def flagLowStatPoints_noLargeErrorCheck(graph, debug):
#stick all the indices of the bad points in this vector
points = []
#loop on all data points of the graph
nPoints = graph.GetN()
for iPoint in xrange(0, nPoints):
dataPointX = Double(0)
dataPointY = Double(0)
error = Double(0)
errorX = graph.GetErrorX(iPoint)
errorY = graph.GetErrorY(iPoint)
graph.GetPoint(iPoint, dataPointX, dataPointY)
##eliminate and flag NaN points (better if numpy module installed)
if isnan(dataPointY):
dataPointY = 0.000001
#if dataPointY < 0.000001 :
#dataPointY = 0.000001
points.append(iPoint)
##flag points where the error is zero (this means that there was just one entry in the histogram)
if errorY < 0.00000001:
points.append(iPoint)
if debug == True:
print "DEBUG: List of bad points for plot", graph.GetName(), ":"
print points
return points
def _integral(self, graph, x_lo, x_hi):
""" Return integral of TGraph from x_lo to x_hi. This is done by using
triangles. Note that TGraph::Integral() does not return the integral of
the TGraph, but the area of the polygon spanned by the TGraph. """
integral = 0.
for point in range(0, graph.GetN()):
# Check if we are in the range requested by the user
if point < x_lo:
continue
if point >= x_hi:
break
# Use ROOT::Double() for x_n, y_n and x_n+1, y_n+1
x_n1 = Double()
y_n1 = Double()
x_n2 = Double()
y_n2 = Double()
graph.GetPoint(int(point), x_n1, y_n1)
graph.GetPoint(int(point) + 1, x_n2, y_n2)
# Calculate new integral segment and add it to old one
integral += 1. / 2 * (y_n2 + y_n1) * (x_n2 - x_n1)
return integral
def plotEtaPhiForDeltaPhiOne(self):
canvas = TCanvas("cEtaPhiDeltaPhiOne", "Eta Phi For DPhi 1", 1200,
1200)
graph = self.fileHandler.getGraph('graphs/averageEnergyDeltaPhi1')
halfbinwidth = L1_PHI_BIN / 2.
hist = TH2D('hEtaPhiDeltaPhi1', "#eta#phi of #Delta#phi=1 evts.", 30,
-15 * L1_ETA_BIN, 15 * L1_ETA_BIN, 289,
-math.pi - halfbinwidth, math.pi + halfbinwidth)
x = Double(0)
y = Double(0)
for i in range(0, graph.GetN()):
graph.GetPoint(i, x, y)
hist.Fill(x, y)
hist.SetStats(0)
hist.GetXaxis().SetRangeUser(-1, 1)
hist.SetTitle(hist.GetTitle() + ';#eta;#phi;Entries')
setupAxes(hist)
hist.Draw('colz')
canvas.Update()
setupPalette(hist)
label = self.drawLabel()
canvas.Update()
self.storeCanvas(canvas, 'etaPhiForDeltaPhiOne')
canvas.SaveAs('plots/etaPhiForDeltaPhiOne.pdf')
return canvas, hist, label
def __drawGraph__(self, graph):
