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math Directory Reference

Directory dependency graph for math:

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## 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.ch/root/htmldoc/guides/users-guide/MathLibraries.html#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 | |