meanap.pipeline.probabilistic_threshold

Probabilistic edge thresholding, port of adjM_thr_parallel.m.

Generates synthetic spike trains by circularly shifting each channel’s real spike times by a random offset, recomputes STTC on the shuffled data, and zeroes out any edge whose real STTC value doesn’t clear the tail significance cutoff across rep_num repetitions.

Not bit-reproducible against MATLAB. The shuffling is driven by a random number generator and MATLAB’s adjM_thr_parallel.m never seeds one explicitly, so even two MATLAB runs of the same recording can produce different adjMci matrices. Only the deterministic, unthresholded STTC matrix (meanap.pipeline.sttc.get_sttc()) has exact parity coverage — see python/test_pipeline_step3.py. This module is validated structurally (shuffled spike counts are conserved, thresholding only ever removes edges, etc.), not against a specific MATLAB run’s random outcome.

Functions

adjm_thr(spike_times_dict, n_channels, ...)

Compute the raw and probabilistically-thresholded STTC adjacency matrices.

circular_shift_spikes(spike_times_dict, ...)

Circularly shift each channel's spike times by an independent random offset.
meanap.pipeline.probabilistic_threshold.adjm_thr(spike_times_dict, n_channels, lag_ms, tail, fs, duration_s, rep_num, rng=None)[source]

Compute the raw and probabilistically-thresholded STTC adjacency matrices.

Returns:

  • adj_m ((n, n) raw STTC matrix (deterministic, exact MATLAB parity))

  • adj_m_ci ((n, n) thresholded matrix — edges not significant at tail) – (one-sided, upper-tail) across rep_num circular-shift surrogates are zeroed.

Parameters:

  • spike_times_dict (dict[int, ndarray])

  • n_channels (int)

  • lag_ms (float)

  • tail (float)

  • fs (float)

  • duration_s (float)

  • rep_num (int)

  • rng (Generator | None)

Return type:

tuple[ndarray, ndarray]

meanap.pipeline.probabilistic_threshold.circular_shift_spikes(spike_times_dict, n_channels, fs, duration_s, rng)[source]

Circularly shift each channel’s spike times by an independent random offset.

Port of the per-repetition synthetic-spike-train loop in adjM_thr_parallel.m (operates in frames, matching MATLAB’s spk_vec = times*fs + k; overhang wraps around num_frames).

Parameters:

  • spike_times_dict (dict[int, ndarray])

  • n_channels (int)

  • fs (float)

  • duration_s (float)

  • rng (Generator)

Return type:

dict[int, ndarray]