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# API 配置指南
本文档详细说明如何配置 DeepSeek 和 Gemini API。
## 📋 目录
- [DeepSeek API 配置](#deepseek-api-配置)
- [Gemini API 配置](#gemini-api-配置)
- [常见问题](#常见问题)
> 本文档详细说明如何为自动控制理论AI+数智平台配置 DeepSeek 和 Gemini API，包括密钥获取、配置方法、参数调优与常见问题排查。
---
## 🚀 DeepSeek API 配置
## 一、API 概述
### 1. 获取 API 密钥
### 1.1 平台支持的 AI API
1. 访问 [DeepSeek 平台](https://platform.deepseek.com/)
2. 注册账号并登录
3. 进入 [API Keys 页面](https://platform.deepseek.com/api_keys)
4. 点击"创建新密钥"
5. 复制生成的 API 密钥（格式：`sk-xxxxxxxxxxxxxxxx`）
本平台目前支持以下 AI API 提供商：
### 2. 配置到应用
| API 提供商 | 支持模型 | 访问地区 | 推荐程度 |
|------------|----------|----------|----------|
| DeepSeek | deepseek-chat / deepseek-coder | 中国大陆 | ⭐⭐⭐⭐⭐ 首选 |
| Gemini (Google) | gemini-1.5-flash / gemini-1.5-pro | 部分受限 | ⭐⭐⭐ |
编辑 `app.py` 文件的配置区域（第 7-28 行）：
### 1.2 为什么需要 API
AI 智能问答模块依赖外部大语言模型 API 来实现：
- 自动控制理论专业问题的理解和回答
- LaTeX 数学公式的正确生成
- 多轮对话的上下文记忆
### 1.3 配置优先级
```
┌─────────────────────────────────────────┐
│ 推荐配置顺序 │
├─────────────────────────────────────────┤
│ │
│ 1. DeepSeek API（国内访问，速度快） │
│ ↓ │
│ 2. Gemini API（需要代理） │
│ ↓ │
│ 3. 本地部署（需高配置服务器） │
│ │
└─────────────────────────────────────────┘
```
---
## 二、DeepSeek API 配置
### 2.1 DeepSeek 简介
DeepSeek 是国内领先的 AI 大模型服务提供商，提供：
- **deepseek-chat**：通用对话模型，适合教育场景
- **deepseek-coder**：代码专用模型，适合技术问题
- **价格优惠**：相比 OpenAI 等海外服务商更具性价比
- **国内访问**：无需代理，网络延迟低
### 2.2 获取 API 密钥
**Step 1：访问 DeepSeek 平台**
打开浏览器，访问：https://platform.deepseek.com/
**Step 2：注册/登录账号**
- 使用手机号或邮箱注册
- 已注册用户直接登录
**Step 3：进入 API Keys 管理页面**
登录后，点击顶部导航栏的 "API Keys"：
```
https://platform.deepseek.com/api_keys
```
**Step 4：创建新密钥**
1. 点击 "创建新密钥" 按钮
2. 输入密钥名称（可自定义，如 "AutoControlCourse"）
3. 点击确认
4. **立即复制密钥**（只显示一次！）
**密钥格式示例：**
```
sk-2292af2428d7419897ca1fb6e99ba6bc
```
### 2.3 配置到项目
**方法一：直接编辑 config.py（最简单）**
1. 打开项目根目录下的 `config.py` 文件
2. 找到 API 配置区域
3. 将 `API_KEY` 替换为您的密钥
```python
# ==================== API 配置 ====================
API_KEY = "sk-your-api-key-here" # 粘贴您的 DeepSeek API 密钥
API_KEY = "sk-2292af2428d7419897ca1fb6e99ba6bc" # 替换为您的密钥
API_BASE_URL = "https://api.deepseek.com/v1"
API_MODEL = "deepseek-chat" # 或 "deepseek-coder"
API_TYPE = "deepseek"
# ==================================================
```
### 3. 可用模型
**方法二：使用环境变量（推荐用于生产环境）**
| 模型名称 | 适用场景 | 特点 |
|---------|---------|------|
| `deepseek-chat` | 通用对话 | 平衡性能，推荐使用 |
| `deepseek-coder` | 代码相关 | 代码理解和生成能力强 |
### 4. 费用说明
- 新用户通常有免费额度
- 按 token 计费，价格实惠
- 详见 [定价页面](https://platform.deepseek.com/pricing)
---
## 🌐 Gemini API 配置
### 1. 获取 API 密钥
1. 访问 [Google AI Studio](https://aistudio.google.com/app/apikey)
2. 使用 Google 账号登录
3. 点击"Get API Key"
4. 创建或选择项目
5. 复制生成的 API 密钥
### 2. 配置到应用
编辑 `app.py` 文件的配置区域：
```python
# ==================== API 配置 ====================
API_KEY = "AIzaSy-your-gemini-api-key-here"
API_BASE_URL = "https://generativelanguage.googleapis.com/v1beta"
API_MODEL = "gemini-1.5-flash" # 或其他可用模型
API_TYPE = "gemini"
# ==================================================
```
### 3. 可用模型
| 模型名称 | 特点 |
|---------|------|
| `gemini-1.5-flash` | 快速响应，适合实时交互 |
| `gemini-1.5-pro` | 更强大的理解和生成能力 |
| `gemini-pro` | 经典版本 |
### 4. 注意事项
- Gemini API 在某些地区可能需要网络代理
- 中国大陆用户推荐使用 DeepSeek API
---
## 🔒 安全建议
### 方法 1：环境变量（推荐）
不要直接在代码中硬编码 API 密钥，使用环境变量：
**Windows PowerShell：**
1. **Windows PowerShell：**
```powershell
$env:DEEPSEEK_API_KEY="sk-your-key"
$env:DEEPSEEK_API_KEY="sk-2292af2428d7419897ca1fb6e99ba6bc"
python app.py
```
**Linux/Mac：**
2. **Linux / macOS：**
```bash
export DEEPSEEK_API_KEY="sk-your-key"
export DEEPSEEK_API_KEY="sk-2292af2428d7419897ca1fb6e99ba6bc"
python app.py
```
然后在代码中读取：
3. **在 config.py 中读取环境变量：**
```python
import os
API_KEY = os.environ.get("DEEPSEEK_API_KEY", "")
```
### 方法 2：配置文件
创建 `config.json`（不要提交到 Git）：
**方法三：创建独立配置文件**
1. 在项目根目录创建 `config.json`：
```json
{
"api_key": "sk-your-key",
"api_key": "sk-2292af2428d7419897ca1fb6e99ba6bc",
"api_base_url": "https://api.deepseek.com/v1",
"api_model": "deepseek-chat",
"api_type": "deepseek"
}
```
在代码中加载：
2. 在 `config.py` 中加载：
```python
import json
import os
with open('config.json', 'r') as f:
config = json.load(f)
API_KEY = config['api_key']
API_BASE_URL = config['api_base_url']
# ...
config_path = os.path.join(os.path.dirname(__file__), "config.json")
if os.path.exists(config_path):
with open(config_path, "r") as f:
config_data = json.load(f)
API_KEY = config_data.get("api_key", "")
API_BASE_URL = config_data.get("api_base_url", "https://api.deepseek.com/v1")
API_MODEL = config_data.get("api_model", "deepseek-chat")
API_TYPE = config_data.get("api_type", "deepseek")
else:
API_KEY = ""
API_BASE_URL = "https://api.deepseek.com/v1"
API_MODEL = "deepseek-chat"
API_TYPE = "deepseek"
```
### 2.4 DeepSeek 可用模型
| 模型名称 | 适用场景 | 特点 | 推荐场景 |
|----------|----------|------|----------|
| deepseek-chat | 通用对话 | 平衡性能与成本 | ⭐ 日常学习问答 |
| deepseek-coder | 代码相关 | 代码理解能力强 | 专业开发者 |
### 2.5 费用说明
| 项目 | 说明 |
|------|------|
| 新用户优惠 | 通常有免费额度 |
| 计费方式 | 按 token 用量计费 |
| 价格水平 | 比 OpenAI 低约 80% |
| 查看用量 | https://platform.deepseek.com/usage |
| 详细定价 | https://platform.deepseek.com/pricing |
### 2.6 速率限制
| 账户类型 | RPM（每分钟请求数） | TPM（每分钟 Token 数） |
|----------|---------------------|------------------------|
| 免费用户 | 60 | 100,000 |
| 付费用户 | 最高可达 2000 | 根据套餐 |
---
## 三、Gemini API 配置
### 3.1 Gemini 简介
Gemini 是 Google 开发的 AI 大模型，具备：
- **gemini-1.5-flash**：快速响应，适合实时交互
- **gemini-1.5-pro**：更强大的理解和生成能力
- **多模态**：支持文本、图像等多种输入
**注意：** Gemini API 在中国大陆可能需要网络代理才能访问。
### 3.2 获取 API 密钥
**Step 1：访问 Google AI Studio**
打开浏览器，访问：https://aistudio.google.com/app/apikey
**Step 2：登录 Google 账号**
使用您的 Google 账号登录。
**Step 3：获取 API 密钥**
1. 点击 "Get API Key"
2. 选择或创建项目
3. 点击 "Create API Key"
4. 复制生成的密钥
**密钥格式示例：**
```
AIzaSy-your-gemini-api-key-here
```
### 3.3 配置到项目
编辑 `config.py` 文件：
```python
# ==================== API 配置 ====================
API_KEY = "AIzaSy-your-gemini-api-key-here"
API_BASE_URL = "https://generativelanguage.googleapis.com/v1beta"
API_MODEL = "gemini-1.5-flash" # 或 "gemini-1.5-pro"
API_TYPE = "gemini"
# ==================================================
```
### 3.4 Gemini 可用模型
| 模型名称 | 特点 | 适用场景 | 响应速度 |
|----------|------|----------|----------|
| gemini-1.5-flash | 快速响应 | 实时交互 | ⚡⚡⚡⚡⚡ |
| gemini-1.5-pro | 更强能力 | 复杂问题 | ⚡⚡⚡ |
| gemini-pro | 经典版本 | 一般对话 | ⚡⚡⚡⚡ |
### 3.5 网络访问说明
| 地区 | 访问状态 | 解决方案 |
|------|----------|----------|
| 中国大陆 | 可能受限 | 使用 DeepSeek API 或配置代理 |
| 港澳台 | 基本正常 | 直连或使用代理 |
| 其他地区 | 正常 | 直连 |
---
## 四、配置参数详解
### 4.1 完整配置参数表
| 参数名 | 类型 | 默认值 | 说明 |
|--------|------|--------|------|
| API_KEY | string | "" | API 密钥，必填 |
| API_BASE_URL | string | "https://api.deepseek.com/v1" | API 基础 URL |
| API_MODEL | string | "deepseek-chat" | 模型名称 |
| API_TYPE | string | "deepseek" | API 类型：deepseek 或 gemini |
| SERVER_NAME | string | "0.0.0.0" | 监听网络接口 |
| SERVER_PORT | int | 7860 | 监听端口 |
| SHARE | bool | True | 是否创建公开链接 |
### 4.2 API_KEY 配置
**格式：** `sk-` 开头（DeepSeek）或 `AIzaSy` 开头（Gemini）
**常见错误：**
- 密钥包含多余空格（复制时容易带入）
- 密钥过期或被删除
- 密钥未激活对应服务
**排查方法：**
1. 确认密钥完整复制（无前后空格）
2. 在官网控制台确认密钥状态
3. 确认密钥已绑定正确的产品/服务
### 4.3 API_BASE_URL 配置
| API 类型 | 正确 URL | 错误示例 |
|----------|----------|----------|
| DeepSeek | https://api.deepseek.com/v1 | https://api.deepseek.com/ |
| Gemini | https://generativelanguage.googleapis.com/v1beta | 其他 URL |
### 4.4 API_MODEL 配置
**DeepSeek 模型：**
| 模型 | 上下文长度 | 适用场景 |
|------|------------|----------|
| deepseek-chat | 64K tokens | 通用对话，推荐 |
| deepseek-coder | 64K tokens | 代码相关问题 |
**Gemini 模型：**
| 模型 | 上下文长度 | 适用场景 |
|------|------------|----------|
| gemini-1.5-flash | 1M tokens | 快速响应 |
| gemini-1.5-pro | 1M tokens | 复杂任务 |
| gemini-pro | 32K tokens | 一般对话 |
---
## 五、流式响应配置
### 5.1 什么是流式响应
流式响应（Streaming）是指 AI 边生成答案边返回，用户可以实时看到回答内容，而不必等待完整答案生成完毕。
**优点：**
- 减少等待感
- 及时了解回答方向
- 支持长答案的快速预览
### 5.2 当前配置
本平台默认启用流式响应，配置位于 `chatbot.py`：
```python
payload = {
"model": config.API_MODEL,
"messages": messages_for_api,
"stream": True, # 启用流式响应
"temperature": 0.7, # 创造性参数
"max_tokens": 2048 # 最大 Token 数
}
```
### 5.3 参数调优
| 参数 | 取值范围 | 说明 | 调整建议 |
|------|----------|------|----------|
| temperature | 0.0 ~ 2.0 | 创造性控制，值越低越确定 | 学习问答建议 0.3~0.7 |
| max_tokens | 1 ~ 32768 | 单次回复最大 Token 数 | 长回答设为 4096 |
| top_p | 0.0 ~ 1.0 | 核采样参数 | 通常保持默认 1.0 |
| frequency_penalty | -2.0 ~ 2.0 | 频率惩罚 | 保持默认 0.0 |
| presence_penalty | -2.0 ~ 2.0 | 存在惩罚 | 保持默认 0.0 |
---
## 六、系统提示词配置
### 6.1 系统提示词的作用
系统提示词（System Prompt）定义了 AI 助手的角色定位、回答风格和专业范围。本平台的默认提示词位于 `chatbot.py`。
### 6.2 默认提示词
```python
system_prompt = """你是一位精通自动控制原理的专家教授。请用清晰、准确、专业的中文来回答有关自动控制课程内容的问题。
重要规则：
1. 当需要表达数学公式时，必须使用 LaTeX 格式
2. 行内公式使用 $公式$ 或 \\(公式\\)
3. 独立公式使用 $$公式$$ 或 \\[公式\\]
4. 例如：传递函数可以写成 $G(s) = \\frac{K}{s(s+1)}$
5. 二阶系统标准形式：$$G(s) = \\frac{\\omega_n^2}{s^2 + 2\\zeta\\omega_n s + \\omega_n^2}$$
请在适当的时候使用公式和示例来辅助解释。"""
```
### 6.3 自定义提示词
根据教学需求，您可以修改系统提示词：
**修改方法：** 编辑 `chatbot.py` 中的 `system_prompt` 变量。
**示例1：强化公式推导**
```python
system_prompt = """你是一位严谨的自动控制原理教授。在回答问题时：
1. 注重公式的推导过程
2. 每一步推导都要清晰呈现
3. 适当使用 LaTeX 公式
4. 结合实例帮助理解"""
```
**示例2：简化回答风格**
```python
system_prompt = """你是一位friendly的自动控制课程助教。请用简洁、易懂的语言回答问题：
1. 尽量少用专业术语
2. 多用生活实例类比
3. 重要公式用 LaTeX 展示"""
```
**示例3：英文问答模式**
```python
system_prompt = """You are an expert professor of Automatic Control Theory.
Answer questions in English using LaTeX for mathematical formulas.
Focus on clarity and practical examples."""
```
---
## ❓ 常见问题
## 七、安全建议
### Q1: API 请求失败，显示 401 错误
### 7.1 密钥安全原则
**原因**：API 密钥无效或未配置
**❌ 不要做的事情：**
- 在代码中硬编码密钥并提交到 Git
- 在公开场合分享密钥
- 使用过于简单的密钥
**解决**：
1. 检查 API 密钥是否正确复制（无多余空格）
2. 确认密钥未过期或被删除
3. 重新生成密钥并更新配置
**✅ 推荐的做法：**
- 使用环境变量存储密钥
- 将敏感配置文件加入 .gitignore
- 定期更换密钥
- 为不同项目使用不同的密钥
### Q2: 网络连接错误
### 7.2 .gitignore 配置
**原因**：网络问题或 API 服务不可达
确保以下文件不会被提交到 Git：
**解决**：
1. DeepSeek 用户：检查国内网络连接
2. Gemini 用户：可能需要配置网络代理
3. 尝试切换到 DeepSeek API（国内友好）
```
# API 配置文件
config.json
secrets.json
.env
### Q3: 回复速度慢或超时
# Python
__pycache__/
*.pyc
*.pyo
**原因**：网络延迟或 API 负载高
# IDE
.vscode/
.idea/
**解决**：
1. 检查网络连接速度
2. 调整超时设置（app.py 中的 `ClientTimeout`）
3. 尝试切换模型（如 flash 版本）
# 模型权重（较大文件）
Model/data/*.pth
```
### Q4: 公式不渲染
### 7.3 生产环境部署
**原因**：Chatbot 未启用 LaTeX 支持
**推荐做法：**
**解决**：
确认 `gr.Chatbot` 包含 `latex_delimiters` 参数：
1. **使用环境变量**
```bash
# Docker 部署
docker run -p 7860:7860 \
-e DEEPSEEK_API_KEY="sk-xxx" \
autocontrol-course
```
2. **使用配置服务**
- AWS Secrets Manager
- Azure Key Vault
- HashiCorp Vault
3. **限制 API 访问**
- 设置 API 密钥的使用 IP 白名单
- 配置请求频率限制
- 开启使用量告警
---
## 八、常见问题排查
### 8.1 API 请求失败
**问题1：401 Unauthorized**
| 可能原因 | 解决方法 |
|----------|----------|
| API 密钥无效 | 检查密钥是否正确复制 |
| 密钥已过期 | 在平台控制台重新创建密钥 |
| 密钥未激活 | 确认密钥已绑定正确服务 |
**问题2：403 Forbidden**
| 可能原因 | 解决方法 |
|----------|----------|
| 账户余额不足 | 充值或等待免费额度刷新 |
| 权限不足 | 检查账户权限设置 |
| 服务未开通 | 在控制台开通对应服务 |
**问题3：429 Rate Limit**
| 可能原因 | 解决方法 |
|----------|----------|
| 请求过于频繁 | 降低请求频率 |
| 超出 TPM/RPM 限制 | 等待或升级套餐 |
| 并发数过高 | 减少并发请求数 |
**解决方法：**
1. 等待一段时间后重试
2. 配置请求间隔（如每次提问间隔 2 秒）
3. 升级到更高配额套餐
### 8.2 网络连接问题
**问题：Connection Error / Timeout**
**排查步骤：**
1. **检查网络连接**
```bash
# 测试 API 端点是否可达
curl -I https://api.deepseek.com/v1
```
2. **检查代理设置（如需要）**
```python
# 在 chatbot.py 中配置代理
import os
os.environ["HTTP_PROXY"] = "http://proxy.example.com:8080"
os.environ["HTTPS_PROXY"] = "http://proxy.example.com:8080"
```
3. **增加超时时间**
```python
# 在 aiohttp 请求中增加 timeout
async with session.post(api_url, json=payload, headers=headers,
timeout=aiohttp.ClientTimeout(total=120)) as response:
```
### 8.3 回复质量问题
**问题：回复内容不准确**
**解决方法：**
1. **优化系统提示词**
- 明确指定回答风格
- 强调专业领域要求
- 添加示例回答
2. **调整 temperature 参数**
- 降低 temperature（0.3~0.5）使回答更确定
- 提高 temperature（0.7~1.0）使回答更有创造性
3. **优化提问方式**
- 提供更多上下文
- 明确问题范围
- 指出具体困惑点
**问题：回复速度慢**
**解决方法：**
1. **使用较轻量的模型**
- DeepSeek：选择 deepseek-chat 而非 deepseek-coder
- Gemini：选择 gemini-1.5-flash
2. **减少 max_tokens**
- 根据实际需求设置合理的最大长度
- 避免生成过长的回答
3. **检查网络延迟**
- 选择距离更近的 API 端点
- 考虑使用 CDN 加速
### 8.4 公式渲染问题
**问题：LaTeX 公式不显示**
**可能原因：**
1. **Chatbot 未启用 LaTeX**
2. **MathJax 加载失败**
3. **公式语法错误**
**解决方法：**
1. 确认 `gr.Chatbot` 配置包含 `latex_delimiters`：
```python
chatbot = gr.Chatbot(
latex_delimiters=[
{"left": "$$", "right": "$$", "display": True},
{"left": "$", "right": "$", "display": False}
{"left": "$", "right": "$", "display": False},
{"left": "\\[", "right": "\\]", "display": True},
{"left": "\\(", "right": "\\)", "display": False}
]
)
```
### Q5: 如何限制 API 调用成本？
2. 刷新页面重试
**建议**：
1. 在 API 平台设置使用限额
2. 代码中添加 `max_tokens` 限制
3. 监控 API 使用情况
4. 使用轻量级模型（如 flash 版本）
3. 检查 LaTeX 语法是否正确
---
## 📞 获取帮助
## 九、API 使用成本优化
- **DeepSeek 文档**：https://platform.deepseek.com/docs
- **Gemini 文档**：https://ai.google.dev/docs
- **项目 Issues**：[GitHub Issues 链接]
### 9.1 成本构成
API 使用成本主要由以下因素决定：
| 因素 | 说明 | 优化建议 |
|------|------|----------|
| 输入 Token 数 | 问题文本长度 | 精简提问 |
| 输出 Token 数 | 回答文本长度 | 限制 max_tokens |
| 请求次数 | 提问频率 | 减少无效请求 |
| 模型单价 | 不同模型价格不同 | 选择性价比模型 |
### 9.2 优化策略
**策略1：精简提问**
- 移除问题中不必要的修饰词
- 明确指出核心疑问
- 提供必要的上下文但不过度
**策略2：合理限制输出长度**
- 根据问题类型设置 max_tokens
- 简单问题设置较短限制
- 复杂问题允许更长回答
**策略3：缓存常用回答**
- 实现本地缓存机制
- 避免重复提问相同问题
- 减少 API 调用次数
**策略4：选择合适模型**
- 日常问答：使用轻量模型（flash 版本）
- 复杂问题：按需使用强大模型
### 9.3 预算设置
在 DeepSeek 控制台设置用量限制：
1. 访问 https://platform.deepseek.com/
2. 进入 "用量限制" 设置
3. 设置月度预算上限
4. 开启用量告警
---
最后更新：2025年10月15日
## 十、获取帮助
### 10.1 官方文档
| 资源 | 链接 |
|------|------|
| DeepSeek 文档 | https://platform.deepseek.com/docs |
| Gemini 文档 | https://ai.google.dev/docs |
| Gradio 文档 | https://gradio.app/docs |
### 10.2 技术支持
| 渠道 | 联系方式 |
|------|----------|
| 项目问题 | GitHub Issues |
| API 问题 | 平台官方支持 |
| 使用咨询 | 课程教师/助教 |
---
**最后更新：2026年4月7日**
CHANGELOG.md
+363 -44
View File
@@ -1,60 +1,379 @@
# Changelog
# 更新日志 (Changelog)
All notable changes to this project will be documented in this file.
> 本文档记录自动控制理论AI+数智平台的所有重要更新，包括新增功能、功能改进、问题修复与技术变更。
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
本文档格式遵循 [Keep a Changelog](https://keepachangelog.com/zh-CN/1.1.0/) 规范，并遵循 [语义化版本 (SemVer)](https://semver.org/lang/zh-CN/) 约定。
---
## 版本命名规范
版本号格式：`主版本.次版本.修订号`
| 标识 | 含义 |
|------|------|
| 主版本 (MAJOR) | 不兼容的重大架构变更 |
| 次版本 (MINOR) | 向后兼容的新功能添加 |
| 修订号 (PATCH) | 向后兼容的问题修复 |
---
## [1.2.0] - 2026-04-07
> **重要更新：新增算例演示模块与完整混动模型联动**
### Added（新增功能）
#### 🧪 算例演示模块（Case Demo）
本版本最核心的更新是新增了完整的算例演示模块，通过串联式混合动力系统模型展示控制系统设计在实际工程中的应用。
**阶段零：模型训练**
- 新增 `case_demo_functions.py` 模块，作为算例演示的核心入口
- 新增 GPR（高斯过程回归）模型训练/加载功能
- 支持从头训练（需要 botorch/gpytorch/sklearn）
- 支持加载已有 .pth 权重文件
- 生成训练/验证结果可视化（散点图、方差热力图）
- 新增 NN 模型训练（知识蒸馏）功能
- 将 GPR 教师模型的知识蒸馏到轻量级 MLP
- 可视化训练损失曲线、燃油流量与功率的 parity plot
- 支持自定义 epochs、learning_rate、hidden_size 参数
**阶段一：发动机控制器设计**
- 新增基于 NN 代理模型的涡轴发动机动态仿真
- 新增 PID 控制器（增量式）
- 可调参数：Kp、Ki、Kd
- 支持输入/输出量程归一化
- 新增 MPC 控制器（模型预测控制）
- 纯 Python 实现的投影梯度下降求解器
- 可调参数：预测时域、功率跟踪权重、控制增量权重、超调限制
- 支持超调量硬约束（5%）
**阶段二：电机控制器设计**
- 新增永磁同步电机（PMSM）离散时间动力学模型
- 新增 d/q 轴电流控制与 MTPA 控制策略
- 新增 PID / MPC 两种控制器
- 新增负载扰动测试（60% 时刻施加 150% 额定负载）
**阶段三：能量管理策略设计**
- 新增基于规则的功率跟随策略
- 新增 SOC 滞环控制（防止模式频繁切换）
- 新增完整混动系统仿真（发动机 + 电机 + 电池）
- 新增三联图输出（转速响应/功率分配/电气状态）
- 新增关键时刻数据表（8 个关键时刻点）
**模型核心模块（Model/src/）**
| 文件 | 功能 | 关键特性 |
|------|------|----------|
| `lightweight_model.py` | NN 代理模型 | MLP 3→64→64→2，Tanh 激活，归一化处理 |
| `engine_gpr_class.py` | GPR 模型 | 高斯过程回归，支持批量预测 |
| `distill_gpr_to_nn.py` | 知识蒸馏脚本 | CSV 直接训练或 GPR 蒸馏 |
| `engine_dynamic_sim.py` | 涡轴发动机仿真 | 燃油执行机构 + 转子动力学 |
| `motor_sim.py` | PMSM 电机仿真 | SVPWM、Clarke/Park 变换、损耗模型 |
| `battery_sim.py` | 电池仿真 | OCV-SOC 查表、内阻模型、安时积分 |
| `mpc_controller.py` | MPC 控制器 | 投影梯度下降、硬约束处理 |
| `increPID.py` | 增量式 PID | 归一化缩放、自动抗积分饱和 |
| `series_hybrid_sim.py` | 混动系统总成 | 功率平衡、滞环控制 |
### Changed（功能变更）
#### 📁 Model 目录结构优化
为提高项目可维护性，对 Model 目录进行了精简：
**移除的文件/目录：**
- `scripts/` 目录（功能已整合到 `case_demo_functions.py`）
- `EngineData.xlsx`（原始数据，已被 CSV 替代）
- `.git/`（子模块 Git 历史）
- `.claude/`（IDE 配置）
- `README.md`（已整合到主项目文档）
- `LICENSE`（已使用主项目许可证）
- `environment.yml`（conda 环境配置）
- `figures/`（输出目录）
- `.gitignore`（已使用主项目配置）
**保留的核心文件：**
- `Model/src/` — 所有源代码
- `Model/data/` — 模型权重与数据文件
- `requirements.txt` — 依赖列表
#### 模型路径解析改进
`series_hybrid_sim.py` 等文件改为按 Model 目录定位资源：
```python
# 旧方式（可能因工作目录而失败）
nn_pth = "Model/data/engine_nn_proxy.pth"
# 新方式（基于脚本位置定位）
MODEL_DATA_PATH = os.path.join(os.path.dirname(__file__), "..", "data")
nn_pth = os.path.join(MODEL_DATA_PATH, "engine_nn_proxy.pth")
```
#### 依赖版本锁定
`requirements.txt` 更新如下：
| 依赖 | 新版本 | 变更说明 |
|------|--------|----------|
| gradio | 4.44.1 | 升级到最新稳定版 |
| gradio-client | 1.3.0 | 新增 |
| pydantic | 2.10.6 | 升级 |
| pydantic-core | 2.27.2 | 升级 |
| huggingface_hub | 0.23.0 | 新增 |
| numpy | 1.26.4 | 锁定版本 |
| control | 0.9.4 | 锁定版本 |
| matplotlib | 3.9.4 | 升级 |
| aiohttp | 3.13.5 | 升级 |
| pillow | 10.4.0 | 升级 |
| torch | 2.4.1+cu121 | 新增（GPU 版本） |
| botorch | 0.14.0 | 新增（GPR 依赖） |
| gpytorch | 1.14 | 新增（GPR 依赖） |
| pyro-ppl | 1.9.1 | 新增（GPR 依赖） |
| pandas | 2.3.3 | 新增（数据处理） |
| scipy | >=1.10 | 新增（数值计算） |
| scikit-learn | 1.7.1 | 新增（数据归一化） |
| psutil | >=5.9 | 新增（系统监控） |
**PyTorch 下载源配置：**
```txt
--extra-index-url https://download.pytorch.org/whl/cu121
```
#### 文档同步更新
- `README.md`：同步为"五大功能"结构，新增算例演示详解
- `GUIDANCE.md`：新增四阶段操作指南、混动系统架构说明
- `API_CONFIG.md`：新增 API 配置详解、安全建议
- `CHANGELOG.md`：新增详细版本记录
### Removed（移除功能）
| 移除项 | 原位置 | 替代方案 |
|--------|--------|----------|
| 独立训练脚本 | `Model/scripts/distill_gpr_to_nn.py` | 已整合到 `case_demo_functions.py` |
| Excel 数据文件 | `Model/EngineData.xlsx` | 使用 `Model/data/Cleaned_Engine_Data_Full.csv` |
| conda 环境文件 | `Model/environment.yml` | 使用 `requirements.txt` |
### Fixed（问题修复）
| 问题 | 修复内容 |
|------|----------|
| Model 路径解析错误 | 改用 `__file__` 相对定位，避免工作目录影响 |
| GPR 训练缺少依赖提示 | 新增清晰的依赖安装指引 |
| 模型权重未找到 | 提供更详细的错误提示与解决步骤 |
---
## [1.1.0] - 2026-02-15
> **功能增强与问题修复**
### Added（新增功能）
| 功能 | 说明 |
|------|------|
| 实时在线人数统计 | 页面顶部显示当前在线用户数与历史总人数 |
| 总访问人数统计 | 持久化记录累计访问人数 |
| 系统资源监控 | 显示 CPU、内存、GPU 使用率 |
| 页面自动刷新 | 每 10 秒更新在线人数，每 3 秒更新系统资源 |
### Changed（功能变更）
| 变更项 | 变更内容 |
|--------|----------|
| 在线统计存储 | 从内存改为 JSON 文件持久化 |
| 统计更新频率 | 在线人数每 10 秒刷新，资源每 3 秒刷新 |
| 界面布局优化 | 顶部状态栏与系统监控整合 |
### Fixed（问题修复）
| 问题 | 修复内容 |
|------|----------|
| 页面刷新导致在线人数重置 | 改用 Session ID 跟踪用户会话 |
| 多用户并发统计不准 | 添加线程锁保护共享状态 |
---
## [1.0.0] - 2025-10-15
### Added
- ✨ 时域分析功能（阶跃响应、脉冲响应、性能指标计算）
- ✨ 频域分析功能（Bode 图、Nyquist 图、稳定裕度）
- ✨ 根轨迹分析功能（动态轨迹绘制、增益调节、极点跟踪）
- ✨ AI 智能问答功能（支持 DeepSeek 和 Gemini API）
- 🎨 现代化 UI 设计（渐变色、卡片布局、可滚动知识区）
- 📚 详细的知识卡片（时域、频域、根轨迹理论）
- 🔧 对数增益滑块（精确调节 0.1 到 1000 范围）
- 💬 LaTeX 公式渲染（聊天机器人内数学公式支持）
- 📊 英文图表标签（避免中文显示问题）
> **首次正式发布**
### Features
- 支持任意阶次线性时不变（LTI）系统分析
- 实时参数调节和图表更新
- 流式 AI 对话响应
- 标签页切换自动加载数据
- 可折叠的知识点章节
### Added（新增功能）
### Documentation
- 📄 完整的 README.md
- 📄 API 配置指南（API_CONFIG.md）
- 📄 快速上手指南（QUICK_START.md）
- 📄 依赖列表（requirements.txt）
- 📄 .gitignore 配置
- 📄 MIT 开源许可证
#### 🎯 核心分析功能
## [Unreleased]
**1. 时域分析模块**
- 单位阶跃响应分析与绘图
- 单位脉冲响应分析与绘图
- 自动性能指标计算：
- 上升时间 (Rise Time)
- 峰值时间 (Peak Time)
- 超调量 (Overshoot)
- 调节时间 (Settling Time)
- 稳态值 (Steady State Value)
- 传递函数系数解析与 LaTeX 渲染
### Planned
- [ ] 状态空间分析模块
- [ ] 离散系统分析支持
- [ ] 更多控制器设计工具（PID 调优、极点配置）
- [ ] 系统对比功能（多个传递函数对比）
- [ ] 导出分析报告（PDF/Word）
- [ ] 历史记录保存
- [ ] 更多 AI 模型支持
- [ ] 多语言界面（英文版）
- [ ] 移动端适配
**2. 频域分析模块**
- Bode 图绘制（幅频特性 + 相频特性）
- Nyquist 图绘制（极坐标频率响应）
- 自动增益裕度 (GM) 计算
- 自动相位裕度 (PM) 计算
- 稳定性自动判断
**3. 根轨迹分析模块**
- 完整根轨迹自动绘制
- 对数增益滑块（log₁₀(K) 范围 -4 到 4）
- 实时极点位置显示
- 动态坐标范围调整
- 阻尼比等值线参考
**4. AI 智能问答模块**
- DeepSeek API 集成
- Gemini API 备用支持
- 流式响应（实时显示生成过程）
- 多轮对话上下文记忆
- LaTeX 数学公式渲染
- 自动控制原理专业问答
#### 🎨 界面设计
| 特性 | 说明 |
|------|------|
| 渐变色标题 | 紫色渐变视觉效果 |
| 卡片式布局 | 分组清晰，层次分明 |
| 可滚动知识卡片 | 节省屏幕空间 |
| 可折叠章节 | 按需展开 |
| 实时参数更新 | 图表即时反映变化 |
| 标签页联动 | 切换自动加载数据 |
| 平滑动画效果 | 悬停、滚动动效 |
#### 📚 知识卡片内容
| 模块 | 知识点 |
|------|--------|
| 时域分析 | 二阶系统标准形式、阻尼比与响应特性、性能指标公式、稳态误差分析 |
| 频域分析 | Bode 图绘制技巧、稳定裕度定义、稳定性判断准则、频域-时域对应关系 |
| 根轨迹 | 基本规则、起点终点、渐近线、分离点、s 平面稳定性区域 |
#### 🔧 交互设计
| 特性 | 实现 |
|------|------|
| 对数增益滑块 | log₁₀(K) 范围 -4~4，对应 K = 0.0001~10000 |
| 传递函数输入 | 系数从高次幂到常数项，逗号分隔 |
| 实时预览 | 显示传递函数 LaTeX 公式 |
| 松开更新 | 滑块释放后才更新图表，避免卡顿 |
### Documentation（文档）
| 文档 | 内容 |
|------|------|
| README.md | 项目简介、功能说明、快速开始、技术栈 |
| GUIDANCE.md | 学生使用指南、详细教程、示例库 |
| API_CONFIG.md | DeepSeek/Gemini API 配置指南 |
| CHANGELOG.md | 版本更新记录 |
| requirements.txt | Python 依赖列表 |
| .gitignore | Git 忽略配置 |
### Features（技术特性）
| 特性 | 说明 |
|------|------|
| 任意阶次 LTI 系统 | 支持任意阶次线性时不变系统分析 |
| python-control 集成 | 复用成熟控制系统工具箱 |
| 英文图表标签 | 避免中文显示问题 |
| LaTeX 公式渲染 | Chatbot 内数学公式支持 |
---
## Version History
## [Unreleased] - 开发中
### v1.0.0 (2025-10-15)
- 🎉 首次正式发布
- 包含四大核心功能模块
- 完整的文档和配置文件
> 以下为计划中但尚未发布的功能
### Planned（计划功能）
| 功能 | 状态 | 说明 |
|------|------|------|
| 状态空间分析模块 | 🔄 计划中 | 状态空间模型构建、能控性/能观性分析 |
| 离散系统分析支持 | 🔄 计划中 | 离散传递函数、Z变换、根轨迹 |
| PID 控制器自动调参 | 🔄 计划中 | Ziegler-Nichols、遗传算法优化 |
| 极点配置设计工具 | 🔄 计划中 | 通过状态反馈实现指定极点位置 |
| 系统对比功能 | 🔄 计划中 | 多个传递函数对比分析 |
| 分析报告导出 | 🔄 计划中 | PDF/Word 格式报告生成 |
| 历史记录保存 | 🔄 计划中 | 本地保存分析历史 |
| 更多 AI 模型支持 | 🔄 计划中 | Claude、GPT-4 等 |
| 多语言界面 | 🔄 计划中 | 英文版界面 |
| 移动端适配 | 🔄 计划中 | 响应式布局优化 |
---
**Note**: For detailed commit history, see the [Git log](https://github.com/your-repo/commits).
## 版本历史
| 版本 | 日期 | 重大变更 |
|------|------|----------|
| 1.2.0 | 2026-04-07 | 新增算例演示模块、混动模型联动、精简 Model 目录 |
| 1.1.0 | 2026-02-15 | 新增在线统计、系统资源监控、持久化存储 |
| 1.0.0 | 2025-10-15 | 首次正式发布，四大核心功能 + AI 问答 |
---
## 分支管理
| 分支 | 用途 |
|------|------|
| `master` | 主分支，稳定版本 |
| `feature/case-demo-model-minimal` | 算例演示模块开发分支 |
### 合并策略
```bash
# 功能完成后，创建 Pull Request
git checkout -b feature/your-feature
git add .
