def DivideTGraphErrors(num, denom, name):
nPoints = num.GetN()
nSkipPoints = 0 # count of when we can't divide
newTGraph = TGraphErrors(nPoints)
newTGraph.SetName(name)
for i in range(nPoints):
x = num.GetX()[i]
x_err = num.GetEX()[i]
y1 = num.GetY()[i]
y2 = denom.GetY()[i]
y = 0.0
y_err = 0.0
if ((y1 != 0) and (y2 != 0)):
# technically could make this work for y1 = 0
y = y1 / y2
y1_err = num.GetEY()[i]
y2_err = denom.GetEY()[i]
y_err = y * math.sqrt(
math.pow(y1_err / y1, 2) + math.pow(y2_err / y2, 2))
newTGraph.SetPoint(i - nSkipPoints, x, y)
newTGraph.SetPointError(i - nSkipPoints, x_err, y_err)
else:
newTGraph.RemovePoint(i - nSkipPoints)
nSkipPoints += 1
newTGraph.SetLineColor(num.GetLineColor())
newTGraph.SetMarkerColor(num.GetMarkerColor())
newTGraph.SetMarkerStyle(num.GetMarkerStyle())
return newTGraph
def make_tgrapherrors(name,
title,
color=1,
marker=20,
marker_size=1,
width=1,
asym_err=False,
style=1,
x=None,
y=None):
if (x and y) is None:
gr = TGraphErrors() if not asym_err else TGraphAsymmErrors()
else:
gr = TGraphErrors(len(x), array(x, 'd'), array(
y, 'd')) if not asym_err else TGraphAsymmErrors(
len(x), array(x, 'd'), array(y), 'd')
gr.SetTitle(title)
gr.SetName(name)
gr.SetMarkerStyle(marker)
gr.SetMarkerColor(color)
gr.SetLineColor(color)
gr.SetMarkerSize(marker_size)
gr.SetLineWidth(width)
gr.SetLineStyle(style)
return gr
def load_cck():
#model by Guillermo at. al.
f = open("data/data-dtdy-y_0-RHIC-clean_CCK.dat", "read")
sigma = []
for line in f:
if line[0] == "#": continue
p = line.split("\t")
t = float(p[3])
nucl = float(p[4])
hs = float(p[8])
sigma.append({"t": t, "nucl": nucl, "hs": hs})
#correction from gamma-Au to AuAu by Michal
k_auau = 9.0296
#scaling to XnXn
kx = 0.1702
gCCK = TGraphErrors(len(sigma))
for i in xrange(len(sigma)):
gCCK.SetPoint(i, sigma[i]["t"], sigma[i]["hs"] * k_auau * kx)
gCCK.SetLineColor(rt.kRed)
gCCK.SetLineWidth(3)
gCCK.SetLineStyle(rt.kDashed) # 9
return gCCK
def testIthr():
lines = get_lines('DAC_scan_ithr_0x40to0xf0.dat')
gr1 = TGraphErrors()
gr2 = TGraphErrors()
fUnit = 1000. / 0.7
yUnit = 'e^{-}'
for line in lines:
if len(line) == 0: continue
if line[0] in ['#', '\n']: continue
fs = line.rstrip().split()
ix = int(fs[0])
gr1.SetPoint(ix, float(fs[1]), float(fs[2]) * fUnit)
gr1.SetPointError(ix, 0, float(fs[3]) * fUnit)
gr2.SetPoint(ix, float(fs[1]), float(fs[4]) * fUnit)
gr2.SetPointError(ix, 0, float(fs[5]) * fUnit)
useAtlasStyle()
gStyle.SetMarkerStyle(20)
gr1.SetMarkerStyle(20)
gr1.Draw('AP')
h1 = gr1.GetHistogram()
h1.GetYaxis().SetTitle("Threshold [" + yUnit + "]")
h1.GetXaxis().SetTitle("I_{Thre} code")
# h1.GetYaxis().SetRangeUser(0,0.2)
gPad.SetTicks(1, 0)
gPad.SetRightMargin(0.16)
y1b = 0
y2b = 15
x1 = h1.GetXaxis().GetXmax()
y1 = h1.GetYaxis().GetXmin()
y2 = h1.GetYaxis().GetXmax()
raxis = TGaxis(x1, y1, x1, y2, y1b, y2b, 506, "+L")
raxis.SetLineColor(2)
raxis.SetLabelColor(2)
raxis.SetTitleColor(2)
raxis.SetTitle("ENC [" + yUnit + "]")
raxis.Draw()
nP = gr2.GetN()
Ys = gr2.GetY()
EYs = gr2.GetEY()
Y = array(
'd', [y1 + (y2 - y1) / (y2b - y1b) * (Ys[i] - y1b) for i in range(nP)])
EY = array('d', [(y2 - y1) / (y2b - y1b) * EYs[i] for i in range(nP)])
gr2x = TGraphErrors(nP, gr2.GetX(), Y, gr2.GetEX(), EY)
gr2x.SetMarkerStyle(24)
gr2x.SetLineColor(2)
gr2x.SetMarkerColor(2)
gr2x.Draw('Psame')
waitRootCmdX()
class GraphicsObject:
def __init__(self, data, name):
self._data = data
self._graphics = None
self._style = Style(kBlack, 20)
self._plotrange = {"Min": None, "Max": None}
self._name = name
def SetPlotRange(self, min, max):
self._plotrange[min] = min
self._plotrange[max] = max
def SetStyle(self, style):
self._style = style
def SetName(self, name):
self._name = name
def GetData(self):
return self._data
def GetGraphics(self):
return self._graphics
def GetStyle(self):
return self._style
def Draw(self):
if not self._graphics:
self._graphics = TGraphErrors()
np = 0
for bin in range(1, self._data.GetXaxis().GetNbins() + 1):
if self._plotrange["Min"] and self._data.GetXaxis(
).GetBinLowEdge(bin) < self._plotrange["Min"]:
continue
if self._plotrange["Max"] and self._data.GetXaxis(
).GetBinUpEdge(bin) > self._plotrange["Max"]:
break
self._graphics.SetPoint(
np,
self._data.GetXaxis().GetBinCenter(bin),
self._data.GetBinContent(bin))
self._graphics.SetPointError(
np,
self._data.GetXaxis().GetBinWidth(bin) / 2.,
self._data.GetBinError(bin))
np = np + 1
self._graphics.SetMarkerColor(self._style.GetColor())
self._graphics.SetLineColor(self._style.GetColor())
self._graphics.SetMarkerStyle(self._style.GetMarker())
self._graphics.Draw("epsame")
def AddToLegend(self, legend, title=None):
if self._graphics:
tit = self._name
if title:
tit = title
legend.AddEntry(self._graphics, tit, "lep")
def GetData(file, scale=1., sed=True, title='SED', barUL=True):
GetData.Ng += 1
g = TGraphErrors(file)
gUL = TGraphErrors()
if sed:
for i in range(g.GetN()):
g.GetY()[i] = pow(g.GetX()[i], 2) * g.GetY()[i] * 1e-6 / scale
g.GetEY()[i] = pow(g.GetX()[i], 2) * g.GetEY()[i] * 1e-6 / scale
g.GetX()[i] *= 1e-3
idel = 0
nUL = 0
while idel < g.GetN():
if g.GetEY()[idel] < 0:
gUL.SetPoint(nUL, g.GetX()[idel], g.GetY()[idel])
if barUL:
gUL.SetPointError(nUL, 0, g.GetY()[idel] * 1e-5)
nUL += 1
g.RemovePoint(idel)
else:
idel += 1
if sed:
g.SetTitle(title + ";Energy [GeV];E^{2}dN/dE [TeV cm^{-2} s^{-1}]")
else:
g.SetTitle(title + ";Energy [MeV];dN/dE [cm^{-2} s^{-1} MeV^{-1}]")
g.SetLineColor(kRed)
g.SetMarkerColor(kRed)
g.SetMarkerStyle(20)
g.SetName("g%d" % GetData.Ng)
gUL.SetLineColor(g.GetLineColor())
gUL.SetName("gUL%d" % GetData.Ng)
return g, gUL
def Plot2DHist( th2, slope, offset, x_points, y_points, error_points, outname, xlabel='',
etBinIdx=None, etaBinIdx=None, etBins=None,etaBins=None):
from ROOT import TCanvas, gStyle, TLegend, kRed, kBlue, kBlack,TLine,kBird, kOrange,kGray
from ROOT import TGraphErrors,TF1,TColor
import array
def AddTopLabels(can, etlist = None, etalist = None, etidx = None, etaidx = None, logger=None):
extraText = [GetAtlasInternalText()]
if etlist and etidx is not None:
etlist=copy(etlist)
if etlist[-1]>9999: etlist[-1]='#infty'
binEt = (str(etlist[etidx]) + ' < E_{T} [GeV] < ' + str(etlist[etidx+1]) if etidx+1 < len(etlist) else
'E_{T} > ' + str(etlist[etidx]) + ' GeV')
extraText.append(binEt)
if etalist and etaidx is not None:
binEta = (str(etalist[etaidx]) + ' < #eta < ' + str(etalist[etaidx+1]) if etaidx+1 < len(etalist) else
str(etalist[etaidx]) + ' < #eta < 2.47')
extraText.append(binEta)
DrawText(can,extraText,.14,.68,.35,.93,totalentries=4)
gStyle.SetPalette(kBird)
drawopt='lpE2'
canvas = TCanvas('canvas','canvas',500, 500)
canvas.SetRightMargin(0.15)
th2.GetXaxis().SetTitle('Neural Network output (Discriminant)')
th2.GetYaxis().SetTitle(xlabel)
th2.GetZaxis().SetTitle('Count')
th2.Draw('colz')
canvas.SetLogz()
g = TGraphErrors(len(x_points), array.array('d',x_points), array.array('d',y_points), array.array('d',error_points), array.array('d',[0]*len(x_points)))
g.SetLineWidth(1)
g.SetLineColor(kBlue)
g.SetMarkerColor(kBlue)
g.Draw("P same")
line = TLine(slope*th2.GetYaxis().GetXmin()+offset,th2.GetYaxis().GetXmin(), slope*th2.GetYaxis().GetXmax()+offset, th2.GetYaxis().GetXmax())
line.SetLineColor(kBlack)
line.SetLineWidth(2)
line.Draw()
AddTopLabels( canvas, etlist=etBins,etalist=etaBins,etidx=etBinIdx,etaidx=etaBinIdx)
FormatCanvasAxes(canvas, XLabelSize=16, YLabelSize=16, XTitleOffset=0.87, ZLabelSize=14,ZTitleSize=14, YTitleOffset=0.87, ZTitleOffset=1.1)
SetAxisLabels(canvas,'Neural Network output (Discriminant)',xlabel)
canvas.SaveAs(outname+'.pdf')
canvas.SaveAs(outname+'.C')
return outname+'.pdf'
def graphTruth():
fname = "PionMinusG4.txt"
kineticEnergy = []
crossSec = []
crossSec_el = []
crossSec_inel = []
zero = []
title = ""
with open(fname) as f:
for fLine in f.readlines():
w = fLine.split()
if is_number(w[0]):
runIn = int(w[0])
ke = float(w[1])
xstot = float(w[4])
kineticEnergy.append(ke)
crossSec.append(xstot)
zero.append(0.)
else:
if "for" not in fLine:
continue
title = fLine[9:]
#define some data points . . .
x = array('f', kineticEnergy)
y = array('f', crossSec)
y_el = array('f', crossSec_el)
y_inel = array('f', crossSec_inel)
exl = array('f', zero)
exr = array('f', zero)
nPoints = len(x)
# . . . and hand over to TGraphErros object
gr = TGraphErrors(nPoints, x, y, exl, exr)
gr.SetTitle(title + "; Kinetic Energy [MeV]; Cross Section [barn]")
gr.GetXaxis().SetRangeUser(0, 1000)
gr.GetYaxis().SetRangeUser(0, 2.)
gr.SetLineWidth(2)
gr.SetLineColor(kGreen - 2)
gr.SetFillColor(0)
return gr
def load_sartre():
sartre = TFile.Open(
"/home/jaroslav/sim/sartre_tx/sartre_AuAu_200GeV_Jpsi_coh_2p7Mevt.root"
)
sartre_tree = sartre.Get("sartre_tree")
hSartre = ut.prepare_TH1D("hSartre", 0.002, 0., 0.12)
sartre_tree.Draw("-tval >> hSartre", "rapidity>-1 && rapidity<1")
ut.norm_to_integral(hSartre, 0.025) # now same as Starlight
gSartre = TGraphErrors(hSartre.GetNbinsX())
for ibin in xrange(1, hSartre.GetNbinsX() + 1):
gSartre.SetPoint(ibin - 1, hSartre.GetBinCenter(ibin),
hSartre.GetBinContent(ibin))
gSartre.SetLineColor(rt.kYellow + 1)
gSartre.SetLineWidth(3)
#gSartre.SetLineStyle(rt.kDashDotted)
return gSartre
def load_ms():
#model by Heikki and Bjorn
f = open("data/to_star_ms.txt", "read")
t_sigma = []
for line in f:
if line[0] == "#": continue
point = line.split(" ")
t_sigma.append([float(point[0]), float(point[1])])
#scaling to XnXn
kx = 0.1702
gMS = TGraphErrors(len(t_sigma))
for i in xrange(len(t_sigma)):
gMS.SetPoint(i, t_sigma[i][0], t_sigma[i][1] * kx)
gMS.SetLineColor(rt.kViolet)
gMS.SetLineWidth(3)
gMS.SetLineStyle(rt.kDashDotted) # kDashed
return gMS
def MeasurePSF_in_Sections(data, fitted_line, nsecs = 3, tgraph_filename = '', DEBUG = False, DEBUG_Filenum = 0):
#get new coefficients as distance to line uses straight line of form ax + by + c = 0
a = -1. * fitted_line.a
b = 1
c = -1. * fitted_line.b
histmin = -1 * max(data.shape[0], data.shape[1])
histmax = max(data.shape[0], data.shape[1])
nbins = (histmax - histmin) * 2
hists = []
for i in range(nsecs):
hists.append(TH1F('Track section ' + str(i), 'Track section ' + str(i), nbins, histmin, histmax))
for xcoord in range(data.shape[0]):
for ycoord in range(data.shape[1]):
x = xcoord + 0.5 #adjust for bin centers - NB This is important!
y = ycoord + 0.5 #adjust for bin centers - NB This is important!
secnum = GetSecNum(data, x, y, nsecs, None) # TODO!
value = float(data[xcoord,ycoord])
# if value < 800:
non_abs_distance = (a*x + b*y + c) / (a**2 + b**2)**0.5
hists[secnum].Fill(non_abs_distance, value)
sigmas, sigma_errors = [], []
for i, hist in enumerate(hists):
fitmin, fitmax = GetLastBinAboveX(hist, 0.1)
# viewmin, viewmax = GetLastBinAboveX(hist, 0.1)
viewmin, viewmax = -2,2
hist.GetXaxis().SetRangeUser(viewmin,viewmax)
fit_func = TF1("gaus", "gaus", fitmin, fitmax)
fit_func.SetNpx(1000)
hist.Fit(fit_func, "MEQ", "", fitmin, fitmax)
legend_text = []
sigma = fit_func.GetParameter(2)
sigma_error = fit_func.GetParError(2)
sigmas.append(abs(15*sigma)) #15 for the 15um per pixel
sigma_errors.append(abs(15*sigma_error)) #15 for the 15um per pixel
mean = fit_func.GetParameter(15*1) #15 for the 15um per pixel
mean_error = fit_func.GetParError(15*1) #10 for the 15um per pixel
chisq = fit_func.GetChisquare()
NDF = fit_func.GetNDF()
try:
chisqr_over_NDF = chisq/NDF
except:
chisqr_over_NDF = -1
# if chisqr_over_NDF > 500 or chisqr_over_NDF <= 1:
# return [], [], []
legend_text.append('mean = ' + str(mean) + ' #pm ' + str(mean_error) + " #mum")
legend_text.append('#sigma = ' + str(round(sigma,4)) + ' #pm ' + str(round(sigma_error,4)) + " #mum")
if DEBUG: #For showing each of n PSF *Histograms* per track
c1 = TCanvas( 'canvas', 'canvas', CANVAS_WIDTH,CANVAS_HEIGHT)
hist.Draw("")
if legend_text != '':
from ROOT import TPaveText
textbox = TPaveText(0.0,1.0,0.2,0.8,"NDC")
for line in legend_text:
textbox.AddText(line)
textbox.SetFillColor(0)
textbox.Draw("same")
c1.SaveAs(OUTPUT_PATH + str(DEBUG_Filenum) + "psf_section_" + str(i) + FILE_TYPE)
del c1
from ROOT import TGraphErrors
c2 = TCanvas( 'canvas', 'canvas', CANVAS_WIDTH,CANVAS_HEIGHT)
assert nsecs == len(sigmas) == len(sigma_errors)
xpoints = GenXPoints(nsecs, 250.)
