meta_analysis_utils.py

import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('Agg')  # 使用非互動式後端
import numpy as np
import pandas as pd
import arviz as az
import io
from PIL import Image
from scipy import stats

# 設定 Matplotlib 中文字體
plt.rcParams['font.sans-serif'] = ['Microsoft JhengHei', 'Microsoft YaHei', 'SimHei', 'Arial Unicode MS', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False

def plot_trace_combined(results):
    """
    繪製完整的 Trace Plot（warmup + posterior）
    
    Args:
        results: 分析結果字典
        
    Returns:
        PIL Image
    """
    try:
        trace_data = results['trace_data']
        warmup_end = trace_data['warmup_end']
        total_draws = results['model_params']['total_draws']
        n_chains = results['model_params']['n_chains']
        
        # 準備資料（處理多鏈）
        d_samples = np.array(trace_data['d'])
        sigma_samples = np.array(trace_data['sigma'])
        delta_new_samples = np.array(trace_data['delta_new'])
        
        # 如果是多鏈，reshape 成 (n_chains, total_draws)
        if d_samples.ndim == 1:
            d_samples = d_samples.reshape(n_chains, -1)
            sigma_samples = sigma_samples.reshape(n_chains, -1)
            delta_new_samples = delta_new_samples.reshape(n_chains, -1)
        
        # 創建 3x2 的子圖
        fig, axes = plt.subplots(3, 2, figsize=(14, 10))
        
        # 定義繪圖參數
        params = [
            ('d', d_samples, 'd (Log-OR)'),
            ('sigma', sigma_samples, 'sigma (Between-study SD)'),
            ('delta_new', delta_new_samples, 'delta_new (Predictive)')
        ]
        
        # 定義顏色
        colors = plt.cm.tab10.colors
        

        for idx, (param_name, samples, label) in enumerate(params):
            # 左圖: KDE 密度圖（分別顯示每條鏈）
            for chain_idx in range(n_chains):
                chain_color = colors[chain_idx % len(colors)]
                # 只用該鏈的 posterior 部分
                posterior_samples_chain = samples[chain_idx, warmup_end:]
                
                density = stats.gaussian_kde(posterior_samples_chain)
                xs = np.linspace(posterior_samples_chain.min(), posterior_samples_chain.max(), 200)
                axes[idx, 0].plot(xs, density(xs), color=chain_color, linewidth=2, 
                                 alpha=0.8, label=f'Chain {chain_idx+1}' if idx == 0 else '')
                axes[idx, 0].fill_between(xs, density(xs), alpha=0.2, color=chain_color)
            
            axes[idx, 0].set_xlabel(label, fontsize=12)
            axes[idx, 0].set_ylabel('Density', fontsize=12)
            axes[idx, 0].set_title(f'{label} Posterior Distribution', fontsize=13, fontweight='bold')
            axes[idx, 0].grid(alpha=0.3)
            if idx == 0 and n_chains > 1:
                axes[idx, 0].legend(loc='upper right', fontsize=9)            
            
            # 右圖: 完整 Trace（warmup + posterior），分別畫每條鏈
            x_vals = np.arange(total_draws)
            
            for chain_idx in range(n_chains):
                chain_color = colors[chain_idx % len(colors)]
                axes[idx, 1].plot(x_vals, samples[chain_idx], 
                                color=chain_color, 
                                alpha=0.7, 
                                linewidth=0.8,
                                label=f'Chain {chain_idx+1}' if idx == 0 else '')
            
            # 標記 warmup 結束
            ylim = axes[idx, 1].get_ylim()
            axes[idx, 1].axvline(x=warmup_end, color='red', linestyle='--', 
                               linewidth=2.5, alpha=0.8, 
                               label='Burn-in end' if idx == 0 else '')
            axes[idx, 1].text(warmup_end + 50, ylim[0] + (ylim[1]-ylim[0])*0.95, 
                            'Burn-in', color='red', fontsize=10, va='top', 
                            bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))
            
            axes[idx, 1].set_xlabel('Iteration', fontsize=12)
            axes[idx, 1].set_ylabel(label, fontsize=12)
            axes[idx, 1].set_title(f'{label} Trace', fontsize=13, fontweight='bold')
            axes[idx, 1].grid(alpha=0.3)
            if idx == 0:
                axes[idx, 1].legend(loc='upper right', fontsize=9)
        
        plt.tight_layout()
        