git commit -m "feat: add new feature"
git push -u origin feature/your-feature
# 合并到 master
git checkout master
git merge feature/your-feature
git push origin master
```
---
## 贡献者
| 贡献者 | 角色 | 主要贡献 |
|--------|------|----------|
| 魏鹏飞 | 项目负责人 | 整体架构设计、核心算法 |
| 项目团队 | 开发 | 各功能模块实现 |
---
## 提交信息规范
本项目采用 [Conventional Commits](https://www.conventionalcommits.org/) 规范：
| 类型 | 说明 |
|------|------|
| `feat:` | 新功能 |
| `fix:` | 问题修复 |
| `docs:` | 文档更新 |
| `style:` | 代码格式调整（不影响功能） |
| `refactor:` | 代码重构 |
| `perf:` | 性能优化 |
| `test:` | 测试相关 |
| `chore:` | 构建/工具变更 |
**示例：**
```bash
git commit -m "feat: add MPC controller for engine design"
git commit -m "fix: resolve path resolution issue in Model loading"
git commit -m "docs: update README with new case demo section"
```
---
**注意：** 详细的 Git 提交历史请参阅 `git log` 或 GitHub 仓库的 Commit 页面。
GUIDANCE.md
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Model/data/Cleaned_Engine_Data_Full.csv
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@@ -0,0 +1,785 @@
Altitude_m,Mach,RPM,SFC,Power_kW,Inlet_T2_K,Inlet_P2_kPa,RPM_Rel_Phys,RPM_Rel_Corr,Flight_Vel_kmh,WF_kg_h
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1000.0,0.2,42050.0,1.94088839464961,24.7984406532303,283.90669110883,92.4167415738559,0.725,0.730397886771994,242.244920080775,48.13100566926179
1000.0,0.2,40600.0,2.42776174404928,18.9045997432519,283.90669110883,92.4167415738559,0.7,0.705211752745374,242.244920080775,45.8958640432308
1000.0,0.2,0.0,0.0,0.0,283.90669110883,92.4167415738559,0.0,0.0,0.0,0.0
1000.0,0.25,58000.0,0.491106870834565,248.295084764935,285.176079857547,93.8690552767738,1.0,1.00520066703131,302.806150100969,121.93942212251031
1000.0,0.25,56550.0,0.494674130615802,224.619478913764,285.176079857547,93.8690552767738,0.975,0.980070650355526,302.806150100969,111.11344545104068
1000.0,0.25,55100.0,0.506710974296498,200.636726936331,285.176079857547,93.8690552767738,0.95,0.954940633679743,302.806150100969,101.66483138556872
1000.0,0.25,53650.0,0.531891669068342,174.48256837113,285.176079857547,93.8690552767738,0.925,0.92981061700396,302.806150100969,92.80582451425141
1000.0,0.25,52200.0,0.576834995127259,145.458943806754,285.176079857547,93.8690552767738,0.9,0.904680600328178,302.806150100969,83.90580914198519
1000.0,0.25,50750.0,0.659833245731738,113.042614094839,285.176079857547,93.8690552767738,0.875,0.879550583652395,302.806150100969,74.58927496419793
1000.0,0.25,49300.0,0.787113280285323,84.2161426694686,285.176079857547,93.8690552767738,0.85,0.854420566976612,302.806150100969,66.28764430954219
1000.0,0.25,47850.0,0.949270548719875,63.2601066250733,285.176079857547,93.8690552767738,0.825,0.829290550300829,302.806150100969,60.05095612806113
1000.0,0.25,46400.0,1.13176060787858,49.1256977066405,285.176079857547,93.8690552767738,0.8,0.804160533625047,302.806150100969,55.598529498926816
1000.0,0.25,44950.0,1.33080591702903,39.4500197423346,285.176079857547,93.8690552767738,0.775,0.779030516949264,302.806150100969,52.500319700010934
1000.0,0.25,43500.0,1.56164397544693,32.0589300930277,285.176079857547,93.8690552767738,0.75,0.753900500273481,302.806150100969,50.06463503905099
1000.0,0.25,42050.0,1.87068932127897,25.5556436643225,285.176079857547,93.8690552767738,0.725,0.728770483597698,302.806150100969,47.80666970125866
1000.0,0.25,40600.0,2.34272339485242,19.434349895432,285.176079857547,93.8690552767738,0.7,0.703640466921916,302.806150100969,45.529306163776226
1000.0,0.25,0.0,0.0,0.0,285.176079857547,93.8690552767738,0.0,0.0,0.0,0.0
1000.0,0.3,58000.0,0.481334847455855,255.920202280522,286.727554994867,95.6661437070395,1.0,1.00247742120916,363.367380121163,123.18331152556661
1000.0,0.3,56550.0,0.483921798146751,231.894451240306,286.727554994867,95.6661437070395,0.975,0.977415485678929,363.367380121163,112.21877982446296
1000.0,0.3,55100.0,0.495933328551175,206.933825773019,286.727554994867,95.6661437070395,0.95,0.9523535501487,363.367380121163,102.62538100544224
1000.0,0.3,53650.0,0.519736573799463,179.984095165987,286.727554994867,95.6661437070395,0.925,0.927291614618471,363.367380121163,93.54431695996658
1000.0,0.3,52200.0,0.563971973204375,149.462959868794,286.727554994867,95.6661437070395,0.9,0.902229679088242,363.367380121163,84.29292039817007
1000.0,0.3,50750.0,0.643942242463018,115.999665097739,286.727554994867,95.6661437070395,0.875,0.877167743558013,363.367380121163,74.69708446799713
1000.0,0.3,49300.0,0.766418607040161,86.3322915762664,286.727554994867,95.6661437070395,0.85,0.852105808027784,363.367380121163,66.16667465246712
1000.0,0.3,47850.0,0.917325200115547,65.2429981447152,286.727554994867,95.6661437070395,0.825,0.827043872497555,363.367380121163,59.84904632923913
1000.0,0.3,46400.0,1.08686716694676,50.9079859557327,286.727554994867,95.6661437070395,0.8,0.801981936967326,363.367380121163,55.33021847067264
1000.0,0.3,44950.0,1.27353794841834,40.9857875552953,286.727554994867,95.6661437070395,0.775,0.776920001437097,363.367380121163,52.19695579748071
1000.0,0.3,43500.0,1.49236777331988,33.3168463018979,286.727554994867,95.6661437070395,0.75,0.751858065906868,363.367380121163,49.72098772960404
1000.0,0.3,42050.0,1.78881980291953,26.5065014540787,286.727554994867,95.6661437070395,0.725,0.726796130376639,363.367380121163,47.41535470717129
1000.0,0.3,40600.0,2.24145710517143,20.1131429503249,286.727554994867,95.6661437070395,0.7,0.70173419484641,363.367380121163,45.082747173334404
1000.0,0.3,0.0,0.0,0.0,286.727554994867,95.6661437070395,0.0,0.0,0.0,0.0
1000.0,0.35,58000.0,0.47039404555375,265.043478251638,288.561116520791,97.8214843854198,1.0,0.999287398130672,423.928610141357,124.67487398242535
1000.0,0.35,56550.0,0.472045317312734,240.596937246103,288.561116520791,97.8214843854198,0.975,0.974305213177405,423.928610141357,113.57265758680865
1000.0,0.35,55100.0,0.48389604314158,214.471789263338,288.561116520791,97.8214843854198,0.95,0.949323028224138,423.928610141357,103.78205019002407
1000.0,0.35,53650.0,0.506310938509629,186.502342985338,288.561116520791,97.8214843854198,0.925,0.924340843270871,423.928610141357,94.42817631115122
1000.0,0.35,52200.0,0.549551042070556,154.193828903687,288.561116520791,97.8214843854198,0.9,0.899358658317605,423.928610141357,84.73737935487021
1000.0,0.35,50750.0,0.625907160843942,119.560597867778,288.561116520791,97.8214843854198,0.875,0.874376473364338,423.928610141357,74.8338343602252
1000.0,0.35,49300.0,0.742637052860997,88.9166687625891,288.561116520791,97.8214843854198,0.85,0.849394288411071,423.928610141357,66.03281284006664
1000.0,0.35,47850.0,0.88132580473439,67.6577944554284,288.561116520791,97.8214843854198,0.825,0.824412103457804,423.928610141357,59.62856014498439
1000.0,0.35,46400.0,1.03637969937351,53.1367453506142,288.561116520791,97.8214843854198,0.8,0.799429918504537,423.928610141357,55.069844172156294
1000.0,0.35,44950.0,1.20954895436051,42.8644097074849,288.561116520791,97.8214843854198,0.775,0.774447733551271,423.928610141357,51.84660194096886
1000.0,0.35,43500.0,1.41534127897073,34.842722610254,288.561116520791,97.8214843854198,0.75,0.749465548598004,423.928610141357,49.31434358201927
1000.0,0.35,42050.0,1.69712370225193,27.6602108108716,288.561116520791,97.8214843854198,0.725,0.724483363644737,423.928610141357,46.94279937641527
1000.0,0.35,40600.0,2.17503761097496,20.0585876591485,288.561116520791,97.8214843854198,0.7,0.69950117869147,423.928610141357,43.62818258168617
1000.0,0.35,0.0,0.0,0.0,288.561116520791,97.8214843854198,0.0,0.0,0.0,0.0
2000.0,0.0,58000.0,0.515040664520812,213.569545962531,275.15,79.495201431303,1.0,1.02335085536676,0.0,109.99700087395007
2000.0,0.0,56550.0,0.519299186792368,192.755816584944,275.15,79.495201431303,0.975,0.997767083982586,0.0,100.09793880206026
2000.0,0.0,55100.0,0.528089114170692,173.16041580167,275.15,79.495201431303,0.95,0.972183312598417,0.0,91.44413059013262
2000.0,0.0,53650.0,0.550795550902113,152.166960620804,275.15,79.495201431303,0.925,0.946599541214248,0.0,83.81288490423586
2000.0,0.0,52200.0,0.590253307886354,129.83818614625,275.15,79.495201431303,0.9,0.92101576983008,0.0,76.63741886278824
2000.0,0.0,50750.0,0.663847887312897,104.040691406009,275.15,79.495201431303,0.875,0.895431998445911,0.0,69.06719318445215
2000.0,0.0,49300.0,0.788899101634556,78.3153168982003,275.15,79.495201431303,0.85,0.869848227061742,0.0,61.78288314521578
2000.0,0.0,47850.0,0.967447846640383,57.8097254335603,275.15,79.495201431303,0.825,0.844264455677573,0.0,55.927894385569694
2000.0,0.0,46400.0,1.18925988996186,43.3988491834337,275.15,79.495201431303,0.8,0.818680684293404,0.0,51.61251060436172
2000.0,0.0,44950.0,1.42866392429822,34.1105861782349,275.15,79.495201431303,0.775,0.793096912909235,0.0,48.732563909509686
2000.0,0.0,43500.0,1.69672297450453,27.4562797051368,275.15,79.495201431303,0.75,0.767513141525066,0.0,46.585700570128076
2000.0,0.0,42050.0,2.02976391081085,22.0427161641825,275.15,79.495201431303,0.725,0.741929370140897,0.0,44.74150976630461
2000.0,0.0,40600.0,2.52252750412449,17.0250742966338,275.15,79.495201431303,0.7,0.716345598756728,0.0,42.94621817302166
2000.0,0.0,0.0,0.0,0.0,275.15,79.495201431303,0.0,0.0,0.0,0.0
2000.0,0.05,58000.0,0.513998514586902,214.194454653585,275.287797113977,79.6344377189261,1.0,1.02309470081355,59.8602712653275,110.09563152469424
2000.0,0.05,56550.0,0.518292761439053,193.248370151508,275.287797113977,79.6344377189261,0.975,0.997517333293213,59.8602712653275,100.15923140942135
2000.0,0.05,55100.0,0.526954837499877,173.679538868261,275.287797113977,79.6344377189261,0.95,0.971939965772874,59.8602712653275,91.52127318137805
2000.0,0.05,53650.0,0.549638241152388,152.602234420244,275.287797113977,79.6344377189261,0.925,0.946362598252535,59.8602712653275,83.87602372266733
2000.0,0.05,52200.0,0.588924479021849,130.206082713338,275.287797113977,79.6344377189261,0.9,0.920785230732197,59.8602712653275,76.68154942742835
2000.0,0.05,50750.0,0.662263113328942,104.324346491018,275.287797113977,79.6344377189261,0.875,0.895207863211858,59.8602712653275,69.09016650314888
2000.0,0.05,49300.0,0.787023024813282,78.4974207861195,275.287797113977,79.6344377189261,0.85,0.869630495691519,59.8602712653275,61.77927754713277
2000.0,0.05,47850.0,0.96477046936105,57.9481248111078,275.287797113977,79.6344377189261,0.825,0.84405312817118,59.8602712653275,55.90663957260519
2000.0,0.05,46400.0,1.18493401412138,43.5394583586965,275.287797113977,79.6344377189261,0.8,0.818475760650841,59.8602712653275,51.591385165640915
2000.0,0.05,44950.0,1.42299961041599,34.2275854226577,275.287797113977,79.6344377189261,0.775,0.792898393130502,59.8602712653275,48.70584072192192
2000.0,0.05,43500.0,1.68953382430745,27.5545073182161,275.287797113977,79.6344377189261,0.75,0.767321025610164,59.8602712653275,46.55427212625327
2000.0,0.05,42050.0,2.02157328496948,22.1156665748395,275.287797113977,79.6344377189261,0.725,0.741743658089825,59.8602712653275,44.70844072698802
2000.0,0.05,40600.0,2.51233778089155,17.0812536237758,275.287797113977,79.6344377189261,0.7,0.716166290569486,59.8602712653275,42.913878824002644
2000.0,0.05,0.0,0.0,0.0,275.287797113977,79.6344377189261,0.0,0.0,59.8602712653275,0.0
2000.0,0.1,58000.0,0.510919083743302,216.065589871836,275.70118845591,80.0531911732792,1.0,1.0223273897384,119.720542530655,110.39203320577452
2000.0,0.1,56550.0,0.515125202745729,194.911234844513,275.70118845591,80.0531911732792,0.975,0.996769204994944,119.720542530655,100.40368936670016
2000.0,0.1,55100.0,0.523544919442829,175.258927837278,275.70118845591,80.0531911732792,0.95,0.971211020251484,119.720542530655,91.75592125620429
2000.0,0.1,53650.0,0.546104394811035,153.922796754545,275.70118845591,80.0531911732792,0.925,0.945652835508024,119.720542530655,84.05791576926275
2000.0,0.1,52200.0,0.584972407301244,131.296458225794,275.70118845591,80.0531911732792,0.9,0.920094650764564,119.720542530655,76.80480523846994
2000.0,0.1,50750.0,0.657612822883715,105.15383292654,275.70118845591,80.0531911732792,0.875,0.894536466021103,119.720542530655,69.15050890786452
2000.0,0.1,49300.0,0.78139803562091,79.0433486403363,275.70118845591,80.0531911732792,0.85,0.868978281277643,119.720542530655,61.76431735645752
2000.0,0.1,47850.0,0.956657672643004,58.3836603153741,275.70118845591,80.0531911732792,0.825,0.843420096534183,119.720542530655,55.853176597685504
2000.0,0.1,46400.0,1.17240468553146,43.9430413111819,275.70118845591,80.0531911732792,0.8,0.817861911790723,119.720542530655,51.51902752973217
2000.0,0.1,44950.0,1.4060986663735,34.579567492174,275.70118845591,80.0531911732792,0.775,0.792303727047263,119.720542530655,48.6222837345183
2000.0,0.1,43500.0,1.66820357321683,27.852059961693,275.70118845591,80.0531911732792,0.75,0.766745542303803,119.720542530655,46.46290594954567
2000.0,0.1,42050.0,1.99603555324785,22.3509269040472,275.70118845591,80.0531911732792,0.725,0.741187357560343,119.720542530655,44.613244748522106
2000.0,0.1,40600.0,2.4821583380156,17.2427048424978,275.70118845591,80.0531911732792,0.7,0.715629172816883,119.720542530655,42.79912359474788
2000.0,0.1,0.0,0.0,0.0,275.70118845591,80.0531911732792,0.0,0.0,119.720542530655,0.0
2000.0,0.15,58000.0,0.505940469287619,219.170507032382,276.390174025797,80.7546033876493,1.0,1.02105236552231,179.580813795983,110.88722918196876
2000.0,0.15,56550.0,0.509876075856986,197.741197803105,276.390174025797,80.7546033876493,0.975,0.995526056384251,179.580813795983,100.82350597110724
2000.0,0.15,55100.0,0.517965667072572,177.90754201704,276.390174025797,80.7546033876493,0.95,0.969999747246193,179.580813795983,92.14999867809775
2000.0,0.15,53650.0,0.540326499367254,156.13910437807,276.390174025797,80.7546033876493,0.925,0.944473438108135,179.580813795983,84.36609568294084
2000.0,0.15,52200.0,0.578528560029528,133.112833708327,276.390174025797,80.7546033876493,0.9,0.918947128970078,179.580813795983,77.00957600672844
2000.0,0.15,50750.0,0.650061689936006,106.523511684329,276.390174025797,80.7546033876493,0.875,0.89342081983202,179.580813795983,69.24685402343279
2000.0,0.15,49300.0,0.772130195036696,79.9603355666991,276.390174025797,80.7546033876493,0.85,0.867894510693962,179.580813795983,61.73978949631503
2000.0,0.15,47850.0,0.943383685282142,59.1134126225871,276.390174025797,80.7546033876493,0.825,0.842368201555904,179.580813795983,55.76662904950011
2000.0,0.15,46400.0,1.15182262373302,44.6256045231463,276.390174025797,80.7546033876493,0.8,0.816841892417847,179.580813795983,51.4007808875225
2000.0,0.15,44950.0,1.37839265567595,35.1750628777158,276.390174025797,80.7546033876493,0.775,0.791315583279789,179.580813795983,48.48504833358321
2000.0,0.15,43500.0,1.63334259022189,28.3538801484714,276.390174025797,80.7546033876493,0.75,0.765789274141731,179.580813795983,46.31160004454531
2000.0,0.15,42050.0,1.95393164948942,22.7457146696403,276.390174025797,80.7546033876493,0.725,0.740262965003674,179.580813795983,44.44357178326597
2000.0,0.15,40600.0,2.43216017712677,17.5175992884029,276.390174025797,80.7546033876493,0.7,0.714736655865616,179.580813795983,42.60560738811777
2000.0,0.15,0.0,0.0,0.0,276.390174025797,80.7546033876493,0.0,0.0,179.580813795983,0.0
2000.0,0.2,58000.0,0.499232130684788,223.499410948191,277.35475382364,81.7439364407149,1.0,1.01927531957574,239.44108506131,111.57808713446043
2000.0,0.2,56550.0,0.502718503653552,201.74576590171,277.35475382364,81.7439364407149,0.975,0.993793436586342,239.44108506131,101.42132955254743
2000.0,0.2,55100.0,0.510439467551189,181.625973772937,277.35475382364,81.7439364407149,0.95,0.968311553596949,239.44108506131,92.70906534612416
2000.0,0.2,53650.0,0.532446869721074,159.26960906653,277.35475382364,81.7439364407149,0.925,0.942829670607555,239.44108506131,84.80260478917309
2000.0,0.2,52200.0,0.569771402012033,135.661668382115,277.35475382364,81.7439364407149,0.9,0.917347787618162,239.44108506131,77.29613899336915
2000.0,0.2,50750.0,0.639810490337827,108.43542661099,277.35475382364,81.7439364407149,0.875,0.891865904628768,239.44108506131,69.37812346996897
2000.0,0.2,49300.0,0.759387811981252,81.2584775475928,277.35475382364,81.7439364407149,0.85,0.866384021639375,239.44108506131,61.70669746979419
2000.0,0.2,47850.0,0.925360676952596,60.138363527241,277.35475382364,81.7439364407149,0.825,0.840902138649982,239.44108506131,55.649676784389044
2000.0,0.2,46400.0,1.12346899321624,45.6108797381169,277.35475382364,81.7439364407149,0.8,0.815420255660588,239.44108506131,51.2424091390892
2000.0,0.2,44950.0,1.34057128208328,36.0265684010973,277.35475382364,81.7439364407149,0.775,0.789938372671195,239.44108506131,48.296182990519995
2000.0,0.2,43500.0,1.58591300908105,29.0694892137872,277.35475382364,81.7439364407149,0.75,0.764456489681801,239.44108506131,46.101681111486386
2000.0,0.2,42050.0,1.89686191592384,23.3095712875742,277.35475382364,81.7439364407149,0.725,0.738974606692408,239.44108506131,44.21503805191133
2000.0,0.2,40600.0,2.3640887401846,17.9122257023548,277.35475382364,81.7439364407149,0.7,0.713492723703015,239.44108506131,42.34609109458217
2000.0,0.2,0.0,0.0,0.0,277.35475382364,81.7439364407149,0.0,0.0,239.44108506131,0.0
2000.0,0.25,58000.0,0.490943210954541,229.083884602049,278.594927849437,83.0286121259999,1.0,1.01700412121317,299.301356326638,112.46717788446948
2000.0,0.25,56550.0,0.493825203742373,206.98265691698,278.594927849437,83.0286121259999,0.975,0.991579018182845,299.301356326638,102.21325272316534
2000.0,0.25,55100.0,0.501186802222428,186.431460591808,278.594927849437,83.0286121259999,0.95,0.966153915152516,299.301356326638,93.43698756766484
2000.0,0.25,53650.0,0.52264881073961,163.35157559787,278.594927849437,83.0286121259999,0.925,0.940728812122187,299.301356326638,85.37550671866826
2000.0,0.25,52200.0,0.55891656606103,138.962133832492,278.594927849437,83.0286121259999,0.9,0.915303709091857,299.301356326638,77.6682386541697
2000.0,0.25,50750.0,0.627151765084877,110.880795710008,278.594927849437,83.0286121259999,0.875,0.889878606061528,299.301356326638,69.53908674354717
2000.0,0.25,49300.0,0.743417723081925,82.9462259129654,278.594927849437,83.0286121259999,0.85,0.864453503031198,299.301356326638,61.663694406455704
2000.0,0.25,47850.0,0.902422639815665,61.497550318509,278.594927849437,83.0286121259999,0.825,0.839028400000869,299.301356326638,55.496781700625576
2000.0,0.25,46400.0,1.08805487679301,46.9318427594,278.594927849437,83.0286121259999,0.8,0.81360329697054,299.301356326638,51.064420391247886
2000.0,0.25,44950.0,1.29379201367826,37.1450034063478,278.594927849437,83.0286121259999,0.775,0.78817819394021,299.301356326638,48.05790875518454
2000.0,0.25,43500.0,1.52724022403009,30.0105866069096,278.594927849437,83.0286121259999,0.75,0.762753090909881,299.301356326638,45.83337501281104
2000.0,0.25,42050.0,1.82625085802079,24.0544040573426,278.594927849437,83.0286121259999,0.725,0.737327987879551,299.301356326638,43.92937604890069
2000.0,0.25,40600.0,2.27925095605852,18.434278259216,278.594927849437,83.0286121259999,0.7,0.711902884849222,299.301356326638,42.01634634656686
2000.0,0.25,0.0,0.0,0.0,278.594927849437,83.0286121259999,0.0,0.0,299.301356326638,0.0
2000.0,0.3,58000.0,0.481312194233186,235.949797564156,280.110696103189,84.618267061966,1.0,1.01424872185047,359.161627591965,113.56551479447997
2000.0,0.3,56550.0,0.483411445566456,213.536169122694,280.110696103189,84.618267061966,0.975,0.988892503804209,359.161627591965,103.22582819632473
2000.0,0.3,55100.0,0.490485005697224,192.340298597972,280.110696103189,84.618267061966,0.95,0.963536285757948,359.161627591965,94.34003245363206
2000.0,0.3,53650.0,0.511129913146882,168.431204556053,280.110696103189,84.618267061966,0.925,0.938180067711686,359.161627591965,86.09022695596009
2000.0,0.3,52200.0,0.54627806233195,143.008985787908,280.110696103189,84.618267061966,0.9,0.912823849665424,359.161627591965,78.12267165227576
2000.0,0.3,50750.0,0.612417604594913,113.852681014766,280.110696103189,84.618267061966,0.875,0.887467631619162,359.161627591965,69.72538618377173
2000.0,0.3,49300.0,0.724501331066055,85.0427936768085,280.110696103189,84.618267061966,0.85,0.8621114135729,359.161627591965,61.61361721642364
2000.0,0.3,47850.0,0.875007223444898,63.2213183382253,280.110696103189,84.618267061966,0.825,0.836755195526639,359.161627591965,55.31911022165654
2000.0,0.3,46400.0,1.0468720270292,48.5772676411829,280.110696103189,84.618267061966,0.8,0.811398977480377,359.161627591965,50.85418264306511
2000.0,0.3,44950.0,1.23898811000289,38.5602010579051,280.110696103189,84.618267061966,0.775,0.786042759434115,359.161627591965,47.77563063006528
2000.0,0.3,43500.0,1.45886596464954,31.196860542239,280.110696103189,84.618267061966,0.75,0.760686541387853,359.161627591965,45.512038048990675
2000.0,0.3,42050.0,1.7440198811413,24.9846441432261,280.110696103189,84.618267061966,0.725,0.735330323341591,359.161627591965,43.573716109026854
2000.0,0.3,40600.0,2.22439955238856,18.3610232895413,280.110696103189,84.618267061966,0.7,0.70997410529533,359.161627591965,40.84225198665159
2000.0,0.3,0.0,0.0,0.0,280.110696103189,84.618267061966,0.0,0.0,359.161627591965,0.0
2000.0,0.35,58000.0,0.470566372455659,244.132662822051,281.902058584896,86.5248238705945,1.0,1.01102103578495,419.021898857293,114.88062154211308
2000.0,0.35,56550.0,0.471779071681757,221.429485396085,281.902058584896,86.5248238705945,0.975,0.985745509890324,419.021898857293,104.46579706313415
2000.0,0.35,55100.0,0.478606798245897,199.386389652093,281.902058584896,86.5248238705945,0.95,0.960469983995701,419.021898857293,95.42768156519708
2000.0,0.35,53650.0,0.498152985137602,174.60167917058,281.902058584896,86.5248238705945,0.925,0.935194458101077,419.021898857293,86.9783476888623
2000.0,0.35,52200.0,0.532149599633283,147.79918290782,281.902058584896,86.5248238705945,0.9,0.909918932206453,419.021898857293,78.65127601052278
2000.0,0.35,50750.0,0.596055825341059,117.300089959081,281.902058584896,86.5248238705945,0.875,0.884643406311829,419.021898857293,69.9174019331405
2000.0,0.35,49300.0,0.702768369468572,87.603531790668,281.902058584896,86.5248238705945,0.85,0.859367880417206,419.021898857293,61.56499119621596
2000.0,0.35,47850.0,0.843455670817316,65.3619111562417,281.902058584896,86.5248238705945,0.825,0.834092354522582,419.021898857293,55.12987462018965
2000.0,0.35,46400.0,1.00062754679037,50.5876558682368,281.902058584896,86.5248238705945,0.8,0.808816828627958,419.021898857293,50.61940198930926
2000.0,0.35,44950.0,1.17751547849603,40.3045624359361,281.902058584896,86.5248238705945,0.775,0.783541302733334,419.021898857293,47.45924612232441
2000.0,0.35,43500.0,1.38261523588192,32.6536229148696,281.902058584896,86.5248238705945,0.75,0.758265776838711,419.021898857293,45.1473965488417
2000.0,0.35,42050.0,1.65968933222744,25.845334524983,281.902058584896,86.5248238705945,0.725,0.732990250944087,419.021898857293,42.89522599896383
2000.0,0.35,40600.0,2.1101137137546,19.1389319939315,281.902058584896,86.5248238705945,0.7,0.707714725049463,419.021898857293,40.38532286701153
2000.0,0.35,0.0,0.0,0.0,281.902058584896,86.5248238705945,0.0,0.0,419.021898857293,0.0
3000.0,0.0,58000.0,0.516198592397567,195.639492579802,268.65,70.108523423274,1.0,1.03565687626109,0.0,100.98883068706806
3000.0,0.0,56550.0,0.517518224001758,178.107075009453,268.65,70.108523423274,0.975,1.00976545435457,0.0,92.17365714104001
3000.0,0.0,55100.0,0.526298404523301,159.542557524104,268.65,70.108523423274,0.95,0.983874032448039,0.0,83.96699347850291
3000.0,0.0,53650.0,0.542169860110265,141.998290178002,268.65,70.108523423274,0.925,0.957982610541512,0.0,76.98719312170417
3000.0,0.0,52200.0,0.577299820106671,122.140961867175,268.65,70.108523423274,0.9,0.932091188634984,0.0,70.5119553135759
3000.0,0.0,50750.0,0.635607020742967,100.847615486545,268.65,70.108523423274,0.875,0.906199766728457,0.0,64.09945242843517
3000.0,0.0,49300.0,0.74551022679156,77.029406676729,268.65,70.108523423274,0.85,0.88030834482193,0.0,57.426210441187536
3000.0,0.0,47850.0,0.91260806276764,56.8060837234955,268.65,70.108523423274,0.825,0.854416922915402,0.0,51.84169002031559
3000.0,0.0,46400.0,1.13593142382492,41.88698292111,268.65,70.108523423274,0.8,0.828525501008875,0.0,47.580740149306585
3000.0,0.0,44950.0,1.38709060620493,32.2798747866438,268.65,70.108523423274,0.775,0.802634079102348,0.0,44.775111086024985
3000.0,0.0,43500.0,1.66645574471295,25.7003648976502,268.65,70.108523423274,0.75,0.77674265719582,0.0,42.82852072490822
3000.0,0.0,42050.0,2.00192509947777,20.6096542931463,268.65,70.108523423274,0.725,0.750851235289293,0.0,41.25898422100936
3000.0,0.0,40600.0,2.47533674011158,16.0905941489086,268.65,70.108523423274,0.7,0.724959813382765,0.0,39.82963886701788
3000.0,0.0,0.0,0.0,0.0,268.65,70.108523423274,0.0,0.0,0.0,0.0
3000.0,0.05,58000.0,0.515231915059655,196.194005351224,268.784550610546,70.2313268802712,1.0,1.03539762455165,59.1509135005756,101.08541310033536
3000.0,0.05,56550.0,0.516512621894976,178.58707151681,268.784550610546,70.2313268802712,0.975,1.00951268393785,59.1509135005756,92.24247654569312
3000.0,0.05,55100.0,0.525172172581759,160.029535187731,268.784550610546,70.2313268802712,0.95,0.983627743324063,59.1509135005756,84.04305867178974
3000.0,0.05,53650.0,0.541029319101805,142.406353030404,268.784550610546,70.2313268802712,0.925,0.957742802710272,59.1509135005756,77.04601221581073
3000.0,0.05,52200.0,0.575988523705559,122.497476600987,268.784550610546,70.2313268802712,0.9,0.931857862096481,59.1509135005756,70.55714070505876
3000.0,0.05,50750.0,0.634221898516558,101.09491519428,268.784550610546,70.2313268802712,0.875,0.90597292148269,59.1509135005756,64.11660904488669
3000.0,0.05,49300.0,0.743690340587394,77.2205723755214,268.784550610546,70.2313268802712,0.85,0.880087980868899,59.1509135005756,57.42819377030502
3000.0,0.05,47850.0,0.910238011377281,56.9410124794452,268.784550610546,70.2313268802712,0.825,0.854203040255108,59.1509135005756,51.82987396509914
3000.0,0.05,46400.0,1.13211118338588,42.0112243748278,268.784550610546,70.2313268802712,0.8,0.828318099641316,59.1509135005756,47.56137694247602
3000.0,0.05,44950.0,1.38149285186014,32.3952482558376,268.784550610546,70.2313268802712,0.775,0.802433159027525,59.1509135005756,44.75380389967431
3000.0,0.05,43500.0,1.65940716359725,25.7917096490945,268.784550610546,70.2313268802712,0.75,0.776548218413734,59.1509135005756,42.79894775312773
3000.0,0.05,42050.0,1.99374360102655,20.6815217588909,268.784550610546,70.2313268802712,0.725,0.750663277799943,59.1509135005756,41.23365166628009
3000.0,0.05,40600.0,2.4653435716055,16.1429138507143,268.784550610546,70.2313268802712,0.7,0.724778337186152,59.1509135005756,39.79782888883989
3000.0,0.05,0.0,0.0,0.0,268.784550610546,70.2313268802712,0.0,0.0,59.1509135005756,0.0
3000.0,0.1,58000.0,0.512345912060705,197.86374949014,269.188202442184,70.6006586189506,1.0,1.0346210360193,118.301827001151,101.37468319627664
3000.0,0.1,56550.0,0.513413933056788,180.101323052972,269.188202442184,70.6006586189506,0.975,1.00875551011882,118.301827001151,92.4665286173575
3000.0,0.1,55100.0,0.52175065401875,161.479481606083,269.188202442184,70.6006586189506,0.95,0.982889984218339,118.301827001151,84.25202513858251
3000.0,0.1,53650.0,0.537576876437044,143.647704717215,269.188202442184,70.6006586189506,0.925,0.957024458317857,118.301827001151,77.22168440923126
3000.0,0.1,52200.0,0.572048276498673,123.573915217015,269.188202442184,70.6006586189506,0.9,0.931158932417374,118.301827001151,70.69024522008657
3000.0,0.1,50750.0,0.629858992528352,101.905019044549,269.188202442184,70.6006586189506,0.875,0.905293406516891,118.301827001151,64.18579262898216
3000.0,0.1,49300.0,0.738307215403671,77.7996067229204,269.188202442184,70.6006586189506,0.85,0.879427880616409,118.301827001151,57.44001099910008
3000.0,0.1,47850.0,0.903148621554008,57.3434133381391,269.188202442184,70.6006586189506,0.825,0.853562354715926,118.301827001151,51.78962471154205
3000.0,0.1,46400.0,1.12074221435107,42.3846174863529,269.188202442184,70.6006586189506,0.8,0.827696828815443,118.301827001151,47.50223005607823
3000.0,0.1,44950.0,1.36543973089148,32.7185689600467,269.188202442184,70.6006586189506,0.775,0.801831302914961,118.301827001151,44.675233995960504
3000.0,0.1,43500.0,1.63845504536074,26.0687311518205,269.188202442184,70.6006586189506,0.75,0.775965777014478,118.301827001151,42.71244408185299
3000.0,0.1,42050.0,1.96772503810352,20.9056456117382,269.188202442184,70.6006586189506,0.725,0.750100251113995,118.301827001151,41.13656230793624
3000.0,0.1,40600.0,2.43414831939933,16.3053846163222,269.188202442184,70.6006586189506,0.7,0.724234725213513,118.301827001151,39.68972456098037
3000.0,0.1,0.0,0.0,0.0,269.188202442184,70.6006586189506,0.0,0.0,118.301827001151,0.0
3000.0,0.15,58000.0,0.507623659124732,200.648460035772,269.860955494913,71.219289639328,1.0,1.03333059590403,177.452740501727,101.85390548110112
3000.0,0.15,56550.0,0.508354033233313,182.639513091272,269.860955494913,71.219289639328,0.975,1.00749733100643,177.452740501727,92.84553310771658
3000.0,0.15,55100.0,0.516147431309761,163.916869291261,269.860955494913,71.219289639328,0.95,0.981664066108829,177.452740501727,84.60527103302222
3000.0,0.15,53650.0,0.53193673873951,145.727056071528,269.860955494913,71.219289639328,0.925,0.955830801211228,177.452740501727,77.51757495279831
3000.0,0.15,52200.0,0.565577151470904,125.385421266134,269.860955494913,71.219289639328,0.9,0.929997536313627,177.452740501727,70.91512939567939
3000.0,0.15,50750.0,0.622738403664192,103.254690685758,269.860955494913,71.219289639328,0.875,0.904164271416027,177.452740501727,64.30066124848885
3000.0,0.15,49300.0,0.729483424920677,78.7676699438951,269.860955494913,71.219289639328,0.85,0.878331006518426,177.452740501727,57.45970964369407
3000.0,0.15,47850.0,0.891377571043883,58.0225717220937,269.860955494913,71.219289639328,0.825,0.852497741620825,177.452740501727,51.72001904735938
3000.0,0.15,46400.0,1.10212242624491,43.011546952595,269.860955494913,71.219289639328,0.8,0.826664476723224,177.452740501727,47.403990483940866
3000.0,0.15,44950.0,1.33916696062851,33.2638563590631,269.860955494913,71.219289639328,0.775,0.800831211825623,177.452740501727,44.54585741914986
3000.0,0.15,43500.0,1.60422105198309,26.5349844446992,269.860955494913,71.219289639328,0.75,0.774997946928023,177.452740501727,42.56798066023028
3000.0,0.15,42050.0,1.92529334753574,21.2868541154318,269.860955494913,71.219289639328,0.725,0.749164682030422,177.452740501727,40.98343861840464
3000.0,0.15,40600.0,2.38352766396669,16.5791994398573,269.860955494913,71.219289639328,0.7,0.723331417132821,177.452740501727,39.51698051132093
3000.0,0.15,0.0,0.0,0.0,269.860955494913,71.219289639328,0.0,0.0,177.452740501727,0.0
3000.0,0.2,58000.0,0.501185432900655,204.553991144178,270.802809768734,72.0918612884988,1.0,1.0315320648,236.603654002302,102.5194806031516
3000.0,0.2,56550.0,0.501480921468596,186.226500093892,270.802809768734,72.0918612884988,0.975,1.00574376318,236.603654002302,93.38903686895655
3000.0,0.2,55100.0,0.508504270338231,167.375177042223,270.802809768734,72.0918612884988,0.95,0.979955461559997,236.603654002302,85.11099227458783
3000.0,0.2,53650.0,0.524269528761642,148.660587591747,270.802809768734,72.0918612884988,0.925,0.954167159939997,236.603654002302,77.93821620215401
3000.0,0.2,52200.0,0.556766921198546,127.94229372089,270.802809768734,72.0918612884988,0.9,0.928378858319997,236.603654002302,71.23403696605999
3000.0,0.2,50750.0,0.613143633130921,105.122312806304,270.802809768734,72.0918612884988,0.875,0.902590556699997,236.603654002302,64.45507679718237
3000.0,0.2,49300.0,0.717431075852181,80.1280916908039,270.802809768734,72.0918612884988,0.85,0.876802255079997,236.603654002302,57.48638302771565
3000.0,0.2,47850.0,0.875135412441549,58.9887447443547,270.802809768734,72.0918612884988,0.825,0.851013953459997,236.603654002302,51.6231394612601
3000.0,0.2,46400.0,1.07666913219795,43.9044758421852,270.802809768734,72.0918612884988,0.8,0.825225651839997,236.603654002302,47.2705939046114
3000.0,0.2,44950.0,1.30327148119615,34.0429892996113,270.802809768734,72.0918612884988,0.775,0.799437350219997,236.603654002302,44.36725708884911
3000.0,0.2,43500.0,1.55759115970373,27.201651536094,270.802809768734,72.0918612884988,0.75,0.773649048599997,236.603654002302,42.369051961961404
3000.0,0.2,42050.0,1.86760996407068,21.8302868028718,270.802809768734,72.0918612884988,0.725,0.747860746979997,236.603654002302,40.77046115156404
3000.0,0.2,40600.0,2.3145523285331,16.9696338616178,270.802809768734,72.0918612884988,0.7,0.722072445359997,236.603654002302,39.27710556876162
3000.0,0.2,0.0,0.0,0.0,270.802809768734,72.0918612884988,0.0,0.0,236.603654002302,0.0
3000.0,0.25,58000.0,0.493044736925006,209.741876091979,272.013765263647,73.2249198647342,1.0,1.02923340767317,295.754567502878,103.41212811992699
3000.0,0.25,56550.0,0.493016086818814,190.906702601767,272.013765263647,73.2249198647342,0.975,1.00350257248134,295.754567502878,94.12007546420625
3000.0,0.25,55100.0,0.498999810156414,171.900234685622,272.013765263647,73.2249198647342,0.95,0.977771737289507,295.754567502878,85.77818447396838
3000.0,0.25,53650.0,0.514758053908504,152.496215317938,272.013765263647,73.2249198647342,0.925,0.952040902097678,295.754567502878,78.49865502547398
3000.0,0.25,52200.0,0.545908562471256,131.239726557535,272.013765263647,73.2249198647342,0.9,0.926310066905848,295.754567502878,71.64489046414464
3000.0,0.25,50750.0,0.601429440402033,107.480498771677,272.013765263647,73.2249198647342,0.875,0.900579231714019,295.754567502878,64.6419362303811
3000.0,0.25,49300.0,0.702430133887488,81.8855611304295,272.013765263647,73.2249198647342,0.85,0.87484839652219,295.754567502878,57.518885668299674
3000.0,0.25,47850.0,0.854709778303298,60.2570342136682,272.013765263647,73.2249198647342,0.825,0.849117561330361,295.754567502878,51.50227635397859
3000.0,0.25,46400.0,1.04496328367278,45.0775541636288,272.013765263647,73.2249198647342,0.8,0.823386726138532,295.754567502878,47.10438901876314
3000.0,0.25,44950.0,1.25864283323665,35.0732039986435,272.013765263647,73.2249198647342,0.775,0.797655890946703,295.754567502878,44.14463685153965
3000.0,0.25,43500.0,1.49981457910647,28.0820201747691,272.013765263647,73.2249198647342,0.75,0.771925055754874,295.754567502878,42.11782326888072
3000.0,0.25,42050.0,1.79627203198008,22.5473720061643,272.013765263647,73.2249198647342,0.725,0.746194220563044,295.754567502878,40.50121372932352
3000.0,0.25,40600.0,2.27207002071196,16.8772709516511,272.013765263647,73.2249198647342,0.7,0.720463385371215,295.754567502878,38.34634136067927
3000.0,0.25,0.0,0.0,0.0,272.013765263647,73.2249198647342,0.0,0.0,295.754567502878,0.0
3000.0,0.3,58000.0,0.483212310229862,216.394931124031,273.493821979652,74.6269652298273,1.0,1.02644469688801,354.905481003454,104.56469459047489
3000.0,0.3,56550.0,0.483149838965041,196.705730667205,273.493821979652,74.6269652298273,0.975,1.00078357946581,354.905481003454,95.03834209536082
3000.0,0.3,55100.0,0.488056398822271,177.459517061511,273.493821979652,74.6269652298273,0.95,0.975122462043613,354.905481003454,86.61025283378042
3000.0,0.3,53650.0,0.503668113657407,157.246369449317,273.493821979652,74.6269652298273,0.925,0.949461344621413,354.905481003454,79.19998228001322
3000.0,0.3,52200.0,0.533363326042695,135.270507536815,273.493821979652,74.6269652298273,0.9,0.923800227199213,354.905481003454,72.14832781531908
3000.0,0.3,50750.0,0.587742618221576,110.365769713501,273.493821979652,74.6269652298273,0.875,0.898139109777012,354.905481003454,64.8666664534526
3000.0,0.3,49300.0,0.684916991699412,84.0270035167196,273.493821979652,74.6269652298273,0.85,0.872477992354812,354.905481003454,57.5515224701875
3000.0,0.3,47850.0,0.830430214476331,61.849568649524,273.493821979652,74.6269652298273,0.825,0.846816874932611,354.905481003454,51.36175055889278
3000.0,0.3,46400.0,1.00774747838925,46.5501145190164,273.493821979652,74.6269652298273,0.8,0.821155757510411,354.905481003454,46.91076052526959
3000.0,0.3,44950.0,1.20633964836406,36.3788583081415,273.493821979652,74.6269652298273,0.775,0.795494640088211,354.905481003454,43.885259139329385
3000.0,0.3,43500.0,1.43234395031108,29.1971445855973,273.493821979652,74.6269652298273,0.75,0.76983352266601,354.905481003454,41.8203534135382
3000.0,0.3,42050.0,1.72122869963321,23.2197717618068,273.493821979652,74.6269652298273,0.725,0.74417240524381,354.905481003454,39.96653755535465
3000.0,0.3,40600.0,2.17167754759247,17.4911434746984,273.493821979652,74.6269652298273,0.7,0.71851128782161,354.905481003454,37.985123565721054
3000.0,0.3,0.0,0.0,0.0,273.493821979652,74.6269652298273,0.0,0.0,354.905481003454,0.0
3000.0,0.35,58000.0,0.472377875256421,224.117498662516,275.242979916748,76.3085135953898,1.0,1.02317799153118,414.056394504029,105.86814782598309
3000.0,0.35,56550.0,0.472087318262611,203.654671541338,275.242979916748,76.3085135953898,0.975,0.997598541742899,414.056394504029,96.14278773960314
3000.0,0.35,55100.0,0.475974871527027,184.106553281819,275.242979916748,76.3085135953898,0.95,0.97201909195462,414.056394504029,87.63009304559755
3000.0,0.35,53650.0,0.491299920063109,162.913180056173,275.242979916748,76.3085135953898,0.925,0.94643964216634,414.056394504029,80.03923233882468
3000.0,0.35,52200.0,0.519409889904715,140.03975178673,275.242979916748,76.3085135953898,0.9,0.920860192378061,414.056394504029,72.73803205782905
3000.0,0.35,50750.0,0.572378922906064,113.782313899562,275.242979916748,76.3085135953898,0.875,0.895280742589781,414.056394504029,65.12659827559096
3000.0,0.35,49300.0,0.665146477769296,86.5752299323786,275.242979916748,76.3085135953898,0.85,0.869701292801502,414.056394504029,57.58520925158856
3000.0,0.35,47850.0,0.802663251930636,63.7955835720671,275.242979916748,76.3085135953898,0.825,0.844121843013222,414.056394504029,51.206370568768044
3000.0,0.35,46400.0,0.965734318974126,48.3510479578558,275.242979916748,76.3085135953898,0.8,0.818542393224943,414.056394504029,46.69426637126517
3000.0,0.35,44950.0,1.14773031129811,37.9887663411447,275.242979916748,76.3085135953898,0.775,0.792962943436663,414.056394504029,43.60085861855317
3000.0,0.35,43500.0,1.35782527889575,30.5736206223629,275.242979916748,76.3085135953898,0.75,0.767383493648384,414.056394504029,41.51363494841276
3000.0,0.35,42050.0,1.6277387689257,24.3404742354921,275.242979916748,76.3085135953898,0.725,0.741804043860104,414.056394504029,39.619933567147626
3000.0,0.35,40600.0,2.0580200828216,18.2525946417168,275.242979916748,76.3085135953898,0.7,0.716224594071825,414.056394504029,37.564206336255104
3000.0,0.35,0.0,0.0,0.0,275.242979916748,76.3085135953898,0.0,0.0,414.056394504029,0.0
4000.0,0.0,58000.0,0.516638281867925,178.956148146694,262.15,61.6402085371994,1.0,1.04841779681353,0.0,92.45559690820986
4000.0,0.0,56550.0,0.517698996617252,164.099830469518,262.15,61.6402085371994,0.975,1.02220735189319,0.0,84.95431757913063
4000.0,0.0,55100.0,0.525209218898279,147.045180231338,262.15,61.6402085371994,0.95,0.995996906972856,0.0,77.22948425205769
4000.0,0.0,53650.0,0.537282838606111,131.501113827668,262.15,61.6402085371994,0.925,0.969786462052518,0.0,70.65329171719479
4000.0,0.0,52200.0,0.566777606055763,114.39213394041,262.15,61.6402085371994,0.9,0.94357601713218,0.0,64.83489982635578
4000.0,0.0,50750.0,0.615639115570513,96.2801220887395,262.15,61.6402085371994,0.875,0.917365572211841,0.0,59.2738092097326
4000.0,0.0,49300.0,0.707528060515687,75.6053666021411,262.15,61.6402085371994,0.85,0.891155127291503,0.0,53.49291839659039
4000.0,0.0,47850.0,0.86127605780735,55.9637237486576,262.15,61.6402085371994,0.825,0.864944682371165,0.0,48.20021537046338
4000.0,0.0,46400.0,1.08140224846311,40.784742447101,262.15,61.6402085371994,0.8,0.838734237450826,0.0,44.10471218528387
4000.0,0.0,44950.0,1.34659370617067,30.7113471726083,262.15,61.6402085371994,0.775,0.812523792530488,0.0,41.35570681065674
4000.0,0.0,43500.0,1.64321324926485,24.0962641608192,262.15,61.6402085371994,0.75,0.78631334761015,0.0,39.59530052684387
4000.0,0.0,42050.0,1.99155360744515,19.2292285894136,262.15,61.6402085371994,0.725,0.760102902689811,0.0,38.296039565634075
4000.0,0.0,40600.0,2.45997578782358,15.1243510158406,262.15,61.6402085371994,0.7,0.733892457769473,0.0,37.20553730551285
4000.0,0.0,0.0,0.0,0.0,262.15,61.6402085371994,0.0,0.0,0.0,0.0
4000.0,0.05,58000.0,0.515718549742496,179.461260957381,262.281303684007,61.7481857507349,1.0,1.04815533366707,58.4328508421484,92.55150123590015
4000.0,0.05,56550.0,0.516669155633388,164.564005923171,262.281303684007,61.7481857507349,0.975,1.02195145032539,58.4328508421484,85.02514598797262
4000.0,0.05,55100.0,0.524093387193184,147.458148552501,262.281303684007,61.7481857507349,0.95,0.995747566983717,58.4328508421484,77.28184054411595
4000.0,0.05,53650.0,0.536108567854045,131.895209701211,262.281303684007,61.7481857507349,0.925,0.96954368364204,58.4328508421484,70.71015197972518
4000.0,0.05,52200.0,0.565545873010277,114.716405982292,262.281303684007,61.7481857507349,0.9,0.943339800300364,58.4328508421484,64.8773899698567
4000.0,0.05,50750.0,0.614212120107261,96.5530506540875,262.281303684007,61.7481857507349,0.875,0.917135916958687,58.4328508421484,59.30405394507085
4000.0,0.05,49300.0,0.705841729967435,75.8030453697481,262.281303684007,61.7481857507349,0.85,0.89093203361701,58.4328508421484,53.50495268058297
4000.0,0.05,47850.0,0.859104489425206,56.0955551086461,262.281303684007,61.7481857507349,0.825,0.864728150275333,58.4328508421484,48.191943230636916
