def produceFamilyPlot(family, surname):
canvas = TCanvas(surname, "", 2400, 800)
legend = TLegend(0.1, 0.5, 0.4, 0.87)
legend.SetBorderSize(0)
legend.SetFillStyle(0)
counter = 0
markers = []
for person in family:
if len(person) < 2:
print "error"
continue
name = person[0]
sex = person[1]
if len(person) >= 3:
birth = person[2]
else:
birth = 0
plot = makePlot(name, sex)
normalizePlot(plot)
plot.SetTitle("")
plot.GetYaxis().SetLabelSize(0)
plot.GetXaxis().SetLabelSize(0.07)
plot.GetYaxis().SetTickSize(0)
plot.GetYaxis().SetTitle("normalized popularity")
plot.GetYaxis().SetTitleSize(0.07)
plot.GetYaxis().SetTitleOffset(0.22)
plot.SetLineColor(colors[counter])
legend.AddEntry(plot, name, "L")
plot.Draw("same C")
if birth > 0:
marker = TMarker(birth+0.5,
plot.GetBinContent(plot.FindBin(birth)), 20)
marker.SetMarkerColor(colors[counter])
marker.SetMarkerSize(5)
markers.append(marker)
markers[counter].Draw("same")
counter += 1
legend.Draw()
canvas.SaveAs(surname + ".pdf")
def plotCombinedEfficiency():
hl = plotEfficiency('.')
canv = TCanvas("canvasCombinedEfficiency", "Combined efficiency plot",
1200, 1200)
leg = TLegend(0.55, 0.1, 0.9, 0.5)
for i, val in enumerate(hl):
if (i == 0):
val[0].Draw()
val[0].SetTitle("Efficiency for several p_{T}")
else:
val[0].Draw('same')
val[1].Draw('same')
hl[0][1].SetFillColor(colorRwthMagenta)
hl[0][0].SetFillColor(colorRwthDarkBlue)
leg.AddEntry(hl[0][0], "L1", "f")
leg.AddEntry(hl[0][1], "L1 + HO", "f")
markers = []
for i, pair in enumerate(markerpairs):
markers.append(TMarker(1, 1, pair[0]))
markers[i].SetMarkerSize(3)
leg.AddEntry(markers[i], "p_{T} = " + str((i + 1) * 5) + " GeV", "p")
leg.Draw()
label = drawLabelCmsPrivateSimulation()
if (not os.path.exists('plots')):
os.mkdir('plots')
if (not os.path.exists('plots/efficiency')):
os.mkdir('plots/efficiency')
canv.SaveAs('plots/efficiency/combinedEfficiency.png')
canv.SaveAs('plots/efficiency/combinedEfficiency.pdf')
canv.SaveAs('plots/efficiency/combinedEfficiency.root')
return canv, leg, hl, label
def _doProfile2d( solver, ipar1=0, type1="u", ipar2=1, type2= "m" ):
from ConstrainedFit import clsq
from ROOT import TGraph2D, TMarker, gPad
from array import array
global tg2d, hist, te1, te2, te3, tm
par1= clsq.createClsqPar( ipar1, type1, solver )
par2= clsq.createClsqPar( ipar2, type2, solver )
parval1, parerr1, name1= _getUMParErrName( par1 )
parval2, parerr2, name2= _getUMParErrName( par2 )
print( "\nChi^2 profile plot " + name1 + " - " + name2 + ":" )
corr= solver.getCorrMatrix()
icorr1= ipar1
icorr2= ipar2
if type1 == "u" or type2 == "u":
nmpar= len(solver.getMpars())
if type1 == "u":
icorr1= nmpar + ipar1
if type2 == "u":
icorr2= nmpar + ipar2
rho= corr[icorr1,icorr2]
te1= _makeEllipse( parval1, parval2, parerr1, parerr2, rho )
te2= _makeEllipse( parval1, parval2, 2.0*parerr1, 2.0*parerr2, rho )
te3= _makeEllipse( parval1, parval2, 3.0*parerr1, 3.0*parerr2, rho )
ca= clsq.clsqAnalysis( solver )
points= ca.profile2d( par1, par2 )
npoints= len(points)
tg2d= TGraph2D( npoints )
for i in range( npoints ):
point= points[i]
tg2d.SetPoint( i, point[0], point[1], point[2] )
