TGraph::SavesAs() for .csv, .tsv and .txt for text output separated by comma, tab, and space, respectively.TGraph2DAsymmErrors to create TGraph2D with asymmetric errors. 
ROOT version 6.28/00 was released on February 03, 2023.
For more information, see:
The following people have contributed to this new version:
Guilherme Amadio, CERN/SFT,
Rahul Balasubramanian, NIKHEF/ATLAS,
Bertrand Bellenot, CERN/SFT,
Jakob Blomer, CERN/SFT,
Patrick Bos, Netherlands eScience Center,
Rene Brun, CERN/SFT,
Carsten D. Burgard, TU Dortmund University/ATLAS,
Artem Busorgin,
Will Buttinger, RAL/ATLAS,
Philippe Canal, FNAL,
Olivier Couet, CERN/SFT,
Michel De Cian, EPFL/LHCb,
Hans Dembinski, TU Dortmund,
Simeon Ehrig, HZDR,
Mattias Ellert, Uppsala University,
Matthew Feickert, UW Madison/ATLAS,
Markus Frank, CERN/LHCb,
Oliver Freyermuth, U Bonn,
Gerri Ganis, CERN/SFT,
Andrei Gheata, CERN/SFT,
Sayandeep Ghosh, Jadavpur U Calcutta,
Konstantin Gizdov, University of Edinburgh/LHCb,
Max Goblirsch, CERN/ATLAS,
Stefan Gränitz,
Enrico Guiraud, CERN/SFT,
Stephan Hageboeck, CERN/IT,
Jonas Hahnfeld, CERN/SFT,
Ahmat Mahamat Hamdan, CERN/SFT,
Fernando Hueso-González, University of Valencia,
Pawan Johnson, Indian Institute of Technology Kharagpur,
Subham Jyoti, ITER Bhubaneswar,
Ivan Kabadzhov, CERN/SFT,
Baidyanath Kundu, Princeton/SFT,
Giovanna Lazzari Miotto, CERN/SFT,
Yueh-Shun Li, National Central U Taiwan/CMS,
Sergey Linev, GSI,
Jerry Ling, Harvard University,
Javier Lopez-Gomez, CERN/SFT,
Enrico Lusiani, INFN/CMS,
Julia Mathe, CERN/SFT,
Pere Mato, CERN/SFT,
Lorenzo Moneta, CERN/SFT,
Nicolas Morange, CNRS/ATLAS,
Alja Mrak Tadel, UCSD/CMS,
Axel Naumann, CERN/SFT,
Duncan Ogilvie,
Hanna Olvhammar, CERN/SFT,
Vincenzo Eduardo Padulano, CERN/SFT and UPV,
Tapasweni Pathak,
Danilo Piparo, CERN/SFT,
David Poulton, Wits/SFT,
Fons Rademakers, CERN/SFT,
Jonas Rembser, CERN/SFT,
Mathias Schmitt,
Neel Shah, VJTI,
Petr Stepanov, Catholic University of America,
Enric Tejedor Saavedra, CERN/SFT,
Neel Shah, GSOC,
Sanjiban Sengupta, CERN/SFT,
Harshal Shende, GSOC,
Garima Singh, Princeton/SFT,
Surya Somayyajula, UW Madison,
Matevz Tadel, UCSD/CMS,
Vassil Vassilev, Princeton/CMS,
Andrii Verbytskyi, MPP Munich/ATLAS,
Wouter Verkerke, NIKHEF/ATLAS,
Frank Winklmeier, U Oregon/ATLAS,
Zef Wolffs, NIKHEF/ATLAS,
Jun Zhang
ROOT::Experimental::TBufferMerger and ROOT::Experimental::TBufferMergerFile are removed. Please use their non-experimental counterparts ROOT::TBufferMerger and ROOT::TBufferMergerFile instead.ROOT::RVec::shrink_to_fit() has now been removed after deprecation; it is not needed.ROOT::RVec::emplace() has now been removed after deprecation; please use ROOT::RVec::insert() instead.ROOT::Detail::RDF::RActionImpl<Helper>::GetDataBlockCallback() is removed; please use GetSampleCallback() instead.RooHashTable, RooNameSet, RooSetPair, and RooList are removed. Please use STL container classes instead, like std::unordered_map, std::set, and std::vector.RooFit::FitOptions(const char*) command to steer RooAbsPdf::fitTo() with an option string was removed. This way of configuring the fit was deprecated since at least since ROOT 5.02. Subsequently, the RooMinimizer::fit(const char*) function and the RooMCStudy constructor that takes an option string were removed as well.RooAbsData::createHistogram that takes integer