gr = TGraphErrors(nsecs, np.asarray(xpoints,dtype = float), np.asarray(sigmas,dtype = float), np.asarray([0 for i in range(nsecs)],dtype = float), np.asarray(sigma_errors,dtype = float)) #populate graph with data points
gr.SetLineColor(2)
gr.SetMarkerColor(2)
gr.Draw("AP")
fit_func = TF1("line","[1]*x + [0]", -1, nsecs+1)
fit_func.SetNpx(1000)
gr.Fit(fit_func, "MEQ", "")
a = fit_func.GetParameter(1)
a_error = fit_func.GetParError(1)
if DEBUG:
if tgraph_filename == '': tgraph_filename = OUTPUT_PATH + 'psf_graph' + '.png'
gr.SetTitle("")
gr.GetYaxis().SetTitle('PSF #sigma (#mum)')
gr.GetXaxis().SetTitle('Av. Si Depth (#mum)')
c2.SaveAs(tgraph_filename)
# if a_error >= a:
# print "Inconclusive muon directionality - skipped track %s"%tgraph_filename
# return [],[],[]
del c2, hists, gr
import gc
gc.collect()
for j in range(nsecs):
if sigma_errors[j] > sigmas[j]:
print "bad fit skipped"
return [],[],[]
if a < 0:
sigmas.reverse()
sigma_errors.reverse()
return xpoints, sigmas, sigma_errors
else:
return xpoints, sigmas, sigma_errors
def plot_2(var,cuts):
for s in attr:
c1 = TCanvas("c1", "Signals", 1200, 800)
if log_y == 1:
c1.SetLogy()
c1.SetTopMargin(0.08)#0.12
c1.SetBottomMargin(0.11)#0.12
c1.SetLeftMargin(0.14)
c1.SetRightMargin(0.14)#0.24
c1.cd()
#c1.SetGrid()
gStyle.SetTitleFontSize(0.04)
if ct_dep == 0:
if s in ('elf','muf','cm','nm','chm'):
c1.SetLogx()
for cc in channel:
#hist[cc][s].SetMaximum(0.44)
if 'combind' in cc:
fc = 30
#hist[cc][s].SetFillStyle()#3005)
elif 'VBF' in cc:
fc = 38
hist[cc][s].SetFillStyle(3444)
elif 'HT50' in cc:
fc = 7
hist[cc][s].SetFillStyle(3001)
elif 'HT100' in cc:
fc = 4
hist[cc][s].SetFillStyle(3002)
elif 'HT200' in cc:
fc = 6
hist[cc][s].SetFillStyle(3003)
elif 'HT300' in cc:
fc = 9
hist[cc][s].SetFillStyle(3004)
if histFillColOn == 1:
pass
#hist[cc][s].SetFillColor(fc)
hist[cc][s].Draw(histStyl)
#legend = TLegend(0.76, 0.56, 0.99, 0.88)
legend = TLegend(0.60, 0.9-0.04*2, 0.85, 0.9) #x_left y_bottom x_right y_top
legend.SetBorderSize(0)
legend.SetFillStyle(0)#1001
legend.SetFillColor(0)
#legend.SetHeader( entry['entries'] )
for cc in channel:
legend.AddEntry(hist[cc][s],cc)
legend.Draw()
for ct in cut_text:
cut_text[ct].Draw()
#>>>>>>>>>>>>>>>>>>>>>>>>>>>>>Line for critical value
if s == 'chm':
l = TLine(11.4,0.0,11.4,0.084)
elif s == 'cHadEFrac':
l = TLine(0.38,0.0,0.38,0.027)
elif s == 'FracCal':
l = TLine(30,0.0,30,0.44)
#l.SetLineColor(2)
#l.SetLineWidth(3)
if critical_line == 1:
l.SetLineColor(46)#4,2
l.SetLineWidth(3)
l.Draw('same')
c1.Print(path1 + s + var + cuts.replace('(','_').replace(')','_').replace('&&','_').replace('>','LG').replace('<','LS').replace('=','EQ').replace('.','P').replace('-','N').replace('Jet','J').replace('GenBquark','GBQ') + ".pdf")
elif ct_dep == 1:
eac0 = str( entries_after_cut['ct0'][s] )
c1.SetLogx()
#gr = TGraph( len_of_lt , x , yy['sgn'][s] )
gr = TGraphErrors( len_of_lt , x , yy['sgn'][s] , ex , ey['sgn'][s] )
gr.SetMarkerSize(1.5)
gr.SetMarkerStyle(1)
gr.GetYaxis().SetTitleOffset(1.6)
gr.SetLineColor(4)
gr.SetLineWidth(4)
gr.SetTitle('mean ' + s )
gr.GetXaxis().SetTitle('decaying length (mm)')
gr.GetYaxis().SetTitle('mean normalized number of events')
gr.GetXaxis().SetTitleOffset(1.4)
gr.SetMaximum( plotrange[s] * 1.12 )
gr.SetName('sgn')
gr.Draw('ACP') # '' sets up the scattering style
gr1 = TGraphErrors( len_of_lt , x , yy['QCD'][s] , ex , ey['QCD'][s] )
gr1.SetMarkerSize(1.0)
gr1.SetMarkerStyle(1)
gr.GetYaxis().SetTitleOffset(1.6)
gr1.SetLineColor(2)
gr1.SetLineWidth(2)
gr1.SetName('QCD')
#gr1.SetTitle('averaged ' + s)
#gr1.GetXaxis().SetTitle('decaying length (mm)')
#gr1.GetYaxis().SetTitle('mean frequency')
gr1.Draw('CP') # '' sets up the scattering style
legend = TLegend(0.76, 0.56, 0.99, 0.88)
legend.SetHeader( 'Entries: ' + eac0 )
legend.AddEntry('QCD', legendb, 'l')
legend.AddEntry('sgn', legends, 'l')
legend.Draw()
for ct in cut_text:
cut_text[ct].Draw()
c1.Print(path1 + 'mean_' + s + var + cuts.replace('(','_').replace(')','_').replace('&&','_').replace('>','LG').replace('<','LS').replace('=','EQ').replace('.','P').replace('-','N').replace('Jet','J').replace('GenBquark','GBQ') + ".pdf")
c1.Update()
c1.Close()
print('|||||||||||||||||||||||||||||||||||||||||||||||||||')
err5s = array( 'f', [0.251, 0.172, 0.153, 0.142, 0.155, 0.156, 0.253, 0.273, 0.259, 0.267, 0.343, 0.287, 0.248, 0.251, 0.255, 0.154, 0.148, 0.150, 0.148, 0.182, 0.239])
err5 = array( 'f', [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ,0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
#40x40
Arr6 = array( 'f', [-2.0, -1.8, -1.6, -1.4, -1.2, -1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0])
det6s = array( 'f', [3.002, 6.100, 6.024, 6.364, 6.701, 6.940, 7.706, 7.920, 8.293, 10.141, 11.660, 10.456, 8.198, 7.676, 7.642, 6.966, 6.677, 6.378, 6.243, 6.122, 3.024])
err6s = array( 'f', [0.102, 0.070, 0.073, 0.088, 0.078, 0.076, 0.080, 0.081, 0.100, 0.155, 0.189, 0.180, 0.107, 0.101, 0.086, 0.083, 0.076, 0.080, 0.075, 0.080, 0.110])
err6 = array( 'f', [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ,0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
n4s = 21
nxs = 33
n2s = 21
c1 = TCanvas( 'c1', 'Y', 200, 10, 1400, 1000 )
g1s = TGraphErrors(nxs, Arr1, det1s, err1, err1s)
g1s.SetTitle( 'Y=0 Detected PE' )
g1s.SetMarkerColor( 4 )
g1s.SetLineColor( 4 )
g1s.SetMarkerStyle( 21 )
g1s.Draw('APC')
g1s.GetYaxis().SetRangeUser(0,45)
g1s.GetYaxis().SetTitle("Hits [PE]")
g1s.GetXaxis().SetTitle("X [cm]")
g2xs = TGraphErrors(nxs, Arr2x, det2xs, err2x, err2xs)
g2xs.SetTitle( 'Y=0 Detected PE' )
g2xs.SetMarkerColor( 9 )
g2xs.SetLineColor( 9 )
g2xs.SetMarkerStyle( 5 )
g2xs.Draw('PC SAME')
#c1.Print("xs.png")
#c2 = TCanvas( 'c1', 'Y', 200, 10, 700, 500 )
'''
g2s = TGraphErrors(nxs, Arr2, det2s, err2, err2s)
jetToMETTriggerRate.SetPointError(3, 5, 9161739)
jetToMETTriggerRate.SetPointError(4, 5, 2492531)
jetToMETTriggerRate.SetPointError(5, 5, 592722)
jetToMETTriggerRate.SetPointError(6, 5, 304389)
jetToMETTriggerRate.SetPointError(7, 5, 193864)
jetToMETTriggerRate.SetPointError(8, 5, 176138)
jetToMETTriggerRate.SetPointError(9, 5, 120598)
jetToMETTriggerRate.SetPointError(10, 5, 91404)
jetToMETTriggerRate.SetPointError(11, 5, 75712)
jetToMETTriggerRate.SetPointError(12, 5, 58571)
jetToMETTriggerRate.SetPointError(13, 5, 45729)
jetToMETTriggerRate.SetPointError(14, 5, 39702)
jetToMETTriggerRate.Draw("AP")
jetToMETTriggerRate.SetMarkerColor(4)
jetToMETTriggerRate.SetLineColor(1)
jetToMETTriggerRate.SetMarkerStyle(21)
text.Draw()
canvas.Update()
canvas.Print("jetToMETTriggerRate.svg", "svg")
canvas.Print("jetToMETTriggerRate.png", "png")
canvas.Print("jetToMETTriggerRate.pdf", "pdf")
print "Retrieving efficiency vs MET plot"
print "Building efficiency vs rate plots"
canvas.SetLogx(0)
canvas.SetLogy(0)
text.SetX1NDC(0.5)
text.SetX2NDC(0.9)
metTriggerEfficiency_ff_H_WW_enuenu_1000events = DelphesSim_ff_H_WW_enuenu_1000events_efficiency_file.Get("metTriggerEfficiency_errors")
_NumberOfProtonsErr.append(0.0)
canvas1 = TCanvas('c1', "mycanvas", 200, 10, 700, 500)
effGraph = TGraphErrors(len(_NumberOfProtons), _NumberOfProtons, _Efficiency,
_NumberOfProtonsErr, _EfficiencyErr)
effGraph.SetMarkerStyle(2)
effGraph.SetMarkerSize(2)
effGraph.GetXaxis().SetTitle("Mean N Proton")
effGraph.GetYaxis().SetTitle("Efficiency/Purity (%)")
effGraph.SetTitle("Efficiency")
purGraph = TGraphErrors(len(_NumberOfProtons), _NumberOfProtons, _Purity,
_NumberOfProtonsErr, _PurityErr)
purGraph.SetMarkerStyle(5)
purGraph.SetLineColor(2)
purGraph.SetMarkerColor(2)
purGraph.SetMarkerSize(2)
purGraph.GetXaxis().SetTitle("Mean N Proton")
purGraph.GetYaxis().SetTitle("Purity")
purGraph.SetTitle("Purity")
MyFile = TFile(
"effVsPurity_bs" + (str)(beamSpread) + "mrad_geo" + geoName +
"_RestrictTracks" + (str)(restrictNTracks) + "_pitch" + (str)(pitch) +
".root", "RECREATE")
mg = TMultiGraph()
mg.SetTitle("Efficiency and Purity vs nProtons;Mean N Protons; (%)")
mg.Add(effGraph, "lp")
mg.Add(purGraph, "lp")
mg.Draw("a")
linFunc = TF1('linFunc', '[0]+[1]*x', 0.0, 0.5)
linFunc.SetParameters(0.0, 1.0)
for i in range(rawfitresultList.GetSize()):
#tageffValVList[i] = rawfitresultList.At(i).getValV();
#tageffErrorList[i] = rawfitresultList.At(i).getError();
#etaAvgValList[i] = etaAvgValVarList.At(i).getValV();
#etaAvgErrorList[i] = etaAvgValVarList.At(i).getError();
tageffVsEtaGraph.SetPoint(i,
etaAvgValVarList.At(i).getValV(),
rawfitresultList.At(i).getValV())
tageffVsEtaGraph.SetPointError(i,
etaAvgValVarList.At(i).getError(),
rawfitresultList.At(i).getError())
tageffVsEtaGraph.SetLineColor(currentColor)
linFunc.SetLineColor(currentColor)
tageffVsEtaGraph.Fit(linFunc)
graphHolder.Add(tageffVsEtaGraph)
currentColor += 1
#tageffVsEtaGraph = TGraph(rawfitresultList.GetSize(),etaAvgValList,tageffValVList)#,etaAvgErrorList,tageffErrorList);
#tageffVsEtaGraph.
#ROOT.gSystem.ProcessEvents();
#img.FromPad(theCanvas);
os.chdir("..")
theCanvas = TCanvas()
gnorm.SetMarkerColor(1)
gnorm.SetMaximum(0)
inorm = TGraphErrors()
inorm.SetMarkerStyle(24)
fnorm = TF1("fnorm", "pol9", 700, 3000)
fnorm.SetLineColor(920)
fnorm.SetLineStyle(7)
fnorm.SetFillColor(2)
fnorm.SetLineColor(cColor)
# Mean
gmean = TGraphErrors()
gmean.SetTitle(";m_{X} (GeV);gaussian mean (GeV)")
gmean.SetMarkerStyle(20)
gmean.SetMarkerColor(cColor)
gmean.SetLineColor(cColor)
imean = TGraphErrors()
imean.SetMarkerStyle(24)
fmean = TF1("fmean", "pol1", 0, 10000)
fmean.SetLineColor(2)
fmean.SetFillColor(2)
# Width
gsigma = TGraphErrors()
gsigma.SetTitle(";m_{X} (GeV);gaussian width (GeV)")
gsigma.SetMarkerStyle(20)
gsigma.SetMarkerColor(cColor)
gsigma.SetLineColor(cColor)
isigma = TGraphErrors()
isigma.SetMarkerStyle(24)
fsigma = TF1("fsigma", "pol1", 0, 10000)
def signal(category):
interPar = True
n = len(genPoints)
cColor = color[category] if category in color else 4
nBtag = category.count('b')
isAH = False #relict from using Alberto's more complex script
if not os.path.exists(PLOTDIR + "MC_signal_" + YEAR):
os.makedirs(PLOTDIR + "MC_signal_" + YEAR)
#*******************************************************#
# #
# Variables and selections #
# #
#*******************************************************#
X_mass = RooRealVar("jj_mass_widejet", "m_{jj}", X_min, X_max, "GeV")
j1_pt = RooRealVar("jpt_1", "jet1 pt", 0., 13000., "GeV")
jj_deltaEta = RooRealVar("jj_deltaEta_widejet", "", 0., 5.)
jbtag_WP_1 = RooRealVar("jbtag_WP_1", "", -1., 4.)
jbtag_WP_2 = RooRealVar("jbtag_WP_2", "", -1., 4.)
fatjetmass_1 = RooRealVar("fatjetmass_1", "", -1., 2500.)
fatjetmass_2 = RooRealVar("fatjetmass_2", "", -1., 2500.)
jid_1 = RooRealVar("jid_1", "j1 ID", -1., 8.)
jid_2 = RooRealVar("jid_2", "j2 ID", -1., 8.)
jnmuons_1 = RooRealVar("jnmuons_1", "j1 n_{#mu}", -1., 8.)
jnmuons_2 = RooRealVar("jnmuons_2", "j2 n_{#mu}", -1., 8.)
jnmuons_loose_1 = RooRealVar("jnmuons_loose_1", "jnmuons_loose_1", -1., 8.)
jnmuons_loose_2 = RooRealVar("jnmuons_loose_2", "jnmuons_loose_2", -1., 8.)
nmuons = RooRealVar("nmuons", "n_{#mu}", -1., 10.)
nelectrons = RooRealVar("nelectrons", "n_{e}", -1., 10.)
HLT_AK8PFJet500 = RooRealVar("HLT_AK8PFJet500", "", -1., 1.)
HLT_PFJet500 = RooRealVar("HLT_PFJet500", "", -1., 1.)
HLT_CaloJet500_NoJetID = RooRealVar("HLT_CaloJet500_NoJetID", "", -1., 1.)
HLT_PFHT900 = RooRealVar("HLT_PFHT900", "", -1., 1.)
HLT_AK8PFJet550 = RooRealVar("HLT_AK8PFJet550", "", -1., 1.)
HLT_PFJet550 = RooRealVar("HLT_PFJet550", "", -1., 1.)
HLT_CaloJet550_NoJetID = RooRealVar("HLT_CaloJet550_NoJetID", "", -1., 1.)
HLT_PFHT1050 = RooRealVar("HLT_PFHT1050", "", -1., 1.)
