␛[1mRooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby␛[0m
Copyright (C) 2000-2013 NIKHEF, University of California & Stanford University
All rights reserved, please read http://roofit.sourceforge.net/license.txt
RooDataSet::modelData[x,y] = 6850 entries
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: activating const optimization
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** 1 **SET PRINT 1
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** 2 **SET NOGRAD
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PARAMETER DEFINITIONS:
NO. NAME VALUE STEP SIZE LIMITS
1 a0 -5.00000e-01 1.00000e+00 -5.00000e+00 5.00000e+00
2 a1 -5.00000e-01 2.00000e-01 -1.00000e+00 1.00000e+00
3 sigma 5.00000e-01 1.90000e-01 1.00000e-01 2.00000e+00
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** 3 **SET ERR 0.5
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** 4 **SET PRINT 1
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** 5 **SET STR 1
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NOW USING STRATEGY 1: TRY TO BALANCE SPEED AGAINST RELIABILITY
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** 6 **MIGRAD 1500 1
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FIRST CALL TO USER FUNCTION AT NEW START POINT, WITH IFLAG=4.
START MIGRAD MINIMIZATION. STRATEGY 1. CONVERGENCE WHEN EDM .LT. 1.00e-03
FCN=421420 FROM MIGRAD STATUS=INITIATE 12 CALLS 13 TOTAL
EDM= unknown STRATEGY= 1 NO ERROR MATRIX
EXT PARAMETER CURRENT GUESS STEP FIRST
NO. NAME VALUE ERROR SIZE DERIVATIVE
1 a0 -5.00000e-01 1.00000e+00 2.02430e-01 -7.46265e+04
2 a1 -5.00000e-01 2.00000e-01 2.35352e-01 -6.95347e+05
3 sigma 5.00000e-01 1.90000e-01 2.52163e-01 -1.29056e+06
ERR DEF= 0.5
MIGRAD MINIMIZATION HAS CONVERGED.
MIGRAD WILL VERIFY CONVERGENCE AND ERROR MATRIX.
COVARIANCE MATRIX CALCULATED SUCCESSFULLY
FCN=9659.64 FROM MIGRAD STATUS=CONVERGED 101 CALLS 102 TOTAL
EDM=8.18253e-05 STRATEGY= 1 ERROR MATRIX ACCURATE
EXT PARAMETER STEP FIRST
NO. NAME VALUE ERROR SIZE DERIVATIVE
1 a0 9.03100e-03 1.19768e-02 1.62548e-04 -2.31829e+00
2 a1 5.02815e-01 2.21631e-03 1.74026e-04 -2.33580e+00
3 sigma 9.91234e-01 8.46817e-03 6.05957e-04 -4.27913e-01
ERR DEF= 0.5
EXTERNAL ERROR MATRIX. NDIM= 25 NPAR= 3 ERR DEF=0.5
1.434e-04 1.880e-07 4.948e-08
1.880e-07 4.912e-06 8.995e-09
4.948e-08 8.995e-09 7.171e-05
PARAMETER CORRELATION COEFFICIENTS
NO. GLOBAL 1 2 3
1 0.00710 1.000 0.007 0.000
2 0.00710 0.007 1.000 0.000
3 0.00068 0.000 0.000 1.000
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** 7 **SET ERR 0.5
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** 8 **SET PRINT 1
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** 9 **HESSE 1500
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COVARIANCE MATRIX CALCULATED SUCCESSFULLY
FCN=9659.64 FROM HESSE STATUS=OK 16 CALLS 118 TOTAL
EDM=8.17764e-05 STRATEGY= 1 ERROR MATRIX ACCURATE
EXT PARAMETER INTERNAL INTERNAL
NO. NAME VALUE ERROR STEP SIZE VALUE
1 a0 9.03100e-03 1.19768e-02 3.25095e-05 1.80620e-03
2 a1 5.02815e-01 2.21631e-03 3.48052e-05 2.61474e+00
3 sigma 9.91234e-01 8.46818e-03 1.21191e-04 -6.18987e-02
ERR DEF= 0.5
EXTERNAL ERROR MATRIX. NDIM= 25 NPAR= 3 ERR DEF=0.5
1.434e-04 1.880e-07 2.151e-09
1.880e-07 4.912e-06 2.334e-10
2.151e-09 2.334e-10 7.171e-05
PARAMETER CORRELATION COEFFICIENTS
NO. GLOBAL 1 2 3
1 0.00708 1.000 0.007 0.000
2 0.00708 0.007 1.000 0.000
3 0.00002 0.000 0.000 1.000
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: deactivating const optimization
