# simsopt
[](https://codecov.io/gh/hiddenSymmetries/simsopt)
[](https://zenodo.org/badge/latestdoi/247710081)
`simsopt` is a framework for optimizing
[stellarators](https://en.wikipedia.org/wiki/Stellarator).
The high-level routines of `simsopt` are in python, with calls to C++
or fortran where needed for performance. Several types of components
are included:
- Interfaces to physics codes, e.g. for MHD equilibrium.
- Tools for defining objective functions and parameter spaces for
optimization.
- Geometric objects that are important for stellarators - surfaces and
curves - with several available parameterizations.
- Efficient implementations of the Biot-Savart law and other magnetic
field representations, including derivatives.
- Tools for parallelized finite-difference gradient calculations.
The design of `simsopt` is guided by several principles:
- Thorough unit testing, regression testing, and continuous
integration.
- Extensibility: It should be possible to add new codes and terms to
the objective function without editing modules that already work,
i.e. the [open-closed principle](https://en.wikipedia.org/wiki/Open%E2%80%93closed_principle).
This is because any edits to working code can potentially introduce bugs.
- Modularity: Physics modules that are not needed for your
optimization problem do not need to be installed. For instance, to
optimize SPEC equilibria, the VMEC module need not be installed.
- Flexibility: The components used to define an objective function can
be re-used for applications other than standard optimization. For
instance, a `simsopt` objective function is a standard python
function that can be plotted, passed to optimization packages
outside of `simsopt`, etc.
`simsopt` is fully open-source, and anyone is welcome to use it, make
suggestions, and contribute.
Several methods are available for installing `simsopt`. One
recommended approach is to use pip:
pip install simsopt
For detailed installation instructions on some specific systems, see
[the wiki](https://github.com/hiddenSymmetries/simsopt/wiki).
Also, a Docker container is available with `simsopt` and its components pre-installed, which
can be started using
docker run -it --rm hiddensymmetries/simsopt
More [installation
options](https://simsopt.readthedocs.io/en/latest/installation.html),
[instructions for the Docker
container](https://simsopt.readthedocs.io/en/latest/containers.html), and
other information can be found in the [main simsopt documentation
here.](https://simsopt.readthedocs.io)
Some of the physics modules with compiled code reside in separate
repositories. These separate modules include
- [VMEC](https://github.com/hiddenSymmetries/VMEC2000), for MHD
equilibrium.
- [SPEC](https://github.com/PrincetonUniversity/SPEC), for MHD
equilibrium.
- [booz_xform](https://hiddensymmetries.github.io/booz_xform), for
Boozer coordinates.
If you use `simsopt` in your research, kindly cite the code using
[this reference](https://doi.org/10.21105/joss.03525):
[1] M Landreman, B Medasani, F Wechsung, A Giuliani, R Jorge, and C Zhu,
"SIMSOPT: A flexible framework for stellarator optimization",
*J. Open Source Software* **6**, 3525 (2021).
See also [the simsopt publications page](https://simsopt.readthedocs.io/en/latest/publications.html).
We gratefully acknowledge funding from the [Simons Foundation's Hidden
symmetries and fusion energy
project](https://hiddensymmetries.princeton.edu).
Raw data
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"description": "# simsopt\n\n\n[](https://codecov.io/gh/hiddenSymmetries/simsopt)\n[](https://zenodo.org/badge/latestdoi/247710081)\n\n\n\n\n`simsopt` is a framework for optimizing\n[stellarators](https://en.wikipedia.org/wiki/Stellarator).\nThe high-level routines of `simsopt` are in python, with calls to C++\nor fortran where needed for performance. Several types of components\nare included:\n\n- Interfaces to physics codes, e.g. for MHD equilibrium.\n- Tools for defining objective functions and parameter spaces for\n optimization.\n- Geometric objects that are important for stellarators - surfaces and\n curves - with several available parameterizations.\n- Efficient implementations of the Biot-Savart law and other magnetic\n field representations, including derivatives.\n- Tools for parallelized finite-difference gradient calculations.\n\nThe design of `simsopt` is guided by several principles:\n\n- Thorough unit testing, regression testing, and continuous\n integration.\n- Extensibility: It should be possible to add new codes and terms to\n the objective function without editing modules that already work,\n i.e. the [open-closed principle](https://en.wikipedia.org/wiki/Open%E2%80%93closed_principle).\n This is because any edits to working code can potentially introduce bugs.\n- Modularity: Physics modules that are not needed for your\n optimization problem do not need to be installed. For instance, to\n optimize SPEC equilibria, the VMEC module need not be installed.\n- Flexibility: The components used to define an objective function can\n be re-used for applications other than standard optimization. For\n instance, a `simsopt` objective function is a standard python\n function that can be plotted, passed to optimization packages\n outside of `simsopt`, etc.\n\n`simsopt` is fully open-source, and anyone is welcome to use it, make\nsuggestions, and contribute.\n\nSeveral methods are available for installing `simsopt`. One\nrecommended approach is to use pip:\n\n pip install simsopt\n\nFor detailed installation instructions on some specific systems, see\n[the wiki](https://github.com/hiddenSymmetries/simsopt/wiki).\nAlso, a Docker container is available with `simsopt` and its components pre-installed, which\ncan be started using\n\n docker run -it --rm hiddensymmetries/simsopt\n\nMore [installation\noptions](https://simsopt.readthedocs.io/en/latest/installation.html),\n[instructions for the Docker\ncontainer](https://simsopt.readthedocs.io/en/latest/containers.html), and\nother information can be found in the [main simsopt documentation\nhere.](https://simsopt.readthedocs.io)\n\nSome of the physics modules with compiled code reside in separate\nrepositories. These separate modules include\n\n- [VMEC](https://github.com/hiddenSymmetries/VMEC2000), for MHD\n equilibrium.\n- [SPEC](https://github.com/PrincetonUniversity/SPEC), for MHD\n equilibrium.\n- [booz_xform](https://hiddensymmetries.github.io/booz_xform), for\n Boozer coordinates.\n \nIf you use `simsopt` in your research, kindly cite the code using\n[this reference](https://doi.org/10.21105/joss.03525):\n\n[1] M Landreman, B Medasani, F Wechsung, A Giuliani, R Jorge, and C Zhu,\n \"SIMSOPT: A flexible framework for stellarator optimization\",\n *J. Open Source Software* **6**, 3525 (2021).\n\nSee also [the simsopt publications page](https://simsopt.readthedocs.io/en/latest/publications.html).\n\nWe gratefully acknowledge funding from the [Simons Foundation's Hidden\nsymmetries and fusion energy\nproject](https://hiddensymmetries.princeton.edu). \n",
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