#HLT_DoublePFJets100_CaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets100_CaloBTagDeepCSV_p71" , "", -1., 1. )
#HLT_DoublePFJets116MaxDeta1p6_DoubleCaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets116MaxDeta1p6_DoubleCaloBTagDeepCSV_p71", "", -1., 1. )
#HLT_DoublePFJets128MaxDeta1p6_DoubleCaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets128MaxDeta1p6_DoubleCaloBTagDeepCSV_p71", "", -1., 1. )
#HLT_DoublePFJets200_CaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets200_CaloBTagDeepCSV_p71" , "", -1., 1. )
#HLT_DoublePFJets350_CaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets350_CaloBTagDeepCSV_p71" , "", -1., 1. )
#HLT_DoublePFJets40_CaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets40_CaloBTagDeepCSV_p71" , "", -1., 1. )
weight = RooRealVar("eventWeightLumi", "", -1.e9, 1.e9)
# Define the RooArgSet which will include all the variables defined before
# there is a maximum of 9 variables in the declaration, so the others need to be added with 'add'
variables = RooArgSet(X_mass)
variables.add(
RooArgSet(j1_pt, jj_deltaEta, jbtag_WP_1, jbtag_WP_2, fatjetmass_1,
fatjetmass_2, jnmuons_1, jnmuons_2, weight))
variables.add(
RooArgSet(nmuons, nelectrons, jid_1, jid_2, jnmuons_loose_1,
jnmuons_loose_2))
variables.add(
RooArgSet(HLT_AK8PFJet500, HLT_PFJet500, HLT_CaloJet500_NoJetID,
HLT_PFHT900, HLT_AK8PFJet550, HLT_PFJet550,
HLT_CaloJet550_NoJetID, HLT_PFHT1050))
#variables.add(RooArgSet(HLT_DoublePFJets100_CaloBTagDeepCSV_p71, HLT_DoublePFJets116MaxDeta1p6_DoubleCaloBTagDeepCSV_p71, HLT_DoublePFJets128MaxDeta1p6_DoubleCaloBTagDeepCSV_p71, HLT_DoublePFJets200_CaloBTagDeepCSV_p71, HLT_DoublePFJets350_CaloBTagDeepCSV_p71, HLT_DoublePFJets40_CaloBTagDeepCSV_p71))
X_mass.setRange("X_reasonable_range", X_mass.getMin(), X_mass.getMax())
X_mass.setRange("X_integration_range", X_mass.getMin(), X_mass.getMax())
if VARBINS:
binsXmass = RooBinning(len(abins) - 1, abins)
X_mass.setBinning(binsXmass)
plot_binning = RooBinning(
int((X_mass.getMax() - X_mass.getMin()) / 100.), X_mass.getMin(),
X_mass.getMax())
else:
X_mass.setBins(int((X_mass.getMax() - X_mass.getMin()) / 10))
binsXmass = RooBinning(int((X_mass.getMax() - X_mass.getMin()) / 100.),
X_mass.getMin(), X_mass.getMax())
plot_binning = binsXmass
X_mass.setBinning(plot_binning, "PLOT")
#X_mass.setBins(int((X_mass.getMax() - X_mass.getMin())/10))
#binsXmass = RooBinning(int((X_mass.getMax() - X_mass.getMin())/100), X_mass.getMin(), X_mass.getMax())
#X_mass.setBinning(binsXmass, "PLOT")
massArg = RooArgSet(X_mass)
# Cuts
if BTAGGING == 'semimedium':
SRcut = aliasSM[category]
#SRcut = aliasSM[category+"_vetoAK8"]
else:
SRcut = alias[category].format(WP=working_points[BTAGGING])
#SRcut = alias[category+"_vetoAK8"].format(WP=working_points[BTAGGING])
if ADDSELECTION: SRcut += SELECTIONS[options.selection]
print " Cut:\t", SRcut
#*******************************************************#
# #
# Signal fits #
# #
#*******************************************************#
treeSign = {}
setSignal = {}
vmean = {}
vsigma = {}
valpha1 = {}
vslope1 = {}
valpha2 = {}
vslope2 = {}
smean = {}
ssigma = {}
salpha1 = {}
sslope1 = {}
salpha2 = {}
sslope2 = {}
sbrwig = {}
signal = {}
signalExt = {}
signalYield = {}
signalIntegral = {}
signalNorm = {}
signalXS = {}
frSignal = {}
frSignal1 = {}
frSignal2 = {}
frSignal3 = {}
# Signal shape uncertainties (common amongst all mass points)
xmean_jes = RooRealVar(
"CMS" + YEAR + "_sig_" + category + "_p1_scale_jes",
"Variation of the resonance position with the jet energy scale", 0.02,
-1., 1.) #0.001
smean_jes = RooRealVar(
"CMS" + YEAR + "_sig_" + category + "_p1_jes",
"Change of the resonance position with the jet energy scale", 0., -10,
10)
xsigma_jer = RooRealVar(
"CMS" + YEAR + "_sig_" + category + "_p2_scale_jer",
"Variation of the resonance width with the jet energy resolution",
0.10, -1., 1.)
ssigma_jer = RooRealVar(
"CMS" + YEAR + "_sig_" + category + "_p2_jer",
"Change of the resonance width with the jet energy resolution", 0.,
-10, 10)
xmean_jes.setConstant(True)
smean_jes.setConstant(True)
xsigma_jer.setConstant(True)
ssigma_jer.setConstant(True)
for m in massPoints:
signalMass = "%s_M%d" % (stype, m)
signalName = "ZpBB_{}_{}_M{}".format(YEAR, category, m)
sampleName = "ZpBB_M{}".format(m)
signalColor = sample[sampleName][
'linecolor'] if signalName in sample else 1
# define the signal PDF
vmean[m] = RooRealVar(signalName + "_vmean", "Crystal Ball mean", m,
m * 0.96, m * 1.05)
smean[m] = RooFormulaVar(signalName + "_mean", "@0*(1+@1*@2)",
RooArgList(vmean[m], xmean_jes, smean_jes))
vsigma[m] = RooRealVar(signalName + "_vsigma", "Crystal Ball sigma",
m * 0.0233, m * 0.019, m * 0.025)
ssigma[m] = RooFormulaVar(
signalName + "_sigma", "@0*(1+@1*@2)",
RooArgList(vsigma[m], xsigma_jer, ssigma_jer))
valpha1[m] = RooRealVar(
signalName + "_valpha1", "Crystal Ball alpha 1", 0.2, 0.05, 0.28
) # number of sigmas where the exp is attached to the gaussian core. >0 left, <0 right
salpha1[m] = RooFormulaVar(signalName + "_alpha1", "@0",
RooArgList(valpha1[m]))
#vslope1[m] = RooRealVar(signalName + "_vslope1", "Crystal Ball slope 1", 10., 0.1, 20.) # slope of the power tail
vslope1[m] = RooRealVar(signalName + "_vslope1",
"Crystal Ball slope 1", 13., 10.,
20.) # slope of the power tail
sslope1[m] = RooFormulaVar(signalName + "_slope1", "@0",
RooArgList(vslope1[m]))
valpha2[m] = RooRealVar(signalName + "_valpha2",
"Crystal Ball alpha 2", 1.)
valpha2[m].setConstant(True)
salpha2[m] = RooFormulaVar(signalName + "_alpha2", "@0",
RooArgList(valpha2[m]))
#vslope2[m] = RooRealVar(signalName + "_vslope2", "Crystal Ball slope 2", 6., 2.5, 15.) # slope of the higher power tail
## FIXME test FIXME
vslope2_estimation = -5.88111436852 + m * 0.00728809389442 + m * m * (
-1.65059568762e-06) + m * m * m * (1.25128996309e-10)
vslope2[m] = RooRealVar(signalName + "_vslope2",
"Crystal Ball slope 2", vslope2_estimation,
vslope2_estimation * 0.9, vslope2_estimation *
1.1) # slope of the higher power tail
## FIXME end FIXME
sslope2[m] = RooFormulaVar(
signalName + "_slope2", "@0",
RooArgList(vslope2[m])) # slope of the higher power tail
signal[m] = RooDoubleCrystalBall(signalName,
"m_{%s'} = %d GeV" % ('X', m), X_mass,
smean[m], ssigma[m], salpha1[m],
sslope1[m], salpha2[m], sslope2[m])
# extend the PDF with the yield to perform an extended likelihood fit
signalYield[m] = RooRealVar(signalName + "_yield", "signalYield", 50,
0., 1.e15)
signalNorm[m] = RooRealVar(signalName + "_norm", "signalNorm", 1., 0.,
1.e15)
signalXS[m] = RooRealVar(signalName + "_xs", "signalXS", 1., 0., 1.e15)
signalExt[m] = RooExtendPdf(signalName + "_ext", "extended p.d.f",
signal[m], signalYield[m])
# ---------- if there is no simulated signal, skip this mass point ----------
if m in genPoints:
if VERBOSE: print " - Mass point", m
# define the dataset for the signal applying the SR cuts
treeSign[m] = TChain("tree")
if YEAR == 'run2':
pd = sample[sampleName]['files']
if len(pd) > 3:
print "multiple files given than years for a single masspoint:", pd
sys.exit()
for ss in pd:
if not '2016' in ss and not '2017' in ss and not '2018' in ss:
print "unknown year given in:", ss
sys.exit()
else:
pd = [x for x in sample[sampleName]['files'] if YEAR in x]
if len(pd) > 1:
print "multiple files given for a single masspoint/year:", pd
sys.exit()
for ss in pd:
if options.unskimmed:
j = 0
while True:
if os.path.exists(NTUPLEDIR + ss + "/" + ss +
"_flatTuple_{}.root".format(j)):
treeSign[m].Add(NTUPLEDIR + ss + "/" + ss +
"_flatTuple_{}.root".format(j))
j += 1
else:
print "found {} files for sample:".format(j), ss
break
else:
if os.path.exists(NTUPLEDIR + ss + ".root"):
treeSign[m].Add(NTUPLEDIR + ss + ".root")
else:
print "found no file for sample:", ss
if treeSign[m].GetEntries() <= 0.:
if VERBOSE:
print " - 0 events available for mass", m, "skipping mass point..."
signalNorm[m].setVal(-1)
vmean[m].setConstant(True)
vsigma[m].setConstant(True)
salpha1[m].setConstant(True)
sslope1[m].setConstant(True)
salpha2[m].setConstant(True)
sslope2[m].setConstant(True)
signalNorm[m].setConstant(True)
signalXS[m].setConstant(True)
continue
#setSignal[m] = RooDataSet("setSignal_"+signalName, "setSignal", variables, RooFit.Cut(SRcut), RooFit.WeightVar("eventWeightLumi*BTagAK4Weight_deepJet"), RooFit.Import(treeSign[m]))
setSignal[m] = RooDataSet("setSignal_" + signalName, "setSignal",
variables, RooFit.Cut(SRcut),
RooFit.WeightVar(weight),
RooFit.Import(treeSign[m]))
if VERBOSE:
print " - Dataset with", setSignal[m].sumEntries(
), "events loaded"
# FIT
entries = setSignal[m].sumEntries()
if entries < 0. or entries != entries: entries = 0
signalYield[m].setVal(entries)
# Instead of eventWeightLumi
#signalYield[m].setVal(entries * LUMI / (300000 if YEAR=='run2' else 100000) )
if treeSign[m].GetEntries(SRcut) > 5:
if VERBOSE: print " - Running fit"
frSignal[m] = signalExt[m].fitTo(setSignal[m], RooFit.Save(1),
RooFit.Extended(True),
RooFit.SumW2Error(True),
RooFit.PrintLevel(-1))
if VERBOSE:
print "********** Fit result [", m, "] **", category, "*" * 40, "\n", frSignal[
m].Print(), "\n", "*" * 80
if VERBOSE: frSignal[m].correlationMatrix().Print()
drawPlot(signalMass + "_" + category, stype + category, X_mass,
signal[m], setSignal[m], frSignal[m])
else:
print " WARNING: signal", stype, "and mass point", m, "in category", category, "has 0 entries or does not exist"
# Remove HVT cross sections
#xs = getCrossSection(stype, channel, m)
xs = 1.
signalXS[m].setVal(xs * 1000.)
signalIntegral[m] = signalExt[m].createIntegral(
massArg, RooFit.NormSet(massArg),
RooFit.Range("X_integration_range"))
boundaryFactor = signalIntegral[m].getVal()
if boundaryFactor < 0. or boundaryFactor != boundaryFactor:
boundaryFactor = 0
if VERBOSE:
print " - Fit normalization vs integral:", signalYield[
m].getVal(), "/", boundaryFactor, "events"
signalNorm[m].setVal(boundaryFactor * signalYield[m].getVal() /
signalXS[m].getVal()
) # here normalize to sigma(X) x Br = 1 [fb]
vmean[m].setConstant(True)
vsigma[m].setConstant(True)
valpha1[m].setConstant(True)
vslope1[m].setConstant(True)
valpha2[m].setConstant(True)
vslope2[m].setConstant(True)
signalNorm[m].setConstant(True)
signalXS[m].setConstant(True)
#*******************************************************#
# #
# Signal interpolation #
# #
#*******************************************************#
### FIXME FIXME just for a test FIXME FIXME
#print
#print
#print "slope2 fit results:"
#print
#y_vals = []
#for m in genPoints:
# y_vals.append(vslope2[m].getVal())
#print "m =", genPoints
#print "y =", y_vals
#sys.exit()
### FIXME FIXME test end FIXME FIXME
# ====== CONTROL PLOT ======
color_scheme = [
636, 635, 634, 633, 632, 633, 636, 635, 634, 633, 632, 633, 636, 635,
634, 633, 632, 633, 636, 635, 634, 633, 632, 633, 636, 635, 634, 633,
632, 633, 636, 635, 634, 633, 632, 633, 636, 635, 634, 633, 632, 633
]
c_signal = TCanvas("c_signal", "c_signal", 800, 600)
c_signal.cd()
frame_signal = X_mass.frame()
for j, m in enumerate(genPoints):
if m in signalExt.keys():
#print "color:",(j%9)+1
#print "signalNorm[m].getVal() =", signalNorm[m].getVal()
#print "RooAbsReal.NumEvent =", RooAbsReal.NumEvent
signal[m].plotOn(
frame_signal, RooFit.LineColor(color_scheme[j]),
RooFit.Normalization(signalNorm[m].getVal(),
RooAbsReal.NumEvent),
RooFit.Range("X_reasonable_range"))
frame_signal.GetXaxis().SetRangeUser(0, 10000)
frame_signal.Draw()
drawCMS(-1, "Simulation Preliminary", year=YEAR)
#drawCMS(-1, "Work in Progress", year=YEAR, suppressCMS=True)
#drawCMS(-1, "", year=YEAR, suppressCMS=True)
drawAnalysis(category)
drawRegion(category)
c_signal.SaveAs(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" +
category + "_Signal.pdf")
c_signal.SaveAs(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" +
category + "_Signal.png")
#if VERBOSE: raw_input("Press Enter to continue...")
