| import torch |
| import kernels |
|
|
| physarum = kernels.get_kernel("phanerozoic/physarum", version=1, trust_remote_code=True) |
|
|
|
|
| def test_network_develops(): |
| sim = physarum.Physarum(width=256, height=256, agents=20000, seed=1) |
| sim.step(120) |
| t = sim.trail |
| assert torch.isfinite(t).all() |
| assert float(t.max()) > 0.0 |
| |
| assert float(t.std()) > 0.4 * float(t.mean()) |
|
|
|
|
| def test_image_shape(): |
| sim = physarum.Physarum(width=128, height=128, agents=5000, seed=2) |
| sim.step(60) |
| img = sim.image() |
| assert tuple(img.shape) == (128, 128, 3) |
| assert img.dtype == torch.uint8 |
|
|
|
|
| def test_deterministic(): |
| a = physarum.Physarum(width=128, height=128, agents=5000, seed=7); a.step(40) |
| b = physarum.Physarum(width=128, height=128, agents=5000, seed=7); b.step(40) |
| assert torch.equal(a.trail, b.trail) |
|
|
|
|
| def _bfs_len(net, a, b): |
| import collections |
| H, W = net.shape |
| seen = torch.zeros(H, W, dtype=torch.bool); seen[a[1], a[0]] = True |
| q = collections.deque([(a[0], a[1], 0)]) |
| while q: |
| x, y, d = q.popleft() |
| if (x, y) == b: |
| return d |
| for dx, dy in ((1, 0), (-1, 0), (0, 1), (0, -1)): |
| nx, ny = x + dx, y + dy |
| if 0 <= nx < W and 0 <= ny < H and bool(net[ny, nx]) and not seen[ny, nx]: |
| seen[ny, nx] = True; q.append((nx, ny, d + 1)) |
| return None |
|
|
|
|
| def test_flow_solves_maze(): |
| mk = physarum.maze(65, 65, seed=3) |
| src, goal = (2, 2), (62, 62) |
| f = physarum.PhysarumFlow(mk).solve([src, goal], [1, -1], iters=150, cg_iters=100) |
| path = f.path(src, goal) |
| assert path is not None |
| assert len(path) - 1 == _bfs_len(mk.bool(), src, goal) |
|
|
|
|
| def test_flow_network_connects(): |
| open_mask = torch.ones(80, 80); open_mask[0] = 0; open_mask[-1] = 0 |
| open_mask[:, 0] = 0; open_mask[:, -1] = 0 |
| terms = [(40, 40), (12, 12), (68, 12), (12, 68), (68, 68)] |
| f = physarum.PhysarumFlow(open_mask).solve(terms, [4, -1, -1, -1, -1], iters=200) |
| assert all(f.path(terms[0], t) is not None for t in terms[1:]) |
|
|
