This VMEC repo is to build VMEC as isolated piece of software. It also comes with support for building a python extension, so VMEC can be run directly from python, with all variables passed directly in memory.
Download the package from git. And change to the root directory of VMEC source code by running
cd <VMEC_ROOT>
Compiling VMEC requires MPI, parallel version of HDF5, netcdf-c, netcdf-fortran, numerical libraries such as BLAS, LAPACK, BLACS, and SCALAPACK.
If you want to build the python extension, skip down to the "Python extension compiling" section below. Building the python extension will also build the VMEC executable.
On PPPL cluster, the dependencies can be enabled using module
command as
module load intel openmpi blacs scalapack netcdf-c netcdf-fortran
The location of the libraries associated with the packages can be found from
echo $LD_LIBRARY_PATH
(Optional) If interested in using cmake, load cmake also using module command.
The one could use either make
or cmake
to build VMEC.
If using make, edit the Makefile
to correctly reflect the location of the packages and the compilers. Then run
make all
The following command was used to configure cmake build setup for VMEC on PPPL cluster
cmake -S. -Bbuild -GNinja -DCMAKE_Fortran_COMPILER=mpifort -DNETCDF_INC_PATH=/usr/pppl/intel/2019-pkgs/netcdf-fortran-4.5.2/include/ -DNETCDF_LIB_PATH=/usr/pppl/intel/2019-pkgs/netcdf-fortran-4.5.2/lib -DSCALAPACK_LIB_NAME=scalapack -DBLACS_LIB_NAME=mpiblacs -DBLACS_CINIT_NAME=mpiblacsCinit -DBLACS_F77INIT_NAME=mpiblacsF77init --trace-source=CMakeLists.txt 2>&1 | tee log
There are few points to note on the above command
log
file. After successful completion of cmake configuration step, go to build
directory and run ninja
command as
cd build; ninja
In the VMEC root folder, edit the cmake_config_file.json
as necessary for your system. Several example .json
files are provided in the cmake/machines
folder. The default one works for CentOS 8 with netcdf and openmpi installed from CentOS repos and Intel MKL.
There are two ways to build the python extension.
You could simply run
pip install .
Sometimes you may get error associated with f90wrap installation. In that case, install numpy before running the above command:
pip install numpy
pip install .
If you are using conda virtual environment, the numpy version used by pip to build the VMEC extension could be different from the numpy version installed in the conda virtaul environment. The numpy version mismatch could pose some issues. For such cases, use the second approach given below.
In this approach, we directly run the setup.py script present in the VMEC root folder. Ensure that the following modules are installed: cmake
, scikit-build
, ninja
, numpy
, and f90wrap
. These packages could be installed by running the following commands:
pip install numpy
pip install cmake scikit-build ninja f90wrap
Some times you may have to install numpy
before trying to install f90wrap
.
Then, run
python setup.py build_ext
python setup.py install
in succession. At this point, you should be able to import the vmec
module in python. To test this, you can try the following command from the shell:
python -c "import vmec; print('success')"