parameters for the bin numbers is now deprecated and will be removed in ROOT 6.30. This was done to avoid confusion with inconsistent behavior when compared to other createHistogram overloads. Please use the verson of createHistogram that takes RooFit command arguments.RooAbsData::valid() method to cache valid entries in the variable range was removed. It was not implemented in RooDataSet, so it never worked as intended. Related to it was the RooDataHist::cacheValidEntries() function, which is removed as well. The preferred way to reduce RooFit datasets to subranges is RooAbsData::reduce().RooStats::HistFactory::EstimateSummary class is removed, including the functions that use it. The information that it was meant to store is managed by the RooStats::HistFactory::Measurement object since many years.RooSuperCategory::MakeIterator() function that was deprecated since 6.22 is now removed. Please use range-based loops to iterate over the category states.HybridCalculatorOriginal and HypoTestInverterOriginal classes in RooStats that were deprecated for a very long time aleady are removed. Please use HybridCalculator and HypoTestInverter.RooSimPdfBuilder that was deprecated in ROOT 5.20 and replaced by the RooSimWSTool is removed.MakeNumpyDataFrame, MakeCsvDataFrame, MakeArrowDataFrame, MakeNTupleDataFrame and MakeSqliteDataFrame are now deprecated in favor of FromNumpy, FromCSV, FromArrow, FromRNTuple and FromSqlite respectively.TDirectory::EncodeNameCycle() is deprecated; it cannot be used safely. Use name + ';' + std::to_string(cycle) instead.
alien has been removed.The build options gfal, gsl_shared, jemalloc, monalisa, pyroot_legacy, tcmalloc, and xproofd have been deprecated. Please complain with root-dev@cern.ch should you still need one!
This version adds the new rootreadspeed CLI tool. This tool can be used to help identify bottlenecks in analysis runtimes, by providing time and throughput measurements when reading ROOT files via file systems or XRootD. More detailed information can be found in the tool’s help information.
To see help information, install and source a recent enough version of ROOT, and run the command rootreadspeed --help in your terminal.
$ rootreadspeed --files <local-folder>/File1.root xrootd://<url-folder>/File2.root --trees Events --all-branches --threads 8llvm and clang have been upgraded to version 13. C++20 support will be provided as part of this release (not yet available for 6.28/00). cling has been upgraded to use llvm’s new just-in-time compilation engive ORCv2.
Cling checks pointer validity now only in interactive mode, improving performance for jitted code in batch systems.
This version of ROOT adds an LLVM JIT event listener to create perf map files during runtime. This allows profiling of interpreted/JITted code generated by cling. Instead of function addresses, the perf data will contain full function names. In addition, stack frame pointers are enabled in JITted code, so full stack traces can be generated. Debugging is aided by switching off optimisations and adding frame pointers for better stack traces. However, since both have a runtime cost, they are disabled by default. Similar to LD_DEBUG and LD_PROFILE for ld.so, the environment variables CLING_DEBUG=1 and/or CLING_PROFILE=1 can be set to enable debugging and/or profiling.