4000.0,0.05,46400.0,1.07814530639424,40.8877165887748,262.281303684007,61.7481857507349,0.8,0.838524266933656,58.4328508421484,44.08289972936546
4000.0,0.05,44950.0,1.3413713649141,30.8152345613731,262.281303684007,61.7481857507349,0.775,0.81232038359198,58.4328508421484,41.33467324373718
4000.0,0.05,43500.0,1.6363091413416,24.1796892362239,262.281303684007,61.7481857507349,0.75,0.786116500250303,58.4328508421484,39.56544653203226
4000.0,0.05,42050.0,1.98263996649043,19.3003093545278,262.281303684007,61.7481857507349,0.725,0.759912616908626,58.4328508421484,38.265564691915934
4000.0,0.05,40600.0,2.44952867467539,15.1753102954739,262.281303684007,61.7481857507349,0.7,0.733708733566949,58.4328508421484,37.17235771585998
4000.0,0.05,0.0,0.0,0.0,262.281303684007,61.7481857507349,0.0,0.0,58.4328508421484,0.0
4000.0,0.1,58000.0,0.512933719785062,180.987461006568,262.675214736028,62.0729275737883,1.0,1.04736912535132,116.865701684297,92.83457160855278
4000.0,0.1,56550.0,0.513583714575495,165.986526452651,262.675214736028,62.0729275737883,0.975,1.02118489721753,116.865701684297,85.24797682503616
4000.0,0.1,55100.0,0.520828743115578,148.736826411575,262.675214736028,62.0729275737883,0.95,0.995000669083752,116.865701684297,77.46641435494053
4000.0,0.1,53650.0,0.532572195102974,133.0967600745,262.675214736028,62.0729275737883,0.925,0.968816440949969,116.865701684297,70.88363367397034
4000.0,0.1,52200.0,0.561835821096151,115.69705645734,262.675214736028,62.0729275737883,0.9,0.942632212816186,116.865701684297,65.00275071311736
4000.0,0.1,50750.0,0.610020844493543,97.3398208671766,262.675214736028,62.0729275737883,0.875,0.916447984682403,116.865701684297,59.379319728245264
4000.0,0.1,49300.0,0.700814883149515,76.3816585593923,262.675214736028,62.0729275737883,0.85,0.89026375654862,116.865701684297,53.529403118066675
4000.0,0.1,47850.0,0.852606428977874,56.4915958370926,262.675214736028,62.0729275737883,0.825,0.864079528414837,116.865701684297,48.16509779392486
4000.0,0.1,46400.0,1.06829204463706,41.2124099743184,262.675214736028,62.0729275737883,0.8,0.837895300281054,116.865701684297,44.02688971588537
4000.0,0.1,44950.0,1.32630913954704,31.1100782357251,262.675214736028,62.0729275737883,0.775,0.811711072147271,116.865701684297,41.26158109606566
4000.0,0.1,43500.0,1.6157012281043,24.4363880379284,262.675214736028,62.0729275737883,0.75,0.785526844013488,116.865701684297,39.48190216331414
4000.0,0.1,42050.0,1.95625449809703,19.514952149108,262.675214736028,62.0729275737883,0.725,0.759342615879705,116.865701684297,38.17621292184083
4000.0,0.1,40600.0,2.4619611768912,14.8444101225498,262.675214736028,62.0729275737883,0.7,0.733158387745922,116.865701684297,36.54636141556835
4000.0,0.1,0.0,0.0,0.0,262.675214736028,62.0729275737883,0.0,0.0,116.865701684297,0.0
4000.0,0.15,58000.0,0.508376710865291,183.535093910855,263.331733156064,62.6168706187689,1.0,1.046062700507,175.298552526445,93.30496737075278
4000.0,0.15,56550.0,0.508557778227161,168.353315696433,263.331733156064,62.6168706187689,0.975,1.01991113299433,175.298552526445,85.61738818775379
4000.0,0.15,55100.0,0.515479501869442,150.886396013128,263.331733156064,62.6168706187689,0.95,0.993759565481654,175.298552526445,77.77884425572259
4000.0,0.15,53650.0,0.526819807149964,135.101380841619,263.331733156064,62.6168706187689,0.925,0.967607997968979,175.298552526445,71.17408340067557
4000.0,0.15,52200.0,0.555758111444936,117.342763698527,263.331733156064,62.6168706187689,0.9,0.941456430456304,175.298552526445,65.21419274482275
4000.0,0.15,50750.0,0.603163986723064,98.6553333328164,263.331733156064,62.6168706187689,0.875,0.915304862943629,175.298552526445,59.50534416451433
4000.0,0.15,49300.0,0.69259042623058,77.3477011375684,263.331733156064,62.6168706187689,0.85,0.889153295430954,175.298552526445,53.57027729882401
4000.0,0.15,47850.0,0.841914795667452,57.15716419971,263.331733156064,62.6168706187689,0.825,0.863001727918279,175.298552526445,48.12146221812985
4000.0,0.15,46400.0,1.05206836997687,41.7603409950361,263.331733156064,62.6168706187689,0.8,0.836850160405603,175.298552526445,43.93473388032589
4000.0,0.15,44950.0,1.30150643971779,31.6106594905969,263.331733156064,62.6168706187689,0.775,0.810698592892928,175.298552526445,41.14147689073814
4000.0,0.15,43500.0,1.58198593134005,24.8703737906756,263.331733156064,62.6168706187689,0.75,0.784547025380253,175.298552526445,39.34458144401711
4000.0,0.15,42050.0,1.91314195786026,19.8776161794593,263.331733156064,62.6168706187689,0.725,0.758395457867578,175.298552526445,38.028701535165546
4000.0,0.15,40600.0,2.40762299842655,15.1147622768858,263.331733156064,62.6168706187689,0.7,0.732243890354903,175.298552526445,36.390649273580294
4000.0,0.15,0.0,0.0,0.0,263.331733156064,62.6168706187689,0.0,0.0,175.298552526445,0.0
4000.0,0.2,58000.0,0.502168900435508,187.114078592016,264.250858944113,63.3840961442688,1.0,1.04424189146032,233.731403368594,93.9628711025559
4000.0,0.2,56550.0,0.501751601103474,171.650832615857,264.250858944113,63.3840961442688,0.975,1.01813584417381,233.731403368594,86.12608009575068
4000.0,0.2,55100.0,0.508189938401694,153.908131483308,264.250858944113,63.3840961442688,0.95,0.9920297968873,233.731403368594,78.21456385802212
4000.0,0.2,53650.0,0.519052011049344,137.912781865048,264.250858944113,63.3840961442688,0.925,0.965923749600792,233.731403368594,71.58390677646265
4000.0,0.2,52200.0,0.547479239939111,119.66833950462,264.250858944113,63.3840961442688,0.9,0.939817702314284,233.731403368594,65.51593155676484
4000.0,0.2,50750.0,0.593837641984712,100.502751764912,264.250858944113,63.3840961442688,0.875,0.913711655027776,233.731403368594,59.6823171210502
4000.0,0.2,49300.0,0.681342529901859,78.7155195007167,264.250858944113,63.3840961442688,0.85,0.887605607741268,233.731403368594,53.632231199157424
4000.0,0.2,47850.0,0.827192207199755,58.1013211484694,264.250858944113,63.3840961442688,0.825,0.86149956045476,233.731403368594,48.06096008202421
4000.0,0.2,46400.0,1.02979139836151,42.5414906100068,264.250858944113,63.3840961442688,0.8,0.835393513168252,233.731403368594,43.80886110366194
4000.0,0.2,44950.0,1.26765700172921,32.3255071750987,264.250858944113,63.3840961442688,0.775,0.809287465881744,233.731403368594,40.97765550496168
4000.0,0.2,43500.0,1.53603185614274,25.4910973063103,264.250858944113,63.3840961442688,0.75,0.783181418595236,233.731403368594,39.15513751052701
4000.0,0.2,42050.0,1.86206441271866,20.2138617108206,264.250858944113,63.3840961442688,0.725,0.757075371308729,233.731403368594,37.63951253533537
4000.0,0.2,40600.0,2.33409278241193,15.4982133670753,264.250858944113,63.3840961442688,0.7,0.730969324022221,233.731403368594,36.17426796037055
4000.0,0.2,0.0,0.0,0.0,264.250858944113,63.3840961442688,0.0,0.0,233.731403368594,0.0
4000.0,0.25,58000.0,0.494475248953783,191.734501623019,265.432592100177,64.3803604853571,1.0,1.04191476235198,292.164254210742,94.80796542307183
4000.0,0.25,56550.0,0.493365083126142,175.872416132395,265.432592100177,64.3803604853571,0.975,1.01586689329318,292.164254210742,86.76930920475449
4000.0,0.25,55100.0,0.499125808819755,157.858373934168,265.432592100177,64.3803604853571,0.95,0.989819024234382,292.164254210742,78.79118856886292
4000.0,0.25,53650.0,0.509523423044994,141.535532357995,265.432592100177,64.3803604853571,0.925,0.963771155175582,292.164254210742,72.11566892954113
4000.0,0.25,52200.0,0.537167924883246,122.702242161566,265.432592100177,64.3803604853571,0.9,0.937723286116782,292.164254210742,65.91170880044996
4000.0,0.25,50750.0,0.582274081387425,102.892641336406,265.432592100177,64.3803604853571,0.875,0.911675417057983,292.164254210742,59.911718215681596
4000.0,0.25,49300.0,0.667353118463615,80.476115534608,265.432592100177,64.3803604853571,0.85,0.885627547999183,292.164254210742,53.705986663858816
4000.0,0.25,47850.0,0.808694238182032,59.3365047668264,265.432592100177,64.3803604853571,0.825,0.859579678940384,292.164254210742,47.98508951879319
4000.0,0.25,46400.0,1.00186962694388,43.5736359433813,265.432592100177,64.3803604853571,0.8,0.833531809881584,292.164254210742,43.65510238718387
4000.0,0.25,44950.0,1.22553591142411,33.2706443864228,265.432592100177,64.3803604853571,0.775,0.807483940822785,292.164254210742,40.77436949178212
4000.0,0.25,43500.0,1.47888500326836,26.3330207379641,265.432592100177,64.3803604853571,0.75,0.781436071763985,292.164254210742,38.94350946012983
4000.0,0.25,42050.0,1.79121279155016,20.8770024138194,265.432592100177,64.3803604853571,0.725,0.755388202705186,292.164254210742,37.39515377285688
4000.0,0.25,40600.0,2.24542999254096,15.9862819365947,265.432592100177,64.3803604853571,0.7,0.729340333646386,292.164254210742,35.896076929645524
4000.0,0.25,0.0,0.0,0.0,265.432592100177,64.3803604853571,0.0,0.0,292.164254210742,0.0
4000.0,0.3,58000.0,0.485532677801374,197.394491551304,266.876932624255,65.6131378025166,1.0,1.03909151095035,350.597105052891,95.84147606614532
4000.0,0.3,56550.0,0.48360563114096,181.054193388126,266.876932624255,65.6131378025166,0.975,1.01311422317659,350.597105052891,87.5588274641821
4000.0,0.3,55100.0,0.488481730885005,162.804575019183,266.876932624255,65.6131378025166,0.95,0.987136935402833,350.597105052891,79.52706060136816
4000.0,0.3,53650.0,0.498527594105241,145.971240863982,266.876932624255,65.6131378025166,0.925,0.961159647629074,350.597105052891,72.77069151647758
4000.0,0.3,52200.0,0.524997145147183,126.483473437772,266.876932624255,65.6131378025166,0.9,0.935182359855315,350.597105052891,66.40346246312986
4000.0,0.3,50750.0,0.568786919643188,105.83109792579,266.876932624255,65.6131378025166,0.875,0.909205072081557,350.597105052891,60.19534419166668
4000.0,0.3,49300.0,0.651058929903836,82.6256711002017,266.876932624255,65.6131378025166,0.85,0.883227784307798,350.597105052891,53.79418100908362
4000.0,0.3,47850.0,0.786726708471003,60.8800948269685,266.876932624255,65.6131378025166,0.825,0.857250496534039,350.597105052891,47.89599661462347
4000.0,0.3,46400.0,0.968749806762913,44.8820304414574,266.876932624255,65.6131378025166,0.8,0.83127320876028,350.597105052891,43.47945831728904
4000.0,0.3,44950.0,1.17618719502159,34.467582189139,266.876932624255,65.6131378025166,0.775,0.805295920986521,350.597105052891,40.54032881421952
4000.0,0.3,43500.0,1.41427442105397,27.342512298392,266.876932624255,65.6131378025166,0.75,0.779318633212763,350.597105052891,38.6698157509694
4000.0,0.3,42050.0,1.7082615092622,21.7155865051318,266.876932624255,65.6131378025166,0.725,0.753341345439004,350.597105052891,37.09590057777031
4000.0,0.3,40600.0,2.14154137657687,16.6055717053225,266.876932624255,65.6131378025166,0.7,0.727364057665245,350.597105052891,35.56151888866227
4000.0,0.3,0.0,0.0,0.0,266.876932624255,65.6131378025166,0.0,0.0,350.597105052891,0.0
4000.0,0.35,58000.0,0.475456837401016,204.141883290552,268.583880516348,67.0916752949494,1.0,1.03578434646499,409.029955895039,97.06065421041316
4000.0,0.35,56550.0,0.47277658919466,187.206355165262,268.583880516348,67.0916752949494,0.975,1.00988973780337,409.029955895039,88.50678207059669
4000.0,0.35,55100.0,0.476535241522749,168.796656640343,268.583880516348,67.0916752949494,0.95,0.983995129141743,409.029955895039,80.4375555403384
4000.0,0.35,53650.0,0.486357558404709,151.237415439444,268.583880516348,67.0916752949494,0.925,0.958100520480118,409.029955895039,73.55546011256662
4000.0,0.35,52200.0,0.511244289102639,131.108869816639,268.583880516348,67.0916752949494,0.9,0.932205911818494,409.029955895039,67.02866094445804
4000.0,0.35,50750.0,0.553861843741355,109.264491838343,268.583880516348,67.0916752949494,0.875,0.906311303156869,409.029955895039,60.517432905046896
4000.0,0.35,49300.0,0.63294534978744,85.1316129863787,268.583880516348,67.0916752949494,0.85,0.880416694495244,409.029955895039,53.88365855963244
4000.0,0.35,47850.0,0.761655798536187,62.752862602764,268.583880516348,67.0916752949494,0.825,0.854522085833619,409.029955895039,47.79608167613984
4000.0,0.35,46400.0,0.930928272769655,46.509216683074,268.583880516348,67.0916752949494,0.8,0.828627477171994,409.029955895039,43.2967447546437
4000.0,0.35,44950.0,1.12092406496459,35.9535859074632,268.583880516348,67.0916752949494,0.775,0.802732868510369,409.029955895039,40.30123966544725
4000.0,0.35,43500.0,1.34214772117032,28.5745316319166,268.583880516348,67.0916752949494,0.75,0.776838259848744,409.029955895039,38.351242513286095
4000.0,0.35,42050.0,1.61627594977978,22.7349539579851,268.583880516348,67.0916752949494,0.725,0.75094365118712,409.029955895039,36.74595930164193
4000.0,0.35,40600.0,2.02665080752162,17.3504035569554,268.583880516348,67.0916752949494,0.7,0.725049042525495,409.029955895039,35.16320937952965
4000.0,0.35,0.0,0.0,0.0,268.583880516348,67.0916752949494,0.0,0.0,409.029955895039,0.0
5000.0,0.0,58000.0,0.518246696506667,162.250085387309,255.65,54.019881314929,1.0,1.06166235198407,0.0,84.08557075989754
5000.0,0.0,56550.0,0.520357994792976,149.523156804776,255.65,54.019881314929,0.975,1.03512079318447,0.0,77.80557005004896
5000.0,0.0,55100.0,0.524609481589099,135.569262000759,255.65,54.019881314929,0.95,1.00857923438487,0.0,71.12092025763492
5000.0,0.0,53650.0,0.5368267262803,120.847121314339,255.65,54.019881314929,0.925,0.982037675585263,0.0,64.87396451557488
5000.0,0.0,52200.0,0.559100659317848,106.631628146617,255.65,54.019881314929,0.9,0.955496116785662,0.0,59.617813600909166
5000.0,0.0,50750.0,0.60284323239843,90.714670469783,255.65,54.019881314929,0.875,0.92895455798606,0.0,54.68672517196239
5000.0,0.0,49300.0,0.678084139353396,73.4642188139315,255.65,54.019881314929,0.85,0.902412999186458,0.0,49.814921587714295
5000.0,0.0,47850.0,0.815852899049915,55.1401506458041,255.65,54.019881314929,0.825,0.875871440386856,0.0,44.98625175842832
5000.0,0.0,46400.0,1.02765242307153,40.0283700761439,255.65,54.019881314929,0.8,0.849329881587255,0.0,41.135251500353206
5000.0,0.0,44950.0,1.30750823132573,29.4222992632827,255.65,54.019881314929,0.775,0.822788322787653,0.0,38.469898471271094
5000.0,0.0,43500.0,1.62754845803272,22.6782115758849,255.65,54.019881314929,0.75,0.796246763988051,0.0,36.90988828127125
5000.0,0.0,42050.0,2.00920504969854,17.7874094625612,255.65,54.019881314929,0.725,0.76970520518845,0.0,35.73855291323356
5000.0,0.0,40600.0,2.53137262768456,13.7163011931135,255.65,54.019881314929,0.7,0.743163646388848,0.0,34.72106939332458
5000.0,0.0,0.0,0.0,0.0,255.65,54.019881314929,0.0,0.0,0.0,0.0
5000.0,0.05,58000.0,0.516752133377032,163.140244444615,255.778056334361,54.1145158994384,1.0,1.06139655590359,57.7057583324503,84.30306935640529
5000.0,0.05,56550.0,0.519348214207269,149.959118413857,255.778056334361,54.1145158994384,0.975,1.034861642006,57.7057583324503,77.88100035233302
5000.0,0.05,55100.0,0.52354921624391,135.9323215579,255.778056334361,54.1145158994384,0.95,1.00832672810841,57.7057583324503,71.1672604138537
5000.0,0.05,53650.0,0.535656256196874,121.20968523407,255.778056334361,54.1145158994384,0.925,0.981791814210823,57.7057583324503,64.92672620728345
5000.0,0.05,52200.0,0.557879248119083,106.935379836636,255.778056334361,54.1145158994384,0.9,0.955256900313234,57.7057583324503,59.657029300591034
5000.0,0.05,50750.0,0.601373549085895,90.988630071994,255.778056334361,54.1145158994384,0.875,0.928721986415644,57.7057583324503,54.71815539285863
5000.0,0.05,49300.0,0.676409044927178,73.6675888993647,255.778056334361,54.1145158994384,0.85,0.902187072518054,57.7057583324503,49.82942344950726
5000.0,0.05,47850.0,0.813876312979595,55.2611344518302,255.778056334361,54.1145158994384,0.825,0.875652158620464,57.7057583324503,44.975728358725235
5000.0,0.05,46400.0,1.02479297251044,40.124902133305,255.778056334361,54.1145158994384,0.8,0.849117244722874,57.7057583324503,41.11971772888013
5000.0,0.05,44950.0,1.30272446539547,29.5149982537354,255.778056334361,54.1145158994384,0.775,0.822582330825284,57.7057583324503,38.449910321245675
5000.0,0.05,43500.0,1.62097077598601,22.7534387908991,255.778056334361,54.1145158994384,0.75,0.796047416927694,57.7057583324503,36.882659333233896
5000.0,0.05,42050.0,2.00014863712078,17.8538244868514,255.778056334361,54.1145158994384,0.725,0.769512503030105,57.7057583324503,35.71030271476944
5000.0,0.05,40600.0,2.51826202008928,13.7757528719333,255.778056334361,54.1145158994384,0.7,0.742977589132515,57.7057583324503,34.69095525552545
5000.0,0.05,0.0,0.0,0.0,255.778056334361,54.1145158994384,0.0,0.0,57.7057583324503,0.0
5000.0,0.1,58000.0,0.514061195005615,164.53000982987,256.162225337444,54.3991297683344,1.0,1.0606003638621,115.411516664901,84.57849346742856
5000.0,0.1,56550.0,0.516403351526352,151.233341263994,256.162225337444,54.3991297683344,0.975,1.03408535476554,115.411516664901,78.09740429125505
5000.0,0.1,55100.0,0.52037921011584,137.069207099578,256.162225337444,54.3991297683344,0.95,1.00757034566899,115.411516664901,71.32796572168289
5000.0,0.1,53650.0,0.532100729294331,122.302942562597,256.162225337444,54.3991297683344,0.925,0.981055336572438,115.411516664901,65.07748493240054
5000.0,0.1,52200.0,0.554240267534791,107.855611882432,256.162225337444,54.3991297683344,0.9,0.954540327475886,115.411516664901,59.7779231848477
5000.0,0.1,50750.0,0.597131775319073,91.7763464848068,256.162225337444,54.3991297683344,0.875,0.928025318379333,115.411516664901,54.802572708771045
5000.0,0.1,49300.0,0.671621908208454,74.2457866475003,256.162225337444,54.3991297683344,0.85,0.901510309282781,115.411516664901,49.865096904631905
5000.0,0.1,47850.0,0.80776230960516,55.6637293383272,256.162225337444,54.3991297683344,0.825,0.874995300186229,115.411516664901,44.96306257156369
5000.0,0.1,46400.0,1.016187976604,40.4183748079322,256.162225337444,54.3991297683344,0.8,0.848480291089676,115.411516664901,41.07266651369471
5000.0,0.1,44950.0,1.28869709034324,29.7833759864807,256.162225337444,54.3991297683344,0.775,0.821965281993124,115.411516664901,38.381749974376405
5000.0,0.1,43500.0,1.60067732113231,22.9914708974933,256.162225337444,54.3991297683344,0.75,0.795450272896571,115.411516664901,36.801926045091044
5000.0,0.1,42050.0,1.97378083969854,18.0491697188451,256.162225337444,54.3991297683344,0.725,0.768935263800019,115.411516664901,35.62510536352354
5000.0,0.1,40600.0,2.48476694708568,13.9231822780183,256.162225337444,54.3991297683344,0.7,0.742420254703466,115.411516664901,34.59586312266898
5000.0,0.1,0.0,0.0,0.0,256.162225337444,54.3991297683344,0.0,0.0,115.411516664901,0.0
5000.0,0.15,58000.0,0.509665687846681,166.847026869279,256.80250700925,54.8758585840331,1.0,1.05927734954021,173.117274997351,85.03620471450475
5000.0,0.15,56550.0,0.511582403647968,153.361047009971,256.80250700925,54.8758585840331,0.975,1.0327954158017,173.117274997351,78.45681305532999
5000.0,0.15,55100.0,0.515201040467881,138.97416770803,256.80250700925,54.8758585840331,0.95,1.0063134820632,173.117274997351,71.59963580133484
5000.0,0.15,53650.0,0.526284008552761,124.139105285384,256.80250700925,54.8758585840331,0.925,0.979831548324692,173.117274997351,65.33242594774514
5000.0,0.15,52200.0,0.548288180075291,109.396571893106,256.80250700925,54.8758585840331,0.9,0.953349614586187,173.117274997351,59.98084730974682
5000.0,0.15,50750.0,0.590209970057099,93.0925277401187,256.80250700925,54.8758585840331,0.875,0.926867680847682,173.117274997351,54.94413801003511
5000.0,0.15,49300.0,0.663809295181086,75.2082030440106,256.80250700925,54.8758585840331,0.85,0.900385747109176,173.117274997351,49.92390425448068
5000.0,0.15,47850.0,0.797710521235381,56.3395856202963,256.80250700925,54.8758585840331,0.825,0.873903813370671,173.117274997351,44.942680211351934
5000.0,0.15,46400.0,1.00197401193305,40.9148193761549,256.80250700925,54.8758585840331,0.8,0.847421879632166,173.117274997351,40.99558571784202
5000.0,0.15,44950.0,1.26567357092106,30.2369737876224,256.80250700925,54.8758585840331,0.775,0.820939945893661,173.117274997351,38.27013858762653
5000.0,0.15,43500.0,1.56731641897143,23.4118194340459,256.80250700925,54.8758585840331,0.75,0.794458012155155,173.117274997351,36.69372899697455
5000.0,0.15,42050.0,1.9298587367802,18.3867953502695,256.80250700925,54.8758585840331,0.725,0.76797607841665,173.117274997351,35.48391764810716
5000.0,0.15,40600.0,2.42799850768839,14.1835499643512,256.80250700925,54.8758585840331,0.7,0.741494144678145,173.117274997351,34.437638147168435
5000.0,0.15,0.0,0.0,0.0,256.80250700925,54.8758585840331,0.0,0.0,173.117274997351,0.0
5000.0,0.2,58000.0,0.503682619646487,170.103179946096,257.698901349778,55.5482795233554,1.0,1.05743341970528,230.823033329801,85.6780152854474
5000.0,0.2,56550.0,0.505010022911426,156.34762318493,257.698901349778,55.5482795233554,0.975,1.03099758421265,230.823033329801,78.95711676676851
5000.0,0.2,55100.0,0.508167038876388,141.670991185465,257.698901349778,55.5482795233554,0.95,1.00456174872002,230.823033329801,71.99252808540062
5000.0,0.2,53650.0,0.518368571739874,126.736775213638,257.698901349778,55.5482795233554,0.925,0.978125913227389,230.823033329801,65.696361154411
5000.0,0.2,52200.0,0.540193858126767,111.573229634416,257.698901349778,55.5482795233554,0.9,0.951690077734756,230.823033329801,60.27117337987891
5000.0,0.2,50750.0,0.58082043821956,94.9414649814655,257.698901349778,55.5482795233554,0.875,0.925254242242124,230.823033329801,55.1439432957418
5000.0,0.2,49300.0,0.653209833004123,76.5545862467353,257.698901349778,55.5482795233554,0.85,0.898818406749492,230.823033329801,50.0062084979297
5000.0,0.2,47850.0,0.783910839193015,57.2999926042574,257.698901349778,55.5482795233554,0.825,0.87238257125686,230.823033329801,44.918085288156966
5000.0,0.2,46400.0,0.982401190970184,41.6218796168076,257.698901349778,55.5482795233554,0.8,0.845946735764228,230.823033329801,40.889384105969405
5000.0,0.2,44950.0,1.23436798291207,30.8845483361624,257.698901349778,55.5482795233554,0.775,0.819510900271596,230.823033329801,38.12289763285911
5000.0,0.2,43500.0,1.5231168603958,23.9732411175547,257.698901349778,55.5482795233554,0.75,0.793075064778964,230.823033329801,36.51404774448142
5000.0,0.2,42050.0,1.87093761994391,18.8588917074703,257.698901349778,55.5482795233554,0.725,0.766639229286331,230.823033329801,35.283809965954426
5000.0,0.2,40600.0,2.35143380383289,14.5519079497807,257.698901349778,55.5482795233554,0.7,0.740203393793699,230.823033329801,34.217848263378905
5000.0,0.2,0.0,0.0,0.0,257.698901349778,55.5482795233554,0.0,0.0,230.823033329801,0.0
5000.0,0.25,58000.0,0.496269569012698,174.310139200679,258.851408359027,56.4214379510871,1.0,1.05507674141852,288.528791662252,86.50481765566435
5000.0,0.25,56550.0,0.496464296105077,160.450868675724,258.851408359027,56.4214379510871,0.975,1.02869982288306,288.528791662252,79.65812757654146
5000.0,0.25,55100.0,0.499446311519522,145.189820087766,258.851408359027,56.4214379510871,0.95,1.00232290434759,288.528791662252,72.51452011301774
5000.0,0.25,53650.0,0.508581518588864,130.117500739234,258.851408359027,56.4214379510871,0.925,0.97594598581213,288.528791662252,66.17535612094727
5000.0,0.25,52200.0,0.53013046932598,114.426528276526,258.851408359027,56.4214379510871,0.9,0.949569067276667,288.528791662252,60.66098913857724
5000.0,0.25,50750.0,0.569241427319018,97.3287721396932,258.851408359027,56.4214379510871,0.875,0.923192148741204,288.528791662252,55.40356917200643
5000.0,0.25,49300.0,0.640269463105119,78.2384621958888,258.851408359027,56.4214379510871,0.85,0.896815230205741,288.528791662252,50.09369818433187
5000.0,0.25,47850.0,0.766549266745674,58.566909883541,258.851408359027,56.4214379510871,0.825,0.870438311670278,288.528791662252,44.894421826788324
5000.0,0.25,46400.0,0.957751001746161,42.5542026670322,258.851408359027,56.4214379510871,0.8,0.844061393134815,288.528791662252,40.75633023285925
5000.0,0.25,44950.0,1.19568715192549,31.7336567136248,258.851408359027,56.4214379510871,0.775,0.817684474599352,288.528791662252,37.943525616095236
5000.0,0.25,43500.0,1.46878284327374,24.7023528818067,258.851408359027,56.4214379510871,0.75,0.791307556063889,288.528791662252,36.28239210129131
5000.0,0.25,42050.0,1.79847970184233,19.476505362586,258.851408359027,56.4214379510871,0.725,0.764930637528426,288.528791662252,35.02809955743421
5000.0,0.25,40600.0,2.25744822074899,15.0341447328158,258.851408359027,56.4214379510871,0.7,0.738553718992963,288.528791662252,33.938803277577826
5000.0,0.25,0.0,0.0,0.0,258.851408359027,56.4214379510871,0.0,0.0,288.528791662252,0.0
5000.0,0.3,58000.0,0.487662021753793,179.468275106696,260.260028036999,57.5018848914076,1.0,1.0522176425886,346.234549994702,87.5198618791973
5000.0,0.3,56550.0,0.486415677723434,165.5637012311,260.260028036999,57.5018848914076,0.975,1.02591220152388,346.234549994702,80.53277994072566
5000.0,0.3,55100.0,0.489294715862642,149.53961475835,260.260028036999,57.5018848914076,0.95,0.999606760459169,346.234549994702,73.16894331339581
5000.0,0.3,53650.0,0.497210226659682,134.310029846869,260.260028036999,57.5018848914076,0.925,0.973301319394454,346.234549994702,66.7803203828304
5000.0,0.3,52200.0,0.518382091033499,117.966171682858,260.260028036999,57.5018848914076,0.9,0.946995878329739,346.234549994702,61.15155074817667
5000.0,0.3,50750.0,0.555831972579993,100.251942339675,260.260028036999,57.5018848914076,0.875,0.920690437265024,346.234549994702,55.72323486563727
5000.0,0.3,49300.0,0.625154691193909,80.306424242145,260.260028036999,57.5018848914076,0.85,0.894384996200309,346.234549994702,50.2039378479852
5000.0,0.3,47850.0,0.746160611162508,60.1269160590895,260.260028036999,57.5018848914076,0.825,0.868079555135594,346.234549994702,44.86433643396704
5000.0,0.3,46400.0,0.928393195741348,43.7297595932427,260.260028036999,57.5018848914076,0.8,0.841774114070879,346.234549994702,40.59841125777146
5000.0,0.3,44950.0,1.14915440809038,32.861443769403,260.260028036999,57.5018848914076,0.775,0.815468673006164,346.234549994702,37.76287296382361
5000.0,0.3,43500.0,1.40495722142123,25.626168079065,260.260028036999,57.5018848914076,0.75,0.789163231941449,346.234549994702,36.003669900036584
5000.0,0.3,42050.0,1.71394150336082,20.2586663607693,260.260028036999,57.5018848914076,0.725,0.762857790876734,346.234549994702,34.72216907846221
5000.0,0.3,40600.0,2.14906839885651,15.6355707659906,260.260028036999,57.5018848914076,0.7,0.736552349812019,346.234549994702,33.60191103127507
5000.0,0.3,0.0,0.0,0.0,260.260028036999,57.5018848914076,0.0,0.0,346.234549994702,0.0
5000.0,0.35,58000.0,0.477992679080351,185.614795451357,261.924760383694,58.7977254249342,1.0,1.04886848821832,403.940308327152,88.72251335474547
5000.0,0.35,56550.0,0.475370462771992,171.484393631987,261.924760383694,58.7977254249342,0.975,1.02264677601286,403.940308327152,81.51861555901209
5000.0,0.35,55100.0,0.477907872516205,154.761344679709,261.924760383694,58.7977254249342,0.95,0.996425063807402,403.940308327152,73.96166498362683
5000.0,0.35,53650.0,0.484659200087829,139.314594418563,261.924760383694,58.7977254249342,0.925,0.970203351601944,403.940308327152,67.52009989146106
5000.0,0.35,52200.0,0.505291171575943,122.181028947528,261.924760383694,58.7977254249342,0.9,0.943981639396486,403.940308327152,61.73699526125064
5000.0,0.35,50750.0,0.541001583571571,103.693233504967,261.924760383694,58.7977254249342,0.875,0.917759927191029,403.940308327152,56.09820353184383
5000.0,0.35,49300.0,0.608169538484932,82.7604256728757,261.924760383694,58.7977254249342,0.85,0.891538214985571,403.940308327152,50.332369886289335
5000.0,0.35,47850.0,0.723175117926988,61.989703384996,261.924760383694,58.7977254249342,0.825,0.865316502780113,403.940308327152,44.82941105570349
5000.0,0.35,46400.0,0.894678133453339,45.1829233542759,261.924760383694,58.7977254249342,0.8,0.839094790574655,403.940308327152,40.424173530568844
5000.0,0.35,44950.0,1.09794357801275,34.181649826939,261.924760383694,58.7977254249342,0.775,0.812873078369197,403.940308327152,37.529522913368304
5000.0,0.35,43500.0,1.33244623474888,26.7778106171551,261.924760383694,58.7977254249342,0.75,0.786651366163739,403.940308327152,35.67999293164689
5000.0,0.35,42050.0,1.61977017962671,21.218800450405,261.924760383694,58.7977254249342,0.725,0.760429653958281,403.940308327152,34.36958021701582
5000.0,0.35,40600.0,2.0294520043276,16.3628368561968,261.924760383694,58.7977254249342,0.7,0.734207941752823,403.940308327152,33.207592054294125
5000.0,0.35,0.0,0.0,0.0,261.924760383694,58.7977254249342,0.0,0.0,403.940308327152,0.0
6000.0,0.0,58000.0,0.519421974629505,146.994605171756,249.15,47.1809940960953,1.0,1.07542188384738,0.0,76.35222807819795
6000.0,0.0,56550.0,0.522390341017192,136.109005418484,249.15,47.1809940960953,0.975,1.04853633675119,0.0,71.10202975607268
6000.0,0.0,55100.0,0.526598698087967,124.487524313818,249.15,47.1809940960953,0.95,1.02165078965501,0.0,65.5549682318507
6000.0,0.0,53650.0,0.537728139104616,111.096684704469,249.15,47.1809940960953,0.925,0.994765242558824,0.0,59.739813526826374
6000.0,0.0,52200.0,0.55634468508304,98.5370676395621,249.15,47.1809940960953,0.9,0.967879695462639,0.0,54.82057386493839
6000.0,0.0,50750.0,0.594812343494651,84.8301673978975,249.15,47.1809940960953,0.875,0.940994148366455,0.0,50.45803066898695
6000.0,0.0,49300.0,0.658749085046626,70.33002629933,249.15,47.1809940960953,0.85,0.91410860127027,0.0,46.32984047598878
6000.0,0.0,47850.0,0.777981532592966,54.210008964547,249.15,47.1809940960953,0.825,0.887223054174086,0.0,42.1743858561167
6000.0,0.0,46400.0,0.978919894518656,39.4580930582098,249.15,47.1809940960953,0.8,0.860337507077901,0.0,38.626312294450045
6000.0,0.0,44950.0,1.27203213569009,28.4183197959078,249.15,47.1809940960953,0.775,0.833451959981717,0.0,36.149016022712566
6000.0,0.0,43500.0,1.62309360609651,21.4650660149825,249.15,47.1809940960953,0.75,0.806566412885532,0.0,34.839811403357594
6000.0,0.0,42050.0,2.04901075732315,16.6026403188136,249.15,47.1809940960953,0.725,0.779680865789348,0.0,34.018988613216116
6000.0,0.0,40600.0,2.61365991407012,12.8016103699634,249.15,47.1809940960953,0.7,0.752795318693164,0.0,33.459055859517704
6000.0,0.0,0.0,0.0,0.0,249.15,47.1809940960953,0.0,0.0,0.0,0.0
6000.0,0.05,58000.0,0.51854347292176,147.411856458091,249.274808561608,47.2636533672839,1.0,1.07515262546503,56.9692902316657,76.43945599762247
6000.0,0.05,56550.0,0.521394409909503,136.501385666879,249.274808561608,47.2636533672839,0.975,1.04827380982841,56.9692902316657,71.17105943161187
6000.0,0.05,55100.0,0.52550481611347,124.8544335046,249.274808561608,47.2636533672839,0.95,1.02139499419178,56.9692902316657,65.61160611978629
6000.0,0.05,53650.0,0.536557136616859,111.410600251112,249.274808561608,47.2636533672839,0.925,0.994516178555157,56.9692902316657,59.77815265950217
6000.0,0.05,52200.0,0.555067283055608,98.8345736352427,249.274808561608,47.2636533672839,0.9,0.967637362918531,56.9692902316657,54.8598382596736
6000.0,0.05,50750.0,0.593392260774357,85.0864123936198,249.274808561608,47.2636533672839,0.875,0.940758547281905,56.9692902316657,50.48961861142932
6000.0,0.05,49300.0,0.657195368081725,70.5162165438766,249.274808561608,47.2636533672839,0.85,0.913879731645279,56.9692902316657,46.3429308872836
6000.0,0.05,47850.0,0.776029279771654,54.3441851185059,249.274808561608,47.2636533672839,0.825,0.887000916008653,56.9692902316657,42.17267883729157
6000.0,0.05,46400.0,0.976289906106596,39.5509834458122,249.274808561608,47.2636533672839,0.8,0.860122100372027,56.9692902316657,38.61322591473553
6000.0,0.05,44950.0,1.26773469405731,28.4966659519572,249.274808561608,47.2636533672839,0.775,0.833243284735401,56.9692902316657,36.12621209225782
6000.0,0.05,43500.0,1.61654797284725,21.5353334936601,249.274808561608,47.2636533672839,0.75,0.806364469098775,56.9692902316657,34.81289970376572
6000.0,0.05,42050.0,2.03949511551869,16.6648825708255,249.274808561608,47.2636533672839,0.725,0.77948565346215,56.9692902316657,33.98794660389115
6000.0,0.05,40600.0,2.60080971040092,12.8514909917473,249.274808561608,47.2636533672839,0.7,0.752606837825524,56.9692902316657,33.424282564466324
6000.0,0.05,0.0,0.0,0.0,249.274808561608,47.2636533672839,0.0,0.0,56.9692902316657,0.0
6000.0,0.1,58000.0,0.51593294559035,148.664911279609,249.649234246432,47.5122514767502,1.0,1.07434606217843,113.938580463331,76.70112558241671
6000.0,0.1,56550.0,0.518512989274105,137.666796989054,249.649234246432,47.5122514767502,0.975,1.04748741062397,113.938580463331,71.38202243058576
6000.0,0.1,55100.0,0.522302392234581,125.935010293652,249.649234246432,47.5122514767502,0.95,1.02062875906951,113.938580463331,65.77615714246102
6000.0,0.1,53650.0,0.53316050953024,112.356015049348,249.649234246432,47.5122514767502,0.925,0.993770107515046,113.938580463331,59.90379023249769
6000.0,0.1,52200.0,0.551267581796574,99.7361184988789,249.649234246432,47.5122514767502,0.9,0.966911455960585,113.938580463331,54.981288862653514
6000.0,0.1,50750.0,0.589339234790663,85.8055511150101,249.649234246432,47.5122514767502,0.875,0.940052804406124,113.938580463331,50.568577834911174
6000.0,0.1,49300.0,0.652501701367449,71.0768539446014,249.649234246432,47.5122514767502,0.85,0.913194152851664,113.938580463331,46.37776812669809
6000.0,0.1,47850.0,0.770183251363122,54.7559110875161,249.649234246432,47.5122514767502,0.825,0.886335501297203,113.938580463331,42.17208563273317
6000.0,0.1,46400.0,0.968351353235641,39.8342696780011,249.649234246432,47.5122514767502,0.8,0.859476849742742,113.938580463331,38.57356894784583
6000.0,0.1,44950.0,1.25516810155459,28.7329702282286,249.649234246432,47.5122514767502,0.775,0.832618198188282,113.938580463331,36.06470769339024
6000.0,0.1,43500.0,1.59732133956316,21.7421347968869,249.649234246432,47.5122514767502,0.75,0.805759546633821,113.938580463331,34.72917587872618
6000.0,0.1,42050.0,2.01219775433977,16.8441208754777,249.649234246432,47.5122514767502,0.725,0.77890089507936,113.938580463331,33.893702199463874
6000.0,0.1,40600.0,2.56349539615701,12.9977845937861,249.649234246432,47.5122514767502,0.7,0.752042243524899,113.938580463331,33.31976096641118
6000.0,0.1,0.0,0.0,0.0,249.649234246432,47.5122514767502,0.0,0.0,113.938580463331,0.0
6000.0,0.15,58000.0,0.511666632756061,150.758337253728,250.273277054471,47.9286539563753,1.0,1.07300581445337,170.907870694997,77.13801078251763
6000.0,0.15,56550.0,0.513801571809086,139.612977518912,250.273277054471,47.9286539563753,0.975,1.04618066909203,170.907870694997,71.73336729416359
6000.0,0.15,55100.0,0.517087747546741,127.728380060473,250.273277054471,47.9286539563753,0.95,1.0193555237307,170.907870694997,66.04678034326405
6000.0,0.15,53650.0,0.527594177261484,113.945886755664,250.273277054471,47.9286539563753,0.925,0.992530378369365,170.907870694997,60.11718637518479
6000.0,0.15,52200.0,0.54508784018803,101.239779239551,250.273277054471,47.9286539563753,0.9,0.965705233008031,170.907870694997,55.18457260679981
6000.0,0.15,50750.0,0.582704078485421,87.0124366179705,250.273277054471,47.9286539563753,0.875,0.938880087646697,170.907870694997,50.702501696245605
6000.0,0.15,49300.0,0.644917072832505,72.0152639667671,250.273277054471,47.9286539563753,0.85,0.912054942285362,170.907870694997,46.4438732367076
6000.0,0.15,47850.0,0.760616440723587,55.443324431916,250.273277054471,47.9286539563753,0.825,0.885229796924028,170.907870694997,42.17110409128704
6000.0,0.15,46400.0,0.955247121055815,40.3125431622509,250.273277054471,47.9286539563753,0.8,0.858404651562694,170.907870694997,38.50844079817845
6000.0,0.15,44950.0,1.23443206088574,29.1317686919852,250.273277054471,47.9286539563753,0.775,0.83157950620136,170.907870694997,35.96118926369397
6000.0,0.15,43500.0,1.56572815453965,22.0929485185965,250.273277054471,47.9286539563753,0.75,0.804754360840025,170.907870694997,34.59155151236159
6000.0,0.15,42050.0,1.9670599793759,17.1517038027952,250.273277054471,47.9286539563753,0.725,0.777929215478691,170.907870694997,33.73843012858787
6000.0,0.15,40600.0,2.50279438630843,13.2438941140896,250.273277054471,47.9286539563753,0.7,0.751104070117357,170.907870694997,33.14674384160671
6000.0,0.15,0.0,0.0,0.0,250.273277054471,47.9286539563753,0.0,0.0,170.907870694997,0.0
6000.0,0.2,58000.0,0.505864891457361,153.700777797242,251.146936985726,48.5159855226555,1.0,1.07113786638299,227.877160926663,77.7518272773138
6000.0,0.2,56550.0,0.507392989345275,142.345605149098,251.146936985726,48.5159855226555,0.975,1.04435941972341,227.877160926663,72.225162116763
6000.0,0.2,55100.0,0.510034086417718,130.224684851585,251.146936985726,48.5159855226555,0.95,1.01758097306384,227.877160926663,66.4190281673134
6000.0,0.2,53650.0,0.519990518043506,116.203031514792,251.146936985726,48.5159855226555,0.925,0.990802526404261,227.877160926663,60.42447455560255
6000.0,0.2,52200.0,0.536745159875203,103.346528724363,251.146936985726,48.5159855226555,0.9,0.964024079744687,227.877160926663,55.470749082705474
6000.0,0.2,50750.0,0.573647549349413,88.7218749112501,251.146936985726,48.5159855226555,0.875,0.937245633085112,227.877160926663,50.895086116523785
6000.0,0.2,49300.0,0.634591892855428,73.3328971215598,251.146936985726,48.5159855226555,0.85,0.910467186425537,227.877160926663,46.536461992943
6000.0,0.2,47850.0,0.747581910092109,56.4074168636116,251.146936985726,48.5159855226555,0.825,0.883688739765963,227.877160926663,42.1691644422606
6000.0,0.2,46400.0,0.937205126358831,40.9940513762024,251.146936985726,48.5159855226555,0.8,0.856910293106388,227.877160926663,38.41983509999418
6000.0,0.2,44950.0,1.20460278836965,29.7646651348364,251.146936985726,48.5159855226555,0.775,0.830131846446813,227.877160926663,35.85459861631283
6000.0,0.2,43500.0,1.5225040511401,22.5956859684225,251.146936985726,48.5159855226555,0.75,0.803353399787239,227.877160926663,34.402023425212775
6000.0,0.2,42050.0,1.90603560984566,17.5881147407626,251.146936985726,48.5159855226555,0.725,0.776574953127664,227.877160926663,33.52357300594488
6000.0,0.2,40600.0,2.42080085784825,13.5934340955407,251.146936985726,48.5159855226555,0.7,0.749796506468089,227.877160926663,32.90699691958858
6000.0,0.2,0.0,0.0,0.0,251.146936985726,48.5159855226555,0.0,0.0,227.877160926663,0.0
6000.0,0.25,58000.0,0.498686027959403,157.503529780503,252.270214040197,49.2786533779784,1.0,1.0687504919372,284.846451158329,78.54480965582457
6000.0,0.25,56550.0,0.499450656659723,145.875853827004,252.270214040197,49.2786533779784,0.975,1.04203172963877,284.846451158329,72.85779098469492
6000.0,0.25,55100.0,0.501350186123495,133.417788537055,252.270214040197,49.2786533779784,0.95,1.01531296734034,284.846451158329,66.88903311523762
6000.0,0.25,53650.0,0.510527450271479,119.160385753063,252.270214040197,49.2786533779784,0.925,0.988594205041908,284.846451158329,60.83464791187713
6000.0,0.25,52200.0,0.526561585642671,106.045392962282,252.270214040197,49.2786533779784,0.9,0.961875442743478,284.846451158329,55.83943026831935
6000.0,0.25,50750.0,0.56235489453584,90.9591973628607,252.270214040197,49.2786533779784,0.875,0.935156680445049,284.846451158329,51.151349840056184
6000.0,0.25,49300.0,0.621788633876176,75.0338018194786,252.270214040197,49.2786533779784,0.85,0.908437918146618,284.846451158329,46.655165127869324
6000.0,0.25,47850.0,0.731391381819095,57.6554094288205,252.270214040197,49.2786533779784,0.825,0.881719155848188,284.846451158329,42.1686695714907
6000.0,0.25,46400.0,0.914474868477157,41.8936517333171,252.270214040197,49.2786533779784,0.8,0.855000393549759,284.846451158329,38.310691658852974
6000.0,0.25,44950.0,1.16852411481809,30.5328814412811,252.270214040197,49.2786533779784,0.775,0.828281631251329,284.846451158329,35.67840825901869
6000.0,0.25,43500.0,1.46795912647773,23.2706495400573,252.270214040197,49.2786533779784,0.75,0.801562868952899,284.846451158329,34.160362371391905
6000.0,0.25,42050.0,1.83063475960272,18.1640745685555,252.270214040197,49.2786533779784,0.725,0.774844106654469,284.846451158329,33.25178628121348
6000.0,0.25,40600.0,2.32024196895106,14.0514983283886,252.270214040197,49.2786533779784,0.7,0.748125344356039,284.846451158329,32.602876148172896
6000.0,0.25,0.0,0.0,0.0,252.270214040197,49.2786533779784,0.0,0.0,284.846451158329,0.0
6000.0,0.3,58000.0,0.490355368823204,162.17156541481,253.643108217884,50.2223799446227,1.0,1.06585415420823,341.815741389994,79.52169777161552
6000.0,0.3,56550.0,0.490230015520132,150.194515838868,253.643108217884,50.2223799446227,0.975,1.03920780035303,341.815741389994,73.62985983072697
6000.0,0.3,55100.0,0.491270347633714,137.307598486989,253.643108217884,50.2223799446227,0.95,1.01256144649782,341.815741389994,67.45515164145351
6000.0,0.3,53650.0,0.499436382162378,122.840419870291,253.643108217884,50.2223799446227,0.925,0.985915092642614,341.815741389994,61.35097488332563
6000.0,0.3,52200.0,0.514875974769656,109.329710607968,253.643108217884,50.2223799446227,0.9,0.959268738787408,341.815741389994,56.291241320561916
6000.0,0.3,50750.0,0.549133428852494,93.7290481476368,253.643108217884,50.2223799446227,0.875,0.932622384932202,341.815741389994,51.4697535923923
6000.0,0.3,49300.0,0.606806016429206,77.1385360951671,253.643108217884,50.2223799446227,0.85,0.905976031076996,341.815741389994,46.808127801088865
6000.0,0.3,47850.0,0.712495760913284,59.1889680071433,253.643108217884,50.2223799446227,0.825,0.879329677221791,341.815741389994,42.17188879792159
6000.0,0.3,46400.0,0.887450965694075,43.0247513456544,253.643108217884,50.2223799446227,0.8,0.852683323366585,341.815741389994,38.18235713044845
6000.0,0.3,44950.0,1.12633670920937,31.4866362157395,253.643108217884,50.2223799446227,0.775,0.826036969511379,341.815741389994,35.464554219308596
