Read various format into SI

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

Internally, to read differents formats, spikeinterface either uses:

• a wrapper to the 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 as si
import spikeinterface.extractors as se

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

• MEArec: an 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)

Out:

/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 ww need let’s load them into SI.

The read_spike2 function returns one object, a RecordingExtractor.

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):

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

Out:

This extractor based on  neo.Spike2RawIO have strange units not in (V, mV, uV) ['']
Spike2RecordingExtractor: 1 channels - 1 segments - 20.8kHz - 198.066s
  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)

Out:

This extractor based on  neo.Spike2RawIO have strange units not in (V, mV, uV) ['']
Spike2RecordingExtractor: 1 channels - 1 segments - 20.8kHz - 198.066s
  file_path: /home/docs/spikeinterface_datasets/ephy_testing_data/spike2/130322-1LY.smr

The read_mearec function returns two objects, a RecordingExtractor and a SortingExtractor:

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

Out:

MEArecRecordingExtractor: 32 channels - 1 segments - 32.0kHz - 10.000s
  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 (RecordingExtractor and SortingExtractor) object can be plotted quickly with the widgets submodule:

import spikeinterface.widgets as sw

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

plt.show()

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