feat(dashboard): aquarium 2D visualization (fish per agent, size by fitness)

Nuova pagina Streamlit "Aquarium" che renderizza ogni genoma come pesce
animato su canvas HTML5: dimensione proporzionale alla fitness, colore
per cognitive_style, halo per i top-3. Helper puro-Python testabile per
costruire dataset e HTML self-contained (no CDN).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

tests/integration/test_streamlit_smoke.py

@@ -1,5 +1,7 @@
 import importlib
 
+import pandas as pd
+
 
 def test_streamlit_app_imports():
     importlib.import_module("multi_swarm.dashboard.data")
@@ -12,3 +14,74 @@ def test_dashboard_data_helpers_signatures():
     assert hasattr(data, "generations_df")
     assert hasattr(data, "evaluations_df")
     assert hasattr(data, "genomes_df")
+
+
+def test_aquarium_helper_builds_html():
+    from multi_swarm.dashboard.aquarium import build_aquarium_html
+
+    fish = [
+        {
+            "id": "abc123",
+            "fitness": 0.8,
+            "cognitive_style": "physicist",
+            "n_trades": 30,
+            "dsr": 0.7,
+        },
+        {
+            "id": "def456",
+            "fitness": 0.0,
+            "cognitive_style": "biologist",
+            "n_trades": 0,
+            "dsr": 0.0,
+        },
+    ]
+    html = build_aquarium_html(fish, canvas_w=800, canvas_h=400, show_labels=True)
+    assert "canvas" in html
+    assert "abc123" in html
+    assert "physicist" in html or "4cc9f0" in html
+    assert "requestAnimationFrame" in html
+
+
+def test_aquarium_build_fish_dataset_sorts_and_caps():
+    from multi_swarm.dashboard.aquarium import build_fish_dataset
+
+    df = pd.DataFrame(
+        [
+            {"genome_id": "low", "fitness": 0.1, "cognitive_style": "physicist",
+             "n_trades": 1, "dsr": 0.0},
+            {"genome_id": "high", "fitness": 0.9, "cognitive_style": "biologist",
+             "n_trades": 10, "dsr": 0.5},
+            {"genome_id": "mid", "fitness": 0.5, "cognitive_style": "engineer",
+             "n_trades": 5, "dsr": 0.2},
+        ]
+    )
+    out = build_fish_dataset(df, max_fish=2)
+    assert len(out) == 2
+    assert out[0]["id"] == "high"
+    assert out[1]["id"] == "mid"
+    assert out[0]["cognitive_style"] == "biologist"
+
+
+def test_aquarium_build_fish_dataset_drops_nan_fitness():
+    from multi_swarm.dashboard.aquarium import build_fish_dataset
+
+    df = pd.DataFrame(
+        [
+            {"genome_id": "ok", "fitness": 0.4, "cognitive_style": "historian",
+             "n_trades": 2, "dsr": 0.1},
+            {"genome_id": "bad", "fitness": float("nan"), "cognitive_style": "ecologist",
+             "n_trades": 0, "dsr": 0.0},
+        ]
+    )
+    out = build_fish_dataset(df)
+    assert len(out) == 1
+    assert out[0]["id"] == "ok"
+
+
+def test_aquarium_empty_input_returns_empty():
+    from multi_swarm.dashboard.aquarium import build_aquarium_html, build_fish_dataset
+
+    assert build_fish_dataset(pd.DataFrame()) == []
+    html = build_aquarium_html([], canvas_w=400, canvas_h=200)
+    assert "canvas" in html
+    assert "Acquario vuoto" in html