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.
docker - A docker containers with the complete NESTML software pipeline installed. Once based on the latest release of NESTML. One that builds the latest development version of NESTML.
models - Example neuron models in NESTML format
src - The source code of NESTML
To install NESTML, the following requirements need to be met:
Compilation can then be issued by running the following commands
cd <nestml_clone>
mvn clean install
If everything ran without errors, then the nestml.jar artifact will be created in <nestml_clone>/target subfolder. Run the nestml tool using
java -jar nestml.jar <models> --target <build_dir>
where <models> is a directory containing one or more .nestml files and build_dir is the directory, into which the C++ are put together with an extension module and the corresponding build infrastructure for NEST.
As NESTML has quite some dependencies, which makes it a bit complicated to install and run it. To lower the burden, we have created a Docker container for you. The Dockerfiles and corresponding helper scripts can be found in the docker folder. In order to use this method, you have to have Docker installed on your machine. Please refer to the installation instructions or use the packages from your Linux distribution's software manager.
The container can be provisioned (created) by first changing to the docker directory of your clone of the nestml Git repository and then running the nestml_docker.sh script:
cd <nestml_clone>/docker
./nestml_docker.sh provision
This will download all required packages and libraries and create a container that uses the pre-built version of the latest release of NESTML. If you are interested in using the bleeding edge version of NESTML (a.k.a. Git master), you can add the argument --dev to the invocation of the ./nestml_docker.sh script.
If everything goes well, the list printed by the command 'docker images' should now contain the 'nestml_release' container. If you experience an error, please open an issue so we can look into and fix it.
To actually convert your model files written in NESTML to NEST C++, you have to run the Docker container. This is again done using the nestml_docker.sh script, which for this purpos gets the command run as first argument and one or more folders containing one or more .nestml files (the folder is called <models> in the following description):
./nestml_docker.sh run <models>
This run creates a subfolder build in the <models> directory that contains the generated code and all infrastructure and source files for an extension module for NESTML, which can be dynamically loaded. The module will have the same name as the folder in which you stored the .nestml files.
As an example, let's consider the folder <nestml_clone>/models that contains all models bundled with NESTML. Among others, it contains the files aeif_cond_alpha.nestml and hh_psc_alpha.nestml. The resulting module will be called models.
In order to compile the module and install it into the NEST installation directory, you have to use the following commands:
cd <models>/build
cmake -Dwith-nest=<nest_install_dir>/bin/nest-config .
make all
make install
Again, if everything goes well, you can now use the generated module in your SLI and PyNEST scripts by using the corresponding version of the Install command. For SLI the invocation looks like this: (<models>) Install, for PyNEST it reads nest.Install("<models>"). After loading the module, the contained models can be instantiated just as the built-in models using the Create command in SLI and PyNEST, respectively.
With the previous example, this results in a module called models, which can be loaded by NEST and gives access to (among others) the two afforementioned neuron models:
nest.Install("models")
aeif_cond_alpha = nest.Create("aeif_cond_alpha_neuron")
hh_psc_alpha = nest.Create("hh_psc_alpha_neuron")