"""Tests para render_eda_pdf.

Importa el módulo directo (sys.path), igual que el resto de tests del grupo eda,
para no depender del registro en __init__.py (lo añade el orquestador al integrar).
"""

import os
import sys

sys.path.insert(0, os.path.dirname(__file__))

from render_eda_pdf import (
    render_eda_pdf,
    render_eda_pdf_relational,
    _models_pages,
    _series_pages,
    _caveats_pages,
)


class _StubPdf:
    """Captura pdf.savefig sin escribir nada — para testear builders aislados."""

    def __init__(self):
        self.figs = 0

    def savefig(self, fig):
        self.figs += 1


def _synthetic_profile() -> dict:
    """TableProfile sintético mínimo: 2 numéricas + 1 categórica + overview."""
    return {
        "table": "ventas",
        "source": "data/ventas.csv",
        "profiled_at": "2026-06-28 10:00 UTC",
        "n_rows": 1000,
        "n_cols": 3,
        "null_cell_pct": 0.02,
        "duplicate_rows": 5,
        "duplicate_pct": 0.005,
        "quality_score": 92.5,
        "type_breakdown": {"numeric": 2, "categorical": 1},
        "key_candidates": ["id"],
        "columns": [
            {
                "name": "precio",
                "inferred_type": "numeric",
                "semantic_type": "currency",
                "null_pct": 0.0,
                "distinct_count": 850,
                "unique_pct": 0.85,
                "quality_score": 95.0,
                "flags": [],
                "numeric": {
                    "min": 1.0, "max": 100.0, "median": 40.0, "mean": 42.5,
                    "std": 12.3, "p25": 30.0, "p75": 55.0, "outlier_pct": 1.2,
                    "distribution_type": "right-skewed",
                    "histogram": [
                        {"lo": 0.0, "hi": 25.0, "count": 100},
                        {"lo": 25.0, "hi": 50.0, "count": 500},
                        {"lo": 50.0, "hi": 75.0, "count": 300},
                        {"lo": 75.0, "hi": 100.0, "count": 50},
                    ],
                },
            },
            {
                "name": "unidades",
                "inferred_type": "numeric",
                "semantic_type": "integer",
                "null_pct": 0.01,
                "distinct_count": 40,
                "unique_pct": 0.04,
                "quality_score": 88.0,
                "flags": ["has_nulls"],
                "numeric": {
                    "min": 1.0, "max": 12.0, "median": 4.0, "mean": 4.8,
                    "std": 2.1, "outlier_pct": 0.0,
                    "distribution_type": "normal",
                    "histogram": [
                        {"lo": 1.0, "hi": 4.0, "count": 400},
                        {"lo": 4.0, "hi": 8.0, "count": 450},
                        {"lo": 8.0, "hi": 12.0, "count": 150},
                    ],
                },
            },
            {
                "name": "categoria",
                "inferred_type": "categorical",
                "semantic_type": "",
                "null_pct": 0.0,
                "distinct_count": 3,
                "unique_pct": 0.003,
                "quality_score": 99.0,
                "flags": [],
                "categorical": {
                    "entropy": 1.05,
                    "top": [
                        {"value": "neumaticos", "count": 500, "pct": 0.5},
                        {"value": "aceite", "count": 300, "pct": 0.3},
                        {"value": "filtros", "count": 200, "pct": 0.2},
                    ],
                },
            },
        ],
        "correlations": {
            "pairs": [
                {"a": "precio", "b": "unidades", "value": -0.42, "method": "pearson"},
            ],
        },
    }


def test_golden_genera_pdf_multipagina(tmp_path):
    """Caso real: profile completo -> PDF existe, pesa >0 y tiene varias páginas."""
    out = str(tmp_path / "eda_ventas.pdf")
    res = render_eda_pdf(_synthetic_profile(), out, title="EDA — ventas")

