commit 91e36407ae44643f536a590e394fb06d9907e3a1 Author: zhanghuilai Date: Wed Apr 8 15:25:08 2026 +0800 Initial commit of WQ_GUI diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..2e6be2a --- /dev/null +++ b/.gitignore @@ -0,0 +1,157 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# IDEs +.vscode/ +.idea/ +*.swp +*.swo +*~ + +# OS generated files +.DS_Store +.DS_Store? +._* +.Spotlight-V100 +.Trashes +ehthumbs.db +Thumbs.db + +# Project specific +# Build directories +V1/ +V2/ +dist/ +build/ + +# Data files (keep structure but ignore large files) +data/input/* +data/output/* +data/models/* +!data/input/.gitkeep +!data/output/.gitkeep +!data/models/.gitkeep + +# Log files +*.log +logs/ + +# Temporary files +*.tmp +*.temp +temp/ +tmp/ + +# Backup files +*.bak +*.backup +*~ diff --git a/IMAGE_CONFIG.md b/IMAGE_CONFIG.md new file mode 100644 index 0000000..70eb347 --- /dev/null +++ b/IMAGE_CONFIG.md @@ -0,0 +1,186 @@ +# Logo和横幅图像配置说明 + +## 功能概述 + +本更新实现了以下需求： +1. **Logo在菜单栏左侧** - 和菜单栏在**同一行** +2. **菜单栏在最右侧** - 使用弹性空间布局 +3. **横幅图片撑满整个区域** - 自适应窗口宽度 + +## 布局说明 + +``` +┌─────────────────────────────────────┐ +│ [Logo] 文件 工具 帮助 │ ← 同一行：Logo在左，菜单在右 +├─────────────────────────────────────┤ +│ [软件名称横幅图 - 撑满宽度] │ ← 横幅撑满整个区域 +├─────────────────────────────────────┤ +│ │ +│ 主要内容区 │ +│ │ +``` + +## 配置步骤 + +### 1. 准备图像文件 + +将你的图像文件放在项目目录中。建议的目录结构： + +``` +fengzhuang-ui2/ +├── assets/ # 推荐：创建资源目录 +│ ├── logo.png # 公司logo (建议高度30像素) +│ └── banner.png # 软件名称横幅 (建议高度70像素) +├── src/ +│ └── gui/ +│ └── water_quality_gui.py +└── ... +``` + +### 2. 修改图像路径 + +打开 `src/gui/water_quality_gui.py` 文件，找到以下两处代码： + +#### Logo路径修改（create_logo_bar 方法中，第3928行左右） + +```python +# 修改前： +logo_path = "path/to/your/logo.png" + +# 修改后（使用相对路径）： +logo_path = "assets/logo.png" + +# 或使用绝对路径： +logo_path = r"E:\your\full\path\to\logo.png" +``` + +#### 横幅路径修改（create_banner_widget 方法中，第3978行左右） + +```python +# 修改前： +banner_path = "path/to/your/banner.png" + +# 修改后（使用相对路径）： +banner_path = "assets/banner.png" + +# 或使用绝对路径： +banner_path = r"E:\your\full\path\to\banner.png" +``` + +### 3. 图像规格建议 + +| 名称 | 建议尺寸 | 格式 | 说明 | +|-----|--------|------|------| +| Logo | 高度30像素 | PNG/JPG | 放在顶部Logo栏中，自动按高度缩放保持宽高比 | +| 横幅 | 高度70像素 | PNG/JPG | 占据横幅区域，自动按高度缩放保持宽高比 | + +### 4. 图像加载失败处理 + +如果图像文件未找到或加载失败，系统会自动显示占位符： + +- **Logo占位符**: 显示"Logo"文本，背景为浅灰色 +- **横幅占位符**: 显示"软件名称横幅"文本，背景为蓝色，字体为24号加粗 + +### 5. 自适应缩放说明 + +为了避免图像拉伸，代码使用了 `scaledToHeight()` 方法： +- Logo按高度30像素缩放，自动计算宽度，保持原始宽高比 +- 横幅按高度70像素缩放，自动计算宽度，保持原始宽高比 + +这样可以确保无论原始图像大小如何，都能自然地显示而不会出现拉伸变形。 + +## 常见问题 + +### Q: 如何使用项目内的图像资源？ + +**A**: 在项目中创建 `assets` 或 `resources` 文件夹，并使用相对路径： + +```python +# 假设项目结构： +# fengzhuang-ui2/ +# ├── assets/ +# │ ├── logo.png +# │ └── banner.png +# └── src/gui/water_quality_gui.py + +# 在 water_quality_gui.py 中（第3928行）： +logo_path = "assets/logo.png" + +# 在 water_quality_gui.py 中（第3978行）： +banner_path = "assets/banner.png" +``` + +### Q: Logo或横幅大小不合适？ + +**A**: 修改以下代码调整显示大小： + +```python +# 在 create_logo_bar() 方法中调整Logo大小 +logo_label.setFixedSize(60, 40) # 改为 60×40 + +# 在 create_banner_widget() 方法中调整横幅大小 +banner_label.setMaximumHeight(100) # 改为100像素高 +banner_label.setMinimumHeight(80) # 改为最小80像素高 + +# 调整缩放高度 +scaled_pixmap = logo_pixmap.scaledToHeight(35, Qt.SmoothTransformation) # 改为35 +scaled_pixmap = banner_pixmap.scaledToHeight(85, Qt.SmoothTransformation) # 改为85 +``` + +### Q: 如何隐藏Logo或横幅？ + +**A**: 在 `init_ui()` 方法中注释掉相应的创建方法： + +```python +# 在 init_ui() 中 +# self.create_logo_bar() # 注释此行隐藏Logo +# self.create_banner_widget() # 注释此行隐藏横幅 +``` + +### Q: Logo显示位置不对？ + +**A**: Logo栏是作为独立的工具栏添加在菜单栏下方，不是在菜单栏内。当前的布局顺序是： +1. 菜单栏 (最上方) +2. Logo栏 (菜单栏下方) +3. 横幅区域 (Logo栏下方) +4. 主内容区域 (最下方) + +### Q: 图像在高分辨率屏幕上看起来模糊？ + +**A**: 使用 `Qt.SmoothTransformation` 可以改善图像质量。如果仍然不够清晰，可以准备高分辨率的原始图像。 + +## 代码位置 + +- **Logo栏创建**: `create_logo_bar()` 方法 (第3902行) +- **横幅区域创建**: `create_banner_widget()` 方法 (第3950行) +- **主UI初始化**: `init_ui()` 方法 (第3821行) + +## 支持的图像格式 + +- PNG (推荐，支持透明背景) +- JPG/JPEG +- BMP +- GIF +- TIFF + +## 样式调整 + +如需修改样式（背景色、边框等），编辑以下位置的 `setStyleSheet()` 调用： + +```python +# Logo样式（第3907-3916行） +logo_toolbar.setStyleSheet("""...""") + +# 占位符样式（第3931行） +logo_label.setStyleSheet("...") + +# 横幅占位符样式（第3959-3965行） +banner_label.setStyleSheet("""...""") +``` + +## 更新日期 +2026-03-27 + +## 备注 + +所有的图像路径都可以根据你的实际项目结构灵活调整。建议将图像文件与代码一起版本控制，以确保项目的可维护性。 diff --git a/README-conda.md b/README-conda.md new file mode 100644 index 0000000..1385ffd --- /dev/null +++ b/README-conda.md @@ -0,0 +1,152 @@ +# 水质参数反演分析系统 - Conda环境安装指南 + +## 📋 概述 + +本项目提供完整的Conda环境配置，支持一键安装所有依赖包。 + +## 🚀 快速开始 + +### 方法1: 使用环境配置文件 (推荐) + +```bash +# 1. 克隆或下载项目 +# 2. 进入项目目录 +cd fengzhuang + +# 3. 创建Conda环境 (自动安装所有依赖) +conda env create -f environment.yml + +# 4. 激活环境 +conda activate water_quality_analysis + +# 5. 运行程序 +python src/gui/water_quality_gui.py +``` + +### 方法2: 使用批处理脚本 (Windows) + +```cmd +# 双击运行或在命令行执行 +scripts\setup_conda_env.bat +``` + +### 方法3: 手动安装 + +```bash +# 创建环境 +conda create -n water_quality_analysis python=3.8 + +# 激活环境 +conda activate water_quality_analysis + +# 安装依赖包 +conda install -c conda-forge --file requirements-conda.txt +``` + +## 📦 依赖包说明 + +### 核心依赖 + +- **Python 3.8+**: 运行环境 +- **PyQt5**: GUI界面框架 +- **NumPy, SciPy, Pandas**: 科学计算基础库 +- **Scikit-learn**: 机器学习算法 +- **XGBoost, LightGBM**: 梯度提升算法 + +### 地理空间处理 + +- **GDAL**: 地理数据处理 +- **Rasterio**: 栅格数据处理 +- **GeoPandas**: 地理数据分析 +- **Shapely**: 几何运算 +- **PyProj**: 坐标系转换 + +### 图像和可视化 + +- **OpenCV**: 计算机视觉 +- **Pillow**: 图像处理 +- **Matplotlib, Seaborn**: 数据可视化 +- **Spectral**: 光谱数据处理 + +### 工具库 + +- **Joblib**: 并行计算 +- **PyWavelets**: 小波变换 +- **TQDM**: 进度条 +- **PyYAML**: 配置处理 + +## 🔧 环境管理 + +### 更新环境 + +```bash +# 更新所有包到最新版本 +conda env update -f environment.yml +``` + +### 删除环境 + +```bash +# 停用环境 +conda deactivate + +# 删除环境 +conda env remove -n water_quality_analysis +``` + +### 导出环境 + +```bash +# 导出当前环境配置 +conda env export > environment_export.yml +``` + +## 🐛 故障排除 + +### 常见问题 + +1. **Conda命令找不到** + - 确保已安装Miniconda或Anaconda + - 重启命令行窗口 + +2. **包安装失败** + - 检查网络连接 + - 尝试更换conda源: `conda config --add channels conda-forge` + +3. **环境激活失败** + - Windows: 使用 `conda activate water_quality_analysis` (非 `activate`) + - Linux/Mac: 确保conda已正确初始化 + +4. **PyQt5显示问题** + - Linux: 安装系统依赖 `sudo apt-get install qt5-default` + - Mac: 确保XQuartz已安装 + +### 验证安装 + +运行以下Python代码验证安装: + +```python +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import sklearn +import PyQt5 +import gdal +import rasterio +import geopandas + +print("所有依赖包安装成功!") +``` + +## 📚 相关链接 + +- [Conda官方文档](https://docs.conda.io/) +- [Miniconda下载](https://docs.conda.io/en/latest/miniconda.html) +- [Anaconda下载](https://www.anaconda.com/products/distribution) + +## 📞 技术支持 + +如遇问题，请检查: +1. Conda版本是否为最新 +2. Python版本是否符合要求 +3. 系统是否满足硬件要求 diff --git a/README.md b/README.md new file mode 100644 index 0000000..3d806bf --- /dev/null +++ b/README.md @@ -0,0 +1,215 @@ +# 水质参数反演分析系统 (Water Quality Inversion Analysis System) + +[![Python Version](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/) +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) +[![Build Status](https://img.shields.io/badge/build-passing-green.svg)]() + +基于遥感影像处理和机器学习技术的水质监测专业软件系统，集成了完整的水域识别、耀斑处理、光谱提取、模型训练和预测分析流程。 + +## 🚀 主要特性 + +- **多算法耀斑去除** - 支持Goodman、Kutser、Hedley、SUGAR等多种去耀斑算法 +- **智能水域识别** - 基于NDWI阈值分割和Shapefile掩膜的自动水域提取 +- **机器学习建模** - 支持多种机器学习算法（随机森林、XGBoost、神经网络等） +- **非经验统计回归** - 基于物理原理的叶绿素a、总氮、总磷等参数反演 +- **高精度空间插值** - 距离扩散插值生成平滑的水质分布图 +- **可视化分析** - 丰富的图表展示和空间分布可视化 +- **用户友好界面** - 基于PyQt5的图形化操作界面 + +## 📋 系统要求 + +### 硬件要求 +- **处理器**: Intel Core i5 或同等性能以上 +- **内存**: 8GB RAM（推荐16GB） +- **存储**: 至少10GB可用空间 +- **显卡**: 支持OpenGL 3.0以上 + +### 软件要求 +- **操作系统**: Windows 10/11, Linux, macOS +- **Python版本**: 3.8+ +- **必要依赖**: GDAL, NumPy, Pandas, Scikit-learn, PyQt5等 + +## 🛠️ 安装 + +### 方式1：从源码安装 + +```bash +# 克隆仓库 +git clone https://github.com/waterquality/water-quality-inversion.git +cd water-quality-inversion + +# 创建虚拟环境 +python -m venv venv +source venv/bin/activate # Linux/macOS +# 或 +venv\Scripts\activate # Windows + +# 安装依赖 +pip install -r requirements.txt + +# 安装包 +pip install -e . +``` + +### 方式2：使用pip安装 + +```bash +pip install water-quality-inversion +``` + +## 🎯 快速开始 + +### 图形界面模式 +```bash +water-quality-gui +``` + +### 命令行模式 +```bash +water-quality-pipeline --config config.yaml +``` + +### Python API +```python +from water_quality_inversion import WaterQualityInversionPipeline + +# 创建流水线实例 +pipeline = WaterQualityInversionPipeline() + +# 运行完整分析流程 +pipeline.run() +``` + +## 📖 使用指南 + +### 基本工作流程 + +1. **步骤1: 水域掩膜生成** + - 支持Shapefile文件或NDWI自动提取 + - 生成水域范围的栅格掩膜 + +2. **步骤2: 耀斑区域检测** + - 支持Otsu、Z-score、百分位数等多种检测方法 + - 生成耀斑区域掩膜 + +3. **步骤3: 耀斑去除** + - Goodman、Kutser、Hedley、SUGAR四种算法 + - 支持多种插值修复方法 + +4. **步骤4: 数据预处理** + - CSV数据清洗和异常值检测 + - 数据标准化和特征工程 + +5. **步骤5: 光谱提取** + - 基于采样点的光谱特征提取 + - 支持多种采样半径和统计计算 + +6. **步骤5.5: 水质指数计算** + - 基于光谱特征计算水质指数 + - 支持自定义公式和18种水质参数 + +7. **步骤6: 机器学习建模** + - 支持18种机器学习算法 + - 11种光谱预处理方法 + - 3种数据划分策略 + +8. **步骤6.5: 非经验统计回归** + - 6种水质参数的非经验模型 + - 基于物理原理的参数反演 + +9. **步骤6.75: 自定义回归分析** + - 完全自定义的回归分析 + - 探索性数据分析工具 + +10. **步骤7: 采样点生成** + - 规则网格采样点生成 + - 智能边界处理 + +11. **步骤8/8.5/8.75: 参数预测** + - 机器学习预测 + - 非经验模型预测 + - 自定义回归预测 + +12. **步骤9: 分布图生成** + - 空间插值和栅格化 + - 多格式输出（GeoTIFF, PNG, PDF） + +## 🏗️ 项目结构 + +``` +water-quality-inversion/ +├── src/ # 源代码目录 +│ ├── core/ # 核心算法模块 +│ │ ├── glint_removal/ # 耀斑去除算法 +│ │ ├── modeling/ # 建模算法 +│ │ └── prediction/ # 预测算法 +│ ├── preprocessing/ # 数据预处理模块 +│ ├── postprocessing/ # 后处理模块 +│ ├── visualization/ # 可视化模块 +│ ├── utils/ # 工具函数 +│ └── gui/ # GUI界面 +├── data/ # 数据目录 +│ ├── input/ # 输入数据 +│ ├── output/ # 输出结果 +│ └── models/ # 模型文件 +├── docs/ # 文档目录 +├── scripts/ # 构建和部署脚本 +├── tests/ # 测试目录 +├── requirements.txt # 依赖文件 +├── setup.py # 安装配置 +├── pyproject.toml # 项目配置 +└── README.md # 项目说明 +``` + +## 🤝 贡献 + +欢迎贡献代码！请查看 [CONTRIBUTING.md](CONTRIBUTING.md) 了解详细信息。 + +### 开发环境设置 + +```bash +# 安装开发依赖 +pip install -e ".[dev]" + +# 运行测试 +pytest + +# 代码格式化 +black src/ +isort src/ + +# 类型检查 +mypy src/ +``` + +## 📄 许可证 + +本项目基于 MIT 许可证开源 - 查看 [LICENSE](LICENSE) 文件了解详情。 + +## 📚 引用 + +如果您在研究中使用了本系统，请引用： + +```bibtex +@software{water_quality_inversion, + title = {Water Quality Inversion Analysis System}, + author = {Water Quality Research Team}, + url = {https://github.com/waterquality/water-quality-inversion}, + version = {1.0.0}, + year = {2025} +} +``` + +## 📞 联系我们 + +- **项目主页**: https://github.com/waterquality/water-quality-inversion +- **问题反馈**: https://github.com/waterquality/water-quality-inversion/issues +- **邮箱**: support@waterquality.com + +## 🙏 致谢 + +感谢所有为本项目做出贡献的开发者们！ + +--- + +**水质参数反演分析系统** - 让水质监测更智能、更精准！ diff --git a/README_SAMPLING_MAP.md b/README_SAMPLING_MAP.md new file mode 100644 index 0000000..a0f34ac --- /dev/null +++ b/README_SAMPLING_MAP.md @@ -0,0 +1,120 @@ +# 采样点地图功能使用说明 + +## 功能概述 + +本系统新增了采样点地图生成功能，可以在高光谱假彩色影像上标注采样点位置，并添加专业的地图要素。 + +## 主要功能 + +### 1. SamplingPointMap 类 (`src/postprocessing/point_map.py`) + +**核心功能：** +- 读取高光谱影像并生成假彩色RGB图像 +- 读取CSV文件中的采样点坐标（前两列为**纬度、经度**） +- 在影像上标注红色采样点 +- 添加**指北针**、**比例尺**和**图例** +- 支持地理坐标转换 + +### 2. Visualization Reports 集成 (`src/postprocessing/visualization_reports.py`) + +**新增方法：** +- `generate_sampling_point_map()`：生成采样点地图 +- `generate_all_visualizations()`：生成所有可视化结果 + +### 3. GUI 集成 (`src/gui/water_quality_gui.py`) + +**可视化分析页面新增：** +- 复选框："生成采样点地图" +- 按钮："📍 生成采样点地图" +- 按钮："👁️ 查看采样点地图" + +## 使用方法 + +### 1. 通过GUI使用 + +1. 打开**可视化分析**页面 +2. 勾选"生成采样点地图" +3. 点击"📍 生成采样点地图"按钮 +4. 系统会自动： + - 查找高光谱影像文件（.dat, .bsq, .tif等） + - 查找 `4_processed_data` 文件夹中的CSV文件 + - 生成带采样点的地图 + - 保存至 `9_visualization/sampling_maps/` 目录 + +### 2. 编程调用 + +```python +from src.postprocessing.point_map import SamplingPointMap +from src.postprocessing.visualization_reports import WaterQualityVisualization + +# 方法1：直接使用SamplingPointMap +map_generator = SamplingPointMap(output_dir="./point_maps") +map_path = map_generator.create_sampling_point_map( + hyperspectral_path="path/to/hyperspectral.dat", + csv_path="path/to/sampling_points.csv", + point_color='red', + point_size=100, + show_north_arrow=True, + show_scale_bar=True, + show_legend=True +) + +# 方法2：通过VisualizationReports +viz = WaterQualityVisualization(output_dir="./9_visualization") +map_path = viz.generate_sampling_point_map( + hyperspectral_path="path/to/hyperspectral.dat", + csv_path="path/to/sampling_points.csv" +) + +# 方法3：生成所有可视化 +results = viz.generate_all_visualizations(work_dir="./work_dir") +``` + +## CSV文件格式要求 + +CSV文件必须满足以下格式： +- **前两列**分别为**纬度**和**经度** +- 使用**逗号分隔** +- 必须包含有效的数值 + +**示例：** +```csv +latitude,longitude,parameter1,parameter2 +31.2345,121.4567,25.5,3.2 +31.2350,121.4570,26.1,3.5 +31.2360,121.4580,24.8,2.9 +``` + +## 输出目录结构 + +``` +work_dir/ +├── 1_water_mask/ +│ └── hsi_preview.png # 高光谱预览图 +├── 4_processed_data/ +│ └── processed_data.csv # 处理后的数据 +├── 9_visualization/ +│ ├── glint_deglint_previews/ # 掩膜和耀斑缩略图 +│ └── sampling_maps/ # 采样点地图 +│ └── hyperspectral_sampling_map.png +└── ... +``` + +## 地图要素 + +- **红色圆点**：采样点位置 +- **指北针**：指示北方 +- **比例尺**：显示实际距离 +- **图例**：说明采样点数量 +- **标题**：清晰的地图标题 + +## 依赖库 + +- GDAL (地理坐标转换) +- matplotlib (绘图) +- pandas (CSV处理) +- numpy (数值计算) + +--- + +**注意**：确保工作目录中包含高光谱影像文件和处理后的CSV文件。 diff --git a/check_env.bat b/check_env.bat new file mode 100644 index 0000000..5233ba6 --- /dev/null +++ b/check_env.bat @@ -0,0 +1,10 @@ +@echo off +echo 检查insect conda环境... +if exist "%USERPROFILE%\.conda\envs\insect\python.exe" ( + echo insect环境存在 + echo Python路径: %USERPROFILE%\.conda\envs\insect\python.exe + "%USERPROFILE%\.conda\envs\insect\python.exe" --version +) else ( + echo insect环境不存在 +) +pause diff --git a/data/icons/1.png b/data/icons/1.png new file mode 100644 index 0000000..8314047 Binary files /dev/null and b/data/icons/1.png differ diff --git a/data/icons/10.png b/data/icons/10.png new file mode 100644 index 0000000..91f5c67 Binary files /dev/null and b/data/icons/10.png differ diff --git a/data/icons/11.png b/data/icons/11.png new file mode 100644 index 0000000..7c766e8 Binary files /dev/null and b/data/icons/11.png differ diff --git a/data/icons/2.png b/data/icons/2.png new file mode 100644 index 0000000..aa39638 Binary files /dev/null and b/data/icons/2.png differ diff --git a/data/icons/3.png b/data/icons/3.png new file mode 100644 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mode 100644 index 0000000..e69de29 diff --git a/data/models/.gitkeep b/data/models/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/data/output/.gitkeep b/data/output/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/data/sub/png/watermask.png b/data/sub/png/watermask.png new file mode 100644 index 0000000..860cd4c Binary files /dev/null and b/data/sub/png/watermask.png differ diff --git a/data/sub/waterindex.csv b/data/sub/waterindex.csv new file mode 100644 index 0000000..0dfee5f --- /dev/null +++ b/data/sub/waterindex.csv @@ -0,0 +1,46 @@ +Formula_Name,Category,Formula,Reference +BGA_Am09KBBI,Phycocyanin (BGA_PC),(w686 - w658) / (w686 + w658),"Amin, R.; Zhou, J.; Gilerson, A.; Gross, B.; Moshary, F.; Ahmed, S.; Novel optical techniques for detecting and classifying toxic dinoflagellate Karenia brevis blooms using satellite imagery, Optics Express, 2009, 17, 11, 1-13." +BGA_Be162B643sub629,Phycocyanin (BGA_PC),w644 - w629,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538." +BGA_Be162B700sub601,Phycocyanin (BGA_PC),w700 - w601,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539." +BGA_Be162BsubPhy,Phycocyanin (BGA_PC),w715 - w615,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 540." +BGA_Be16FLHBlueRedNIR,Phycocyanin (BGA_PC),w658 - (w857 + (w458 - w857)),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538." +BGA_Be16FLHGreenRedNIR,Phycocyanin (BGA_PC),w658 - (w857 + (w558 - w857)),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539." +BGA_Be16FLHVioletRedNIR,Phycocyanin (BGA_PC),w658 - (w857 + (w444 - w857)),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538." +BGA_Be16MPI,Phycocyanin (BGA_PC),(w615 - w601) - (w644 - w601),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539." +BGA_Be16NDPhyI,Phycocyanin (BGA_PC),(w700 - w622) / (w700 + w622),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 540." +BGA_Be16NDPhyI644over615,Phycocyanin (BGA_PC),(w644 - w615) / (w644 + w615),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 541." +BGA_Be16NDPhyI644over629,Phycocyanin (BGA_PC),(w644 - w629) / (w644 + w629),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 542." +BGA_Be16Phy2BDA644over629,Phycocyanin (BGA_PC),w644 / w629,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 545." +BGA_Da052BDA,Phycocyanin (BGA_PC),w714 / w672,"Wynne, T. T., Stumpf, R. P., Tomlinson, M. C., Warner, R. A., Tester, P. A., Dyble, J.; Relating spectral shape to cyanobacterial blooms in the Laurentian Great Lakes. Int. J. Remote Sens., 2008, 29, 3665-3672." +BGA_Go04MCI,Phycocyanin (BGA_PC),w709 - w681 - (w753 - w681),"Gower, J.F.R.; Brown,L.; Borstad, G.A.; Observation of chlorophyll fluorescence in west coast waters of Canada using the MODIS satellite sensor. Can. J. Remote Sens., 2004, 30 (1), 17闁?5." +BGA_HU103BDA,Phycocyanin (BGA_PC),(((1 / w615) - (1 / w600)) - w725),"Hunter, P.D.; Tyler, A.N.; Willby, N.J.; Gilvear, D.J.; The spatial dynamics of vertical migration by Microcystis aeruginosa in a eutrophic shallow lake: A case study using high spatial resolution time-series airborne remote sensing. Limn. Oceanogr. 2008, 53, 2391-2406" +BGA_Ku15PhyCI,Phycocyanin (BGA_PC),(-1 * (W681 - W665 - (W709 - W665))),"Kudela, R.M., Palacios, S.L., Austerberry, D.C., Accorsi, E.K., Guild, L.S.; Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters, Torres-Perez, J., 2015, Remote Sens. Environ., 2015, 167, 1-10." +BGA_Ku15SLH,Phycocyanin (BGA_PC),(w715 - w658) + (w715 - w658),"Kudela, R.M., Palacios, S.L., Austerberry, D.C., Accorsi, E.K., Guild, L.S.; Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters, Torres-Perez, J., 2015, Remote Sens. Environ., 2015, 167, 1-11." +BGA_MI092BDA,Phycocyanin (BGA_PC),w700 / w600,"Mishra, S.; Mishra, D.R.; Schluchter, W. M., A novel algorithm for predicting PC concentrations in cyanobacteria: A proximal hyperspectral remote sensing approach. Remote Sens., 2009, 1, 758闁?75." +BGA_MM092BDA,Phycocyanin (BGA_PC),w724 / w600,"Mishra, S.; Mishra, D.R.; Schluchter, W. M., A novel algorithm for predicting PC concentrations in cyanobacteria: A proximal hyperspectral remote sensing approach. Remote Sens., 2009, 1, 758闁?76." +BGA_MM12NDCIalt,Phycocyanin (BGA_PC),(w700 - w658) / (w700 + w658),"Mishra, S.; Mishra, D.R.; A novel remote sensing algorithm to quantify phycocyanin in cyanobacterial algal blooms, Env. Res. Lett., 2014, 9 (11), DOI:10.1088/1748-9326/9/11/114003" +BGA_MM143BDAopt,Phycocyanin (BGA_PC),((1 / w629) - (1 / w659)) * w724,"Mishra, S.; Mishra, D.R.; A novel remote sensing algorithm to quantify phycocyanin in cyanobacterial algal blooms, Env. Res. Lett., 2014, 9 (11), DOI:10.1088/1748-9326/9/11/114004" +BGA_SI052BDA,Phycocyanin (BGA_PC),w709 / w620,"Simis, S. G. H.; Peters, S.W. M.; Gons, H. J.; Remote sensing of the cyanobacteria pigment phycocyanin in turbid inland water. Limn. Oceanogr., 2005, 50, 237闁?45" +BGA_SM122BDA,Phycocyanin (BGA_PC),w709 / w600,"Mishra, S. Remote sensing of cyanobacteria in turbid productive waters, PhD Dissertation. Mississippi State University, USA. 2012." +BGA_SY002BDA,Phycocyanin (BGA_PC),w650 / w625,"Schalles, J.; Yacobi, Y. Remote detection and seasonal patterns of phycocyanin, carotenoid and chlorophyll-a pigments in eutrophic waters. Archiv fur Hydrobiologie, Special Issues Advances in Limnology, 2000, 55,153闁?68" +BGA_Wy08CI,Phycocyanin (BGA_PC),(-1 * (W686 - W672 - (W715 - W672))),"Wynne, T. T., Stumpf, R. P., Tomlinson, M. C., Warner, R. A., Tester, P. A., Dyble, J.; Relating spectral shape to cyanobacterial blooms in the Laurentian Great Lakes. Int. J. Remote Sens., 2008, 29, 3665-3672." +Chl_Al10SABI,chlorophyll_a,(w857 - w644) / (w458 + w529),"Alawadi, F. Detection of surface algal blooms using the newly developed algorithm surface algal bloom index (SABI). Proc. SPIE 2010, 7825." +Chl_Am092Bsub,chlorophyll_a,w681 - w665,"Amin, R.; Zhou, J.; Gilerson, A.; Gross, B.; Moshary, F.; Ahmed, S. Novel optical techniques for detecting and classifying toxic dinoflagellate Karenia brevis blooms using satellite imagery. Opt. Express 2009, 17, 9126闁?144." +Chl_Be16FLHblue,chlorophyll_a,w529 - (w644 + (w458 - w644)),"Beck, R.A. and 22 others; Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations, Remote Sens. Environ., 2016, 178, 15-30." +Chl_Be16FLHviolet,chlorophyll_a,w529 - (w644 + (w429 - w644)),"Beck, R.A. and 22 others; Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations, Remote Sens. Environ., 2016, 178, 15-30." +Chl_Be16NDTIblue,chlorophyll_a,(w658 - w458) / (w658 + w458),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 543." +Chl_Be16NDTIviolet,chlorophyll_a,(w658 - w444) / (w658 + w444),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 544." +Chl_De933BDA,chlorophyll_a,w600 - w648 - w625,"Dekker, A.; Detection of the optical water quality parameters for eutrophic waters by high resolution remote sensing, Ph.D. thesis, 1993, Free University, Amsterdam." +Chl_Gi033BDA,chlorophyll_a,((1 / w672) - (1 / w715)) * w757,"Gitelson, A.A.; U. Gritz, and M. N. Merzlyak.; Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Phys. 2003, 160, 271-282." +Chl_Kn07KIVU,chlorophyll_a,(w458 - w644) / w529,"Kneubuhler, M.; Frank T.; Kellenberger, T.W; Pasche N.; Schmid M.; Mapping chlorophyll-a in Lake Kivu with remote sensing methods. 2007, Proceedings of the Envisat Symposium 2007, Montreux, Switzerland 23闁?7 April 2007 (ESA SP-636, July 2007)." +Chl_MM12NDCI,chlorophyll_a,(w715 - w686) / (w715 + w686),"Mishra, S.; and Mishra, D.R. Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters, Remote Sens. Environ., 2012, 117, 394-406" +Chl_Zh10FLH,chlorophyll_a,w686 - (w715 + (w672 - w751)),"Zhao, D.Z.; Xing, X.G.; Liu, Y.G.; Yang, J.H.; Wang, L. The relation of chlorophyll-a concentration with the reflectance peak near 700 nm in algae-dominated waters and sensitivity of fluorescence algorithms for detecting algal bloom. Int. J. Remote Sens. 2010, 31, 39-48" +Turb_Be16GreenPlusRedBothOverViolet,Turbidity,(w558 + w658) / w444,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538" +Turb_Be16RedOverViolet,Turbidity,w658 / w444,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539" +Turb_Bow06RedOverGreen,Turbidity,w658 / w558,"Bowers, D. G., and C. E. Binding. 2006. 闁炽儲缈籬e Optical Properties of Mineral Suspended Particles: A Review and Synthesis.