meanap.pipeline.step4

Step 4: network activity metrics, port of ExtractNetMet.m (see network_metrics.py for exactly which metrics are and aren’t in scope, and which are deterministic vs. dependent on a stochastic null model).

Functions

compute_network_metrics(adj_m, spike_counts, ...)

Compute deterministic network metrics for one (recording, lag) adjacency matrix.
meanap.pipeline.step4.compute_network_metrics(adj_m, spike_counts, duration_s, min_activity_level, min_nodes, exclude_edges_below_threshold=True, params=None, rng=None)[source]

Compute deterministic network metrics for one (recording, lag) adjacency matrix.

Mirrors the active-node subsetting + metric calls in ExtractNetMet.m (weighted adjM path).

Parameters:

  • adj_m (ndarray)

  • spike_counts (ndarray)

  • duration_s (float)

  • min_activity_level (float)

  • min_nodes (int)

  • exclude_edges_below_threshold (bool)

  • params (Params | None)

  • rng (Generator | None)

Return type:

dict