std namespace is now diagnosed. Specifically, given that ROOT injects using namespace std directive, all the names in the std namespace become available in the global scope. However, in some circumstances users inadvertently introduce a declaration that conflicts with a name in std making references to the former declaration result in ambiguous lookup. A fairly common case is trying to declare a global variable named data which conflict with std::data [C++17]. See ROOT-5971 for a discussion. As of v6.28, such declarations result inroot [] int data;
ROOT_prompt_0:1:1: warning: 'data' shadows a declaration with the same name in the 'std' namespace; use '::data' to reference this declaration
int data;
^root [] .help editTFile by TTreeProcessorMT.A new cross-protocol redirection has been added to allow files on EOS mounts to be opened by TFile::Open via XRootD protocol rather than via FUSE when that is possible. The redirection uses the eos.url.xroot extended file attribute that is present on files in EOS. The attribute can be viewed with getfattr -n eos.url.xroot [file] on the command line. When the URL passed into TFile::Open is a for a file on an EOS mount, the extended attribute is used to attempt the redirection to XRootD protocol. If the redirection fails, the file is opened using the plain file path as before. This feature is controlled by the pre-existing configuration option TFile.CrossProtocolRedirects and is enabled by default. It can be disabled by setting TFile.CrossProtocolRedirects to 0 in rootrc.
ROOT’s experimental successor of TTree has seen many updates during the last few months. Specifically, v6.28 includes the following changes:
Complete support for big-endian architectures (PR #10402).
Support for std::pair<T1, T2> and std::tuple<Ts...> fields
Support for C array fields whose type is of the form T[N]. Note that only single-dimension arrays are currently supported.
RNTupleReader or RDataFrame object can be created from a TFile instance as followsauto f = TFile::Open("data.root");
auto ntpl = f->Get<ROOT::Experimental::RNTuple>("Events");
auto reader = ROOT::Experimental::RNTupleReader::Open(ntpl);
// or for RDataFrame
auto rdf = ROOT::Experimental::MakeNTupleDataFrame(ntpl);If buffered write is enabled, vector writes are used where possible. In particular, this yields important improvements in storage backends leveraging parallel writes, e.g. in object storages.
Large read/write throughput improvements in the experimental Intel DAOS backend.
RNTupleWriter::Fill() now returns the number of uncompressed bytes written, which is align with TTree behavior.
Support for user-defined classes that behave as a collection via the TVirtualCollectionProxy interface. Fields created via RFieldBase::Create() automatically detect the presence of a collection proxy at run-time. However, if RField<T> (T being a class) is used instead, the trait IsCollectionProxy<T> must be set for the given type (see PR #11525 for details). Note that associative collections are not yet supported.
Some internal support for per field post-read callbacks. This functionality will be presented in upcoming releases through custom I/O rules.
Please, report any issues regarding the abovementioned features should you encounter them. RNTuple is still experimental and is scheduled to become production grade in 2024. Thus, we appreciate feedback and suggestions for improvement.
GraphAsymmErrors action that fills a TGraphAsymmErrors object.RDatasetSpec as an experimental class to specify the input dataset to an RDataFrame.RDatasetSpec. The metadata of each sample can then be retrieved during the execution by calling DefinePerSample.SaveGraph, where previously cling was getting wrong static initialization.Graph action (that fills a TGraph object) to properly handle containers and non-container types.RCsvDS class now allows users to specify column types, and can properly read empty entries of csv files.Display operation would not show the correct amount of entries requested by the user if called together with other operations (PR).Stats results with VariationsFor is now supported.MakeCsvDataFrame is now FromCSV. The old wording is still available but deprecated.Sums and Means of single-precision floating point values has been greatly improved by employing Kahan summations.Vary and VariationsFor operations) in distributed mode.Snapshot) is purposely made lazy by the user, distributed RDataFrame now respects this and avoids triggering the computations right away.Histo*D actions is now required in distributed mode. See the relative PR for more discussion.npartitions argument is not set by the user, the default number of tasks created by a distributed RDataFrame is equal to the number of cores specified by the user when connecting to the cluster.std::exception and derived) are now correctly propagated from the processes of the computing nodes to the user side.dask version required to support distributed RDataFrame is 2022.8.1, since a series of critical bugs present before that version were hindering the normal execution of the tool. Consequently, the minimum Python version needed to include distributed RDataFrame in the ROOT build is Python 3.8. More information in the relative github issue.Stats and StdDev operations are now available in distributed mode.GetColumnNames operation is now available in distributed mode.TGraph::SavesAs() for .csv, .tsv and .txt for text output separated by comma, tab, and space, respectively.TGraph2DAsymmErrors to create TGraph2D with asymmetric errors. TLorentzVector can now convert to ROOT::Math::PxPyPzEVector.Some improvements and small fixes to the internal object memory management have been applied to the ROOT::Fit::Fitter class.
