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roofit Directory Reference

Directory dependency graph for roofit:

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## Files | |

rf101_basics.C | |

Basic functionality: fitting, plotting, toy data generation on one-dimensional PDFs. | |

rf101_basics.py | |

This tutorial illustrates the basic features of RooFit. | |

rf102_dataimport.C | |

Basic functionality: importing data from ROOT TTrees and THx histograms. | |

rf102_dataimport.py | |

'BASIC FUNCTIONALITY' RooFit tutorial macro #102 Importing data from ROOT TTrees and THx histograms | |

rf103_interprfuncs.C | |

Basic functionality: interpreted functions and PDFs. | |

rf103_interprfuncs.py | |

Basic functionality: interpreted functions and pdfs | |

rf104_classfactory.C | |

Basic functionality: The class factory for functions and pdfs | |

rf104_classfactory.py | |

Basic functionality: the class factory for functions and pdfs | |

rf105_funcbinding.C | |

Basic functionality: binding ROOT math functions as RooFit functions and pdfs | |

rf105_funcbinding.py | |

'BASIC FUNCTIONALITY' RooFit tutorial macro #105 Demonstration of binding ROOT Math functions as RooFit functions and pdfs | |

rf106_plotdecoration.C | |

Basic functionality: adding boxes with parameters, statistics to RooPlots, decorating with arrows, text etc... | |

rf106_plotdecoration.py | |

Basic functionality: adding boxes with parameters to RooPlots and decorating with arrows, etc... | |

rf107_plotstyles.C | |

Basic functionality: various plotting styles of data, functions in a RooPlot | |

rf107_plotstyles.py | |

Basic functionality: demonstration of various plotting styles of data, functions in a RooPlot | |

rf108_plotbinning.C | |

Basic functionality: plotting unbinned data with alternate and variable binnings | |

rf108_plotbinning.py | |

Basic functionality: plotting unbinned data with alternate and variable binnings | |

rf109_chi2residpull.C | |

Basic functionality: Calculating chi^2 from histograms and curves in RooPlots, making histogram of residual and pull distributions | |

rf109_chi2residpull.py | |

'BASIC FUNCTIONALITY' RooFit tutorial macro #109 Calculating chi^2 from histograms and curves in ROOT.RooPlots, making histogram of residual and pull distributions | |

rf110_normintegration.C | |

Basic functionality: normalization and integration of pdfs, construction of cumulative distribution monodimensional functions | |

rf110_normintegration.py | |

Basic functionality: examples on normalization and integration of pdfs, construction of cumulative distribution functions from monodimensional pdfs | |

rf111_derivatives.C | |

Basic functionality: numerical 1st,2nd and 3rd order derivatives w.r.t. | |

rf111_derivatives.py | |

Basic functionality: numerical 1st, and 3rd order derivatives w.r.t. | |

rf201_composite.C | |

Addition and convolution: composite pdf with signal and background component | |

rf201_composite.py | |

Addition and convolution: composite pdf with signal and background component | |

rf202_extendedmlfit.C | |

Setting up an extended maximum likelihood fit. | |

rf202_extendedmlfit.py | |

Addition and convolution: setting up an extended maximum likelihood fit | |

rf203_ranges.C | |

Fitting and plotting in sub ranges. | |

rf203_ranges.py | |

Addition and convolution: fitting and plotting in sub ranges | |

rf204a_extendedLikelihood.C | |

Extended maximum likelihood fit in multiple ranges. | |

rf204a_extendedLikelihood.py | |

Extended maximum likelihood fit in multiple ranges. | |

rf204b_extendedLikelihood_rangedFit.C | |

This macro demonstrates how to set up a fit in two ranges for plain likelihoods and extended likelihoods. | |

rf204b_extendedLikelihood_rangedFit.py | |

This macro demonstrates how to set up a fit in two ranges for plain likelihoods and extended likelihoods. | |

rf205_compplot.C | |

Addition and convolution: options for plotting components of composite pdfs. | |

rf205_compplot.py | |

Addition and convolution: options for plotting components of composite pdfs. | |

rf206_treevistools.C | |

Addition and convolution: tools for visualization of RooAbsArg expression trees | |

rf206_treevistools.py | |

Addition and convolution: tools for visualization of ROOT.RooAbsArg expression trees | |

rf207_comptools.C | |

Addition and convolution: tools and utilities for manipulation of composite objects | |

rf207_comptools.py | |

'ADDITION AND CONVOLUTION' RooFit tutorial macro #207 Tools and utilities for manipulation of composite objects | |

