A tool for reading, writing and generally working with 9ML objects and files.
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.
$ brew install hdf5
On Ubuntu/Debian HDF5 can be installed via the following packages:
- libhdf5-serial-dev (serial)
- libhdf5-openmpi-dev (parallel with Open MPI)
- libhdf5-mpich-dev (parallel with MPICH)
The NineML Python Library can be installed using pip
$ pip install nineml
Relation to the NineML Specification
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 https://github.com/INCF/nineml/issues/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=2.0 keyword argument is used.
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
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.
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