Files | |
Bessel.C | |
Show the different kinds of Bessel functions available in ROOT To execute the macro type in: | |
Bessel.py | |
Show the different kinds of Bessel functions available in ROOT To execute the macro type in: | |
binomial.C | |
tutorial illustrating the use of TMath::Binomial can be run with: | |
BreitWigner.C | |
Tutorial illustrating how to create a plot comparing a Breit Wigner to a Relativistic Breit Wigner | |
ChebyshevPol.C | |
Example of Chebyshev polynomials using new TFormula pre-defined definitions of chebyshev polynomials | |
chi2test.C | |
Example to use chi2 test for comparing two histograms One unweighted histogram is compared with a weighted histogram. | |
CrystalBall.C | |
Example of CrystalBall Function and its distribution (pdf and cdf) | |
exampleFunction.py | |
Example of using Python functions and input to numerical algorithm using the ROOT Functor class | |
exampleFunctor.C | |
Tutorial illustrating how creating a TF1 class using functor or class member functions | |
exampleMultiRoot.C | |
Example of using multiroot finder based on GSL algorithm. | |
exampleTKDE.C | |
Example of using the TKDE class (kernel density estimator) | |
FeldmanCousins.C | |
Example macro of using the TFeldmanCousins class in root. | |
GammaFun.C | |
Example showing the usage of the major special math functions (gamma, beta, erf) in ROOT To execute the macro type in: | |
goftest.C | |
GoFTest tutorial macro | |
hlquantiles.C | |
Demo for quantiles (with highlight mode) | |
kdTreeBinning.C | |
kdTreeBinning tutorial: bin the data in cells of equal content using a kd-tree | |
Legendre.C | |
Example of first few Legendre Polynomials | |
Legendre.py | |
Example of first few Legendre Polynomials. | |
LegendreAssoc.C | |
Example describing the usage of different kinds of Associate Legendre Polynomials To execute the macro type in: | |
limit.C | |
This program demonstrates the computation of 95 % C.L. | |
mathBeta.C | |
Test the TMath::BetaDist and TMath::BetaDistI functions | |
mathcoreCDF.C | |
Example describing how to use the different cumulative distribution functions in ROOT. | |
mathcoreGenVector.C | |
Example macro testing available methods and operation of the GenVector classes. | |
mathcoreSpecFunc.C | |
Example macro describing how to use the special mathematical functions taking full advantage of the precision and speed of the C99 compliant environments. | |
mathcoreStatFunc.C | |
Example macro showing some major probability density functions in ROOT. | |
mathcoreStatFunc.py | |
Example macro showing some major probability density functions in ROOT. | |
mathcoreVectorCollection.C | |
Example showing how to write and read a std vector of ROOT::Math LorentzVector in a ROOT tree. | |
mathcoreVectorFloatIO.C | |
Macro illustrating I/O with Lorentz Vectors of floats The dictionary for LorentzVector of float is not in the libMathCore, therefore is generated when parsed the file with CLING. | |
mathcoreVectorIO.C | |
Example of I/O of a mathcore Lorentz Vectors in a Tree and comparison with a TLorentzVector. | |
mathGammaNormal.C | |
Tutorial illustrating the use of TMath::GammaDist and TMath::LogNormal | |
mathLaplace.C | |
Test the TMath::LaplaceDist and TMath::LaplaceDistI functions | |
mathmoreIntegration.C | |
Example on the usage of the adaptive 1D integration algorithm of MathMore it calculates the numerically cumulative integral of a distribution (like in this case the BreitWigner) to execute the macro type it (you need to compile with AClic) | |
mathmoreIntegrationMultidim.C | |
Example on the usage of the multidimensional integration algorithm of MathMore Please refer to the web documentation for further details: https://root.cern/manual/math/#numerical-integration To execute the macro type the following: | |
mathStudent.C | |
Tutorial illustrating the use of the Student and F distributions | |
multidimSampling.C | |
Example of sampling a multi-dim distribution using the DistSampler class NOTE: This tutorial must be run with ACLIC | |
multivarGaus.C | |
Tutorial illustrating the multivariate gaussian random number generation | |
normalDist.C | |
Tutorial illustrating the new statistical distributions functions (pdf, cdf and quantile) | |
normalDist.py | |
Tutorial illustrating the new statistical distributions functions (pdf, cdf and quantile) | |
permute.C | |
Tutorial illustrating the use of TMath::Permute can be run with: | |
principal.C | |
Principal Components Analysis (PCA) example | |
principal.py | |
Principal Components Analysis (PCA) example | |
quantiles.C | |
Demo for quantiles | |
quasirandom.C | |
Example of generating quasi-random numbers | |
Rolke.C | |
Example of the usage of the TRolke class The TRolke class computes the profile likelihood confidence limits for 7 different model assumptions on systematic/statistical uncertainties | |
testrandom.C | |
Performance test of all the ROOT random generator (TRandom, TRandom1, TRandom2 and TRandom3) Tests the generator TRandom3 against some ref values and creates a timing table against TRandom, TRandom1 and TRandom2. | |
tStudent.C | |
Example macro describing the student t distribution | |
tStudent.py | |
Example macro describing the student t distribution | |
TSVDUnfoldExample.C | |
Data unfolding using Singular Value Decomposition | |
vavilov.C | |
Test of the TMath::Vavilov distribution | |