## Takes care of drawing a graph
# Also takes care of calculating axis ranges, etc.
if self.combineStatsAndSyst and graph in self.systematicsGraphs.values(
):
return
drawOption = self.optionsDict[graph].upper()
title = graph.GetTitle()
if title:
legendOption = ''
if any(x in drawOption for x in ['L', '0']):
legendOption += 'L'
if any(x in drawOption for x in ['2', '3', '4', '5']):
legendOption += 'F'
if any(x in drawOption for x in ['P']):
legendOption += 'P'
self.legendElements.append(
LegendElement(graph, title, legendOption))
if not self.firstDrawnObject:
self.firstDrawnObject = self.xVariable.createHistogram()
self.firstDrawnObject.Draw()
if graph.GetN() > 0:
from ROOT import Double
xMin, xMax, yMin, yMax = (Double(), Double(), Double(), Double())
graph.ComputeRange(xMin, yMin, xMax, yMax)
if self.minimum > yMin:
self.minimum = yMin
if self.maximum < yMax:
self.maximum = yMax
graph.Draw(drawOption)
def tau_from_L_curve(unfoldingObject):
'''
Get best tau via l curve method
Not tested
'''
lCurve = TGraph()
logTauX = TSpline3()
logTauY = TSpline3()
iBest = unfoldingObject.ScanLcurve(500, 0., 0., lCurve, logTauX, logTauY)
# Additional info, plots
t = Double(0)
x = Double(0)
y = Double(0)
logTauX.GetKnot(iBest, t, x)
logTauY.GetKnot(iBest, t, y)
bestLcurve = Graph(1)
bestLcurve.SetPoint(1, x, y)
lCurve.SetMarkerColor(600)
lCurve.SetMarkerSize(1)
lCurve.SetMarkerStyle(5)
# lCurve.Draw("AP");
bestLcurve.markercolor = 'red'
# bestLcurve.Draw("*");
return unfoldingObject.GetTau()
def rescaleJetPlot(plot):
#loop on all data points
nPoints = plot.GetN()
# print "old:",plot.GetName(), nPoints
#create another TGraph
newPlot = TGraphErrors()
for i in xrange(0, nPoints):
dataPointX = Double(0)
dataPointY = Double(0)
error = Double(0)
plot.GetPoint(i, dataPointX, dataPointY)
#if numpy.isnan(dataPointY) : dataPointY = 0.000001
if dataPointY < 0.000001: dataPointY = 0.000001
errorX = plot.GetErrorX(i)
errorY = plot.GetErrorY(i)
newPlot.SetPoint(i, dataPointX / 1000., dataPointY)
newPlot.SetPointError(i, errorX / 1000., errorY)
# print "new:", newPlot.GetName(), newPlot.GetN()
return newPlot
def extendLastGoodBin_2011(graph, warn):
#loop on all data points of the graph
nPoints = graph.GetN()
#it should not happen that the first bin is bad, but just in case we keep it obvious
lastGoodBin = -999
lastGoodError = -999
for iPoint in xrange(0, nPoints):
dataPointX = Double(0)
dataPointY = Double(0)
error = Double(0)
errorX = graph.GetErrorX(iPoint)
errorY = graph.GetErrorY(iPoint)
graph.GetPoint(iPoint, dataPointX, dataPointY)
#HACK: this is rather arbitrary!
if dataPointY > 2. or errorY > 0.1:
if warn:
print "WARNING in JESUncertainty_RatioUtils::extendLastGoodBin: "
print "found bad point for graph:", graph.GetName(
), "at point: ", iPoint, ", X:", dataPointX, "Y:", dataPointY
print "extending previous point:", lastGoodBin
newDataPointY = lastGoodBin
newErrorY = lastGoodError
graph.SetPoint(iPoint, dataPointX, newDataPointY)
graph.SetPointError(iPoint, errorX, newErrorY)
else:
lastGoodBin = dataPointY
lastGoodError = errorY
def correctIncompleteShowers(self, limits):
"""
Arguments:
-> fname: Name of the root file where the incomplete energy per layer
distributions are stored.
-> hnames: Name of the histograms to be corrected.
