2026-02-05 09:04:10 +08:00
|
|
|
|
import os
|
|
|
|
|
|
import re
|
|
|
|
|
|
import sys
|
|
|
|
|
|
import time
|
|
|
|
|
|
import pandas as pd
|
|
|
|
|
|
import matplotlib.pyplot as plt
|
2026-02-24 11:09:48 +08:00
|
|
|
|
import seaborn as sns
|
2026-02-05 09:04:10 +08:00
|
|
|
|
from datetime import datetime
|
|
|
|
|
|
from matplotlib.lines import Line2D
|
|
|
|
|
|
from typing import Optional, Tuple, List, Dict, Any, Union
|
|
|
|
|
|
from pathlib import Path
|
|
|
|
|
|
import numpy as np
|
|
|
|
|
|
import base64
|
|
|
|
|
|
from io import BytesIO
|
|
|
|
|
|
from jinja2 import Template
|
|
|
|
|
|
from colorama import Fore, Style, init
|
|
|
|
|
|
import multiprocessing as mp
|
|
|
|
|
|
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
|
|
|
|
|
|
import psutil
|
|
|
|
|
|
|
|
|
|
|
|
# 初始化colorama
|
|
|
|
|
|
init(autoreset=True)
|
|
|
|
|
|
|
|
|
|
|
|
# 避免 SettingWithCopy 警告影响输出可读性
|
|
|
|
|
|
pd.options.mode.chained_assignment = None
|
|
|
|
|
|
|
|
|
|
|
|
# 设置中文字体支持
|
|
|
|
|
|
plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans', 'Arial Unicode MS', 'Microsoft YaHei']
|
|
|
|
|
|
plt.rcParams['axes.unicode_minus'] = False
|
|
|
|
|
|
|
|
|
|
|
|
# HTML模板 - 更新为支持多关键词
|
|
|
|
|
|
# 性能优化配置
|
|
|
|
|
|
OPTIMIZATION_CONFIG = {
|
|
|
|
|
|
'max_workers': min(mp.cpu_count(), 8), # 限制最大工作线程数
|
|
|
|
|
|
'chunk_size': 50000, # 分块读取大小
|
|
|
|
|
|
'use_threading': True, # 使用多线程
|
|
|
|
|
|
'memory_limit_gb': psutil.virtual_memory().available // (1024 ** 3) * 0.7, # 内存限制
|
|
|
|
|
|
}
|
|
|
|
|
|
|
2026-02-24 11:09:48 +08:00
|
|
|
|
# HTML模板 - 增加汇总对比报告部分
|
2026-02-05 09:04:10 +08:00
|
|
|
|
HTML_TEMPLATE = """
|
|
|
|
|
|
<!DOCTYPE html>
|
|
|
|
|
|
<html lang="zh-CN">
|
|
|
|
|
|
<head>
|
|
|
|
|
|
<meta charset="UTF-8">
|
|
|
|
|
|
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
2026-02-24 11:09:48 +08:00
|
|
|
|
<title>测试报告分析 - {{ keyword }}</title>
|
2026-02-05 09:04:10 +08:00
|
|
|
|
<style>
|
|
|
|
|
|
/* 样式保持不变，根据需要调整 */
|
|
|
|
|
|
body {
|
|
|
|
|
|
font-family: Arial, sans-serif;
|
|
|
|
|
|
margin: 0;
|
|
|
|
|
|
padding: 20px;
|
|
|
|
|
|
background-color: #f5f5f5;
|
|
|
|
|
|
}
|
|
|
|
|
|
.header {
|
|
|
|
|
|
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
|
|
|
|
|
|
color: white;
|
|
|
|
|
|
padding: 20px;
|
|
|
|
|
|
border-radius: 10px;
|
|
|
|
|
|
margin-bottom: 20px;
|
|
|
|
|
|
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
|
|
|
|
|
|
}
|
|
|
|
|
|
.test-card {
|
|
|
|
|
|
background: white;
|
|
|
|
|
|
border-radius: 10px;
|
|
|
|
|
|
padding: 20px;
|
|
|
|
|
|
margin-bottom: 20px;
|
|
|
|
|
|
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
|
|
|
|
|
|
transition: transform 0.2s ease;
|
|
|
|
|
|
}
|
|
|
|
|
|
.test-card:hover {
|
|
|
|
|
|
transform: translateY(-2px);
|
|
|
|
|
|
box-shadow: 0 4px 8px rgba(0, 0, 0, 0.15);
|
|
|
|
|
|
}
|
|
|
|
|
|
.test-header {
|
|
|
|
|
|
display: flex;
|
|
|
|
|
|
justify-content: space-between;
|
|
|
|
|
|
align-items: center;
|
|
|
|
|
|
margin-bottom: 15px;
|
|
|
|
|
|
padding-bottom: 10px;
|
|
|
|
|
|
border-bottom: 2px solid #eaeaea;
|
|
|
|
|
|
}
|
|
|
|
|
|
.test-title {
|
|
|
|
|
|
font-size: 18px;
|
|
|
|
|
|
font-weight: bold;
|
|
|
|
|
|
color: #333;
|
|
|
|
|
|
}
|
|
|
|
|
|
.test-stats {
|
|
|
|
|
|
display: grid;
|
|
|
|
|
|
grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
|
|
|
|
|
|
gap: 15px;
|
|
|
|
|
|
margin-bottom: 15px;
|
|
|
|
|
|
}
|
|
|
|
|
|
.stat-item {
|
|
|
|
|
|
background: #f8f9fa;
|
|
|
|
|
|
padding: 12px;
|
|
|
|
|
|
border-radius: 8px;
|
|
|
|
|
|
text-align: center;
|
|
|
|
|
|
}
|
|
|
|
|
|
.stat-label {
|
|
|
|
|
|
font-size: 12px;
|
|
|
|
|
|
color: #666;
|
|
|
|
|
|
margin-bottom: 5px;
|
|
|
|
|
|
}
|
|
|
|
|
|
.stat-value {
|
|
|
|
|
|
font-size: 16px;
|
|
|
|
|
|
font-weight: bold;
|
|
|
|
|
|
color: #333;
|
|
|
|
|
|
}
|
|
|
|
|
|
.plot-container {
|
|
|
|
|
|
text-align: center;
|
|
|
|
|
|
margin: 20px 0;
|
|
|
|
|
|
}
|
|
|
|
|
|
.plot-image {
|
|
|
|
|
|
max-width: 100%;
|
|
|
|
|
|
height: auto;
|
|
|
|
|
|
border-radius: 8px;
|
|
|
|
|
|
box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
|
|
|
|
|
|
}
|
2026-02-24 11:09:48 +08:00
|
|
|
|
.sn-plots-container {
|
2026-02-05 09:04:10 +08:00
|
|
|
|
display: grid;
|
2026-02-24 11:09:48 +08:00
|
|
|
|
grid-template-columns: repeat(auto-fit, minmax(400px, 1fr));
|
2026-02-05 09:04:10 +08:00
|
|
|
|
gap: 20px;
|
|
|
|
|
|
margin: 20px 0;
|
|
|
|
|
|
}
|
2026-02-24 11:09:48 +08:00
|
|
|
|
.sn-plot-item {
|
|
|
|
|
|
background: #f8f9fa;
|
2026-02-05 09:04:10 +08:00
|
|
|
|
padding: 15px;
|
2026-02-24 11:09:48 +08:00
|
|
|
|
border-radius: 8px;
|
2026-02-05 09:04:10 +08:00
|
|
|
|
text-align: center;
|
|
|
|
|
|
}
|
2026-02-24 11:09:48 +08:00
|
|
|
|
.sn-plot-title {
|
|
|
|
|
|
font-size: 14px;
|
2026-02-05 09:04:10 +08:00
|
|
|
|
font-weight: bold;
|
|
|
|
|
|
margin-bottom: 10px;
|
|
|
|
|
|
color: #555;
|
|
|
|
|
|
}
|
|
|
|
|
|
.summary {
|
|
|
|
|
|
background: white;
|
|
|
|
|
|
border-radius: 10px;
|
|
|
|
|
|
padding: 20px;
|
|
|
|
|
|
margin-top: 20px;
|
|
|
|
|
|
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
|
|
|
|
|
|
}
|
|
|
|
|
|
.summary-item {
|
|
|
|
|
|
margin: 10px 0;
|
|
|
|
|
|
padding: 10px;
|
|
|
|
|
|
background: #f8f9fa;
|
|
|
|
|
|
border-radius: 6px;
|
|
|
|
|
|
}
|
|
|
|
|
|
.timestamp {
|
|
|
|
|
|
text-align: center;
|
|
|
|
|
|
color: #666;
|
|
|
|
|
|
margin-top: 30px;
|
|
|
|
|
|
font-size: 12px;
|
|
|
|
|
|
}
|
|
|
|
|
|
.badge {
|
|
|
|
|
|
padding: 4px 8px;
|
|
|
|
|
|
border-radius: 12px;
|
|
|
|
|
|
font-size: 12px;
|
|
|
|
|
|
font-weight: bold;
|
|
|
|
|
|
}
|
|
|
|
|
|
.badge-success {
|
|
|
|
|
|
background: #d4edda;
|
|
|
|
|
|
color: #155724;
|
|
|
|
|
|
}
|
|
|
|
|
|
.badge-warning {
|
|
|
|
|
|
background: #fff3cd;
|
|
|
|
|
|
color: #856404;
|
|
|
|
|
|
}
|
|
|
|
|
|
.badge-danger {
|
|
|
|
|
|
background: #f8d7da;
|
|
|
|
|
|
color: #721c24;
|
|
|
|
|
|
}
|
|
|
|
|
|
.section-title {
|
|
|
|
|
|
font-size: 16px;
|
|
|
|
|
|
font-weight: bold;
|
|
|
|
|
|
margin: 20px 0 10px 0;
|
|
|
|
|
|
color: #333;
|
|
|
|
|
|
border-left: 4px solid #667eea;
|
|
|
|
|
|
padding-left: 10px;
|
|
|
|
|
|
}
|
|
|
|
|
|
.file-info {
|
|
|
|
|
|
background: #e7f3ff;
|
|
|
|
|
|
padding: 10px;
|
|
|
|
|
|
border-radius: 6px;
|
|
|
|
|
|
margin: 10px 0;
|
|
|
|
|
|
}
|
2026-02-24 11:09:48 +08:00
|
|
|
|
.comparison-section {
|
|
|
|
|
|
background: white;
|
|
|
|
|
|
border-radius: 10px;
|
|
|
|
|
|
padding: 20px;
|
|
|
|
|
|
margin-top: 30px;
|
|
|
|
|
|
}
|
|
|
|
|
|
.comparison-plots {
|
|
|
|
|
|
display: grid;
|
|
|
|
|
|
grid-template-columns: repeat(auto-fit, minmax(500px, 1fr));
|
|
|
|
|
|
gap: 20px;
|
|
|
|
|
|
margin: 20px 0;
|
|
|
|
|
|
}
|
|
|
|
|
|
.keyword-group {
|
|
|
|
|
|
background: #f0f8ff;
|
|
|
|
|
|
border-left: 4px solid #4169e1;
|
|
|
|
|
|
padding: 15px;
|
|
|
|
|
|
margin: 20px 0;
|
|
|
|
|
|
border-radius: 8px;
|
|
|
|
|
|
}
|
|
|
|
|
|
.keyword-title {
|
|
|
|
|
|
font-size: 18px;
|
|
|
|
|
|
font-weight: bold;
|
|
|
|
|
|
color: #4169e1;
|
|
|
|
|
|
margin-bottom: 10px;
|
|
|
|
|
|
}
|
|
|
|
|
|
.sub-test-card {
|
|
|
|
|
|
background: #f8f9fa;
|
|
|
|
|
|
border-radius: 8px;
|
|
|
|
|
|
padding: 15px;
|
|
|
|
|
|
margin: 10px 0;
|
|
|
|
|
|
border-left: 3px solid #667eea;
|
2026-02-05 09:04:10 +08:00
|
|
|
|
}
|
|
|
|
|
|
</style>
|
|
|
|
|
|
</head>
|
|
|
|
|
|
<body>
|
|
|
|
|
|
<div class="header">
|
|
|
|
|
|
<h1>📊 测试报告分析 (多文件合并)</h1>
|
2026-02-24 11:09:48 +08:00
|
|
|
|
<p>关键词: <strong>{{ keyword }}</strong> | 生成时间: {{ timestamp }}</p>
|
2026-02-05 09:04:10 +08:00
|
|
|
|
<p>共分析 {{ test_count }} 个测试项，{{ total_points }} 个数据点，来自 {{ file_count }} 个文件</p>
|
2026-02-24 11:09:48 +08:00
|
|
|
|
{% if keyword_groups|length > 1 %}
|
|
|
|
|
|
<p>关键词分组: {{ keyword_groups|length }} 组</p>
|
|
|
|
|
|
{% endif %}
|
2026-02-05 09:04:10 +08:00
|
|
|
|
</div>
|
|
|
|
|
|
|
|
|
|
|
|
<div class="summary">
|
|
|
|
|
|
<h3>📁 处理的文件列表</h3>
|
|
|
|
|
|
{% for file_info in file_infos %}
|
|
|
|
|
|
<div class="file-info">
|
|
|
|
|
|
<strong>{{ loop.index }}. {{ file_info.filename }}</strong><br>
|
|
|
|
|
|
路径: {{ file_info.path }}<br>
|
|
|
|
|
|
数据行数: {{ file_info.rows }} | 测试项数: {{ file_info.tests }}
|
