defect-analysis/app.py

"""
缺陷集中性分析 - Streamlit 交互式可视化页面
"""

import pandas as pd
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
import seaborn as sns
import streamlit as st
import plotly.express as px
import plotly.graph_objects as go
import os
from datetime import datetime
from sklearn.cluster import DBSCAN
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from defect_analysis.data_quality import build_data_quality_report
from app_utils import (
    apply_defect_filters,
    build_diagnostic_dashboard,
    build_ml_factor_insights,
    calculate_kpis,
    calculate_spc_metrics,
    generate_industry_diagnosis,
    get_missing_required_columns,
    normalize_defect_schema,
    TEMPLATE_COLUMNS,
)

# --- 中文字体设置 ---
def setup_chinese_font():
    """设置中文字体"""
    font_paths = [
        r"C:\Windows\Fonts\msyh.ttc",       # 微软雅黑
        r"C:\Windows\Fonts\simhei.ttf",      # 黑体
        r"C:\Windows\Fonts\simsun.ttc",      # 宋体
        r"C:\Windows\Fonts\malgun.ttf",      # Malgun Gothic
    ]
    for fp in font_paths:
        if os.path.exists(fp):
            font_prop = fm.FontProperties(fname=fp)
            plt.rcParams["font.family"] = font_prop.get_name()
            plt.rcParams["axes.unicode_minus"] = False
            return font_prop
    # fallback
    plt.rcParams["font.sans-serif"] = ["SimHei", "Microsoft YaHei", "Arial Unicode MS"]
    plt.rcParams["axes.unicode_minus"] = False
    return None

setup_chinese_font()

# --- 页面配置 ---
st.set_page_config(
    page_title="屏幕缺陷集中性分析",
    page_icon="🔍",
    layout="wide",
    initial_sidebar_state="expanded"
)

# --- 侧边栏 ---
st.sidebar.title("🔍 筛选条件")

# --- 数据源切换 ---
st.sidebar.divider()
st.sidebar.subheader("📂 数据源")
data_source = st.sidebar.radio("选择数据源", ["内置模拟数据", "上传CSV文件"], label_visibility="collapsed")

uploaded_df = None
if data_source == "上传CSV文件":
    uploaded_file = st.sidebar.file_uploader("上传CSV文件", type=["csv"], accept_multiple_files=False)
    if uploaded_file is not None:
        try:
            uploaded_df = pd.read_csv(uploaded_file, parse_dates=["timestamp"])
            uploaded_df["timestamp"] = pd.to_datetime(uploaded_df["timestamp"])
            missing = get_missing_required_columns(uploaded_df)
            if missing:
                st.sidebar.error(f"缺少字段: {', '.join(missing)}")
                uploaded_df = None
            else:
                uploaded_df = normalize_defect_schema(uploaded_df)
                st.sidebar.success(f"已加载 {len(uploaded_df)} 条记录")
                st.sidebar.caption("已自动补齐缺陷几何、多工序机台、治具和材料批次等可选行业字段")
                # 下载模板
                template_df = pd.DataFrame(columns=TEMPLATE_COLUMNS)
                csv_template = template_df.to_csv(index=False, encoding="utf-8-sig")
                st.sidebar.download_button(
                    label="📋 下载数据格式模板",
                    data=csv_template,
                    file_name="defect_data_template.csv",
                    mime="text/csv"
                )
        except Exception as e:
            st.sidebar.error(f"CSV解析失败: {e}")
            uploaded_df = None
    else:
        st.sidebar.info("请选择一个CSV文件上传")

# --- 加载数据 ---
@st.cache_data(ttl=300)
def load_data_from_csv():
    """加载内置模拟数据"""
    if not os.path.exists("defect_data.csv"):
        st.error("未找到 defect_data.csv，请先运行 generate_data.py 生成数据")
        return None
    df = pd.read_csv("defect_data.csv", parse_dates=["timestamp"])
    return normalize_defect_schema(df)


@st.cache_data(ttl=300, show_spinner=False)
def build_cached_ml_factor_insights(data, target_defect_type, model_name, top_n):
    """缓存 ML 训练洞察，避免页面交互时重复训练。"""
    return build_ml_factor_insights(
        data,
        target_defect_type=target_defect_type,
        model_name=model_name,
        top_n=top_n,
    )

if data_source == "上传CSV文件" and uploaded_df is not None:
    df = uploaded_df
else:
    df = load_data_from_csv()
if df is None:
    st.stop()

# --- 角色视图 ---
st.sidebar.divider()
st.sidebar.subheader("👤 视图模式")
view_mode = st.sidebar.selectbox(
    "选择视图模式",
    options=["操作员", "工程师", "管理者"],
    index=1,
    help="操作员: 基础分析 | 工程师: 全部功能 | 管理者: KPI+SPC+健康评分"
)

# 各角色可见的 Tab
tab_visibility = {
    "操作员": {
        "tabs": ["🗺️ 空间集中性", "📊 类型集中性 (帕累托)", "📈 时间集中性",
                 "🏗️ 设备座号集中性", "🔬 缺陷模式识别", "🧭 诊断驾驶舱"],
        "show_kpi": True,
        "show_export": True,
    },
    "工程师": {
        "tabs": "all",
        "show_kpi": True,
        "show_export": True,
    },
    "管理者": {
        "tabs": ["🚨 SPC 控制图与预警", "🔬 缺陷模式识别", "💚 设备健康与共性分析",
                 "📊 类型集中性 (帕累托)", "📈 时间集中性", "🧭 诊断驾驶舱"],
        "show_kpi": True,
        "show_export": True,
    },
}

# 应用 Tab 可见性
current_config = tab_visibility[view_mode]

# --- 筛选条件 ---
# 日期范围
min_date = df["timestamp"].min().date()
max_date = df["timestamp"].max().date()
date_range = st.sidebar.date_input(
    "日期范围",
    value=[min_date, max_date],
    min_value=min_date,
    max_value=max_date
)

if len(date_range) == 2:
    start_date, end_date = pd.Timestamp(date_range[0]), pd.Timestamp(date_range[1])
else:
    start_date, end_date = pd.Timestamp(min_date), pd.Timestamp(max_date)

# 缺陷类型
all_types = sorted(df["defect_type"].unique())
selected_types = st.sidebar.multiselect("缺陷类型", options=all_types, default=all_types)

# 班次
shift_options = ["全部", "白班", "夜班"]
selected_shift = st.sidebar.radio("班次", options=shift_options)

# 批次
all_batches = sorted(df["batch_id"].unique())
selected_batches = st.sidebar.multiselect("批次", options=all_batches, default=all_batches)

# 严重程度
all_severities = ["全部", "轻微", "中等", "严重"]
selected_severity = st.sidebar.selectbox("严重程度", options=all_severities)

# 设备
all_equipment = sorted(df["equipment_id"].unique())
selected_equipment = st.sidebar.multiselect("前贴附设备", options=all_equipment, default=all_equipment)

# 座号（随设备联动）
if selected_equipment:
    eq_seats = sorted(df[df["equipment_id"].isin(selected_equipment)]["seat_id"].unique())
    selected_seats = st.sidebar.multiselect("座号", options=eq_seats, default=eq_seats)
else:
    selected_seats = []

filtered_df = apply_defect_filters(
    df,
    start_date=start_date,
    end_date=end_date,
    selected_types=selected_types,
    selected_batches=selected_batches,
    selected_equipment=selected_equipment,
    selected_seats=selected_seats,
    selected_shift=selected_shift,
    selected_severity=selected_severity,
)

# ========== KPI 看板 ==========
kpis = calculate_kpis(df, filtered_df)
total_panels_inspected = kpis["total_panels_inspected"]
defective_panels = kpis["defective_panels"]
yield_rate = kpis["yield_rate"]
total_defects = kpis["total_defects"]
critical_defects = kpis["critical_defects"]
top_defect_type = kpis["top_defect_type"]

kpi1, kpi2, kpi3, kpi4, kpi5, kpi6 = st.columns(6)
kpi1.metric("检测面板数", f"{total_panels_inspected} 块")
kpi2.metric("不良面板数", f"{defective_panels} 块", delta=f"{defective_panels/total_panels_inspected*100:.1f}%" if total_panels_inspected > 0 else "0%")
kpi3.metric("综合良率", f"{yield_rate:.1f}%", delta=f"{yield_rate - 95:.1f}%", delta_color="normal" if yield_rate >= 95 else "inverse")
kpi4.metric("缺陷总数", f"{total_defects} 个")
kpi5.metric("严重缺陷", f"{critical_defects} 个", delta=f"{critical_defects/max(total_defects,1)*100:.1f}%" if total_defects > 0 else "0%")
kpi6.metric("主要缺陷类型", top_defect_type)

# 第二排 KPI
eq_concentrated = False
if "equipment_id" in filtered_df.columns:
    eq_stats = filtered_df.groupby("equipment_id").size()
    top_eq = eq_stats.idxmax() if len(eq_stats) > 0 else "-"
    top_eq_count = eq_stats.max() if len(eq_stats) > 0 else 0
else:
    top_eq, top_eq_count = "-", 0

seat_concentrated = False
if "seat_id" in filtered_df.columns and len(filtered_df) > 0:
    seat_stats = filtered_df.groupby("seat_id").size()
    if len(seat_stats) > 0:
        top_seat = seat_stats.idxmax()
        top_seat_count = seat_stats.max()
        avg_seat_count = seat_stats.mean()
        if top_seat_count > avg_seat_count * 2:
            seat_concentrated = True
    else:
        top_seat, top_seat_count = "-", 0
else:
    top_seat, top_seat_count = "-", 0

kpi7, kpi8, kpi9 = st.columns(3)
kpi7.metric("最高缺陷设备", str(top_eq), f"{top_eq_count} 个缺陷")
kpi8.metric("最高缺陷座号", str(top_seat), f"{top_seat_count} 个缺陷")
if seat_concentrated:
    kpi9.metric("座号集中性", "⚠️ 存在集中", delta="需关注", delta_color="inverse")
else:
    kpi9.metric("座号集中性", "✅ 正常分布")

# --- 主标题 ---
st.title("📊 屏幕缺陷集中性分析系统")
st.markdown(f"**数据范围**: {start_date.strftime('%Y-%m-%d')} ~ {end_date.strftime('%Y-%m-%d')} | "
            f"**筛选后缺陷数**: {len(filtered_df)} 条 | "
            f"**涉及面板**: {filtered_df['panel_id'].nunique()} 块")

st.divider()

if filtered_df.empty:
    st.warning("当前筛选条件下没有缺陷记录，请放宽日期、批次、设备或缺陷类型筛选。")
    st.stop()

# --- Tab 布局 (按角色动态) ---
ALL_TABS = [
    "🧭 诊断驾驶舱",
    "🔬 ML 因子分析",
    "🗺️ 空间集中性",
    "📊 类型集中性 (帕累托)",
    "📈 时间集中性",
    "🏭 批次集中性",
    "🏗️ 设备座号集中性",
    "🔗 关联分析",
    "🧠 智能缺陷聚类 (DBSCAN)",
    "🚨 SPC 控制图与预警",
    "🔬 缺陷模式识别",
    "💚 设备健康与共性分析",
    "🔲 多层叠加分析"
]

if current_config["tabs"] == "all":
    visible_tabs = ALL_TABS
else:
    visible_tabs = [t for t in ALL_TABS if t in current_config["tabs"]]

tab_containers = st.tabs(visible_tabs)
tab_map = {name: container for name, container in zip(visible_tabs, tab_containers)}

def get_tab(name):
    """获取指定 Tab 容器，如果不可见则返回 None"""
    return tab_map.get(name)

# ========== Tab 0: 诊断驾驶舱 ==========
_t = get_tab("🧭 诊断驾驶舱")
if _t:
    with _t:
        dashboard = build_diagnostic_dashboard(filtered_df)
        industry_diagnosis = generate_industry_diagnosis(filtered_df, dashboard)
        quality_report = build_data_quality_report(filtered_df)
        level_colors = {
            "严重": ("#7f1d1d", "#fee2e2"),
            "关注": ("#92400e", "#fef3c7"),
            "正常": ("#14532d", "#dcfce7"),
        }
        level_fg, level_bg = level_colors.get(dashboard["severity_level"], ("#334155", "#e2e8f0"))

        st.markdown(
            """
            <style>
            .diag-hero {
                padding: 24px 28px;
                border-radius: 24px;
                background:
                    radial-gradient(circle at 15% 15%, rgba(20, 184, 166, .18), transparent 28%),
                    linear-gradient(135deg, #0f172a 0%, #12343b 52%, #294936 100%);
                color: #f8fafc;
                box-shadow: 0 18px 45px rgba(15, 23, 42, .18);
                margin-bottom: 18px;
            }
            .diag-hero h2 { margin: 0 0 8px 0; font-size: 30px; letter-spacing: .02em; }
            .diag-hero p { margin: 0; color: #cbd5e1; font-size: 15px; }
            .diag-badge {
                display: inline-flex;
                align-items: center;
                padding: 6px 12px;
                border-radius: 999px;
                font-weight: 700;
                margin-bottom: 12px;
            }
            .diag-card {
                padding: 18px 18px;
                border-radius: 18px;
                border: 1px solid #dbe4e7;
                background: linear-gradient(180deg, #ffffff 0%, #f8fafc 100%);
                min-height: 128px;
            }
            .diag-card .label { color: #64748b; font-size: 13px; margin-bottom: 8px; }
            .diag-card .value { color: #0f172a; font-size: 26px; font-weight: 800; line-height: 1.1; }
            .diag-card .hint { color: #475569; font-size: 13px; margin-top: 10px; }
            </style>
            """,
            unsafe_allow_html=True,
        )

        st.markdown(
            f"""
            <div class="diag-hero">
                <div class="diag-badge" style="color:{level_fg}; background:{level_bg};">
                    当前诊断等级：{dashboard["severity_level"]}
                </div>
                <h2>缺陷诊断驾驶舱</h2>
                <p>{dashboard["primary_recommendation"]}</p>
            </div>
            """,
            unsafe_allow_html=True,
        )

