How to contribute

There are various ways to contribute to SpikeInterface as a user or developer. Some tasks you can help us with include:

  • Developing a new extractor for a different file format.

  • Creating a new spike sorter.

  • Designing a new post-processing algorithm.

  • Enhancing documentation, including docstrings, tutorials, and examples.

  • Crafting tutorials for common workflows (e.g., spike sorting, post-processing, etc.).

  • Writing unit tests to expand code coverage and use case scenarios.

  • Reporting bugs and issues.

We use a forking workflow <>_ to manage contributions. Here’s a summary of the steps involved, with more details available in the provided link:

  • Fork the SpikeInterface repository.

  • Create a new branch (e.g., git switch -c my-contribution).

  • Modify the code, commit, and push changes to your fork.

  • Open a pull request from the “Pull Requests” tab of your fork to spikeinterface/main.

  • By following this process, we can review the code and even make changes as necessary.

While we appreciate all the contributions please be mindful of the cost of reviewing pull requests <>_ .

How to run tests locally

Before submitting a pull request, we recommend running the tests locally. In the CI we use pytest to run the tests so it is a good idea to do the same. To run the tests locally, you can use the following command:


From your local repository. This will run all the tests in the repository. If you want to run a specific test, you can use the following command:

pytest path/to/

For example, if you want to run the tests for the spikeinterface.extractors module, you can use the following command:

pytest src/spikeinterface/extractors

If you want to run a specific test in a specific file, you can use the following command:

pytest pytest src/spikeinterface/core/tests/

We also mantain pytest markers to run specific tests. For example, if you want to run only the tests for the spikeinterface.extractors module, you can use the following command:

pytest -m "extractors"

The markers are located in the pyproject.toml file in the root of the repository.

Note that you should install the package before running the tests. You can do this by running the following command:

pip install -e .[test,extractors,full]

You can change the [test,extractors,full] to install only the dependencies you need. The dependencies are specified in the pyproject.toml file in the root of the repository.

The specific environment for the CI is specified in the .github/actions/build-test-environment/action.yml and you can find the full tests in the .github/workflows/full_test.yml file.

The extractor tests require datalad for some of the tests. Here are instructions for installing datalad:

Installing Datalad

First install the datalad-installer package using pip:

pip install datalad-installer

The following instructions depend on the operating system you are using:


datalad-installer --sudo ok git-annex --method datalad/packages

Mac OS

datalad-installer --sudo ok git-annex --method brew


datalad-installer --sudo ok git-annex --method datalad/git-annex:release

The following steps are common to all operating systems:

pip install datalad

(Optional) Configure Git to use git-annex for large files for efficiency:

git config --global filter.annex.process "git-annex filter-process"

Stylistic conventions

SpikeInterface maintains a consistent coding style across the project, leveraging the black Python code formatter. This helps to ensure readability and maintainability of the code, making it easier for contributors to collaborate.

To install black, you can use pip, the Python package installer. Run the following command in your terminal:

pip install black

This will install black into your current Python environment.

In addition to black, we use pre-commit to manage a suite of code formatting. Pre-commit helps to automate the process of running these tools before every commit, ensuring that all code is checked for style.

You can install pre-commit using pip as well:

pip install pre-commit

Once pre-commit is installed, you can set up the pre-commit hooks for your local repository. These hooks are scripts that pre-commit will run prior to each commit. To install the pre-commit hooks, navigate to your local repository in your terminal and run the following command:

pre-commit install

Now, each time you make a commit, pre-commit will automatically run black and any other configured hooks. If the hooks make changes or if there are any issues, the commit will be stopped, and you’ll be able to review and add the changes.

If you want black to omit a line from formatting, you can add the following comment to the end of the line:

# fmt: off

As described in the black documentation,

The following are some styling conventions that we follow in SpikeInterface:

  1. Avoid using abreviations in variable names (e.g., use recording instead of rec). It is especially important to avoid single letter variables.

  2. Use index as singular and indices for plural following Numpy. Avoid idx or indexes. Plus, id and ids are reserved for identifiers (i.e. channel_ids)

  3. We use file_path and folder_path (instead of file_name and folder_name) for clarity.

  4. Use the numpy docstring standard in all the docstrings.

How to build the documentation

We use Sphinx to build the documentation. To build the documentation locally, you can use the following command:

sphinx-build -b html doc ./doc/_build/

This will build the documentation in the doc/_build/html folder. You can open the index.html file in your browser to see the documentation.