|
|
|
|
|
</div>
|
|
|
|
|
|
{% endfor %}
|
|
|
|
|
|
</div>
|
|
|
|
|
|
|
2026-02-24 11:09:48 +08:00
|
|
|
|
<!-- 按关键词分组显示测试项 -->
|
|
|
|
|
|
{% for group_name, group_tests in keyword_groups.items() %}
|
|
|
|
|
|
<div class="keyword-group">
|
|
|
|
|
|
<div class="keyword-title">🔍 关键词组: {{ group_name }}</div>
|
|
|
|
|
|
|
|
|
|
|
|
<!-- 显示该关键词组下的所有测试项 -->
|
|
|
|
|
|
{% for test_name, test_list in group_tests.items() %}
|
|
|
|
|
|
<div class="sub-test-card">
|
|
|
|
|
|
<div class="test-header">
|
|
|
|
|
|
<div class="test-title">📋 {{ test_name }}</div>
|
|
|
|
|
|
{% if test_list[0].status %}
|
|
|
|
|
|
<div class="badge badge-{{ test_list[0].status }}">
|
|
|
|
|
|
{{ test_list[0].status_display }}
|
|
|
|
|
|
</div>
|
|
|
|
|
|
{% endif %}
|
2026-02-05 09:04:10 +08:00
|
|
|
|
</div>
|
|
|
|
|
|
|
2026-02-24 11:09:48 +08:00
|
|
|
|
{% for test in test_list %}
|
|
|
|
|
|
<div class="test-stats">
|
|
|
|
|
|
<div class="stat-item">
|
|
|
|
|
|
<div class="stat-label">数据点数</div>
|
|
|
|
|
|
<div class="stat-value">{{ test.stats.count }}</div>
|
|
|
|
|
|
</div>
|
|
|
|
|
|
<div class="stat-item">
|
|
|
|
|
|
<div class="stat-label">平均值</div>
|
|
|
|
|
|
<div class="stat-value">{{ "%.4f"|format(test.stats.mean) }}</div>
|
|
|
|
|
|
</div>
|
|
|
|
|
|
<div class="stat-item">
|
|
|
|
|
|
<div class="stat-label">中位数</div>
|
|
|
|
|
|
<div class="stat-value">{{ "%.4f"|format(test.stats.median) }}</div>
|
|
|
|
|
|
</div>
|
|
|
|
|
|
<div class="stat-item">
|
|
|
|
|
|
<div class="stat-label">标准差</div>
|
|
|
|
|
|
<div class="stat-value">{{ "%.4f"|format(test.stats.std) }}</div>
|
|
|
|
|
|
</div>
|
|
|
|
|
|
<div class="stat-item">
|
|
|
|
|
|
<div class="stat-label">最小值</div>
|
|
|
|
|
|
<div class="stat-value">{{ "%.4f"|format(test.stats.min) }}</div>
|
|
|
|
|
|
</div>
|
|
|
|
|
|
<div class="stat-item">
|
|
|
|
|
|
<div class="stat-label">最大值</div>
|
|
|
|
|
|
<div class="stat-value">{{ "%.4f"|format(test.stats.max) }}</div>
|
|
|
|
|
|
</div>
|
2026-02-05 09:04:10 +08:00
|
|
|
|
</div>
|
|
|
|
|
|
|
2026-02-24 11:09:48 +08:00
|
|
|
|
{% if test.limits.lower is not none or test.limits.upper is not none %}
|
|
|
|
|
|
<div class="test-stats">
|
|
|
|
|
|
{% if test.limits.lower is not none %}
|
|
|
|
|
|
<div class="stat-item">
|
|
|
|
|
|
<div class="stat-label">下限值</div>
|
|
|
|
|
|
<div class="stat-value">{{ "%.4f"|format(test.limits.lower) }}</div>
|
|
|
|
|
|
</div>
|
|
|
|
|
|
{% endif %}
|
|
|
|
|
|
{% if test.limits.upper is not none %}
|
|
|
|
|
|
<div class="stat-item">
|
|
|
|
|
|
<div class="stat-label">上限值</div>
|
|
|
|
|
|
<div class="stat-value">{{ "%.4f"|format(test.limits.upper) }}</div>
|
|
|
|
|
|
</div>
|
|
|
|
|
|
{% endif %}
|
2026-02-05 09:04:10 +08:00
|
|
|
|
</div>
|
2026-02-24 11:09:48 +08:00
|
|
|
|
{% endif %}
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
2026-02-24 11:09:48 +08:00
|
|
|
|
<!-- 汇总图 -->
|
|
|
|
|
|
<div class="section-title">📈 汇总视图 (所有SN)</div>
|
|
|
|
|
|
<div class="plot-container">
|
|
|
|
|
|
<img src="data:image/png;base64,{{ test.summary_plot_image }}" alt="{{ test.name }} 汇总散点图" class="plot-image">
|
2026-02-05 09:04:10 +08:00
|
|
|
|
</div>
|
2026-02-24 11:09:48 +08:00
|
|
|
|
|
|
|
|
|
|
<!-- SN独立图 -->
|
|
|
|
|
|
{% if test.sn_plot_images %}
|
|
|
|
|
|
<div class="section-title">🔍 SN独立视图 ({{ test.sn_plot_images|length }}个SN)</div>
|
|
|
|
|
|
<div class="sn-plots-container">
|
|
|
|
|
|
{% for sn_plot in test.sn_plot_images %}
|
|
|
|
|
|
<div class="sn-plot-item">
|
2026-03-27 14:32:56 +08:00
|
|
|
|
<div class="sn-plot-title">
|
|
|
|
|
|
{% if sn_plot.cell_no %}
|
|
|
|
|
|
Cell: {{ sn_plot.cell_no }}, SN: {{ sn_plot.sn_no }}
|
|
|
|
|
|
{% else %}
|
|
|
|
|
|
SN: {{ sn_plot.sn }}
|
|
|
|
|
|
{% endif %}
|
|
|
|
|
|
</div>
|
|
|
|
|
|
{% if sn_plot.has_cell_data %}
|
|
|
|
|
|
<div class="cell-info">
|
|
|
|
|
|
<small>Cell编号信息:</small>
|
|
|
|
|
|
<table style="width:100%; font-size:12px; margin:10px 0; border-collapse: collapse;">
|
|
|
|
|
|
<tr style="background-color:#f0f0f0;">
|
|
|
|
|
|
<th style="padding:5px; border:1px solid #ddd;">Cell编号</th>
|
|
|
|
|
|
<th style="padding:5px; border:1px solid #ddd;">数据点</th>
|
|
|
|
|
|
<th style="padding:5px; border:1px solid #ddd;">均值</th>
|
|
|
|
|
|
<th style="padding:5px; border:1px solid #ddd;">标准差</th>
|
|
|
|
|
|
<th style="padding:5px; border:1px solid #ddd;">最小值</th>
|
|
|
|
|
|
<th style="padding:5px; border:1px solid #ddd;">最大值</th>
|
|
|
|
|
|
</tr>
|
|
|
|
|
|
{% for cell_no, stats_dict in sn_plot.cell_info.items() %}
|
|
|
|
|
|
<tr>
|
|
|
|
|
|
<td style="padding:5px; border:1px solid #ddd;">{{ cell_no }}</td>
|
|
|
|
|
|
<td style="padding:5px; border:1px solid #ddd;">{{ stats_dict.count }}</td>
|
|
|
|
|
|
<td style="padding:5px; border:1px solid #ddd;">{{ "%.4f"|format(stats_dict.mean) }}</td>
|
|
|
|
|
|
<td style="padding:5px; border:1px solid #ddd;">{{ "%.4f"|format(stats_dict.std) }}</td>
|
|
|
|
|
|
<td style="padding:5px; border:1px solid #ddd;">{{ "%.4f"|format(stats_dict.min) }}</td>
|
|
|
|
|
|
<td style="padding:5px; border:1px solid #ddd;">{{ "%.4f"|format(stats_dict.max) }}</td>
|
|
|
|
|
|
</tr>
|
|
|
|
|
|
{% endfor %}
|
|
|
|
|
|
|
|
|
|
|
|
</table>
|
|
|
|
|
|
</div>
|
|
|
|
|
|
{% endif %}
|
2026-02-24 11:09:48 +08:00
|
|
|
|
<img src="data:image/png;base64,{{ sn_plot.image }}" alt="{{ test.name }} - SN {{ sn_plot.sn }} 散点图" class="plot-image">
|
|
|
|
|
|
</div>
|
|
|
|
|
|
{% endfor %}
|
2026-02-05 09:04:10 +08:00
|
|
|
|
</div>
|
|
|
|
|
|
{% endif %}
|
|
|
|
|
|
|
2026-03-27 14:32:56 +08:00
|
|
|
|
|
2026-02-24 11:09:48 +08:00
|
|
|
|
{% if not loop.last %}
|
|
|
|
|
|
<hr style="margin: 20px 0; border: none; border-top: 1px solid #e0e0e0;">
|
|
|
|
|
|
{% endif %}
|
|
|
|
|
|
{% endfor %}
|
2026-02-05 09:04:10 +08:00
|
|
|
|
</div>
|
2026-02-24 11:09:48 +08:00
|
|
|
|
{% endfor %}
|
|
|
|
|
|
</div>
|
|
|
|
|
|
{% endfor %}
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
2026-02-24 11:09:48 +08:00
|
|
|
|
<!-- 汇总对比报告 -->
|
|
|
|
|
|
{% if comparison_plots %}
|
|
|
|
|
|
<div class="comparison-section">
|
|
|
|
|
|
<h2>📊 汇总对比报告</h2>
|
|
|
|
|
|
<div class="comparison-plots">
|
|
|
|
|
|
{% for plot in comparison_plots %}
|
|
|
|
|
|
<div class="plot-container">
|
|
|
|
|
|
<h3>{{ plot.title }}</h3>
|
|
|
|
|
|
<img src="data:image/png;base64,{{ plot.image }}" alt="{{ plot.title }}" class="plot-image">
|
|
|
|
|
|
{% if plot.description %}
|
|
|
|
|
|
<p>{{ plot.description }}</p>
|
|
|
|
|
|
{% endif %}
|
2026-02-05 09:04:10 +08:00
|
|
|
|
</div>
|
|
|
|
|
|
{% endfor %}
|
|
|
|
|
|
</div>
|
|
|
|
|
|
</div>
|
2026-02-24 11:09:48 +08:00
|
|
|
|
{% endif %}
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
|
|
|
|
|
<div class="summary">
|
|
|
|
|
|
<h3>📈 分析摘要</h3>
|
|
|
|
|
|
<div class="summary-item">
|
|
|
|
|
|
<strong>文件夹路径:</strong> {{ folder_path }}
|
|
|
|
|
|
</div>
|
|
|
|
|
|
<div class="summary-item">
|
|
|
|
|
|
<strong>分析时间:</strong> {{ analysis_time }}秒
|
|
|
|
|
|
</div>
|
|
|
|
|
|
<div class="summary-item">
|
2026-02-24 11:09:48 +08:00
|
|
|
|
<strong>关键词分组:</strong> {{ keyword_groups|length }} 组
|
|
|
|
|
|
<ul>
|
|
|
|
|
|
{% for group_name, group_tests in keyword_groups.items() %}
|
|
|
|
|
|
<li>{{ group_name }}: {{ group_tests|length }} 个测试项</li>
|
|
|
|
|
|
{% endfor %}
|
|
|
|
|
|
</ul>
|
2026-02-05 09:04:10 +08:00
|
|
|
|
</div>
|
|
|
|
|
|
<div class="summary-item">
|
|
|
|
|
|
<strong>测试项分布:</strong>
|
|
|
|
|
|
<ul>
|
|
|
|
|
|
<li>正常: {{ status_counts.normal }} 个</li>
|
|
|
|
|
|
<li>警告: {{ status_counts.warning }} 个</li>
|
|
|
|
|
|
<li>异常: {{ status_counts.abnormal }} 个</li>
|
|
|
|
|
|
</ul>
|
|
|
|
|
|
</div>
|
|
|
|
|
|
<div class="summary-item">
|
|
|
|
|
|
<strong>数据摘要:</strong>
|
|
|
|
|
|
<ul>
|
|
|
|
|
|
<li>总文件数: {{ file_count }} 个</li>
|
|
|
|
|
|
<li>总数据行数: {{ total_rows }} 行</li>
|
|
|
|
|
|
<li>总测试项数: {{ test_count }} 个</li>
|
|
|
|
|
|
<li>总数据点数: {{ total_points }} 个</li>
|
|
|
|
|
|
</ul>
|
|
|
|
|
|
</div>
|
|
|
|
|
|
</div>
|
|
|
|
|
|
|
|
|
|
|
|
<div class="timestamp">
|
|
|
|
|
|
报告生成于 {{ timestamp }} | 多文件测试报告分析系统
|
|
|
|
|
|
</div>
|
|
|
|
|
|
</body>
|
|
|
|
|
|
</html>
|
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class MultiFileTestReportScatterPlotter:
|
|
|
|
|
|
def __init__(self):
|
|
|
|
|
|
self.folder_path: Optional[str] = None
|
|
|
|
|
|
self.df: Optional[pd.DataFrame] = None
|
|
|
|
|
|
self.output_dir: Optional[str] = None
|
2026-03-27 14:32:56 +08:00
|
|
|
|
# self.required_columns = ["Test Name New", "SN", "Measurement", "Test Time", "Lower Limit", "Upper Limit"]
|
|
|
|
|
|
# 更新required_columns，增加Cell编号
|
|
|
|
|
|
self.required_columns = [
|
|
|
|
|
|
"Test Name New",
|
|
|
|
|
|
"SN",
|
|
|
|
|
|
"Measurement",
|
|
|
|
|
|
"Test Time",
|
|
|
|
|
|
"Lower Limit",
|
|
|
|
|
|
"Upper Limit",
|
|
|
|
|
|
"Cell" # 新增Cell编号列
|
|
|
|
|
|
]
|
2026-02-05 09:04:10 +08:00
|
|
|
|
self.col_lower: Optional[str] = None
|
|
|
|
|
|
self.col_upper: Optional[str] = None
|
|
|
|
|
|
self.html_report_path: Optional[str] = None
|
2026-02-24 11:09:48 +08:00
|
|
|
|
self.comparison_report_path: Optional[str] = None
|
2026-02-05 09:04:10 +08:00
|
|
|
|
self.file_infos: List[Dict[str, Any]] = []
|
|
|
|
|
|
|
|
|
|
|
|
# 缓存处理过的数据
|
|
|
|
|
|
self._processed_data_cache: Dict[str, Any] = {}
|
|
|
|
|
|
|
|
|
|
|
|
# 性能监控
|
|
|
|
|
|
self.performance_stats = {
|
|
|
|
|
|
'load_times': [],
|
|
|
|
|
|
'memory_usage': [],
|
|
|
|
|
|
'file_sizes': []
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
def _print_stage(self, msg: str, color=Fore.CYAN) -> None:
|
|
|
|
|
|
"""统一的阶段信息输出"""
|
|
|
|
|
|
print(f"\n{color}{'=' * 50}")
|
|
|
|
|
|
print(f"📋 {msg}")
|
|
|
|
|
|
print(f"{'=' * 50}{Style.RESET_ALL}")
|
|
|
|
|
|
|
|
|
|
|
|
def _print_progress(self, current: int, total: int, prefix: str = "进度",
|
|
|
|
|
|
color=Fore.YELLOW) -> None:
|
|
|
|
|
|
"""改进的进度条显示"""
|
|
|
|
|
|
if total <= 0:
|
|
|
|
|
|
return
|
|
|
|
|
|
|
|
|
|
|
|
percent = (current / total) * 100
|
|
|
|
|
|
bar_len = 40
|
|
|
|
|
|
filled = int(bar_len * current / total)
|
|
|
|
|
|
bar = "█" * filled + "░" * (bar_len - filled)
|
|
|
|
|
|
|
|
|
|
|
|
sys.stdout.write(f"\r{color}{prefix}: [{bar}] {current}/{total} ({percent:.1f}%){Style.RESET_ALL}")
|
|
|
|
|
|
sys.stdout.flush()
|
|
|
|
|
|
|
|
|
|
|
|
if current == total:
|
|
|
|
|
|
print(f"{Fore.GREEN} ✅ 完成{Style.RESET_ALL}")
|
|
|
|
|
|
|
|
|
|
|
|
def _print_warning(self, msg: str) -> None:
|
|
|
|
|
|
"""警告信息输出"""
|
|
|
|
|
|
print(f"{Fore.YELLOW}⚠️ {msg}{Style.RESET_ALL}")
|
|
|
|
|
|
|
|
|
|
|
|
def _print_success(self, msg: str) -> None:
|
|
|
|
|
|
"""成功信息输出"""
|
|
|
|
|
|
print(f"{Fore.GREEN}✅ {msg}{Style.RESET_ALL}")
|
|
|
|
|
|
|
|
|
|
|
|
def _print_error(self, msg: str) -> None:
|
|
|
|
|
|
"""错误信息输出"""
|
|
|
|
|
|
print(f"{Fore.RED}❌ {msg}{Style.RESET_ALL}")
|
|
|
|
|
|
|
|
|
|
|
|
def _get_memory_usage(self) -> float:
|
|
|
|
|
|
"""获取当前内存使用量（GB）"""
|
|
|
|
|
|
process = psutil.Process()
|
|
|
|
|
|
return process.memory_info().rss / (1024 ** 3)
|
|
|
|
|
|
|
|
|
|
|
|
def _check_memory_safe(self, file_size_mb: float) -> bool:
|
|
|
|
|
|
"""检查内存是否安全"""
|
|
|
|
|
|
available_memory = psutil.virtual_memory().available / (1024 ** 3)
|
|
|
|
|
|
estimated_need = file_size_mb * 5 / 1024 # 估算需要的内存（GB）
|
|
|
|
|
|
return available_memory > estimated_need + 1 # 保留1GB安全空间
|
|
|
|
|
|
|
|
|
|
|
|
def _load_single_file_optimized(self, file_info: Dict[str, Any]) -> Optional[pd.DataFrame]:
|
|
|
|
|
|
"""优化单文件加载方法"""
|
|
|
|
|
|
file_path = file_info['path']
|
|
|
|
|
|
filename = file_info['filename']
|
|
|
|
|
|
|
|
|
|
|
|
try:
|
|
|
|
|
|
start_time = time.time()
|
|
|
|
|
|
file_size_mb = os.path.getsize(file_path) / (1024 ** 2)
|
|
|
|
|
|
|
|
|
|
|
|
# 内存安全检查
|
|
|
|
|
|
if not self._check_memory_safe(file_size_mb):
|
|
|
|
|
|
self._print_warning(f"内存不足，跳过大文件: {filename} ({file_size_mb:.1f}MB)")
|
|
|
|
|
|
return None
|
|
|
|
|
|
|
|
|
|
|
|
# 选择合适的引擎
|
|
|
|
|
|
file_ext = file_path.lower()
|
|
|
|
|
|
if file_ext.endswith('.xlsx'):
|
|
|
|
|
|
engine = 'openpyxl'
|
|
|
|
|
|
elif file_ext.endswith('.xls'):
|
|
|
|
|
|
engine = 'xlrd'
|
|
|
|
|
|
else:
|
|
|
|
|
|
self._print_warning(f"不支持的文件格式: {filename}")
|
|
|
|
|
|
return None
|
|
|
|
|
|
|
|
|
|
|
|
# 快速获取工作表信息
|
|
|
|
|
|
try:
|
|
|
|
|
|
excel_file = pd.ExcelFile(file_path, engine=engine)
|
|
|
|
|
|
sheet_names = excel_file.sheet_names
|
|
|
|
|
|
|
|
|
|
|
|
# 选择工作表
|
|
|
|
|
|
target_sheets = ["Merged All Tests", "All Tests", sheet_names[0] if sheet_names else None]
|
|
|
|
|
|
selected_sheet = next((s for s in target_sheets if s and s in sheet_names), None)
|
|
|
|
|
|
|
|
|
|
|
|
if not selected_sheet:
|
|
|
|
|
|
self._print_warning(f"未找到目标工作表: {filename}")
|
|
|
|
|
|
return None
|
|
|
|
|
|
|
|
|
|
|
|
except Exception as e:
|
|
|
|
|
|
self._print_warning(f"无法读取工作表信息 {filename}: {e}")
|
|
|
|
|
|
return None
|
|
|
|
|
|
|
|
|
|
|
|
# 优化读取参数
|
|
|
|
|
|
read_kwargs = {
|
|
|
|
|
|
'io': file_path,
|
|
|
|
|
|
'sheet_name': selected_sheet,
|
|
|
|
|
|
'engine': engine,
|
|
|
|
|
|
'dtype': 'object',
|
|
|
|
|
|
'na_filter': False,
|
|
|
|
|
|
'usecols': self.required_columns, # 只读取需要的列
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
# 对于大文件，使用分块读取
|
|
|
|
|
|
if file_size_mb > 50: # 50MB以上使用分块读取
|
|
|
|
|
|
chunks = []
|
|
|
|
|
|
for chunk in pd.read_excel(**read_kwargs, chunksize=OPTIMIZATION_CONFIG['chunk_size']):
|
|
|
|
|
|
chunks.append(chunk)
|
|
|
|
|
|
|
|
|
|
|
|
if chunks:
|
|
|
|
|
|
df = pd.concat(chunks, ignore_index=True)
|
|
|
|
|
|
else:
|
|
|
|
|
|
df = pd.DataFrame()
|
|
|
|
|
|
else:
|
|
|
|
|
|
df = pd.read_excel(**read_kwargs)
|
|
|
|
|
|
|
|
|
|
|
|
if df.empty:
|
|
|
|
|
|
self._print_warning(f"文件为空: {filename}")
|
|
|
|
|
|
return None
|
|
|
|
|
|
|
|
|
|
|
|
# 检查必要列
|
|
|
|
|
|
missing_columns = [col for col in self.required_columns if col not in df.columns]
|
|
|
|
|
|
if missing_columns:
|
|
|
|
|
|
self._print_warning(f"缺少必要列 {filename}: {missing_columns}")
|
|
|
|
|
|
return None
|
|
|
|
|
|
|
|
|
|
|
|
# 添加文件标识
|
|
|
|
|
|
df['_source_file'] = filename
|
|
|
|
|
|
|
|
|
|
|
|
load_time = time.time() - start_time
|
|
|
|
|
|
file_info.update({
|
|
|
|
|
|
'load_time': round(load_time, 2),
|
|
|
|
|
|
'file_size_mb': round(file_size_mb, 2),
|
|
|
|
|
|
'engine': engine,
|
|
|
|
|
|
'rows': len(df)
|
|
|
|
|
|
})
|
|
|
|
|
|
|
|
|
|
|
|
self.performance_stats['load_times'].append(load_time)
|
|
|
|
|
|
self.performance_stats['file_sizes'].append(file_size_mb)
|
|
|
|
|
|
self.performance_stats['memory_usage'].append(self._get_memory_usage())
|
|
|
|
|
|
|
|
|
|
|
|
self._print_success(f"加载完成: {filename} ({len(df)}行, {load_time:.2f}s)")
|
|
|
|
|
|
return df
|
|
|
|
|
|
|
|
|
|
|
|
except Exception as e:
|
|
|
|
|
|
self._print_error(f"加载文件失败 {filename}: {e}")
|
|
|
|
|
|
return None
|
|
|
|
|
|
|
|
|
|
|
|
def _find_column_case_insensitive(self, candidates: List[str]) -> Optional[str]:
|
|
|
|
|
|
"""优化的大小写不敏感列查找"""
|
|
|
|
|
|
if self.df is None:
|
|
|
|
|
|
return None
|
|
|
|
|
|
|
|
|
|
|
|
columns_lower = {col.lower().strip(): col for col in self.df.columns}
|
|
|
|
|
|
for candidate in candidates:
|
|
|
|
|
|
key = candidate.lower().strip()
|
|
|
|
|
|
if key in columns_lower:
|
|
|
|
|
|
return columns_lower[key]
|
|
|
|
|
|
return None
|
|
|
|
|
|
|
|
|
|
|
|
# 以下方法保持不变（为节省空间省略部分重复代码）
|
|
|
|
|
|
def get_folder_path(self) -> None:
|
|
|
|
|
|
"""获取文件夹路径"""
|
|
|
|
|
|
self._print_stage("输入文件夹路径")
|
|
|
|
|
|
|
|
|
|
|
|
while True:
|
|
|
|
|
|
print(f"{Fore.WHITE}请输入包含Excel文件的文件夹路径: ")
|
|
|
|
|
|
folder_path = input("> ").strip()
|
|
|
|
|
|
|
|
|
|
|
|
if not folder_path:
|
|
|
|
|
|
continue
|
|
|
|
|
|
|
|
|
|
|
|
path_obj = Path(folder_path)
|
|
|
|
|
|
if path_obj.exists() and path_obj.is_dir():
|
|
|
|
|
|
self.folder_path = str(path_obj.resolve())
|
|
|
|
|
|
print(f"{Fore.GREEN}已选择文件夹: {self.folder_path}{Style.RESET_ALL}")
|
|
|
|
|
|
break
|
|
|
|
|
|
else:
|
|
|
|
|
|
self._print_error(f"文件夹不存在: {folder_path}，请重新输入")
|
|
|
|
|
|
|
|
|
|
|
|
def find_excel_files(self) -> List[str]:
|
|
|
|
|
|
"""查找文件夹中的所有Excel文件"""
|
|
|
|
|
|
self._print_stage("扫描Excel文件")
|
|
|
|
|
|
|
|
|
|
|
|
excel_files = []
|
|
|
|
|
|
valid_extensions = ('.xlsx', '.xls')
|
|
|
|
|
|
|
|
|
|
|
|
try:
|
|
|
|
|
|
for file_path in Path(self.folder_path).rglob('*'):
|
|
|
|
|
|
if file_path.suffix.lower() in valid_extensions and file_path.is_file():
|
|
|
|
|
|
