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corr.py

Code adapted from International Brain Laboratory, T. (2021). ibllib [Computer software]. https://github.com/int-brain-lab/ibllib

xcorr(spike_times, spike_clusters, bin_size, window_size)

Compute all pairwise cross-correlograms among the clusters appearing in spike_clusters.

Parameters:

Name Type Description Default
spike_times ndarray

Spike times in seconds.

 required
spike_clusters ndarray

Spike-cluster mapping.

 required
bin_size float

Size of the time bin in seconds.

 required
window_size int

Size of the correlogram window in seconds.

 required

Returns:

Type Description
ndarray

np.ndarray: cross-correlogram array
Source code in element_array_ephys/plotting/corr.py 
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def xcorr(
    spike_times: np.ndarray,
    spike_clusters: np.ndarray,
    bin_size: float,
    window_size: int,
) -> np.ndarray:
    """Compute all pairwise cross-correlograms among the clusters appearing in `spike_clusters`.

    Args:
        spike_times (np.ndarray): Spike times in seconds.
        spike_clusters (np.ndarray): Spike-cluster mapping.
        bin_size (float): Size of the time bin in seconds.
        window_size (int): Size of the correlogram window in seconds.

    Returns:
         np.ndarray: cross-correlogram array
    """
    assert np.all(np.diff(spike_times) >= 0), "The spike times must be increasing."
    assert spike_times.ndim == 1
    assert spike_times.shape == spike_clusters.shape

    # Find `binsize`.
    bin_size = np.clip(bin_size, 1e-5, 1e5)  # in seconds

    # Find `winsize_bins`.
    window_size = np.clip(window_size, 1e-5, 1e5)  # in seconds
    winsize_bins = 2 * int(0.5 * window_size / bin_size) + 1

    # Take the cluster order into account.
    clusters = np.unique(spike_clusters)
    n_clusters = len(clusters)

    # Like spike_clusters, but with 0..n_clusters-1 indices.
    spike_clusters_i = _index_of(spike_clusters, clusters)

    # Shift between the two copies of the spike trains.
    shift = 1

    # At a given shift, the mask precises which spikes have matching spikes
    # within the correlogram time window.
    mask = np.ones_like(spike_times, dtype=bool)

    correlograms = _create_correlograms_array(n_clusters, winsize_bins)

    # The loop continues as long as there is at least one spike with
    # a matching spike.
    while mask[:-shift].any():
        # Interval between spike i and spike i+shift.
        spike_diff = _diff_shifted(spike_times, shift)

        # Binarize the delays between spike i and spike i+shift.
        spike_diff_b = np.round(spike_diff / bin_size).astype(np.int64)

        # Spikes with no matching spikes are masked.
        mask[:-shift][spike_diff_b > (winsize_bins / 2)] = False

        # Cache the masked spike delays.
        m = mask[:-shift].copy()
        d = spike_diff_b[m]

        # Find the indices in the raveled correlograms array that need
        # to be incremented, taking into account the spike clusters.
        indices = np.ravel_multi_index(
            (spike_clusters_i[:-shift][m], spike_clusters_i[+shift:][m], d),
            correlograms.shape,
        )

        # Increment the matching spikes in the correlograms array.
        _increment(correlograms.ravel(), indices)

        shift += 1

    return _symmetrize_correlograms(correlograms)

acorr(spike_times, bin_size, window_size)

Compute the auto-correlogram of a unit.

Parameters:

Name Type Description Default
spike_times ndarray

Spike times in seconds.

 required
bin_size float

Size of the time bin in seconds.

 required
window_size int

Size of the correlogram window in seconds.

 required

Returns:

Type Description
ndarray

np.ndarray: auto-correlogram array (winsize_samples,)
Source code in element_array_ephys/plotting/corr.py 
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def acorr(spike_times: np.ndarray, bin_size: float, window_size: int) -> np.ndarray:
    """Compute the auto-correlogram of a unit.

    Args:
        spike_times (np.ndarray): Spike times in seconds.
        bin_size (float, optional): Size of the time bin in seconds.
        window_size (int, optional): Size of the correlogram window in seconds.

    Returns:
        np.ndarray: auto-correlogram array (winsize_samples,)
    """
    xc = xcorr(
        spike_times,
        np.zeros_like(spike_times, dtype=np.int32),
        bin_size=bin_size,
        window_size=window_size,
    )
    return xc[0, 0, :]