def DrawHistogram( self, h, _LowerEdge, _UpperEdge ):
CanvasName = h.GetName()
CanvasName.replace("h_", "c_")
c = TCanvas(CanvasName+"M%.0lfto%.0lf"%(_LowerEdge, _UpperEdge), "", 800, 800)
c.cd()
if (_UpperEdge - _LowerEdge) > 20:
c.SetLogy()
c.SetTopMargin(0.03)
c.SetLeftMargin(0.13)
c.SetRightMargin(0.05)
c.SetBottomMargin(0.1)
if _UpperEdge > 1000:
c.SetLogx()
h.Draw()
h.SetStats(0)
h.SetMarkerColor(2)
h.SetLineColor(2) # -- red -- #
h.SetFillColorAlpha(0, 0) # -- white with 100% transparency -- #
h.GetXaxis().SetRangeUser( _LowerEdge, _UpperEdge )
h.GetXaxis().SetMoreLogLabels()
h.GetXaxis().SetNoExponent()
h.GetYaxis().SetTitleOffset(1.3)
h.SetXTitle("m(#mu#mu) [GeV]")
h.SetYTitle("d#sigma/dm [pb/GeV]")
latex = TLatex()
latex.SetTextSize(0.028);
# if _LowerEdge == 15 and _UpperEdge==3000:
latex.DrawLatexNDC(0.50, 0.85, "Z/#gamma* #rightarrow #mu#mu @ E_{CM} = 8 TeV")
latex.DrawLatexNDC(0.50, 0.80, "FEWZ (%s)" % (self.PDF) )
latex.DrawLatexNDC(0.50, 0.77, "QCD@NNLO, EWK@NLO")
c.SaveAs(".pdf")
def draw(self, canvas=None):
if canvas is None:
canvas = self
canvas.Divide(1, 2)
canvas.cd(1).SetMargin(0.1, 0.1, 0.05, 0.2)
self._rad.Draw()
canvas.cd(2).SetMargin(0.1, 0.1, 0.2, 0.05)
self._ang.Draw()
canvas.cd()
canvas.Update()
for c, axis in [('r', self._rad.GetXaxis()),
('y1', self._rad.GetYaxis()),
('p', self._ang.GetXaxis()),
('y2', self._ang.GetYaxis())]:
axis.SetLabelSize(0.05)
axis.SetTitleSize(0.07)
axis.SetLabelOffset(0.01)
rng = getattr(self, '_{0}range'.format(c))
if rng is not None:
axis.SetRangeUser(rng[0], rng[1])
ttl = getattr(self, '_{0}title'.format(c))
if c.startswith('y') and ttl is not None:
axis.SetTitle(ttl)
if c.startswith('y'):
axis.SetTitleOffset(0.6)
axis.SetNdivisions(505)
canvas.cd()
self._pad = TPad('newpad', '', 0, 0, 1, 1)
self._pad.SetFillStyle(4000)
self._pad.Draw()
self._pad.cd()
if self._drawCMS:
self.draw_cms()
text = TLatex()
text.SetNDC()
text.SetTextFont(42)
text.SetTextSize(0.035)
text.SetTextAlign(13)
text.SetTextAngle(90)
if self._rtitle is not None:
text.DrawLatexNDC(0.905, 0.54, self._rtitle)
if self._ptitle is not None:
text.DrawLatexNDC(0.905, 0.115, self._ptitle)
for obj in self._container_draw:
obj.Draw('SAME')
for c in (canvas.cd(1), canvas.cd(2), canvas):
c.Modified()
c.Update()
canvas.cd()
def show_sample(self,
fnameP='/data/Samples/TMSPlane/Dec26/sample_{0:d}.adc',
Ns=10,
ich=8):
from ROOT import TGraph, gPad, TLatex
s1 = SigProc(nSamples=16384, nAdcCh=20, nSdmCh=19, adcSdmCycRatio=5)
data1 = s1.generate_adcDataBuf()
data2 = s1.generate_sdmDataBuf()
data3 = ((s1.ANALYSIS_WAVEFORM_BASE_TYPE * s1.nSamples) * Ns)()
dataList = []
for i in range(Ns):
fname = fnameP.format(i)
s1.read_data([fname, ''], data1, data2)
x = array.array('f', range(s1.nSamples))
# s1.filters_trapezoidal(data1[ich], data3[i], [100,100,200,-1])
s1.filters_trapezoidal(data1[ich], data3[i],
[100, 100, 100, 0.000722])
g1 = TGraph(s1.nSamples, x, data3[i])
# g1 = TGraph(s1.nSamples, x, data1[ich])
opt = 'AP' if i == 0 else "PSame"
g1.Draw(opt + ' PLC PMC')
dataList.append(g1)
lt = TLatex()
lt.DrawLatexNDC(0.2, 0.8, "Chan={0:d}".format(ich))
gPad.Update()
waitRootCmdX()
def Plot2DEfficiency(num, den, plotname, topTitle, xAxisTitle, xAxisRangeLow,
xAxisRangeHigh, yAxisTitle, yAxisRangeLow, yAxisRangeHigh,
effMin, effMax):
c = TCanvas("cv", "cv", 800, 800)
ratio = num.Clone("ratio")
ratio.Divide(den)
ratio.Draw("colz")
ratio.SetMaximum(effMax)
ratio.SetMinimum(effMin)
ratio.GetZaxis().SetTitle("Efficiency")
ratio.GetZaxis().SetTitleSize(0.05)
ratio.GetZaxis().SetTitleOffset(1.25)
ratio.Draw("colz")
ratio.SetStats(0)
ratio.GetXaxis().SetTitle(xAxisTitle)
ratio.GetXaxis().SetTitleSize(0.05)
ratio.GetXaxis().SetTitleOffset(0.90)
ratio.GetXaxis().SetLabelSize(0.03)
ratio.GetXaxis().SetRangeUser(xAxisRangeLow, xAxisRangeHigh)
ratio.GetYaxis().SetTitle(yAxisTitle)
ratio.GetYaxis().SetTitleSize(0.05)
ratio.GetYaxis().SetTitleOffset(1.05)
ratio.GetYaxis().SetLabelSize(0.03)
ratio.GetYaxis().SetRangeUser(yAxisRangeLow, yAxisRangeHigh)
c.SetRightMargin(0.18)
c.SetLeftMargin(0.12)
title = TLatex()
title.SetTextSize(0.05)
#title.SetTextAlign(13);
title.DrawLatexNDC(.2, .93, topTitle)
c.SaveAs(plotname + ".gif")
def makeROC(fpr, tpr, thresholds, AUC, outfile, signal_label,
background_label):
c = TCanvas("c", "c", 700, 600)
gPad.SetMargin(0.15, 0.07, 0.15, 0.05)
gPad.SetLogy(0)
gPad.SetGrid(1, 1)
gStyle.SetGridColor(15)
roc = TGraph(len(fpr), tpr, fpr)
roc.SetLineColor(2)
roc.SetLineWidth(2)
roc.SetTitle(";Signal efficiency (%s); Background efficiency (%s)" %
(signal_label, background_label))
roc.GetXaxis().SetTitleOffset(1.4)
roc.GetXaxis().SetTitleSize(0.045)
roc.GetYaxis().SetTitleOffset(1.4)
roc.GetYaxis().SetTitleSize(0.045)
roc.GetXaxis().SetRangeUser(0, 1)
roc.GetYaxis().SetRangeUser(0.000, 1)
roc.Draw("AL")
latex = TLatex()
latex.SetTextFont(42)
latex.SetTextSize(0.05)
latex.DrawLatexNDC(0.2, 0.88, 'AUC = %.3f' % AUC)
c.SaveAs(outfile)
def drawText(x, y, t, isNDC=False):
text = TLatex()
text.SetTextColor(1)
text.SetTextFont(42)
text.SetTextAlign(23)
text.SetTextSize(0.04)
if isNDC: text.DrawLatexNDC(x, y, t)
else: text.DrawLatex(x, y, t)
text.Draw()
return text
def checkStablibity(isensor=8):
qt1 = QuickTuner(mode=0)
cd1 = qt1.sensorConfig.cd
cd1.readBackPars(isensor)
dataX = []
for i in range(10):
time.sleep(1)
cd1.fetch()
dataX.append(cd1.adcData[isensor][:])
max1 = -1
min1 = 999
ndata = len(dataX[0])
gr0 = None
option = 'AL'
icolor = 1
grs = []
for x in dataX:
max2 = max(x)
if max2>max1: max1 = max2
min2 = min(x)
if min2<min1: min1 = min2
gr1 = TGraph(ndata, array('d',[i*0.2*0.001 for i in range(ndata)]), array('d', x))
gr1.Draw(option)
if gr0 == None:
gr0 = gr1
option = 'L'
gr1.SetLineColor(icolor)
icolor += 1
grs.append(gr1)
# waitRootCmdX()
h1 = gr0.GetHistogram()
print min1, max1
h1.GetYaxis().SetRangeUser(min1,max1)
h1.GetYaxis().SetTitle("V")
h1.GetXaxis().SetTitle("t [ms]")
h1.Draw("axissame")
lt = TLatex()
lt.DrawLatexNDC(0.2,0.92, "C{0:d}: ".format(isensor)+' '.join(['{0:.3f}'.format(x) for x in cd1.inputVs]))
waitRootCmdX()
print len(dataX), len(grs)
def draw_study_label(x=0.5, y=0.9):
latex = TLatex()
latex.SetTextAlign(13)
drawn.append(latex.DrawLatexNDC(x, y, " ".join(study_label)))
def main(reader_name,
tags,
lut_path,
ptmin,
ptmax,
ymin=None,
ymax=None,
tag_name=None,
logx=False,
logy=False,
leg_pos=[0.74, 0.2, 0.90, 0.4],
particles=None,
eta=0,
dnch_deta=100,
rmin=None,
add_eta_label=True,
add_alice3_label=True,
save=None,
background=False,
use_p_over_z=True,
aod=None,
study_label="ALICE 3 study"):
gROOT.LoadMacro(reader_name)
gROOT.LoadMacro("style.C")
reader_name = reader_name.split(".")[-2]
reader = getattr(__import__('ROOT', fromlist=[reader_name]), reader_name)
style = getattr(__import__('ROOT', fromlist=["style"]), "style")
style()
p = {
"el": "e",
"pi": "#pi",
"ka": "K",
"pr": "p",
"de": "d",
"tr": "t",
"he3": "^{3}He"
}
charge = {"el": 1, "pi": 1, "ka": 1, "pr": 1, "de": 1, "tr": 1, "he3": 2}
p_colors = {
"el": "#e41a1c",
"pi": "#377eb8",
"ka": "#4daf4a",
"pr": "#984ea3",
"de": "#ff7f00",
"tr": "#999999",
"he3": "#a65628"
}
if particles is not None:
to_remove = []
for i in p:
if i not in particles:
to_remove.append(i)
for i in to_remove:
p.pop(i)
latex = TLatex()
latex.SetTextAlign(33)
canvas = reader_name
canvas = canvas.replace("lutRead_", "")
canvas = TCanvas(canvas, canvas, 800, 800)
canvas.Divide(2, 2)
drawn = [canvas]
drawn_graphs = {}
drawn_frames = {}
if ymin is None:
if "_dca" in reader_name:
ymin = 0.1
elif "_pt" in reader_name:
ymin = 1.
elif "_eff" in reader_name:
ymin = 0.
if ymax is None:
if "_dca" in reader_name:
ymax = 1e4
elif "_pt" in reader_name:
ymax = 100.
elif "_eff" in reader_name:
ymax = 115.
def adjust_pad():
if logx:
gPad.SetLogx()
if logy:
gPad.SetLogy()
counter = 1
leg = None
if tag_name is not None:
leg = TLegend(*leg_pos)
if add_eta_label:
label = f"#eta = {int(eta)}"
label += " dN_{Ch}/d#eta ="
label += f" {int(dnch_deta)}"
if rmin is not None:
label += " R_{min} = " + rmin
else:
leg.SetHeader()
leg.SetHeader(label)
leg.SetLineColor(0)
drawn.append(leg)
def draw_study_label(x=0.5, y=0.9):
latex = TLatex()
latex.SetTextAlign(13)
drawn.append(latex.DrawLatexNDC(x, y, " ".join(study_label)))
for i in p:
c = f"{canvas.GetName()}_{i}"
c = TCanvas(c, c, 800, 800)
drawn.append(c)
adjust_pad()
frame = c.DrawFrame(ptmin, ymin, ptmax, ymax, "")
frame.SetDirectory(0)
drawn_frames[i] = frame
g_list = []
extra = {}
cols = [
'#e41a1c', '#377eb8', '#4daf4a', '#984ea3', '#ff7f00', '#ffff33'
]
for k, j in enumerate(tags):
lut = f"{lut_path}/lutCovm.{i}.{j}.dat"
if j == "":
lut = f"{lut_path}/lutCovm.{i}.dat"
if not path.isfile(lut):
print("LUT file", lut, "does not exist")
return
g = reader(lut, eta, dnch_deta)
if g.GetN() <= 0:
print("Skipping", g.GetName(), "because empty graph")
continue
if len(g_list) == 0:
frame.GetXaxis().SetTitle(g.GetXaxis().GetTitle())
frame.GetYaxis().SetTitle(g.GetYaxis().GetTitle())
if use_p_over_z:
for j in range(g.GetN()):
if "_pt" in reader_name:
g.SetPoint(j,
g.GetPointX(j) / charge[i],
g.GetPointY(j) / charge[i])
else:
g.SetPoint(j,
g.GetPointX(j) / charge[i], g.GetPointY(j))
frame.GetXaxis().SetTitle("#it{p}_{T}/z (GeV/#it{c})")
col = TColor.GetColor(cols[len(g_list)])
g.SetLineColor(col)
g.SetLineStyle(1)
g.SetLineWidth(3)
g.Draw("samel")
if aod is not None:
f_aod = TFile(aod, "READ")
if "_eff" in reader_name:
extra[g.GetName()] = f_aod.Get(
"qa-tracking-efficiency-kaon/pt/num")
extra[g.GetName()].Divide(
f_aod.Get("qa-tracking-efficiency-kaon/pt/num"),
f_aod.Get("qa-tracking-efficiency-kaon/pt/den"), 1, 1,
"B")
extra[g.GetName()].Scale(100)
extra[g.GetName()].Draw("SAME")
extra[g.GetName()].SetDirectory(0)
elif "_pt" in reader_name:
extra[g.GetName()] = f_aod.Get(
"qa-tracking-efficiency-kaon/pt/num")
extra[g.GetName()].Divide(
f_aod.Get("qa-tracking-efficiency-kaon/pt/num"),
f_aod.Get("qa-tracking-efficiency-kaon/pt/den"), 1, 1,
"B")
extra[g.GetName()].Scale(100)
extra[g.GetName()].Draw("SAME")
extra[g.GetName()].SetDirectory(0)
f_aod.Close()
print("Drawing", g.GetName())
if tag_name is not None and counter == 1:
leg.AddEntry(g, tag_name[k], "l")
g_list.append(g)
drawn_graphs[i] = g_list
if len(g_list) <= 0:
print("Nothing drawn!")