        card1, card2, card3, card4 = st.columns(4)
        with card1:
            st.markdown(
                f"""
                <div class="diag-card">
                    <div class="label">筛选后缺陷</div>
                    <div class="value">{len(filtered_df)}</div>
                    <div class="hint">涉及 {filtered_df["panel_id"].nunique()} 块面板</div>
                </div>
                """,
                unsafe_allow_html=True,
            )
        with card2:
            st.markdown(
                f"""
                <div class="diag-card">
                    <div class="label">主导缺陷类型</div>
                    <div class="value">{dashboard["top_defect_type"]}</div>
                    <div class="hint">占全部缺陷 {dashboard["top_defect_share"]:.1%}</div>
                </div>
                """,
                unsafe_allow_html=True,
            )
        with card3:
            st.markdown(
                f"""
                <div class="diag-card">
                    <div class="label">严重缺陷占比</div>
                    <div class="value">{dashboard["serious_share"]:.1%}</div>
                    <div class="hint">高于 20% 建议立即复盘</div>
                </div>
                """,
                unsafe_allow_html=True,
            )
        with card4:
            top_root = dashboard["root_causes"].iloc[0] if len(dashboard["root_causes"]) else None
            root_name = top_root["根因候选"] if top_root is not None else "-"
            root_share = top_root["占比"] if top_root is not None else 0
            root_lift = top_root["异常倍数"] if top_root is not None else 0
            st.markdown(
                f"""
                <div class="diag-card">
                    <div class="label">首要根因候选</div>
                    <div class="value" style="font-size:22px;">{root_name}</div>
                    <div class="hint">贡献 {root_share:.1%} 缺陷，异常 {root_lift:.2f}x</div>
                </div>
                """,
                unsafe_allow_html=True,
            )

        st.markdown(
            f"""
            <div style="
                margin-top: 16px;
                padding: 18px 20px;
                border-radius: 18px;
                border: 1px solid #c7d2fe;
                background: linear-gradient(135deg, #eef2ff 0%, #f8fafc 55%, #ecfeff 100%);
            ">
                <div style="font-size: 13px; color: #475569; font-weight: 700; margin-bottom: 6px;">
                    3C 面板行业诊断结论
                </div>
                <div style="font-size: 18px; color: #0f172a; font-weight: 800;">
                    {industry_diagnosis["headline"]}
                </div>
            </div>
            """,
            unsafe_allow_html=True,
        )

        diag_col1, diag_col2 = st.columns([1, 1])
        with diag_col1:
            st.subheader("识别到的缺陷模式")
            for pattern in industry_diagnosis["patterns"]:
                st.markdown(f"- {pattern}")
        with diag_col2:
            st.subheader("行业化排查建议")
            for idx, recommendation in enumerate(industry_diagnosis["recommendations"], 1):
                st.markdown(f"{idx}. {recommendation}")

        quality_cols = st.columns(5)
        quality_cols[0].metric("数据质量分", f"{quality_report['score']:.1f}")
        quality_cols[1].metric("必填完整率", f"{quality_report['required_complete_rate']:.1%}")
        quality_cols[2].metric("坐标合法率", f"{quality_report['coordinate_valid_rate']:.1%}")
        quality_cols[3].metric("枚举合法率", f"{quality_report['enum_valid_rate']:.1%}")
        quality_cols[4].metric("追溯覆盖率", f"{quality_report['traceability_rate']:.1%}")
        if quality_report["issues"] != ["数据质量良好"]:
            st.warning("数据质量提示：" + "；".join(quality_report["issues"]))

        st.divider()
        left, right = st.columns([1.25, 1])
        with left:
            st.subheader("交互式面板数字孪生")
            panel_w = float(df["panel_width_mm"].iloc[0])
            panel_h = float(df["panel_height_mm"].iloc[0])
            fig_map = go.Figure()
            fig_map.add_shape(
                type="rect",
                x0=0,
                y0=0,
                x1=panel_w,
                y1=panel_h,
                line=dict(color="#0f172a", width=2),
                fillcolor="#f8fafc",
                layer="below",
            )
            fig_map.add_trace(
                go.Scatter(
                    x=filtered_df["x_mm"],
                    y=filtered_df["y_mm"],
                    mode="markers",
                    marker=dict(
                        size=7,
                        color=filtered_df["severity"].map({"轻微": 1, "中等": 2, "严重": 3}),
                        colorscale=[[0, "#38bdf8"], [0.5, "#f59e0b"], [1, "#dc2626"]],
                        showscale=True,
                        colorbar=dict(title="严重度"),
                        opacity=0.72,
                        line=dict(width=0.4, color="#ffffff"),
                    ),
                    text=filtered_df["defect_id"],
                    customdata=filtered_df[["defect_type", "severity", "equipment_id", "seat_id", "batch_id"]],
                    hovertemplate=(
                        "缺陷ID: %{text}<br>"
                        "坐标: (%{x:.1f}, %{y:.1f}) mm<br>"
                        "类型: %{customdata[0]}<br>"
                        "严重度: %{customdata[1]}<br>"
                        "设备/座号: %{customdata[2]} / %{customdata[3]}<br>"
                        "批次: %{customdata[4]}<extra></extra>"
                    ),
                    name="缺陷点",
                )
            )
            fig_map.add_vrect(x0=0, x1=panel_w * 0.1, fillcolor="#f97316", opacity=0.08, line_width=0)
            fig_map.add_vrect(x0=panel_w * 0.9, x1=panel_w, fillcolor="#f97316", opacity=0.08, line_width=0)
            fig_map.add_hrect(y0=panel_h * 0.72, y1=panel_h * 0.88, fillcolor="#14b8a6", opacity=0.09, line_width=0)
            fig_map.update_layout(
                height=560,
                margin=dict(l=18, r=18, t=30, b=18),
                plot_bgcolor="#ffffff",
                paper_bgcolor="#ffffff",
                xaxis=dict(title="X (mm)", range=[0, panel_w], showgrid=True, gridcolor="#e2e8f0"),
                yaxis=dict(title="Y (mm)", range=[0, panel_h], scaleanchor="x", scaleratio=1, showgrid=True, gridcolor="#e2e8f0"),
                title="按真实屏幕比例定位缺陷，橙色为边缘敏感区，青色为 FPC 关注区",
            )
            st.plotly_chart(fig_map, use_container_width=True)

            fig_density = px.density_heatmap(
                filtered_df,
                x="x_mm",
                y="y_mm",
                nbinsx=28,
                nbinsy=42,
                color_continuous_scale="YlOrRd",
                title="密度热区视图",
                labels={"x_mm": "X (mm)", "y_mm": "Y (mm)"},
            )
            fig_density.update_layout(height=300, margin=dict(l=18, r=18, t=42, b=18))
            st.plotly_chart(fig_density, use_container_width=True)

        with right:
            st.subheader("根因候选榜")
            root_causes = dashboard["root_causes"].copy()
            fig_root = px.bar(
                root_causes.sort_values("风险分", ascending=True),
                x="风险分",
                y="根因候选",
                orientation="h",
                color="异常倍数",
                color_continuous_scale="Tealrose",
                text="风险分",
                hover_data={
                    "缺陷数": True,
                    "占比": ":.1%",
                    "异常倍数": ":.2f",
                    "涉及面板": True,
                    "主要缺陷": True,
                    "严重占比": ":.1%",
                    "风险分": ":.1f",
                },
                labels={"风险分": "风险分", "根因候选": ""},
            )
            fig_root.update_traces(texttemplate="%{text:.1f}", textposition="outside")
            fig_root.update_layout(height=360, margin=dict(l=8, r=20, t=20, b=20))
            st.plotly_chart(fig_root, use_container_width=True)

            root_table = root_causes.copy()
            root_table["占比"] = root_table["占比"].map(lambda v: f"{v:.1%}")
            root_table["异常倍数"] = root_table["异常倍数"].map(lambda v: f"{v:.2f}x")
            root_table["严重占比"] = root_table["严重占比"].map(lambda v: f"{v:.1%}")
            st.dataframe(root_table, use_container_width=True, hide_index=True)
            st.caption("风险分 = 贡献规模 + 异常倍数 + 严重占比 + 涉及面板数。先查高贡献且高偏离的组合。")

        trend_col, pareto_col = st.columns([1, 1])
        with trend_col:
            st.subheader("每日缺陷走势")
            daily_trend = dashboard["daily_trend"]
            fig_trend_dash = px.area(
                daily_trend,
                x="day",
                y="缺陷数",
                markers=True,
                color_discrete_sequence=["#0f766e"],
                labels={"day": "日期", "缺陷数": "缺陷数"},
            )
            fig_trend_dash.update_traces(line=dict(width=3), fillcolor="rgba(20, 184, 166, .22)")
            fig_trend_dash.update_layout(height=350, margin=dict(l=18, r=18, t=20, b=18))
            st.plotly_chart(fig_trend_dash, use_container_width=True)

        with pareto_col:
            st.subheader("缺陷类型 Pareto")
            pareto = dashboard["pareto"].head(8)
            fig_pareto_dash = go.Figure()
            fig_pareto_dash.add_trace(
                go.Bar(
                    x=pareto["缺陷类型"],
                    y=pareto["缺陷数"],
                    marker_color="#334155",
                    name="缺陷数",
                    hovertemplate="%{x}<br>缺陷数: %{y}<extra></extra>",
                )
            )
            fig_pareto_dash.add_trace(
                go.Scatter(
                    x=pareto["缺陷类型"],
                    y=pareto["累计占比"],
                    yaxis="y2",
                    mode="lines+markers",
                    line=dict(color="#dc2626", width=3),
                    name="累计占比",
                    hovertemplate="%{x}<br>累计占比: %{y:.1%}<extra></extra>",
                )
            )
            fig_pareto_dash.update_layout(
                height=350,
                margin=dict(l=18, r=18, t=20, b=18),
                yaxis=dict(title="缺陷数"),
                yaxis2=dict(title="累计占比", overlaying="y", side="right", tickformat=".0%"),
                legend=dict(orientation="h", y=1.12),
            )
            st.plotly_chart(fig_pareto_dash, use_container_width=True)

# ========== Tab 0.5: ML 因子分析 ==========
_t = get_tab("🔬 ML 因子分析")
if _t:
    with _t:
        dashboard = build_diagnostic_dashboard(filtered_df)
        extended_root_causes = dashboard.get("extended_root_causes")

        st.header("根因与关键因子分析")
        st.markdown("综合规则评分、统计分析、机器学习验证与行业维度，输出可解释的异常候选。")
        ml_col1, ml_col2, ml_col3 = st.columns([1, 1, 1])
        with ml_col1:
            ml_target_type = st.selectbox(
                "目标缺陷",
                options=sorted(filtered_df["defect_type"].dropna().unique()),
                index=sorted(filtered_df["defect_type"].dropna().unique()).index(dashboard["top_defect_type"])
                if dashboard["top_defect_type"] in sorted(filtered_df["defect_type"].dropna().unique())
                else 0,
            )
        with ml_col2:
            ml_model_name = st.selectbox(
                "ML 模型",
                options=["random_forest", "logistic_regression", "xgboost", "lightgbm"],
                format_func=lambda name: {
                    "random_forest": "RandomForest",
                    "logistic_regression": "LogisticRegression",
                    "xgboost": "XGBoost",
                    "lightgbm": "LightGBM",
                }[name],
            )
        with ml_col3:
            ml_top_n = st.slider("候选因子数", min_value=5, max_value=30, value=10, step=5)

        ml_insights = build_cached_ml_factor_insights(
            filtered_df,
            ml_target_type,
            ml_model_name,
            ml_top_n,
        )
        st.divider()

        if extended_root_causes is not None and not extended_root_causes.empty:
            st.subheader("扩展根因候选")
            extended_table = extended_root_causes.copy()
            extended_table["占比"] = extended_table["占比"].map(lambda v: f"{v:.1%}")
            extended_table["异常倍数"] = extended_table["异常倍数"].map(lambda v: f"{v:.2f}x")
            extended_table["严重占比"] = extended_table["严重占比"].map(lambda v: f"{v:.1%}")
            st.dataframe(extended_table, use_container_width=True, hide_index=True)
            st.caption("覆盖治具、吸嘴、材料批次、清洗/绑定等维度，用于多前制程链路追溯。")

        if ml_insights["error"]:
            st.warning(f"ML 模型暂不可用：{ml_insights['error']}")
        else:
            metric_train = ml_insights["metrics"]
            metric_valid = ml_insights["validation_metrics"]
            m1, m2, m3, m4 = st.columns(4)
            m1.metric("训练准确率", f"{metric_train.get('train_accuracy', 0):.1%}")
            m2.metric("训练 AUC", f"{metric_train.get('train_auc', 0):.3f}")
            m3.metric("验证准确率", f"{metric_valid.get('validation_accuracy', 0):.1%}")
            m4.metric("验证 AUC", f"{metric_valid.get('validation_auc', 0):.3f}")

            importance_df = pd.DataFrame(ml_insights["feature_importance"])
            if not importance_df.empty:
                st.subheader("模型特征贡献 TOP")
                importance_df["importance"] = importance_df["importance"].map(lambda v: round(v, 4))
                st.dataframe(importance_df.head(15), use_container_width=True, hide_index=True)
                st.caption("用于判断模型主要依赖哪些设备、座号、材料批次、坐标或缺陷几何特征。")

        key_factors = ml_insights["key_factors"]
        if not key_factors.empty:
            st.subheader(f"关键因子分析：{ml_insights['target_defect_type']}")
            key_factor_table = key_factors.copy()
            key_factor_table["目标占比"] = key_factor_table["目标占比"].map(lambda v: f"{v:.1%}")
            key_factor_table["基线占比"] = key_factor_table["基线占比"].map(lambda v: f"{v:.1%}")
            key_factor_table["异常倍数"] = key_factor_table["异常倍数"].map(lambda v: f"{v:.2f}x")
            key_factor_table["支持度"] = key_factor_table["支持度"].map(lambda v: f"{v:.1%}")
            if "ml_probability" in key_factor_table.columns:
                key_factor_table["ml_probability"] = key_factor_table["ml_probability"].map(lambda v: f"{v:.1%}")
            st.dataframe(key_factor_table, use_container_width=True, hide_index=True)
            st.caption("关键因子按目标缺陷占比、异常倍数、样本数、支持度和模型概率综合排序。")
        else:
            st.info("当前数据未找到显著关键因子，可放宽筛选条件或增加样本量。")