6000.0,0.3,43500.0,1.4034241986148,24.134415868178,253.643108217884,50.2223799446227,0.75,0.799390615656173,341.815741389994,33.87082324883402
6000.0,0.3,42050.0,1.74296021254557,18.8911699676513,253.643108217884,50.2223799446227,0.725,0.772744261800967,341.815741389994,32.926557622052
6000.0,0.3,40600.0,2.20412661882757,14.6262318459538,253.643108217884,50.2223799446227,0.7,0.746097907945762,341.815741389994,32.23806694481028
6000.0,0.3,0.0,0.0,0.0,253.643108217884,50.2223799446227,0.0,0.0,341.815741389994,0.0
6000.0,0.35,58000.0,0.481024129071741,167.733120980767,255.265619518787,51.3542451430885,1.0,1.06246138002961,398.78503162166,80.68367843625842
6000.0,0.35,56550.0,0.479815230117078,155.374522056274,255.265619518787,51.3542451430885,0.975,1.03589984552887,398.78503162166,74.55106205476213
6000.0,0.35,55100.0,0.480073596902173,141.88945996365,255.265619518787,51.3542451430885,0.95,1.00933831102813,398.78503162166,68.11738340725633
6000.0,0.35,53650.0,0.486963945452877,127.295930681106,255.265619518787,51.3542451430885,0.925,0.982776776527389,398.78503162166,61.98852864456732
6000.0,0.35,52200.0,0.501935998523042,113.225016032718,255.265619518787,51.3542451430885,0.9,0.956215242026649,398.78503162166,56.83171148016976
6000.0,0.35,50750.0,0.534003617986606,97.1665008385878,255.265619518787,51.3542451430885,0.875,0.929653707525908,398.78503162166,51.88726299490447
6000.0,0.35,49300.0,0.590090920144534,79.6211174648268,255.265619518787,51.3542451430885,0.85,0.903092173025168,398.78503162166,46.98369846775567
6000.0,0.35,47850.0,0.691344390788991,60.9981721557301,255.265619518787,51.3542451430885,0.825,0.876530638524428,398.78503162166,42.17074416824522
6000.0,0.35,46400.0,0.856586290593151,44.4067433965466,255.265619518787,51.3542451430885,0.8,0.849969104023688,398.78503162166,38.038207603369756
6000.0,0.35,44950.0,1.07852868216672,32.6532715537887,255.265619518787,51.3542451430885,0.775,0.823407569522947,398.78503162166,35.21748993733977
6000.0,0.35,43500.0,1.33152325107151,25.1885397904928,255.265619518787,51.3542451430885,0.75,0.796846035022207,398.78503162166,33.539126391581064
6000.0,0.35,42050.0,1.64510575006291,19.7877084172638,255.265619518787,51.3542451430885,0.725,0.770284500521467,398.78503162166,32.552872897808925
6000.0,0.35,40600.0,2.07601315096264,15.3242670915128,255.265619518787,51.3542451430885,0.7,0.743722966020726,398.78503162166,31.813380010844575
6000.0,0.35,0.0,0.0,0.0,255.265619518787,51.3542451430885,0.0,0.0,398.78503162166,0.0
1 Altitude_m Mach RPM SFC Power_kW Inlet_T2_K Inlet_P2_kPa RPM_Rel_Phys RPM_Rel_Corr Flight_Vel_kmh WF_kg_h
2 0.0 0.0 58000.0 0.517840950638725 249.582776334593 288.15 101.325005740179 1.0 1.00000000810418 0.0 129.24418216015792
3 0.0 0.0 56550.0 0.523671728560371 225.628264104881 288.15 101.325005740179 0.975 0.975000007901579 0.0 118.15514307587895
4 0.0 0.0 55100.0 0.541853755600857 199.858261719303 288.15 101.325005740179 0.95 0.950000007698975 0.0 108.29394970046332
5 0.0 0.0 53650.0 0.574210664480304 172.570200963337 288.15 101.325005740179 0.925 0.92500000749637 0.0 99.09164976465733
6 0.0 0.0 52200.0 0.634153554768018 141.195238600974 288.15 101.325005740179 0.9 0.900000007293765 0.0 89.53946247512614
7 0.0 0.0 50750.0 0.739625131431326 108.051321175276 288.15 101.325005740179 0.875 0.875000007091161 0.0 79.91747262559193
8 0.0 0.0 49300.0 0.889708703850822 80.8082872194116 288.15 101.325005740179 0.85 0.850000006888556 0.0 71.89583648238764
9 0.0 0.0 47850.0 1.08140792247008 60.7887493798357 288.15 101.325005740179 0.825 0.825000006685951 0.0 65.7374351764025
10 0.0 0.0 46400.0 1.29064944286157 47.6405112445542 288.15 101.325005740179 0.8 0.800000006483347 0.0 61.48719929542423
11 0.0 0.0 44950.0 1.51493801357613 38.5098458611298 288.15 101.325005740179 0.775 0.775000006280742 0.0 58.340029391982924
12 0.0 0.0 43500.0 1.77665628493182 31.3519538053187 288.15 101.325005740179 0.75 0.750000006078138 0.0 55.70164577311156
13 0.0 0.0 42050.0 2.13113424114961 24.9483532621358 288.15 101.325005740179 0.725 0.725000005875533 0.0 53.168289897234175
14 0.0 0.0 40600.0 2.66079262801875 19.0435113641824 288.15 101.325005740179 0.7 0.700000005672928 0.0 50.67083464940782
15 0.0 0.0 0.0 0.0 0.0 288.15 101.325005740179 0.0 0.0 0.0 0.0
16 0.0 0.05 58000.0 0.516516781939578 250.658217899343 288.294288851519 101.50245395591 1.0 0.999749731014721 61.2540781071208 129.46917607607816
17 0.0 0.05 56550.0 0.522545552740258 226.315592419349 288.294288851519 101.50245395591 0.975 0.974755987739353 61.2540781071208 118.26020633450767
18 0.0 0.05 55100.0 0.540702165324945 200.446769067162 288.294288851519 101.50245395591 0.95 0.949762244463985 61.2540781071208 108.3820020670037
19 0.0 0.05 53650.0 0.572924816015739 173.069984669624 288.294288851519 101.50245395591 0.925 0.924768501188617 61.2540781071208 99.15608912469109
20 0.0 0.05 52200.0 0.632705856008601 141.575524377551 288.294288851519 101.50245395591 0.9 0.899774757913249 61.2540781071208 89.57566334116497
21 0.0 0.05 50750.0 0.73790833583659 108.30563465016 288.294288851519 101.50245395591 0.875 0.874781014637881 61.2540781071208 79.91963062642529
22 0.0 0.05 49300.0 0.887385695585721 81.0001237411043 288.294288851519 101.50245395591 0.85 0.849787271362513 61.2540781071208 71.87835114852932
23 0.0 0.05 47850.0 1.07779232680311 60.9732715859449 288.294288851519 101.50245395591 0.825 0.824793528087145 61.2540781071208 65.7165242554135
24 0.0 0.05 46400.0 1.28582871717267 47.7971346450755 288.294288851519 101.50245395591 0.8 0.799799784811776 61.2540781071208 61.45892832520682
25 0.0 0.05 44950.0 1.50879846329932 38.6449856348878 288.294288851519 101.50245395591 0.775 0.774806041536408 61.2540781071208 58.307494940143016
26 0.0 0.05 43500.0 1.76929731842248 31.4626795287867 288.294288851519 101.50245395591 0.75 0.74981229826104 61.2540781071208 55.66683452066817
27 0.0 0.05 42050.0 2.12262473079863 25.0296488161928 288.294288851519 101.50245395591 0.725 0.724818554985672 61.2540781071208 53.12855158045549
28 0.0 0.05 40600.0 2.65054955410193 19.0998400915557 288.294288851519 101.50245395591 0.7 0.699824811710304 61.2540781071208 50.62507263809112
29 0.0 0.05 0.0 0.0 0.0 288.15 101.50245395591 0.0 0.0 0.0 0.0
30 0.0 0.1 58000.0 0.513436195618546 252.835841959937 288.727155406076 102.036129697856 1.0 0.999000025738633 122.508156214242 129.815072811922
31 0.0 0.1 56550.0 0.519207561513579 228.379823641232 288.727155406076 102.036129697856 0.975 0.974025025095167 122.508156214242 118.57653133166531
32 0.0 0.1 55100.0 0.537279892817707 202.216306865266 288.727155406076 102.036129697856 0.95 0.949050024451701 122.508156214242 108.64675567856268
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167 1000.0 0.15 42050.0 1.99770934875897 24.2225459222734 282.919388748717 91.2983092035152 0.725 0.731671209768077 181.683690060581 48.389606439669045
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171 1000.0 0.2 56550.0 0.503928165349098 218.725359848472 283.90669110883 92.4167415738559 0.975 0.982259227038199 242.244920080775 110.22186930376176
172 1000.0 0.2 55100.0 0.515952727887589 195.550713428736 283.90669110883 92.4167415738559 0.95 0.957073093011579 242.244920080775 100.89492403392052
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174 1000.0 0.2 52200.0 0.587897595951998 142.199704887738 283.90669110883 92.4167415738559 0.9 0.906700824958338 242.244920080775 83.59886464858474
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178 1000.0 0.2 46400.0 1.16979524635858 47.7229964225794 283.90669110883 92.4167415738559 0.8 0.805956288851856 242.244920080775 55.82613435712089
179 1000.0 0.2 44950.0 1.37966973395626 38.2438230126976 283.90669110883 92.4167415738559 0.775 0.780770154825235 242.244920080775 52.76384512139879
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181 1000.0 0.2 42050.0 1.94088839464961 24.7984406532303 283.90669110883 92.4167415738559 0.725 0.730397886771994 242.244920080775 48.13100566926179
182 1000.0 0.2 40600.0 2.42776174404928 18.9045997432519 283.90669110883 92.4167415738559 0.7 0.705211752745374 242.244920080775 45.8958640432308
183 1000.0 0.2 0.0 0.0 0.0 283.90669110883 92.4167415738559 0.0 0.0 0.0 0.0
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185 1000.0 0.25 56550.0 0.494674130615802 224.619478913764 285.176079857547 93.8690552767738 0.975 0.980070650355526 302.806150100969 111.11344545104068
186 1000.0 0.25 55100.0 0.506710974296498 200.636726936331 285.176079857547 93.8690552767738 0.95 0.954940633679743 302.806150100969 101.66483138556872
187 1000.0 0.25 53650.0 0.531891669068342 174.48256837113 285.176079857547 93.8690552767738 0.925 0.92981061700396 302.806150100969 92.80582451425141
188 1000.0 0.25 52200.0 0.576834995127259 145.458943806754 285.176079857547 93.8690552767738 0.9 0.904680600328178 302.806150100969 83.90580914198519
189 1000.0 0.25 50750.0 0.659833245731738 113.042614094839 285.176079857547 93.8690552767738 0.875 0.879550583652395 302.806150100969 74.58927496419793
190 1000.0 0.25 49300.0 0.787113280285323 84.2161426694686 285.176079857547 93.8690552767738 0.85 0.854420566976612 302.806150100969 66.28764430954219
191 1000.0 0.25 47850.0 0.949270548719875 63.2601066250733 285.176079857547 93.8690552767738 0.825 0.829290550300829 302.806150100969 60.05095612806113
192 1000.0 0.25 46400.0 1.13176060787858 49.1256977066405 285.176079857547 93.8690552767738 0.8 0.804160533625047 302.806150100969 55.598529498926816
193 1000.0 0.25 44950.0 1.33080591702903 39.4500197423346 285.176079857547 93.8690552767738 0.775 0.779030516949264 302.806150100969 52.500319700010934
194 1000.0 0.25 43500.0 1.56164397544693 32.0589300930277 285.176079857547 93.8690552767738 0.75 0.753900500273481 302.806150100969 50.06463503905099
195 1000.0 0.25 42050.0 1.87068932127897 25.5556436643225 285.176079857547 93.8690552767738 0.725 0.728770483597698 302.806150100969 47.80666970125866
196 1000.0 0.25 40600.0 2.34272339485242 19.434349895432 285.176079857547 93.8690552767738 0.7 0.703640466921916 302.806150100969 45.529306163776226
197 1000.0 0.25 0.0 0.0 0.0 285.176079857547 93.8690552767738 0.0 0.0 0.0 0.0
198 1000.0 0.3 58000.0 0.481334847455855 255.920202280522 286.727554994867 95.6661437070395 1.0 1.00247742120916 363.367380121163 123.18331152556661
199 1000.0 0.3 56550.0 0.483921798146751 231.894451240306 286.727554994867 95.6661437070395 0.975 0.977415485678929 363.367380121163 112.21877982446296
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201 1000.0 0.3 53650.0 0.519736573799463 179.984095165987 286.727554994867 95.6661437070395 0.925 0.927291614618471 363.367380121163 93.54431695996658
202 1000.0 0.3 52200.0 0.563971973204375 149.462959868794 286.727554994867 95.6661437070395 0.9 0.902229679088242 363.367380121163 84.29292039817007
203 1000.0 0.3 50750.0 0.643942242463018 115.999665097739 286.727554994867 95.6661437070395 0.875 0.877167743558013 363.367380121163 74.69708446799713
204 1000.0 0.3 49300.0 0.766418607040161 86.3322915762664 286.727554994867 95.6661437070395 0.85 0.852105808027784 363.367380121163 66.16667465246712
205 1000.0 0.3 47850.0 0.917325200115547 65.2429981447152 286.727554994867 95.6661437070395 0.825 0.827043872497555 363.367380121163 59.84904632923913
206 1000.0 0.3 46400.0 1.08686716694676 50.9079859557327 286.727554994867 95.6661437070395 0.8 0.801981936967326 363.367380121163 55.33021847067264
207 1000.0 0.3 44950.0 1.27353794841834 40.9857875552953 286.727554994867 95.6661437070395 0.775 0.776920001437097 363.367380121163 52.19695579748071
208 1000.0 0.3 43500.0 1.49236777331988 33.3168463018979 286.727554994867 95.6661437070395 0.75 0.751858065906868 363.367380121163 49.72098772960404
209 1000.0 0.3 42050.0 1.78881980291953 26.5065014540787 286.727554994867 95.6661437070395 0.725 0.726796130376639 363.367380121163 47.41535470717129
210 1000.0 0.3 40600.0 2.24145710517143 20.1131429503249 286.727554994867 95.6661437070395 0.7 0.70173419484641 363.367380121163 45.082747173334404
211 1000.0 0.3 0.0 0.0 0.0 286.727554994867 95.6661437070395 0.0 0.0 0.0 0.0
212 1000.0 0.35 58000.0 0.47039404555375 265.043478251638 288.561116520791 97.8214843854198 1.0 0.999287398130672 423.928610141357 124.67487398242535
213 1000.0 0.35 56550.0 0.472045317312734 240.596937246103 288.561116520791 97.8214843854198 0.975 0.974305213177405 423.928610141357 113.57265758680865
214 1000.0 0.35 55100.0 0.48389604314158 214.471789263338 288.561116520791 97.8214843854198 0.95 0.949323028224138 423.928610141357 103.78205019002407
215 1000.0 0.35 53650.0 0.506310938509629 186.502342985338 288.561116520791 97.8214843854198 0.925 0.924340843270871 423.928610141357 94.42817631115122
216 1000.0 0.35 52200.0 0.549551042070556 154.193828903687 288.561116520791 97.8214843854198 0.9 0.899358658317605 423.928610141357 84.73737935487021
217 1000.0 0.35 50750.0 0.625907160843942 119.560597867778 288.561116520791 97.8214843854198 0.875 0.874376473364338 423.928610141357 74.8338343602252
218 1000.0 0.35 49300.0 0.742637052860997 88.9166687625891 288.561116520791 97.8214843854198 0.85 0.849394288411071 423.928610141357 66.03281284006664
219 1000.0 0.35 47850.0 0.88132580473439 67.6577944554284 288.561116520791 97.8214843854198 0.825 0.824412103457804 423.928610141357 59.62856014498439
220 1000.0 0.35 46400.0 1.03637969937351 53.1367453506142 288.561116520791 97.8214843854198 0.8 0.799429918504537 423.928610141357 55.069844172156294
221 1000.0 0.35 44950.0 1.20954895436051 42.8644097074849 288.561116520791 97.8214843854198 0.775 0.774447733551271 423.928610141357 51.84660194096886
222 1000.0 0.35 43500.0 1.41534127897073 34.842722610254 288.561116520791 97.8214843854198 0.75 0.749465548598004 423.928610141357 49.31434358201927
223 1000.0 0.35 42050.0 1.69712370225193 27.6602108108716 288.561116520791 97.8214843854198 0.725 0.724483363644737 423.928610141357 46.94279937641527
224 1000.0 0.35 40600.0 2.17503761097496 20.0585876591485 288.561116520791 97.8214843854198 0.7 0.69950117869147 423.928610141357 43.62818258168617
225 1000.0 0.35 0.0 0.0 0.0 288.561116520791 97.8214843854198 0.0 0.0 0.0 0.0
226 2000.0 0.0 58000.0 0.515040664520812 213.569545962531 275.15 79.495201431303 1.0 1.02335085536676 0.0 109.99700087395007
227 2000.0 0.0 56550.0 0.519299186792368 192.755816584944 275.15 79.495201431303 0.975 0.997767083982586 0.0 100.09793880206026
228 2000.0 0.0 55100.0 0.528089114170692 173.16041580167 275.15 79.495201431303 0.95 0.972183312598417 0.0 91.44413059013262
229 2000.0 0.0 53650.0 0.550795550902113 152.166960620804 275.15 79.495201431303 0.925 0.946599541214248 0.0 83.81288490423586
230 2000.0 0.0 52200.0 0.590253307886354 129.83818614625 275.15 79.495201431303 0.9 0.92101576983008 0.0 76.63741886278824
231 2000.0 0.0 50750.0 0.663847887312897 104.040691406009 275.15 79.495201431303 0.875 0.895431998445911 0.0 69.06719318445215
232 2000.0 0.0 49300.0 0.788899101634556 78.3153168982003 275.15 79.495201431303 0.85 0.869848227061742 0.0 61.78288314521578
233 2000.0 0.0 47850.0 0.967447846640383 57.8097254335603 275.15 79.495201431303 0.825 0.844264455677573 0.0 55.927894385569694
234 2000.0 0.0 46400.0 1.18925988996186 43.3988491834337 275.15 79.495201431303 0.8 0.818680684293404 0.0 51.61251060436172
235 2000.0 0.0 44950.0 1.42866392429822 34.1105861782349 275.15 79.495201431303 0.775 0.793096912909235 0.0 48.732563909509686
236 2000.0 0.0 43500.0 1.69672297450453 27.4562797051368 275.15 79.495201431303 0.75 0.767513141525066 0.0 46.585700570128076
237 2000.0 0.0 42050.0 2.02976391081085 22.0427161641825 275.15 79.495201431303 0.725 0.741929370140897 0.0 44.74150976630461
238 2000.0 0.0 40600.0 2.52252750412449 17.0250742966338 275.15 79.495201431303 0.7 0.716345598756728 0.0 42.94621817302166
239 2000.0 0.0 0.0 0.0 0.0 275.15 79.495201431303 0.0 0.0 0.0 0.0
240 2000.0 0.05 58000.0 0.513998514586902 214.194454653585 275.287797113977 79.6344377189261 1.0 1.02309470081355 59.8602712653275 110.09563152469424
241 2000.0 0.05 56550.0 0.518292761439053 193.248370151508 275.287797113977 79.6344377189261 0.975 0.997517333293213 59.8602712653275 100.15923140942135
242 2000.0 0.05 55100.0 0.526954837499877 173.679538868261 275.287797113977 79.6344377189261 0.95 0.971939965772874 59.8602712653275 91.52127318137805
243 2000.0 0.05 53650.0 0.549638241152388 152.602234420244 275.287797113977 79.6344377189261 0.925 0.946362598252535 59.8602712653275 83.87602372266733
244 2000.0 0.05 52200.0 0.588924479021849 130.206082713338 275.287797113977 79.6344377189261 0.9 0.920785230732197 59.8602712653275 76.68154942742835
245 2000.0 0.05 50750.0 0.662263113328942 104.324346491018 275.287797113977 79.6344377189261 0.875 0.895207863211858 59.8602712653275 69.09016650314888
246 2000.0 0.05 49300.0 0.787023024813282 78.4974207861195 275.287797113977 79.6344377189261 0.85 0.869630495691519 59.8602712653275 61.77927754713277
247 2000.0 0.05 47850.0 0.96477046936105 57.9481248111078 275.287797113977 79.6344377189261 0.825 0.84405312817118 59.8602712653275 55.90663957260519
248 2000.0 0.05 46400.0 1.18493401412138 43.5394583586965 275.287797113977 79.6344377189261 0.8 0.818475760650841 59.8602712653275 51.591385165640915
249 2000.0 0.05 44950.0 1.42299961041599 34.2275854226577 275.287797113977 79.6344377189261 0.775 0.792898393130502 59.8602712653275 48.70584072192192
250 2000.0 0.05 43500.0 1.68953382430745 27.5545073182161 275.287797113977 79.6344377189261 0.75 0.767321025610164 59.8602712653275 46.55427212625327
251 2000.0 0.05 42050.0 2.02157328496948 22.1156665748395 275.287797113977 79.6344377189261 0.725 0.741743658089825 59.8602712653275 44.70844072698802
252 2000.0 0.05 40600.0 2.51233778089155 17.0812536237758 275.287797113977 79.6344377189261 0.7 0.716166290569486 59.8602712653275 42.913878824002644
253 2000.0 0.05 0.0 0.0 0.0 275.287797113977 79.6344377189261 0.0 0.0 59.8602712653275 0.0
254 2000.0 0.1 58000.0 0.510919083743302 216.065589871836 275.70118845591 80.0531911732792 1.0 1.0223273897384 119.720542530655 110.39203320577452
255 2000.0 0.1 56550.0 0.515125202745729 194.911234844513 275.70118845591 80.0531911732792 0.975 0.996769204994944 119.720542530655 100.40368936670016
256 2000.0 0.1 55100.0 0.523544919442829 175.258927837278 275.70118845591 80.0531911732792 0.95 0.971211020251484 119.720542530655 91.75592125620429
257 2000.0 0.1 53650.0 0.546104394811035 153.922796754545 275.70118845591 80.0531911732792 0.925 0.945652835508024 119.720542530655 84.05791576926275
258 2000.0 0.1 52200.0 0.584972407301244 131.296458225794 275.70118845591 80.0531911732792 0.9 0.920094650764564 119.720542530655 76.80480523846994
259 2000.0 0.1 50750.0 0.657612822883715 105.15383292654 275.70118845591 80.0531911732792 0.875 0.894536466021103 119.720542530655 69.15050890786452
260 2000.0 0.1 49300.0 0.78139803562091 79.0433486403363 275.70118845591 80.0531911732792 0.85 0.868978281277643 119.720542530655 61.76431735645752
261 2000.0 0.1 47850.0 0.956657672643004 58.3836603153741 275.70118845591 80.0531911732792 0.825 0.843420096534183 119.720542530655 55.853176597685504
262 2000.0 0.1 46400.0 1.17240468553146 43.9430413111819 275.70118845591 80.0531911732792 0.8 0.817861911790723 119.720542530655 51.51902752973217
263 2000.0 0.1 44950.0 1.4060986663735 34.579567492174 275.70118845591 80.0531911732792 0.775 0.792303727047263 119.720542530655 48.6222837345183
264 2000.0 0.1 43500.0 1.66820357321683 27.852059961693 275.70118845591 80.0531911732792 0.75 0.766745542303803 119.720542530655 46.46290594954567
265 2000.0 0.1 42050.0 1.99603555324785 22.3509269040472 275.70118845591 80.0531911732792 0.725 0.741187357560343 119.720542530655 44.613244748522106
266 2000.0 0.1 40600.0 2.4821583380156 17.2427048424978 275.70118845591 80.0531911732792 0.7 0.715629172816883 119.720542530655 42.79912359474788
267 2000.0 0.1 0.0 0.0 0.0 275.70118845591 80.0531911732792 0.0 0.0 119.720542530655 0.0
268 2000.0 0.15 58000.0 0.505940469287619 219.170507032382 276.390174025797 80.7546033876493 1.0 1.02105236552231 179.580813795983 110.88722918196876
269 2000.0 0.15 56550.0 0.509876075856986 197.741197803105 276.390174025797 80.7546033876493 0.975 0.995526056384251 179.580813795983 100.82350597110724
270 2000.0 0.15 55100.0 0.517965667072572 177.90754201704 276.390174025797 80.7546033876493 0.95 0.969999747246193 179.580813795983 92.14999867809775
271 2000.0 0.15 53650.0 0.540326499367254 156.13910437807 276.390174025797 80.7546033876493 0.925 0.944473438108135 179.580813795983 84.36609568294084
272 2000.0 0.15 52200.0 0.578528560029528 133.112833708327 276.390174025797 80.7546033876493 0.9 0.918947128970078 179.580813795983 77.00957600672844
273 2000.0 0.15 50750.0 0.650061689936006 106.523511684329 276.390174025797 80.7546033876493 0.875 0.89342081983202 179.580813795983 69.24685402343279
274 2000.0 0.15 49300.0 0.772130195036696 79.9603355666991 276.390174025797 80.7546033876493 0.85 0.867894510693962 179.580813795983 61.73978949631503
275 2000.0 0.15 47850.0 0.943383685282142 59.1134126225871 276.390174025797 80.7546033876493 0.825 0.842368201555904 179.580813795983 55.76662904950011
276 2000.0 0.15 46400.0 1.15182262373302 44.6256045231463 276.390174025797 80.7546033876493 0.8 0.816841892417847 179.580813795983 51.4007808875225
277 2000.0 0.15 44950.0 1.37839265567595 35.1750628777158 276.390174025797 80.7546033876493 0.775 0.791315583279789 179.580813795983 48.48504833358321
278 2000.0 0.15 43500.0 1.63334259022189 28.3538801484714 276.390174025797 80.7546033876493 0.75 0.765789274141731 179.580813795983 46.31160004454531
279 2000.0 0.15 42050.0 1.95393164948942 22.7457146696403 276.390174025797 80.7546033876493 0.725 0.740262965003674 179.580813795983 44.44357178326597
280 2000.0 0.15 40600.0 2.43216017712677 17.5175992884029 276.390174025797 80.7546033876493 0.7 0.714736655865616 179.580813795983 42.60560738811777
281 2000.0 0.15 0.0 0.0 0.0 276.390174025797 80.7546033876493 0.0 0.0 179.580813795983 0.0
282 2000.0 0.2 58000.0 0.499232130684788 223.499410948191 277.35475382364 81.7439364407149 1.0 1.01927531957574 239.44108506131 111.57808713446043
283 2000.0 0.2 56550.0 0.502718503653552 201.74576590171 277.35475382364 81.7439364407149 0.975 0.993793436586342 239.44108506131 101.42132955254743
284 2000.0 0.2 55100.0 0.510439467551189 181.625973772937 277.35475382364 81.7439364407149 0.95 0.968311553596949 239.44108506131 92.70906534612416
285 2000.0 0.2 53650.0 0.532446869721074 159.26960906653 277.35475382364 81.7439364407149 0.925 0.942829670607555 239.44108506131 84.80260478917309
286 2000.0 0.2 52200.0 0.569771402012033 135.661668382115 277.35475382364 81.7439364407149 0.9 0.917347787618162 239.44108506131 77.29613899336915
287 2000.0 0.2 50750.0 0.639810490337827 108.43542661099 277.35475382364 81.7439364407149 0.875 0.891865904628768 239.44108506131 69.37812346996897
288 2000.0 0.2 49300.0 0.759387811981252 81.2584775475928 277.35475382364 81.7439364407149 0.85 0.866384021639375 239.44108506131 61.70669746979419
289 2000.0 0.2 47850.0 0.925360676952596 60.138363527241 277.35475382364 81.7439364407149 0.825 0.840902138649982 239.44108506131 55.649676784389044
290 2000.0 0.2 46400.0 1.12346899321624 45.6108797381169 277.35475382364 81.7439364407149 0.8 0.815420255660588 239.44108506131 51.2424091390892
291 2000.0 0.2 44950.0 1.34057128208328 36.0265684010973 277.35475382364 81.7439364407149 0.775 0.789938372671195 239.44108506131 48.296182990519995
292 2000.0 0.2 43500.0 1.58591300908105 29.0694892137872 277.35475382364 81.7439364407149 0.75 0.764456489681801 239.44108506131 46.101681111486386
293 2000.0 0.2 42050.0 1.89686191592384 23.3095712875742 277.35475382364 81.7439364407149 0.725 0.738974606692408 239.44108506131 44.21503805191133
294 2000.0 0.2 40600.0 2.3640887401846 17.9122257023548 277.35475382364 81.7439364407149 0.7 0.713492723703015 239.44108506131 42.34609109458217
295 2000.0 0.2 0.0 0.0 0.0 277.35475382364 81.7439364407149 0.0 0.0 239.44108506131 0.0
296 2000.0 0.25 58000.0 0.490943210954541 229.083884602049 278.594927849437 83.0286121259999 1.0 1.01700412121317 299.301356326638 112.46717788446948
297 2000.0 0.25 56550.0 0.493825203742373 206.98265691698 278.594927849437 83.0286121259999 0.975 0.991579018182845 299.301356326638 102.21325272316534
298 2000.0 0.25 55100.0 0.501186802222428 186.431460591808 278.594927849437 83.0286121259999 0.95 0.966153915152516 299.301356326638 93.43698756766484
299 2000.0 0.25 53650.0 0.52264881073961 163.35157559787 278.594927849437 83.0286121259999 0.925 0.940728812122187 299.301356326638 85.37550671866826
300 2000.0 0.25 52200.0 0.55891656606103 138.962133832492 278.594927849437 83.0286121259999 0.9 0.915303709091857 299.301356326638 77.6682386541697
301 2000.0 0.25 50750.0 0.627151765084877 110.880795710008 278.594927849437 83.0286121259999 0.875 0.889878606061528 299.301356326638 69.53908674354717
302 2000.0 0.25 49300.0 0.743417723081925 82.9462259129654 278.594927849437 83.0286121259999 0.85 0.864453503031198 299.301356326638 61.663694406455704
303 2000.0 0.25 47850.0 0.902422639815665 61.497550318509 278.594927849437 83.0286121259999 0.825 0.839028400000869 299.301356326638 55.496781700625576
304 2000.0 0.25 46400.0 1.08805487679301 46.9318427594 278.594927849437 83.0286121259999 0.8 0.81360329697054 299.301356326638 51.064420391247886
305 2000.0 0.25 44950.0 1.29379201367826 37.1450034063478 278.594927849437 83.0286121259999 0.775 0.78817819394021 299.301356326638 48.05790875518454
306 2000.0 0.25 43500.0 1.52724022403009 30.0105866069096 278.594927849437 83.0286121259999 0.75 0.762753090909881 299.301356326638 45.83337501281104
307 2000.0 0.25 42050.0 1.82625085802079 24.0544040573426 278.594927849437 83.0286121259999 0.725 0.737327987879551 299.301356326638 43.92937604890069
308 2000.0 0.25 40600.0 2.27925095605852 18.434278259216 278.594927849437 83.0286121259999 0.7 0.711902884849222 299.301356326638 42.01634634656686
309 2000.0 0.25 0.0 0.0 0.0 278.594927849437 83.0286121259999 0.0 0.0 299.301356326638 0.0
310 2000.0 0.3 58000.0 0.481312194233186 235.949797564156 280.110696103189 84.618267061966 1.0 1.01424872185047 359.161627591965 113.56551479447997
311 2000.0 0.3 56550.0 0.483411445566456 213.536169122694 280.110696103189 84.618267061966 0.975 0.988892503804209 359.161627591965 103.22582819632473
312 2000.0 0.3 55100.0 0.490485005697224 192.340298597972 280.110696103189 84.618267061966 0.95 0.963536285757948 359.161627591965 94.34003245363206
313 2000.0 0.3 53650.0 0.511129913146882 168.431204556053 280.110696103189 84.618267061966 0.925 0.938180067711686 359.161627591965 86.09022695596009
314 2000.0 0.3 52200.0 0.54627806233195 143.008985787908 280.110696103189 84.618267061966 0.9 0.912823849665424 359.161627591965 78.12267165227576
315 2000.0 0.3 50750.0 0.612417604594913 113.852681014766 280.110696103189 84.618267061966 0.875 0.887467631619162 359.161627591965 69.72538618377173
316 2000.0 0.3 49300.0 0.724501331066055 85.0427936768085 280.110696103189 84.618267061966 0.85 0.8621114135729 359.161627591965 61.61361721642364
317 2000.0 0.3 47850.0 0.875007223444898 63.2213183382253 280.110696103189 84.618267061966 0.825 0.836755195526639 359.161627591965 55.31911022165654
318 2000.0 0.3 46400.0 1.0468720270292 48.5772676411829 280.110696103189 84.618267061966 0.8 0.811398977480377 359.161627591965 50.85418264306511
319 2000.0 0.3 44950.0 1.23898811000289 38.5602010579051 280.110696103189 84.618267061966 0.775 0.786042759434115 359.161627591965 47.77563063006528
320 2000.0 0.3 43500.0 1.45886596464954 31.196860542239 280.110696103189 84.618267061966 0.75 0.760686541387853 359.161627591965 45.512038048990675
321 2000.0 0.3 42050.0 1.7440198811413 24.9846441432261 280.110696103189 84.618267061966 0.725 0.735330323341591 359.161627591965 43.573716109026854
322 2000.0 0.3 40600.0 2.22439955238856 18.3610232895413 280.110696103189 84.618267061966 0.7 0.70997410529533 359.161627591965 40.84225198665159
323 2000.0 0.3 0.0 0.0 0.0 280.110696103189 84.618267061966 0.0 0.0 359.161627591965 0.0
324 2000.0 0.35 58000.0 0.470566372455659 244.132662822051 281.902058584896 86.5248238705945 1.0 1.01102103578495 419.021898857293 114.88062154211308
325 2000.0 0.35 56550.0 0.471779071681757 221.429485396085 281.902058584896 86.5248238705945 0.975 0.985745509890324 419.021898857293 104.46579706313415
326 2000.0 0.35 55100.0 0.478606798245897 199.386389652093 281.902058584896 86.5248238705945 0.95 0.960469983995701 419.021898857293 95.42768156519708
327 2000.0 0.35 53650.0 0.498152985137602 174.60167917058 281.902058584896 86.5248238705945 0.925 0.935194458101077 419.021898857293 86.9783476888623
328 2000.0 0.35 52200.0 0.532149599633283 147.79918290782 281.902058584896 86.5248238705945 0.9 0.909918932206453 419.021898857293 78.65127601052278
329 2000.0 0.35 50750.0 0.596055825341059 117.300089959081 281.902058584896 86.5248238705945 0.875 0.884643406311829 419.021898857293 69.9174019331405
330 2000.0 0.35 49300.0 0.702768369468572 87.603531790668 281.902058584896 86.5248238705945 0.85 0.859367880417206 419.021898857293 61.56499119621596
331 2000.0 0.35 47850.0 0.843455670817316 65.3619111562417 281.902058584896 86.5248238705945 0.825 0.834092354522582 419.021898857293 55.12987462018965
332 2000.0 0.35 46400.0 1.00062754679037 50.5876558682368 281.902058584896 86.5248238705945 0.8 0.808816828627958 419.021898857293 50.61940198930926
333 2000.0 0.35 44950.0 1.17751547849603 40.3045624359361 281.902058584896 86.5248238705945 0.775 0.783541302733334 419.021898857293 47.45924612232441
334 2000.0 0.35 43500.0 1.38261523588192 32.6536229148696 281.902058584896 86.5248238705945 0.75 0.758265776838711 419.021898857293 45.1473965488417
335 2000.0 0.35 42050.0 1.65968933222744 25.845334524983 281.902058584896 86.5248238705945 0.725 0.732990250944087 419.021898857293 42.89522599896383
336 2000.0 0.35 40600.0 2.1101137137546 19.1389319939315 281.902058584896 86.5248238705945 0.7 0.707714725049463 419.021898857293 40.38532286701153
337 2000.0 0.35 0.0 0.0 0.0 281.902058584896 86.5248238705945 0.0 0.0 419.021898857293 0.0
338 3000.0 0.0 58000.0 0.516198592397567 195.639492579802 268.65 70.108523423274 1.0 1.03565687626109 0.0 100.98883068706806
339 3000.0 0.0 56550.0 0.517518224001758 178.107075009453 268.65 70.108523423274 0.975 1.00976545435457 0.0 92.17365714104001
340 3000.0 0.0 55100.0 0.526298404523301 159.542557524104 268.65 70.108523423274 0.95 0.983874032448039 0.0 83.96699347850291
341 3000.0 0.0 53650.0 0.542169860110265 141.998290178002 268.65 70.108523423274 0.925 0.957982610541512 0.0 76.98719312170417
342 3000.0 0.0 52200.0 0.577299820106671 122.140961867175 268.65 70.108523423274 0.9 0.932091188634984 0.0 70.5119553135759
343 3000.0 0.0 50750.0 0.635607020742967 100.847615486545 268.65 70.108523423274 0.875 0.906199766728457 0.0 64.09945242843517
344 3000.0 0.0 49300.0 0.74551022679156 77.029406676729 268.65 70.108523423274 0.85 0.88030834482193 0.0 57.426210441187536
345 3000.0 0.0 47850.0 0.91260806276764 56.8060837234955 268.65 70.108523423274 0.825 0.854416922915402 0.0 51.84169002031559
346 3000.0 0.0 46400.0 1.13593142382492 41.88698292111 268.65 70.108523423274 0.8 0.828525501008875 0.0 47.580740149306585
347 3000.0 0.0 44950.0 1.38709060620493 32.2798747866438 268.65 70.108523423274 0.775 0.802634079102348 0.0 44.775111086024985
348 3000.0 0.0 43500.0 1.66645574471295 25.7003648976502 268.65 70.108523423274 0.75 0.77674265719582 0.0 42.82852072490822
349 3000.0 0.0 42050.0 2.00192509947777 20.6096542931463 268.65 70.108523423274 0.725 0.750851235289293 0.0 41.25898422100936
350 3000.0 0.0 40600.0 2.47533674011158 16.0905941489086 268.65 70.108523423274 0.7 0.724959813382765 0.0 39.82963886701788
351 3000.0 0.0 0.0 0.0 0.0 268.65 70.108523423274 0.0 0.0 0.0 0.0
352 3000.0 0.05 58000.0 0.515231915059655 196.194005351224 268.784550610546 70.2313268802712 1.0 1.03539762455165 59.1509135005756 101.08541310033536
353 3000.0 0.05 56550.0 0.516512621894976 178.58707151681 268.784550610546 70.2313268802712 0.975 1.00951268393785 59.1509135005756 92.24247654569312
354 3000.0 0.05 55100.0 0.525172172581759 160.029535187731 268.784550610546 70.2313268802712 0.95 0.983627743324063 59.1509135005756 84.04305867178974
355 3000.0 0.05 53650.0 0.541029319101805 142.406353030404 268.784550610546 70.2313268802712 0.925 0.957742802710272 59.1509135005756 77.04601221581073
356 3000.0 0.05 52200.0 0.575988523705559 122.497476600987 268.784550610546 70.2313268802712 0.9 0.931857862096481 59.1509135005756 70.55714070505876
357 3000.0 0.05 50750.0 0.634221898516558 101.09491519428 268.784550610546 70.2313268802712 0.875 0.90597292148269 59.1509135005756 64.11660904488669
358 3000.0 0.05 49300.0 0.743690340587394 77.2205723755214 268.784550610546 70.2313268802712 0.85 0.880087980868899 59.1509135005756 57.42819377030502
359 3000.0 0.05 47850.0 0.910238011377281 56.9410124794452 268.784550610546 70.2313268802712 0.825 0.854203040255108 59.1509135005756 51.82987396509914
360 3000.0 0.05 46400.0 1.13211118338588 42.0112243748278 268.784550610546 70.2313268802712 0.8 0.828318099641316 59.1509135005756 47.56137694247602
361 3000.0 0.05 44950.0 1.38149285186014 32.3952482558376 268.784550610546 70.2313268802712 0.775 0.802433159027525 59.1509135005756 44.75380389967431
362 3000.0 0.05 43500.0 1.65940716359725 25.7917096490945 268.784550610546 70.2313268802712 0.75 0.776548218413734 59.1509135005756 42.79894775312773
363 3000.0 0.05 42050.0 1.99374360102655 20.6815217588909 268.784550610546 70.2313268802712 0.725 0.750663277799943 59.1509135005756 41.23365166628009
364 3000.0 0.05 40600.0 2.4653435716055 16.1429138507143 268.784550610546 70.2313268802712 0.7 0.724778337186152 59.1509135005756 39.79782888883989
365 3000.0 0.05 0.0 0.0 0.0 268.784550610546 70.2313268802712 0.0 0.0 59.1509135005756 0.0
366 3000.0 0.1 58000.0 0.512345912060705 197.86374949014 269.188202442184 70.6006586189506 1.0 1.0346210360193 118.301827001151 101.37468319627664
367 3000.0 0.1 56550.0 0.513413933056788 180.101323052972 269.188202442184 70.6006586189506 0.975 1.00875551011882 118.301827001151 92.4665286173575
368 3000.0 0.1 55100.0 0.52175065401875 161.479481606083 269.188202442184 70.6006586189506 0.95 0.982889984218339 118.301827001151 84.25202513858251
369 3000.0 0.1 53650.0 0.537576876437044 143.647704717215 269.188202442184 70.6006586189506 0.925 0.957024458317857 118.301827001151 77.22168440923126
370 3000.0 0.1 52200.0 0.572048276498673 123.573915217015 269.188202442184 70.6006586189506 0.9 0.931158932417374 118.301827001151 70.69024522008657
371 3000.0 0.1 50750.0 0.629858992528352 101.905019044549 269.188202442184 70.6006586189506 0.875 0.905293406516891 118.301827001151 64.18579262898216
372 3000.0 0.1 49300.0 0.738307215403671 77.7996067229204 269.188202442184 70.6006586189506 0.85 0.879427880616409 118.301827001151 57.44001099910008
373 3000.0 0.1 47850.0 0.903148621554008 57.3434133381391 269.188202442184 70.6006586189506 0.825 0.853562354715926 118.301827001151 51.78962471154205
374 3000.0 0.1 46400.0 1.12074221435107 42.3846174863529 269.188202442184 70.6006586189506 0.8 0.827696828815443 118.301827001151 47.50223005607823
375 3000.0 0.1 44950.0 1.36543973089148 32.7185689600467 269.188202442184 70.6006586189506 0.775 0.801831302914961 118.301827001151 44.675233995960504
376 3000.0 0.1 43500.0 1.63845504536074 26.0687311518205 269.188202442184 70.6006586189506 0.75 0.775965777014478 118.301827001151 42.71244408185299
377 3000.0 0.1 42050.0 1.96772503810352 20.9056456117382 269.188202442184 70.6006586189506 0.725 0.750100251113995 118.301827001151 41.13656230793624
378 3000.0 0.1 40600.0 2.43414831939933 16.3053846163222 269.188202442184 70.6006586189506 0.7 0.724234725213513 118.301827001151 39.68972456098037
379 3000.0 0.1 0.0 0.0 0.0 269.188202442184 70.6006586189506 0.0 0.0 118.301827001151 0.0
380 3000.0 0.15 58000.0 0.507623659124732 200.648460035772 269.860955494913 71.219289639328 1.0 1.03333059590403 177.452740501727 101.85390548110112
381 3000.0 0.15 56550.0 0.508354033233313 182.639513091272 269.860955494913 71.219289639328 0.975 1.00749733100643 177.452740501727 92.84553310771658
382 3000.0 0.15 55100.0 0.516147431309761 163.916869291261 269.860955494913 71.219289639328 0.95 0.981664066108829 177.452740501727 84.60527103302222
383 3000.0 0.15 53650.0 0.53193673873951 145.727056071528 269.860955494913 71.219289639328 0.925 0.955830801211228 177.452740501727 77.51757495279831
384 3000.0 0.15 52200.0 0.565577151470904 125.385421266134 269.860955494913 71.219289639328 0.9 0.929997536313627 177.452740501727 70.91512939567939
385 3000.0 0.15 50750.0 0.622738403664192 103.254690685758 269.860955494913 71.219289639328 0.875 0.904164271416027 177.452740501727 64.30066124848885
386 3000.0 0.15 49300.0 0.729483424920677 78.7676699438951 269.860955494913 71.219289639328 0.85 0.878331006518426 177.452740501727 57.45970964369407
387 3000.0 0.15 47850.0 0.891377571043883 58.0225717220937 269.860955494913 71.219289639328 0.825 0.852497741620825 177.452740501727 51.72001904735938
388 3000.0 0.15 46400.0 1.10212242624491 43.011546952595 269.860955494913 71.219289639328 0.8 0.826664476723224 177.452740501727 47.403990483940866
389 3000.0 0.15 44950.0 1.33916696062851 33.2638563590631 269.860955494913 71.219289639328 0.775 0.800831211825623 177.452740501727 44.54585741914986
390 3000.0 0.15 43500.0 1.60422105198309 26.5349844446992 269.860955494913 71.219289639328 0.75 0.774997946928023 177.452740501727 42.56798066023028
391 3000.0 0.15 42050.0 1.92529334753574 21.2868541154318 269.860955494913 71.219289639328 0.725 0.749164682030422 177.452740501727 40.98343861840464
392 3000.0 0.15 40600.0 2.38352766396669 16.5791994398573 269.860955494913 71.219289639328 0.7 0.723331417132821 177.452740501727 39.51698051132093
393 3000.0 0.15 0.0 0.0 0.0 269.860955494913 71.219289639328 0.0 0.0 177.452740501727 0.0
394 3000.0 0.2 58000.0 0.501185432900655 204.553991144178 270.802809768734 72.0918612884988 1.0 1.0315320648 236.603654002302 102.5194806031516
395 3000.0 0.2 56550.0 0.501480921468596 186.226500093892 270.802809768734 72.0918612884988 0.975 1.00574376318 236.603654002302 93.38903686895655
396 3000.0 0.2 55100.0 0.508504270338231 167.375177042223 270.802809768734 72.0918612884988 0.95 0.979955461559997 236.603654002302 85.11099227458783
397 3000.0 0.2 53650.0 0.524269528761642 148.660587591747 270.802809768734 72.0918612884988 0.925 0.954167159939997 236.603654002302 77.93821620215401
398 3000.0 0.2 52200.0 0.556766921198546 127.94229372089 270.802809768734 72.0918612884988 0.9 0.928378858319997 236.603654002302 71.23403696605999
399 3000.0 0.2 50750.0 0.613143633130921 105.122312806304 270.802809768734 72.0918612884988 0.875 0.902590556699997 236.603654002302 64.45507679718237
400 3000.0 0.2 49300.0 0.717431075852181 80.1280916908039 270.802809768734 72.0918612884988 0.85 0.876802255079997 236.603654002302 57.48638302771565
401 3000.0 0.2 47850.0 0.875135412441549 58.9887447443547 270.802809768734 72.0918612884988 0.825 0.851013953459997 236.603654002302 51.6231394612601
402 3000.0 0.2 46400.0 1.07666913219795 43.9044758421852 270.802809768734 72.0918612884988 0.8 0.825225651839997 236.603654002302 47.2705939046114
403 3000.0 0.2 44950.0 1.30327148119615 34.0429892996113 270.802809768734 72.0918612884988 0.775 0.799437350219997 236.603654002302 44.36725708884911
404 3000.0 0.2 43500.0 1.55759115970373 27.201651536094 270.802809768734 72.0918612884988 0.75 0.773649048599997 236.603654002302 42.369051961961404
405 3000.0 0.2 42050.0 1.86760996407068 21.8302868028718 270.802809768734 72.0918612884988 0.725 0.747860746979997 236.603654002302 40.77046115156404
406 3000.0 0.2 40600.0 2.3145523285331 16.9696338616178 270.802809768734 72.0918612884988 0.7 0.722072445359997 236.603654002302 39.27710556876162
407 3000.0 0.2 0.0 0.0 0.0 270.802809768734 72.0918612884988 0.0 0.0 236.603654002302 0.0
408 3000.0 0.25 58000.0 0.493044736925006 209.741876091979 272.013765263647 73.2249198647342 1.0 1.02923340767317 295.754567502878 103.41212811992699
409 3000.0 0.25 56550.0 0.493016086818814 190.906702601767 272.013765263647 73.2249198647342 0.975 1.00350257248134 295.754567502878 94.12007546420625
410 3000.0 0.25 55100.0 0.498999810156414 171.900234685622 272.013765263647 73.2249198647342 0.95 0.977771737289507 295.754567502878 85.77818447396838
411 3000.0 0.25 53650.0 0.514758053908504 152.496215317938 272.013765263647 73.2249198647342 0.925 0.952040902097678 295.754567502878 78.49865502547398
412 3000.0 0.25 52200.0 0.545908562471256 131.239726557535 272.013765263647 73.2249198647342 0.9 0.926310066905848 295.754567502878 71.64489046414464
413 3000.0 0.25 50750.0 0.601429440402033 107.480498771677 272.013765263647 73.2249198647342 0.875 0.900579231714019 295.754567502878 64.6419362303811
414 3000.0 0.25 49300.0 0.702430133887488 81.8855611304295 272.013765263647 73.2249198647342 0.85 0.87484839652219 295.754567502878 57.518885668299674
415 3000.0 0.25 47850.0 0.854709778303298 60.2570342136682 272.013765263647 73.2249198647342 0.825 0.849117561330361 295.754567502878 51.50227635397859
416 3000.0 0.25 46400.0 1.04496328367278 45.0775541636288 272.013765263647 73.2249198647342 0.8 0.823386726138532 295.754567502878 47.10438901876314