# ====== CONTROL PLOT ======
# Normalization
gnorm = TGraphErrors()
gnorm.SetTitle(";m_{X} (GeV);integral (GeV)")
gnorm.SetMarkerStyle(20)
gnorm.SetMarkerColor(1)
gnorm.SetMaximum(0)
inorm = TGraphErrors()
inorm.SetMarkerStyle(24)
fnorm = TF1("fnorm", "pol9", 700, 3000)
fnorm.SetLineColor(920)
fnorm.SetLineStyle(7)
fnorm.SetFillColor(2)
fnorm.SetLineColor(cColor)
# Mean
gmean = TGraphErrors()
gmean.SetTitle(";m_{X} (GeV);gaussian mean (GeV)")
gmean.SetMarkerStyle(20)
gmean.SetMarkerColor(cColor)
gmean.SetLineColor(cColor)
imean = TGraphErrors()
imean.SetMarkerStyle(24)
fmean = TF1("fmean", "pol1", 0, 10000)
fmean.SetLineColor(2)
fmean.SetFillColor(2)
# Width
gsigma = TGraphErrors()
gsigma.SetTitle(";m_{X} (GeV);gaussian width (GeV)")
gsigma.SetMarkerStyle(20)
gsigma.SetMarkerColor(cColor)
gsigma.SetLineColor(cColor)
isigma = TGraphErrors()
isigma.SetMarkerStyle(24)
fsigma = TF1("fsigma", "pol1", 0, 10000)
fsigma.SetLineColor(2)
fsigma.SetFillColor(2)
# Alpha1
galpha1 = TGraphErrors()
galpha1.SetTitle(";m_{X} (GeV);crystal ball lower alpha")
galpha1.SetMarkerStyle(20)
galpha1.SetMarkerColor(cColor)
galpha1.SetLineColor(cColor)
ialpha1 = TGraphErrors()
ialpha1.SetMarkerStyle(24)
falpha1 = TF1("falpha", "pol1", 0, 10000) #pol0
falpha1.SetLineColor(2)
falpha1.SetFillColor(2)
# Slope1
gslope1 = TGraphErrors()
gslope1.SetTitle(";m_{X} (GeV);exponential lower slope (1/Gev)")
gslope1.SetMarkerStyle(20)
gslope1.SetMarkerColor(cColor)
gslope1.SetLineColor(cColor)
islope1 = TGraphErrors()
islope1.SetMarkerStyle(24)
fslope1 = TF1("fslope", "pol1", 0, 10000) #pol0
fslope1.SetLineColor(2)
fslope1.SetFillColor(2)
# Alpha2
galpha2 = TGraphErrors()
galpha2.SetTitle(";m_{X} (GeV);crystal ball upper alpha")
galpha2.SetMarkerStyle(20)
galpha2.SetMarkerColor(cColor)
galpha2.SetLineColor(cColor)
ialpha2 = TGraphErrors()
ialpha2.SetMarkerStyle(24)
falpha2 = TF1("falpha", "pol1", 0, 10000) #pol0
falpha2.SetLineColor(2)
falpha2.SetFillColor(2)
# Slope2
gslope2 = TGraphErrors()
gslope2.SetTitle(";m_{X} (GeV);exponential upper slope (1/Gev)")
gslope2.SetMarkerStyle(20)
gslope2.SetMarkerColor(cColor)
gslope2.SetLineColor(cColor)
islope2 = TGraphErrors()
islope2.SetMarkerStyle(24)
fslope2 = TF1("fslope", "pol1", 0, 10000) #pol0
fslope2.SetLineColor(2)
fslope2.SetFillColor(2)
n = 0
for i, m in enumerate(genPoints):
if not m in signalNorm.keys(): continue
if signalNorm[m].getVal() < 1.e-6: continue
if gnorm.GetMaximum() < signalNorm[m].getVal():
gnorm.SetMaximum(signalNorm[m].getVal())
gnorm.SetPoint(n, m, signalNorm[m].getVal())
#gnorm.SetPointError(i, 0, signalNorm[m].getVal()/math.sqrt(treeSign[m].GetEntriesFast()))
gmean.SetPoint(n, m, vmean[m].getVal())
gmean.SetPointError(n, 0,
min(vmean[m].getError(), vmean[m].getVal() * 0.02))
gsigma.SetPoint(n, m, vsigma[m].getVal())
gsigma.SetPointError(
n, 0, min(vsigma[m].getError(), vsigma[m].getVal() * 0.05))
galpha1.SetPoint(n, m, valpha1[m].getVal())
galpha1.SetPointError(
n, 0, min(valpha1[m].getError(), valpha1[m].getVal() * 0.10))
gslope1.SetPoint(n, m, vslope1[m].getVal())
gslope1.SetPointError(
n, 0, min(vslope1[m].getError(), vslope1[m].getVal() * 0.10))
galpha2.SetPoint(n, m, salpha2[m].getVal())
galpha2.SetPointError(
n, 0, min(valpha2[m].getError(), valpha2[m].getVal() * 0.10))
gslope2.SetPoint(n, m, sslope2[m].getVal())
gslope2.SetPointError(
n, 0, min(vslope2[m].getError(), vslope2[m].getVal() * 0.10))
#tmpVar = w.var(var+"_"+signalString)
#print m, tmpVar.getVal(), tmpVar.getError()
n = n + 1
gmean.Fit(fmean, "Q0", "SAME")
gsigma.Fit(fsigma, "Q0", "SAME")
galpha1.Fit(falpha1, "Q0", "SAME")
gslope1.Fit(fslope1, "Q0", "SAME")
galpha2.Fit(falpha2, "Q0", "SAME")
gslope2.Fit(fslope2, "Q0", "SAME")
# gnorm.Fit(fnorm, "Q0", "", 700, 5000)
#for m in [5000, 5500]: gnorm.SetPoint(gnorm.GetN(), m, gnorm.Eval(m, 0, "S"))
#gnorm.Fit(fnorm, "Q", "SAME", 700, 6000)
gnorm.Fit(fnorm, "Q", "SAME", 1800, 8000) ## adjusted recently
for m in massPoints:
if vsigma[m].getVal() < 10.: vsigma[m].setVal(10.)
# Interpolation method
syield = gnorm.Eval(m)
spline = gnorm.Eval(m, 0, "S")
sfunct = fnorm.Eval(m)
#delta = min(abs(1.-spline/sfunct), abs(1.-spline/syield))
delta = abs(1. - spline / sfunct) if sfunct > 0 else 0
syield = spline
if interPar:
#jmean = gmean.Eval(m)
#jsigma = gsigma.Eval(m)
#jalpha1 = galpha1.Eval(m)
#jslope1 = gslope1.Eval(m)
#jalpha2 = galpha2.Eval(m)
#jslope2 = gslope2.Eval(m)
jmean = gmean.Eval(m, 0, "S")
jsigma = gsigma.Eval(m, 0, "S")
jalpha1 = galpha1.Eval(m, 0, "S")
jslope1 = gslope1.Eval(m, 0, "S")
jalpha2 = galpha2.Eval(m, 0, "S")
jslope2 = gslope2.Eval(m, 0, "S")
else:
jmean = fmean.GetParameter(
0) + fmean.GetParameter(1) * m + fmean.GetParameter(2) * m * m
jsigma = fsigma.GetParameter(0) + fsigma.GetParameter(
1) * m + fsigma.GetParameter(2) * m * m
jalpha1 = falpha1.GetParameter(0) + falpha1.GetParameter(
1) * m + falpha1.GetParameter(2) * m * m
jslope1 = fslope1.GetParameter(0) + fslope1.GetParameter(
1) * m + fslope1.GetParameter(2) * m * m
jalpha2 = falpha2.GetParameter(0) + falpha2.GetParameter(
1) * m + falpha2.GetParameter(2) * m * m
jslope2 = fslope2.GetParameter(0) + fslope2.GetParameter(
1) * m + fslope2.GetParameter(2) * m * m
inorm.SetPoint(inorm.GetN(), m, syield)
signalNorm[m].setVal(max(0., syield))
imean.SetPoint(imean.GetN(), m, jmean)
if jmean > 0: vmean[m].setVal(jmean)
isigma.SetPoint(isigma.GetN(), m, jsigma)
if jsigma > 0: vsigma[m].setVal(jsigma)
ialpha1.SetPoint(ialpha1.GetN(), m, jalpha1)
if not jalpha1 == 0: valpha1[m].setVal(jalpha1)
islope1.SetPoint(islope1.GetN(), m, jslope1)
if jslope1 > 0: vslope1[m].setVal(jslope1)
ialpha2.SetPoint(ialpha2.GetN(), m, jalpha2)
if not jalpha2 == 0: valpha2[m].setVal(jalpha2)
islope2.SetPoint(islope2.GetN(), m, jslope2)
if jslope2 > 0: vslope2[m].setVal(jslope2)
#### newly introduced, not yet sure if helpful:
vmean[m].removeError()
vsigma[m].removeError()
valpha1[m].removeError()
valpha2[m].removeError()
vslope1[m].removeError()
vslope2[m].removeError()
#signalNorm[m].setConstant(False) ## newly put here to ensure it's freely floating in the combine fit
#c1 = TCanvas("c1", "Crystal Ball", 1200, 1200) #if not isAH else 1200
#c1.Divide(2, 3)
c1 = TCanvas("c1", "Crystal Ball", 1800, 800)
c1.Divide(3, 2)
c1.cd(1)
gmean.SetMinimum(0.)
gmean.Draw("APL")
imean.Draw("P, SAME")
drawRegion(category)
drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME
c1.cd(2)
gsigma.SetMinimum(0.)
gsigma.Draw("APL")
isigma.Draw("P, SAME")
drawRegion(category)
drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME
c1.cd(3)
galpha1.Draw("APL")
ialpha1.Draw("P, SAME")
drawRegion(category)
drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME
galpha1.GetYaxis().SetRangeUser(0., 1.1) #adjusted upper limit from 5 to 2
c1.cd(4)
gslope1.Draw("APL")
islope1.Draw("P, SAME")
drawRegion(category)
drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME
gslope1.GetYaxis().SetRangeUser(0.,
150.) #adjusted upper limit from 125 to 60
if True: #isAH:
c1.cd(5)
galpha2.Draw("APL")
ialpha2.Draw("P, SAME")
drawRegion(category)
drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME
galpha2.GetYaxis().SetRangeUser(0., 2.)
c1.cd(6)
gslope2.Draw("APL")
islope2.Draw("P, SAME")
drawRegion(category)
drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME
gslope2.GetYaxis().SetRangeUser(0., 20.)
c1.Print(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" + category +
"_SignalShape.pdf")
c1.Print(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" + category +
"_SignalShape.png")
c2 = TCanvas("c2", "Signal Efficiency", 800, 600)
c2.cd(1)
gnorm.SetMarkerColor(cColor)
gnorm.SetMarkerStyle(20)
gnorm.SetLineColor(cColor)
gnorm.SetLineWidth(2)
gnorm.Draw("APL")
inorm.Draw("P, SAME")
gnorm.GetXaxis().SetRangeUser(genPoints[0] - 100, genPoints[-1] + 100)
gnorm.GetYaxis().SetRangeUser(0., gnorm.GetMaximum() * 1.25)
drawCMS(-1, "Simulation Preliminary", year=YEAR)
#drawCMS(-1, "Work in Progress", year=YEAR, suppressCMS=True)
#drawCMS(-1, "", year=YEAR, suppressCMS=True)
drawAnalysis(category)
drawRegion(category)
c2.Print(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" + category +
"_SignalNorm.pdf")
c2.Print(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" + category +
"_SignalNorm.png")
#*******************************************************#
# #
# Generate workspace #
# #
#*******************************************************#
# create workspace
w = RooWorkspace("Zprime_" + YEAR, "workspace")
for m in massPoints:
getattr(w, "import")(signal[m], RooFit.Rename(signal[m].GetName()))
getattr(w, "import")(signalNorm[m],
RooFit.Rename(signalNorm[m].GetName()))
getattr(w, "import")(signalXS[m], RooFit.Rename(signalXS[m].GetName()))
w.writeToFile(WORKDIR + "MC_signal_%s_%s.root" % (YEAR, category), True)
print "Workspace", WORKDIR + "MC_signal_%s_%s.root" % (
YEAR, category), "saved successfully"
small=1.e-5
c1.Divide(1,2,small,small)
c1.cd(1)
gPad.SetPad(small, 0.3, 1.-small, 1.-small)
gPad.SetBottomMargin(small)
labelSize=10
makerSize = 0.3
gStyle.SetEndErrorSize(0)
gr = TGraphErrors( n, energyVec, recoEnergyVec, energyErrorVec, recoEnergyErrorVec )
gr.SetName('gr')
gr.SetLineColor( 1 )
gr.SetLineWidth( 1 )
gr.SetLineStyle( 2 )
gr.SetMarkerColor( 2 )
gr.SetMarkerStyle( 20 )
gr.SetMarkerSize( makerSize )
textsize = labelSize/(gPad.GetWh()*gPad.GetAbsHNDC())
gr.SetTitle( '' )
gr.GetXaxis().SetTitle( 'E_{particle} (GeV)' )
gr.GetYaxis().SetTitle( 'E_{reco} (GeV)' )
gr.GetYaxis().SetTitleSize(textsize)
gr.GetYaxis().SetLabelSize(textsize)
gr.GetYaxis().SetTitleOffset(1.4)
gr.GetYaxis().SetRangeUser(2, 100)
gPad.SetLeftMargin(0.15)
gr.Draw( 'AP' )
g_purity = TGraphErrors(len(purity_list), array(tolerance_list),
array(purity_list), no_err, array(purity_err_list))
g_acceptance = TGraphErrors(len(acceptance), array(tolerance_list),
array(acceptance), no_err, array(purity_err_list))
g_reco_eff_data = TGraphErrors(len(reco_eff_list), array(tolerance_list),
array(reco_eff_list), no_err,
array(eff_err_list))
g_data_eff = TGraphErrors(len(reco_eff_list), array(tolerance_list),
array(data_eff_list), no_err, array(eff_err_list))
g_eff.Draw("APL")
g_eff.GetXaxis().SetTitle("#it{d}_{max} [cm]")
g_eff.GetYaxis().SetTitle("[%]")
g_eff.GetYaxis().SetRangeUser(90, 100)
g_eff.SetMarkerStyle(20)
g_eff.SetLineColor(kRed + 1)
g_eff.SetLineWidth(2)
g_purity.Draw("PL")
g_purity.SetMarkerStyle(21)
g_purity.SetLineColor(kBlue + 1)
g_purity.SetLineWidth(2)
g_acceptance.Draw("PL")
g_acceptance.SetMarkerStyle(23)
g_acceptance.SetLineColor(kGreen + 2)
g_acceptance.SetLineWidth(2)
g_reco_eff.Draw("PL")
g_reco_eff.SetLineColor(kGray + 2)
g_reco_eff.SetLineWidth(2)
g_reco_eff.SetMarkerStyle(29)
g_reco_eff.SetMarkerSize(1.5)
g_reco_eff_data.Draw("PL")
legend.SetBorderSize(0)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetTextFont(42)
legend.SetTextSize(0.035)
legend.AddEntry(GraphL0Unc, "L0", "LP")
legend.AddEntry(GraphNomUnc, "nominal", "LP")
GraphNomUnc.Draw("")
GraphNomUnc.GetXaxis().SetRangeUser(minMass, maxMass)
GraphNomUnc.GetYaxis().SetRangeUser(0, 0.005)
GraphNomUnc.SetTitle("Unconstrained Invariant Mass Resolution Prompt A's")
GraphNomUnc.GetXaxis().SetTitle("mass [GeV]")
GraphNomUnc.GetYaxis().SetTitle("mass resolution [GeV]")
GraphL0Unc.Draw("same")
GraphL0Unc.SetLineColor(2)
gStyle.SetOptStat(0)
legend.Draw()
c.Print("uncInvMass.pdf", "Title:histoTitle")
c.Clear()
GraphNomTarg.Draw("")
GraphNomTarg.GetXaxis().SetRangeUser(minMass, maxMass)
GraphNomTarg.GetYaxis().SetRangeUser(0, 0.005)
GraphNomTarg.SetTitle(
"Target Constrained Invariant Mass Resolution Prompt A's")
GraphNomTarg.GetXaxis().SetTitle("mass [GeV]")
GraphNomTarg.GetYaxis().SetTitle("mass resolution [GeV]")
def closureTest_plots(filename):
f = BareRootFile(filename)
tree = f.Get('closureTest')
gStyle.SetStatX(0.9)
gStyle.SetStatY(0.9)
gStyle.SetStatW(0.2)
gStyle.SetStatH(0.14)
hist1 = TH1F('hist1', '', 55, 0.9, 2.0)
hist2 = TH1F('hist2', '', 55, 0.9, 2.0)
condition = ('fit_overlapDiff>=0 && temp_overlapDiff>=0 && '
'fit_chisq/fit_dof<=2 && temp_chisq/temp_dof<=2')
tree.Draw('temp_chisq/temp_dof>>hist1', condition)
tree.Draw('fit_chisq/fit_dof>>hist2', condition)
for i, hist in [(0, hist1), (1, hist2)]:
hist.SetLineColor(colors[i])
hist.SetLineWidth(3)
maxi = max(hist1.GetMaximum(), hist2.GetMaximum()) * 1.1
plot = SingleHistBase(hist1,
'hist_numberPerChisq',
fill=None,
workinprogress=wip)
gStyle.SetOptStat(10)
plot._xtitle = '#chi^{2}/d.o.f.'