# ========== Tab 1: 空间集中性 ==========
_t = get_tab("🗺️ 空间集中性")
if _t:
    with _t:
        st.header("缺陷空间分布热力图")
        col1, col2 = st.columns([2, 1])

        with col1:
        # 热力图分辨率
            grid_size = st.slider("热力图网格分辨率", min_value=5, max_value=50, value=20)

            fig, axes = plt.subplots(1, 2, figsize=(14, 6))

        # 左图：2D 热力图
            x_edges = np.linspace(0, df["panel_width_mm"].iloc[0], grid_size + 1)
            y_edges = np.linspace(0, df["panel_height_mm"].iloc[0], grid_size + 1)

            H, _, _ = np.histogram2d(
                filtered_df["x_mm"], filtered_df["y_mm"],
                bins=[x_edges, y_edges]
            )

            im = axes[0].imshow(
                H.T, origin="lower", aspect="auto",
                extent=[0, df["panel_width_mm"].iloc[0], 0, df["panel_height_mm"].iloc[0]],
                cmap="YlOrRd"
            )
            axes[0].set_title(f"缺陷密度热力图 (总 {len(filtered_df)} 个)")
            axes[0].set_xlabel("X (mm)")
            axes[0].set_ylabel("Y (mm)")
            plt.colorbar(im, ax=axes[0], label="缺陷数量")

        # 右图：散点图（叠加）
            axes[1].scatter(
                filtered_df["x_mm"], filtered_df["y_mm"],
                alpha=0.3, s=5, c="red", edgecolors="none"
            )
            axes[1].set_title("缺陷位置散点图")
            axes[1].set_xlabel("X (mm)")
            axes[1].set_ylabel("Y (mm)")
            axes[1].set_aspect("equal")

            st.pyplot(fig)
            plt.close()

        with col2:
            st.subheader("区域统计")
        # 将面板分为 9 宫格
            x_bins = pd.cut(filtered_df["x_mm"], bins=3, labels=["左", "中", "右"])
            y_bins = pd.cut(filtered_df["y_mm"], bins=3, labels=["上", "中", "下"])
            region_df = pd.DataFrame({"X区域": x_bins, "Y区域": y_bins})
            region_counts = region_df.groupby(["X区域", "Y区域"], observed=False).size().unstack(fill_value=0)
            st.dataframe(region_counts, use_container_width=True)

        # 高频缺陷区域 TOP5
            st.subheader("高频缺陷区域 TOP5")
            region_df["区域"] = region_df["X区域"].astype(str) + "-" + region_df["Y区域"].astype(str)
            top_regions = region_df["区域"].value_counts().head(5)
            for i, (region, count) in enumerate(top_regions.items(), 1):
                st.metric(f"#{i} {region}", f"{count} 个缺陷")

    # --- 模拟面板缺陷标注图 ---
        st.divider()
        st.subheader("🖼️ 模拟面板缺陷标注图")
        st.markdown("选择批次和面板，查看缺陷在面板上的实际分布标注（按缺陷类型用不同颜色/形状区分）")

        ann_col1, ann_col2, ann_col3 = st.columns(3)
        with ann_col1:
            ann_batch = st.selectbox("选择批次", options=sorted(filtered_df["batch_id"].unique()), key="ann_batch")
        with ann_col2:
            panels_in_batch = sorted(filtered_df[filtered_df["batch_id"] == ann_batch]["panel_id"].unique())
            ann_panel = st.selectbox("选择面板", options=panels_in_batch, key="ann_panel")
        with ann_col3:
            ann_show_label = st.checkbox("显示缺陷标签", value=True)

        panel_defects = filtered_df[(filtered_df["batch_id"] == ann_batch) & (filtered_df["panel_id"] == ann_panel)]

        if len(panel_defects) == 0:
            st.warning(f"当前面板 **{ann_panel}** (批次 {ann_batch}) 在筛选条件下无缺陷记录，请调整筛选条件或选择其他面板")
        else:
            pw = df["panel_width_mm"].iloc[0]
            ph = df["panel_height_mm"].iloc[0]

        # 缺陷类型 → 颜色/形状映射
            type_style = {
                "划痕": {"color": "red", "marker": "x", "size": 80},
                "亮点": {"color": "yellow", "marker": "o", "size": 60},
                "暗点": {"color": "black", "marker": "x", "size": 60},
                "气泡": {"color": "cyan", "marker": "o", "size": 100},
                "色差": {"color": "magenta", "marker": "s", "size": 70},
                "漏光": {"color": "orange", "marker": "D", "size": 80},
                "裂纹": {"color": "darkred", "marker": "v", "size": 90},
                "异物": {"color": "green", "marker": "P", "size": 80},
            }

            fig_ann, ax_ann = plt.subplots(figsize=(3.5, 5))

        # 面板背景（模拟屏幕灰色渐变）
            ax_ann.add_patch(plt.Rectangle((0, 0), pw, ph, facecolor="#1a1a2e", edgecolor="#444", linewidth=2))
        # 内框（模拟屏幕可视区域）
            margin = 8
            ax_ann.add_patch(plt.Rectangle((margin, margin), pw - 2*margin, ph - 2*margin,
                                           facecolor="#16213e", edgecolor="#0f3460", linewidth=1.5))
        # FPC绑定区域标注
            fpc_y = ph * 0.7
            ax_ann.axhline(y=fpc_y, color="#555", linestyle="--", alpha=0.4, linewidth=0.5)
            ax_ann.text(pw/2, fpc_y + 2, "FPC区", color="#666", fontsize=7, ha="center", alpha=0.5)

        # 绘制缺陷标注
            for _, row in panel_defects.iterrows():
                style = type_style.get(row["defect_type"], {"color": "white", "marker": "o", "size": 50})
                severity_size = {"轻微": 0.7, "中等": 1.0, "严重": 1.4}.get(row["severity"], 1.0)
                ax_ann.scatter(row["x_mm"], row["y_mm"],
                               c=style["color"], marker=style["marker"],
                               s=style["size"] * severity_size,
                               edgecolors="white", linewidth=0.3, alpha=0.85, zorder=3)
                if ann_show_label:
                    ax_ann.annotate(row["defect_type"][:2],
                                    (row["x_mm"], row["y_mm"]),
                                    fontsize=5, color="white",
                                    ha="center", va="bottom", alpha=0.7, zorder=4)

        # 图例
            legend_elements = [plt.Line2D([0], [0], marker=type_style[t]["marker"], color="w",
                                           markerfacecolor=type_style[t]["color"], markersize=8,
                                           label=t, markeredgewidth=0.5, markeredgecolor="white")
                               for t in type_style]
            ax_ann.legend(handles=legend_elements, loc="upper right", fontsize=7,
                          framealpha=0.7, facecolor="#222", edgecolor="#555")

            ax_ann.set_xlim(-5, pw + 5)
            ax_ann.set_ylim(-5, ph + 5)
            ax_ann.set_title(f"面板 {ann_panel} | 批次 {ann_batch} | {len(panel_defects)} 个缺陷",
                             fontsize=11, pad=10)
            ax_ann.set_xlabel("X (mm)")
            ax_ann.set_ylabel("Y (mm)")
            ax_ann.set_aspect("equal")
            ax_ann.grid(True, alpha=0.1, color="gray")

            st.pyplot(fig_ann)
            plt.close()

# ========== Tab 2: 帕累托分析 ==========
_t = get_tab("📊 类型集中性 (帕累托)")
if _t:
    with _t:
        st.header("缺陷类型帕累托分析")

        type_counts = filtered_df["defect_type"].value_counts().reset_index()
        type_counts.columns = ["缺陷类型", "数量"]
        type_counts = type_counts.sort_values("数量", ascending=False).reset_index(drop=True)
        type_counts["累计占比"] = type_counts["数量"].cumsum() / type_counts["数量"].sum() * 100
        type_counts["占比"] = type_counts["数量"] / type_counts["数量"].sum() * 100

        fig, ax1 = plt.subplots(figsize=(10, 5))

    # 柱状图
        bars = ax1.bar(type_counts["缺陷类型"], type_counts["数量"], color="steelblue", alpha=0.8)
        ax1.set_xlabel("缺陷类型")
        ax1.set_ylabel("数量", color="steelblue")
        ax1.set_title("帕累托图 - 缺陷类型分布")

    # 累计占比折线
        ax2 = ax1.twinx()
        ax2.plot(type_counts["缺陷类型"], type_counts["累计占比"], color="red", marker="o", linewidth=2)
        ax2.axhline(y=80, color="green", linestyle="--", alpha=0.5, label="80%线")
        ax2.set_ylabel("累计占比 (%)", color="red")
        ax2.set_ylim(0, 110)

    # 标注数值
        for bar, count in zip(bars, type_counts["数量"]):
            ax1.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 2,
                     str(count), ha="center", va="bottom", fontsize=9)

        st.pyplot(fig)
        plt.close()

    # 数据表格
        st.subheader("详细数据")
        st.dataframe(type_counts, use_container_width=True)

    # 严重程度分布
        st.subheader("按严重程度分布")
        sev_counts = filtered_df["severity"].value_counts()
        fig2, ax = plt.subplots(figsize=(6, 4))
        colors = {"轻微": "#4CAF50", "中等": "#FF9800", "严重": "#F44336"}
        sev_counts.plot(kind="bar", ax=ax, color=[colors.get(s, "gray") for s in sev_counts.index])
        ax.set_title("缺陷严重程度分布")
        ax.set_ylabel("数量")
        st.pyplot(fig2)
        plt.close()

# ========== Tab 3: 时间集中性 ==========
_t = get_tab("📈 时间集中性")
if _t:
    with _t:
        st.header("缺陷时间分布趋势")

        col1, col2 = st.columns(2)

        with col1:
        # 按天趋势
            daily = filtered_df.groupby("day").size().reset_index(name="缺陷数")
            daily["day"] = pd.to_datetime(daily["day"])

            fig1, ax1 = plt.subplots(figsize=(10, 4))
            ax1.plot(daily["day"], daily["缺陷数"], marker="o", markersize=3, linewidth=1.5, color="steelblue")
            ax1.fill_between(daily["day"], daily["缺陷数"], alpha=0.2, color="steelblue")
            ax1.set_title("每日缺陷数量趋势")
            ax1.set_ylabel("缺陷数量")
            ax1.tick_params(axis="x", rotation=45)

        # 移动平均
            if len(daily) > 3:
                daily["移动平均(3天)"] = daily["缺陷数"].rolling(window=3, min_periods=1).mean()
                ax1.plot(daily["day"], daily["移动平均(3天)"], color="red", linestyle="--",
                         linewidth=2, alpha=0.7, label="3日移动平均")
                ax1.legend()

            st.pyplot(fig1)
            plt.close()

        with col2:
        # 按小时分布
            hourly = filtered_df.groupby("hour").size().reindex(range(24), fill_value=0)
            fig2, ax2 = plt.subplots(figsize=(10, 4))
            colors = ["#FF6B6B" if (h >= 17 or h < 8) else "#4ECDC4" for h in hourly.index]
            ax2.bar(hourly.index, hourly.values, color=colors, alpha=0.8)
            ax2.set_title("每小时缺陷分布 (红色=夜班)")
            ax2.set_xlabel("小时")
            ax2.set_ylabel("缺陷数量")
            st.pyplot(fig2)
            plt.close()

    # 班次对比
        st.subheader("班次对比")
        shift_stats = filtered_df.groupby("shift").agg({
            "defect_id": "count",
            "panel_id": "nunique"
        }).rename(columns={"defect_id": "缺陷数", "panel_id": "涉及面板数"})
        st.dataframe(shift_stats, use_container_width=True)

    # 每周分布
        st.subheader("按星期分布")
        filtered_df_copy = filtered_df.copy()
        filtered_df_copy["weekday"] = filtered_df_copy["timestamp"].dt.day_name()
        weekday_order = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
        weekday_cn = {"Monday": "周一", "Tuesday": "周二", "Wednesday": "周三",
                      "Thursday": "周四", "Friday": "周五", "Saturday": "周六", "Sunday": "周日"}
        filtered_df_copy["星期"] = filtered_df_copy["weekday"].map(weekday_cn)
        weekday_counts = filtered_df_copy.groupby("星期").size().reindex(
            [weekday_cn[d] for d in weekday_order], fill_value=0
        )

        fig3, ax3 = plt.subplots(figsize=(8, 4))
        ax3.bar(range(7), weekday_counts.values, color="steelblue", alpha=0.8)
        ax3.set_xticks(range(7))
        ax3.set_xticklabels(weekday_counts.index)
        ax3.set_title("按星期分布")
        ax3.set_ylabel("缺陷数量")
        st.pyplot(fig3)
        plt.close()

# ========== Tab 4: 批次集中性 ==========
_t = get_tab("🏭 批次集中性")
if _t:
    with _t:
        st.header("批次缺陷集中性分析")

        batch_stats = filtered_df.groupby("batch_id").agg({
            "defect_id": "count",
            "panel_id": "nunique",
            "severity": lambda x: (x == "严重").sum()
        }).rename(columns={"defect_id": "缺陷数", "panel_id": "面板数", "severity": "严重缺陷数"})
        batch_stats["缺陷率"] = batch_stats["缺陷数"] / batch_stats["面板数"]
        batch_stats = batch_stats.sort_index()

        col1, col2 = st.columns(2)

        with col1:
            fig1, ax1 = plt.subplots(figsize=(10, 4))
            ax1.bar(range(len(batch_stats)), batch_stats["缺陷数"], color="steelblue", alpha=0.8)
            ax1.set_title("各批次缺陷数量")
            ax1.set_xlabel("批次")
            ax1.set_ylabel("缺陷数")
            ax1.set_xticks(range(len(batch_stats)))
            ax1.set_xticklabels(batch_stats.index, rotation=90, fontsize=7)
            st.pyplot(fig1)
            plt.close()

        with col2:
            fig2, ax2 = plt.subplots(figsize=(10, 4))
            ax2.plot(range(len(batch_stats)), batch_stats["缺陷率"], marker="o", markersize=3,
                     color="red", linewidth=1.5)
            ax2.axhline(y=batch_stats["缺陷率"].mean(), color="green", linestyle="--",
                         label=f"平均缺陷率: {batch_stats['缺陷率'].mean():.2%}")
            ax2.set_title("各批次缺陷率趋势")
            ax2.set_xlabel("批次")
            ax2.set_ylabel("缺陷率")
            ax2.set_xticks(range(len(batch_stats)))
            ax2.set_xticklabels(batch_stats.index, rotation=90, fontsize=7)
            ax2.legend()
            st.pyplot(fig2)
            plt.close()