rf208_convolution.C | |

Addition and convolution: one-dimensional numeric convolution | |

rf208_convolution.py | |

'ADDITION AND CONVOLUTION' RooFit tutorial macro #208 One-dimensional numeric convolution (require ROOT to be compiled with –enable-fftw3) | |

rf209_anaconv.C | |

Addition and convolution: decay function pdfs with optional B physics effects (mixing and CP violation) | |

rf209_anaconv.py | |

Addition and convolution: decay function pdfs with optional B physics effects (mixing and CP violation) that can be analytically convolved with e.g. | |

rf210_angularconv.C | |

Addition and convolution: convolution in cyclical angular observables theta | |

rf210_angularconv.py | |

Convolution in cyclical angular observables theta, and construction of p.d.f in terms of transformed angular coordinates, e.g. | |

rf211_paramconv.C | |

Addition and convolution: working with a pdf with a convolution operator in terms of a parameter | |

rf211_paramconv.py | |

'ADDITION AND CONVOLUTION' RooFit tutorial macro #211 Working a with a p.d.f. | |

rf212_plottingInRanges_blinding.C | |

Plot a PDF in disjunct ranges, and get normalisation right. | |

rf212_plottingInRanges_blinding.py | |

Plot a PDF in disjunct ranges, and get normalisation right. | |

rf301_composition.C | |

Multidimensional models: multi-dimensional pdfs through composition e.g. | |

rf301_composition.py | |

Multidimensional models: multi-dimensional pdfs through composition, e.g. | |

rf302_utilfuncs.C | |

Multidimensional models: utility functions classes available for use in tailoring of composite (multidimensional) pdfs | |

rf302_utilfuncs.py | |

Multidimensional models: utility functions classes available for use in tailoring of composite (multidimensional) pdfs | |

rf303_conditional.C | |

Multidimensional models: use of tailored pdf as conditional pdfs.s | |

rf303_conditional.py | |

'MULTIDIMENSIONAL MODELS' RooFit tutorial macro #303 Use of tailored p.d.f as conditional p.d.fs.s | |

rf304_uncorrprod.C | |

Multidimensional models: simple uncorrelated multi-dimensional pdfs | |

rf304_uncorrprod.py | |

Multidimensional models: simple uncorrelated multi-dimensional pdfs | |

rf305_condcorrprod.C | |

Multidimensional models: multi-dimensional pdfs with conditional pdfs in product | |

rf305_condcorrprod.py | |

Multidimensional models: multi-dimensional pdfs with conditional pdfs in product | |

rf306_condpereventerrors.C | |

Multidimensional models: conditional pdf with per-event errors | |

rf306_condpereventerrors.py | |

Multidimensional models: complete example with use of conditional pdf with per-event errors | |

rf307_fullpereventerrors.C | |

Multidimensional models: full pdf with per-event errors | |

rf307_fullpereventerrors.py | |

Multidimensional models: usage of full pdf with per-event errors | |

rf308_normintegration2d.C | |

Multidimensional models: normalization and integration of pdfs, construction of cumulative distribution functions from pdfs in two dimensions | |

rf308_normintegration2d.py | |

Multidimensional models: normalization and integration of pdfs, construction of cumulative distribution functions from pdfs in two dimensions | |

rf309_ndimplot.C | |

Multidimensional models: making 2/3 dimensional plots of pdfs and datasets | |

rf309_ndimplot.py | |

Multidimensional models: making 2/3 dimensional plots of pdfs and datasets | |

rf310_sliceplot.C | |

Multidimensional models: projecting pdf and data slices in discrete observables | |

rf310_sliceplot.py | |

Multidimensional models: projecting pdf and data slices in discrete observables | |

rf311_rangeplot.C | |

Multidimensional models: projecting pdf and data ranges in continuous observables | |

rf311_rangeplot.py | |

Multidimensional models: projecting pdf and data ranges in continuous observables | |

rf312_multirangefit.C | |

Multidimensional models: performing fits in multiple (disjoint) ranges in one or more dimensions | |

rf312_multirangefit.py | |

Multidimensional models: performing fits in multiple (disjoint) ranges in one or more dimensions | |

rf313_paramranges.C | |

Multidimensional models: working with parametrized ranges to define non-rectangular regions for fitting and integration | |

rf313_paramranges.py | |

Multidimensional models: working with parameterized ranges to define non-rectangular regions for fitting and integration | |

