activity op• Data kinds: points × matrix → rgbvideo
• Call: import fullseye as fs; fs.ledger.points_activity_video(P: 'Any', X: 'Any', colors: 'Any' = None, size: 'int' = 480, aspect: 'float' = 0.75, pitch: 'float' = 15.0, yaw_start: 'float' = 0.0, yaw_span: 'float' = 360.0, substeps: 'int' = 1, point_px: 'int' = 2, gain: 'float' = 100.0, background: 'Any' = None, views: 'Any' = None) -> 'np.ndarray' (to call the implementation directly, import conngraph; conngraph.points_activity_video(P: 'Any', X: 'Any', colors: 'Any' = None, size: 'int' = 480, aspect: 'float' = 0.75, pitch: 'float' = 15.0, yaw_start: 'float' = 0.0, yaw_span: 'float' = 360.0, substeps: 'int' = 1, point_px: 'int' = 2, gain: 'float' = 100.0, background: 'Any' = None, views: 'Any' = None) -> 'np.ndarray'; from the registry, opsconngraph.get("points_activity_video"))
A color video (F, H, W, 3) that puts the activity onto the point cloud and rotates it, values in [0, 1]. F = T × substeps, H = size, W = size × aspect.
Passing a sequence of (yaw, pitch) (degrees) in views places the frames seen from those directions side by side without rotating (W = number of views × size × aspect + 4 px gaps; yaw_start / yaw_span are not used). For comparing the same instant from several directions (e.g. dorsal (0, 0) / lateral (90, 0) / axial (0, 90)).
P = (n, 3) coordinates, X = (T, n) state sequence (reservoir_states). Frame k orthographically projects the state at time k / substeps (linear interpolation between adjacent steps) with a camera at yaw = yaw_start + yaw_span × k / F. Brightness b = log1p(gain · |x| / max|X|) / log1p(gain) -- 1 scale across all frames (stretching per frame would make things that do not move flicker). Node colour = colors ((n, 3), [0, 1], white if None) × (0.2 + 0.8 b); nodes with b > 0.5 are drawn one size larger and brighter. background lays an (m, 3) point cloud down first in light grey (e.g. putting selected nodes on top of all somata of the brain). The field of view is set by the box enclosing both P and background. The output is limited to F×H×W×3 ≤ MAX_VIDEO_ELEMENTS (2^27).
• 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_connectome_motor_bottleneck — py -3.11 examples/poc_connectome_motor_bottleneck.py
• poc_em_branch_territory — py -3.11 examples/poc_em_branch_territory.py
• poc_em_second_opinion — py -3.11 examples/poc_em_second_opinion.py
• poc_eye_to_brain — py -3.11 examples/poc_eye_to_brain.py
• poc_malecns_activity_wave — py -3.11 examples/poc_malecns_activity_wave.py
• poc_microns_brain_wave — py -3.11 examples/poc_microns_brain_wave.py
rgbvideo as input)—
activity)graph_activation_latency · graph_activity_spread
*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.