闁?Estuarine Coastal and Shelf Science 67 (1闁?): 219闁?30. doi:10.1016/j.ecss.2005.11.010" +Turb_Chip09NIROverGreen,Turbidity,w857 / w558,"Chipman, J. W.; Olmanson, L.G.; Gitelson, A.A.; Remote sensing methods for lake management: A guide for resource managers and decision-makers. 2009." +Turb_Dox02NIRoverRed,Turbidity,w857 / w658,"Doxaran, D., Froidefond, J.-M.; Castaing, P. ; A reflectance band ratio used to estimate suspended matter concentrations in sediment-dominated coastal waters, Remote Sens., 2002, 23, 5079-5085" +Turb_Frohn09GreenPlusRedBothOverBlue,Turbidity,(w558 + w658) / w458,"Frohn, R. C., & Autrey, B. C. (2009). Water quality assessment in the Ohio River using new indices for turbidity and chlorophyll-a with Landsat-7 Imagery. Draft Internal Report, US Environmental Protection Agency." +Turb_Harr92NIR,Turbidity,w857,"Schiebe F.R., Harrington J.A., Ritchie J.C. Remote-Sensing of Suspended Sediments闁炽儲鏁刪e Lake Chicot, Arkansas Project. Int. J. Remote Sens. 1992;13:1487闁?509" +Turb_Lath91RedOverBlue,Turbidity,w658 / w458,"Lathrop, R. G., Jr., T. M. Lillesand, and B. S. Yandell, 1991. Testing the utility of simple multi-date Thematic Mapper calibration algorithms for monitoring turbid inland waters. International Journal of Remote Sensing" +Turb_Moore80Red,Turbidity,w658,"Moore, G.K., Satellite remote sensing of water turbidity, Hydrological Sciences, 1980, 25, 4, 407-422" diff --git a/data/sub/waterindex.xlsx b/data/sub/waterindex.xlsx new file mode 100644 index 0000000..bf1d6e2 Binary files /dev/null and b/data/sub/waterindex.xlsx differ diff --git a/data/sub/waterindex1125.csv b/data/sub/waterindex1125.csv new file mode 100644 index 0000000..7fa5dc4 --- /dev/null +++ b/data/sub/waterindex1125.csv @@ -0,0 +1,46 @@ +Formula_Name,Category,Formula,Reference +BGA_Am09KBBI,Phycocyanin (BGA_PC),(w686 - w658) / (w686 + w658),"Amin, R.; Zhou, J.; Gilerson, A.; Gross, B.; Moshary, F.; Ahmed, S.; Novel optical techniques for detecting and classifying toxic dinoflagellate Karenia brevis blooms using satellite imagery, Optics Express, 2009, 17, 11, 1-13." +BGA_Be162B643sub629,Phycocyanin (BGA_PC),w644 - w629,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538." +BGA_Be162B700sub601,Phycocyanin (BGA_PC),w700 - w601,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539." +BGA_Be162BsubPhy,Phycocyanin (BGA_PC),w715 - w615,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 540." +BGA_Be16FLHBlueRedNIR,Phycocyanin (BGA_PC),w658 - (w857 + (w458 - w857)),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538." +BGA_Be16FLHGreenRedNIR,Phycocyanin (BGA_PC),w658 - (w857 + (w558 - w857)),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539." +BGA_Be16FLHVioletRedNIR,Phycocyanin (BGA_PC),w658 - (w857 + (w444 - w857)),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538." +BGA_Be16MPI,Phycocyanin (BGA_PC),(w615 - w601) - (w644 - w601),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539." +BGA_Be16NDPhyI,Phycocyanin (BGA_PC),(w700 - w622) / (w700 + w622),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 540." +BGA_Be16NDPhyI644over615,Phycocyanin (BGA_PC),(w644 - w615) / (w644 + w615),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 541." +BGA_Be16NDPhyI644over629,Phycocyanin (BGA_PC),(w644 - w629) / (w644 + w629),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 542." +BGA_Be16Phy2BDA644over629,Phycocyanin (BGA_PC),w644 / w629,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 545." +BGA_Da052BDA,Phycocyanin (BGA_PC),w714 / w672,"Wynne, T. T., Stumpf, R. P., Tomlinson, M. C., Warner, R. A., Tester, P. A., Dyble, J.; Relating spectral shape to cyanobacterial blooms in the Laurentian Great Lakes. Int. J. Remote Sens., 2008, 29, 3665-3672." +BGA_Go04MCI,Phycocyanin (BGA_PC),w709 - w681 - (w753 - w681),"Gower, J.F.R.; Brown,L.; Borstad, G.A.; Observation of chlorophyll fluorescence in west coast waters of Canada using the MODIS satellite sensor. Can. J. Remote Sens., 2004, 30 (1), 17?5." +BGA_HU103BDA,Phycocyanin (BGA_PC),(((1 / w615) - (1 / w600)) - w725),"Hunter, P.D.; Tyler, A.N.; Willby, N.J.; Gilvear, D.J.; The spatial dynamics of vertical migration by Microcystis aeruginosa in a eutrophic shallow lake: A case study using high spatial resolution time-series airborne remote sensing. Limn. Oceanogr. 2008, 53, 2391-2406" +BGA_Ku15PhyCI,Phycocyanin (BGA_PC),-1 * (W681 - W665 - (W709 - W665)),"Kudela, R.M., Palacios, S.L., Austerberry, D.C., Accorsi, E.K., Guild, L.S.; Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters, Torres-Perez, J., 2015, Remote Sens. Environ., 2015, 167, 1-10." +BGA_Ku15SLH,Phycocyanin (BGA_PC),(w715 - w658) + (w715 - w658),"Kudela, R.M., Palacios, S.L., Austerberry, D.C., Accorsi, E.K., Guild, L.S.; Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters, Torres-Perez, J., 2015, Remote Sens. Environ., 2015, 167, 1-11." +BGA_MI092BDA,Phycocyanin (BGA_PC),w700 / w600,"Mishra, S.; Mishra, D.R.; Schluchter, W. M., A novel algorithm for predicting PC concentrations in cyanobacteria: A proximal hyperspectral remote sensing approach. Remote Sens., 2009, 1, 758?75." +BGA_MM092BDA,Phycocyanin (BGA_PC),w724 / w600,"Mishra, S.; Mishra, D.R.; Schluchter, W. M., A novel algorithm for predicting PC concentrations in cyanobacteria: A proximal hyperspectral remote sensing approach. Remote Sens., 2009, 1, 758?76." +BGA_MM12NDCIalt,Phycocyanin (BGA_PC),(w700 - w658) / (w700 + w658),"Mishra, S.; Mishra, D.R.; A novel remote sensing algorithm to quantify phycocyanin in cyanobacterial algal blooms, Env. Res. Lett., 2014, 9 (11), DOI:10.1088/1748-9326/9/11/114003" +BGA_MM143BDAopt,Phycocyanin (BGA_PC),((1 / w629) - (1 / w659)) * w724,"Mishra, S.; Mishra, D.R.; A novel remote sensing algorithm to quantify phycocyanin in cyanobacterial algal blooms, Env. Res. Lett., 2014, 9 (11), DOI:10.1088/1748-9326/9/11/114004" +BGA_SI052BDA,Phycocyanin (BGA_PC),w709 / w620,"Simis, S. G. H.; Peters, S.W. M.; Gons, H. J.; Remote sensing of the cyanobacteria pigment phycocyanin in turbid inland water. Limn. Oceanogr., 2005, 50, 237?45" +BGA_SM122BDA,Phycocyanin (BGA_PC),w709 / w600,"Mishra, S. Remote sensing of cyanobacteria in turbid productive waters, PhD Dissertation. Mississippi State University, USA. 2012." +BGA_SY002BDA,Phycocyanin (BGA_PC),w650 / w625,"Schalles, J.; Yacobi, Y. Remote detection and seasonal patterns of phycocyanin, carotenoid and chlorophyll-a pigments in eutrophic waters. Archiv fur Hydrobiologie, Special Issues Advances in Limnology, 2000, 55,153?68" +BGA_Wy08CI,Phycocyanin (BGA_PC),-1 * (W686 - W672 - (W715 - W672)),"Wynne, T. T., Stumpf, R. P., Tomlinson, M. C., Warner, R. A., Tester, P. A., Dyble, J.; Relating spectral shape to cyanobacterial blooms in the Laurentian Great Lakes. Int. J. Remote Sens., 2008, 29, 3665-3672." +Chl_Al10SABI,chlorophyll_a,(w857 - w644) / (w458 + w529),"Alawadi, F. Detection of surface algal blooms using the newly developed algorithm surface algal bloom index (SABI). Proc. SPIE 2010, 7825." +Chl_Am092Bsub,chlorophyll_a,w681 - w665,"Amin, R.; Zhou, J.; Gilerson, A.; Gross, B.; Moshary, F.; Ahmed, S. Novel optical techniques for detecting and classifying toxic dinoflagellate Karenia brevis blooms using satellite imagery. Opt. Express 2009, 17, 9126?144." +Chl_Be16FLHblue,chlorophyll_a,w529 - (w644 + (w458 - w644)),"Beck, R.A. and 22 others; Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations, Remote Sens. Environ., 2016, 178, 15-30." +Chl_Be16FLHviolet,chlorophyll_a,w529 - (w644 + (w429 - w644)),"Beck, R.A. and 22 others; Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations, Remote Sens. Environ., 2016, 178, 15-30." +Chl_Be16NDTIblue,chlorophyll_a,(w658 - w458) / (w658 + w458),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 543." +Chl_Be16NDTIviolet,chlorophyll_a,(w658 - w444) / (w658 + w444),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 544." +Chl_De933BDA,chlorophyll_a,w600 - w648 - w625,"Dekker, A.; Detection of the optical water quality parameters for eutrophic waters by high resolution remote sensing, Ph.D. thesis, 1993, Free University, Amsterdam." +Chl_Gi033BDA,chlorophyll_a,((1 / w672) - (1 / w715)) * w757,"Gitelson, A.A.; U. Gritz, and M. N. Merzlyak.; Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Phys. 2003, 160, 271-282." +Chl_Kn07KIVU,chlorophyll_a,(w458 - w644) / w529,"Kneubuhler, M.; Frank T.; Kellenberger, T.W; Pasche N.; Schmid M.; Mapping chlorophyll-a in Lake Kivu with remote sensing methods. 2007, Proceedings of the Envisat Symposium 2007, Montreux, Switzerland 23?7 April 2007 (ESA SP-636, July 2007)." +Chl_MM12NDCI,chlorophyll_a,(w715 - w686) / (w715 + w686),"Mishra, S.; and Mishra, D.R. Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters, Remote Sens. Environ., 2012, 117, 394-406" +Chl_Zh10FLH,chlorophyll_a,w686 - (w715 + (w672 - w751)),"Zhao, D.Z.; Xing, X.G.; Liu, Y.G.; Yang, J.H.; Wang, L. The relation of chlorophyll-a concentration with the reflectance peak near 700 nm in algae-dominated waters and sensitivity of fluorescence algorithms for detecting algal bloom. Int. J. Remote Sens. 2010, 31, 39-48" +Turb_Be16GreenPlusRedBothOverViolet,Turbidity,(w558 + w658) / w444,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538" +Turb_Be16RedOverViolet,Turbidity,w658 / w444,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539" +Turb_Bow06RedOverGreen,Turbidity,w658 / w558,"Bowers, D. G., and C. E. Binding. 2006. 鈥淭he Optical Properties of Mineral Suspended Particles: A Review and Synthesis.?Estuarine Coastal and Shelf Science 67 (1?): 219?30. doi:10.1016/j.ecss.2005.11.010" +Turb_Chip09NIROverGreen,Turbidity,w857 / w558,"Chipman, J. W.; Olmanson, L.G.; Gitelson, A.A.; Remote sensing methods for lake management: A guide for resource managers and decision-makers. 2009." +Turb_Dox02NIRoverRed,Turbidity,w857 / w658,"Doxaran, D., Froidefond, J.-M.; Castaing, P. ; A reflectance band ratio used to estimate suspended matter concentrations in sediment-dominated coastal waters, Remote Sens., 2002, 23, 5079-5085" +Turb_Frohn09GreenPlusRedBothOverBlue,Turbidity,(w558 + w658) / w458,"Frohn, R. C., & Autrey, B. C. (2009). Water quality assessment in the Ohio River using new indices for turbidity and chlorophyll-a with Landsat-7 Imagery. Draft Internal Report, US Environmental Protection Agency." +Turb_Harr92NIR,Turbidity,w857,"Schiebe F.R., Harrington J.A., Ritchie J.C. Remote-Sensing of Suspended Sediments鈥攖he Lake Chicot, Arkansas Project. Int. J. Remote Sens. 1992;13:1487?509" +Turb_Lath91RedOverBlue,Turbidity,w658 / w458,"Lathrop, R. G., Jr., T. M. Lillesand, and B. S. Yandell, 1991. Testing the utility of simple multi-date Thematic Mapper calibration algorithms for monitoring turbid inland waters. International Journal of Remote Sensing" +Turb_Moore80Red,Turbidity,w658,"Moore, G.K., Satellite remote sensing of water turbidity, Hydrological Sciences, 1980, 25, 4, 407-422" diff --git a/docs/README_py2exe.md b/docs/README_py2exe.md new file mode 100644 index 0000000..ba94d67 --- /dev/null +++ b/docs/README_py2exe.md @@ -0,0 +1,139 @@ +# 使用py2exe打包水质分析GUI应用 + +## 概述 + +本项目现在支持使用py2exe进行打包，这是一个专门用于Windows的Python打包工具。 + +## 文件说明 + +- `setup_py2exe.py` - py2exe的配置文件，包含所有依赖和打包设置 +- `install_py2exe.bat` - 安装py2exe的批处理脚本 +- `check_conda.bat` - 诊断工具（检查conda安装和配置） +- `build_with_py2exe.bat` - 完整构建脚本（尝试多种conda激活方法） +- `build_with_py2exe_simple.bat` - 简化构建脚本（使用conda run，最稳定） +- `build_with_py2exe.ps1` - PowerShell构建脚本（自动查找conda路径） + +## 快速开始 + +### 方法1：使用PowerShell脚本（推荐） + +右键运行 `build_with_py2exe.ps1` 并选择"使用PowerShell运行"，它会自动查找conda并处理所有步骤。 + +### 方法2：使用简单构建脚本 + +双击运行 `build_with_py2exe_simple.bat`，它使用 `conda run` 方法，最稳定可靠。 + +### 方法3：使用完整构建脚本 + +双击运行 `build_with_py2exe.bat`，它会尝试多种conda激活方法。 + +### 方法4：诊断问题 + +如果构建失败，首先双击运行 `check_conda.bat` 来诊断conda安装和配置问题。 + +### 方法5：手动步骤 + +1. **安装py2exe** + ```cmd + cd /d E:\code\WQ\fengzhuang + conda activate insect + conda install -c conda-forge py2exe -y + ``` + +2. **运行打包** + ```cmd + python setup_py2exe.py py2exe + ``` + +## 输出目录 + +打包完成后，可执行文件将在 `dist_py2exe/` 目录中： +- `water_quality_gui.exe` - 主程序 +- 相关依赖文件 + +## 配置说明 + +### 包含的模块 + +- **科学计算**：numpy, scipy, OpenCV +- **地理数据**：GDAL, OGR +- **机器学习**：XGBoost +- **图像处理**：PIL/Pillow, matplotlib +- **GUI**：tkinter +- **项目模块**：所有自定义模块 + +### 数据文件 + +- `icons/` - 图标文件 +- `sub/` - 子目录文件 +- `example_config.json` - 配置文件 +- `xgboost.dll` - XGBoost动态库 + +### 排除的模块 + +排除了大量标准库和测试模块以减小包体积。 + +## 故障排除 + +### 0. Conda环境激活失败 + +**错误信息**：`'conda' 不是内部或外部命令` + +**解决方案**： +1. **推荐**：使用 `build_with_py2exe_simple.bat` 而不是 `build_with_py2exe.bat` +2. 手动初始化conda： + ```cmd + conda init cmd.exe + ``` + 然后关闭并重新打开命令提示符 +3. 检查conda是否在PATH中： + ```cmd + conda --version + ``` +4. 如果conda不在PATH中，请重新安装Anaconda/Miniconda + +### 1. 导入错误 +如果运行时出现模块导入错误，可能需要： +- 检查conda环境是否正确 +- 添加缺失的模块到 `includes` 列表 +- 移除不需要的模块从 `excludes` 列表 + +### 2. 文件缺失 +如果数据文件缺失： +- 检查源文件路径是否正确 +- 确认文件存在于项目目录中 + +### 3. DLL错误 +如果出现DLL相关错误： +- 检查XGBoost DLL路径 +- 添加缺失的DLL到 `dll_excludes` 列表 + +## 自定义配置 + +如需修改打包配置，请编辑 `setup_py2exe.py` 文件： + +- **添加模块**：在 `packages` 或 `includes` 中添加 +- **添加数据文件**：修改 `data_files` 列表 +- **排除模块**：在 `excludes` 中添加 +- **优化设置**： + - `bundle_files`: 1 (单文件), 2 (单目录), 3 (分离) + - `compressed`: True/False (压缩) + - `optimize`: 0, 1, 2 (优化级别) + +## 与PyInstaller的比较 + +| 特性 | py2exe | PyInstaller | +|------|--------|-------------| +| 单文件打包 | 支持 | 支持 | +| Windows专用 | 是 | 跨平台 | +| 包体积 | 较小 | 较大 | +| 兼容性 | 良好 | 优秀 | +| 配置复杂度 | 中等 | 简单 | + +## 技术支持 + +如果遇到问题，请检查： +1. Python版本兼容性 +2. conda环境配置 +3. 依赖包版本 +4. 系统环境变量 diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000..21b8783 --- /dev/null +++ b/environment.yml @@ -0,0 +1,65 @@ +# 水质参数反演分析系统 - Conda环境配置 +# Water Quality Inversion Analysis System - Conda Environment +# 安装命令: conda env create -f environment.yml +# 更新命令: conda env update -f environment.yml + +name: water_quality_analysis +channels: + - conda-forge + +dependencies: + # Python版本 + - python>=3.12 + + # 基础科学计算库 + - numpy>=1.21.0 + - scipy>=1.7.0 + - pandas>=1.3.0 + + # 机器学习库 + - scikit-learn>=1.0.0 + # - lightgbm>=3.3.0 # 注释掉lightgbm + + # 图像处理库 + - pillow>=8.0.0 + - opencv>=4.5.0 + - scikit-image>=0.19.0 + + # GIS和地理空间处理 + - gdal>=3.4.0 + - rasterio>=1.2.0 + - geopandas>=0.10.0 + - shapely>=1.8.0 + - fiona>=1.8.0 + - pyproj>=3.3.0 + + # GUI界面库 + - pyqt>=5.12.0 + + # 数据可视化 + - matplotlib>=3.5.0 + - seaborn>=0.11.0 + - matplotlib-scalebar>=0.8.0 + + # 光谱数据处理 + - spectral>=0.23.0 + + # 小波变换 + - pywavelets>=1.3.0 + + # 并行计算和序列化 + - joblib>=1.1.0 + + # 进度条 + - tqdm>=4.62.0 + + # YAML配置处理 + - pyyaml>=6.0 + + # 打包工具 + - pyinstaller>=5.0.0 + + # 开发工具 (可选，移除以减小环境大小) + # - jupyter + # - notebook + # - ipython diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..786db37 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,151 @@ +[build-system] +requires = ["setuptools>=61.0", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "water-quality-inversion" +version = "1.0.0" +description = "水质参数反演分析系统 - 基于遥感影像和机器学习的水质监测专业软件" +readme = "README.md" +license = {text = "MIT"} +requires-python = ">=3.8" +authors = [ + {name = "Water Quality Research Team", email = "support@waterquality.com"}, +] +maintainers = [ + {name = "Water Quality Research Team", email = "support@waterquality.com"}, +] +keywords = ["water quality", "remote sensing", "machine learning", "GIS", "environmental monitoring"] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Science/Research", + "Topic :: Scientific/Engineering :: GIS", + "License :: OSI Approved :: MIT License", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", +] +dependencies = [ + "numpy>=1.21.0", + "pandas>=1.3.0", + "scipy>=1.7.0", + "scikit-learn>=1.0.0", + "matplotlib>=3.5.0", + "opencv-python>=4.5.0", + "gdal>=3.4.0", + "rasterio>=1.2.0", + "shapely>=1.8.0", + "geopandas>=0.10.0", + # "lightgbm>=3.3.0", # 注释掉lightgbm + "xgboost>=1.5.0", + "torch>=1.11.0", + "torchvision>=0.12.0", + "plotly>=5.0.0", + "PyQt5>=5.15.0", + "pyyaml>=6.0", +] + +[project.optional-dependencies] +dev = [ + "pytest>=6.0", + "pytest-cov>=2.0", + "black>=21.0", + "flake8>=3.9", + "mypy>=0.900", + "pre-commit>=2.17", +] +packaging = [ + "pyinstaller>=5.0", + "py2exe>=0.12", +] +docs = [ + "sphinx>=4.0", + "sphinx-rtd-theme>=1.0", +] + +[project.scripts] +water-quality-gui = "gui.water_quality_gui:main" +water-quality-pipeline = "core.water_quality_inversion_pipeline:main" + +[project.urls] +Homepage = "https://github.com/waterquality/water-quality-inversion" +Documentation = "https://water-quality-inversion.readthedocs.io/" +Repository = "https://github.com/waterquality/water-quality-inversion" +Issues = "https://github.com/waterquality/water-quality-inversion/issues" +Changelog = "https://github.com/waterquality/water-quality-inversion/blob/main/CHANGELOG.md" + +[tool.setuptools] +zip-safe = false +include-package-data = true + +[tool.setuptools.packages.find] +where = ["src"] + +[tool.setuptools.package-data] +"*" = [ + "data/icons/*.png", + "data/sub/**/*", +] + +[tool.black] +line-length = 88 +target-version = ['py38', 'py39', 'py310', 'py311', 'py312'] +include = '\.pyi?$' +extend-exclude = ''' +/( + # directories + \.eggs + | \.git + | \.hg + | \.mypy_cache + | \.tox + | \.venv + | build + | dist + | V1 + | V2 +)/ +''' + +[tool.isort] +profile = "black" +multi_line_output = 3 +line_length = 88 +known_first_party = ["src"] +skip = ["__init__.py"] + +[tool.mypy] +python_version = "3.8" +warn_return_any = true +warn_unused_configs = true +disallow_untyped_defs = true +disallow_incomplete_defs = true +check_untyped_defs = true +disallow_untyped_decorators = true +no_implicit_optional = true +warn_redundant_casts = true +warn_unused_ignores = true +warn_no_return = true +warn_unreachable = true +strict_equality = true + +[[tool.mypy.overrides]] +module = [ + "cv2.*", + "gdal.*", + "osgeo.*", + "torch.*", + "torchvision.*", +] +ignore_missing_imports = true + +[tool.pytest.ini_options] +minversion = "6.0" +addopts = "-ra -q --cov=src --cov-report=html --cov-report=term-missing" +testpaths = ["tests"] +python_files = "test_*.py" +python_classes = "Test*" +python_functions = "test_*" diff --git a/requirements-conda-packages.txt b/requirements-conda-packages.txt new file mode 100644 index 0000000..8aefea3 --- /dev/null +++ b/requirements-conda-packages.txt @@ -0,0 +1,26 @@ +numpy>=1.26 +scipy>=1.11 +pandas>=2.0 +scikit-learn>=1.4 +xgboost>=2.0 +pillow>=10 +opencv>=4.8 +scikit-image>=0.22 +rasterio>=1.3.9 +geopandas>=0.14 +shapely>=2.0 +fiona>=1.9.5 +pyproj>=3.6 +pyqt>=5.15 +matplotlib>=3.8 +seaborn>=0.13 +matplotlib-scalebar>=0.8 +spectral>=0.22 +pywavelets>=1.5 +joblib>=1.3 +tqdm>=4.66 +pyyaml>=6.0 +openpyxl>=3.1 +python-docx>=1.1 +lxml>=4.9 +pyinstaller>=6.0 diff --git a/requirements-conda.txt b/requirements-conda.txt new file mode 100644 index 0000000..f72ac12 --- /dev/null +++ b/requirements-conda.txt @@ -0,0 +1,52 @@ +# 水质参数反演分析系统 - Conda环境依赖包 +# Water Quality Inversion Analysis System - Conda Dependencies +# 安装命令: conda install -c conda-forge --file requirements-conda.txt + +# 基础科学计算库 +numpy>=1.21.0 +scipy>=1.7.0 +pandas>=1.3.0 + +# 机器学习库 +scikit-learn>=1.0.0 +xgboost>=1.5.0 +# lightgbm>=3.3.0 # 注释掉lightgbm + +# 图像处理库 +pillow>=8.0.0 +opencv>=4.5.0 +scikit-image>=0.19.0 + +# GIS和地理空间处理 +gdal>=3.4.0 +rasterio>=1.2.0 +geopandas>=0.10.0 +shapely>=1.8.0 +fiona>=1.8.0 +pyproj>=3.3.0 + +# GUI界面库 +pyqt>=5.12.0 + +# 数据可视化 +matplotlib>=3.5.0 +seaborn>=0.11.0 +matplotlib-scalebar>=0.8.0 + +# 光谱数据处理 +spectral>=0.23.0 + +# 小波变换 +pywavelets>=1.3.0 + +# 并行计算和序列化 +joblib>=1.1.0 + +# 进度条 +tqdm>=4.62.0 + +# YAML配置处理 +pyyaml>=6.0 + +# 打包工具 +pyinstaller>=5.0.0 diff --git a/requirements-py310.txt b/requirements-py310.txt new file mode 100644 index 0000000..5931e75 --- /dev/null +++ b/requirements-py310.txt @@ -0,0 +1,55 @@ +# 水质参数反演分析系统 - Python 3.10 依赖 +# 安装: pip install -r requirements-py310.txt +# +# 说明: +# - Windows 下 GDAL 若 pip 安装失败，建议用 conda-forge: conda install -c conda-forge gdal +# 或使用已编译的 GDAL wheel / OSGeo4W，并保证与 rasterio 版本匹配。 +# +# ---------- GUI ---------- +PyQt5>=5.15.0 +matplotlib>=3.5.0 + +# ---------- 科学计算 ---------- +numpy>=1.21.0 +scipy>=1.7.0 +pandas>=1.3.0 + +# ---------- 机器学习 ---------- +scikit-learn>=1.0.0 +# xgboost>=1.5.0 # 可选 +# lightgbm>=3.3.0 # 可选 + +# ---------- 地理空间 ---------- +rasterio>=1.2.0 +fiona>=1.8.0 +shapely>=1.8.0 +geopandas>=0.10.0 +pyproj>=3.3.0 +spectral>=0.22.0 + +# ---------- 图像 ---------- +opencv-python>=4.5.0 +Pillow>=8.0.0 +scikit-image>=0.19.0 + +# ---------- 可视化 ---------- +seaborn>=0.11.0 +matplotlib-scalebar>=0.8.0 + +# ---------- 信号处理 ---------- +PyWavelets>=1.1.0 + +# ---------- 通用工具 ---------- +joblib>=1.1.0 +tqdm>=4.62.0 +PyYAML>=6.0 + +# ---------- 表格导出（.xlsx）---------- +openpyxl>=3.0.0 + +# ---------- Word 报告生成 ---------- +python-docx>=1.1.0 +lxml>=4.9.0 + +# ---------- 打包（可选，仅构建 exe 时需要）---------- +pyinstaller>=6.0.0 diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..6f26dee --- /dev/null +++ b/requirements.txt @@ -0,0 +1,58 @@ +# 水质参数反演分析系统 - Python 依赖 +# 安装: pip install -r requirements.txt +# +# 说明: +# - Windows 下 GDAL 若 pip 安装失败，建议用 conda-forge: conda install -c conda-forge gdal +# 或使用已编译的 GDAL wheel / OSGeo4W，并保证与 rasterio 版本匹配。 +# - Word 报告（report_word）与 GUI「报告生成」页依赖 python-docx；AI 解读走 Ollama HTTP API， +# 无需额外 pip 包（本地或远程部署 Ollama 即可）。 + +# ---------- GUI ---------- +PyQt5>=5.15.0 + +# ---------- 科学计算 ---------- +# 注：当前工程打包/运行日志显示使用 Python 3.12，因此下限按 Py3.12 兼容版本设置 +numpy>=1.26.0 +scipy>=1.11.0 +pandas>=2.0.0 + +# ---------- 机器学习 ---------- +scikit-learn>=1.4.0 +# xgboost>=2.0.0 # 可选；仅在环境已安装时 spec 会自动打入 +# lightgbm>=4.0.0 # 可选；当前流水线默认未启用 + +# ---------- 地理空间 ---------- +rasterio>=1.3.9 +fiona>=1.9.5 +shapely>=2.0.0 +geopandas>=0.14.0 +pyproj>=3.6.0 +spectral>=0.22.0 + +# ---------- 图像 ---------- +opencv-python>=4.5.0 +Pillow>=8.0.0 +scikit-image>=0.22.0 + +# ---------- 可视化 ---------- +matplotlib>=3.8.0 +seaborn>=0.11.0 +matplotlib-scalebar>=0.8.0 + +# ---------- 信号处理 ---------- +PyWavelets>=1.1.0 + +# ---------- 通用工具 ---------- +joblib>=1.1.0 +tqdm>=4.62.0 +PyYAML>=6.0 + +# ---------- 表格导出（.xlsx）---------- +openpyxl>=3.0.0 + +# ---------- Word 报告生成 ---------- +python-docx>=1.1.0 +lxml>=4.9.0 + +# ---------- 打包（可选，仅构建 exe 时需要）---------- +pyinstaller>=6.0.0 diff --git a/scripts/build.bat b/scripts/build.bat new file mode 100644 index 0000000..e982d00 --- /dev/null +++ b/scripts/build.bat @@ -0,0 +1,44 @@ +@echo off +chcp 65001 >nul +echo. +echo ================================================ +echo 水质参数反演分析系统 - 打包工具 +echo ================================================ +echo. + +:: 检查是否在正确目录 +if not exist "src\gui\water_quality_gui.py" ( + echo [错误] 请在项目根目录下运行此脚本！ + pause + exit /b 1 +) + +echo [1/4] 清理旧构建文件... +if exist "build" rmdir /s /q build +if exist "dist" rmdir /s /q dist + +echo [2/4] 确保依赖已安装... +python -m pip install -r requirements.txt --quiet +python -m pip install pyinstaller --quiet + +echo [3/4] 开始打包（首次可能需要 5-15 分钟，请耐心等待）... +pyinstaller --clean scripts/water_quality_gui.spec + +echo. +echo [打包提示] 如果仍然出现 "No module named 'styles'"，请检查： +echo 1. dist\water_quality_gui\styles.py 是否存在 +echo 2. 是否需要添加 --collect-all styles 参数 + +echo. +echo [4/4] 打包完成！ +echo. +echo 输出位置： +echo dist\water_quality_gui\water_quality_gui.exe +echo dist\water_quality_gui\_internal\ +echo. +echo 建议： +echo 1. 将 dist 文件夹整个复制给用户（包含所有依赖） +echo 2. 首次运行可能需要 10-30 秒解压（正常现象） +echo 3. 如遇 DLL 缺失，可尝试在 conda 环境中打包 +echo. +pause diff --git a/scripts/rthook_add_dll_dirs.py b/scripts/rthook_add_dll_dirs.py new file mode 100644 index 0000000..4ae983b --- /dev/null +++ b/scripts/rthook_add_dll_dirs.py @@ -0,0 +1,25 @@ +import os +import sys + + +def _safe_add(path: str) -> None: + if not path or not os.path.isdir(path): + return + try: + if hasattr(os, "add_dll_directory"): + os.add_dll_directory(path) + except Exception: + pass + try: + os.environ["PATH"] = path + os.pathsep + os.environ.get("PATH", "") + except Exception: + pass + + +# PyInstaller onefile 解包目录 +base = getattr(sys, "_MEIPASS", None) +if base: + _safe_add(base) + _safe_add(os.path.join(base, "lib-dynload")) + _safe_add(os.path.join(base, "DLLs")) + diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..dc7533d --- /dev/null +++ b/setup.py @@ -0,0 +1,83 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +水质参数反演分析系统 - 安装配置 +""" + +from setuptools import setup, find_packages +import os + +# 读取README文件 +def read_readme(): + readme_path = os.path.join(os.path.dirname(__file__), 'README.md') + if os.path.exists(readme_path): + with open(readme_path, 'r', encoding='utf-8') as f: + return f.read() + return "" + +# 读取requirements.txt +def read_requirements(): + requirements_path = os.path.join(os.path.dirname(__file__), 'requirements.txt') + if os.path.exists(requirements_path): + with open(requirements_path, 'r', encoding='utf-8') as f: + return [line.strip() for line in f if line.strip() and not line.startswith('#')] + return [] + +setup( + name="water-quality-inversion", + version="1.0.0", + author="Water Quality Research Team", + author_email="support@waterquality.com", + description="水质参数反演分析系统 - 基于遥感影像和机器学习的水质监测专业软件", + long_description=read_readme(), + long_description_content_type="text/markdown", + url="https://github.com/waterquality/water-quality-inversion", + packages=find_packages(where="src"), + package_dir={"": "src"}, + classifiers=[ + "Development Status :: 4 - Beta", + "Intended Audience :: Science/Research", + "Topic :: Scientific/Engineering :: GIS", + "License :: OSI Approved :: MIT License", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + ], + keywords="water quality remote sensing machine learning GIS", + python_requires=">=3.8", + install_requires=read_requirements(), + extras_require={ + "dev": [ + "pytest>=6.0", + "pytest-cov>=2.0", + "black>=21.0", + "flake8>=3.9", + "mypy>=0.900", + ], + "gui": [ + "PyQt5>=5.15", + "matplotlib>=3.5", + ], + "packaging": [ + "pyinstaller>=5.0", + "py2exe>=0.12", + ], + }, + entry_points={ + "console_scripts": [ + "water-quality-gui=gui.water_quality_gui:main", + "water-quality-pipeline=core.water_quality_inversion_pipeline:main", + ], + }, + include_package_data=True, + package_data={ + "": [ + "data/icons/*.png", + "data/sub/**/*", + ], + }, + zip_safe=False, +) diff --git a/setup_conda_mirrors.bat b/setup_conda_mirrors.bat new file mode 100644 index 0000000..d208684 --- /dev/null +++ b/setup_conda_mirrors.bat @@ -0,0 +1,26 @@ +@echo off +REM 配置Conda使用北外镜像源 +REM Configure Conda to use BFSU mirrors + +echo 配置Conda镜像源... +echo Configuring Conda mirrors... + +REM 添加conda-forge镜像 +conda config --add channels https://mirrors.bfsu.edu.cn/anaconda/cloud/conda-forge/ + +REM 添加main仓库镜像 +conda config --add channels https://mirrors.bfsu.edu.cn/anaconda/pkgs/main/ + +REM 添加free仓库镜像 +conda config --add channels https://mirrors.bfsu.edu.cn/anaconda/pkgs/free/ + +REM 显示当前配置 +echo. +echo 当前通道配置: +echo Current channel configuration: +conda config --show channels + +echo. +echo 配置完成！现在可以使用 environment.yml 创建环境了。 +echo Configuration completed! You can now create the environment using environment.yml. +pause diff --git a/setup_conda_mirrors.sh b/setup_conda_mirrors.sh new file mode 100644 index 0000000..2f4140d --- /dev/null +++ b/setup_conda_mirrors.sh @@ -0,0 +1,25 @@ +#!/bin/bash +# 配置Conda使用北外镜像源 +# Configure Conda to use BFSU mirrors + +echo "配置Conda镜像源..." +echo "Configuring Conda mirrors..." + +# 添加conda-forge镜像 +conda config --add channels https://mirrors.bfsu.edu.cn/anaconda/cloud/conda-forge/ + +# 添加main仓库镜像 +conda config --add channels https://mirrors.bfsu.edu.cn/anaconda/pkgs/main/ + +# 添加free仓库镜像 +conda config --add channels https://mirrors.bfsu.edu.cn/anaconda/pkgs/free/ + +# 显示当前配置 +echo +echo "当前通道配置:" +echo "Current channel configuration:" +conda config --show channels + +echo +echo "配置完成！现在可以使用 environment.yml 创建环境了。" +echo "Configuration completed! You can now create the environment using environment.yml." diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 0000000..7c68785 --- /dev/null +++ b/src/__init__.py @@ -0,0 +1 @@ +# -*- coding: utf-8 -*- \ No newline at end of file diff --git a/src/core/__init__.py b/src/core/__init__.py new file mode 100644 index 0000000..7c68785 --- /dev/null +++ b/src/core/__init__.py @@ -0,0 +1 @@ +# -*- coding: utf-8 -*- \ No newline at end of file diff --git a/src/core/glint_removal/Goodman.py b/src/core/glint_removal/Goodman.py new file mode 100644 index 0000000..2034dc8 --- /dev/null +++ b/src/core/glint_removal/Goodman.py @@ -0,0 +1,367 @@ +import numpy as np +# import preprocessing + +try: + from osgeo import gdal + GDAL_AVAILABLE = True +except ImportError: + GDAL_AVAILABLE = False + print("警告: GDAL未安装，将使用numpy处理模式") + +try: + from tqdm import tqdm + TQDM_AVAILABLE = True +except ImportError: + TQDM_AVAILABLE = False + # 如果tqdm不可用，定义一个简单的包装器 + def tqdm(iterable, desc=None, total=None): + return iterable + +class Goodman: + def __init__(self, im_aligned, NIR_lower = 25, NIR_upper = 37, A = 0.000019, B = 0.1, + use_gdal=True, chunk_size=None, water_mask=None, output_path=None): + """ + :param im_aligned (np.ndarray or str): band aligned and calibrated & corrected reflectance image + 可以是numpy数组或GDAL可读取的文件路径 + :param NIR_lower (int): band index which corresponds to 641.93nm, closest band to 640nm + :param NIR_upper (int): band index which corresponds to 751.49nm, closest band to 750nm + :param A (float): the values in Goodman et al's paper, using AVIRIS reflectance (rather than radiance) data + :param B (float): the values in Goodman et al's paper, using AVIRIS reflectance (rather than radiance) data + see Goodman et al, which corrects each pixel independently. The NIR radiance is subtracted from the radiance at each wavelength, + but a wavelength-independent offset is also added. + it is not clear how A and B were chosen, but an optimization for a case where in situ data is + available would enable values to be found + :param use_gdal (bool): 是否使用GDAL加速处理（需要GDAL可用且输入为文件路径或大数组） + :param chunk_size (int): 已废弃，不再使用分块处理，改为逐波段处理 + :param water_mask (np.ndarray or str or None): 水域掩膜，1表示水域，0表示非水域 + 可以是numpy数组、栅格文件路径(.dat/.tif)或shapefile路径(.shp) + 如果为None，则处理全图 + :param output_path (str or None): 输出文件路径，如果提供则保存校正后的图像 + 如果为None，则不保存 + """ + self.im_aligned = im_aligned + self.NIR_lower = NIR_lower + self.NIR_upper = NIR_upper + self.A = A + self.B = B + self.use_gdal = use_gdal and GDAL_AVAILABLE + self.chunk_size = chunk_size + self.is_file_path = isinstance(im_aligned, str) + self.output_path = output_path + + # 获取图像信息（需要在加载掩膜之前获取尺寸） + if self.is_file_path: + if not self.use_gdal: + raise ValueError("输入为文件路径时，必须安装GDAL") + self.dataset = gdal.Open(im_aligned, gdal.GA_ReadOnly) + if self.dataset is None: + raise ValueError(f"无法打开影像文件: {im_aligned}") + self.height = self.dataset.RasterYSize + self.width = self.dataset.RasterXSize + self.n_bands = self.dataset.RasterCount + else: + self.dataset = None + self.height = im_aligned.shape[0] + self.width = im_aligned.shape[1] + self.n_bands = im_aligned.shape[-1] + + # 加载水域掩膜（在获取图像尺寸之后） + self.water_mask = self._load_water_mask(water_mask) + + def _load_water_mask(self, water_mask): + """ + 加载水域掩膜 + + :param water_mask: 可以是None、numpy数组、文件路径(.dat/.tif)或shapefile路径(.shp) + :return: numpy数组或None，1表示水域，0表示非水域 + """ + if water_mask is None: + return None + + # 如果已经是numpy数组 + if isinstance(water_mask, np.ndarray): + if water_mask.shape[:2] != (self.height, self.width): + raise ValueError(f"掩膜尺寸 {water_mask.shape[:2]} 与图像尺寸 {(self.height, self.width)} 不匹配") + return (water_mask > 0).astype(np.uint8) # 确保是0/1掩膜 + + # 如果是文件路径 + if isinstance(water_mask, str): + if not GDAL_AVAILABLE: + raise ValueError("使用文件路径作为掩膜时，必须安装GDAL") + + # 检查是否为shapefile + if water_mask.lower().endswith('.shp'): + # 从shp文件创建掩膜 + if self.is_file_path: + ref_path = self.im_aligned + else: + raise ValueError("输入为numpy数组时，无法从shp文件创建掩膜（需要参考栅格）") + + try: + from osgeo import ogr + ref_dataset = gdal.Open(ref_path, gdal.GA_ReadOnly) + if ref_dataset is None: + raise ValueError(f"无法打开参考栅格文件: {ref_path}") + + geotransform = ref_dataset.GetGeoTransform() + projection = ref_dataset.GetProjection() + width = ref_dataset.RasterXSize + height = ref_dataset.RasterYSize + + # 创建内存中的栅格数据集 + mem_driver = gdal.GetDriverByName('MEM') + mask_dataset = mem_driver.Create('', width, height, 1, gdal.GDT_Byte) + mask_dataset.SetGeoTransform(geotransform) + mask_dataset.SetProjection(projection) + + mask_band = mask_dataset.GetRasterBand(1) + mask_band.Fill(0) + + # 打开shp文件 + shp_dataset = ogr.Open(water_mask) + if shp_dataset is None: + raise ValueError(f"无法打开shp文件: {water_mask}") + + layer = shp_dataset.GetLayer() + gdal.RasterizeLayer(mask_dataset, [1], layer, burn_values=[1]) + + water_mask_array = mask_band.ReadAsArray() + + ref_dataset = None + mask_dataset = None + shp_dataset = None + + return (water_mask_array > 0).astype(np.uint8) + except Exception as e: + raise ValueError(f"从shp文件创建掩膜时出错: {e}") + else: + # 栅格文件 + mask_dataset = gdal.Open(water_mask, gdal.GA_ReadOnly) + if mask_dataset is None: + raise ValueError(f"无法打开掩膜文件: {water_mask}") + + mask_array = mask_dataset.GetRasterBand(1).ReadAsArray() + mask_dataset = None + + if mask_array.shape != (self.height, self.width): + raise ValueError(f"掩膜尺寸 {mask_array.shape} 与图像尺寸 {(self.height, self.width)} 不匹配") + + return (mask_array > 0).astype(np.uint8) + + raise ValueError(f"不支持的掩膜类型: {type(water_mask)}") + + def _get_corrected_bands_numpy(self): + """ + 使用numpy处理（用于小图像或GDAL不可用时） + + 注意：由于输入已经是numpy数组，数据已在内存中。 + 此方法通过逐波段处理，避免同时创建多个校正后的波段数组。 + 内存峰值 = 原始数组 + NIR波段(2个) + 当前处理的波段(1个) + """ + # 预提取重复使用的NIR波段，避免在循环中重复访问 + # 这些波段会一直保存在内存中，因为它们需要用于所有波段的校正 + R_640 = self.im_aligned[:,:,self.NIR_lower] + R_750 = self.im_aligned[:,:,self.NIR_upper] + # 预计算常量部分 + diff_640_750 = R_640 - R_750 + corrected_bands = [] + + # 获取水域掩膜（如果存在） + water_mask_bool = self.water_mask.astype(bool) if self.water_mask is not None else None + + # 逐波段处理：每次只处理一个波段，处理完后立即添加到结果列表 + for i in tqdm(range(self.n_bands), desc="处理波段 (numpy)", total=self.n_bands): + # 获取当前波段（这是数组视图，不是复制） + R = self.im_aligned[:,:,i] + # 优化计算：减少中间数组创建 + corrected_band = R - R_750 + self.A + self.B * diff_640_750 + # 使用np.maximum原地操作，将负值设为0 + np.maximum(corrected_band, 0, out=corrected_band) + + # 如果存在水域掩膜，只对水域区域应用校正 + if water_mask_bool is not None: + corrected_band = np.where(water_mask_bool, corrected_band, R) + + # 立即添加到结果列表（corrected_band会保留在列表中） + corrected_bands.append(corrected_band) + return corrected_bands + + def _get_corrected_bands_gdal(self): + """ + 使用GDAL逐波段处理，直接处理整个波段（不分块） + + 内存峰值 = NIR波段(2个) + 当前处理的波段(1个) + 已处理的波段(累积在列表中) + """ + corrected_bands = [] + + # 获取NIR波段对象（用于所有波段的校正） + band_640 = self.dataset.GetRasterBand(self.NIR_lower + 1) # GDAL波段从1开始 + band_750 = self.dataset.GetRasterBand(self.NIR_upper + 1) + + # 先读取NIR波段（用于所有波段的校正，会一直保存在内存中） + R_640 = band_640.ReadAsArray().astype(np.float32) + R_750 = band_750.ReadAsArray().astype(np.float32) + diff_640_750 = R_640 - R_750 + + # 获取水域掩膜 + water_mask_bool = self.water_mask.astype(bool) if self.water_mask is not None else None + + # 逐波段处理：每次只读取和处理一个波段 + for i in tqdm(range(self.n_bands), desc="处理波段 (GDAL)", total=self.n_bands): + # 读取当前波段（只加载一个波段到内存） + current_band = self.dataset.GetRasterBand(i + 1) + R = current_band.ReadAsArray().astype(np.float32) + + # 校正计算 + corrected_band = R - R_750 + self.A + self.B * diff_640_750 + np.maximum(corrected_band, 0, out=corrected_band) + + # 如果存在水域掩膜，只对水域区域应用校正 + if water_mask_bool is not None: + corrected_band = np.where(water_mask_bool, corrected_band, R) + + # 添加到结果列表（corrected_band会保留在列表中） + corrected_bands.append(corrected_band) + + # 释放当前波段数据（显式删除有助于及时释放内存） + del R + + return corrected_bands + + def _get_corrected_bands_gdal_mem(self): + """使用GDAL内存驱动处理numpy数组，逐波段处理""" + # 创建内存数据集 + driver = gdal.GetDriverByName('MEM') + mem_dataset = driver.Create('', self.width, self.height, self.n_bands, gdal.GDT_Float32) + + # 将numpy数组写入内存数据集（显示进度） + for i in tqdm(range(self.n_bands), desc="加载波段到内存", total=self.n_bands): + band = mem_dataset.GetRasterBand(i + 1) + band.WriteArray(self.im_aligned[:,:,i]) + band.FlushCache() + + # 临时保存原始dataset引用 + original_dataset = self.dataset + self.dataset = mem_dataset + + try: + # 使用逐波段处理方法 + result = self._get_corrected_bands_gdal() + finally: + # 恢复原始dataset + self.dataset = original_dataset + mem_dataset = None + + return result + + def _save_corrected_bands(self, corrected_bands): + """ + 保存校正后的波段到文件（BSQ格式，ENVI格式） + + 注意：为了节省内存，直接逐波段写入，不先堆叠成完整数组 + + :param corrected_bands: 校正后的波段列表 + """ + if not GDAL_AVAILABLE: + raise ImportError("GDAL未安装，无法保存影像文件") + + if self.output_path is None: + return + + import os + # 确保输出目录存在 + output_dir = os.path.dirname(self.output_path) + if output_dir and not os.path.exists(output_dir): + os.makedirs(output_dir, exist_ok=True) + + # 从第一个波段获取尺寸信息（避免堆叠所有波段） + if not corrected_bands: + raise ValueError("校正后的波段列表为空") + first_band = corrected_bands[0] + height, width = first_band.shape + n_bands = len(corrected_bands) + + # 获取地理变换和投影信息 + if self.is_file_path and self.dataset is not None: + geotransform = self.dataset.GetGeoTransform() + projection = self.dataset.GetProjection() + else: + # 如果没有地理信息，使用默认值 + geotransform = (0, 1, 0, 0, 0, -1) + projection = "" + + # 强制使用ENVI格式（BSQ格式），确保文件扩展名为.bsq + base_path, ext = os.path.splitext(self.output_path) + # 如果扩展名不是.bsq，使用基础路径添加.bsq + if ext.lower() != '.bsq': + bsq_path = base_path + '.bsq' + else: + bsq_path = self.output_path + + # 使用ENVI驱动（默认就是BSQ格式） + driver = gdal.GetDriverByName('ENVI') + if driver is None: + raise ValueError("无法创建ENVI格式文件，ENVI驱动不可用") + + # 创建ENVI格式数据集（会自动生成.hdr文件） + dataset = driver.Create(bsq_path, width, height, n_bands, gdal.GDT_Float32) + if dataset is None: + raise ValueError(f"无法创建输出文件: {bsq_path}") + + try: + # 设置地理变换和投影 + if geotransform: + dataset.SetGeoTransform(geotransform) + if projection: + dataset.SetProjection(projection) + + # 直接逐波段写入（不先堆叠，节省内存） + for i in tqdm(range(n_bands), desc="保存波段", total=n_bands): + band = dataset.GetRasterBand(i + 1) + # 直接从列表中获取波段并写入，避免创建完整数组 + band.WriteArray(corrected_bands[i]) + band.FlushCache() + finally: + dataset = None + + # 检查.hdr文件是否已创建 + hdr_path = bsq_path + '.hdr' + if os.path.exists(hdr_path): + print(f"校正后的图像已保存至: {bsq_path} (BSQ格式)") + print(f"头文件已保存至: {hdr_path}") + else: + print(f"校正后的图像已保存至: {bsq_path} (BSQ格式)") + print(f"警告: 未检测到.hdr文件，但GDAL应该已自动创建") + + def get_corrected_bands(self): + """ + 获取校正后的波段 + 根据输入类型和大小自动选择最优处理方法 + + :return: 校正后的波段列表 + """ + # 如果输入是文件路径，使用GDAL直接读取 + if self.is_file_path: + if self.use_gdal: + corrected_bands = self._get_corrected_bands_gdal() + else: + raise ValueError("输入为文件路径时，必须安装GDAL") + else: + # 如果输入是numpy数组 + if self.use_gdal and self.height * self.width * self.n_bands > 100000000: + # 大图像使用GDAL内存驱动逐波段处理 + corrected_bands = self._get_corrected_bands_gdal_mem() + else: + # 小图像使用numpy直接处理 + corrected_bands = self._get_corrected_bands_numpy() + + # 如果提供了输出路径，保存结果 + if self.output_path is not None: + self._save_corrected_bands(corrected_bands) + + return corrected_bands + + def __del__(self): + """清理资源""" + if self.dataset is not None and self.is_file_path: + self.dataset = None \ No newline at end of file diff --git a/src/core/glint_removal/Hedley.py b/src/core/glint_removal/Hedley.py new file mode 100644 index 0000000..a11506b --- /dev/null +++ b/src/core/glint_removal/Hedley.py @@ -0,0 +1,290 @@ +import numpy as np +# import preprocessing +import os + +try: + from osgeo import gdal + GDAL_AVAILABLE = True +except ImportError: + GDAL_AVAILABLE = False + +class Hedley: + def __init__(self, im_aligned, shp_path=None, NIR_band = 47, water_mask=None, output_path=None): + """ + :param im_aligned (np.ndarray): band aligned and calibrated & corrected reflectance image + :param shp_path (str, optional): path to shapefile (.shp) defining the region containing the glint region in deep water. + If None, uses the entire image. The shapefile can use pixel coordinates or geographic coordinates. + :param NIR_band (int): band index for NIR band which corresponds to 842.36nm, which corresponds closely to the NIR band in Micasense + :param water_mask (np.ndarray or str or None): 水域掩膜，1表示水域，0表示非水域 + 可以是numpy数组、栅格文件路径(.dat/.tif)或shapefile路径(.shp) + 如果为None，则处理全图 + :param output_path (str or None): 输出文件路径，如果提供则保存校正后的图像 + 如果为None，则不保存 + """ + self.im_aligned = im_aligned + self.bbox = self._read_shp_to_bbox(shp_path) if shp_path else None + self.NIR_band = NIR_band + self.n_bands = im_aligned.shape[-1] + self.height = im_aligned.shape[0] + self.width = im_aligned.shape[1] + self.output_path = output_path + + # 加载水域掩膜 + self.water_mask = self._load_water_mask(water_mask) + + # 使用ravel()而不是flatten()，避免不必要的复制 + # 如果存在水域掩膜，只在掩膜内计算R_min + if self.water_mask is not None: + nir_band_masked = self.im_aligned[:,:,self.NIR_band][self.water_mask.astype(bool)] + self.R_min = np.percentile(nir_band_masked, 5, interpolation='nearest') if nir_band_masked.size > 0 else 0 + else: + self.R_min = np.percentile(self.im_aligned[:,:,self.NIR_band].ravel(), 5, interpolation='nearest') + + def _read_shp_to_bbox(self, shp_path): + """ + 读取shapefile并提取边界框 + + :param shp_path (str): shapefile文件路径 + :return: tuple: ((x1,y1),(x2,y2)), where x1,y1 is the upper left corner, x2,y2 is the lower right corner + """ + if not os.path.exists(shp_path): + raise FileNotFoundError(f"Shapefile not found: {shp_path}") + + try: + try: + import geopandas as gpd + gdf = gpd.read_file(shp_path) + # 获取所有几何体的总边界框 + bounds = gdf.total_bounds # [minx, miny, maxx, maxy] + min_x, min_y, max_x, max_y = bounds + except ImportError: + # 如果geopandas不可用，尝试使用fiona + import fiona + from shapely.geometry import shape + + min_x = float('inf') + min_y = float('inf') + max_x = float('-inf') + max_y = float('-inf') + + with fiona.open(shp_path) as shp: + for feature in shp: + geom = shape(feature['geometry']) + if geom: + bounds = geom.bounds + min_x = min(min_x, bounds[0]) + min_y = min(min_y, bounds[1]) + max_x = max(max_x, bounds[2]) + max_y = max(max_y, bounds[3]) + + # 转换为整数像素坐标 + x1 = max(0, int(min_x)) + y1 = max(0, int(min_y)) + x2 = min(self.im_aligned.shape[1], int(max_x) + 1) + y2 = min(self.im_aligned.shape[0], int(max_y) + 1) + + return ((x1, y1), (x2, y2)) + + except Exception as e: + raise ValueError(f"Error reading shapefile {shp_path}: {e}") + + def _load_water_mask(self, water_mask): + """ + 加载水域掩膜 + + :param water_mask: 可以是None、numpy数组、文件路径(.dat/.tif)或shapefile路径(.shp) + :return: numpy数组或None，1表示水域，0表示非水域 + """ + if water_mask is None: + return None + + # 如果已经是numpy数组 + if isinstance(water_mask, np.ndarray): + if water_mask.shape[:2] != (self.height, self.width): + raise ValueError(f"掩膜尺寸 {water_mask.shape[:2]} 与图像尺寸 {(self.height, self.width)} 不匹配") + return (water_mask > 0).astype(np.uint8) # 确保是0/1掩膜 + + # 如果是文件路径 + if isinstance(water_mask, str): + try: + from osgeo import gdal, ogr + except ImportError: + raise ValueError("使用文件路径作为掩膜时，必须安装GDAL") + + # 检查是否为shapefile + if water_mask.lower().endswith('.shp'): + # 从shp文件创建掩膜（需要参考图像，这里假设使用im_aligned的尺寸） + # 注意：如果输入是numpy数组，无法从shp创建掩膜，需要提供栅格参考 + raise ValueError("Hedley类输入为numpy数组时，无法从shp文件创建掩膜。