project
/
lanu-android


			
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							#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
生成日志分析HTML可视化报告

功能特性:
    - 自动根据输入文件名生成输出报告文件名
    - 统计和标记超过400字符的超长日志
    - 可视化展示TAG使用情况和超长日志分布
    - 提供优化建议

使用方法:
    # 自动生成报告文件名（推荐）
    python generate_log_report_html.py com.partyjoy.yoki_20250922.xlog.log
    # 输出: com.partyjoy.yoki_20250922_report.html
    
    # 指定输出文件名
    python generate_log_report_html.py logcat.txt --output custom_report.html
"""

import sys
import argparse
from pathlib import Path
from analyze_log_tags import LogTagAnalyzer


HTML_TEMPLATE = """
<!DOCTYPE html>
<html lang="zh-CN">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>日志TAG分析报告</title>
    <style>
        * {{{{
            margin: 0;
            padding: 0;
            box-sizing: border-box;
        }}}}
        
        body {{
            font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif;
            background: #f5f5f5;
            padding: 20px;
            color: #333;
        }}
        
        .container {{
            max-width: 1400px;
            margin: 0 auto;
            background: white;
            border-radius: 8px;
            box-shadow: 0 2px 8px rgba(0,0,0,0.1);
            padding: 30px;
        }}
        
        h1 {{
            color: #1a73e8;
            margin-bottom: 10px;
            font-size: 28px;
        }}
        
        .subtitle {{
            color: #666;
            margin-bottom: 30px;
            font-size: 14px;
        }}
        
        .summary {{
            display: grid;
            grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
            gap: 20px;
            margin-bottom: 30px;
        }}
        
        .summary-card {{
            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
            color: white;
            padding: 20px;
            border-radius: 8px;
            box-shadow: 0 2px 4px rgba(0,0,0,0.1);
        }}
        
        .summary-card.info {{
            background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%);
        }}
        
        .summary-card.success {{
            background: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%);
        }}
        
        .summary-card.warning {{
            background: linear-gradient(135deg, #ffa751 0%, #ffe259 100%);
        }}
        
        .summary-card h3 {{
            font-size: 14px;
            margin-bottom: 10px;
            opacity: 0.9;
        }}
        
        .summary-card .value {{
            font-size: 32px;
            font-weight: bold;
        }}
        
        .summary-card .sub-value {{
            font-size: 14px;
            margin-top: 5px;
            opacity: 0.9;
        }}
        
        .chart-container {{
            margin: 30px 0;
            background: #fafafa;
            padding: 20px;
            border-radius: 8px;
        }}
        
        .chart-title {{
            font-size: 18px;
            font-weight: bold;
            margin-bottom: 15px;
            color: #333;
        }}
        
        .bar-chart {{
            margin: 20px 0;
        }}
        
        .bar-item {{
            margin-bottom: 12px;
        }}
        
        .bar-label {{
            display: flex;
            justify-content: space-between;
            margin-bottom: 5px;
            font-size: 13px;
        }}
        
        .tag-name {{
            font-weight: 500;
            color: #333;
        }}
        
        .tag-stats {{
            color: #666;
        }}
        
        .bar-bg {{
            background: #e0e0e0;
            height: 24px;
            border-radius: 4px;
            overflow: hidden;
            position: relative;
        }}
        
        .bar-fill {{
            height: 100%;
            background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
            display: flex;
            align-items: center;
            justify-content: flex-end;
            padding-right: 8px;
            color: white;
            font-size: 12px;
            font-weight: bold;
            transition: width 0.3s ease;
        }}
        
        table {{
            width: 100%;
            border-collapse: collapse;
            margin: 20px 0;
            font-size: 14px;
        }}
        
        th, td {{
            padding: 12px;
            text-align: left;
            border-bottom: 1px solid #e0e0e0;
        }}
        
        th {{
            background: #f5f5f5;
            font-weight: 600;
            color: #333;
            position: sticky;
            top: 0;
        }}
        
        tr:hover {{
            background: #fafafa;
        }}
        
        .rank {{
            font-weight: bold;
            color: #666;
        }}
        
        .percentage {{
            font-weight: 600;
        }}
        
        .high-freq {{
            color: #d32f2f;
        }}
        
        .med-freq {{
            color: #f57c00;
        }}
        
        .low-freq {{
            color: #388e3c;
        }}
        
        .level-badge {{
            display: inline-block;
            padding: 2px 6px;
            border-radius: 3px;
            font-size: 11px;
            font-weight: bold;
            margin-right: 4px;
        }}
        