-> weights: weights to correct the incomplete showers
-> limits: limits on the x axis where the correction should stop being applied
"""
h, corr_graphs = ([] for _ in range(2))
for ireg in range(self.nsr):
h.append([])
corr_graphs.append([])
for iw in range(self.ndiscrintervals_):
h[ireg].append(self.f_.Get(self.hn_bckg[ireg][iw]))
corr_graphs[ireg].append(TGraph(h[ireg][iw].GetNbinsX()))
for ireg in range(self.nsr):
graphs = RootHistograms(h[ireg]).toGraph()
assert len(graphs) == self.ndiscrintervals_
for iw in range(self.ndiscrintervals_):
for j in range(graphs[iw].GetN()):
x, y = Double(0.), Double(0.) #pass by reference
graphs[iw].GetPoint(j, x, y)
if x < limits[ireg] and self.weights_[ireg][iw][j] != 0:
corr_graphs[ireg][iw].SetPoint(
j, x, y / self.weights_[ireg][iw][j])
else:
corr_graphs[ireg][iw].SetPoint(j, x, y)
return corr_graphs
def grrScale(gr1, scale):
x1 = Double()
x_ = Double()
x1e = Double()
y1 = Double()
y1e = Double()
y = []
ex = []
ey = []
NC = gr1.GetN()
x = [0 for i in range(NC)]
for ii in range(NC):
x1e = gr1.GetErrorX(ii)
y1e = gr1.GetErrorY(ii)
gr1.GetPoint(ii, x_, y1)
x1 = x_ * 1.0
x[ii] = x1
y.append(y1 * scale)
ex.append(x1e)
ey.append(y1e * scale)
gr = Graph(NC)
for x0, y0, x0e, y0e, i in zip(x, y, ex, ey, range(NC)):
gr.SetPoint(i, x0, y0)
gr.SetPointError(i, x0e, x0e, y0e, y0e)
return gr
def get_integrals_and_errors(condor_dir,channel,dataset,histogram,threshold,minimum,maximum):
out = []
inputFile = TFile("condor/"+condor_dir+"/"+dataset+".root")
Histogram = inputFile.Get("OSUAnalysis/"+channel+"/"+histogram).Clone()
Histogram.SetDirectory(0)
inputFile.Close()
thresholdBin = Histogram.FindBin (threshold)
if Histogram.GetXaxis().GetBinLowEdge(thresholdBin) - threshold > 0.00001:
print "WARNING: your threshold value ("+str(threshold)+") is not on a bin edge"
print "the edge of its bin is " + str(Histogram.GetXaxis().GetBinLowEdge(thresholdBin))
minimumBin = Histogram.FindBin (minimum)
if Histogram.GetXaxis().GetBinLowEdge(minimumBin) - minimum > 0.00001:
print "WARNING: your minimum value ("+str(minimum)+") is not on a bin edge"
print "the edge of its bin is " + str(Histogram.GetXaxis().GetBinLowEdge(minimumBin))
maximumBin = Histogram.FindBin (maximum)
if Histogram.GetXaxis().GetBinLowEdge(maximumBin) - maximum > 0.00001:
print "WARNING: your maximum value ("+str(maximum)+") is not on a bin edge"
print "the edge of its bin is " + str(Histogram.GetXaxis().GetBinLowEdge(maximumBin))
isolatedIntegral = Double(0)
isolatedIntegralError = Double(0)
isolatedIntegral = Histogram.IntegralAndError(0,thresholdBin,isolatedIntegralError)
out.append(isolatedIntegral)
out.append(isolatedIntegralError)
nonIsolatedIntegral = Double(0)
nonIsolatedIntegralError = Double(0)
nonIsolatedIntegral = Histogram.IntegralAndError(minimumBin,maximumBin,nonIsolatedIntegralError)
out.append(nonIsolatedIntegral)
out.append(nonIsolatedIntegralError)
return out
def getRateInPb (self, seed):
if seed not in numerator[self._label]:
print "ETM" + seed + " not found in results!"
return
self._denominatorHist = TH1D ("total", "", 1, -0.5, 0.5)
self._numeratorHist = TH1D ("pass", "", 1, -0.5, 0.5)
self._denominatorHist.SetBinContent (1, self._denominator)
self._denominatorHist.SetBinError (1, math.sqrt (self._denominator))
self._numeratorHist.SetBinContent (1, numerator[self._label][seed])
self._numeratorHist.SetBinError (1, math.sqrt (numerator[self._label][seed]))
self._rateGraph = TGraphAsymmErrors (self._numeratorHist, self._denominatorHist)
x = Double (0.0)
y = Double (0.0)
self._rateGraph.GetPoint (0, x, y)
eLow = self._rateGraph.GetErrorYlow (0)
eHigh = self._rateGraph.GetErrorYhigh (0)
y *= self._crossSectionInPb
eLow *= self._crossSectionInPb
eHigh *= self._crossSectionInPb
return (y, eLow, eHigh)
def makeErrorGraph(graph):
'''Make graphs of the low and high error of a given graph.'''