continue
drawn.append(latex.DrawLatexNDC(0.9, 0.9, p[i]))
if leg is not None:
leg.Draw()
draw_study_label(.4, .91)
gPad.Update()
canvas.cd(counter)
clone = c.DrawClonePad()
if counter != 1:
l = gPad.GetListOfPrimitives()
for i in l:
cn = i.ClassName()
if cn == "TLegend":
l.Remove(i)
elif cn == "TLatex":
if "ALICE" in i.GetTitle():
l.Remove(i)
drawn.append(clone)
c.SaveAs(f"/tmp/{c.GetName()}.png")
gPad.Update()
counter += 1
canvas_all_species = None
if len(tags) == 1:
canvas_all_species = TCanvas("all_spec_" + canvas.GetName(),
"all_spec_" + canvas.GetName(), 800, 800)
drawn.append(canvas_all_species)
canvas_all_species.cd()
drawn_graphs_all_spec = {}
leg_all_spec = TLegend(*leg_pos)
leg_all_spec.SetNColumns(2)
leg_all_spec.SetLineColor(0)
drawn.append(leg_all_spec)
for i in drawn_graphs:
if canvas_all_species.GetListOfPrimitives().GetEntries() == 0:
drawn_frames[i].Draw()
g_list = []
for j in drawn_graphs[i]:
g_list.append(j.Clone())
g_list[-1].SetName(g_list[-1].GetName() + "_color")
g_list[-1].SetLineColor(TColor.GetColor(p_colors[i]))
g_list[-1].Draw("same")
leg_all_spec.AddEntry(g_list[-1], p[i], "L")
drawn_graphs_all_spec[i] = g_list
for i in drawn_graphs_all_spec:
drawn_graphs[i + "_allspec"] = drawn_graphs_all_spec[i]
leg_all_spec.Draw()
if add_alice3_label:
draw_study_label()
latex = TLatex()
latex.SetTextAlign(13)
latex.SetTextSize(0.04)
if tag_name is not None:
drawn.append(latex.DrawLatexNDC(0.5, 0.80, tag_name[0]))
drawn.append(
latex.DrawLatexNDC(
0.4, 0.82, f"#eta = {int(eta)}" + "\n dN_{Ch}/d#eta =" +
f" {int(dnch_deta)}" +
("\n R_{min} = " + rmin if rmin is not None else "")))
adjust_pad()
canvas_all_species.Update()
canvas_all_species.SaveAs(f"/tmp/{canvas_all_species.GetName()}.png")
if save is None:
canvas.SaveAs(f"/tmp/lut_{canvas.GetName()}.root")
else:
fo = TFile(save, "RECREATE")
fo.cd()
canvas.Write()
if canvas_all_species is not None:
canvas_all_species.Write()
for i in drawn_graphs:
for j in drawn_graphs[i]:
j.Write()
if not background:
input("Done, press enter to continue")
def Plot1DEfficiencyWithFit(tree, plotname, topTitle, xAxisTitle,
xAxisRangeLow, xAxisRangeHigh):
#make amp histogram
c = TCanvas("c", "c", 800, 800)
ampHist = TH1F("ampHist", ";Amplitude [mV]; Number of Events", 25, 0, 50)
tree.Draw(
"amp[3]>>ampHist",
" x_dut[2] > 19.6 && x_dut[2] < 19.7 && y_dut[2] > 23.5 && y_dut[2] < 24.0 && ((t_peak[3] - t_peak[0])*1e9 > 6 && (t_peak[3] - t_peak[0])*1e9 < 16)"
)
# create function for fitting
fitFunction = TF1("NoisePlusLandauGaus", NoisePlusLandauGaus, 0, 50, 5)
fitFunction.SetParameters(10, 0.95, 20.0, 2.5, 3.0)
fitFunction.SetParNames("a", "f", "mpv", "sigmaLandau", "sigmaGaus")
#fitFunction.SetParLimits(0, -1, -4)
#fitFunction.SetParLimits(1, 0.01, 0.2)
#fitFunction.SetParLimits(2, 0, 2)
#fitFunction.SetParLimits(3, 0, 1000)
ampHist.Fit("NoisePlusLandauGaus")
c.SaveAs("fit.gif")
return
nbins = num.GetXaxis().GetNbins()
x = list()
y = list()
xErrLow = list()
xErrHigh = list()
yErrLow = list()
yErrHigh = list()
for b in range(1, nbins):
xtemp = num.GetXaxis().GetBinCenter(b + 1)
xerrlow = num.GetXaxis().GetBinCenter(
b + 1) - num.GetXaxis().GetBinLowEdge(b + 1)
xerrhigh = num.GetXaxis().GetBinUpEdge(
b + 1) - num.GetXaxis().GetBinCenter(b + 1)
ratio = 0
errLow = 0
errHigh = 0
n1 = int(num.GetBinContent(b + 1))
n2 = int(den.GetBinContent(b + 1))
print "numerator: " + str(n1) + " and denominator: " + str(n2)
if (n1 > n2):
n1 = n2
if (n2 > 0):
ratio = float(n1) / float(n2)
if (ratio > 1):
ratio = 1
errLow = ratio - TEfficiency.ClopperPearson(
n2, n1, 0.68269, False)
errHigh = TEfficiency.ClopperPearson(n2, n1, 0.68269, True) - ratio
print " done bin " + str(
b) + " " + str(xtemp) + " : " + str(n1) + "(" + str(
num.GetBinContent(b + 1)) + ")" + " / " + str(n2) + "(" + str(
den.GetBinContent(b + 1)) + ")" + " = " + str(
ratio) + " " + str(errLow) + " " + str(errHigh)
ytemp = ratio
yerrlowtemp = errLow
yerrhightemp = errHigh
print "x: " + str(xtemp) + " and y: " + str(ytemp)
x.append(xtemp)
y.append(ytemp)
xErrLow.append(xerrlow)
xErrHigh.append(xerrhigh)
yErrLow.append(yerrlowtemp)
yErrHigh.append(yerrhightemp)
c = TCanvas("cv", "cv", 800, 800)
c.SetLeftMargin(0.12)
#must convert list into array for TGraphAsymmErrors to work
xArr = array.array('f', x)
yArr = array.array('f', y)
xErrLowArr = array.array('f', xErrLow)
xErrHighArr = array.array('f', xErrHigh)
yErrLowArr = array.array('f', yErrLow)
yErrHighArr = array.array('f', yErrHigh)
effGraph = TGraphAsymmErrors(nbins, xArr, yArr, xErrLowArr, xErrHighArr,
yErrLowArr, yErrHighArr)
effGraph.Draw("APE")
effGraph.SetTitle("")
effGraph.GetXaxis().SetTitle(xAxisTitle)
effGraph.GetXaxis().SetTitleSize(0.05)
effGraph.GetXaxis().SetTitleOffset(0.90)
effGraph.GetXaxis().SetLabelSize(0.03)
effGraph.GetXaxis().SetRangeUser(xAxisRangeLow, xAxisRangeHigh)
effGraph.GetYaxis().SetTitle("Efficiency")
effGraph.GetYaxis().SetTitleSize(0.05)
effGraph.GetYaxis().SetTitleOffset(1.05)
effGraph.GetYaxis().SetLabelSize(0.03)
title = TLatex()
title.SetTextSize(0.05)
title.DrawLatexNDC(.2, .93, topTitle)
c.Update()
c.SaveAs(plotname + ".gif")
class QuickTuner:
def __init__(self, mode=0):
self.mode = mode # 0: normal, 1: testing
self.sensorConfig = None
self.showPlot = True
self.gainRef = None
self.tuneRef = [0.6, -1, 0.3, 0.1]
self.logFile = None
self.lt = TLatex()
self.stepI = 0
self.sDir = sDir
self.sTag = sTag
self.autoSave = sDirectly
self.halfPeriod = 2500
### To save the results a list of [((),())]
self.results = None
self.setup()
def setupLogFile(self,fname):
self.logFile = open(fname,'a')
def setup(self):
### get connected
data_ip_port = "192.168.2.3:1024"
control_ip_port = "192.168.2.3:1025"
dataIpPort = data_ip_port.split(':')
sD = socket.socket(socket.AF_INET,socket.SOCK_STREAM)
ctrlIpPort = control_ip_port.split(':')
sC = socket.socket(socket.AF_INET,socket.SOCK_STREAM)
cmd = Cmd()
cd = CommonData(cmd, dataSocket=sD, ctrlSocket=sC)
cd.aoutBuf = 1
cd.x2gain = 2
cd.sdmMode = 0
cd.bufferTest = 0
if self.mode == 0:
sD.connect((dataIpPort[0],int(dataIpPort[1])))
sC.connect((ctrlIpPort[0],int(ctrlIpPort[1])))
self.sensorConfig = SensorConfig(cd)
cd.getCurrentBest()
for i in range(len(cd.sensorVcodes)):
print i, [cd.tms1mmReg.dac_code2volt(vx) for vx in cd.sensorVcodes[i]]
def get_score(self, data):
score = 0
## is there any structure?
### Find the jumps
nP = 10 # use 10 previous points if exist
nTh = 4
pPoints = []
nPoints = []
nData = len(data)
for i in range(nData):
### get the average of previous nP points -- these nP should be similar
av = 0.
av2 = 0.
nPp = 0
for j in range(nP):
jx = i-1-j
if jx < 0: break
jv = data[jx]
nPp += 1
av += jv
av2 += jv*jv
if nPp <2: continue ### not enough points
av /= nPp
av2 /= nPp
av2 -= av*av
av2 = sqrt(av2)
nOut = 0
for j in range(nP):
jx = i-1-j
if jx>=0 and abs(data[jx]-av)/av2 > 4: nOut += 1
if i<50:
print i, av, av2, nOut
if nOut > 0 or av/av2<2: # not good average
continue ## skip as we can't judge...
if (data[i]-av)/av2 > 5: ### good positive point
pPoints.append((i, data[i]-av))
elif (data[i]-av)/av2 < -5:
nPoints.append((i, av-data[i]))
### Compare with current one
print pPoints
print nPoints
## is it a signal structure?
## How good is the signal?
return score;
# def assess(self, isensor=None, inputVs=[1.379, 1.546, 1.626, 1.169, 1.357, 2.458], adjustDecayTime=10):
def assess(self, isensor=None, inputVs=None, adjustDecayTime=10, adjustRef=True):
tname = self.__class__.__name__ + ':' + sys._getframe().f_code.co_name
cd = self.sensorConfig.cd
cd.updatePars(isensor, inputVs)
isensor = cd.currentSensor
self.sensorConfig.update_sensor(isensor)
print("Checking:", isensor, cd.inputVs)
### get new data
time.sleep(5)
data = cd.adcData[isensor]
a1 = SigInfo()
a1.sTag = self.sTag
r = (0,0,0,0,0)
nTest = 10
if self.mode==0:
var = 99
nTried = 0
while nTried<4:
rAva = []
rL = [0,0,0,0,0]
for k in range(nTest):
cd.fetch()
r = a1.getQuickInfo(data)
rAva.append(r[1])
for i in range(len(r)): rL[i] += r[i]/nTest
mu,var = getMeanVar(rAva)
# print mu, var
r = tuple(rL)
if var<0.002:
print "Convereged."
break
print "Not stable"
time.sleep(10)
nTried += 1
if self.logFile:
infox = "{1:d}> P{0:d}:".format(isensor, self.stepI)
infox += ', '.join(['{0:.3f}'.format(x) for x in cd.inputVs])
infox += '->'+' '.join('{0:.4f}'.format(x) for x in r)
self.logFile.write(infox+'\n')
self.stepI += 1
print "temp results: r=", r
if self.showPlot:
ref = self.gainRef if self.gainRef else -1
a1.show(data, "P{1:d}: A={0:.4f} ({2:.4f})".format(r[1],isensor, ref),', '.join(['{0:.3f}'.format(x) for x in cd.inputVs]))
if self.gainRef and self.gainRef - r[1]>0.002:
Warning(tname, "Will not fine tune")
return None
if adjustRef:
inputVs_t = cd.inputVs[:]
if r[0]+r[1]>1.25:
print '-----r[0]=', r[0], cd.voltsNames[5], ':',cd.inputVs[5],'->',
cd.inputVs[5] -= 0.01
print cd.inputVs[5]
r_t = self.assess(isensor, cd.inputVs, adjustDecayTime, True)
### check the results: the gain should not degrade much, the avarage should get smaller
if r_t[1]-r[1]>-0.002 and r_t[0]<r[0]:
r = r_t
else:
cd.inputVs = inputVs_t
Error(tname, "Failed to reduce r[0], reverting...")
elif r[0]<self.tuneRef[0] and cd.inputVs[5]<2.99:
print '-----r[0]=', r[0], cd.voltsNames[5], ':',cd.inputVs[5],'->',
cd.inputVs[5] += 0.01
print cd.inputVs[5]
r_t = self.assess(isensor, cd.inputVs, adjustDecayTime, True)
### check the results
if r_t[1]-r[1]<-0.002 and r_t[0]>r[0]:
r = r_t
else:
cd.inputVs = inputVs_t
Error(tname, "Failed to increase r[0], reverting...")
if adjustDecayTime and r[1]>0.004:
inputVs_t = cd.inputVs[:]
adjustDecayTime -= 1
print r[4], float(abs(r[4])%self.halfPeriod)/self.halfPeriod
if r[3]>self.tuneRef[3] or r[3]<-0.1 or float(abs(r[4])%self.halfPeriod)/self.halfPeriod>0.1:
print '-----r[3]=', r[3], cd.voltsNames[4], ':',cd.inputVs[4],'->',
cd.inputVs[4] += 0.01
print cd.inputVs[4]
r_t = self.assess(isensor, cd.inputVs, adjustDecayTime, adjustRef)
### check the results
if r_t and r_t[1]-r[1]<-0.002 and r_t[3]<r[3]:
r = r_t
else:
cd.inputVs = inputVs_t
Error(tname, "Failed to reduce r[3], reverting...")
elif r[2]<self.tuneRef[2]:
print '-----r[2]=', r[2], cd.voltsNames[4], ':',cd.inputVs[4],'->',
cd.inputVs[4] -= 0.01
print cd.inputVs[4]
r_t = self.assess(isensor, cd.inputVs, adjustDecayTime, adjustRef)
### check the results
if r_t and r_t[1]-r[1]<-0.002 and r_t[2]>r[2]:
r = r_t
else:
cd.inputVs = inputVs_t
Error(tname, "Failed to increase r[2], reverting...")
return r
def scanForStructure(self, chan=18, xtag=''):
self.sTag = "sfs_"+xtag+str(chan)+'_'
for vdis in range(9):
for vref in range(10):
cd = self.sensorConfig.cd
cd.currentSensor = chan
cd.readBackPars(chan)
cd.inputVs[4] = 1.+0.1*vdis
cd.inputVs[5] = 2.+0.1*vref
x0 = self.assess(adjustDecayTime=0, adjustRef=False)
def scanForStructure2(self, chan=18, xtag=''):
self.sTag = "sfs_"+xtag+str(chan)+'_'
for vref in range(10):
for vdis in range(9):
cd = self.sensorConfig.cd
cd.currentSensor = chan
cd.readBackPars(chan)
cd.inputVs[4] = 1.+0.1*vdis
cd.inputVs[5] = 2.+0.1*vref
x0 = self.assess(adjustDecayTime=0, adjustRef=False)
def scan(self):
'''Scan the given range with given steps'''
pass
def smartScan(self):
'''Increase the number of points denpending on the situation'''
pass
def tryExisting(self):
'''Try the existing parameters'''
isendor = cd.currentSensor
# currentValues =
# for i in range(len(cd.sensorVcodes)):
# print i, [cd.tms1mmReg.dac_code2volt(vx) for vx in cd.sensorVcodes[i]]
pass
def tune2(self, chan):
self.sTag = "plot_{0:d}_".format(chan)
cd = self.sensorConfig.cd
cd.currentSensor = chan
cd.readBackPars(chan)
print "starting with pars:", cd.inputVs
### check it
# x0 = self.assess(adjustDecayTime=0, adjustRef=False)
x0 = self.assess()
self.gainRef = x0[1]
inputVs0 = cd.inputVs[:]
for i in range(cd.nCh):
if cd.isGood[i]:
cd.sensorVcodes[chan] = cd.sensorVcodes[i][:]
# xi = self.assess(adjustDecayTime=0, adjustRef=False)
xi = self.assess()
if xi[1]>self.gainRef:
cd.readBackPars(chan)
self.gainRef = xi[1]
print "find better starting point:", cd.inputVs, 'with gain:',self.gainRef
if self.logFile: self.logFile.write('# new base\n')
else:
cd.sensorVcodes[chan] = inputVs0
print "worse gain:", xi[1], "going back to the old one"
### first step: get high gain with loose shape requirement
### scan all parameters execpt VDIS
self.tuneRef = [0.6, -1, 0.2, 0.1] ## loose requirement for high gain
# changeList = [0.001, 0.001, 0.003, 0.005, 0.02, 0.03, 0.05, 0.2, 0.3, 0.5]
changeList = [0.001, 0.001, 0.002, 0.003, 0.005, 0.02, 0.03, 0.05, 0.1, 0.1, 0.1,0.1, 0.1, 0.1, 0.2]
tunePars = [5,1,2,0,3]
updated = True
tuned = []
tuneVref = (5 not in tunePars)
for ipar in growList(tunePars):
if ipar == -1: break ### reach the end of the list
print "===> Start tune", cd.voltsNames[ipar]
### find the best point
updated = False
while True:
direction = 0
inputVs0 = cd.inputVs[:]
while direction == 0:
print '------', cd.voltsNames[ipar], '[U]:',cd.inputVs[ipar],'->',
for d in changeList:
cd.inputVs[ipar] += d
if(cd.inputVs[ipar]>3): break
print cd.inputVs[ipar],
x1 = self.assess(adjustRef=tuneVref)
if x1 is None: continue
print "dx=", cd.inputVs[ipar]-inputVs0[ipar],", dA=", x1[1]-x0[1],
print x0, x1, self.gainRef
if abs(x1[1]-self.gainRef)>0.001: break
if (x1 is None) or x1[1]<self.gainRef+0.0008:
cd.inputVs = inputVs0
print 'reverting...'
