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.
copied from cf-staging / rootconda install conda-forge::root
conda install conda-forge/label/broken::root
conda install conda-forge/label/cf202003::root
conda install conda-forge/label/gcc7::root
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
.