excel_files.append(str(file_path.resolve()))
|
|
|
|
|
|
|
|
|
|
|
|
# 按文件名排序
|
|
|
|
|
|
excel_files.sort()
|
|
|
|
|
|
|
|
|
|
|
|
self._print_success(f"找到 {len(excel_files)} 个Excel文件")
|
|
|
|
|
|
for i, file_path in enumerate(excel_files, 1):
|
|
|
|
|
|
print(f" {i:2d}. {os.path.basename(file_path)}")
|
|
|
|
|
|
|
|
|
|
|
|
return excel_files
|
|
|
|
|
|
|
|
|
|
|
|
except Exception as e:
|
|
|
|
|
|
self._print_error(f"扫描文件夹时发生错误: {e}")
|
|
|
|
|
|
return []
|
|
|
|
|
|
|
|
|
|
|
|
def load_multiple_files_optimized(self, excel_files: List[str]) -> None:
|
|
|
|
|
|
"""优化多文件加载方法"""
|
|
|
|
|
|
self._print_stage("并行加载Excel文件")
|
|
|
|
|
|
start_time = time.time()
|
|
|
|
|
|
|
|
|
|
|
|
# 准备文件信息
|
|
|
|
|
|
file_infos = [{'path': path, 'filename': os.path.basename(path)} for path in excel_files]
|
|
|
|
|
|
|
|
|
|
|
|
all_dataframes = []
|
|
|
|
|
|
self.file_infos = []
|
|
|
|
|
|
|
|
|
|
|
|
if OPTIMIZATION_CONFIG['use_threading'] and len(excel_files) > 1:
|
|
|
|
|
|
# 使用多线程并行加载
|
|
|
|
|
|
with ThreadPoolExecutor(max_workers=OPTIMIZATION_CONFIG['max_workers']) as executor:
|
|
|
|
|
|
futures = {executor.submit(self._load_single_file_optimized, file_info): file_info
|
|
|
|
|
|
for file_info in file_infos}
|
|
|
|
|
|
|
|
|
|
|
|
completed = 0
|
|
|
|
|
|
for future in futures:
|
|
|
|
|
|
try:
|
|
|
|
|
|
df = future.result(timeout=300) # 5分钟超时
|
|
|
|
|
|
if df is not None:
|
|
|
|
|
|
all_dataframes.append(df)
|
|
|
|
|
|
self.file_infos.append(futures[future])
|
|
|
|
|
|
completed += 1
|
|
|
|
|
|
self._print_progress(completed, len(excel_files), "并行加载文件")
|
|
|
|
|
|
except Exception as e:
|
|
|
|
|
|
file_info = futures[future]
|
|
|
|
|
|
self._print_error(f"加载失败 {file_info['filename']}: {e}")
|
|
|
|
|
|
else:
|
|
|
|
|
|
# 顺序加载
|
|
|
|
|
|
for i, file_info in enumerate(file_infos, 1):
|
2026-02-24 11:09:48 +08:00
|
|
|
|
self._print_progress(i, len(file_infos), "加载文件")
|
2026-02-05 09:04:10 +08:00
|
|
|
|
df = self._load_single_file_optimized(file_info)
|
|
|
|
|
|
if df is not None:
|
|
|
|
|
|
all_dataframes.append(df)
|
|
|
|
|
|
self.file_infos.append(file_info)
|
|
|
|
|
|
|
|
|
|
|
|
if not all_dataframes:
|
|
|
|
|
|
raise ValueError("没有成功加载任何Excel文件")
|
|
|
|
|
|
|
|
|
|
|
|
# 合并数据
|
|
|
|
|
|
self._print_stage("合并数据")
|
|
|
|
|
|
merge_start = time.time()
|
|
|
|
|
|
|
|
|
|
|
|
try:
|
|
|
|
|
|
self.df = pd.concat(all_dataframes, ignore_index=True, sort=False)
|
|
|
|
|
|
merge_time = time.time() - merge_start
|
|
|
|
|
|
|
|
|
|
|
|
total_time = time.time() - start_time
|
|
|
|
|
|
avg_load_time = np.mean(self.performance_stats['load_times']) if self.performance_stats['load_times'] else 0
|
|
|
|
|
|
|
|
|
|
|
|
self._print_success(f"合并完成: {len(self.df)}行, {len(all_dataframes)}个文件")
|
|
|
|
|
|
self._print_success(f"加载耗时: {total_time:.2f}s (平均: {avg_load_time:.2f}s/文件)")
|
|
|
|
|
|
self._print_success(f"合并耗时: {merge_time:.2f}s")
|
|
|
|
|
|
|
|
|
|
|
|
# 显示性能统计
|
|
|
|
|
|
print(f"\n{Fore.CYAN}📊 性能统计:")
|
|
|
|
|
|
print(f" 平均加载时间: {avg_load_time:.2f}s")
|
|
|
|
|
|
print(f" 峰值内存使用: {max(self.performance_stats['memory_usage']):.2f}GB")
|
|
|
|
|
|
print(f" 总文件大小: {sum(self.performance_stats['file_sizes']):.1f}MB{Style.RESET_ALL}")
|
|
|
|
|
|
|
|
|
|
|
|
except Exception as e:
|
|
|
|
|
|
self._print_error(f"合并数据失败: {e}")
|
|
|
|
|
|
raise
|
|
|
|
|
|
|
|
|
|
|
|
# 记录上下限列名
|
|
|
|
|
|
self.col_lower = self._find_column_case_insensitive([
|
|
|
|
|
|
"Lower Limit", "lower limit", "lower_limit", "ll", "lower"
|
|
|
|
|
|
])
|
|
|
|
|
|
self.col_upper = self._find_column_case_insensitive([
|
|
|
|
|
|
"Upper Limit", "upper limit", "upper_limit", "ul", "upper"
|
|
|
|
|
|
])
|
2026-02-24 11:09:48 +08:00
|
|
|
|
|
|
|
|
|
|
def get_keywords(self) -> Tuple[pd.DataFrame, str, List[str], Dict[str, List[str]]]:
|
|
|
|
|
|
"""获取用户输入的多个关键词并筛选数据，返回关键词分组信息"""
|
2026-02-05 09:04:10 +08:00
|
|
|
|
self._print_stage("筛选关键词")
|
|
|
|
|
|
|
|
|
|
|
|
while True:
|
2026-02-24 11:09:48 +08:00
|
|
|
|
keyword_input = input("请输入筛选关键词(多个关键词用','分割，匹配 'Test Name New'): ").strip()
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
|
|
|
|
|
if not keyword_input:
|
|
|
|
|
|
print("❌ 关键词不能为空，请重新输入")
|
|
|
|
|
|
continue
|
|
|
|
|
|
|
|
|
|
|
|
# 检查数据框是否为空
|
2026-02-24 11:09:48 +08:00
|
|
|
|
if self.df.empty:
|
2026-02-05 09:04:10 +08:00
|
|
|
|
print("⚠️ 数据框为空，无法进行筛选")
|
2026-02-24 11:09:48 +08:00
|
|
|
|
return pd.DataFrame(), keyword_input, [], {}
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
|
|
|
|
|
# 检查列是否存在
|
|
|
|
|
|
if "Test Name New" not in self.df.columns:
|
|
|
|
|
|
print("❌ 列 'Test Name New' 不存在于数据框中")
|
|
|
|
|
|
print(f"可用列: {list(self.df.columns)}")
|
2026-02-24 11:09:48 +08:00
|
|
|
|
return pd.DataFrame(), keyword_input, [], {}
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
|
|
|
|
|
try:
|
2026-02-24 11:09:48 +08:00
|
|
|
|
# 分割关键词
|
|
|
|
|
|
keywords = [k.strip() for k in keyword_input.split(',') if k.strip()]
|
|
|
|
|
|
|
|
|
|
|
|
# 构建多个关键词的筛选条件
|
2026-02-05 09:04:10 +08:00
|
|
|
|
mask = pd.Series(False, index=self.df.index)
|
2026-02-24 11:09:48 +08:00
|
|
|
|
keyword_groups = {}
|
|
|
|
|
|
|
2026-02-05 09:04:10 +08:00
|
|
|
|
for keyword in keywords:
|
|
|
|
|
|
keyword_mask = self.df["Test Name New"].astype(str).str.contains(keyword, case=False, na=False)
|
|
|
|
|
|
mask = mask | keyword_mask
|
|
|
|
|
|
|
2026-02-24 11:09:48 +08:00
|
|
|
|
# 记录每个关键词匹配的测试项
|
|
|
|
|
|
matched_tests = self.df.loc[keyword_mask, "Test Name New"].unique().tolist()
|
|
|
|
|
|
keyword_groups[keyword] = matched_tests
|
|
|
|
|
|
|
2026-02-05 09:04:10 +08:00
|
|
|
|
filtered_df = self.df.loc[mask].copy()
|
|
|
|
|
|
|
|
|
|
|
|
if filtered_df.empty:
|
|
|
|
|
|
# 提供友好的提示和建议
|
2026-02-24 11:09:48 +08:00
|
|
|
|
print(f"⚠️ 没有找到包含关键词 '{keyword_input}' 的测试项")
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
|
|
|
|
|
# 显示部分可用的测试项作为参考
|
|
|
|
|
|
available_tests = self.df["Test Name New"].dropna().unique()
|
|
|
|
|
|
if len(available_tests) > 0:
|
|
|
|
|
|
print("📋 可用的测试项示例:")
|
|
|
|
|
|
for test in available_tests[:5]:
|
|
|
|
|
|
print(f" - {test}")
|
|
|
|
|
|
if len(available_tests) > 5:
|
|
|
|
|
|
print(f" ... 还有 {len(available_tests) - 5} 个测试项")
|
|
|
|
|
|
|
|
|
|
|
|
# 提供重新输入或退出的选项
|
|
|
|
|
|
choice = input("请选择: 1-重新输入关键词 2-使用所有数据 3-退出当前操作: ")
|
|
|
|
|
|
if choice == "1":
|
|
|
|
|
|
continue
|
|
|
|
|
|
elif choice == "2":
|
|
|
|
|
|
filtered_df = self.df.copy()
|
|
|
|
|
|
unique_tests = filtered_df["Test Name New"].unique().tolist()
|
2026-02-24 11:09:48 +08:00
|
|
|
|
# 为所有数据创建一个默认分组
|
|
|
|
|
|
keyword_groups = {"所有测试项": unique_tests}
|
2026-02-05 09:04:10 +08:00
|
|
|
|
print(f"✅ 使用所有数据: {len(filtered_df)} 行，{len(unique_tests)} 个测试项")
|
2026-02-24 11:09:48 +08:00
|
|
|
|
return filtered_df, "", unique_tests, keyword_groups
|
2026-02-05 09:04:10 +08:00
|
|
|
|
else:
|
|
|
|
|
|
print("👋 退出筛选操作")
|
2026-02-24 11:09:48 +08:00
|
|
|
|
return pd.DataFrame(), keyword_input, [], {}
|
2026-02-05 09:04:10 +08:00
|
|
|
|
else:
|
|
|
|
|
|
unique_tests = filtered_df["Test Name New"].unique().tolist()
|
|
|
|
|
|
print(f"✅ 匹配到 {len(filtered_df)} 行数据，涉及 {len(unique_tests)} 个不同测试项")
|
2026-02-24 11:09:48 +08:00
|
|
|
|
print(f"📊 关键词分组: {len(keyword_groups)} 组")
|
|
|
|
|
|
for keyword, tests in keyword_groups.items():
|
|
|
|
|
|
print(f" - '{keyword}': {len(tests)} 个测试项")
|
|
|
|
|
|
|
|
|
|
|
|
return filtered_df, keyword_input, unique_tests, keyword_groups
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
|
|
|
|
|
except Exception as e:
|
|
|
|
|
|
print(f"❌ 筛选过程中发生错误: {e}")
|
|
|
|
|
|
print("请检查数据格式或重新输入关键词")
|
|
|
|
|
|
continue
|
|
|
|
|
|
|
|
|
|
|
|
def create_output_dir(self, keyword) -> None:
|
|
|
|
|
|
"""创建输出目录"""
|
|
|
|
|
|
self._print_stage("创建输出目录")
|
|
|
|
|
|
|
|
|
|
|
|
if not self.folder_path:
|
|
|
|
|
|
raise ValueError("文件夹路径未设置")
|
|
|
|
|
|
|
|
|
|
|
|
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
|
|
|
|
|
self.output_dir = os.path.join(self.folder_path, f"scatter_report_out")
|
|
|
|
|
|
safe_keyword = self._safe_filename(keyword) if keyword else "all_data"
|
|
|
|
|
|
self.html_report_path = os.path.join(self.output_dir, f"{safe_keyword}_report_{timestamp}.html")
|
|
|
|
|
|
|