GSLMultiFit class.TLinearMinimizer class).Support for providing the second derivatives (Hessian matrix) from the model function is added to the Fitter class and the corresponding function interfaces. The functionality it is then propagated in the implementation of the FitMethod classes and it is also added to the Minimizer classes for providing a user computed Hessian of the objective functions to the minimizers. Only Minuit2 (see below) has the capabilities of using this external Hessian.
The GradFunctor class has been improved by providing a new constructor taking an std::function implementing the full gradient calculations instead of the single partial derivative.
The specialized methods for least-square/likelihood functions such as Fumili, Fumili2 and GSLMultiFit have been improved in case of binned likelihood fits, where a better approximation is used than before. This makes these method work better (conerging with less number of function calls) for these types of fits.
The support for using an External Hessian calculator has been added. The external Hessian can be used for both the initial seeding, using only the diagonal part, if the strategy is equal to 1 (the default value) and in MnHesse, after the minimization, to compute the covariance and correlation matrices.
The print log of Minuit2 has been improved, especially when printing vector and matrices with large number of parameters (when the print level = 3).
The ROOT::Math::KahanSum class was slightly modified:
operator-= and operator+= on a KahanSum were not symmetric, leading to slight bit-wise inaccuracies. In fits, where such operations are done a lot of times (e.g. through the offsetting mechanism in RooFit which subtracts a constant KahanSum term after each likelihood evaluation), this can add up to significant numerical divergence. An improved algorithm was implemented, based on an algorithm for combining Kahan sums and carry terms (Tian et al. 2012). (PR #11940)T and implicit type T constructor in KahanSum made it hard to debug KahanSum, because it is easy to overlook implicit conversions in code, especially in lines where the type of the return value is auto. These auto-conversions were removed. Where necessary, they should be replaced with an explicit construction or explicit conversion to double via Sum(). (PR #11941)operator== and operator!= were added.The usage of TRef in the TFoamCell class has ben replaced with array indices. This avoids, when generating a large number of toys requiring a re-initialization of TFoam an increase in the memory usage caused by TRef.
In previous releases, the default minimizer type that RooFit used was hardcoded to be the original Minuit, while RooStats used the default minimizer specified by ROOT::Math::MinimizerOptions::DefaultMinimizerType(). Now it is possible to centrally define the global minimizer for all RooFit libraries via ROOT::Math::MinimizerOptions::SetDefaultMinimizer(), or alternatively in the .rootrc file by adding for example Root.Fitter: Minuit2 to select Minuit2.
std::string in RooFit interfacesThe following lesser-used RooFit functions now return a std::string instead of a const char*, potentially requiring the update of your code:
Before v6.28, it was ensured that no RooArgSet and RooDataSet objects on the heap were located at an address that had already been used for an instance of the same class before. With v6.28, this is not guaranteed anymore. Hence, if your code uses pointer comparisons to uniquely identify RooArgSet or RooDataSet instances, please consider using the new RooArgSet::uniqueId() or RooAbsData::uniqueId().
In a binned likelihood fit, it is possible to skip the PDF normalization when the unnormalized binned PDF can be interpreted directly in terms of event yields. This is now done by default for HistFactory models, which results in great speedups for binned fits with many channels. Some RooFit users like ATLAS were already using this for a long time.
To disable this optimization when using the hist2workspace executable, add the -disable_binned_fit_optimization command line argument. Directly in C++, you can also set the binnedFitOptimization to false in the HistFactory configuration as follows:
If your compiler doesn’t support aggregate initialization with designators, you need to create and edit the configuration struct explicitely:
RooStats::HistFactory::HistoToWorkspaceFactoryFast::Configuration hfCfg;
hfCfg.binnedFitOptimization = false;
RooStats::HistFactory::MakeModelAndMeasurementFast(measurement, hfCfg);Copy assignment for RooAbsArgs was implemented in an unexpected and inconsistent way. While one would expect that the copy assignment is copying the object, it said in the documentation of RooAbsArg::operator= that it will “assign all boolean and string properties of the original bject. Transient properties and client-server links are not assigned.” This contradicted with the implementation, where the server links were actually copied too. Furthermore, in RooAbsRealLValue, the assigment operator was overloaded by a function that only assigns the value of another RooAbsReal.
