"""根因候选分析。"""

import pandas as pd


EXTENDED_ROOT_CAUSE_DIMENSIONS = [
    "lam_fixture_id",
    "lam_jig_id",
    "lam_nozzle_id",
    "material_lot_oca",
    "material_lot_glass",
    "material_lot_polarizer",
    "clean_equipment_id",
    "clean_slot_id",
    "bond_equipment_id",
    "bond_head_id",
    "recipe_id",
]


def build_extended_root_causes(df, dimensions=None):
    """按治具、吸嘴、材料批次等行业维度生成扩展根因候选。"""
    dimensions = EXTENDED_ROOT_CAUSE_DIMENSIONS if dimensions is None else dimensions
    total_defects = max(len(df), 1)
    rows = []
    for dimension in dimensions:
        if dimension not in df.columns:
            continue
        series = df[dimension].fillna("").astype(str)
        valid = df[series != ""].copy()
        if valid.empty:
            continue
        counts = valid.groupby(dimension).agg(
            缺陷数=("defect_id", "count"),
            涉及面板=("panel_id", "nunique"),
            主要缺陷=("defect_type", lambda s: s.mode().iloc[0] if not s.mode().empty else "-"),
            严重数=("severity", lambda s: int((s == "严重").sum())),
        ).reset_index()
        expected = len(valid) / max(valid[dimension].nunique(), 1)
        counts["维度"] = dimension
        counts["候选值"] = counts[dimension].astype(str)
        counts["占比"] = counts["缺陷数"] / total_defects
        counts["严重占比"] = counts["严重数"] / counts["缺陷数"].clip(lower=1)
        counts["异常倍数"] = (counts["缺陷数"] / max(expected, 0.001)).round(2)
        count_score = counts["缺陷数"] / counts["缺陷数"].max()
        lift_score = (counts["异常倍数"] / 3).clip(upper=1)
        counts["风险分"] = (count_score * 55 + lift_score * 30 + counts["严重占比"] * 15).round(1)
        rows.append(
            counts[["维度", "候选值", "缺陷数", "占比", "异常倍数", "涉及面板", "主要缺陷", "严重占比", "风险分"]]
        )

    if not rows:
        return pd.DataFrame(
            columns=["维度", "候选值", "缺陷数", "占比", "异常倍数", "涉及面板", "主要缺陷", "严重占比", "风险分"]
        )

    return (
        pd.concat(rows, ignore_index=True)
        .sort_values(["风险分", "缺陷数"], ascending=False)
        .head(12)
        .reset_index(drop=True)
    )