    # 异常批次
        st.subheader("异常批次 (缺陷率 > 平均值 + 1倍标准差)")
        threshold = batch_stats["缺陷率"].mean() + batch_stats["缺陷率"].std()
        abnormal = batch_stats[batch_stats["缺陷率"] > threshold].sort_values("缺陷率", ascending=False)
        if len(abnormal) > 0:
            st.dataframe(abnormal, use_container_width=True)
        else:
            st.success("未发现异常批次")

# ========== Tab 5: 设备座号集中性 ==========
_t = get_tab("🏗️ 设备座号集中性")
if _t:
    with _t:
        st.header("🏗️ 前贴附制程设备座号集中性分析")
        st.markdown(
            "分析缺陷是否集中在特定设备的特定座号（工位）。"
            "如果某个座号缺陷明显多于其他座号，说明该座号对应的设备局部存在问题（如吸嘴老化、加热不均、压力异常等）。"
        )

    # --- 设备对比 ---
        st.subheader("设备级别对比")
        eq_stats = filtered_df.groupby("equipment_id").agg({
            "defect_id": "count",
            "panel_id": "nunique",
            "severity": lambda x: (x == "严重").sum()
        }).rename(columns={"defect_id": "缺陷数", "panel_id": "面板数", "severity": "严重缺陷"})
        eq_stats["缺陷率"] = eq_stats["缺陷数"] / eq_stats["面板数"]
        eq_stats = eq_stats.sort_values("缺陷数", ascending=False)

        col_eq1, col_eq2 = st.columns(2)

        with col_eq1:
            fig_eq1, ax_eq1 = plt.subplots(figsize=(8, 4))
            bars1 = ax_eq1.bar(range(len(eq_stats)), eq_stats["缺陷数"], color=["#FF6B6B", "#4ECDC4", "#45B7D1"][:len(eq_stats)], alpha=0.8)
            ax_eq1.set_xticks(range(len(eq_stats)))
            ax_eq1.set_xticklabels(eq_stats.index, fontsize=10)
            ax_eq1.set_ylabel("缺陷数量")
            ax_eq1.set_title("各设备缺陷总数")
            for bar, count in zip(bars1, eq_stats["缺陷数"]):
                ax_eq1.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 3,
                            str(count), ha="center", va="bottom", fontsize=10, fontweight="bold")
            st.pyplot(fig_eq1)
            plt.close()

        with col_eq2:
            fig_eq2, ax_eq2 = plt.subplots(figsize=(8, 4))
            bars2 = ax_eq2.bar(range(len(eq_stats)), eq_stats["缺陷率"] * 100,
                               color=["#FF6B6B", "#4ECDC4", "#45B7D1"][:len(eq_stats)], alpha=0.8)
            ax_eq2.set_xticks(range(len(eq_stats)))
            ax_eq2.set_xticklabels(eq_stats.index, fontsize=10)
            ax_eq2.set_ylabel("缺陷率 (%)")
            ax_eq2.set_title("各设备缺陷率")
            for bar, rate in zip(bars2, eq_stats["缺陷率"] * 100):
                ax_eq2.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.3,
                            f"{rate:.1f}%", ha="center", va="bottom", fontsize=10, fontweight="bold")
            st.pyplot(fig_eq2)
            plt.close()

        st.dataframe(eq_stats, use_container_width=True)

    # --- 座号级别分析 ---
        st.divider()
        st.subheader("座号级别缺陷分布")

    # 选择设备查看座号
        eq_for_seat = st.selectbox("选择设备查看座号分布", options=sorted(filtered_df["equipment_id"].unique()), key="eq_seat")

        eq_data = filtered_df[filtered_df["equipment_id"] == eq_for_seat]
        eq_info = None
        for eq_name, info in [("LAM-A01", {"rows": 4, "cols": 5}), ("LAM-A02", {"rows": 4, "cols": 5}), ("LAM-B01", {"rows": 5, "cols": 4})]:
            if eq_name == eq_for_seat:
                eq_info = info
                break

        seat_counts = eq_data.groupby("seat_id").size().reset_index(name="缺陷数")
        seat_counts = seat_counts.sort_values("缺陷数", ascending=False)

        if eq_info:
        # 网格热力图
            grid = np.zeros((eq_info["rows"], eq_info["cols"]))
            seat_to_defects = eq_data.groupby("seat_id").size().to_dict()

            for r in range(1, eq_info["rows"] + 1):
                for c in range(1, eq_info["cols"] + 1):
                    seat_name = f"R{r}C{c}"
                    grid[r - 1, c - 1] = seat_to_defects.get(seat_name, 0)

            fig_grid, ax_grid = plt.subplots(figsize=(8, 6))
            im = ax_grid.imshow(grid, cmap="YlOrRd", aspect="equal")
            ax_grid.set_title(f"{eq_for_seat} 座号缺陷热力图")
            ax_grid.set_xlabel("列号")
            ax_grid.set_ylabel("行号")
            ax_grid.set_xticks(range(eq_info["cols"]))
            ax_grid.set_xticklabels([f"C{i+1}" for i in range(eq_info["cols"])])
            ax_grid.set_yticks(range(eq_info["rows"]))
            ax_grid.set_yticklabels([f"R{i+1}" for i in range(eq_info["rows"])])

        # 标注数值
            for r in range(eq_info["rows"]):
                for c in range(eq_info["cols"]):
                    val = int(grid[r, c])
                    color = "white" if val > grid.max() * 0.7 else "black"
                    ax_grid.text(c, r, str(val), ha="center", va="center", fontsize=10,
                                 color=color, fontweight="bold")

            plt.colorbar(im, ax=ax_grid, label="缺陷数量")
            st.pyplot(fig_grid)
            plt.close()
        else:
            fig_bar, ax_bar = plt.subplots(figsize=(10, 4))
            ax_bar.bar(range(len(seat_counts)), seat_counts["缺陷数"], color="steelblue", alpha=0.8)
            ax_bar.set_xticks(range(len(seat_counts)))
            ax_bar.set_xticklabels(seat_counts["seat_id"], rotation=45, fontsize=8)
            ax_bar.set_ylabel("缺陷数量")
            ax_bar.set_title("座号缺陷分布")
            st.pyplot(fig_bar)
            plt.close()

    # 座号数据表格
        st.dataframe(seat_counts, use_container_width=True)

    # --- 异常座号检测 ---
        st.divider()
        st.subheader("异常座号检测")
        all_seat_stats = filtered_df.groupby(["equipment_id", "seat_id"]).size().reset_index(name="缺陷数")
        overall_mean = all_seat_stats["缺陷数"].mean()
        overall_std = all_seat_stats["缺陷数"].std()

        threshold_1x = overall_mean + overall_std
        threshold_2x = overall_mean + 2 * overall_std

        st.info(f"📊 全局统计: 平均每个座号 **{overall_mean:.1f}** 个缺陷 | 标准差 **{overall_std:.1f}**")

        col_anom1, col_anom2 = st.columns(2)

        with col_anom1:
            st.markdown(f"**⚠️ 1σ 预警座号** (缺陷数 > {threshold_1x:.0f})")
            warning_seats = all_seat_stats[all_seat_stats["缺陷数"] > threshold_1x].sort_values("缺陷数", ascending=False)
            if len(warning_seats) > 0:
                st.dataframe(warning_seats.reset_index(drop=True), use_container_width=True)
            else:
                st.success("无预警座号")

        with col_anom2:
            st.markdown(f"**🔴 2σ 异常座号** (缺陷数 > {threshold_2x:.0f})")
            critical_seats = all_seat_stats[all_seat_stats["缺陷数"] > threshold_2x].sort_values("缺陷数", ascending=False)
            if len(critical_seats) > 0:
                st.dataframe(critical_seats.reset_index(drop=True), use_container_width=True)
            else:
                st.success("无异常座号")

    # --- 座号 × 缺陷类型 交叉分析 ---
        st.divider()
        st.subheader("座号 × 缺陷类型 交叉分析")
        st.markdown("识别哪些座号偏向产生特定类型的缺陷（如 R2C3 座号主要产生气泡 → 吸嘴问题）")

        if eq_info:
            eq_seat_type = eq_data.groupby(["seat_id", "defect_type"]).size().unstack(fill_value=0)
            fig_ct, ax_ct = plt.subplots(figsize=(10, 6))
            sns.heatmap(eq_seat_type, annot=True, fmt="d", cmap="YlOrRd", ax=ax_ct,
                        linewidths=0.5, linecolor="white")
            ax_ct.set_title(f"{eq_for_seat} 座号 × 缺陷类型 热力图")
            st.pyplot(fig_ct)
            plt.close()

# ========== Tab 6: 关联分析 ==========
_t = get_tab("🔗 关联分析")
if _t:
    with _t:
        st.header("缺陷关联分析")

        col1, col2 = st.columns(2)

        with col1:
        # 缺陷类型 x 严重程度 交叉表
            ct = pd.crosstab(filtered_df["defect_type"], filtered_df["severity"])
            fig1, ax1 = plt.subplots(figsize=(8, 5))
            sns.heatmap(ct, annot=True, fmt="d", cmap="YlOrRd", ax=ax1,
                        linewidths=0.5, linecolor="white")
            ax1.set_title("缺陷类型 × 严重程度 热力图")
            st.pyplot(fig1)
            plt.close()

        with col2:
        # 缺陷类型 x 班次 交叉表
            ct2 = pd.crosstab(filtered_df["defect_type"], filtered_df["shift"])
            fig2, ax2 = plt.subplots(figsize=(8, 5))
            sns.heatmap(ct2, annot=True, fmt="d", cmap="Blues", ax=ax2,
                        linewidths=0.5, linecolor="white")
            ax2.set_title("缺陷类型 × 班次 热力图")
            st.pyplot(fig2)
            plt.close()

    # 面板缺陷 TOP10
        st.subheader("缺陷最多的面板 TOP10")
        panel_defects = filtered_df.groupby("panel_id").agg({
            "defect_id": "count",
            "defect_type": lambda x: x.mode().iloc[0] if len(x) > 0 else "N/A"
        }).rename(columns={"defect_id": "缺陷数", "defect_type": "主要缺陷类型"})
        panel_defects = panel_defects.sort_values("缺陷数", ascending=False).head(10)
        st.dataframe(panel_defects, use_container_width=True)

    # 面板缺陷分布
        fig3, ax3 = plt.subplots(figsize=(8, 4))
        panel_counts = filtered_df.groupby("panel_id").size()
        ax3.hist(panel_counts, bins=20, color="steelblue", alpha=0.8, edgecolor="white")
        ax3.set_title("单面板缺陷数量分布")
        ax3.set_xlabel("缺陷数/面板")
        ax3.set_ylabel("面板数量")
        ax3.axvline(x=panel_counts.mean(), color="red", linestyle="--", label=f"平均: {panel_counts.mean():.1f}")
        ax3.legend()
        st.pyplot(fig3)
        plt.close()

# --- 智能缺陷聚类 (DBSCAN + PCA) ---
_t = get_tab("🧠 智能缺陷聚类 (DBSCAN)")
if _t:
    with _t:
        st.header("🧠 DBSCAN 智能缺陷空间聚类")
        st.markdown(
            "**原理**: DBSCAN 是基于密度的空间聚类算法，能自动识别任意形状的缺陷聚集区域，"
            "无需预设聚类数量，自动过滤随机散落的噪声缺陷。"
            "行业标准：半导体晶圆/面板缺陷模式识别首选算法。"
        )

        col1, col2 = st.columns([2, 1])

        with col1:
        # --- 参数控制 ---
            st.subheader("参数设置")
            p_col1, p_col2 = st.columns(2)

            with p_col1:
                eps = st.slider(
                    "eps (邻域半径 mm)",
                    min_value=5.0, max_value=100.0, value=25.0, step=5.0,
                    help="两个点被视为'邻居'的最大距离。值越大，簇越大。"
                )
            with p_col2:
                min_samples = st.slider(
                    "min_samples (最小簇点数)",
                    min_value=3, max_value=50, value=10,
                    help="形成一个簇所需的最小点数。值越大，越严格的聚集才算簇。"
                )

        # --- 执行聚类 ---
            coords = filtered_df[["x_mm", "y_mm"]].values

            scaler = StandardScaler()
            coords_scaled = scaler.fit_transform(coords)

            dbscan = DBSCAN(eps=eps / scaler.scale_[0], min_samples=min_samples)
            filtered_df["cluster"] = dbscan.fit_predict(coords_scaled)

        # 统计聚类结果
            n_clusters = len(set(dbscan.labels_)) - (1 if -1 in dbscan.labels_ else 0)
            n_noise = list(dbscan.labels_).count(-1)

            st.info(f"📊 **聚类结果**: 发现 **{n_clusters}** 个缺陷聚集区域，**{n_noise}** 个噪声点（随机散落缺陷）")

        # --- 可视化 ---
            fig, axes = plt.subplots(1, 2, figsize=(14, 6))

        # 左图：聚类结果（空间位置）
            labels = filtered_df["cluster"].values
            unique_labels = set(labels)
            colors = plt.cm.get_cmap("tab20", len(unique_labels) if len(unique_labels) > 0 else 1)

            for k in unique_labels:
                if k == -1:
                # 噪声点
                    xy = filtered_df[labels == k][["x_mm", "y_mm"]].values
                    axes[0].scatter(xy[:, 0], xy[:, 1], c="lightgray", s=3, alpha=0.3, label="噪声")
                else:
                    xy = filtered_df[labels == k][["x_mm", "y_mm"]].values
                    axes[0].scatter(xy[:, 0], xy[:, 1], c=[colors(k)], s=15, alpha=0.7,
                                    label=f"簇 {k+1} ({len(xy)} 点)")

            axes[0].set_title(f"DBSCAN 空间聚类结果 (eps={eps}, min_samples={min_samples})")
            axes[0].set_xlabel("X (mm)")
            axes[0].set_ylabel("Y (mm)")
            axes[0].set_aspect("equal")
            axes[0].legend(fontsize=7, loc="upper right", ncol=2)