With all these inconsistencies, it was deemed safer to disable copy assignment of RooAbsArgs from now on.
This experimental new feature utilises the technology from ROOT’s familiar TBrowser in order to create an interface for graphically exploring and visualizing the content of a workspace, as well as perform basic fitting operations with the models and datasets.
The original HistoToWorkspaceFactory produced models that consisted of a Poisson term for each bin. In this “number counting form” the dataset has one row and the collumns corresponded to the number of events for each bin. This led to severe performance problems in statistical tools that generated pseudo-experiments and evaluated likelihood ratio test statistics.
Nowadays, everyone uses the faster HistoToWorkspaceFactoryFast implementation that produces a model in the “standard form” where the dataset has one row for each event, and the column corresponds to the value of the observable in the histogram.
Therefore, the original HistoToWorkspaceFactory is now removed to avoid confusion and maintainance burden.
As printed out by the HistFactory in a warning message for a long time already, setting the Const attribute to the <NormFactor> tag is deprecated and it will be ignored. Instead, add <ParamSetting Const="True"> myparam </ParamSetting> to your top-level XML’s <Measurement> entry.
This deprecation implied that the constant parameter flag in the RooStats:HistFactory::NormFactor class had no effect as well. To avoid ambiguity in the future, the possibility to set and retrieve this flag with NormFactor::SetConst() and NormFactor::GetConst() was removed, as well as the Sample::AddNormFactor(std::string Name, double Val, double Low, double High, bool Const) overload. Also, the aforementioned deprecation warning is not printed anymore.
RooAbsMinimizerFcn and RooMinimizerFcn from the public interfaceThe RooAbsMinimizerFcn class and its implementation RooMinimizerFcn were removed from the public interface. These classes are implementation details of the RooMinimizer and should not be used in your code. In the unlikely case that this causes any problem for you, please open a GitHub issue requesting to extend the RooMinimizer by the needed functionality.
RooAbsBinning interface for bin index lookupsThe RooAbsBinning interface for bin index lookups was changed to enable vectorized implementations. Instead of having the override RooAbsBinning::binNumber(), the binning implementations now have to override the RooAbsBinning::binNumbers() function to evaluate the bin indices of multiple values in one function call.
RooAbsRealLValue::inRange()So far, the RooAbsRealLValue::inRange() function used the following undocumented convention to check whether a value x is in the range with limits a and b: test if [x - eps * x, x + eps * x] overlaps with [a, b], where the parameter eps is defined as max(epsRel * x, epsAbs).
The values of the relative and absolute epsilons were inconsistent among the overloads:
epsRel = 1e-8, epsAbs = 0epsRel = 0, epsAbs = 1e-6epsRel = 0, epsAbs = 1e-6With this release, the default absolute and relative epsilon is zero to avoid confusion. You can change them with RooNumber::setRangeEpsRel(epsRel) and RooNumber::setRangeEpsAbs(epsAbs).
A large number of new features have been added in the TMVA SOFIE library. The list of all operators supported in the RModel class is the one provided below for the ONNX parser.
The interface of RModel::Generate has been changed to
RModel::Generate(Options options = Options::kDefault, int batchsize = 1)`where Options is a new enumeration having 3 different values:
kDefault = 0x0 : default case, a session class is generated and the weights are stored in a separate .dat file (in text format).kNoSession = 0x1 : no session class is generated and the internal intermediate tensors are declared in the global namespace TMVA_SOFIE_$ModelName.kNoWeightFile = 0x2 the weight values are not written in a separate .dat file, but they are included in the generated header file.In addition, the RModel::Generate function takes as an additional optional argument the batch size (default is = 1) and the inference code can then be generated for the desired batch size.