hist= tg2d.GetHistogram()
contourlevels= array( "d", [ 1.0, 4.0, 9.0 ] )
hist.SetContour( 3, contourlevels )
hist.GetXaxis().SetTitle( name1 )
hist.GetYaxis().SetTitle( name2 )
hist.SetTitle( "Triangle fit "+name2+" vs "+name1 )
hist.Draw( "cont1" )
tm= TMarker( parval1, parval2, 20 )
tm.Draw( "s" )
te1.Draw( "s" )
te2.Draw( "s" )
te3.Draw( "s" )
gPad.Print( "triangle_errorellipse.png" )
return
def findIntersect(g1,g2,x1,x2):
orientation = (g1.Eval(x1)<g2.Eval(x1))
for iX in xrange(1000):
x = (x2-x1)*iX/1000.+x1
if orientation != (g1.Eval(x)<g2.Eval(x)):
# return TGraph(1,array('f',[x]),array('f',[g1.Eval(x)]))
return TMarker(x,g1.Eval(x),1)
# return x
print 'Could not find intersection!'
return None
def draw_potential():
markers = []
c1 = TCanvas("c1", "Prova")
h2 = TH2F("h2", "h2", 1000, -100, 1200, 1000, 0, 1000)
m = re.compile("(\d+)\s(\d+)\s([\-0-9\.]+)\s([\-0-9]+)")
file = open("mappa_piazza.dat")
while 1:
line = file.readline()
if (not line):
break
n = m.match(line)
if (n):
po = TMarker(float(n.group(1)), float(n.group(2)), 6)
po.SetMarkerColor(abs(int(n.group(4))) + 1)
markers.append(po)
h2.Draw()
for i in markers:
i.Draw("SAME")
c1.SaveAs("potenziale_piazza.png")
sys.exit(1)
def calculateCentralFractionInTight(self):
hSum = self.fileHandler.getHistogram('deltaEtaDeltaPhiEnergy/averageEMaxAroundPointpatTightToL1Muons_2dSummedWeights')
hCounter = self.fileHandler.getHistogram('deltaEtaDeltaPhiEnergy/averageEMaxAroundPointpatTightToL1Muons_2dCounter')
points = []
sum = 0
for x in range(0,5):
for y in range(0,5):
sum += hCounter.GetBinContent(hSum.FindBin(x*0.0435 - 0.087,y*0.0435 - 0.087))
p = TMarker(x*0.0435 - 0.087,y*0.0435 - 0.087,20)
# p.Draw('same')
points.append(p)
print sum
print hCounter.Integral()
print '-----'
print sum/hCounter.Integral()
def plot_event(name, canvas):
primitives = canvas.GetListOfPrimitives()
histo = None
point = None
for primitive in primitives:
if primitive.InheritsFrom(TH2Poly.Class()):
histo = primitive
if primitive.InheritsFrom(TMarker.Class()):
point = primitive
histo.SetXTitle('x [cm]')
histo.SetYTitle('y [cm]')
canvas_new = Canvas(700, 700)
histo.SetAxisRange(77,100,'X')
histo.Draw('colz')
point.Draw()
canvas_new.SetLogz()
canvas_new.Print('./plots/event_{}.png'.format(name))
def GetCanvasWithHistAndPeakMarkers(Hist, Peaks, Name):
"Take the histogram and peaks to make a nice plot"
c = TCanvas(Name, Name)
c.cd()
Hist.Draw("hist")
Markers = []
for peak in Peaks:
print 'peak found at ', peak, Peaks[peak]
Markers.append(TMarker(peak, Peaks[peak], 26))
Markers[-1].Draw('same')
c.SetLogy(1)
c.SaveAs(Name + '.pdf')
return c
def myMarkerTextSmall2(x, y, lcolor, lstyle, mcolor, mstyle, msize, size,
text):
l = TLatex()
l.SetTextAlign(12)
l.SetTextSize(size / 2.5)
l.SetNDC()
l.DrawLatex(x, y, text)
yb1 = y - 0.15 * size
yb2 = y + 0.15 * size
xb1 = x - 0.28 * size - 0.45 * size
xb2 = x + 0.28 * size - 0.45 * size
print "box = ", xb1, yb1, xb2, yb2
#print "line = ",xl1,yl1,xl2,yl2
mline1 = TLine(xb1, yb1, xb2, yb2)
mline1.SetLineColor(lcolor)
mline1.SetLineStyle(lstyle)
mline1.SetLineWidth(2)
y_new = (yb1 + yb2) / 2.