请先栅格化shp文件或提供numpy数组掩膜") + else: + # 栅格文件 + mask_dataset = gdal.Open(water_mask, gdal.GA_ReadOnly) + if mask_dataset is None: + raise ValueError(f"无法打开掩膜文件: {water_mask}") + + mask_array = mask_dataset.GetRasterBand(1).ReadAsArray() + mask_dataset = None + + if mask_array.shape != (self.height, self.width): + raise ValueError(f"掩膜尺寸 {mask_array.shape} 与图像尺寸 {(self.height, self.width)} 不匹配") + + return (mask_array > 0).astype(np.uint8) + + raise ValueError(f"不支持的掩膜类型: {type(water_mask)}") + + def covariance_NIR(self,NIR,b): + """ + NIR & b are vectors + reflectance for band i + """ + n = len(NIR) + # 优化：减少重复计算，使用更高效的numpy操作 + nir_mean = np.mean(NIR) + b_mean = np.mean(b) + # 使用更高效的协方差计算 + pij = np.mean((NIR - nir_mean) * (b - b_mean)) + pjj = np.mean((NIR - nir_mean) ** 2) + # 避免除零错误 + return pij / pjj if pjj != 0 else 0.0 + + def correlation_bands_reflectance(self): + """ + calculate correlation between NIR and other bands for reflectance + NIR_band is 750 nm + """ + # If bbox is None, use the entire image + if self.bbox is None: + # 使用ravel()而不是flatten()，避免不必要的复制 + # 直接使用视图，只在需要时创建扁平数组 + im_region = self.im_aligned + mask_region = self.water_mask + else: + ((x1,y1),(x2,y2)) = self.bbox + im_region = self.im_aligned[y1:y2,x1:x2,:] + mask_region = self.water_mask[y1:y2,x1:x2] if self.water_mask is not None else None + + # 如果存在水域掩膜，只在掩膜内计算相关性 + if mask_region is not None: + mask_bool = mask_region.astype(bool) + if mask_bool.any(): + # 只在掩膜内提取数据 + NIR_reflectance = im_region[:,:,self.NIR_band][mask_bool] + else: + # 如果掩膜内没有有效像素，使用全区域 + NIR_reflectance = im_region[:,:,self.NIR_band].ravel() + mask_bool = None + else: + NIR_reflectance = im_region[:,:,self.NIR_band].ravel() + mask_bool = None + + # 优化：一次性计算所有波段的相关性，减少循环开销 + corr_list = [] + for v in range(self.n_bands): + if mask_bool is not None and mask_bool.any(): + band_reflectance = im_region[:,:,v][mask_bool] + else: + band_reflectance = im_region[:,:,v].ravel() + corr = self.covariance_NIR(NIR_reflectance, band_reflectance) + corr_list.append(corr) + + return corr_list + + def _save_corrected_bands(self, corrected_bands): + """ + 保存校正后的波段到文件（BSQ格式，ENVI格式） + + :param corrected_bands: 校正后的波段列表 + """ + if not GDAL_AVAILABLE: + raise ImportError("GDAL未安装，无法保存影像文件") + + if self.output_path is None: + return + + # 确保输出目录存在 + output_dir = os.path.dirname(self.output_path) + if output_dir and not os.path.exists(output_dir): + os.makedirs(output_dir, exist_ok=True) + + # 将波段列表转换为数组 + corrected_array = np.stack(corrected_bands, axis=2) + + # 如果没有地理信息，使用默认值 + geotransform = (0, 1, 0, 0, 0, -1) + projection = "" + + # 强制使用ENVI格式（BSQ格式），确保文件扩展名为.bsq + base_path, ext = os.path.splitext(self.output_path) + # 如果扩展名不是.bsq，使用基础路径添加.bsq + if ext.lower() != '.bsq': + bsq_path = base_path + '.bsq' + else: + bsq_path = self.output_path + + # 使用ENVI驱动（默认就是BSQ格式） + driver = gdal.GetDriverByName('ENVI') + if driver is None: + raise ValueError("无法创建ENVI格式文件，ENVI驱动不可用") + + height, width, n_bands = corrected_array.shape + # 创建ENVI格式数据集（会自动生成.hdr文件） + dataset = driver.Create(bsq_path, width, height, n_bands, gdal.GDT_Float32) + if dataset is None: + raise ValueError(f"无法创建输出文件: {bsq_path}") + + try: + # 设置地理变换和投影 + if geotransform: + dataset.SetGeoTransform(geotransform) + if projection: + dataset.SetProjection(projection) + + # 写入每个波段（BSQ格式：按波段顺序存储） + for i in range(n_bands): + band = dataset.GetRasterBand(i + 1) + band.WriteArray(corrected_array[:, :, i]) + band.FlushCache() + finally: + dataset = None + + # 检查.hdr文件是否已创建 + hdr_path = bsq_path + '.hdr' + if os.path.exists(hdr_path): + print(f"校正后的图像已保存至: {bsq_path} (BSQ格式)") + print(f"头文件已保存至: {hdr_path}") + else: + print(f"校正后的图像已保存至: {bsq_path} (BSQ格式)") + print(f"警告: 未检测到.hdr文件，但GDAL应该已自动创建") + + def get_corrected_bands(self): + """ + correction is done in reflectance + + :return: 校正后的波段列表 + """ + corr = self.correlation_bands_reflectance() + NIR_reflectance = self.im_aligned[:,:,self.NIR_band] + # 预计算NIR-R_min，避免在循环中重复计算 + NIR_diff = NIR_reflectance - self.R_min + + # 获取水域掩膜（如果存在） + water_mask_bool = self.water_mask.astype(bool) if self.water_mask is not None else None + + corrected_bands = [] + for band_number in range(self.n_bands): #iterate across bands + b = corr[band_number] + R = self.im_aligned[:,:,band_number] + # 优化：减少中间数组创建 + corrected_band = R - b * NIR_diff + + # 如果存在水域掩膜，只对水域区域应用校正 + if water_mask_bool is not None: + corrected_band = np.where(water_mask_bool, corrected_band, R) + + corrected_bands.append(corrected_band) + + # 如果提供了输出路径，保存结果 + if self.output_path is not None: + self._save_corrected_bands(corrected_bands) + + return corrected_bands diff --git a/src/core/glint_removal/Kutser.py b/src/core/glint_removal/Kutser.py new file mode 100644 index 0000000..7a659ac --- /dev/null +++ b/src/core/glint_removal/Kutser.py @@ -0,0 +1,313 @@ +import numpy as np +# import preprocessing +import os + +try: + from osgeo import gdal + GDAL_AVAILABLE = True +except ImportError: + GDAL_AVAILABLE = False + +class Kutser: + def __init__(self, im_aligned, shp_path=None, oxy_band = 38,lower_oxy = 36, upper_oxy = 49, NIR_band = 47, water_mask=None, output_path=None): + """ + :param im_aligned (np.ndarray): band aligned and calibrated & corrected reflectance image + :param shp_path (str, optional): path to shapefile (.shp) defining the region containing the glint region in deep water. + If None, uses the entire image. The shapefile can use pixel coordinates or geographic coordinates. + :param oxy_band (int): band index for oxygen absorption band, which corresponds to 760.6nm + :param lower_oxy (int): band index for outside oxygen absorption band, which corresponds to 742.39nm + :param upper_oxy (int): band index for outside oxygen absorption band, which corresponds to 860.48nm + see Kutser, Vahtmäe and Praks + :param water_mask (np.ndarray or str or None): 水域掩膜，1表示水域，0表示非水域 + 可以是numpy数组、栅格文件路径(.dat/.tif)或shapefile路径(.shp) + 如果为None，则处理全图 + :param output_path (str or None): 输出文件路径，如果提供则保存校正后的图像 + 如果为None，则不保存 + """ + self.im_aligned = im_aligned + self.bbox = self._read_shp_to_bbox(shp_path) if shp_path else None + self.oxy_band = oxy_band + self.lower_oxy = lower_oxy + self.upper_oxy = upper_oxy + self.NIR_band = NIR_band + self.n_bands = im_aligned.shape[-1] + self.height = im_aligned.shape[0] + self.width = im_aligned.shape[1] + self.output_path = output_path + + # 加载水域掩膜 + self.water_mask = self._load_water_mask(water_mask) + + # 使用ravel()而不是flatten()，避免不必要的复制 + # 如果存在水域掩膜，只在掩膜内计算R_min + if self.water_mask is not None: + nir_band_masked = self.im_aligned[:,:,self.NIR_band][self.water_mask.astype(bool)] + self.R_min = np.percentile(nir_band_masked, 5, interpolation='nearest') if nir_band_masked.size > 0 else 0 + else: + self.R_min = np.percentile(self.im_aligned[:,:,self.NIR_band].ravel(), 5, interpolation='nearest') + + def _read_shp_to_bbox(self, shp_path): + """ + 读取shapefile并提取边界框 + + :param shp_path (str): shapefile文件路径 + :return: tuple: ((x1,y1),(x2,y2)), where x1,y1 is the upper left corner, x2,y2 is the lower right corner + """ + if not os.path.exists(shp_path): + raise FileNotFoundError(f"Shapefile not found: {shp_path}") + + try: + try: + import geopandas as gpd + gdf = gpd.read_file(shp_path) + # 获取所有几何体的总边界框 + bounds = gdf.total_bounds # [minx, miny, maxx, maxy] + min_x, min_y, max_x, max_y = bounds + except ImportError: + # 如果geopandas不可用，尝试使用fiona + import fiona + from shapely.geometry import shape + + min_x = float('inf') + min_y = float('inf') + max_x = float('-inf') + max_y = float('-inf') + + with fiona.open(shp_path) as shp: + for feature in shp: + geom = shape(feature['geometry']) + if geom: + bounds = geom.bounds + min_x = min(min_x, bounds[0]) + min_y = min(min_y, bounds[1]) + max_x = max(max_x, bounds[2]) + max_y = max(max_y, bounds[3]) + + # 转换为整数像素坐标 + x1 = max(0, int(min_x)) + y1 = max(0, int(min_y)) + x2 = min(self.im_aligned.shape[1], int(max_x) + 1) + y2 = min(self.im_aligned.shape[0], int(max_y) + 1) + + return ((x1, y1), (x2, y2)) + + except Exception as e: + raise ValueError(f"Error reading shapefile {shp_path}: {e}") + + def _load_water_mask(self, water_mask): + """ + 加载水域掩膜 + + :param water_mask: 可以是None、numpy数组、文件路径(.dat/.tif)或shapefile路径(.shp) + :return: numpy数组或None，1表示水域，0表示非水域 + """ + if water_mask is None: + return None + + # 如果已经是numpy数组 + if isinstance(water_mask, np.ndarray): + if water_mask.shape[:2] != (self.height, self.width): + raise ValueError(f"掩膜尺寸 {water_mask.shape[:2]} 与图像尺寸 {(self.height, self.width)} 不匹配") + return (water_mask > 0).astype(np.uint8) # 确保是0/1掩膜 + + # 如果是文件路径 + if isinstance(water_mask, str): + try: + from osgeo import gdal, ogr + except ImportError: + raise ValueError("使用文件路径作为掩膜时，必须安装GDAL") + + # 检查是否为shapefile + if water_mask.lower().endswith('.shp'): + # 从shp文件创建掩膜（需要参考图像，这里假设使用im_aligned的尺寸） + # 注意：如果输入是numpy数组，无法从shp创建掩膜，需要提供栅格参考 + raise ValueError("Kutser类输入为numpy数组时，无法从shp文件创建掩膜。请先栅格化shp文件或提供numpy数组掩膜") + else: + # 栅格文件 + mask_dataset = gdal.Open(water_mask, gdal.GA_ReadOnly) + if mask_dataset is None: + raise ValueError(f"无法打开掩膜文件: {water_mask}") + + mask_array = mask_dataset.GetRasterBand(1).ReadAsArray() + mask_dataset = None + + if mask_array.shape != (self.height, self.width): + raise ValueError(f"掩膜尺寸 {mask_array.shape} 与图像尺寸 {(self.height, self.width)} 不匹配") + + return (mask_array > 0).astype(np.uint8) + + raise ValueError(f"不支持的掩膜类型: {type(water_mask)}") + + def get_depth_D(self): + """ + Assume the amount of glint is proportional to the depth of the oxygen absorption feature, D + returns the normalised D by dividing it by the maximum D found in a deep water region + """ + # 优化：减少中间数组创建，使用更高效的计算 + lower_oxy_band = self.im_aligned[:,:,self.lower_oxy] + upper_oxy_band = self.im_aligned[:,:,self.upper_oxy] + oxy_band = self.im_aligned[:,:,self.oxy_band] + D = (lower_oxy_band + upper_oxy_band) * 0.5 - oxy_band + + # 确定用于计算D_max的区域 + if self.bbox is None: + search_region = D + else: + ((x1,y1),(x2,y2)) = self.bbox + search_region = D[y1:y2,x1:x2] + + # 如果存在水域掩膜，只在掩膜内搜索最大值 + if self.water_mask is not None: + if self.bbox is None: + mask_region = self.water_mask.astype(bool) + else: + ((x1,y1),(x2,y2)) = self.bbox + mask_region = self.water_mask[y1:y2,x1:x2].astype(bool) + + if mask_region.any(): + D_max = search_region[mask_region].max() + else: + D_max = search_region.max() + else: + D_max = search_region.max() # assumed to be the maximum glint value + + # 避免除零错误 + if D_max == 0: + return np.zeros_like(D) + return D / D_max + + def get_glint_G(self): + """ + The spectral variation of glint G is found by subtracting the spectrum at the darkest (ie. lowest D) NIR deep-water pixel from the brightest + returns G as a function of wavelength + """ + # If bbox is None, use the entire image + if self.bbox is None: + im_region = self.im_aligned + mask_region = self.water_mask + else: + ((x1,y1),(x2,y2)) = self.bbox + im_region = self.im_aligned[y1:y2,x1:x2,:] + mask_region = self.water_mask[y1:y2,x1:x2] if self.water_mask is not None else None + + # 如果存在水域掩膜，只在掩膜内计算最大最小值 + if mask_region is not None: + mask_bool = mask_region.astype(bool) + if mask_bool.any(): + # 对每个波段，只在掩膜内计算最大最小值 + G_list = [] + for i in range(self.n_bands): + band_data = im_region[:,:,i] + G_max = band_data[mask_bool].max() + G_min = band_data[mask_bool].min() + G_list.append(G_max - G_min) + else: + # 如果掩膜内没有有效像素，使用全区域 + G_max = np.amax(im_region, axis=(0, 1)) + G_min = np.amin(im_region, axis=(0, 1)) + G_list = (G_max - G_min).tolist() + else: + # 优化：一次性计算所有波段的最大最小值，减少循环开销 + # 使用numpy的amax和amin沿最后一个轴计算 + G_max = np.amax(im_region, axis=(0, 1)) # 沿空间维度计算最大值 + G_min = np.amin(im_region, axis=(0, 1)) # 沿空间维度计算最小值 + G_list = (G_max - G_min).tolist() + return G_list + + def _save_corrected_bands(self, corrected_bands): + """ + 保存校正后的波段到文件（BSQ格式，ENVI格式） + + :param corrected_bands: 校正后的波段列表 + """ + if not GDAL_AVAILABLE: + raise ImportError("GDAL未安装，无法保存影像文件") + + if self.output_path is None: + return + + # 确保输出目录存在 + output_dir = os.path.dirname(self.output_path) + if output_dir and not os.path.exists(output_dir): + os.makedirs(output_dir, exist_ok=True) + + # 将波段列表转换为数组 + corrected_array = np.stack(corrected_bands, axis=2) + + # 如果没有地理信息，使用默认值 + geotransform = (0, 1, 0, 0, 0, -1) + projection = "" + + # 强制使用ENVI格式（BSQ格式），确保文件扩展名为.bsq + base_path, ext = os.path.splitext(self.output_path) + # 如果扩展名不是.bsq，使用基础路径添加.bsq + if ext.lower() != '.bsq': + bsq_path = base_path + '.bsq' + else: + bsq_path = self.output_path + + # 使用ENVI驱动（默认就是BSQ格式） + driver = gdal.GetDriverByName('ENVI') + if driver is None: + raise ValueError("无法创建ENVI格式文件，ENVI驱动不可用") + + height, width, n_bands = corrected_array.shape + # 创建ENVI格式数据集（会自动生成.hdr文件） + dataset = driver.Create(bsq_path, width, height, n_bands, gdal.GDT_Float32) + if dataset is None: + raise ValueError(f"无法创建输出文件: {bsq_path}") + + try: + # 设置地理变换和投影 + if geotransform: + dataset.SetGeoTransform(geotransform) + if projection: + dataset.SetProjection(projection) + + # 写入每个波段（BSQ格式：按波段顺序存储） + for i in range(n_bands): + band = dataset.GetRasterBand(i + 1) + band.WriteArray(corrected_array[:, :, i]) + band.FlushCache() + finally: + dataset = None + + # 检查.hdr文件是否已创建 + hdr_path = bsq_path + '.hdr' + if os.path.exists(hdr_path): + print(f"校正后的图像已保存至: {bsq_path} (BSQ格式)") + print(f"头文件已保存至: {hdr_path}") + else: + print(f"校正后的图像已保存至: {bsq_path} (BSQ格式)") + print(f"警告: 未检测到.hdr文件，但GDAL应该已自动创建") + + def get_corrected_bands(self): + """ + correction is done in reflectance + + :return: 校正后的波段列表 + """ + g_list = self.get_glint_G() + D = self.get_depth_D() + + # 获取水域掩膜（如果存在） + water_mask_bool = self.water_mask.astype(bool) if self.water_mask is not None else None + + corrected_bands = [] + for band_number in range(self.n_bands): #iterate across bands + G = g_list[band_number] + R = self.im_aligned[:,:,band_number] + # 优化：减少中间数组创建，直接计算 + corrected_band = R - G * D + + # 如果存在水域掩膜，只对水域区域应用校正 + if water_mask_bool is not None: + corrected_band = np.where(water_mask_bool, corrected_band, R) + + corrected_bands.append(corrected_band) + + # 如果提供了输出路径，保存结果 + if self.output_path is not None: + self._save_corrected_bands(corrected_bands) + + return corrected_bands diff --git a/src/core/glint_removal/SUGAR.py b/src/core/glint_removal/SUGAR.py new file mode 100644 index 0000000..54eb88c --- /dev/null +++ b/src/core/glint_removal/SUGAR.py @@ -0,0 +1,572 @@ +import cv2 +import os +import numpy as np +from scipy import ndimage +from scipy.optimize import minimize_scalar + +try: + from osgeo import gdal + GDAL_AVAILABLE = True +except ImportError: + GDAL_AVAILABLE = False + +# SUn-Glint-Aware Restoration (SUGAR):A sweet and simple algorithm for correcting sunglint +class SUGAR: + def __init__(self, im_aligned,bounds=[(1,2)],sigma=1,estimate_background=True, glint_mask_method="cdf", water_mask=None, output_path=None): + """ + :param im_aligned (np.ndarray): band aligned and calibrated & corrected reflectance image + :param bounds (a list of tuple): lower and upper bound for optimisation of b for each band + :param sigma (float): smoothing sigma for LoG + :param estimate_background (bool): whether to estimate background spectra using median filtering + :param glint_mask_method (str): choose either "cdf" or "otsu", "cdf" is set as the default + :param water_mask (np.ndarray or str or None): 水域掩膜，1表示水域，0表示非水域 + 可以是numpy数组、栅格文件路径(.dat/.tif)或shapefile路径(.shp) + 如果为None，则处理全图 + :param output_path (str or None): 输出文件路径，如果提供则保存校正后的图像 + 如果为None，则不保存 + """ + self.im_aligned = im_aligned + self.sigma = sigma + self.estimate_background = estimate_background + self.n_bands = im_aligned.shape[-1] + self.bounds = bounds*self.n_bands + self.glint_mask_method = glint_mask_method + self.height = im_aligned.shape[0] + self.width = im_aligned.shape[1] + self.output_path = output_path + + # 加载水域掩膜 + self.water_mask = self._load_water_mask(water_mask) + + def _load_water_mask(self, water_mask): + """ + 加载水域掩膜 + + :param water_mask: 可以是None、numpy数组、文件路径(.dat/.tif)或shapefile路径(.shp) + :return: numpy数组或None，1表示水域，0表示非水域 + """ + if water_mask is None: + return None + + # 如果已经是numpy数组 + if isinstance(water_mask, np.ndarray): + if water_mask.shape[:2] != (self.height, self.width): + raise ValueError(f"掩膜尺寸 {water_mask.shape[:2]} 与图像尺寸 {(self.height, self.width)} 不匹配") + return (water_mask > 0).astype(np.uint8) # 确保是0/1掩膜 + + # 如果是文件路径 + if isinstance(water_mask, str): + try: + from osgeo import gdal, ogr + except ImportError: + raise ValueError("使用文件路径作为掩膜时，必须安装GDAL") + + # 检查是否为shapefile + if water_mask.lower().endswith('.shp'): + # 从shp文件创建掩膜（需要参考图像，这里假设使用im_aligned的尺寸） + # 注意：如果输入是numpy数组，无法从shp创建掩膜，需要提供栅格参考 + raise ValueError("SUGAR类输入为numpy数组时，无法从shp文件创建掩膜。请先栅格化shp文件或提供numpy数组掩膜") + else: + # 栅格文件 + mask_dataset = gdal.Open(water_mask, gdal.GA_ReadOnly) + if mask_dataset is None: + raise ValueError(f"无法打开掩膜文件: {water_mask}") + + mask_array = mask_dataset.GetRasterBand(1).ReadAsArray() + mask_dataset = None + + if mask_array.shape != (self.height, self.width): + raise ValueError(f"掩膜尺寸 {mask_array.shape} 与图像尺寸 {(self.height, self.width)} 不匹配") + + return (mask_array > 0).astype(np.uint8) + + raise ValueError(f"不支持的掩膜类型: {type(water_mask)}") + + def otsu_thresholding(self,im): + """ + :param im (np.ndarray) of shape mxn. Note that it is the LoG of image + otsu thresholding with Brent's minimisation of a univariate function + returns the value of the threshold for input + """ + auto_bins = int(0.005*im.shape[0]*im.shape[1]) + # 使用ravel()而不是flatten()，避免不必要的复制（如果可能） + # 如果存在无效值（如NaN或极大值），过滤掉它们 + im_flat = im.ravel() + # 过滤掉NaN和无穷大值 + valid_mask = np.isfinite(im_flat) + if not valid_mask.all(): + im_flat = im_flat[valid_mask] + count, bin_edges = np.histogram(im_flat, bins=auto_bins) + bin = (bin_edges[:-1] + bin_edges[1:]) * 0.5 # bin centers，使用乘法替代除法 + + count_sum = count.sum() + hist_norm = count / count_sum # normalised histogram + Q = hist_norm.cumsum() # CDF function ranges from 0 to 1 + N = count.shape[0] + N_negative = np.sum(bin < 0) + bins = np.arange(N, dtype=np.float32) # 使用float32减少内存 + + def otsu_thresh(x): + x = int(x) + # 使用切片而不是hsplit，避免创建新数组 + p1 = hist_norm[:x] + p2 = hist_norm[x:] + q1 = Q[x] + q2 = Q[N-1] - Q[x] + b1 = bins[:x] + b2 = bins[x:] + # finding means and variances + m1 = np.sum(p1 * b1) / q1 if q1 > 0 else 0 + m2 = np.sum(p2 * b2) / q2 if q2 > 0 else 0 + v1 = np.sum(((b1 - m1) ** 2) * p1) / q1 if q1 > 0 else 0 + v2 = np.sum(((b2 - m2) ** 2) * p2) / q2 if q2 > 0 else 0 + # calculates the minimization function + fn = v1 * q1 + v2 * q2 + return fn + + # brent method is used to minimise an univariate function + # bounded minimisation + # we can just limit the search to negative values since we know thresh should be negative as L<0 for glint pixels + if N_negative <= 1: + # 如果没有足够的负值，使用默认阈值 + return bin[np.argmax(count)] + res = minimize_scalar(otsu_thresh, bounds=(1, N_negative), method='bounded') + thresh = bin[int(res.x)] + + return thresh + + # def cdf_thresholding(self,im, percentile=0.05): + # """ + # :param im (np.ndarray) of shape mxn + # :param percentile (float): lower and upper percentile values are potential glint pixels + # """ + # lower_perc = percentile + # upper_perc = 1-percentile + # im_flatten = im.flatten() + # H,X1 = np.histogram(im_flatten, bins = int(0.005*im.shape[0]*im.shape[1]), density=True ) + # dx = X1[1] - X1[0] + # F1 = np.cumsum(H)*dx + # F_lower = X1[1:][F1upper_perc] + # while((F_lower.size == 0) or (F_upper.size == 0)): + # if (F_lower.size == 0): + # lower_perc += 0.01 + # F_lower = X1[1:][F1upper_perc] + + # lower_thresh = F_lower[-1] + # upper_thresh = F_upper[0] + + # return lower_thresh,upper_thresh + + def cdf_thresholding(self,im,auto_bins=10): + """ + :param im (np.ndarray) of shape mxn. Note that it is the LoG of image + :param percentile (float): lower and upper percentile values are potential glint pixels + """ + # 使用ravel()而不是flatten()，避免不必要的复制 + im_flat = im.ravel() + # 过滤掉NaN和无穷大值 + valid_mask = np.isfinite(im_flat) + if not valid_mask.all(): + im_flat = im_flat[valid_mask] + count, bin_edges = np.histogram(im_flat, bins=auto_bins) + bin = (bin_edges[:-1] + bin_edges[1:]) * 0.5 # bin centers，使用乘法替代除法 + thresh = bin[np.argmax(count)] + return thresh + + def glint_list(self): + """ + returns a list of np.ndarray, where each item is an extracted glint for each band based on get_glint_mask + """ + glint_mask = self.glint_mask_list() + extracted_glint_list = [] + for i in range(self.im_aligned.shape[-1]): + gm = glint_mask[i] + extracted_glint = gm*self.im_aligned[:,:,i] + extracted_glint_list.append(extracted_glint) + + return extracted_glint_list + + def glint_mask_list(self): + """ + get glint mask using laplacian of gaussian image. + returns a list of np.ndarray + """ + glint_mask_list = [] + for i in range(self.im_aligned.shape[-1]): + glint_mask = self.get_glint_mask(self.im_aligned[:,:,i]) + glint_mask_list.append(glint_mask) + + return glint_mask_list + + def log_image_list(self): + """ + get Laplacian of Gaussian (LoG) images for all bands. + returns a list of np.ndarray + """ + log_image_list = [] + for i in range(self.im_aligned.shape[-1]): + log_im = self.get_log_image(self.im_aligned[:,:,i]) + log_image_list.append(log_im) + return log_image_list + + def get_log_image(self, im): + """ + get Laplacian of Gaussian (LoG) image for a single band. + returns a np.ndarray + """ + LoG_im = ndimage.gaussian_laplace(im, sigma=self.sigma) + return LoG_im + + def get_glint_mask(self,im): + """ + get glint mask using laplacian of gaussian image. + We assume that water constituents and features follow a smooth continuum, + but glint pixels vary a lot spatially and in intensities + Note that for very extensive glint, this method may not work as well <--:TODO use U-net to identify glint mask + returns a np.ndarray + """ + LoG_im = ndimage.gaussian_laplace(im,sigma=self.sigma) + + # 如果存在水域掩膜，只在掩膜内计算阈值 + if self.water_mask is not None: + mask_bool = self.water_mask.astype(bool) + if mask_bool.any(): + # 只在掩膜内提取LoG值用于阈值计算 + LoG_masked = LoG_im[mask_bool] + # 将非掩膜区域设为极大值，确保不影响阈值计算 + LoG_for_thresh = LoG_im.copy() + LoG_for_thresh[~mask_bool] = LoG_masked.max() + 1 + else: + LoG_for_thresh = LoG_im + else: + LoG_for_thresh = LoG_im + + #threshold mask + if (self.glint_mask_method == "otsu"): + thresh = self.otsu_thresholding(LoG_for_thresh) + elif (self.glint_mask_method == "cdf"): + thresh = self.cdf_thresholding(LoG_for_thresh) + else: + raise ValueError('Enter only cdf or otsu as glint_mask_method') + # 使用更高效的方式创建mask，避免np.where的开销 + glint_mask = (LoG_im < thresh).astype(np.uint8) + + # 如果存在水域掩膜，将非水域区域设为0 + if self.water_mask is not None: + glint_mask = glint_mask * self.water_mask + + return glint_mask + + def get_est_background(self, im,k_size=5): + """ + :param im (np.ndarray): image of a band + estimate background spectra + returns a np.ndarray + """ + kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(k_size,k_size)) + dst = cv2.erode(im, kernel) + + return dst + + def optimise_correction_by_band(self,im,glint_mask,R_BG,bounds): + """ + :param im (np.ndarray): image of a band + :param glint_mask (np.ndarray): glint mask, where glint area is 1 and non-glint area is 0 + use brent method to get the optimimum b which minimises the variation (i.e. variance) in the entire image + returns regression slope b + """ + # 预计算常量，避免在优化函数中重复计算 + glint_mask_bool = glint_mask.astype(bool) + R_BG_flat = R_BG if isinstance(R_BG, (int, float)) else R_BG[glint_mask_bool] + + def optimise_b(b): + # 优化计算：只在glint区域计算校正 + if isinstance(R_BG, (int, float)): + im_corrected = im.copy() + im_corrected[glint_mask_bool] = im[glint_mask_bool] - glint_mask[glint_mask_bool] * (im[glint_mask_bool] / b - R_BG) + else: + im_corrected = im.copy() + im_corrected[glint_mask_bool] = im[glint_mask_bool] - glint_mask[glint_mask_bool] * (im[glint_mask_bool] / b - R_BG[glint_mask_bool]) + return np.var(im_corrected) + + res = minimize_scalar(optimise_b, bounds=bounds, method='bounded') + return res.x + + def divide_and_conquer(self): + """ + instead of computing b_list for each window, use the previous b_list to narrow the bounds, + because of the strong spatial autocorrelation, we know that the b (correction magnitude) cannot diff too much + this can optimise the run time + """ + + + def optimise_correction(self): + """ + returns a list of slope in band order i.e. 0,1,2,3,4,5,6,7,8,9 through optimisation + """ + b_list = [] + glint_mask_list = [] + est_background_list = [] + for i in range(self.n_bands): + glint_mask = self.get_glint_mask(self.im_aligned[:,:,i]) + glint_mask_list.append(glint_mask) + if self.estimate_background is True: + est_background = self.get_est_background(self.im_aligned[:,:,i]) + est_background_list.append(est_background) + else: + est_background = np.percentile(self.im_aligned[:,:,i], 5, interpolation='nearest') + est_background_list.append(est_background) + bounds = self.bounds[i] + b = self.optimise_correction_by_band(self.im_aligned[:,:,i],glint_mask,est_background,bounds) + b_list.append(b) + + # add