break
x0 = x1
self.gainRef = x0[1]
print 'Good. Moving on...'
if self.logFile: self.logFile.write('# new base\n')
direction = 1
inputVs0 = cd.inputVs[:]
while direction == 0:
print '------', cd.voltsNames[ipar], '[D]:',cd.inputVs[ipar],'->',
for d in changeList:
cd.inputVs[ipar] -= d
if(cd.inputVs[ipar]<0): break
print cd.inputVs[ipar],
x1 = self.assess(adjustRef=tuneVref)
# x1 = self.assess()
if x1 is None: continue
print "dx=", cd.inputVs[ipar]-inputVs0[ipar],", dA=", x1[1]-x0[1],
print x0, x1, self.gainRef
if abs(x1[1]-self.gainRef)>0.001: break
if (x1 is None) or x1[1]<self.gainRef+0.0008:
cd.inputVs = inputVs0
print 'reverting...'
break
x0 = x1
self.gainRef = x0[1]
print 'Good. Moving on...'
if self.logFile: self.logFile.write('# new base\n')
direction = -1
if direction != 0:
updated = True
else: break
### redo other parameters if
if updated:
tunePars += tuned ### if the parameter changed, the tuned ones need to be revisited
tuned = []
else:
tuned.append(ipar)
print "gain tune done:", cd.inputVs
# x1 = self.assess()
# print x1
# print ['{0:.3f}'.format(x) for x in cd.inputVs]
print "------------------------"
# ### second step: tune the [VDIS, VREF, VCASN, VBIASN] with loose gain requirement
# self.tuneRef = [0.6, -1, 0.4, 0.05] ## loose requirement for high gain
# # changeList = [0.001, 0.001, 0.003, 0.005, 0.02, 0.03, 0.05, 0.2, 0.3, 0.5]
# changeList = [0.001, 0.001, 0.002, 0.003, 0.005, 0.02, 0.03, 0.05, 0.1, 0.1, 0.1,0.1, 0.1, 0.1, 0.2]
# tunePars = [4,0,2,5]
#
# updated = True
# tuned = []
# tuneVref = (5 in tunePars)
# tuneDT = 0 if 4 in tunePars else 10
# for ipar in growList(tunePars):
# if ipar == -1: break ### reach the end of the list
# print "===> Start tune", cd.voltsNames[ipar]
#
# ### find the best point
# updated = False
# while True:
# direction = 0
# inputVs0 = cd.inputVs[:]
# while direction == 0:
# print '------', cd.voltsNames[ipar], '[U]:',cd.inputVs[ipar],'->',
# for d in changeList:
# cd.inputVs[ipar] += d
# if(cd.inputVs[ipar]>3): break
# print cd.inputVs[ipar],
# x1 = self.assess(adjustDecayTime=tuneDT, adjustRef=tuneVref)
# if x1 is None: continue
# print "dx=", cd.inputVs[ipar]-inputVs0[ipar],", dA=", x1[1]-x0[1],
# print x0, x1, self.gainRef
# if abs(x1[1]-self.gainRef)>0.001: break
# if (x1 is None) or x1[1]<x0[1]+0.0008:
# cd.inputVs = inputVs0
# print 'reverting...'
# break
# x0 = x1
# self.gainRef = x0[1]
# print 'Good. Moving on...'
# if self.logFile: self.logFile.write('# new base\n')
# direction = 1
# inputVs0 = cd.inputVs[:]
# while direction == 0:
# print '------', cd.voltsNames[ipar], '[D]:',cd.inputVs[ipar],'->',
# for d in changeList:
# cd.inputVs[ipar] -= d
# if(cd.inputVs[ipar]<0): break
# print cd.inputVs[ipar],
# x1 = self.assess(adjustDecayTime=tuneDT, adjustRef=tuneVref)
# # x1 = self.assess()
# if x1 is None: continue
# print "dx=", cd.inputVs[ipar]-inputVs0[ipar],", dA=", x1[1]-x0[1],
# print x0, x1, self.gainRef
# if abs(x1[1]-self.gainRef)>0.001: break
# if (x1 is None) or x1[1]<x0[1]+0.0008:
# cd.inputVs = inputVs0
# print 'reverting...'
# break
# x0 = x1
# self.gainRef = x0[1]
# print 'Good. Moving on...'
# if self.logFile: self.logFile.write('# new base\n')
# direction = -1
# if direction != 0:
# updated = True
# else: break
#
# ### redo other parameters if
# if updated:
# tunePars += tuned ### if the parameter changed, the tuned ones need to be revisited
# tuned = []
# else:
# tuned.append(ipar)
print "final:", cd.inputVs
x1 = self.assess(adjustDecayTime=0, adjustRef=False)
print x1
print ['{0:.3f}'.format(x) for x in cd.inputVs]
def tune(self, chan):
## give a set of parameters and get a quantity of goodness
### pass the parameters and get data
### assess the data -- what's good? What's better?
## Move the next set of parameters
cd = self.sensorConfig.cd
cd.currentSensor = chan
cd.readBackPars(chan)
# cd.inputVs = [1.029,1.106,1.676,1.169,0.8,2.99]
print "starting with pars:", cd.inputVs
### check it
x0 = self.assess()
self.gainRef = x0[1]
## tune the first four VDIS for higher gain
tuned = []
px = [None]*6 ## to save the results of the 6 parameters
mx = 0
updated = True
changeList = [0.001, 0.001, 0.003, 0.005, 0.02, 0.03, 0.05, 0.2, 0.3, 0.5]
tunePars = [5,1,2,0,3]
for ipar in growList(tunePars):
if ipar == -1: break ### reach the end of the list
print "===> Start tune", cd.voltsNames[ipar]
### find the best point
updated = False
while True:
direction = 0
inputVs0 = cd.inputVs[:]
while direction == 0:
print '------', cd.voltsNames[ipar], '[U]:',cd.inputVs[ipar],'->',
for d in changeList:
cd.inputVs[ipar] += d
if(cd.inputVs[ipar]>3): break
print cd.inputVs[ipar],
x1 = self.assess()
if x1 is None: continue
print "dx=", cd.inputVs[ipar]-inputVs0[ipar],", dA=", x1[1]-x0[1],
print x0, x1, self.gainRef
if abs(x1[1]-self.gainRef)>0.001: break
if (x1 is None) or x1[1]<x0[1]+0.0008:
cd.inputVs = inputVs0
print 'reverting...'
break
x0 = x1
self.gainRef = x0[1]
print 'Good. Moving on...'
direction = 1
inputVs0 = cd.inputVs[:]
while direction == 0:
print '------', cd.voltsNames[ipar], '[D]:',cd.inputVs[ipar],'->',
for d in changeList:
cd.inputVs[ipar] -= d
if(cd.inputVs[ipar]<0): break
print cd.inputVs[ipar],
x1 = self.assess()
if x1 is None: continue
print "dx=", cd.inputVs[ipar]-inputVs0[ipar],", dA=", x1[1]-x0[1],
print x0, x1, self.gainRef
if abs(x1[1]-self.gainRef)>0.001: break
if (x1 is None) or x1[1]<x0[1]+0.0008:
cd.inputVs = inputVs0
print 'reverting...'
break
x0 = x1
self.gainRef = x0[1]
print 'Good. Moving on...'
direction = -1
if direction != 0:
updated = True
else: break
### redo other parameters if
if updated:
tunePars += tuned ### if the parameter changed, the tuned ones need to be revisited
else:
tuned.append(ipar)
print "final:", cd.inputVs
x1 = self.assess()
print x1
print ['{0:.3f}'.format(x) for x in cd.inputVs]
def handTune(self, chan):
self.sensorConfig.cd.currentSensor = chan
inputVs = None
while True:
### check it
x0 = self.assess(None, inputVs)
print x0
args = raw_input("inputVs:")
if args in ['e','q','exit','quit','.q']:
break
inputVs = [float(x) for x in args.split(',')]
def test(self):
print "testing"
dat1 = None
ichan = 5 if len(sys.argv)<2 else int(sys.argv[1])
with open('adc_test.dat') as f1:
dat1 = [float(l.split()[ichan]) for l in f1.readlines() if l.find('#')==-1]
# a1 = SigInfo(dat1)
a1 = SigInfo()
print a1.getQuickInfo(dat1, 20)
# self.get_score(dat1)
# self.check(dat1)
def fullTune(self):
'''Do everything automatically'''
### First find the initial values for fine tune
### Dead channels will be marked
self.getInitialValues()
### Continue with the fine tune
## Could be done in prallel in future
nChan = 19 # number of channels
for i in range(nChan):
# don't waste time on the dead ones
if self.isDead[i]: continue
self.tune2(i)
### Finish. The output is a list of parameters and the characteristics
### Where to save the output? Inside the class, or return from this method? -- save to the method.
def compareInputs(self, chan, inputs, dt=10, info=None, saveName='testing'):
'''Compare a list of inputs
Each input is a tuple: (tag, tex, [pars]), tex will be shown in the legend and tag should plain text used to distinguish the entry.
For each entry, a TGraph will be made.
'''
cd1 = self.sensorConfig.cd
grs = []
dataX = []
ic = 1
max0 = -1
min0 = 999
lg = TLegend(0.7,0.8,0.9,0.9)
lg.SetFillStyle(0)
for ip in inputs:
cd1.updatePars(chan, ip[2])
print ip[2]
self.sensorConfig.update_sensor(chan)
print("Checking:", cd1.currentSensor, cd1.inputVs)
time.sleep(dt)
cd1.fetch()
# dataX.append(cd1.adcData[chan][:])
dataX = cd1.adcData[chan][:]
for i in range(10): print dataX[i]
# ndata = len(dataX[-1])
# gr1 = TGraph(ndata, array('d',[i*0.2*0.001 for i in range(ndata)]), array('d', dataX[-1]))
ndata = len(dataX)
gr1 = TGraph(ndata, array('d',[i*0.2*0.001 for i in range(ndata)]), array('d', dataX))
gr1.SetLineColor(ic)
gr1.SetMarkerColor(ic)
gr1.SetMarkerStyle(20+ic)
ic += 1
lg.AddEntry(gr1, ip[1],'lp')
grs.append(gr1)
# max0 = max([max0, max(dataX[-1])])
# min0 = min([min0, min(dataX[-1])])
max0 = max([max0, max(dataX)])
min0 = min([min0, min(dataX)])
# gr1.Draw("AP")
# waitRootCmdX()
gr0 = grs[0]
gr0.Draw("AP")
dm = max0 - min0
h1 = gr0.GetHistogram()
h1.GetYaxis().SetRangeUser(min([min0*0.8,min0-0.1*dm]), max([max0*1.2,max0+0.1*dm]))
h1.GetXaxis().SetTitle("t [ms]")
h1.GetYaxis().SetTitle("V_{out} [V]")
for gr in grs[1:]: gr.Draw("Psame")
h1.Draw('axissame')
lg.Draw()
if info:
self.lt.DrawLatexNDC(0.2,0.8, info)
waitRootCmdX(self.sDir+self.sTag+saveName, self.autoSave)
def scanX(chans=[i for i in range(19)]):
'''Used to test the scan of one or more parameters'''
nChips = 19 # the magic number
nChan = len(chans)
iP = 1
fixedPars = [1.5, 1.5]
cd = CommonData()
cd.setupConnection()
inputs = cd.inputVs
sc1 = SensorConfig(cd)
### setup the style list
mkColors = [2,3,4,6,7,8]
mkMarkers = [20,25,23,26,21,22,24,27,32]
styleList = [(None,None)]*nChips
for i,j in sc1.tms1mmX19chainSensors.iteritems():
for k,l in enumerate(j):
styleList[l] = (mkColors[i],mkMarkers[k])
### get list of chains to be updated
chains = set([sc1.tms1mmX19chainSensors[sc1.tms1mmX19sensorInChain[c]][0] for c in chans])
print chains
### setup the elements for plotting
lg = TLegend(0.84,0.11,0.998,0.89)
lg.SetFillStyle(0)
grs = [TGraphErrors() for chan in chans]
for ic in range(len(chans)):
chan = chans[ic]
grs[ic].SetMarkerColor(styleList[chan][0])
grs[ic].SetLineColor(styleList[chan][0])
grs[ic].SetMarkerStyle(styleList[chan][1])
lg.AddEntry(grs[ic],str(chan),'lp')
### variables to store the results
pValues = [None]*nChan
changeP = [(None,None)]*nChan ## save (where, how_large) for each channel
### perform the scan
max1 = -1
min1 = 999
for ir in range(11): ## run ir
vr = 0.33*ir
### update channels
for chan in chans:
cd.readBackPars(chan)
for f in range(len(fixedPars)):
if fixedPars[f] is not None: inputs[f] = fixedPars[f]
inputs[iP] = vr
cd.updatePars(chan, inputs, False)
for c in chains: sc1.update_sensor(c)
time.sleep(2)
cd.fetch()
# x = ''
# while x!='m':
# cd.fetch()
# a1 = SigInfo()
# a1.showMore(dat1)
# x = raw_input("'m' to move to next|")
for ic in range(len(chans)):
chan = chans[ic]
m,v = getMeanVar(cd.adcData[chan])
if m+v>max1: max1 = m+v
if m-v<min1: min1 = m-v
print chan, m,v
grs[ic].SetPoint(ir, vr, m)
grs[ic].SetPointError(ir, 0, v)
### get the values
if pValues[ic] is not None:
if (changeP[ic][1] is None) or (m - pValues[ic] < changeP[ic][1]):
changeP[ic] = (ir, m - pValues[ic])
pValues[ic] = m
gr1 = grs[0]
print gr1.GetN()
gr1.Draw("APL")
h1 = gr1.GetHistogram()
h1.GetXaxis().SetTitle(cd.voltsNames[iP]+' [V]')
h1.GetYaxis().SetTitle('V_{out} [V]')
dm = 0.05*(max1-min1)
h1.GetYaxis().SetRangeUser(min1-dm,max1+dm)
for gr in grs[1:]: gr.Draw("PLsame")
lg.Draw("same")
### add info of other parameters
inputT = ''
for i,x in enumerate(fixedPars):
if i == iP: continue
if x is not None: inputT += cd.voltsNames[i]+'='+str(x)
lt = TLatex()
lt.DrawLatexNDC(0.2,0.92,' '.join(inputT))
# inputT = ['{0:.3f}'.format(x) for x in inputs]
# inputT[iP] = '--'
# lt.DrawLatexNDC(0.2,0.92,' '.join(inputT))
for x in changeP: print x
waitRootCmdX()
def GetAndDrawHistograms(DataFileName,
DataTreeName,
MCFileName,
MCTreeName,
noStack=True):
fMC = TFile(MCFileName)
tMC = fMC.Get(MCTreeName)
fData = TFile(DataFileName)
tData = fData.Get(DataTreeName)
histoListe = setupHistograms()
HistDic = {}
for i, h in enumerate(histoListe):
legende = TLegend(0.7, 0.7, 0.89, 0.89)
CanvasName = "c" + str(i)
CanvasName = TCanvas(CanvasName, 'Defining histogram size')
g = THStack("", "")
#tMC.Draw(histoListe[i][3],CutMC)
#hist_MC = tMC.GetHistogram().Clone("hist_MC")
#hist_MC.SetLineColor(6)
#hist_MC.SetLineWidth(1)
##hist_MC.SetMarkerStyle(21)
#legende.AddEntry(hist_MC, "MC", "lp")
#hist_MC.GetXaxis().SetTitle(h[1])
#hist_MC.GetYaxis().SetTitle(h[2])
#hist_MC.Scale(1/hist_MC.Integral())
#MaxMC = hist_MC.GetMaximum()
#g.Add(hist_MC)
tData.Draw(histoListe[i][3], CutData)
hist_data = tData.GetHistogram().Clone("hist_data")
hist_data.SetLineColor(9)
hist_data.SetLineWidth(1)
#hist_data.SetMarkerStyle(21)
legende.AddEntry(hist_data, "Data", "lp")
hist_data.GetXaxis().SetTitle(h[1])
hist_data.GetYaxis().SetTitle(h[2])
#hist_data.Scale(1/hist_data.Integral())
MaxData = hist_data.GetMaximum()
g.Add(hist_data)
#tData.Draw(histoListe[i][3],CutData_RS)
#hist_RS = tData.GetHistogram().Clone("hist_RS")
#hist_RS.SetLineColor(4)
#hist_RS.SetLineWidth(1)
##hist_RS.SetMarkerStyle(21)
#legende.AddEntry(hist_RS, "Data RS", "lp")
#hist_RS.Scale(1/hist_RS.Integral())
#MaxRS = hist_RS.GetMaximum()
#g.Add(hist_RS)
#if noStack:
# if (MaxMC > MaxData):
# g.SetMaximum(MaxMC*1.1)
# else:
# g.SetMaximum(MaxData*1.1)
# g.Draw("nostackhist")
#else:
# g.SetMaximum(g.GetMaximum()*1.1)
# g.Draw("hist")
g.SetMaximum(MaxData * 1.1)
g.Draw("hist")
#legende.Draw()
g.GetXaxis().SetTitle(h[1])
g.GetYaxis().SetTitle(h[2])
g.GetYaxis().SetTitleOffset(1.4)
g.GetXaxis().SetTitleOffset(1.1)
my_Latex = TLatex()
my_Latex.SetTextSize(0.04)
my_Latex.DrawLatexNDC(0.13, 0.85, "LHCb Data 2012")
CanvasName.Update()
outDir = '/sps/lhcb/volle/BMass'
if not os.path.isdir(outDir):
os.makedirs(outDir)
CanvasName.SaveAs("{}/{}_{}.pdf".format(outDir, histoListe[i][0],
comment))
CanvasName.SaveAs("{}/{}_{}.png".format(outDir, histoListe[i][0],
comment))
for i, h in histories.iteritems():
histos["q_peak"].Fill(max([e.Q for i, e in h.iteritems()]))
canvases["q_peak"] = TCanvas("q_peak", "Peak of quarantines", 400, 400)
histos["q_peak"].SetStats(0)
histos["q_peak"].SetTitle("; Maximum of quarantined people; A.U.")