mline1.DrawLineNDC(xb1, y_new, xb2, y_new)
mline2 = TLine(xb1, yb1, xb2, yb2)
mline2.SetLineColor(lcolor)
mline2.SetLineStyle(lstyle)
mline2.SetLineWidth(2)
x_new = (xb1 + xb2) / 2.
mline2.DrawLineNDC(x_new, yb1, x_new, yb2)
marker = TMarker((xb1 + xb2) / 2.0, y, mstyle)
marker.SetNDC()
marker.SetMarkerColor(mcolor)
marker.SetMarkerStyle(mstyle)
marker.SetMarkerSize(msize)
marker.Draw()
return marker
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 plotFailedHoMatchesNoTrg():
c = TCanvas("cFailedHoMatchesNoTrg","cFailedHoMatchesNoTrg",1200,1200)
c.cd().SetRightMargin(0.25)
c.cd().SetLeftMargin(0.08)
#Graph for in events not in geometric acceptance
grNotInGaNC = file.Get("hoMuonAnalyzer/graphs/NoTrgTdmiNotInGA")
grNotInGa = PlotStyle.convertToHcalCoords(grNotInGaNC)
grNotInGa.GetYaxis().SetTitle("i#phi / a.u.")
grNotInGa.GetXaxis().SetTitle("i#eta / a.u.")
grNotInGa.GetYaxis().SetTitleFont(62)
grNotInGa.GetYaxis().SetLabelFont(62)
grNotInGa.SetMarkerStyle(6)
grNotInGa.SetMarkerColor(PlotStyle.colorRwthDarkBlue)
grNotInGa.SetTitle("#eta #phi plot failed HO matches in no Single #mu Trg. events")
grNotInGa.Draw("AP")
#Graph of events with HO match below threshold
grHoBelowThrNC = file.Get("hoMuonAnalyzer/graphs/NoTrgTdmiBelowThr")
grHoBelowThr = PlotStyle.convertToHcalCoords(grHoBelowThrNC)
grHoBelowThr.SetMarkerStyle(20)
grHoBelowThr.SetMarkerSize(1.2)
grHoBelowThr.SetMarkerColor( PlotStyle.colorRwthGruen )
grHoBelowThr.Draw("samep")
#Graph for events where HO matching failed
grHoMatchFailNC = file.Get("hoMuonAnalyzer/graphs/NoTrgHoMatchFail")
grHoMatchFail = PlotStyle.convertToHcalCoords(grHoMatchFailNC)
grHoMatchFail.SetMarkerStyle(21)
grHoMatchFail.SetMarkerSize(1)
grHoMatchFail.SetMarkerColor( PlotStyle.colorRwthRot )
grHoMatchFail.Draw("samep")
#Draw chimneys
chimney1Converted = PlotStyle.convertToHcalCoords(PlotStyle.chimney1)
chimney2Converted = PlotStyle.convertToHcalCoords(PlotStyle.chimney2)
chimney1Converted.SetLineColor(PlotStyle.colorRwthMagenta)
chimney2Converted.SetLineColor(PlotStyle.colorRwthMagenta)
chimney1Converted.Draw('same')
chimney2Converted.Draw('same')
#cms private label
label = TPaveText(PlotStyle.getLabelCmsPrivateSimulation(x1ndc=0.5,x2ndc=0.75))
label.Draw()
#create extra marker for the legend
marker = TMarker(1,1,2)
marker.SetMarkerSize(3)
marker.SetMarkerColor(PlotStyle.colorRwthDarkBlue)
#legend
legend = TLegend(0.75,0.8,0.99,0.9)
legend.AddEntry(chimney2Converted,"chimney","l")
legend.AddEntry(marker,'Not in GA','p')
legend.AddEntry(grHoMatchFail,'HO match fail','p')
legend.AddEntry(grHoBelowThr,'HO match < 0.2 GeV','p')