N = graph.GetN()
graphElow = TGraph(N)
graphEup = TGraph(N)
graphElow.SetName(graph.GetName() + "_graphElow")
graphEup.SetName(graph.GetName() + "_graphEup")
graphElow.SetTitle(graph.GetTitle() + " low error")
graphEup.SetTitle(graph.GetTitle() + " high error")
for i in xrange(0, N):
x, y = Double(), Double()
graph.GetPoint(i, x, y)
graphElow.SetPoint(i, x, graph.GetErrorYlow(i))
graphEup.SetPoint(i, x, graph.GetErrorYhigh(i))
graphElow.SetLineWidth(graph.GetLineWidth())
graphEup.SetLineWidth(graph.GetLineWidth())
graphElow.SetLineColor(graph.GetLineColor())
graphEup.SetLineColor(graph.GetLineColor())
graphElow.SetLineStyle(graph.GetLineStyle())
graphEup.SetLineStyle(graph.GetLineStyle())
graphElow.SetMarkerSize(graph.GetMarkerSize())
graphEup.SetMarkerSize(graph.GetMarkerSize())
graphElow.SetMarkerColor(graph.GetMarkerColor())
graphEup.SetMarkerColor(graph.GetMarkerColor())
graphElow.SetMarkerStyle(graph.GetMarkerStyle())
graphEup.SetMarkerStyle(graph.GetMarkerStyle())
return [graphElow, graphEup]
def ScaleGraphY(graph, factor):
'''Scales the y values of a graph with the factor in the argument'''
for i in range(graph.GetN()):
x, y = Double(0), Double(0)
graph.GetPoint(i, x, y)
y *= factor
graph.SetPoint(i, x, y)
def generate(self, add_particle):
#electron polar angle theta and energy E
theta = -1.
E = 0.
while theta < 0. or theta > pi or E**2 < self.me**2:
#values of the x and y from the cross section
x = Double(0)
y = Double(0)
self.eq.GetRandom2(x, y)
#electron angle and energy
theta = math.sqrt(x * y * self.s / ((1. - y) * self.Ee**2))
E = self.Ee * (1. - y)
#tree output with generator kinematics
self.out.gen_x = x
self.out.gen_y = y
self.out.gen_Q2 = x * y * self.s
self.out.gen_theta = theta
self.out.gen_E = E
#uniform azimuthal angle
phi = 2. * TMath.Pi() * self.rand.Rndm()
self.out.gen_phi = phi
#put the electron to the event
el = add_particle(electron(E, theta, phi))
el.pxyze_prec = 9
def getHistoContentInJson(histo):
xBins={}
histoName=histo.GetName()
xaxisName = re.split("_",histoName)[0]
yaxisName = re.split("_",histoName)[1]
#if (histo.GetYaxis().GetNbins()==1):
if (histo.GetYaxis().GetNbins()==1):
print "this is a 1D histo"
#for i in range(0,histo.GetXaxis().GetNbins()+1):
for i in range(0,histo.GetN()):
print 'i is', i
x = Double(999)
x_hi = 999
x_low = 999
y = Double(999)
y_hi = 999
y_low = 999
histo.GetPoint(i,x,y)
x_hi = x+ histo.GetErrorXhigh(i)
x_low = x - histo.GetErrorXlow(i)
y_hi = histo.GetErrorYhigh(i)
y_low = histo.GetErrorYlow(i)
xBinValue=xaxisName+":["+str(x_low)+","+str(x_hi)+"]"
print 'xBinValue is', xBinValue
xBins[xBinValue]=getValueError(y, max(y_low,y_hi))
return xBins
def get_bins_errors(binned, include_overflow=False):
"""Get a list of bin content and bin centers of a TH1 or TGraph.