    assert isinstance(res, dict)
    assert set(res.keys()) == {"pdf_path", "n_pages", "note"}
    assert res["pdf_path"] == out
    assert os.path.exists(out)
    assert os.path.getsize(out) > 0
    # Cover + overview + numéricas + categóricas + calidad + correlaciones >= 5.
    assert res["n_pages"] >= 5
    # Cabecera de archivo PDF.
    with open(out, "rb") as fh:
        assert fh.read(4) == b"%PDF"


def test_edge_profile_vacio_no_revienta(tmp_path):
    """Edge: dict vacío -> 1 página garantizada, sin excepción."""
    out = str(tmp_path / "vacio.pdf")
    res = render_eda_pdf({}, out)
    assert os.path.exists(out)
    assert os.path.getsize(out) > 0
    assert res["n_pages"] >= 1
    assert res["pdf_path"] == out


def test_edge_profile_none_no_revienta(tmp_path):
    """Edge: None -> tratado como vacío, 1 página, sin excepción."""
    out = str(tmp_path / "none.pdf")
    res = render_eda_pdf(None, out)
    assert os.path.exists(out)
    assert res["n_pages"] >= 1


def test_edge_solo_numericas(tmp_path):
    """Edge: profile sólo con columnas numéricas (sin categóricas ni corr)."""
    prof = {
        "table": "t",
        "n_rows": 10,
        "n_cols": 1,
        "columns": [
            {
                "name": "x",
                "inferred_type": "numeric",
                "quality_score": 80.0,
                "numeric": {
                    "median": 2.0, "mean": 2.0,
                    "histogram": [{"lo": 0.0, "hi": 4.0, "count": 10}],
                },
            },
        ],
    }
    out = str(tmp_path / "num.pdf")
    res = render_eda_pdf(prof, out)
    assert os.path.exists(out)
    assert res["n_pages"] >= 2  # cover + numéricas al menos.


def test_forward_compat_seccion_desconocida(tmp_path):
    """Error/forward-compat: un bloque nuevo del profile se vuelca, no rompe."""
    prof = {
        "table": "t",
        "n_rows": 5,
        "columns": [],
        # Bloques que este renderer no conoce (otros agentes los añaden):
        "models": {"kmeans": {"k": 3, "silhouette": 0.55}},
        "caveats": ["muestra pequeña", "fechas como texto"],
    }
    out = str(tmp_path / "fwd.pdf")
    res = render_eda_pdf(prof, out)
    assert os.path.exists(out)
    assert res["n_pages"] >= 1
    # No se perdió ninguna sección por error.
    assert "omitida" not in res["note"]


# --------------------------------------------------------------------------- #
# H4: builders dedicados para models / series / caveats (antes caían al volcado
# genérico como str(dict) truncado). Se testean aislados con un stub de pdf.
# --------------------------------------------------------------------------- #
def _sample_models() -> dict:
    return {
        "n_numeric_cols": 3,
        "pca": {
            "n_components": 2, "n_rows_used": 1000, "n_features": 3,
            "explained_variance_ratio": [0.62, 0.21],
            "cumulative": [0.62, 0.83],
            "top_loadings": [
                {"component": 0, "feature": "precio", "loading": 0.71},
                {"component": 1, "feature": "unidades", "loading": -0.55},
            ],
        },
        "kmeans": {
            "best_k": 3, "silhouette": 0.48, "cluster_sizes": [500, 300, 200],
            "scores_by_k": [{"k": 3, "silhouette": 0.48, "inertia": 900.0}],
        },
        "outliers": {"n_outliers": 35, "outlier_pct": 3.5, "threshold": -0.51},
        "normality": {"precio": {"jarque_bera": {"p": 0.0001}, "is_normal": False}},
        "note": "",
    }


def _sample_series() -> dict:
    return {
        "precio": {
            "stationarity": {"verdict": "non_stationary"},
            "acf_pacf": {"is_autocorrelated": True},
            "stl": {"trend_strength": 0.95, "seasonal_strength": 0.10, "period": 7},
            "levels_suggested": True, "levels_kind": "returns",
        },
    }


