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 Simulate(self, save=None, draw=None):
'''
:param save: if True: saves the root file as well as the true signal distribution
:param draw: if True draws the signal distribution
:param ask:
:return:
'''
# Settings:
MPV = self.MCAttributes['Landau_MPV_bkg'] # background for Landau MPV distribution
sigma = self.MCAttributes['Landau_Sigma'] # Landau scaling sigma
NPeaks = self.MCAttributes['NPeaks']
MCRunPath = self.MCAttributes['MCRunPath'].format(self.RunNumber)
xmin = self.diamond.Position['xmin'] + 0.01
xmax = self.diamond.Position['xmax'] - 0.01
ymin = self.diamond.Position['ymin'] + 0.01
ymax = self.diamond.Position['ymax'] - 0.01
center_x = (xmax + xmin) / 2.
center_y = (ymax + ymin) / 2.
if draw != None:
assert (type(draw) == t.BooleanType), "draw has to be boolean type"
self.MCAttributes['DrawRealDistribution'] = draw
if save != None:
assert (type(save) == t.BooleanType), "save argument has to be of type boolean"
self.MCAttributes['Save'] = save
def ManualHitDistribution(x, par):
'''
Probability density function for hit distribution based on
6 2d gaussian in a rectangular pattern. The PDF is NOT
normalized to 1
:param x: array, x[0]: x position, x[1]: y position
:param par: parameter array;
:return:
'''
result = 0.1 # bkg
norm = 1.
sigma = 0.04
for i in range(len(par) / 2):
result += norm * TMath.Gaus(x[0], par[2 * i], sigma) * TMath.Gaus(x[1], par[2 * i + 1], sigma)
return result
def CreateRandomPeaksConfig(xmin, xmax, ymin, ymax, bkg=120, peak_height=0.5, npeaks=NPeaks):
'''
Creates the input parameters for SignalShape, which describes the peak distribution in the
Signal distribution
:param xmin: window parameter
:param xmax: window parameter
:param ymin: window parameter
:param ymax: window parameter
:param bkg: background of signal response
:param peak_height: height of one peak in % of bkg, if bkg==0: the peaks heights are set to 1
:param npeaks: number of peaks in window - if none it picks random between 0 and 15
:return: array containing the parameters
'''
if npeaks == None:
npeaks = int(round(gRandom.Uniform(0, 15)))
if self.MCAttributes['SpecialDistribution'] in ["Central", "central"]:
npeaks = 1
dxy = 0.02
parameters = np.zeros(7)
parameters[0] = npeaks
parameters[1] = bkg
parameters[2] = peak_height
parameters[3] = gRandom.Uniform(center_x - dxy, center_x + dxy)
parameters[4] = gRandom.Uniform(center_y - dxy, center_y + dxy)
parameters[5] = gRandom.Uniform(self.MCAttributes['PeakSigmaX_min'], self.MCAttributes['PeakSigmaX_max'])
parameters[6] = gRandom.Uniform(self.MCAttributes['PeakSigmaY_min'], self.MCAttributes['PeakSigmaY_max'])
elif self.MCAttributes['SpecialDistribution'] in ["4Peaks", "4peaks", "L", "3Peaks"]:
npeaks = 4
dxy = 0.02
parameters = np.zeros(3 + 4 * npeaks)
parameters[0] = npeaks
parameters[1] = bkg
parameters[2] = peak_height
# peaks:
peaknr = 0
for x in [center_x - 0.07, center_x + 0.07]:
for y in [center_y - 0.07, center_y + 0.07]:
parameters[3 + 4 * peaknr] = gRandom.Uniform(x - dxy, x + dxy)
parameters[4 + 4 * peaknr] = gRandom.Uniform(y - dxy, y + dxy)
parameters[5 + 4 * peaknr] = gRandom.Uniform(self.MCAttributes['PeakSigmaX_min'], self.MCAttributes['PeakSigmaX_max'])