2026-02-24 11:09:48 +08:00
|
|
|
|
# 为汇总对比报告创建单独的文件路径
|
|
|
|
|
|
self.comparison_report_path = os.path.join(self.output_dir,
|
|
|
|
|
|
f"{safe_keyword}_comparison_report_{timestamp}.html")
|
|
|
|
|
|
|
2026-02-05 09:04:10 +08:00
|
|
|
|
os.makedirs(self.output_dir, exist_ok=True)
|
|
|
|
|
|
print(f"输出目录: {self.output_dir}")
|
|
|
|
|
|
|
|
|
|
|
|
@staticmethod
|
|
|
|
|
|
def _safe_filename(name: str) -> str:
|
|
|
|
|
|
"""生成安全的文件名"""
|
2026-02-24 11:10:06 +08:00
|
|
|
|
safe = "".join(c for c in str(name) if c.isalnum() or c in (","," ", "_", "-")).strip()
|
2026-02-05 09:04:10 +08:00
|
|
|
|
return safe or "Unknown_Test"
|
|
|
|
|
|
|
|
|
|
|
|
def _extract_limits(self, df_one_test: pd.DataFrame) -> Tuple[
|
|
|
|
|
|
Optional[float], Optional[float], List[float], List[float]]:
|
|
|
|
|
|
"""提取某个测试项的上下限数值"""
|
|
|
|
|
|
lower_plot = upper_plot = None
|
|
|
|
|
|
lower_set = []
|
|
|
|
|
|
upper_set = []
|
|
|
|
|
|
|
|
|
|
|
|
if self.col_lower and self.col_lower in df_one_test.columns:
|
|
|
|
|
|
lower_vals = self._clean_and_convert_series(df_one_test[self.col_lower], 'numeric').dropna().unique()
|
|
|
|
|
|
lower_set = sorted(lower_vals.tolist()) if len(lower_vals) > 0 else []
|
|
|
|
|
|
if lower_set:
|
|
|
|
|
|
lower_plot = min(lower_set)
|
|
|
|
|
|
|
|
|
|
|
|
if self.col_upper and self.col_upper in df_one_test.columns:
|
|
|
|
|
|
upper_vals = self._clean_and_convert_series(df_one_test[self.col_upper], 'numeric').dropna().unique()
|
|
|
|
|
|
upper_set = sorted(upper_vals.tolist()) if len(upper_vals) > 0 else []
|
|
|
|
|
|
if upper_set:
|
|
|
|
|
|
upper_plot = max(upper_set)
|
|
|
|
|
|
|
|
|
|
|
|
return lower_plot, upper_plot, lower_set, upper_set
|
|
|
|
|
|
|
|
|
|
|
|
@staticmethod
|
|
|
|
|
|
def _clean_and_convert_series(series: pd.Series, target_type: str = 'numeric') -> pd.Series:
|
2026-02-24 11:09:48 +08:00
|
|
|
|
"""统一的系列清洗和转换方法"""
|
2026-02-05 09:04:10 +08:00
|
|
|
|
if series.empty:
|
|
|
|
|
|
return series
|
|
|
|
|
|
|
|
|
|
|
|
if target_type == 'numeric':
|
|
|
|
|
|
# 数值转换优化
|
|
|
|
|
|
if pd.api.types.is_numeric_dtype(series):
|
|
|
|
|
|
return series.astype(float)
|
|
|
|
|
|
|
2026-02-24 11:09:48 +08:00
|
|
|
|
# 批量字符串处理
|
2026-02-05 09:04:10 +08:00
|
|
|
|
cleaned = series.astype(str).str.replace(r'[, ]', '', regex=True).str.strip()
|
|
|
|
|
|
return pd.to_numeric(cleaned, errors='coerce')
|
|
|
|
|
|
|
|
|
|
|
|
elif target_type == 'datetime':
|
|
|
|
|
|
return MultiFileTestReportScatterPlotter._convert_to_datetime(series)
|
|
|
|
|
|
|
|
|
|
|
|
return series
|
|
|
|
|
|
|
|
|
|
|
|
@staticmethod
|
|
|
|
|
|
def _convert_to_datetime(series: pd.Series) -> pd.Series:
|
|
|
|
|
|
"""优化的日期时间转换"""
|
|
|
|
|
|
if pd.api.types.is_datetime64_any_dtype(series):
|
|
|
|
|
|
return series
|
|
|
|
|
|
|
|
|
|
|
|
# 预处理：转换为数值和字符串两种形式
|
|
|
|
|
|
numeric_series = pd.to_numeric(series, errors='coerce')
|
|
|
|
|
|
string_series = series.astype(str).str.strip()
|
|
|
|
|
|
|
|
|
|
|
|
result = pd.Series(pd.NaT, index=series.index, dtype='datetime64[ns]')
|
|
|
|
|
|
|
|
|
|
|
|
# 数值时间戳处理 - 优化逻辑
|
|
|
|
|
|
masks = {
|
|
|
|
|
|
'ms': numeric_series >= 1e12, # 调整为更合理的阈值
|
|
|
|
|
|
's_ms': (numeric_series >= 1e9) & (numeric_series < 1e12), # 包含秒和毫秒的混合情况
|
|
|
|
|
|
'excel': (numeric_series > 20000) & (numeric_series < 60000)
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
for mask_type, mask in masks.items():
|
|
|
|
|
|
if mask.any():
|
|
|
|
|
|
if mask_type == 'ms':
|
|
|
|
|
|
result.loc[mask] = pd.to_datetime(numeric_series.loc[mask], unit='ms')
|
|
|
|
|
|
elif mask_type == 's_ms':
|
|
|
|
|
|
# 对有小数部分的时间戳使用浮点数处理
|
|
|
|
|
|
timestamp_values = numeric_series.loc[mask]
|
|
|
|
|
|
|
|
|
|
|
|
# 检查是否有小数部分
|
|
|
|
|
|
has_decimal = (timestamp_values % 1 != 0)
|
|
|
|
|
|
|
|
|
|
|
|
# 对整数部分（秒级时间戳）处理
|
|
|
|
|
|
if (~has_decimal).any():
|
|
|
|
|
|
integer_mask = mask & (~has_decimal)
|
|
|
|
|
|
result.loc[integer_mask] = pd.to_datetime(
|
|
|
|
|
|
numeric_series.loc[integer_mask].astype('int64'), unit='s'
|
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
# 对小数部分（可能是毫秒级）处理
|
|
|
|
|
|
if has_decimal.any():
|
|
|
|
|
|
decimal_mask = mask & has_decimal
|
|
|
|
|
|
# 尝试毫秒单位转换
|
|
|
|
|
|
result.loc[decimal_mask] = pd.to_datetime(
|
|
|
|
|
|
numeric_series.loc[decimal_mask] * 1000, unit='ms'
|
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
elif mask_type == 'excel':
|
|
|
|
|
|
origin = pd.Timestamp('1899-12-30')
|
|
|
|
|
|
result.loc[mask] = origin + pd.to_timedelta(numeric_series.loc[mask], unit='D')
|
|
|
|
|
|
|
|
|
|
|
|
# 字符串日期处理
|
|
|
|
|
|
remaining_mask = result.isna()
|
|
|
|
|
|
if remaining_mask.any():
|
|
|
|
|
|
remaining_strings = string_series.loc[remaining_mask]
|
|
|
|
|
|
|
|
|
|
|
|
# 特定格式优先处理
|
|
|
|
|
|
format_patterns = [
|
|
|
|
|
|
(r'^\d{4}-\d{2}-\d{2} \d{2}-\d{2}-\d{2}$', '%Y-%m-%d %H-%M-%S'),
|
|
|
|
|
|
]
|
|
|
|
|
|
|
|
|
|
|
|
for pattern, date_format in format_patterns:
|
|
|
|
|
|
format_mask = remaining_strings.str.match(pattern)
|
|
|
|
|
|
if format_mask.any():
|
|
|
|
|
|
result.loc[remaining_mask[remaining_mask].index[format_mask]] = pd.to_datetime(
|
|
|
|
|
|
remaining_strings.loc[format_mask], format=date_format, errors='coerce'
|
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
# 通用解析
|
|
|
|
|
|
still_na_mask = result.isna() & remaining_mask
|
|
|
|
|
|
if still_na_mask.any():
|
|
|
|
|
|
result.loc[still_na_mask] = pd.to_datetime(
|
|
|
|
|
|
string_series.loc[still_na_mask], errors='coerce'
|
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
return result
|
|
|
|
|
|
|
|
|
|
|
|
def _preprocess_test_data(self, test_data: pd.DataFrame) -> pd.DataFrame:
|
|
|
|
|
|
"""数据预处理"""
|
|
|
|
|
|
# 数值转换
|
|
|
|
|
|
test_data['Measurement_num'] = self._clean_and_convert_series(
|
|
|
|
|
|
test_data['Measurement'], 'numeric'
|
|
|
|
|
|
)
|
|
|
|
|
|
test_data['TestTime_dt'] = self._clean_and_convert_series(
|
|
|
|
|
|
test_data['Test Time'], 'datetime'
|
|
|
|
|
|
)
|
2026-03-27 14:32:56 +08:00
|
|
|
|
# 确保Cell编号存在（如果原数据中有）
|
|
|
|
|
|
if 'Cell' in test_data.columns:
|
|
|
|
|
|
# 清理数据：去除首尾空格，并尝试转换为数值类型
|
|
|
|
|
|
test_data['Cell编号'] = test_data['Cell'].astype(str).str.strip()
|
|
|
|
|
|
|
|
|
|
|
|
# 尝试将清理后的字符串转换为数值（例如整数）
|
|
|
|
|
|
# errors='coerce' 会将无法转换的值设为NaN（非数字）
|
|
|
|
|
|
test_data['Cell编号_数值'] = pd.to_numeric(test_data['Cell编号'], errors='coerce')
|
|
|
|
|
|
|
|
|
|
|
|
# 检查是否存在转换失败的值（即NaN）
|
|
|
|
|
|
failed_conversions = test_data['Cell编号_数值'].isna().sum()
|
|
|
|
|
|
if failed_conversions > 0:
|
|
|
|
|
|
print(f"警告：发现 {failed_conversions} 个 'Cell' 值无法转换为数字，这些条目将保留为字符串或根据业务逻辑处理。")
|
|
|
|
|
|
# 业务决策：对于无法转换的，可以保留原字符串，或使用一个默认值
|
|
|
|
|
|
# 例如，将无法转换的条目其数值编号设为-1或一个特定的标识值
|
|
|
|
|
|
# test_data.loc[test_data['Cell编号_数值'].isna(), 'Cell编号_数值'] = -1
|
|
|
|
|
|
|
|
|
|
|
|
# 此时，您可以根据需求选择使用 'Cell编号'（字符串）或 'Cell编号_数值'（数字）进行后续分组和可视化
|
|
|
|
|
|
# 对于绘图着色和排序，使用 'Cell编号_数值' 列
|
|
|
|
|
|
grouping_column = 'Cell编号_数值'
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
|
|
|
|
|
# 去除无效数据
|
|
|
|
|
|
valid_data = test_data.dropna(subset=['Measurement_num', 'TestTime_dt'])
|
|
|
|
|
|
return valid_data.sort_values('TestTime_dt')
|
|
|
|
|
|
|
|
|
|
|
|
def _calculate_statistics(self, y_data: pd.Series) -> Dict[str, float]:
|
|
|
|
|
|
"""计算统计信息"""
|
|
|
|
|
|
stats = {
|
|
|
|
|
|
'count': len(y_data),
|
|
|
|
|
|
'mean': y_data.mean(),
|
|
|
|
|
|
'median': y_data.median(),
|
|
|
|
|
|
'min': y_data.min(),
|
|
|
|
|
|
'max': y_data.max(),
|
|
|
|
|
|
'std': y_data.std(),
|
|
|
|
|
|
'q1': y_data.quantile(0.25),
|
|
|
|
|
|
'q3': y_data.quantile(0.75)
|
|
|
|
|
|
}
|
|
|
|
|
|
return stats
|
|
|
|
|
|
|
|
|
|
|
|
def _plot_to_base64(self, fig) -> str:
|
|
|
|
|
|
"""将图表转换为base64编码"""
|
|
|
|
|
|
buf = BytesIO()
|
|
|
|
|
|
fig.savefig(buf, format='png', dpi=150, bbox_inches='tight')
|
|
|
|
|
|
buf.seek(0)
|
|
|
|
|
|
img_str = base64.b64encode(buf.read()).decode('utf-8')
|
|
|
|
|
|
plt.close(fig)
|
|
|
|
|
|
return img_str
|
|
|
|
|
|
|
|
|
|
|
|
def _create_summary_plot(self, test_data: pd.DataFrame, test_name: str,
|
|
|
|
|
|
lower_plot: Optional[float], upper_plot: Optional[float]) -> str:
|
|
|
|
|
|
"""创建汇总图（所有SN在一个图中）"""
|
|
|
|
|
|
fig, ax = plt.subplots(figsize=(12, 8))