Args:
binned: The TH1 or TGraph object
Returns:
A list of (x, y, y_err) values
"""
points = []
if isinstance(binned, TGraph):
x = Double()
y = Double()
for i in range(binned.GetN()):
binned.GetPoint(i, x, y)
points.append((float(x), float(y), binned.GetErrorYhigh(i)))
elif isinstance(binned, TH1):
offset = 1 if include_overflow else 0
points = [(binned.GetBinCenter(i + 1), binned.GetBinContent(i + 1),
binned.GetBinError(i + 1))
for i in range(0 - offset,
binned.GetNbinsX() + offset)]
else:
raise RuntimeError('Unsupported object: {0}'.format(type(binned)))
return points
def makeCoordinatePlot(self, source):
c = TCanvas(source, source, 1200, 1200)
graphDt = self.fileHandler.getGraph('graphs/L1MuonPresent_' + source)
histAll = TH2D('hEtaPhi' + source, ";#eta_{L1};#phi_{L1};#", 30,
-15 * L1_ETA_BIN, 15 * L1_ETA_BIN, 144, -math.pi,
math.pi)
fillGraphIn2DHist(graphDt, histAll)
###
'''
Temporary stuff to check the eta coordinates in the graphs
'''
x = Double(0)
y = Double(0)
listeDt = []
for i in range(0, graphDt.GetN()):
graphDt.GetPoint(i, x, y)
listeDt.append(float(x))
histAll.SetStats(0)
histAll.Draw('colz')
c.Update()
setupAxes(histAll)
label = self.drawLabel()
c.Update()
return c, histAll, label
def plcLimit(obs_, poi_, model, ws, data, CL=0.95, verbose=False):
# obs : observable variable or RooArgSet of observables
# poi : parameter of interest or RooArgSet of parameters
# model : RooAbsPdf of model to consider including any constraints
# data : RooAbsData of the data
# CL : confidence level for interval
# returns a dictionary with the upper and lower limits for the first/only
# parameter in poi_ as well as the interval object and status flag
obs = RooArgSet(obs_)
obs.setName('observables')
poi = RooArgSet(poi_)
poi.setName('poi')
poi.setAttribAll('Constant', False)
nuis = model.getParameters(obs)
nuis.remove(poi)
nuis.remove(nuis.selectByAttrib('Constant', True))
nuis.setName('nuisance')
if verbose:
print 'observables'
obs.Print('v')
print 'parameters of interest'
poi.Print('v')
print 'nuisance parameters'
nuis.Print('v')
mc = RooStats.ModelConfig('mc')
mc.SetWorkspace(ws)
mc.SetPdf(model)
mc.SetObservables(obs)
mc.SetParametersOfInterest(poi)
mc.SetNuisanceParameters(nuis)
plc = RooStats.ProfileLikelihoodCalculator(data, mc)
plc.SetConfidenceLevel(CL)
interval = plc.GetInterval()
upperLimit = Double(999.)
lowerLimit = Double(0.)
Limits = {}
paramIter = poi.createIterator()
param = paramIter.Next()
while param:
ok = interval.FindLimits(param, lowerLimit, upperLimit)
Limits[param.GetName()] = {
'ok': ok,
'upper': float(upperLimit),
'lower': float(lowerLimit)
}
param = paramIter.Next()
if verbose:
print '%.0f%% CL limits' % (interval.ConfidenceLevel() * 100)
print Limits
Limits['interval'] = interval
return Limits
def w_lowHighMTRatio( var, anaDir,
comp, weights,
cut, weight, lowMTMax, highMTMin, highMTMax, chargeRequirement, treeName = None):
cutWithChargeReq = ' && '.join([cut, chargeRequirement])
max = 5000
mt = shape(var, anaDir,
comp, weights, max, 0, max,
cutWithChargeReq, weight,
None, None, treeName = treeName)
mt_low = mt.Integral(True, 0, lowMTMax)
mt_high = mt.Integral(True, highMTMin, highMTMax)
mt_ratio = mt_low / mt_high
print 'W MT ratio, mt ranges', lowMTMax, highMTMin, highMTMax
error_low = Double(0.)
error_high = Double(0.)