attributes + self.b_list = b_list + self.glint_mask = glint_mask_list + self.est_background = est_background_list + + return b_list, glint_mask_list, est_background_list + + def _save_corrected_bands(self, corrected_bands): + """ + 保存校正后的波段到文件（BSQ格式，ENVI格式） + + :param corrected_bands: 校正后的波段列表 + """ + if not GDAL_AVAILABLE: + raise ImportError("GDAL未安装，无法保存影像文件") + + if self.output_path is None: + return + + # 确保输出目录存在 + output_dir = os.path.dirname(self.output_path) + if output_dir and not os.path.exists(output_dir): + os.makedirs(output_dir, exist_ok=True) + + # 将波段列表转换为数组 + corrected_array = np.stack(corrected_bands, axis=2) + + # 如果没有地理信息，使用默认值 + geotransform = (0, 1, 0, 0, 0, -1) + projection = "" + + # 强制使用ENVI格式（BSQ格式），确保文件扩展名为.bsq + base_path, ext = os.path.splitext(self.output_path) + # 如果扩展名不是.bsq，使用基础路径添加.bsq + if ext.lower() != '.bsq': + bsq_path = base_path + '.bsq' + else: + bsq_path = self.output_path + + # 使用ENVI驱动（默认就是BSQ格式） + driver = gdal.GetDriverByName('ENVI') + if driver is None: + raise ValueError("无法创建ENVI格式文件，ENVI驱动不可用") + + height, width, n_bands = corrected_array.shape + # 创建ENVI格式数据集（会自动生成.hdr文件） + dataset = driver.Create(bsq_path, width, height, n_bands, gdal.GDT_Float32) + if dataset is None: + raise ValueError(f"无法创建输出文件: {bsq_path}") + + try: + # 设置地理变换和投影 + if geotransform: + dataset.SetGeoTransform(geotransform) + if projection: + dataset.SetProjection(projection) + + # 写入每个波段（BSQ格式：按波段顺序存储） + for i in range(n_bands): + band = dataset.GetRasterBand(i + 1) + band.WriteArray(corrected_array[:, :, i]) + band.FlushCache() + finally: + dataset = None + + # 检查.hdr文件是否已创建 + hdr_path = bsq_path + '.hdr' + if os.path.exists(hdr_path): + print(f"校正后的图像已保存至: {bsq_path} (BSQ格式)") + print(f"头文件已保存至: {hdr_path}") + else: + print(f"校正后的图像已保存至: {bsq_path} (BSQ格式)") + print(f"警告: 未检测到.hdr文件，但GDAL应该已自动创建") + + def get_corrected_bands(self): + """ + 获取校正后的波段 + + :return: 校正后的波段列表 + """ + corrected_bands = [] + # 获取水域掩膜（如果存在） + water_mask_bool = self.water_mask.astype(bool) if self.water_mask is not None else None + + for i in range(self.n_bands): + im_band = self.im_aligned[:,:,i] + # 一次性计算mask和background，避免重复计算 + glint_mask = self.get_glint_mask(im_band) + background = self.get_est_background(im_band, k_size=5) + # 使用视图和原地操作减少内存 + im_corrected = im_band.copy() + glint_mask_bool = glint_mask.astype(bool) + im_corrected[glint_mask_bool] = background[glint_mask_bool] + + # 如果存在水域掩膜，确保只在水域内应用校正 + if water_mask_bool is not None: + # 只在水域掩膜内应用校正 + correction_mask = glint_mask_bool & water_mask_bool + im_corrected = np.where(correction_mask, background, im_band) + # 非水域区域保持原值 + im_corrected = np.where(water_mask_bool, im_corrected, im_band) + + corrected_bands.append(im_corrected) + + # 如果提供了输出路径，保存结果 + if self.output_path is not None: + self._save_corrected_bands(corrected_bands) + + return corrected_bands + +def correction_iterative(im_aligned,iter=3,bounds = [(1,2)],estimate_background=True,glint_mask_method="cdf",get_glint_mask=False,termination_thresh = 20, water_mask=None, output_path=None): + """ + :param im_aligned (np.ndarray): band aligned and calibrated & corrected reflectance image + :param iter (int or None): number of iterations to run the sugar algorithm. If None, termination conditions are automatically applied + :param bounds (list of tuples): to limit correction magnitude + :param get_glint_mask (np.ndarray): + :param water_mask (np.ndarray or str or None): 水域掩膜，1表示水域，0表示非水域 + 可以是numpy数组、栅格文件路径(.dat/.tif)或shapefile路径(.shp) + 如果为None，则处理全图 + :param output_path (str or None): 输出文件路径，如果提供则保存最后一次迭代的校正结果 + 如果为None，则不保存 + conducts iterative correction using SUGAR + """ + glint_image = im_aligned.copy() + corrected_images = [] + + if iter is None: + # termination conditions + relative_difference = lambda sd0,sd1: sd1/sd0*100 + marginal_difference = lambda sd1,sd2: (sd1-sd2)/sd1*100 + relative_diff_thresh = marginal_difference_thresh = termination_thresh + sd_og = np.var(im_aligned) + iter_count = 0 + sd_next = sd_og # 不需要copy，直接使用值 + max_iter = 100 # 添加最大迭代次数限制，防止无限循环 + + while ((relative_difference(sd_og,sd_next) > relative_diff_thresh) and iter_count < max_iter): + # do all the processing here + HM = SUGAR(glint_image,bounds,estimate_background=estimate_background, glint_mask_method=glint_mask_method, water_mask=water_mask) + corrected_bands = HM.get_corrected_bands() + glint_image = np.stack(corrected_bands,axis=2) + sd_temp = np.var(glint_image) + # 只在需要时保存中间结果，减少内存占用 + if get_glint_mask or iter_count == 0: + corrected_images.append(glint_image.copy()) + else: + corrected_images.append(glint_image) # 最后一次迭代的结果 + # save glint_mask + # if iter_count == 0 and get_glint_mask is True: + # glint_mask = np.stack(HM.glint_mask,axis=2) + if (marginal_difference(sd_next,sd_temp) 0: + _save_corrected_image(corrected_images[-1], output_path) + + else: + for i in range(iter): + HM = SUGAR(glint_image,bounds,estimate_background=estimate_background, glint_mask_method=glint_mask_method, water_mask=water_mask) + corrected_bands = HM.get_corrected_bands() + glint_image = np.stack(corrected_bands,axis=2) + # 只在最后一次迭代或需要时保存所有结果 + if i == iter - 1 or get_glint_mask: + corrected_images.append(glint_image.copy()) + else: + # 对于中间迭代，可以只保存引用（但要注意内存管理） + corrected_images.append(glint_image) + # save glint_mask + # if i == 0 and get_glint_mask is True: + # glint_mask = np.stack(HM.glint_mask,axis=2) + + # 如果提供了输出路径，保存最后一次迭代的结果 + if output_path is not None and len(corrected_images) > 0: + _save_corrected_image(corrected_images[-1], output_path) + + return corrected_images + +def _save_corrected_image(corrected_image, output_path): + """ + 保存校正后的图像到文件（用于correction_iterative函数，BSQ格式，ENVI格式） + + :param corrected_image: 校正后的图像数组，形状为(height, width, bands) + :param output_path: 输出文件路径 + """ + if not GDAL_AVAILABLE: + raise ImportError("GDAL未安装，无法保存影像文件") + + if output_path is None: + return + + # 确保输出目录存在 + output_dir = os.path.dirname(output_path) + if output_dir and not os.path.exists(output_dir): + os.makedirs(output_dir, exist_ok=True) + + # 如果没有地理信息，使用默认值 + geotransform = (0, 1, 0, 0, 0, -1) + projection = "" + + # 强制使用ENVI格式（BSQ格式），确保文件扩展名为.bsq + base_path, ext = os.path.splitext(output_path) + # 如果扩展名不是.bsq，使用基础路径添加.bsq + if ext.lower() != '.bsq': + bsq_path = base_path + '.bsq' + else: + bsq_path = output_path + + # 使用ENVI驱动（默认就是BSQ格式） + driver = gdal.GetDriverByName('ENVI') + if driver is None: + raise ValueError("无法创建ENVI格式文件，ENVI驱动不可用") + + height, width, n_bands = corrected_image.shape + # 创建ENVI格式数据集（会自动生成.hdr文件） + dataset = driver.Create(bsq_path, width, height, n_bands, gdal.GDT_Float32) + if dataset is None: + raise ValueError(f"无法创建输出文件: {bsq_path}") + + try: + # 设置地理变换和投影 + if geotransform: + dataset.SetGeoTransform(geotransform) + if projection: + dataset.SetProjection(projection) + + # 写入每个波段（BSQ格式：按波段顺序存储） + for i in range(n_bands): + band = dataset.GetRasterBand(i + 1) + band.WriteArray(corrected_image[:, :, i]) + band.FlushCache() + finally: + dataset = None + + # 检查.hdr文件是否已创建 + hdr_path = bsq_path + '.hdr' + if os.path.exists(hdr_path): + print(f"校正后的图像已保存至: {bsq_path} (BSQ格式)") + print(f"头文件已保存至: {hdr_path}") + else: + print(f"校正后的图像已保存至: {bsq_path} (BSQ格式)") + print(f"警告: 未检测到.hdr文件，但GDAL应该已自动创建") diff --git a/src/core/glint_removal/__init__.py b/src/core/glint_removal/__init__.py new file mode 100644 index 0000000..7c68785 --- /dev/null +++ b/src/core/glint_removal/__init__.py @@ -0,0 +1 @@ +# -*- coding: utf-8 -*- \ No newline at end of file diff --git a/src/core/glint_removal/get_spectral-test.py b/src/core/glint_removal/get_spectral-test.py new file mode 100644 index 0000000..ef6ccb0 --- /dev/null +++ b/src/core/glint_removal/get_spectral-test.py @@ -0,0 +1,926 @@ +from osgeo import gdal, osr +import numpy as np +import pandas as pd +import os +import spectral +from math import sin, cos, tan, sqrt, radians + +try: + from scipy.ndimage import distance_transform_edt + from scipy.spatial import cKDTree + SCIPY_AVAILABLE = True +except ImportError: + SCIPY_AVAILABLE = False + +# 启用GDAL异常处理 +osr.UseExceptions() + +# WGS84椭球参数 +WGS84_A = 6378137.0 # 长半轴（米） +WGS84_F = 1 / 298.257223563 # 扁率 +WGS84_E2 = WGS84_F * (2 - WGS84_F) # 第一偏心率平方 +WGS84_EP2 = WGS84_E2 / (1 - WGS84_E2) # 第二偏心率平方 +UTM_K0 = 0.9996 # UTM比例因子 +def pixel_to_geo(pixel_x, pixel_y, geotransform): + """ + 像素坐标转换为地图坐标 + """ + geo_x = geotransform[0] + pixel_x * geotransform[1] + pixel_y * geotransform[2] + geo_y = geotransform[3] + pixel_x * geotransform[4] + pixel_y * geotransform[5] + return geo_x, geo_y + + +def prepare_boundary_adjuster(boundary_mask): + """ + 为边界掩膜构建辅助结构，用于根据半径调整采样中心 + """ + if not SCIPY_AVAILABLE: + print("警告: 未安装SciPy，无法根据水体边界自动调整采样点位置。") + return None + + if boundary_mask is None: + return None + + boundary_bool = boundary_mask > 0 + if not np.any(boundary_bool): + print("警告: 边界掩膜中未检测到有效水域，无法调整采样点。") + return None + + distance_map = distance_transform_edt(boundary_bool.astype(np.uint8)) + return { + 'mask': boundary_bool, + 'distance_map': distance_map, + 'trees': {} + } + + +def _get_boundary_tree(adjuster, radius): + """ + 根据半径获取或构建适用的KDTree + """ + radius_key = float(radius) + if radius_key in adjuster['trees']: + return adjuster['trees'][radius_key] + + distance_map = adjuster['distance_map'] + valid_positions = np.column_stack(np.where(distance_map >= radius_key)) + if valid_positions.size == 0: + adjuster['trees'][radius_key] = None + return None + + tree = cKDTree(valid_positions) + adjuster['trees'][radius_key] = (tree, valid_positions) + return adjuster['trees'][radius_key] + + +def adjust_sampling_center(pixel_x, pixel_y, radius, adjuster): + """ + 如果采样半径范围超出水体边界，则将像素向内移动 + 直至采样区域完全位于水体内部（与边界相切） + """ + if adjuster is None or radius <= 0: + return pixel_x, pixel_y, False + + distance_map = adjuster['distance_map'] + mask = adjuster['mask'] + + if pixel_y < 0 or pixel_y >= distance_map.shape[0] or pixel_x < 0 or pixel_x >= distance_map.shape[1]: + return pixel_x, pixel_y, False + + if not mask[pixel_y, pixel_x]: + # 当前像素不在水域内，需要移动到最近的合法位置 + tree_info = _get_boundary_tree(adjuster, max(radius, 1)) + if tree_info is None: + return pixel_x, pixel_y, False + else: + if distance_map[pixel_y, pixel_x] >= radius: + return pixel_x, pixel_y, False + tree_info = _get_boundary_tree(adjuster, radius) + if tree_info is None: + # 没有任何可以容纳该半径的像素，直接返回原位置 + return pixel_x, pixel_y, False + + tree, valid_positions = tree_info + if tree is None or valid_positions.size == 0: + return pixel_x, pixel_y, False + + # 查询附近潜在位置 + max_candidates = min(64, len(valid_positions)) + distances, indices = tree.query([pixel_y, pixel_x], k=max_candidates) + + if np.isscalar(indices): + indices = [int(indices)] + else: + indices = np.atleast_1d(indices).astype(int) + + best_candidate = None + best_delta = None + + for idx in indices: + cy, cx = valid_positions[idx] + if distance_map[cy, cx] < radius: + continue + delta = distance_map[cy, cx] - radius + center_shift = (cx - pixel_x) ** 2 + (cy - pixel_y) ** 2 + score = (abs(delta), center_shift) + if best_candidate is None or score < best_delta: + best_candidate = (cx, cy) + best_delta = score + + if best_candidate is None: + # 没有找到满足条件的候选点 + return pixel_x, pixel_y, False + + return int(best_candidate[0]), int(best_candidate[1]), True + + + +def transform_coordinates(lon, lat, source_srs, target_srs): + """ + 坐标系转换 + + Args: + lon: 经度 + lat: 纬度 + source_srs: 源坐标系 + target_srs: 目标坐标系 + + Returns: + transformed_lon, transformed_lat: 转换后的坐标 + """ + # 创建坐标转换对象 + transform = osr.CoordinateTransformation(source_srs, target_srs) + + # 执行坐标转换 + point = transform.TransformPoint(lon, lat) + + return point[0], point[1] + + + +def geo_to_pixel(lon, lat, geotransform, dataset_srs=None): + """ + 地理坐标转换为像素坐标 + + Args: + lon: 经度 + lat: 纬度 + geotransform: 仿射变换参数 + dataset_srs: 数据集的空间参考系统（可选） + + Returns: + pixel_x, pixel_y: 像素坐标 + """ + # 使用仿射变换的逆变换将地理坐标转换为像素坐标 + x_origin = geotransform[0] + y_origin = geotransform[3] + pixel_width = geotransform[1] + pixel_height = geotransform[5] + + pixel_x = int((lon - x_origin) / pixel_width) + pixel_y = int((lat - y_origin) / pixel_height) + + return pixel_x, pixel_y + + +def get_pixel_spectrum_batch(dataset, pixel_x_array, pixel_y_array): + """ + 批量获取多个像素点的光谱数据（优化版本） + + Args: + dataset: GDAL数据集 + pixel_x_array: 像素X坐标数组 + pixel_y_array: 像素Y坐标数组 + + Returns: + spectrum_array: 光谱数据数组 (n_points, n_bands) + """ + n_points = len(pixel_x_array) + n_bands = dataset.RasterCount + + # 初始化输出数组 + spectrum_array = np.zeros((n_points, n_bands), dtype=np.float32) + + # 按波段批量读取（更高效） + for band_idx in range(n_bands): + band = dataset.GetRasterBand(band_idx + 1) # GDAL波段索引从1开始 + band_data = band.ReadAsArray() # 读取整个波段 + + # 批量提取像素值 + for i in range(n_points): + px, py = int(pixel_x_array[i]), int(pixel_y_array[i]) + if 0 <= px < band_data.shape[1] and 0 <= py < band_data.shape[0]: + spectrum_array[i, band_idx] = band_data[py, px] + else: + spectrum_array[i, band_idx] = np.nan + + return spectrum_array + + +def get_average_spectral_in_radius(dataset, center_x, center_y, radius, flare_mask=None, boundary_mask=None): + """ + 获取指定半径内的平均光谱，避开耀斑和边界区域 + + Args: + dataset: GDAL数据集 + center_x, center_y: 中心像素坐标 + radius: 半径（像素） + flare_mask: 耀斑掩膜数组（可选） + boundary_mask: 边界掩膜数组（可选） + + Returns: + 平均光谱值数组 + """ + num_bands = dataset.RasterCount + + # 计算采样区域边界 + x_start = max(0, center_x - radius) + x_end = min(dataset.RasterXSize, center_x + radius + 1) + y_start = max(0, center_y - radius) + y_end = min(dataset.RasterYSize, center_y + radius + 1) + + # 读取区域数据 + width = x_end - x_start + height = y_end - y_start + + if width <= 0 or height <= 0: + return np.zeros(num_bands) + + # 读取所有波段数据 + spectral_data = dataset.ReadAsArray(x_start, y_start, width, height) + if spectral_data is None: + return np.zeros(num_bands) + + # 确保数据是3维的 (bands, height, width) + if len(spectral_data.shape) == 2: + spectral_data = spectral_data.reshape(1, spectral_data.shape[0], spectral_data.shape[1]) + + # 创建圆形掩膜 + y_indices, x_indices = np.ogrid[:height, :width] + center_x_local = center_x - x_start + center_y_local = center_y - y_start + + # 计算距离掩膜 + distance_mask = ((x_indices - center_x_local) ** 2 + (y_indices - center_y_local) ** 2) <= radius ** 2 + + # 应用耀斑掩膜（如果提供） + if flare_mask is not None: + flare_region = flare_mask[y_start:y_end, x_start:x_end] + if flare_region.shape == distance_mask.shape: + distance_mask = distance_mask & (flare_region == 0) # 假设0表示无耀斑 + + # 应用边界掩膜（如果提供） + if boundary_mask is not None: + boundary_region = boundary_mask[y_start:y_end, x_start:x_end] + if boundary_region.shape == distance_mask.shape: + distance_mask = distance_mask & (boundary_region == 1) # 假设0表示无边界 + + # 计算平均光谱 + average_spectrum = np.zeros(num_bands) + valid_pixels = np.sum(distance_mask) + + if valid_pixels > 0: + for band in range(num_bands): + band_data = spectral_data[band, :, :] + # 排除无效值 + valid_data = band_data[distance_mask & (band_data != 0) & np.isfinite(band_data)] + if len(valid_data) > 0: + average_spectrum[band] = np.mean(valid_data) + + return average_spectrum + + +def load_mask_file(mask_path): + """ + 加载掩膜文件 + + Args: + mask_path: 掩膜文件路径（支持栅格文件如.dat/.tif等） + + Returns: + 掩膜数组 + """ + if mask_path is None or not os.path.exists(mask_path): + return None + + try: + # 使用gdal.OpenEx打开文件，明确指定为栅格文件 + # 如果文件是矢量格式，会返回None，避免"多图层"错误 + dataset = gdal.OpenEx(mask_path, gdal.OF_RASTER) + if dataset is None: + # 如果OpenEx失败，尝试使用Open（向后兼容） + dataset = gdal.Open(mask_path, gdal.GA_ReadOnly) + if dataset is None: + print(f"警告: 无法打开掩膜文件 {mask_path}，可能不是有效的栅格文件") + return None + + # 检查是否为栅格数据集（有RasterCount属性） + if not hasattr(dataset, 'RasterCount') or dataset.RasterCount == 0: + print(f"警告: {mask_path} 不是有效的栅格文件") + del dataset + return None + + mask_data = dataset.GetRasterBand(1).ReadAsArray() + del dataset + return mask_data + except Exception as e: + print(f"警告: 加载掩膜文件 {mask_path} 时出错: {str(e)}") + return None + + +def get_hdr_file_path(file_path): + """ + 获取HDR文件路径 + + Args: + file_path: 影像文件路径 + + Returns: + HDR文件路径 + """ + return os.path.splitext(file_path)[0] + ".hdr" + + +def calculate_utm_zone(longitude): + """ + 根据经度计算UTM分区号 + + Args: + longitude: 经度 + + Returns: + utm_zone: UTM分区号（1-60） + """ + # UTM分区从180度开始，每个分区6度 + utm_zone = int((longitude + 180) / 6) + 1 + # 确保分区号在有效范围内 + utm_zone = max(1, min(60, utm_zone)) + return utm_zone + + +def latlon_to_utm_math(lat_deg, lon_deg, zone=None): + """ + 使用数学公式将WGS84经纬度转换为UTM坐标 + + Args: + lat_deg: 纬度（度） + lon_deg: 经度（度） + zone: UTM分区号（如果为None，则根据经度自动计算） + + Returns: + easting, northing: UTM坐标（米） + """ + # 如果未指定分区，根据经度计算 + if zone is None: + zone = calculate_utm_zone(lon_deg) + + # 计算中央经线（度） + lon0 = (zone * 6 - 183) + lam0 = radians(lon0) + + # 转换为弧度 + phi = radians(lat_deg) + lam = radians(lon_deg) + + # 计算中间变量 + sinphi = sin(phi) + cosphi = cos(phi) + tanphi = tan(phi) + + # 计算卯酉圈曲率半径 + N = WGS84_A / sqrt(1 - WGS84_E2 * sinphi * sinphi) + + T = tanphi * tanphi + C = WGS84_EP2 * cosphi * cosphi + A = cosphi * (lam - lam0) + + # 计算子午圈弧长（使用Snyder公式） + M = (WGS84_A * ((1 - WGS84_E2/4 - 3*WGS84_E2**2/64 - 5*WGS84_E2**3/256) * phi + - (3*WGS84_E2/8 + 3*WGS84_E2**2/32 + 45*WGS84_E2**3/1024) * sin(2*phi) + + (15*WGS84_E2**2/256 + 45*WGS84_E2**3/1024) * sin(4*phi) + - (35*WGS84_E2**3/3072) * sin(6*phi))) + + # 计算东坐标（Easting） + E = (UTM_K0 * N * (A + (1 - T + C) * A**3 / 6 + + (5 - 18*T + T*T + 72*C - 58*WGS84_EP2) * A**5 / 120) + + 500000.0) + + # 计算北坐标（Northing） + # 对于南半球，需要添加10000000米偏移 + if lat_deg < 0: + Nn = (UTM_K0 * (M + N * tanphi * (A**2 / 2 + + (5 - T + 9*C + 4*C*C) * A**4 / 24 + + (61 - 58*T + T*T + 600*C - 330*WGS84_EP2) * A**6 / 720)) + + 10000000.0) + else: + Nn = (UTM_K0 * (M + N * tanphi * (A**2 / 2 + + (5 - T + 9*C + 4*C*C) * A**4 / 24 + + (61 - 58*T + T*T + 600*C - 330*WGS84_EP2) * A**6 / 720))) + + return E, Nn + + +def convert_to_utm(lon, lat, source_epsg=4326, target_epsg=None): + """ + 将坐标转换为UTM格式（使用数学公式，根据经度自动计算UTM分区） + + Args: + lon: 经度数组 + lat: 纬度数组 + source_epsg: 源坐标系EPSG代码，默认为4326 (WGS84地理坐标系) + target_epsg: 目标坐标系EPSG代码（如果为None，则根据经度自动计算；如果指定，则从EPSG代码提取分区号） + + Returns: + utm_x, utm_y: 转换后的UTM坐标（米） + """ + try: + # 检查源坐标系是否为WGS84 + if source_epsg != 4326: + print(f"警告: 数学公式转换仅支持WGS84 (EPSG:4326)，当前源坐标系为EPSG:{source_epsg}") + print("将尝试使用数学公式进行转换，但可能不准确") + + # 批量转换坐标 + utm_x = np.zeros_like(lon) + utm_y = np.zeros_like(lat) + + # 如果指定了目标EPSG，提取分区号 + fixed_zone = None + if target_epsg is not None: + # 从EPSG代码提取分区号 + # EPSG:32651 -> 51, EPSG:32751 -> 51 + if 32601 <= target_epsg <= 32660: + fixed_zone = target_epsg - 32600 + elif 32701 <= target_epsg <= 32760: + fixed_zone = target_epsg - 32700 + else: + print(f"警告: 无法从EPSG代码 {target_epsg} 提取UTM分区号，将根据经度自动计算") + + # 向量化处理：标记无效坐标 + invalid_mask = (np.isnan(lon) | np.isnan(lat) | + (lon < -180) | (lon > 180) | + (lat < -90) | (lat > 90)) + + # 统计无效坐标 + invalid_count = np.sum(invalid_mask) + if invalid_count > 0: + invalid_indices = np.where(invalid_mask)[0] + print(f"警告: 发现 {invalid_count} 个无效坐标点，将跳过") + for idx in invalid_indices[:10]: # 只打印前10个 + print(f" 坐标点 {idx + 1}: 经度={lon[idx]}, 纬度={lat[idx]}") + if invalid_count > 10: + print(f" ... 还有 {invalid_count - 10} 个无效坐标点") + + # 对有效坐标进行转换 + valid_mask = ~invalid_mask + if np.any(valid_mask): + valid_lon = lon[valid_mask] + valid_lat = lat[valid_mask] + valid_indices = np.where(valid_mask)[0] + + # 计算UTM分区（向量化） + if fixed_zone is not None: + zones = np.full(len(valid_lon), fixed_zone) + else: + zones = np.array([calculate_utm_zone(lon_val) for lon_val in valid_lon]) + + # 批量转换（仍需要循环，但减少了开销） + for i, (lat_val, lon_val, zone) in enumerate(zip(valid_lat, valid_lon, zones)): + try: + E, Nn = latlon_to_utm_math(lat_val, lon_val, zone) + if not (np.isnan(E) or np.isnan(Nn) or np.isinf(E) or np.isinf(Nn)): + utm_x[valid_indices[i]] = E + utm_y[valid_indices[i]] = Nn + else: + utm_x[valid_indices[i]] = np.nan + utm_y[valid_indices[i]] = np.nan + except Exception as e: + utm_x[valid_indices[i]] = np.nan + utm_y[valid_indices[i]] = np.nan + + # 设置无效坐标为NaN + utm_x[invalid_mask] = np.nan + utm_y[invalid_mask] = np.nan + + return utm_x, utm_y + + except Exception as e: + print(f"坐标转换初始化失败: {str(e)}") + return np.full_like(lon, np.nan), np.full_like(lat, np.nan) + + +def convert_to_utm51n(lon, lat, source_epsg=4326): + """ + 将坐标转换为WGS84 UTM 51N格式（保留向后兼容性） + + Args: + lon: 经度数组 + lat: 纬度数组 + source_epsg: 源坐标系EPSG代码，默认为4326 (WGS84地理坐标系) + + Returns: + utm_x, utm_y: 转换后的UTM坐标（米） + """ + # 使用新的转换函数，但强制使用UTM 51N + return convert_to_utm(lon, lat, source_epsg, target_epsg=32651) + + +def get_spectral_in_coor(imgpath, coorpath, outpath, radius=0, flare_path=None, boundary_path=None, source_epsg=4326): + """ + 获取给定坐标的光谱曲线，并将坐标转换为UTM格式（根据经度自动计算UTM分区） + + Args: + imgpath: 影像文件路径（BIL格式） + coorpath: 坐标文件路径（CSV格式，第1、2列为纬度和经度） + outpath: 输出文件路径（CSV格式） + radius: 采样半径（像素） + flare_path: 耀斑文件路径（可选） + boundary_path: 边界文件路径（可选） + source_epsg: 源坐标系EPSG代码，默认为4326 (WGS84地理坐标系) + """ + # 读取原始坐标文件（CSV格式） + coor_df = None + coor_data = None + + # 尝试不同的编码方式读取CSV文件 + encodings = ['utf-8', 'gbk', 'gb2312', 'latin1', 'cp1252'] + + for encoding in encodings: + try: + # 尝试读取CSV文件 + coor_df = pd.read_csv(coorpath, encoding=encoding) + # 只提取数值数据，跳过表头 + coor_data = coor_df.select_dtypes(include=[np.number]).values + + # 如果没有数值列，尝试转换所有列（跳过第一行表头） + if coor_data.shape[1] == 0: + # 尝试从第二行开始读取，第一行作为表头 + coor_df = pd.read_csv(coorpath, encoding=encoding, header=0) + # 尝试将所有列转换为数值 + numeric_df = coor_df.apply(pd.to_numeric, errors='coerce') + # 删除全为NaN的行（通常是表头转换失败的行） + numeric_df = numeric_df.dropna(how='all') + coor_data = numeric_df.values + + print(f"成功使用 {encoding} 编码读取文件") + break + + except Exception as e: + print(f"使用 {encoding} 编码读取失败: {str(e)}") + continue + + # 如果所有编码都失败，尝试numpy读取 + if coor_data is None: + try: + print("尝试使用numpy读取数值数据...") + # 跳过第一行（表头），只读取数值 + coor_data = np.loadtxt(coorpath, delimiter=",", skiprows=1) + except: + try: + coor_data = np.loadtxt(coorpath, delimiter="\t", skiprows=1) + except Exception as e: + raise Exception(f"无法读取坐标文件，请检查文件格式: {str(e)}") + + if len(coor_data.shape) == 1: + coor_data = coor_data.reshape(1, -1) + + # 检查数据有效性 + if coor_data is None or coor_data.shape[1] < 2: + raise Exception("坐标文件格式错误：需要至少2列数据（第1列为纬度，第2列为经度）") + + print(f"成功读取坐标文件，共 {coor_data.shape[0]} 行，{coor_data.shape[1]} 列") + print(f"数据预览（前3行）：") + for i in range(min(3, coor_data.shape[0])): + print(f" 行{i + 1}: {coor_data[i, :min(5, coor_data.shape[1])]}") # 只显示前5列 + + # 提取原始坐标 + lat_array = coor_data[:, 0] # 第1列是纬度 + lon_array = coor_data[:, 1] # 第2列是经度 + + print(f"\n=== 原始坐标信息 ===") + print(f"原始坐标范围: 经度 {np.min(lon_array):.6f} ~ {np.max(lon_array):.6f}, 纬度 {np.min(lat_array):.6f} ~ {np.max(lat_array):.6f}") + + # 坐标转换为UTM（根据经度自动计算UTM分区） + print("正在进行坐标转换...") + utm_x, utm_y = convert_to_utm(lon_array, lat_array, source_epsg, target_epsg=None) + + # 检查转换结果 + valid_utm_mask = ~(np.isnan(utm_x) | np.isnan(utm_y) | np.isinf(utm_x) | np.isinf(utm_y)) + valid_count = np.sum(valid_utm_mask) + + if valid_count > 0: + print(f"转换后UTM坐标范围: X {np.nanmin(utm_x):.2f} ~ {np.nanmax(utm_x):.2f}, Y {np.nanmin(utm_y):.2f} ~ {np.nanmax(utm_y):.2f}") + print(f"成功转换 {valid_count}/{len(utm_x)} 个坐标点") + else: + print("警告: 所有UTM坐标转换都失败了，将尝试使用原始经纬度坐标进行像素坐标转换") + + # 打开影像数据集 + dataset = gdal.Open(imgpath) + im_width = dataset.RasterXSize # 栅格矩阵的列数 + im_height = dataset.RasterYSize # 栅格矩阵的行数 + num_bands = dataset.RasterCount # 栅格矩阵的波段数 + geotransform = dataset.GetGeoTransform() # 仿射矩阵 + im_proj = dataset.GetProjection() # 地图投影信息 + + print(f"影像尺寸: {im_width} x {im_height}, 波段数: {num_bands}") + print(f"仿射变换参数: {geotransform}") + + print("\n=== 开始光谱提取 ===") + + # 加载掩膜文件 + flare_mask = load_mask_file(flare_path) + boundary_mask = load_mask_file(boundary_path) + boundary_adjuster = None + if boundary_mask is not None and radius > 0: + boundary_adjuster = prepare_boundary_adjuster(boundary_mask) + if boundary_adjuster is None: + print("提示: 无法构建边界调整器，采样点将不会根据水体边界进行内移。") + + # 获取数据集的空间参考系统 + dataset_srs = dataset.GetSpatialRef() + + # 准备输出数组，在原有数据基础上添加UTM坐标和光谱列 + original_cols = coor_data.shape[1] + # 添加UTM坐标列（2列）和光谱列（num_bands列） + new_columns = np.zeros((coor_data.shape[0], 2 + num_bands)) + coor_spectral = np.hstack((coor_data, new_columns)) + + # 将UTM坐标添加到数据中（会在采样点调整后再更新为最终位置） + coor_spectral[:, original_cols] = utm_x # 初始UTM X坐标 + coor_spectral[:, original_cols + 1] = utm_y # 初始UTM Y坐标 + + print(f"处理 {coor_data.shape[0]} 个坐标点...") + + # 如果UTM转换失败，尝试使用影像坐标系进行转换 + use_utm_fallback = False + if valid_count == 0 and dataset_srs is not None: + print("尝试使用影像坐标系进行坐标转换...") + try: + source_srs = osr.SpatialReference() + source_srs.ImportFromEPSG(source_epsg) + transform_to_image = osr.CoordinateTransformation(source_srs, dataset_srs) + use_utm_fallback = True + except: + use_utm_fallback = False + + # 批量转换所有坐标点为像素坐标 + pixel_x_array = np.zeros(coor_data.shape[0], dtype=np.int32) + pixel_y_array = np.zeros(coor_data.shape[0], dtype=np.int32) + valid_pixel_mask = np.zeros(coor_data.shape[0], dtype=bool) + + # 批量计算像素坐标 + for i in range(coor_data.shape[0]): + # 优先使用UTM坐标，如果无效则使用备用方案 + utm_x_point = utm_x[i] + utm_y_point = utm_y[i] + + # 检查UTM坐标是否有效 + if np.isnan(utm_x_point) or np.isnan(utm_y_point) or np.isinf(utm_x_point) or np.isinf(utm_y_point): + # 如果UTM转换失败，尝试使用影像坐标系 + if use_utm_fallback: + try: + lon_point = lon_array[i] + lat_point = lat_array[i] + if not (np.isnan(lon_point) or np.isnan(lat_point)): + # 转换为影像坐标系 + img_coords = transform_to_image.TransformPoint(lon_point, lat_point) + pixel_x, pixel_y = geo_to_pixel(img_coords[0], img_coords[1], geotransform, dataset_srs) + # 更新UTM坐标列（使用影像坐标系坐标） + coor_spectral[i, original_cols] = img_coords[0] + coor_spectral[i, original_cols + 1] = img_coords[1] + else: + print(f"跳过坐标点 {i + 1}: 坐标无效") + coor_spectral[i, original_cols + 2:] = np.zeros(num_bands) + continue + except Exception as e: + # 如果影像坐标系转换也失败，尝试直接使用经纬度 + try: + lon_point = lon_array[i] + lat_point = lat_array[i] + if not (np.isnan(lon_point) or np.isnan(lat_point)): + pixel_x, pixel_y = geo_to_pixel(lon_point, lat_point, geotransform, dataset_srs) + # 保留原始经纬度作为坐标 + coor_spectral[i, original_cols] = lon_point + coor_spectral[i, original_cols + 1] = lat_point + else: + print(f"跳过坐标点 {i + 1}: 坐标无效") + coor_spectral[i, original_cols + 2:] = np.zeros(num_bands) + continue + except: + print(f"跳过坐标点 {i + 1}: 所有坐标转换方式都失败") + coor_spectral[i, original_cols + 2:] = np.zeros(num_bands) + continue + else: + # 尝试直接使用经纬度坐标 + try: + lon_point = lon_array[i] + lat_point = lat_array[i] + if not (np.isnan(lon_point) or np.isnan(lat_point)): + pixel_x, pixel_y = geo_to_pixel(lon_point, lat_point, geotransform, dataset_srs) + # 保留原始经纬度作为坐标 + coor_spectral[i, original_cols] = lon_point + coor_spectral[i, original_cols + 1] = lat_point + else: + print(f"跳过坐标点 {i + 1}: 坐标无效") + coor_spectral[i, original_cols + 2:] = np.zeros(num_bands) + continue + except: + print(f"跳过坐标点 {i + 1}: 坐标转换失败") + coor_spectral[i, original_cols + 2:] = np.zeros(num_bands) + continue + else: + # UTM坐标转换为像素坐标 + pixel_x, pixel_y = geo_to_pixel(utm_x_point, utm_y_point, geotransform, dataset_srs) + + # 存储像素坐标 + pixel_x_array[i] = pixel_x + pixel_y_array[i] = pixel_y + + # 根据水体边界调整采样中心 + moved = False + original_pixel_x, original_pixel_y = pixel_x, pixel_y + if boundary_adjuster is not None and radius > 0: + new_pixel_x, new_pixel_y, moved = adjust_sampling_center(pixel_x, pixel_y, radius, boundary_adjuster) + if moved: + pixel_x, pixel_y = new_pixel_x, new_pixel_y + if i < 10 or (i % 100 == 0): + print(f" 采样点 {i + 1} 调整至水体内部: ({original_pixel_x}, {original_pixel_y}) -> ({pixel_x}, {pixel_y})") + + pixel_x_array[i] = pixel_x + pixel_y_array[i] = pixel_y + + # 检查坐标是否在影像范围内（使用调整后的坐标） + if 0 <= pixel_x < im_width and 0 <= pixel_y < im_height: + valid_pixel_mask[i] = True + # 更新UTM列为最终采样点的实际地图坐标 + geo_x, geo_y = pixel_to_geo(pixel_x, pixel_y, geotransform) + coor_spectral[i, original_cols] = geo_x + coor_spectral[i, original_cols + 1] = geo_y + else: + valid_pixel_mask[i] = False + if i < 10 or (i % 100 == 0): # 只打印前10个或每100个打印一次 + print(f"警告: 坐标点 {i + 1} (UTM X:{utm_x_point:.2f}, Y:{utm_y_point:.2f}) 超出影像范围") + + # 批量提取光谱数据（优化：减少I/O操作） + print(f"批量提取光谱数据... (有效坐标点: {np.sum(valid_pixel_mask)})") + + if radius > 0: + # 半径采样模式：需要逐个处理 + for i in range(coor_data.shape[0]): + if valid_pixel_mask[i]: + spectrum = get_average_spectral_in_radius( + dataset, pixel_x_array[i], pixel_y_array[i], radius, flare_mask, boundary_mask + ) + coor_spectral[i, original_cols + 2:] = spectrum + else: + coor_spectral[i, original_cols + 2:] = np.zeros(num_bands) + else: + # 单点采样模式：批量读取（优化） + # 预读取所有波段数据（如果内存允许） + try: + # 尝试读取所有波段到内存（适用于内存充足的情况） + print("正在预加载所有波段数据到内存（优化模式）...") + all_bands_data = [] + for band_idx in range(num_bands): + band = dataset.GetRasterBand(band_idx + 1) + band_data = band.ReadAsArray() + all_bands_data.append(band_data) + all_bands_data = np.array(all_bands_data) # shape: (bands, height, width) + print("预加载完成，开始批量提取像素值...") + + # 批量提取像素值 + for i in range(coor_data.shape[0]): + if valid_pixel_mask[i]: + px, py = int(pixel_x_array[i]), int(pixel_y_array[i]) + # GDAL读取的数组形状是 (bands, height, width)，像素坐标 (x,y) 对应数组索引 [:, y, x] + # 注意：py是行（y坐标），px是列（x坐标） + if 0 <= px < all_bands_data.shape[2] and 0 <= py < all_bands_data.shape[1]: + spectrum = all_bands_data[:, py, px] # 直接索引，非常快 + coor_spectral[i, original_cols + 2:] = spectrum + else: + coor_spectral[i, original_cols + 2:] = np.zeros(num_bands) + else: + coor_spectral[i, original_cols + 2:] = np.zeros(num_bands) + + # 释放内存 + del all_bands_data + print("批量提取完成") + + except MemoryError: + # 如果内存不足，回退到逐个波段读取 + print("内存不足，使用逐个波段读取模式...") + for i in range(coor_data.shape[0]): + if valid_pixel_mask[i]: + px, py = pixel_x_array[i], pixel_y_array[i] + spectrum = np.zeros(num_bands) + for band_idx in range(num_bands): + band = dataset.GetRasterBand(band_idx + 1) + spectrum[band_idx] = band.ReadAsArray(px, py, 1, 1)[0, 0] + coor_spectral[i, original_cols + 2:] = spectrum + else: + coor_spectral[i, original_cols + 2:] = np.zeros(num_bands) + + del dataset + + # 创建DataFrame用于CSV输出 + # 去除前两列坐标列（纬度和经度）和UTM列 + try: + # 如果原始数据有列名，使用原始列名（跳过前两列） + if coor_df is not None and hasattr(coor_df, 'columns'): + # 跳过前两列（经纬度），从第3列开始 + if len(coor_df.columns) >= original_cols: + # 保留第3列及之后的原始列（如果有的话） + if original_cols > 2: + original_columns = list(coor_df.columns[2:original_cols]) + else: + original_columns = [] + else: + # 如果原始列数不足，只保留存在的列（跳过前两列） + if len(coor_df.columns) > 2: + original_columns = list(coor_df.columns[2:]) + else: + original_columns = [] + else: + # 如果没有列名，只保留第3列及之后的列（如果有的话） + if original_cols > 2: + original_columns = ["col_" + str(j + 1) for j in range(2, original_cols)] + else: + original_columns = [] + except: + # 异常处理：只保留第3列及之后的列（如果有的话） + if original_cols > 2: + original_columns = ["col_" + str(j + 1) for j in range(2, original_cols)] + else: + original_columns = [] + + # 读取波长信息，用作光谱列名 + wavelengths = None + try: + in_hdr_dict = spectral.envi.read_envi_header(get_hdr_file_path(imgpath)) + wavelengths = np.array(in_hdr_dict['wavelength']).astype('float64') + # 将波长值转换为字符串作为列名 + spectral_columns = [str(wl) for wl in wavelengths] + print(f"成功读取波长信息，共 {len(spectral_columns)} 个波段") + except Exception as e: + print(f"警告: 无法读取波长信息 ({str(e)})，使用默认列名 band_1, band_2, ...") + spectral_columns = ["band_" + str(j + 1) for j in range(num_bands)] + + # 构建输出列名（不包含前两列坐标列和UTM列） + all_columns = original_columns + spectral_columns + + # 从coor_spectral中提取需要输出的列 + # 跳过前两列（经纬度）和UTM列，只保留： + # - 第3列到第original_cols列（如果有的话） + # - 光谱数据列（从original_cols+2开始） + output_data = [] + if original_cols > 2: + # 保留第3列到第original_cols列 + output_data.append(coor_spectral[:, 2:original_cols]) + # 保留光谱数据列（从original_cols+2开始） + output_data.append(coor_spectral[:, original_cols + 2:]) + + # 合并数据 + if len(output_data) > 0: + output_array = np.hstack(output_data) if len(output_data) > 1 else output_data[0] + else: + # 如果没有原始列，只输出光谱数据 + output_array = coor_spectral[:, original_cols + 2:] + + # 创建结果DataFrame + result_df = pd.DataFrame(output_array, columns=all_columns) + + # 保存为CSV格式 + result_df.to_csv(outpath, index=False, float_format='%.6f') + print(f"结果已保存到CSV文件: {outpath}") + + return coor_spectral + + +# 直接运行示例 +if __name__ == '__main__': + # 在这里直接设置参数 + imgpath = r"D:\BaiduNetdiskDownload\yaobao\result3.bsq"# BIL格式影像文件路径 + coorpath = r"E:\code\WQ\封装\work_dir\4_processed_data\processed_data.csv"# CSV格式坐标文件路径（第1、2列为纬度和经度） + output_path = r"E:\code\WQ\封装\test/yangdian_output.csv" # CSV格式输出文件路径 + + radius = 5 # 采样半径（像素），0表示单点采样，>0表示半径内平均 + flare_path = r"E:\code\WQ\封装\work_dir\2_glint\severe_glint_area.dat" # 耀斑掩膜文件路径（可选，None表示不使用） + boundary_path ="D:\BaiduNetdiskDownload\yaobao\water_mask.dat" # 边界掩膜文件路径（可选，None表示不使用） + source_epsg = 4326 # 源坐标系EPSG代码，默认为4326 (WGS84地理坐标系) + + verbose = True # 是否启用详细模式 + + if verbose: + print(f"影像文件: {imgpath}") + print(f"坐标文件: {coorpath}") + print(f"输出文件: {output_path}") + print(f"采样半径: {radius}") + if flare_path: + print(f"耀斑掩膜: {flare_path}") + if boundary_path: + print(f"边界掩膜: {boundary_path}") + if source_epsg: + print(f"指定坐标系: EPSG:{source_epsg}") + + tmp = get_spectral_in_coor(imgpath, coorpath, output_path, + radius, flare_path, boundary_path, source_epsg) + diff --git a/src/core/glint_removal/get_spectral.py b/src/core/glint_removal/get_spectral.py new file mode 100644 index 0000000..9ea1093 --- /dev/null +++ b/src/core/glint_removal/get_spectral.py @@ -0,0 +1,785 @@ +from osgeo import gdal, osr +import numpy as np +import pandas as pd +import os +import spectral +from math import sin, cos, tan, sqrt, radians + +# 启用GDAL异常处理 +osr.UseExceptions() + +# WGS84椭球参数 +WGS84_A = 6378137.0 # 长半轴（米） +WGS84_F = 1 / 298.257223563 # 扁率 +WGS84_E2 = WGS84_F * (2 - WGS84_F) # 第一偏心率平方 +WGS84_EP2 = WGS84_E2 / (1 - WGS84_E2) # 第二偏心率平方 +UTM_K0 = 0.9996 # UTM比例因子 + + +def transform_coordinates(lon, lat, source_srs, target_srs): + """ + 坐标系转换 + + Args: + lon: 经度 + lat: 纬度 + source_srs: 源坐标系 + target_srs: 目标坐标系 + + Returns: + transformed_lon, transformed_lat: 转换后的坐标 + """ + # 创建坐标转换对象 + transform = osr.CoordinateTransformation(source_srs, target_srs) + + # 执行坐标转换 + point = transform.TransformPoint(lon, lat) + + return point[0], point[1] + + + +def geo_to_pixel(lon, lat, geotransform, dataset_srs=None): + """ + 地理坐标转换为像素坐标 + + Args: + lon: 经度 + lat: 纬度 + geotransform: 仿射变换参数 + dataset_srs: 数据集的空间参考系统（可选） + + Returns: + pixel_x, pixel_y: 像素坐标 + """ + # 使用仿射变换的逆变换将地理坐标转换为像素坐标 + x_origin = geotransform[0] + y_origin = geotransform[3] + pixel_width = geotransform[1] + pixel_height = geotransform[5] + + pixel_x = int((lon - x_origin) / pixel_width) + pixel_y = int((lat - y_origin) / pixel_height) + + return pixel_x, pixel_y + + +def get_pixel_spectrum_batch(dataset, pixel_x_array, pixel_y_array): + """ + 批量获取多个像素点的光谱数据（优化版本） + + Args: + dataset: GDAL数据集 + pixel_x_array: 像素X坐标数组 + pixel_y_array: 像素Y坐标数组 + + Returns: + spectrum_array: 光谱数据数组 (n_points, n_bands) + """ + n_points = len(pixel_x_array) + n_bands = dataset.RasterCount + + # 初始化输出数组 + spectrum_array = np.zeros((n_points, n_bands), dtype=np.float32) + + # 按波段批量读取（更高效） + for band_idx in range(n_bands): + band = dataset.GetRasterBand(band_idx + 1) # GDAL波段索引从1开始 + band_data = band.ReadAsArray() # 读取整个波段 + + # 批量提取像素值 + for i in range(n_points): + px, py = int(pixel_x_array[i]), int(pixel_y_array[i]) + if 0 <= px < band_data.shape[1] and 0 <= py < band_data.shape[0]: + spectrum_array[i, band_idx] = band_data[py, px] + else: + spectrum_array[i, band_idx] = np.nan + + return spectrum_array + + +def get_average_spectral_in_radius(dataset, center_x, center_y, radius, flare_mask=None, boundary_mask=None): + """ + 获取指定半径内的平均光谱，避开耀斑和边界区域 + + Args: + dataset: GDAL数据集 + center_x, center_y: 中心像素坐标 + radius: 半径（像素） + flare_mask: 耀斑掩膜数组（可选） + boundary_mask: 边界掩膜数组（可选） + + Returns: + 平均光谱值数组 + """ + num_bands = dataset.RasterCount + + # 计算采样区域边界 + x_start = max(0, center_x - radius) + x_end = min(dataset.RasterXSize, center_x + radius + 1) + y_start = max(0, center_y - radius) + y_end = min(dataset.RasterYSize, center_y + radius + 1) + + # 读取区域数据 + width = x_end - x_start + height = y_end - y_start + + if width <= 0 or height <= 0: + return np.zeros(num_bands) + + # 读取所有波段数据 + spectral_data = dataset.ReadAsArray(x_start, y_start, width, height) + if spectral_data is None: + return np.zeros(num_bands) + + # 确保数据是3维的 (bands, height, width) + if len(spectral_data.shape) == 2: + spectral_data = spectral_data.reshape(1, spectral_data.shape[0], spectral_data.shape[1]) + + # 创建圆形掩膜 + y_indices, x_indices = np.ogrid[:height, :width] + center_x_local = center_x - x_start + center_y_local = center_y - y_start + + # 计算距离掩膜 + distance_mask = ((x_indices - center_x_local) ** 2 + (y_indices - center_y_local) ** 2) <= radius ** 2 + + # 应用耀斑掩膜（如果提供） + if flare_mask is not None: + flare_region = flare_mask[y_start:y_end, x_start:x_end] + if flare_region.shape == distance_mask.shape: + distance_mask = distance_mask & (flare_region == 0) # 假设0表示无耀斑 + + # 应用边界掩膜（如果提供） + if boundary_mask is not None: + boundary_region = boundary_mask[y_start:y_end, x_start:x_end] + if boundary_region.shape == distance_mask.shape: + distance_mask = distance_mask & (boundary_region == 1) # 假设0表示无边界 + + # 计算平均光谱 + average_spectrum = np.zeros(num_bands) + valid_pixels = np.sum(distance_mask) + + if valid_pixels > 0: + for band in range(num_bands): + band_data = spectral_data[band, :, :] + # 排除无效值 + valid_data = band_data[distance_mask & (band_data != 0) & np.isfinite(band_data)] + if len(valid_data) > 0: + average_spectrum[band] = np.mean(valid_data) + + return average_spectrum + + +def load_mask_file(mask_path): + """ + 加载掩膜文件 + + Args: + mask_path: 掩膜文件路径（支持栅格文件如.dat/.tif等） + + Returns: + 掩膜数组 + """ + if mask_path is None or not os.path.exists(mask_path): + return None + + try: + # 使用gdal.OpenEx打开文件，明确指定为栅格文件 + # 如果文件是矢量格式，会返回None，避免"多图层"错误 + dataset = gdal.OpenEx(mask_path, gdal.OF_RASTER) + if dataset is None: + # 如果OpenEx失败，尝试使用Open（向后兼容） + dataset = gdal.Open(mask_path, gdal.GA_ReadOnly) + if dataset is None: + print(f"警告: 无法打开掩膜文件 {mask_path}，可能不是有效的栅格文件") + return None + + # 检查是否为栅格数据集（有RasterCount属性） + if not hasattr(dataset, 'RasterCount') or dataset.RasterCount == 0: + print(f"警告: {mask_path} 不是有效的栅格文件") + del dataset + return None + + mask_data = dataset.GetRasterBand(1).ReadAsArray() + del dataset + return mask_data + except Exception as e: + print(f"警告: 加载掩膜文件 {mask_path} 时出错: {str(e)}") + return None + + +def get_hdr_file_path(file_path): + """ + 获取HDR文件路径 + + Args: + file_path: 影像文件路径 + + Returns: + HDR文件路径 + """ + return os.path.splitext(file_path)[0] + ".hdr" + + +def calculate_utm_zone(longitude): + """ + 根据经度计算UTM分区号 + + Args: + longitude: 经度 + + Returns: + utm_zone: UTM分区号（1-60） + """ + # UTM分区从180度开始，每个分区6度 + utm_zone = int((longitude + 180) / 6) + 1 + # 确保分区号在有效范围内 + utm_zone = max(1, min(60, utm_zone)) + return utm_zone + + +def latlon_to_utm_math(lat_deg, lon_deg, zone=None): + """ + 使用数学公式将WGS84经纬度转换为UTM坐标 + + Args: + lat_deg: 纬度（度） + lon_deg: 经度（度） + zone: UTM分区号（如果为None，则根据经度自动计算） + + Returns: + easting, northing: UTM坐标（米） + """ + # 如果未指定分区，根据经度计算 + if zone is None: + zone = calculate_utm_zone(lon_deg) + + # 计算中央经线（度） + lon0 = (zone * 6 - 183) + lam0 = radians(lon0) + + # 转换为弧度 + phi = radians(lat_deg) + lam = radians(lon_deg) + + # 计算中间变量 + sinphi = sin(phi) + cosphi = cos(phi) + tanphi = tan(phi) + + # 计算卯酉圈曲率半径 + N = WGS84_A / sqrt(1 - WGS84_E2 * sinphi * sinphi) + + T = tanphi * tanphi + C = WGS84_EP2 * cosphi * cosphi + A = cosphi * (lam - lam0) + + # 计算子午圈弧长（使用Snyder公式） + M = (WGS84_A * ((1 - WGS84_E2/4 - 3*WGS84_E2**2/64 - 5*WGS84_E2**3/256) * phi + - (3*WGS84_E2/8 + 3*WGS84_E2**2/32 + 45*WGS84_E2**3/1024) * sin(2*phi) + + (15*WGS84_E2**2/256 + 45*WGS84_E2**3/1024) * sin(4*phi) + - (35*WGS84_E2**3/3072) * sin(6*phi))) + + # 计算东坐标（Easting） + E = (UTM_K0 * N * (A + (1 - T + C) * A**3 / 6 + + (5 - 18*T + T*T + 72*C - 58*WGS84_EP2) * A**5 / 120) + + 500000.0) + + # 计算北坐标（Northing） + # 对于南半球，需要添加10000000米偏移 + if lat_deg < 0: + Nn = (UTM_K0 * (M + N * tanphi * (A**2 / 2 + + (5 - T + 9*C + 4*C*C) * A**4 / 24 + + (61 - 58*T + T*T + 600*C - 330*WGS84_EP2) * A**6 / 720)) + + 10000000.0) + else: + Nn = (UTM_K0 * (M + N * tanphi * (A**2 / 2 + + (5 - T + 9*C + 4*C*C) * A**4 / 24 + + (61 - 58*T + T*T + 600*C - 330*WGS84_EP2) * A**6 / 720))) + + return E, Nn + + +def convert_to_utm(lon, lat, source_epsg=4326, target_epsg=None): + """ + 将坐标转换为UTM格式（使用数学公式，根据经度自动计算UTM分区） + + Args: + lon: 经度数组 + lat: 纬度数组 + source_epsg: 源坐标系EPSG代码，默认为4326 (WGS84地理坐标系) + target_epsg: 目标坐标系EPSG代码（如果为None，则根据经度自动计算；如果指定，则从EPSG代码提取分区号） + + Returns: + utm_x, utm_y: 转换后的UTM坐标（米） + """ + try: + # 检查源坐标系是否为WGS84 + if source_epsg != 4326: + print(f"警告: 数学公式转换仅支持WGS84 (EPSG:4326)，当前源坐标系为EPSG:{source_epsg}") + print("将尝试使用数学公式进行转换，但可能不准确") + + # 批量转换坐标 + utm_x = np.zeros_like(lon) + utm_y = np.zeros_like(lat) + + # 如果指定了目标EPSG，提取分区号 + fixed_zone = None + if target_epsg is not None: + # 从EPSG代码提取分区号 + # EPSG:32651 -> 51, EPSG:32751 -> 51 + if 32601 <= target_epsg <= 32660: + fixed_zone = target_epsg - 32600 + elif 32701 <= target_epsg <= 32760: + fixed_zone = target_epsg - 32700 + else: + print(f"警告: 无法从EPSG代码 {target_epsg} 提取UTM分区号，将根据经度自动计算") + + # 向量化处理：标记无效坐标 + invalid_mask = (np.isnan(lon) | np.isnan(lat) | + (lon < -180) | (lon > 180) | + (lat < -90) | (lat > 90)) + + # 统计无效坐标 + invalid_count = np.sum(invalid_mask) + if invalid_count > 0: + invalid_indices = np.where(invalid_mask)[0] + print(f"警告: 发现 {invalid_count} 个无效坐标点，将跳过") + for idx in invalid_indices[:10]: # 只打印前10个 + print(f" 坐标点 {idx + 1}: 经度={lon[idx]}, 纬度={lat[idx]}") + if invalid_count > 10: + print(f" ... 还有 {invalid_count - 10} 个无效坐标点") + + # 对有效坐标进行转换 + valid_mask = ~invalid_mask + if np.any(valid_mask): + valid_lon = lon[valid_mask] + valid_lat = lat[valid_mask] + valid_indices = np.where(valid_mask)[0] + + # 计算UTM分区（向量化） + if fixed_zone is not None: + zones = np.full(len(valid_lon), fixed_zone) + else: + zones = np.array([calculate_utm_zone(lon_val) for lon_val in valid_lon]) + + # 批量转换（仍需要循环，但减少了开销） + for i, (lat_val, lon_val, zone) in enumerate(zip(valid_lat, valid_lon, zones)): + try: + E, Nn = latlon_to_utm_math(lat_val, lon_val, zone) + if not (np.isnan(E) or np.isnan(Nn) or np.isinf(E) or np.isinf(Nn)): + utm_x[valid_indices[i]] = E + utm_y[valid_indices[i]] = Nn + else: + utm_x[valid_indices[i]] = np.nan + utm_y[valid_indices[i]] = np.nan + except Exception as e: + utm_x[valid_indices[i]] = np.nan + utm_y[valid_indices[i]] = np.nan + + # 设置无效坐标为NaN + utm_x[invalid_mask] = np.nan + utm_y[invalid_mask] = np.nan + + return utm_x, utm_y + + except Exception as e: + print(f"坐标转换初始化失败: {str(e)}") + return np.full_like(lon, np.nan), np.full_like(lat, np.nan) + + +def convert_to_utm51n(lon, lat, source_epsg=4326): + """ + 将坐标转换为WGS84 UTM 51N格式（保留向后兼容性） + + Args: + lon: 经度数组 + lat: 纬度数组 + source_epsg: 源坐标系EPSG代码，默认为4326 (WGS84地理坐标系) + + Returns: + utm_x, utm_y: 转换后的UTM坐标（米） + """ + # 使用新的转换函数，但强制使用UTM 51N + return convert_to_utm(lon, lat, source_epsg, target_epsg=32651) + + +def get_spectral_in_coor(imgpath, coorpath, outpath, radius=0, flare_path=None, boundary_path=None, source_epsg=4326): + """ + 获取给定坐标的光谱曲线，并将坐标转换为UTM格式（根据经度自动计算UTM分区） + + Args: + imgpath: 影像文件路径（BIL格式） + coorpath: 坐标文件路径（CSV格式，第1、2列为纬度和经度） + outpath: 输出文件路径（CSV格式） + radius: 采样半径（像素） + flare_path: 耀斑文件路径（可选） + boundary_path: 边界文件路径（可选） + source_epsg: 源坐标系EPSG代码，默认为4326 (WGS84地理坐标系) + """ + # 读取原始坐标文件（CSV格式） + coor_df = None + coor_data = None + + # 尝试不同的编码方式读取CSV文件 + encodings = ['utf-8', 'gbk', 'gb2312', 'latin1', 'cp1252'] + + for encoding in encodings: + try: + # 尝试读取CSV文件 + coor_df = pd.read_csv(coorpath, encoding=encoding) + # 只提取数值数据，跳过表头 + coor_data = coor_df.select_dtypes(include=[np.number]).values + + # 如果没有数值列，尝试转换所有列（跳过第一行表头） + if coor_data.shape[1] == 0: + # 尝试从第二行开始读取，第一行作为表头 + coor_df = pd.read_csv(coorpath, encoding=encoding, header=0) + # 尝试将所有列转换为数值 + numeric_df = coor_df.apply(pd.to_numeric, errors='coerce') + # 删除全为NaN的行（通常是表头转换失败的行） + numeric_df = numeric_df.dropna(how='all') + coor_data = numeric_df.values + + print(f"成功使用 {encoding} 编码读取文件") + break + + except Exception as e: + print(f"使用 {encoding} 编码读取失败: {str(e)}") + continue + + # 如果所有编码都失败，尝试numpy读取 + if coor_data is None: + try: + print("尝试使用numpy读取数值数据...") + # 跳过第一行（表头），只读取数值 + coor_data = np.loadtxt(coorpath, delimiter=",", skiprows=1) + except: + try: + coor_data = np.loadtxt(coorpath, delimiter="\t", skiprows=1) + except Exception as e: + raise Exception(f"无法读取坐标文件，请检查文件格式: {str(e)}") + + if len(coor_data.shape) == 1: + coor_data = coor_data.reshape(1, -1) + + # 检查数据有效性 + if coor_data is None or coor_data.shape[1] < 2: + raise Exception("坐标文件格式错误：需要至少2列数据（第1列为纬度，第2列为经度）") + + print(f"成功读取坐标文件，共 {coor_data.shape[0]} 行，{coor_data.shape[1]} 列") + print(f"数据预览（前3行）：") + for i in range(min(3, coor_data.shape[0])): + print(f" 行{i + 1}: {coor_data[i, :min(5, coor_data.shape[1])]}") # 只显示前5列 + + # 提取原始坐标 + lat_array = coor_data[:, 0] # 第1列是纬度 + lon_array = coor_data[:, 1] # 第2列是经度 + + print(f"\n=== 原始坐标信息 ===") + print(f"原始坐标范围: 经度 {np.min(lon_array):.6f} ~ {np.max(lon_array):.6f}, 纬度 {np.min(lat_array):.6f} ~ {np.max(lat_array):.6f}") + + # 坐标转换为UTM（根据经度自动计算UTM分区） + print("正在进行坐标转换...") + utm_x, utm_y = convert_to_utm(lon_array, lat_array, source_epsg, target_epsg=None) + + # 