NestML is a domain specific language that supports the specification of neuron models in a precise and concise syntax, based on the syntax of Python. Model equations can either be given as a simple string of mathematical notation or as an algorithm written in the built-in procedural language. The equations are analyzed by NESTML to compute an exact solution if possible or use an appropriate numeric solver otherwise.

models - Example neuron models in NestML format.

pynestml - The source code of PyNestML.

tests - A collection of tests for testing of the toolchains behavior.

doc - The documentation of the modeling language NestML as well as processing toolchain PyNestML.

In order to execute the language tool-chain, Python in version 2 or 3 is required. A setup file is provided and can be installed by

python2 setup.py install --user

For Python in version 3, respectively:

python3 setup.py install --user

Correct installation can be tested by

python2 setup.py test
\# respectively python3 setup.py test 

In order to ensure correct installation and resolving of dependencies, Python's package manager pip, the distribution tool setuptools as well as the python-dev package are required and should be installed in advance. The setup file additionally installs the following components:

• SymPy in the version >= 1.1.1

• NumPy in the version >=1.8.2

• Antlr4 runtime environment in the version >= 4.7

In the case that no 'enum' package is found, additionally, enum34 has to be updated by

pip install --upgrade pip enum34

All requirements are stored in the requirements.txt and can be installed in one step by pip

pip install -r requirements.txt

After the installation, the toolchain can be executed by the following command.

python PyNestML.py -ARGUMENTS

where arguments are:

Generated artifacts are copied to the selected target directory (default is /target). In order to install the models into NEST, the following commands have to be executed:

cmake -Dwith-nest=<nest_install_dir>/bin/nest-config .
make all
make install

where nest_install_dir points to the installation directory of NEST (e.g. work/nest-install). Subsequently, PyNEST can be used to set up and execute a simulation.

PyNestML is also available as a component and can therefore be used from within other Python tools and scripts. After the PyNestML has been installed, the following modules have to be imported:

from pynestml.frontend.pynestml_frontend import to_nest,install_nest

Subsequently, it is possible to call PyNestML from other Python tools and scripts via:

to_nest(path, target, dry, logging_level, module_name, store_log, dev)    

This operation expects the same set of arguments as in the case of the shell/CMD call, with the following default values being used, where only the path is mandatory:

where no values provided indicates the same behavior as listed for default values in arguments table. If no errors occur, the output will be generated to the specified target directory. In order to avoid an execution of all required module-installation routines by hand, PyNestML features a function for an installation of NEST models directly into NEST:

install_nest(models_path,nest_path)

where the models_path has to point as previously selected for the target argument, while nest_path has to point to the directory where NEST is installed (e.g., "/home/nest/work/nest-install"). The second argument is hereby equivalent to the nest_install_dir argument from the manual installation of models (see above). The simulation can then be started. A typical script, therefore, can look like the following:

from pynestml.frontend.pynestml_frontend import to_nest,install_nest

to_nest(path="/home/nest/work/pynestml/models", target="/home/nest/work/pynestml/target",dev=True)

install_nest("/home/nest/work/pynestml/target", "/home/nest/work/nest-install")

nest.install(<the module name>)    
...
nest.Simulate(400.0)

For an in-depth introduction to the underlying modeling language NestML, we refer to the following introduction. For those interested in the implementation of PyNestML or the general structure of a DSL-processing toolchain, a documentation of all implemented components is provided.