plot._ytitle = 'number of toys'
plot.yrange(0.0, maxi)
leg = TLegend(0.65, 0.75, 0.89, 0.85)
leg.SetBorderSize(0)
leg.AddEntry(hist1, 'DG fit', 'L')
leg.AddEntry(hist2, 'SupDG fit', 'L')
plot.draw()
hist2.Draw('SAMEH')
leg.Draw()
plot.save_pdf()
plot.Close()
hist3 = TH2F('hist3', '', 31, -0.05, 3.05, 31, -0.05, 3.05)
tree.Draw('100*temp_overlapDiff:100*fit_overlapDiff>>hist3', condition)
one = TF1('one', 'x', -10.0, 10.0)
one.SetLineColor(1)
plot = SingleHistBase(hist3,
'correctionDGvsSupDG',
fill=None,
workinprogress=wip)
plot.add(one)
plot._drawoption = 'BOX'
plot._xtitle = 'correction [%] from SupDG fit'
plot._ytitle = 'correction [%] from DG fit'
plot._above = True
plot.draw()
plot.save_pdf()
plot.Close()
hist4 = TH2F('hist4', '', 22, 0.95, 1.5, 22, 0.95, 1.5)
tree.Draw('temp_chisq/temp_dof:fit_chisq/fit_dof>>hist4', condition)
plot = SingleHistBase(hist4,
'chisqDGvsSupDG',
fill=None,
workinprogress=wip)
plot.add(one)
plot._drawoption = 'BOX'
plot._xtitle = '#chi^{2}/d.o.f. of SupDG fit'
plot._ytitle = '#chi^{2}/d.o.f. of DG fit'
plot._above = True
plot.draw()
plot.save_pdf()
plot.Close()
gStyle.SetStatX(0.9)
gStyle.SetStatY(0.83)
gStyle.SetStatW(0.3)
gStyle.SetStatH(0.08)
#bins_csq = [(0.0, 1.1), (1.1, 1.3), (1.3, 1.6), (1.6, 2.0)]
#bins_cor = [(0.0, 0.5), (0.5, 1.0), (1.0, 1.5), (1.5, 2.0), (2.0, 2.5)]
#bins_csq = [(1.07, 1.13), (1.06, 1.11), (1.3, 1.5), (1.08, 1.17), (1.1, 1.2)]
#bins_cor = [(0.3, 0.8), (0.9, 1.2), (0.1, 0.5), (0.4, 0.7), (0.3, 1.1)]
bins_csq = [(0.99, 1.06), (0.98, 1.07), (0.99, 1.10), (1.02, 1.06),
(1.00, 1.06), (1.00, 1.04)]
bins_cor = [(0.9, 1.9), (0.4, 1.6), (0.6, 1.5), (0.6, 1.9), (1.0, 1.3),
(0.3, 1.5)]
means = {
mod: [[0.0 for __ in bins_cor] for __ in bins_csq]
for mod in ('DG', 'SupDG')
}
meane = {
mod: [[0.0 for __ in bins_cor] for __ in bins_csq]
for mod in ('DG', 'SupDG')
}
rmses = {
mod: [[0.0 for __ in bins_cor] for __ in bins_csq]
for mod in ('DG', 'SupDG')
}
rmser = {
mod: [[0.0 for __ in bins_cor] for __ in bins_csq]
for mod in ('DG', 'SupDG')
}
#for i, (csq_lo, csq_hi) in enumerate(bins_csq):
# for j, (cor_lo, cor_hi) in enumerate(bins_cor):
for i, ((csq_lo, csq_hi), (cor_lo,
cor_hi)) in enumerate(zip(bins_csq, bins_cor)):
j = i
name = 'hist_{{0}}_{0}csq{1}_{2}cor{3}' \
.format(csq_lo, csq_hi, cor_lo, cor_hi)
fields = '100*({0}_overlapDiff-toy_overlapDiff)>>hist'
condition = (
'100*{{0}}_overlapDiff>={0} && 100*{{0}}_overlapDiff<{1} && '
'{{0}}_chisq/{{0}}_dof>={2} && {{0}}_chisq/{{0}}_dof<{3} && '
'fit_overlapDiff>=0 && temp_overlapDiff>=0 && '
'fit_chisq/fit_dof<=2 && temp_chisq/temp_dof<=2').format(
cor_lo, cor_hi, csq_lo, csq_hi)
xtitle = 'correction [%] from {0} fit #minus true correction [%]'
line1 = '{0} < correction < {1}'.format(cor_lo, cor_hi)
line2 = '{0} < #chi^{{2}}/d.o.f. < {1}'.format(csq_lo, csq_hi)
gStyle.SetOptStat(2210)
for prefix, modname in (('temp', 'DG'), ('fit', 'SupDG')):
hist = TH1F('hist', '', 41, -2.05, 2.05)
hist.StatOverflows()
tree.Draw(fields.format(prefix), condition.format(prefix))
plot = SingleHistBase(hist,
name.format(modname),
fill=None,
workinprogress=wip)
gStyle.SetOptStat(2210)
plot._xtitle = xtitle.format(modname)
plot._ytitle = 'number of toys'
plot.xrange(-2.05, 2.05)
plot.yrange(0.0, plot._graph.GetMaximum() * 1.2)
pave = plot.add_pave(0.6, 0.83, 0.9, 0.9, border=True)
pave(line1)
pave(line2)
plot.draw()
plot.save_pdf()
means[modname][i][j] = plot._graph.GetMean()
meane[modname][i][j] = plot._graph.GetMeanError()
rmses[modname][i][j] = plot._graph.GetRMS()
rmser[modname][i][j] = plot._graph.GetRMSError()
plot.Close()
multi = TMultiGraph('multi', '')
for k, modname in enumerate(('DG', 'SupDG')):
for i, (csq_lo, csq_hi) in enumerate(bins_csq):
xval = array(
'd',
[j - 0.35 + k * 0.4 + i * 0.1 for j in range(len(bins_cor))])
xerr = array('d', [0.0] * len(bins_cor))
yval = array('d', means[modname][i])
yerr = array('d', rmses[modname][i])
graph = TGraphErrors(len(bins_cor), xval, yval, xerr, yerr)
graph.SetName('graph{0}{1}'.format(k, i))
graph.SetMarkerStyle(22 + k)
graph.SetMarkerColor(colors[i])
graph.SetLineColor(colors[i])
multi.Add(graph)
minvalues = [
means[mod][i][j] - rmses[mod][i][j] for j in range(len(bins_cor))
for i in range(len(bins_csq)) for mod in ('DG', 'SupDG')
]
maxvalues = [
means[mod][i][j] + rmses[mod][i][j] for j in range(len(bins_cor))
for i in range(len(bins_csq)) for mod in ('DG', 'SupDG')
]
mini, maxi = min(minvalues), max(maxvalues)
mini, maxi = mini - 0.1 * (maxi - mini), maxi + 0.3 * (maxi - mini)
hist = TH2F('axishist', '', len(bins_cor), -0.5,
len(bins_cor) - 0.5, 100, mini, maxi)
for j, (cor_lo, cor_hi) in enumerate(bins_cor):
hist.GetXaxis().SetBinLabel(j + 1,
'{0}% #minus {1}%'.format(cor_lo, cor_hi))
leg1 = TLegend(0.53, 0.78, 0.63, 0.86)
leg1.SetBorderSize(0)
dgmarker = TMarker(0.0, 0.0, 22)
supdgmarker = TMarker(0.0, 0.0, 23)
leg1.AddEntry(dgmarker, 'DG', 'P')
leg1.AddEntry(supdgmarker, 'SupDG', 'P')
leg2 = TLegend(0.65, 0.75, 0.89, 0.89)
leg2.SetBorderSize(0)
csqmarker = TMarker(0.0, 0.0, 1)
for i, (csq_lo, csq_hi) in enumerate(bins_csq):
title = '{0} < #chi^{{2}}/d.o.f. < {1}'.format(csq_lo, csq_hi)
entry = leg2.AddEntry(csqmarker, title, 'L')
entry.SetMarkerColor(colors[i])
entry.SetLineColor(colors[i])
zero = TF1('zero', '0.0', -1.0, 10.0)
zero.SetLineColor(1)
zero.SetLineStyle(2)
plot = SingleHistBase(hist,
name='differenceDoubleDifferential',
fill=None,
workinprogress=wip)
plot._xtitle = 'correction from fit'
plot._ytitle = 'correction [%] from fit #minus true correction [%]'
plot.xrange(-0.5, len(bins_cor) - 0.5)
plot.yrange(mini, maxi)
plot._drawoption = 'AXIS'
plot.draw()
plot.xaxis().SetNdivisions(len(bins_cor), False)
plot.xaxis().SetLabelSize(0.03)
zero.Draw('SAME')
multi.Draw('P')
leg1.Draw()
leg2.Draw()
plot.save_pdf()
plot.Close()
bcid = (41, 281, 872, 1783, 2063)
DGcor1 = array('d', [0.809, 0.392, 0.846, 0.731, 0.497])
DGerr1 = array('d', [0.548, 0.567, 0.984, 0.984, 1.018])
DGcor2 = array('d', [1.145, 0.799, 1.58, 1.465, 1.281])
DGerr2 = array('d', [0.432, 0.395, 0.656, 0.656, 0.649])
DGxval = array('d', [j - 0.1 for j in range(5)])
SupDGcor1 = array('d', [0.823, 0.761, 1.458, 0.986, 1.012])
SupDGerr1 = array('d', [0.513, 0.513, 0.499, 0.513, 0.499])
SupDGcor2 = array('d', [0.978, 0.916, 1.532, 1.141, 1.086])
SupDGerr2 = array('d', [0.489, 0.489, 0.493, 0.489, 0.493])
SupDGxval = array('d', [j + 0.1 for j in range(5)])
xerr = array('d', [0.0] * 5)
for fill, values in [
(4266, {
'DGcor': [1.021, 1.057, 0.968, 1.084, 1.114],
'DGerr': [(e**2 + 0.74**2)**0.5
for e in (0.118, 0.124, 0.119, 0.117, 0.119)],
'SupDGcor': [1.402, 1.411, 1.164, 1.549, 1.589],
'SupDGerr': [(e**2 + 0.45**2)**0.5
for e in (0.106, 0.110, 0.108, 0.106, 0.115)],
'bcids': [51, 771, 1631, 2211, 2674]
}),
(4954, {
'DGcor': [0.799, 0.398, 0.845, 0.724, 0.502],
'DGerr': [(e**2 + 0.79**2)**0.5
for e in (0.137, 0.124, 0.122, 0.130, 0.116)],
'SupDGcor': [0.794, 0.694, 1.642, 0.983, 0.993],
'SupDGerr': [(e**2 + 0.50**2)**0.5
for e in (0.126, 0.112, 0.186, 0.102, 0.144)],
'bcids': [41, 281, 872, 1783, 2063]
}),
(4937, {
'DGcor': [0.649, 0.494, 0.575, 0.527, 0.602],
'DGerr':
[(e**2 + 0.85**2)**0.5 for e in (0.127, 0.115, 0.120, 0.125)],
'SupDGcor': [0.377, 0.611, 1.137, 0.453, 1.840],
'SupDGerr': [(e**2 + 0.56**2)**0.5
for e in (0.100, 0.105, 0.288, 0.161, 0.207)],
'bcids': [81, 875, 1610, 1690, 1730]
}),
(6016, {
'DGcor': [0.146, 0.394, 0.377, 0.488, 0.184],
'DGerr': [(e**2 + 1.15**2)**0.5
for e in (0.110, 0.114, 0.118, 0.123, 0.109)],
'SupDGcor': [0.760, 0.953, 1.048, 0.847, 0.373],
'SupDGerr': [(e**2 + 0.79**2)**0.5
for e in (0.219, 0.094, 0.189, 0.098, 0.169)],
'bcids': [41, 281, 872, 1783, 2063]
})
]:
bcid = values['bcids']
DGcor = array('d', values['DGcor'])
DGerr = array('d', values['DGerr'])
DGxvl = array('d', [j - 0.1 for j in range(len(bcid))])
SupDGcor = array('d', values['SupDGcor'])
SupDGerr = array('d', values['SupDGerr'])
SupDGxvl = array('d', [j + 0.1 for j in range(len(bcid))])
xerr = array('d', [0.0] * len(bcid))
maxi = max(max([v + e for v, e in zip(DGcor, DGerr)]),
max([v + e for v, e in zip(SupDGcor, SupDGerr)]))
mini = min(min([v - e for v, e in zip(DGcor, DGerr)]),
min([v - e for v, e in zip(SupDGcor, SupDGerr)]))
maxi, mini = maxi + 0.2 * (maxi - mini), mini - 0.1 * (maxi - mini)
graphDG = TGraphErrors(len(bcid), DGxvl, DGcor, xerr, DGerr)
graphDG.SetName('graphDG')
graphSupDG = TGraphErrors(len(bcid), SupDGxvl, SupDGcor, xerr,
SupDGerr)
graphSupDG.SetName('graphSupDG')
multi = TMultiGraph('multi', '')
for i, graph in [(0, graphDG), (1, graphSupDG)]:
graph.SetMarkerStyle(22 + i)
graph.SetMarkerColor(colors[i])
graph.SetLineColor(colors[i])
multi.Add(graph)
leg = TLegend(0.15, 0.80, 0.5, 0.83)
leg.SetNColumns(2)
leg.SetBorderSize(0)
leg.AddEntry(graphDG, 'DG fit', 'PL')
leg.AddEntry(graphSupDG, 'SupDG fit', 'PL')
axishist = TH2F('axishist', '', len(bcid), -0.5,
len(bcid) - 0.5, 100, mini, maxi)
for i, bx in enumerate(bcid):
axishist.GetXaxis().SetBinLabel(i + 1, '{0}'.format(bx))
plot = SingleHistBase(axishist,
name='Fill{0}biased'.format(fill),
fill=fill,
workinprogress=wip)
plot._xtitle = 'BCID'
plot._ytitle = 'correction [%] from fit'
plot.xrange(-0.5, len(bcid) - 0.5)
plot.yrange(mini, maxi)
plot._drawoption = 'AXIS'
plot.draw()
plot.xaxis().SetNdivisions(len(bcid), False)
plot.xaxis().SetLabelSize(0.03)
multi.Draw('P')
leg.Draw()
plot.save_pdf()
plot.Close()
print
print 'Fill', fill
for bx, cor, err in zip(bcid, SupDGcor, SupDGerr):
print 'BCID {0}:\t{1:.2f} +- {2:.2f}'.format(bx, cor, err)
print
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 signal(channel, stype):
if 'VBF' in channel:
stype = 'XZHVBF'
else:
stype = 'XZH'
# HVT model
if stype.startswith('X'):
signalType = 'HVT'
genPoints = [800, 1000, 1200, 1400, 1600, 1800, 2000, 2500, 3000, 3500, 4000, 4500, 5000]
massPoints = [x for x in range(800, 5000+1, 100)]
interPar = True
else:
print "Signal type", stype, "not recognized"
return
n = len(genPoints)
category = channel
cColor = color[category] if category in color else 1
nElec = channel.count('e')
nMuon = channel.count('m')
nLept = nElec + nMuon
nBtag = channel.count('b')
if '0b' in channel:
nBtag = 0
X_name = "VH_mass"
if not os.path.exists(PLOTDIR+stype+category): os.makedirs(PLOTDIR+stype+category)
#*******************************************************#
# #
# Variables and selections #
# #
#*******************************************************#
X_mass = RooRealVar( "X_mass", "m_{ZH}", XBINMIN, XBINMAX, "GeV")
J_mass = RooRealVar( "H_mass", "jet mass", LOWMIN, HIGMAX, "GeV")
V_mass = RooRealVar( "V_mass", "V jet mass", -9., 1.e6, "GeV")
CSV1 = RooRealVar( "H_csv1", "", -999., 2. )
CSV2 = RooRealVar( "H_csv2", "", -999., 2. )
DeepCSV1= RooRealVar( "H_deepcsv1", "", -999., 2. )
DeepCSV2= RooRealVar( "H_deepcsv2", "", -999., 2. )
H_ntag = RooRealVar( "H_ntag", "", -9., 9. )
H_dbt = RooRealVar( "H_dbt", "", -2., 2. )
H_tau21 = RooRealVar( "H_tau21", "", -9., 2. )
H_eta = RooRealVar( "H_eta", "", -9., 9. )
H_tau21_ddt = RooRealVar( "H_ddt", "", -9., 2. )
MaxBTag = RooRealVar( "MaxBTag", "", -10., 2. )
H_chf = RooRealVar( "H_chf", "", -1., 2. )
MinDPhi = RooRealVar( "MinDPhi", "", -1., 99. )
DPhi = RooRealVar( "DPhi", "", -1., 99. )
DEta = RooRealVar( "DEta", "", -1., 99. )
Mu1_relIso = RooRealVar( "Mu1_relIso", "", -1., 99. )
Mu2_relIso = RooRealVar( "Mu2_relIso", "", -1., 99. )
nTaus = RooRealVar( "nTaus", "", -1., 99. )
Vpt = RooRealVar( "V.Pt()", "", -1., 1.e6 )
V_pt = RooRealVar( "V_pt", "", -1., 1.e6 )
H_pt = RooRealVar( "H_pt", "", -1., 1.e6 )
VH_deltaR=RooRealVar( "VH_deltaR", "", -1., 99. )
isZtoNN = RooRealVar( "isZtoNN", "", 0., 2. )
isZtoEE = RooRealVar( "isZtoEE", "", 0., 2. )
isZtoMM = RooRealVar( "isZtoMM", "", 0., 2. )
isHtobb = RooRealVar( "isHtobb", "", 0., 2. )
isVBF = RooRealVar( "isVBF", "", 0., 2. )
isMaxBTag_loose = RooRealVar( "isMaxBTag_loose", "", 0., 2. )
weight = RooRealVar( "eventWeightLumi", "", -1.e9, 1.e9 )
Xmin = XBINMIN
Xmax = XBINMAX
# Define the RooArgSet which will include all the variables defined before
# there is a maximum of 9 variables in the declaration, so the others need to be added with 'add'
variables = RooArgSet(X_mass, J_mass, V_mass, CSV1, CSV2, H_ntag, H_dbt, H_tau21)
variables.add(RooArgSet(DEta, DPhi, MaxBTag, MinDPhi, nTaus, Vpt))
variables.add(RooArgSet(DeepCSV1, DeepCSV2,VH_deltaR, H_tau21_ddt))
variables.add(RooArgSet(isZtoNN, isZtoEE, isZtoMM, isHtobb, isMaxBTag_loose, weight))
variables.add(RooArgSet(isVBF, Mu1_relIso, Mu2_relIso, H_chf, H_pt, V_pt,H_eta))
#X_mass.setRange("X_extended_range", X_mass.getMin(), X_mass.getMax())
X_mass.setRange("X_reasonable_range", X_mass.getMin(), X_mass.getMax())
X_mass.setRange("X_integration_range", Xmin, Xmax)
X_mass.setBins(int((X_mass.getMax() - X_mass.getMin())/100))
binsXmass = RooBinning(int((X_mass.getMax() - X_mass.getMin())/100), X_mass.getMin(), X_mass.getMax())
X_mass.setBinning(binsXmass, "PLOT")
massArg = RooArgSet(X_mass)
# Cuts
SRcut = selection[category]+selection['SR']
print " Cut:\t", SRcut
#*******************************************************#
# #
# Signal fits #
# #
#*******************************************************#
treeSign = {}
setSignal = {}
vmean = {}
vsigma = {}
valpha1 = {}
vslope1 = {}
smean = {}
ssigma = {}
salpha1 = {}
sslope1 = {}
salpha2 = {}
sslope2 = {}
a1 = {}
a2 = {}
sbrwig = {}
signal = {}
signalExt = {}
signalYield = {}
signalIntegral = {}
signalNorm = {}
signalXS = {}
frSignal = {}
frSignal1 = {}
frSignal2 = {}
frSignal3 = {}
# Signal shape uncertainties (common amongst all mass points)
xmean_fit = RooRealVar("sig_p1_fit", "Variation of the resonance position with the fit uncertainty", 0.005, -1., 1.)