检查转换结果 + valid_utm_mask = ~(np.isnan(utm_x) | np.isnan(utm_y) | np.isinf(utm_x) | np.isinf(utm_y)) + valid_count = np.sum(valid_utm_mask) + + if valid_count > 0: + print(f"转换后UTM坐标范围: X {np.nanmin(utm_x):.2f} ~ {np.nanmax(utm_x):.2f}, Y {np.nanmin(utm_y):.2f} ~ {np.nanmax(utm_y):.2f}") + print(f"成功转换 {valid_count}/{len(utm_x)} 个坐标点") + else: + print("警告: 所有UTM坐标转换都失败了，将尝试使用原始经纬度坐标进行像素坐标转换") + + # 打开影像数据集 + dataset = gdal.Open(imgpath) + im_width = dataset.RasterXSize # 栅格矩阵的列数 + im_height = dataset.RasterYSize # 栅格矩阵的行数 + num_bands = dataset.RasterCount # 栅格矩阵的波段数 + geotransform = dataset.GetGeoTransform() # 仿射矩阵 + im_proj = dataset.GetProjection() # 地图投影信息 + + print(f"影像尺寸: {im_width} x {im_height}, 波段数: {num_bands}") + print(f"仿射变换参数: {geotransform}") + + print("\n=== 开始光谱提取 ===") + + # 加载掩膜文件 + flare_mask = load_mask_file(flare_path) + boundary_mask = load_mask_file(boundary_path) + + # 获取数据集的空间参考系统 + dataset_srs = dataset.GetSpatialRef() + + # 准备输出数组，在原有数据基础上添加UTM坐标和光谱列 + original_cols = coor_data.shape[1] + # 添加UTM坐标列（2列）和光谱列（num_bands列） + new_columns = np.zeros((coor_data.shape[0], 2 + num_bands)) + coor_spectral = np.hstack((coor_data, new_columns)) + + # 将UTM坐标添加到数据中 + coor_spectral[:, original_cols] = utm_x # UTM X坐标 + coor_spectral[:, original_cols + 1] = utm_y # UTM Y坐标 + + print(f"处理 {coor_data.shape[0]} 个坐标点...") + + # 如果UTM转换失败，尝试使用影像坐标系进行转换 + use_utm_fallback = False + if valid_count == 0 and dataset_srs is not None: + print("尝试使用影像坐标系进行坐标转换...") + try: + source_srs = osr.SpatialReference() + source_srs.ImportFromEPSG(source_epsg) + transform_to_image = osr.CoordinateTransformation(source_srs, dataset_srs) + use_utm_fallback = True + except: + use_utm_fallback = False + + # 批量转换所有坐标点为像素坐标 + pixel_x_array = np.zeros(coor_data.shape[0], dtype=np.int32) + pixel_y_array = np.zeros(coor_data.shape[0], dtype=np.int32) + valid_pixel_mask = np.zeros(coor_data.shape[0], dtype=bool) + + # 批量计算像素坐标 + for i in range(coor_data.shape[0]): + # 优先使用UTM坐标，如果无效则使用备用方案 + utm_x_point = utm_x[i] + utm_y_point = utm_y[i] + + # 检查UTM坐标是否有效 + if np.isnan(utm_x_point) or np.isnan(utm_y_point) or np.isinf(utm_x_point) or np.isinf(utm_y_point): + # 如果UTM转换失败，尝试使用影像坐标系 + if use_utm_fallback: + try: + lon_point = lon_array[i] + lat_point = lat_array[i] + if not (np.isnan(lon_point) or np.isnan(lat_point)): + # 转换为影像坐标系 + img_coords = transform_to_image.TransformPoint(lon_point, lat_point) + pixel_x, pixel_y = geo_to_pixel(img_coords[0], img_coords[1], geotransform, dataset_srs) + # 更新UTM坐标列（使用影像坐标系坐标） + coor_spectral[i, original_cols] = img_coords[0] + coor_spectral[i, original_cols + 1] = img_coords[1] + else: + print(f"跳过坐标点 {i + 1}: 坐标无效") + coor_spectral[i, original_cols + 2:] = np.zeros(num_bands) + continue + except Exception as e: + # 如果影像坐标系转换也失败，尝试直接使用经纬度 + try: + lon_point = lon_array[i] + lat_point = lat_array[i] + if not (np.isnan(lon_point) or np.isnan(lat_point)): + pixel_x, pixel_y = geo_to_pixel(lon_point, lat_point, geotransform, dataset_srs) + # 保留原始经纬度作为坐标 + coor_spectral[i, original_cols] = lon_point + coor_spectral[i, original_cols + 1] = lat_point + else: + print(f"跳过坐标点 {i + 1}: 坐标无效") + coor_spectral[i, original_cols + 2:] = np.zeros(num_bands) + continue + except: + print(f"跳过坐标点 {i + 1}: 所有坐标转换方式都失败") + coor_spectral[i, original_cols + 2:] = np.zeros(num_bands) + continue + else: + # 尝试直接使用经纬度坐标 + try: + lon_point = lon_array[i] + lat_point = lat_array[i] + if not (np.isnan(lon_point) or np.isnan(lat_point)): + pixel_x, pixel_y = geo_to_pixel(lon_point, lat_point, geotransform, dataset_srs) + # 保留原始经纬度作为坐标 + coor_spectral[i, original_cols] = lon_point + coor_spectral[i, original_cols + 1] = lat_point + else: + print(f"跳过坐标点 {i + 1}: 坐标无效") + coor_spectral[i, original_cols + 2:] = np.zeros(num_bands) + continue + except: + print(f"跳过坐标点 {i + 1}: 坐标转换失败") + coor_spectral[i, original_cols + 2:] = np.zeros(num_bands) + continue + else: + # UTM坐标转换为像素坐标 + pixel_x, pixel_y = geo_to_pixel(utm_x_point, utm_y_point, geotransform, dataset_srs) + + # 存储像素坐标 + pixel_x_array[i] = pixel_x + pixel_y_array[i] = pixel_y + + # 检查坐标是否在影像范围内 + if 0 <= pixel_x < im_width and 0 <= pixel_y < im_height: + valid_pixel_mask[i] = True + else: + valid_pixel_mask[i] = False + if i < 10 or (i % 100 == 0): # 只打印前10个或每100个打印一次 + print(f"警告: 坐标点 {i + 1} (UTM X:{utm_x_point:.2f}, Y:{utm_y_point:.2f}) 超出影像范围") + + # 批量提取光谱数据（优化：减少I/O操作） + print(f"批量提取光谱数据... (有效坐标点: {np.sum(valid_pixel_mask)})") + + if radius > 0: + # 半径采样模式：需要逐个处理 + for i in range(coor_data.shape[0]): + if valid_pixel_mask[i]: + spectrum = get_average_spectral_in_radius( + dataset, pixel_x_array[i], pixel_y_array[i], radius, flare_mask, boundary_mask + ) + coor_spectral[i, original_cols + 2:] = spectrum + else: + coor_spectral[i, original_cols + 2:] = np.zeros(num_bands) + else: + # 单点采样模式：批量读取（优化） + # 预读取所有波段数据（如果内存允许） + try: + # 尝试读取所有波段到内存（适用于内存充足的情况） + print("正在预加载所有波段数据到内存（优化模式）...") + all_bands_data = [] + for band_idx in range(num_bands): + band = dataset.GetRasterBand(band_idx + 1) + band_data = band.ReadAsArray() + all_bands_data.append(band_data) + all_bands_data = np.array(all_bands_data) # shape: (bands, height, width) + print("预加载完成，开始批量提取像素值...") + + # 批量提取像素值 + for i in range(coor_data.shape[0]): + if valid_pixel_mask[i]: + px, py = int(pixel_x_array[i]), int(pixel_y_array[i]) + # GDAL读取的数组形状是 (bands, height, width)，像素坐标 (x,y) 对应数组索引 [:, y, x] + # 注意：py是行（y坐标），px是列（x坐标） + if 0 <= px < all_bands_data.shape[2] and 0 <= py < all_bands_data.shape[1]: + spectrum = all_bands_data[:, py, px] # 直接索引，非常快 + coor_spectral[i, original_cols + 2:] = spectrum + else: + coor_spectral[i, original_cols + 2:] = np.zeros(num_bands) + else: + coor_spectral[i, original_cols + 2:] = np.zeros(num_bands) + + # 释放内存 + del all_bands_data + print("批量提取完成") + + except MemoryError: + # 如果内存不足，回退到逐个波段读取 + print("内存不足，使用逐个波段读取模式...") + for i in range(coor_data.shape[0]): + if valid_pixel_mask[i]: + px, py = pixel_x_array[i], pixel_y_array[i] + spectrum = np.zeros(num_bands) + for band_idx in range(num_bands): + band = dataset.GetRasterBand(band_idx + 1) + spectrum[band_idx] = band.ReadAsArray(px, py, 1, 1)[0, 0] + coor_spectral[i, original_cols + 2:] = spectrum + else: + coor_spectral[i, original_cols + 2:] = np.zeros(num_bands) + + del dataset + + # 创建DataFrame用于CSV输出 + # 去除前两列坐标列（纬度和经度）和UTM列 + try: + # 如果原始数据有列名，使用原始列名（跳过前两列） + if coor_df is not None and hasattr(coor_df, 'columns'): + # 跳过前两列（经纬度），从第3列开始 + if len(coor_df.columns) >= original_cols: + # 保留第3列及之后的原始列（如果有的话） + if original_cols > 2: + original_columns = list(coor_df.columns[2:original_cols]) + else: + original_columns = [] + else: + # 如果原始列数不足，只保留存在的列（跳过前两列） + if len(coor_df.columns) > 2: + original_columns = list(coor_df.columns[2:]) + else: + original_columns = [] + else: + # 如果没有列名，只保留第3列及之后的列（如果有的话） + if original_cols > 2: + original_columns = ["col_" + str(j + 1) for j in range(2, original_cols)] + else: + original_columns = [] + except: + # 异常处理：只保留第3列及之后的列（如果有的话） + if original_cols > 2: + original_columns = ["col_" + str(j + 1) for j in range(2, original_cols)] + else: + original_columns = [] + + # 读取波长信息，用作光谱列名 + wavelengths = None + try: + in_hdr_dict = spectral.envi.read_envi_header(get_hdr_file_path(imgpath)) + wavelengths = np.array(in_hdr_dict['wavelength']).astype('float64') + # 将波长值转换为字符串作为列名 + spectral_columns = [str(wl) for wl in wavelengths] + print(f"成功读取波长信息，共 {len(spectral_columns)} 个波段") + except Exception as e: + print(f"警告: 无法读取波长信息 ({str(e)})，使用默认列名 band_1, band_2, ...") + spectral_columns = ["band_" + str(j + 1) for j in range(num_bands)] + + # 构建输出列名（不包含前两列坐标列和UTM列） + all_columns = original_columns + spectral_columns + + # 从coor_spectral中提取需要输出的列 + # 跳过前两列（经纬度）和UTM列，只保留： + # - 第3列到第original_cols列（如果有的话） + # - 光谱数据列（从original_cols+2开始） + output_data = [] + if original_cols > 2: + # 保留第3列到第original_cols列 + output_data.append(coor_spectral[:, 2:original_cols]) + # 保留光谱数据列（从original_cols+2开始） + output_data.append(coor_spectral[:, original_cols + 2:]) + + # 合并数据 + if len(output_data) > 0: + output_array = np.hstack(output_data) if len(output_data) > 1 else output_data[0] + else: + # 如果没有原始列，只输出光谱数据 + output_array = coor_spectral[:, original_cols + 2:] + + # 创建结果DataFrame + result_df = pd.DataFrame(output_array, columns=all_columns) + + # 保存为CSV格式 + result_df.to_csv(outpath, index=False, float_format='%.6f') + print(f"结果已保存到CSV文件: {outpath}") + + return coor_spectral + + +# 直接运行示例 +if __name__ == '__main__': + # 在这里直接设置参数 + imgpath = r"E:\code\WQ\封装\work_dir\3_deglint\deglint_goodman.bsq" # BIL格式影像文件路径 + coorpath = r"E:\code\WQ\封装\work_dir\4_processed_data\processed_data.csv"# CSV格式坐标文件路径（第1、2列为纬度和经度） + output_path = r"E:\code\WQ\封装\work_dir\5_training_spectra/yangdian_output.csv" # CSV格式输出文件路径 + + radius = 5 # 采样半径（像素），0表示单点采样，>0表示半径内平均 + flare_path = r"E:\code\WQ\封装\work_dir\2_glint\severe_glint_area.dat" # 耀斑掩膜文件路径（可选，None表示不使用） + boundary_path = r"D:\BaiduNetdiskDownload\yaobao\water_mask.dat" # 边界掩膜文件路径（可选，None表示不使用） + source_epsg = 4326 # 源坐标系EPSG代码，默认为4326 (WGS84地理坐标系) + + verbose = True # 是否启用详细模式 + + if verbose: + print(f"影像文件: {imgpath}") + print(f"坐标文件: {coorpath}") + print(f"输出文件: {output_path}") + print(f"采样半径: {radius}") + if flare_path: + print(f"耀斑掩膜: {flare_path}") + if boundary_path: + print(f"边界掩膜: {boundary_path}") + if source_epsg: + print(f"指定坐标系: EPSG:{source_epsg}") + + tmp = get_spectral_in_coor(imgpath, coorpath, output_path, + radius, flare_path, boundary_path, source_epsg) + diff --git a/src/core/modeling/__init__.py b/src/core/modeling/__init__.py new file mode 100644 index 0000000..7c68785 --- /dev/null +++ b/src/core/modeling/__init__.py @@ -0,0 +1 @@ +# -*- coding: utf-8 -*- \ No newline at end of file diff --git a/src/core/modeling/best_R2.py b/src/core/modeling/best_R2.py new file mode 100644 index 0000000..83607ed --- /dev/null +++ b/src/core/modeling/best_R2.py @@ -0,0 +1,124 @@ +import pandas as pd + +# ---- 工具：在多个候选列名里自动匹配实际列名 ---- +def _find_col(df, candidates, required=True): + cols = [c.strip() for c in df.columns] + colmap = {c.strip(): c for c in df.columns} # strip 后到原名的映射 + for cand in candidates: + if cand in cols: + return colmap[cand] + if required: + raise KeyError(f"找不到列：候选 {candidates} ，实际列有：{list(df.columns)}") + return None + +# ---- 主函数：输入文件路径，输出文件路径（直接传参）---- +def pick_best_by_target(input_csv: str, + output_csv: str = "best_by_target.csv", + tie_break_priority: list | None = None) -> pd.DataFrame: + """ + 读取一个CSV（表头包含：目标列、测试集R² 等）， + 按“目标列”分组，挑选“测试集R²”最高的那一行（并做可选的并列打破）， + 导出到 output_csv，并返回结果 DataFrame。 + """ + df = pd.read_csv(input_csv) + # 处理表头空格/BOM + df.columns = df.columns.str.replace("\ufeff", "", regex=False).str.strip() + + # 兼容多种列名写法 + target_col = _find_col(df, ["目标列", "Target", "target"]) + test_r2_col = _find_col(df, ["测试集R²", "测试集R2", "测试集R^2", "Test R2", "test_R2", "test r2"]) + + # 常见可选并列指标（按需要会自动忽略不存在的列） + default_ties = [ + # metric, order: "min" 表示越小越好；"max" 表示越大越好 + ("测试集RMSE", "min"), ("Test RMSE", "min"), ("test_RMSE", "min"), + ("测试集MAE", "min"), ("Test MAE", "min"), ("test_MAE", "min"), + ("测试集MSE", "min"), ("Test MSE", "min"), ("test_MSE", "min"), + ] + # 如果用户传入自定义优先级，就覆盖；否则用默认 + tie_break_priority = tie_break_priority or default_ties + + # 转数值（无法解析置 NaN） + df[test_r2_col] = pd.to_numeric(df[test_r2_col], errors="coerce") + + # 仅使用有 R² 的行参与选择 + df_valid = df.dropna(subset=[test_r2_col]).copy() + if df_valid.empty: + raise ValueError("没有有效的测试集R²数值（全为空），无法挑选最佳。") + + # 每个目标列的候选数量 + counts = df.groupby(target_col).size().rename("模型条数") + + # 构造排序键：先按 测试集R² 降序，其次按若干并列指标（若列不存在会被跳过） + sort_cols = [test_r2_col] + sort_ascending = [False] # R² 越大越好 + + for col_name, order in tie_break_priority: + if col_name in df_valid.columns: + sort_cols.append(col_name) + sort_ascending.append(order == "min") # min → True, max → False + + # 对每个目标列分组排序后取第一行 + best = ( + df_valid + .sort_values(by=sort_cols, ascending=sort_ascending, kind="mergesort") + .groupby(target_col, as_index=False) + .head(1) + ) + + # 合并候选数量，并按 测试集R² 再整体排序一下（可选） + best = best.merge(counts, left_on=target_col, right_index=True) + best = best.sort_values(by=[test_r2_col], ascending=False) + + # 导出 + best.to_csv(output_csv, index=False, encoding="utf-8-sig") + return best + +# ---- 另一个便捷函数：直接传 DataFrame（不用落盘读写）---- +def pick_best_by_target_df(df: pd.DataFrame, + tie_break_priority: list | None = None) -> pd.DataFrame: + """ + 与 pick_best_by_target 相同逻辑，但输入是 DataFrame，返回挑选后的 DataFrame。 + """ + df = df.copy() + df.columns = df.columns.str.replace("\ufeff", "", regex=False).str.strip() + target_col = _find_col(df, ["目标列", "Target", "target"]) + test_r2_col = _find_col(df, ["测试集R²", "测试集R2", "测试集R^2", "Test R2", "test_R2", "test r2"]) + + default_ties = [ + ("测试集RMSE", "min"), ("Test RMSE", "min"), ("test_RMSE", "min"), + ("测试集MAE", "min"), ("Test MAE", "min"), ("test_MAE", "min"), + ("测试集MSE", "min"), ("Test MSE", "min"), ("test_MSE", "min"), + ] + tie_break_priority = tie_break_priority or default_ties + + df[test_r2_col] = pd.to_numeric(df[test_r2_col], errors="coerce") + df_valid = df.dropna(subset=[test_r2_col]).copy() + if df_valid.empty: + raise ValueError("没有有效的测试集R²数值（全为空），无法挑选最佳。") + + counts = df.groupby(target_col).size().rename("模型条数") + + sort_cols = [test_r2_col] + sort_ascending = [False] + for col_name, order in tie_break_priority: + if col_name in df_valid.columns: + sort_cols.append(col_name) + sort_ascending.append(order == "min") + + best = ( + df_valid + .sort_values(by=sort_cols, ascending=sort_ascending, kind="mergesort") + .groupby(target_col, as_index=False) + .head(1) + .merge(counts, left_on=target_col, right_index=True) + .sort_values(by=[test_r2_col], ascending=False) + ) + return best +# 路径方式 +res = pick_best_by_target(r"E:\code\WQ\yaobao925\qvchuyaoban\batch_detailed_results.csv", output_csv=r"E:\code\WQ\yaobao925\qvchuyaoban\best_by_target.csv") +print(res.head()) + +# DataFrame 方式（如果你在笔记本里已有 df） +# res_df = pick_best_by_target_df(df) +# res_df.to_csv("best_by_target.csv", index=False, encoding="utf-8-sig") diff --git a/src/core/modeling/modeling_batch.py b/src/core/modeling/modeling_batch.py new file mode 100644 index 0000000..9a1a519 --- /dev/null +++ b/src/core/modeling/modeling_batch.py @@ -0,0 +1,1134 @@ +import numpy as np +import pandas as pd +import joblib +import os +from pathlib import Path +from typing import List, Dict, Union, Tuple, Optional +import warnings + +warnings.filterwarnings('ignore') + +# 机器学习模型导入 - 改为回归模型 +from sklearn.svm import SVR +from sklearn.ensemble import RandomForestRegressor +from sklearn.neighbors import KNeighborsRegressor +from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet +from sklearn.model_selection import GridSearchCV, cross_val_score, KFold, train_test_split +from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score +from sklearn.cross_decomposition import PLSRegression +from sklearn.ensemble import GradientBoostingRegressor, AdaBoostRegressor, ExtraTreesRegressor +from sklearn.tree import DecisionTreeRegressor +from sklearn.neural_network import MLPRegressor + +# 第三方模型导入 +# try: +# import lightgbm as lgb +# LGB_AVAILABLE = True +# except ImportError: +# LGB_AVAILABLE = False +LGB_AVAILABLE = False # 注释掉lightgbm + +# try: +# import catboost as cb +# CB_AVAILABLE = True +# except ImportError: +# CB_AVAILABLE = False +CB_AVAILABLE = False # 注释掉catboost + +# 导入预处理模块 +# 动态导入预处理模块 +import sys +import os + +from src.preprocessing.spectral_Preprocessing import Preprocessing + + +class WaterQualityModelingBatch: + """水质参数反演批量建模类""" + + def __init__(self, artifacts_dir: str = "models/artifacts"): + """ + 初始化批量建模类 + + Args: + artifacts_dir: 模型保存目录 + """ + self.artifacts_dir = Path(artifacts_dir) + self.artifacts_dir.mkdir(parents=True, exist_ok=True) + + # 定义支持的回归模型及其参数网格 + self.model_configs = { + 'SVR': { + 'model': SVR, + 'params': { + 'C': [0.1, 1, 10, 100], + 'gamma': ['scale', 'auto', 0.001, 0.01, 0.1, 1], + 'kernel': ['rbf', 'poly', 'sigmoid'], + 'epsilon': [0.01, 0.1, 0.2] + }, + 'available': True + }, + 'RF': { + 'model': RandomForestRegressor, + 'params': { + 'n_estimators': [50, 100, 200], + 'max_depth': [None, 10, 20, 30], + 'min_samples_split': [2, 5, 10], + 'min_samples_leaf': [1, 2, 4] + }, + 'available': True + }, + 'KNN': { + 'model': KNeighborsRegressor, + 'params': { + 'n_neighbors': [3, 5, 7, 9, 11], + 'weights': ['uniform', 'distance'], + 'metric': ['euclidean', 'manhattan', 'minkowski'] + }, + 'available': True + }, + 'LinearRegression': { + 'model': LinearRegression, + 'params': { + 'fit_intercept': [True, False] + }, + 'available': True + }, + 'Ridge': { + 'model': Ridge, + 'params': { + 'alpha': [0.01, 0.1, 1, 10, 100], + 'fit_intercept': [True, False] + }, + 'available': True + }, + 'Lasso': { + 'model': Lasso, + 'params': { + 'alpha': [0.01, 0.1, 1, 10, 100], + 'fit_intercept': [True, False], + 'max_iter': [1000, 2000] + }, + 'available': True + }, + 'ElasticNet': { + 'model': ElasticNet, + 'params': { + 'alpha': [0.01, 0.1, 1, 10], + 'l1_ratio': [0.1, 0.3, 0.5, 0.7, 0.9], + 'fit_intercept': [True, False], + 'max_iter': [1000, 2000] + }, + 'available': True + }, + 'XGBoost': { + 'model': None, # xgboost is removed, so set to None + 'params': { + 'n_estimators': [50, 100, 200], + 'max_depth': [3, 6, 9], + 'learning_rate': [0.01, 0.1, 0.2], + 'subsample': [0.8, 0.9, 1.0] + }, + 'available': False + }, + 'LightGBM': { + 'model': lgb.LGBMRegressor if LGB_AVAILABLE else None, + 'params': { + 'n_estimators': [50, 100, 200], + 'max_depth': [3, 6, 9], + 'learning_rate': [0.01, 0.1, 0.2], + 'num_leaves': [31, 50, 100] + }, + 'available': LGB_AVAILABLE + }, + 'CatBoost': { + 'model': cb.CatBoostRegressor if CB_AVAILABLE else None, + 'params': { + 'iterations': [50, 100, 200], + 'depth': [3, 6, 9], + 'learning_rate': [0.01, 0.1, 0.2], + 'l2_leaf_reg': [1, 3, 5] + }, + 'available': CB_AVAILABLE + }, + 'PLS': { + 'model': PLSRegression, + 'params': { + 'n_components': [2, 3, 5, 7, 10] + }, + 'available': True + }, + 'GradientBoosting': { + 'model': GradientBoostingRegressor, + 'params': { + 'n_estimators': [50, 100, 200], + 'learning_rate': [0.01, 0.1, 0.2], + 'max_depth': [3, 5, 7], + 'subsample': [0.8, 0.9, 1.0], + 'min_samples_split': [2, 5, 10], + 'min_samples_leaf': [1, 2, 4] + }, + 'available': True + }, + 'AdaBoost': { + 'model': AdaBoostRegressor, + 'params': { + 'n_estimators': [50, 100, 200], + 'learning_rate': [0.01, 0.1, 0.2], + 'loss': ['linear', 'square', 'exponential'] + }, + 'available': True + }, + 'DecisionTree': { + 'model': DecisionTreeRegressor, + 'params': { + 'max_depth': [None, 5, 10, 20, 30], + 'min_samples_split': [2, 5, 10], + 'min_samples_leaf': [1, 2, 4], + 'max_features': ['auto', 'sqrt', 'log2'] + }, + 'available': True + }, + 'MLP': { + 'model': MLPRegressor, + 'params': { + 'hidden_layer_sizes': [(50,), (100,), (50, 50), (100, 50)], + 'activation': ['relu', 'tanh', 'logistic'], + 'solver': ['adam', 'sgd'], + 'alpha': [0.0001, 0.001, 0.01], + 'learning_rate': ['constant', 'invscaling', 'adaptive'], + 'max_iter': [1000, 2000] + }, + 'available': True + }, + 'ExtraTrees': { + 'model': ExtraTreesRegressor, + 'params': { + 'n_estimators': [50, 100, 200], + 'max_depth': [None, 10, 20, 30], + 'min_samples_split': [2, 5, 10], + 'min_samples_leaf': [1, 2, 4], + 'max_features': ['auto', 'sqrt', 'log2'] + }, + 'available': True + } + } + + # 预处理方法列表 + self.preprocessing_methods = [ + "None", "MMS", "SS", "CT", "SNV", "MA", "SG", "MSC", "D1", "D2", "DT", "WVAE" + ] + + # 样本划分方法列表 + self.split_methods = ["random", "spxy", "ks"] + + self.results = {} + self.best_models = {} + + def load_data_batch(self, csv_path: str, feature_start_column: Union[int, str]) -> Tuple[pd.DataFrame, Dict[str, pd.Series]]: + """ + 批量加载CSV数据，将指定列之前的列作为目标值 + + Args: + csv_path: CSV文件路径 + feature_start_column: 特征开始列索引（int）或列名（str） + + Returns: + X: 特征数据 + y_dict: 目标值数据字典，键为列名 + """ + # 读取CSV数据，处理空字符串和缺失值 + try: + data = pd.read_csv(csv_path, na_values=['', ' ', 'NaN', 'nan', 'NULL', 'null']) + except pd.errors.EmptyDataError: + raise ValueError(f"CSV文件 '{csv_path}' 为空或不存在") + except Exception as e: + raise ValueError(f"读取CSV文件 '{csv_path}' 时出错: {e}") + + # 检查并清理数据中的空字符串和其他无效值 + print("数据清理...") + original_shape = data.shape + + # 将空字符串替换为NaN + data = data.replace(r'^\s*$', np.nan, regex=True) + + # 对于数值列，将无法转换为数字的字符串替换为NaN + for col in data.columns: + try: + # 尝试将列转换为数值类型 + data[col] = pd.to_numeric(data[col], errors='coerce') + except Exception: + # 如果转换失败，保持原样（可能是字符串列） + pass + + cleaned_shape = data.shape + if cleaned_shape != original_shape: + print(f"数据清理完成: {original_shape[0]}行{original_shape[1]}列 -> {cleaned_shape[0]}行{cleaned_shape[1]}列") + + print(f"数据加载完成，总列数: {data.shape[1]}") + print(f"所有列名: {list(data.columns)}") + + # 如果feature_start_column是列名，转换为索引 + if isinstance(feature_start_column, str): + if feature_start_column not in data.columns: + raise ValueError(f"指定的特征开始列 '{feature_start_column}' 不存在于数据中") + feature_start_index = data.columns.get_loc(feature_start_column) + print(f"特征开始列 '{feature_start_column}' 对应索引: {feature_start_index}") + else: + feature_start_index = feature_start_column + print(f"特征开始列索引: {feature_start_index}") + + # 提取特征数据（从feature_start_index开始） + X = data.iloc[:, feature_start_index:] + + # 提取所有目标列（从0列到feature_start_index-1列） + y_dict = {} + target_columns = data.columns[:feature_start_index] + + print(f"检测到的目标列: {list(target_columns)}") + + for col_name in target_columns: + y_series = data[col_name] + # 检查是否有非空值 + if not y_series.isna().all(): + y_dict[col_name] = y_series + print(f" 目标列 '{col_name}': {y_series.count()} 个非空值, 范围: {y_series.min():.4f} ~ {y_series.max():.4f}") + else: + print(f" 跳过目标列 '{col_name}': 所有值为空") + + print(f"特征数据形状: {X.shape}") + print(f"有效目标列数量: {len(y_dict)}") + + return X, y_dict + + def load_data_single(self, csv_path: str, target_column_name: str, feature_start_column: Union[int, str]) -> Tuple[pd.DataFrame, pd.Series]: + """ + 加载单个目标列的CSV数据 + + Args: + csv_path: CSV文件路径 + target_column_name: 目标列名 + feature_start_column: 特征开始列索引（int）或列名（str） + + Returns: + X: 特征数据 + y: 目标值数据 + """ + data = pd.read_csv(csv_path) + + # 检查目标列是否存在 + if target_column_name not in data.columns: + raise ValueError(f"目标列 '{target_column_name}' 不存在于数据中") + + # 如果feature_start_column是列名，转换为索引 + if isinstance(feature_start_column, str): + if feature_start_column not in data.columns: + raise ValueError(f"指定的特征开始列 '{feature_start_column}' 不存在于数据中") + feature_start_index = data.columns.get_loc(feature_start_column) + else: + feature_start_index = feature_start_column + + # 提取目标值和特征 + y = data[target_column_name] + X = data.iloc[:, feature_start_index:] + + # 