histos["q_peak"].SetLineColor(1)
histos["q_peak"].SetLineWidth(2)
histos["q_peak"].GetYaxis().SetRangeUser(
0, 1.5 * histos["q_peak"].GetMaximum())
histos["q_peak"].Draw()
canvases["q_peak"].SetBottomMargin(0.15)
canvases["q_peak"].SetTopMargin(0.05)
canvases["q_peak"].SetRightMargin(0.05)
canvases["q_peak"].SetLeftMargin(0.12)
latex.DrawLatexNDC(
0.2, 0.85,
"Max. quarantined people = " + str(int(histos["q_peak"].GetMean())) +
" #pm " + str(int(histos["q_peak"].GetRMS())))
canvases["q_peak"].SaveAs(plot_dir + "QuarantinePeak.pdf")
#
# time of peak of quarantined
#
histos["tq_peak"] = TH1F("tq_peak", "", 20, 30, 50)
for i, h in histories.iteritems():
max_q = max([e.Q for i, e in h.iteritems()])
for i, e in h.iteritems():
if e.Q == max_q:
histos["tq_peak"].Fill(e.time)
break
canvases["tq_peak"] = TCanvas("tq_peak", "Peak of quarantines", 400, 400)
def DrawSplotHistograms(DataFileName,
DataTreeName,
SFileName,
STreeName,
noStack=True):
fData = TFile(DataFileName)
tData = fData.Get(DataTreeName)
tData.AddFriend(STreeName, SFileName)
histoListe = setupHistograms()
HistDic = {}
for i, h in enumerate(histoListe):
legende = TLegend(0.75, 0.83, 0.97, 0.97)
CanvasName = "c" + str(i)
CanvasName = TCanvas(CanvasName, 'Defining histogram size')
g = THStack("", "")
#tMC.Draw(histoListe[i][3],CutMC)
#hist_MC = tMC.GetHistogram().Clone("hist_MC")
#hist_MC.SetLineColor(6)
#hist_MC.SetLineWidth(1)
##hist_MC.SetMarkerStyle(21)
#legende.AddEntry(hist_MC, "MC", "lp")
#hist_MC.GetXaxis().SetTitle(h[1])
#hist_MC.GetYaxis().SetTitle(h[2])
#hist_MC.Scale(1/hist_MC.Integral())
#MaxMC = hist_MC.GetMaximum()
#g.Add(hist_MC)
tData.Draw(histoListe[i][3], "B_M>5300")
hist_data = tData.GetHistogram().Clone("hist_data")
hist_data.SetLineColor(5)
hist_data.SetLineWidth(1)
#hist_data.SetMarkerStyle(21)
legende.AddEntry(hist_data, "Data", "lp")
hist_data.GetXaxis().SetTitle(h[1])
hist_data.GetYaxis().SetTitle(h[2])
MaxData = hist_data.GetMaximum()
g.Add(hist_data)
tData.Draw(histoListe[i][3], "nsig_sw")
hist_sw = tData.GetHistogram().Clone("hist_sw")
hist_sw.SetLineColor(4)
hist_sw.SetLineWidth(1)
##hist_RS.SetMarkerStyle(21)
legende.AddEntry(hist_sw, "sweighted Data", "lp")
MaxSW = hist_sw.GetMaximum()
g.Add(hist_sw)
if noStack:
if (MaxSW > MaxData):
g.SetMaximum(MaxSW * 1.2)
else:
g.SetMaximum(MaxData * 1.2)
g.Draw("nostackhist")
else:
g.SetMaximum(g.GetMaximum() * 1.2)
g.Draw("hist")
legende.Draw()
g.GetXaxis().SetTitle(h[1])
g.GetYaxis().SetTitle(h[2])
g.GetYaxis().SetTitleOffset(1.4)
g.GetXaxis().SetTitleOffset(1.1)
my_Latex = TLatex()
my_Latex.SetTextSize(0.04)
my_Latex.DrawLatexNDC(0.13, 0.85, "LHCb Data 2012")
CanvasName.Update()
outDir = '/sps/lhcb/volle/FitBMass/v1_Min5120'
if not os.path.isdir(outDir):
os.makedirs(outDir)
CanvasName.SaveAs("{}/{}_both.pdf".format(outDir,
histoListe[i][0])) #_both
CanvasName.SaveAs("{}/{}_both.png".format(outDir,
histoListe[i][0])) #_both
def Plot1DEfficiency(num, den, plotname, topTitle, xAxisTitle, xAxisRangeLow,
xAxisRangeHigh):
nbins = num.GetXaxis().GetNbins()
x = list()
y = list()
xErrLow = list()
xErrHigh = list()
yErrLow = list()
yErrHigh = list()
for b in range(1, nbins):
xtemp = num.GetXaxis().GetBinCenter(b + 1)
xerrlow = num.GetXaxis().GetBinCenter(
b + 1) - num.GetXaxis().GetBinLowEdge(b + 1)
xerrhigh = num.GetXaxis().GetBinUpEdge(
b + 1) - num.GetXaxis().GetBinCenter(b + 1)
ratio = 0
errLow = 0
errHigh = 0
n1 = int(num.GetBinContent(b + 1))
n2 = int(den.GetBinContent(b + 1))
print "numerator: " + str(n1) + " and denominator: " + str(n2)
if (n1 > n2):
n1 = n2
if (n2 > 0):
ratio = float(n1) / float(n2)
if (ratio > 1):
ratio = 1
errLow = ratio - TEfficiency.ClopperPearson(
n2, n1, 0.68269, False)
errHigh = TEfficiency.ClopperPearson(n2, n1, 0.68269, True) - ratio
print " done bin " + str(
b) + " " + str(xtemp) + " : " + str(n1) + "(" + str(
num.GetBinContent(b + 1)) + ")" + " / " + str(n2) + "(" + str(
den.GetBinContent(b + 1)) + ")" + " = " + str(
ratio) + " " + str(errLow) + " " + str(errHigh)
ytemp = ratio
yerrlowtemp = errLow
yerrhightemp = errHigh
print "x: " + str(xtemp) + " and y: " + str(ytemp)
x.append(xtemp)
y.append(ytemp)
xErrLow.append(xerrlow)
xErrHigh.append(xerrhigh)
yErrLow.append(yerrlowtemp)
yErrHigh.append(yerrhightemp)
c = TCanvas("cv", "cv", 800, 800)
c.SetLeftMargin(0.12)
#must convert list into array for TGraphAsymmErrors to work
xArr = array.array('f', x)
yArr = array.array('f', y)
xErrLowArr = array.array('f', xErrLow)
xErrHighArr = array.array('f', xErrHigh)
yErrLowArr = array.array('f', yErrLow)
yErrHighArr = array.array('f', yErrHigh)
effGraph = TGraphAsymmErrors(nbins, xArr, yArr, xErrLowArr, xErrHighArr,
yErrLowArr, yErrHighArr)
effGraph.Draw("APE")
effGraph.SetTitle("")
effGraph.GetXaxis().SetTitle(xAxisTitle)
effGraph.GetXaxis().SetTitleSize(0.05)
effGraph.GetXaxis().SetTitleOffset(0.90)
effGraph.GetXaxis().SetLabelSize(0.03)
effGraph.GetXaxis().SetRangeUser(xAxisRangeLow, xAxisRangeHigh)
effGraph.GetYaxis().SetTitle("Efficiency")
effGraph.GetYaxis().SetTitleSize(0.05)
effGraph.GetYaxis().SetTitleOffset(1.05)
effGraph.GetYaxis().SetLabelSize(0.03)
title = TLatex()
title.SetTextSize(0.05)
title.DrawLatexNDC(.2, .93, topTitle)
c.Update()
c.SaveAs(plotname + ".gif")
def Plot1DEfficiencyWithBkgSubtraction(tree, num, den, axis, pixel, plotname,
topTitle, xAxisTitle, xAxisRangeLow,
xAxisRangeHigh):
#make amp histogram
c = TCanvas("c", "c", 800, 800)
#Each pixel has slightly different time distribution and efficiency of time window cut differs a bit
#We derive corresponding efficiency corrections for each pixel
timeWindowCutEfficiency = 1.0
if (pixel == "5_3"):
timeWindowCutEfficiency = 0.989
if (pixel == "5_4"):
timeWindowCutEfficiency = 0.9478
if (pixel == "5_10"):
timeWindowCutEfficiency = 0.9643
#ampHist = TH1F("ampHist",";Amplitude [mV]; Number of Events", 25,0,50)
#tree.Draw("amp[3]>>ampHist"," x_dut[2] > 19.6 && x_dut[2] < 19.7 && y_dut[2] > 23.5 && y_dut[2] < 24.0 && ((t_peak[3] - t_peak[0])*1e9 > 6 && (t_peak[3] - t_peak[0])*1e9 < 16)")
nbins = num.GetXaxis().GetNbins()
x = list()
y = list()
xErrLow = list()
xErrHigh = list()
yErrLow = list()
yErrHigh = list()
for b in range(1, nbins):
xtemp = num.GetXaxis().GetBinCenter(b + 1)
xerrlow = num.GetXaxis().GetBinCenter(
b + 1) - num.GetXaxis().GetBinLowEdge(b + 1)
xerrhigh = num.GetXaxis().GetBinUpEdge(
b + 1) - num.GetXaxis().GetBinCenter(b + 1)
#Noise templates:
#Pixel 5,3 : amp <= 6mV gives 0.4417 of the total and has 0 signal contamination
#Pixel 5,4 : amp <= 10mV gives 0.0404 of the total and has 0 signal contamination
#Pixel 5,10 : amp < gives 0. of the total and has 0 signal contamination
#We will assume that bins 0+1+2 (0-6mV) do not contain ANY signal.
#We count the number of events in those bins and divide by 0.4417 to get
#the total number of noise events. We subtract those from the numerator.
