Adriano
70b8bc3d6c
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>
88 lines
2.6 KiB
Python
88 lines
2.6 KiB
Python
import importlib
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import pandas as pd
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def test_streamlit_app_imports():
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importlib.import_module("multi_swarm.dashboard.data")
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def test_dashboard_data_helpers_signatures():
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from multi_swarm.dashboard import data
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assert hasattr(data, "list_runs_df")
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assert hasattr(data, "generations_df")
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assert hasattr(data, "evaluations_df")
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assert hasattr(data, "genomes_df")
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def test_aquarium_helper_builds_html():
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from multi_swarm.dashboard.aquarium import build_aquarium_html
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fish = [
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{
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"id": "abc123",
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"fitness": 0.8,
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"cognitive_style": "physicist",
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"n_trades": 30,
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"dsr": 0.7,
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},
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{
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"id": "def456",
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"fitness": 0.0,
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"cognitive_style": "biologist",
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"n_trades": 0,
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"dsr": 0.0,
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},
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]
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html = build_aquarium_html(fish, canvas_w=800, canvas_h=400, show_labels=True)
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assert "canvas" in html
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assert "abc123" in html
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assert "physicist" in html or "4cc9f0" in html
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assert "requestAnimationFrame" in html
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def test_aquarium_build_fish_dataset_sorts_and_caps():
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from multi_swarm.dashboard.aquarium import build_fish_dataset
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df = pd.DataFrame(
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[
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{"genome_id": "low", "fitness": 0.1, "cognitive_style": "physicist",
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"n_trades": 1, "dsr": 0.0},
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{"genome_id": "high", "fitness": 0.9, "cognitive_style": "biologist",
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"n_trades": 10, "dsr": 0.5},
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{"genome_id": "mid", "fitness": 0.5, "cognitive_style": "engineer",
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"n_trades": 5, "dsr": 0.2},
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]
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)
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out = build_fish_dataset(df, max_fish=2)
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assert len(out) == 2
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assert out[0]["id"] == "high"
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assert out[1]["id"] == "mid"
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assert out[0]["cognitive_style"] == "biologist"
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def test_aquarium_build_fish_dataset_drops_nan_fitness():
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from multi_swarm.dashboard.aquarium import build_fish_dataset
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df = pd.DataFrame(
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[
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{"genome_id": "ok", "fitness": 0.4, "cognitive_style": "historian",
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"n_trades": 2, "dsr": 0.1},
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{"genome_id": "bad", "fitness": float("nan"), "cognitive_style": "ecologist",
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"n_trades": 0, "dsr": 0.0},
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]
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)
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out = build_fish_dataset(df)
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assert len(out) == 1
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assert out[0]["id"] == "ok"
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def test_aquarium_empty_input_returns_empty():
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from multi_swarm.dashboard.aquarium import build_aquarium_html, build_fish_dataset
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assert build_fish_dataset(pd.DataFrame()) == []
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html = build_aquarium_html([], canvas_w=400, canvas_h=200)
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assert "canvas" in html
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assert "Acquario vuoto" in html
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