from __future__ import annotations

import json
from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd
import pytest


def _write_dummy_png(path: Path) -> Path:
    path.parent.mkdir(parents=True, exist_ok=True)
    fig, ax = plt.subplots(figsize=(1.5, 1.0))
    ax.plot([0, 1], [0, 1])
    fig.savefig(path, dpi=100)
    plt.close(fig)
    return path


def test_run_appendix_artifacts_writes_bundle(tmp_path: Path) -> None:
    from sepsis_mcp.neurips_appendix_artifacts import run_appendix_artifacts

    score_dir = tmp_path / "score"
    eta_dir = tmp_path / "eta"
    calsize_dir = tmp_path / "calsize"
    model_swap_dir = tmp_path / "swap"
    stress_dir = tmp_path / "stress"
    output_dir = tmp_path / "artifacts"

    score_dir.mkdir()
    eta_dir.mkdir()
    calsize_dir.mkdir()
    model_swap_dir.mkdir()
    stress_dir.mkdir()

    pd.DataFrame(
        [
            {"dataset": "gossis", "model_type": "xgboost", "seed": 0, "split_kind": "calibration", "group": 0, "group_label": "low", "score": 0.1},
            {"dataset": "gossis", "model_type": "xgboost", "seed": 0, "split_kind": "calibration", "group": 1, "group_label": "high", "score": 0.8},
            {"dataset": "gossis", "model_type": "xgboost", "seed": 0, "split_kind": "test", "group": 0, "group_label": "low", "score": 0.2},
            {"dataset": "gossis", "model_type": "xgboost", "seed": 0, "split_kind": "test", "group": 1, "group_label": "high", "score": 0.9},
        ]
    ).to_csv(score_dir / "score_records.csv", index=False)
    pd.DataFrame(
        [
            {
                "model_type": "xgboost",
                "group_label": "low",
                "mean_eta": 0.20,
                "std_eta": 0.01,
                "mean_delta": 0.05,
                "std_delta": 0.01,
                "mean_cost": 0.02,
                "advantage_rate": 1.0,
            }
        ]
    ).to_csv(eta_dir / "eta_aggregate.csv", index=False)
    pd.DataFrame(
        [
            {"cal_size": 250, "cal_size_label": "250", "method": "standard", "coverage_mean": 0.88, "max_gap_mean": 0.06, "set_size_mean": 0.92},
            {"cal_size": 250, "cal_size_label": "250", "method": "missingness_aware", "coverage_mean": 0.90, "max_gap_mean": 0.03, "set_size_mean": 0.94},
        ]
    ).to_csv(calsize_dir / "calsize_summary.csv", index=False)
    _write_dummy_png(calsize_dir / "calsize_panels.png")
    pd.DataFrame(
        [
            {
                "calibrate_model": "logistic_regression",
                "deploy_model": "xgboost",
                "grouping": "coverage_gap_variable",
                "mean_gap": 0.04,
                "mean_coverage": 0.90,
                "mean_test_group_overlap_vs_self": 1.0,
                "mean_coverage_drop_vs_self": 0.01,
                "mean_gap_increase_vs_self": 0.00,
            },
            {
                "calibrate_model": "xgboost",
                "deploy_model": "xgboost",
                "grouping": "coverage_gap_variable",
                "mean_gap": 0.02,
                "mean_coverage": 0.91,
                "mean_test_group_overlap_vs_self": 1.0,
                "mean_coverage_drop_vs_self": 0.00,
                "mean_gap_increase_vs_self": 0.00,
            },
        ]
    ).to_csv(model_swap_dir / "swap_matrix.csv", index=False)
    pd.DataFrame(
        [
            {
                "comparison": "coverage_gap_variable_vs_predicted_risk_tercile_frozen",
                "win_grouping": "coverage_gap_variable",
                "win_count": 3,
                "mean_gap_difference": 0.02,
            }
        ]
    ).to_csv(model_swap_dir / "swap_win_summary.csv", index=False)
    pd.DataFrame(
        [
            {"perturbation_type": "mar_dropout", "perturbation_strength": 0.0, "drop_rate": 0.0, "method": "standard", "mean_gap": 0.03, "std_gap": 0.0, "run_count": 1},
            {"perturbation_type": "mar_dropout", "perturbation_strength": 0.3, "drop_rate": 0.3, "method": "standard", "mean_gap": 0.06, "std_gap": 0.0, "run_count": 1},
            {"perturbation_type": "mar_dropout", "perturbation_strength": 0.0, "drop_rate": 0.0, "method": "missingness_aware", "mean_gap": 0.02, "std_gap": 0.0, "run_count": 1},
            {"perturbation_type": "mar_dropout", "perturbation_strength": 0.3, "drop_rate": 0.3, "method": "missingness_aware", "mean_gap": 0.03, "std_gap": 0.0, "run_count": 1},
        ]
    ).to_csv(stress_dir / "stress_aggregate.csv", index=False)

    paths = run_appendix_artifacts(
        score_dir=score_dir,
        eta_dir=eta_dir,
        calsize_dir=calsize_dir,
        model_swap_dir=model_swap_dir,
        stress_dir=stress_dir,
        output_dir=output_dir,
    )

    assert Path(paths["score_figure"]).exists()
    assert Path(paths["eta_figure"]).exists()
    assert Path(paths["swap_figure"]).exists()
    assert Path(paths["stress_figure"]).exists()

    manifest = json.loads((output_dir / "manifest.json").read_text(encoding="utf-8"))
    assert "score_figure" in manifest
    assert "provenance" in manifest
    assert (output_dir / "source_tables" / "score_records.csv").exists()
    assert (output_dir / "source_tables" / "appendix_eta_focus_table.csv").exists()
    eta_focus = pd.read_csv(output_dir / "source_tables" / "appendix_eta_focus_table.csv")
    assert "mean_eta_minus_cost" in eta_focus.columns
    assert "mean_eta_minus_delta" not in eta_focus.columns
    assert eta_focus.loc[0, "mean_eta_minus_cost"] == pytest.approx(0.18)
    assert (output_dir / "appendix" / "calsize_panels.png").exists()

    provenance = json.loads((output_dir / "provenance.json").read_text(encoding="utf-8"))
    assert "git" in provenance
    assert "inputs" in provenance
    assert "artifacts" in provenance
    assert "appendix_score_ecdfs.png" in provenance["artifacts"]
    score_info = provenance["artifacts"]["appendix_score_ecdfs.png"]
    assert score_info["derived_from"] == ["score_records.csv"]
    assert (output_dir / "TRACEABILITY.md").exists()