parameters[6 + 4 * peaknr] = gRandom.Uniform(self.MCAttributes['PeakSigmaY_min'], self.MCAttributes['PeakSigmaY_max'])
peaknr += 1
if self.MCAttributes['SpecialDistribution'] in ["L", "3Peaks"]:
npeaks = 3
parameters[0] = npeaks
parameters = parameters[:3 + 4 * npeaks]
elif self.MCAttributes['SpecialDistribution'] in ["Testpattern", "testpattern", "Test", "test"]:
npeaks = 8
Center_x = gRandom.Uniform(center_x - 0.01, center_x + 0.01)
Center_y = gRandom.Uniform(center_y - 0.01, center_y + 0.01)
parameters = np.zeros(3 + 4 * npeaks)
parameters[0] = npeaks
parameters[1] = bkg
parameters[2] = peak_height
parameters[3] = Center_x - 0.1
parameters[4] = Center_y + 0.08
parameters[5] = 0.02
parameters[6] = 0.02
parameters[7] = Center_x - 0.04
parameters[8] = Center_y + 0.08
parameters[9] = 0.02
parameters[10] = 0.02
parameters[11] = Center_x - 0.1
parameters[12] = Center_y
parameters[13] = 0.025
parameters[14] = 0.025
parameters[15] = Center_x
parameters[16] = Center_y
parameters[17] = 0.02
parameters[18] = 0.02
parameters[19] = Center_x + 0.1
parameters[20] = Center_y
parameters[21] = 0.03
parameters[22] = 0.03
parameters[23] = Center_x - 0.04
parameters[24] = Center_y - 0.06
parameters[25] = 0.015
parameters[26] = 0.015
parameters[27] = Center_x - 0.12
parameters[28] = Center_y - 0.12
parameters[29] = 0.03
parameters[30] = 0.03
parameters[31] = Center_x + 0.08
parameters[32] = Center_y - 0.12
parameters[33] = 0.04
parameters[34] = 0.04
else:
parameters = np.zeros(3 + 4 * npeaks)
parameters[0] = npeaks
parameters[1] = bkg
parameters[2] = peak_height
for i in range(npeaks):
parameters[3 + 4 * i] = gRandom.Uniform(xmin, xmax)
parameters[4 + 4 * i] = gRandom.Uniform(ymin, ymax)
parameters[5 + 4 * i] = gRandom.Uniform(0.02, 0.07)
parameters[6 + 4 * i] = gRandom.Uniform(0.02, 0.07)
self.MCAttributes['NPeaks'] = npeaks
return parameters
def SignalShape(x, par):
'''
:param x: x[0]: x position
x[1]: y position
:param par: par[0]: number of peaks
par[1]: mean bkg signal
par[2]: peak height in percent of bkg
par[3]: peak1 x position
par[4]: peak1 y position
par[5]: peak1 sigma in x
par[6]: peak1 sigma in y
...
par[3+4*n]: peakn x position
par[4+4*n]: peakn y position
par[5+4*n]: peakn sigma in x
par[6+4*n]: peakn sigma in y
:return:
'''
norm = par[1] * par[2]
result = par[1]
if par[1] == 0:
norm = 1
for i in range(int(par[0])):
result += norm * TMath.Gaus(x[0], par[3 + 4 * i], par[5 + 4 * i]) * TMath.Gaus(x[1], par[4 + 4 * i], par[6 + 4 * i])
return result
# Set seed for random number generator:
today = datetime.today()
seed = int((today - datetime(today.year, today.month, today.day, 0, 0, 0, 0)).total_seconds() % 1800 * 1e6)
gRandom.SetSeed(seed)
# create track_info ROOT file
if not os.path.exists(MCRunPath):
os.makedirs(MCRunPath)
if self.MCAttributes['Save']:
file = TFile(MCRunPath + 'track_info.root', 'RECREATE')
self.track_info = TTree('track_info', 'MC track_info')
track_x = array('f', [0])
track_y = array('f', [0])
integral50 = array('f', [0])
calibflag = array('i', [0])
calib_offset = array('i', [0])
time_stamp = array('f', [0])
self.track_info.Branch('track_x', track_x, 'track_x/F')
self.track_info.Branch('track_y', track_y, 'track_y/F')
self.track_info.Branch('integral50', integral50, 'integral50/F')
self.track_info.Branch('calibflag', calibflag, 'calibflag/I')