        # 轉換為圖片
        buf = io.BytesIO()
        plt.savefig(buf, format='png', dpi=150, bbox_inches='tight')
        buf.seek(0)
        img = Image.open(buf)
        plt.close()
        
        return img
    
    except Exception as e:
        print(f"Error in plot_trace_combined: {e}")
        import traceback
        traceback.print_exc()
        return None


def plot_posterior(results):
    """
    繪製後驗分佈圖
    
    Args:
        results: 分析結果字典
        
    Returns:
        PIL Image
    """
    try:
        trace_data = results['trace_data']
        warmup_end = trace_data['warmup_end']
        n_chains = results['model_params']['n_chains']
        
        # 準備後驗樣本（去除 warmup，處理多鏈）
        d_arr = np.array(trace_data['d'])
        sigma_arr = np.array(trace_data['sigma'])
        delta_new_arr = np.array(trace_data['delta_new'])
        or_arr = np.array(trace_data['or'])
        
        # 如果是多鏈，reshape 成 (n_chains, total_draws)
        if d_arr.ndim == 1:
            d_arr = d_arr.reshape(n_chains, -1)
            sigma_arr = sigma_arr.reshape(n_chains, -1)
            delta_new_arr = delta_new_arr.reshape(n_chains, -1)
            or_arr = or_arr.reshape(n_chains, -1)
        
        # 取 posterior 部分並 flatten（合併所有鏈）
        d_post = d_arr[:, warmup_end:].flatten()
        sigma_post = sigma_arr[:, warmup_end:].flatten()
        delta_new_post = delta_new_arr[:, warmup_end:].flatten()
        or_post = or_arr[:, warmup_end:].flatten()    

        
        # 創建 2x2 的子圖
        fig, axes = plt.subplots(2, 2, figsize=(14, 10))
        axes = axes.flatten()
        
        params = [
            ('d', d_post, 'd (Log-OR)', results['overall']['d_mean']),
            ('sigma', sigma_post, 'sigma (Between-study SD)', results['overall']['sigma_mean']),
            ('delta_new', delta_new_post, 'delta_new (Predictive)', results['predictive']['delta_new_mean']),
            ('or', or_post, 'OR (Odds Ratio)', results['overall']['or_mean'])
        ]
        
        for idx, (param_name, samples, label, mean_val) in enumerate(params):
            ax = axes[idx]
            
            # 計算 HDI
            hdi_low = np.percentile(samples, 2.5)
            hdi_high = np.percentile(samples, 97.5)
            
            # 繪製密度圖
            density = stats.gaussian_kde(samples)
            xs = np.linspace(samples.min(), samples.max(), 300)
            ys = density(xs)
            
            ax.plot(xs, ys, color='steelblue', linewidth=2)
            
            # 填充 HDI 區域
            mask = (xs >= hdi_low) & (xs <= hdi_high)
            ax.fill_between(xs[mask], ys[mask], alpha=0.3, color='steelblue', label='95% HDI')
            
            # 標記平均值
            ax.axvline(mean_val, color='red', linestyle='--', linewidth=2, label=f'Mean = {mean_val:.3f}')
            
            # 設定標籤
            ax.set_xlabel(label, fontsize=12)
            ax.set_ylabel('Density', fontsize=12)
            ax.set_title(f'{label}', fontsize=13, fontweight='bold')
            ax.legend(loc='upper right', fontsize=9)
            ax.grid(alpha=0.3)
            
            # 添加 HDI 文字註解
            ax.text(0.02, 0.98, f'95% HDI:\n[{hdi_low:.3f}, {hdi_high:.3f}]',
                   transform=ax.transAxes, fontsize=9,
                   verticalalignment='top',
                   bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
        
        plt.tight_layout()
        
        # 轉換為圖片
        buf = io.BytesIO()
        plt.savefig(buf, format='png', dpi=150, bbox_inches='tight')
        buf.seek(0)
        img = Image.open(buf)
        plt.close()
        
        return img
    
    except Exception as e:
        print(f"Error in plot_posterior: {e}")
        return None


def plot_forest(results):
    """
    繪製 Forest Plot（各研究效應）
    
    Args:
        results: 分析結果字典
        
    Returns:
        PIL Image
    """

    try:      
        
        by_study = results['by_study']
        n_studies = results['n_studies']
        treatment_type = results['treatment_type']
        control_type = results['control_type']
        
        delta_mean = np.array(by_study['delta_mean'])
        delta_hdi_low = np.array(by_study['delta_hdi_low'])
        delta_hdi_high = np.array(by_study['delta_hdi_high'])
        