        .level-v {{ background: #9e9e9e; color: white; }}
        .level-d {{ background: #2196f3; color: white; }}
        .level-i {{ background: #4caf50; color: white; }}
        .level-w {{ background: #ff9800; color: white; }}
        .level-e {{ background: #f44336; color: white; }}
        
        .section {{
            margin: 40px 0;
        }}
        
        .section-title {{
            font-size: 20px;
            font-weight: bold;
            margin-bottom: 20px;
            color: #333;
            border-left: 4px solid #1a73e8;
            padding-left: 12px;
        }}
        
        .suggestion-box {{
            background: #fff3cd;
            border-left: 4px solid #ffc107;
            padding: 15px;
            border-radius: 4px;
            margin: 20px 0;
        }}
        
        .suggestion-title {{
            font-weight: bold;
            margin-bottom: 10px;
            color: #856404;
        }}
        
        .suggestion-list {{
            margin-left: 20px;
            color: #856404;
        }}
        
        footer {{
            margin-top: 40px;
            padding-top: 20px;
            border-top: 1px solid #e0e0e0;
            text-align: center;
            color: #666;
            font-size: 12px;
        }}
    </style>
</head>
<body>
    <div class="container">
        <h1>📊 日志TAG分析报告</h1>
        <div class="subtitle">生成时间: {timestamp} | 文件: {filename}</div>
        
        <div class="summary">
            <div class="summary-card">
                <h3>总日志行数</h3>
                <div class="value">{total_lines:,}</div>
            </div>
            <div class="summary-card info">
                <h3>有效日志行</h3>
                <div class="value">{matched_lines:,}</div>
            </div>
            <div class="summary-card success">
                <h3>不同TAG数量</h3>
                <div class="value">{unique_tags}</div>
            </div>
            <div class="summary-card warning">
                <h3>超长日志数</h3>
                <div class="value">{long_logs_count:,}</div>
                <div class="sub-value">占比 {long_logs_percentage:.2f}%</div>
            </div>
        </div>
        
        <div class="section">
            <div class="section-title">TOP 20 高频TAG</div>
            <div class="chart-container">
                <div class="bar-chart">
                    {top20_bars}
                </div>
            </div>
        </div>
        
        <div class="section">
            <div class="section-title">超长日志TOP 10（>400字符）</div>
            <div class="chart-container">
                <div class="bar-chart">
                    {long_logs_bars}
                </div>
            </div>
        </div>
        
        <div class="section">
            <div class="section-title">完整TAG列表</div>
            <table>
                <thead>
                    <tr>
                        <th>排名</th>
                        <th>TAG名称</th>
                        <th>出现次数</th>
                        <th>占比</th>
                        <th>超长日志</th>
                        <th>日志级别分布</th>
                    </tr>
                </thead>
                <tbody>
                    {table_rows}
                </tbody>
            </table>
        </div>
        
        <div class="section">
            <div class="suggestion-box">
                <div class="suggestion-title">💡 优化建议</div>
                <div class="suggestion-list">
                    {suggestions}
                </div>
            </div>
        </div>
        
        <footer>
            报告由 analyze_log_tags.py 生成
        </footer>
    </div>
</body>
</html>
"""


def generate_html_report(analyzer: LogTagAnalyzer, filename: str, output_path: str):
    """生成HTML报告"""
    from datetime import datetime
    
    stats = analyzer.get_statistics()
    
