# Log-radial MOND scan 1D chain with **log-spaced** positions (base × scale^i). More nodes near the mass, fewer in deep space — informational density roughly constant (distance ∝ 1/μ in RRT). Goal: plot acceleration vs distance across orders of magnitude to see the MOND "kink" ($a_0$ scale). ## Implementation Entrypoint: `rst_reality_engine_test_suite.run_log_radial_scan`. Builds a log-spaced radial chain, runs the graph engine for `steps` ticks, and computes acceleration from graph-distance change at probe nodes: ```python # rst_reality_engine_test_suite.py (CLI: --run log_radial) def run_log_radial_scan(config_path=None, n_nodes=30, steps=80, ...): nodes_spec, edges_spec, _, deficit_name = build_log_radial_spec(n_nodes=n_nodes) # positions base*scale^i engine = GraphRealityEngine(W_total=1e5, initial_H=..., deficit_node_name=deficit_name, ...) for step in range(steps): log = engine.update_cycle(step=step) # graph distance from each node to deficit (via 1/μ edge weights) D = graph_distance_matrix(n_nodes, edges_ij, edge_mu_from_log(log)) dist_to_deficit.append(D[:, deficit_idx]) # acceleration = second derivative of distance at each probe → result["position"], result["acceleration"] # plot: scatter(position, acceleration), xscale log; writes log_radial_data.csv ``` **Run:** From Reality Engine directory: ```bash python rst_reality_engine_test_suite.py --run log_radial --micro # 15 nodes, 25 steps python rst_reality_engine_test_suite.py --run log_radial # long: 80 nodes, 250 steps ``` Or `--run all --micro` / `--run all` to include with other tests. **Quick:** 30 nodes, 80 steps. **Outputs:** `tests/results/log_radial_acceleration_vs_distance.png`, `log_radial_data.csv`, config `config/log_radial_mond.json`. **Interpretation:** X-axis log scale (position). Look for transition from high-SNR (Newtonian) to low-SNR (MOND) regime.