|
|
| import sys |
| import os |
| import numpy as np |
| import networkx as nx |
| import matplotlib.pyplot as plt |
| import matplotlib.animation as animation |
|
|
| |
| sys.path.append(os.path.join(os.path.dirname(__file__), '../tests/tensor_lenia/lib')) |
|
|
| from hypergraph import Hypergraph |
| from operators import apply_asymmetric_laplacian, apply_laplacian |
|
|
| def create_maze_graph(rows=10, cols=20): |
| G = nx.grid_2d_graph(rows, cols) |
| walls = [] |
| for r in range(rows): |
| if r != 5: walls.append((r, 6)) |
| if r != 2 and r != 3: walls.append((r, 13)) |
| G.remove_nodes_from(walls) |
| return G |
|
|
| def run_collective_maze(sim_steps=800): |
| output_path = "/home/daroch/SOLITONES/EXPERIMENTOS/" |
| log_file = output_path + "exp06_collective_maze.log" |
| |
| with open(log_file, "w") as f: |
| f.write("--- LEGACY EXP 03: COLLECTIVE MAZE ---\n") |
|
|
| print("Running High-Speed Collective Maze Navigation...") |
| with open(log_file, "a") as f: f.write("Running High-Speed Collective Maze Navigation...\n") |
| |
| G_raw = create_maze_graph() |
| mapping = {node: i for i, node in enumerate(G_raw.nodes())} |
| reverse_mapping = {i: node for node, i in mapping.items()} |
| G = nx.relabel_nodes(G_raw, mapping) |
| nodes = list(G.nodes()) |
| adj = {n: set(G.neighbors(n)) for n in nodes} |
| |
| class MazeSystem: |
| def get_adjacency_list(self): return adj |
| system = MazeSystem() |
| |
| biomass = {n: 0.0 for n in nodes} |
| st_nodes = [n for n, (r, c) in reverse_mapping.items() if c < 2] |
| for n in st_nodes: biomass[n] = 8.0 |
| |
| goal_nodes = [n for n, (r, c) in reverse_mapping.items() if c > 18] |
| pheromone = {n: 0.0 for n in nodes} |
| |
| DT = 0.2 |
| history = [] |
| pos = {n: (reverse_mapping[n][1], -reverse_mapping[n][0]) for n in nodes} |
| |
| for t in range(sim_steps): |
| for n in goal_nodes: pheromone[n] = 20.0 |
| |
| lap_p = apply_laplacian(pheromone, system) |
| new_pheromone = {} |
| for n in nodes: |
| p = pheromone.get(n, 0) |
| b = biomass.get(n, 0) |
| |
| dp = (lap_p.get(n, 0) * 1.5) - (0.05 * p) + (b * 0.5) |
| new_pheromone[n] = np.clip(p + dp * DT, 0, 30.0) |
| pheromone = new_pheromone |
| |
| flow_weights = {} |
| for u in nodes: |
| pu = pheromone.get(u, 0) |
| for v in adj[u]: |
| pv = pheromone.get(v, 0) |
| |
| w = 0.05 + max(0, pv - pu) * 10.0 |
| flow_weights[(u, v)] = w |
| |
| adv_b = apply_asymmetric_laplacian(biomass, system, flow_weights) |
| new_biomass = {} |
| for n in nodes: |
| b = biomass.get(n, 0) |
| |
| g = 2.0 * np.exp(-((b - 3.0)**2) / 2.0) - 0.5 |
| db = (adv_b.get(n, 0) * 4.0) + g - (0.02 * b) |
| new_biomass[n] = np.clip(b + db * DT, 0, 15.0) |
| biomass = new_biomass |
| |
| if t % 20 == 0: |
| history.append({'biomass': biomass.copy()}) |
| cur_goal = sum(biomass[n] for n in goal_nodes) |
| if t % 200 == 0: |
| msg = f" T={t}: Progress to Goal = {cur_goal:.2f}" |
| print(msg) |
| with open(log_file, "a") as f: f.write(msg + "\n") |
|
|
| print(f"Generating Fast Animation...") |
| fig, ax = plt.subplots(figsize=(10, 5)) |
| def update(frame_idx): |
| ax.clear() |
| data = history[frame_idx] |
| local_b = data['biomass'] |
| node_colors = [local_b.get(n, 0) for n in nodes] |
| nx.draw_networkx_nodes(G, pos, node_size=40, node_color=node_colors, cmap='inferno', vmin=0, vmax=10, ax=ax) |
| nx.draw_networkx_edges(G, pos, alpha=0.1, ax=ax) |
| nx.draw_networkx_nodes(G, pos, nodelist=goal_nodes, node_size=100, edgecolors='cyan', node_color='none', ax=ax) |
| ax.set_title(f"Collective Navigation: T={frame_idx*20}") |
| ax.axis('off') |
| ani = animation.FuncAnimation(fig, update, frames=len(history), interval=100) |
| save_file = output_path + 'exp06_collective_maze.gif' |
| ani.save(save_file, writer='pillow', fps=10) |
| print(f"Saved {save_file}") |
| with open(log_file, "a") as f: f.write(f"Saved {save_file}\n") |
|
|
| if __name__ == "__main__": |
| run_collective_maze() |
|
|