        # 右图：PCA 降维可视化（加入更多特征维度）
            if len(filtered_df) > 2:
            # 构建多维特征：x, y, hour, defect_type编码, severity编码
                feature_df = filtered_df[["x_mm", "y_mm", "hour"]].copy()
            # 缺陷类型编码
                type_map = {t: i for i, t in enumerate(filtered_df["defect_type"].unique())}
                feature_df["type_code"] = filtered_df["defect_type"].map(type_map).astype(float)
            # 严重程度编码
                sev_map = {"轻微": 0, "中等": 1, "严重": 2}
                feature_df["sev_code"] = filtered_df["severity"].map(sev_map).astype(float)

                features = feature_df.values
                features_scaled = StandardScaler().fit_transform(features)

            # PCA 降维到 2D
                n_components = min(2, features_scaled.shape[1])
                pca = PCA(n_components=n_components)
                pca_result = pca.fit_transform(features_scaled)

                explained_var = pca.explained_variance_ratio_

                for k in unique_labels:
                    mask_k = labels == k
                    if k == -1:
                        axes[1].scatter(pca_result[mask_k, 0], pca_result[mask_k, 1],
                                        c="lightgray", s=3, alpha=0.3, label="噪声")
                    else:
                        axes[1].scatter(pca_result[mask_k, 0], pca_result[mask_k, 1],
                                        c=[colors(k)], s=15, alpha=0.7, label=f"簇 {k+1}")

                axes[1].set_title(
                    f"PCA 多维特征降维\n"
                    f"PC1: {explained_var[0]*100:.1f}% | PC2: {explained_var[1]*100:.1f}%"
                )
                axes[1].set_xlabel("主成分 1")
                axes[1].set_ylabel("主成分 2")
                axes[1].legend(fontsize=7, loc="upper right")

            st.pyplot(fig)
            plt.close()

        # --- 簇特征统计 ---
            if n_clusters > 0:
                st.divider()
                st.subheader("各簇特征分析")

                cluster_data = []
                for k in sorted([c for c in unique_labels if c != -1]):
                    cluster_df = filtered_df[labels == k]
                    cluster_data.append({
                        "簇编号": k + 1,
                        "缺陷数量": len(cluster_df),
                        "占比": f"{len(cluster_df)/len(filtered_df)*100:.1f}%",
                        "中心X(mm)": round(cluster_df["x_mm"].mean(), 1),
                        "中心Y(mm)": round(cluster_df["y_mm"].mean(), 1),
                        "X范围": f"{cluster_df['x_mm'].min():.0f}~{cluster_df['x_mm'].max():.0f}",
                        "Y范围": f"{cluster_df['y_mm'].min():.0f}~{cluster_df['y_mm'].max():.0f}",
                        "主要缺陷": cluster_df["defect_type"].mode().iloc[0] if len(cluster_df) > 0 else "-",
                        "主要严重度": cluster_df["severity"].mode().iloc[0] if len(cluster_df) > 0 else "-",
                        "涉及批次": cluster_df["batch_id"].nunique(),
                        "涉及面板": cluster_df["panel_id"].nunique(),
                    })

                st.dataframe(pd.DataFrame(cluster_data), use_container_width=True)

        with col2:
        # --- 聚类结果说明 ---
            st.subheader("📖 结果解读")
            st.markdown(
                f"""
                **当前参数**: eps={eps}mm, min_samples={min_samples}

                **聚类统计**:
                - 缺陷聚集区域: {n_clusters} 个
                - 随机散落噪声: {n_noise} 个
                - 噪声占比: {n_noise/len(filtered_df)*100:.1f}%

                **参数调优建议**:
                - **eps 调大** → 簇数量减少，簇变大
                - **eps 调小** → 簇数量增加，更精细
                - **min_samples 调大** → 只有高度密集区域才算簇
                - **min_samples 调小** → 更多区域被识别为簇

                **工业应用**:
                - 每个"簇"代表一个**系统性缺陷源**
                  （如某台设备、某道工序、某个物料批次）
                - "噪声"点是随机缺陷，通常无需特别关注
                - 重点关注**缺陷数量多、涉及批次集中**的簇
                """
            )

        # --- 簇分布饼图 ---
            if n_clusters > 0:
                st.subheader("簇规模分布")
                cluster_counts = filtered_df[labels >= 0]["cluster"].value_counts().sort_index()
                fig_pie, ax_pie = plt.subplots(figsize=(5, 5))
                pie_labels = [f"簇{i+1}" for i in cluster_counts.index]
                ax_pie.pie(cluster_counts.values, labels=pie_labels, autopct="%1.1f%%",
                           colors=plt.cm.tab20.colors[:len(cluster_counts)], startangle=90)
                ax_pie.set_title("各簇缺陷占比")
                st.pyplot(fig_pie)
                plt.close()

        # --- DBSCAN vs K-Means 对比 ---
            st.subheader("为什么选 DBSCAN?")
            st.markdown(
                """
                | 维度 | DBSCAN | K-Means |
                |------|--------|---------|
                | 形状适应 | ✅ 任意形状 | ❌ 仅球形 |
                | 预设K值 | ❌ 不需要 | ✅ 必须 |
                | 噪声处理 | ✅ 自动过滤 | ❌ 干扰聚类 |
                | 环形/线形缺陷 | ✅ 能识别 | ❌ 识别不了 |
                """
            )

# ========== Tab 8: SPC 控制图与预警 ==========
_t = get_tab("🚨 SPC 控制图与预警")
if _t:
    with _t:
        st.header("🚨 SPC 统计过程控制")
        st.markdown(
            "基于统计过程控制(SPC)方法，监控每日缺陷率是否在控制限内，"
            "自动检测异常趋势并给出改善/恶化结论。"
        )

    # --- 数据准备：按天计算缺陷率 ---
    # 需要知道每天检测了多少面板才能算缺陷率
    # 用 batch_id 近似日期
        spc_metrics = calculate_spc_metrics(df)
        daily_all = spc_metrics["daily"]

        if len(daily_all) < 2:
            st.warning("数据天数不足，无法生成控制图")
        else:
        # 控制限计算
            p_bar = spc_metrics["p_bar"]
            sigma_p = spc_metrics["sigma_p"]
            UCL = spc_metrics["ucl"]
            LCL = spc_metrics["lcl"]
            UWL = spc_metrics["uwl"]
            LWL = spc_metrics["lwl"]

        # --- Western Electric 规则检测 ---
            we_violations = []

        # 规则1: 单点超出 3σ 控制限
            for i, row in daily_all.iterrows():
                if row["defect_rate"] > UCL or row["defect_rate"] < LCL:
                    we_violations.append({
                        "日期": row["day"].strftime("%Y-%m-%d"),
                        "规则": "Rule 1: 超出3σ控制限",
                        "值": f"{row['defect_rate']:.2%}"
                    })

        # 规则2: 连续7点上升或下降
            rates = daily_all["defect_rate"].values
            if len(rates) >= 7:
                for i in range(len(rates) - 6):
                    window = rates[i:i+7]
                    if all(window[j] < window[j+1] for j in range(6)):
                        we_violations.append({
                            "日期": daily_all.loc[i+6, "day"].strftime("%Y-%m-%d"),
                            "规则": "Rule 2: 连续7点上升",
                            "值": f"{rates[i]:.2%} → {rates[i+6]:.2%}"
                        })
                    elif all(window[j] > window[j+1] for j in range(6)):
                        we_violations.append({
                            "日期": daily_all.loc[i+6, "day"].strftime("%Y-%m-%d"),
                            "规则": "Rule 2: 连续7点下降",
                            "值": f"{rates[i]:.2%} → {rates[i+6]:.2%}"
                        })

        # 规则3: 连续7点在中心线同一侧
            for i in range(len(rates) - 6):
                window = rates[i:i+7]
                if all(v > p_bar for v in window):
                    we_violations.append({
                        "日期": daily_all.loc[i+6, "day"].strftime("%Y-%m-%d"),
                        "规则": "Rule 3: 连续7点在CL上方",
                        "值": f"持续偏高"
                    })
                elif all(v < p_bar for v in window):
                    we_violations.append({
                        "日期": daily_all.loc[i+6, "day"].strftime("%Y-%m-%d"),
                        "规则": "Rule 3: 连续7点在CL下方",
                        "值": f"持续偏低"
                    })

        # --- 趋势分析 ---
            from numpy.polynomial import polynomial as P
            x = np.arange(len(daily_all))
            coeffs = np.polyfit(x, rates, 1)
            slope = coeffs[0]
            daily_all["trend"] = np.polyval(coeffs, x)

            if abs(slope) < sigma_p * 0.1:
                trend_status = "稳定"
                trend_icon = "➡️"
                trend_color = "normal"
            elif slope > 0:
                trend_status = "恶化中"
                trend_icon = "📈"
                trend_color = "inverse"
            else:
                trend_status = "改善中"
                trend_icon = "📉"
                trend_color = "normal"

        # --- KPI 行 ---
            kpi_spc1, kpi_spc2, kpi_spc3, kpi_spc4 = st.columns(4)
            kpi_spc1.metric("平均缺陷率", f"{p_bar:.2%}")
            kpi_spc2.metric("控制限 (UCL/LCL)", f"{UCL:.2%} / {LCL:.2%}")
            kpi_spc3.metric("趋势判断", f"{trend_icon} {trend_status}", delta=f"斜率: {slope*100:.3f}%/天", delta_color=trend_color)
            kpi_spc4.metric("Western Electric 告警", f"{len(we_violations)} 次", delta="需关注" if len(we_violations) > 0 else "正常")

        # --- 控制图 ---
            st.divider()
            st.subheader("X-bar 控制图 (每日缺陷率)")

            fig_spc, ax_spc = plt.subplots(figsize=(14, 5))

        # 数据点
            ax_spc.plot(daily_all["day"], daily_all["defect_rate"],
                         marker="o", markersize=4, linewidth=1.5, color="steelblue", label="日缺陷率")
            ax_spc.fill_between(daily_all["day"], daily_all["defect_rate"], alpha=0.15, color="steelblue")

        # 控制限线
            ax_spc.axhline(y=p_bar, color="green", linestyle="-", linewidth=1.5, label=f"CL (中心线): {p_bar:.2%}")
            ax_spc.axhline(y=UCL, color="red", linestyle="--", linewidth=1, label=f"UCL: {UCL:.2%}")
            ax_spc.axhline(y=LCL, color="red", linestyle="--", linewidth=1, label=f"LCL: {LCL:.2%}")
            ax_spc.axhline(y=UWL, color="orange", linestyle=":", linewidth=1, alpha=0.6, label=f"UWL (2σ): {UWL:.2%}")
            ax_spc.axhline(y=LWL, color="orange", linestyle=":", linewidth=1, alpha=0.6, label=f"LWL (2σ): {LWL:.2%}")

        # 标注异常点
            for v in we_violations:
                if "Rule 1" in v["规则"]:
                    anomaly_date = pd.Timestamp(v["日期"])
                    val = float(v["值"].rstrip("%")) / 100
                    ax_spc.annotate("⚠️", (anomaly_date, val), fontsize=12,
                                   ha="center", va="bottom", color="red")

            ax_spc.set_title("SPC 控制图 - 每日缺陷率")
            ax_spc.set_ylabel("缺陷率")
            ax_spc.tick_params(axis="x", rotation=45)
            ax_spc.legend(fontsize=8, loc="upper right")
            ax_spc.grid(True, alpha=0.3)

            st.pyplot(fig_spc)
            plt.close()

        # --- 趋势图 ---
            st.subheader("缺陷率趋势 (含线性回归)")

            fig_trend, ax_trend = plt.subplots(figsize=(14, 4))
            ax_trend.plot(daily_all["day"], daily_all["defect_rate"],
                          marker="o", markersize=3, linewidth=1.5, color="steelblue", label="日缺陷率")
            ax_trend.plot(daily_all["day"], daily_all["trend"],
                          color="red", linestyle="--", linewidth=2, label=f"趋势线 (斜率: {slope*100:.3f}%/天)")
            ax_trend.fill_between(daily_all["day"], daily_all["defect_rate"], alpha=0.1, color="steelblue")
            ax_trend.axhline(y=p_bar, color="green", linestyle="--", alpha=0.5, label=f"平均: {p_bar:.2%}")
            ax_trend.set_ylabel("缺陷率")
            ax_trend.tick_params(axis="x", rotation=45)
            ax_trend.legend(fontsize=8)
            ax_trend.grid(True, alpha=0.3)

            st.pyplot(fig_trend)
            plt.close()

        # --- 告警清单 ---
            st.divider()
            st.subheader("⚠️ Western Electric 规则告警清单")
            if we_violations:
                we_df = pd.DataFrame(we_violations)
                st.dataframe(we_df, use_container_width=True)
                st.warning(f"共发现 **{len(we_violations)}** 次统计异常，建议关注对应日期的工艺参数和人员排班")
            else:
                st.success("✅ 未触发 Western Electric 规则告警，过程处于统计控制状态")

        # --- 结论 ---
            st.divider()
            st.subheader("📋 过程能力结论")

            if trend_status == "改善中":
                st.success(
                    f"**趋势改善中** 📉\n\n"
                    f"每日缺陷率以平均 {abs(slope)*100:.3f}%/天 的速度下降。\n"
                    f"当前平均缺陷率为 {p_bar:.2%}，控制上限 {UCL:.2%}。\n"
                    f"{'已触发' if we_violations else '未触发'} Western Electric 规则告警。"
                )
            elif trend_status == "恶化中":
                st.error(
                    f"**趋势恶化中** 📈\n\n"
                    f"每日缺陷率以平均 {slope*100:.3f}%/天 的速度上升。\n"
                    f"当前平均缺陷率为 {p_bar:.2%}，控制上限 {UCL:.2%}。\n"
                    f"{'已触发' if we_violations else '未触发'} Western Electric 规则告警。\n\n"
                    f"建议：检查近期工艺参数变化、设备状态和原材料批次。"
                )
            else:
                st.info(
                    f"**过程稳定** ➡️\n\n"
                    f"缺陷率趋势平稳，斜率 {slope*100:.3f}%/天，无显著上升或下降。\n"
                    f"当前平均缺陷率为 {p_bar:.2%}，控制限 [{LCL:.2%}, {UCL:.2%}]。\n"
                    f"{'已触发' if we_violations else '未触发'} Western Electric 规则告警。"
                )


