construct op• Data kinds: synapse_table → conn_graph
• Call: import fullseye as fs; fs.ledger.graph_from_synapses(syn: 'Any', n: 'int | None' = None, sign: 'float' = 1.0) -> 'np.ndarray' (to call the implementation directly, import conngraph; conngraph.graph_from_synapses(syn: 'Any', n: 'int | None' = None, sign: 'float' = 1.0) -> 'np.ndarray'; from the registry, opsconngraph.get("graph_from_synapses"))
Turns a synapse table (m, 3) = (pre_id, post_id, count) into an n×n weighted directed adjacency matrix.
W[pre, post] += count * sign. Rows with the same (pre, post) are summed. If n is omitted, max(id) + 1. Ids are 0-based integer values (they may be stored as float); out-of-range, non-integer and negative ids are rejected. sign is a coefficient for when you want to enter inhibitory synapses as negative (count itself is treated as an unsigned quantity).
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
• poc_fly_optomotor_steering — py -3.11 examples/poc_fly_optomotor_steering.py
• poc_microns_brain_wave — py -3.11 examples/poc_microns_brain_wave.py
conn_graph as input)graph_degree_preserving_shuffle · graph_binarize · graph_degree_table · graph_clustering_coefficient · graph_betweenness · graph_laplacian_spectrum · graph_spectral_radius · graph_components
construct)graph_degree_preserving_shuffle · graph_binarize
*Provenance: conngraph.py — CONNGRAPH operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.