mt_low_jan = mt.weighted.IntegralAndError(1, mt.weighted.FindBin(lowMTMax)-1, error_low)
mt_high_jan = mt.weighted.IntegralAndError(mt.weighted.FindBin(highMTMin), mt.weighted.FindBin(highMTMax), error_high)
print mt.weighted.FindBin(lowMTMax)-1, mt.weighted.FindBin(highMTMin), mt.weighted.FindBin(highMTMax)
if mt_high > 0. and mt_low > 0.:
print 'MT ratio', mt_ratio, '+-', math.sqrt(error_low**2/mt_low**2 + error_high**2/mt_high**2) * mt_ratio
# print 'MT ratio', mt_low_jan/mt_high_jan, '+-', math.sqrt(error_low**2/mt_low**2 + error_high**2/mt_high**2) * mt_ratio
print 'Integrals: low', mt_low, '+-', error_low, 'high', mt_high, '+-', error_high
# print 'Integrals: low', mt_low_jan, '+-', error_low, 'high', mt_high_jan, '+-', error_high
print 'Relative errors: low', error_low/mt_low, 'high', error_high/mt_high
print 'TOTAL RELATIVE ERROR:', math.sqrt(error_low**2/mt_low**2 + error_high**2/mt_high**2), '\n'
return mt_ratio
def getGraph(self,graphname,filesToProcess = -1):
rootfile = TFile(self.getFile(0),'READ')
graphname = self.getFullPlotPath(graphname)
try:
graph = rootfile.Get(graphname)
nTotal = 0
counter = graph.GetN()
except AttributeError:
if self.DEBUG: commandLine.warning('Could not get graph %s' % graphname)
return None
#Check whether a number of result files to process is given
fileRange = filesToProcess if (filesToProcess != -1) else len(self.fileNameList)
#Then check that we don't try process more files than available
fileRange = min(len(self.fileNameList), fileRange )
for i in range(0,fileRange):
rootfile = TFile(self.getFile(i),'READ')
g = rootfile.Get(graphname)
nTotal += g.GetN()
commandLine.output('getGraph(%s) found %d points to process' % (graphname,nTotal))
for i in range(1,fileRange):
rootfile = TFile(self.getFile(i),'READ')
g2 = rootfile.Get(graphname)
x = Double(0)
y = Double(0)
for j in range(0,g2.GetN()):
counter += 1
if (counter % 10000 == 0):
commandLine.printProgress(counter,nTotal)
g2.GetPoint(j,x,y)
graph.SetPoint(graph.GetN(),x,y)
commandLine.printProgress(counter,nTotal)
return graph
def fixpar(massmin, massmax, masspeak, range_signal):
par_fix1 = Double(massmax-massmin)
par_fix2 = Double(massmax+massmin)
par_fix3 = Double(massmax*massmax*massmax-massmin*massmin*massmin)
par_fix4 = Double(masspeak)
par_fix5 = Double(range_signal)
return par_fix1, par_fix2, par_fix3, par_fix4, par_fix5
def draw_l_shape( l_shape, best_tau, x_value, y_value, channel, variable ):
total = l_shape.GetN()
x_values = []
y_values = []
add_x = x_values.append
add_y = y_values.append
for i in range( 0, total ):
x = Double( 0 )
y = Double( 0 )
l_shape.GetPoint( i, x, y )
add_x( x )
add_y( y )
plt.figure( figsize = ( 16, 16 ), dpi = 200, facecolor = 'white' )
plt.plot( x_values, y_values )
plt.xlabel( r'log10($\chi^2$)', fontsize = 40 )
plt.ylabel( 'log10(curvature)', fontsize = 40 )
ax = plt.axes()
ax.annotate( r"$\tau = %.3g$" % best_tau,
xy = ( x_value, y_value ), xycoords = 'data',
xytext = ( 0.3, 0.3 ), textcoords = 'figure fraction',
arrowprops = dict( arrowstyle = "fancy,head_length=0.4,head_width=0.4,tail_width=0.4",
connectionstyle = "arc3" ),
size = 40,
)
if use_data:
plt.savefig( 'plots/tau_from_L_shape_%s_channel_%s_DATA.png' % ( channel, variable ) )
else:
plt.savefig( 'plots/tau_from_L_shape_%s_channel_%s_MC.png' % ( channel, variable ) )
def profile(self, parameter_name, bins=21, bound=2, args=None, subtract_min=False):
if self.__gMinuit is None:
raise MinimizerROOTTMinuitException("Need to perform a fit before calling profile()!")