self.track_info.Branch('calib_offset', calib_offset, 'calib_offset/I')
self.track_info.Branch('time_stamp', time_stamp, 'time_stamp/F')
# Create Manual Hit Distribution:
if self.MCAttributes['HitDistributionMode'] == 'Manual':
dx = 0.08
dy = 0.07
f_lateral = TF2('f_lateral', ManualHitDistribution, xmin, xmax, ymin, ymax, 12)
f_lateral.SetNpx(80)
f_lateral.SetNpy(80)
# 6 gaus centers:
par = np.array([center_x - dx / 2., # x1
center_y + dy, # y1
center_x + dx / 2., # x2
center_y + dy, # y2
center_x + dx / 2., # x3
center_y, # y3
center_x - dx / 2., # x4
center_y, # y4
center_x - dx / 2., # x5
center_y - dy, # y5
center_x + dx / 2., # x6
center_y - dy # y6
])
f_lateral.SetParameters(par)
a = Double()
b = Double()
# Generate Signal Distribution:
if self.MCAttributes['SignalMode'] == 'Landau':
self.SignalParameters = CreateRandomPeaksConfig(xmin, xmax, ymin, ymax, peak_height=self.MCAttributes['PeakHeight'], bkg=MPV)
else:
self.SignalParameters = CreateRandomPeaksConfig(xmin, xmax, ymin, ymax, peak_height=self.MCAttributes['PeakHeight'], bkg=100)
f_signal = TF2('f_signal', SignalShape, xmin, xmax, ymin, ymax, len(self.SignalParameters))
f_signal.SetNpx(40) # Resolution
f_signal.SetNpy(40)
f_signal.SetParameters(self.SignalParameters)
if self.MCAttributes['DrawRealDistribution']:
canvas = TCanvas('canvas', 'canvas')
canvas.cd()
gStyle.SetPalette(55) # a Rain Bow palette == used.
gStyle.SetNumberContours(8)
f_signal.Draw('surf1')
gPad.Print(MCRunPath + 'RealSignalDistribution.png')
if self.ShowAndWait:
answer = input('for data creation, type `yes`: ')
else:
answer = 'yes'
else:
answer = 'yes'
# Set the Hit distribution for Manual or Import
if self.MCAttributes['HitDistributionMode'] == 'Manual':
HitsTemplate = f_lateral
elif self.MCAttributes['HitDistributionMode'] == 'Import':
HitsTemplate = self.counthisto
mc_start_timestamp = 42. # arbitrary timestamp for the first MC event
MCEventDeltaTime = 30. * 60. / 300000. # 1800s/300000Hits = 0.006s/Hit
tmp_time = mc_start_timestamp
# Generate Toy Data:
if answer == 'yes':
if self.verbose:
self.ShowMCConfig()
self.verbose_print('Creating Toy Data with {0} Hits'.format(self.NumberOfHits))
integral50_max = self.MCAttributes['integral50_max'] # Maximum of Signal response allowed (data: 500 ?)
i = 0
j = 0
while i < self.NumberOfHits and j < 2 * self.NumberOfHits:
# Get x and y
if self.MCAttributes['HitDistributionMode'] == 'Uniform':
track_x[0] = gRandom.Uniform(xmin, xmax)
track_y[0] = gRandom.Uniform(ymin, ymax)
else:
HitsTemplate.GetRandom2(a, b)
track_x[0] = gRandom.Gaus(a, self.MCAttributes['TrackResolution']) # 0.002 = 20mu track resolution (first guess)
track_y[0] = gRandom.Gaus(b, self.MCAttributes['TrackResolution'])
# Get Signal at x and y
if self.MCAttributes['SignalMode'] == 'Landau':
integral50[0] = gRandom.Landau(f_signal(track_x[0], track_y[0]), sigma)
else:
integral50[0] = gRandom.Gaus(f_signal(track_x[0], track_y[0]), 0.6 * f_signal(track_x[0], track_y[0]) - 33)
# check if found values fulfill requirements
if xmin < track_x[0] < xmax and ymin < track_y[0] < ymax and integral50[0] < integral50_max:
tmp_time += MCEventDeltaTime
time_stamp[0] = tmp_time
self.track_info.Fill()
self.Data['track_x'].append(track_x[0])
self.Data['track_y'].append(track_y[0])
self.Data['integral50'].append(integral50[0])
