Add "3D Auto-correlograms" (3D ACGs) postprocessing extension #3860
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| from spikeinterface.core.waveforms_extractor_backwards_compatibility import MockWaveformExtractor | ||
| from joblib import Parallel, delayed |
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@fededagos joblib is not a requirement. Instead, we normally use the built-in ProcessPoolExecutor
Is it ok if I push to this PR directly to refactor the parallelization?
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Done :) You can check the changes if you're interested!
…into feature/3d_acg
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A couple initial comments. I guess one other approach would be to try to numba this instead for a speed gain since we already use numba for the standard correlograms. I saw that you pushed changes Alessio while I was typing this so if you changed something I marked feel free to ignore.
| n_jobs : int, default: -1. | ||
| Number of parallel jobs to spawn to compute the 3D-ACGS on different units. |
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| n_jobs : int, default: -1. | |
| Number of parallel jobs to spawn to compute the 3D-ACGS on different units. |
If we want the job kwargs we need to do the {} trick and pull the shared kwarg docs. We can help with that.
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| # pre-compute time bins | ||
| winsize_bins = 2 * int(0.5 * window_ms * 1.0 / bin_ms) + 1 | ||
| bin_times_ms = np.linspace(-window_ms / 2, window_ms / 2, num=winsize_bins) |
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| bin_times_ms = np.linspace(-window_ms / 2, window_ms / 2, num=winsize_bins) | |
| bin_times_ms = np.linspace(-window_ms / 2, window_ms / 2, num=winsize_bins) |
should we automatically make that the user can't specify a binsize that is inappropriate. So if I give a bin_size of like 0.0003 ms then most recording equipment don't have that resolution.
| acgs_3d[unit_index, :, :] = acg_3d | ||
| firing_quantiles[unit_index, :] = firing_quantile | ||
| else: | ||
| # Process units in serial |
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| # Process units in serial | |
| # Process units serially |
:)
| assert winsize_bins >= 1 | ||
| assert winsize_bins % 2 == 1 |
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We should have some error output for this case for ease of debugging in the future.
| try: | ||
| import npyx | ||
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| HAVE_NPYX = True | ||
| except ModuleNotFoundError: | ||
| HAVE_NPYX = False | ||
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importlib.util.find_spec
But I haven't fixed testing yet so I can go back and fix that later to try to gain a tiny speed boost in testing.
Following conversations at Cosyne 2025 with @alejoe91, here is a first potential implementation of the "3D autocorrelograms" as an analyzer postprocessing extension.
First introduced in Beau et al., 2025, 3D ACGs provide a rich representation of a neuron's firing rate which accounts for different firing modes.
They were also recently used by Yu et al., (2025) again as a powerful representation of firing rates that can be used e.g. by representation learning methods, the same way that Beau et al. did.
The implementation is adapted directly from NeuroPyxels (mainly maintained by @m-beau), which contains the original Python version.
The original conceptualization is from @herzfeldd, who I am also tagging here for visibility.