MAX_ITERATIONS = 6000
_error_code = Long(0)
_minuit_id = Long(self.parameter_names.index(parameter_name) + 1)
_par_min = Double(0)
_par_err = Double(0)
self.__gMinuit.GetParameter(_minuit_id - 1, _par_min, _par_err)
_x = np.linspace(start=_par_min - bound * _par_err, stop=_par_min + bound * _par_err, num=bins, endpoint=True)
self.__gMinuit.mnexcm("FIX", arr('d', [_minuit_id]), 1, _error_code)
_y = np.zeros(bins)
for i in range(bins):
self.__gMinuit.mnexcm("SET PAR", arr('d', [_minuit_id, Double(_x[i])]), 2, _error_code)
self.__gMinuit.mnexcm("MIGRAD", arr('d', [MAX_ITERATIONS, self.tolerance]), 2, _error_code)
_y[i] = self.get_fit_info("fcn")
self.__gMinuit.mnexcm("RELEASE", arr('d', [_minuit_id]), 1, _error_code)
self._migrad()
self.__gMinuit.mnexcm("SET PAR", arr('d', [_minuit_id, Double(_par_min)]), 2, _error_code)
return np.asarray((_x, _y))
def normalize(self):
""" Normalize S-curves.
First point is at y=1., last point is at y=0. """
# List to store normalize graps
graphs_normalized = []
for graph in self._scurves:
# Last point of TGraph needed for normalization
x_N = Double()
y_N = Double()
graph.GetPoint(graph.GetN() - 1, x_N, y_N)
# Need arrays for TGraph constructor
a_pts = array('d', range(0, graph.GetN()))
a_nrm = array('d', [])
for point in a_pts:
# Use ROOT::Double() for x_n, y_n
x_n1 = Double()
y_n1 = Double()
graph.GetPoint(int(point), x_n1, y_n1)
# Normalize to 1 and invert
a_nrm.append(-safe_divide(y_n1, y_N))
graphs_normalized.append(TGraph(len(a_pts), a_pts, a_nrm))
# Overwrite class member list
self._scurves = graphs_normalized
def properTime(particle, vertex, fitter):
tau = Double(-999999 / SystemOfUnits.picosecond)
if vertex:
error = Double(0.)
chi2 = Double(0.)
fitter.fit(vertex, particle, tau, error, chi2)
return tau / SystemOfUnits.picosecond
def correctIncompleteShowers(fname, hnames, weights, limits):
"""
Arguments:
-> fname: Name of the root file where the incomplete energy per layer
distributions are stored.
-> hnames: Name of the histograms to be corrected.
-> weights: weights to correct the incomplete showers
-> limits: limits on the x axis where the correction should stop being applied
"""
assert len(hnames) == len(weights)
fIn = TFile.Open(fname)
h = []
for i in hnames:
h.append(fIn.Get(i))
correctedGraphs = [TGraph(h[i].GetNbinsX()) for i in range(len(h))]
g = RootHistograms(h).toGraph()
g = RootGraphs(g)
ilim = -1
for it,ig in enumerate(g.getObjects()):
if ilim%3 == 0:
ilim += 1
for j in range(ig.GetN()):
x, y = Double(0.), Double(0.) #pass by reference
ig.GetPoint(j,x,y)
if x <= limits[ilim] and weights[it][j] != 0:
correctedGraphs[it].SetPoint(j, x, y/weights[it][j])
else:
correctedGraphs[it].SetPoint(j, x, y)
return correctedGraphs
def getVariationGraph(graph):
varGraph = graph.Clone()
for iPoint in xrange(0, graph.GetN()):
# Retrieve points
graphPointX = Double(0)
graphPointY = Double(0)
graphErrorX = graph.GetErrorX(iPoint)
graphErrorY = graph.GetErrorY(iPoint)
graph.GetPoint(iPoint, graphPointX, graphPointY)
newPoint = Double(0)
newPoint = fabs(1 - graphPointY)
newPointErrorX = graphErrorX
newPointErrorY = graphErrorY
#set the point in the new plot
varGraph.SetPoint(iPoint, graphPointX, newPoint)
varGraph.SetPointError(iPoint, newPointErrorX, newPointErrorY)
return varGraph
def addHistToTGraphPoisson(self, h, g, w=1.):
for i in range(0, g.GetN()):
# shifted by +1, i.e. over/underflow
err = h.GetBinError(i + 1)
y = h.GetBinContent(i + 1)
binWidth = h.GetBinWidth(i + 1)
# From https://twiki.cern.ch/twiki/bin/view/CMS/PoissonErrorBars
alpha = 1. - 0.6827
n = int(
round(y * binWidth /
w)) # Round is necessary due to, well, rounding errors
l = Math.gamma_quantile(alpha / 2., n, 1.) if n != 0. else 0.
