def __init__(self, config_path:str=None, sorters:list=[]) -> None:
config_path = Path(config_path)
self._config_files = [config_path / f for f in os.listdir(config_path) if os.path.isfile(config_path / f)]
assert set(sorters).issubset(set(ss.available_sorters())), """
Please check the input sorter names and the installed sorters.
"""
self._sorter_names = sorters
##############################################################################
# Using the :code:`toolkit`, you can perform pre-processing on the recordings. Each pre-processing function also returns
# a :code:`RecordingExtractor`, which makes it easy to build pipelines. Here, we filter the recording and apply common
# median reference (CMR)
recording_f = st.preprocessing.bandpass_filter(recording,
freq_min=300,
freq_max=6000)
recording_cmr = st.preprocessing.common_reference(recording_f,
reference='median')
##############################################################################
# Now you are ready to spikesort using the :code:`sorters` module!
# Let's first check which sorters are implemented and which are installed
print('Available sorters', ss.available_sorters())
print('Installed sorters', ss.installed_sorter_list)
##############################################################################
# The :code:`ss.installed_sorter_list` will list the sorters installed in the machine. Each spike sorter
# is implemented as a class. We can see we have Klusta and Mountainsort4 installed.
# Spike sorters come with a set of parameters that users can change. The available parameters are dictionaries and
# can be accessed with:
print(ss.get_default_params('mountainsort4'))
print(ss.get_default_params('klusta'))
##############################################################################
# Let's run mountainsort4 and change one of the parameter, the detection_threshold:
sorting_MS4 = ss.run_mountainsort4(recording=recording_cmr, detect_threshold=6)
""" import spikeinterface.extractors as se import spikeinterface.sorters as ss ############################################################################## # First, let's create a toy example: recording, sorting_true = se.example_datasets.toy_example(duration=10, seed=0) ############################################################################## # Check available sorters # -------------------------- # print(ss.available_sorters()) ############################################################################## # This will list the sorters available through SpikeInterface. To see which sorters are installed on the machine # you can run: print(ss.installed_sorters()) ############################################################################## # Change sorter parameters # ----------------------------------- # default_ms4_params = ss.Mountainsort4Sorter.default_params() print(default_ms4_params)
def list_sorters():
"""Print a list of spikeinterface sorters."""
print('Available sorters', ss.available_sorters())
print('Installed sorters', ss.installed_sorter_list)