[#1] INFO:Plotting -- RooAbsReal::plotOn(model) plot on x averages using data variables (y)
[#1] INFO:Plotting -- RooDataWeightedAverage::ctor(modelDataWgtAvg) constructing data weighted average of function model_Norm[x] over 6850 data points of (y) with a total weight of 6850
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[#1] INFO:Plotting -- RooAbsReal::plotOn(model) plot on x averages using data variables (y)
[#1] INFO:Plotting -- RooDataWeightedAverage::ctor(modelDataWgtAvg) constructing data weighted average of function model_Norm[x] over 100 data points of (y) with a total weight of 6850
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[#1] INFO:Plotting -- RooAbsReal::plotOn(model) plot on x averages using data variables (y)
[#1] INFO:Plotting -- RooDataWeightedAverage::ctor(modelDataWgtAvg) constructing data weighted average of function model_Norm[x] over 5 data points of (y) with a total weight of 6850
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{
RooDataSet *data = model.generate(
x, ProtoData(*expDataY));
model.fitTo(*expDataXY, ConditionalObservables(
y));
model.plotOn(xframe, ProjWData(*expDataY));
model.
plotOn(xframe, ProjWData(*binnedDataY2), LineColor(
kRed));
new TCanvas(
"rf303_conditional",
"rf303_conditional", 600, 460);
gPad->SetLeftMargin(0.15);
}
{
for (int i = 0; i < 10000; i++) {
if (fabs(tmpy) < 10 && fabs(tmpx) < 10) {
}
}
}
R__EXTERN TRandom * gRandom
RooAbsArg * find(const char *name) const
Find object with given name in list.
RooAbsData is the common abstract base class for binned and unbinned datasets.
virtual void Print(Option_t *options=0) const
Print TNamed name and title.
virtual RooPlot * plotOn(RooPlot *frame, const RooCmdArg &arg1=RooCmdArg::none(), const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none(), const RooCmdArg &arg7=RooCmdArg::none(), const RooCmdArg &arg8=RooCmdArg::none()) const
RooAbsData * reduce(const RooCmdArg &arg1, const RooCmdArg &arg2=RooCmdArg(), const RooCmdArg &arg3=RooCmdArg(), const RooCmdArg &arg4=RooCmdArg(), const RooCmdArg &arg5=RooCmdArg(), const RooCmdArg &arg6=RooCmdArg(), const RooCmdArg &arg7=RooCmdArg(), const RooCmdArg &arg8=RooCmdArg())
Create a reduced copy of this dataset.
RooArgSet is a container object that can hold multiple RooAbsArg objects.
RooPlot * plotOn(RooPlot *frame, PlotOpt o) const override
Back end function to plotting functionality.
RooDataSet is a container class to hold unbinned data.
virtual const RooArgSet * get(Int_t index) const override
Return RooArgSet with coordinates of event 'index'.
RooDataHist * binnedClone(const char *newName=0, const char *newTitle=0) const
Return binned clone of this dataset.
A RooPlot is a plot frame and a container for graphics objects within that frame.
static RooPlot * frame(const RooAbsRealLValue &var, Double_t xmin, Double_t xmax, Int_t nBins)
Create a new frame for a given variable in x.
virtual void Draw(Option_t *options=0)
Draw this plot and all of the elements it contains.
Class RooPolyVar is a RooAbsReal implementing a polynomial in terms of a list of RooAbsReal coefficie...
RooRealVar represents a variable that can be changed from the outside.
virtual void SetTitleOffset(Float_t offset=1)
Set distance between the axis and the axis title.
virtual Double_t Gaus(Double_t mean=0, Double_t sigma=1)
Samples a random number from the standard Normal (Gaussian) Distribution with the given mean and sigm...
The namespace RooFit contains mostly switches that change the behaviour of functions of PDFs (or othe...