def _sample_caveats() -> dict:
    return {
        "n": 1,
        "caveats": [
            {"id": "exploratory_nature", "topic": "naturaleza exploratoria",
             "message": "El EDA genera hipótesis, no conclusiones."},
        ],
    }


def test_models_builder_produces_pages():
    pdf = _StubPdf()
    assert _models_pages(pdf, _sample_models()) >= 1
    assert pdf.figs >= 1


def test_series_builder_produces_pages():
    pdf = _StubPdf()
    assert _series_pages(pdf, _sample_series()) >= 1
    assert pdf.figs >= 1


def test_caveats_builder_produces_pages():
    pdf = _StubPdf()
    assert _caveats_pages(pdf, _sample_caveats()) >= 1
    assert pdf.figs >= 1


def test_builders_tolerate_none_and_empty():
    pdf = _StubPdf()
    # None / vacío -> 0 páginas, sin excepción.
    assert _models_pages(pdf, None) == 0
    assert _series_pages(pdf, {}) == 0
    assert _caveats_pages(pdf, None) == 0
    assert pdf.figs == 0


def test_models_series_caveats_no_caen_al_generico(tmp_path):
    # Con builder dedicado, models/series/caveats NO se vuelcan en "Otras
    # secciones" (genérico). El profile completo se renderiza sin error.
    prof = _synthetic_profile()
    prof["models"] = _sample_models()
    prof["series"] = _sample_series()
    prof["caveats"] = _sample_caveats()
    out = str(tmp_path / "full.pdf")
    res = render_eda_pdf(prof, out)
    assert os.path.exists(out)
    assert os.path.getsize(out) > 0
    assert "omitida" not in res["note"]
    # Cover+overview+num+cat+calidad+corr + models + series + caveats.
    assert res["n_pages"] >= 8


# --------------------------------------------------------------------------- #
# H9: render_eda_pdf_relational — PDF DB-level (resumen de tablas + join graph).
# --------------------------------------------------------------------------- #
def _synthetic_db_profile() -> dict:
    return {
        "db_path": "data/shop.duckdb",
        "profiled_at": "2026-06-29 01:00 UTC",
        "n_tables": 2,
        "tables": [
            {"table": "customers", "n_rows": 4, "n_cols": 3, "quality_score": 98.0,
             "key_candidates": ["id"]},
            {"table": "orders", "n_rows": 6, "n_cols": 3, "quality_score": 95.0,
             "key_candidates": ["order_id"]},
        ],
        "fk_candidates": [
            {"from_table": "orders", "from_col": "customer_id",
             "to_table": "customers", "to_col": "id",
             "inclusion": 1.0, "cardinality": "N:1"},
        ],
        "join_graph": {"mermaid": "graph LR\n  orders --> customers"},
    }


def test_relational_golden_genera_pdf(tmp_path):
    out = str(tmp_path / "eda_db.pdf")
    res = render_eda_pdf_relational(_synthetic_db_profile(), out, title="EDA base")
    assert isinstance(res, dict)
    assert set(res.keys()) == {"pdf_path", "n_pages", "note"}
    assert res["pdf_path"] == out
    assert os.path.exists(out)
    assert os.path.getsize(out) > 0
    # cover + tablas + relaciones >= 3.
    assert res["n_pages"] >= 3
    with open(out, "rb") as fh:
        assert fh.read(4) == b"%PDF"


def test_relational_edge_vacio_no_revienta(tmp_path):
    out = str(tmp_path / "db_vacio.pdf")
    res = render_eda_pdf_relational({}, out)
    assert os.path.exists(out)
    assert res["n_pages"] >= 1


def test_relational_edge_none_no_revienta(tmp_path):
    out = str(tmp_path / "db_none.pdf")
    res = render_eda_pdf_relational(None, out)
    assert os.path.exists(out)
    assert res["n_pages"] >= 1