ROOT is a modular scientific software toolkit. It provides all the functionalities needed to deal with big data processing, statistical analysis, visualisation and storage. It is mainly written in C++ but integrated with other languages such as Python and R.
Almost everything in ROOT should be supported in this Conda package; ROOT was built with lots of options turned on. Here are a few things to try:
root: you can start up a session and see the splash screen; Control-D to exit.python followed by import ROOT will load PyROOT.root --notebook will start a notebook server with a ROOT kernel choice.rootbrowse will open a TBrowser session so you can look through files.root -l -q $ROOTSYS/tutorials/dataframe/df013_InspectAnalysis.C will run a DataFrame example with an animated plot.root -b -q -l -n -e "std::cout << TROOT::GetTutorialDir() << std::endl;" will print the tutorial dir.root -b -l -q -e 'std::cout << (float) TPython::Eval("1+1") << endl;' will run Python from C++ ROOT.See the post here for more information about using this Conda package.
The ROOT package will prepare the required compilers. Everything in Conda is symlinked into
$CONDA_PREFIX if you build things by hand; tools like CMake should find it automatically. The thisroot.*
scripts should not be used and are not provided. Graphics, rootbrowse, etc. all should work. OpenGL is enabled.
There is also a root_base package, with minimal dependecies, that libraries should depend on this to avoid
having a runtime dependency on the compilers package. root-dependencies and root-binaries are also available. In most cases users should use the root package directly, since it adds both of these, along with compilers, Jupyter, and a few other things to facilitate using ROOT or PyROOT.
ROOT was built with and will report -std=c++17 from root-config.