smean_fit = RooRealVar("CMSRunII_sig_p1_fit", "Change of the resonance position with the fit uncertainty", 0., -10, 10)
xmean_jes = RooRealVar("sig_p1_scale_jes", "Variation of the resonance position with the jet energy scale", 0.010, -1., 1.) #0.001
smean_jes = RooRealVar("CMSRunII_sig_p1_jes", "Change of the resonance position with the jet energy scale", 0., -10, 10)
xmean_e = RooRealVar("sig_p1_scale_e", "Variation of the resonance position with the electron energy scale", 0.001, -1., 1.)
smean_e = RooRealVar("CMSRunII_sig_p1_scale_e", "Change of the resonance position with the electron energy scale", 0., -10, 10)
xmean_m = RooRealVar("sig_p1_scale_m", "Variation of the resonance position with the muon energy scale", 0.001, -1., 1.)
smean_m = RooRealVar("CMSRunII_sig_p1_scale_m", "Change of the resonance position with the muon energy scale", 0., -10, 10)
xsigma_fit = RooRealVar("sig_p2_fit", "Variation of the resonance width with the fit uncertainty", 0.02, -1., 1.)
ssigma_fit = RooRealVar("CMSRunII_sig_p2_fit", "Change of the resonance width with the fit uncertainty", 0., -10, 10)
xsigma_jes = RooRealVar("sig_p2_scale_jes", "Variation of the resonance width with the jet energy scale", 0.010, -1., 1.) #0.001
ssigma_jes = RooRealVar("CMSRunII_sig_p2_jes", "Change of the resonance width with the jet energy scale", 0., -10, 10)
xsigma_jer = RooRealVar("sig_p2_scale_jer", "Variation of the resonance width with the jet energy resolution", 0.020, -1., 1.)
ssigma_jer = RooRealVar("CMSRunII_sig_p2_jer", "Change of the resonance width with the jet energy resolution", 0., -10, 10)
xsigma_e = RooRealVar("sig_p2_scale_e", "Variation of the resonance width with the electron energy scale", 0.001, -1., 1.)
ssigma_e = RooRealVar("CMSRunII_sig_p2_scale_e", "Change of the resonance width with the electron energy scale", 0., -10, 10)
xsigma_m = RooRealVar("sig_p2_scale_m", "Variation of the resonance width with the muon energy scale", 0.040, -1., 1.)
ssigma_m = RooRealVar("CMSRunII_sig_p2_scale_m", "Change of the resonance width with the muon energy scale", 0., -10, 10)
xalpha1_fit = RooRealVar("sig_p3_fit", "Variation of the resonance alpha with the fit uncertainty", 0.03, -1., 1.)
salpha1_fit = RooRealVar("CMSRunII_sig_p3_fit", "Change of the resonance alpha with the fit uncertainty", 0., -10, 10)
xslope1_fit = RooRealVar("sig_p4_fit", "Variation of the resonance slope with the fit uncertainty", 0.10, -1., 1.)
sslope1_fit = RooRealVar("CMSRunII_sig_p4_fit", "Change of the resonance slope with the fit uncertainty", 0., -10, 10)
xmean_fit.setConstant(True)
smean_fit.setConstant(True)
xmean_jes.setConstant(True)
smean_jes.setConstant(True)
xmean_e.setConstant(True)
smean_e.setConstant(True)
xmean_m.setConstant(True)
smean_m.setConstant(True)
xsigma_fit.setConstant(True)
ssigma_fit.setConstant(True)
xsigma_jes.setConstant(True)
ssigma_jes.setConstant(True)
xsigma_jer.setConstant(True)
ssigma_jer.setConstant(True)
xsigma_e.setConstant(True)
ssigma_e.setConstant(True)
xsigma_m.setConstant(True)
ssigma_m.setConstant(True)
xalpha1_fit.setConstant(True)
salpha1_fit.setConstant(True)
xslope1_fit.setConstant(True)
sslope1_fit.setConstant(True)
# the alpha method is now done.
for m in massPoints:
signalString = "M%d" % m
signalMass = "%s_M%d" % (stype, m)
signalName = "%s%s_M%d" % (stype, category, m)
signalColor = sample[signalMass]['linecolor'] if signalName in sample else 1
# define the signal PDF
vmean[m] = RooRealVar(signalName + "_vmean", "Crystal Ball mean", m, m*0.5, m*1.25)
smean[m] = RooFormulaVar(signalName + "_mean", "@0*(1+@1*@2)*(1+@3*@4)*(1+@5*@6)*(1+@7*@8)", RooArgList(vmean[m], xmean_e, smean_e, xmean_m, smean_m, xmean_jes, smean_jes, xmean_fit, smean_fit))
vsigma[m] = RooRealVar(signalName + "_vsigma", "Crystal Ball sigma", m*0.035, m*0.01, m*0.4)
sigmaList = RooArgList(vsigma[m], xsigma_e, ssigma_e, xsigma_m, ssigma_m, xsigma_jes, ssigma_jes, xsigma_jer, ssigma_jer)
sigmaList.add(RooArgList(xsigma_fit, ssigma_fit))
ssigma[m] = RooFormulaVar(signalName + "_sigma", "@0*(1+@1*@2)*(1+@3*@4)*(1+@5*@6)*(1+@7*@8)*(1+@9*@10)", sigmaList)
valpha1[m] = RooRealVar(signalName + "_valpha1", "Crystal Ball alpha", 1., 0., 5.) # number of sigmas where the exp is attached to the gaussian core. >0 left, <0 right
salpha1[m] = RooFormulaVar(signalName + "_alpha1", "@0*(1+@1*@2)", RooArgList(valpha1[m], xalpha1_fit, salpha1_fit))
vslope1[m] = RooRealVar(signalName + "_vslope1", "Crystal Ball slope", 10., 1., 60.) # slope of the power tail #10 1 60
sslope1[m] = RooFormulaVar(signalName + "_slope1", "@0*(1+@1*@2)", RooArgList(vslope1[m], xslope1_fit, sslope1_fit))
salpha2[m] = RooRealVar(signalName + "_alpha2", "Crystal Ball alpha", 2, 1., 5.) # number of sigmas where the exp is attached to the gaussian core. >0 left, <0 right
sslope2[m] = RooRealVar(signalName + "_slope2", "Crystal Ball slope", 10, 1.e-1, 115.) # slope of the power tail
#define polynomial
#a1[m] = RooRealVar(signalName + "_a1", "par 1 for polynomial", m, 0.5*m, 2*m)
a1[m] = RooRealVar(signalName + "_a1", "par 1 for polynomial", 0.001*m, 0.0005*m, 0.01*m)
a2[m] = RooRealVar(signalName + "_a2", "par 2 for polynomial", 0.05, -1.,1.)
#if channel=='nnbbVBF' or channel=='nn0bVBF':
# signal[m] = RooPolynomial(signalName,"m_{%s'} = %d GeV" % (stype[1], m) , X_mass, RooArgList(a1[m],a2[m]))
#else:
# signal[m] = RooCBShape(signalName, "m_{%s'} = %d GeV" % (stype[1], m), X_mass, smean[m], ssigma[m], salpha1[m], sslope1[m]) # Signal name does not have the channel
signal[m] = RooCBShape(signalName, "m_{%s'} = %d GeV" % (stype[1], m), X_mass, smean[m], ssigma[m], salpha1[m], sslope1[m]) # Signal name does not have the channel
# extend the PDF with the yield to perform an extended likelihood fit
signalYield[m] = RooRealVar(signalName+"_yield", "signalYield", 100, 0., 1.e6)
signalNorm[m] = RooRealVar(signalName+"_norm", "signalNorm", 1., 0., 1.e6)
signalXS[m] = RooRealVar(signalName+"_xs", "signalXS", 1., 0., 1.e6)
signalExt[m] = RooExtendPdf(signalName+"_ext", "extended p.d.f", signal[m], signalYield[m])
vslope1[m].setMax(50.)
vslope1[m].setVal(20.)
#valpha1[m].setVal(1.0)
#valpha1[m].setConstant(True)
if 'bb' in channel and 'VBF' not in channel:
if 'nn' in channel:
valpha1[m].setVal(0.5)
elif '0b' in channel and 'VBF' not in channel:
if 'nn' in channel:
if m==800:
valpha1[m].setVal(2.)
vsigma[m].setVal(m*0.04)
elif 'ee' in channel:
valpha1[m].setVal(0.8)
if m==800:
#valpha1[m].setVal(1.2)
valpha1[m].setVal(2.5)
vslope1[m].setVal(50.)
elif 'mm' in channel:
if m==800:
valpha1[m].setVal(2.)
vsigma[m].setVal(m*0.03)
else:
vmean[m].setVal(m*0.9)
vsigma[m].setVal(m*0.08)
elif 'bb' in channel and 'VBF' in channel:
if 'nn' in channel:
if m!=1800:
vmean[m].setVal(m*0.8)
vsigma[m].setVal(m*0.08)
valpha1[m].setMin(1.)
elif 'ee' in channel:
valpha1[m].setVal(0.7)
elif 'mm' in channel:
if m==800:
vslope1[m].setVal(50.)
valpha1[m].setVal(0.7)
elif '0b' in channel and 'VBF' in channel:
if 'nn' in channel:
valpha1[m].setVal(3.)
vmean[m].setVal(m*0.8)
vsigma[m].setVal(m*0.08)
valpha1[m].setMin(1.)
elif 'ee' in channel:
if m<2500:
valpha1[m].setVal(2.)
if m==800:
vsigma[m].setVal(m*0.05)
elif m==1000:
vsigma[m].setVal(m*0.03)
elif m>1000 and m<1800:
vsigma[m].setVal(m*0.04)
elif 'mm' in channel:
if m<2000:
valpha1[m].setVal(2.)
if m==1000 or m==1800:
vsigma[m].setVal(m*0.03)
elif m==1200 or m==1600:
vsigma[m].setVal(m*0.04)
#if m < 1000: vsigma[m].setVal(m*0.06)
# If it's not the proper channel, make it a gaussian
#if nLept==0 and 'VBF' in channel:
# valpha1[m].setVal(5)
# valpha1[m].setConstant(True)
# vslope1[m].setConstant(True)
# salpha2[m].setConstant(True)
# sslope2[m].setConstant(True)
# ---------- if there is no simulated signal, skip this mass point ----------
if m in genPoints:
if VERBOSE: print " - Mass point", m
# define the dataset for the signal applying the SR cuts
treeSign[m] = TChain("tree")
for j, ss in enumerate(sample[signalMass]['files']):
treeSign[m].Add(NTUPLEDIR + ss + ".root")
if treeSign[m].GetEntries() <= 0.:
if VERBOSE: print " - 0 events available for mass", m, "skipping mass point..."
signalNorm[m].setVal(-1)
vmean[m].setConstant(True)
vsigma[m].setConstant(True)
salpha1[m].setConstant(True)
sslope1[m].setConstant(True)
salpha2[m].setConstant(True)
sslope2[m].setConstant(True)
signalNorm[m].setConstant(True)
signalXS[m].setConstant(True)
continue
setSignal[m] = RooDataSet("setSignal_"+signalName, "setSignal", variables, RooFit.Cut(SRcut), RooFit.WeightVar(weight), RooFit.Import(treeSign[m]))
if VERBOSE: print " - Dataset with", setSignal[m].sumEntries(), "events loaded"
# FIT
signalYield[m].setVal(setSignal[m].sumEntries())
if treeSign[m].GetEntries(SRcut) > 5:
if VERBOSE: print " - Running fit"
frSignal[m] = signalExt[m].fitTo(setSignal[m], RooFit.Save(1), RooFit.Extended(True), RooFit.SumW2Error(True), RooFit.PrintLevel(-1))
if VERBOSE: print "********** Fit result [", m, "] **", category, "*"*40, "\n", frSignal[m].Print(), "\n", "*"*80
if VERBOSE: frSignal[m].correlationMatrix().Print()
drawPlot(signalMass, stype+channel, X_mass, signal[m], setSignal[m], frSignal[m])
else:
print " WARNING: signal", stype, "and mass point", m, "in channel", channel, "has 0 entries or does not exist"
# Remove HVT cross section (which is the same for Zlep and Zinv)
if stype == "XZHVBF":
sample_name = 'Zprime_VBF_Zh_Zlephinc_narrow_M-%d' % m
else:
sample_name = 'ZprimeToZHToZlepHinc_narrow_M%d' % m
xs = xsection[sample_name]['xsec']
signalXS[m].setVal(xs * 1000.)
signalIntegral[m] = signalExt[m].createIntegral(massArg, RooFit.NormSet(massArg), RooFit.Range("X_integration_range"))
boundaryFactor = signalIntegral[m].getVal()
if VERBOSE:
print " - Fit normalization vs integral:", signalYield[m].getVal(), "/", boundaryFactor, "events"
if channel=='nnbb' and m==5000:
signalNorm[m].setVal(2.5)
elif channel=='nn0b' and m==5000:
signalNorm[m].setVal(6.7)
else:
signalNorm[m].setVal( boundaryFactor * signalYield[m].getVal() / signalXS[m].getVal()) # here normalize to sigma(X) x Br(X->VH) = 1 [fb]
a1[m].setConstant(True)
a2[m].setConstant(True)
vmean[m].setConstant(True)
vsigma[m].setConstant(True)
valpha1[m].setConstant(True)
vslope1[m].setConstant(True)
salpha2[m].setConstant(True)
sslope2[m].setConstant(True)
signalNorm[m].setConstant(True)
signalXS[m].setConstant(True)
#*******************************************************#
# #
# Signal interpolation #
# #
#*******************************************************#
# ====== CONTROL PLOT ======
c_signal = TCanvas("c_signal", "c_signal", 800, 600)
c_signal.cd()
frame_signal = X_mass.frame()
for m in genPoints[:-2]:
if m in signalExt.keys():
signal[m].plotOn(frame_signal, RooFit.LineColor(sample["%s_M%d" % (stype, m)]['linecolor']), RooFit.Normalization(signalNorm[m].getVal(), RooAbsReal.NumEvent), RooFit.Range("X_reasonable_range"))
frame_signal.GetXaxis().SetRangeUser(0, 6500)
frame_signal.Draw()
drawCMS(-1, YEAR, "Simulation")
drawAnalysis(channel)
drawRegion(channel)
c_signal.SaveAs(PLOTDIR+"/"+stype+category+"/"+stype+"_Signal.pdf")
c_signal.SaveAs(PLOTDIR+"/"+stype+category+"/"+stype+"_Signal.png")
#if VERBOSE: raw_input("Press Enter to continue...")