rf314_paramfitrange.C | |

Multidimensional models: working with parametrized ranges in a fit. | |

rf314_paramfitrange.py | |

Multidimensional models: working with parameterized ranges in a fit. | |

rf315_projectpdf.C | |

Multidimensional models: marginizalization of multi-dimensional pdfs through integration | |

rf315_projectpdf.py | |

Multidimensional models: marginizalization of multi-dimensional pdfs through integration | |

rf316_llratioplot.C | |

Multidimensional models: using the likelihood ratio technique to construct a signal enhanced one-dimensional projection of a multi-dimensional pdf | |

rf316_llratioplot.py | |

Multidimensional models: using the likelihood ratio technique to construct a signal enhanced one-dimensional projection of a multi-dimensional pdf | |

rf401_importttreethx.C | |

Data and categories: advanced options for importing data from ROOT TTree and THx histograms | |

rf401_importttreethx.py | |

| |

rf402_datahandling.C | |

Data and categories: tools for manipulation of (un)binned datasets | |

rf402_datahandling.py | |

Data and categories: tools for manipulation of (un)binned datasets | |

rf403_weightedevts.C | |

Data and categories: using weights in unbinned datasets | |

rf403_weightedevts.py | |

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rf404_categories.C | |

Data and categories: working with RooCategory objects to describe discrete variables | |

rf404_categories.py | |

Data and categories: working with ROOT.RooCategory objects to describe discrete variables | |

rf405_realtocatfuncs.C | |

Data and categories: demonstration of real-->discrete mapping functions | |

rf405_realtocatfuncs.py | |

Data and categories: demonstration of real-discrete mapping functions | |

rf406_cattocatfuncs.C | |

Data and categories: demonstration of discrete-->discrete (invertible) functions | |

rf406_cattocatfuncs.py | |

Data and categories: demonstration of discrete-discrete (invertable) functions | |

rf407_latextables.C | |

Data and categories: latex printing of lists and sets of RooArgSets | |

rf407_latextables.py | |

Data and categories: latex printing of lists and sets of RooArgSets | |

rf408_RDataFrameToRooFit.C | |

Fill RooDataSet/RooDataHist in RDataFrame. | |

rf408_RDataFrameToRooFit.py | |

Fill RooDataSet/RooDataHist in RDataFrame. | |

rf409_NumPyPandasToRooFit.py | |

Convert between NumPy arrays or Pandas DataFrames and RooDataSets. | |

rf501_simultaneouspdf.C | |

Organisation and simultaneous fits: using simultaneous pdfs to describe simultaneous fits to multiple datasets | |

rf501_simultaneouspdf.py | |

Organization and simultaneous fits: using simultaneous pdfs to describe simultaneous fits to multiple datasets | |

rf502_wspacewrite.C | |

Organisation and simultaneous fits: creating and writing a workspace | |

rf502_wspacewrite.py | |

Organization and simultaneous fits: creating and writing a workspace | |

rf503_wspaceread.C | |

Organisation and simultaneous fits: reading and using a workspace | |

rf503_wspaceread.py | |

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rf504_simwstool.C | |

Organisation and simultaneous fits: using RooSimWSTool to construct a simultaneous pdf that is built of variations of an input pdf | |

rf504_simwstool.py | |

Organization and simultaneous fits: using RooSimWSTool to construct a simultaneous pdf that is built of variations of an input pdf | |

rf505_asciicfg.C | |

Organisation and simultaneous fits: reading and writing ASCII configuration files | |

rf505_asciicfg.py | |

Organization and simultaneous fits: reading and writing ASCII configuration files | |

rf506_msgservice.C | |

Organisation and simultaneous fits: tuning and customizing the RooFit message logging facility | |

rf506_msgservice.py | |

Organization and simultaneous fits: tuning and customizing the ROOT.RooFit message logging facility | |

rf508_listsetmanip.C | |

Organization and simultaneous fits: RooArgSet and RooArgList tools and tricks | |

rf508_listsetmanip.py | |

| |

rf509_wsinteractive.C | |

Organization and simultaneous fits: easy interactive access to workspace contents - CINT to CLING code migration | |

rf509_wsinteractive.py | |

Organization and simultaneous fits: easy interactive access to workspace contents - CINT to CLING code migration | |

rf510_wsnamedsets.C | |

Organization and simultaneous fits: working with named parameter sets and parameter snapshots in workspaces | |

rf510_wsnamedsets.py | |

| |

rf511_wsfactory_basic.C | |

Organization and simultaneous fits: basic use of the 'object factory' associated with a workspace to rapidly build pdfs functions and their parameter components | |

