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SHyFT is an OpenSource hydrological toolbox developed by Statkraft.
It is optimized for highly efficient modeling of hydrologic processes following the paradigm of distributed, lumped parameter models -- though recent developments have introduced more physically based / process-level methods.
The code is based on an early initiative for distributed hydrological simulation, called ENKI developed at Sintef by Sjur Kolberg with contributions from Kolbjorn Engeland and Oddbjorn Bruland.
Wiki for SHyFT includes details for how to get prebuilt binaries, how to build and contribute.
For compiling and running SHyFT, you will need:
- A C++11 compiler
- The BLAS and LAPACK libraries (development packages)
- A Python3 (3.4 or higher) interpreter
- The NumPy package (>= 1.8.0)
- The netCDF4 package (>= 1.2.1)
- The CMake building tool (2.8.7 or higher)
In addition, a series of Python packages are needed mainly for running the tests. These can be easily installed via:
$ pip install -r requeriments.txt
or, if you are using conda:
$ cat requirements.txt | xargs conda install
NOTE: the compiling instructions below have been mainly tested on Linux platforms. SHyFT can also be compiled (and it is actively maintained) for Windows, but the building instructions are not covered here (yet).
You can compile SHyFT by using the typical procedure for Python packages:
$ python setup.py build
from the root directory.
Although SHyFT tests are meant to be run from the sources directory (e.g. it expects the shyft-data repo to be cloned locally next to the shyft repo sources), you can also install it with:
$ python setup.py install
Although you won't be able to run the tests except in a very
restricted scenario (i.e. shyft-data
should be a sibling of the
current working directory), this won't prevent you to use SHyFT on top
of your own datasets.
Although (at least on Linux) the setup.py
method above uses the
CMake building tool behind the scenes, you can also compile it
manually (in fact, this is recommended because you will be able to run
the integrated C++ tests). The steps are the usual ones:
$ export SHYFT_SOURCES=shyft_root_directory # absolute path required!
$ cd $SHYFT_SOURCES
$ mkdir build
$ cd build
$ export SHYFT_DEPENDENCIES_DIR=directory_to_keep_dependencies # absolute path
$ cmake .. # configuration step; or "ccmake .." for curses interface
$ make -j 4 # do the actual compilation of C++ sources (using 4 processes)
$ make install # copy Python extensions somewhere in $SHYFT_SOURCES
We have the beast compiled by now. For testing:
$ export LD_LIBRARY_PATH=$SHYFT_DEPENDENCIES_DIR/local/lib
$ make test # run the C++ tests
$ export PYTHONPATH=$SHYFT_SOURCES
$ nosetests .. # run the Python tests
If all the tests pass, then you have an instance of SHyFT that is fully functional. In case this directory is going to act as a long-term installation it is recommended to persist your $LD_LIBRARY_PATH and $PYTHONPATH environment variables (in ~/.bashrc or similar).
The way to test SHyFT is by running:
$ nosetests
from the root directory.
The test suite is not very comprehensive yet, but at least would provide indications that your installation is sane.
Once you tested you SHyFT package you can install it in your system via::
$ python setup.py install
SHyFT is developed by Statkraft, and the two main initial authors to the C++ core were Sigbjørn Helset [email protected] and Ola Skavhaug [email protected].
Orchestration and the Python wrappers were originally developed by John F. Burkhart [email protected]
Several of the methods implemented are rewrites of corresponding code in ENKI
Contributors and current project participants include:
- Sigbjørn Helset [email protected]
- Ola Skavhaug [email protected]
- John Burkhart [email protected]
- Yisak Sultan Abdella [email protected]
- Felix Matt [email protected]
- Francesc Alted [email protected]
SHyFT is released under LGPL V.3 See LICENCE