Read various format into SpikeInterface

SpikeInterface can read various formats of “recording” (traces) and “sorting” (spike train) data.

Internally, to read different formats, SpikeInterface either uses:

• a wrapper to neo rawio classes

• or a direct implementation

Note that:

• file formats contain a “recording”, a “sorting”, or “both”

• file formats can be file-based (NWB, …) or folder based (SpikeGLX, OpenEphys, …)

In this example we demonstrate how to read different file formats into SI

import matplotlib.pyplot as plt

import spikeinterface.core as si
import spikeinterface.extractors as se

Let’s download some datasets in different formats from the ephy_testing_data repo:

• MEArec: a simulator format which is hdf5-based. It contains both a “recording” and a “sorting” in the same file.

• Spike2: file from spike2 devices. It contains “recording” information only.

spike2_file_path = si.download_dataset(remote_path="spike2/130322-1LY.smr")
print(spike2_file_path)

mearec_folder_path = si.download_dataset(remote_path="mearec/mearec_test_10s.h5")
print(mearec_folder_path)

/home/docs/spikeinterface_datasets/ephy_testing_data/spike2/130322-1LY.smr
/home/docs/spikeinterface_datasets/ephy_testing_data/mearec/mearec_test_10s.h5

Now that we have downloaded the files, let’s load them into SI.

The read_spike2() function returns one object, a BaseRecording.

Note that internally this file contains 2 data streams (‘0’ and ‘1’), so we need to specify which one we want to retrieve (‘0’ in our case). the stream information can be retrieved by using the get_neo_streams() function.

stream_names, stream_ids = se.get_neo_streams("spike2", spike2_file_path)
print(stream_names)
print(stream_ids)
stream_id = stream_ids[0]
print("stream_id", stream_id)

recording = se.read_spike2(spike2_file_path, stream_id="0")
print(recording)
print(type(recording))
print(isinstance(recording, si.BaseRecording))

['Signal stream 0', 'Signal stream 1']
['0', '1']
stream_id 0
Spike2RecordingExtractor: 1 channels - 20.8kHz - 1 segments - 4,126,365 samples
                          198.07s (3.30 minutes) - int16 dtype - 7.87 MiB
  file_path: /home/docs/spikeinterface_datasets/ephy_testing_data/spike2/130322-1LY.smr
<class 'spikeinterface.extractors.neoextractors.spike2.Spike2RecordingExtractor'>
True

The read_spike2`() function is equivalent to instantiating a Spike2RecordingExtractor object:

recording = se.Spike2RecordingExtractor(spike2_file_path, stream_id="0")
print(recording)

Spike2RecordingExtractor: 1 channels - 20.8kHz - 1 segments - 4,126,365 samples
                          198.07s (3.30 minutes) - int16 dtype - 7.87 MiB
  file_path: /home/docs/spikeinterface_datasets/ephy_testing_data/spike2/130322-1LY.smr

The read_mearec() function returns two objects, a BaseRecording and a BaseSorting:

recording, sorting = se.read_mearec(mearec_folder_path)
print(recording)
print(type(recording))
print()
print(sorting)
print(type(sorting))

MEArecRecordingExtractor: 32 channels - 32.0kHz - 1 segments - 320,000 samples - 10.00s
                          float32 dtype - 39.06 MiB
  file_path: /home/docs/spikeinterface_datasets/ephy_testing_data/mearec/mearec_test_10s.h5
<class 'spikeinterface.extractors.neoextractors.mearec.MEArecRecordingExtractor'>

MEArecSortingExtractor: 10 units - 1 segments - 32.0kHz
  file_path: /home/docs/spikeinterface_datasets/ephy_testing_data/mearec/mearec_test_10s.h5
<class 'spikeinterface.extractors.neoextractors.mearec.MEArecSortingExtractor'>

The read_mearec() function is equivalent to:

recording = se.MEArecRecordingExtractor(mearec_folder_path)
sorting = se.MEArecSortingExtractor(mearec_folder_path)

SI objects (BaseRecording and BaseSorting) can be plotted quickly with the spikeinterface.widgets submodule:

import spikeinterface.widgets as sw

w_ts = sw.plot_traces(recording, time_range=(0, 5))
w_rs = sw.plot_rasters(sorting, time_range=(0, 5))

plt.show()

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