This is a python-based tool for gradient-based optimization of VMEC equilibria using adjoint methods. Details and examples are discussed in https://arxiv.org/abs/2012.10028.

Required python packages and variants I currently use:

• mpi4py (3.0.3)
• numpy (1.19.1)
• scipy (1.5.2)
• nlopt (2.6.2)
• numba (0.51.2)
• scanf (1.5.2)
• f90nml (1.2)

Many objectives rely on the VMEC code. This can be installed from https://github.com/PrincetonUniversity/STELLOPT.

• In order to run VMEC from the command line, the environment variable VMEC_PATH must be set to the absolute path to the executable.
• VMEC can alternatively be run from a python interface. This can be installed from https://github.com/ejpaul/simsopt. Once the interface is built, the directory simsopt.modules.vmec must be in your pythonpath. (The python interface to VMEC is currently maintained at https://gitlab.com/mattland/VMEC2000/-/tree/master)

The axis ripple objective relies on a variant of the ANIMEC code. This can be installed from https://github.com/ejpaul/ANIMEC_adjoint_ripple.

• In order to run VMEC from the command line, the environment variable ANIMEC_PATH must be set to the absolute path to the executable.

Several optimization examples are contained in the examples directory.

Each of the modules have unittesting in the test directory. These can be run with : python -m unittest test_*.py