# ========== 重复缺陷坐标检测 ==========
_t = get_tab("🗺️ 空间集中性")
if _t:
    with _t:
        st.divider()
        st.subheader("🎯 重复缺陷坐标检测")
        st.markdown(
            "检测在不同面板上重复出现的缺陷坐标。随机缺陷不会在同一位置反复出现，"
            "而设备硬伤（如吸嘴划伤、夹具压痕）会在相同位置持续产生缺陷。"
            "这是从'描述分析'跨入'根因诊断'的关键一步。"
        )

    # 坐标分桶：将面板划分为网格，找出跨面板重复的缺陷桶
        repeat_bin_size = st.slider("坐标分桶大小 (mm)", min_value=5, max_value=50, value=15, step=5,
                                     help="将坐标按此大小分桶，同一桶内出现于不同面板的缺陷视为'重复'")

        pw = df["panel_width_mm"].iloc[0]
        ph = df["panel_height_mm"].iloc[0]

    # 计算桶ID
        df_copy = filtered_df.copy()
        df_copy["x_bin"] = (df_copy["x_mm"] // repeat_bin_size).astype(int)
        df_copy["y_bin"] = (df_copy["y_mm"] // repeat_bin_size).astype(int)
        df_copy["bin_key"] = df_copy["x_bin"].astype(str) + "_" + df_copy["y_bin"].astype(str)

    # 统计每个桶出现在多少不同面板上
        bin_panels = df_copy.groupby("bin_key").agg(
            panel_count=("panel_id", "nunique"),
            defect_count=("defect_id", "count"),
            x_center=("x_mm", "mean"),
            y_center=("y_mm", "mean"),
            dominant_type=("defect_type", lambda x: x.mode().iloc[0] if len(x) > 0 else "-"),
            dominant_severity=("severity", lambda x: x.mode().iloc[0] if len(x) > 0 else "-"),
        ).reset_index()

        repeat_threshold = st.slider("重复判定阈值 (跨面板数)", min_value=2, max_value=10, value=3)
        repeated_bins = bin_panels[bin_panels["panel_count"] >= repeat_threshold].sort_values("panel_count", ascending=False)

        col_repeat1, col_repeat2 = st.columns([1, 2])

        with col_repeat1:
            st.metric("重复缺陷桶数", f"{len(repeated_bins)}",
                      delta=f"阈值: ≥{repeat_threshold} 块面板")

            if len(repeated_bins) > 0:
                st.dataframe(
                    repeated_bins[["panel_count", "defect_count", "x_center", "y_center", "dominant_type", "dominant_severity"]]
                    .rename(columns={"panel_count": "涉及面板", "defect_count": "缺陷总数",
                                     "x_center": "中心X", "y_center": "中心Y",
                                     "dominant_type": "主要类型", "dominant_severity": "主要严重度"}),
                    use_container_width=True, height=400
                )
            else:
                st.info(f"未发现跨 {repeat_threshold}+ 块面板的重复缺陷坐标")

        with col_repeat2:
            if len(repeated_bins) > 0:
            # 在面板图上标注重复缺陷桶
                fig_repeat, ax_repeat = plt.subplots(figsize=(4, 6))

            # 面板背景
                ax_repeat.add_patch(plt.Rectangle((0, 0), pw, ph, facecolor="#1a1a2e", edgecolor="#444", linewidth=2))
                ax_repeat.add_patch(plt.Rectangle((8, 8), pw-16, ph-16, facecolor="#16213e", edgecolor="#0f3460", linewidth=1.5))

            # 所有缺陷散点（淡）
                ax_repeat.scatter(filtered_df["x_mm"], filtered_df["y_mm"],
                                 alpha=0.1, s=2, c="gray", edgecolors="none", zorder=1)

            # 重复缺陷桶标注重叠圈
                max_count = repeated_bins["panel_count"].max()
                for _, row in repeated_bins.iterrows():
                    size = 100 + (row["panel_count"] / max_count) * 400
                    ax_repeat.scatter(row["x_center"], row["y_center"],
                                     s=size, c="red", alpha=0.3, edgecolors="red",
                                     linewidth=2, zorder=3)
                    ax_repeat.text(row["x_center"], row["y_center"],
                                  str(row["panel_count"]), ha="center", va="center",
                                  fontsize=8, color="white", fontweight="bold", zorder=4)

                ax_repeat.set_xlim(-5, pw + 5)
                ax_repeat.set_ylim(-5, ph + 5)
                ax_repeat.set_title(f"重复缺陷坐标 (≥{repeat_threshold} 块面板)", fontsize=11)
                ax_repeat.set_xlabel("X (mm)")
                ax_repeat.set_ylabel("Y (mm)")
                ax_repeat.set_aspect("equal")
                ax_repeat.grid(True, alpha=0.1, color="gray")

                st.pyplot(fig_repeat)
                plt.close()
            else:
                st.info("调整分桶大小或阈值以检测重复缺陷")

# ========== Tab 9: 缺陷模式识别 ==========
_t = get_tab("🔬 缺陷模式识别")
if _t:
    with _t:
        st.header("🔬 缺陷空间模式自动识别")
        st.markdown(
            "参考 WM811K 晶圆缺陷图谱分类标准，对每块面板的缺陷分布进行模式评分。"
            "不同模式对应不同的根因机制（如边缘型→贴合工艺，角落型→夹具应力，"
            "中心型→压力不均，线条型→机械刮伤，随机型→来料污染）。"
        )

        from scipy.spatial import ConvexHull
        from scipy.spatial.distance import cdist

        pw = df["panel_width_mm"].iloc[0]
        ph = df["panel_height_mm"].iloc[0]

    # 按面板分组，逐块分析模式
        panel_groups = filtered_df.groupby("panel_id")

        patterns_results = []
        for panel_id, panel_data in panel_groups:
            if len(panel_data) < 3:
                continue

            coords = panel_data[["x_mm", "y_mm"]].values

        # 归一化坐标到 [0,1]
            x_norm = panel_data["x_mm"].values / pw
            y_norm = panel_data["y_mm"].values / ph

        # --- 模式1: 边缘型 (缺陷靠近面板四边) ---
        # 计算每个点到最近边缘的距离比例
            edge_dist = np.minimum(np.minimum(x_norm, 1 - x_norm),
                                   np.minimum(y_norm, 1 - y_norm))
            edge_ratio = (edge_dist < 0.12).mean()  # 12% 以内的点视为边缘点
            edge_score = edge_ratio

        # --- 模式2: 角落型 (缺陷集中在四个角落) ---
            corner_threshold = 0.15  # 15% 范围
            in_corner = (
                ((x_norm < corner_threshold) & (y_norm < corner_threshold)) |  # 左下
                ((x_norm < corner_threshold) & (y_norm > 1 - corner_threshold)) |  # 左上
                ((x_norm > 1 - corner_threshold) & (y_norm < corner_threshold)) |  # 右下
                ((x_norm > 1 - corner_threshold) & (y_norm > 1 - corner_threshold))  # 右上
            )
            corner_score = in_corner.mean()

        # --- 模式3: 中心型 (缺陷集中在面板中心区域) ---
            center_x, center_y = 0.5, 0.5
            dist_to_center = np.sqrt((x_norm - center_x)**2 + (y_norm - center_y)**2)
            center_radius = 0.18  # 18% 半径
            center_score = (dist_to_center < center_radius).mean()

        # --- 模式4: 线条型 (缺陷沿一条线分布) ---
        # 用 PCA 第一主成分占比来判断线性程度
            if len(coords) >= 3:
                from sklearn.decomposition import PCA
                pca = PCA(n_components=2)
                pca.fit(coords)
                linearity = pca.explained_variance_ratio_[0]  # 第一主成分占比
                line_score = linearity
            else:
                line_score = 0

        # --- 模式5: 随机型 (均匀分布，无明显模式) ---
        # 用空间变异系数：将面板分为网格，计算各格缺陷数的变异系数
            grid_n = 5
            x_edges = np.linspace(0, pw, grid_n + 1)
            y_edges = np.linspace(0, ph, grid_n + 1)
            H, _, _ = np.histogram2d(panel_data["x_mm"].values, panel_data["y_mm"].values,
                                      bins=[x_edges, y_edges])
            if H.sum() > 0 and H.std() > 0:
                cv = H.std() / H.mean() if H.mean() > 0 else 999
            # cv 越小越均匀（随机）
                randomness_score = max(0, 1 - cv / 3)  # 归一化到 [0,1]
            else:
                randomness_score = 0

        # --- 主导模式判定 ---
            scores = {
                "边缘型": edge_score,
                "角落型": corner_score,
                "中心型": center_score,
                "线条型": line_score,
                "随机型": randomness_score,
            }
            dominant_pattern = max(scores, key=scores.get)

            patterns_results.append({
                "面板ID": panel_id,
                "缺陷数": len(panel_data),
                "主导模式": dominant_pattern,
                "边缘型": round(edge_score, 2),
                "角落型": round(corner_score, 2),
                "中心型": round(center_score, 2),
                "线条型": round(line_score, 2),
                "随机型": round(randomness_score, 2),
            })

        if patterns_results:
            pattern_df = pd.DataFrame(patterns_results)

        # --- 模式统计 ---
            col_pat1, col_pat2, col_pat3 = st.columns([1, 1, 2])

            with col_pat1:
                pattern_counts = pattern_df["主导模式"].value_counts()
                fig_pat, ax_pat = plt.subplots(figsize=(8, 5))
                colors_pat = {"边缘型": "#FF6B6B", "角落型": "#FFA500", "中心型": "#4ECDC4",
                              "线条型": "#9B59B6", "随机型": "#95A5A6"}
                bars = ax_pat.bar(pattern_counts.index, pattern_counts.values,
                                 color=[colors_pat.get(p, "#888") for p in pattern_counts.index],
                                 alpha=0.8)
                for bar, count in zip(bars, pattern_counts.values):
                    ax_pat.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.5,
                               str(count), ha="center", va="bottom", fontsize=11, fontweight="bold")
                ax_pat.set_title("缺陷模式分布")
                ax_pat.set_ylabel("面板数量")
                st.pyplot(fig_pat)
                plt.close()

            with col_pat2:
                st.subheader("模式占比")
                total_panels = len(pattern_df)
                for pattern in ["边缘型", "角落型", "中心型", "线条型", "随机型"]:
                    count = (pattern_df["主导模式"] == pattern).sum()
                    pct = count / total_panels * 100
                    st.metric(pattern, f"{count} 块", f"{pct:.1f}%")

            with col_pat3:
            # --- 模式-根因映射 ---
                st.subheader("模式 → 可能根因")
                root_cause_map = {
                    "边缘型": {
                        "可能原因": "贴合工艺参数异常、边缘夹具压力不均、涂胶厚度不均",
                        "建议排查": "检查贴合压力、边缘密封工艺、涂胶均匀性"
                    },
                    "角落型": {
                        "可能原因": "夹具应力集中、面板放置定位偏差、角落散热不良",
                        "建议排查": "检查夹具对齐、面板定位精度、角落温度分布"
                    },
                    "中心型": {
                        "可能原因": "压力中心不均、FPC绑定区域工艺异常、中心温度过高",
                        "建议排查": "检查压力分布曲线、FPC绑定参数、加热板温度"
                    },
                    "线条型": {
                        "可能原因": "机械刮伤、传送带划痕、清洗刷毛磨损、吸嘴移动轨迹",
                        "建议排查": "检查传送带状态、清洗设备、吸嘴运动轨迹"
                    },
                    "随机型": {
                        "可能原因": "来料污染、环境尘埃、化学药液杂质",
                        "建议排查": "检查洁净室等级、来料检验记录、药液过滤状态"
                    },
                }

                for pattern in ["边缘型", "角落型", "中心型", "线条型", "随机型"]:
                    count = (pattern_df["主导模式"] == pattern).sum()
                    if count == 0:
                        continue
                    rc = root_cause_map[pattern]
                    with st.expander(f"{pattern} ({count} 块面板)"):
                        st.markdown(f"**可能原因**: {rc['可能原因']}")
                        st.markdown(f"**建议排查**: {rc['建议排查']}")

        # --- 详细数据表 ---
            st.divider()
            st.subheader("面板模式评分明细")
            st.dataframe(pattern_df, use_container_width=True, height=400)

        else:
            st.warning("当前筛选条件下无足够面板数据进行模式分析（需至少 3 个缺陷/面板）")

# ========== Tab 10: 设备健康与共性分析 ==========
_t = get_tab("💚 设备健康与共性分析")
if _t:
    with _t:
        st.header("💚 设备健康评分 & 共性分析")
        st.markdown(
            "综合评估各台设备的健康状态，并在发现异常批次时自动分析其共性特征。"
        )

    # --- 设备健康评分 ---
        st.subheader("设备健康评分 (0-100)")
        st.markdown("评分维度：缺陷率(40%) + 座号集中度(30%) + 严重度分布(30%)")

        health_data = []
        for eq_id in sorted(df["equipment_id"].unique()):
            eq_all = df[df["equipment_id"] == eq_id]
            eq_filtered = filtered_df[filtered_df["equipment_id"] == eq_id]

        # 维度1: 缺陷率评分 (40%)
            eq_panels = eq_all["panel_id"].nunique()
            eq_defects = len(eq_all)
            eq_defect_rate = eq_defects / max(eq_panels, 1)
        # 缺陷率越低分越高，线性归一化
        # 以 5 个缺陷/面板为最差(0分)，0 为最好(100分)
            rate_score = max(0, 100 * (1 - eq_defect_rate / 5))

        # 维度2: 座号集中度评分 (30%)
        # 座号分布越均匀分越高，集中分越低
            eq_seat_counts = eq_all.groupby("seat_id").size()
            if len(eq_seat_counts) > 1:
                seat_cv = eq_seat_counts.std() / max(eq_seat_counts.mean(), 0.001)
            # cv 越小越均匀，得分越高
                seat_score = max(0, 100 * (1 - seat_cv / 3))
            else:
                seat_score = 50

        # 维度3: 严重度评分 (30%)
            eq_sev = eq_all["severity"].value_counts()
            severe_ratio = eq_sev.get("严重", 0) / max(len(eq_all), 1)
            sev_score = max(0, 100 * (1 - severe_ratio * 3))  # 严重占比 33% 时为 0 分

