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 classes provided in the spikesorters package.

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 myspikesorter.py. Additional configuration files must be placed in the same folder.

You can start by importing the base class:

import spikeextractors 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:

try:
    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:
        https://myspikesorterwebsite.com
"""

Now you can start filling out the required methods:

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

# optional
@staticmethod
def get_sorter_version():
    return myspikesorter.__version__

@classmethod
def is_installed(cls):

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

def _setup_recording(self, recording, output_folder):

    # 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 _run(self, recording, output_folder):

    # 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, tridescous), pure Python code (e.g. Mountainsort4,
    # Herding Spikes), or even MATLAB code (e.g. Kilosort, Kilosort2, Ironclust)

@staticmethod
def get_result_from_folder(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 sorterlist.py. Moreover, you have to add a launcher function:

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

When you are done you can optionally write a test in tests/test_myspikesorter.py. 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.

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