# ====== CONTROL PLOT ======
# Normalization
gnorm = TGraphErrors()
gnorm.SetTitle(";m_{X} (GeV);integral (GeV)")
gnorm.SetMarkerStyle(20)
gnorm.SetMarkerColor(1)
gnorm.SetMaximum(0)
inorm = TGraphErrors()
inorm.SetMarkerStyle(24)
fnorm = TF1("fnorm", "pol9", 800, 5000) #"pol5" if not channel=="XZHnnbb" else "pol6" #pol5*TMath::Floor(x-1800) + ([5]*x + [6]*x*x)*(1-TMath::Floor(x-1800))
fnorm.SetLineColor(920)
fnorm.SetLineStyle(7)
fnorm.SetFillColor(2)
fnorm.SetLineColor(cColor)
# Mean
gmean = TGraphErrors()
gmean.SetTitle(";m_{X} (GeV);gaussian mean (GeV)")
gmean.SetMarkerStyle(20)
gmean.SetMarkerColor(cColor)
gmean.SetLineColor(cColor)
imean = TGraphErrors()
imean.SetMarkerStyle(24)
fmean = TF1("fmean", "pol1", 0, 5000)
fmean.SetLineColor(2)
fmean.SetFillColor(2)
# Width
gsigma = TGraphErrors()
gsigma.SetTitle(";m_{X} (GeV);gaussian width (GeV)")
gsigma.SetMarkerStyle(20)
gsigma.SetMarkerColor(cColor)
gsigma.SetLineColor(cColor)
isigma = TGraphErrors()
isigma.SetMarkerStyle(24)
fsigma = TF1("fsigma", "pol1", 0, 5000)
fsigma.SetLineColor(2)
fsigma.SetFillColor(2)
# Alpha1
galpha1 = TGraphErrors()
galpha1.SetTitle(";m_{X} (GeV);crystal ball lower alpha")
galpha1.SetMarkerStyle(20)
galpha1.SetMarkerColor(cColor)
galpha1.SetLineColor(cColor)
ialpha1 = TGraphErrors()
ialpha1.SetMarkerStyle(24)
falpha1 = TF1("falpha", "pol0", 0, 5000)
falpha1.SetLineColor(2)
falpha1.SetFillColor(2)
# Slope1
gslope1 = TGraphErrors()
gslope1.SetTitle(";m_{X} (GeV);exponential lower slope (1/Gev)")
gslope1.SetMarkerStyle(20)
gslope1.SetMarkerColor(cColor)
gslope1.SetLineColor(cColor)
islope1 = TGraphErrors()
islope1.SetMarkerStyle(24)
fslope1 = TF1("fslope", "pol0", 0, 5000)
fslope1.SetLineColor(2)
fslope1.SetFillColor(2)
# Alpha2
galpha2 = TGraphErrors()
galpha2.SetTitle(";m_{X} (GeV);crystal ball upper alpha")
galpha2.SetMarkerStyle(20)
galpha2.SetMarkerColor(cColor)
galpha2.SetLineColor(cColor)
ialpha2 = TGraphErrors()
ialpha2.SetMarkerStyle(24)
falpha2 = TF1("falpha", "pol0", 0, 5000)
falpha2.SetLineColor(2)
falpha2.SetFillColor(2)
# Slope2
gslope2 = TGraphErrors()
gslope2.SetTitle(";m_{X} (GeV);exponential upper slope (1/Gev)")
gslope2.SetMarkerStyle(20)
gslope2.SetMarkerColor(cColor)
gslope2.SetLineColor(cColor)
islope2 = TGraphErrors()
islope2.SetMarkerStyle(24)
fslope2 = TF1("fslope", "pol0", 0, 5000)
fslope2.SetLineColor(2)
fslope2.SetFillColor(2)
n = 0
for i, m in enumerate(genPoints):
if not m in signalNorm.keys(): continue
if signalNorm[m].getVal() < 1.e-6: continue
signalString = "M%d" % m
signalName = "%s_M%d" % (stype, m)
if gnorm.GetMaximum() < signalNorm[m].getVal(): gnorm.SetMaximum(signalNorm[m].getVal())
gnorm.SetPoint(n, m, signalNorm[m].getVal())
gmean.SetPoint(n, m, vmean[m].getVal())
gmean.SetPointError(n, 0, min(vmean[m].getError(), vmean[m].getVal()*0.02))
gsigma.SetPoint(n, m, vsigma[m].getVal())
gsigma.SetPointError(n, 0, min(vsigma[m].getError(), vsigma[m].getVal()*0.05))
galpha1.SetPoint(n, m, valpha1[m].getVal())
galpha1.SetPointError(n, 0, min(valpha1[m].getError(), valpha1[m].getVal()*0.10))
gslope1.SetPoint(n, m, vslope1[m].getVal())
gslope1.SetPointError(n, 0, min(vslope1[m].getError(), vslope1[m].getVal()*0.10))
galpha2.SetPoint(n, m, salpha2[m].getVal())
galpha2.SetPointError(n, 0, min(salpha2[m].getError(), salpha2[m].getVal()*0.10))
gslope2.SetPoint(n, m, sslope2[m].getVal())
gslope2.SetPointError(n, 0, min(sslope2[m].getError(), sslope2[m].getVal()*0.10))
n = n + 1
print "fit on gmean:"
gmean.Fit(fmean, "Q0", "SAME")
print "fit on gsigma:"
gsigma.Fit(fsigma, "Q0", "SAME")
print "fit on galpha:"
galpha1.Fit(falpha1, "Q0", "SAME")
print "fit on gslope:"
gslope1.Fit(fslope1, "Q0", "SAME")
galpha2.Fit(falpha2, "Q0", "SAME")
gslope2.Fit(fslope2, "Q0", "SAME")
#for m in [5000, 5500]: gnorm.SetPoint(gnorm.GetN(), m, gnorm.Eval(m, 0, "S"))
gnorm.Fit(fnorm, "Q", "SAME", 700, 5000)
for m in massPoints:
signalName = "%s_M%d" % (stype, m)
if vsigma[m].getVal() < 10.: vsigma[m].setVal(10.)
# Interpolation method
syield = gnorm.Eval(m)
spline = gnorm.Eval(m, 0, "S")
sfunct = fnorm.Eval(m)
#delta = min(abs(1.-spline/sfunct), abs(1.-spline/syield))
delta = abs(1.-spline/sfunct) if sfunct > 0 else 0
syield = spline
if interPar:
jmean = gmean.Eval(m)
jsigma = gsigma.Eval(m)
jalpha1 = galpha1.Eval(m)
jslope1 = gslope1.Eval(m)
else:
jmean = fmean.GetParameter(0) + fmean.GetParameter(1)*m + fmean.GetParameter(2)*m*m
jsigma = fsigma.GetParameter(0) + fsigma.GetParameter(1)*m + fsigma.GetParameter(2)*m*m
jalpha1 = falpha1.GetParameter(0) + falpha1.GetParameter(1)*m + falpha1.GetParameter(2)*m*m
jslope1 = fslope1.GetParameter(0) + fslope1.GetParameter(1)*m + fslope1.GetParameter(2)*m*m
inorm.SetPoint(inorm.GetN(), m, syield)
signalNorm[m].setVal(syield)
imean.SetPoint(imean.GetN(), m, jmean)
if jmean > 0: vmean[m].setVal(jmean)
isigma.SetPoint(isigma.GetN(), m, jsigma)
if jsigma > 0: vsigma[m].setVal(jsigma)
ialpha1.SetPoint(ialpha1.GetN(), m, jalpha1)
if not jalpha1==0: valpha1[m].setVal(jalpha1)
islope1.SetPoint(islope1.GetN(), m, jslope1)
if jslope1 > 0: vslope1[m].setVal(jslope1)
c1 = TCanvas("c1", "Crystal Ball", 1200, 800)
c1.Divide(2, 2)
c1.cd(1)
gmean.SetMinimum(0.)
gmean.Draw("APL")
imean.Draw("P, SAME")
drawRegion(channel)
c1.cd(2)
gsigma.SetMinimum(0.)
gsigma.Draw("APL")
isigma.Draw("P, SAME")
drawRegion(channel)
c1.cd(3)
galpha1.Draw("APL")
ialpha1.Draw("P, SAME")
drawRegion(channel)
galpha1.GetYaxis().SetRangeUser(0., 5.)
c1.cd(4)
gslope1.Draw("APL")
islope1.Draw("P, SAME")
drawRegion(channel)
gslope1.GetYaxis().SetRangeUser(0., 125.)
if False:
c1.cd(5)
galpha2.Draw("APL")
ialpha2.Draw("P, SAME")
drawRegion(channel)
c1.cd(6)
gslope2.Draw("APL")
islope2.Draw("P, SAME")
drawRegion(channel)
gslope2.GetYaxis().SetRangeUser(0., 10.)
c1.Print(PLOTDIR+stype+category+"/"+stype+"_SignalShape.pdf")
c1.Print(PLOTDIR+stype+category+"/"+stype+"_SignalShape.png")
c2 = TCanvas("c2", "Signal Efficiency", 800, 600)
c2.cd(1)
gnorm.SetMarkerColor(cColor)
gnorm.SetMarkerStyle(20)
gnorm.SetLineColor(cColor)
gnorm.SetLineWidth(2)
gnorm.Draw("APL")
inorm.Draw("P, SAME")
gnorm.GetXaxis().SetRangeUser(genPoints[0]-100, genPoints[-1]+100)
gnorm.GetYaxis().SetRangeUser(0., gnorm.GetMaximum()*1.25)
drawCMS(-1,YEAR , "Simulation")
drawAnalysis(channel)
drawRegion(channel)
c2.Print(PLOTDIR+stype+category+"/"+stype+"_SignalNorm.pdf")
c2.Print(PLOTDIR+stype+category+"/"+stype+"_SignalNorm.png")
#*******************************************************#
# #
# Generate workspace #
# #
#*******************************************************#
# create workspace
w = RooWorkspace("ZH_RunII", "workspace")
for m in massPoints:
getattr(w, "import")(signal[m], RooFit.Rename(signal[m].GetName()))
getattr(w, "import")(signalNorm[m], RooFit.Rename(signalNorm[m].GetName()))
getattr(w, "import")(signalXS[m], RooFit.Rename(signalXS[m].GetName()))
w.writeToFile("%s%s.root" % (WORKDIR, stype+channel), True)
print "Workspace", "%s%s.root" % (WORKDIR, stype+channel), "saved successfully"
sys.exit()
c1.SetLogy(False)
y.append(1.08)
y.append(1.1)
y.append(1.15)
y.append(1.2)
y.append(1.2)
y.append(1.22)
y.append(1.22)
n = len(x)
gr0 = TGraphErrors(n, x, y, ex, ey)
gr0.SetMarkerColor(ROOT.kBlue)
gr0.SetMarkerStyle(20)
gr0.SetLineColor(ROOT.kBlue)
gr0.SetMarkerSize(1.5)
gr0.Draw()
p1 = TF1("pol1", "pol1", 200, 900.)
# p1 = TF1("pol2","pol2",200,2000.);
p1.SetLineColor(ROOT.kMagenta)
f1 = TF1("f1", "[0] +[1]*x", 200, 2000)
f1.SetParameters(0.99601, 0.00241919)
f1.SetLineColor(ROOT.kBlue)
gr0.Fit(p1, "0R")
p1.Draw("sames")
z[i] = ts_correctmass[i].GetMean()
yerr[i] = ts_correct[i].GetMeanError()
zerr[i] = ts_correctmass[i].GetMeanError()
graph1.SetPoint(i, x[i], y[i])
graph2.SetPoint(i, x[i], z[i])
graph1.SetPointError(i, 0, yerr[i])
graph2.SetPointError(i, 0, zerr[i])
c2.cd()
graph1.Draw("AP")
graph1.GetYaxis().SetRangeUser(52., 62.)
graph1.GetYaxis().SetTitle("Mean value")
graph1.GetXaxis().SetTitle("Top M(GeV)")
graph1.Draw("AP")
graph1.SetTitle("Mean value of the histogram vs top mass")
graph2.SetLineColor(2)
graph2.SetMarkerColor(2)
graph2.Draw("P")
# fit
graph1.Fit("pol1")
graph2.Fit("pol1")
pol1 = TF1()
pol1 = graph1.GetFunction("pol1")
pol2 = TF1()
pol2 = graph2.GetFunction("pol1")
a0 = str(round(pol1.GetParameter(0), 2))
a1 = str(round(pol1.GetParameter(1), 2))
def generator(nuslice_tree, rootfile, pset):
n_bins = pset.n_bins
drift_distance = pset.DriftDistance
bin_width = drift_distance/n_bins
half_bin_width = bin_width/2.
xvals = np.arange(half_bin_width, drift_distance, bin_width)
xerrs = np.array([half_bin_width] * len(xvals))
dist_to_anode_bins = n_bins
dist_to_anode_low = 0.
dist_to_anode_up = drift_distance
profile_bins = n_bins
profile_option = 's' # errors are the standard deviation
dy_spreads = [None] * n_bins
dy_means = [None] * n_bins
dy_hist = TH2D("dy_hist", "#Delta y",
dist_to_anode_bins, dist_to_anode_low, dist_to_anode_up,
pset.dy_bins, pset.dy_low, pset.dy_up)
dy_hist.GetXaxis().SetTitle("distance from anode (cm)")
dy_hist.GetYaxis().SetTitle("y_flash - y_TPC (cm)")
dy_prof = TProfile("dy_prof", "Profile of dy_spreads in #Delta y",
profile_bins, dist_to_anode_low, dist_to_anode_up,
pset.dy_low*2, pset.dy_up*2, profile_option)
dy_prof.GetXaxis().SetTitle("distance from anode (cm)")
dy_prof.GetYaxis().SetTitle("y_flash - y_TPC (cm)")
dy_h1 = TH1D("dy_h1", "",
profile_bins, dist_to_anode_low, dist_to_anode_up)
dy_h1.GetXaxis().SetTitle("distance from anode (cm)")
dy_h1.GetYaxis().SetTitle("y_flash - y_TPC (cm)")
dz_spreads = [None] * n_bins
dz_means = [None] * n_bins
dz_hist = TH2D("dz_hist", "#Delta z",
dist_to_anode_bins, dist_to_anode_low, dist_to_anode_up,
pset.dz_bins, pset.dz_low, pset.dz_up)
dz_hist.GetXaxis().SetTitle("distance from anode (cm)")
dz_hist.GetYaxis().SetTitle("z_flash - z_TPC (cm)")
dz_prof = TProfile("dz_prof", "Profile of dz_spreads in #Delta z",
profile_bins, dist_to_anode_low, dist_to_anode_up,
pset.dz_low*2.5, pset.dz_up*2.5, profile_option)
dz_prof.GetXaxis().SetTitle("distance from anode (cm)")
dz_prof.GetYaxis().SetTitle("z_flash - z_TPC (cm)")
dz_h1 = TH1D("dz_h1", "",
profile_bins, dist_to_anode_low, dist_to_anode_up)
dz_h1.GetXaxis().SetTitle("distance from anode (cm)")
dz_h1.GetYaxis().SetTitle("z_flash - z_TPC (cm)")
rr_spreads = [None] * n_bins
rr_means = [None] * n_bins
rr_hist = TH2D("rr_hist", "PE Spread",
dist_to_anode_bins, dist_to_anode_low, dist_to_anode_up,
pset.rr_bins, pset.rr_low, pset.rr_up)
rr_hist.GetXaxis().SetTitle("distance from anode (cm)")
rr_hist.GetYaxis().SetTitle("RMS flash (cm)")
rr_prof = TProfile("rr_prof", "Profile of PE Spread",
profile_bins, dist_to_anode_low, dist_to_anode_up,
pset.rr_low, pset.rr_up, profile_option)
rr_prof.GetXaxis().SetTitle("distance from anode (cm)")
rr_prof.GetYaxis().SetTitle("RMS flash (cm)")
rr_h1 = TH1D("rr_h1", "",
profile_bins, dist_to_anode_low, dist_to_anode_up)
rr_h1.GetXaxis().SetTitle("distance from anode (cm)")
rr_h1.GetYaxis().SetTitle("RMS flash (cm)")
if detector == "sbnd":
pe_spreads = [None] * n_bins
pe_means = [None] * n_bins
pe_hist = TH2D("pe_hist", "Uncoated/Coated Ratio",
dist_to_anode_bins, dist_to_anode_low, dist_to_anode_up,
pset.pe_bins, pset.pe_low, pset.pe_up)
pe_hist.GetXaxis().SetTitle("distance from anode (cm)")
pe_hist.GetYaxis().SetTitle("ratio_{uncoated/coated}")
pe_prof = TProfile("pe_prof", "Profile of Uncoated/Coated Ratio",
profile_bins, dist_to_anode_low, dist_to_anode_up,
pset.pe_low, pset.pe_up, profile_option)
pe_prof.GetXaxis().SetTitle("distance from anode (cm)")
pe_prof.GetYaxis().SetTitle("ratio_{uncoated/coated}")
pe_h1 = TH1D("pe_h1", "",
profile_bins, dist_to_anode_low, dist_to_anode_up)
pe_h1.GetXaxis().SetTitle("distance from anode (cm)")
pe_h1.GetYaxis().SetTitle("ratio_{uncoated/coated}")
match_score_scatter = TH2D("match_score_scatter", "Scatter plot of match scores",
dist_to_anode_bins, dist_to_anode_low, dist_to_anode_up,
pset.score_hist_bins, pset.score_hist_low, pset.score_hist_up*(3./5.))
match_score_scatter.GetXaxis().SetTitle("distance from anode (cm)")
match_score_scatter.GetYaxis().SetTitle("match score (arbitrary)")
match_score_hist = TH1D("match_score", "Match Score",
pset.score_hist_bins, pset.score_hist_low, pset.score_hist_up)
match_score_hist.GetXaxis().SetTitle("match score (arbitrary)")
for e in nuslice_tree:
slice = e.charge_x
dy_hist.Fill(slice, e.flash_y - e.charge_y)
dy_prof.Fill(slice, e.flash_y - e.charge_y)
dz_hist.Fill(slice, e.flash_z - e.charge_z)
dz_prof.Fill(slice, e.flash_z - e.charge_z)
rr_hist.Fill(slice, e.flash_r)
rr_prof.Fill(slice, e.flash_r)
if detector == "sbnd":
pe_hist.Fill(slice, e.flash_ratio)
pe_prof.Fill(slice, e.flash_ratio)
# fill histograms for match score calculation from profile histograms
for ib in list(range(0, profile_bins)):
ibp = ib + 1
dy_h1.SetBinContent(ibp, dy_prof.GetBinContent(ibp))
dy_h1.SetBinError(ibp, dy_prof.GetBinError(ibp))
dy_means[int(ib)] = dy_prof.GetBinContent(ibp)
dy_spreads[int(ib)] = dy_prof.GetBinError(ibp)
dz_h1.SetBinContent(ibp, dz_prof.GetBinContent(ibp))
dz_h1.SetBinError(ibp, dz_prof.GetBinError(ibp))
dz_means[int(ib)] = dz_prof.GetBinContent(ibp)
dz_spreads[int(ib)] = dz_prof.GetBinError(ibp)
rr_h1.SetBinContent(ibp, rr_prof.GetBinContent(ibp))
rr_h1.SetBinError(ibp, rr_prof.GetBinError(ibp))
rr_means[int(ib)] = rr_prof.GetBinContent(ibp)
rr_spreads[int(ib)] = rr_prof.GetBinError(ibp)
if detector == "sbnd":
pe_h1.SetBinContent(ibp, pe_prof.GetBinContent(ibp))
pe_h1.SetBinError(ibp, pe_prof.GetBinError(ibp))
pe_means[int(ib)] = pe_prof.GetBinContent(ibp)
pe_spreads[int(ib)] = pe_prof.GetBinError(ibp)
for e in nuslice_tree:
slice = e.charge_x
# calculate match score
isl = int(slice/bin_width)
score = 0.