legend.Draw()
nNotMatching = grHoMatchFail.GetN()
nNotInGa = grNotInGa.GetN()
nBelowThr = grHoBelowThr.GetN()
nTotal = nNotMatching + nNotInGa + nBelowThr
print 80*'#'
print 'Not Matching:\t%5d/%d\t=> %5.2f%% +- %f%%' % (nNotMatching,nTotal,nNotMatching/float(nTotal)*100,PlotStyle.calcSigma(nNotMatching,float(nTotal)))
print 'Not in GA:\t%5d/%d\t=> %5.2f%% +- %f%%' % (nNotInGa,nTotal,nNotInGa/float(nTotal)*100,PlotStyle.calcSigma(nNotInGa,float(nTotal)))
print 'Below Thr:\t%5d/%d\t=> %5.2f%% +- %f%%' % (nBelowThr,nTotal,nBelowThr/float(nTotal)*100,PlotStyle.calcSigma(nBelowThr,float(nTotal)))
print 80*'#'
c.Update()
c.SaveAs('plots/graphsEtaPhi/gNoTrgHoMatchingFailed.png')
c.SaveAs('plots/graphsEtaPhi/gNoTrgHoMatchingFailed.pdf')
return c,grNotInGa,label,chimney1Converted,chimney2Converted,legend,grHoMatchFail,grHoBelowThr
ROCwmass.SetMaximum(1.0)
ROCwmass.GetXaxis().SetRangeUser(0.0,1.0)
ROCwmass.SetLineColor(2)
ROCwmass.SetLineWidth(2)
ROCwmass.SetTitle(";signal efficiency;background rejection")
c2 = TCanvas("c2")
ptlimstrs = [str(int(2000.0*ptlims[0])),str(int(2000.0*ptlims[1]))]
ROCwmass.Draw()
prelim.DrawLatex( 0.15, 0.85, ptlimstrs[0]+" GeV < p_{T} < "+ptlimstrs[1]+" GeV" )
if (cust=="w" or cust=="top") and Drawmarker:
if cust=="w":
prelim.DrawLatex( 0.44, 0.55, "N_{2}^{DDT}+msd" )
TM = TMarker(0.4, 0.01, 23)
if cust=="top":
prelim.DrawLatex( 0.44, 0.60, "#tau_{32}+msd+subjetb" )
TM = TMarker(0.4, 0.017, 23)
TM.SetMarkerSize(2)
TM.SetMarkerColor(4)
TM.Draw()
c2.SetLogy()
c2.Write("rocwmass")
ROC.Draw()
c2.Write("roccomp")
dnnsig.Write()
dnnbkg.Write()
output.Write()
def analyze(datasets, inputDir='.'):
gROOT.SetBatch()
gStyle.SetOptStat(0)
gStyle.SetOptTitle(0)
canvas = TCanvas("c1", "c1", 800, 800)
input_ele = TFile(inputDir + '/output_electrons.root', 'read')
input_muon = TFile(inputDir + '/output_muons.root', 'read')
input_fake = TFile(inputDir + '/output_fake.root', 'read')
h_disc_ele = input_ele.Get('disc')
h_disc_muon = input_muon.Get('disc')
h_disc_fake = input_fake.Get('disc')
h_sigma_ele = input_ele.Get('sigma')
h_sigma_muon = input_muon.Get('sigma')
h_sigma_fake = input_fake.Get('sigma')
h_disc_signal = {}
h_sigma_signal = {}
for dataset in datasets:
if not dataset.startswith('higgsino'): continue
fin = TFile(inputDir + '/output_' + dataset + '.root', 'read')
h_disc = fin.Get('disc')
h_sigma = fin.Get('sigma')
h_disc.SetDirectory(0)
h_sigma.SetDirectory(0)
h_disc_signal[dataset] = h_disc
h_sigma_signal[dataset] = h_sigma
fin.Close()