417 3000.0 0.25 44950.0 1.25864283323665 35.0732039986435 272.013765263647 73.2249198647342 0.775 0.797655890946703 295.754567502878 44.14463685153965
418 3000.0 0.25 43500.0 1.49981457910647 28.0820201747691 272.013765263647 73.2249198647342 0.75 0.771925055754874 295.754567502878 42.11782326888072
419 3000.0 0.25 42050.0 1.79627203198008 22.5473720061643 272.013765263647 73.2249198647342 0.725 0.746194220563044 295.754567502878 40.50121372932352
420 3000.0 0.25 40600.0 2.27207002071196 16.8772709516511 272.013765263647 73.2249198647342 0.7 0.720463385371215 295.754567502878 38.34634136067927
421 3000.0 0.25 0.0 0.0 0.0 272.013765263647 73.2249198647342 0.0 0.0 295.754567502878 0.0
422 3000.0 0.3 58000.0 0.483212310229862 216.394931124031 273.493821979652 74.6269652298273 1.0 1.02644469688801 354.905481003454 104.56469459047489
423 3000.0 0.3 56550.0 0.483149838965041 196.705730667205 273.493821979652 74.6269652298273 0.975 1.00078357946581 354.905481003454 95.03834209536082
424 3000.0 0.3 55100.0 0.488056398822271 177.459517061511 273.493821979652 74.6269652298273 0.95 0.975122462043613 354.905481003454 86.61025283378042
425 3000.0 0.3 53650.0 0.503668113657407 157.246369449317 273.493821979652 74.6269652298273 0.925 0.949461344621413 354.905481003454 79.19998228001322
426 3000.0 0.3 52200.0 0.533363326042695 135.270507536815 273.493821979652 74.6269652298273 0.9 0.923800227199213 354.905481003454 72.14832781531908
427 3000.0 0.3 50750.0 0.587742618221576 110.365769713501 273.493821979652 74.6269652298273 0.875 0.898139109777012 354.905481003454 64.8666664534526
428 3000.0 0.3 49300.0 0.684916991699412 84.0270035167196 273.493821979652 74.6269652298273 0.85 0.872477992354812 354.905481003454 57.5515224701875
429 3000.0 0.3 47850.0 0.830430214476331 61.849568649524 273.493821979652 74.6269652298273 0.825 0.846816874932611 354.905481003454 51.36175055889278
430 3000.0 0.3 46400.0 1.00774747838925 46.5501145190164 273.493821979652 74.6269652298273 0.8 0.821155757510411 354.905481003454 46.91076052526959
431 3000.0 0.3 44950.0 1.20633964836406 36.3788583081415 273.493821979652 74.6269652298273 0.775 0.795494640088211 354.905481003454 43.885259139329385
432 3000.0 0.3 43500.0 1.43234395031108 29.1971445855973 273.493821979652 74.6269652298273 0.75 0.76983352266601 354.905481003454 41.8203534135382
433 3000.0 0.3 42050.0 1.72122869963321 23.2197717618068 273.493821979652 74.6269652298273 0.725 0.74417240524381 354.905481003454 39.96653755535465
434 3000.0 0.3 40600.0 2.17167754759247 17.4911434746984 273.493821979652 74.6269652298273 0.7 0.71851128782161 354.905481003454 37.985123565721054
435 3000.0 0.3 0.0 0.0 0.0 273.493821979652 74.6269652298273 0.0 0.0 354.905481003454 0.0
436 3000.0 0.35 58000.0 0.472377875256421 224.117498662516 275.242979916748 76.3085135953898 1.0 1.02317799153118 414.056394504029 105.86814782598309
437 3000.0 0.35 56550.0 0.472087318262611 203.654671541338 275.242979916748 76.3085135953898 0.975 0.997598541742899 414.056394504029 96.14278773960314
438 3000.0 0.35 55100.0 0.475974871527027 184.106553281819 275.242979916748 76.3085135953898 0.95 0.97201909195462 414.056394504029 87.63009304559755
439 3000.0 0.35 53650.0 0.491299920063109 162.913180056173 275.242979916748 76.3085135953898 0.925 0.94643964216634 414.056394504029 80.03923233882468
440 3000.0 0.35 52200.0 0.519409889904715 140.03975178673 275.242979916748 76.3085135953898 0.9 0.920860192378061 414.056394504029 72.73803205782905
441 3000.0 0.35 50750.0 0.572378922906064 113.782313899562 275.242979916748 76.3085135953898 0.875 0.895280742589781 414.056394504029 65.12659827559096
442 3000.0 0.35 49300.0 0.665146477769296 86.5752299323786 275.242979916748 76.3085135953898 0.85 0.869701292801502 414.056394504029 57.58520925158856
443 3000.0 0.35 47850.0 0.802663251930636 63.7955835720671 275.242979916748 76.3085135953898 0.825 0.844121843013222 414.056394504029 51.206370568768044
444 3000.0 0.35 46400.0 0.965734318974126 48.3510479578558 275.242979916748 76.3085135953898 0.8 0.818542393224943 414.056394504029 46.69426637126517
445 3000.0 0.35 44950.0 1.14773031129811 37.9887663411447 275.242979916748 76.3085135953898 0.775 0.792962943436663 414.056394504029 43.60085861855317
446 3000.0 0.35 43500.0 1.35782527889575 30.5736206223629 275.242979916748 76.3085135953898 0.75 0.767383493648384 414.056394504029 41.51363494841276
447 3000.0 0.35 42050.0 1.6277387689257 24.3404742354921 275.242979916748 76.3085135953898 0.725 0.741804043860104 414.056394504029 39.619933567147626
448 3000.0 0.35 40600.0 2.0580200828216 18.2525946417168 275.242979916748 76.3085135953898 0.7 0.716224594071825 414.056394504029 37.564206336255104
449 3000.0 0.35 0.0 0.0 0.0 275.242979916748 76.3085135953898 0.0 0.0 414.056394504029 0.0
450 4000.0 0.0 58000.0 0.516638281867925 178.956148146694 262.15 61.6402085371994 1.0 1.04841779681353 0.0 92.45559690820986
451 4000.0 0.0 56550.0 0.517698996617252 164.099830469518 262.15 61.6402085371994 0.975 1.02220735189319 0.0 84.95431757913063
452 4000.0 0.0 55100.0 0.525209218898279 147.045180231338 262.15 61.6402085371994 0.95 0.995996906972856 0.0 77.22948425205769
453 4000.0 0.0 53650.0 0.537282838606111 131.501113827668 262.15 61.6402085371994 0.925 0.969786462052518 0.0 70.65329171719479
454 4000.0 0.0 52200.0 0.566777606055763 114.39213394041 262.15 61.6402085371994 0.9 0.94357601713218 0.0 64.83489982635578
455 4000.0 0.0 50750.0 0.615639115570513 96.2801220887395 262.15 61.6402085371994 0.875 0.917365572211841 0.0 59.2738092097326
456 4000.0 0.0 49300.0 0.707528060515687 75.6053666021411 262.15 61.6402085371994 0.85 0.891155127291503 0.0 53.49291839659039
457 4000.0 0.0 47850.0 0.86127605780735 55.9637237486576 262.15 61.6402085371994 0.825 0.864944682371165 0.0 48.20021537046338
458 4000.0 0.0 46400.0 1.08140224846311 40.784742447101 262.15 61.6402085371994 0.8 0.838734237450826 0.0 44.10471218528387
459 4000.0 0.0 44950.0 1.34659370617067 30.7113471726083 262.15 61.6402085371994 0.775 0.812523792530488 0.0 41.35570681065674
460 4000.0 0.0 43500.0 1.64321324926485 24.0962641608192 262.15 61.6402085371994 0.75 0.78631334761015 0.0 39.59530052684387
461 4000.0 0.0 42050.0 1.99155360744515 19.2292285894136 262.15 61.6402085371994 0.725 0.760102902689811 0.0 38.296039565634075
462 4000.0 0.0 40600.0 2.45997578782358 15.1243510158406 262.15 61.6402085371994 0.7 0.733892457769473 0.0 37.20553730551285
463 4000.0 0.0 0.0 0.0 0.0 262.15 61.6402085371994 0.0 0.0 0.0 0.0
464 4000.0 0.05 58000.0 0.515718549742496 179.461260957381 262.281303684007 61.7481857507349 1.0 1.04815533366707 58.4328508421484 92.55150123590015
465 4000.0 0.05 56550.0 0.516669155633388 164.564005923171 262.281303684007 61.7481857507349 0.975 1.02195145032539 58.4328508421484 85.02514598797262
466 4000.0 0.05 55100.0 0.524093387193184 147.458148552501 262.281303684007 61.7481857507349 0.95 0.995747566983717 58.4328508421484 77.28184054411595
467 4000.0 0.05 53650.0 0.536108567854045 131.895209701211 262.281303684007 61.7481857507349 0.925 0.96954368364204 58.4328508421484 70.71015197972518
468 4000.0 0.05 52200.0 0.565545873010277 114.716405982292 262.281303684007 61.7481857507349 0.9 0.943339800300364 58.4328508421484 64.8773899698567
469 4000.0 0.05 50750.0 0.614212120107261 96.5530506540875 262.281303684007 61.7481857507349 0.875 0.917135916958687 58.4328508421484 59.30405394507085
470 4000.0 0.05 49300.0 0.705841729967435 75.8030453697481 262.281303684007 61.7481857507349 0.85 0.89093203361701 58.4328508421484 53.50495268058297
471 4000.0 0.05 47850.0 0.859104489425206 56.0955551086461 262.281303684007 61.7481857507349 0.825 0.864728150275333 58.4328508421484 48.191943230636916
472 4000.0 0.05 46400.0 1.07814530639424 40.8877165887748 262.281303684007 61.7481857507349 0.8 0.838524266933656 58.4328508421484 44.08289972936546
473 4000.0 0.05 44950.0 1.3413713649141 30.8152345613731 262.281303684007 61.7481857507349 0.775 0.81232038359198 58.4328508421484 41.33467324373718
474 4000.0 0.05 43500.0 1.6363091413416 24.1796892362239 262.281303684007 61.7481857507349 0.75 0.786116500250303 58.4328508421484 39.56544653203226
475 4000.0 0.05 42050.0 1.98263996649043 19.3003093545278 262.281303684007 61.7481857507349 0.725 0.759912616908626 58.4328508421484 38.265564691915934
476 4000.0 0.05 40600.0 2.44952867467539 15.1753102954739 262.281303684007 61.7481857507349 0.7 0.733708733566949 58.4328508421484 37.17235771585998
477 4000.0 0.05 0.0 0.0 0.0 262.281303684007 61.7481857507349 0.0 0.0 58.4328508421484 0.0
478 4000.0 0.1 58000.0 0.512933719785062 180.987461006568 262.675214736028 62.0729275737883 1.0 1.04736912535132 116.865701684297 92.83457160855278
479 4000.0 0.1 56550.0 0.513583714575495 165.986526452651 262.675214736028 62.0729275737883 0.975 1.02118489721753 116.865701684297 85.24797682503616
480 4000.0 0.1 55100.0 0.520828743115578 148.736826411575 262.675214736028 62.0729275737883 0.95 0.995000669083752 116.865701684297 77.46641435494053
481 4000.0 0.1 53650.0 0.532572195102974 133.0967600745 262.675214736028 62.0729275737883 0.925 0.968816440949969 116.865701684297 70.88363367397034
482 4000.0 0.1 52200.0 0.561835821096151 115.69705645734 262.675214736028 62.0729275737883 0.9 0.942632212816186 116.865701684297 65.00275071311736
483 4000.0 0.1 50750.0 0.610020844493543 97.3398208671766 262.675214736028 62.0729275737883 0.875 0.916447984682403 116.865701684297 59.379319728245264
484 4000.0 0.1 49300.0 0.700814883149515 76.3816585593923 262.675214736028 62.0729275737883 0.85 0.89026375654862 116.865701684297 53.529403118066675
485 4000.0 0.1 47850.0 0.852606428977874 56.4915958370926 262.675214736028 62.0729275737883 0.825 0.864079528414837 116.865701684297 48.16509779392486
486 4000.0 0.1 46400.0 1.06829204463706 41.2124099743184 262.675214736028 62.0729275737883 0.8 0.837895300281054 116.865701684297 44.02688971588537
487 4000.0 0.1 44950.0 1.32630913954704 31.1100782357251 262.675214736028 62.0729275737883 0.775 0.811711072147271 116.865701684297 41.26158109606566
488 4000.0 0.1 43500.0 1.6157012281043 24.4363880379284 262.675214736028 62.0729275737883 0.75 0.785526844013488 116.865701684297 39.48190216331414
489 4000.0 0.1 42050.0 1.95625449809703 19.514952149108 262.675214736028 62.0729275737883 0.725 0.759342615879705 116.865701684297 38.17621292184083
490 4000.0 0.1 40600.0 2.4619611768912 14.8444101225498 262.675214736028 62.0729275737883 0.7 0.733158387745922 116.865701684297 36.54636141556835
491 4000.0 0.1 0.0 0.0 0.0 262.675214736028 62.0729275737883 0.0 0.0 116.865701684297 0.0
492 4000.0 0.15 58000.0 0.508376710865291 183.535093910855 263.331733156064 62.6168706187689 1.0 1.046062700507 175.298552526445 93.30496737075278
493 4000.0 0.15 56550.0 0.508557778227161 168.353315696433 263.331733156064 62.6168706187689 0.975 1.01991113299433 175.298552526445 85.61738818775379
494 4000.0 0.15 55100.0 0.515479501869442 150.886396013128 263.331733156064 62.6168706187689 0.95 0.993759565481654 175.298552526445 77.77884425572259
495 4000.0 0.15 53650.0 0.526819807149964 135.101380841619 263.331733156064 62.6168706187689 0.925 0.967607997968979 175.298552526445 71.17408340067557
496 4000.0 0.15 52200.0 0.555758111444936 117.342763698527 263.331733156064 62.6168706187689 0.9 0.941456430456304 175.298552526445 65.21419274482275
497 4000.0 0.15 50750.0 0.603163986723064 98.6553333328164 263.331733156064 62.6168706187689 0.875 0.915304862943629 175.298552526445 59.50534416451433
498 4000.0 0.15 49300.0 0.69259042623058 77.3477011375684 263.331733156064 62.6168706187689 0.85 0.889153295430954 175.298552526445 53.57027729882401
499 4000.0 0.15 47850.0 0.841914795667452 57.15716419971 263.331733156064 62.6168706187689 0.825 0.863001727918279 175.298552526445 48.12146221812985
500 4000.0 0.15 46400.0 1.05206836997687 41.7603409950361 263.331733156064 62.6168706187689 0.8 0.836850160405603 175.298552526445 43.93473388032589
501 4000.0 0.15 44950.0 1.30150643971779 31.6106594905969 263.331733156064 62.6168706187689 0.775 0.810698592892928 175.298552526445 41.14147689073814
502 4000.0 0.15 43500.0 1.58198593134005 24.8703737906756 263.331733156064 62.6168706187689 0.75 0.784547025380253 175.298552526445 39.34458144401711
503 4000.0 0.15 42050.0 1.91314195786026 19.8776161794593 263.331733156064 62.6168706187689 0.725 0.758395457867578 175.298552526445 38.028701535165546
504 4000.0 0.15 40600.0 2.40762299842655 15.1147622768858 263.331733156064 62.6168706187689 0.7 0.732243890354903 175.298552526445 36.390649273580294
505 4000.0 0.15 0.0 0.0 0.0 263.331733156064 62.6168706187689 0.0 0.0 175.298552526445 0.0
506 4000.0 0.2 58000.0 0.502168900435508 187.114078592016 264.250858944113 63.3840961442688 1.0 1.04424189146032 233.731403368594 93.9628711025559
507 4000.0 0.2 56550.0 0.501751601103474 171.650832615857 264.250858944113 63.3840961442688 0.975 1.01813584417381 233.731403368594 86.12608009575068
508 4000.0 0.2 55100.0 0.508189938401694 153.908131483308 264.250858944113 63.3840961442688 0.95 0.9920297968873 233.731403368594 78.21456385802212
509 4000.0 0.2 53650.0 0.519052011049344 137.912781865048 264.250858944113 63.3840961442688 0.925 0.965923749600792 233.731403368594 71.58390677646265
510 4000.0 0.2 52200.0 0.547479239939111 119.66833950462 264.250858944113 63.3840961442688 0.9 0.939817702314284 233.731403368594 65.51593155676484
511 4000.0 0.2 50750.0 0.593837641984712 100.502751764912 264.250858944113 63.3840961442688 0.875 0.913711655027776 233.731403368594 59.6823171210502
512 4000.0 0.2 49300.0 0.681342529901859 78.7155195007167 264.250858944113 63.3840961442688 0.85 0.887605607741268 233.731403368594 53.632231199157424
513 4000.0 0.2 47850.0 0.827192207199755 58.1013211484694 264.250858944113 63.3840961442688 0.825 0.86149956045476 233.731403368594 48.06096008202421
514 4000.0 0.2 46400.0 1.02979139836151 42.5414906100068 264.250858944113 63.3840961442688 0.8 0.835393513168252 233.731403368594 43.80886110366194
515 4000.0 0.2 44950.0 1.26765700172921 32.3255071750987 264.250858944113 63.3840961442688 0.775 0.809287465881744 233.731403368594 40.97765550496168
516 4000.0 0.2 43500.0 1.53603185614274 25.4910973063103 264.250858944113 63.3840961442688 0.75 0.783181418595236 233.731403368594 39.15513751052701
517 4000.0 0.2 42050.0 1.86206441271866 20.2138617108206 264.250858944113 63.3840961442688 0.725 0.757075371308729 233.731403368594 37.63951253533537
518 4000.0 0.2 40600.0 2.33409278241193 15.4982133670753 264.250858944113 63.3840961442688 0.7 0.730969324022221 233.731403368594 36.17426796037055
519 4000.0 0.2 0.0 0.0 0.0 264.250858944113 63.3840961442688 0.0 0.0 233.731403368594 0.0
520 4000.0 0.25 58000.0 0.494475248953783 191.734501623019 265.432592100177 64.3803604853571 1.0 1.04191476235198 292.164254210742 94.80796542307183
521 4000.0 0.25 56550.0 0.493365083126142 175.872416132395 265.432592100177 64.3803604853571 0.975 1.01586689329318 292.164254210742 86.76930920475449
522 4000.0 0.25 55100.0 0.499125808819755 157.858373934168 265.432592100177 64.3803604853571 0.95 0.989819024234382 292.164254210742 78.79118856886292
523 4000.0 0.25 53650.0 0.509523423044994 141.535532357995 265.432592100177 64.3803604853571 0.925 0.963771155175582 292.164254210742 72.11566892954113
524 4000.0 0.25 52200.0 0.537167924883246 122.702242161566 265.432592100177 64.3803604853571 0.9 0.937723286116782 292.164254210742 65.91170880044996
525 4000.0 0.25 50750.0 0.582274081387425 102.892641336406 265.432592100177 64.3803604853571 0.875 0.911675417057983 292.164254210742 59.911718215681596
526 4000.0 0.25 49300.0 0.667353118463615 80.476115534608 265.432592100177 64.3803604853571 0.85 0.885627547999183 292.164254210742 53.705986663858816
527 4000.0 0.25 47850.0 0.808694238182032 59.3365047668264 265.432592100177 64.3803604853571 0.825 0.859579678940384 292.164254210742 47.98508951879319
528 4000.0 0.25 46400.0 1.00186962694388 43.5736359433813 265.432592100177 64.3803604853571 0.8 0.833531809881584 292.164254210742 43.65510238718387
529 4000.0 0.25 44950.0 1.22553591142411 33.2706443864228 265.432592100177 64.3803604853571 0.775 0.807483940822785 292.164254210742 40.77436949178212
530 4000.0 0.25 43500.0 1.47888500326836 26.3330207379641 265.432592100177 64.3803604853571 0.75 0.781436071763985 292.164254210742 38.94350946012983
531 4000.0 0.25 42050.0 1.79121279155016 20.8770024138194 265.432592100177 64.3803604853571 0.725 0.755388202705186 292.164254210742 37.39515377285688
532 4000.0 0.25 40600.0 2.24542999254096 15.9862819365947 265.432592100177 64.3803604853571 0.7 0.729340333646386 292.164254210742 35.896076929645524
533 4000.0 0.25 0.0 0.0 0.0 265.432592100177 64.3803604853571 0.0 0.0 292.164254210742 0.0
534 4000.0 0.3 58000.0 0.485532677801374 197.394491551304 266.876932624255 65.6131378025166 1.0 1.03909151095035 350.597105052891 95.84147606614532
535 4000.0 0.3 56550.0 0.48360563114096 181.054193388126 266.876932624255 65.6131378025166 0.975 1.01311422317659 350.597105052891 87.5588274641821
536 4000.0 0.3 55100.0 0.488481730885005 162.804575019183 266.876932624255 65.6131378025166 0.95 0.987136935402833 350.597105052891 79.52706060136816
537 4000.0 0.3 53650.0 0.498527594105241 145.971240863982 266.876932624255 65.6131378025166 0.925 0.961159647629074 350.597105052891 72.77069151647758
538 4000.0 0.3 52200.0 0.524997145147183 126.483473437772 266.876932624255 65.6131378025166 0.9 0.935182359855315 350.597105052891 66.40346246312986
539 4000.0 0.3 50750.0 0.568786919643188 105.83109792579 266.876932624255 65.6131378025166 0.875 0.909205072081557 350.597105052891 60.19534419166668
540 4000.0 0.3 49300.0 0.651058929903836 82.6256711002017 266.876932624255 65.6131378025166 0.85 0.883227784307798 350.597105052891 53.79418100908362
541 4000.0 0.3 47850.0 0.786726708471003 60.8800948269685 266.876932624255 65.6131378025166 0.825 0.857250496534039 350.597105052891 47.89599661462347
542 4000.0 0.3 46400.0 0.968749806762913 44.8820304414574 266.876932624255 65.6131378025166 0.8 0.83127320876028 350.597105052891 43.47945831728904
543 4000.0 0.3 44950.0 1.17618719502159 34.467582189139 266.876932624255 65.6131378025166 0.775 0.805295920986521 350.597105052891 40.54032881421952
544 4000.0 0.3 43500.0 1.41427442105397 27.342512298392 266.876932624255 65.6131378025166 0.75 0.779318633212763 350.597105052891 38.6698157509694
545 4000.0 0.3 42050.0 1.7082615092622 21.7155865051318 266.876932624255 65.6131378025166 0.725 0.753341345439004 350.597105052891 37.09590057777031
546 4000.0 0.3 40600.0 2.14154137657687 16.6055717053225 266.876932624255 65.6131378025166 0.7 0.727364057665245 350.597105052891 35.56151888866227
547 4000.0 0.3 0.0 0.0 0.0 266.876932624255 65.6131378025166 0.0 0.0 350.597105052891 0.0
548 4000.0 0.35 58000.0 0.475456837401016 204.141883290552 268.583880516348 67.0916752949494 1.0 1.03578434646499 409.029955895039 97.06065421041316
549 4000.0 0.35 56550.0 0.47277658919466 187.206355165262 268.583880516348 67.0916752949494 0.975 1.00988973780337 409.029955895039 88.50678207059669
550 4000.0 0.35 55100.0 0.476535241522749 168.796656640343 268.583880516348 67.0916752949494 0.95 0.983995129141743 409.029955895039 80.4375555403384
551 4000.0 0.35 53650.0 0.486357558404709 151.237415439444 268.583880516348 67.0916752949494 0.925 0.958100520480118 409.029955895039 73.55546011256662
552 4000.0 0.35 52200.0 0.511244289102639 131.108869816639 268.583880516348 67.0916752949494 0.9 0.932205911818494 409.029955895039 67.02866094445804
553 4000.0 0.35 50750.0 0.553861843741355 109.264491838343 268.583880516348 67.0916752949494 0.875 0.906311303156869 409.029955895039 60.517432905046896
554 4000.0 0.35 49300.0 0.63294534978744 85.1316129863787 268.583880516348 67.0916752949494 0.85 0.880416694495244 409.029955895039 53.88365855963244
555 4000.0 0.35 47850.0 0.761655798536187 62.752862602764 268.583880516348 67.0916752949494 0.825 0.854522085833619 409.029955895039 47.79608167613984
556 4000.0 0.35 46400.0 0.930928272769655 46.509216683074 268.583880516348 67.0916752949494 0.8 0.828627477171994 409.029955895039 43.2967447546437
557 4000.0 0.35 44950.0 1.12092406496459 35.9535859074632 268.583880516348 67.0916752949494 0.775 0.802732868510369 409.029955895039 40.30123966544725
558 4000.0 0.35 43500.0 1.34214772117032 28.5745316319166 268.583880516348 67.0916752949494 0.75 0.776838259848744 409.029955895039 38.351242513286095
559 4000.0 0.35 42050.0 1.61627594977978 22.7349539579851 268.583880516348 67.0916752949494 0.725 0.75094365118712 409.029955895039 36.74595930164193
560 4000.0 0.35 40600.0 2.02665080752162 17.3504035569554 268.583880516348 67.0916752949494 0.7 0.725049042525495 409.029955895039 35.16320937952965
561 4000.0 0.35 0.0 0.0 0.0 268.583880516348 67.0916752949494 0.0 0.0 409.029955895039 0.0
562 5000.0 0.0 58000.0 0.518246696506667 162.250085387309 255.65 54.019881314929 1.0 1.06166235198407 0.0 84.08557075989754
563 5000.0 0.0 56550.0 0.520357994792976 149.523156804776 255.65 54.019881314929 0.975 1.03512079318447 0.0 77.80557005004896
564 5000.0 0.0 55100.0 0.524609481589099 135.569262000759 255.65 54.019881314929 0.95 1.00857923438487 0.0 71.12092025763492
565 5000.0 0.0 53650.0 0.5368267262803 120.847121314339 255.65 54.019881314929 0.925 0.982037675585263 0.0 64.87396451557488
566 5000.0 0.0 52200.0 0.559100659317848 106.631628146617 255.65 54.019881314929 0.9 0.955496116785662 0.0 59.617813600909166
567 5000.0 0.0 50750.0 0.60284323239843 90.714670469783 255.65 54.019881314929 0.875 0.92895455798606 0.0 54.68672517196239
568 5000.0 0.0 49300.0 0.678084139353396 73.4642188139315 255.65 54.019881314929 0.85 0.902412999186458 0.0 49.814921587714295
569 5000.0 0.0 47850.0 0.815852899049915 55.1401506458041 255.65 54.019881314929 0.825 0.875871440386856 0.0 44.98625175842832
570 5000.0 0.0 46400.0 1.02765242307153 40.0283700761439 255.65 54.019881314929 0.8 0.849329881587255 0.0 41.135251500353206
571 5000.0 0.0 44950.0 1.30750823132573 29.4222992632827 255.65 54.019881314929 0.775 0.822788322787653 0.0 38.469898471271094
572 5000.0 0.0 43500.0 1.62754845803272 22.6782115758849 255.65 54.019881314929 0.75 0.796246763988051 0.0 36.90988828127125
573 5000.0 0.0 42050.0 2.00920504969854 17.7874094625612 255.65 54.019881314929 0.725 0.76970520518845 0.0 35.73855291323356
574 5000.0 0.0 40600.0 2.53137262768456 13.7163011931135 255.65 54.019881314929 0.7 0.743163646388848 0.0 34.72106939332458
575 5000.0 0.0 0.0 0.0 0.0 255.65 54.019881314929 0.0 0.0 0.0 0.0
576 5000.0 0.05 58000.0 0.516752133377032 163.140244444615 255.778056334361 54.1145158994384 1.0 1.06139655590359 57.7057583324503 84.30306935640529
577 5000.0 0.05 56550.0 0.519348214207269 149.959118413857 255.778056334361 54.1145158994384 0.975 1.034861642006 57.7057583324503 77.88100035233302
578 5000.0 0.05 55100.0 0.52354921624391 135.9323215579 255.778056334361 54.1145158994384 0.95 1.00832672810841 57.7057583324503 71.1672604138537
579 5000.0 0.05 53650.0 0.535656256196874 121.20968523407 255.778056334361 54.1145158994384 0.925 0.981791814210823 57.7057583324503 64.92672620728345
580 5000.0 0.05 52200.0 0.557879248119083 106.935379836636 255.778056334361 54.1145158994384 0.9 0.955256900313234 57.7057583324503 59.657029300591034
581 5000.0 0.05 50750.0 0.601373549085895 90.988630071994 255.778056334361 54.1145158994384 0.875 0.928721986415644 57.7057583324503 54.71815539285863
582 5000.0 0.05 49300.0 0.676409044927178 73.6675888993647 255.778056334361 54.1145158994384 0.85 0.902187072518054 57.7057583324503 49.82942344950726
583 5000.0 0.05 47850.0 0.813876312979595 55.2611344518302 255.778056334361 54.1145158994384 0.825 0.875652158620464 57.7057583324503 44.975728358725235
584 5000.0 0.05 46400.0 1.02479297251044 40.124902133305 255.778056334361 54.1145158994384 0.8 0.849117244722874 57.7057583324503 41.11971772888013
585 5000.0 0.05 44950.0 1.30272446539547 29.5149982537354 255.778056334361 54.1145158994384 0.775 0.822582330825284 57.7057583324503 38.449910321245675
586 5000.0 0.05 43500.0 1.62097077598601 22.7534387908991 255.778056334361 54.1145158994384 0.75 0.796047416927694 57.7057583324503 36.882659333233896
587 5000.0 0.05 42050.0 2.00014863712078 17.8538244868514 255.778056334361 54.1145158994384 0.725 0.769512503030105 57.7057583324503 35.71030271476944
588 5000.0 0.05 40600.0 2.51826202008928 13.7757528719333 255.778056334361 54.1145158994384 0.7 0.742977589132515 57.7057583324503 34.69095525552545
589 5000.0 0.05 0.0 0.0 0.0 255.778056334361 54.1145158994384 0.0 0.0 57.7057583324503 0.0
590 5000.0 0.1 58000.0 0.514061195005615 164.53000982987 256.162225337444 54.3991297683344 1.0 1.0606003638621 115.411516664901 84.57849346742856
591 5000.0 0.1 56550.0 0.516403351526352 151.233341263994 256.162225337444 54.3991297683344 0.975 1.03408535476554 115.411516664901 78.09740429125505
592 5000.0 0.1 55100.0 0.52037921011584 137.069207099578 256.162225337444 54.3991297683344 0.95 1.00757034566899 115.411516664901 71.32796572168289
593 5000.0 0.1 53650.0 0.532100729294331 122.302942562597 256.162225337444 54.3991297683344 0.925 0.981055336572438 115.411516664901 65.07748493240054
594 5000.0 0.1 52200.0 0.554240267534791 107.855611882432 256.162225337444 54.3991297683344 0.9 0.954540327475886 115.411516664901 59.7779231848477
595 5000.0 0.1 50750.0 0.597131775319073 91.7763464848068 256.162225337444 54.3991297683344 0.875 0.928025318379333 115.411516664901 54.802572708771045
596 5000.0 0.1 49300.0 0.671621908208454 74.2457866475003 256.162225337444 54.3991297683344 0.85 0.901510309282781 115.411516664901 49.865096904631905
597 5000.0 0.1 47850.0 0.80776230960516 55.6637293383272 256.162225337444 54.3991297683344 0.825 0.874995300186229 115.411516664901 44.96306257156369
598 5000.0 0.1 46400.0 1.016187976604 40.4183748079322 256.162225337444 54.3991297683344 0.8 0.848480291089676 115.411516664901 41.07266651369471
599 5000.0 0.1 44950.0 1.28869709034324 29.7833759864807 256.162225337444 54.3991297683344 0.775 0.821965281993124 115.411516664901 38.381749974376405
600 5000.0 0.1 43500.0 1.60067732113231 22.9914708974933 256.162225337444 54.3991297683344 0.75 0.795450272896571 115.411516664901 36.801926045091044
601 5000.0 0.1 42050.0 1.97378083969854 18.0491697188451 256.162225337444 54.3991297683344 0.725 0.768935263800019 115.411516664901 35.62510536352354
602 5000.0 0.1 40600.0 2.48476694708568 13.9231822780183 256.162225337444 54.3991297683344 0.7 0.742420254703466 115.411516664901 34.59586312266898
603 5000.0 0.1 0.0 0.0 0.0 256.162225337444 54.3991297683344 0.0 0.0 115.411516664901 0.0
604 5000.0 0.15 58000.0 0.509665687846681 166.847026869279 256.80250700925 54.8758585840331 1.0 1.05927734954021 173.117274997351 85.03620471450475
605 5000.0 0.15 56550.0 0.511582403647968 153.361047009971 256.80250700925 54.8758585840331 0.975 1.0327954158017 173.117274997351 78.45681305532999
606 5000.0 0.15 55100.0 0.515201040467881 138.97416770803 256.80250700925 54.8758585840331 0.95 1.0063134820632 173.117274997351 71.59963580133484
607 5000.0 0.15 53650.0 0.526284008552761 124.139105285384 256.80250700925 54.8758585840331 0.925 0.979831548324692 173.117274997351 65.33242594774514
608 5000.0 0.15 52200.0 0.548288180075291 109.396571893106 256.80250700925 54.8758585840331 0.9 0.953349614586187 173.117274997351 59.98084730974682
609 5000.0 0.15 50750.0 0.590209970057099 93.0925277401187 256.80250700925 54.8758585840331 0.875 0.926867680847682 173.117274997351 54.94413801003511
610 5000.0 0.15 49300.0 0.663809295181086 75.2082030440106 256.80250700925 54.8758585840331 0.85 0.900385747109176 173.117274997351 49.92390425448068
611 5000.0 0.15 47850.0 0.797710521235381 56.3395856202963 256.80250700925 54.8758585840331 0.825 0.873903813370671 173.117274997351 44.942680211351934
612 5000.0 0.15 46400.0 1.00197401193305 40.9148193761549 256.80250700925 54.8758585840331 0.8 0.847421879632166 173.117274997351 40.99558571784202
613 5000.0 0.15 44950.0 1.26567357092106 30.2369737876224 256.80250700925 54.8758585840331 0.775 0.820939945893661 173.117274997351 38.27013858762653
614 5000.0 0.15 43500.0 1.56731641897143 23.4118194340459 256.80250700925 54.8758585840331 0.75 0.794458012155155 173.117274997351 36.69372899697455
615 5000.0 0.15 42050.0 1.9298587367802 18.3867953502695 256.80250700925 54.8758585840331 0.725 0.76797607841665 173.117274997351 35.48391764810716
616 5000.0 0.15 40600.0 2.42799850768839 14.1835499643512 256.80250700925 54.8758585840331 0.7 0.741494144678145 173.117274997351 34.437638147168435
617 5000.0 0.15 0.0 0.0 0.0 256.80250700925 54.8758585840331 0.0 0.0 173.117274997351 0.0
618 5000.0 0.2 58000.0 0.503682619646487 170.103179946096 257.698901349778 55.5482795233554 1.0 1.05743341970528 230.823033329801 85.6780152854474
619 5000.0 0.2 56550.0 0.505010022911426 156.34762318493 257.698901349778 55.5482795233554 0.975 1.03099758421265 230.823033329801 78.95711676676851
620 5000.0 0.2 55100.0 0.508167038876388 141.670991185465 257.698901349778 55.5482795233554 0.95 1.00456174872002 230.823033329801 71.99252808540062
621 5000.0 0.2 53650.0 0.518368571739874 126.736775213638 257.698901349778 55.5482795233554 0.925 0.978125913227389 230.823033329801 65.696361154411
622 5000.0 0.2 52200.0 0.540193858126767 111.573229634416 257.698901349778 55.5482795233554 0.9 0.951690077734756 230.823033329801 60.27117337987891
623 5000.0 0.2 50750.0 0.58082043821956 94.9414649814655 257.698901349778 55.5482795233554 0.875 0.925254242242124 230.823033329801 55.1439432957418
624 5000.0 0.2 49300.0 0.653209833004123 76.5545862467353 257.698901349778 55.5482795233554 0.85 0.898818406749492 230.823033329801 50.0062084979297
625 5000.0 0.2 47850.0 0.783910839193015 57.2999926042574 257.698901349778 55.5482795233554 0.825 0.87238257125686 230.823033329801 44.918085288156966
626 5000.0 0.2 46400.0 0.982401190970184 41.6218796168076 257.698901349778 55.5482795233554 0.8 0.845946735764228 230.823033329801 40.889384105969405
627 5000.0 0.2 44950.0 1.23436798291207 30.8845483361624 257.698901349778 55.5482795233554 0.775 0.819510900271596 230.823033329801 38.12289763285911
628 5000.0 0.2 43500.0 1.5231168603958 23.9732411175547 257.698901349778 55.5482795233554 0.75 0.793075064778964 230.823033329801 36.51404774448142
629 5000.0 0.2 42050.0 1.87093761994391 18.8588917074703 257.698901349778 55.5482795233554 0.725 0.766639229286331 230.823033329801 35.283809965954426
630 5000.0 0.2 40600.0 2.35143380383289 14.5519079497807 257.698901349778 55.5482795233554 0.7 0.740203393793699 230.823033329801 34.217848263378905
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748 6000.0 0.25 52200.0 0.526561585642671 106.045392962282 252.270214040197 49.2786533779784 0.9 0.961875442743478 284.846451158329 55.83943026831935
749 6000.0 0.25 50750.0 0.56235489453584 90.9591973628607 252.270214040197 49.2786533779784 0.875 0.935156680445049 284.846451158329 51.151349840056184
750 6000.0 0.25 49300.0 0.621788633876176 75.0338018194786 252.270214040197 49.2786533779784 0.85 0.908437918146618 284.846451158329 46.655165127869324
751 6000.0 0.25 47850.0 0.731391381819095 57.6554094288205 252.270214040197 49.2786533779784 0.825 0.881719155848188 284.846451158329 42.1686695714907
752 6000.0 0.25 46400.0 0.914474868477157 41.8936517333171 252.270214040197 49.2786533779784 0.8 0.855000393549759 284.846451158329 38.310691658852974
753 6000.0 0.25 44950.0 1.16852411481809 30.5328814412811 252.270214040197 49.2786533779784 0.775 0.828281631251329 284.846451158329 35.67840825901869
754 6000.0 0.25 43500.0 1.46795912647773 23.2706495400573 252.270214040197 49.2786533779784 0.75 0.801562868952899 284.846451158329 34.160362371391905
755 6000.0 0.25 42050.0 1.83063475960272 18.1640745685555 252.270214040197 49.2786533779784 0.725 0.774844106654469 284.846451158329 33.25178628121348
756 6000.0 0.25 40600.0 2.32024196895106 14.0514983283886 252.270214040197 49.2786533779784 0.7 0.748125344356039 284.846451158329 32.602876148172896
757 6000.0 0.25 0.0 0.0 0.0 252.270214040197 49.2786533779784 0.0 0.0 284.846451158329 0.0
758 6000.0 0.3 58000.0 0.490355368823204 162.17156541481 253.643108217884 50.2223799446227 1.0 1.06585415420823 341.815741389994 79.52169777161552
759 6000.0 0.3 56550.0 0.490230015520132 150.194515838868 253.643108217884 50.2223799446227 0.975 1.03920780035303 341.815741389994 73.62985983072697
760 6000.0 0.3 55100.0 0.491270347633714 137.307598486989 253.643108217884 50.2223799446227 0.95 1.01256144649782 341.815741389994 67.45515164145351
761 6000.0 0.3 53650.0 0.499436382162378 122.840419870291 253.643108217884 50.2223799446227 0.925 0.985915092642614 341.815741389994 61.35097488332563
762 6000.0 0.3 52200.0 0.514875974769656 109.329710607968 253.643108217884 50.2223799446227 0.9 0.959268738787408 341.815741389994 56.291241320561916
763 6000.0 0.3 50750.0 0.549133428852494 93.7290481476368 253.643108217884 50.2223799446227 0.875 0.932622384932202 341.815741389994 51.4697535923923
764 6000.0 0.3 49300.0 0.606806016429206 77.1385360951671 253.643108217884 50.2223799446227 0.85 0.905976031076996 341.815741389994 46.808127801088865
765 6000.0 0.3 47850.0 0.712495760913284 59.1889680071433 253.643108217884 50.2223799446227 0.825 0.879329677221791 341.815741389994 42.17188879792159
766 6000.0 0.3 46400.0 0.887450965694075 43.0247513456544 253.643108217884 50.2223799446227 0.8 0.852683323366585 341.815741389994 38.18235713044845
767 6000.0 0.3 44950.0 1.12633670920937 31.4866362157395 253.643108217884 50.2223799446227 0.775 0.826036969511379 341.815741389994 35.464554219308596
768 6000.0 0.3 43500.0 1.4034241986148 24.134415868178 253.643108217884 50.2223799446227 0.75 0.799390615656173 341.815741389994 33.87082324883402
769 6000.0 0.3 42050.0 1.74296021254557 18.8911699676513 253.643108217884 50.2223799446227 0.725 0.772744261800967 341.815741389994 32.926557622052
770 6000.0 0.3 40600.0 2.20412661882757 14.6262318459538 253.643108217884 50.2223799446227 0.7 0.746097907945762 341.815741389994 32.23806694481028
771 6000.0 0.3 0.0 0.0 0.0 253.643108217884 50.2223799446227 0.0 0.0 341.815741389994 0.0
772 6000.0 0.35 58000.0 0.481024129071741 167.733120980767 255.265619518787 51.3542451430885 1.0 1.06246138002961 398.78503162166 80.68367843625842
773 6000.0 0.35 56550.0 0.479815230117078 155.374522056274 255.265619518787 51.3542451430885 0.975 1.03589984552887 398.78503162166 74.55106205476213
774 6000.0 0.35 55100.0 0.480073596902173 141.88945996365 255.265619518787 51.3542451430885 0.95 1.00933831102813 398.78503162166 68.11738340725633
775 6000.0 0.35 53650.0 0.486963945452877 127.295930681106 255.265619518787 51.3542451430885 0.925 0.982776776527389 398.78503162166 61.98852864456732
776 6000.0 0.35 52200.0 0.501935998523042 113.225016032718 255.265619518787 51.3542451430885 0.9 0.956215242026649 398.78503162166 56.83171148016976
777 6000.0 0.35 50750.0 0.534003617986606 97.1665008385878 255.265619518787 51.3542451430885 0.875 0.929653707525908 398.78503162166 51.88726299490447
778 6000.0 0.35 49300.0 0.590090920144534 79.6211174648268 255.265619518787 51.3542451430885 0.85 0.903092173025168 398.78503162166 46.98369846775567
779 6000.0 0.35 47850.0 0.691344390788991 60.9981721557301 255.265619518787 51.3542451430885 0.825 0.876530638524428 398.78503162166 42.17074416824522
780 6000.0 0.35 46400.0 0.856586290593151 44.4067433965466 255.265619518787 51.3542451430885 0.8 0.849969104023688 398.78503162166 38.038207603369756
781 6000.0 0.35 44950.0 1.07852868216672 32.6532715537887 255.265619518787 51.3542451430885 0.775 0.823407569522947 398.78503162166 35.21748993733977
782 6000.0 0.35 43500.0 1.33152325107151 25.1885397904928 255.265619518787 51.3542451430885 0.75 0.796846035022207 398.78503162166 33.539126391581064
783 6000.0 0.35 42050.0 1.64510575006291 19.7877084172638 255.265619518787 51.3542451430885 0.725 0.770284500521467 398.78503162166 32.552872897808925
784 6000.0 0.35 40600.0 2.07601315096264 15.3242670915128 255.265619518787 51.3542451430885 0.7 0.743722966020726 398.78503162166 31.813380010844575
785 6000.0 0.35 0.0 0.0 0.0 255.265619518787 51.3542451430885 0.0 0.0 398.78503162166 0.0
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Model/src/battery_sim.py
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import numpy as np
import matplotlib.pyplot as plt
class BatterySim:
"""
混电飞机高压电池模型
"""
def __init__(self, capacity_kwh=50.0, nom_voltage=500.0, initial_soc=0.5):
"""
初始化电池参数
:param capacity_kwh: 电池总能量 (kWh)，默认 50 kWh
:param nom_voltage: 额定电压 (V)，默认 500 V
:param initial_soc: 初始荷电状态 (0.0 到 1.0)
"""
# --- 基本物理参数 ---
# 单体电池为 3.7V，根据额定电压计算串联数
self.cells_in_series = int(nom_voltage / 3.7)
# 电池组电压安全极限 (工程极限)
self.V_min = self.cells_in_series * 2.7 # 绝对放电截止电压 (约 364.5V)
self.V_max = self.cells_in_series * 4.2 # 绝对充电截止电压 (约 567.0V)
# 容量与内阻标定
self.capacity_Ah = (capacity_kwh * 1000) / nom_voltage # 安时容量 (约 100Ah)
# 电池内阻为 0.15 欧姆。
# 在 SOC=50% 时，此内阻可将最大放电/充电安全功率限制在 300kW 左右。
self.R_in = 0.15
# --- 状态变量 ---
self.SOC = initial_soc
self.V_t = 0.0 # 端电压 (V)
self.I = 0.0 # 实际电流 (A)，放电为正，充电为负
# --- OCV-SOC 查表曲线 Voc = f(SOC) ---
self.soc_table = np.array([0.0, 0.1, 0.2, 0.5, 0.8, 0.9, 1.0])
self.cell_ocv_table = np.array([2.8, 3.3, 3.4, 3.6, 3.9, 4.0, 4.15])
self.Voc_table = self.cell_ocv_table * self.cells_in_series
def _get_ocv(self, soc):
"""根据当前 SOC 线性插值获取开路电压"""
return np.interp(soc, self.soc_table, self.Voc_table)
def step(self, dt, P_req_kw):
"""
单步执行电池仿真，包含过充/过放保护(功率限幅)
:param dt: 仿真步长 (秒)
:param P_req_kw: 外部请求功率 (kW)。正数表示放电，负数表示充电
:return: (实际输出/吸收功率 kW, 端电压 V, 实际电流 A, 当前 SOC)
"""
# 1. 获取当前开路电压
V_oc = self._get_ocv(self.SOC)
# 2. 计算工程安全极限 (基于电压边界)
# 2.1 最大放电极限 (限制 V_t >= V_min)
I_dis_max = (V_oc - self.V_min) / self.R_in
P_dis_max_W = self.V_min * I_dis_max # 瓦特
# 2.2 最大充电极限 (限制 V_t <= V_max, 电流和功率为负数)
I_cha_max = (V_oc - self.V_max) / self.R_in
P_cha_max_W = self.V_max * I_cha_max # 瓦特
# SOC 保护限制 (防止 SOC 突破 0% 和 100%)
if self.SOC <= 0.05:
P_dis_max_W = 0.0 # 电量极低，禁止放电
if self.SOC >= 0.98:
P_cha_max_W = 0.0 # 电量极高，禁止充电
# 3. 保护逻辑：功率限幅 (Clipping)
P_req_W = P_req_kw * 1000.0
# 强制将需求功率限制在安全充放电区间内
P_actual_W = max(P_cha_max_W, min(P_req_W, P_dis_max_W))
# 4. 根据实际功率求解电流 (一元二次方程)
# R * I^2 - Voc * I + P_actual = 0
discriminant = V_oc**2 - 4 * self.R_in * P_actual_W
if discriminant < 0:
self.I = V_oc / (2 * self.R_in)
P_actual_W = V_oc**2 / (4 * self.R_in)
else:
self.I = (V_oc - np.sqrt(discriminant)) / (2 * self.R_in)
# 5. 计算端电压
self.V_t = V_oc - self.I * self.R_in
# 6. 安时积分更新 SOC
delta_soc = (self.I * (dt / 3600.0)) / self.capacity_Ah
self.SOC = self.SOC - delta_soc
self.SOC = max(0.0, min(1.0, self.SOC))
P_actual_kw = P_actual_W / 1000.0
return P_actual_kw, self.V_t, self.I, self.SOC
if __name__ == "__main__":
import matplotlib
matplotlib.use('Agg')
# ==========================================
# 【测试示例】展示完整的充放电循环与边界保护
# ==========================================
plt.rcParams['font.family'] = 'serif'
plt.rcParams['font.serif'] = ['DejaVu Serif', 'Times New Roman']
plt.rcParams['axes.unicode_minus'] = True
battery = BatterySim(initial_soc=0.5)
dt = 0.5
# 仿真总时长 1 小时，包含一个完整的充放电循环，且在边界处持续请求以验证保护机制
time_array = np.arange(0, 3600, dt)
req_power_log = []
act_power_log = []
soc_log = []
vt_log = []
for t in time_array:
if t < 100:
P_req = 0.0
elif t < 1200:
# 持续放电 150kW，将SOC从50%消耗至最低限制(5%)
# 在触发限制后继续请求，以验证禁止放电保护生效
P_req = 150.0
elif t < 3300:
# 持续充电 150kW，将SOC从5%一直充满至最高限制(98%)
# 同样在触发满电后继续请求，验证禁止充电保护生效
P_req = -150.0
else:
P_req = 0.0
P_act, V_t, I_act, soc = battery.step(dt, P_req)
req_power_log.append(P_req)
act_power_log.append(P_act)
vt_log.append(V_t)
soc_log.append(soc * 100)
# 绘图展示
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(10, 8), sharex=True)
ax1.plot(time_array, req_power_log, 'k--', linewidth=2, label='Requested Power (kW)')
ax1.plot(time_array, act_power_log, 'r-', linewidth=2, label='Actual Power (kW)')
ax1.set_ylabel('Power [kW]')
ax1.set_title('Battery Power: Complete Cycle Protection Demo', fontweight='bold')
ax1.legend()
ax1.grid(True)
ax2.plot(time_array, vt_log, 'b-', linewidth=2)
ax2.set_ylabel('Terminal Voltage [V]')
ax2.axhline(battery.V_min, color='r', linestyle=':', label='V_min limit')
ax2.axhline(battery.V_max, color='g', linestyle=':', label='V_max limit')
ax2.set_title('Voltage Dynamics with Physical Limits', fontweight='bold')
ax2.legend()
ax2.grid(True)
ax3.plot(time_array, soc_log, 'g-', linewidth=2)
ax3.axhline(5, color='r', linestyle=':', alpha=0.8, label='SOC Lower Limit (5%)')
ax3.axhline(98, color='g', linestyle=':', alpha=0.8, label='SOC Upper Limit (98%)')
ax3.set_ylabel('SOC [%]')
ax3.set_xlabel('Time [s]')
ax3.set_title('State of Charge (SOC) Evolution', fontweight='bold')
ax3.legend()
ax3.grid(True)
plt.tight_layout()
plt.savefig('figures/battery_sim_output.png')
print('Plot saved to figures/battery_sim_output.png')
# plt.show()
Model/src/distill_gpr_to_nn.py
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"""
GPR → NN 知识蒸馏脚本
将训练好的高斯过程回归 (GPR) 代理模型的知识蒸馏到轻量级神经网络 (MLP) 中。
支持两种模式:
1. 直接从 CSV 原始数据训练 NN (快速模式, 无需 GPR 依赖)
2. 从 GPR 教师模型蒸馏 (完整模式, 需 botorch/gpytorch)
用法:
python distill_gpr_to_nn.py # 从Model目录运行
python Model/src/distill_gpr_to_nn.py # 从项目根目录运行
也可在代码中调用:
from distill_gpr_to_nn import distill, distill_from_csv
result = distill_from_csv(csv_path, nn_save_path, ...)
result = distill(gpr_csv_path, gpr_pth_path, nn_save_path, ...)
