()
# h4.SetLineColor(2) # red
# h4.Draw("same")
#
# l2 = TLegend(0.6,0.6,0.87,0.92)
# l2.AddEntry(h3,"basic","l")
# l2.AddEntry(h4,"+fitSlo < 20","l")
# l2.AddEntry(h4,"Retention: %.3f" % retention2,"")
# l2.Draw("same")
c1.Print("./plots/ds1-fitSlo.pdf")