u = Math.gamma_quantile_c(alpha, n + 1, 1) if n != 0. else 0.
g.SetPointEYlow(
i,
math.sqrt(((n - l) / binWidth * w)**2 + g.GetErrorYlow(i)**2))
g.SetPointEYhigh(
i,
math.sqrt(((u - n) / binWidth * w)**2 + g.GetErrorYhigh(i)**2))
gx = Double(0.)
gy = Double(0.)
g.GetPoint(i, gx, gy)
g.SetPoint(i, gx, y + gy)
def shiftAlongX(tGraph, numberOfGraphs, index):
for binNumber in xrange(tGraph.GetN()):
x = Double(-1)
y = Double(-1)
tGraph.GetPoint(binNumber, x, y)
shift = (tGraph.GetErrorXhigh(binNumber)) / (numberOfGraphs + 1)
x = x + shift * index
tGraph.SetPoint(binNumber, x, y)
def test1MinuitFit(self):
"""Test minuit callback and fit"""
# setup minuit and callback
gMinuit = TMinuit(5)
gMinuit.SetPrintLevel(-1) # quiet
gMinuit.SetGraphicsMode(ROOT.kFALSE)
gMinuit.SetFCN(fcn)
arglist = array('d', 10 * [0.])
ierflg = Long()
arglist[0] = 1
gMinuit.mnexcm("SET ERR", arglist, 1, ierflg)
# set starting values and step sizes for parameters
vstart = array('d', [3, 1, 0.1, 0.01])
step = array('d', [0.1, 0.1, 0.01, 0.001])
gMinuit.mnparm(0, "a1", vstart[0], step[0], 0, 0, ierflg)
gMinuit.mnparm(1, "a2", vstart[1], step[1], 0, 0, ierflg)
gMinuit.mnparm(2, "a3", vstart[2], step[2], 0, 0, ierflg)
gMinuit.mnparm(3, "a4", vstart[3], step[3], 0, 0, ierflg)
# now ready for minimization step
arglist[0] = 500
arglist[1] = 1.
gMinuit.mnexcm("MIGRAD", arglist, 2, ierflg)
# verify results
amin, edm, errdef = Double(), Double(), Double()
nvpar, nparx, icstat = Long(), Long(), Long()
gMinuit.mnstat(amin, edm, errdef, nvpar, nparx, icstat)
# gMinuit.mnprin( 3, amin )
self.assertEqual(nvpar, 4)
self.assertEqual(nparx, 4)
# success means that full covariance matrix is available (icstat==3)
self.assertEqual(icstat, 3)
# check results (somewhat debatable ... )
par, err = Double(), Double()
gMinuit.GetParameter(0, par, err)
self.assertEqual(round(par - 2.15, 2), 0.)
self.assertEqual(round(err - 0.10, 2), 0.)
gMinuit.GetParameter(1, par, err)
self.assertEqual(round(par - 0.81, 2), 0.)
self.assertEqual(round(err - 0.25, 2), 0.)
gMinuit.GetParameter(2, par, err)
self.assertEqual(round(par - 0.17, 2), 0.)
self.assertEqual(round(err - 0.40, 2), 0.)
gMinuit.GetParameter(3, par, err)
self.assertEqual(round(par - 0.10, 2), 0.)
self.assertEqual(round(err - 0.16, 2), 0.)
