Add read_nwb_sorting_analyzer: build a SortingAnalyzer from an NWB Units table

[h-mayorquin]

read_nwb_sorting_analyzer builds a curatable SortingAnalyzer directly from an NWB (Neurodata Without Borders) Units table, populating each extension from what the file already stores rather than recomputing it. When the Units table has waveform_mean, the analyzer is built recordingless from those stored templates, plus the per-unit metrics and the electrodes region for sparsity; when the file has an accessible ElectricalSeries, it is used as the recording. It mirrors read_kilosort_as_analyzer in structure, injecting the templates, quality and template metrics, sparsity, and random_spikes extensions from the file's contents. This supersedes the earlier draft #4270.

The reads are deliberate: Units columns are classified from metadata and only the templates, the electrodes region, and the scalar metric/label columns are materialized, while the large per-spike ragged columns (spike times, amplitudes, depths) are never touched at build. The sorting is kept lazy so its spike times are read only on demand, which builds on #4662 (single bulk-read NWB spike vector) and the copy_sorting option (#4668); the recordingless case builds a lightweight placeholder recording from the electrode rel_x/rel_y geometry (via generate_ground_truth_recording, #4588) purely to carry probe geometry into the standard constructor, then drops it. Together these keep the build small and memory-light regardless of file size.

Because the reads are deliberate and the sorting stays lazy, the function works the same whether the file is local or streamed (stream_mode is passed through to the extractors). That makes the streamed case cheap: as a check, building from a real IBL (International Brain Laboratory) processed file on dandiset 000409, session 6713a4a7-faed-4df2-acab-ee4e63326f8d (898 units, 20.7M spikes), produced a curatable analyzer in about 14 s while transferring only ~22 MB, with the ~130 MB spike read deferred until a spike-based view needs it. This PR is the reader itself; a separate how-to PR will cover the streaming-from-DANDI workflow in depth. It depends on #4662 and the copy_sorting PR (#4668), which should merge first so this branch rebases down to just the reader.

from dandi.dandiapi import DandiAPIClient
from spikeinterface.extractors import read_nwb_sorting_analyzer

# IBL Brain Wide Map (dandiset 000409), one session's processed file (units + templates, no raw traces)
SESSION = "6713a4a7-faed-4df2-acab-ee4e63326f8d"
with DandiAPIClient() as client:
    dandiset = client.get_dandiset("000409", "draft")
    asset = next(a for a in dandiset.get_assets_by_glob(f"*{SESSION}*.nwb") if "desc-processed" in a.path)
    s3_url = asset.get_content_url(follow_redirects=1, strip_query=True)

# build the analyzer directly from the streamed Units table (no full download)
analyzer = read_nwb_sorting_analyzer(s3_url, stream_mode="remfile")

print(analyzer)
print("num_units:", analyzer.get_num_units())
print("templates:", analyzer.get_extension("templates").get_data().shape)
print("quality metrics:", list(analyzer.get_extension("quality_metrics").get_data().columns))

# open in spikeinterface-gui for curation (needs a display and `pip install spikeinterface-gui`)
# import spikeinterface_gui
# spikeinterface_gui.run_mainwindow(analyzer)

[alejoe91]

This is failing on https://dandiarchive.s3.amazonaws.com/blobs/c41/fae/c41fae67-e6e2-4dc9-adcb-6131d530b6cd since it has two probes and when you select groups it calls a select_units, which doesn't have the spike_times_data

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[4], line 1
----> 1 analyzer = se.read_nwb_sorting_analyzer(dandi_path_sorting, stream_mode="remfile", group_name="Probe00")

File ~/Documents/codes/spike_sorting/spikeinterface/spikeinterface/src/spikeinterface/extractors/nwbextractors.py:2108, in read_nwb_sorting_analyzer(file_path, t_start, sampling_frequency, electrical_series_path, unit_table_path, stream_mode, stream_cache_path, cache, storage_options, use_pynwb, group_name, compute_extra, compute_extra_params, extension_map, verbose)
   2106     analyzer_channel_ids = list(recording.get_channel_ids())
   2107 else:
-> 2108     analyzer_recording, analyzer_channel_ids = _make_placeholder_recording_from_electrodes(
   2109         sorting, electrodes_table, electrodes_indices, verbose=verbose
   2110     )
   2112 # Per-unit sparsity and channel map from the Units `electrodes` region. Each unit's `waveform_mean`
   2113 # is stored only on a subset of channels (near its peak); the region gives which channels those are.
   2114 # We use it both for the analyzer sparsity and to scatter the waveforms onto their true channel
   2115 # positions instead of stacking the sparse block densely.
   2116 sparsity, unit_local_channels = _make_sparsity_from_electrodes(
   2117     sorting, electrodes_table, electrodes_indices, analyzer_channel_ids
   2118 )

