Prerequisites and installation#

This section lists the prerequisites of pyALF and how to set things up to be able to use it in a productive manner. \(\phantom{\xi}\)

ALF prerequisites#

Since pyALF builds on ALF, we also want to satisfy its requirements. Note, however, that pyALF’s postprocessing features are independent from ALF. This might be relevant, for example, when performing Monte Carlo runs and analysis on different machines.

The minimal ALF prerequisites are:

  • The Unix shell Bash

  • Make

  • A recent Fortran Compiler (e. g. Submodules must be supported)


  • Python 3

For parallelization, an MPI development library, e. g. Open MPI, is necessary.

Results from ALF can either be saved in a plain text format or HDF5, but full pyALF support is only provided for the latter, which is why in pyALF, HDF5 is enabled by default. ALF automatically downloads and compiles HDF5. For this to succeed, the following is needed:

  • A C compiler (which is most often automatically included when installing a Fortran Compiler)

  • A C++ preprocessor

  • Curl or Wget

  • gzip development libraries

The recommended way for obtaining the source code is through git.

Finally, the ALF testsuite needs:

  • CMake

As an example, the requirements mentioned above can be satisfied on a Debian, Ubuntu, or similar operating system using the APT package manager, by executing the command:

sudo apt install make gfortran libblas-dev liblapack-dev \
           python3 libopenmpi-dev g++ curl libghc-zlib-dev \
           git ca-certificates cmake bash

The above installs compilers from the GNU compiler collection. Other supported and tested compiler frameworks are from the Intel® oneAPI Toolkits and the NVIDIA HPC SDK. The latter is denoted as PGI in ALF.

pyALF installation#


In previous versions of pyALF, the installation instructions asked the users to set the environment variable PYTHONPATH. This conflicts with the newer pip package, therefore you should remove definitions of the PYTHONPATH environment variable related to pyALF.

pyALF can be installed via the Python package installer pip.

pip install pyALF

It automatically installs all requirements, but in case you want to install them in a different way, e.g. through apt or conda, these are the Python packages pyALF depends on:

  • f90nml

  • h5py

  • ipympl

  • ipywidgets

  • matplotlib

  • numba

  • numpy

  • pandas

  • scipy

  • tkinter

Development installation#

If you want to develop pyALF, you can clone the repository and install it in development mode, which allows you to edit the files while using them like an installed package. For this, it is highly recommended to use a dedicated Python environment using e.g. Python venv or a conda environment. The following example shows how to install pyALF in development mode using venv.

git clone
cd pyALF
python -m venv .venv
source .venv/bin/activate

pip install --editable .

Setting ALF directory through environment variable#

Since pyALF is set up to automatically clone ALF with git, it is not strictly necessary to download ALF manually, but pyALF will download ALF every time it does not find it. Therefore it is recommended to clone ALF once manually from here and saving location in the environment variable ALF_DIR. This way, pyALF will use the same ALF source code directory every time.

ALF can be cloned with the Unix shell command

git clone

this will create a folder called ALF in the current working directory of the terminal and download the repository there[1].

The environment variable can the be set with the command

export ALF_DIR="/path/to/ALF"

where /path/to/ALF is the location of the ALF code, for example /home/jonas/Programs/ALF. To not have to repeat this command in every terminal session, it is advisable to add it to a file sourced when starting the shell, e.g. ~/.bashrc or ~/.zshrc.

Check setup#

To check if most things have been set up correctly, the script minimal_ALF_run can be used. It executes the same commands as the Minimal example. One should therefore be able to run it by executing

in the Unix shell. If it does clone the ALF repository, ALF_DIR has not been set up correctly. Note that on the first compilation, ALF downloads and compiles HDF5, which can take up to ~15 minutes.

Using Jupyter Notebooks#

A convenient way to work with pyALF (and Python in general) is through Jupyter Notebooks. These are interactively usable documents that combine source code, results and narration (through Markdown) in one file. pyALF includes example notebooks, online available from here, or by cloning the pyALF repository.

The canonical way to use the Jupyter Notebooks, is through a JupyterLab, which can for example be installed via pip (for more details see here):

pip install jupyterlab

A JupyterLab can then be started with the shell command jupyter-lab, which launches a web server that should be automatically opened in your default browser.

