wflow consists of a set of Python programs that can be run on the command line and perform hydrological simulations. The models are based on the PCRaster Python framework. In wflow this framework is extended (the wf_DynamicFramework) so that models build using the framework can be controlled using the API. Links to BMI, OpenMI and OpenDAP have been made.
A link to the latest version can always be found at https://github.com/openstreams/wflow
Reference documentation at:
Go to https://github.com/openstreams/wflow. There you can download the source or a release. Also make sure you get the required third party models first (see below).
The master branch can change rapidly (and break functionality without warning) so please use one of the releases if possible. If you want to adjust things in the model(s) we assume you should be comfortable using the master branch.
The main dependencies for wflow are an installation of Python 3.6, and PCRaster 4.2+. Only 64 bit OS/Python is supported.
Installing Python
For Python we recommend using the Anaconda Distribution for Python 3, which is available for download from https://www.anaconda.com/download/. The installer gives the option to add python to your PATH environment variable. We will assume in the instructions below that it is available in the path, such that python, pip, and conda are all available from the command line.
Note that there is no hard requirement specifically for Anaconda's Python, but often it makes installation of required dependencies easier using the conda package manager.
Installing pcraster
- Download pcraster from http://pcraster.geo.uu.nl/ website (version 4.2+)
- Follow the installation instructions at http://pcraster.geo.uu.nl/quick-start-guide/
The easiest and most robust way to install wflow is by installing it in a separate conda environment. In the root repository directory there is an environment.yml file. This file lists all dependencies, except PCRaster, which must be installed manually as described above. Either use the environment.yml file from the master branch (please note that the master branch can change rapidly and break functionality without warning) , or from one of the releases {release}.
Run this command to start installing all wflow dependencies:
conda env create -f environment.yml
This creates a new environment with the name wflow. To activate this environment in a session, run:
activate wflow
For the installation of wflow there are two options (from the Python Package Index (PyPI) or from Github). To install a release of wflow from the PyPI (available from release 2018.1):
pip install wflow=={release}
To install directly from GitHub (from the HEAD of the master branch):
pip install git+https://github.com/openstreams/wflow.git
or from Github from a specific release:
pip install git+https://github.com/openstreams/wflow.git@{release}
Now you should be able to start this environment's Python with python, try import wflow to see if the package is installed.
More details on how to work with conda environments can be found here: https://conda.io/docs/user-guide/tasks/manage-environments.html
If you are planning to make changes and contribute to the development of wflow, it is best to make a git clone of the repository, and do a editable install in the location of you clone. This will not move a copy to your Python installation directory, but instead create a link in your Python installation pointing to the folder you installed it from, such that any changes you make there are directly reflected in your install.
git clone https://github.com/openstreams/wflow.gitcd wflowactivate wflowpip install -e .
Alternatively, if you want to avoid using git and simply want to test the latest version from the master branch, you can replace the first line with downloading a zip archive from GitHub: https://github.com/openstreams/wflow/archive/master.zip
Besides the recommended conda environment setup described above, you can also install wflow with pip. For the more difficult to install Python dependencies, it is best to use the conda package manager:
conda install numpy scipy gdal netcdf4 cftime xarray pyproj numba python-dateutil
Then install a release {release} of wflow (available from release 2018.1) with pip:
pip install wflow=={release}
To check it the install is successful, go to the examples directory and run the following command:
python -m wflow.wflow_sbm -C wflow_rhine_sbm -R testing
This should run without errors.
Although you can get everything with the python packages bundled with most linux distributions (CentOS, Ubuntu, etc) we have found that the easiest way is to install the linux version of Anaconda and use the conda tool to install all requirements apart from pcraster which has to be installed manually.
Since version 4.2, compiled versions of PCRaster are no longer distributed, so it will need to be built following the instructions given at http://pcraster.geo.uu.nl/getting-started/pcraster-on-linux/
Unfortunately there is no pcraster build for osx yet. If anybody wants to pick this up please let the guys at pcraster.eu know!
- The stats.py script was made by Keith Cherkauer (https://engineering.purdue.edu/~cherkaue/software.htm)
- pcraster is developed and maintained by Utrecht University (http://www.pcraster.eu)
- netCDF4 is developed by unidata (http://unidata.github.io/netcdf4-python/)
- GDAL is released under an X/MIT style Open Source license by the Open Source Geospatial Foundation (http://www.gdal.org).
See doi of the release you use. If you use a snapshot of the development (without a DOI) cite as follows:
Jaap Schellekens, Willem van Verseveld, Martijn Visser, Hessel Winsemius, Tanja Euser, Laurène Bouaziz, Christophe Thiange, Sander de Vries, Hélène Boisgontier, Dirk Eilander, Daniel Tollenaar, Albrecht Weerts, Fedor Baart, Pieter Hazenberg, Arthur Lutz, Corine ten Velden, Mischa Jansen, Imme Benedict, YEAR. openstreams/wflow: unstable-master. https://github.com/openstreams/wflow, obtained: DATE_OF_DOWNLOAD
To check the doi of releases you use: https://doi.org/10.5281/zenodo.593510