#Noise template is made from this data (outside of sensor region AND outside of time window):
#/eos/uscms/store/user/cmstestbeam/2019_04_April_CMSTiming/KeySightScope/RecoData/TimingDAQRECO/RecoWithTracks/v6_CACTUSSkim/Completed/Data_CACTUSAnalog_Pixel5_3_16216-16263.root
#pulse->Draw("amp[3]>>ampHist(25,0,50)","!(x_dut[2] > 19.4 && x_dut[2] < 20.6 && y_dut[2] > 23.4 && y_dut[2] < 24.1) && !((t_peak[3] - t_peak[0])*1e9 > 6 && (t_peak[3] - t_peak[0])*1e9 < 16)")
noiseSelection = ""
noiseSelectionCRFraction = 1
xPositionSelection = ""
yPositionSelection = ""
if (pixel == "5_3"):
noiseSelection = " && amp[3] <= 6"
noiseSelectionCRFraction = 0.4417
xPositionSelection = " && x_dut[2] > 19.5 && x_dut[2] < 20.5 "
yPositionSelection = " && y_dut[2] > 23.5 && y_dut[2] < 24.0 "
if (pixel == "5_4"):
noiseSelection = " && amp[3] <= 14"
noiseSelectionCRFraction = 0.4053
xPositionSelection = " && x_dut[2] > 18.5 && x_dut[2] < 19.5 "
yPositionSelection = " && y_dut[2] > 23.5 && y_dut[2] < 24.0 "
if (pixel == "5_10"):
noiseSelection = " && amp[3] <= 13"
noiseSelectionCRFraction = 0.3802
xPositionSelection = " && x_dut[2] > 19.5 && x_dut[2] < 20.5 "
yPositionSelection = " && y_dut[2] > 23.0 && y_dut[2] < 23.5 "
print "noise selection = " + noiseSelection + " " + str(
noiseSelectionCRFraction)
positionSelectionString = ""
if (axis == "x"):
positionSelectionString = " && x_dut[2] > " + str(
num.GetXaxis().GetBinLowEdge(b + 1)) + " && x_dut[2] < " + str(
num.GetXaxis().GetBinUpEdge(b + 1)) + yPositionSelection
if (axis == "y"):
positionSelectionString = xPositionSelection + " && y_dut[2] > " + str(
num.GetXaxis().GetBinLowEdge(b + 1)) + " && y_dut[2] < " + str(
num.GetXaxis().GetBinUpEdge(b + 1)) + " "
print "numerator: " + "ntracks==1 && y_dut[0] > 0 && npix>0 && nback>0 " + positionSelectionString + " && ((t_peak[3] - t_peak[0])*1e9 > 6 && (t_peak[3] - t_peak[0])*1e9 < 16)"
ampHist = TH1F("ampHist" + "_" + str(b),
";Amplitude [mV]; Number of Events", 25, 0, 50)
denominatorCount = tree.GetEntries(
"ntracks==1 && y_dut[0] > 0 && npix>0 && nback>0 " +
positionSelectionString + " ")
tmpNumeratorTotalCount = tree.GetEntries(
"ntracks==1 && y_dut[0] > 0 && npix>0 && nback>0 " +
positionSelectionString +
" && ((t_peak[3] - t_peak[0])*1e9 > 6 && (t_peak[3] - t_peak[0])*1e9 < 16)"
)
tmpNumeratorNoiseControlRegionCount = tree.GetEntries(
"ntracks==1 && y_dut[0] > 0 && npix>0 && nback>0 " +
positionSelectionString +
" && ((t_peak[3] - t_peak[0])*1e9 > 6 && (t_peak[3] - t_peak[0])*1e9 < 16) "
+ noiseSelection)
tmpNumeratorSignalCount = tmpNumeratorTotalCount - tmpNumeratorNoiseControlRegionCount / noiseSelectionCRFraction
print "ntracks==1 && y_dut[0] > 0 && npix>0 && nback>0 " + positionSelectionString + " && ((t_peak[3] - t_peak[0])*1e9 > 6 && (t_peak[3] - t_peak[0])*1e9 < 16) " + noiseSelection
ratio = 0
errLow = 0
errHigh = 0
n1 = int(tmpNumeratorSignalCount)
n2 = int(denominatorCount)
print "numerator: " + str(n1) + " and denominator: " + str(n2)
if (n1 > n2):
n1 = n2
if (n2 > 0):
ratio = float(n1) / float(n2)
if (ratio > 1):
ratio = 1
errLow = ratio - TEfficiency.ClopperPearson(
n2, n1, 0.68269, False)
errHigh = TEfficiency.ClopperPearson(n2, n1, 0.68269, True) - ratio
print " done bin " + str(b) + " : " + str(
num.GetXaxis().GetBinLowEdge(b + 1)) + " - " + str(
num.GetXaxis().GetBinUpEdge(b + 1))
print " num = " + str(n1) + " = " + str(
tmpNumeratorTotalCount) + " - " + str(
tmpNumeratorNoiseControlRegionCount) + " / " + str(
noiseSelectionCRFraction) + " | den = " + str(n2)
print "ratio = " + str(ratio) + " " + str(errLow) + " " + str(errHigh)
ytemp = ratio / timeWindowCutEfficiency #here we correct for the time window cut inefficiency
yerrlowtemp = errLow
yerrhightemp = errHigh
print "x: " + str(xtemp) + " and y: " + str(ytemp)
x.append(xtemp)
y.append(ytemp)
xErrLow.append(xerrlow)
xErrHigh.append(xerrhigh)
yErrLow.append(yerrlowtemp)
yErrHigh.append(yerrhightemp)
c = TCanvas("cv", "cv", 800, 800)
c.SetLeftMargin(0.12)
#must convert list into array for TGraphAsymmErrors to work
xArr = array.array('f', x)
yArr = array.array('f', y)
xErrLowArr = array.array('f', xErrLow)
xErrHighArr = array.array('f', xErrHigh)
yErrLowArr = array.array('f', yErrLow)
yErrHighArr = array.array('f', yErrHigh)
effGraph = TGraphAsymmErrors(nbins, xArr, yArr, xErrLowArr, xErrHighArr,
yErrLowArr, yErrHighArr)
effGraph.Draw("APE")
effGraph.SetTitle("")
effGraph.GetXaxis().SetTitle(xAxisTitle)
effGraph.GetXaxis().SetTitleSize(0.05)
effGraph.GetXaxis().SetTitleOffset(0.90)
effGraph.GetXaxis().SetLabelSize(0.03)
effGraph.GetXaxis().SetRangeUser(xAxisRangeLow, xAxisRangeHigh)
effGraph.GetYaxis().SetTitle("Efficiency")
effGraph.GetYaxis().SetTitleSize(0.05)
effGraph.GetYaxis().SetTitleOffset(1.05)
effGraph.GetYaxis().SetLabelSize(0.03)
title = TLatex()
title.SetTextSize(0.05)
title.DrawLatexNDC(.2, .93, topTitle)
c.Update()
c.SaveAs(plotname + ".gif")
h_sumDiboson.SetLineColor(923)
h_sumDiboson.GetXaxis().SetTitle("Double b score")
h_sumDiboson.GetYaxis().SetTitle("Events/Bin")
leg.AddEntry(h_sumDiboson, "Diboson", "f")
#-------Draw Histogram in c1---------#
h_TopMatchFinal.Draw("hist")
h_WmatchFinal.Draw("histsame")
h_unmatchFinal.Draw("histsame")
h_sumWJetsFinal.Draw("histsame")
h_sumDiboson.Draw("histsame")
leg.Draw()
lt = TLatex()
lt.DrawLatexNDC(0.23, 0.85,
"#scale[0.8]{CMS} #scale[0.65]{#bf{#it{Preliminary}}}")
lt.DrawLatexNDC(0.23, 0.8, "#scale[0.7]{#bf{t#bar{t} CR (e)}}")
lt.DrawLatexNDC(0.23, 0.75, "#scale[0.5]{#bf{2-prong (bq) enriched}}")
lt.DrawLatexNDC(0.71, 0.92, "#scale[0.7]{#bf{41.0 fb^{-1} (13 TeV)}}")
c1.cd()
c1.Modified()
c1.Update()
c1.SaveAs("test.pdf")
#------------Overlap histograms in c2-------------#
c2 = TCanvas("c2", "c2", 900, 700) #width-height
c2.SetLeftMargin(0.15)
gStyle.SetOptStat(0)
def mistagSFtopEMu(year_, ana_):
if year_ == 2017:
dir = "monohbb.v06.00.05.2017_NCU/withSingleTop/" + ana_ + "/"
if year_ == 2018:
dir = "monohbb.v06.00.05.2018_NCU/withSingleTop/" + ana_ + "/"
print "Top Electron Region"
print " "
openfile1 = TFile(dir + "TopE.root") #
topmatchTopE = openfile1.Get("h_TopMatch")
WmatchTopE = openfile1.Get("h_Wmatch")
unmatchTopE = openfile1.Get("h_unmatch")
wjetsTopE = openfile1.Get("h_sumWJets")
dibosonTopE = openfile1.Get("h_sumDiboson")
unsubtractedDataTopE = openfile1.Get("h_Data")
failMCsubtractTopE = openfile1.Get("h_ttFailed")
passMCsubtractTopE = openfile1.Get("h_ttPassed")
subtractedDataTopE = openfile1.Get("SubtractedData")
datafailTopE = openfile1.Get("SubtractedDataFailed")
datapassTopE = openfile1.Get("SubtractedDataPassed") #
totaldataTopE = openfile1.Get("h_totaldata")
totalMCtopE = openfile1.Get("h_tt")
passedTopEdataBfrSubtract = openfile1.Get("h_Data_Passed")
wjetsTopEpassed = openfile1.Get("h_sumWJetsPassed")
dibosonTopEpassed = openfile1.Get("h_sumDibosonPassed")
failedTopEdataBfrSubtract = openfile1.Get("h_Data_Failed")
wjetsTopEfailed = openfile1.Get("h_sumWJetsFailed")
dibosonTopEfailed = openfile1.Get("h_sumDibosonFailed")
stTopE = openfile1.Get("h_sumST")
stTopEpassed = openfile1.Get("h_sumSTPassed")
stTopEfailed = openfile1.Get("h_sumSTFailed")
print "Top Muon Region"
print " "
openfile2 = TFile(dir + "TopMu.root") #
topmatchTopMu = openfile2.Get("h_TopMatch")
WmatchTopMu = openfile2.Get("h_Wmatch")
unmatchTopMu = openfile2.Get("h_unmatch")
wjetsTopMu = openfile2.Get("h_sumWJets")
dibosonTopMu = openfile2.Get("h_sumDiboson")
unsubtractedDataTopMu = openfile2.Get("h_Data")
failMCsubtractTopMu = openfile2.Get("h_ttFailed")
passMCsubtractTopMu = openfile2.Get("h_ttPassed")
subtractedDataTopMu = openfile2.Get("SubtractedData")
datafailTopMu = openfile2.Get("SubtractedDataFailed")
datapassTopMu = openfile2.Get("SubtractedDataPassed") #
totaldataTopMu = openfile2.Get("h_totaldata")
totalMCtopMu = openfile2.Get("h_tt")
passedTopMudataBfrSubtract = openfile2.Get("h_Data_Passed")
wjetsTopMupassed = openfile2.Get("h_sumWJetsPassed")
dibosonTopMupassed = openfile2.Get("h_sumDibosonPassed")
failedTopMudataBfrSubtract = openfile2.Get("h_Data_Failed")
wjetsTopMufailed = openfile2.Get("h_sumWJetsFailed")
dibosonTopMufailed = openfile2.Get("h_sumDibosonFailed")
stTopMu = openfile2.Get("h_sumST")
stTopMupassed = openfile2.Get("h_sumSTPassed")
stTopMufailed = openfile2.Get("h_sumSTFailed")
print "get histograms from root files: done"
print " "
SubtractedDataPassedTopE = datapassTopE.Clone("SubtractedDataPassedTopE")
SubtractedDataPassedTopMu = datapassTopMu.Clone(
"SubtractedDataPassedTopMu")
#merge histogram"
print "merge histograms"
print " "
topmatchMerge = topmatchTopE + topmatchTopMu
WmatchMerge = WmatchTopE + WmatchTopMu
unmatchMerge = unmatchTopE + unmatchTopMu
wjetsMerge = wjetsTopE + wjetsTopMu
stMerge = stTopE + stTopMu
dibosonMerge = dibosonTopE + dibosonTopMu
unsubtractedDataMerge = unsubtractedDataTopE + unsubtractedDataTopMu
failMCsubtractMerge = failMCsubtractTopE.Clone("failMCsubtractMerge")
failMCsubtractMerge = failMCsubtractMerge + failMCsubtractTopMu
passMCsubtractMerge = passMCsubtractTopE.Clone("passMCsubtractMerge")
passMCsubtractMerge = passMCsubtractMerge + passMCsubtractTopMu
subtractedDataMerge = subtractedDataTopE + subtractedDataTopMu
ttData_fraction = arr.array('d')
ttData_error = arr.array('d')
totaldataMerge = totaldataTopE + totaldataTopMu
totaldata = totaldataMerge.Integral()
totaldataMerge.Rebin(14)
datafailMerge = datafailTopE + datafailTopMu
faildata = datafailMerge.Integral()
datafailMerge.Rebin(14)
datafailMerge.Sumw2()
datafailMerge.Divide(totaldataMerge)
frac_ttData_fail = datafailMerge.Integral()
ttData_fraction.append(frac_ttData_fail)
ttData_error.append(datafailMerge.GetBinError(1))
datapassMerge = datapassTopE + datapassTopMu
passdata = datapassMerge.Integral()
datapassMerge.Rebin(14)
datapassMerge.Sumw2()
datapassMerge.Divide(totaldataMerge)
frac_ttData_pass = datapassMerge.Integral()
ttData_fraction.append(frac_ttData_pass)
ttData_error.append(datapassMerge.GetBinError(1))
ttMC_fraction = arr.array('d')
ttMC_error = arr.array('d')
totalMCmerge = totalMCtopE + totalMCtopMu
totalMCmerge.Rebin(14)
MCfailTopE = failMCsubtractTopE.Clone("MCfailTopE")
MCfailTopMu = failMCsubtractTopMu.Clone("MCfailTopMu")
MCfailMerge = MCfailTopE + MCfailTopMu
MCfailMerge.Rebin(14)
MCfailMerge.Sumw2()
MCfailMerge.Divide(totalMCmerge)
frac_Failed_fin = MCfailMerge.Integral()
ttMC_fraction.append(frac_Failed_fin)
ttMC_error.append(MCfailMerge.GetBinError(1))
MCpassTopE = passMCsubtractTopE.Clone("MCpassTopE")
MCpassTopMu = passMCsubtractTopMu.Clone("MCpassTopMu")
MCpassMerge = MCpassTopE + MCpassTopMu
MCpassMerge.Rebin(14)
MCpassMerge.Sumw2()
MCpassMerge.Divide(totalMCmerge)
frac_Passed_fin = MCpassMerge.Integral()
ttMC_fraction.append(frac_Passed_fin)
ttMC_error.append(MCpassMerge.GetBinError(1))
#print "\nttMC_fraction:", ttMC_fraction
#print "ttMC_error:", ttMC_error
sfMerge = datapassMerge.Clone("sfMerge")
sfMerge.Sumw2()
sfMerge.Divide(MCpassMerge)
stacklist = []
stacklist.append(dibosonMerge)
stacklist.append(stMerge)
stacklist.append(wjetsMerge)
stacklist.append(unmatchMerge)
stacklist.append(WmatchMerge)
stacklist.append(topmatchMerge)
print "merge histograms: done"
print " "
print "draw histograms"
print " "
#----------------------- canvas 1 -----------------------#
c1 = TCanvas("c1", "", 800, 900) #width-height
c1.SetTopMargin(0.4)
c1.SetBottomMargin(0.05)
c1.SetRightMargin(0.1)
c1.SetLeftMargin(0.15)
gStyle.SetOptStat(0)
binvalues1 = []
for i in range(14):
binvalue = unsubtractedDataMerge.GetBinContent(i)
binvalues1.append(binvalue)
totalmax = max(binvalues1) + 100
padMain = TPad("padMain", "", 0.0, 0.25, 1.0, 0.97)
padMain.SetTopMargin(0.4)
padMain.SetRightMargin(0.05)
padMain.SetLeftMargin(0.17)
padMain.SetBottomMargin(0.03)
padMain.SetTopMargin(0.1)
padRatio = TPad("padRatio", "", 0.0, 0.0, 1.0, 0.25)
padRatio.SetRightMargin(0.05)
padRatio.SetLeftMargin(0.17)
padRatio.SetTopMargin(0.05)
padRatio.SetBottomMargin(0.3)
padMain.Draw()
padRatio.Draw()
padMain.cd()
gPad.GetUymax()
leg1 = myLegend(coordinate=[0.45, 0.57, 0.65, 0.6])
unsubtractedDataMerge.SetMaximum(totalmax)
unsubtractedDataMerge.SetLineColor(1)
unsubtractedDataMerge.SetMarkerStyle(20)
unsubtractedDataMerge.SetMarkerSize(1.5)
unsubtractedDataMerge.GetXaxis().SetLabelSize(0)
unsubtractedDataMerge.GetXaxis().SetTitleSize(0)
unsubtractedDataMerge.GetXaxis().SetTitle("DDB")
unsubtractedDataMerge.GetYaxis().SetTitle("Events/Bin")
leg1.AddEntry(unsubtractedDataMerge, "Data", "lep")
unsubtractedDataMerge.Draw("e1")
stackhisto = myStack(colorlist_=[627, 800, 854, 813, 822, 821],
backgroundlist_=stacklist,
legendname_=[
"Diboson", "Single Top", "W+Jets",
"Top (unmtch.)", "Top (W-mtch.)", "Top (mtch.)"
