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TVB Contributors manual

So you want to contribute to TVB. Maybe add a model or another feature. Thank you for helping! We welcome and appreciate contributions.

Get in touch with the TheVirtualBrain team, we are glad to help Sign up for the mailing list and introduce yourself.

Read trough these docs. Get to know TVB by installing a TVB Distribution (from TVB Web Page) and playing with the GUI or following tutorials first.

Have a look at the open tasks in our Jira open issues.

Finally revisit this document and find out how to set up TheVirtualBrain as a developer.

The source code

TVB’s source code is hosted on github . Fork the tvb-pack into your account. You will need to have git installed locally and a Github account prepared. Then clone the repo locally.

The work environment

We recommend preparing your local Python environment for TVB with anaconda, then install TVB from Pypi. Using a virtual environment inside Anaconda is a good idea.

$ envname="tvb-run"
$ conda create -y -n $envname numpy
$ source activate $envname
$ conda config --add channels conda-forge
$ pip install tvb-framework  # This will bring tvb and all its dependencies
$ python -m WEB_PROFILE  # Launch TVB web server locally

Similarly as using conda-forge repo above, you could install from Pypi tvb-framework and/or tvb-library.

The above setup will bring you the latest release code of TVB into your Anaconda env. As last step, you should replace that released TVB link from your env, with your local clone of the code. You can use the script install_full_tvb from tvb root repo.


If you have problems, send us an email, and we will do our best to help you. You can see open issues on TVB’s Jira open issues. You may also create a new issue.

Test suite

TVB’s test suite takes a long time to run, but a patch will have to pass it. We recommend running tests before submitting changes that touch code that you have not written:

$ pip install pytest
$ pytest --pyargs tvb.tests.library
$ pytest --pyargs tvb.tests.framework

Contribution guidelines

You should put explanatory comments and documentation in your code. Document every public function with a docstring. Use english for both comments and names.

Avoid cryptic short names. You may relax this if implementing a mathematical formula. But then please document it using latex docstrings.

Try to adhere to the Python code style. Indent with 4 spaces. We are ok with 120 long lines. Naming: module_name, ClassName, function_name, CONSTANT_NAME function_parameter_name, local_var_name

You should attach unit-tests for your new code, to prove that it is correct and that it fits into the overall architecture of TVB.

Prefer small commits. Add a meaningful commit message. We strongly recommend that the commit message start with the Jira task id. (e.g. TVB-1963 Add FCT analyser).

Use logging instead of print statements.

If code is indented more than 6 levels your function is too complex. If a function has more than 50 lines it is too long. Split these functions.

Do not copy paste code. Avoid reinventing the wheel. Use the python built in functions, the standard library and numpy.

Git guidelines

By default, the only branch available is ‘trunk’. You should always create a separate branch with a self-explanatory name for the new features you want to add to TVB.

While making your modifications/contributions, make sure that

  1. you are working in the right branch and
  2. you make pull requests from master often, in order to quickly solve any conflicts which might appear.
  3. You follow the Contribution guidelines

Once you are done with your changes and you believe that they can be integrated into TVB master repository, go to your GitHub repository, switch to your feature branch and issue a pull request, describing the improvements you did. We will later test that your changes are fit to be included, and notify you of the integration process.


We use pycharm to develop and debug TVB. To test quick ideas we like ipython notebooks.

Technologies used by TVB

TVB uses numpy extensively. Numpy is quite different from other python libraries. Learn a bit about it before trying to understand TVB code.

The TVB framework uses sqlalchemy for ORM mapping, cherrypy as a web framework and server and genshi for html templating. Numeric arrays are stored in the hdf5 format. Client side we use jquery, d3 and webgl.

TVB uses some advanced python features to implement it’s Traits system: metaclasses and data descriptors.

Glossary of terms used by TVB code


The way TVB represents data. Similar to entities in a database model. They usually contain numeric arrays. Many algorithms receive and produce Datatypes.

Tvb framework organizes them into projects, stores the numeric data in .h5 files and metadata in tables in a relational database (sqlite or postgresql).

Example: Surface, Connectivity Code: scientific_library/tvb/datatypes/


A TVB framework plugin, similar to a runnable task. It has a launch method. It declares what inputs it requires and what Datatypes it produces. Asynchronous Adapters will be run in a different process, possibly on a cluster.

Adapters may be of different types: analysers, creators, uploaders, visualizers

These plugins are discovered at TVB startup and recorded in the database table ALGORITHMS.

Example: SimulatorAdapter code: framework_tvb/tvb/adapters


Running an Adapter produces an Operation. It will contain the Datatypes produced by the Adapter.


Organizes the data of an user. It will contain all Operations and Datatypes. Stored on disk in ~/TVB/PROJECTS. The numerically named folders correspond to operations with that id, the h5 files in them correspond to datatypes.