rf511_wsfactory_basic.py | |

Organization and simultaneous fits: basic use of the 'object factory' associated with a workspace to rapidly build pdfs functions and their parameter components | |

rf512_wsfactory_oper.C | |

Organization and simultaneous fits: operator expressions and expression-based basic pdfs in the workspace factory syntax | |

rf512_wsfactory_oper.py | |

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rf513_wsfactory_tools.C | |

Organization and simultaneous fits: RooCustomizer and RooSimWSTool interface in factory workspace tool in a complex standalone B physics example | |

rf513_wsfactory_tools.py | |

Organization and simultaneous fits: illustration use of ROOT.RooCustomizer and ROOT.RooSimWSTool interface in factory workspace tool in a complex standalone B physics example | |

rf514_RooCustomizer.C | |

Using the RooCustomizer to create multiple PDFs that share a lot of properties, but have unique parameters for each category. | |

rf514_RooCustomizer.py | |

Using the RooCustomizer to create multiple PDFs that share a lot of properties, but have unique parameters for each category. | |

rf515_hfJSON.py | |

Code HistFactory Models in JSON. | |

rf601_intminuit.C | |

Likelihood and minimization: interactive minimization with MINUIT | |

rf601_intminuit.py | |

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rf602_chi2fit.C | |

Likelihood and minimization: setting up a chi^2 fit to a binned dataset | |

rf602_chi2fit.py | |

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rf603_multicpu.C | |

Likelihood and minimization: setting up a multi-core parallelized unbinned maximum likelihood fit | |

rf603_multicpu.py | |

Likelihood and minimization: setting up a multi-core parallelized unbinned maximum likelihood fit | |

rf604_constraints.C | |

Likelihood and minimization: fitting with constraints | |

rf604_constraints.py | |

Likelihood and minimization: fitting with constraints | |

rf605_profilell.C | |

Likelihood and minimization: working with the profile likelihood estimator | |

rf605_profilell.py | |

| |

rf606_nllerrorhandling.C | |

Likelihood and minimization: understanding and customizing error handling in likelihood evaluations | |

rf606_nllerrorhandling.py | |

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rf607_fitresult.C | |

Likelihood and minimization: demonstration of options of the RooFitResult class | |

rf607_fitresult.py | |

Likelihood and minimization: demonstration of options of the RooFitResult class | |

rf608_fitresultaspdf.C | |

Likelihood and minimization: representing the parabolic approximation of the fit as a multi-variate Gaussian on the parameters of the fitted pdf | |

rf608_fitresultaspdf.py | |

Likelihood and minimization: representing the parabolic approximation of the fit as a multi-variate Gaussian on the parameters of the fitted pdf | |

rf609_xychi2fit.C | |

Likelihood and minimization: setting up a chi^2 fit to an unbinned dataset with X,Y,err(Y) values (and optionally err(X) values) | |

rf609_xychi2fit.py | |

Likelihood and minimization: setting up a chi^2 fit to an unbinned dataset with X,Y,err(Y) values (and optionally err(X) values) | |

rf610_visualerror.C | |

Likelihood and minimization: visualization of errors from a covariance matrix | |

rf610_visualerror.py | |

Likelihood and minimization: visualization of errors from a covariance matrix | |

rf611_weightedfits.C | |

Likelihood and minimization: Parameter uncertainties for weighted unbinned ML fits | |

rf612_recoverFromInvalidParameters.C | |

Likelihood and minimization: Recover from regions where the function is not defined. | |

rf612_recoverFromInvalidParameters.py | |

Likelihood and minimization: Recover from regions where the function is not defined. | |

rf613_global_observables.C | |

This tutorial explains the concept of global observables in RooFit, and showcases how their values can be stored either in the model or in the dataset. | |

rf613_global_observables.py | |

This tutorial explains the concept of global observables in RooFit, and showcases how their values can be stored either in the model or in the dataset. | |

rf614_binned_fit_problems.C | |

A tutorial that explains you how to solve problems with binning effects and numerical stability in binned fits. | |

rf614_binned_fit_problems.py | |

A tutorial that explains you how to solve problems with binning effects and numerical stability in binned fits. | |

rf701_efficiencyfit.C | |

Special pdf's: unbinned maximum likelihood fit of an efficiency eff(x) function | |

rf701_efficiencyfit.py | |

Special pdf's: unbinned maximum likelihood fit of an efficiency eff(x) function to a dataset D(x,cut), cut is a category encoding a selection, which the efficiency as function of x should be described by eff(x) | |