|
|
|
|
|
|
|
2026-03-27 14:32:56 +08:00
|
|
|
|
# 检查是否有Cell编号列
|
|
|
|
|
|
has_cell_no = 'Cell编号' in test_data.columns
|
|
|
|
|
|
|
2026-02-05 09:04:10 +08:00
|
|
|
|
# 分组绘制
|
2026-03-27 14:32:56 +08:00
|
|
|
|
if has_cell_no and not test_data['Cell编号'].isna().all():
|
|
|
|
|
|
# 先按Cell编号_数值排序，再按SN排序
|
|
|
|
|
|
test_data_sorted = test_data.sort_values(['Cell编号_数值', 'SN'])
|
|
|
|
|
|
|
|
|
|
|
|
# 按Cell编号_数值和SN分组
|
|
|
|
|
|
cell_sn_groups = list(test_data_sorted.groupby(['Cell编号_数值', 'SN']))
|
|
|
|
|
|
|
|
|
|
|
|
# 生成颜色映射
|
|
|
|
|
|
colors = plt.cm.Set3(np.linspace(0, 1, len(cell_sn_groups)))
|
|
|
|
|
|
|
|
|
|
|
|
for idx, ((cell_no, sn), group) in enumerate(cell_sn_groups):
|
|
|
|
|
|
label = f"Cell:{cell_no}, SN:{sn}"
|
|
|
|
|
|
ax.scatter(group['TestTime_dt'], group['Measurement_num'],
|
|
|
|
|
|
color=colors[idx], alpha=0.7, s=25, label=label)
|
|
|
|
|
|
else:
|
|
|
|
|
|
# 只按SN分组排序
|
|
|
|
|
|
test_data_sorted = test_data.sort_values('SN')
|
|
|
|
|
|
sn_groups = [(f"{sn}_no_cell", group) for sn, group in test_data_sorted.groupby("SN")]
|
|
|
|
|
|
|
|
|
|
|
|
# 生成颜色映射
|
|
|
|
|
|
colors = plt.cm.Set3(np.linspace(0, 1, len(sn_groups)))
|
|
|
|
|
|
|
|
|
|
|
|
for idx, (group_key, group) in enumerate(sn_groups):
|
|
|
|
|
|
sn = str(group_key).replace('_no_cell', '')
|
|
|
|
|
|
label = f"SN: {sn}"
|
|
|
|
|
|
ax.scatter(group['TestTime_dt'], group['Measurement_num'],
|
|
|
|
|
|
color=colors[idx], alpha=0.7, s=25, label=label)
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
|
|
|
|
|
# 计算统计信息
|
|
|
|
|
|
y_data = test_data['Measurement_num']
|
|
|
|
|
|
stats = self._calculate_statistics(y_data)
|
|
|
|
|
|
|
|
|
|
|
|
# 绘制限值线和统计线
|
|
|
|
|
|
x_min, x_max = test_data['TestTime_dt'].min(), test_data['TestTime_dt'].max()
|
|
|
|
|
|
|
|
|
|
|
|
if lower_plot is not None:
|
|
|
|
|
|
ax.axhline(y=lower_plot, color='green', linestyle='--', linewidth=1.2, label="Lower Limit")
|
|
|
|
|
|
if upper_plot is not None:
|
|
|
|
|
|
ax.axhline(y=upper_plot, color='red', linestyle='--', linewidth=1.2, label="Upper Limit")
|
|
|
|
|
|
|
|
|
|
|
|
# 添加统计线
|
|
|
|
|
|
ax.hlines(y=stats['mean'], xmin=x_min, xmax=x_max, colors='orange',
|
|
|
|
|
|
linestyles='-', linewidth=1.5, alpha=0.7, label='Mean')
|
|
|
|
|
|
ax.hlines(y=stats['median'], xmin=x_min, xmax=x_max, colors='purple',
|
|
|
|
|
|
linestyles='-.', linewidth=1.5, alpha=0.7, label='Median')
|
|
|
|
|
|
|
|
|
|
|
|
# 设置图形属性
|
2026-03-27 14:32:56 +08:00
|
|
|
|
title = f"汇总图 - {test_name}"
|
|
|
|
|
|
if has_cell_no and not test_data['Cell编号'].isna().all():
|
|
|
|
|
|
title += " (按Cell→SN排序)"
|
|
|
|
|
|
ax.set_title(title)
|
2026-02-05 09:04:10 +08:00
|
|
|
|
ax.set_xlabel("Test Time")
|
|
|
|
|
|
ax.set_ylabel("Measurement Value")
|
|
|
|
|
|
ax.grid(True, alpha=0.3)
|
|
|
|
|
|
ax.tick_params(axis='x', rotation=45)
|
|
|
|
|
|
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
|
|
|
|
|
|
|
|
|
|
|
|
return self._plot_to_base64(fig)
|
|
|
|
|
|
|
|
|
|
|
|
def _create_sn_plots(self, test_data: pd.DataFrame, test_name: str,
|
|
|
|
|
|
lower_plot: Optional[float], upper_plot: Optional[float]) -> List[Dict[str, str]]:
|
|
|
|
|
|
"""为每个SN创建独立图表"""
|
|
|
|
|
|
sn_plots = []
|
|
|
|
|
|
|
|
|
|
|
|
if "SN" not in test_data.columns:
|
|
|
|
|
|
return sn_plots
|
|
|
|
|
|
|
2026-03-27 14:32:56 +08:00
|
|
|
|
# 检查是否有Cell编号列
|
|
|
|
|
|
has_cell_no = 'Cell编号' in test_data.columns
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
2026-03-27 14:32:56 +08:00
|
|
|
|
# 如果有Cell编号，先按Cell编号_数值排序，再按SN排序
|
|
|
|
|
|
if has_cell_no and not test_data['Cell编号'].isna().all():
|
|
|
|
|
|
# 先按Cell编号_数值排序，再按SN排序
|
|
|
|
|
|
test_data_sorted = test_data.sort_values(['Cell编号_数值', 'SN'])
|
|
|
|
|
|
|
|
|
|
|
|
# 按Cell编号_数值分组，然后对每个Cell内的数据按SN排序
|
|
|
|
|
|
cell_groups = test_data_sorted.groupby('Cell编号_数值')
|
|
|
|
|
|
|
|
|
|
|
|
for cell_no, cell_group in cell_groups:
|
|
|
|
|
|
if cell_group.empty:
|
|
|
|
|
|
continue
|
|
|
|
|
|
|
|
|
|
|
|
# 对当前Cell内的数据按SN排序
|
|
|
|
|
|
cell_group_sorted = cell_group.sort_values('SN')
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
2026-03-27 14:32:56 +08:00
|
|
|
|
# 按SN分组
|
|
|
|
|
|
sn_groups = cell_group_sorted.groupby('SN')
|
|
|
|
|
|
|
|
|
|
|
|
for sn, group in sn_groups:
|
|
|
|
|
|
if group.empty:
|
|
|
|
|
|
continue
|
|
|
|
|
|
|
|
|
|
|
|
fig, ax = plt.subplots(figsize=(10, 6))
|
|
|
|
|
|
|
|
|
|
|
|
# 绘制当前SN和Cell的数据点
|
|
|
|
|
|
ax.scatter(group['TestTime_dt'], group['Measurement_num'],
|
|
|
|
|
|
color='blue', alpha=0.7, s=30, label=f"SN:{sn}, Cell:{cell_no}")
|
|
|
|
|
|
|
|
|
|
|
|
# 计算当前SN和Cell组合的统计信息
|
|
|
|
|
|
y_data = group['Measurement_num']
|
|
|
|
|
|
stats = self._calculate_statistics(y_data)
|
|
|
|
|
|
|
|
|
|
|
|
# 绘制限值线
|
|
|
|
|
|
x_min, x_max = group['TestTime_dt'].min(), group['TestTime_dt'].max()
|
|
|
|
|
|
|
|
|
|
|
|
if lower_plot is not None:
|
|
|
|
|
|
ax.axhline(y=lower_plot, color='green', linestyle='--', linewidth=1.2, label="Lower Limit")
|
|
|
|
|
|
if upper_plot is not None:
|
|
|
|
|
|
ax.axhline(y=upper_plot, color='red', linestyle='--', linewidth=1.2, label="Upper Limit")
|
|
|
|
|
|
|
|
|
|
|
|
# 添加统计线
|
|
|
|
|
|
ax.hlines(y=stats['mean'], xmin=x_min, xmax=x_max, colors='orange',
|
|
|
|
|
|
linestyles='-', linewidth=1.5, alpha=0.7, label='Mean')
|
|
|
|
|
|
ax.hlines(y=stats['median'], xmin=x_min, xmax=x_max, colors='purple',
|
|
|
|
|
|
linestyles='-.', linewidth=1.5, alpha=0.7, label='Median')
|
|
|
|
|
|
|
|
|
|
|
|
# 设置图形属性
|
|
|
|
|
|
ax.set_title(f"SN独立图 - {test_name} (Cell: {cell_no}, SN: {sn})")
|
|
|
|
|
|
ax.set_xlabel("Test Time")
|
|
|
|
|
|
ax.set_ylabel("Measurement Value")
|
|
|
|
|
|
ax.grid(True, alpha=0.3)
|
|
|
|
|
|
ax.tick_params(axis='x', rotation=45)
|
|
|
|
|
|
ax.legend()
|
|
|
|
|
|
|
|
|
|
|
|
# 转换为base64
|
|
|
|
|
|
plot_image = self._plot_to_base64(fig)
|
|
|
|
|
|
|
|
|
|
|
|
# 收集当前Cell编号的统计信息 - 修复格式
|
|
|
|
|
|
cell_info = {}
|
|
|
|
|
|
if has_cell_no:
|
|
|
|
|
|
# 计算当前Cell编号的详细统计信息
|
|
|
|
|
|
cell_stats_dict = {
|
|
|
|
|
|
'count': len(group),
|
|
|
|
|
|
'mean': float(group['Measurement_num'].mean()),
|
|
|
|
|
|
'std': float(group['Measurement_num'].std()),
|
|
|
|
|
|
'min': float(group['Measurement_num'].min()),
|
|
|
|
|
|
'max': float(group['Measurement_num'].max())
|
|
|
|
|
|
}
|
|
|
|
|
|
cell_info = {str(cell_no): cell_stats_dict}
|
|
|
|
|
|
|
|
|
|
|
|
sn_plots.append({
|
|
|
|
|
|
"sn": f"Cell_{cell_no}_SN_{sn}",
|
|
|
|
|
|
"image": plot_image,
|
|
|
|
|
|
"cell_info": cell_info if cell_info else None,
|
|
|
|
|
|
"has_cell_data": True,
|
|
|
|
|
|
"cell_no": str(cell_no),
|
|
|
|
|
|
"sn_no": str(sn)
|
|
|
|
|
|
})
|
|
|
|
|
|
else:
|
|
|
|
|
|
# 没有Cell编号，只按SN分组排序
|
|
|
|
|
|
test_data_sorted = test_data.sort_values('SN')
|
|
|
|
|
|
sn_groups = test_data_sorted.groupby("SN")
|
|
|
|
|
|
|
|
|
|
|
|
for sn, group in sn_groups:
|
|
|
|
|
|
if group.empty:
|
|
|
|
|
|
continue
|
|
|
|
|
|
|
|
|
|
|
|
fig, ax = plt.subplots(figsize=(10, 6))
|
|
|
|
|
|
|
|
|
|
|
|
# 没有Cell编号，按SN着色
|
|
|
|
|
|
ax.scatter(group['TestTime_dt'], group['Measurement_num'],
|
|
|
|
|
|
color='blue', alpha=0.7, s=30, label=f"SN: {sn}")
|
|
|
|
|
|
|
|
|
|
|
|
# 计算当前SN的统计信息
|
|
|
|
|
|
y_data = group['Measurement_num']
|
|
|
|
|
|
stats = self._calculate_statistics(y_data)
|
|
|
|
|
|
|
|
|
|
|
|
# 绘制限值线
|
|
|
|
|
|
x_min, x_max = group['TestTime_dt'].min(), group['TestTime_dt'].max()
|
|
|
|
|
|
|
|
|
|
|
|
if lower_plot is not None:
|
|
|
|
|
|
ax.axhline(y=lower_plot, color='green', linestyle='--', linewidth=1.2, label="Lower Limit")
|
|
|
|
|
|
if upper_plot is not None:
|
|
|
|
|
|
ax.axhline(y=upper_plot, color='red', linestyle='--', linewidth=1.2, label="Upper Limit")
|
|
|
|
|
|
|
|
|
|
|
|
# 添加统计线
|
|
|
|
|
|
ax.hlines(y=stats['mean'], xmin=x_min, xmax=x_max, colors='orange',
|
|
|
|
|
|
linestyles='-', linewidth=1.5, alpha=0.7, label='Mean')
|
|
|
|
|
|
ax.hlines(y=stats['median'], xmin=x_min, xmax=x_max, colors='purple',
|
|
|
|
|
|
linestyles='-.', linewidth=1.5, alpha=0.7, label='Median')
|
|
|
|
|
|
|
|
|
|
|
|
# 设置图形属性
|
|
|
|
|
|
ax.set_title(f"SN独立图 - {test_name} (SN: {sn})")
|
|
|
|
|
|
ax.set_xlabel("Test Time")
|
|
|
|
|
|
ax.set_ylabel("Measurement Value")