Another convenient way to work with the notebooks is with Visual Studio Code, a versatile an extendable source-code editor.

Ready-to-use container image#

For a ready-to-use environment, one can use the Docker image alfcollaboration/jupyter-pyalf-full, which has the above mentioned dependencies, ALF and pyALF installed. With a suitable container runtime e.g. Docker or Podman, it can be used run ALF and pyALF without any further setup. It is derived from the Jupyter Docker Stacks, therefore this documentation applies. For example, one could run a container like this:

docker run -it --rm -p -v "$PWD":/home/jovyan/work \
  • The -p flag is used to expose port 8888 and you can access a JupyterLab running within the container by navigating to http://localhost:8888/lab?token=<token> with you browser, where <token> has to be replaced by the token echoed to the terminal on startup.

  • The -v flag mounts the current working directory to /home/jovyan/work within the container, allowing to work on the same data in- and outside of the container.

  • The –rm flag instructs Docker to automatically remove the container after it exits, avoiding cluttering up the system with unused containers.

  • The -i and -t flags keep the container’s STDIN open and attach a pseudo-terminal, allowing interactive input on the terminal.

It is also possible to use the container without launching the included JupyterLab. The following command launches a container, which executes minimal_ALF_run, saving the results in the current working directory and removing the container right after that.

docker run -it --rm -v "$PWD":/home/jovyan/work \ \
    bash -c 'cd /home/jovyan/work && minimal_ALF_run'

Some SSH port forwarding applications#

ALF simulations are often performed on remote clusters that are accessed via SSH. Notably, SSH can be used for much more than running a remote shell. In this section, I will show how one can use SSH port forwarding to download data to HPC clusters with restrictive firewalls and how to access a JupyterLab launched on a HPC cluster.

Use remote forwarding to circumvent restrictive firewalls#

If one wanted to git clone the ALF source code, this could usually be done with one of the following commands, using HTTPS or SSH, respectively.

git clone
git clone

But on some systems with very restrictive firewalls, this approach might not work. This is where the ssh option -R might come in handy, which maps a port on the remote machine to a an address connected to from the local machine that executed the SSH command. To facilitate a connection to, the following commands can be used, connecting to port 443 or 22, for the HTTPS or SSH protocol, respectively.

ssh -R <PortNum> <username>@<servername>
ssh -R <PortNum> <username>@<servername>

Here <PortNum> refers to a port on the remote machine, a value in the range from 49152 to 65535 would be best here [3]. And <username>@<servername> is the usual SSH address. Alternatively to the command line option -R, the SSH config file option RemoteForward can be used.

With these port forwarding options, the ALF source code can the be clone on the remote machine with:

git clone -c http.sslVerify=false https://localhost:<PortNum>/ALF/ALF.git
git clone ssh://git@localhost:<PortNum>/ALF/ALF.git

The HTTPS version needs the option -c http.sslVerify=false because the SSL certificate for does not apply to localhost.

One can omit the host value in the -R option (in the example above which will set up a dynamic SOCKS proxy, able to connect to arbitrary addresses. This can be used, for example, to download and install packages with pip.


Ports on the remote machine opened with -R / RemoteForward can not only be used by you, but possibly also by other users of the machine. Therefore one should be careful when using the options, in particular without specifying a host.

Using -R without a host to install pyALF with pip:

ssh -R <PortNum> <username>@<servername>

That pip can use the SOCKS proxy, the python package pysocks is necessary. If the package is not yet available, is it enough to to get the file from here and have Python find it, e.g. with the environment variable PYTHONPATH.

Then pyALF can be installed with:

pip install --proxy socks4://localhost:<PortNum> pyALF

Using Jupyter via SSH tunnel#

When launching JupyterLab, it sets up a webserver and prints out how to access it locally, like:


Where <remote_port_number> is some port number (default 8888) and <token> is the password to access the server.

Now, to access this web server on the remote machine, one can forward this port to the local machine using the SSH option -L and open it with the browser.

ssh -L <local_port_number>:localhost:<remote_port_number> <username>@<servername>

With the command from above, a remote JupyterLab will be accessible trough the address http://localhost:<local_port_number>:/lab?token=<token>.

Using SSH in Visual Studio Code#