        # 創建圖表
        fig, ax = plt.subplots(figsize=(12, max(8, n_studies * 0.3)))
        
        y_pos = np.arange(n_studies)
        
        # 繪製信賴區間（橫線）
        for i in range(n_studies):
            ax.hlines(y_pos[i], delta_hdi_low[i], delta_hdi_high[i], 
                     color='steelblue', linewidth=2.5, alpha=0.8)
        
        # 繪製平均值（點）
        ax.scatter(delta_mean, y_pos, color='darkblue', s=100, zorder=3, 
                  edgecolors='white', linewidth=1.5, label='Mean Effect')
        
        # 標註顯著的點
        for i in range(n_studies):
            if delta_hdi_low[i] > 0:  # 顯著正效應
                ax.scatter(delta_mean[i], y_pos[i], marker='*', s=300, 
                          color='gold', zorder=4, edgecolors='black', linewidth=1)
            elif delta_hdi_high[i] < 0:  # 顯著負效應
                ax.scatter(delta_mean[i], y_pos[i], marker='*', s=300, 
                          color='red', zorder=4, edgecolors='black', linewidth=1)
        
        # 設定軸
        ax.set_yticks(y_pos)
        ax.set_yticklabels([f'Gym {i+1}' for i in range(n_studies)], fontsize=10)
        ax.invert_yaxis()
        ax.axvline(0, color='red', linestyle='--', linewidth=2, alpha=0.7, label='No Effect (δ=0)')
        ax.set_xlabel('Delta (Log Odds Ratio)', fontsize=13, fontweight='bold')
        ax.set_title(f'Study-specific Treatment Effects\n{treatment_type} vs {control_type}', 
                    fontsize=15, fontweight='bold', pad=20)
        ax.legend(loc='lower right', fontsize=10)
        ax.grid(axis='x', alpha=0.3)
        
        plt.tight_layout()
        
        # 轉換為圖片
        buf = io.BytesIO()
        plt.savefig(buf, format='png', dpi=150, bbox_inches='tight')
        buf.seek(0)
        img = Image.open(buf)
        plt.close()
        
        return img
    
    except Exception as e:
        print(f"Error in plot_forest: {e}")
        return None


def plot_dag(model, results):
    """
    繪製 WinBUGS 風格的模型 DAG 圖
    將 Binomial 拆成 r[i]（觀測勝場）與 n[i]（總場數常數）
    節點標籤動態帶入實際選擇的屬性名稱
    指向 Deterministic 節點的邊以虛線表示

    Args:
        model: PyMC 模型物件（保留介面相容，實際不使用）
        results: 分析結果字典

    Returns:
        PIL Image 或 None
    """
    try:
        import graphviz

        treatment_type = results['treatment_type']
        control_type = results['control_type']
        n_studies = results['n_studies']

        t = treatment_type
        c = control_type

        dot = graphviz.Digraph(
            'BayesianMetaAnalysis',
            format='png',
            graph_attr={
                'rankdir': 'TB',
                'fontsize': '14',
                'fontname': 'Arial',
                'bgcolor': 'white',
                'pad': '0.6',
                'nodesep': '0.8',
                'ranksep': '1.0',
                'dpi': '150',
            },
            node_attr={
                'fontname': 'Arial',
                'fontsize': '12',
            },
            edge_attr={
                'arrowsize': '0.8',
                'color': '#333333',
            }
        )

        # === 節點樣式 ===
        # 隨機變數 (stochastic) → 橢圓，白底
        stochastic = {
            'shape': 'ellipse', 'style': 'filled',
            'fillcolor': '#FFFFFF', 'color': '#333333', 'penwidth': '2'
        }
        # 確定性變數 (deterministic) → 方框，白底
        deterministic = {
            'shape': 'rect', 'style': 'filled',
            'fillcolor': '#FFFFFF', 'color': '#333333', 'penwidth': '2'
        }
        # 觀測資料 (observed) → 橢圓，灰底
        observed = {
            'shape': 'ellipse', 'style': 'filled',
            'fillcolor': '#D0D0D0', 'color': '#333333', 'penwidth': '2'
        }
        # 常數/資料 (constant) → 方框，灰底
        constant = {
            'shape': 'rect', 'style': 'filled',
            'fillcolor': '#D0D0D0', 'color': '#333333', 'penwidth': '2'
        }