|
|
|
|
|
|
ax.grid(True, alpha=0.3)
|
|
|
|
|
|
ax.tick_params(axis='x', rotation=45)
|
|
|
|
|
|
ax.legend()
|
|
|
|
|
|
|
|
|
|
|
|
# 转换为base64
|
|
|
|
|
|
plot_image = self._plot_to_base64(fig)
|
|
|
|
|
|
|
|
|
|
|
|
sn_plots.append({
|
|
|
|
|
|
"sn": str(sn),
|
|
|
|
|
|
"image": plot_image,
|
|
|
|
|
|
"cell_info": None,
|
|
|
|
|
|
"has_cell_data": False,
|
|
|
|
|
|
"cell_no": None,
|
|
|
|
|
|
"sn_no": str(sn)
|
|
|
|
|
|
})
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
|
|
|
|
|
return sn_plots
|
|
|
|
|
|
|
|
|
|
|
|
def _determine_test_status(self, stats: Dict[str, float],
|
|
|
|
|
|
lower_limit: Optional[float],
|
|
|
|
|
|
upper_limit: Optional[float]) -> Dict[str, Any]:
|
|
|
|
|
|
"""确定测试状态"""
|
|
|
|
|
|
status = "success"
|
|
|
|
|
|
status_display = "正常"
|
|
|
|
|
|
|
|
|
|
|
|
if lower_limit is not None and upper_limit is not None:
|
|
|
|
|
|
# 检查是否超出限值
|
|
|
|
|
|
if stats['min'] < lower_limit or stats['max'] > upper_limit:
|
|
|
|
|
|
status = "danger"
|
|
|
|
|
|
status_display = "异常"
|
|
|
|
|
|
elif (stats['mean'] < lower_limit * 1.1 or stats['mean'] > upper_limit * 0.9 or
|
|
|
|
|
|
stats['std'] > (upper_limit - lower_limit) * 0.2):
|
|
|
|
|
|
status = "warning"
|
|
|
|
|
|
status_display = "警告"
|
|
|
|
|
|
|
|
|
|
|
|
return {"status": status, "status_display": status_display}
|
|
|
|
|
|
|
2026-02-24 11:09:48 +08:00
|
|
|
|
def _create_comparison_plots(self, filtered_df: pd.DataFrame, keyword_groups: Dict[str, List[str]]) -> List[
|
|
|
|
|
|
Dict[str, str]]:
|
|
|
|
|
|
"""创建汇总对比报告图表，按关键词和测试项分组显示"""
|
2026-02-05 09:04:10 +08:00
|
|
|
|
comparison_plots = []
|
|
|
|
|
|
|
2026-02-24 11:09:48 +08:00
|
|
|
|
if filtered_df.empty or "Test Name New" not in filtered_df.columns:
|
|
|
|
|
|
return comparison_plots
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
2026-02-24 11:09:48 +08:00
|
|
|
|
# 预处理数据
|
|
|
|
|
|
processed_data = self._preprocess_test_data(filtered_df.copy())
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
2026-02-24 11:09:48 +08:00
|
|
|
|
if processed_data.empty:
|
|
|
|
|
|
return comparison_plots
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
2026-02-24 11:09:48 +08:00
|
|
|
|
# 1. 时间序列散点图 - 按关键词和测试项分别显示
|
|
|
|
|
|
num_tests_total = sum(len(tests) for tests in keyword_groups.values())
|
|
|
|
|
|
if num_tests_total > 0:
|
|
|
|
|
|
# 计算适当的子图布局
|
|
|
|
|
|
max_cols = 2
|
|
|
|
|
|
num_rows = (num_tests_total + max_cols - 1) // max_cols
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
2026-02-24 11:09:48 +08:00
|
|
|
|
fig1, axes = plt.subplots(num_rows, max_cols, figsize=(16, 6 * num_rows))
|
|
|
|
|
|
if num_rows == 1:
|
|
|
|
|
|
axes = axes.reshape(1, -1)
|
|
|
|
|
|
elif num_tests_total == 1:
|
|
|
|
|
|
axes = np.array([[axes]])
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
2026-02-24 11:09:48 +08:00
|
|
|
|
axes = axes.flatten()
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
2026-02-24 11:09:48 +08:00
|
|
|
|
plot_idx = 0
|
|
|
|
|
|
colors = plt.cm.Set3(np.linspace(0, 1, 12))
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
2026-02-24 11:09:48 +08:00
|
|
|
|
for keyword, test_names in keyword_groups.items():
|
|
|
|
|
|
for test_name in test_names:
|
|
|
|
|
|
if plot_idx >= len(axes):
|
|
|
|
|
|
break
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
2026-02-24 11:09:48 +08:00
|
|
|
|
# 筛选当前测试项的数据
|
|
|
|
|
|
test_data = processed_data[processed_data["Test Name New"] == test_name]
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
2026-02-24 11:09:48 +08:00
|
|
|
|
if test_data.empty:
|
|
|
|
|
|
continue
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
2026-02-24 11:09:48 +08:00
|
|
|
|
ax = axes[plot_idx]
|
|
|
|
|
|
|
|
|
|
|
|
# 按SN分组绘制
|
|
|
|
|
|
sn_groups = test_data.groupby("SN")
|
|
|
|
|
|
color_idx = 0
|
|
|
|
|
|
|
|
|
|
|
|
for sn, group in sn_groups:
|
|
|
|
|
|
ax.scatter(group['TestTime_dt'], group['Measurement_num'],
|
|
|
|
|
|
c=[colors[color_idx % len(colors)]], label=str(sn), alpha=0.7, s=30)
|
|
|
|
|
|
color_idx += 1
|
|
|
|
|
|
|
|
|
|
|
|
# 添加上下限
|
|
|
|
|
|
lower_plot, upper_plot, _, _ = self._extract_limits(test_data)
|
|
|
|
|
|
if lower_plot is not None:
|
|
|
|
|
|
ax.axhline(y=lower_plot, color='green', linestyle='--', linewidth=2, label="Lower Limit")
|
|
|
|
|
|
if upper_plot is not None:
|
|
|
|
|
|
ax.axhline(y=upper_plot, color='red', linestyle='--', linewidth=2, label="Upper Limit")
|
|
|
|
|
|
|
|
|
|
|
|
ax.set_title(f"{test_name}\n(关键词: {keyword})", fontsize=10)
|
|
|
|
|
|
ax.set_xlabel("测试时间")
|
|
|
|
|
|
ax.set_ylabel("测量值")
|
|
|
|
|
|
ax.grid(True, alpha=0.3)
|
|
|
|
|
|
ax.tick_params(axis='x', rotation=45)
|
|
|
|
|
|
ax.legend(fontsize=8)
|
|
|
|
|
|
|
|
|
|
|
|
plot_idx += 1
|
|
|
|
|
|
|
|
|
|
|
|
# 隐藏多余的子图
|
|
|
|
|
|
for idx in range(plot_idx, len(axes)):
|
|
|
|
|
|
axes[idx].set_visible(False)
|
|
|
|
|
|
|
|
|
|
|
|
plt.tight_layout()
|
|
|
|
|
|
comparison_plots.append({
|
|
|
|
|
|
"title": "时间序列散点图（按测试项分组）",
|
|
|
|
|
|
"image": self._plot_to_base64(fig1),
|
|
|
|
|
|
"description": "每个测试项单独显示，按SN区分不同数据点"
|
|
|
|
|
|
})
|
|
|
|
|
|
|
|
|
|
|
|
# 2. 箱线图 - 按关键词分组，每个关键词组内的测试项分别显示
|
|
|
|
|
|
if len(keyword_groups) > 0:
|
|
|
|
|
|
fig2, axes = plt.subplots(len(keyword_groups), 1, figsize=(14, 6 * len(keyword_groups)))
|
|
|
|
|
|
if len(keyword_groups) == 1:
|
|
|
|
|
|
axes = [axes]
|
|
|
|
|
|
|
|
|
|
|
|
for idx, (keyword, test_names) in enumerate(keyword_groups.items()):
|
|
|
|
|
|
ax = axes[idx]
|
|
|
|
|
|
|
|
|
|
|
|
boxplot_data = []
|
|
|
|
|
|
boxplot_labels = []
|
|
|
|
|
|
|
|
|
|
|
|
for test_name in test_names:
|
|
|
|
|
|
test_data = processed_data[processed_data["Test Name New"] == test_name]
|
|
|
|
|
|
if not test_data.empty:
|
|
|
|
|
|
boxplot_data.append(test_data['Measurement_num'].values)
|
|
|
|
|
|
boxplot_labels.append(test_name)
|
|
|
|
|
|
|
|
|
|
|
|
if boxplot_data:
|
|
|
|
|
|
box_plot = ax.boxplot(boxplot_data, tick_labels=boxplot_labels, patch_artist=True)
|
|
|
|
|
|
|
|
|
|
|
|
# 设置箱线图颜色
|
|
|
|
|
|
colors_box = plt.cm.Set3(np.linspace(0, 1, len(boxplot_data)))
|
|
|
|
|
|
for patch, color in zip(box_plot['boxes'], colors_box):
|
|
|
|
|
|
patch.set_facecolor(color)
|
|
|
|
|
|
|
|
|
|
|
|
# 添加上下限线（使用第一个测试项的数据）
|
|
|
|
|
|
if boxplot_data:
|
|
|
|
|
|
first_test_data = processed_data[processed_data["Test Name New"] == test_names[0]]
|
|
|
|
|
|
lower_plot, upper_plot, _, _ = self._extract_limits(first_test_data)
|
|
|
|
|
|
if lower_plot is not None:
|
|
|
|
|
|
ax.axhline(y=lower_plot, color='green', linestyle='--', linewidth=2, label="Lower Limit")
|
|
|
|
|
|
if upper_plot is not None:
|
|
|
|
|
|
ax.axhline(y=upper_plot, color='red', linestyle='--', linewidth=2, label="Upper Limit")
|
|
|
|
|
|
|
|
|
|
|
|
ax.set_title(f"箱线图 - {keyword}组", fontsize=12)
|
|
|
|
|
|
ax.set_ylabel("测量值")
|
|
|
|
|
|
ax.tick_params(axis='x', rotation=45)
|
|
|
|
|
|
ax.grid(True, alpha=0.3)
|
|
|
|
|
|
ax.legend()
|
|
|
|
|
|
|
|
|
|
|
|
plt.tight_layout()
|
|
|
|
|
|
comparison_plots.append({
|
|
|
|
|
|
"title": "箱线图（按关键词分组）",
|
|
|
|
|
|
"image": self._plot_to_base64(fig2),
|
|
|
|
|
|
"description": "每个关键词组单独显示，组内测试项分别绘制箱线图"
|
|
|
|
|
|
})
|
|
|
|
|
|
|
|
|
|
|
|
# 3. 概率分布直方图 - 按测试项分组显示
|
|
|
|
|
|
if num_tests_total > 0:
|
|
|
|
|
|
max_cols = 2
|
|
|
|
|
|
num_rows = (num_tests_total + max_cols - 1) // max_cols
|
|
|
|
|
|
|
|
|
|
|
|
fig3, axes = plt.subplots(num_rows, max_cols, figsize=(16, 6 * num_rows))
|
|
|
|
|
|
if num_rows == 1:
|
|
|
|
|
|
axes = axes.reshape(1, -1)
|
|
|
|
|
|
elif num_tests_total == 1:
|
|
|
|
|
|
axes = np.array([[axes]])
|
|
|
|
|
|
|
|
|
|
|
|
axes = axes.flatten()
|
|
|
|
|
|
|
|
|
|
|
|
plot_idx = 0
|
|
|
|
|
|
colors_hist = plt.cm.Set3(np.linspace(0, 1, 12))
|
|
|
|
|
|
|
|
|
|
|
|
for keyword, test_names in keyword_groups.items():
|
|
|
|
|
|
for test_name in test_names:
|
|
|
|
|
|
if plot_idx >= len(axes):
|
|
|
|
|
|
break
|
|
|
|
|
|
|
|
|
|
|
|
test_data = processed_data[processed_data["Test Name New"] == test_name]
|
|
|
|
|
|
|
|
|
|
|
|
if test_data.empty or len(test_data) < 2:
|
|
|
|
|
|
continue
|
|
|
|
|
|
|
|
|
|
|
|
ax = axes[plot_idx]
|
|
|
|
|
|
|
|
|
|
|
|
# 绘制概率分布直方图
|
|
|
|
|
|