roc_curves = { 'disc_%d_%s' % (i, sig) : getROC(h_disc_ele, h_disc_muon, h_disc_fake, h_disc_signal[sig], i) for i in [4, 5, 6] for sig in h_disc_signal }
roc_curves.update(
{ 'sigma_%d_%s' % (i, sig) : getROC(h_sigma_ele, h_sigma_muon, h_sigma_fake, h_sigma_signal[sig], i) for i in [4, 5, 6] for sig in h_disc_signal }
)
fout = TFile('durp.root', 'recreate')
for curve_name in roc_curves:
roc_curves[curve_name][0].Write(curve_name)
print 'Optimal cut for', curve_name, '=', roc_curves[curve_name][1]
m = TMarker(roc_curves[curve_name][2], roc_curves[curve_name][3], 29)
m.Write(curve_name + '_optimal')
fout.Close()
for i in [4, 5, 6]:
plotStack(h_disc_ele, h_disc_muon, h_disc_fake, h_disc_signal['higgsino_300_1000'], i, 'discriminant', canvas, rebin=5)
plotStack(h_sigma_ele, h_sigma_muon, h_sigma_fake, h_sigma_signal['higgsino_300_1000'], i, 'sigma', canvas, rebin=5)
for dataset in h_disc_signal:
for i in [4, 5, 6]:
wp = getWP(h_disc_ele, h_sigma_ele, h_disc_muon, h_sigma_muon, h_disc_fake, h_sigma_fake, h_disc_signal[dataset], h_sigma_signal[dataset], i)
print dataset, '--', wp
iClone = -1
print '\nComparing disc < 0.3 to fiducial map method:\n'
printChanges('Electrons nLayers=4', h_sigma_ele, h_disc_ele, 0.3, 4, iClone)
printChanges('Electrons nLayers=5', h_sigma_ele, h_disc_ele, 0.3, 5, iClone)
printChanges('Electrons nLayers>=6', h_sigma_ele, h_disc_ele, 0.3, 6, iClone)
printChanges('Muons nLayers=4', h_sigma_muon, h_disc_muon, 0.3, 4, iClone)
printChanges('Muons nLayers=5', h_sigma_muon, h_disc_muon, 0.3, 5, iClone)
printChanges('Muons nLayers>=6', h_sigma_muon, h_disc_muon, 0.3, 6, iClone)
printChanges('Fakes nLayers=4', h_sigma_fake, h_disc_fake, 0.3, 4, iClone)
printChanges('Fakes nLayers=5', h_sigma_fake, h_disc_fake, 0.3, 5, iClone)
printChanges('Fakes nLayers>=6', h_sigma_fake, h_disc_fake, 0.3, 6, iClone)
extra_samples = {
10 : ['0p' + str(i) for i in range(2, 10)] + [str(i) for i in range(0, 10)],
100 : [str(i*10) for i in range(2, 10)],
1000 : [str(i*100) for i in range(2, 10)],
10000 : [str(i*1000) for i in range(2, 10)],
}
for i in [4, 5, 6]:
print 'NLAYERS: ', i
for mass in range(100, 1000, 100):
for lifetime in [10, 100, 1000, 10000]:
datasetName = 'higgsino_%d_%d' % (mass, lifetime)
change_string = printChanges('Higgsino_%dGeV_%dcm_94X' % (mass, lifetime),
h_sigma_signal[datasetName],
h_disc_signal[datasetName],
0.3,
i,
iClone)
for extra_lifetime in extra_samples[lifetime]:
print change_string.replace('Higgsino_%dGeV_%dcm_94X' % (mass, lifetime), 'Higgsino_%dGeV_%scm_94X' % (mass, extra_lifetime))
for i in xrange(1, h_coupling.GetNbinsX() + 1):
h_coupling.SetBinContent(i, 1. / h_coupling.GetXaxis().GetBinCenter(i)**2)