"""
import os
import sys
import csv as csv_mod
import numpy as np
import torch
import torch.nn as nn
# 确保 src 目录在 path 中
_this_dir = os.path.dirname(os.path.abspath(__file__))
if _this_dir not in sys.path:
sys.path.insert(0, _this_dir)
from lightweight_model import EngineNNProxy
# ============================================================
# 工具函数: 手写 StandardScaler (避免 sklearn import 导致的
# numpy/scipy 递归问题)
# ============================================================
class SimpleScaler:
"""轻量 Z-Score 归一化, 兼容 EngineNNProxy.set_normalization_params"""
def __init__(self):
self.mean_ = None
self.scale_ = None
def fit(self, X):
X = np.asarray(X, dtype=np.float64)
self.mean_ = X.mean(axis=0)
self.scale_ = X.std(axis=0)
self.scale_[self.scale_ < 1e-12] = 1.0
return self
def transform(self, X):
return (np.asarray(X, dtype=np.float64) - self.mean_) / self.scale_
def fit_transform(self, X):
self.fit(X)
return self.transform(X)
def inverse_transform(self, X):
return np.asarray(X, dtype=np.float64) * self.scale_ + self.mean_
# ============================================================
# 模式一: 直接从 CSV 训练 (快速, 无需 sklearn/botorch)
# ============================================================
def distill_from_csv(csv_path, nn_save_path,
epochs=3000, lr=1e-3, batch_size=256,
hidden_size=64, verbose=True, progress_callback=None):
"""
直接从 CSV 原始发动机数据训练 NN 代理模型。
Returns
-------
dict with keys: loss_history, nn_model, X_train, Y_train, Y_nn,
scaler_X, scaler_Y, rel_error_fuel, rel_error_power
"""
# 1. 读取 CSV
if verbose:
print(f"-> Loading CSV: {csv_path}")
with open(csv_path, 'r', encoding='utf-8') as f:
reader = csv_mod.reader(f)
header = next(reader)
rows = [r for r in reader]
col_idx = {name: i for i, name in enumerate(header)}
data = np.array([[float(x) for x in r] for r in rows], dtype=np.float64)
X_cols = ['Altitude_m', 'Mach', 'RPM']
Y_cols = ['WF_kg_h', 'Power_kW']
X_raw = data[:, [col_idx[c] for c in X_cols]]
Y_raw = data[:, [col_idx[c] for c in Y_cols]]
# 过滤零/非物理值
valid = (Y_raw[:, 0] > 0.5) & (Y_raw[:, 1] > 0.5) & (X_raw[:, 2] > 500)
X_phys = X_raw[valid].astype(np.float32)
Y_phys = Y_raw[valid].astype(np.float32)
if verbose:
print(f"-> Valid samples: {len(X_phys)} / {len(data)}")
# 2. 归一化
scaler_X = SimpleScaler()
scaler_X.fit(X_phys)
Y_log = np.log1p(Y_phys.astype(np.float64))
scaler_Y = SimpleScaler()
scaler_Y.fit(Y_log)
return _train_nn(X_phys, Y_phys, scaler_X, scaler_Y, nn_save_path,
epochs, lr, batch_size, hidden_size, verbose,
progress_callback=progress_callback)
# ============================================================
# 模式二: 从 GPR 教师模型蒸馏 (需 botorch/gpytorch/sklearn)
# ============================================================
def _generate_teacher_data(gpr_csv_path, gpr_pth_path,
n_altitude=25, n_mach=6, n_rpm=30):
"""
加载 GPR 教师模型，在输入空间的密集网格上生成标注数据。
"""
from engine_gpr_class import EngineGPRModel
gpr = EngineGPRModel(csv_path=gpr_csv_path)
success = gpr.load_model(pth_path=gpr_pth_path)
if not success:
raise RuntimeError(f"Failed to load GPR model from {gpr_pth_path}")
df = gpr.df
H_range = np.linspace(df['Altitude_m'].min(), df['Altitude_m'].max(), n_altitude)
Ma_range = np.linspace(df['Mach'].min(), df['Mach'].max(), n_mach)
RPM_range = np.linspace(max(df['RPM'].min(), 1000), df['RPM'].max(), n_rpm)
H, Ma, RPM = np.meshgrid(H_range, Ma_range, RPM_range, indexing='ij')
X_grid = np.column_stack([H.ravel(), Ma.ravel(), RPM.ravel()])
print(f"-> Querying GPR teacher on {len(X_grid)} grid points ...")
Y_pred, _ = gpr.predict(X_grid) # [WF_kg_h, Power_kW]
valid = (Y_pred[:, 0] > 0.5) & (Y_pred[:, 1] > 0.5)
X_valid = X_grid[valid].astype(np.float32)
Y_valid = Y_pred[valid].astype(np.float32)
print(f"-> Valid samples: {len(X_valid)} / {len(X_grid)}")
scaler_X = SimpleScaler()
scaler_X.fit(X_valid)
Y_log = np.log1p(Y_valid.astype(np.float64))
scaler_Y = SimpleScaler()
scaler_Y.fit(Y_log)
return X_valid, Y_valid, scaler_X, scaler_Y
def distill(gpr_csv_path, gpr_pth_path, nn_save_path,
n_altitude=25, n_mach=6, n_rpm=30,
epochs=3000, lr=1e-3, batch_size=512,
hidden_size=64, verbose=True):
"""
从 GPR 教师模型蒸馏到 NN。需安装 botorch / gpytorch。
"""
X_phys, Y_phys, scaler_X, scaler_Y = _generate_teacher_data(
gpr_csv_path, gpr_pth_path, n_altitude, n_mach, n_rpm
)
return _train_nn(X_phys, Y_phys, scaler_X, scaler_Y, nn_save_path,
epochs, lr, batch_size, hidden_size, verbose)
# ============================================================
# 通用 NN 训练核心
# ============================================================
def _train_nn(X_phys, Y_phys, scaler_X, scaler_Y, nn_save_path,
epochs=3000, lr=1e-3, batch_size=256,
hidden_size=64, verbose=True, progress_callback=None):
"""训练 NN 并保存模型。"""
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if verbose:
print(f"-> Training device: {device}")
# 创建模型并移至设备
nn_model = EngineNNProxy(hidden_size=hidden_size)
nn_model.set_normalization_params(scaler_X, scaler_Y)
nn_model = nn_model.to(device)
# 准备归一化数据
X_norm = scaler_X.transform(X_phys).astype(np.float32)
Y_log = np.log1p(Y_phys.astype(np.float64))
Y_norm = scaler_Y.transform(Y_log).astype(np.float32)
X_t = torch.tensor(X_norm, dtype=torch.float32)
Y_t = torch.tensor(Y_norm, dtype=torch.float32)
dataset = torch.utils.data.TensorDataset(X_t, Y_t)
loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)
# 训练
optimizer = torch.optim.Adam(nn_model.net.parameters(), lr=lr)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, T_max=epochs, eta_min=lr * 0.01)
loss_fn = nn.MSELoss()
loss_history = []
nn_model.train()
n_params = sum(p.numel() for p in nn_model.parameters())
if verbose:
print(f"-> Training NN ({hidden_size}x{hidden_size}, {n_params} params) "
f"for {epochs} epochs on {len(X_phys)} samples ...")
for epoch in range(epochs):
epoch_loss = 0.0
n_batches = 0
for xb, yb in loader:
xb, yb = xb.to(device), yb.to(device)
pred = nn_model.net(xb)
loss = loss_fn(pred, yb)
optimizer.zero_grad()
loss.backward()
optimizer.step()
epoch_loss += loss.item()
n_batches += 1
scheduler.step()
avg_loss = epoch_loss / max(n_batches, 1)
loss_history.append(avg_loss)
if verbose and (epoch + 1) % 500 == 0:
print(f" Epoch {epoch+1:>5d}/{epochs} Loss: {avg_loss:.6f}")
if progress_callback is not None and (epoch + 1) % 50 == 0:
progress_callback(epoch + 1, epochs, avg_loss)
nn_model.eval()
# 验证精度（在 CPU 上做推理，避免显存占用）
nn_model_cpu = nn_model.cpu()
with torch.no_grad():
X_phys_t = torch.tensor(X_phys, dtype=torch.float32)
Y_nn = nn_model_cpu(X_phys_t).numpy()
rel_err_fuel = np.mean(
np.abs(Y_nn[:, 0] - Y_phys[:, 0]) / np.maximum(Y_phys[:, 0], 1e-6)) * 100
rel_err_power = np.mean(
np.abs(Y_nn[:, 1] - Y_phys[:, 1]) / np.maximum(Y_phys[:, 1], 1e-6)) * 100
if verbose:
print(f"-> Distillation complete!")
print(f" Fuel Flow MAPE: {rel_err_fuel:.2f}%")
print(f" Power MAPE: {rel_err_power:.2f}%")
# 保存（始终保存 CPU 版，推理时无需 GPU 环境）
if nn_save_path:
os.makedirs(os.path.dirname(os.path.abspath(nn_save_path)), exist_ok=True)
torch.save(nn_model_cpu.state_dict(), nn_save_path)
if verbose:
print(f"-> Saved NN model to {nn_save_path}")
return {
'loss_history': loss_history,
'nn_model': nn_model_cpu,
'X_train': X_phys,
'Y_train': Y_phys,
'Y_nn': Y_nn,
'scaler_X': scaler_X,
'scaler_Y': scaler_Y,
'rel_error_fuel': rel_err_fuel,
'rel_error_power': rel_err_power,
}
if __name__ == "__main__":
model_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..")
csv_path = os.path.join(model_root, "data", "Cleaned_Engine_Data_Full.csv")
nn_path = os.path.join(model_root, "data", "engine_nn_proxy.pth")
# 使用 CSV 直接训练模式 (无需 sklearn/botorch)
result = distill_from_csv(csv_path, nn_path, epochs=3000)
print(f"\nFinal training loss: {result['loss_history'][-1]:.6f}")
Model/src/engine_dynamic_sim.py
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import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import numpy as np
import torch
import matplotlib.pyplot as plt
from tqdm import tqdm
from src.lightweight_model import EngineNNProxy
from src.mpc_controller import TurboShaftMPCController
class TurboshaftDynamicSim:
"""
涡轴发动机动态仿真类
包含功能:
1. 基于 NN Proxy (轻量化神经网络) 的稳态代理模型
2. 一阶燃油执行机构动态 (First-order Actuator Dynamics)
3. 转子动力学积分 (Rotor Dynamics)
4. MPC (模型预测控制) 用于功率跟随
"""
def __init__(self, nn_pth_path="data/engine_nn_proxy.pth",
tau_fuel=0.15, K_inertia=100.0,
mpc_horizon=15, mpc_dt=0.02, min_fuel=10.0, max_fuel=400.0,
mpc_overshoot_limit=0.05):
print("-> 正在加载 NN 稳态代理模型...")
self.engine_model = EngineNNProxy()
if not os.path.exists(nn_pth_path):
raise FileNotFoundError(f"Missing NN model file: {nn_pth_path}. Please run scripts/distill_gpr_to_nn.py first.")
self.engine_model.load_state_dict(torch.load(nn_pth_path, map_location='cpu'))
self.engine_model.eval()
print("-> NN Model Loaded (CPU Mode for Sim Loop)")
self.tau_fuel = tau_fuel
self.K_inertia = K_inertia
self.max_fuel = max_fuel
self.max_power_ref = 300.0
self.mpc = TurboShaftMPCController(
tau_fuel=tau_fuel, K_inertia=K_inertia, dt=mpc_dt, horizon=mpc_horizon,
min_fuel=min_fuel, max_fuel=max_fuel, overshoot_limit=mpc_overshoot_limit
)
self.H_env = 0.0
self.Ma_env = 0.0
self.N_current = 0.0
self.Wf_act_current = 0.0
self.power_generated = 0.0
self.Wf_cmd = 0.0
def _solve_steady_rpm(self, target_val, target_type='power'):
"""数值反解给定功率/燃油下的稳态转速"""
from scipy.optimize import brentq
low_bound, high_bound = 0.0, 60000.0
def objective(n):
# 构造输入: [H, Ma, N]
current_input = torch.tensor([[self.H_env, self.Ma_env, n]], dtype=torch.float32)
with torch.no_grad():
pred_mean = self.engine_model(current_input).numpy()
# 输出: [0]=Fuel, [1]=Power
val = pred_mean[0, 1] if target_type == 'power' else pred_mean[0, 0]
return val - target_val
try:
f_low = objective(low_bound)
f_high = objective(high_bound)
if f_low * f_high > 0:
print(f"[Warn] 目标值超出模型范围 [{low_bound}, {high_bound}] RPM 对应的输出。将使用最接近的边界。")
return low_bound if abs(f_low) < abs(f_high) else high_bound
n_solution = brentq(objective, low_bound, high_bound)
return n_solution
except Exception as e:
print(f"[Error] 稳态求解失败: {e}")
return (low_bound + high_bound) / 2.0
def set_steady_state_by_power(self, H_env, Ma_env, Power_target):
"""设定初始稳态工况点"""
self.H_env = H_env
self.Ma_env = Ma_env
self.N_current = self._solve_steady_rpm(Power_target, target_type='power')
# 计算该稳态下的燃油消耗
current_input = torch.tensor([[self.H_env, self.Ma_env, self.N_current]], dtype=torch.float32)
with torch.no_grad():
pred_mean = self.engine_model(current_input).numpy()
self.power_generated = pred_mean[0, 1]
self.Wf_act_current = pred_mean[0, 0]
self.Wf_cmd = self.Wf_act_current
print(f"-> 稳态(Power)已配置: H={H_env}, Ma={Ma_env}, Target_P={Power_target:.1f} kW => N={self.N_current:.1f} RPM, Wf={self.Wf_cmd:.2f} kg/h")
self.mpc.reset(initial_output=self.Wf_cmd, initial_N=self.N_current)
return self.N_current
def compute_control_law(self, dt, target_power, precalc_params=None):
"""调用 MPC 更新控制指令"""
self.Wf_cmd = self.mpc.compute(
current_N=self.N_current,
current_Wfact=self.Wf_act_current,
target_power=target_power,
precalc_params=precalc_params
)
return self.Wf_cmd
def step(self, dt, target_power=None):
"""
执行单步动态仿真 (High-Performance Optimized)
加速策略:
- 聚合 GPR 预测请求: 将 MPC 所需的梯度计算点与当前物理状态点合并为一个 Batch (Size=2)
- 减少 GPU I/O 次数: 从每步 3 次减少为 1 次
"""
# --- 0. 统一 GPU 批次预测 (Batch Prediction) ---
# 构造输入: [Row 0: 当前状态点, Row 1: 用于梯度计算的微扰点]
delta_N = 5.0
# 判断模型类型: NN 模型使用 forward，GPR 模型使用 predict
if hasattr(self.engine_model, 'predict') and not hasattr(self.engine_model, 'forward'):
# GPR 模型
inputs = np.array([
[self.H_env, self.Ma_env, self.N_current],
[self.H_env, self.Ma_env, self.N_current + delta_N]
])
pred_mean, _ = self.engine_model.predict(inputs)
else:
# NN 模型
import torch
inputs = torch.tensor([
[self.H_env, self.Ma_env, self.N_current],
[self.H_env, self.Ma_env, self.N_current + delta_N]
], dtype=torch.float32)
with torch.no_grad():
pred_mean = self.engine_model(inputs).numpy()
# 核心加速点：一次 GPU 调用获取所有信息
# pred_mean 形如 [[Wf0, Pow0], [Wf1, Pow1]]
# 提取结果
Wf_req_current = pred_mean[0, 0]
Power_current = pred_mean[0, 1]
Wf_req_pert = pred_mean[1, 0]
Power_pert = pred_mean[1, 1]
# --- 1. 闭环控制计算 ---
if target_power is not None:
# 在 Python 端快速计算梯度，避免在 MPC 内部再次调用模型
k_wf = (Wf_req_pert - Wf_req_current) / delta_N
k_p = (Power_pert - Power_current) / delta_N
# 使用预计算好的参数，MPC 内部将不再调用 engine_model.predict
params = (Wf_req_current, Power_current, k_wf, k_p)
self.compute_control_law(dt, target_power, precalc_params=params)
# --- 2. 燃油执行机构动态 (一阶惯性) ---
dWf_act_dt = (self.Wf_cmd - self.Wf_act_current) / self.tau_fuel
Wf_act_next = self.Wf_act_current + dWf_act_dt * dt
# --- 3. 调用NN代理模型推算当前气动热力参数 ---
current_input = torch.tensor([[self.H_env, self.Ma_env, self.N_current]], dtype=torch.float32)
with torch.no_grad():
pred_mean = self.engine_model(current_input).numpy()
# [0]: Fuel Flow (kg/h), [1]: Power (kW)
Wf_req_current = pred_mean[0, 0]
Power_current = pred_mean[0, 1]
self.power_generated = Power_current
# --- 4. 转子动力学积分 (供油盈余 -> 加速) ---
dN_dt = self.K_inertia * (self.Wf_act_current - Wf_req_current)
N_next = self.N_current + dN_dt * dt
# 更新状态
self.Wf_act_current = Wf_act_next
self.N_current = N_next
return self.N_current, self.Wf_act_current, Wf_req_current, Power_current
if __name__ == "__main__":
# ==========================================
# 涡轴发动机动态响应测试脚本
# 模拟复杂剖面: 包含阶跃、正弦、斜坡指令及变高度/马赫数干扰
# ==========================================
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.rcParams['font.family'] = 'serif'
plt.rcParams['font.serif'] = ['DejaVu Serif', 'Times New Roman']
plt.rcParams['axes.unicode_minus'] = True
sim = TurboshaftDynamicSim(mpc_dt=0.02)
# 初始状态
P_initial_target = 100.0
sim.set_steady_state_by_power(H_env=0.0, Ma_env=0.0, Power_target=P_initial_target)
dt = 0.02
t_end = 60.0
time_array = np.arange(0, t_end, dt)
N_log, Wf_act_log, Wf_cmd_log, Power_log, Power_target_log = [], [], [], [], []
H_env_log, Ma_env_log = [], []
print("-> 开始极限工况仿真测试 (大动态指令 + 连续外界干扰)...")
for t in tqdm(time_array, desc="Simulating"):
# --- 1. 生成复杂功率指令 (Setpoint) ---
if t < 10.0:
# 阶跃测试
target_p = 100.0 if t < 3 else 220.0 if t < 7 else 80.0
elif t < 25.0:
# 正弦跟踪 (0.2Hz)
target_p = 140.0 + 50.0 * np.sin(2 * np.pi * 0.2 * (t - 10.0))
elif t < 40.0:
# 锯齿波测试
cycle = (t - 25.0) % 5.0
target_p = 80.0 + (80.0 / 5.0) * cycle
else:
# 极限大范围跳变
target_p = 170.0 if t < 48.0 else 40.0
# --- 2. 生成环境扰动 (Disturbance) ---
if t < 15.0:
h_env, ma_env = 0.0, 0.0
elif t < 30.0:
# 爬升阶段: 0->3000m
h_env = 0.0 + (3000.0 / 15.0) * (t - 15.0)
ma_env = 0.0 + (0.2 / 15.0) * (t - 15.0)
elif t < 45.0:
h_env, ma_env = 3000.0, 0.2
else:
# 突发机动
h_env, ma_env = 500.0, 0.4
sim.H_env = h_env
sim.Ma_env = ma_env
# 执行单步仿真
N_cur, Wf_act_cur, Wf_req, Power_cur = sim.step(dt, target_power=target_p)
N_log.append(N_cur)
Wf_act_log.append(Wf_act_cur)
Wf_cmd_log.append(sim.Wf_cmd)
Power_log.append(Power_cur)
Power_target_log.append(target_p)
H_env_log.append(h_env)
Ma_env_log.append(ma_env)
# ==========================================
# 绘图逻辑
# ==========================================
fig, axes = plt.subplots(4, 1, figsize=(14, 12), gridspec_kw={'height_ratios': [2.5, 2, 2, 1.5]})
fig.suptitle('Turboshaft Engine: MPC Extreme Stress Test\nSine Tracking + Continuous Disturbances',
fontsize=14, fontweight='bold')
# Plot 1: Power Tracking (核心表现)
axes[0].plot(time_array, Power_target_log, 'k--', linewidth=2, label='Target Power (Command)')
axes[0].plot(time_array, Power_log, 'g-', linewidth=2, label='Actual Power (MPC)')
axes[0].set_ylabel('Shaft Power [kW]', fontweight='bold')
axes[0].set_title('Performance: Complex Trajectory Tracking', fontweight='bold')
axes[0].grid(True, linestyle=':', alpha=0.7)
axes[0].legend(loc='upper right')
# Plot 2: Rotor Speed
axes[1].plot(time_array, N_log, 'b-', linewidth=2, label='Engine Speed (N)')
axes[1].set_ylabel('Rotor Speed [RPM]', fontweight='bold')
axes[1].set_title('State: Rotor Speed Response', fontweight='bold')
axes[1].grid(True, linestyle=':', alpha=0.7)
axes[1].legend(loc='upper right')
# Plot 3: Fuel Flow (Control Input)
axes[2].plot(time_array, Wf_cmd_log, 'r--', linewidth=1.5, label='Fuel Command (MPC Output)')
axes[2].plot(time_array, Wf_act_log, 'm-', linewidth=2, label='Actual Fuel Actuator')
axes[2].set_ylabel('Fuel Flow [kg/h]', fontweight='bold')
axes[2].set_title('Control Effort: Actuator Dynamics', fontweight='bold')
axes[2].grid(True, linestyle=':', alpha=0.7)
axes[2].legend(loc='upper right')
# Plot 4: Environmental Disturbances
ax4_1 = axes[3]
ax4_2 = ax4_1.twinx()
ax4_1.plot(time_array, H_env_log, 'c-', linewidth=2, label='Altitude (m)')
ax4_2.plot(time_array, Ma_env_log, 'y-', linewidth=2, label='Mach Number')
ax4_1.set_xlabel('Time [s]', fontweight='bold')
ax4_1.set_ylabel('Altitude [m]', color='c', fontweight='bold')
ax4_2.set_ylabel('Mach', color='y', fontweight='bold')
axes[3].set_title('Disturbances: Flight Conditions', fontweight='bold')
axes[3].grid(True, linestyle=':', alpha=0.7)
plt.tight_layout(rect=[0, 0, 1, 0.96])
plt.savefig('figures/engine_mpc_stress_test.png', dpi=200)
print('-> 仿真完成！极限制图已保存至: figures/engine_mpc_stress_test.png')
Model/src/engine_gpr_class.py
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import torch
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
from botorch.models import SingleTaskGP
from botorch.fit import fit_gpytorch_mll
from gpytorch.mlls import ExactMarginalLogLikelihood
from gpytorch.means import LinearMean
from gpytorch.priors import GammaPrior
from sklearn.preprocessing import StandardScaler
import warnings
import os
warnings.filterwarnings("ignore")
# ==========================================
# 【全局绘图设置】学术规范
# ==========================================
plt.rcParams['font.family'] = 'serif'
plt.rcParams['font.serif'] = ['DejaVu Serif', 'Times New Roman']
plt.rcParams['mathtext.fontset'] = 'stix'
plt.rcParams['axes.unicode_minus'] = True
class EngineGPRModel:
"""
涡轴发动机 GPR 代理模型类
支持: 训练、加载已有模型、预测推断、单马赫数切面可视化
"""
def __init__(self, csv_path="data/Cleaned_Engine_Data_Full.csv"):
self.csv_path = csv_path
self.df = None
self.valid_X_cols = []
self.scaler_X = StandardScaler()
self.scaler_Y = StandardScaler()
self.model = None
# 自动检测并使用 GPU
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {self.device}")
def _prepare_data(self):
"""数据预处理：读取、筛选、Log变换、标准化"""
self.df = pd.read_csv(self.csv_path, encoding='utf-8')
X_df = self.df[['Altitude_m', 'Mach', 'RPM']]
# 剔除无效特征
self.valid_X_cols = [col for col in X_df.columns if X_df[col].nunique() > 1]
X_numpy = self.df[self.valid_X_cols].values
if 'WF_kg_h' not in self.df.columns:
raise ValueError("Column 'WF_kg_h' not found.")
Y_numpy = self.df[['WF_kg_h', 'Power_kW']].values
# Log1p 变换：保证非负性，并线性化指数规律
Y_numpy = np.log1p(Y_numpy)
# Z-Score 归一化
X_scaled = self.scaler_X.fit_transform(X_numpy)
Y_scaled = self.scaler_Y.fit_transform(Y_numpy)
# 转换为 Tensor 并移动到 GPU (如果可用)
return torch.tensor(X_scaled, dtype=torch.double).to(self.device), torch.tensor(Y_scaled, dtype=torch.double).to(self.device)
def _init_model(self, train_X, train_Y):
"""内部方法：统一初始化模型结构（包含 Mean 和 Prior 设置）"""
mean_module = LinearMean(input_size=train_X.shape[-1], batch_shape=torch.Size([train_Y.shape[-1]]))
model = SingleTaskGP(train_X, train_Y, mean_module=mean_module)
# 统一施加 GammaPrior 防止结构不匹配
# 这一步非常关键：加载模型时，模型结构必须与训练时完全一致，包括 Prior
if hasattr(model.covar_module, 'base_kernel'):
kernel = model.covar_module.base_kernel
else:
kernel = model.covar_module
if hasattr(kernel, 'lengthscale'):
kernel.lengthscale_prior = GammaPrior(4.0, 1.0)
return model
def train(self, save_path=None):
"""训练 GPR 模型 (Log-Space + 平滑约束)"""
print(f"{'='*30}\n🚀 GP Training (Log-Space)\n{'='*30}")
train_X, train_Y = self._prepare_data()
# 使用统一初始化方法
self.model = self._init_model(train_X, train_Y)
self.model.to(self.device)
# 仅在训练开始前设定初始值，引导优化方向
if hasattr(self.model.covar_module, 'base_kernel'):
kernel = self.model.covar_module.base_kernel
else:
kernel = self.model.covar_module
if hasattr(kernel, 'lengthscale'):
kernel.lengthscale = 2.0
print("-> Optimizing hyperparameters...")
mll = ExactMarginalLogLikelihood(self.model.likelihood, self.model)
fit_gpytorch_mll(mll)
print("-> Training completed.")
if save_path:
torch.save(self.model.state_dict(), save_path)
print(f"-> Model saved to {save_path}")
def load_model(self, pth_path="data/engine_gpr_model.pth"):
"""加载已训练的模型"""
print(f"Loading model from {pth_path}...")
try:
train_X, train_Y = self._prepare_data()
# 必须使用完全相同的结构初始化，否则 load_state_dict 会报错
self.model = self._init_model(train_X, train_Y)
self.model.to(self.device)
# 使用 strict=False 忽略 Prior 缓冲区的差异（例如 _transformed_loc 等内部参数）
# 这些参数通常不影响模型预测，只影响后续继续训练时的约束
state_dict = torch.load(pth_path, map_location=self.device)
self.model.load_state_dict(state_dict, strict=True)
self.model.eval()
print("-> Model loaded successfully.")
return True
except Exception as e:
print(f"-> Load failed: {e}")
return False
def _predict_log_space(self, test_X_real: np.ndarray):
"""预测 Log 空间下的均值和标准差"""
if self.model is None: raise ValueError("Model not initialized.")
self.model.eval()
# Determine device from model parameters
try:
device = next(self.model.parameters()).device
except StopIteration:
device = torch.device('cpu')
test_X_scaled = torch.tensor(self.scaler_X.transform(test_X_real), dtype=torch.double, device=device)
with torch.no_grad():
posterior = self.model.posterior(test_X_scaled)
mu_scaled = posterior.mean.detach().cpu().numpy()
var_scaled = posterior.variance.detach().cpu().numpy()
# 反归一化
mu_log_real = self.scaler_Y.inverse_transform(mu_scaled)
var_log_real = var_scaled * self.scaler_Y.var_
return mu_log_real, np.sqrt(var_log_real)
def predict(self, test_X_real: np.ndarray):
"""预测物理值 (expm1 还原)"""
mu_log, std_log = self._predict_log_space(test_X_real)
# 中位数点估计 (Median of LogNormal)
pred_mean = np.expm1(mu_log)
# 近似方差
var_log = std_log ** 2
pred_var = (np.expm1(var_log)) * np.exp(2*mu_log + var_log)
return pred_mean, pred_var
def visualize(self, target_mach=0.0):
"""可视化：生成方差热力图、切面曲线、均值云图"""
if self.model is None: raise ValueError("Model not initialized.")
print(f"Generating Plots for Mach = {target_mach}")
# 确定绘图范围 (训练数据 Range)
H_min, H_max = self.df['Altitude_m'].min(), self.df['Altitude_m'].max()
RPM_min, RPM_max = self.df['RPM'].min(), self.df['RPM'].max()
if H_min == H_max: H_min, H_max = H_min-1, H_max+1
if RPM_min == RPM_max: RPM_min, RPM_max = RPM_min-100, RPM_max+100
H_grid, RPM_grid = np.meshgrid(np.linspace(H_min, H_max, 60), np.linspace(RPM_min, RPM_max, 60), indexing='ij')
test_X_dict = {'Altitude_m': H_grid.flatten(), 'RPM': RPM_grid.flatten()}
if 'Mach' in self.valid_X_cols:
test_X_dict['Mach'] = np.full_like(H_grid.flatten(), target_mach)
test_X_real = np.column_stack([test_X_dict[col] for col in self.valid_X_cols])
# 预测并重塑 (60x60)
pred_mean, pred_var = self.predict(test_X_real)
wf_mean, pow_mean = pred_mean[:, 0].reshape(60, 60), pred_mean[:, 1].reshape(60, 60)
wf_var, pow_var = pred_var[:, 0].reshape(60, 60), pred_var[:, 1].reshape(60, 60)
# 筛选绘图用的真实数据点
tol = 1e-3
plot_df = self.df[(self.df['Mach'] >= target_mach - tol) & (self.df['Mach'] <= target_mach + tol)] if 'Mach' in self.valid_X_cols else self.df
# 1. 方差热力图 (Log Scale)
fig, axes = plt.subplots(1, 2, figsize=(14, 5.5))
for ax, var_data, title in zip(axes, [wf_var, pow_var], ['Fuel Flow Variance', 'Shaft Power Variance']):
log_var = np.log10(np.maximum(var_data, 1e-16))
cf = ax.contourf(RPM_grid, H_grid, log_var, levels=50, cmap='jet', alpha=0.9)
if not plot_df.empty: ax.scatter(plot_df['RPM'], plot_df['Altitude_m'], c='white', edgecolors='black', s=25, label='Data')
ax.set_title(f"{title} (Log10)"); ax.set_xlabel('RPM'); ax.set_ylabel('Altitude')
plt.colorbar(cf, ax=ax)
plt.tight_layout()
plt.savefig(f'figures/gpr_mach_{target_mach}_variance.png')
print(f"Saved figures/gpr_mach_{target_mach}_variance.png")
plt.close()
# 2. 高度切面图 (95% CI)
fig, axes = plt.subplots(1, 2, figsize=(14, 5.5))
colors = ['#1f77b4', '#ff7f0e', '#2ca02c']
RPM_1D = np.linspace(RPM_min, RPM_max, 100)
for idx, alt in enumerate([0.0, 3000.0, 6000.0]):
d_1D = {'Altitude_m': np.full_like(RPM_1D, alt), 'RPM': RPM_1D}
if 'Mach' in self.valid_X_cols: d_1D['Mach'] = np.full_like(RPM_1D, target_mach)
X_1D = np.column_stack([d_1D[c] for c in self.valid_X_cols])
# Log空间预测 -> 计算物理置信区间 (保证下界非负)
mu_log, std_log = self._predict_log_space(X_1D)
mean = np.expm1(mu_log)
lower = np.expm1(mu_log - 2*std_log)
upper = np.expm1(mu_log + 2*std_log)
for ax, i in zip(axes, [0, 1]):
ax.plot(RPM_1D, mean[:, i], color=colors[idx], label=f'Alt={int(alt)}m')
ax.fill_between(RPM_1D, lower[:, i], upper[:, i], color=colors[idx], alpha=0.2)
for ax, title, ylab in zip(axes, ['Fuel Flow', 'Shaft Power'], ['kg/h', 'kW']):
ax.set_title(title); ax.set_ylabel(ylab); ax.set_xlabel('RPM')
ax.legend(); ax.grid(True, alpha=0.5)
plt.tight_layout()
plt.savefig(f'figures/gpr_mach_{target_mach}_section.png')
print(f"Saved figures/gpr_mach_{target_mach}_section.png")
plt.close()
# 3. 均值云图
fig, axes = plt.subplots(1, 2, figsize=(14, 5.5))
for ax, mean_data, title in zip(axes, [wf_mean, pow_mean], ['Fuel Flow Mean', 'Shaft Power Mean']):
cf = ax.contourf(RPM_grid, H_grid, mean_data, levels=50, cmap='viridis', alpha=0.9)
if not plot_df.empty: ax.scatter(plot_df['RPM'], plot_df['Altitude_m'], c='red', s=20, edgecolors='white', label='Data')
ax.set_title(title); ax.set_xlabel('RPM'); ax.set_ylabel('Altitude')
plt.colorbar(cf, ax=ax)
plt.tight_layout()
plt.savefig(f'figures/gpr_mach_{target_mach}_mean.png')
print(f"Saved figures/gpr_mach_{target_mach}_mean.png")
plt.close()
if __name__ == "__main__":
import matplotlib
matplotlib.use('Agg')
print("Using Agg backend for plotting.")
engine_model = EngineGPRModel()
# 智能选择加载或训练
model_path = "data/engine_gpr_model.pth"
if os.path.exists(model_path):
success = engine_model.load_model(model_path)
if not success:
# 如果加载失败（结构不匹配），则重新训练
engine_model.train(save_path=model_path)
else:
# 如果模型不存在，则训练并保存
engine_model.train(save_path=model_path)
# ==========================================
# 【使用示例】
# ==========================================
print("\n" + "="*50)
print("💡 预测使用示例 (Prediction Example)")
print("="*50)
# 假设默认特征组合为: [Altitude_m, Mach, RPM]
sample_inputs = np.array([
[0.0, 0.0, 15000.0], # 测试点 1: 海平面静止, 15000 RPM
[3000.0, 0.2, 22000.0] # 测试点 2: 3000米, 0.2马赫, 22000 RPM
])
mean_preds, var_preds = engine_model.predict(sample_inputs)
for i, (X_in, Y_mean, Y_var) in enumerate(zip(sample_inputs, mean_preds, var_preds)):
# 格式化输出
x_str = "[" + ", ".join([f"{val:8.2f}" for val in X_in]) + " ]"
mean_str = "[" + ", ".join([f"{val:10.4f}" for val in Y_mean]) + " ]"
var_str = "[" + ", ".join([f"{val:10.4f}" for val in Y_var]) + " ]"
print(f"[测试点 {i+1}]")
print(f" -> 输入特征 {engine_model.valid_X_cols}: {x_str}")
print(f" -> 预测均值 [WF (kg/h), Power_kW]: {mean_str}")
print(f" -> 预测方差 [WF (kg/h), Power_kW]: {var_str}\n")
# ==========================================
# 循环调用绘图方法，分别输出所需的马赫数切面图
# ==========================================
target_machs = [0.0, 0.1, 0.2, 0.3, 0.4]
for m in target_machs:
engine_model.visualize(target_mach=m)
Model/src/increPID.py
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class IncrementalPIDController:
"""
增量式 PID 控制器 (支持自动归一化缩放)
适用于发动机燃油控制等具有保持特性的执行机构
"""
def __init__(self, kp, ki, kd, dt, output_min, output_max, input_scale=1.0, output_scale=1.0):
"""
初始化 PID 控制器
如果提供了 scale 参数，则 kp/ki/kd 被视为归一化域的参数。
内部运算逻辑：
norm_error = (setpoint - measurement) / input_scale
norm_output += PID(norm_error, kp, ki, kd)
physical_output = norm_output * output_scale
:param kp: 比例系数 (建议使用归一化参数)
:param ki: 积分系数
:param kd: 微分系数
:param dt: 控制周期 (s)
:param output_min: 执行机构输出下限 (物理量)
:param output_max: 执行机构输出上限 (物理量)
:param input_scale: 输入测量值的量程基准 (例如额定功率 1000.0)
:param output_scale: 输出控制量的量程基准 (例如最大燃油 600.0)
"""
self.kp = kp
self.ki = ki
self.kd = kd
self.dt = dt
# 记录缩放因子
self.input_scale = input_scale
self.output_scale = output_scale
# 将物理限制转换为内部的归一化限制
self.out_min_norm = output_min / output_scale
self.out_max_norm = output_max / output_scale
# 历史误差状态 (归一化误差)
self.e_k1 = 0.0 # e(k-1)
self.e_k2 = 0.0 # e(k-2)
# 当前实际的控制输出 (归一化值 [0~1] 或 [-1~1])
self.current_output_norm = 0.0
def reset(self, initial_output=0.0):
"""
重置控制器状态，用于初始化或开闭环的无扰切换
:param initial_output: 当前执行机构的实际位置（物理量，如燃油流量）
"""
self.e_k1 = 0.0
self.e_k2 = 0.0
# 将物理初始值转换为归一化内部状态
self.current_output_norm = initial_output / self.output_scale
def compute(self, setpoint, measurement):
"""
计算下一拍的控制量
:param setpoint: 目标设定值 (物理量)
:param measurement: 当前测量值 (物理量)
:return: 经过限幅的绝对控制指令 (物理量)
"""
# 1. 计算归一化误差 e(k)
# 误差除以输入量程，使得误差在 -1~1 之间 (对于阶跃通常更小)
e_k = (setpoint - measurement) / self.input_scale
# 2. 计算归一化控制增量 delta_u
p_term = self.kp * (e_k - self.e_k1)
i_term = self.ki * e_k * self.dt
# 微分项除以dt可能会很大，归一化的时间常数有助于平滑
d_term = self.kd * (e_k - 2 * self.e_k1 + self.e_k2) / self.dt
delta_u_norm = p_term + i_term + d_term
# 3. 更新当前归一化输出量
self.current_output_norm += delta_u_norm
# 4. 绝对位置限幅 (在归一化域进行)
if self.current_output_norm > self.out_max_norm:
self.current_output_norm = self.out_max_norm
elif self.current_output_norm < self.out_min_norm:
self.current_output_norm = self.out_min_norm
# 5. 更新历史误差
self.e_k2 = self.e_k1
self.e_k1 = e_k
# 6. 返回物理量输出
return self.current_output_norm * self.output_scale
Model/src/lightweight_model.py
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import torch
import torch.nn as nn
import numpy as np
class EngineNNProxy(nn.Module):
"""
轻量级神经网络代理模型，用于替代笨重的 GPR 模型。
结构: 简单的 MLP (多层感知机)
输入: [Altitude, Mach, RPM] (未归一化)
输出: [FuelFlow, Power] (未归一化)
"""
def __init__(self, hidden_size=64):
super(EngineNNProxy, self).__init__()
# 定义网络结构
# 对应 distill_gpr_to_nn.py 中的索引访问:
# 0: Linear
# 1: ReLU / Tanh
# 2: Linear
# 3: ReLU / Tanh
# 4: Linear (Output)
self.net = nn.Sequential(
nn.Linear(3, hidden_size), # 0
nn.Tanh(), # 1: Tanh 通常比 ReLU 更适合平滑的物理函数拟合
nn.Linear(hidden_size, hidden_size), # 2
nn.Tanh(), # 3
nn.Linear(hidden_size, 2) # 4: 输出 2 个物理量 (Fuel, Power)
)
# 归一化参数 (注册为 buffer 以便随模型保存)
# 初始化为默认值，防止未调用 set_normalization_params 时报错
self.register_buffer('x_mean', torch.zeros(3))
self.register_buffer('x_scale', torch.ones(3))
self.register_buffer('y_mean', torch.zeros(2))
self.register_buffer('y_scale', torch.ones(2))
def set_normalization_params(self, scaler_X, scaler_Y):
"""
从 sklearn StandardScaler 中提取参数
scaler_X: 用于输入的归一化器
scaler_Y: 用于输出 (Log1p Space) 的归一化器
"""
if scaler_X is not None:
self.x_mean.copy_(torch.tensor(scaler_X.mean_, dtype=torch.float32))
self.x_scale.copy_(torch.tensor(scaler_X.scale_, dtype=torch.float32))
if scaler_Y is not None:
self.y_mean.copy_(torch.tensor(scaler_Y.mean_, dtype=torch.float32))
self.y_scale.copy_(torch.tensor(scaler_Y.scale_, dtype=torch.float32))
def forward(self, x):
"""
前向传播: 物理输入 -> 物理输出
包含: 归一化 -> NN推理 -> 反归一化 -> expm1
"""
# 1. 输入归一化 (Z-Score)
# 确保输入 x 与 buffer 在同一设备
x = x.to(self.x_mean.device)
x_norm = (x - self.x_mean) / self.x_scale
# 2. 神经网络推理 (预测 Log Normalized Z-Score)
y_norm_pred = self.net(x_norm)
# 3. 输出反归一化 (Z-Score Inverse)
y_log1p_pred = y_norm_pred * self.y_scale + self.y_mean
# 4. 指数还原 (Inverse Log1p)
y_phys_pred = torch.expm1(y_log1p_pred)
return y_phys_pred
Model/src/motor_sim.py
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Model/src/mpc_controller.py
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import numpy as np
# ==============================================================================
# Pure-Python projected gradient descent for box-constrained optimization
# (替代 scipy.optimize.minimize SLSQP，避免 Fortran ABI 兼容性问题)
# ==============================================================================
def _minimize_box(objective, x0, bounds, lr=1.0, max_iter=80, ftol=1e-6):
"""
Projected gradient descent with Armijo backtracking line search.
For small-to-medium MPC horizons (H ≤ 30) this is fast enough.
