Contributing to PyNN¶

Mailing list¶

Discussions about PyNN take place in the NeuralEnsemble Google Group.

Setting up a development environment¶

We strongly suggest you work in a virtual environment, e.g. using virtualenv or Anaconda.

Requirements¶

In addition to the requirements listed in Installation, you will need to install:

nose

mock

coverage

to run tests, and:

Sphinx

matplotlib

to build the documentation.

Code checkout¶

PyNN development is based around GitHub. Once you have a GitHub account, you should fork the official PyNN repository, and then clone your fork to your local machine:

$ git clone https://github.com/<username>/PyNN.git pyNN_dev

To work on the development version:

$ git checkout master

To work on the latest stable release (for bug-fixes):

$ git checkout --track origin/0.8

To keep your PyNN repository up-to-date with respect to the official repository, add it as a remote:

$ git remote add upstream https://github.com/NeuralEnsemble/PyNN.git

and then you can pull in any upstream changes:

$ git pull upstream master

To get PyNN onto your PYTHONPATH there are many options, such as:

pip editable mode (pip install -e /path/to/PyNN)

creating a symbolic link named pyNN from somewhere that is already on your PYTHONPATH, such as the site-packages directory, to the pyNN_trunk/pyNN directory.

If you are developing with NEURON, don’t forget to compile the NMODL files in pyNN/neuron/nmodl by running nrnivmodl, and to recompile any time you modify any of them.

Coding style¶

We try to stay fairly close to PEP8. Please note in particular:

indentation of four spaces, no tabs

single space around most operators, but no space around the ‘=’ sign when used to indicate a keyword argument or a default parameter value.

some function/method names in PyNN use mixedCase, but these will gradually be deprecated and replaced with lower_case_with_underscores. Any new functions or methods should use the latter.

we currently target versions 2.7 and 3.6+

Testing¶

Running the PyNN test suite requires the nose_, mock_ and nose-testconfig packages, and optionally the coverage_ package. To run the entire test suite, in the test subdirectory of the source tree:

$ nosetests

To see how well the codebase is covered by the tests, run:

$ nosetests --with-coverage --cover-package=pyNN --cover-erase --cover-html

There are currently two sorts of tests, unit tests, which aim to exercise small pieces of code such as individual functions and methods, and system tests, which aim to test that all the pieces of the system work together as expected.

If you add a new feature to PyNN, or fix a bug, you should write both unit and system tests.

Unit tests should where necessary make use of mock/fake/stub/dummy objects to isolate the component under test as well as possible. The pyNN.mock module is a complete mock simulator backend that may be used for this purpose. Except when testing a specific simulator interface, unit tests should be able to run without a simulator installed.

System tests should be written so that they can run with any of the simulators. The suggested way to do this is to write test functions, in a separate file, that take a simulator module as an argument, and then call these functions from test_neuron.py, test_nest.py, etc.

System tests defined in the scenarios directory are treated as a single test (test_scenarios()) while running nosetests. To run only the tests within a file named ‘test_electrodes’ located inside system/scenarios, use:

$ nosetests -s --tc=testFile:test_electrodes test_nest.py

To run a single specific test named ‘test_changing_electrode’ located within some file (and added to registry) inside system/scenarios, use:

$ nosetests -s --tc=testName:test_changing_electrode test_nest.py

Note that this would also run the tests specified within the simulator specific files such as test_brian.py, test_nest.py and test_neuron.py. To avoid this, specify the ‘test_scenarios function’ on the command line:

$ nosetests -s --tc=testName:test_changing_electrode test_nest.py:test_scenarios

The test/unsorted directory contains a number of old tests that are either no longer useful or have not yet been adapted to the nose framework. These are not part of the test suite, but we are gradually adapting those tests that are useful and deleting the others.

Submitting code¶

The best way to get started with contributing code to PyNN is to fix a small bug (bugs marked “minor” in the bug tracker) in your checkout of the code. Once you are happy with your changes, run the test suite again to check that you have not introduced any new bugs. If this is your first contribution to the project, please add your name and affiliation/employer to AUTHORS.

After committing the changes to your local repository:

$ git commit -m 'informative commit message'

first pull in any changes from the upstream repository:

$ git pull upstream master

then push to your own account on GitHub:

$ git push

Now, via the GitHub web interface, open a pull request.

Documentation¶

PyNN documentation is generated using Sphinx.

To build the documentation in HTML format, run:

$ make html

in the doc subdirectory of the source tree. Many of the files contain examples of interactive Python sessions. The validity of this code can be tested by running:

$ make doctest

PyNN documentation is hosted at http://neuralensemble.org/docs/PyNN

Making a release¶

To make a release of PyNN requires you to have permissions to upload PyNN packages to the Python Package Index, and to upload documentation to the neuralensemble.org server. If you are interested in becoming release manager for PyNN, please contact us via the mailing list.

When you think a release is ready, run through the following checklist one last time:

do all the tests pass? This means running nosetests in test/unittests and test/system and running make doctest in doc. You should do this on at least two Linux systems – one a very recent version and one at least a year old, and on at least one version of Mac OS X. You should also do this with Python 2.7 and 3.4, 3.5 or 3.6.

do all the example scripts generate the correct output? Run the run_all_examples.py script in examples/tools and then visually check the .png files generated in examples/tools/Results. Again, you should do this on at least two Linux systems and one Mac OS X system.

does the documentation build without errors? You should then at least skim the generated HTML pages to check for obvious problems.

have you updated the version numbers in setup.py, pyNN/__init__.py, doc/conf.py and doc/installation.txt?

have you updated the changelog?

Once you’ve confirmed all the above, create a source package using:

$ python setup.py sdist

and check that it installs properly (you will find it in the dist subdirectory.

Now you should commit any changes, then tag with the release number as follows:

$ git tag x.y.z

where x.y.z is the release number. You should now upload the documentation to http://neuralensemble.org/docs/PyNN/ by running:

$ make zip

in the doc directory, and then unpacking the resulting archive on the NeuralEnsemble server.

If this is a development release (i.e. an alpha or beta), the final step is to upload the source package to the INCF Software Center. Do not upload development releases to PyPI.

To upload a package to the INCF Software Center, log-in, and then go to the Contents tab. Click on “Add new…” then “File”, then fill in the form and upload the source package.

If this is a final release, there are a few more steps:

if it is a major release (i.e. an x.y.0 release), create a new bug-fix branch:
$ git branch x.y
upload the source package to PyPI:
$ python setup.py sdist upload
make an announcement on the mailing list

if it is a major release, write a blog post about it with a focus on the new features and major changes

go home, take a headache pill and lie down for a while in a darkened room (-;