The ONNX parser supports now several new ONNX operators. The list of the current supported ONNX operators is the following:
In addition a Custom (user defined) operator is supported. An example of using a Custom operator is the program tmva/pymva/test/EmitCustomModel.cxx.
The ONNX parser supports also the fusing of the operators MatMul + Add in a Gemm operator and fusing Conv + Add and ConvTranspose + Add.
The Keras parser supports now model with input batch size not defined (e.g bathsize=-1), and by default the model is generated with batchsize=1. The Keras parser supports now in addition to the Dense layer the Conv2D layer, several activation functions (Relu, Selu, Sigmoid, Softmax, Tanh, LeakyRelu) and these other layers: BatchNormalization, Reshape, Convatenate, Add, Subtract, Multiply. Models with Dropout layers are supported in case the Dropout is used only during training and not inference.
For model having operators not yet supported in the Keras parser it is then reccomended to convert the Keras model to ONNX using the python tf2onnx tool.
If using PyTorch it is recommended to save the model directly in ONNX format instad of the native .pt format by using the torch.onnx.export function of PyTorch. The support for parsing directly .pt files is limited to the Gemm, Conv, Relu, Selu, Sigmoid and Transpose operators.
The SOFIE inference is now integrated with RDataFrame, where a model can be evaluated on the columns of an input TTree with RDataFrame using the adapter functor class SofieFunctor. Examples of using SOFIE with RDataFrame are the new tutorials (in the tutorials/tmva directory) TMVA_SOFIE_RDataFrame.C or TMVA_SOFIE_RDataFrame.py. TMVA_SOFIE_RDataFrame_JIT.C is an example where the SOFIE model is generated and compiled at runtime using ROOT Cling and evaluated using RDataFrame.
RSofieReader is a new class, which takes as input a model file (in ONNX, Keras, PyTorch or ROOT format) and generates and compiles the C++ code for the inference at run time using the ROOT JITing capabilities of CLING. An example of using this class is the tutorial TMVA_SOFIE_RSofieReader.C.
New Pythonizations are available for TMVA allowing to replace the option string passed to several TMVA functions such as the TMVA::Factory constructor, the DataLoader::PrepareTrainingAndTestTree and Factory::BookMethod using Python function arguments. For example instead of writing an option string "NTrees=500:BoostType=AdaBoost" one can use in Python NTrees=500,BoostType='AdaBoost'. The new tmva tutorials TMVA_Higgs_Classification.py, TMVA_CNN_Classificaion.py and TMVA_RNN_Classificaton.py provide examples of using these new pythonizations.
Implement the option “File”: The current file name is painted on the bottom right of each plot if the option File is set on via gStyle->SetOptFile().
import matplotlib.pyplot as plt
import numpy as np
points = np.array([3, 8, 1, 10, 5, 7])
plt.plot(ypoints)
plt. show()It is now possible to do the same with the ROOT TGraph:
REve now uses RenderCore to visualize 3D objects in JavaScript, replacing the use of Three.js. RenderCore is an open source WebGL rendering engine maintained by University of Ljubljana and tailored to the needs of the REve visualization framework. The minimized version is now included in ROOT source as built-in. REve with use of RendeCore gains:
This runs normal WebGL rendering in headless web browser (Chrome or Firefox) and creates png or jpeg image out of it.
rootssh script for tunnel to remote sessionrootssh shell script to simplify use of web-based widgets on remote nodes: [localnode] rootssh user@remotenode
[remotenode] root --web -e 'new TBrowser'Script automatically configures ssh tunnel between local and remote nodes, one the remote node unix socket with strict 0700 mode is used. When ROOT running on remote node wants to display new web widget, script will automatically start web browser on local node with appropriate URL, accessing widget via configured ssh tunnel.
tutorial/tmva directory. Tutorials like TMVA_Higgs_Classification.py shows the new pythonizations available in TMVA and new TMVA_SOFIE_... tutorials show th eusage of SOFIE in both C++ or Python.__cplusplus preprocessor macro w.r.t. when ROOT was configured.override..rootlogon.py file will be searched both in the current working directory and in the user’s home directory. This file is the Python equivalent of rootlogon.C and can be used to tweak ROOT settings when using PyROOT.TFile now enables its usage as a Python context manager:from ROOT import TFile
with TFile("file1.root", "recreate") as outfile:
hout = ROOT.TH1F(...)
outfile.WriteObject(hout, "myhisto")TDirectory::TContext now enables its usage as a Python context manager:with TDirectory.TContext():
# Open some file here
file = ROOT.TFile(...)