        # ========== 第 1 層：超參數 ==========
        with dot.subgraph() as s:
            s.attr(rank='same')
            s.node('tau', 'τ\nGamma', **stochastic)
            s.node('d', 'd\nNormal', **stochastic)

        # ========== 第 2 層：衍生超參數 + 預測 ==========
        with dot.subgraph() as s:
            s.attr(rank='same')
            #s.node('sigma', 'σ = 1/√τ', **deterministic)
            #s.node('delta_new', 'δ_new\nNormal(d, 1/√τ)', **stochastic)
            s.node('sigma', 'σ = 1/√τ', **deterministic)
            s.node('delta_new', 'δ_new\nNormal', **stochastic)            
            s.node('or', 'OR = exp(d)', **deterministic)

        # ========== Plate（研究 i = 1..N） ==========
        with dot.subgraph(name='cluster_studies') as plate:
            plate.attr(
                label=f'  i = 1 ... {n_studies}  ',
                labelloc='b',
                labeljust='r',
                style='rounded',
                color='#555555',
                penwidth='2',
                fontsize='14',
                fontname='Arial',
                margin='20',
            )

            # 第 3 層：研究特定隨機效應
            with plate.subgraph() as s:
                s.attr(rank='same')
                #s.node('mu_i', 'μ[i]\nNormal(0, 100)', **stochastic)
                #s.node('delta_i', 'δ[i]\nNormal(d, 1/√τ)', **stochastic)
                s.node('mu_i', 'μ[i]\nNormal', **stochastic)
                s.node('delta_i', 'δ[i]\nNormal', **stochastic)                

            # 第 4 層：勝率（確定性）— 帶屬性名稱
            with plate.subgraph() as s:
                s.attr(rank='same')
                s.node('pc_i', f'p_{c}[i]', **deterministic)
                s.node('pt_i', f'p_{t}[i]', **deterministic)

            # 第 5 層：觀測資料 r[i] 和常數 n[i] — 帶屬性名稱
            with plate.subgraph() as s:
                s.attr(rank='same')
                s.node('nc_i', f'n_{c}[i]', **constant)
                s.node('rc_i', f'r_{c}[i]', **observed)
                s.node('rt_i', f'r_{t}[i]', **observed)
                s.node('nt_i', f'n_{t}[i]', **constant)

        # ========== 邊（依賴關係） ==========
        # 虛線邊樣式（指向 Deterministic 節點）
        dashed = {'style': 'dashed'}

        # --- 虛線：指向 Deterministic 節點的邊 ---
        dot.edge('tau', 'sigma', **dashed)          # τ → σ
        dot.edge('d', 'or', **dashed)               # d → OR
        dot.edge('mu_i', 'pc_i', **dashed)          # μ[i] → p_c[i]
        dot.edge('mu_i', 'pt_i', **dashed)          # μ[i] → p_t[i]
        dot.edge('delta_i', 'pt_i', **dashed)       # δ[i] → p_t[i]

        # --- 實線：其餘所有邊 ---
        dot.edge('d', 'delta_new')
        dot.edge('tau', 'delta_new')
        dot.edge('d', 'delta_i')
        dot.edge('tau', 'delta_i')
        dot.edge('pc_i', 'rc_i')
        dot.edge('nc_i', 'rc_i')
        dot.edge('pt_i', 'rt_i')
        dot.edge('nt_i', 'rt_i')

        # 渲染為 PNG → PIL Image
        png_bytes = dot.pipe(format='png')
        img = Image.open(io.BytesIO(png_bytes))

        return img

    except Exception as e:
        print(f"Error in plot_dag: {e}")
        return None


def create_dag_legend_table(results):
    """
    創建 DAG 中文對照表
    
    Args:
        results: 分析結果字典
        
    Returns:
        PIL Image
    """
    
    try:
        treatment_type = results['treatment_type']
        control_type = results['control_type']
        