    # 计算超长日志统计
    total_long_logs = sum(analyzer.long_log_counter.values())
    long_logs_percentage = (total_long_logs / analyzer.matched_lines * 100) if analyzer.matched_lines > 0 else 0
    
    # 生成超长日志TOP 10柱状图
    long_logs_bars = ""
    if analyzer.long_log_counter:
        long_log_stats = analyzer.long_log_counter.most_common(10)
        max_long_count = long_log_stats[0][1] if long_log_stats else 1
        
        for i, (tag, long_count) in enumerate(long_log_stats, 1):
            tag_total = analyzer.tag_counter[tag]
            long_percentage_in_tag = (long_count / tag_total) * 100
            bar_width = (long_count / max_long_count) * 100
            
            long_logs_bars += f"""
            <div class="bar-item">
                <div class="bar-label">
                    <span class="tag-name">{i}. {tag}</span>
                    <span class="tag-stats high-freq">{long_count:,} 条 ({long_percentage_in_tag:.1f}% 的该TAG)</span>
                </div>
                <div class="bar-bg">
                    <div class="bar-fill" style="width: {bar_width}%; background: linear-gradient(90deg, #ff9800 0%, #ff5722 100%);">{long_count:,}</div>
                </div>
            </div>
            """
    else:
        long_logs_bars = '<p style="color: #666; text-align: center; padding: 20px;">未发现超长日志</p>'
    
    # 生成TOP 20柱状图
    top20_bars = ""
    for i, (tag, count, percentage) in enumerate(stats[:20], 1):
        freq_class = "high-freq" if percentage > 5 else ("med-freq" if percentage > 1 else "low-freq")
        top20_bars += f"""
        <div class="bar-item">
            <div class="bar-label">
                <span class="tag-name">{i}. {tag}</span>
                <span class="tag-stats {freq_class}">{count:,} 次 ({percentage:.2f}%)</span>
            </div>
            <div class="bar-bg">
                <div class="bar-fill" style="width: {min(percentage * 2, 100)}%">{percentage:.2f}%</div>
            </div>
        </div>
        """
    
    # 生成表格行
    table_rows = ""
    for i, (tag, count, percentage) in enumerate(stats, 1):
        freq_class = "high-freq" if percentage > 5 else ("med-freq" if percentage > 1 else "low-freq")
        
        # 超长日志数量
        long_count = analyzer.long_log_counter.get(tag, 0)
        if long_count > 0:
            long_percentage = (long_count / count) * 100
            long_info = f'<span class="high-freq">{long_count:,} ({long_percentage:.1f}%)</span>'
        else:
            long_info = '<span style="color: #999;">-</span>'
        
        # 级别分布
        level_badges = ""
        if tag in analyzer.tag_level_counter:
            for level in ['V', 'D', 'I', 'W', 'E']:
                if level in analyzer.tag_level_counter[tag]:
                    level_count = analyzer.tag_level_counter[tag][level]
                    level_badges += f'<span class="level-badge level-{level.lower()}">{level}:{level_count}</span>'
        
        table_rows += f"""
        <tr>
            <td class="rank">{i}</td>
            <td><code>{tag}</code></td>
            <td>{count:,}</td>
            <td class="percentage {freq_class}">{percentage:.2f}%</td>
            <td>{long_info}</td>
            <td>{level_badges}</td>
        </tr>
        """
    
    # 生成优化建议
    suggestions_html = "<ul>"
    
    # 超长日志建议
    if total_long_logs > 0:
        suggestions_html += f"<li><strong>⚠️ 超长日志优化：</strong>发现 {total_long_logs:,} 条超长日志（{long_logs_percentage:.2f}%），建议优化以下TAG："
        suggestions_html += "<ul>"
        top_long_tags = analyzer.long_log_counter.most_common(5)
        for tag, long_count in top_long_tags:
            tag_total = analyzer.tag_counter[tag]
            long_pct = (long_count / tag_total) * 100
            suggestions_html += f"<li>{tag}: {long_count:,} 条超长日志（占该TAG的 {long_pct:.1f}%）</li>"
        suggestions_html += "</ul></li>"
    