n_points = h_width.GetNbinsX()
g_width = TGraph(n_points)
g_partial_width = TGraph(n_points)
for i in xrange(n_points):
g_width.SetPoint(i, h_coupling.GetBinContent(i + 1),
h_width.GetBinContent(i + 1))
g_partial_width.SetPoint(
i, h_coupling.GetBinContent(i + 1),
h_width.GetBinContent(i + 1) * h_br.GetBinContent(i + 1))
# Points/lines for simulated samples
p_5percent = TMarker(1., 0.05 * mass, 1)
p_10percent = TMarker(1., 0.1 * mass, 2)
p_25percent = TMarker(1., 0.025 * mass, 3)
def findX(graph, y):
'''Find x value, assume monotonously decreasing y'''
xvals = graph.GetX()
yvals = graph.GetY()
for i in xrange(graph.GetN()):
yval = yvals[i]
xval = xvals[i]
print xval, yval, y
if yval < y:
if i == 0:
return xval
def plot_rocs(plotObjects, kwargs):
from ROOT import kCyan, kRed, kGreen, kBlue, kBlack, kMagenta, kGray, kWhite, kYellow
Colors = [kBlue, kRed, kMagenta, kBlack, kCyan, kGreen]
from RingerCore import StdPair as std_pair
from util import line, minmax
dset = kwargs['set']
ref = kwargs['reference']
refVal = kwargs['refVal']
eps = kwargs['eps']
savename = kwargs['cname'] + '.pdf'
#Some protection
if not ('operation' in dset or 'tst' in dset):
raise ValueError('Option set must be: tst (test) or val (validation)')
if not ('SP' in ref or 'Pd' in ref or 'Pf' in ref):
raise ValueError('Option reference must be: SP, Pd or Pf')
#Create dict to hold all list plots
curves = dict()
#list of dicts to dict of lists
for name in plotObjects.keys():
curves[name] = plotObjects.tolist(name)
paintIdx = kwargs['paintListIdx'] # [best, worst]
paintCurves = [
std_pair(plotObjects.index_correction(paintIdx[0]), kBlack),
std_pair(plotObjects.index_correction(paintIdx[1]), kRed)
]
curves['roc'] = curves['roc_' + dset]
#Start to build all ROOT objects
from ROOT import TCanvas, gROOT, kTRUE
gROOT.SetBatch(kTRUE)
canvas = TCanvas('canvas', 'canvas', 1600, 1300)
x_limits = [0.00, 0.40]
y_limits = [0.6, 1.03]
#create dummy graph
dummy = curves['roc'][0]
dummy.SetTitle('Receive Operation Curve')
dummy.GetXaxis().SetTitle('False Alarm')
dummy.GetYaxis().SetTitle('Detection')
dummy.GetHistogram().SetAxisRange(y_limits[0], y_limits[1], 'Y')
dummy.GetHistogram().SetAxisRange(x_limits[0], x_limits[1], 'X')
dummy.Draw('AL')
corredor = None
target = None
from ROOT import TBox
if ref == 'Pf':
corredor = TBox(refVal - eps, y_limits[0], refVal + eps, y_limits[1])
target = line(refVal, y_limits[0], refVal, y_limits[1], kBlack, 2, 1,
'')
elif ref == 'Pd':
corredor = TBox(x_limits[0], refVal - eps, x_limits[1], refVal + eps)