rf702_efficiencyfit_2D.C | |

Special pdf's: unbinned maximum likelihood fit of an efficiency eff(x) function to a dataset D(x,cut), cut is a category encoding a selection whose efficiency as function of x should be described by eff(x) | |

rf702_efficiencyfit_2D.py | |

Special pdf's: unbinned maximum likelihood fit of an efficiency eff(x) function to a dataset D(x,cut), cut is a category encoding a selection whose efficiency as function of x should be described by eff(x) | |

rf703_effpdfprod.C | |

Special pdf's: using a product of an (acceptance) efficiency and a pdf as pdf | |

rf703_effpdfprod.py | |

Special pdf's: using a product of an (acceptance) efficiency and a pdf as pdf | |

rf704_amplitudefit.C | |

Special pdf's: using a pdf defined by a sum of real-valued amplitude components | |

rf704_amplitudefit.py | |

Special pdf's: using a pdf defined by a sum of real-valued amplitude components | |

rf705_linearmorph.C | |

Special pdf's: linear interpolation between pdf shapes using the 'Alex Read' algorithm | |

rf705_linearmorph.py | |

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rf706_histpdf.C | |

Special pdf's: histogram-based pdfs and functions | |

rf706_histpdf.py | |

Special pdf's: histogram based pdfs and functions | |

rf707_kernelestimation.C | |

Special pdf's: using non-parametric (multi-dimensional) kernel estimation pdfs | |

rf707_kernelestimation.py | |

Special pdf's: using non-parametric (multi-dimensional) kernel estimation pdfs | |

rf708_bphysics.C | |

Special pdf's: special decay pdf for B physics with mixing and/or CP violation | |

rf708_bphysics.py | |

Special pdf's: special decay pdf for B physics with mixing and/or CP violation | |

rf709_BarlowBeeston.C | |

Implementing the Barlow-Beeston method for taking into account the statistical uncertainty of a Monte-Carlo fit template. | |

rf709_BarlowBeeston.py | |

Implementing the Barlow-Beeston method for taking into account the statistical uncertainty of a Monte-Carlo fit template. | |

rf710_roopoly.C | |

Taylor expansion of RooFit functions using the taylorExpand function with RooPolyFunc | |

rf710_roopoly.py | |

Taylor expansion of RooFit functions using the taylorExpand function | |

rf711_lagrangianmorph.C | |

Morphing effective field theory distributions with RooLagrangianMorphFunc A morphing function as a function of one coefficient is setup and can be used to obtain the distribution for any value of the coefficient. | |

rf711_lagrangianmorph.py | |

Morphing effective field theory distributions with RooLagrangianMorphFunc. | |

rf712_lagrangianmorphfit.C | |

Performing a simple fit with RooLagrangianMorphFunc. | |

rf712_lagrangianmorphfit.py | |

Performing a simple fit with RooLagrangianMorphFunc | |

rf801_mcstudy.C | |

Validation and MC studies: toy Monte Carlo study that perform cycles of event generation and fitting | |

rf801_mcstudy.py | |

Validation and MC studies: toy Monte Carlo study that perform cycles of event generation and fitting | |

rf802_mcstudy_addons.C | |

Validation and MC studies: RooMCStudy - using separate fit and generator models, using the chi^2 calculator model Running a biased fit model against an optimal fit. | |

rf802_mcstudy_addons.py | |

rf803_mcstudy_addons2.C | |

Validation and MC studies: RooMCStudy - Using the randomizer and profile likelihood add-on models | |

rf803_mcstudy_addons2.py | |

rf804_mcstudy_constr.C | |

Validation and MC studies: using RooMCStudy on models with constrains | |

rf804_mcstudy_constr.py | |

rf901_numintconfig.C | |

Numeric algorithm tuning: configuration and customization of how numeric (partial) integrals are executed | |

rf901_numintconfig.py | |

Numeric algorithm tuning: configuration and customization of how numeric (partial) integrals are executed | |

rf902_numgenconfig.C | |

Numeric algorithm tuning: configuration and customization of how MC sampling algorithms on specific pdfs are executed | |

rf902_numgenconfig.py | |

Numeric algorithm tuning: configuration and customization of how MC sampling algorithms on specific pdfs are executed | |

rf903_numintcache.C | |

Numeric algorithm tuning: caching of slow numeric integrals and parameterization of slow numeric integrals | |

rf903_numintcache.py | |

Numeric algorithm tuning: caching of slow numeric integrals and parameterizations of slow numeric integrals | |