        # 設定中文字體（雲端部署用）
        import matplotlib.font_manager as fm
        
        # 嘗試找到可用的中文字體
        available_fonts = [f.name for f in fm.fontManager.ttflist]
        chinese_fonts = ['Noto Sans CJK TC', 'Noto Sans CJK SC', 'Noto Sans TC', 'Noto Sans SC',
                         'Microsoft JhengHei', 'Microsoft YaHei', 'SimHei', 'WenQuanYi Micro Hei',
                         'AR PL UMing TW', 'DejaVu Sans']
        
        selected_font = None
        for font in chinese_fonts:
            if font in available_fonts:
                selected_font = font
                break
        
        if selected_font:
            plt.rcParams['font.family'] = selected_font
        plt.rcParams['axes.unicode_minus'] = False
        
        fig, ax = plt.subplots(figsize=(12, 7))

        ax.axis('off')
        
        # 準備表格資料
        table_data = [
            ['節點符號', '統計意義', '寶可夢道館情境'],
            ['d', '整體平均效果\n(log-odds ratio)', f'{treatment_type}相比{control_type}的平均勝率差異\n(對數尺度)'],
            ['tau', '精確度參數\n(precision)', '道館間變異的精確度\n(tau越大代表各道館越一致)'],
            ['sigma', '標準差\n(standard deviation)', '道館間效果的標準差\n(不同道館之間的結果波動幅度)'],
            ['delta[i]', '研究i的效果\n(study-specific effect)', f'第i間道館內，{treatment_type}相對{control_type}的勝率優勢'],
            ['delta_new', '預測新研究效果\n(predictive effect)', '預測未來新開的第31間道館的對抗結果'],
            ['mu[i]', '基線參數\n(baseline logit)', f'第i間道館內，{control_type}的基礎勝率(logit尺度)'],
            ['OR', '勝算比\n(odds ratio)', f'{treatment_type}相比{control_type}的勝算倍數 (OR=exp(d))'],
            [f'p_{treatment_type}', f'{treatment_type}勝率\n(win rate)', f'第i間道館內，{treatment_type}寶可夢的勝率'],
            [f'p_{control_type}', f'{control_type}勝率\n(win rate)', f'第i間道館內，{control_type}寶可夢的勝率'],
            [f'{treatment_type}_wins', '觀測資料\n(observed data)', f'第i間道館內，{treatment_type}的實際勝場數'],
            [f'{control_type}_wins', '觀測資料\n(observed data)', f'第i間道館內，{control_type}的實際勝場數'],
        ]
        
        # 創建表格
        table = ax.table(cellText=table_data, loc='center', cellLoc='left',
                        colWidths=[0.15, 0.28, 0.57])
        table.auto_set_font_size(False)
        table.set_fontsize(9.5)
        table.scale(1, 2.8)
        
        # 標題列格式
        for i in range(3):
            cell = table[(0, i)]
            cell.set_facecolor('#4472C4')
            cell.set_text_props(weight='bold', color='white', fontsize=11)
            cell.set_height(0.08)
        
        # 其他列交替顏色
        for i in range(1, len(table_data)):
            color = '#E7E6E6' if i % 2 == 0 else 'white'
            for j in range(3):
                cell = table[(i, j)]
                cell.set_facecolor(color)
                cell.set_edgecolor('#CCCCCC')
                if j == 0:  # 第一列（符號）加粗
                    cell.set_text_props(weight='bold', fontsize=10)
        
        # 加上標題和說明
        plt.suptitle(f'貝葉斯後設分析模型節點說明', 
                    fontsize=16, weight='bold', y=0.98)
        plt.title(f'比較情境: {treatment_type} (實驗組) vs {control_type} (對照組)', 
                 fontsize=12, pad=20)
        
        # 加上註解
        fig.text(0.5, 0.02, 
                f'註: 灰色底的節點 (如 {treatment_type}_wins, {control_type}_wins) 為觀測資料；白色圓圈節點為隨機變數；方框節點為確定性變數',
                ha='center', fontsize=9, style='italic', color='gray')
        
        plt.tight_layout()
        