target = line(x_limits[0], refVal, x_limits[1], refVal, kBlack, 2, 1,
'')
if ref != 'SP':
corredor.SetFillColor(kYellow - 9)
corredor.Draw('same')
target.Draw('same')
canvas.Modified()
canvas.Update()
#Plot curves
for c in curves['roc']:
c.SetLineColor(kGray + 1)
#c.SetMarkerStyle(7)
#c.SetMarkerColor(kBlue)
c.SetLineWidth(1)
c.SetLineStyle(3)
#c.Draw('PLsame')
c.Draw('same')
marker = list()
#Paint a specifical curve
for pair in paintCurves:
curves['roc'][pair.first].SetLineWidth(1)
curves['roc'][pair.first].SetLineStyle(1)
#curves['roc'][pair.first].SetMarkerStyle(7)
#curves['roc'][pair.first].SetMarkerColor(kBlue)
curves['roc'][pair.first].SetLineColor(pair.second)
#curves['roc'][pair.first].Draw('PLsame')
curves['roc'][pair.first].Draw('same')
if ref == 'SP':
faVec = curves['roc'][pair.first].GetX()
detVec = curves['roc'][pair.first].GetY()
from RingerCore import calcSP
spVec = [
calcSP(detVec[i], 1 - faVec[i])
for i in range(curves['roc'][pair.first].GetN())
]
imax = spVec.index(max(spVec))
from ROOT import TMarker
marker.append(TMarker(faVec[imax], detVec[imax], 4))
marker[-1].SetMarkerColor(pair.second)
marker[-1].Draw('same')
#Update Canvas
canvas.Modified()
canvas.Update()
canvas.SaveAs(savename)
del canvas
return savename
nIterations) + ") trying hit inefficiency of " + str(i) + "..."
chi2, N = getChi2(i)
if N == 0:
continue
chi2 /= N
if chi2 < minChi2 or minChi2 < 0.0:
minChi2 = chi2
minChi2HitAndTOBDrop = i
minChi2Index = copy.deepcopy(index)
g.SetPoint(n, i, chi2)
n += 1
print "(" + str(iteration + 1) + " / " + str(
nIterations) + ") minimum chi2: " + str(minChi2) + " at " + str(
minChi2HitAndTOBDrop)
m = TMarker(minChi2HitAndTOBDrop, minChi2, 29)
m.SetMarkerSize(3)
hitDropGridLower = hitDropGrid[
minChi2Index - 1] if minChi2Index > 0 else hitDropGrid[
minChi2Index] - hitDropGrid[minChi2Index + 1]
hitDropGridUpper = hitDropGrid[minChi2Index + 1] if minChi2Index < len(
hitDropGrid) - 1 else hitDropGrid[minChi2Index] + hitDropGrid[
minChi2Index - 1]
hitDropGrid = numpy.linspace(max(hitDropGridLower, 0.0),
hitDropGridUpper, 5)
foutMode = "update" if iteration > 0 else "recreate"
fout = TFile.Open("hipChi2.root", foutMode)
fout.cd()
g.Write("chi2VsHitDropProbability_" + str(iteration))
xp = -xp
xm = math.sqrt(r2**2 - ym**2)
if abs((xm - x1)**2 + (ym - y1)**2 - r1**2) > 1e-9:
xm = -xm
if switchxy:
xm, ym = ym, xm
xp, yp = yp, xp
return xm, ym, xp, yp
if __name__ == '__main__':
from ROOT import TEllipse, TH2F, TCanvas, TMarker
can = TCanvas("can", "", 600, 600)
suph = TH2F("suph", "", 10, -5, 5, 10, -5, 5)
suph.Draw()
x1, y1, r1, r2 = 0., 1.8, 1., 2.