])
stackhisto[0].Draw("histsame")
unsubtractedDataMerge.Draw("e1same")
stackhisto[1].Draw()
leg1.Draw()
lt = TLatex()
lt.DrawLatexNDC(0.23, 0.85,
"#scale[0.8]{CMS} #scale[0.65]{#bf{#it{Internal}}}")
if ana_ == "Inclusive":
lt.DrawLatexNDC(0.17, 0.92, "#scale[0.7]{#bf{" + ana_ + "}}")
if ana_ == "PT-200-350" or ana_ == "PT-350-500" or ana_ == "PT-500-2000":
words = ana_.split("-")
if words[2] == "2000":
lt.DrawLatexNDC(0.17, 0.92,
"#scale[0.7]{#bf{p_{T} " + words[1] + "-Inf GeV}}")
else:
lt.DrawLatexNDC(
0.17, 0.92, "#scale[0.7]{#bf{p_{T} " + words[1] + "-" +
words[2] + " GeV}}")
else:
words = ana_.split("-")
lt.DrawLatexNDC(
0.17, 0.92, "#scale[0.7]{#bf{" + words[0] + " " + words[1] + "-" +
words[2] + " GeV}}")
lt.DrawLatexNDC(0.23, 0.8, "#scale[0.7]{#bf{t#bar{t} CR (e+#mu)}}")
lt.DrawLatexNDC(0.23, 0.75, "#scale[0.5]{#bf{2-prong (bq) enriched}}")
if year_ == 2017:
lt.DrawLatexNDC(0.71, 0.92, "#scale[0.7]{#bf{41.5 fb^{-1} (13 TeV)}}")
if year_ == 2018:
lt.DrawLatexNDC(0.71, 0.92,
"#scale[0.7]{#bf{58.827 fb^{-1} (13 TeV)}}")
padRatio.cd()
gPad.GetUymax()
h_totalBkg = topmatchMerge.Clone("h_totalBkg")
h_totalBkg = h_totalBkg + WmatchMerge + unmatchMerge + wjetsMerge + dibosonMerge
ratio = dataPredRatio(data_=unsubtractedDataMerge, totalBkg_=h_totalBkg)
ratio.SetLineColor(1)
ratio.SetLineWidth(3)
ratio.SetMarkerSize(1.5)
ratio.GetXaxis().SetLabelSize(0.13)
ratio.GetXaxis().SetTitleOffset(1)
ratio.GetXaxis().SetTitleSize(0.13)
ratio.GetXaxis().SetTickLength(0.1)
ratio.GetYaxis().SetLabelSize(0.12)
ratio.GetYaxis().SetTitleOffset(0.5)
ratio.GetYaxis().SetTitleSize(0.13)
ratio.GetYaxis().SetNdivisions(405)
ratio.GetYaxis().SetTitle("#frac{Data-Pred}{Pred}")
ratio.GetXaxis().SetTitle("DDB")
ratio.Draw("e1")
c1.SaveAs(dir + "Merge_all.pdf") #
#----------------------- canvas 2 -----------------------#
c2 = myCanvas(c="c2")
gPad.GetUymax()
leg2 = myLegend()
binvalues2 = []
for i in range(14):
binvalue = subtractedDataMerge.GetBinContent(i)
binvalues2.append(binvalue)
ttmax = max(binvalues2) + 50
failMCsubtractMerge.SetMaximum(ttmax)
failMCsubtractMerge.SetFillColor(821)
failMCsubtractMerge.SetLineColor(821) #922
failMCsubtractMerge.GetXaxis().SetTitle("DDB")
failMCsubtractMerge.GetYaxis().SetTitle("Events/Bin")
leg2.AddEntry(failMCsubtractMerge, "t#bar{t}", "f")
passMCsubtractMerge.SetFillColor(622)
passMCsubtractMerge.SetLineColor(622)
#passMCsubtractMerge.GetXaxis().SetTitle("DDB")
#passMCsubtractMerge.GetYaxis().SetTitle("Events/Bin")
leg2.AddEntry(passMCsubtractMerge, "t#bar{t} mistag", "f")
subtractedDataMerge.SetLineColor(1)
subtractedDataMerge.SetMarkerStyle(20)
subtractedDataMerge.SetMarkerSize(1.5)
#subtractedDataMerge.GetXaxis().SetTitle("DDB")
#subtractedDataMerge.GetYaxis().SetTitle("Events/Bin")
leg2.AddEntry(subtractedDataMerge, "Subtracted Data", "lep")
failMCsubtractMerge.Draw("hist")
passMCsubtractMerge.Draw("histsame")
subtractedDataMerge.Draw("e1same")
leg2.Draw()
lt2 = TLatex()
if ana_ == "Inclusive":
lt2.DrawLatexNDC(0.17, 0.92, "#scale[0.7]{#bf{" + ana_ + "}}")
if ana_ == "PT-200-350" or ana_ == "PT-350-500" or ana_ == "PT-500-2000":
words = ana_.split("-")
if words[2] == "2000":
lt2.DrawLatexNDC(
0.17, 0.92, "#scale[0.7]{#bf{p_{T} " + words[1] + "-Inf GeV}}")
else:
lt2.DrawLatexNDC(
0.17, 0.92, "#scale[0.7]{#bf{p_{T} " + words[1] + "-" +
words[2] + " GeV}}")
else:
words = ana_.split("-")
lt2.DrawLatexNDC(
0.17, 0.92, "#scale[0.7]{#bf{" + words[0] + " " + words[1] + "-" +
words[2] + " GeV}}")
lt2.DrawLatexNDC(0.23, 0.85,
"#scale[0.8]{CMS} #scale[0.65]{#bf{#it{Internal}}}")
lt2.DrawLatexNDC(0.23, 0.8, "#scale[0.7]{#bf{t#bar{t} CR (e+#mu)}}")
lt2.DrawLatexNDC(0.23, 0.75, "#scale[0.5]{#bf{2-prong (bq) enriched}}")
if year_ == 2017:
lt2.DrawLatexNDC(0.71, 0.92, "#scale[0.7]{#bf{41.5 fb^{-1} (13 TeV)}}")
if year_ == 2018:
lt2.DrawLatexNDC(0.71, 0.92,
"#scale[0.7]{#bf{58.827 fb^{-1} (13 TeV)}}")
c2.SaveAs(dir + "Merged_subtrac.pdf") #
#----------------------- canvas 3 -----------------------#
c3 = myCanvas(c="c3", size=[700, 900])
pad1 = TPad("pad1", "", 0.01, 0.25, 0.93, 1.0)
pad1.SetTopMargin(0.1)
pad1.SetRightMargin(0.05)
pad1.SetLeftMargin(0.17)
pad1.SetBottomMargin(0.05)
pad2 = TPad("pad2", "", 0.0, 0.0, 0.375, 0.24)
pad2.SetTopMargin(0.0)
pad2.SetRightMargin(0.0)
pad2.SetLeftMargin(0.0)
pad2.SetBottomMargin(0.0)
pad3 = TPad("pad3", "", 0.38, 0.025, 0.94, 0.25)
pad2.SetTopMargin(0.05)
pad2.SetRightMargin(0.0)
pad2.SetLeftMargin(0.45)
pad2.SetBottomMargin(0.2)
pad1.Draw()
pad2.Draw()
pad3.Draw()
#* Pad 1 *#
pad1.cd()
leg3 = myLegend(coordinate=[0.65, 0.4, 0.75, 0.5])
xaxisname = arr.array('d', [1, 2])
zero1 = np.zeros(2)
gPad.Modified()
gPad.SetGridy()
gr1 = TGraphErrors(2, xaxisname, ttMC_fraction, zero1, ttMC_error)
gr1.SetTitle("t#bar{t}")
gr1.SetLineColor(870)
gr1.SetLineWidth(3)
gr1.SetMarkerStyle(20)
gr1.SetMarkerColor(870)
leg3.AddEntry(gr1, "t#bar{t}", "lep")
gr2 = TGraphErrors(2, xaxisname, ttData_fraction, zero1, ttData_error)
gr2.SetTitle("t#bar{t} Data")
gr2.SetLineColor(1)
gr2.SetLineWidth(2)
gr2.SetMarkerStyle(20)
gr2.SetMarkerColor(1)
leg3.AddEntry(gr2, "t#bar{t} Data", "lep")
mg = TMultiGraph("mg", "")
mg.Add(gr1)
mg.Add(gr2)
mg.GetHistogram().SetMaximum(1.5)
mg.GetHistogram().SetMinimum(0)
mg.GetYaxis().SetTitle("Fraction")
mg.GetXaxis().SetLimits(0, 3)
mg.GetXaxis().SetTickLength(0.03)
mg.GetXaxis().SetNdivisions(103)
mg.GetXaxis().ChangeLabel(2, -1, -1, -1, -1, -1, "Fail")
mg.GetXaxis().ChangeLabel(1, -1, 0)
mg.GetXaxis().ChangeLabel(-1, -1, 0)
mg.GetXaxis().ChangeLabel(3, -1, -1, -1, -1, -1, "Pass")
mg.Draw("AP")
leg3.Draw()
lt3 = TLatex()
if ana_ == "Inclusive":
lt3.DrawLatexNDC(0.17, 0.92, "#scale[0.7]{#bf{" + ana_ + "}}")
if ana_ == "PT-200-350" or ana_ == "PT-350-500" or ana_ == "PT-500-2000":
words = ana_.split("-")
if words[2] == "2000":
lt3.DrawLatexNDC(
0.17, 0.92, "#scale[0.7]{#bf{p_{T} " + words[1] + "-Inf GeV}}")
else:
lt3.DrawLatexNDC(
0.17, 0.92, "#scale[0.7]{#bf{p_{T} " + words[1] + "-" +
words[2] + " GeV}}")
else:
words = ana_.split("-")
lt3.DrawLatexNDC(
0.17, 0.92, "#scale[0.7]{#bf{" + words[0] + " " + words[1] + "-" +
words[2] + " GeV}}")
lt3.DrawLatexNDC(0.19, 0.855,
"#scale[0.8]{CMS} #scale[0.65]{#bf{#it{Internal}}}")
lt3.DrawLatexNDC(0.19, 0.805, "#scale[0.7]{#bf{t#bar{t} CR (e+#mu)}}")
lt3.DrawLatexNDC(0.19, 0.755, "#scale[0.5]{#bf{2-prong (bq) enriched}}")
if year_ == 2017:
lt3.DrawLatexNDC(0.71, 0.92, "#scale[0.7]{#bf{41.5 fb^{-1} (13 TeV)}}")
if year_ == 2018:
lt3.DrawLatexNDC(0.71, 0.92,
"#scale[0.7]{#bf{58.827 fb^{-1} (13 TeV)}}")
lt3.Draw()
pad1.Update()
#* Pad 2 *#
pad2.cd()
MCpassMerge.SetFillColor(0)
MCpassMerge.SetLineColor(870)
MCpassMerge.SetLineWidth(3)
MCpassMerge.SetMarkerColor(870)
MCpassMerge.SetMarkerStyle(20)
MCpassMerge.GetYaxis().SetTitle("Fraction")
MCpassMerge.GetYaxis().SetTitleSize(0.09)
MCpassMerge.GetYaxis().SetLabelSize(0.1)
MCpassMerge.GetYaxis().SetNdivisions(404)
MCpassMerge.SetMaximum(0.3)
MCpassMerge.SetMinimum(0.0)
MCpassMerge.GetXaxis().SetTitle("")
MCpassMerge.GetXaxis().SetLabelOffset(0.02)
MCpassMerge.GetXaxis().SetLabelSize(0.09)
MCpassMerge.GetXaxis().SetNdivisions(104)
MCpassMerge.GetXaxis().ChangeLabel(2, -1, -1, -1, -1, -1, "Pass")
MCpassMerge.GetXaxis().ChangeLabel(1, -1, 0)
MCpassMerge.GetXaxis().ChangeLabel(-1, -1, 0)
datapassMerge.SetFillColor(0)
datapassMerge.SetLineColor(1)
datapassMerge.SetLineWidth(2)
datapassMerge.SetMarkerColor(1)
datapassMerge.SetMarkerStyle(20)
MCpassMerge.Draw("e1")
datapassMerge.Draw("e1histsame")
#* Pad 3 *#
pad3.cd()
SF = sfMerge.Integral()
print "******"
print "mistag SF:", SF
SFfinal = round(SF, 3)
SFtext = "SF = " + str(SFfinal)
mistagSFmax = SF + 0.2
mistagSFmin = SF - 0.2
sfMerge.SetLineColor(797)
sfMerge.SetMarkerColor(797)
sfMerge.SetLineWidth(3)
sfMerge.SetMaximum(mistagSFmax)
sfMerge.SetMinimum(mistagSFmin)
sfMerge.GetXaxis().SetTitle(" ")
sfMerge.GetXaxis().SetLabelOffset(999)
sfMerge.GetXaxis().SetLabelSize(0)
sfMerge.GetXaxis().SetTickLength(0)
sfMerge.GetYaxis().SetLabelSize(0.1)
sfMerge.GetYaxis().SetNdivisions(404)
sfMerge.GetYaxis().SetTitle(" ")
sfMerge.Draw("e1hist")
pt = TPaveText(0.21, 0.72, 0.31, 0.8, "brNDC")
pt.SetBorderSize(0)
pt.SetTextAlign(12)
pt.SetFillStyle(0)
pt.SetTextFont(42)
pt.SetTextSize(0.1)
pt.AddText(SFtext)
pt.Draw()
c3.SaveAs(dir + "Merge_SF.pdf") #
passedBfrSubtactDataMerge = (passedTopEdataBfrSubtract +
passedTopMudataBfrSubtract).Integral()
failedBfrSubtractDataMerge = (failedTopEdataBfrSubtract +
failedTopMudataBfrSubtract).Integral()
passbackground = (wjetsTopEpassed + wjetsTopMupassed + dibosonTopEpassed +
dibosonTopMupassed + stTopEpassed +
stTopMupassed).Integral()
failbackground = (wjetsTopEfailed + wjetsTopMufailed + dibosonTopEfailed +
dibosonTopMufailed + stTopEfailed +
stTopMufailed).Integral()
totalbackground = (wjetsMerge + dibosonMerge + stMerge).Integral()
#get the statistical uncertainty#
dx = ttData_error[1]
print "data efficiency error", dx
dy = ttMC_error[1]
print "MC efficiency error", dy
x = datapassMerge.Integral()
y = MCpassMerge.Integral()
statUnc = TMath.Sqrt(((dx**2) / (y**2)) + ((x**2) * (dy**2) / (y**4)))
#print "statistical Uncertainty in Top (e+muon) CR", statUnc
#print " "
print "relative statistical Uncertainty in Top (e+muon) CR", statUnc / SF * 100, " %"
print " "
print "DDB Mistag SF and stat: ", round(SF, 3), " +- ", round(statUnc,
3), " (stat)"
#print "theoretical statistical uncertainty of data efficiency", TMath.Sqrt((x*(1-x))/(subtractedDataMerge.Integral()))
#print "theoretical statistical uncertainty of MC efficiency", TMath.Sqrt((y*(1-y))/(totalMCmerge.Integral()))
#print " "
header = ["Process", "Number of Events", "Top (e+muon)"]
row1 = [
" ", "DDB mistag SF",
str(SFfinal) + " +- " + str(round(statUnc, 3)) + " (stat)"
]
row2 = ["tt MC", "Pass (not normalized)", ""]
row3 = [
" ", "Pass (normalized)",
str(round(passMCsubtractMerge.Integral(), 2))
]
row4 = [" ", "Fail (not normalized)", ""]
row5 = [
" ", "Fail (normalized)",
str(round(failMCsubtractMerge.Integral(), 2))
]
row6 = [" ", "Total (not normalized)", ""]
row7 = [" ", "Total (normalized)", str(round(totalMCmerge.Integral(), 2))]
inforMC = [row2, row3, row4, row5, row6, row7]
row8 = [
"tt DATA", "Pass (before subtraction)",
str(round(passedBfrSubtactDataMerge, 2))
]
row9 = [" ", "Pass (after subtraction)", str(round(passdata, 2))]
row10 = [
" ", "Fail (before subtraction)",
str(round(failedBfrSubtractDataMerge, 2))
]
row11 = [" ", "Fail (after subtraction)", str(round(faildata, 2))]
row12 = [
" ", "Total (before subtraction)",
str(round(unsubtractedDataMerge.Integral(), 2))
]
row13 = [" ", "Total (after subtraction)", str(round(totaldata, 2))]
inforDATA = [row8, row9, row10, row11, row12, row13]
row14 = ["Background", "Pass (normalized)", str(round(passbackground, 2))]
row15 = [" ", "Fail (normalized)", str(round(failbackground, 2))]
row16 = [" ", "Total (normalized)", str(round(totalbackground, 2))]
inforBKG = [row14, row15, row16]
DDB_mistagSF.makeTable(header, row1, inforMC, inforDATA, inforBKG)
def drawLatex(x=0.5, y=0.50, text="text", size=0.035, font=132, color=1):
tl = TLatex(x, y, text)
tl.SetTextSize(size)
tl.SetTextFont(font)
tl.SetTextColor(color)
tl.DrawLatexNDC(x, y, text)
def scanKK():
'''Used to test the scan of one or more parameters'''
nChips = 19 # the magic number
# nChan = len(chans)
iP = 1
cd = CommonData()
cd.setupConnection()
sc1 = SensorConfig(cd)
### get list of chains to be updated
# chains = set([sc1.tms1mmX19chainSensors[sc1.tms1mmX19sensorInChain[c]][0] for c in chans])
chan = 5
# cd.inputVs = [3., 0., 3., 0., 0., 0.]
# cd.inputVs = [3., 0., 0.732, 1.68, 0., 0.]
# cd.inputVs = [0.75, 2.15, 0.7, 0., 0., 0.]
show = True
g1 = None
if show:
g1 = TGraphErrors()
xj = open('scan_KK_6.ttl','w')
### point insert scan
ipar = 2
pts = [(0.,None),(3.,None)]
while True:
print pts
pt, xy, r1, k = insertPoint(pts)
print '-'*30
print pt,xy,r1, k
print '-'*30
if xy<3 and (r1<0.01 or xy/r1<0.1 or k<0.007): break
cd.inputVs[ipar] = pt
cd.updatePars(chan, None, False)
sc1.update_sensor(chan)
time.sleep(3)
cd.fetch()
m,v = getMeanVar(cd.adcData[chan])
print ' '.join([str(x) for x in [chan, m, v]+cd.inputVs])
xj.write(' '.join([str(x) for x in [chan, m, v]+cd.inputVs])+'\n')
if g1:
n1 = g1.GetN()
g1.SetPoint(n1, pt, m)
g1.SetPointError(n1, 0, v)
if xy == 999:
pts[r1] = (pt,m)
else:
pts.append((pt,m))
### simple scan
# while cd.inputVs[0]>0.001:
# cd.updatePars(chan, None, False)
# sc1.update_sensor(chan)
# time.sleep(2)
# cd.fetch()
#
# m,v = getMeanVar(cd.adcData[chan])
# print ' '.join([str(x) for x in [chan, m, v]+cd.inputVs])
# xj.write(' '.join([str(x) for x in [chan, m, v]+cd.inputVs])+'\n')
#
# if g1:
# g1.SetPoint(g1.GetN(), cd.inputVs[0], m)
# cd.inputVs[0] *= 2./3
if g1:
g1.SetMarkerStyle(4)
g1.SetMarkerColor(2)
g1.SetLineColor(2)
g1.Draw("AP")
h1 = g1.GetHistogram()
h1.GetXaxis().SetTitle(cd.voltsNames[ipar]+' [V]')
h1.GetYaxis().SetTitle("V_{out} [V]")
lt = TLatex()
lt.DrawLatexNDC(0.2,0.92,"Chip %d"%chan)
waitRootCmdX()
class encChecker:
def __init__(self):
self.dataFiles = None
self.bins = (50, 0, 1)
self.Info = None
self.lt = TLatex()
def showENC(self):
tree1 = TTree()
header = 'idX/i:vL/F:vH:A:D/i:R:W'
first = True
for f in self.dataFiles:
if first: tree1.ReadFile(f, header)
else: tree1.ReadFile(f)
p1 = TProfile('p1', 'p1;#DeltaU [V];Prob', self.bins[0],
tree1.GetMinimum('vH-vL') * 0.8,
tree1.GetMaximum('vH-vL') * 1.2)
tree1.Draw("D:(vH-vL)>>p1", "", "profE")
### change it to tgraph
g1 = TGraphErrors()
for i in range(p1.GetNbinsX() + 2):
N = p1.GetBinEntries(i)
if N > 0:
print i, N, p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(
i), p1.GetBinError(i)
n = g1.GetN()
g1.SetPoint(n,
p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(i))
g1.SetPointError(n, 0, p1.GetBinError(i))
p1.Draw("axis")
g1.Draw('Psame')
fun1 = TF1('fun1', '0.5*(1+TMath::Erf((x-[0])/(TMath::Sqrt(2)*[1])))',
0.05, 0.3)
fun1.SetParameter(0, 0.155)
fun1.SetParameter(1, 0.005)
g1.Fit(fun1)
fun1a = g1.GetFunction('fun1')
fun1a.SetLineColor(2)
v0 = fun1a.GetParameter(0)
e0 = fun1a.GetParError(0)
v1 = fun1a.GetParameter(1)
e1 = fun1a.GetParError(1)
print v0, v1
fUnit = 1000.
