Installation

Using Pip

On most systems with a Python installation, libNeuroML can be installed using the default Python package manager, Pip:

pip install libNeuroML

It is recommended to use a virtual environment when installing Python packages using pip to prevent these from conflicting with other system libraries.

This will support the default XML serialization. Note that you may be required to install libhdf5 using your package manager because PyTables’ wheels for python 3.7+ depend on the system libhdf5 to build.

# On Ubuntu based systems
sudo apt-get install libhdf5-dev
pip install libNeuroML

On Fedora based systems

On Fedora Linux systems, the NeuroFedora community provides libNeuroML in the standard Fedora repos and can be installed using the following commands:

sudo dnf install python3-libNeuroML

Install from source

You can clone the GitHub repository and also build libNeuroML from the sources. For this, you will need git:

git clone git://github.com/NeuralEnsemble/libNeuroML.git
cd libNeuroML

More details about the git repository and making your own branch/fork are here. To build and install libNeuroML, you can use the standard install method for Python packages (preferably in a virtual environment):

pip install .

To use the latest development version of libNeuroML, switch to the development branch:

git checkout development
pip install .

To install developer dependencies and other extras, use the extras declared in the setup.cfg file. E.g.:

pip install .[dev]
pip install .[test]
pip install .[doc]

Run an example

Some sample scripts are included in neuroml/examples, e.g. :

cd neuroml/examples
python build_network.py

The standard examples can also be found Examples.

Unit tests

To run unit tests cd to the directory neuroml/test and use the Python unittest module discover method:

cd neuroml/test/
python -m unittest discover

If all tests passed correctly, your output should look something like this:

.......
----------------------------------------------------------------------
Ran 55 tests in 40.1s

OK

You can also use PyTest to run tests.

pip install pytest
pytest -v --strict -W all