Installing current development version of ANUGA research code on Windows


anuga is developed on Linux (Ubuntu) and hence the installation on Linux (Ubuntu) is more streamlined. But it is possible to install on windows. First you need to install the required python environment, then download the anuga source and then compile and test. The most time consuming part of the process is getting the python environment setup.


We use python as our programming environment together with a number of standard python packages such as numpy, scipy, matplotlib, netcdf4. One way to install all the required packages is to use a distribution like python_xy, Anaconda and the related miniconda environments. Also it should be possible to download the appropriate pre-compiled packages from

I suggest miniconda as it allows you to only install the packages that are needed (so is a bit quicker).


Download and run the Miniconda installer.

Be sure to choose the win32 python 2.7 version. This is the version for which we are developing.

At the moment anuga has memory problems with win64 so avoid that for the time being. You are most welcome to help track down the problems and provide a bug fix :-).

Anuga python package dependencies

Assuming conda is now installed, we need to install a number of other packages, via the conda install command.

Fire up cmd.exe and install all the packages via

conda install python=2.7 pip nose numpy scipy netcdf4 matplotlib gdal

GCC compiler

We need a gcc compiler with openmp support to install anuga.

We suggest that you download and install the version of MinGW provided by TDM-GCC. Mark the openmp and gfortran options in the "Choose Components" part of the installation.

We need to force the python installation scripts to use this compiler. Add a configuration file named pydistutils.cfg to your home directory C:\Users\yourName with the contents



Our preferred method of installing anuga and updating is to use git (Though you can use subverson or download a zipped file).

To install git use the windows download link

Manually install dependencies

It is possible to install the required environment manually. You would need to install Mingw to provide a compiler, the standard distribution of python27 and then precompiled python libraries for numpy, scipy, matplotlib, netcdf4 from a site like It would be interesting to hear feedback on this option, as there is an opportunity to use versions using the Intel Math Kernel Library, which should provide a useful increase in speed.

Download ANUGA

You should now have all the dependencies installed. You should fire up a new cmd.exe and install anuga

You can just install the anuga library using pip or download the full repo with example code and manual using git.

pip install

If just want the latest released version of the package then use pip via

pip install anuga

If the install is successful, test the instal via

python -c "import anuga; anuga.test()"

Install via source

First we need to get the anuga source code.

Clone anuga via git

To obtain the anuga repo from github use git clone i.e.

git clone

Checkout anuga via svn

You can also check out the anuga repository using subversion. I suggest installing tortoise svn downloads and then checking out the following svn repository. When you installed tortoise svn it creates a few extra menu items to your right click menu in the file manager. Just choose "tortoise" checkout to download the code.

Zipped version

Finally a zipped version of the latest development version of anuga is available from github at and the latest released version at sourceforge You will need to unzip the file into a directory called anuga_core perhaps in your home directory.

Installing ANUGA

You should now have an anuga_core directory.

Now go to the directory anuga_core and build and install anuga. Fire up a cmd terminal, change to the anuga_core directory and run

python install

Hopefully no errors.

Run Unit tests

From the anuga_core directory run the unit tests via:



At present the Anaconda (conda) version of gdal is missing the gdal data directory (presumably to save space). This will cause some error messages of the form "ERROR 4: Unable to open EPSG support file gcs.csv". It should be fine to disregard these errors messages. It would be possible to download the gdal data directory and point the GDAL_DATA environment variable to the data directory.


Hopefully all the unit tests pass. As this is bleeding edge there are sometimes a small number of failures as this is a work in progress. Have a look at the demos in the directory anuga_core/documentation/user_manual/demos (along with the user manual) to see how to use anuga.


From time to time you should update your version of anuga. This is fairly easy if you used subversion or git to obtain the source. You will then just need to pull (with git) or update (with subversion).

I.e. if using git fire up cmd.exe, change into the anuga_core directory and run

git pull

Then re-install the latest version of the code and check the unit tests via

python install
Last modified 3 years ago Last modified on Mar 28, 2015, 2:41:54 PM