Adriano
3688611a40
Rimuove sidebar acquario (slider max-pesci, toggle label): la dimensione popolazione è già definita dal GA, le label sono ridondanti col pannello di ispezione. Mostra tutti i pesci della generazione selezionata. Aggiunge `build_lineage_index` (mappa ogni genome_id della run ai suoi attributi) e `trace_ancestors` (BFS sui parent_ids fino a max_levels, guardia su cicli). `build_fish_dataset` accetta ora il lineage_index e allega il campo `ancestors` ad ogni pesce; conserva la firma legacy per compat con i fixture di test esistenti. `build_aquarium_html` perde `show_labels`. Embedda click handler con hit-test in canvas pixel space (account per CSS scaling) + pannello info top-right con stile, fitness/DSR/Sharpe/maxDD/trades, prompt e albero discendenza colorato per cognitive_style. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
88 lines
2.4 KiB
Python
88 lines
2.4 KiB
Python
from __future__ import annotations
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import streamlit as st
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import streamlit.components.v1 as components
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from multi_swarm.dashboard.aquarium import (
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STYLE_COLORS,
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build_aquarium_html,
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build_fish_dataset,
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build_lineage_index,
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)
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from multi_swarm.dashboard.data import (
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evaluations_df,
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genomes_df,
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get_repo,
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list_runs_df,
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)
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st.title("Aquarium 2D")
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st.caption(
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"Pesci colorati per stile cognitivo, dimensione proporzionale a fitness. "
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"Click su un pesce per dettaglio + discendenza."
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)
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db_path = st.session_state.get("db_path", "./runs.db")
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repo = get_repo(db_path)
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runs = list_runs_df(repo)
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if runs.empty:
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st.info("Nessuna run nel database.")
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st.stop()
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selected_run = st.selectbox("Run", runs["id"].tolist())
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# Fetch ALL genomes of the run (no gen filter): needed to build the lineage
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# index across generations. The active set is filtered afterwards.
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all_genomes = genomes_df(repo, selected_run)
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all_evals = evaluations_df(repo, selected_run)
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if all_genomes.empty:
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st.warning("Nessun genoma per questa run.")
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st.stop()
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available_gens = sorted(all_genomes["generation_idx"].unique().tolist())
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selected_gen = st.selectbox(
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"Generazione",
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available_gens,
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index=len(available_gens) - 1, # default ultima
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)
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active_genomes = all_genomes[all_genomes["generation_idx"] == selected_gen]
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active_evals = (
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all_evals[all_evals["genome_id"].isin(active_genomes["id"])]
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if not all_evals.empty
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else all_evals
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)
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if not active_evals.empty:
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active_merged = active_genomes.merge(
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active_evals,
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left_on="id",
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right_on="genome_id",
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how="left",
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suffixes=("", "_eval"),
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)
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else:
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active_merged = active_genomes.copy()
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active_merged["genome_id"] = active_merged["id"]
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lineage = build_lineage_index(all_genomes, all_evals)
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fish = build_fish_dataset(active_merged, lineage, max_lineage_levels=5)
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if not fish:
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st.warning("Nessun agente attivo in questa generazione.")
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st.stop()
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st.caption(f"{len(fish)} agenti in generazione {selected_gen}")
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html_str = build_aquarium_html(fish, canvas_w=1000, canvas_h=600)
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components.html(html_str, height=620, scrolling=False)
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with st.expander("Legenda colori"):
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legend_md = "\n".join(
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f"- <span style='color:{color};font-weight:bold;'>●</span> "
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f"`{style}`"
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for style, color in STYLE_COLORS.items()
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)
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st.markdown(legend_md, unsafe_allow_html=True)
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