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>

src/multi_swarm/dashboard/pages/04_aquarium.py

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+from __future__ import annotations
+
+import streamlit as st
+import streamlit.components.v1 as components
+
+from multi_swarm.dashboard.aquarium import (
+    STYLE_COLORS,
+    build_aquarium_html,
+    build_fish_dataset,
+)
+from multi_swarm.dashboard.data import (
+    evaluations_df,
+    generations_df,
+    genomes_df,
+    get_repo,
+    list_runs_df,
+)
+
+st.title("Aquarium 2D")
+st.caption(
+    "Ogni genoma è un pesce: dimensione proporzionale alla fitness, "
+    "colore per cognitive_style."
+)
+
+db_path = st.session_state.get("db_path", "./runs.db")
+repo = get_repo(db_path)
+
+runs = list_runs_df(repo)
+if runs.empty:
+    st.info("Nessuna run.")
+    st.stop()
+
+selected = st.selectbox("Run", runs["id"].tolist())
+
+gens = generations_df(repo, selected)
+gen_options: list[int | None] = [None]
+if not gens.empty:
+    gen_options.extend(sorted(gens["generation_idx"].unique().tolist()))
+
+def _fmt_gen(v: int | None) -> str:
+    return "tutte le generazioni" if v is None else f"gen {v}"
+
+# Default to latest gen if available.
+default_idx = len(gen_options) - 1 if len(gen_options) > 1 else 0
+gen_choice = st.selectbox(
+    "Generazione", gen_options, index=default_idx, format_func=_fmt_gen
+)
+
+with st.sidebar:
+    st.subheader("Aquarium controls")
+    max_fish = st.slider("Max pesci", min_value=5, max_value=100, value=30, step=1)
+    show_labels = st.toggle("Mostra ID genoma", value=False)
+    if st.button("Refresh"):
+        st.rerun()
+
+evals = evaluations_df(repo, selected)
+genomes = genomes_df(repo, selected, generation_idx=gen_choice)
+
+if evals.empty or genomes.empty:
+    st.warning("Nessun dato disponibile per questa run/generazione.")
+    st.stop()
+
+merged = evals.merge(
+    genomes, left_on="genome_id", right_on="id", how="inner", suffixes=("", "_g")
+)
+if merged.empty:
+    st.warning("Nessuna evaluation associata ai genomi della generazione scelta.")
+    st.stop()
+
+fish = build_fish_dataset(merged, max_fish=max_fish)
+st.write(f"Visualizzati {len(fish)} pesci (top per fitness).")
+
+html_str = build_aquarium_html(
+    fish, canvas_w=1000, canvas_h=600, show_labels=show_labels
+)
+components.html(html_str, height=620, scrolling=False)
+
+with st.expander("Legenda colori"):
+    legend_md = "\n".join(
+        f"- <span style='color:{color};font-weight:bold;'>&#9679;</span> "
+        f"`{style}`"
+        for style, color in STYLE_COLORS.items()
+    )
+    st.markdown(legend_md, unsafe_allow_html=True)