Population Recurrence

driada.recurrence.population.population_recurrence_graph(recurrence_graphs, method='joint', threshold=1.0, binarize_threshold=None)

Combine per-neuron recurrence graphs into a population graph.

Parameters:

• recurrence_graphs (list of RecurrenceGraph) – Individual recurrence graphs. All must have the same number of nodes.

• method ({'joint', 'mean'}) –

  Combination strategy: - ‘joint’: Thresholded element-wise AND (JRP). A point (i,j) is kept

  if at least threshold fraction of neurons recur there. threshold=1.0 is strict AND, threshold=0.5 is majority voting.

  • ’mean’: Average recurrence matrices. Values in [0, 1]. Optionally binarize with binarize_threshold.

• threshold (float, default=1.0) – For method=’joint’: fraction of neurons that must recur.

• binarize_threshold (float, optional) – For method=’mean’: if given, binarize the averaged matrix.

Returns:

Population-level recurrence graph (via from_adjacency).

Return type:

RecurrenceGraph

Raises:

ValueError – If graphs have different sizes or method is unknown.

driada.recurrence.population.pairwise_jaccard_sparse(matrices, trim_to_min=False)

Compute pairwise Jaccard similarity for sparse binary matrices.

Uses sparse matrix multiplication to compute all pairwise intersection counts in a single operation, avoiding O(N²) Python loops.

Parameters:

• matrices (list of scipy.sparse matrices or RecurrenceGraph objects) – Square binary adjacency matrices. RecurrenceGraph objects are accepted (uses .adj).

• trim_to_min (bool, default=False) – If True, matrices with different sizes (e.g., from per-neuron embeddings with different tau/dim) are trimmed to the smallest common size before comparison. If False (default), different sizes raise ValueError.

Returns:

Symmetric matrix of Jaccard indices. Diagonal is 1.0 for non-empty matrices, 0.0 for empty ones.

Return type:

ndarray of shape (N, N)

Raises:

ValueError – If list is empty, or if matrices have different shapes and trim_to_min is False.