        # 综合得分
            total_score = rate_score * 0.4 + seat_score * 0.3 + sev_score * 0.3

            health_data.append({
                "设备ID": eq_id,
                "缺陷总数": eq_defects,
                "缺陷率": f"{eq_defect_rate:.2f}",
                "座号集中度(CV)": f"{seat_cv:.2f}" if len(eq_seat_counts) > 1 else "N/A",
                "严重占比": f"{severe_ratio:.1%}",
                "缺陷率分(40%)": round(rate_score, 1),
                "座号分(30%)": round(seat_score, 1),
                "严重度分(30%)": round(sev_score, 1),
                "健康总分": round(total_score, 1),
            })

        health_df = pd.DataFrame(health_data).sort_values("健康总分", ascending=False)

    # 显示健康评分
        col_h1, col_h2 = st.columns([3, 2])

        with col_h1:
            st.dataframe(health_df, use_container_width=True, hide_index=True)

        with col_h2:
        # 可视化排名
            fig_health, ax_health = plt.subplots(figsize=(6, 4))
            health_sorted = health_df.sort_values("健康总分", ascending=True)
            colors_health = ["#4CAF50" if s >= 70 else "#FF9800" if s >= 40 else "#F44336"
                             for s in health_sorted["健康总分"]]
            bars = ax_health.barh(health_sorted["设备ID"], health_sorted["健康总分"],
                                  color=colors_health, alpha=0.8, height=0.5)
            for bar, score in zip(bars, health_sorted["健康总分"]):
                ax_health.text(bar.get_width() + 1, bar.get_y() + bar.get_height()/2,
                              f"{score:.0f}", ha="left", va="center", fontsize=12, fontweight="bold")
            ax_health.set_xlabel("健康评分 (0-100)")
            ax_health.set_title("设备健康排名")
            ax_health.set_xlim(0, 110)
            st.pyplot(fig_health)
            plt.close()

    # --- 共性分析 ---
        st.divider()
        st.subheader("🔍 异常批次共性分析")
        st.markdown("选中异常批次后，自动分析这些批次的共同特征（设备/时段/座号/缺陷类型）。")

    # 自动检测异常批次（基于缺陷率）
        batch_stats = df.groupby("batch_id").agg(
            defects=("defect_id", "count"),
            panels=("panel_id", "nunique")
        )
        batch_stats["defect_rate"] = batch_stats["defects"] / batch_stats["panels"]
        threshold = batch_stats["defect_rate"].mean() + batch_stats["defect_rate"].std()
        abnormal_batches = batch_stats[batch_stats["defect_rate"] > threshold].index.tolist()

        st.info(f"自动检测到的异常批次 (缺陷率 > {threshold:.2%}): **{len(abnormal_batches)}** 个")
        st.write(", ".join(abnormal_batches[:10]))

        if abnormal_batches:
            col_c1, col_c2 = st.columns(2)

            with col_c1:
            # 选择要分析的批次
                selected_abnormal = st.multiselect(
                    "选择要分析的异常批次",
                    options=abnormal_batches,
                    default=abnormal_batches[:3] if len(abnormal_batches) >= 3 else abnormal_batches,
                    key="commonality_batch"
                )

            if selected_abnormal:
                abnormal_df = df[df["batch_id"].isin(selected_abnormal)]
                normal_df = df[~df["batch_id"].isin(selected_abnormal)]

                st.divider()
                st.markdown(f"**分析对象**: {len(selected_abnormal)} 个异常批次, "
                           f"{len(abnormal_df)} 条缺陷记录")

            # 共性分析：设备
                st.subheader("共性特征 TOP3")

                col_common1, col_common2, col_common3 = st.columns(3)

                with col_common1:
                # 设备共性
                    abnormal_eq_rate = abnormal_df.groupby("equipment_id").size() / len(abnormal_df)
                    normal_eq_rate = normal_df.groupby("equipment_id").size() / len(normal_df)
                    eq_boost = {}
                    for eq in abnormal_df["equipment_id"].unique():
                        a_rate = abnormal_eq_rate.get(eq, 0)
                        n_rate = normal_eq_rate.get(eq, 0)
                        if n_rate > 0:
                            eq_boost[eq] = (a_rate - n_rate) / n_rate * 100
                        else:
                            eq_boost[eq] = 999
                    eq_top = sorted(eq_boost.items(), key=lambda x: x[1], reverse=True)[:3]
                    st.markdown("**设备共用性**")
                    for eq, boost in eq_top:
                        st.markdown(f"- {eq}: 异常占比 {abnormal_eq_rate.get(eq, 0):.1%}, "
                                   f"相对正常 **+{boost:.0f}%**")

                with col_common2:
                # 时段共性
                    abnormal_hour = abnormal_df.groupby("hour").size() / len(abnormal_df)
                    normal_hour = normal_df.groupby("hour").size() / len(normal_df)
                # 按班次聚合
                    abnormal_shift = abnormal_df.groupby("shift").size() / len(abnormal_df)
                    normal_shift = normal_df.groupby("shift").size() / len(normal_df)
                    st.markdown("**时段共性**")
                    for shift in ["白班", "夜班"]:
                        a_rate = abnormal_shift.get(shift, 0)
                        n_rate = normal_shift.get(shift, 0)
                        if n_rate > 0:
                            boost = (a_rate - n_rate) / n_rate * 100
                        else:
                            boost = 999
                        st.markdown(f"- {shift}: 异常占比 {a_rate:.1%}, "
                                   f"相对正常 **{'+' if boost > 0 else ''}{boost:.0f}%**")

                with col_common3:
                # 座号共性
                    abnormal_seat = abnormal_df.groupby("seat_id").size() / len(abnormal_df)
                    normal_seat = normal_df.groupby("seat_id").size() / len(normal_df)
                    seat_boost = {}
                    for seat in abnormal_df["seat_id"].unique():
                        a_rate = abnormal_seat.get(seat, 0)
                        n_rate = normal_seat.get(seat, 0)
                        if n_rate > 0:
                            seat_boost[seat] = (a_rate - n_rate) / n_rate * 100
                        else:
                            seat_boost[seat] = 999
                    seat_top = sorted(seat_boost.items(), key=lambda x: x[1], reverse=True)[:3]
                    st.markdown("**座号共性**")
                    for seat, boost in seat_top:
                        st.markdown(f"- {seat}: 异常占比 {abnormal_seat.get(seat, 0):.1%}, "
                                   f"相对正常 **+{boost:.0f}%**")

            # --- 缺陷类型偏差 ---
                st.subheader("异常批次缺陷类型偏差")
                abnormal_type = abnormal_df.groupby("defect_type").size() / len(abnormal_df)
                normal_type = normal_df.groupby("defect_type").size() / len(normal_df)

                type_diff = []
                for t in set(list(abnormal_type.index) + list(normal_type.index)):
                    a_rate = abnormal_type.get(t, 0)
                    n_rate = normal_type.get(t, 0)
                    type_diff.append({
                        "缺陷类型": t,
                        "异常占比": f"{a_rate:.1%}",
                        "正常占比": f"{n_rate:.1%}",
                        "偏差": f"{'+' if a_rate > n_rate else ''}{(a_rate - n_rate) / max(n_rate, 0.001) * 100:.0f}%",
                    })

                st.dataframe(pd.DataFrame(type_diff).sort_values("偏差", key=lambda x: x.str.rstrip("%").astype(float), ascending=False),
                            use_container_width=True, hide_index=True)

# ========== Tab 11: 多层叠加分析 ==========
_t = get_tab("🔲 多层叠加分析")
if _t:
    with _t:
        st.header("🔲 多层叠加分析")
        st.markdown(
            "将缺陷数据与面板物理区域、设备座号、时间维度叠加在同一视图上，"
            "揭示单一维度看不到的深层关联。"
        )

        pw = df["panel_width_mm"].iloc[0]
        ph = df["panel_height_mm"].iloc[0]

    # --- 自定义区域定义 ---
        st.subheader("📐 自定义区域缺陷统计")
        st.markdown("将面板划分为不同功能区域，统计各区域缺陷分布")

    # 定义区域：(名称, 判定函数)
    # 边缘区：距四边 < 15%
    # 中心区：距中心 < 20% 半径
    # 角落区：四个角的 15% 范围
    # FPC区：Y > 70% 高度
    # 上半区/下半区

        def classify_zone(x_norm, y_norm):
            """将每个缺陷点分类到区域"""
            zones = []
            for i in range(len(x_norm)):
                zx, zy = x_norm[i], y_norm[i]
                zone_list = []

            # 边缘区
                if min(zx, 1 - zx, zy, 1 - zy) < 0.15:
                    zone_list.append("边缘区")

            # 中心区
                if np.sqrt((zx - 0.5)**2 + (zy - 0.5)**2) < 0.20:
                    zone_list.append("中心区")

            # 角落区
                if (zx < 0.15 or zx > 0.85) and (zy < 0.15 or zy > 0.85):
                    zone_list.append("角落区")

            # FPC区
                if zy > 0.70:
                    zone_list.append("FPC区")

            # 上半区
                if zy < 0.50:
                    zone_list.append("上半区")

            # 下半区
                if zy > 0.50:
                    zone_list.append("下半区")

                if not zone_list:
                    zone_list.append("其他区域")

                zones.append(", ".join(zone_list))
            return zones

    # 计算每个缺陷的区域归属
        x_norm_arr = filtered_df["x_mm"].values / pw
        y_norm_arr = filtered_df["y_mm"].values / ph
        filtered_df_copy = filtered_df.copy()
        filtered_df_copy["zone"] = classify_zone(x_norm_arr, y_norm_arr)

    # 统计各区域缺陷数
        zone_counts = {}
        zone_types = ["边缘区", "中心区", "角落区", "FPC区", "上半区", "下半区", "其他区域"]
        for z in zone_types:
            count = filtered_df_copy["zone"].str.contains(z).sum()
            zone_counts[z] = count

        col_z1, col_z2 = st.columns([1, 2])

        with col_z1:
            st.subheader("区域缺陷统计")
            for z in zone_types:
                count = zone_counts.get(z, 0)
                pct = count / max(len(filtered_df_copy), 1) * 100
                bar_len = int(pct / 100 * 200)
                bar = "█" * max(bar_len, 0)
                st.markdown(f"{z} | {bar} **{count}** ({pct:.1f}%)")

        with col_z2:
        # 区域可视化
            fig_zone, ax_zone = plt.subplots(figsize=(4, 6))

        # 面板背景
            ax_zone.add_patch(plt.Rectangle((0, 0), pw, ph, facecolor="#1a1a2e", edgecolor="#444", linewidth=2))

        # 区域边界
        # 边缘区 (15% 边界)
            margin_x = pw * 0.15
            margin_y = ph * 0.15
            ax_zone.add_patch(plt.Rectangle((0, 0), margin_x, ph, fill=False, edgecolor="yellow", linewidth=1, alpha=0.4, linestyle="--"))
            ax_zone.add_patch(plt.Rectangle((pw - margin_x, 0), margin_x, ph, fill=False, edgecolor="yellow", linewidth=1, alpha=0.4, linestyle="--"))
            ax_zone.add_patch(plt.Rectangle((0, 0), pw, margin_y, fill=False, edgecolor="yellow", linewidth=1, alpha=0.4, linestyle="--"))
            ax_zone.add_patch(plt.Rectangle((0, ph - margin_y), pw, margin_y, fill=False, edgecolor="yellow", linewidth=1, alpha=0.4, linestyle="--"))

        # 中心区 (20% 半径)
            center_r = 0.20 * max(pw, ph) / 2
            circle = plt.Circle((pw/2, ph/2), center_r, fill=False, edgecolor="cyan", linewidth=1.5, alpha=0.5, linestyle="--")
            ax_zone.add_patch(circle)

        # FPC区
            fpc_y = ph * 0.70
            ax_zone.add_patch(plt.Rectangle((0, fpc_y), pw, ph - fpc_y, fill=False, edgecolor="magenta", linewidth=1.5, alpha=0.5, linestyle="--"))

        # 缺陷散点
            scatter_colors = {"边缘区": "yellow", "中心区": "cyan", "角落区": "orange",
                              "FPC区": "magenta", "上半区": "#4ECDC4", "下半区": "#45B7D1", "其他区域": "gray"}
            for z_name in zone_types:
                z_mask = filtered_df_copy["zone"].str.contains(z_name)
                if z_mask.sum() > 0:
                    z_data = filtered_df_copy[z_mask]
                    ax_zone.scatter(z_data["x_mm"], z_data["y_mm"],
                                   c=scatter_colors.get(z_name, "gray"), s=5, alpha=0.3,
                                   label=f"{z_name} ({z_mask.sum()})", edgecolors="none", zorder=2)

            ax_zone.set_xlim(-5, pw + 5)
            ax_zone.set_ylim(-5, ph + 5)
            ax_zone.set_title("缺陷区域叠加图 (虚线=区域边界)")
            ax_zone.set_xlabel("X (mm)")
            ax_zone.set_ylabel("Y (mm)")
            ax_zone.set_aspect("equal")
            ax_zone.legend(fontsize=7, loc="upper right", ncol=1, framealpha=0.7)

            st.pyplot(fig_zone)
            plt.close()

    # --- 跨批次同座号面板对比 ---
        st.divider()
        st.subheader("🔀 跨批次同座号面板对比")
        st.markdown(
            "选择一台设备和一个座号，查看该座号在不同批次生产的面板上缺陷分布的对比。"
            "如果同一座号持续在相同位置产生缺陷 → 该座号存在系统性问题。"
        )

        col_cmp1, col_cmp2, col_cmp3 = st.columns(3)

        with col_cmp1:
            cmp_eq = st.selectbox("选择设备", options=sorted(df["equipment_id"].unique()), key="cmp_eq")

        with col_cmp2:
            eq_seats = sorted(df[(df["equipment_id"] == cmp_eq)]["seat_id"].unique())
            cmp_seat = st.selectbox("选择座号", options=eq_seats, key="cmp_seat")

        with col_cmp3:
        # 找出有该设备座号缺陷的批次
            eq_seat_batches = sorted(df[(df["equipment_id"] == cmp_eq) & (df["seat_id"] == cmp_seat)]["batch_id"].unique())
            cmp_batches = st.multiselect("选择对比批次", options=eq_seat_batches, default=eq_seat_batches[:3] if len(eq_seat_batches) >= 3 else eq_seat_batches)

        if cmp_batches and len(cmp_batches) >= 2:
            n_cols = min(len(cmp_batches), 3)
            n_rows = (len(cmp_batches) + n_cols - 1) // n_cols
            fig_cmp, axes_cmp = plt.subplots(n_rows, n_cols, figsize=(3.5 * n_cols, 5 * n_rows))
            axes_cmp = axes_cmp.flatten() if n_cols * n_rows > 1 else [axes_cmp]

            for i, batch in enumerate(cmp_batches):
                ax = axes_cmp[i]
                batch_data = df[(df["equipment_id"] == cmp_eq) & (df["seat_id"] == cmp_seat) & (df["batch_id"] == batch)]