    # 高频TAG建议
    high_freq_tags = [(tag, p) for tag, _, p in stats[:10] if p > 10]
    if high_freq_tags:
        suggestions_html += "<li><strong>高频TAG优化：</strong>以下TAG占比较高，建议检查是否有冗余日志<ul>"
        for tag, percentage in high_freq_tags:
            suggestions_html += f"<li>{tag} ({percentage:.2f}%)</li>"
        suggestions_html += "</ul></li>"
    
    # 低频TAG建议
    low_freq_tags = [tag for tag, count, _ in stats if count < 5]
    if len(low_freq_tags) > 10:
        suggestions_html += f"<li><strong>低频TAG清理：</strong>发现 {len(low_freq_tags)} 个出现次数 < 5 的TAG，建议review并移除不必要的日志</li>"
    
    # TOP占比分析
    if len(stats) >= 20:
        top20_percentage = sum(p for _, _, p in stats[:20])
        suggestions_html += f"<li><strong>集中度分析：</strong>TOP 20 TAG占总日志的 {top20_percentage:.2f}%，"
        if top20_percentage > 80:
            suggestions_html += "日志较为集中，重点优化这些TAG即可获得明显效果</li>"
        else:
            suggestions_html += "日志较为分散，建议统一TAG命名规范</li>"
    
    suggestions_html += "</ul>"
    
    # 填充模板
    html = HTML_TEMPLATE.format(
        timestamp=datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
        filename=filename,
        total_lines=analyzer.total_lines,
        matched_lines=analyzer.matched_lines,
        unique_tags=len(analyzer.tag_counter),
        long_logs_count=total_long_logs,
        long_logs_percentage=long_logs_percentage,
        long_logs_bars=long_logs_bars,
        top20_bars=top20_bars,
        table_rows=table_rows,
        suggestions=suggestions_html
    )
    
    # 写入文件
    with open(output_path, 'w', encoding='utf-8') as f:
        f.write(html)
    
    print(f"\n✅ HTML报告已生成: {output_path}")
    print(f"   用浏览器打开查看: file://{Path(output_path).absolute()}")


def generate_output_filename(input_file: str) -> str:
    """根据输入文件名生成输出报告文件名"""
    input_path = Path(input_file)
    
    # 获取文件名（不含扩展名）
    base_name = input_path.stem
    
    # 移除常见的日志文件后缀
    # 例如: com.partyjoy.yoki_20250922.xlog.log -> com.partyjoy.yoki_20250922
    for suffix in ['.xlog', '.log', '.txt']:
        if base_name.endswith(suffix):
            base_name = base_name[:-len(suffix)]
    
    # 生成报告文件名: 原文件名_report.html
    output_name = f"{base_name}_report.html"
    
    # 如果输入文件在某个目录下，输出到同一目录
    if input_path.parent != Path('.'):
        output_path = input_path.parent / output_name
    else:
        output_path = Path(output_name)
    
    return str(output_path)


def main():
    parser = argparse.ArgumentParser(description='生成日志TAG分析的HTML可视化报告')
    parser.add_argument('logfile', help='日志文件路径')
    parser.add_argument('--output', '-o', default=None,
                       help='输出HTML文件路径（默认: 根据输入文件名自动生成）')
    
    args = parser.parse_args()
    
    # 检查文件
    if not Path(args.logfile).exists():
        print(f"❌ 错误: 文件不存在: {args.logfile}")
        sys.exit(1)
    
    # 确定输出文件名
    if args.output:
        output_path = args.output
    else:
        output_path = generate_output_filename(args.logfile)
        print(f"📝 自动生成输出文件名: {output_path}")
    
    # 分析日志
    print(f"正在分析日志文件: {args.logfile}")
    analyzer = LogTagAnalyzer()
    analyzer.analyze_file(args.logfile)
    
    # 生成HTML
    generate_html_report(analyzer, args.logfile, output_path)


if __name__ == "__main__":
    main()