self.lt.DrawLatexNDC(
0.185, 0.89,
'#mu = {0:.1f} #pm {1:.1f} mV'.format(v0 * fUnit, e0 * fUnit))
self.lt.DrawLatexNDC(
0.185, 0.84,
'#sigma = {0:.1f} #pm {1:.1f} mV'.format(v1 * fUnit, e1 * fUnit))
if self.Info:
self.lt.DrawLatexNDC(0.185, 0.6, self.Info)
print 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1)
fun2 = TF1('gaus1', 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1))
fun2.SetLineColor(4)
fun2.SetLineStyle(2)
fun2.Draw('same')
lg = TLegend(0.7, 0.4, 0.95, 0.5)
lg.SetFillStyle(0)
lg.AddEntry(p1, 'Measurement', 'p')
lg.AddEntry(fun1a, 'Fit', 'l')
lg.AddEntry(fun2, 'Gaus', 'l')
lg.Draw()
waitRootCmdX()
class SigInfo:
def __init__(self, data=None):
self.resp = [] # [(t0, dT, tM, A),]
self.lt = TLatex()
self.sTag = sTag
self.sDir = sDir
self.autoSave = sDirectly
if data:
self.extract(data)
else:
self.bkgMu = None
self.bkgVar = None
self.quality = None
def show(self, data, info=None, info2=None):
x = [0.2*i for i in range(len(data))]
g0 = TGraph(len(data), array('d',x), array('d',data))
g0.Draw("AP")
g0.GetHistogram().GetYaxis().SetTitle("V_{out} [V]")
g0.GetHistogram().GetXaxis().SetTitle("t [#mus]")
# g0.GetHistogram().GetYaxis().SetRangeUser(0.9, 1.4)
if info: self.lt.DrawLatexNDC(0.2,0.8,info)
if info2: self.lt.DrawLatexNDC(0.2,0.93,info2)
global plot_count
plot_count += 1
waitRootCmdX(self.sDir+self.sTag+str(plot_count), self.autoSave)
def showMore(self, data, info=None, info2=None):
x = [0.2*i for i in range(len(data))]
g0 = TGraph(len(data), array('d',x), array('d',data))
g0.Draw("AP")
g0.GetHistogram().GetYaxis().SetTitle("V_{out} [V]")
g0.GetHistogram().GetXaxis().SetTitle("t [#mus]")
# g0.GetHistogram().GetYaxis().SetRangeUser(0.9, 1.4)
if info: self.lt.DrawLatexNDC(0.2,0.8,info)
if info2: self.lt.DrawLatexNDC(0.2,0.93,info2)
### add info
n1=500; n2=2000; np=20
maxI, mx = max(enumerate(data[:-n2+np]), key=lambda p:p[1])
minI, mn = min(enumerate(data[:-n2+np]), key=lambda p:p[1])
dx = 0.5*(mx-mn)
dn1 = sum(data[maxI+n1:maxI+n1+np])/np
dn2 = sum(data[maxI+n2:maxI+n2+np])/np
## we want large dx, large #2 and small #3
# return (0.5*(mx+mn), dx, 1-(mx-dn1)/dx, 1-(mx-dn2)/dx, maxI-minI)
### A line for the mean
fun1 = TF1("fun1",str(0.5*(mx+mn)), 0, 99999999)
fun1.SetLineColor(3)
fun1.SetLineStyle(2)
fun1.Draw("same")
### Draw 2 circles for max and min
gr1 = TGraph()
gr1.SetPoint(0, maxI*0.2, mx)
gr1.SetMarkerColor(2)
gr1.SetMarkerStyle(24)
gr1.Draw("Psame")
gr2 = TGraph()
gr2.SetPoint(0, minI*0.2, mn)
gr2.SetMarkerColor(4)
gr2.SetMarkerStyle(24)
gr2.Draw("Psame")
### Add two arrows for the the n50 and n2000
gr3 = TGraph()
gr3.SetPoint(0, (maxI+n1)*0.2, dn1)
gr3.SetMarkerColor(2)
gr3.SetMarkerStyle(23)
gr3.Draw("Psame")
gr4 = TGraph()
gr4.SetPoint(0, (maxI+n2)*0.2, dn2)
gr4.SetMarkerColor(4)
gr4.SetMarkerStyle(23)
gr4.Draw("Psame")
global plot_count
plot_count += 1
waitRootCmdX(self.sDir+self.sTag+str(plot_count), self.autoSave)
def getQuickInfo(self, data, n1=500, n2=2000, np=20):
'''Get some useful infomation quickly'''
maxI, mx = max(enumerate(data[:-n2+np]), key=lambda p:p[1])
minI, mn = min(enumerate(data[:-n2+np]), key=lambda p:p[1])
dx = 0.5*(mx-mn)
dn1 = sum(data[maxI+n1:maxI+n1+np])/np
dn2 = sum(data[maxI+n2:maxI+n2+np])/np
# dn1 = max(data[maxI+n1:maxI+n1+np])
# dn2 = max(data[maxI+n2:maxI+n2+np])
# print mx, dn1, dn2, dx,
# avarged = [sum(data[50*t:min([50*t+50,len(data)])] for t in range(len(data)/50))]
## we want large dx, large #2 and small #3
return (0.5*(mx+mn), dx, 1-(mx-dn1)/dx, 1-(mx-dn2)/dx, maxI-minI)
def extract(self, data):
self.resp = []
nData = len(data)
muS = 0
var2S = 0
for i in range(nData):
muS += data[i]
var2S += data[i]*data[i]
### remove the outliers one by one
minMs = 3*3
data2 = data[:]
mu = muS/nData
var2 = var2S/nData - mu*mu
for i in range(nData):
## get max and idex
mI, ms = max(enumerate([(x and pow(x-mu,2)/var2) for x in data2]), key=lambda p:p[1])
data2[mI] = None
idata = nData-i-1
if isDebug: print mI, 'removed', idata, 'left. Max Zn=', ms
if ms<minMs: break
mV = data[mI]
var2S -= mV*mV
muS -= mV
mu = muS/idata
var2 = var2S/idata - mu*mu
self.bkgMu = mu
self.bkgVar = sqrt(var2)
### get fragments
iStart = None
nLow = 0
for i in range(nData):
if data2[i] is not None and iStart is not None:
if nLow == 0:
nLow += 1
if i+1<nData and data2[i+1] is None: continue ## require at least two consective None
if i+2<nData and data2[i+2] is None: continue ## require at least two consective None
if i-iStart > 10:
mI, mx = max(enumerate(data[iStart:i]), key=lambda p:abs(p[1]-mu))
self.resp.append((iStart, i-iStart, mI, mx-mu))
iStart = None
nLow = 0
elif iStart is None and data2[i] is None:
iStart = i
### remove low quality peaks
if len(self.resp) > 0:
self.quality = max([abs(x[3]) for x in self.resp])
# mx = max(self.resp, key=lambda p:abs(p[3]))
# self.resp = [x for x in self.resp if abs(x[3])>0.3*mx[3]]
# print mx
if isDebug:
print self.resp, self.quality
print mu, var2, len([x for x in data2 if x is not None])
x = [0.2*i for i in range(nData)]
g0 = TGraph(nData, array('d',x), array('d',data))
g0.Draw("AP")
g0.GetHistogram().GetYaxis().SetTitle("V_{out} [V]")
g0.GetHistogram().GetXaxis().SetTitle("t [#mus]")
g1 = TGraph(nData, array('d',[x[0]*0.2 for x in enumerate(data2) if x[1] is not None]), array('d',[x[1] for x in enumerate(data2) if x[1] is not None]))
g1.SetMarkerColor(2)
g1.Draw('Psame')
waitRootCmdX()
pt.SetTextSize(0.04)
for imult, iplot in enumerate(plotbinMB):
if not iplot:
continue
cfPrompt.cd(imult+1).DrawFrame(0, 0, 25, 1.05, \
";#it{p}_{T} (GeV/#it{c});#it{f}_{prompt} %s" % name)
grfPrompt = fileres_MB[imult].Get("gFcConservative")
grfPrompt.SetTitle(";#it{p}_{T} (GeV/#it{c});#it{f}_{prompt} %s" %
name)
grfPrompt.SetLineColor(colors[imult % len(colors)])
grfPrompt.SetMarkerColor(colors[imult % len(colors)])
grfPrompt.SetMarkerStyle(21)
grfPrompt.SetMarkerSize(0.5)
grfPrompt.Draw("ap")
pt.DrawLatexNDC(0.15, 0.15, "%.1f #leq %s < %.1f (MB)" % \
(binsmin[imult], latexbin2var, binsmax[imult]))
for imult, iplot in enumerate(plotbinHM):
if not iplot:
continue
cfPrompt.cd(imult+1).DrawFrame(0, 0, 25, 1.05, \
";#it{p}_{T} (GeV/#it{c});#it{f}_{prompt} %s" % name)
grfPromptHM = fileres_trig[imult].Get("gFcConservative")
grfPromptHM.SetTitle(";#it{p}_{T} (GeV/#it{c});#it{f}_{prompt} %s" %
name)
grfPromptHM.SetLineColor(colors[imult % len(colors)])
grfPromptHM.SetMarkerColor(colors[imult % len(colors)])
grfPromptHM.SetMarkerStyle(21)
grfPromptHM.SetMarkerSize(0.5)
grfPromptHM.Draw("ap")
pt.DrawLatexNDC(0.15, 0.15, "%.1f #leq %s < %.1f (HM)" % \
def showENC():
fname1 = '/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan1.dat'
tree1 = TTree()
tree1.ReadFile(
'/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan1.dat',
'idX/i:vL/F:vH:A:D/i:R:W')
tree1.ReadFile(
'/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan2_mod.dat'
)
tree1.Show(500)
p1 = TProfile('p1', 'p1;#DeltaU [V];Prob', 50, 0.12, 0.2)
tree1.Draw("D:(vH-vL)>>p1", "", "profE")
### change it to tgraph
g1 = TGraphErrors()
for i in range(p1.GetNbinsX() + 2):
N = p1.GetBinEntries(i)
if N > 0:
print i, N, p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(
i), p1.GetBinError(i)
n = g1.GetN()
g1.SetPoint(n, p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(i))
g1.SetPointError(n, 0, p1.GetBinError(i))
# g1.SetMarkerColor(3)
# g1.SetLineColor(3)
p1.Draw("axis")
g1.Draw('Psame')
fun1 = TF1('fun1', '0.5*(1+TMath::Erf((x-[0])/(TMath::Sqrt(2)*[1])))',
0.05, 0.3)
fun1.SetParameter(0, 0.155)
fun1.SetParameter(1, 0.005)
g1.Fit(fun1)
fun1a = g1.GetFunction('fun1')
# p1.Fit(fun1)
# fun1a = p1.GetFunction('fun1')
fun1a.SetLineColor(2)
# p1.Draw("Esame")
v0 = fun1a.GetParameter(0)
e0 = fun1a.GetParError(0)
v1 = fun1a.GetParameter(1)
e1 = fun1a.GetParError(1)
print v0, v1
fUnit = 1000.