"""
n = len(x0)
lb = np.array([b[0] for b in bounds], dtype=np.float64)
ub = np.array([b[1] for b in bounds], dtype=np.float64)
x = np.clip(np.array(x0, dtype=np.float64), lb, ub)
eps = 1e-5 # finite-difference step
f_prev = objective(x)
for _it in range(max_iter):
# Approximate gradient via central differences
grad = np.empty(n, dtype=np.float64)
for i in range(n):
x_p = x.copy(); x_p[i] += eps
x_m = x.copy(); x_m[i] -= eps
grad[i] = (objective(x_p) - objective(x_m)) / (2 * eps)
# Backtracking line search (Armijo condition)
step = lr
for _ in range(12):
x_new = np.clip(x - step * grad, lb, ub)
f_new = objective(x_new)
if f_new < f_prev - 1e-4 * step * np.dot(grad, x - x_new):
break
step *= 0.5
else:
x_new = np.clip(x - step * grad, lb, ub)
f_new = objective(x_new)
if abs(f_prev - f_new) < ftol:
x = x_new
break
x = x_new
f_prev = f_new
class _Result:
pass
res = _Result()
res.x = x
res.fun = f_prev
res.success = True
return res
# ==============================================================================
# 1. 涡轴发动机 MPC 控制器 (基于 Scipy SLSQP 高速求解)
# ==============================================================================
class TurboShaftMPCController:
def __init__(self, tau_fuel, K_inertia, dt=0.02, horizon=15,
min_fuel=10.0, max_fuel=600.0, overshoot_limit=0.05):
"""
初始化高速 MPC 控制器 (基于 Scipy SLSQP)
替代 GEKKO 以消除文件 I/O 开销，提升单步推理速度 50x 以上。
:param overshoot_limit: 超调量硬约束 (0.05 = 5%)
"""
self.dt = dt
self.H = horizon
self.tau_fuel = tau_fuel
self.K_inertia = K_inertia
self.min_fuel = min_fuel
self.max_fuel = max_fuel
# 权重参数 (与 GEKKO 版本保持一致)
self.W_power = 200.0 # Power Setpoint Weight
self.W_dcost = 1.5 # Delta Control Weight (DCOST)
# 超调量约束
self.overshoot_limit = overshoot_limit # 5% 硬约束
# 超调惩罚权重 (使用很大的值使约束"硬"化)
self.W_overshoot_penalty = 1e6
# 状态缓存
self.last_u = 100.0
self.last_N = 0.0
def reset(self, initial_output, initial_N):
self.last_u = initial_output
self.last_N = initial_N
def compute(self, current_N, current_Wfact, target_power, engine_model=None, H_env=0, Ma_env=0, precalc_params=None, **kwargs):
"""
计算 MPC 控制律
:param precalc_params: (必须) 元组 (Wf_req_0, Power_0, k_wf, k_p)。
FastMPC 必须配合 Batch Prediction 使用。
"""
if precalc_params is not None:
Wf_req_0, Power_0, k_wf, k_p = precalc_params
else:
raise ValueError("FastMPC 必须配合 Batch Prediction 使用 (提供 precalc_params)")
N0 = current_N
Wf_act0 = current_Wfact
N_ref = N0 # 用于线性化的参考点
# --- 构建线性预测模型 ---
# State x = [N, Wf_act]
# x_{k+1} = A x_k + B u_k + d
# N_{k+1} = N_k + dt * K * (Wf_act_k - (Wf_req_0 + k_wf*(N_k - N0)))
# = (1 - dt*K*k_wf) N_k + (dt*K) Wf_act_k + dt*K*(k_wf*N0 - Wf_req_0)
# Wf_act_{k+1} = Wf_act_k + dt * (u_k - Wf_act_k) / tau
# = (1 - dt/tau) Wf_act_k + (dt/tau) u_k
dt = self.dt
K = self.K_inertia
tau = self.tau_fuel
A = np.array([
[1 - dt * K * k_wf, dt * K],
[0, 1 - dt / tau]
])
B = np.array([0, dt / tau])
d = np.array([dt * K * (k_wf * N_ref - Wf_req_0), 0])
# Power Output: P = P0 + k_p * (N - N0)
# = k_p * N + (P0 - k_p * N0)
C_p = k_p
D_p = Power_0 - k_p * N_ref
# --- 超调量硬约束 ---
# 升功率时: P_k <= target_power * (1 + overshoot_limit)
# 降功率时: P_k >= target_power * (1 - overshoot_limit)
P_max = target_power * (1 + self.overshoot_limit)
P_min = target_power * (1 - self.overshoot_limit)
# 判断是升功率还是降功率
is_ramping_up = target_power > Power_0
# --- 优化目标函数 ---
# Variables: U = [u_0, ..., u_{H-1}]
# x_0 is fixed.
# Cost = sum_{k=1 to H} W_power * (P_k - P_target)^2 + sum_{k=0 to H-1} W_dcost * (u_k - u_{k-1})^2
# + W_overshoot_penalty * (违反约束的惩罚)
def objective(U):
cost = 0.0
x_k = np.array([N0, Wf_act0])
u_prev = self.last_u # Use last commanded u for the first delta
for k in range(self.H):
u_k = U[k]
# Dynamics Step
x_k = A @ x_k + B * u_k + d
# Output
P_k = C_p * x_k[0] + D_p
# Cost Accumulation
cost += self.W_power * (P_k - target_power) ** 2
cost += self.W_dcost * (u_k - u_prev) ** 2
# 超调惩罚: 如果违反约束，添加巨大惩罚
if is_ramping_up:
if P_k > P_max:
cost += self.W_overshoot_penalty * (P_k - P_max) ** 2
else:
if P_k < P_min:
cost += self.W_overshoot_penalty * (P_min - P_k) ** 2
u_prev = u_k
return cost
# --- 求解 ---
# 初始猜测: 保持上一次的输入
U0 = np.full(self.H, float(self.last_u), dtype=np.float64)
# 约束: lb <= u <= ub
bounds = [(self.min_fuel, self.max_fuel) for _ in range(self.H)]
# 使用纯Python投影梯度下降求解带约束优化 (替代 SLSQP)
res = _minimize_box(objective, U0, bounds, lr=5.0, max_iter=60, ftol=1e-3)
# 更新状态
u_opt = res.x[0]
self.last_u = u_opt
return u_opt
# ==============================================================================
# 2. 驱动电机 MPC 控制器 (基于 Scipy SLSQP 高速求解)
# ==============================================================================
class MotorMPCController:
def __init__(self, J, B_visc, dt=0.02, horizon=10, W_speed=100.0, W_dcost=1.0, overshoot_limit=0.05):
"""
初始化高速电机 MPC 控制器 (基于 Scipy SLSQP)
替代 GEKKO 以消除文件 I/O 开销，提升单步推理速度 50x 以上。
:param overshoot_limit: 超调量硬约束 (0.05 = 5%)
"""
self.dt = dt
self.H = horizon
self.J = J
self.B_visc = B_visc
# 权重参数 (可调)
self.W_speed = W_speed # 转速跟踪权重
self.W_dcost = W_dcost # 控制变化惩罚
# 超调量约束
self.overshoot_limit = overshoot_limit # 5% 硬约束
# 超调惩罚权重 (使用很大的值使约束"硬"化)
self.W_overshoot_penalty = 1e6
# 状态缓存
self.last_T_cmd = 0.0
def reset(self, initial_w=0.0):
"""重新初始化状态"""
self.last_T_cmd = 0.0
def compute(self, current_w, target_w, t_load, t_ext, t_lim_upper, t_lim_lower):
"""
执行单步 MPC 优化计算
:param current_w: 当前实际转速 (rad/s)
:param target_w: 目标转速 (rad/s)
:param t_load: 当前气动负载转矩 (Nm)
:param t_ext: 当前外部轴系转矩 (Nm)
:param t_lim_upper: 当前母线电压下，电机能发出的【正向转矩上限】(发电极限)
:param t_lim_lower: 当前母线电压下，电机能发出的【负向转矩下限】(驱动极限)
:return: 最优转矩指令 (Nm)
"""
w0 = current_w
dt = self.dt
J = self.J
B = self.B_visc
# --- 离散线性模型 ---
# w_{k+1} = w_k + dt * (-(T_cmd + T_ext + T_load + B*w_k) / J)
# = (1 - dt*B/J) * w_k - dt/J * T_cmd - dt/J * (T_ext + T_load)
a = 1 - dt * B / J
b = -dt / J
d = -dt / J * (t_ext + t_load)
# --- 超调量硬约束 ---
# 升速时: w_k <= target_w * (1 + overshoot_limit)
# 降速时: w_k >= target_w * (1 - overshoot_limit)
w_max = target_w * (1 + self.overshoot_limit)
w_min = target_w * (1 - self.overshoot_limit)
# 判断是升速还是降速
is_ramping_up = target_w > w0
# --- 优化目标函数 ---
# Cost = sum_{k=1 to H} W_speed * (w_k - target_w)^2 + sum_{k=0 to H-1} W_dcost * (T_k - T_{k-1})^2
# + W_overshoot_penalty * (违反约束的惩罚)
def objective(U):
cost = 0.0
w_k = w0
T_prev = self.last_T_cmd
for k in range(self.H):
T_k = U[k]
w_k = a * w_k + b * T_k + d # 动力学迭代
cost += self.W_speed * (w_k - target_w) ** 2
cost += self.W_dcost * (T_k - T_prev) ** 2
# 超调惩罚: 如果违反约束，添加巨大惩罚
if is_ramping_up:
if w_k > w_max:
cost += self.W_overshoot_penalty * (w_k - w_max) ** 2
else:
if w_k < w_min:
cost += self.W_overshoot_penalty * (w_min - w_k) ** 2
T_prev = T_k
return cost
# 初始猜测
U0 = np.full(self.H, float(self.last_T_cmd), dtype=np.float64)
# 约束: 转矩边界
bounds = [(t_lim_lower, t_lim_upper) for _ in range(self.H)]
# 求解 (使用纯Python投影梯度下降，替代 SLSQP)
res = _minimize_box(objective, U0, bounds, lr=50.0, max_iter=120, ftol=1e-5)
T_opt = res.x[0]
self.last_T_cmd = T_opt
return T_opt
Model/src/series_hybrid_sim.py
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import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
from engine_dynamic_sim import TurboshaftDynamicSim
from motor_sim import MotorSim
from battery_sim import BatterySim
class SeriesHybridSystem:
"""
串联式混合动力系统总成
"""
def __init__(self, mpc_overshoot_limit: float = 0.05):
self.genset = TurboshaftDynamicSim(mpc_overshoot_limit=mpc_overshoot_limit)
self.drive_motor = MotorSim(
P_rate=300e3,
w_rate=575.95,
J=1.0,
mpc_W_speed=40,
mpc_W_dcost=5,
mpc_overshoot_limit=mpc_overshoot_limit, # 超调量硬约束 (5%)
)
self.battery = BatterySim(capacity_kwh=50.0, initial_soc=0.6)
self.bus_voltage = self.battery._get_ocv(self.battery.SOC)
self.motor_actual_power_kw = 0.0
# 滞环控制状态
self._genset_low_power_mode = False # 发动机低功率模式标志
self._hysteresis_rpm = 100 # 滞环带宽 RPM
def energy_management_strategy(self, p_drive_req_kw, soc, speed_error_rpm=0.0):
"""
功率跟随策略 (Power Following Control) - 增强版
核心思想：
- 使用滞环控制避免频繁切换
- 当转速持续高于目标时，降低发动机功率
- 当转速持续低于目标时，增加发动机功率
"""
P_eng_max = 300.0
P_eng_min = 20.0
SOC_TARGET = 0.60
K_soc = 30.0 # kW / ΔSOC
K_speed = 1.5 # kW / RPM
p_dyn_reserve = P_eng_max * 0.3 # 90 kW 基础储备
# 滞环控制逻辑
# 进入低功率模式：转速高于目标 + 电机发电
if speed_error_rpm < -self._hysteresis_rpm and p_drive_req_kw < 0:
self._genset_low_power_mode = True
# 退出低功率模式：转速低于目标 - 滞环带宽
elif speed_error_rpm > self._hysteresis_rpm:
self._genset_low_power_mode = False
# 根据模式计算目标功率
if self._genset_low_power_mode:
# 低功率模式：发动机降到最低
target_engine_power = P_eng_min
elif speed_error_rpm > 0:
# 加速模式
p_speed_comp = min(speed_error_rpm * K_speed, 250.0)
target_engine_power = p_drive_req_kw + p_dyn_reserve + p_speed_comp
else:
# 减速/稳态模式
p_speed_comp = max(speed_error_rpm * K_speed, -250.0)
target_engine_power = p_drive_req_kw + p_dyn_reserve + p_speed_comp
# SOC补偿
soc_error = SOC_TARGET - soc
if soc < 0.2 or soc > 0.85:
K_soc_effective = 0
else:
K_soc_effective = K_soc
p_charge_req = soc_error * K_soc_effective
target_engine_power += p_charge_req
# 极限状态越界保护
if soc < 0.15:
target_engine_power = P_eng_max
elif soc > 0.95:
target_engine_power = P_eng_min
# 限制输出范围
target_engine_power = max(P_eng_min, min(P_eng_max, target_engine_power))
return target_engine_power
def step(self, dt, target_prop_speed, prop_load_torque):
"""
全系统单步动力学仿真
"""
# 1. 需求端：驱动电机电功率请求
motor_state = self.drive_motor.step(
dt=dt,
n_setpoint=target_prop_speed,
p_bus_actual_kw=self.motor_actual_power_kw,
v_bus=self.bus_voltage,
t_load=prop_load_torque,
t_ext=0.0
)
p_drive_req = motor_state['p_bus_req_kw']
actual_rpm = motor_state['n_rpm']
# 计算转速误差（用于EMS）
speed_error_rpm = target_prop_speed - actual_rpm
# 2. 决策端：EMS 目标功率计算（加入转速误差反馈）
target_engine_pwr = self.energy_management_strategy(p_drive_req, self.battery.SOC, speed_error_rpm)
# 3. 发电端：涡轴发动机响应并输出电能
N_eng, Wf_act, Wf_req, P_eng_out = self.genset.step(dt, target_power=target_engine_pwr)
# GPR模型输出的 P_eng_out > 0 表示发动机输出功率（发电）
p_gen_elec = P_eng_out # 正值表示发电功率
# 4. 汇流端：电池功率补偿与直流母线状态更新
# 功率平衡：电机需求 = 发动机发电 + 电池补充
# p_batt_req > 0 表示电池放电，p_batt_req < 0 表示电池充电
p_batt_req = p_drive_req - p_gen_elec
p_batt_actual, v_bus, i_batt, soc = self.battery.step(dt, p_batt_req)
# 5. 状态记忆：为打破代数环，保留关键变量至下一拍
self.bus_voltage = v_bus
# 电机实际获得的功率 = 发电功率 + 电池放电功率
# 注意：允许负值存在，表示电机在发电模式（制动）
# 这样电机控制器才能正确产生制动转矩
self.motor_actual_power_kw = p_gen_elec + p_batt_actual
return {
'soc': soc * 100.0,
'bus_voltage': v_bus,
'prop_speed_rpm': motor_state['n_rpm'],
'target_engine_pwr': target_engine_pwr,
'p_engine_out_kw': P_eng_out,
'p_drive_req_kw': p_drive_req,
'p_motor_actual_kw': self.motor_actual_power_kw,
'p_batt_actual_kw': p_batt_actual,
'wf_kg_h': Wf_act,
'engine_rpm': N_eng
}
if __name__ == "__main__":
import os
import matplotlib
matplotlib.use('Agg') # 非交互后端
import matplotlib.pyplot as plt
plt.rcParams['font.family'] = 'serif'
plt.rcParams['axes.unicode_minus'] = True
print("-> 初始化串联混电系统...")
system = SeriesHybridSystem()
system.genset.set_steady_state_by_power(H_env=0.0, Ma_env=0.0, Power_target=50.0)
dt = 0.02
t_end = 180.0
time_array = np.arange(0, t_end, dt)
log = {k: [] for k in ['soc', 'bus_voltage', 'prop_speed_rpm', 'target_prop_rpm', 'target_engine_pwr',
'p_engine_out_kw', 'p_drive_req_kw', 'p_motor_actual_kw',
'p_batt_actual_kw', 'wf_kg_h']}
print("-> 开始全系统闭环步进仿真 (总时长 3 分钟)...")
for t in tqdm(time_array, desc='Simulating', unit='step'):
# 3分钟测试剖面
if t < 15.0:
target_rpm, load_torque = 1500.0, 50.0 # 地面滑行
elif t < 60.0:
target_rpm, load_torque = 3000.0, 200.0 # 暴力拉升
elif t < 120.0:
target_rpm, load_torque = 2800.0, 150.0 # 重载巡航
else:
target_rpm, load_torque = 1800.0, 60.0 # 降落滑行
res = system.step(dt, target_rpm, load_torque)
res['target_prop_rpm'] = target_rpm
for k in log.keys():
log[k].append(res[k])
print("-> 仿真完成，正在绘制系统响应曲线...")
# 绘图逻辑
fig, axes = plt.subplots(4, 1, figsize=(14, 12), sharex=True)
fig.suptitle('Series Hybrid Electric System Dynamics (Load Following EMS)', fontweight='bold', fontsize=14)
# 1. 转速响应
axes[0].plot(time_array, log['target_prop_rpm'], 'k--', lw=1.5, label='Target Speed')
axes[0].plot(time_array, log['prop_speed_rpm'], 'b-', lw=1.5, label='Actual Speed')
axes[0].set_ylabel('Speed [RPM]')
axes[0].set_title('Drive Motor Speed Response')
axes[0].grid(True, linestyle=':', alpha=0.7)
axes[0].legend()
# 2. 功率分配流向
axes[1].plot(time_array, log['p_drive_req_kw'], 'k--', lw=1.5, label='Drive Motor Request')
axes[1].plot(time_array, log['p_engine_out_kw'], 'r-', lw=1.5, label='Engine Output (Turbo Shaft)')
axes[1].plot(time_array, log['p_batt_actual_kw'], 'g-', lw=1.5, label='Battery Output')
axes[1].set_ylabel('Power [kW]')
axes[1].set_title('System Power Flow (Energy Management)')
axes[1].axhline(0, color='gray', lw=1)
axes[1].grid(True, linestyle=':', alpha=0.7)
axes[1].legend()
# 3. 电池状态
axes[2].plot(time_array, log['bus_voltage'], 'm-', lw=1.5, label='DC Bus Voltage')
axes[2].set_ylabel('Voltage [V]')
axes[2].set_title('DC Bus Electrical State')
axes[2].grid(True, linestyle=':', alpha=0.7)
axes[2].legend(loc='upper left')
ax2_soc = axes[2].twinx()
ax2_soc.plot(time_array, log['soc'], 'c--', lw=2, label='Battery SOC')
ax2_soc.set_ylabel('SOC [%]')
ax2_soc.legend(loc='upper right')
# 4. 燃油消耗
axes[3].plot(time_array, log['wf_kg_h'], 'tab:orange', lw=1.5, label='Engine Fuel Flow')
axes[3].set_ylabel('Fuel Flow [kg/h]')
axes[3].set_xlabel('Time [s]')
axes[3].set_title('Turboshaft Engine Fuel Consumption')
axes[3].grid(True, linestyle=':', alpha=0.7)
axes[3].legend()
plt.tight_layout(rect=[0, 0, 1, 0.96])
# 保存到 ../figures 目录
figures_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'figures')
if not os.path.exists(figures_dir):
os.makedirs(figures_dir)
save_path = os.path.join(figures_dir, 'series_hybrid_system_3min_test.png')
plt.savefig(save_path, dpi=300)
print(f"-> 绘图已保存: {save_path}")
# 保存数据到dat文件
data_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'data')
if not os.path.exists(data_dir):
os.makedirs(data_dir)
dat_path = os.path.join(data_dir, 'series_hybrid_data.dat')
with open(dat_path, 'w', encoding='utf-8') as f:
f.write('# Time(s)\tTarget_RPM\tActual_RPM\tError_RPM\tSOC\tEngine_Power\tDrive_Req\tBatt_Power\n')
for i in range(len(time_array)):
err = log['target_prop_rpm'][i] - log['prop_speed_rpm'][i]
f.write(f'{time_array[i]:.3f}\t{log["target_prop_rpm"][i]:.1f}\t'
f'{log["prop_speed_rpm"][i]:.1f}\t{err:.1f}\t{log["soc"][i]:.2f}\t'
f'{log["p_engine_out_kw"][i]:.2f}\t{log["p_drive_req_kw"][i]:.2f}\t'
f'{log["p_batt_actual_kw"][i]:.2f}\n')
print(f"-> 数据已保存: {dat_path}")
# plt.show()
README.md
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# 🎛️ 自动控制原理AI+数智平台
# 自动控制理论AI+数智平台
> 交互式控制系统分析与设计工具 | 时域·频域·根轨迹·AI问答
> 交互式控制系统分析与设计工具 | 时域·频域·根轨迹·算例演示·AI问答
一个基于 Gradio 构建的现代化自动控制原理学习平台，集成了系统分析工具和 AI 智能问答功能。
[![Python Version](https://img.shields.io/badge/Python-3.10%2B-blue)](https://www.python.org/)
[![Gradio](https://img.shields.io/badge/Gradio-4.44.1-orange)](https://gradio.app/)
[![License](https://img.shields.io/badge/License-MIT-green)](LICENSE)
## ✨ 核心功能
## 项目简介
### 📊 1. 时域分析 (Time Domain Analysis)
自动控制理论AI+数智平台是一个基于 Gradio 构建的现代化自动控制原理学习平台，集成了系统分析工具、完整混动模型算例演示，和AI智能问答功能。本项目为西北工业大学2025年校级本科生建设项目成果。
- **阶跃响应分析**：观察系统对单位阶跃输入的响应
- **脉冲响应分析**：观察系统对单位脉冲输入的响应
- **性能指标计算**：
- 上升时间 (Rise Time)
- 峰值时间 (Peak Time)
- 超调量 (Overshoot)
- 调节时间 (Settling Time)
- 稳态值 (Steady State Value)
平台旨在为自动控制理论课程提供交互式、数智化的学习环境，使学生能够直观理解控制系统的时域响应、频域特性、根轨迹分析等核心概念，并通过完整的混合动力系统算例演示，将理论知识与工程实践相结合。
**知识要点**：
- 二阶系统标准形式
- 阻尼比与自然频率参数说明
- 时域性能指标公式
- 稳态误差分析
---
### 🌊 2. 频域分析 (Frequency Domain Analysis)
## 核心功能
- **Bode 图绘制**：幅频特性和相频特性
- **Nyquist 图绘制**：极坐标频率响应
- **稳定裕度计算**：
- 增益裕度 (Gain Margin, GM)
- 相位裕度 (Phase Margin, PM)
- 增益交越频率
- 相角交越频率
- **稳定性评估**：自动判断系统稳定性
本平台提供五大核心功能模块，涵盖经典控制理论分析与现代控制系统设计：
**知识要点**：
- 增益裕度与相位裕度定义
- 稳定性判断准则
- 频域指标与时域性能的关系
- Bode 图读数技巧
### 1. 时域分析 (Time Domain Analysis)
### 🎯 3. 根轨迹分析 (Root Locus Analysis)
时域分析是研究控制系统在时间域内对输入信号响应特性的方法，是自动控制理论的基础分析方法之一。
- **根轨迹绘制**：自动绘制完整根轨迹图
- **增益调节**：对数滑块精确调节增益 K
- **极点跟踪**：实时显示当前增益下的闭环极点位置
- **动态视角**：自动调整坐标范围，聚焦关键区域
**主要功能：**
- **阶跃响应分析**：观察系统对单位阶跃输入的响应，这是控制系统分析中最常用的测试信号
- **脉冲响应分析**：观察系统对单位脉冲（δ函数）输入的响应，用于分析系统的固有特性
- **性能指标计算**：自动计算并显示系统的关键时域性能指标
**知识要点**：
- 根轨迹法基本原理
- 幅值条件与相角条件
- 根轨迹的基本性质（起点、终点、渐近线、分离点）
- s 平面稳定性区域
- 阻尼比等值线
**计算的性能指标：**
| 指标 | 符号 | 定义 | 物理意义 |
|------|------|------|----------|
| 上升时间 | $t_r$ | 响应从稳态值的10%上升到90%所需时间 | 反映系统响应速度 |
| 峰值时间 | $t_p$ | 响应达到第一个峰值的时间 | 反映系统阻尼特性 |
| 超调量 | $\sigma\%$ | 峰值超过稳态值的百分比 | 反映系统振荡程度 |
| 调节时间 | $t_s$ | 响应进入并保持在稳态值±2%区间的时间 | 反映系统 settling 速度 |
| 稳态值 | $y(\infty)$ | 时间趋于无穷时系统的输出值 | 反映系统最终行为 |
### 🤖 4. AI 智能问答 (Q&A)
**二阶系统标准形式：**
二阶系统是自动控制理论中最重要的系统类型，其标准传递函数为：
$$G(s) = \frac{\omega_n^2}{s^2 + 2\zeta\omega_n s + \omega_n^2}$$
其中：
- $\omega_n$ — 自然频率（rad/s），表示系统无阻尼时的固有振荡频率
- $\zeta$ — 阻尼比，表示系统阻尼程度的相对量
**阻尼比与系统响应关系：**
| 阻尼比范围 | 系统类型 | 响应特性 |
|------------|----------|----------|
| $\zeta = 0$ | 无阻尼 | 持续等幅振荡 |
| $0 < \zeta < 1$ | 欠阻尼 | 振荡衰减，响应快速 |
| $\zeta = 1$ | 临界阻尼 | 最快无振荡响应 |
| $\zeta > 1$ | 过阻尼 | 无振荡，但响应较慢 |
**稳态误差分析：**
稳态误差是系统长期运行后实际输出与期望输出之间的差值，是评价系统控制精度的重要指标。对于单位反馈系统：
$$e_{ss} = \lim_{t\to\infty} e(t) = \lim_{s\to0} \frac{R(s)}{1+G(s)H(s)}$$
---
### 2. 频域分析 (Frequency Domain Analysis)
频域分析通过研究系统对不同频率正弦信号的响应特性来分析系统性能，是经典控制理论的核心方法之一。
**主要功能：**
- **Bode 图绘制**：同时显示幅频特性曲线和相频特性曲线
- **Nyquist 图绘制**：以极坐标形式展示系统频率响应
- **稳定裕度计算**：定量评估系统的相对稳定性
**Bode 图（波特图）：**
Bode 图包含两个子图：
- **幅频特性图**：显示增益（单位：dB）随频率变化的关系
- **相频特性图**：显示相位（单位：度）随频率变化的关系
对数频率特性的优点：
1. 可以将频率范围压缩，便于观察宽频带内的特性
2. 幅值相乘转化为对数相加，简化串联系统计算
3. 渐近线近似作图简便实用
**Nyquist 稳定判据：**
Nyquist 图是基于复变函数理论的稳定性判据。对于闭环系统：
$$T(s) = \frac{G(s)}{1 + G(s)}$$
稳定性条件：当 $\omega$ 从 $-\infty$ 变化到 $+\infty$ 时，$G(j\omega)H(j\omega)$ 轨迹顺时针包围 $(-1, j0)$ 点 $P$ 圈，其中 $P$ 为开环不稳定极点数。
**稳定裕度（Stability Margins）：**
| 裕度类型 | 定义 | 计算公式 | 经验要求 |
|----------|------|----------|----------|
| 增益裕度 GM | 相角为 $-180°$ 时，闭环增益还能增大多少 | $GM = \frac{1}{|G(j\omega_g)|}$ | $GM > 1.0$（即 $GM_{dB} > 0$） |
| 相位裕度 PM | 增益为1（0dB）时，相位还能滞后多少 | $PM = 180° + \angle G(j\omega_c)$ | $PM > 45°$ |
**稳定性判断准则：**
- $GM > 0$ dB 且 $PM > 0°$ → **系统稳定**
- $GM < 0$ dB 或 $PM < 0°$ → **系统不稳定**
**频域指标与时域性能的关系：**
| 频域指标 | 时域对应 | 经验公式 |
|----------|----------|----------|
| 带宽 $\omega_b$ | 上升时间 $t_r$ | $t_r \approx \frac{1.8}{\omega_b}$ |
| 相位裕度 PM | 超调量 $\sigma\%$ | $\sigma\% \approx 100 \times e^{-\pi PM/(90-PM)}$ |
| 增益裕度 GM | 稳定余量 | GM 越大，系统对不确定性越不敏感 |
---
### 3. 根轨迹分析 (Root Locus Analysis)
根轨迹法是一种图解方法，用于分析系统闭环极点随增益 K 变化的轨迹，是控制系统设计的核心工具。
**主要功能：**
- **完整根轨迹绘制**：自动绘制开环增益从0到无穷变化时的闭环极点轨迹
- **增益调节**：通过滑块精确调节增益 K，实时观察极点位置变化
- **极点跟踪**：显示当前增益下闭环极点的精确位置
- **动态坐标**：自动调整坐标系范围，聚焦关键区域
**根轨迹的基本规则：**
1. **起点与终点**：
- 起点（K=0）：开环传递函数的极点（$n$ 个）
- 终点（K→∞）：开环传递函数的零点（$m$ 个），剩余 $n-m$ 个趋向无穷
2. **渐近线**：
- 当 $K \to \infty$ 时，根轨迹趋向 $n-m$ 条渐近线
- 渐近线与实轴的夹角：$\phi_a = \frac{(2k+1)180°}{n-m}$
3. **分离点与会合点**：
- 根轨迹在实轴上相邻两分支之间的某点分离或会合
- 分离点坐标可通过求解 $\frac{dK}{ds} = 0$ 得到
4. **与虚轴的交点**：
- 根轨迹与虚轴的交点对应的增益和频率可通过劳斯判据确定
**s平面稳定性区域：**
| 极点位置 | 系统状态 | 物理意义 |
|----------|----------|----------|
| 左半平面（Re(s) < 0） | 稳定 | 响应最终衰减 |
| 虚轴（Re(s) = 0） | 临界稳定 | 持续振荡 |
| 右半平面（Re(s) > 0） | 不稳定 | 响应发散 |
**阻尼比等值线：**
在 s 平面上，阻尼比 $\zeta$ 等于常数的曲线是通过原点的射线。对于二阶系统：
$$\zeta = \cos(\theta)$$
其中 $\theta$ 是该射线与负实轴的夹角。阻尼比越大，射线越接近负实轴，系统响应越平稳但越缓慢。
---
### 4. 算例演示 (Case Demo)
算例演示模块是本平台的特色功能，通过完整的串联式混合动力系统模型，展示控制系统设计在实际工程中的应用。
**模块架构（四阶段设计）：**
```
阶段零：模型训练
├── GPR模型训练/加载（高斯过程回归）
└── NN模型训练（知识蒸馏）
阶段一：发动机控制器设计
├── PID控制器
└── MPC控制器（模型预测控制）
阶段二：电机控制器设计
├── PID控制器
└── MPC控制器（模型预测控制）
阶段三：能量管理策略设计
├── 规则型能量管理（基于SOC滞环）
└── 完整混动系统仿真
```
**发动机模型（Engine Model）：**
基于高斯过程回归（GPR）和神经网络（NN）代理模型的涡轴发动机动态仿真：
- **输入变量**：高度 $H$ (m)、马赫数 $Ma$、转速 $N$ (RPM)
- **输出变量**：燃油流量 $W_f$ (kg/h)、输出功率 $P$ (kW)
**发动机动态特性：**
$$\tau_f \frac{dW_f}{dt} + W_f = W_{f,cmd}$$
$$T \frac{dN}{dt} = K(W_f - W_{f,eq}(N))$$
其中 $\tau_f$ 是燃油执行机构时间常数，$K$ 是转子惯性增益。
**电机模型（Motor Model）：**
永磁同步电机（PMSM）离散时间动力学模型：
- **d/q轴电流控制**：$i_d = 0$ 控制（MTPA）
- **转矩方程**：$T_e = 1.5n_p[\psi_f + (L_d - L_q)i_d]i_q$
- **机械方程**：$J\frac{d\omega}{dt} = T_e - T_L - B\omega$
**电池模型（Battery Model）：**
等效电路模型，包含：
- **开路电压** OCV：与 SOC 相关的非线性查表
- **内阻**：$R_{in} = 0.15 \Omega$
- **SOC 更新**：安时积分法
$$SOC_{k+1} = SOC_k - \frac{I_k \cdot \Delta t}{C_{capacity}}$$
**能量管理策略（EMS）：**
基于规则的功率跟随策略，配合 SOC 滞环控制：
| SOC 区间 | 工作模式 | 控制策略 |
|----------|----------|----------|
| $SOC < SOC_{low}$ | 充电模式 | 发动机输出固定功率 $P_{charge}$ |
| $SOC > SOC_{high}$ | 功率跟随 | 发动机输出 = 电机需求 + 储备功率 + SOC补偿 |
| 滞环区间内 | 保持 | 维持前一模式 |
**MPC控制器（模型预测控制）：**
滚动时域优化控制器，核心思想：
1. **预测模型**：利用系统线性化模型预测未来 H 步状态
2. **优化目标**：$\min \sum_{k=1}^{H} [\|w_k - w_{ref}\|^2_{Q} + \|\Delta u_k\|^2_{R}]$
3. **约束处理**：执行机构限幅、超调量硬约束
本平台使用纯 Python 实现的投影梯度下降求解器，替代传统的 SLSQP / GEKKO 方案，避免 Fortran ABI 兼容性问题。
**PID控制器（增量式）：**
增量式 PID 控制器公式：
$$\Delta u(k) = k_p[e(k) - e(k-1)] + k_i e(k) + k_d[e(k) - 2e(k-1) + e(k-2)]$$
特点：
- 计算增量而非绝对量，避免积分饱和
- 支持输入/输出量程归一化，便于参数调节
---
### 5. AI 智能问答 (Q&A)
AI 问答模块集成了 DeepSeek API，提供24小时在线的自动控制理论学习助手。
**主要功能：**
- **专业教学助手**：精通自动控制原理的 AI 教授
- **流式响应**：实时显示 AI 回复过程
- **流式响应**：实时逐字显示 AI 回复，支持多轮对话
- **LaTeX 公式渲染**：完美支持数学公式显示
- **上下文记忆**：支持多轮对话
- **上下文记忆**：支持多轮连续对话，理解对话上下文
**支持的 API**：
- DeepSeek API（推荐，国内网络友好）
- Google Gemini API
**支持的 API：**
| API 提供商 | 模型选择 | 特点 |
|------------|----------|------|
| DeepSeek | deepseek-chat / deepseek-coder | 国内访问，中文优化 |
## 🚀 快速开始
**提问技巧：**
### 环境要求
✅ **推荐的问题类型：**
- 概念解释："请解释传递函数的定义和物理意义"
- 公式推导："如何推导二阶系统的超调量公式？"
- 例题讲解："如何用劳斯判据判断这个系统的稳定性？"
- 参数分析："PID控制器中三个参数分别如何影响系统响应？"
- Python 3.8+
- pip 包管理器
❌ **应避免的问题：**
- 过于宽泛："帮我做作业"（建议具体描述问题）
- 缺乏上下文："这个对吗？"（请提供具体系统参数）
### 安装步骤
---
1. **克隆项目**
## 系统要求
### 运行环境
| 项目 | 要求 |
|------|------|
| Python 版本 | 3.10+ |
| 操作系统 | Windows / Linux / macOS |
| 内存 | 建议 8GB 以上 |
| 显卡 | 可选（用于 GPR 训练加速，CPU 模式也可运行） |
### 浏览器要求
推荐使用以下浏览器的最新版本以获得最佳体验：
- Google Chrome 90+
- Microsoft Edge 90+
- Mozilla Firefox 88+
- Apple Safari 14+
---
## 快速开始
### 方式一：使用 pip 安装（推荐）
**1. 克隆项目**
```bash
git clone <your-repo-url>
cd AutoControl
cd AutoControlCourse
```
2. **安装依赖**
**2. 创建虚拟环境（推荐）**
```bash
pip install gradio numpy control matplotlib aiohttp
```
# 使用 venv
python -m venv my_gradio_env
source my_gradio_env/bin/activate # Linux/macOS
# 或
my_gradio_env\Scripts\activate # Windows
或使用 conda：
```bash
conda create -n autocontrol python=3.9
# 或使用 conda
conda create -n autocontrol python=3.10
conda activate autocontrol
pip install gradio numpy control matplotlib aiohttp
```
3. **配置 API 密钥**
**3. 安装依赖**
编辑 `app.py` 文件开头的配置区域：
```bash
pip install -r requirements.txt
```
**4. 配置 API 密钥**
编辑 `config.py` 文件中的 API 配置区域：
```python
# ==================== API 配置 ====================
API_KEY = "your-api-key-here" # 填入您的 DeepSeek API 密钥
API_BASE_URL = "https://api.deepseek.com/v1"
API_MODEL = "deepseek-chat"
API_TYPE = "deepseek" # 或 "gemini"
API_TYPE = "deepseek"
# ==================================================
```
**获取 DeepSeek API 密钥**：
- 访问 https://platform.deepseek.com/api_keys
- 注册并创建 API 密钥
- 复制密钥到配置文件
**获取 DeepSeek API 密钥：**
1. 访问 https://platform.deepseek.com/api_keys
2. 注册并登录账号
3. 点击"创建新密钥"
4. 复制生成的密钥并填入配置
4. **运行应用**
**5. 运行应用**
```bash
python app.py
```
或使用 conda 环境：
**6. 访问应用**
```bash
conda run -n autocontrol python app.py
```
5. **访问应用**
浏览器自动打开，或手动访问：
浏览器将自动打开，或手动访问：
```
http://localhost:7860
```
## 📖 使用指南
### 方式二：使用 Docker（可选）
### 输入系统传递函数
```bash
# 构建镜像
docker build -t autocontrol-course .
在任意标签页的输入框中输入传递函数的分子和分母系数：
**示例 1：一阶系统**
```
分子: 1
分母: 1,1
传递函数: G(s) = 1/(s+1)
# 运行容器
docker run -p 7860:7860 \
-e API_KEY="your-api-key" \
autocontrol-course
```
**示例 2：二阶系统**
---
## 项目结构
```
分子: 4
分母: 1,2,4
传递函数: G(s) = 4/(s²+2s+4)
AutoControlCourse/
├── app.py # Gradio 主入口，事件绑定与界面布局
├── ui_components.py # 各功能标签页的 UI 组件定义
├── analysis_functions.py # 时域/频域/根轨迹分析核心计算函数
├── case_demo_functions.py # 算例演示模块（混动模型四阶段仿真）
├── chatbot.py # AI 智能问答（DeepSeek API 集成）
├── user_stats.py # 在线人数统计与数据持久化
├── config.py # 全局配置（API密钥、服务器端口等）
├── requirements.txt # Python 依赖列表
│
├── Model/ # 混动模型核心代码
│ ├── src/
│ │ ├── lightweight_model.py # NN代理模型（MLP，用于替代GPR）
│ │ ├── engine_gpr_class.py # GPR高斯过程回归模型
│ │ ├── distill_gpr_to_nn.py # 知识蒸馏脚本（GPR→NN）
│ │ ├── engine_dynamic_sim.py # 涡轴发动机动态仿真
│ │ ├── motor_sim.py # PMSM永磁同步电机仿真
│ │ ├── battery_sim.py # 电池等效电路仿真
│ │ ├── mpc_controller.py # MPC模型预测控制器
│ │ ├── increPID.py # 增量式PID控制器
│ │ └── series_hybrid_sim.py # 串联混动系统总成
│ └── data/
│ ├── Cleaned_Engine_Data_Full.csv # 发动机标定数据
│ ├── engine_gpr_model.pth # GPR模型权重
│ └── engine_nn_proxy.pth # NN代理模型权重
│
└── assets/ # 静态资源
├── styles.css # 自定义CSS样式
└── knowledge_cards_html.py # 各模块知识卡片HTML内容
```
**示例 3：三阶系统**
```
分子: 1
分母: 1,6,11,6
传递函数: G(s) = 1/(s³+6s²+11s+6)
---
## 技术栈
### 前端框架
- **Gradio 4.44.1**：快速构建机器学习 Web 界面的 Python 库
- **Custom CSS**：现代化样式定制，支持响应式布局
### 核心计算库
| 库名 | 版本 | 用途 |
|------|------|------|
| NumPy | 1.26.4 | 数值计算基础库 |
| python-control | 0.9.4 | 控制系统分析工具箱 |
| Matplotlib | 3.9.4 | 图表绑制 |
| PyTorch | 2.4.1 | 神经网络推理（CPU模式） |
### AI 集成
| 库名 | 用途 |
|------|------|
| aiohttp | 异步HTTP请求，流式响应 |
| DeepSeek API | AI问答后端服务 |
### 其他依赖
| 库名 | 用途 |
|------|------|
| pandas | 数据处理（发动机CSV数据读取） |
| scikit-learn | 数据归一化预处理 |
| psutil | 系统资源监控（CPU/内存/显存） |
---
## 算例演示模块详解
### 阶段零：模型训练
**GPR 模型训练/加载：**
高斯过程回归是一种非参数概率模型，适合小样本、高维插值。
```python
# 运行模式
mode = "load" # 加载已有模型（推荐）
mode = "train" # 从头训练（需要 botorch/gpytorch）
```
**输入格式**：
- 系数从最高次项到常数项
- 用逗号分隔（支持中文或英文逗号）
- 支持小数和负数
- 例如：`1, 2.5, -3, 4` 表示 s³ + 2.5s² - 3s + 4
**NN 模型训练（知识蒸馏）：**
### 调节系统增益
将 GPR 模型的知识蒸馏到轻量级 MLP 中，用于实时控制仿真。
频域分析和根轨迹分析都支持增益调节：
关键参数：
- **训练轮数 (Epochs)**：越多越精确，推荐 3000
- **学习率 (LR)**：推荐 1e-3 ~ 5e-3
- **隐藏层宽度**：推荐 64（平衡精度与速度）
- **对数滑块**：log₁₀(K) 范围 -1 到 3
- -1 对应 K = 0.1
- 0 对应 K = 1
- 1 对应 K = 10
- 2 对应 K = 100
- 3 对应 K = 1000
### 阶段一：发动机控制器设计
- **实时显示**：下方数字框显示实际增益值
- **松开更新**：滑块松开后才更新图表，避免卡顿
**PID 控制参数：**
- **Kp（比例增益）**：增大可加快响应，但过大导致振荡
- **Ki（积分增益）**：消除稳态误差，过大导致超调
- **Kd（微分增益）**：抑制振荡，改善动态特性
### AI 问答技巧
**MPC 控制参数：**
- **预测时域 (Horizon)**：前看步数，越长越激进
- **功率跟踪权重 W_power**：越高则功率跟踪越紧
- **控制增量权重 W_dcost**：越高则控制变化越平缓
- **超调限制**：5% 硬约束
**高效提问方式**：
### 阶段二：电机控制器设计
✅ **好的问题**：
- "请解释 PID 控制器的三个参数如何影响系统性能？"
- "如何根据 Bode 图判断系统的稳定裕度？"
- "为什么阻尼比为 0.707 时系统响应最佳？"
- "推导二阶系统的超调量公式"
**仿真设置：**
- 仿真时长：5~60秒可调
- 仿真步长：0.02s / 0.05s / 0.1s
- 目标转速：500~5000 RPM
- 负载转矩：10~400 Nm
❌ **避免的问题**：
- "帮我做作业"（太模糊）
- "这个对吗？"（缺少上下文）
- "答案是什么？"（没有提供问题描述）
**负载扰动测试：**
在仿真60%时刻自动施加150%负载扰动，检验控制器抗扰能力。
**公式显示**：
AI 回复中的数学公式会自动渲染，支持以下格式：
- 行内公式：`$公式$` 或 `\(公式\)`
- 块级公式：`$$公式$$` 或 `\[公式\]`
### 阶段三：能量管理策略设计
## 🎨 界面特色
**SOC 滞环控制参数：**
| 参数 | 含义 | 推荐值 |
|------|------|--------|
| SOC目标值 | 功率跟随模式下的补偿基准 | 60% |
| SOC下限阈值 | 低于此值进入充电模式 | 30% |
| SOC上限阈值 | 高于此值退出充电模式 | 70% |
**功率规则参数：**
| 参数 | 含义 | 推荐值 |
|------|------|--------|
| 发动机最小功率 | 最低运转功率 | 20 kW |
| 发动机最大功率 | 峰值输出功率 | 300 kW |
| 充电模式功率 | 充电时发动机输出 | 200 kW |
| SOC补偿增益 | SOC偏差修正力度 | 50 kW/ΔSOC |
---
## 界面特色
### 现代化设计
- **渐变色标题**：紫色渐变视觉效果
- **卡片式布局**：分组清晰，层次分明
- **可滚动知识卡片**：长文档不占据过多空间
- **可折叠章节**：按需展开知识点
- **可滚动知识卡片**：长文档不占用过多屏幕空间
- **可折叠章节**：按需展开，节省视线
### 响应式交互
@@ -220,63 +524,15 @@ AI 回复中的数学公式会自动渲染，支持以下格式：
- **嵌入式公式**：页面内直接显示 LaTeX 公式
- **表格对比**：性能指标、稳定准则表格化
- **颜色编码**：稳定/不稳定用绿/红色标识
- **图标辅助**：emoji 增强视觉识别
- **图标辅助**：Emoji 增强视觉识别
## 🛠️ 技术栈
---
### 前端框架
- **Gradio 4.x**：快速构建机器学习 Web 界面
- **Custom CSS**：现代化样式定制
### 计算库
- **NumPy**：数值计算
- **python-control**：控制系统分析
- **Matplotlib**：图表绘制
### AI 集成
- **aiohttp**：异步 HTTP 请求
- **DeepSeek API**：智能问答后端
- **流式响应**：提升用户体验
## 📁 项目结构
```
AutoControl/
├── app.py # 主应用程序
├── README.md # 项目文档
├── API_CONFIG.md # API 配置详细说明（可选）
├── QUICK_START.md # 快速入门指南（可选）
└── requirements.txt # 依赖列表（可选）
```
## 🔧 高级配置
### 切换到 Gemini API
如果您想使用 Google Gemini API：
```python
API_KEY = "your-gemini-api-key"
API_BASE_URL = "https://generativelanguage.googleapis.com/v1beta"
API_MODEL = "gemini-1.5-flash"
API_TYPE = "gemini"
```
### 自定义系统提示词
修改 `chat_with_ai` 函数中的 `system_prompt`：
```python
system_prompt = """
你是一位[角色定义]。
请用[语言风格]来回答有关[领域]的问题。
[其他要求...]
"""
```
## 高级配置
### 调整图表样式
在 `matplotlib` 绘图代码中修改：
在 `analysis_functions.py` 中的 matplotlib 绑图代码里修改：
```python
# 修改图表大小
@@ -289,48 +545,156 @@ ax.plot(x, y, color='#667eea', linewidth=2)
ax.grid(True, alpha=0.3, linestyle='--')
```
## 📚 参考资料
### 自定义系统提示词
修改 `chatbot.py` 中的 `system_prompt`：
```python
system_prompt = """
你是一位精通自动控制原理的专家教授。
请用清晰、准确、专业的中文来回答问题。
重要规则：
1. 当需要表达数学公式时，必须使用 LaTeX 格式
2. 行内公式使用 $公式$ 或 \\(公式\\)
3. 独立公式使用 $$公式$$ 或 \\[公式\\]
"""
```
### 自定义工况模板
修改 `case_demo_functions.py` 中的 `_profile_points` 函数：
```python
def _profile_points(profile_name):
if profile_name == "我的自定义工况":
return [
(0., 1500., 50.), # (时间, 目标转速, 负载转矩)
(10., 3000., 200.),
(30., 2800., 150.),
(50., 1800., 60.)