results = circle_intersection(x1, y1, r1, r2)
c1 = TEllipse(x1, y1, r1)
c1.Draw('same')
c2 = TEllipse(0., 0., r2)
c2.Draw('same')
c1.SetFillStyle(0)
c2.SetFillStyle(0)
mm = TMarker(results[0], results[1], 8)
mp = TMarker(results[2], results[3], 21)
mm.Draw('same')
mp.Draw('same')
can.Update()
def MuFit(Nbins,irebin=1.):
# Get histrograms
h_bgr = GetMassDistribution(1)
h_data = GetMassDistribution(2)
h_sig = GetMassDistribution(125)
h_bgr.Rebin(irebin)
h_data.Rebin(irebin)
h_sig.Rebin(irebin)
h_sf = TH2D("scalefactor","title",Nbins,0.5,2.,Nbins,0.,5.)
for i in range(1,h_sf.GetNbinsX()+1):
for j in range(1,h_sf.GetNbinsY()+1):
sf_bgr = h_sf.GetXaxis().GetBinCenter(i)
sf_sig = h_sf.GetYaxis().GetBinCenter(j)
# Loop over bins, compute likelihood
loglik = 0.
for iDataBin in range(1,h_data.GetNbinsX()+1):
m4lepBin = h_data.GetBinCenter(iDataBin)
NObsBin = h_data.GetBinContent(iDataBin)
MeanBin = sf_bgr*h_bgr.GetBinContent(iDataBin) + sf_sig*h_sig.GetBinContent(iDataBin)
if (MeanBin>0): loglik += TMath.Log( TMath.Poisson(NObsBin,MeanBin) )
h_sf.SetBinContent(i,j,-2.*loglik)
# Get best SF parameters
x=ctypes.c_int(0)
y=ctypes.c_int(0)
z=ctypes.c_int(0)
h_sf.GetBinXYZ(h_sf.GetMinimumBin(),x,y,z)
Minimum = h_sf.GetBinContent(x.value,y.value)
best_alpha = h_sf.GetXaxis().GetBinCenter(x.value)
best_mu = h_sf.GetYaxis().GetBinCenter(y.value)
print(Minimum,best_alpha,best_mu)
# Rescale histogram
for i in range(1,h_sf.GetNbinsX()+1):
for j in range(1,h_sf.GetNbinsY()+1):
h_sf.SetBinContent( i,j, h_sf.GetBinContent(i,j)-Minimum )
canvas = TCanvas("canvas","Standard Canvas",600,400)
canvas.cd()
Min = TMarker(best_alpha,best_mu,29)
Min.SetMarkerSize(2)
h_sf.SetStats(kFALSE)
h_sf.SetTitle("HIGGS PRODUCTION CROSS SECTION - PARAMETERS")
h_sf.GetXaxis().SetTitle(r"#alpha")
h_sf.GetYaxis().SetTitle(r"#mu")
h_sf.Draw("COLZ")
h_sigma = h_sf.Clone("h_sigma")
h_sigma.Reset()
for i in range(1,h_sigma.GetNbinsX()+1):
for j in range(1,h_sigma.GetNbinsY()+1):
if( h_sf.GetBinContent(i,j)<=1.): h_sigma.SetBinContent(i,j, 1.)
h_sigma.SetMarkerColorAlpha(kRed,0.40)
h_sigma.SetMarkerSize(10)
h_sigma.Draw("same L")
Min.Draw()
leg1 = TLegend(0.65,0.85,0.85,0.75)
leg1.SetBorderSize(1); leg1.SetFillColor(0);
leg1a = leg1.AddEntry(Min, r"optimal (#alpha,#mu)", "p"); leg1a.SetTextSize(0.04);
leg1.Draw()
canvas.Print("Plots/MuFit.pdf")