lt = TLatex()
lt.DrawLatexNDC(
0.185, 0.89,
'#mu = {0:.1f} #pm {1:.1f} mV'.format(v0 * fUnit, e0 * fUnit))
lt.DrawLatexNDC(
0.185, 0.84,
'#sigma = {0:.1f} #pm {1:.1f} mV'.format(v1 * fUnit, e1 * fUnit))
print 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1)
fun2 = TF1('gaus1', 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1))
fun2.SetLineColor(4)
fun2.SetLineStyle(2)
fun2.Draw('same')
lg = TLegend(0.7, 0.4, 0.95, 0.5)
lg.SetFillStyle(0)
lg.AddEntry(p1, 'Measurement', 'p')
lg.AddEntry(fun1a, 'Fit', 'l')
lg.AddEntry(fun2, 'Gaus', 'l')
lg.Draw()
waitRootCmdX()
def DrawBazil(diagonal=175, doData=False, pname='limits'):
import numpy as np
# 'mstop','mlsp','sm2','sm1','central','sp1','sp2', 'data'
d = GetDic(pname + '.p', diagonal)
x = np.array(d['mstop'])
e = np.array(d['exp'])
y1max = np.array(d['s+1'])
y2max = np.array(d['s+2'])
y1min = np.array(d['s-1'])
y2min = np.array(d['s-2'])
# observed
if (doData): o = np.array(d['obs'])
else: o = np.array(d['exp'])
c1 = TCanvas("c1", "CL", 10, 10, 800, 600)
#c1.SetGrid();
ymax = 7.5
ymin = 0.3
if (diagonal == 'down' or diagonal == 'Down' or diagonal == 'DOWN'):
ymax = 3.1
ymin = 0.30
elif (diagonal == 'up' or diagonal == 'Up' or diagonal == 'UP'):
ymax = 2.5
ymin = 0.15
#c1.DrawFrame(min(x)-2,min(d['sp2']+d['sm2'])-0.2,max(x)+2,max(d['sp2']+d['sm2'])+0.2);
c1.DrawFrame(min(x), ymin, max(x), ymax)
n = len(e)
gr1min = TGraph(n, x, y1min)
gr1max = TGraph(n, x, y1max)
gr2min = TGraph(n, x, y2min)
gr2max = TGraph(n, x, y2max)
gro = TGraph(n, x, o)
gre = TGraph(n, x, e)
gh = TGraph(n, x, np.linspace(0.999, 1, n))
gr1shade = TGraph(2 * n)
gr2shade = TGraph(2 * n)
#color1shade = 3; color2shade = 5;
color1shade = kOrange
color2shade = kGreen + 1
for i in range(n):
gr1shade.SetPoint(i, x[i], y1max[i] * scalefact)
gr1shade.SetPoint(n + i, x[n - i - 1], y1min[n - i - 1] * scalefact)
gr2shade.SetPoint(i, x[i], y2max[i] * scalefact)
gr2shade.SetPoint(n + i, x[n - i - 1], y2min[n - i - 1] * scalefact)
gre.SetPoint(i, x[i], e[i] * scalefact)
gh.SetPoint(i, x[i], np.linspace(0.999, 1, n)[i] * scalefact)
gro.SetPoint(i, x[i], o[i] * scalefact if doData else e[i] * scalefact)
gr2shade.SetFillColor(color2shade)
gr2shade.Draw("f")
gr1shade.SetFillColor(color1shade)
gr1shade.Draw("f")
gh.SetLineWidth(2)
gh.SetMarkerStyle(0)
gh.SetLineColor(46)
gh.SetLineStyle(2)
gh.Draw("LP")
gro.SetLineWidth(2)
gro.SetMarkerStyle(20)
gro.SetMarkerSize(0.7)
gro.SetLineColor(1)
if doData: gro.Draw("LP")
gre.SetLineWidth(2)
gre.SetMarkerStyle(0)
gre.SetLineColor(1)
gre.SetLineStyle(6)
gre.Draw("LP")
gre.SetTitle("Expected")
gro.SetTitle("Observed")
gr1shade.SetTitle("Expected 1#sigma")
gr2shade.SetTitle("Expected 2#sigma")
leg = TLegend(.1, .65, .4, .9)
leg.AddEntry(gro)
leg.AddEntry(gre)
leg.AddEntry(gr1shade, '', 'f')
leg.AddEntry(gr2shade, '', 'f')
leg.SetFillColor(0)
leg.Draw("same")
gre.SetFillColor(0)
gro.SetFillColor(0)
gr1shade.SetLineColor(color1shade)
gr2shade.SetLineColor(color2shade)
tit = "m_{#tilde{t}_{1}} - m_{#tilde{#chi}_{1}^{0}} = "
dm = "0"
ymax = 4.1
ymin = 0.3
if (diagonal == 'down' or diagonal == 'Down' or diagonal == 'DOWN'):
tit += "182.5 GeV" #" + 7.5 GeV"
dm = "m7p5"
ymax = 2.2
ymin = 0.15
elif (diagonal == 'up' or diagonal == 'Up' or diagonal == 'UP'):
tit += "167.5 GeV" #" - 7.5 GeV"
dm = "7p5"
ymax = 3.3
ymin = 0.30
else:
tit += "175 GeV"
Title = TLatex()
Title.SetTextSize(0.060)
Title.DrawLatexNDC(.42, .84, tit)
Xaxis = TLatex()
Xaxis.SetTextFont(42)
Xaxis.DrawLatexNDC(0.8, 0.03, "m_{#tilde{t}_{1}} (GeV)")
Yaxis = TLatex()
Yaxis.SetTextFont(42)
Yaxis.SetTextAngle(90)
Yaxis.DrawLatexNDC(0.05, 0.15, "95% CL limit on signal strength")
textCMS = TLatex()
textCMS.SetTextSize(0.06)
textCMS.SetTextSizePixels(22)
textCMS.SetTextAlign(12)
textCMS.DrawLatexNDC(.12, .93, "CMS")
textLumi = TLatex()
textLumi.SetTextFont(42)
textLumi.SetTextSize(0.06)
textLumi.SetTextSizePixels(22)
textLumi.DrawLatexNDC(.58, .91, "%1.1f fb^{-1} (13 TeV)" % (lumi)) #35.9
name = "brazil_%i" % diagonal
name = "brazil_%s" % pname
for form in ['pdf', 'png']:
c1.Print(outputdir + name + '.%s' % form)
leg.AddEntry(h_MET, "Data", "lep")
#-------Draw Histogram in Full Canvas---------#
h_TopMatchFinal.Draw("hist")
h_WmatchFinal.Draw("histsame")
h_unmatchFinal.Draw("histsame")
#h_ttHadFinal.Draw("histsame")
#h_ttLepFinal.Draw("histsame")
h_sumWJetsFinal.Draw("histsame")
h_sumDiboson.Draw("histsame")
h_MET.Draw("e1same")
leg.Draw()
lt = TLatex()
lt.DrawLatexNDC(0.24, 0.85,
"#scale[0.8]{CMS} #scale[0.65]{#bf{#it{Internal}}}")
lt.DrawLatexNDC(0.24, 0.8, "#scale[0.7]{#bf{t#bar{t} CR (#mu)}}")
lt.DrawLatexNDC(0.24, 0.75, "#scale[0.5]{#bf{2-prong (bq) enriched}}")
lt.DrawLatexNDC(0.71, 0.92, "#scale[0.7]{#bf{41.5 fb^{-1} (13 TeV)}}")
padRatio.cd()
gPad.GetUymax()
ratio = dataPredRatio(data_=h_MET, totalBkg_=h_TopMatchFinal)
ratio.SetLineColor(1)
ratio.SetLineWidth(3)
ratio.SetMarkerSize(1.5)
ratio.GetXaxis().SetLabelSize(0.13)
ratio.GetXaxis().SetTitleOffset(1)
ratio.GetXaxis().SetTitleSize(0.13)
effGraph.SetTitle("")
effGraph.GetXaxis().SetTitle("X [mm]")
effGraph.GetXaxis().SetTitleSize(0.05)
effGraph.GetXaxis().SetTitleOffset(0.90)
effGraph.GetXaxis().SetLabelSize(0.03)
#effGraph.GetXaxis().SetRangeUser(23.0,25.0)
effGraph.GetYaxis().SetTitle("Mean #Delta t [ns]")
effGraph.GetYaxis().SetTitleSize(0.05)
effGraph.GetYaxis().SetTitleOffset(0.92)
effGraph.GetYaxis().SetLabelSize(0.03)
effGraph.GetYaxis().SetRangeUser(-2, 2)
title = TLatex()
title.SetTextSize(0.05)
#title.SetTextAlign(13);
title.DrawLatexNDC(.2, .93, "CACTUS Pixel " + pixelName + " Analog")
c.Update()
c.SaveAs("CACTUS_DeltaTMeanVsX" + outputLabel + ".gif")
##########################
#1D MPV Vs Y
##########################
yLeftBoundary = yMin
yRightBoundary = yMax
yBinWidth = 0.1
yNBins = int((yRightBoundary - yLeftBoundary) / yBinWidth)
tmpHist2D = TH2F("tmpHist2D", ";Y [mm];Amplitude [mV]; NEvt", yNBins,
yLeftBoundary, yRightBoundary, 100, -10, 10)
tree.Draw(
def DrawOverlap(fileVec,
histVec,
titleVec,
legendtext,
pngname,
logstatus=[0, 0],
xRange=[-99999, 99999, 1],
text_="",
x_=0.5,
y_=0.5,
legendloc=[0.53, 0.13, 0.93, 0.39]):
gStyle.SetOptTitle(0)
gStyle.SetOptStat(0)
#gStyle.SetTitleOffset(1.1,"Y");
#gStyle.SetTitleOffset(0.9,"X");
gStyle.SetLineWidth(3)
gStyle.SetFrameLineWidth(3)
i = 0
histList_ = []
histList = []
histList1 = []
maximum = []
## Legend
legpos = legendloc
leg = TLegend(legpos[0], legpos[1], legpos[2], legpos[3])
leg.SetBorderSize(0)
leg.SetNColumns(2)
leg.SetLineColor(1)
leg.SetLineStyle(1)
leg.SetLineWidth(1)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetTextFont(42)
leg.SetTextSize(0.049)
from PlotTemplates import myCanvas1D
c = myCanvas1D()
c.SetLogy(logstatus[1])
c.SetLogx(logstatus[0])
#c.SetBottomMargin(0.15)
#c.SetLeftMargin(0.15)
#c1_2 = TPad("c1_2","newpad",0.04,0.13,1,0.994)
#c1_2.Draw()
print("you have provided " + str(len(fileVec)) + " files and " +
str(len(histVec)) + " histograms to make a overlapping plot")
print "opening rootfiles"
c.cd()
#c1_2.SetBottomMargin(0.013)
#c1_2.SetLogy(logstatus[1])
#c1_2.SetLogx(logstatus[0])
#c1_2.cd()
ii = 0
inputfile = {}
print str(fileVec[(len(fileVec) - 1)])
for ifile_ in range(len(fileVec)):
print("opening file " + fileVec[ifile_])
inputfile[ifile_] = TFile(fileVec[ifile_])
print "fetching histograms"
for ihisto_ in range(len(histVec)):
print("printing histo " + str(histVec[ihisto_]))
histo = inputfile[ifile_].Get(histVec[ihisto_])
#status_ = type(histo) is TGraphAsymmErrors
histList.append(histo)
# for ratio plot as they should nt be normalize
histList1.append(histo)
#print histList[ii].Integral()
#histList[ii].Rebin(xRange[2])
#histList[ii].Scale(1.0/histList[ii].Integral())
maximum.append(histList[ii].GetMaximum())
maximum.sort()
ii = ii + 1
print histList
for ih in range(len(histList)):
tt = type(histList[ih])
if logstatus[1] is 1:
histList[ih].SetMaximum(100) #1.4 for log
histList[ih].SetMinimum(0.00001) #1.4 for log
if logstatus[1] is 0:
histList[ih].SetMaximum(1.4) #1.4 for log
histList[ih].SetMinimum(0.001) #1.4 for log
# print "graph_status =" ,(tt is TGraphAsymmErrors)
# print "hist status =", (tt is TH1D) or (tt is TH1F)
if ih == 0:
if tt is TGraphAsymmErrors:
histList[ih].Draw("APL")
if (tt is TH1D) or (tt is TH1F) or (tt is TH1) or (tt is TH1I):
histList[ih].Draw("hist")
if ih > 0:
#histList[ih].SetLineWidth(2)
if tt is TGraphAsymmErrors:
histList[ih].Draw("PL same")
if (tt is TH1D) or (tt is TH1F) or (tt is TH1) or (tt is TH1I):
histList[ih].Draw("hist same")
if tt is TGraphAsymmErrors:
histList[ih].SetMaximum(10)
histList[ih].SetMarkerColor(colors[ih])
histList[ih].SetLineColor(colors[ih])
histList[ih].SetMarkerStyle(markerStyle[ih])
histList[ih].SetMarkerSize(1)
leg.AddEntry(histList[ih], legendtext[ih], "PL")
if (tt is TH1D) or (tt is TH1F) or (tt is TH1) or (tt is TH1I):
histList[ih].SetLineStyle(linestyle[ih])
histList[ih].SetLineColor(colors[ih])
histList[ih].SetLineWidth(3)
leg.AddEntry(histList[ih], legendtext[ih], "L")
histList[ih].GetYaxis().SetTitle(titleVec[1])
histList[ih].GetYaxis().SetTitleSize(0.052)
histList[ih].GetYaxis().SetTitleOffset(1.08)
histList[ih].GetYaxis().SetLabelSize(.052)
histList[ih].GetXaxis().SetRangeUser(xRange[0], xRange[1])
histList[ih].GetXaxis().SetLabelSize(0.052)
histList[ih].GetXaxis().SetTitle(titleVec[0])
histList[ih].GetXaxis().SetLabelSize(0.052)
histList[ih].GetXaxis().SetTitleSize(0.052)
histList[ih].GetXaxis().SetTitleOffset(1.14)
#histList[ih].GetXaxis().SetTickLength(0.07)
histList[ih].GetXaxis().SetNdivisions(508)
from PlotTemplates import SetCMSAxis
histList[ih] = SetCMSAxis(histList[ih], 1.0, 1.15)
#histList[ih].GetXaxis().SetMoreLogLabels();
#histList[ih].GetXaxis().SetNoExponent();
#histList[ih].GetTGaxis().SetMaxDigits(3);
i = i + 1
'''
pt = TPaveText(0.0877181,0.9,0.9580537,0.96,"brNDC")
pt.SetBorderSize(0)
pt.SetTextAlign(12)
pt.SetFillStyle(0)
pt.SetTextFont(22)
pt.SetTextSize(0.046)
text = pt.AddText(0.2,0.5,"CMS Internal")
'''
from PlotTemplates import drawenergy1D
pt_ = drawenergy1D(False)
for ipt in pt_:
ipt.Draw()
'''
if len(text_) >0:
ltx = TLatex()
ltx.SetTextFont(42)
ltx.SetTextSize(0.049)
#text_f = "#font[42]{Phase Scan}"
ltx.DrawTextNDC(x_,y_,text_)
'''
from PlotTemplates import ExtraText
text_ex = ExtraText(text_, x_, y_)
text_ex.Draw()
ExtraText("ECAL Endcaps", 0.4, 0.85)
ltx_ = TLatex()
ltx_.SetTextFont(42)
ltx_.SetTextSize(0.049)
ltx_.DrawLatexNDC(0.32, 0.81, "L1 e-#gamma object p_{T} > 10 GeV")
#text_ex.Draw()
#pt = TPaveText(0.0877181,0.9,0.9580537,0.96,"brNDC")
#text = pt.AddText(0.65,0.5,"Phase Scan data #sqrt{s} = 13 TeV (2018)")
#pt.Draw()
# t2a = TPaveText(0.0877181,0.81,0.9580537,0.89,"brNDC")
# t2a.SetBorderSize(0)
# t2a.SetFillStyle(0)
# t2a.SetTextSize(0.040)
# t2a.SetTextAlign(12)
# t2a.SetTextFont(62)
# histolabel1= str(fileVec[(len(fileVec)-1)])
# text1 = t2a.AddText(0.06,0.5,"CMS Internal")
# t2a.Draw()
leg.SetHeader("Matched TP energy")
leg.Draw()
#
# c.cd()
outputdirname = './'
histname = outputdirname + pngname
c.SaveAs(histname + '.png')
c.SaveAs(histname + '.pdf')
def plotCorrelation(channel,var,DM,year,*parameters,**kwargs):
"""Calculate and plot correlation between parameters."""
if DM=='DM0' and 'm_2' in var: return
print green("\n>>> plotCorrelation %s, %s"%(DM, var))
if len(parameters)==1 and isinstance(parameters[0],list): parameters = parameters[0]
parameters = [p.replace('$CAT',DM).replace('$CHANNEL',channel) for p in list(parameters)]
title = kwargs.get('title', "" )
name = kwargs.get('name', "" )
indir = kwargs.get('indir', "output_%d"%year )
outdir = kwargs.get('outdir', "postfit_%d"%year )
tag = kwargs.get('tag', "" )
plotlabel = kwargs.get('plotlabel', "" )
order = kwargs.get('order', False )
era = "%d-13TeV"%year
filename = '%s/higgsCombine.%s_%s-%s%s-%s.MultiDimFit.mH90.root'%(indir,channel,var,DM,tag,era)
ensureDirectory(outdir)
print '>>> file "%s"'%(filename)
# HISTOGRAM
parlist = getParameters(filename,parameters)
if order:
parlist.sort(key=lambda x: -x.sigma)
N = len(parlist)
hist = TH2F("corr","corr",N,0,N,N,0,N)
for i in xrange(N): # diagonal
hist.SetBinContent(i+1,N-i,1.0)
hist.GetXaxis().SetBinLabel(1+i,parlist[i].title)
hist.GetYaxis().SetBinLabel(N-i,parlist[i].title)
for xi, yi in combinations(range(N),2): # off-diagonal
r = parlist[xi].corr(parlist[yi])
hist.SetBinContent(1+xi,N-yi,r)
hist.SetBinContent(1+yi,N-xi,r)
#print "%20s - %20s: %5.3f"%(par1,par2,r)
#hist.Fill(par1.title,par2.title,r)
#hist.Fill(par2.title,par1.title,r)
# SCALE
canvasH = 160+64*max(10,N)
canvasW = 300+70*max(10,N)
scaleH = 800./canvasH
scaleW = 1000./canvasW
scaleF = 640./(canvasH-160)
tmargin = 0.06
bmargin = 0.14
lmargin = 0.22
rmargin = 0.12
canvas = TCanvas('canvas','canvas',100,100,canvasW,canvasH)
canvas.SetFillColor(0)
canvas.SetBorderMode(0)
canvas.SetFrameFillStyle(0)
canvas.SetFrameBorderMode(0)
canvas.SetTopMargin( tmargin ); canvas.SetBottomMargin( bmargin )
canvas.SetLeftMargin( lmargin ); canvas.SetRightMargin( rmargin )
canvas.SetTickx(0)
canvas.SetTicky(0)
canvas.SetGrid()
canvas.cd()
frame = hist
frame.GetXaxis().SetLabelSize(0.054*scaleF)
frame.GetYaxis().SetLabelSize(0.072*scaleF)
frame.GetZaxis().SetLabelSize(0.034)
frame.GetXaxis().SetLabelOffset(0.005)
frame.GetYaxis().SetLabelOffset(0.003)
frame.GetXaxis().SetNdivisions(508)
#gStyle.SetPalette(kBlackBody)
#frame.SetContour(3);
gStyle.SetPaintTextFormat(".2f")
frame.SetMarkerSize(1.5*scaleF)
#frame.SetMarkerColor(kRed)
hist.Draw('COLZ TEXT')
# TEXT
if title:
latex = TLatex()
latex.SetTextSize(0.045)
latex.SetTextAlign(33)
latex.SetTextFont(42)
latex.DrawLatexNDC(0.96*lmargin,0.80*bmargin,title)
CMS_lumi.relPosX = 0.14
CMS_lumi.CMS_lumi(canvas,13,0)
gPad.SetTicks(1,1)
gPad.Modified()
frame.Draw('SAMEAXIS')
canvasname = "%s/postfit-correlation_%s_%s%s%s"%(outdir,var,DM,tag,plotlabel)
canvas.SaveAs(canvasname+".png")
if args.pdf: canvas.SaveAs(canvasname+".pdf")
canvas.Close()