if dy_spreads[isl] <= 1.e-8:
print("Warning zero spread.\n",
f"slice: {slice}. isl: {isl}. dy_spreads[isl]: {dy_spreads[isl]} ")
dy_spreads[isl] = dy_spreads[isl+1]
if dz_spreads[isl] <= 1.e-8:
print("Warning zero spread.\n",
f"slice: {slice}. isl: {isl}. dz_spreads[isl]: {dz_spreads[isl]} ")
dz_spreads[isl] = dz_spreads[isl+1]
if rr_spreads[isl] <= 1.e-8:
print("Warning zero spread.\n",
f"slice: {slice}. isl: {isl}. rr_spreads[isl]: {rr_spreads[isl]} ")
rr_spreads[isl] = rr_spreads[isl+1]
if detector == "sbnd" and pe_spreads[isl] <= 1.e-8:
print("Warning zero spread.\n",
f"slice: {slice}. isl: {isl}. pe_spreads[isl]: {pe_spreads[isl]} ")
pe_spreads[isl] = pe_spreads[isl+1]
score += abs(abs(e.flash_y-e.charge_y) - dy_means[isl])/dy_spreads[isl]
score += abs(abs(e.flash_z-e.charge_z) - dz_means[isl])/dz_spreads[isl]
score += abs(e.flash_r-rr_means[isl])/rr_spreads[isl]
if detector == "sbnd" and pset.UseUncoatedPMT:
score += abs(e.flash_ratio-pe_means[isl])/pe_spreads[isl]
match_score_scatter.Fill(slice, score)
match_score_hist.Fill(score)
metrics_filename = 'fm_metrics_' + detector + '.root'
hfile = gROOT.FindObject(metrics_filename)
if hfile:
hfile.Close()
hfile = TFile(metrics_filename, 'RECREATE',
'Simple flash matching metrics for ' + detector.upper())
dy_hist.Write()
dy_prof.Write()
dy_h1.Write()
dz_hist.Write()
dz_prof.Write()
dz_h1.Write()
rr_hist.Write()
rr_prof.Write()
rr_h1.Write()
if detector == "sbnd":
pe_hist.Write()
pe_prof.Write()
pe_h1.Write()
match_score_scatter.Write()
match_score_hist.Write()
hfile.Close()
canv = TCanvas("canv")
dy_hist.Draw()
crosses = TGraphErrors(n_bins,
array('f', xvals), array('f', dy_means),
array('f', xerrs), array('f', dy_spreads))
crosses.SetLineColor(9)
crosses.SetLineWidth(3)
crosses.Draw("Psame")
canv.Print("dy.pdf")
canv.Update()
dz_hist.Draw()
crosses = TGraphErrors(n_bins,
array('f', xvals), array('f', dz_means),
array('f', xerrs), array('f', dz_spreads))
crosses.SetLineColor(9)
crosses.SetLineWidth(3)
crosses.Draw("Psame")
canv.Print("dz.pdf")
canv.Update()
rr_hist.Draw()
crosses = TGraphErrors(n_bins,
array('f', xvals), array('f', rr_means),
array('f', xerrs), array('f', rr_spreads))
crosses.SetLineColor(9)
crosses.SetLineWidth(3)
crosses.Draw("Psame")
canv.Print("rr.pdf")
canv.Update()
if detector == "sbnd":
pe_hist.Draw()
crosses = TGraphErrors(n_bins,
array('f', xvals), array('f', pe_means),
array('f', xerrs), array('f', pe_spreads))
crosses.SetLineColor(9)
crosses.SetLineWidth(3)
crosses.Draw("Psame")
canv.Print("pe.pdf")
canv.Update()
match_score_scatter.Draw()
canv.Print("match_score_scatter.pdf")
canv.Update()
match_score_hist.Draw()
canv.Print("match_score.pdf")
canv.Update()
sleep(20)
def __applyThresholdCorrection( self, refValue, sgn_hist2D, bkg_hist2D, partition_name, output_name, **kwargs):
legend_position = retrieve_kw( kwargs, 'legend_position', (0.36,0.20,0.66,0.40))
useNoActivationFunctionInTheLastLayer=retrieve_kw(kwargs,'useNoActivationFunctionInTheLastLayer',False)
xname = retrieve_kw( kwargs, 'xname', 'n_{vtx}' )
draw = retrieve_kw( kwargs, 'draw', False)
limits = retrieve_kw( kwargs, 'limits', [0,10,20])
dovertical = retrieve_kw( kwargs, 'dovertical' , False)
mumin = limits[0]; mumax=limits[-1]
mubins = mumax-mumin
from TrigEgammaDevelopments.helper.util import *
sgn_hist2D = copy2DRegion(sgn_hist2D,1000,-12,7,mubins,mumin,mumax)
bkg_hist2D = copy2DRegion(bkg_hist2D,1000,-12,7,mubins,mumin,mumax)
from copy import deepcopy
refValue_requested = refValue
false_alarm = 1.0
false_alarm_limit = 0.20
while false_alarm > false_alarm_limit:
# Calculate the original threshold
b0, error = find_threshold(sgn_hist2D.ProjectionX(), refValue )
# Take eff points using uncorrection threshold
discr_points, nvtx_points, error_points = calculate_dependent_discr_points(sgn_hist2D , refValue )
# Calculate eff without correction
sgn_histNum, sgn_histDen, sgn_histEff, det0 = calculate_efficiency(sgn_hist2D, refValue, b0, 0, doCorrection=False)
# Generate correction parameters and produce fixed plots
sgn_histNum_corr, sgn_histDen_corr, sgn_histEff_corr, detection ,b, a = calculate_efficiency( sgn_hist2D,
refValue, b0, 0, limits = limits, doCorretion=True)
# Calculate eff without correction
bkg_histNum, bkg_histDen, bkg_histEff, _ = calculate_efficiency(bkg_hist2D, refValue, b0, 0, doCorrection=False)
# Calculate eff using the correction from signal
bkg_histNum_corr, bkg_histDen_corr, bkg_histEff_corr, false_alarm = calculate_efficiency(bkg_hist2D, refValue, b, a, doCorrection=False)
if false_alarm > false_alarm_limit:
refValue-=0.025
# To np.array
discr_points = np.array(discr_points)
# Plot correction
if draw:
# Retrieve some usefull information
y_max = sgn_hist2D.GetYaxis().GetXmax()
y_min = sgn_hist2D.GetYaxis().GetXmin()
x_min = y_min; x_max = y_max
from ROOT import TCanvas, gStyle, TLegend, kRed, kBlue, kBlack,TLine
from ROOT import TGraphErrors,TF1
gStyle.SetPalette(107)
if dovertical:
canvas = TCanvas('canvas','canvas',1600,2000)
canvas.Divide(2,3)
else:
canvas = TCanvas('canvas','canvas',2800,1600)
canvas.Divide(3,2)
pad1= canvas.cd(1)
sgn_histEff.SetTitle('Signal Efficiency in: '+partition_name)
sgn_histEff.SetLineColor(kRed)
sgn_histEff.SetMarkerColor(kRed)
sgn_histEff.GetYaxis().SetTitle('#epsilon('+xname+')')
sgn_histEff.GetXaxis().SetTitle(xname)
sgn_histEff.GetYaxis().SetRangeUser( 0.6, 1.1 )
sgn_histEff.Draw()
sgn_histEff_corr.SetLineColor(kBlack)
sgn_histEff_corr.SetMarkerColor(kBlack)
sgn_histEff_corr.Draw('sames')
l0 = TLine(x_min,refValue_requested,x_max,refValue_requested)
l0.SetLineColor(kBlack)
l0.Draw()
l1 = TLine(x_min,refValue,x_max,refValue)
l1.SetLineColor(kBlack)
l1.SetLineStyle(9)
l1.Draw()
leg1 = TLegend(legend_position[0],legend_position[1], legend_position[2],legend_position[3])
setLegend1(leg1)
leg1.SetHeader('Signal efficiency in '+partition_name)
leg1.AddEntry(sgn_histEff,('Old: d = %1.3f')%(b0),'p' )
lg1 = leg1.AddEntry(sgn_histEff_corr,('New: d = %1.3f + %s %1.3f')%(b,xname,a),'p' )
lg1.SetTextColor(kRed)
leg1.AddEntry(l1,('Reference: %1.3f')%(refValue) ,'l')
leg1.SetTextSize(0.03)
leg1.SetBorderSize(0)
leg1.Draw()
atlas_template(pad1,**kwargs)
pad2 = canvas.cd(2) if dovertical else canvas.cd(4)
bkg_histEff.SetTitle('Background rejection in: '+partition_name)
bkg_histEff.SetLineColor(kRed)
bkg_histEff.SetMarkerColor(kRed)
bkg_histEff.GetYaxis().SetTitle('#epsilon('+xname+')')
bkg_histEff.GetXaxis().SetTitle(xname)
bkg_histEff.Draw()
bkg_histEff_corr.SetLineColor(kBlack)
bkg_histEff_corr.SetMarkerColor(kBlack)
bkg_histEff_corr.Draw('sames')
l0.Draw()
l1.Draw()
leg2 = TLegend(legend_position[0],legend_position[1]+0.4, legend_position[2],legend_position[3]+0.4)
setLegend1(leg2)
leg2.SetHeader('Background rejection in '+partition_name)
leg2.AddEntry(sgn_histEff,('Old: d = %1.3f')%(b0),'p' )
lg2 = leg2.AddEntry(sgn_histEff_corr,('New: d = %1.3f + %s %1.3f')%(b,xname,a),'p' )
lg2.SetTextColor(kRed)
leg2.SetTextSize(0.03)
leg2.SetBorderSize(0)
leg2.Draw()
atlas_template(pad2,**kwargs)
pad3 = canvas.cd(3) if dovertical else canvas.cd(2)
sgn_hist2D.SetTitle('Neural Network output as a function fo nvtx, '+partition_name)
sgn_hist2D.GetXaxis().SetTitle('Neural Network output (Discriminant)')
sgn_hist2D.GetYaxis().SetTitle(xname)
sgn_hist2D.GetZaxis().SetTitle('')
if not useNoActivationFunctionInTheLastLayer: sgn_hist2D.SetAxisRange(-1,1, 'X' )
sgn_hist2D.Draw('colz')
pad3.SetLogz()
# Add points
g1 = TGraphErrors(len(discr_points), discr_points, np.array(nvtx_points)+limits[0], np.array(error_points), np.zeros(discr_points.shape))
g1.SetLineWidth(1)
g1.SetLineColor(kBlue)
g1.SetMarkerColor(kBlue)
g1.Draw("P same")
# Old threshold line
l2 = TLine(b0,y_min,b0,y_max)
l2.SetLineColor(kRed)
l2.SetLineWidth(2)
l2.Draw()
# New threshold line
l3 = TLine(b,y_min, a*y_max+b, y_max)
l3.SetLineColor(kBlack)
l3.SetLineWidth(2)
l3.Draw()
atlas_template(pad3,**kwargs)
pad4 = canvas.cd(4) if dovertical else canvas.cd(5)
bkg_hist2D.SetTitle('Neural Network output as a function fo nvtx, '+partition_name)
bkg_hist2D.GetXaxis().SetTitle('Neural Network output (Discriminant)')
bkg_hist2D.GetYaxis().SetTitle(xname)
bkg_hist2D.GetZaxis().SetTitle('')
if not useNoActivationFunctionInTheLastLayer: bkg_hist2D.SetAxisRange(-1,1, 'X' )
#sgn_hist2D.SetAxisRange(b+y_max*a-0.2,1, 'X' )
bkg_hist2D.Draw('colz')
pad4.SetLogz()
# Add points
g2 = TGraphErrors(len(discr_points), discr_points, np.array(nvtx_points)+limits[0], np.array(error_points), np.zeros(discr_points.shape))
g2.SetLineWidth(1)
g2.SetLineColor(kBlue)
g2.SetMarkerColor(kBlue)
g2.Draw("P same")
# Old threshold line
l4 = TLine(b0,y_min,b0,y_max)
l4.SetLineColor(kRed)
l4.SetLineWidth(2)
l4.Draw()
# New threshold line
l5 = TLine(b,y_min, a*y_max+b, y_max)
l5.SetLineColor(kBlack)
l5.SetLineWidth(2)
l5.Draw()
atlas_template(pad4,**kwargs)
from ROOT import TH1D, kAzure
from TrigEgammaDevelopments.plots import AutoFixAxes
pad5 = canvas.cd(5) if dovertical else canvas.cd(3)
#pad5.SetLogy()
h5 = TH1D(sgn_hist2D.ProjectionX())
h6 = TH1D(bkg_hist2D.ProjectionX())
if max(h5.GetMaximum(),h6.GetMaximum()) > 10*(min(h5.GetMaximum(),h6.GetMaximum())):
pad5.SetLogy()
h5.Rebin(10)
h6.Rebin(10)
h5.SetLineColor(kAzure+6)
h5.SetFillColor(kAzure+6)
h6.SetLineColor(kRed-7)
h5.Draw()
h6.Draw('sames')
AutoFixAxes(pad5,False,False,1.5)
atlas_template(pad5,**kwargs)
pad6 = canvas.cd(6)
#pad6.SetLogy()
h7 = TH1D(bkg_hist2D.ProjectionX())
h8 = TH1D(sgn_hist2D.ProjectionX())
if max(h7.GetMaximum(),h8.GetMaximum()) > 10*(min(h7.GetMaximum(),h8.GetMaximum())):
pad6.SetLogy()
h7.Rebin(10)
h8.Rebin(10)
h7.SetLineColor(kRed-7)
h7.SetFillColor(kRed-7)
h8.SetLineColor(kAzure+6)
h7.Draw()
h8.Draw('sames')
AutoFixAxes(pad6,False,False,1.5)
atlas_template(pad6,**kwargs)
canvas.SaveAs(output_name+'.pdf')
return b,a,b0
def DrawNoPull(data, bkg, legend, fileName, varName, dirName, lumi, signals,
SUM, ControlRegion, hideData, year):
frame = data.Clone(data.GetName() + '_frame')
frame.Reset()
frame.GetXaxis().SetTitle(varName)
myMax = max(data.GetMaximum(), SUM.GetMaximum())
frame.SetMaximum(myMax * 1.7)
frame.SetMinimum(0.5)
# frame.GetYaxis().SetMaxDigits(2)
frame.GetYaxis().SetTitle("Events")
nbins = frame.GetXaxis().GetNbins()
binslow = frame.GetXaxis().GetBinLowEdge(1)
binsup = frame.GetXaxis().GetBinUpEdge(nbins)
perbin = (float(binsup) - float(binslow)) / float(nbins)
thisLabel = "Events/(" + str(perbin) + ")"
if "GeV" in varName:
thisLabel = "Events/(" + str(perbin) + " GeV)"
if binsup > 999:
frame.GetXaxis().SetLimits(binslow, 999)
frame.GetYaxis().SetTitle(thisLabel)
SetAxisTextSizes(frame)
SetGeneralStyle()
tc = TCanvas('tc', 'tc', 800, 700)
SetPadStyle(tc)
frame.Draw()
bkg.Draw("hist same")
SUM.Draw("E2 same")
if hideData == False:
nbins = data.GetNbinsX()
gData = TGraphErrors(nbins)
for i in range(1, nbins + 1):
xcenter = data.GetBinCenter(i)
ycenter = data.GetBinContent(i)
yerror = data.GetBinError(i)
gData.SetPoint(i - 1, xcenter, ycenter)
gData.SetPointError(i - 1, 0.0001, yerror)
gData.SetLineColor(data.GetLineColor())
gData.SetMarkerColor(data.GetMarkerColor())
gData.SetMarkerStyle(data.GetMarkerStyle())
gData.Draw("PE same")
for si in signals:
# print si
si[0].Draw("hist same")
tc.Update()
tc.RedrawAxis()
for ll in legend:
ll.Draw("same")
Lumi = str(lumi / 1000.)
l1 = DrawCMSLabels(tc, Lumi, 0, 0.08)
tc.Update()
tc.SaveAs(dirName + "/NP_" + fileName + ".pdf")
tc.SaveAs(dirName + "/NP_" + fileName + ".png")
for ll in l1:
ll.Delete()
if 'cos' in varName or 'Cos' in varName:
frame.SetMaximum(myMax * 30)
else:
frame.SetMaximum(myMax * 60)
tc.SetLogy()
tc.Update()
l2 = DrawCMSLabels(tc, Lumi, 0, 0)
tc.SaveAs(dirName + "/NP_LOG_" + fileName + ".pdf")
tc.SaveAs(dirName + "/NP_LOG_" + fileName + ".png")
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()