File ~/Documents/codes/spike_sorting/spikeinterface/spikeinterface/src/spikeinterface/extractors/nwbextractors.py:2200, in _make_placeholder_recording_from_electrodes(sorting, electrodes_table, electrodes_indices, verbose)
   2192 locations = np.array([electrodes_table_sliced["rel_x"][:], electrodes_table_sliced["rel_y"][:]]).T
   2194 # The recording length only needs to loosely bound the timeline for the recordingless GUI; nothing
   2195 # about curation depends on it being exact. Estimate it cheaply from the last stored spike time (one
   2196 # element, i.e. only the last spike_times chunk) rather than scanning the whole array for the true
   2197 # global maximum. spike_times is in seconds, so this is already a duration in seconds. It is only an
   2198 # approximation: because spike_times is concatenated per unit and not globally sorted, this is the
   2199 # last unit's last spike, not necessarily the latest spike overall.
-> 2200 last_spike_time = float(np.asarray(sorting._sorting_segments[0].spike_times_data[-1]))
   2201 duration = last_spike_time + 1.0
   2203 probe = Probe(si_units="um")

AttributeError: 'UnitsSelectionSortingSegment' object has no attribute 'spike_times_data'

[alejoe91]

This is failing on https://dandiarchive.s3.amazonaws.com/blobs/c41/fae/c41fae67-e6e2-4dc9-adcb-6131d530b6cd since it has two probes and when you select groups it calls a select_units, which doesn't have the spike_times_data

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[4], line 1
----> 1 analyzer = se.read_nwb_sorting_analyzer(dandi_path_sorting, stream_mode="remfile", group_name="Probe00")

File ~/Documents/codes/spike_sorting/spikeinterface/spikeinterface/src/spikeinterface/extractors/nwbextractors.py:2108, in read_nwb_sorting_analyzer(file_path, t_start, sampling_frequency, electrical_series_path, unit_table_path, stream_mode, stream_cache_path, cache, storage_options, use_pynwb, group_name, compute_extra, compute_extra_params, extension_map, verbose)
   2106     analyzer_channel_ids = list(recording.get_channel_ids())
   2107 else:
-> 2108     analyzer_recording, analyzer_channel_ids = _make_placeholder_recording_from_electrodes(
   2109         sorting, electrodes_table, electrodes_indices, verbose=verbose
   2110     )
   2112 # Per-unit sparsity and channel map from the Units `electrodes` region. Each unit's `waveform_mean`
   2113 # is stored only on a subset of channels (near its peak); the region gives which channels those are.
   2114 # We use it both for the analyzer sparsity and to scatter the waveforms onto their true channel
   2115 # positions instead of stacking the sparse block densely.
   2116 sparsity, unit_local_channels = _make_sparsity_from_electrodes(
   2117     sorting, electrodes_table, electrodes_indices, analyzer_channel_ids
   2118 )

File ~/Documents/codes/spike_sorting/spikeinterface/spikeinterface/src/spikeinterface/extractors/nwbextractors.py:2200, in _make_placeholder_recording_from_electrodes(sorting, electrodes_table, electrodes_indices, verbose)
   2192 locations = np.array([electrodes_table_sliced["rel_x"][:], electrodes_table_sliced["rel_y"][:]]).T
   2194 # The recording length only needs to loosely bound the timeline for the recordingless GUI; nothing
   2195 # about curation depends on it being exact. Estimate it cheaply from the last stored spike time (one
   2196 # element, i.e. only the last spike_times chunk) rather than scanning the whole array for the true
   2197 # global maximum. spike_times is in seconds, so this is already a duration in seconds. It is only an
   2198 # approximation: because spike_times is concatenated per unit and not globally sorted, this is the
   2199 # last unit's last spike, not necessarily the latest spike overall.
-> 2200 last_spike_time = float(np.asarray(sorting._sorting_segments[0].spike_times_data[-1]))
   2201 duration = last_spike_time + 1.0
   2203 probe = Probe(si_units="um")

AttributeError: 'UnitsSelectionSortingSegment' object has no attribute 'spike_times_data'

Fixed in last couple of commits