# Retrieve contents from the file
histo = file.Get("myhisto")
# After the 'with' statement, the current directory is restored to ROOT.gROOTstd::vector contents at the prompt.roottest standaloneRDataFrame::GraphAsymmErrorsint32 numpy array to Int_t * doesn’t work on 32-bit platformsrootcling does not properly detect the class doc when using a ClassDefOverrideTFile::Close()TAxis::ChangeLabel documentationTColor::GetColorTransparent() does not re-use previously defined transparent colorsROOT::VecOps::RVec<double> as a column typeRBrowser should support SafariRBrowserRooFit banner with CMAKE_CXX_FLAGS option __ROOFIT_NOBANNER by defaultRooAbsPdf::generate()auto is used for pointer typesnbconvertTThreadExecutor and TSeq with specified beginning/rangeRooProduct::Print("v")std::iteratorruntime_cxxmodules fails to build with GCC12RBrowser does not list in-memory objects when written objects are present in TDirectory contextVary to accept StatsGetViewCollection semantics are unclearstd::pairRooMinimizer::contour does not give back the same plotRooAbsPdf::createChi2() and RooChi2Varstd::tupleRBrowser attempts and fails to launch TBrowser despite --web=server:xxxxPreprocessFunction::PrintXML() needs to escape special characters to produce valid XMLRooAbsData::ExpectedRooAddPdf::fixCoefRange cache issue with createIntegralroot-config fails if spaces are part of ROOTSYS pathRooFit::UniqueIdFillColor/FillStyle and VisualizeError to RooAbsReal::plotOn()std::pair<int, int> branch at promptnlohmann_jsonAlias and TTree sub-branchesTGeoMixture::ComputeDerivedQuantitiesTTreeReaderArray does not support Double32_tTMetaUtils::ReSubstTemplateArg with gcc12 headersREAD_WITHOUT_GLOBALREGISTRATION has no effect on remote filesTH1::Merge does not extend axes properly for certain histograms with labelsTHnSparse::Add() does not preserve weightsRooPlot::pullHist only uses upper rangeRange with begin+strideEXTRA_CLING_ARGS to -O2TInterpreter::Calc with no output stack in seemingly random distributed rdf test executionTH1::GetRMS does not mention the fact that it is not the RMS anymorebuiltin_clang=OFFstd::vector<const char*>Sum(vec_of_bool) should not return a boolRooRealSumPdf when using batchmodeRNTupleWriter::Fill() return the number of bytes writtenTVirtualCollectionProxyTFile::Open when filename contains “://”RooPlot::pullHistTFile::DrawMap plots can be improvedplotSamplingHint can cause evaluation outside the “safe” range of an observablechar from a TTreeReader<signed char>_HAS_CONDITIONAL_EXPLICIT=0 won’t work with VS 2022 17.4RooAbsPdf::fitTo: would it be possible to add an option to control maximal number of calls?TExec::SavePrimitive fails when string args used inside commandTColor::SaveColor sometimes does not store colorRNTupleLocatorsTClass::GetMethodWithPrototype-flat_namespace changes user-facing behavior[#12164] - Strange behaviour in interpreter in master/6.28 when initialising vectors
TROOT::ProcessLine()RooHist does not correctly handle negative scale factorRooAbsData reduce with CutRange not applying cut to unselected variablesTTreeReaderArray<T> does not work for VLEN branches whose length branch is not of type intRooHistPdf plotted in range[ROOT-11023] - RDataFrame Remove global state in ROOT::RDF::SaveGraph
Published on March 21, 2023
rootreadspeed --help has “random” linebreaks?# syntax in TChain::AddRVec<T> is broken for non-trivially-constructible TsgPad->SetLogx is combined with Draw("same")TTime class is not splitableThese changes will be part of a future 6.28/04.