i += 1
j += 1
if not j < 2 * self.NumberOfHits: # if too many times requirements were not fulfilled
assert (False), "Bad MC Parameters"
# Save root file and true Signal Shape:
if self.MCAttributes['Save']:
file.Write()
f_signal.SaveAs(MCRunPath + 'RealSignalDistribution.root')
self.verbose_print(MCRunPath + 'track_info.root has been written')
self.TrackingPadAnalysis['ROOTFile'] = MCRunPath + 'track_info.root'
# Print Settings of created data:
if self.verbose:
print("\nToydata containing {:.0f} peaks generated.".format(self.SignalParameters[0]))
if self.MCAttributes['SignalMode'] == 'Landau':
for i in range(int(self.SignalParameters[0])):
x = self.SignalParameters[3 + 4 * i]
y = self.SignalParameters[4 + 4 * i]
print("Peak {0:.0f} at position: ({1:.3f}/{2:.3f}) with Laundau Response MPV: {3:.2f} Sigma: {4:.1f}".format(i + 1, x, y, f_signal(x, y), sigma))
else:
integral50[0] = gRandom.Gaus(f_signal(track_x[0], track_y[0]), 0.6 * f_signal(track_x[0], track_y[0]) - 33)
for i in range(int(self.SignalParameters[0])):
x = self.SignalParameters[3 + 4 * i]
y = self.SignalParameters[4 + 4 * i]
print("Peak {0:.0f} at position: ({1:.3f}/{2:.3f}) with Gaussian Response Mean: {3:.2f} Sigma: {4:.1f}".format(i + 1, x, y, f_signal(x, y), 0.6 * f_signal(x, y) - 33))
self.DataIsMade = True
self.verbose_print('Monte Carlo Toy Data created.\n')
N += model[6] / 2.
Npb = model[6] / model[2]
Nreco = tmpHist.Integral()
print model[3], 'Ngen: %i Nreco: %0.0f xsec: %0.4f pb' % (model[6], Nreco, model[2]), \
'equiv integrated lumi: %0.0f pb^(-1)' % (Npb),
print 'Nreco/(fb^(-1)): %0.3f' % (Nreco * (1000. / Npb))
print model[3], 'acceptance x efficiency = %04.3g' % (Nreco / model[6])
## print model[3], 'acceptance x efficiency = %04.3g' % (Nreco/N)
tmpHist.Scale(1000. / Npb, 'width')
tmpHist.SetLineColor(model[5])
tmpHist.SetLineWidth(3)
if model[4] == 'WH':
HiggsEff = Nreco / N
hists.append(tmpHist)
f = TFile('Mjj_%s_%iJets_NewPhysics_1fb.root' % (modeString, opts.Nj),
'recreate')
for hist in hists:
hist.Print()
hist.Draw('hist')
hist.SetXTitle("m_{jj} (GeV)")
hist.SetYTitle("Arbitrary Units")
gPad.Update()
gPad.Print("Mjj_%s_%iJets_%s.pdf" % (modeString, opts.Nj, hist.GetName()))
gPad.Print("Mjj_%s_%iJets_%s.png" % (modeString, opts.Nj, hist.GetName()))
hist.Write()
f.Close()
fintegral = ratio.createIntegral(RooArgSet(time), 'fullrange').getVal()
hintegral = ratiohist.Integral('width') # has weights, use width
norm = fintegral / hintegral
print '=' * 5, ' Integrals ', '=' * 5
print 'Function integral / histogram integral = %g / %g = %g' % (
fintegral, hintegral, norm)
ratiohist.Scale(norm)
# make dataset from histogram
ratiodset = RooDataHist('ratiodset', '', RooArgList(time), ratiohist)
ratiodset.Print('v')
## Plot
tframe = time.frame(RooFit.Title('Time acceptance ratio'))
paramset = RooArgSet(rturnon, roffset, rbeta)
ratio.plotOn(tframe, RooFit.VisualizeError(fitresult, paramset, 1, False))
ratio.plotOn(tframe)
ratiodset.plotOn(tframe, RooFit.MarkerStyle(kFullDotMedium))
tframe.Draw()
# Print
if doPrint:
print 'Plotting to file: plots/DsK_ratio_%s.{png,pdf}' % timestamp
gPad.Print('plots/DsK_ratio_%s.png' % timestamp)
gPad.Print('plots/DsK_ratio_%s.pdf' % timestamp)
# NB: Do not close file, otherwise plot disappears
# ffile.Close()
