Veros, the versatile ocean simulator, is just that: A powerful tool that makes high-performance ocean modeling approachable and fun. Since it is a pure Python module, the days of struggling with complicated model setup workflows, ancient programming environments, and obscure legacy code are finally over.
Veros supports both a NumPy backend for small-scale problems and a fully parallelized high-performance backend powered by Bohrium using either OpenMP (CPU) or OpenCL (GPU). The underlying numerics are based on pyOM2, an ocean model developed by Carsten Eden (Institut für Meereskunde, Hamburg University). A good starting point to gain an overview of Veros' design, performance, and capabilities are these slides of a talk on Veros held during the 98th Annual Meeting of the American Meteorological Society.
Veros is currently being developed at Niels Bohr Institute, Copenhagen University.
- a fully staggered 3-D grid geometry (C-grid)
- support for both idealized and realistic configurations in Cartesian or pseudo-spherical coordinates
- several friction and advection schemes to choose from
- isoneutral mixing, eddy-kinetic energy, turbulent kinetic energy, and internal wave energy parameterizations
- several pre-implemented diagnostics such as energy fluxes, variable time averages, and a vertical overturning stream function (written to netCDF output)
- pre-configured idealized and realistic set-ups that are ready to run and easy to adapt
- accessibility, readability, and extensibility - thanks to the power of Python!
Veros for the impatient
A minimal example to install and run Veros:
$ pip install veros[all] $ veros copy-setup acc --to /tmp $ cd /tmp/acc $ python acc.py
Veros only has two external library dependencies, namely
netCDF. The installation procedure of those libraries varies between
platforms. The easiest way to install Veros and its
dependencies (including Bohrium) is Anaconda
Python that ships with a package
If you do not want to use Anaconda, the most convenient way is to use your operating system's package manager. On Debian / Ubuntu, you can e.g. use
$ sudo apt-get install libhdf5-dev libnetcdf-dev
As soon as you have a working environment, installing Veros is simple:
Clone the repository to your hard-drive:
$ git clone https://github.com/dionhaefner/veros.git
Install it, preferably with
$ pip install -e veros
If you use the
-eflag, any changes you make to the model code are immediately reflected without having to re-install.
In case you want to use the Bohrium backend, you will have to install
Bohrium, e.g. through
apt-get, or by building it from source.
To run Veros, you need to set up a model - i.e., specify which settings
and model domain you want to use. This is done by subclassing the
Veros base class in a setup script that is written in Python. You
should have a look at the pre-implemented model setups in the
setup folder, or use the
veros copy-setup command
to copy one into your current folder. A good place to start is the
After setting up your model, all you need to do is call the
run methods on your setup class. The pre-implemented setups can
all be executed as scripts, e.g. through
$ python acc.py
For more information on using Veros, have a look at our documentation.
Contributions to Veros are always welcome, no matter if you spotted an inaccuracy in the documentation, wrote a nice setup, fixed a bug, or even extended Veros' core mechanics. There are two ways to contribute:
- If you want to report a bug or request a missing feature, please open an issue. If you are reporting a bug, make sure to include all relevant information for reproducing it (ideally through a minimal code sample).
- If you want to fix the issue yourself, or wrote an extension for Veros - great! You are welcome to submit your code for review by committing it to a repository and opening a pull request. However, before you do so, please check the contribution guide for some tips on testing and benchmarking, and to make sure that your modifications adhere with our style policies. Most importantly, please ensure that you follow the PEP8 guidelines, use meaningful variable names, and document your code using Google-style docstrings.