How to run code coverage locally

To run code coverage locally, you can use the following command:

pytest --cov=spikeinterface --cov-report html

This will run the tests and generate a report in the htmlcov folder. You can open the index.html file in your browser to see the report.

Note, however, that the running time of the command above will be slow. If you want to run the tests for a specific module, you can use the following command:

pytest src/spikeinterface/core/ --cov=spikeinterface/core --cov-report html

Implement a new extractor

SpikeInterface already supports over 30 file formats, but the acquisition system you use might not be among the supported formats list (*ref*). Most of the extractord rely on the NEO package to read information from files. Therefore, to implement a new extractor to handle the unsupported format, we recommend make a new neo.rawio `_ class. Once that is done, the new class can be easily wrapped into SpikeInterface as an extension of the :py:class:`~spikeinterface.extractors.neoextractors.neobaseextractors.NeoBaseRecordingExtractor (for BaseRecording objects) or NeoBaseRecordingExtractor (for py:class:~spikeinterface.core.BaseSorting objects) or with a few lines of code (e.g., see reader for SpikeGLX or Neuralynx).

NOTE: implementing a neo.rawio Class is not required, but recommended. Several extractors (especially) for Sorting objects are implemented directly in SpikeInterface and inherit from the base classes. As examples, see the CompressedBinaryIblExtractor for a BaseRecording object, or the SpykingCircusSortingExtractor for a a BaseSorting object.

Implement a spike sorter

Implementing a new spike sorter for a specific file format is as simple as creating a new subclass based on the predefined base class BaseSorter.

To enable standardization among subclasses, the BaseSorter is base class which require a new subclass to override a few methods.

The contributed extractors are in the spikesorters folder. You can fork the repo and create a new folder myspikesorter there. In the folder, create a new file named Additional configuration files must be placed in the same folder.

You can start by importing the base class:

import spikeinterface.extractors as se
from ..basesorter import BaseSorter

In order to check if your spike sorter is installed, a try - except block is used. For example, if your sorter is implemented in Python (installed with the package myspikesorter), this block will look as follows:

    import myspikesorter
    HAVE_MSS = True
except ImportError:
    HAVE_MSS = False

Then, you can start creating a new class:

class MySpikeSorter(BaseSorter):
Brief description (optional)

sorter_name = 'myspikesorter'
installed = HAVE_MSS

_default_params = {
    'param1': None,
    'param2': 2,

_params_description = {
    'param1': 'Description for param1',
    'param1': 'Description for param1',

installation_mesg = """
    >>> pip install myspikesorter
    More information on MySpikesorter at:

Now you can start filling out the required methods:

def __init__(self, **kargs):
    BaseSorter.__init__(self, **kargs)

# optional
def get_sorter_version(cls):
    return myspikesorter.__version__

def is_installed(cls):

    # Fill code to check sorter installation. It returns a boolean
    return HAVE_MSS

def _setup_recording(cls, recording, output_folder, params, verbose):

    # Fill code to set up the recording: convert to required file, parse config files, etc.
    # The files should be placed in the 'output_folder'

def _check_params(cls, recording, output_folder, params):
    # optional
    # can be implemented in subclass for custom checks
    return params

def _check_apply_filter_in_params(cls, params):
    return False

    #  optional
    # can be implemented in subclass to check if the filter will be applied

def _run_from_folder(cls, output_folder, params, verbose):

    # Fill code to run your spike sorter based on the files created in the _setup_recording()
    # You can run CLI commands (e.g. klusta, spykingcircus, tridesclous), pure Python code (e.g. Mountainsort5,
    # Herding Spikes), or even MATLAB code (e.g. Kilosort, Kilosort2, Ironclust)

def _get_result_from_folder(cls, output_folder):

    # If your spike sorter has a specific file format, you should implement a SortingExtractor in spikeextractors.
    # Let's assume you have done so, and the extractor is called MySpikeSorterSortingExtractor

    sorting = se.MySpikeSorterSortingExtractor(output_folder)
    return sorting

When your spike sorter class is implemented, you have to add it to the list of available spike sorters in the Moreover, you have to add a launcher function like run_XXXX().

def run_myspikesorter(*args, **kwargs):
    return run_sorter('myspikesorter', *args, **kwargs)

When you are done you need to write a test in tests/ In order to be tested, you can install the required packages by changing the .travis.yml. Note that MATLAB based tests cannot be run at the moment, but we recommend testing the implementation locally.

After this you need to add a block in doc/sorters_info.rst

Finally, make a pull request to the spikesorters repo, so we can review the code and merge it to the spikesorters!