        # 轉換為圖片
        buf = io.BytesIO()
        plt.savefig(buf, format='png', dpi=200, bbox_inches='tight', facecolor='white')
        buf.seek(0)
        img = Image.open(buf)
        plt.close()
        
        return img
    
    except Exception as e:
        print(f"Error in create_dag_legend_table: {e}")
        return None
        

def create_dag_legend_html(results):
    """
    創建 DAG 中文對照表（HTML 版本）
    
    Args:
        results: 分析結果字典
        
    Returns:
        str: HTML 表格字串
    """
    treatment_type = results['treatment_type']
    control_type = results['control_type']
    
    html = f"""
    <style>
        .dag-table {{
            width: 100%;
            border-collapse: collapse;
            font-family: Arial, sans-serif;
            margin: 20px 0;
        }}
        .dag-table th {{
            background-color: #4472C4;
            color: white;
            padding: 12px 8px;
            text-align: left;
            font-weight: bold;
        }}
        .dag-table td {{
            padding: 10px 8px;
            border-bottom: 1px solid #ddd;
        }}
        .dag-table tr:nth-child(even) {{
            background-color: #f2f2f2;
        }}
        .dag-table tr:hover {{
            background-color: #e8e8e8;
        }}
        .node-symbol {{
            font-weight: bold;
            font-family: monospace;
            color: #333;
        }}
    </style>
    
    <h3 style="text-align: center;">貝葉斯後設分析模型節點說明</h3>
    <p style="text-align: center; color: #666;">比較情境: {treatment_type} (實驗組) vs {control_type} (對照組)</p>
    
    <table class="dag-table">
        <tr>
            <th style="width: 15%;">節點符號</th>
            <th style="width: 25%;">統計意義</th>
            <th style="width: 60%;">寶可夢道館情境</th>
        </tr>
        <tr>
            <td class="node-symbol">d</td>
            <td>整體平均效果<br><small>(log-odds ratio)</small></td>
            <td>{treatment_type}相比{control_type}的平均勝率差異（對數尺度）</td>
        </tr>
        <tr>
            <td class="node-symbol">tau</td>
            <td>精確度參數<br><small>(precision)</small></td>
            <td>道館間變異的精確度（tau越大代表各道館越一致）</td>
        </tr>
        <tr>
            <td class="node-symbol">sigma</td>
            <td>標準差<br><small>(standard deviation)</small></td>
            <td>道館間效果的標準差（不同道館之間的結果波動幅度）</td>
        </tr>
        <tr>
            <td class="node-symbol">delta[i]</td>
            <td>研究i的效果<br><small>(study-specific effect)</small></td>
            <td>第i間道館內，{treatment_type}相對{control_type}的勝率優勢</td>
        </tr>
        <tr>
            <td class="node-symbol">delta_new</td>
            <td>預測新研究效果<br><small>(predictive effect)</small></td>
            <td>預測未來新開的第31間道館的對抗結果</td>
        </tr>
        <tr>
            <td class="node-symbol">mu[i]</td>
            <td>基線參數<br><small>(baseline logit)</small></td>
            <td>第i間道館內，{control_type}的基礎勝率(logit尺度)</td>
        </tr>
        <tr>
            <td class="node-symbol">OR</td>
            <td>勝算比<br><small>(odds ratio)</small></td>
            <td>{treatment_type}相比{control_type}的勝算倍數 (OR=exp(d))</td>
        </tr>
        <tr>
            <td class="node-symbol">p_{treatment_type}</td>
            <td>{treatment_type}勝率<br><small>(win rate)</small></td>
            <td>第i間道館內，{treatment_type}寶可夢的勝率</td>
        </tr>
        <tr>
            <td class="node-symbol">p_{control_type}</td>
            <td>{control_type}勝率<br><small>(win rate)</small></td>
            <td>第i間道館內，{control_type}寶可夢的勝率</td>
        </tr>
        <tr>
            <td class="node-symbol">{treatment_type}_wins</td>
            <td>觀測資料<br><small>(observed data)</small></td>
            <td>第i間道館內，{treatment_type}的實際勝場數</td>
        </tr>
        <tr>
            <td class="node-symbol">{control_type}_wins</td>
            <td>觀測資料<br><small>(observed data)</small></td>
            <td>第i間道館內，{control_type}的實際勝場數</td>
        </tr>
    </table>
    
