Go to the Download page. Download LFA Lab from this page and unpack it when necessary.

Prepare your System

LFA Lab depends on the following packages:

Furthermore, the following packages provide extra functionality, but are optional:

You can find the commands to install the dependencies for different operating systems below.


To install the dependencies on Debian, run the following command:

sudo apt-get install -y \
    g++ cmake python-dev python-numpy-dev python-six swig \
    libeigen3-dev liblapack-dev python-matplotlib


To install the dependencies on Fedora, run the following command:

sudo dnf install -y \
    gcc-c++ cmake python2-devel python2-numpy python-six swig \
    eigen3-devel lapack-devel python2-matplotlib

Mac OS X

An easy way to install the required (non-python) dependencies of LFA Lab is the Homebrew package manager for Mac. Make sure you have Homebrew installed. Then, change into the source directory of LFA Lab and execute the following command:

brew bundle

Then, you can use PIP to install the remaining (python) dependencies:

easy_install --user pip
python -mpip install --user --upgrade -r requirements.txt

Warning: You might have multiple Python versions on your machine. You have to make sure that all dependencies and LFA Lab are installed with the same version.

Furthermore, ff you want to use Matplotlib you need to make sure that you are using a Framework build of Python. See also here and here.

If you want you can build and install LFA Lab using pip:

python -mpip install --user .

If you want to customize the installation of in case the build fails, see the manual installation below.

Build LFA Lab

Execute the following commands in the source directory of LFA Lab:

cmake [OPTIONS] .

(Do not forget the dot at the end.)

In case the build fails or you want to tweak your installation you can use the following options.


Install into a per-user directory:


Turns the compiler optimization on:


Choose to use LAPACK for certain operations. If a good LAPACK/BLAS implementation is available, this will speed up the program essentially:


Choose to use ARPACK. This will speed up the program if large spectra need to be analyzed. Arpack, however, might not be able to compute the spectra for certain tricky problems:


Set other prefices wich will be searched. For example if you installed some of the libraries in $HOME/.local run:


For example:



To build the documentation you can run:

make sphinx-doc

This command requires Sphinx.

The C++-Core modules can be documented using:

make doxygen


To install LFA Lab just run:

sudo make install

If you just want to use the software without installation, you can run:


instead. This command will setup the current shell session such that you can use LFA Lab.

You can now use LFA Lab. Take a look at the Tutorial page to find out how to use it.