            # 面板背景
                ax.add_patch(plt.Rectangle((0, 0), pw, ph, facecolor="#1a1a2e", edgecolor="#444", linewidth=1))

                if len(batch_data) > 0:
                # 按缺陷类型着色
                    type_colors = {"划痕": "red", "亮点": "yellow", "暗点": "black", "气泡": "cyan",
                                  "色差": "magenta", "漏光": "orange", "裂纹": "darkred", "异物": "green"}
                    for _, row in batch_data.iterrows():
                        c = type_colors.get(row["defect_type"], "white")
                        ax.scatter(row["x_mm"], row["y_mm"], c=c, s=30, alpha=0.7, edgecolors="white", linewidth=0.3, zorder=3)

                ax.set_xlim(-3, pw + 3)
                ax.set_ylim(-3, ph + 3)
                ax.set_title(f"{batch}\n{len(batch_data)} 缺陷", fontsize=9)
                ax.set_aspect("equal")
                ax.grid(True, alpha=0.1, color="gray")
                ax.tick_params(left=False, bottom=False, labelleft=False, labelbottom=False)

        # 隐藏多余子图
            for j in range(len(cmp_batches), len(axes_cmp)):
                axes_cmp[j].set_visible(False)

            fig_cmp.suptitle(f"{cmp_eq} / {cmp_seat} 跨批次对比", fontsize=12, y=1.01)
            plt.tight_layout()
            st.pyplot(fig_cmp)
            plt.close()

        # 对比统计
            st.subheader("对比统计")
            comp_stats = []
            for batch in cmp_batches:
                batch_data = df[(df["equipment_id"] == cmp_eq) & (df["seat_id"] == cmp_seat) & (df["batch_id"] == batch)]
                comp_stats.append({
                    "批次": batch,
                    "缺陷数": len(batch_data),
                    "主要类型": batch_data["defect_type"].mode().iloc[0] if len(batch_data) > 0 else "-",
                    "严重占比": f"{(batch_data['severity']=='严重').sum() / max(len(batch_data), 1):.0%}",
                    "中心X": round(batch_data["x_mm"].mean(), 1) if len(batch_data) > 0 else "-",
                    "中心Y": round(batch_data["y_mm"].mean(), 1) if len(batch_data) > 0 else "-",
                })
            st.dataframe(pd.DataFrame(comp_stats), use_container_width=True, hide_index=True)

        # 趋势判断
            if len(cmp_batches) >= 3:
                defect_counts = [len(df[(df["equipment_id"] == cmp_eq) & (df["seat_id"] == cmp_seat) & (df["batch_id"] == b)]) for b in cmp_batches]
                x_trend = np.arange(len(cmp_batches))
                coeffs = np.polyfit(x_trend, defect_counts, 1)
                slope = coeffs[0]
                if slope > 0.5:
                    st.warning(f"⚠️ **{cmp_eq}/{cmp_seat}** 缺陷数呈**上升趋势** (斜率: {slope:.1f}/批次)，建议安排设备检修")
                elif slope < -0.5:
                    st.success(f"✅ **{cmp_eq}/{cmp_seat}** 缺陷数呈**改善趋势** (斜率: {slope:.1f}/批次)")
                else:
                    st.info(f"➡️ **{cmp_eq}/{cmp_seat}** 缺陷数**平稳** (斜率: {slope:.1f}/批次)")

        else:
            st.info("请选择至少 2 个批次进行对比")

    # --- 缺陷传播追踪 ---
        st.divider()
        st.subheader("📡 缺陷坐标传播追踪")
        st.markdown(
            "追踪同一坐标区域在时间轴上的缺陷演变，识别持续恶化的位置。"
            "如果某坐标的缺陷数量随时间递增 → 该位置存在渐进性损伤（如吸嘴持续磨损）。"
        )

    # 坐标分桶 + 时间维度
        prop_bin = st.slider("传播追踪分桶大小 (mm)", min_value=10, max_value=50, value=20, step=10)

        df_time = df.copy()
        df_time["x_bin"] = (df_time["x_mm"] // prop_bin).astype(int)
        df_time["y_bin"] = (df_time["y_mm"] // prop_bin).astype(int)

    # 按桶 + 日期聚合
        prop_df = df_time.groupby(["x_bin", "y_bin", "day"]).size().reset_index(name="defect_count")

    # 找出至少有 3 天数据的桶
        bucket_days = prop_df.groupby(["x_bin", "y_bin"])["day"].nunique()
        active_buckets = bucket_days[bucket_days >= 3].index.tolist()

        if active_buckets:
        # 选择要追踪的桶
            bucket_options = [f"({bx},{by})" for bx, by in active_buckets]
            bucket_counts = prop_df.groupby(["x_bin", "y_bin"])["defect_count"].sum().sort_values(ascending=False)

        # 默认选缺陷最多的桶
            default_top = bucket_counts.index[0]
            selected_bucket = st.selectbox(
                "选择要追踪的坐标桶",
                options=bucket_options,
                index=0,
                format_func=lambda x: f"{x} (总缺陷: {bucket_counts.loc[tuple(map(int, x.strip('()').split(',')))]:.0f})"
            )

            bx, by = map(int, selected_bucket.strip("()").split(","))
            bucket_timeline = prop_df[(prop_df["x_bin"] == bx) & (prop_df["y_bin"] == by)].sort_values("day")
            bucket_timeline["day"] = pd.to_datetime(bucket_timeline["day"])

        # 传播趋势图
            fig_prop, ax_prop = plt.subplots(figsize=(12, 4))
            ax_prop.bar(bucket_timeline["day"], bucket_timeline["defect_count"],
                        color="steelblue", alpha=0.7, width=0.8)
        # 趋势线
            if len(bucket_timeline) >= 2:
                x_t = np.arange(len(bucket_timeline))
                coeffs_p = np.polyfit(x_t, bucket_timeline["defect_count"].values, 1)
                slope_p = coeffs_p[0]
                trend_y = np.polyval(coeffs_p, x_t)
                ax_prop.plot(bucket_timeline["day"], trend_y, color="red", linestyle="--",
                            linewidth=2, label=f"趋势 (斜率: {slope_p:.2f}/天)")

                if slope_p > 0.3:
                    ax_prop.set_title(f"坐标桶 ({bx},{by}) — 缺陷数上升 (恶化趋势)")
                elif slope_p < -0.3:
                    ax_prop.set_title(f"坐标桶 ({bx},{by}) — 缺陷数下降 (改善趋势)")
                else:
                    ax_prop.set_title(f"坐标桶 ({bx},{by}) — 缺陷数平稳")
            else:
                ax_prop.set_title(f"坐标桶 ({bx},{by})")

            ax_prop.set_ylabel("缺陷数量")
            ax_prop.tick_params(axis="x", rotation=45)
            ax_prop.legend()
            ax_prop.grid(True, alpha=0.3, axis="y")
            st.pyplot(fig_prop)
            plt.close()

        # 该桶的缺陷类型演变
            bucket_data = df_time[(df_time["x_bin"] == bx) & (df_time["y_bin"] == by)]
            st.markdown(f"**坐标桶 ({bx},{by}) 缺陷类型演变** (对应面板区域: X {bx*prop_bin}-{(bx+1)*prop_bin}mm, Y {by*prop_bin}-{(by+1)*prop_bin}mm)")
            bucket_type_timeline = bucket_data.groupby(["day", "defect_type"]).size().unstack(fill_value=0)
            bucket_type_timeline.index = pd.to_datetime(bucket_type_timeline.index)
            st.dataframe(bucket_type_timeline, use_container_width=True, height=300)
        else:
            st.info("当前数据中无足够多天数的连续缺陷坐标桶 (需 ≥3 天)")

# --- 底部：数据导出 ---
st.divider()
if current_config["show_export"]:
    st.subheader("📥 数据导出")

    # 综合报告导出
    st.subheader("📋 一键导出综合报告")
    st.markdown("包含所有分析模块的关键结论，适合汇报和存档。")

    report_parts = []
    report_parts.append("# 缺陷集中性分析综合报告\n")
    report_parts.append(f"**生成时间**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
    report_parts.append(f"**数据范围**: {start_date.strftime('%Y-%m-%d')} ~ {end_date.strftime('%Y-%m-%d')}")
    report_parts.append(f"**筛选后缺陷数**: {len(filtered_df)} 条")
    report_parts.append(f"**涉及面板**: {filtered_df['panel_id'].nunique()} 块")
    report_parts.append(f"**视图模式**: {view_mode}\n")

    # 1. KPI 摘要
    report_parts.append("## 1. KPI 摘要\n")
    report_kpis = calculate_kpis(df, filtered_df)
    total_panels_inspected_r = report_kpis["total_panels_inspected"]
    defective_panels_r = report_kpis["defective_panels"]
    yield_rate_r = report_kpis["yield_rate"]
    report_parts.append(f"- 检测面板数: {total_panels_inspected_r} 块")
    defective_rate_r = defective_panels_r / max(total_panels_inspected_r, 1) * 100
    report_parts.append(f"- 不良面板数: {defective_panels_r} 块 ({defective_rate_r:.1f}%)")
    report_parts.append(f"- 综合良率: {yield_rate_r:.1f}%")
    report_parts.append(f"- 缺陷总数: {len(filtered_df)} 个")
    report_parts.append(f"- 严重缺陷: {(filtered_df['severity']=='严重').sum()} 个\n")

    # 2. 缺陷类型
    report_parts.append("## 2. 缺陷类型分布\n")
    type_counts_r = filtered_df["defect_type"].value_counts()
    for t, c in type_counts_r.items():
        report_parts.append(f"- {t}: {c} ({c/len(filtered_df)*100:.1f}%)")
    report_parts.append("")

    # 3. 设备/座号
    if "equipment_id" in filtered_df.columns:
        report_parts.append("## 3. 设备与座号分布\n")
        eq_counts = filtered_df["equipment_id"].value_counts()
        for e, c in eq_counts.items():
            report_parts.append(f"- {e}: {c} 个缺陷")
        seat_top = filtered_df["seat_id"].value_counts().head(5)
        report_parts.append(f"\n**缺陷座号 TOP5**:")
        for i, (s, c) in enumerate(seat_top.items(), 1):
            report_parts.append(f"  {i}. {s}: {c} 个")
        report_parts.append("")

    # 4. 趋势
    report_parts.append("## 4. 趋势分析\n")
    daily_r = filtered_df.groupby("day").size()
    if len(daily_r) >= 2:
        x_r = np.arange(len(daily_r))
        coeffs_r = np.polyfit(x_r, daily_r.values.astype(float), 1)
        slope_r = coeffs_r[0]
        if slope_r > 0:
            report_parts.append(f"- 缺陷数趋势: **上升** (斜率 {slope_r:.1f}/天)")
        else:
            report_parts.append(f"- 缺陷数趋势: **下降** (斜率 {slope_r:.1f}/天)")
    report_parts.append("")

    # 5. 异常座号
    report_parts.append("## 5. 异常检测\n")
    if "seat_id" in filtered_df.columns:
        all_seat_stats_r = filtered_df.groupby(["equipment_id", "seat_id"]).size()
        mean_r = all_seat_stats_r.mean()
        std_r = all_seat_stats_r.std()
        threshold_2x_r = mean_r + 2 * std_r
        critical_r = all_seat_stats_r[all_seat_stats_r > threshold_2x_r]
        if len(critical_r) > 0:
            report_parts.append(f"- ⚠️ 2σ 异常座号: {len(critical_r)} 个")
            for (eq, seat), count in critical_r.items():
                report_parts.append(f"  - {eq}/{seat}: {count} 个缺陷")
        else:
            report_parts.append("- ✅ 无 2σ 异常座号")
    report_parts.append("")

    # 6. 建议
    report_parts.append("## 6. 建议\n")
    top_type = type_counts_r.index[0] if len(type_counts_r) > 0 else "-"
    top_eq = eq_counts.index[0] if len(eq_counts) > 0 else "-"
    report_parts.append(f"- 重点关注缺陷类型: **{top_type}**")
    report_parts.append(f"- 重点关注设备: **{top_eq}**")
    report_parts.append("- 建议查看 SPC 控制图确认趋势状态")
    report_parts.append("- 建议检查设备健康评分\n")

    report_parts.append("---\n*本报告由缺陷集中性分析系统自动生成*")

    full_report = "\n".join(report_parts)

    col_exp1, col_exp2, col_exp3 = st.columns(3)
    with col_exp1:
        st.download_button(
            label="📥 综合报告 (MD)",
            data=full_report.encode("utf-8"),
            file_name=f"defect_report_{datetime.now().strftime('%Y%m%d')}.md",
            mime="text/markdown",
            use_container_width=True
        )
    with col_exp2:
        csv_data = filtered_df.to_csv(index=False).encode("utf-8-sig")
        st.download_button(
            label="📥 筛选数据 (CSV)",
            data=csv_data,
            file_name=f"defect_data_{datetime.now().strftime('%Y%m%d')}.csv",
            mime="text/csv",
            use_container_width=True
        )
    with col_exp3:
        # 精简版 TXT 报告
        txt_lines = ["缺陷集中性分析报告", f"生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}",
                     f"缺陷数: {len(filtered_df)} | 面板: {filtered_df['panel_id'].nunique()}",
                     f"良率: {yield_rate_r:.1f}%"]
        for t, c in type_counts_r.head(3).items():
            txt_lines.append(f"  TOP: {t} {c}个")
        txt_content = "\n".join(txt_lines)
        st.download_button(
            label="📥 精简报告 (TXT)",
            data=txt_content.encode("utf-8"),
            file_name=f"defect_summary_{datetime.now().strftime('%Y%m%d')}.txt",
            mime="text/plain",
            use_container_width=True
        )