    <p style="text-align: center; font-size: 12px; color: #888; font-style: italic;">
        註: 灰色底的節點 (如 {treatment_type}_wins, {control_type}_wins) 為觀測資料；白色圓圈節點為隨機變數；方框節點為確定性變數
    </p>
    """
    
    return html



def format_summary_stats(results):
    """
    格式化分析結果為文字報告
    
    Args:
        results: 分析結果字典
        
    Returns:
        str: 格式化的文字報告
    """
    overall = results['overall']
    pred = results['predictive']
    diag = results['diagnostics']
    
    report = f"""
==============================================
貝氏後設分析報告
==============================================

分析時間: {results['timestamp']}
實驗組: {results['treatment_type']}
對照組: {results['control_type']}
研究數量: {results['n_studies']} 個道館

----------------------------------------------
1. 整體效應摘要 (Overall Effect)
----------------------------------------------
d (整體對數勝算比):
  - 平均值: {overall['d_mean']:.4f}
  - 標準差: {overall['d_sd']:.4f}
  - 95% HDI: [{overall['d_hdi_low']:.4f}, {overall['d_hdi_high']:.4f}]

勝算比 (Odds Ratio):
  - 平均值: {overall['or_mean']:.4f}
  - 標準差: {overall['or_sd']:.4f}
  - 95% HDI: [{overall['or_hdi_low']:.4f}, {overall['or_hdi_high']:.4f}]

sigma (道館間變異):
  - 平均值: {overall['sigma_mean']:.4f}
  - 標準差: {overall['sigma_sd']:.4f}
  - 95% HDI: [{overall['sigma_hdi_low']:.4f}, {overall['sigma_hdi_high']:.4f}]

----------------------------------------------
2. 預測新研究效果 (Predictive Effect)
----------------------------------------------
delta_new (預測效應):
  - 平均值: {pred['delta_new_mean']:.4f}
  - 標準差: {pred['delta_new_sd']:.4f}
  - 95% HDI: [{pred['delta_new_hdi_low']:.4f}, {pred['delta_new_hdi_high']:.4f}]

預測勝算比:
  - 平均值: {pred['or_new_mean']:.4f}
  - 95% HDI: [{pred['or_new_hdi_low']:.4f}, {pred['or_new_hdi_high']:.4f}]

不確定性增加倍數: {pred['uncertainty_ratio']:.2f}x

----------------------------------------------
3. 模型收斂診斷 (Diagnostics)
----------------------------------------------
R-hat (d): {f"{diag['rhat_d']:.4f}" if diag['rhat_d'] is not None else 'N/A'}
R-hat (sigma): {f"{diag['rhat_sigma']:.4f}" if diag['rhat_sigma'] is not None else 'N/A'}
ESS (d): {int(diag['ess_d']) if diag['ess_d'] is not None else 'N/A'}
ESS (sigma): {int(diag['ess_sigma']) if diag['ess_sigma'] is not None else 'N/A'}
收斂狀態: {'✓ 已收斂' if diag['converged'] else '✗ 未收斂'}

----------------------------------------------
4. MCMC 參數設定
----------------------------------------------
Warmup 樣本數: {results['model_params']['n_warmup']}
Posterior 樣本數: {results['model_params']['n_samples']}
鏈數: {results['model_params']['n_chains']}
總樣本數: {results['model_params']['total_draws']}

----------------------------------------------
5. 結果解釋
----------------------------------------------
"""
    
    # 添加解釋
    or_mean = overall['or_mean']
    or_low = overall['or_hdi_low']
    or_high = overall['or_hdi_high']
    
    if or_low > 1:
        effect_interp = f"{results['treatment_type']} 相對於 {results['control_type']} 有顯著優勢"
    elif or_high < 1:
        effect_interp = f"{results['control_type']} 相對於 {results['treatment_type']} 有顯著優勢"
    else:
        effect_interp = f"{results['treatment_type']} 與 {results['control_type']} 無顯著差異"
    
    sigma_mean = overall['sigma_mean']
    if sigma_mean > 0.5:
        het_interp = "高異質性 - 不同道館的結果差異很大"
    elif sigma_mean > 0.3:
        het_interp = "中等異質性 - 不同道館的結果有一定差異"
    else:
        het_interp = "低異質性 - 不同道館的結果相對一致"
    
    report += f"""
整體效應: {effect_interp}
異質性: {het_interp}

平均而言，{results['treatment_type']} 獲勝的勝算是 {results['control_type']} 的 {or_mean:.3f} 倍
(95% 可信區間: [{or_low:.3f}, {or_high:.3f}])

==============================================
"""
    
    return report