import importlib

import pandas as pd


def test_streamlit_app_imports():
    importlib.import_module("multi_swarm.dashboard.data")


def test_dashboard_data_helpers_signatures():
    from multi_swarm.dashboard import data

    assert hasattr(data, "list_runs_df")
    assert hasattr(data, "generations_df")
    assert hasattr(data, "evaluations_df")
    assert hasattr(data, "genomes_df")


def test_aquarium_helper_builds_html_with_click_handler():
    from multi_swarm.dashboard.aquarium import build_aquarium_html

    fish = [
        {
            "id": "abc123",
            "fitness": 0.8,
            "cognitive_style": "physicist",
            "n_trades": 30,
            "dsr": 0.7,
            "sharpe": 1.2,
            "max_dd": 0.1,
            "system_prompt": "test",
            "temperature": 0.9,
            "lookback_window": 200,
            "feature_access": ["close"],
            "model_tier": "C",
            "generation": 1,
            "parent_ids": [],
            "ancestors": [],
        }
    ]
    html = build_aquarium_html(fish, canvas_w=800, canvas_h=400)
    assert "canvas" in html
    assert "abc123" in html  # fish id present in JSON payload
    assert "addEventListener('click'" in html
    assert "fish-info-panel" in html
    assert "showFishInfo" in html
    assert "Discendenza" in html
    assert "requestAnimationFrame" in html


def test_aquarium_build_fish_dataset_legacy_path():
    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,
            },
        ]
    )
    out = build_fish_dataset(df)
    ids = {f["id"] for f in out}
    assert ids == {"low", "high"}
    high = next(f for f in out if f["id"] == "high")
    assert high["cognitive_style"] == "biologist"
    assert high["ancestors"] == []


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


def test_build_lineage_index_returns_dict_keyed_by_id():
    from multi_swarm.dashboard.aquarium import build_lineage_index

    genomes = pd.DataFrame(
        [
            {
                "id": "g1",
                "generation_idx": 0,
                "generation": 0,
                "system_prompt": "x",
                "feature_access": ["close"],
                "temperature": 0.9,
                "top_p": 0.95,
                "model_tier": "C",
                "lookback_window": 100,
                "cognitive_style": "physicist",
                "parent_ids": [],
            },
            {
                "id": "g2",
                "generation_idx": 1,
                "generation": 1,
                "system_prompt": "y",
                "feature_access": ["close", "volume"],
                "temperature": 1.0,
                "top_p": 0.95,
                "model_tier": "C",
                "lookback_window": 200,
                "cognitive_style": "biologist",
                "parent_ids": ["g1"],
            },
        ]
    )
    evals = pd.DataFrame(
        [
            {
                "genome_id": "g1",
                "fitness": 0.5,
                "dsr": 0.6,
                "sharpe": 1.2,
                "max_dd": 0.1,
                "n_trades": 30,
                "parse_error": None,
                "raw_text": "",
            },
            {
                "genome_id": "g2",
                "fitness": 0.7,
                "dsr": 0.8,
                "sharpe": 1.5,
                "max_dd": 0.05,
                "n_trades": 40,
                "parse_error": None,
                "raw_text": "",
            },
        ]
    )
    idx = build_lineage_index(genomes, evals)
    assert "g1" in idx and "g2" in idx
    assert idx["g2"]["parent_ids"] == ["g1"]
    assert idx["g2"]["fitness"] == 0.7
    assert idx["g1"]["cognitive_style"] == "physicist"
    assert idx["g2"]["feature_access"] == ["close", "volume"]


def test_trace_ancestors_walks_levels():
    from multi_swarm.dashboard.aquarium import trace_ancestors

    idx = {
        "child": {
            "id": "child",
            "parent_ids": ["p1", "p2"],
            "fitness": 0.8,
            "generation": 2,
            "cognitive_style": "physicist",
        },
        "p1": {
            "id": "p1",
            "parent_ids": ["gp1"],
            "fitness": 0.5,
            "generation": 1,
            "cognitive_style": "biologist",
        },
        "p2": {
            "id": "p2",
            "parent_ids": [],
            "fitness": 0.3,
            "generation": 1,
            "cognitive_style": "engineer",
        },
        "gp1": {
            "id": "gp1",
            "parent_ids": [],
            "fitness": 0.2,
            "generation": 0,
            "cognitive_style": "historian",
        },
    }
    levels = trace_ancestors("child", idx, max_levels=5)
    assert len(levels) == 2
    assert {a["id"] for a in levels[0]} == {"p1", "p2"}
    assert {a["id"] for a in levels[1]} == {"gp1"}


def test_trace_ancestors_handles_cycles():
    from multi_swarm.dashboard.aquarium import trace_ancestors

    # Pathological cycle: a <-> b. Should terminate cleanly.
    idx = {
        "a": {
            "id": "a",
            "parent_ids": ["b"],
            "fitness": 0.1,
            "generation": 1,
            "cognitive_style": "physicist",
        },
        "b": {
            "id": "b",
            "parent_ids": ["a"],
            "fitness": 0.2,
            "generation": 0,
            "cognitive_style": "biologist",
        },
    }
    levels = trace_ancestors("a", idx, max_levels=5)
    # a -> b at level 0; b's only parent is a, already seen -> stop.
    assert len(levels) == 1
    assert levels[0][0]["id"] == "b"


def test_trace_ancestors_no_parents_returns_empty():
    from multi_swarm.dashboard.aquarium import trace_ancestors

    idx = {
        "solo": {
            "id": "solo",
            "parent_ids": [],
            "fitness": 0.4,
            "generation": 0,
            "cognitive_style": "engineer",
        },
    }
    assert trace_ancestors("solo", idx) == []


def test_build_fish_dataset_attaches_ancestors():
    from multi_swarm.dashboard.aquarium import build_fish_dataset, build_lineage_index

    genomes = pd.DataFrame(
        [
            {
                "id": "p",
                "generation_idx": 0,
                "generation": 0,
                "system_prompt": "p",
                "feature_access": ["close"],
                "temperature": 0.8,
                "top_p": 0.9,
                "model_tier": "C",
                "lookback_window": 100,
                "cognitive_style": "physicist",
                "parent_ids": [],
            },
            {
                "id": "c",
                "generation_idx": 1,
                "generation": 1,
                "system_prompt": "c",
                "feature_access": ["close"],
                "temperature": 0.8,
                "top_p": 0.9,
                "model_tier": "C",
                "lookback_window": 120,
                "cognitive_style": "biologist",
                "parent_ids": ["p"],
            },
        ]
    )
    evals = pd.DataFrame(
        [
            {"genome_id": "p", "fitness": 0.3, "dsr": 0.0, "sharpe": 0.0,
             "max_dd": 0.0, "n_trades": 0},
            {"genome_id": "c", "fitness": 0.6, "dsr": 0.0, "sharpe": 0.0,
             "max_dd": 0.0, "n_trades": 0},
        ]
    )
    lineage = build_lineage_index(genomes, evals)

    active = genomes[genomes["generation_idx"] == 1].merge(
        evals, left_on="id", right_on="genome_id", how="left"
    )
    fish = build_fish_dataset(active, lineage)
    assert len(fish) == 1
    assert fish[0]["id"] == "c"
    assert len(fish[0]["ancestors"]) == 1
    assert fish[0]["ancestors"][0][0]["id"] == "p"