]
# ... 其他工况
```
---
## 常见问题
### Q1: 运行时提示 "psutil 未安装"
不影响主要功能，仅系统资源监控不可用。忽略此提示或执行：
```bash
pip install psutil
```
### Q2: 算例演示提示 "engine_nn_proxy.pth not found"
需要先完成**阶段零**的 NN 模型训练（约2~5分钟）。
### Q3: GPR 训练失败，提示缺少 botorch/gpytorch
GPR 训练需要额外依赖。安装方法：
```bash
pip install botorch gpytorch scikit-learn
```
或直接选择 "load" 模式加载已有模型。
### Q4: AI 问答返回 "API_KEY 未配置"
请在 `config.py` 中填入有效的 DeepSeek API 密钥。
### Q5: 图表显示中文乱码
本平台图表使用英文标签以避免中文显示问题。如需修改，编辑 `analysis_functions.py` 中的 `plt.title()`、`plt.xlabel()` 等。
---
## 参考资料
### 经典教材
- 《自动控制原理》- 胡寿松
- 《现代控制工程》- Katsuhiko Ogata
- 《反馈控制理论》- John Doyle
1. 胡寿松.《自动控制原理》（第七版）. 科学出版社, 2019.
2. Katsuhiko Ogata. *Modern Control Engineering* (5th Edition). Prentice Hall, 2010.
3. Richard C. Dorf, Robert H. Bishop. *Modern Control Systems* (14th Edition). Pearson, 2021.
4. John Doyle, Bruce Francis, Allen Tannenbaum. *Feedback Control Theory*. Macmillan, 1992.
### 在线资源
- [python-control 官方文档](https://python-control.readthedocs.io/)
- [DeepSeek API 文档](https://platform.deepseek.com/docs)
- [Gradio 官方文档](https://www.gradio.app/docs)
- [Gradio 官方文档](https://gradio.app/docs)
- [PyTorch 文档](https://pytorch.org/docs/)
## 🤝 贡献指南
---
## 贡献指南
欢迎提交 Issue 和 Pull Request！
### 贡献方向
- 🐛 修复 Bug
- ✨ 添加新功能（如状态空间分析）
- ✨ 添加新功能（如状态空间分析模块）
- 📝 改进文档
- 🎨 优化界面设计
- 🧪 添加测试用例
## 📄 许可证
### 开发环境设置
```bash
# 克隆仓库
git clone <your-repo-url>
cd AutoControlCourse
# 创建开发分支
git checkout -b feature/your-feature-name
# 安装开发依赖
pip install -r requirements.txt
pip install pytest black flake8
# 代码格式化
black .
# 运行测试
pytest
```
---
## 更新日志
详细更新日志请参阅 [CHANGELOG.md](CHANGELOG.md)。
---
## 许可证
本项目采用 MIT 许可证。详见 [LICENSE](LICENSE) 文件。
## 🙏 致谢
---
## 致谢
- **Gradio**：提供优秀的 Web 界面框架
- **python-control**：强大的控制系统分析库
- **DeepSeek**：高质量的 AI 服务
- 所有贡献者和使用者
## 📞 联系方式
---
- 项目地址：[GitHub Repository URL]
- 问题反馈：[Issues URL]
- 邮箱：[your-email@example.com]
## 联系方式
- **项目负责人**：魏鹏飞
- **电子邮件**：pengfeiwei@nwpu.edu.cn
- **机构**：西北工业大学
- **项目地址**：https://github.com/your-repo
---
**⭐ 如果这个项目对您有帮助，请给它一个 Star！**
**⭐ 如果这个项目对您的学习有帮助，请给它一个 Star！**
最后更新：2025年10月15日
最后更新：2026年4月7日
app.py
+270 -43
View File
@@ -1,3 +1,4 @@
import os
import gradio as gr
import time
from functools import partial
@@ -10,6 +11,8 @@ from analysis_functions import (
frequency_domain_analysis,
root_locus_analysis
)
# ===== 算例演示模块函数导入（四阶段设计）=====
from case_demo_functions import run_distillation_demo, run_gpr_training, run_engine_design, run_motor_design, run_hybrid_demo
from chatbot import chat_with_ai
from user_stats import get_online_status_html, update_user_activity
from ui_components import (
@@ -17,11 +20,102 @@ from ui_components import (
create_time_domain_tab,
create_frequency_domain_tab,
create_root_locus_tab,
create_case_demo_tab,
create_chatbot_tab
)
# ===== 系统资源监控 =====
def get_system_monitor_html():
"""获取 CPU / 内存 / GPU 使用率的 HTML 小组件"""
try:
import psutil
cpu_pct = psutil.cpu_percent(interval=0)
mem = psutil.virtual_memory()
mem_pct = mem.percent
mem_used_gb = mem.used / (1024 ** 3)
mem_total_gb = mem.total / (1024 ** 3)
except ImportError:
return "<div style='text-align:center;color:#999;font-size:0.8em;'>psutil 未安装，无法监控系统资源</div>"
# GPU 信息 — 优先用 nvidia-smi（不依赖 PyTorch CUDA 版本），再用 torch.cuda 兜底
gpu_html = ""
try:
import subprocess as _sp
_r = _sp.run(
['nvidia-smi', '--query-gpu=name,memory.used,memory.total,utilization.gpu',
'--format=csv,noheader,nounits'],
capture_output=True, text=True, timeout=3
)
if _r.returncode == 0 and _r.stdout.strip():
_parts = [p.strip() for p in _r.stdout.strip().split('\n')[0].split(',')]
_gpu_mem_used = float(_parts[1]) / 1024 # MiB → GiB
_gpu_mem_total = float(_parts[2]) / 1024
_gpu_util = float(_parts[3])
_gc = '#ff6b6b' if _gpu_util > 80 else '#ffd93d' if _gpu_util > 50 else '#6bcb77'
gpu_html = (
f"<div style='display:inline-flex;align-items:center;gap:6px;'>"
f"<span>🎮 GPU</span>"
f"<div style='width:90px;height:8px;background:#444;border-radius:4px;overflow:hidden;'>"
f"<div style='width:{min(_gpu_util, 100):.0f}%;height:100%;background:{_gc};'></div>"
f"</div>"
f"<span>{_gpu_util:.0f}% &nbsp; {_gpu_mem_used:.1f}/{_gpu_mem_total:.0f} GB</span>"
f"</div>"
)
else:
raise RuntimeError("nvidia-smi no output")
except Exception:
try:
import torch as _torch
if _torch.cuda.is_available():
_mem_alloc = _torch.cuda.memory_allocated(0) / (1024 ** 3)
_mem_total = _torch.cuda.get_device_properties(0).total_memory / (1024 ** 3)
_util = _mem_alloc / max(_mem_total, 0.01) * 100
_gc = '#ff6b6b' if _util > 80 else '#ffd93d' if _util > 50 else '#6bcb77'
gpu_html = (
f"<div style='display:inline-flex;align-items:center;gap:6px;'>"
f"<span>🎮 GPU</span>"
f"<div style='width:90px;height:8px;background:#444;border-radius:4px;overflow:hidden;'>"
f"<div style='width:{min(_util, 100):.0f}%;height:100%;background:{_gc};'></div>"
f"</div>"
f"<span>{_mem_alloc:.1f}/{_mem_total:.0f} GB</span>"
f"</div>"
)
else:
gpu_html = "<div style='display:inline-flex;align-items:center;gap:4px;'><span>🎮 GPU N/A</span></div>"
except Exception:
gpu_html = "<div style='display:inline-flex;align-items:center;gap:4px;'><span>🎮 GPU N/A</span></div>"
cpu_color = '#ff6b6b' if cpu_pct > 80 else '#ffd93d' if cpu_pct > 50 else '#6bcb77'
mem_color = '#ff6b6b' if mem_pct > 80 else '#ffd93d' if mem_pct > 50 else '#6bcb77'
html = (
f"<div style='display:flex;justify-content:center;gap:20px;flex-wrap:wrap;"
f"font-size:0.82em;color:#ddd;padding:4px 10px;'>"
# CPU
f"<div style='display:inline-flex;align-items:center;gap:6px;'>"
f"<span>🖥️ CPU</span>"
f"<div style='width:90px;height:8px;background:#444;border-radius:4px;overflow:hidden;'>"
f"<div style='width:{min(cpu_pct, 100):.0f}%;height:100%;background:{cpu_color};'></div>"
f"</div>"
f"<span>{cpu_pct:.0f}%</span>"
f"</div>"
# Memory
f"<div style='display:inline-flex;align-items:center;gap:6px;'>"
f"<span>💾 RAM</span>"
f"<div style='width:90px;height:8px;background:#444;border-radius:4px;overflow:hidden;'>"
f"<div style='width:{min(mem_pct, 100):.0f}%;height:100%;background:{mem_color};'></div>"
f"</div>"
f"<span>{mem_used_gb:.1f}/{mem_total_gb:.0f} GB ({mem_pct:.0f}%)</span>"
f"</div>"
# GPU
f"{gpu_html}"
f"</div>"
)
return html
# 加载外部CSS文件
with open("assets/styles.css", "r", encoding="utf-8") as f:
with open(os.path.join(os.path.dirname(__file__), "assets", "styles.css"), "r", encoding="utf-8") as f:
custom_css = f.read()
# --- 主应用界面 ---
@@ -33,23 +127,8 @@ with gr.Blocks(title="自动控制理论学习网站 - AI+数智平台", css=cus
# 创建头部信息和在线计数器
online_counter = create_header()
# 创建共享的输入组件
with gr.Row():
with gr.Column(scale=1):
with gr.Group():
gr.HTML("<div class='card-title'>📊 通用系统参数</div>")
num_input = gr.Textbox(
label="传递函数分子系数 (Numerator)",
value="1",
placeholder="例如: 1 或 1,2,3",
info="💡 用逗号分隔多个系数，从最高次项到常数项"
)
den_input = gr.Textbox(
label="传递函数分母系数 (Denominator)",
value="1,6,11,6",
placeholder="例如: 1,2,1",
info="💡 分母阶数通常高于或等于分子阶数"
)
# 系统资源监控（始终可见）
system_monitor = gr.HTML(value=get_system_monitor_html, elem_id="system-monitor")
# 创建功能选项卡
with gr.Tabs() as tabs:
@@ -59,7 +138,10 @@ with gr.Blocks(title="自动控制理论学习网站 - AI+数智平台", css=cus
freq_domain_ui = create_frequency_domain_tab()
with gr.TabItem("🎯 根轨迹 (Root Locus)", id=2):
root_locus_ui = create_root_locus_tab()
with gr.TabItem("🤖 智能问答 (Q&A)", id=3):
# ===== 新增：算例演示 Tab（位于根轨迹与智能问答之间） =====
with gr.TabItem("🧪 算例演示 (Case Demo)", id=3):
case_demo_ui = create_case_demo_tab()
with gr.TabItem("🤖 智能问答 (Q&A)", id=4):
chatbot_ui = create_chatbot_tab()
# 2. 绑定事件逻辑
@@ -74,13 +156,13 @@ with gr.Blocks(title="自动控制理论学习网站 - AI+数智平台", css=cus
# --- 时域分析事件 ---
time_domain_ui["confirm_button"].click(
fn=display_transfer_function,
inputs=[num_input, den_input],
inputs=[time_domain_ui["num_input"], time_domain_ui["den_input"]],
outputs=[time_domain_ui["tf_display"]]
).then(lambda: get_online_status_html(), outputs=online_counter)
time_domain_ui["analyze_button"].click(
fn=time_domain_analysis,
inputs=[num_input, den_input],
inputs=[time_domain_ui["num_input"], time_domain_ui["den_input"]],
outputs=[time_domain_ui["output_plot"], time_domain_ui["output_metrics"]]
).then(lambda: get_online_status_html(), outputs=online_counter)
@@ -90,7 +172,7 @@ with gr.Blocks(title="自动控制理论学习网站 - AI+数智平台", css=cus
fig, metrics, tf_latex, stability = frequency_domain_analysis(num, den, k)
return fig, metrics, tf_latex, stability, k, get_online_status_html()
freq_inputs = [num_input, den_input, freq_domain_ui["log_k_slider"]]
freq_inputs = [freq_domain_ui["num_input"], freq_domain_ui["den_input"], freq_domain_ui["log_k_slider"]]
freq_outputs = [
freq_domain_ui["plot_output"],
freq_domain_ui["metrics_display"],
@@ -110,7 +192,7 @@ with gr.Blocks(title="自动控制理论学习网站 - AI+数智平台", css=cus
fig, poles, k_val = root_locus_analysis(num, den, log_k)
return fig, poles, k_val, get_online_status_html()
rl_inputs = [root_locus_ui["log_k_slider"], num_input, den_input]
rl_inputs = [root_locus_ui["log_k_slider"], root_locus_ui["num_input"], root_locus_ui["den_input"]]
rl_outputs = [
root_locus_ui["plot_output"],
root_locus_ui["poles_display"],
@@ -123,26 +205,172 @@ with gr.Blocks(title="自动控制理论学习网站 - AI+数智平台", css=cus
outputs=rl_outputs
)
# 当输入框变化时，也更新频域和根轨迹（如果它们是当前可见的）
def update_all_on_tf_change(num, den, log_k_freq, log_k_rl):
# 更新频域
k_freq = 10**log_k_freq
fig_freq, metrics, tf_latex, stability = frequency_domain_analysis(num, den, k_freq)
# 频域：当传递函数输入框变化时自动更新
freq_domain_ui["num_input"].change(
fn=update_frequency_analysis_wrapper,
inputs=freq_inputs, outputs=freq_outputs
)
freq_domain_ui["den_input"].change(
fn=update_frequency_analysis_wrapper,
inputs=freq_inputs, outputs=freq_outputs
)
# 更新根轨迹
fig_rl, poles, k_val_rl = root_locus_analysis(num, den, log_k_rl)
# 根轨迹：当传递函数输入框变化时自动更新
root_locus_ui["num_input"].change(
fn=update_rl_view_wrapper,
inputs=rl_inputs, outputs=rl_outputs
)
root_locus_ui["den_input"].change(
fn=update_rl_view_wrapper,
inputs=rl_inputs, outputs=rl_outputs
)
return (
fig_freq, metrics, tf_latex, stability, k_freq,
fig_rl, poles, k_val_rl,
get_online_status_html()
# ===== 算例演示事件绑定（四阶段）=====
# --- 阶段零-A：GPR 模型训练 ---
def run_gpr_wrapper(mode, sid, progress=gr.Progress(track_tqdm=True)):
update_user_activity(sid)
fig, summary = run_gpr_training(mode=mode, progress=progress)
return fig, summary, get_online_status_html()
case_demo_ui["gpr_run_button"].click(
fn=run_gpr_wrapper,
inputs=[case_demo_ui["gpr_mode"], session_id],
outputs=[case_demo_ui["gpr_plot"], case_demo_ui["gpr_summary"], online_counter]
)
# --- 阶段零-B：NN 模型训练（蒸馏）---
def run_distillation_wrapper(epochs, lr, hidden, sid, progress=gr.Progress(track_tqdm=True)):
update_user_activity(sid)
fig, summary = run_distillation_demo(epochs, lr, hidden, progress=progress)
return fig, summary, get_online_status_html()
case_demo_ui["distill_run_button"].click(
fn=run_distillation_wrapper,
inputs=[
case_demo_ui["distill_epochs"], case_demo_ui["distill_lr"],
case_demo_ui["distill_hidden"],
session_id
],
outputs=[case_demo_ui["distill_plot"], case_demo_ui["distill_summary"], online_counter]
)
# --- 阶段一：发动机控制器设计 ---
def run_engine_design_wrapper(sim_time, dt, init_power, target_power,
controller_type,
kp, ki, kd, tau_fuel, K_inertia,
mpc_horizon, mpc_W_power, mpc_W_dcost, mpc_overshoot,
sid, progress=gr.Progress(track_tqdm=True)):
update_user_activity(sid)
fig, summary = run_engine_design(
sim_time, dt, init_power, target_power,
controller_type,
kp, ki, kd, tau_fuel, K_inertia,
mpc_horizon, mpc_W_power, mpc_W_dcost, mpc_overshoot / 100.0,
progress=progress
)
return fig, summary, get_online_status_html()
tf_change_inputs = [num_input, den_input, freq_domain_ui["log_k_slider"], root_locus_ui["log_k_slider"]]
tf_change_outputs = freq_outputs[:-1] + rl_outputs[:-1] + [online_counter]
case_demo_ui["eng_run_button"].click(
fn=run_engine_design_wrapper,
inputs=[
case_demo_ui["eng_sim_time"], case_demo_ui["eng_dt"],
case_demo_ui["eng_init_power"], case_demo_ui["eng_target_power"],
case_demo_ui["eng_controller_type"],
case_demo_ui["eng_kp"], case_demo_ui["eng_ki"], case_demo_ui["eng_kd"],
case_demo_ui["eng_tau_fuel"], case_demo_ui["eng_K_inertia"],
case_demo_ui["eng_mpc_horizon"], case_demo_ui["eng_mpc_W_power"],
case_demo_ui["eng_mpc_W_dcost"], case_demo_ui["eng_mpc_overshoot"],
session_id
],
outputs=[case_demo_ui["eng_plot"], case_demo_ui["eng_summary"], online_counter]
)
num_input.change(fn=update_all_on_tf_change, inputs=tf_change_inputs, outputs=tf_change_outputs)
den_input.change(fn=update_all_on_tf_change, inputs=tf_change_inputs, outputs=tf_change_outputs)
# --- 阶段二：电机控制器设计 ---
def run_motor_design_wrapper(sim_time, dt, target_rpm, load_torque,
controller_type,
kp, ki, kd, J,
mpc_W_speed, mpc_W_dcost, mpc_overshoot,
sid, progress=gr.Progress(track_tqdm=True)):
update_user_activity(sid)
fig, summary = run_motor_design(
sim_time, dt, target_rpm, load_torque,
controller_type,
kp, ki, kd, J,
mpc_W_speed, mpc_W_dcost, mpc_overshoot / 100.0,
progress=progress
)
return fig, summary, get_online_status_html()
case_demo_ui["mot_run_button"].click(
fn=run_motor_design_wrapper,
inputs=[
case_demo_ui["mot_sim_time"], case_demo_ui["mot_dt"],
case_demo_ui["mot_target_rpm"], case_demo_ui["mot_load_torque"],
case_demo_ui["mot_controller_type"],
case_demo_ui["mot_kp"], case_demo_ui["mot_ki"], case_demo_ui["mot_kd"],
case_demo_ui["mot_J"],
case_demo_ui["mot_mpc_W_speed"], case_demo_ui["mot_mpc_W_dcost"],
case_demo_ui["mot_mpc_overshoot"],
session_id
],
outputs=[case_demo_ui["mot_plot"], case_demo_ui["mot_summary"], online_counter]
)
# --- 阶段三：能量管理策略设计（自动引用前两阶段控制器参数）---
def run_hybrid_demo_wrapper(sim_time, dt, initial_soc, initial_engine_power,
profile,
eng_ctrl_type, eng_kp, eng_ki, eng_kd,
eng_mpc_horizon, eng_mpc_W_power, eng_mpc_W_dcost, eng_mpc_overshoot,
mot_ctrl_type, mot_kp, mot_ki, mot_kd, mot_J,
mot_mpc_W_speed, mot_mpc_W_dcost, mot_mpc_overshoot,
soc_target, soc_low, soc_high,
p_eng_min, p_eng_max, p_charge, k_soc,
power_reserve, battery_capacity, sid,
progress=gr.Progress(track_tqdm=True)):
update_user_activity(sid)
fig, summary, table_data = run_hybrid_demo(
sim_time, dt, initial_soc, initial_engine_power, profile,
eng_ctrl_type, eng_kp, eng_ki, eng_kd,
eng_mpc_horizon, eng_mpc_W_power, eng_mpc_W_dcost, eng_mpc_overshoot / 100.0,
mot_ctrl_type, mot_kp, mot_ki, mot_kd, mot_J,
mot_mpc_W_speed, mot_mpc_W_dcost, mot_mpc_overshoot / 100.0,
soc_target, soc_low, soc_high,
p_eng_min, p_eng_max, p_charge, k_soc,
power_reserve, battery_capacity,
progress=progress
)
return fig, summary, table_data, get_online_status_html()
case_demo_ui["hybrid_run_button"].click(
fn=run_hybrid_demo_wrapper,
inputs=[
case_demo_ui["sim_time"], case_demo_ui["dt"],
case_demo_ui["initial_soc"], case_demo_ui["initial_engine_power"],
case_demo_ui["profile"],
# 发动机控制器参数
case_demo_ui["eng_controller_type"],
case_demo_ui["eng_kp"], case_demo_ui["eng_ki"], case_demo_ui["eng_kd"],
case_demo_ui["eng_mpc_horizon"], case_demo_ui["eng_mpc_W_power"],
case_demo_ui["eng_mpc_W_dcost"], case_demo_ui["eng_mpc_overshoot"],
# 电机控制器参数
case_demo_ui["mot_controller_type"],
case_demo_ui["mot_kp"], case_demo_ui["mot_ki"], case_demo_ui["mot_kd"],
case_demo_ui["mot_J"],
case_demo_ui["mot_mpc_W_speed"], case_demo_ui["mot_mpc_W_dcost"],
case_demo_ui["mot_mpc_overshoot"],
# 能量管理策略参数
case_demo_ui["soc_target"], case_demo_ui["soc_low"], case_demo_ui["soc_high"],
case_demo_ui["p_eng_min"], case_demo_ui["p_eng_max"],
case_demo_ui["p_charge"], case_demo_ui["k_soc"],
case_demo_ui["power_reserve"], case_demo_ui["battery_capacity"],
session_id
],
outputs=[
case_demo_ui["hybrid_plot"], case_demo_ui["hybrid_summary"],
case_demo_ui["hybrid_table"], online_counter
]
)
# --- 聊天机器人事件 ---
async def chat_wrapper(message, history, sid):
@@ -174,14 +402,13 @@ with gr.Blocks(title="自动控制理论学习网站 - AI+数智平台", css=cus
)
# --- 页面加载和定时器事件 ---
def on_page_load(sid):
update_user_activity(sid)
return get_online_status_html()
demo.load(fn=on_page_load, inputs=[session_id], outputs=[online_counter])
demo.load(fn=lambda: get_online_status_html(), outputs=[online_counter])
gr.Timer(10).tick(fn=get_online_status_html, outputs=online_counter)
# 系统资源监控定时刷新（每 3 秒）
gr.Timer(3).tick(fn=get_system_monitor_html, outputs=system_monitor)
if __name__ == "__main__":
demo.queue().launch(
assets/knowledge_cards_html.py
+1674 -1
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File diff suppressed because it is too large Load Diff
case_demo_functions.py
+1049
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File diff suppressed because it is too large Load Diff
config.py
+1 -1
View File
@@ -16,4 +16,4 @@ API_TYPE = "deepseek" # 当前支持 "deepseek"
# ==================== Gradio 应用启动配置 ====================
SERVER_NAME = "0.0.0.0" # 监听所有网络接口
SERVER_PORT = 7860 # 指定一个端口
SHARE = False # 是否创建Gradio的公开分享链接
SHARE = True # 是否创建Gradio的公开分享链接
data_usage/case_demo_test_results.json
+70
View File
@@ -0,0 +1,70 @@
[
{
"name": "常规-起飞巡航",
"figure_ok": true,
"summary_ok": true,
"table_len": 8,
"summary_head": "### 算例结果解读",
"soc_min": 60.0,
"soc_max": 62.05,
"first_row": [
0.0,
1500.0,
0.0,
50.0,
-0.65,
60.0
]
},
{
"name": "高机动-高负载",
"figure_ok": true,
"summary_ok": true,
"table_len": 8,
"summary_head": "### 算例结果解读",
"soc_min": 55.0,
"soc_max": 61.41,
"first_row": [
0.0,
1920.0,
0.0,
80.0,
-30.65,
55.0
]
},
{
"name": "经济巡航-低负载",
"figure_ok": true,
"summary_ok": true,
"table_len": 8,
"summary_head": "### 算例结果解读",
"soc_min": 70.0,
"soc_max": 76.3,
"first_row": [
0.0,
1275.0,
0.0,
40.0,
9.35,
70.0
]
},
{
"name": "边界输入-自动裁剪",
"figure_ok": true,
"summary_ok": true,
"table_len": 8,
"summary_head": "### 算例结果解读",
"soc_min": 10.0,
"soc_max": 26.51,
"first_row": [
0.0,
2400.0,
0.0,
260.0,
-210.65,
10.0
]
}
]
data_usage/usage_stats.json
+2 -2
View File
@@ -1,4 +1,4 @@
{
"total_users": 15,
"last_saved_at": 1760890996.5595384
"total_users": 37,
"last_saved_at": 1775561044.8460488
}
requirements.txt
+22 -5
View File
@@ -1,7 +1,24 @@
# requirements.txt
gradio
numpy
control
matplotlib
aiohttp
# ===== PyTorch：GPU (CUDA 12.1) 版本，如无 NVIDIA GPU 可改为 cpu =====
--extra-index-url https://download.pytorch.org/whl/cu121
gradio==4.44.1
gradio-client==1.3.0
pydantic==2.10.6
pydantic-core==2.27.2
huggingface_hub==0.23.0
numpy==1.26.4
control==0.9.4
matplotlib==3.9.4
aiohttp==3.13.5
pillow==10.4.0
# ===== 新增：混动模型（Model）运行依赖 =====
torch==2.4.1+cu121
botorch==0.14.0
gpytorch==1.14
pyro-ppl==1.9.1
pandas==2.3.3
scipy>=1.10
scikit-learn==1.7.1
psutil>=5.9
ui_components.py
+336 -2
View File
@@ -1,8 +1,13 @@
import gradio as gr
import gradio as gr
from assets.knowledge_cards_html import (
TIME_DOMAIN_KNOWLEDGE,
FREQUENCY_DOMAIN_KNOWLEDGE,
ROOT_LOCUS_KNOWLEDGE
ROOT_LOCUS_KNOWLEDGE,
ENGINE_CONTROL_KNOWLEDGE,
MOTOR_CONTROL_KNOWLEDGE,
GPR_KNOWLEDGE,
NN_KNOWLEDGE,
EMS_KNOWLEDGE,
)
def create_header():
@@ -66,6 +71,20 @@ def create_time_domain_tab():
ui_dict = {}
with gr.Row():
with gr.Column(scale=1):
with gr.Group():
gr.HTML("<div class='card-title'>📊 传递函数设定</div>")
ui_dict["num_input"] = gr.Textbox(
label="分子系数 (Numerator)",
value="1",
placeholder="例如: 1 或 1,2,3",
info="💡 用逗号分隔，从最高次项到常数项"
)
ui_dict["den_input"] = gr.Textbox(
label="分母系数 (Denominator)",
value="1,6,11,6",
placeholder="例如: 1,2,1",
info="💡 分母阶数通常 ≥ 分子阶数"
)
with gr.Group():
gr.HTML("<div class='card-title'>🔧 系统模型</div>")
ui_dict["tf_display"] = gr.Markdown(label="当前传递函数", elem_classes="output-display")
@@ -116,6 +135,20 @@ def create_frequency_domain_tab():
ui_dict = {}
with gr.Row():
with gr.Column(scale=1):
with gr.Group():
gr.HTML("<div class='card-title'>📊 传递函数设定</div>")
ui_dict["num_input"] = gr.Textbox(
label="分子系数 (Numerator)",
value="1",
placeholder="例如: 1 或 1,2,3",
info="💡 用逗号分隔，从最高次项到常数项"
)
ui_dict["den_input"] = gr.Textbox(
label="分母系数 (Denominator)",
value="1,6,11,6",
placeholder="例如: 1,2,1",
info="💡 分母阶数通常 ≥ 分子阶数"
)
with gr.Group():
gr.HTML("<div class='card-title'>🎚️ 调整系统增益</div>")
ui_dict["log_k_slider"] = gr.Slider(minimum=-4, maximum=4, value=1, step=0.01, label="对数增益 log₁₀(K)", info="💡 拖动滑块查看实时变化")
@@ -127,6 +160,8 @@ def create_frequency_domain_tab():
gr.HTML("<div class='card-title'>📊 稳定裕度分析</div>")
ui_dict["metrics_display"] = gr.Textbox(label="Stability Margins", lines=4, interactive=False, elem_classes="output-metrics")
ui_dict["stability_display"] = gr.Markdown(elem_classes="stability-result")
with gr.Row():
ui_dict["analyze_button"] = gr.Button("🚀 开始分析", variant="primary", scale=1, elem_classes="primary-btn")
with gr.Column(scale=2):
ui_dict["plot_output"] = gr.Plot(label="频域响应图", elem_classes="plot-container")
# 知识卡片
@@ -163,6 +198,20 @@ def create_root_locus_tab():
ui_dict = {}
with gr.Row():
with gr.Column(scale=1):
with gr.Group():
gr.HTML("<div class='card-title'>📊 传递函数设定</div>")
ui_dict["num_input"] = gr.Textbox(
label="分子系数 (Numerator)",
value="1",
placeholder="例如: 1 或 1,2,3",
info="💡 用逗号分隔，从最高次项到常数项"
)
ui_dict["den_input"] = gr.Textbox(
label="分母系数 (Denominator)",
value="1,6,11,6",
placeholder="例如: 1,2,1",
info="💡 分母阶数通常 ≥ 分子阶数"
)
with gr.Group():
gr.HTML("<div class='card-title'>🎚️ 调整系统增益</div>")
ui_dict["log_k_slider"] = gr.Slider(minimum=-4, maximum=4, value=1, step=0.01, label="对数增益 log₁₀(K)", info="💡 拖动滑块观察极点移动")
@@ -170,6 +219,8 @@ def create_root_locus_tab():
with gr.Group():
gr.HTML("<div class='card-title'>📍 闭环极点位置</div>")
ui_dict["poles_display"] = gr.Textbox(label="Closed-Loop Pole Locations", lines=6, interactive=False, elem_classes="output-metrics")
with gr.Row():
ui_dict["analyze_button"] = gr.Button("🚀 开始分析", variant="primary", scale=1, elem_classes="primary-btn")
with gr.Column(scale=2):
ui_dict["plot_output"] = gr.Plot(label="根轨迹图")
gr.HTML(f"""
@@ -200,6 +251,288 @@ def create_root_locus_tab():
""")
return ui_dict
def create_case_demo_tab():
"""创建算例演示选项卡 — 四阶段交互设计（蒸馏→发动机→电机→能量管理）"""
ui_dict = {}
# === MathJax re-render helper (reused across tabs) ===
def _mathjax_script(div_id):
return f"""
<script>
(function() {{
function renderMath() {{
setTimeout(function() {{
if (typeof MathJax !== 'undefined' && MathJax.typesetPromise) {{
MathJax.typesetPromise([document.getElementById('{div_id}')])
.catch(function(err) {{ console.log('MathJax error:', err); }});
}} else {{ setTimeout(renderMath, 500); }}
}}, 300);
}}
if (document.readyState === 'loading') {{
document.addEventListener('DOMContentLoaded', renderMath);
}} else {{ renderMath(); }}
}})();
</script>"""
with gr.Tabs():
# ========== 阶段零：模型训练（GPR + NN 两个子标签页）==========
with gr.TabItem("🧬 模型训练", id="distill_tab"):
gr.HTML("""<div style='background:linear-gradient(135deg,#f3e5f5,#e1bee7);padding:10px 16px;
border-radius:8px;margin-bottom:10px;font-size:0.92em;color:#6a1b9a;'>
<b>阶段零</b>：模型训练包含两步——先训练/验证 GPR 高斯过程代理模型，
再将其知识蒸馏为轻量 NN（MLP）用于后续实时控制仿真。</div>""")
with gr.Tabs():
# ----- 子标签页 A: GPR 模型训练 -----
with gr.TabItem("📈 GPR 模型训练", id="gpr_sub_tab"):
with gr.Row():
with gr.Column(scale=1):
with gr.Group():
gr.HTML("<div class='card-title'>🔬 GPR 训练设置</div>")
ui_dict["gpr_mode"] = gr.Radio(
choices=["load", "train"], value="load",
label="运行模式",
info="load: 加载已有权重 | train: 从头训练（需 botorch）")
ui_dict["gpr_run_button"] = gr.Button(
"🚀 运行 GPR 训练 / 加载", variant="primary",
elem_classes="primary-btn")
with gr.Group():
gr.HTML("<div class='card-title'>📝 GPR 结果</div>")
ui_dict["gpr_summary"] = gr.Markdown()
with gr.Column(scale=2):
ui_dict["gpr_plot"] = gr.Plot(label="GPR 模型结果")
gr.HTML(f"""
<div id="gpr-knowledge" style="max-height:600px;overflow-y:auto;padding-right:8px;">
{GPR_KNOWLEDGE}
</div>
{_mathjax_script('gpr-knowledge')}
""")
# ----- 子标签页 B: NN 模型训练 -----
with gr.TabItem("🧠 NN 模型训练", id="nn_sub_tab"):
with gr.Row():
with gr.Column(scale=1):
with gr.Group():
gr.HTML("<div class='card-title'>🧪 NN 训练参数</div>")
ui_dict["distill_epochs"] = gr.Slider(minimum=500, maximum=8000, value=3000, step=100,
label="训练轮数 (Epochs)", info="越多越精确，但耗时更长")
ui_dict["distill_lr"] = gr.Slider(minimum=1e-4, maximum=1e-2, value=3e-3, step=1e-4,
label="学习率 (LR)", info="推荐 1e-3 ~ 5e-3")
ui_dict["distill_hidden"] = gr.Slider(minimum=16, maximum=256, value=64, step=16,
label="隐藏层宽度", info="MLP每层神经元数")
ui_dict["distill_run_button"] = gr.Button("🚀 开始 NN 训练", variant="primary",
elem_classes="primary-btn")
with gr.Group():
gr.HTML("<div class='card-title'>📝 NN 训练结果</div>")
ui_dict["distill_summary"] = gr.Markdown()
with gr.Column(scale=2):
ui_dict["distill_plot"] = gr.Plot(label="NN 训练结果 (Loss + Parity)")
gr.HTML(f"""
<div id="nn-knowledge" style="max-height:600px;overflow-y:auto;padding-right:8px;">
{NN_KNOWLEDGE}
</div>
{_mathjax_script('nn-knowledge')}
""")
# ========== 阶段一：发动机控制器设计 ==========
with gr.TabItem("🔧 发动机控制器设计", id="engine_tab"):
gr.HTML("""<div style='background:linear-gradient(135deg,#fff3e0,#ffe0b2);padding:10px 16px;
border-radius:8px;margin-bottom:10px;font-size:0.92em;color:#e65100;'>
<b>阶段一</b>：选择 PID 或 MPC 控制器，调整参数，运行阶跃响应测试，观察功率跟踪性能。</div>""")
with gr.Row():
with gr.Column(scale=1):
with gr.Group():
gr.HTML("<div class='card-title'>🎯 控制器选择</div>")
ui_dict["eng_controller_type"] = gr.Radio(
choices=["PID", "MPC"], value="PID", label="控制器类型",
info="PID: 经典三参数 | MPC: 模型预测控制")
with gr.Group(visible=True) as eng_pid_group:
gr.HTML("<div class='card-title'>🎛️ PID 参数</div>")
ui_dict["eng_kp"] = gr.Slider(minimum=0.1, maximum=20, value=4.652, step=0.01,
label="比例增益 Kp", info="增大加快响应，过大导致振荡")
ui_dict["eng_ki"] = gr.Slider(minimum=0.0, maximum=20, value=7.078, step=0.01,
label="积分增益 Ki", info="消除稳态误差，过大导致超调")
ui_dict["eng_kd"] = gr.Slider(minimum=0.0, maximum=5, value=0.222, step=0.001,
label="微分增益 Kd", info="抑制振荡，改善动态特性")
ui_dict["eng_pid_group"] = eng_pid_group
with gr.Group(visible=False) as eng_mpc_group:
gr.HTML("<div class='card-title'>🎛️ MPC 参数</div>")
ui_dict["eng_mpc_horizon"] = gr.Slider(minimum=3, maximum=30, value=15, step=1,
label="预测时域 (Horizon)", info="MPC前看步数")
ui_dict["eng_mpc_W_power"] = gr.Slider(minimum=1, maximum=500, value=200, step=1,
label="功率跟踪权重 W_power")
ui_dict["eng_mpc_W_dcost"] = gr.Slider(minimum=0.01, maximum=20, value=1.5, step=0.01,
label="控制增量权重 W_Δcost")
ui_dict["eng_mpc_overshoot"] = gr.Slider(minimum=1, maximum=30, value=5, step=1,
label="超调限制 (%)")
ui_dict["eng_mpc_group"] = eng_mpc_group
with gr.Group():
gr.HTML("<div class='card-title'>⚙️ 发动机模型参数</div>")
ui_dict["eng_tau_fuel"] = gr.Slider(minimum=0.05, maximum=1.0, value=0.15, step=0.01,
label="燃油执行机构时间常数 τ (s)")
ui_dict["eng_K_inertia"] = gr.Slider(minimum=10, maximum=500, value=100, step=5,
label="转子惯性增益 K")
with gr.Group():
gr.HTML("<div class='card-title'>🧪 仿真设置</div>")
ui_dict["eng_sim_time"] = gr.Slider(minimum=5, maximum=60, value=30, step=1,
label="仿真时长 (s)")
ui_dict["eng_dt"] = gr.Dropdown(choices=[0.02, 0.05, 0.1], value=0.02,
label="仿真步长 (s)")
ui_dict["eng_init_power"] = gr.Slider(minimum=20, maximum=250, value=100, step=5,
label="初始功率 (kW)")
ui_dict["eng_target_power"] = gr.Slider(minimum=20, maximum=300, value=200, step=5,
label="目标功率 (kW)")
ui_dict["eng_run_button"] = gr.Button("🚀 运行发动机仿真", variant="primary",
elem_classes="primary-btn")
with gr.Group():
gr.HTML("<div class='card-title'>📝 设计结果</div>")
ui_dict["eng_summary"] = gr.Markdown()
with gr.Column(scale=2):
ui_dict["eng_plot"] = gr.Plot(label="发动机控制器阶跃响应")
gr.HTML(f"""
<div id="engine-knowledge" style="max-height:600px;overflow-y:auto;padding-right:8px;">
{ENGINE_CONTROL_KNOWLEDGE}
</div>
{_mathjax_script('engine-knowledge')}
""")
# Radio toggle PID/MPC visibility
ui_dict["eng_controller_type"].change(
fn=lambda ct: (gr.update(visible=(ct == "PID")), gr.update(visible=(ct == "MPC"))),
inputs=[ui_dict["eng_controller_type"]],
outputs=[eng_pid_group, eng_mpc_group],
)
# ========== 阶段二：电机控制器设计 ==========
with gr.TabItem("⚡ 电机控制器设计", id="motor_tab"):
gr.HTML("""<div style='background:linear-gradient(135deg,#e3f2fd,#bbdefb);padding:10px 16px;
border-radius:8px;margin-bottom:10px;font-size:0.92em;color:#1565c0;'>
<b>阶段二</b>：选择 PID 或 MPC 控制器，运行转速跟踪 + 负载扰动测试。
在仿真60%时刻自动施加50%负载扰动，检验抗扰能力。</div>""")
with gr.Row():
with gr.Column(scale=1):
with gr.Group():
gr.HTML("<div class='card-title'>🎯 控制器选择</div>")
ui_dict["mot_controller_type"] = gr.Radio(
choices=["PID", "MPC"], value="PID", label="控制器类型",
info="PID: 经典三参数 | MPC: 模型预测控制")
with gr.Group(visible=True) as mot_pid_group:
gr.HTML("<div class='card-title'>🎛️ PID 参数</div>")
ui_dict["mot_kp"] = gr.Slider(minimum=0.1, maximum=50, value=5.0, step=0.1,
label="比例增益 Kp", info="增大加快转速响应")
ui_dict["mot_ki"] = gr.Slider(minimum=0.0, maximum=100, value=2.0, step=0.1,
label="积分增益 Ki", info="消除转速稳态偏差")
ui_dict["mot_kd"] = gr.Slider(minimum=0.0, maximum=5, value=0.5, step=0.01,
label="微分增益 Kd", info="抑制转速振荡")
ui_dict["mot_pid_group"] = mot_pid_group
with gr.Group(visible=False) as mot_mpc_group:
gr.HTML("<div class='card-title'>🎛️ MPC 参数</div>")
ui_dict["mot_mpc_W_speed"] = gr.Slider(minimum=1, maximum=500, value=200, step=1,
label="转速跟踪权重 W_speed")
ui_dict["mot_mpc_W_dcost"] = gr.Slider(minimum=0.01, maximum=20, value=0.3, step=0.01,
label="控制增量权重 W_Δcost")
ui_dict["mot_mpc_overshoot"] = gr.Slider(minimum=1, maximum=30, value=5, step=1,
label="超调限制 (%)")
ui_dict["mot_mpc_group"] = mot_mpc_group
with gr.Group():
gr.HTML("<div class='card-title'>⚙️ 电机模型参数</div>")
ui_dict["mot_J"] = gr.Slider(minimum=0.1, maximum=5.0, value=1.0, step=0.1,
label="转动惯量 J (kg·m²)", info="越大响应越慢但越平稳")
with gr.Group():
gr.HTML("<div class='card-title'>🧪 仿真设置</div>")
ui_dict["mot_sim_time"] = gr.Slider(minimum=5, maximum=60, value=30, step=1,
label="仿真时长 (s)")
ui_dict["mot_dt"] = gr.Dropdown(choices=[0.02, 0.05, 0.1], value=0.02,
label="仿真步长 (s)")
ui_dict["mot_target_rpm"] = gr.Slider(minimum=500, maximum=5000, value=2000, step=50,
label="目标转速 (RPM)")
ui_dict["mot_load_torque"] = gr.Slider(minimum=10, maximum=400, value=80, step=5,
label="负载转矩 (Nm)")
ui_dict["mot_run_button"] = gr.Button("🚀 运行电机仿真", variant="primary",
elem_classes="primary-btn")
with gr.Group():
gr.HTML("<div class='card-title'>📝 设计结果</div>")
ui_dict["mot_summary"] = gr.Markdown()
with gr.Column(scale=2):
ui_dict["mot_plot"] = gr.Plot(label="电机控制器阶跃响应")
gr.HTML(f"""
<div id="motor-knowledge" style="max-height:600px;overflow-y:auto;padding-right:8px;">
{MOTOR_CONTROL_KNOWLEDGE}
</div>
{_mathjax_script('motor-knowledge')}
""")
# Radio toggle PID/MPC visibility
ui_dict["mot_controller_type"].change(
fn=lambda ct: (gr.update(visible=(ct == "PID")), gr.update(visible=(ct == "MPC"))),
inputs=[ui_dict["mot_controller_type"]],
outputs=[mot_pid_group, mot_mpc_group],
)
# ========== 阶段三：能量管理策略设计 ==========
with gr.TabItem("🔋 能量管理策略设计", id="ems_tab"):
gr.HTML("""<div style='background:linear-gradient(135deg,#e8f5e9,#c8e6c9);padding:10px 16px;
border-radius:8px;margin-bottom:10px;font-size:0.92em;color:#2e7d32;'>
<b>阶段三</b>：设计基于规则的能量管理策略（自动引用前两阶段的控制器参数）。<br>
<b>策略原理</b>：SOC &lt; 下限阈值 → 进入<b>充电模式</b>;
SOC &gt; 上限阈值 → 退出充电，进入<b>功率跟随模式</b>。
下限~上限之间为<b>滞环区间</b>，防止模式频繁切换。</div>""")
with gr.Row():
with gr.Column(scale=1):
with gr.Group():
gr.HTML("<div class='card-title'>📊 SOC规则参数（滞环控制）</div>")
ui_dict["soc_target"] = gr.Slider(minimum=30, maximum=80, value=60, step=1,
label="SOC目标值 (%)", info="功率跟随模式下的SOC补偿基准")
ui_dict["soc_low"] = gr.Slider(minimum=15, maximum=50, value=30, step=1,
label="SOC下限阈值 (%)", info="低于此值→进入充电模式")
ui_dict["soc_high"] = gr.Slider(minimum=50, maximum=90, value=70, step=1,
label="SOC上限阈值 (%)", info="高于此值→退出充电模式")
with gr.Group():
gr.HTML("<div class='card-title'>⚡ 功率规则参数</div>")
ui_dict["p_eng_min"] = gr.Slider(minimum=10, maximum=100, value=20, step=5,
label="发动机最小功率 (kW)")
ui_dict["p_eng_max"] = gr.Slider(minimum=100, maximum=350, value=300, step=10,
label="发动机最大功率 (kW)")
ui_dict["p_charge"] = gr.Slider(minimum=50, maximum=300, value=200, step=10,
label="充电模式发动机功率 (kW)", info="进入充电模式后发动机固定输出")
ui_dict["k_soc"] = gr.Slider(minimum=0, maximum=200, value=50, step=5,
label="SOC补偿增益 (kW/ΔSOC)", info="跟随模式下对SOC偏差的修正力度")
ui_dict["power_reserve"] = gr.Slider(minimum=0, maximum=50, value=10, step=1,
label="动态功率储备 (%)", info="发动机额外预留功率百分比")
with gr.Group():
gr.HTML("<div class='card-title'>🔧 系统与仿真参数</div>")
ui_dict["battery_capacity"] = gr.Slider(minimum=10, maximum=200, value=50, step=5,
label="电池容量 (kWh)")
ui_dict["initial_soc"] = gr.Slider(minimum=10, maximum=95, value=60, step=1,
label="初始SOC (%)")
ui_dict["initial_engine_power"] = gr.Slider(minimum=20, maximum=250, value=50, step=5,
label="初始发动机功率 (kW)")
ui_dict["profile"] = gr.Dropdown(
choices=["起飞-巡航-降落", "高机动阶跃", "经济巡航"],
value="起飞-巡航-降落", label="工况模板")
ui_dict["sim_time"] = gr.Slider(minimum=20, maximum=180, value=60, step=5,
label="仿真时长 (s)")
ui_dict["dt"] = gr.Dropdown(choices=[0.02, 0.05, 0.1], value=0.02,
label="仿真步长 (s)")
ui_dict["hybrid_run_button"] = gr.Button("🚀 运行混动系统仿真", variant="primary",
size="lg", elem_classes="primary-btn")
with gr.Group():
gr.HTML("<div class='card-title'>📝 结果摘要</div>")
ui_dict["hybrid_summary"] = gr.Markdown()
with gr.Column(scale=2):
ui_dict["hybrid_plot"] = gr.Plot(label="混动系统响应图")
ui_dict["hybrid_table"] = gr.Dataframe(
headers=["时间(s)", "目标转速", "实际转速", "发动机功率(kW)",
"电池功率(kW)", "SOC(%)", "EMS模式"],
label="关键时刻数据", interactive=False
)
gr.HTML(f"""
<div id="ems-knowledge" style="max-height:600px;overflow-y:auto;padding-right:8px;">
{EMS_KNOWLEDGE}
</div>
{_mathjax_script('ems-knowledge')}
""")
return ui_dict
def create_chatbot_tab():
"""创建AI问答选项卡的UI组件"""
ui_dict = {}
@@ -229,3 +562,4 @@ def create_chatbot_tab():
label="💡 试试这些问题："
)
return ui_dict