sns.histplot(test_data['Measurement_num'], kde=True,
|
|
|
|
|
|
color=colors_hist[plot_idx % len(colors_hist)], alpha=0.7, ax=ax)
|
|
|
|
|
|
|
|
|
|
|
|
# 添加上下限线
|
|
|
|
|
|
lower_plot, upper_plot, _, _ = self._extract_limits(test_data)
|
|
|
|
|
|
if lower_plot is not None:
|
|
|
|
|
|
ax.axvline(x=lower_plot, color='green', linestyle='--', linewidth=2, label="Lower Limit")
|
|
|
|
|
|
if upper_plot is not None:
|
|
|
|
|
|
ax.axvline(x=upper_plot, color='red', linestyle='--', linewidth=2, label="Upper Limit")
|
|
|
|
|
|
|
|
|
|
|
|
ax.set_title(f"{test_name}\n(关键词: {keyword})", fontsize=10)
|
|
|
|
|
|
ax.set_xlabel("测量值")
|
|
|
|
|
|
ax.set_ylabel("频率")
|
|
|
|
|
|
ax.grid(True, alpha=0.3)
|
|
|
|
|
|
ax.legend()
|
|
|
|
|
|
|
|
|
|
|
|
plot_idx += 1
|
|
|
|
|
|
|
|
|
|
|
|
# 隐藏多余的子图
|
|
|
|
|
|
for idx in range(plot_idx, len(axes)):
|
|
|
|
|
|
axes[idx].set_visible(False)
|
|
|
|
|
|
|
|
|
|
|
|
plt.tight_layout()
|
|
|
|
|
|
comparison_plots.append({
|
|
|
|
|
|
"title": "概率分布直方图（按测试项分组）",
|
|
|
|
|
|
"image": self._plot_to_base64(fig3),
|
|
|
|
|
|
"description": "每个测试项单独显示概率分布和上下限"
|
|
|
|
|
|
})
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
|
|
|
|
|
return comparison_plots
|
|
|
|
|
|
|
2026-02-24 11:09:48 +08:00
|
|
|
|
def _organize_tests_by_keyword_groups(self, test_results: List[Dict[str, Any]],
|
|
|
|
|
|
keyword_groups: Dict[str, List[str]]) -> Dict[
|
|
|
|
|
|
str, Dict[str, List[Dict[str, Any]]]]:
|
|
|
|
|
|
"""按关键词分组组织测试结果，每个测试项单独显示"""
|
|
|
|
|
|
organized_groups = {}
|
|
|
|
|
|
|
|
|
|
|
|
# 构建测试名称到测试结果的映射
|
|
|
|
|
|
test_name_to_result = {test['name']: test for test in test_results}
|
|
|
|
|
|
|
|
|
|
|
|
# 为每个关键词组分配测试结果
|
|
|
|
|
|
for keyword, test_names in keyword_groups.items():
|
|
|
|
|
|
group_tests = {}
|
|
|
|
|
|
for test_name in test_names:
|
|
|
|
|
|
if test_name in test_name_to_result:
|
|
|
|
|
|
# 每个测试项单独作为一个分组
|
|
|
|
|
|
group_tests[test_name] = [test_name_to_result[test_name]]
|
|
|
|
|
|
|
|
|
|
|
|
if group_tests:
|
|
|
|
|
|
organized_groups[keyword] = group_tests
|
|
|
|
|
|
|
|
|
|
|
|
return organized_groups
|
|
|
|
|
|
|
|
|
|
|
|
def generate_html_report(self, filtered_df: pd.DataFrame, keyword: str,
|
|
|
|
|
|
unique_tests: List[str], keyword_groups: Dict[str, List[str]]) -> None:
|
|
|
|
|
|
"""生成HTML报告"""
|
2026-02-05 09:04:10 +08:00
|
|
|
|
self._print_stage("生成HTML报告")
|
|
|
|
|
|
start_time = time.time()
|
|
|
|
|
|
|
|
|
|
|
|
test_results = []
|
|
|
|
|
|
total_points = 0
|
|
|
|
|
|
status_counts = {"success": 0, "warning": 0, "danger": 0}
|
|
|
|
|
|
|
|
|
|
|
|
for i, test_name in enumerate(unique_tests, 1):
|
|
|
|
|
|
self._print_progress(i, len(unique_tests), "生成测试报告")
|
|
|
|
|
|
|
|
|
|
|
|
# 获取测试数据
|
|
|
|
|
|
test_data = filtered_df[filtered_df["Test Name New"] == test_name].copy()
|
|
|
|
|
|
test_data = self._preprocess_test_data(test_data)
|
|
|
|
|
|
|
|
|
|
|
|
if test_data.empty:
|
|
|
|
|
|
continue
|
|
|
|
|
|
|
|
|
|
|
|
# 提取限值信息
|
|
|
|
|
|
lower_plot, upper_plot, _, _ = self._extract_limits(test_data)
|
|
|
|
|
|
|
|
|
|
|
|
# 计算统计信息
|
|
|
|
|
|
y_data = test_data['Measurement_num']
|
|
|
|
|
|
stats = self._calculate_statistics(y_data)
|
|
|
|
|
|
total_points += stats['count']
|
|
|
|
|
|
|
|
|
|
|
|
# 生成汇总图表
|
|
|
|
|
|
summary_plot_image = self._create_summary_plot(test_data, test_name, lower_plot, upper_plot)
|
|
|
|
|
|
|
|
|
|
|
|
# 生成SN独立图表
|
|
|
|
|
|
sn_plot_images = self._create_sn_plots(test_data, test_name, lower_plot, upper_plot)
|
|
|
|
|
|
|
|
|
|
|
|
# 确定测试状态
|
|
|
|
|
|
status_info = self._determine_test_status(stats, lower_plot, upper_plot)
|
|
|
|
|
|
status_counts[status_info["status"]] += 1
|
|
|
|
|
|
|
|
|
|
|
|
# 添加到结果列表
|
|
|
|
|
|
test_results.append({
|
|
|
|
|
|
"name": test_name,
|
|
|
|
|
|
"stats": stats,
|
|
|
|
|
|
"limits": {"lower": lower_plot, "upper": upper_plot},
|
|
|
|
|
|
"summary_plot_image": summary_plot_image,
|
|
|
|
|
|
"sn_plot_images": sn_plot_images,
|
|
|
|
|
|
"status": status_info["status"],
|
|
|
|
|
|
"status_display": status_info["status_display"]
|
|
|
|
|
|
})
|
|
|
|
|
|
|
2026-02-24 11:09:48 +08:00
|
|
|
|
# 按关键词分组组织测试结果，每个测试项单独显示
|
|
|
|
|
|
organized_keyword_groups = self._organize_tests_by_keyword_groups(test_results, keyword_groups)
|
|
|
|
|
|
|
|
|
|
|
|
# 生成汇总对比报告图表
|
|
|
|
|
|
comparison_plots = self._create_comparison_plots(filtered_df, keyword_groups)
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
|
|
|
|
|
# 渲染HTML模板
|
|
|
|
|
|
template = Template(HTML_TEMPLATE)
|
|
|
|
|
|
html_content = template.render(
|
2026-02-24 11:09:48 +08:00
|
|
|
|
keyword=keyword if keyword else "所有数据",
|
2026-02-05 09:04:10 +08:00
|
|
|
|
timestamp=datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
|
|
|
|
|
|
test_count=len(test_results),
|
|
|
|
|
|
total_points=total_points,
|
|
|
|
|
|
tests=test_results,
|
|
|
|
|
|
folder_path=self.folder_path,
|
|
|
|
|
|
analysis_time=round(time.time() - start_time, 2),
|
|
|
|
|
|
status_counts={"normal": status_counts["success"], "warning": status_counts["warning"],
|
|
|
|
|
|
"abnormal": status_counts["danger"]},
|
|
|
|
|
|
file_count=len(self.file_infos),
|
|
|
|
|
|
file_infos=self.file_infos,
|
|
|
|
|
|
total_rows=len(self.df) if self.df is not None else 0,
|
2026-02-24 11:09:48 +08:00
|
|
|
|
comparison_plots=comparison_plots,
|
|
|
|
|
|
keyword_groups=organized_keyword_groups
|
2026-02-05 09:04:10 +08:00
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
# 调试：检查生成的HTML内容
|
2026-02-24 11:09:48 +08:00
|
|
|
|
if comparison_plots:
|
|
|
|
|
|
if "comparison_plots" in html_content or "时间序列散点图" in html_content:
|
|
|
|
|
|
self._print_success(f"对比图已成功嵌入HTML")
|
2026-02-05 09:04:10 +08:00
|
|
|
|
else:
|
2026-02-24 11:09:48 +08:00
|
|
|
|
# print("❌ 对比图未正确嵌入HTML")
|
|
|
|
|
|
self._print_error("对比图未正确嵌入HTML")
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
|
|
|
|
|
# 保存HTML文件
|
|
|
|
|
|
with open(self.html_report_path, 'w', encoding='utf-8') as f:
|
|
|
|
|
|
f.write(html_content)
|
|
|
|
|
|
|
|
|
|
|
|
self._print_success(f"HTML报告已生成: {self.html_report_path}")
|
|
|
|
|
|
self._print_success(
|
|
|
|
|
|
f"共处理 {len(self.file_infos)} 个文件，{len(test_results)} 个测试项，{total_points} 个数据点")
|
2026-02-24 11:09:48 +08:00
|
|
|
|
if len(keyword_groups) > 1:
|
|
|
|
|
|
self._print_success(f"已生成 {len(comparison_plots)} 个对比图表，{len(keyword_groups)}个关键词：{", ".join(keyword_groups)}。")
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
|
|
|
|
|
def run(self) -> None:
|
|
|
|
|
|
"""运行主程序"""
|
|
|
|
|
|
try:
|
|
|
|
|
|
self.get_folder_path()
|
|
|
|
|
|
excel_files = self.find_excel_files()
|
|
|
|
|
|
|
|
|
|
|
|
if not excel_files:
|
|
|
|
|
|
self._print_error("没有找到可用的Excel文件")
|
|
|
|
|
|
return
|
|
|
|
|
|
|
|
|
|
|
|
# 使用优化后的加载方法
|
|
|
|
|
|
self.load_multiple_files_optimized(excel_files)
|
|
|
|
|
|
|
|
|
|
|
|
while True:
|
2026-02-24 11:09:48 +08:00
|
|
|
|
# 修改为使用多关键词输入方法，返回关键词分组信息
|
|
|
|
|
|
filtered_df, keyword, unique_tests, keyword_groups = self.get_keywords()
|
2026-02-05 09:04:10 +08:00
|
|
|
|
if filtered_df.empty:
|
|
|
|
|
|
self._print_warning("没有数据可处理，退出程序")
|
|
|
|
|
|
break
|
|
|
|
|
|
|
2026-02-24 11:09:48 +08:00
|
|
|
|
self.create_output_dir(keyword)
|
|
|
|
|
|
self.generate_html_report(filtered_df, keyword, unique_tests, keyword_groups)
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
|
|
|
|
|
self._print_success("分析完成！")
|
|
|
|
|
|
print(f"📊 报告文件: {self.html_report_path}")
|
|
|
|
|
|
print(f"📁 输出目录: {self.output_dir}")
|
2026-02-24 11:09:48 +08:00
|
|
|
|
if len(keyword_groups) > 1:
|
|
|
|
|
|
print(f"🔍 对比关键词/组: {', '.join(keyword_groups)}")
|
2026-02-05 09:04:10 +08:00
|
|
|
|
|
|
|
|
|
|
# 询问是否继续分析其他关键词
|
|
|
|
|
|
continue_choice = input("\n是否继续分析其他关键词？(y/n): ").strip().lower()
|
|
|
|
|
|
if continue_choice not in ['y', 'yes', '是']:
|
|
|
|
|
|
break
|
|
|
|
|
|
|
|
|
|
|
|
except KeyboardInterrupt:
|
|
|
|
|
|
self._print_warning("用户中断程序")
|
|
|
|
|
|
except Exception as e:
|
|
|
|
|
|
self._print_error(f"发生错误: {type(e).__name__}: {str(e)}")
|
|
|
|
|
|
import traceback
|
|
|
|
|
|
traceback.print_exc()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
|
|
|
plotter = MultiFileTestReportScatterPlotter()
|
|
|
|
|
|
plotter.run()
|
