NineML (9ML) is a language for describing the dynamics and connectivity of neuronal network simulations (http://nineml.net), which is defined by the NineML specification.
The NineML Python Library is a software package written in Python, which maps the NineML object model onto Python classes for convenient creation, manipulation and validation of NineML models, as well as handling their serialisation to and from XML, JSON, YAML, and HDF5.
- Documentation: http://nineml-python.readthedocs.org
- Mailing list: NeuralEnsemble Google Group
- Issue tracker: https://github.com/INCF/nineml-python/issues
The layout of the Python modules and classes in the NineML Python Library
relates closely to the structure of the NineML specification (v1.0). However,
there are notable exceptions where the NineML Python Library uses names and
relationships that are planned to be changed in v2.0 of the specification
(the NineML Python Library will be backwards compatible), such as the
renaming of ComponentClass elements to separate Dynamics,
ConnectionRule and RandomDistribution elements
(see INCF/nineml-spec#94).
A full list of changes planned for NineML v2.0 can be found at
https://github.com/INCF/nineml/milestone/3. When serializing 9ML models
the version 1.0 syntax is used unless the version=2 keyword argument is
provided.
In addition to classes that directly correspond to the 9ML object model, a
range of shorthand notations ("syntactic sugar") exist to make writing 9ML
models by hand more convenient (see the nineml.sugar module). These
notations are frequently demonstrated in the examples directory of the
repository.
The NineML Catalog contains a collection of validated NineML models, which can be loaded and maninpulated with the NineML Python Library. If you create a model that you believe will be of wider use to the computational neuroscience community please consider contributing to the catalog via a pull request.
To add support to read or write HDF5 serialisations you must first install a HDF5 dev library (i.e. with the C headers).
On macOS HDF5 can be installed using Homebrew:
$ brew install hdf5
On Ubuntu/Debian HDF5 can be installed by one of the following packages:
- libhdf5-serial-dev (serial)
- libhdf5-openmpi-dev (parallel with Open MPI)
- libhdf5-mpich-dev (parallel with MPICH)
If you don't install HDF5 the other serialisation formats can still be used but you will need to install the package manually (i.e. not use pip).
The NineML Python Library can be installed using pip:
$ pip install nineml
| copyright: | Copyright 20011-2017 by the NineML Python Library team, see AUTHORS. |
|---|---|
| license: | BSD 3, see LICENSE for details. |