Sorting objects

The BaseSorting is the basic class for handling spike sorted data. Here is how it works.

A SortingExtractor handles:

• spike trains retrieval across segments

• dumping to/loading from dict-json

• saving (caching)

import numpy as np
import spikeinterface.extractors as se

Traceback (most recent call last):
  File "/home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/checkouts/0.98.0/examples/modules_gallery/core/plot_2_sorting_extractor.py", line 17, in <module>
    import spikeinterface.extractors as se
  File "/home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/conda/0.98.0/lib/python3.9/site-packages/spikeinterface/extractors/__init__.py", line 1, in <module>
    from .extractorlist import *
  File "/home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/conda/0.98.0/lib/python3.9/site-packages/spikeinterface/extractors/extractorlist.py", line 15, in <module>
    from .neoextractors import *
  File "/home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/conda/0.98.0/lib/python3.9/site-packages/spikeinterface/extractors/neoextractors/__init__.py", line 1, in <module>
    from .alphaomega import AlphaOmegaRecordingExtractor, AlphaOmegaEventExtractor, read_alphaomega, read_alphaomega_event
  File "/home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/conda/0.98.0/lib/python3.9/site-packages/spikeinterface/extractors/neoextractors/alphaomega.py", line 3, in <module>
    from .neobaseextractor import NeoBaseRecordingExtractor, NeoBaseEventExtractor
  File "/home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/conda/0.98.0/lib/python3.9/site-packages/spikeinterface/extractors/neoextractors/neobaseextractor.py", line 332, in <module>
    class NeoBaseSortingExtractor(_NeoBaseExtractor, BaseSorting):
  File "/home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/conda/0.98.0/lib/python3.9/site-packages/spikeinterface/extractors/neoextractors/neobaseextractor.py", line 480, in NeoBaseSortingExtractor
    def _infer_t_start_from_signal_stream(self, segment_index: int, stream_id: Optional[str] = None) -> float | None:
TypeError: unsupported operand type(s) for |: 'type' and 'NoneType'

We will create a SortingExtractor object from scratch using numpy and the NumpySorting

Let’s define the properties of the dataset:

sampling_frequency = 30000.
duration = 20.
num_timepoints = int(sampling_frequency * duration)
num_units = 4
num_spikes = 1000

We generate some random events for 2 segments:

times0 = np.int_(np.sort(np.random.uniform(0, num_timepoints, num_spikes)))
labels0 = np.random.randint(1, num_units + 1, size=num_spikes)

times1 = np.int_(np.sort(np.random.uniform(0, num_timepoints, num_spikes)))
labels1 = np.random.randint(1, num_units + 1, size=num_spikes)

And instantiate a NumpySorting object:

sorting = se.NumpySorting.from_times_labels([times0, times1], [labels0, labels1], sampling_frequency)
print(sorting)

We can now print properties that the SortingExtractor retrieves from the underlying sorted dataset.

print('Unit ids = {}'.format(sorting.get_unit_ids()))
st = sorting.get_unit_spike_train(unit_id=1, segment_index=0)
print('Num. events for unit 1seg0 = {}'.format(len(st)))
st1 = sorting.get_unit_spike_train(unit_id=1, start_frame=0, end_frame=30000, segment_index=1)
print('Num. events for first second of unit 1 seg1 = {}'.format(len(st1)))

Some extractors also implement a write function. We can for example save our newly created sorting object to NPZ format (a simple format based on numpy used in spikeinterface):

file_path = 'my_sorting.npz'
se.NpzSortingExtractor.write_sorting(sorting, file_path)

We can now read it back with the proper extractor:

sorting2 = se.NpzSortingExtractor(file_path)
print(sorting2)

Unit properties are key value pairs that we can store for any unit. We will now calculate unit firing rates and add them as properties to the SortingExtractor object:

firing_rates = []
for unit_id in sorting2.get_unit_ids():
    st = sorting2.get_unit_spike_train(unit_id=unit_id, segment_index=0)
    firing_rates.append(st.size / duration)
sorting2.set_property('firing_rate', firing_rates)

print(sorting2.get_property('firing_rate'))

You can also get a a sorting with a subset of unit. Properties are propagated to the new object:

sorting3 = sorting2.select_units(unit_ids=[1, 4])
print(sorting3)

print(sorting3.get_property('firing_rate'))

# which is equivalent to
from spikeinterface import UnitsSelectionSorting

sorting3 = UnitsSelectionSorting(sorting2, unit_ids=[1, 4])

A sorting can be “dumped” (exported) to:

• a dict

 * a json file

• a pickle file

The “dump” operation is lazy, i.e., the spike trains are not exported. Only the information about how to reconstruct the sorting are dumped:

from spikeinterface import load_extractor
from pprint import pprint

d = sorting2.to_dict()
pprint(d)

sorting2_loaded = load_extractor(d)
print(sorting2_loaded)

The dictionary can also be dumped directly to a JSON file on disk:

sorting2.dump('my_sorting.json')

sorting2_loaded = load_extractor('my_sorting.json')
print(sorting2_loaded)

IMPORTANT: the “dump” operation DOES NOT copy the spike trains to disk!

If you wish to also store the spike trains in a compact way you need to use the save() function:

sorting2.save(folder='./my_sorting')

import os

pprint(os.listdir('./my_sorting'))

sorting2_cached = load_extractor('./my_sorting')
print(sorting2_cached)