ROOT is a unified software package for the storage, processing, and analysis of scientific data: from its acquisition to the final visualization in form of highly customizable, publication-ready plots. It is reliable, performant and well supported, easy to use and obtain, and strives to maximize the quantity and impact of scientific results obtained per unit cost, both of human effort and computing resources. ROOT is performance critical software written in C++ and enables rapid prototyping powered by a unique C++ compliant interpreter called Cling. Cling also enables performant C++ type introspection which is a building block of automatic interoperability with Python. Thanks to its dynamic Python bindings, leveraging the cppyy technology, ROOT offers efficient, on-demand C++/Python interoperability in a uniform cross-language execution environment.
copied from cf-post-staging / root_baseWhile the feature set of the ROOT framework is vast and not everything can be supported in the conda distribution, this package aims at enabling most of the features of ROOT. 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 to access all ROOT functionalities via the Python bindings.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 -b -q -l $ROOTSYS/tutorials/analysis/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.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.
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. In most cases users should use the root package directly,
since it adds more features (taking care of their dependencies) to facilitate usage of the ROOT Python bindings.