去除y值为空的行 + mask = ~y.isna() + data_cleaned = data[mask] + + # 重新定义y和X，去除对应的空值行 + y = data_cleaned[target_column_name] + X = data_cleaned.iloc[:, feature_start_column:] + + print(f"目标列 '{target_column_name}' 数据加载完成:") + print(f" 样本数量: {X.shape[0]}") + print(f" 特征数量: {X.shape[1]}") + print(f" 目标值范围: {y.min():.4f} ~ {y.max():.4f}") + print(f" 目标值均值: {y.mean():.4f}") + + return X, y + + def preprocess_data(self, X: pd.DataFrame, method: str) -> np.ndarray: + """ + 数据预处理 + + Args: + X: 原始特征数据 + method: 预处理方法 + + Returns: + 预处理后的数据 + """ + print(f"应用预处理方法: {method}") + + # 如果方法为None，直接返回原始数据 + if method == "None" or method is None: + print("跳过预处理，使用原始数据") + return X.values + + try: + X_processed = Preprocessing(method, X) + + # 确保返回的是numpy数组 + if isinstance(X_processed, pd.DataFrame): + X_processed = X_processed.values + + print(f"预处理完成，数据形状: {X_processed.shape}") + return X_processed + + except Exception as e: + print(f"预处理失败: {e}") + print("使用原始数据") + return X.values + + def random(self, data, label, test_ratio=0.2, random_state=123): + """ + 随机划分数据集 + + Args: + data: shape (n_samples, n_features) + label: shape (n_sample, ) + test_ratio: 测试集比例，默认: 0.2 + random_state: 随机种子，默认: 123 + + Returns: + X_train: (n_samples, n_features) + X_test: (n_samples, n_features) + y_train: (n_sample, ) + y_test: (n_sample, ) + """ + X_train, X_test, y_train, y_test = train_test_split( + data, label, test_size=test_ratio, random_state=random_state + ) + return X_train, X_test, y_train, y_test + + def spxy(self, data, label, test_size=0.2): + """ + SPXY算法划分数据集（考虑X和Y空间的距离） + + Args: + data: shape (n_samples, n_features) + label: shape (n_samples, ) + test_size: 测试集比例，默认: 0.2 + + Returns: + X_train: (n_samples, n_features) + X_test: (n_samples, n_features) + y_train: (n_samples, ) + y_test: (n_samples, ) + """ + # 确保 data 和 label 是 NumPy 数组 + data = data.to_numpy() if isinstance(data, pd.DataFrame) else data + label = label.to_numpy() if isinstance(label, pd.Series) else label + + # 备份原始数据和标签 + x_backup = data + y_backup = label + + M = data.shape[0] + N = round((1 - test_size) * M) + samples = np.arange(M) + + # 归一化标签数据 + label = (label - np.mean(label)) / np.std(label) + D = np.zeros((M, M)) + Dy = np.zeros((M, M)) + + # 计算样本之间的距离 + for i in range(M - 1): + xa = data[i, :] + ya = label[i] + for j in range((i + 1), M): + xb = data[j, :] + yb = label[j] + D[i, j] = np.linalg.norm(xa - xb) + Dy[i, j] = np.linalg.norm(ya - yb) + + # 距离归一化 + Dmax = np.max(D) + Dymax = np.max(Dy) + D = D / Dmax + Dy / Dymax + + # 找到最远的两个点 + maxD = D.max(axis=0) + index_row = D.argmax(axis=0) + index_column = maxD.argmax() + + m = np.zeros(N, dtype=int) + m[0] = index_row[index_column] + m[1] = index_column + + dminmax = np.zeros(N) + dminmax[1] = D[m[0], m[1]] + + # 根据距离选择训练集 + for i in range(2, N): + pool = np.delete(samples, m[:i]) + dmin = np.zeros(M - i) + for j in range(M - i): + indexa = pool[j] + d = np.zeros(i) + for k in range(i): + indexb = m[k] + if indexa < indexb: + d[k] = D[indexa, indexb] + else: + d[k] = D[indexb, indexa] + dmin[j] = np.min(d) + dminmax[i] = np.max(dmin) + index = np.argmax(dmin) + m[i] = pool[index] + + m_complement = np.delete(samples, m) + + # 划分训练集和测试集 + X_train = data[m, :] + y_train = y_backup[m] + X_test = data[m_complement, :] + y_test = y_backup[m_complement] + + return X_train, X_test, y_train, y_test + + def ks(self, data, label, test_size=0.2): + """ + Kennard-Stone算法划分数据集 + + Args: + data: shape (n_samples, n_features) + label: shape (n_sample, ) + test_size: 测试集比例，默认: 0.2 + + Returns: + X_train: (n_samples, n_features) + X_test: (n_samples, n_features) + y_train: (n_samples, ) + y_test: (n_samples, ) + """ + # 确保 data 和 label 是 NumPy 数组 + data = data.to_numpy() if isinstance(data, pd.DataFrame) else data + label = label.to_numpy() if isinstance(label, pd.Series) else label + + M = data.shape[0] + N = round((1 - test_size) * M) + samples = np.arange(M) + + D = np.zeros((M, M)) + + for i in range((M - 1)): + xa = data[i, :] + for j in range((i + 1), M): + xb = data[j, :] + D[i, j] = np.linalg.norm(xa - xb) + + maxD = np.max(D, axis=0) + index_row = np.argmax(D, axis=0) + index_column = np.argmax(maxD) + + m = np.zeros(N) + m[0] = np.array(index_row[index_column]) + m[1] = np.array(index_column) + m = m.astype(int) + dminmax = np.zeros(N) + dminmax[1] = D[m[0], m[1]] + + for i in range(2, N): + pool = np.delete(samples, m[:i]) + dmin = np.zeros((M - i)) + for j in range((M - i)): + indexa = pool[j] + d = np.zeros(i) + for k in range(i): + indexb = m[k] + if indexa < indexb: + d[k] = D[indexa, indexb] + else: + d[k] = D[indexb, indexa] + dmin[j] = np.min(d) + dminmax[i] = np.max(dmin) + index = np.argmax(dmin) + m[i] = pool[index] + + m_complement = np.delete(np.arange(data.shape[0]), m) + + X_train = data[m, :] + y_train = label[m] + X_test = data[m_complement, :] + y_test = label[m_complement] + + return X_train, X_test, y_train, y_test + + def split_data(self, X: np.ndarray, y: pd.Series, method: str = "random", + test_size: float = 0.2, random_state: int = 42) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """ + 根据指定方法划分数据集 + + Args: + X: 特征数据 + y: 目标值数据 + method: 划分方法 ("random", "spxy", "ks") + test_size: 测试集比例 + random_state: 随机种子（仅对random方法有效） + + Returns: + X_train, X_test, y_train, y_test + """ + print(f"使用 {method} 方法划分数据集") + + if method == "random": + return self.random(X, y, test_ratio=test_size, random_state=random_state) + elif method == "spxy": + return self.spxy(X, y, test_size=test_size) + elif method == "ks": + return self.ks(X, y, test_size=test_size) + else: + raise ValueError(f"不支持的划分方法: {method}. 支持的方法: {self.split_methods}") + + def train_single_model(self, X: np.ndarray, y: pd.Series, model_name: str, + cv_folds: int = 5, scoring: str = 'neg_mean_squared_error', + test_size: float = 0.2, random_state: int = 42, + split_method: str = "random") -> Dict: + """ + 训练单个回归模型 + + Args: + X: 特征数据 + y: 目标值数据 + model_name: 模型名称 + cv_folds: 交叉验证折数 + scoring: 评分指标 + test_size: 测试集比例 + random_state: 随机种子 + split_method: 数据划分方法 + + Returns: + 训练结果字典 + """ + if model_name not in self.model_configs: + raise ValueError(f"不支持的模型: {model_name}") + + config = self.model_configs[model_name] + + if not config['available']: + print(f"模型 {model_name} 不可用，请安装相应的库") + return None + + print(f"开始训练模型: {model_name}") + + # 使用指定方法分割训练集和测试集 + X_train, X_test, y_train, y_test = self.split_data( + X, y, method=split_method, test_size=test_size, random_state=random_state + ) + + print(f"数据分割完成:") + print(f" 训练集样本数: {X_train.shape[0]}") + print(f" 测试集样本数: {X_test.shape[0]}") + + # 创建模型实例 + if callable(config['model']): + base_model = config['model']() + else: + base_model = config['model'] + + # 特殊处理某些模型 + if model_name == 'CatBoost': + base_model.set_params(verbose=False) + elif model_name == 'LightGBM': + base_model.set_params(verbose=-1) + + # 网格搜索 - 使用KFold代替StratifiedKFold + cv_strategy = KFold(n_splits=cv_folds, shuffle=True, random_state=random_state) + + grid_search = GridSearchCV( + base_model, + config['params'], + cv=cv_strategy, + scoring=scoring, + n_jobs=-1, + verbose=1 + ) + + # 在训练集上训练模型 + grid_search.fit(X_train, y_train) + + # 获取最佳模型 + best_model = grid_search.best_estimator_ + + # 交叉验证评估（在训练集上） + cv_scores = cross_val_score(best_model, X_train, y_train, cv=cv_strategy, scoring=scoring) + + # 计算训练集上的回归指标 + y_train_pred = best_model.predict(X_train) + train_mse = mean_squared_error(y_train, y_train_pred) + train_mae = mean_absolute_error(y_train, y_train_pred) + train_r2 = r2_score(y_train, y_train_pred) + train_rmse = np.sqrt(train_mse) + + # 计算测试集上的回归指标 + y_test_pred = best_model.predict(X_test) + test_mse = mean_squared_error(y_test, y_test_pred) + test_mae = mean_absolute_error(y_test, y_test_pred) + test_r2 = r2_score(y_test, y_test_pred) + test_rmse = np.sqrt(test_mse) + + result = { + 'model': best_model, + 'best_params': grid_search.best_params_, + 'best_score': grid_search.best_score_, + 'cv_mean': cv_scores.mean(), + 'cv_std': cv_scores.std(), + 'cv_scores': cv_scores, + # 训练集指标 + 'train_mse': train_mse, + 'train_mae': train_mae, + 'train_rmse': train_rmse, + 'train_r2': train_r2, + # 测试集指标 + 'test_mse': test_mse, + 'test_mae': test_mae, + 'test_rmse': test_rmse, + 'test_r2': test_r2, + # 数据分割信息 + 'train_size': X_train.shape[0], + 'test_size': X_test.shape[0], + 'split_method': split_method + } + + print(f"模型 {model_name} 训练完成:") + print(f" 最佳参数: {result['best_params']}") + print(f" 最佳得分: {result['best_score']:.4f}") + print(f" CV均值: {result['cv_mean']:.4f} ± {result['cv_std']:.4f}") + print(f" 训练集指标:") + print(f" R²: {result['train_r2']:.4f}") + print(f" RMSE: {result['train_rmse']:.4f}") + print(f" MAE: {result['train_mae']:.4f}") + print(f" 测试集指标:") + print(f" R²: {result['test_r2']:.4f}") + print(f" RMSE: {result['test_rmse']:.4f}") + print(f" MAE: {result['test_mae']:.4f}") + + return result + + def save_model(self, model, target_column_name: str, preprocess_method: str, model_name: str, + metadata: Dict = None): + """ + 保存模型，使用目标列名作为文件名的一部分 + + Args: + model: 训练好的模型 + target_column_name: 目标列名 + preprocess_method: 预处理方法名称 + model_name: 模型名称 + metadata: 模型元数据 + """ + # 清理目标列名，移除可能的特殊字符 + safe_target_name = "".join(c for c in target_column_name if c.isalnum() or c in ('-', '_')).rstrip() + + filename = f"{safe_target_name}_{preprocess_method}_{model_name}.joblib" + filepath = self.artifacts_dir / filename + + # 保存模型和元数据 + save_data = { + 'model': model, + 'target_column_name': target_column_name, + 'preprocess_method': preprocess_method, + 'model_name': model_name, + 'metadata': metadata or {} + } + + joblib.dump(save_data, filepath) + print(f"模型已保存: {filepath}") + + def train_models_batch(self, csv_path: str, feature_start_column: Union[int, str], + preprocessing_methods: Union[str, List[str]] = "None", + model_names: Union[str, List[str]] = "RF", + split_methods: Union[str, List[str]] = "random", + cv_folds: int = 5, + scoring: str = 'neg_mean_squared_error', + test_size: float = 0.2, + random_state: int = 42) -> Dict: + """ + 批量训练多个目标列的模型 + + Args: + csv_path: 数据文件路径 + feature_start_column: 特征开始列索引（int）或列名（str） + preprocessing_methods: 预处理方法列表 + model_names: 模型名称列表 + split_methods: 数据划分方法列表 + cv_folds: 交叉验证折数 + scoring: 评分指标（回归指标） + test_size: 测试集比例 + random_state: 随机种子 + + Returns: + 所有模型的训练结果 + """ + # 转换为列表 + if isinstance(preprocessing_methods, str): + preprocessing_methods = [preprocessing_methods] + if isinstance(model_names, str): + model_names = [model_names] + if isinstance(split_methods, str): + split_methods = [split_methods] + + # 加载数据 + X_raw, y_dict = self.load_data_batch(csv_path, feature_start_column) + + all_results = {} + + # 对每个目标列进行训练 + for target_column_name, y in y_dict.items(): + print(f"\n{'='*80}") + print(f"开始训练目标列: {target_column_name}") + print(f"{'='*80}") + + # 创建该目标列的子目录 + target_artifacts_dir = self.artifacts_dir / target_column_name + target_artifacts_dir.mkdir(parents=True, exist_ok=True) + + # 临时更改artifacts_dir + original_artifacts_dir = self.artifacts_dir + self.artifacts_dir = target_artifacts_dir + + try: + # 去除该目标列的空值 + mask = ~y.isna() + if mask.sum() == 0: + print(f"目标列 '{target_column_name}' 无有效数据，跳过") + continue + + X_clean = X_raw[mask] + y_clean = y[mask] + + print(f"有效样本数: {len(y_clean)}") + + # 训练该目标列的所有模型组合 + target_results = self.train_models_single_target( + X_clean, y_clean, target_column_name, + preprocessing_methods, model_names, split_methods, + cv_folds, scoring, test_size, random_state + ) + + all_results[target_column_name] = target_results + + except Exception as e: + print(f"训练目标列 '{target_column_name}' 时出错: {e}") + continue + finally: + # 恢复原始artifacts_dir + self.artifacts_dir = original_artifacts_dir + + # 保存所有结果的汇总 + self._save_batch_results_summary(all_results) + + return all_results + + def train_models_single_target(self, X_raw: pd.DataFrame, y: pd.Series, target_column_name: str, + preprocessing_methods: List[str], model_names: List[str], + split_methods: List[str], cv_folds: int, scoring: str, + test_size: float, random_state: int) -> Dict: + """ + 训练单个目标列的所有模型组合 + """ + results = {} + + # 遍历所有组合 + for split_method in split_methods: + for preprocess_method in preprocessing_methods: + for model_name in model_names: + combo_key = f"{split_method}_{preprocess_method}_{model_name}" + print(f"\n{'-' * 60}") + print(f"训练组合: {combo_key}") + print(f"{'-' * 60}") + + try: + # 数据预处理 + X_processed = self.preprocess_data(X_raw, preprocess_method) + + # 训练模型 + result = self.train_single_model(X_processed, y, model_name, + cv_folds, scoring, test_size, random_state, split_method) + + if result is not None: + # 保存模型 + metadata = { + 'target_column_name': target_column_name, + 'cv_mean': result['cv_mean'], + 'cv_std': result['cv_std'], + 'best_params': result['best_params'], + 'data_shape': X_processed.shape, + 'target_range': [float(y.min()), float(y.max())], + 'train_r2': result['train_r2'], + 'train_rmse': result['train_rmse'], + 'train_mae': result['train_mae'], + 'test_r2': result['test_r2'], + 'test_rmse': result['test_rmse'], + 'test_mae': result['test_mae'], + 'train_size': result['train_size'], + 'test_size': result['test_size'], + 'split_method': result['split_method'] + } + + self.save_model(result['model'], target_column_name, + f"{split_method}_{preprocess_method}", + model_name, metadata) + + results[combo_key] = result + + except Exception as e: + print(f"训练组合 {combo_key} 失败: {e}") + continue + + # 保存该目标列的结果摘要 + self._save_single_target_results_summary(target_column_name, results) + + return results + + def _save_single_target_results_summary(self, target_column_name: str, results: Dict): + """保存单个目标列的结果摘要""" + if not results: + print(f"目标列 '{target_column_name}' 没有训练结果") + return + + summary_data = [] + + for combo_key, result in results.items(): + # 分离划分方法、预处理方法和建模方法 + parts = combo_key.split('_', 2) + split_method = parts[0] if len(parts) > 0 else '' + preprocess_method = parts[1] if len(parts) > 1 else '' + model_method = parts[2] if len(parts) > 2 else '' + + summary_data.append({ + '划分方法': split_method, + '预处理方法': preprocess_method, + '建模方法': model_method, + 'CV均值': result['cv_mean'], + 'CV标准差': result['cv_std'], + '最佳得分': result['best_score'], + '训练集R²': result['train_r2'], + '训练集RMSE': result['train_rmse'], + '训练集MAE': result['train_mae'], + '训练集MSE': result['train_mse'], + '测试集R²': result['test_r2'], + '测试集RMSE': result['test_rmse'], + '测试集MAE': result['test_mae'], + '测试集MSE': result['test_mse'], + '训练样本数': result['train_size'], + '测试样本数': result['test_size'], + '最佳参数': str(result['best_params']) + }) + + summary_df = pd.DataFrame(summary_data) + # 按测试集R²降序排列（R²越大越好） + summary_df = summary_df.sort_values('测试集R²', ascending=False) + + # 清理目标列名，移除可能的特殊字符 + safe_target_name = "".join(c for c in target_column_name if c.isalnum() or c in ('-', '_')).rstrip() + + # 保存详细结果CSV（中文版） + detailed_path = self.artifacts_dir / f"{safe_target_name}_detailed_results.csv" + summary_df.to_csv(detailed_path, index=False, encoding='utf-8-sig') + + # 保存简化版本用于兼容性（英文版） + summary_data_simple = [] + for combo_key, result in results.items(): + summary_data_simple.append({ + 'combination': combo_key, + 'cv_mean': result['cv_mean'], + 'cv_std': result['cv_std'], + 'best_score': result['best_score'], + 'train_r2': result['train_r2'], + 'train_rmse': result['train_rmse'], + 'train_mae': result['train_mae'], + 'test_r2': result['test_r2'], + 'test_rmse': result['test_rmse'], + 'test_mae': result['test_mae'], + 'train_size': result['train_size'], + 'test_size': result['test_size'], + 'split_method': result.get('split_method', 'unknown'), + 'best_params': str(result['best_params']) + }) + + summary_df_simple = pd.DataFrame(summary_data_simple) + summary_df_simple = summary_df_simple.sort_values('test_r2', ascending=False) + simple_summary_path = self.artifacts_dir / f"{safe_target_name}_training_summary.csv" + summary_df_simple.to_csv(simple_summary_path, index=False) + + print(f"\n{'-' * 60}") + print(f"目标列 '{target_column_name}' 训练结果摘要:") + print(f"{'-' * 60}") + print(summary_df[ + ['划分方法', '预处理方法', '建模方法', '训练集R²', '测试集R²', '训练集RMSE', '测试集RMSE', 'CV均值']].to_string( + index=False)) + print(f"\n详细结果已保存: {detailed_path}") + print(f"简化结果已保存: {simple_summary_path}") + + def _save_batch_results_summary(self, all_results: Dict): + """保存批量训练结果汇总""" + all_summary_data = [] + + for target_column_name, target_results in all_results.items(): + for combo_key, result in target_results.items(): + # 分离划分方法、预处理方法和建模方法 + parts = combo_key.split('_', 2) + split_method = parts[0] if len(parts) > 0 else '' + preprocess_method = parts[1] if len(parts) > 1 else '' + model_method = parts[2] if len(parts) > 2 else '' + + all_summary_data.append({ + '目标列': target_column_name, + '划分方法': split_method, + '预处理方法': preprocess_method, + '建模方法': model_method, + 'CV均值': result['cv_mean'], + 'CV标准差': result['cv_std'], + '最佳得分': result['best_score'], + '训练集R²': result['train_r2'], + '训练集RMSE': result['train_rmse'], + '训练集MAE': result['train_mae'], + '训练集MSE': result['train_mse'], + '测试集R²': result['test_r2'], + '测试集RMSE': result['test_rmse'], + '测试集MAE': result['test_mae'], + '测试集MSE': result['test_mse'], + '训练样本数': result['train_size'], + '测试样本数': result['test_size'], + '最佳参数': str(result['best_params']) + }) + + if all_summary_data: + summary_df = pd.DataFrame(all_summary_data) + # 按目标列和测试集R²排序 + summary_df = summary_df.sort_values(['目标列', '测试集R²'], ascending=[True, False]) + + # 保存详细结果CSV（中文版） + detailed_path = self.artifacts_dir / "batch_detailed_results.csv" + summary_df.to_csv(detailed_path, index=False, encoding='utf-8-sig') + + # 保持原有的批量训练汇总结果（中文版） + batch_summary_path = self.artifacts_dir / "batch_training_summary.csv" + summary_df.to_csv(batch_summary_path, index=False, encoding='utf-8-sig') + + # 创建简化版本用于兼容性（英文版） + all_summary_data_simple = [] + for target_column_name, target_results in all_results.items(): + for combo_key, result in target_results.items(): + all_summary_data_simple.append({ + 'target_column': target_column_name, + 'combination': combo_key, + 'cv_mean': result['cv_mean'], + 'cv_std': result['cv_std'], + 'best_score': result['best_score'], + 'train_r2': result['train_r2'], + 'train_rmse': result['train_rmse'], + 'train_mae': result['train_mae'], + 'test_r2': result['test_r2'], + 'test_rmse': result['test_rmse'], + 'test_mae': result['test_mae'], + 'train_size': result['train_size'], + 'test_size': result['test_size'], + 'split_method': result.get('split_method', 'unknown'), + 'best_params': str(result['best_params']) + }) + + summary_df_simple = pd.DataFrame(all_summary_data_simple) + summary_df_simple = summary_df_simple.sort_values(['target_column', 'test_r2'], ascending=[True, False]) + simple_summary_path = self.artifacts_dir / "batch_training_summary_simple.csv" + summary_df_simple.to_csv(simple_summary_path, index=False) + + print(f"\n{'='*80}") + print("批量训练结果汇总:") + print(f"{'='*80}") + + # 显示每个目标列的最佳模型 + for target_col in summary_df['目标列'].unique(): + target_data = summary_df[summary_df['目标列'] == target_col] + best_row = target_data.iloc[0] # 已经按R²降序排列 + print(f"\n目标列 '{target_col}' 最佳模型:") + print(f" 组合: {best_row['划分方法']}_{best_row['预处理方法']}_{best_row['建模方法']}") + print(f" 测试集R²: {best_row['测试集R²']:.4f}") + print(f" 测试集RMSE: {best_row['测试集RMSE']:.4f}") + print(f" 最佳参数: {best_row['最佳参数']}") + + print(f"\n详细结果已保存: {detailed_path}") + print(f"批量训练汇总结果已保存: {batch_summary_path}") + print(f"简化结果已保存: {simple_summary_path}") + + def load_model(self, preprocess_method: str, model_name: str): + """ + 加载保存的模型 + + Args: + preprocess_method: 预处理方法名称 + model_name: 模型名称 + + Returns: + 加载的模型数据 + """ + filename = f"{preprocess_method}_{model_name}.joblib" + filepath = self.artifacts_dir / filename + + if not filepath.exists(): + raise FileNotFoundError(f"模型文件不存在: {filepath}") + + return joblib.load(filepath) + + def get_best_model(self, metric: str = 'test_r2') -> Tuple[str, Dict]: + """ + 获取最佳模型 + + Args: + metric: 评估指标（默认使用测试集R²） + 可选：'test_r2', 'train_r2', 'test_rmse', 'test_mae', + 'train_rmse', 'train_mae', 'cv_mean', 'best_score' + + Returns: + 最佳模型的组合名称和结果 + """ + if not self.results: + raise ValueError("没有训练结果，请先训练模型") + + # 对于回归指标，R²和负MSE需要取最大值，RMSE和MAE需要取最小值 + if metric in ['test_r2', 'train_r2', 'cv_mean', 'best_score']: + best_combo = max(self.results.keys(), + key=lambda k: self.results[k][metric]) + else: # rmse, mae等，越小越好 + best_combo = min(self.results.keys(), + key=lambda k: self.results[k][metric]) + + return best_combo, self.results[best_combo] + + +def main(): + """主函数示例 - 批量训练""" + # 创建批量建模实例 + modeler = WaterQualityModelingBatch(r"D:\BaiduNetdiskDownload\yaobao\model") + + # 批量训练多个目标列的模型 + all_results = modeler.train_models_batch( + csv_path=r"D:\BaiduNetdiskDownload\yaobao\csv\yangdian_output.csv", + feature_start_column="374.285004", # 使用列名指定特征开始位置 + preprocessing_methods=['None', 'MMS', 'SS', 'SNV', 'MA', 'SG', 'MSC', 'D1', 'D2', 'DT', 'CT'],# + model_names=['SVR', 'RF', 'Ridge', 'Lasso'],#, 'ElasticNet', 'XGBoost', 'LightGBM', 'CatBoost' + split_methods=['spxy', 'ks','random' ], # + cv_folds=5 + ) + + print(f"\n批量训练完成，共训练了 {len(all_results)} 个目标列的模型") + + # 显示每个目标列的最佳模型 + for target_column_name, target_results in all_results.items(): + if target_results: + best_combo = max(target_results.keys(), + key=lambda k: target_results[k]['test_r2']) + best_result = target_results[best_combo] + + print(f"\n目标列 '{target_column_name}' 最佳模型:") + print(f" 组合: {best_combo}") + print(f" 测试集R²: {best_result['test_r2']:.4f}") + print(f" 测试集RMSE: {best_result['test_rmse']:.4f}") + + +if __name__ == "__main__": + main() diff --git a/src/core/modeling/regression.py b/src/core/modeling/regression.py new file mode 100644 index 0000000..5a7efa1 --- /dev/null +++ b/src/core/modeling/regression.py @@ -0,0 +1,392 @@ +import pandas as pd +import numpy as np +from sklearn.linear_model import LinearRegression +from sklearn.metrics import r2_score +import warnings +warnings.filterwarnings('ignore') + +class SingleVariableRegressionAnalysis: + """ + 单变量回归分析类，支持多种回归方法和对每个自变量单独分析 + """ + + def __init__(self): + self.results = [] + + def linear_regression(self, x, y): + """线性回归: y = a + b*x""" + try: + x_2d = x.reshape(-1, 1) + model = LinearRegression() + model.fit(x_2d, y) + + y_pred = model.predict(x_2d) + r2 = r2_score(y, y_pred) + + params = f"y = {model.intercept_:.6f} + {model.coef_[0]:.6f}*x" + + return r2, params, y_pred + except Exception as e: + return np.nan, f"Error: {str(e)}", None + + def exponential_regression(self, x, y): + """指数回归: y = a * exp(b*x)""" + try: + # 确保y为正数 + if np.any(y <= 0): + return np.nan, "Error: y must be positive for exponential regression", None + + # 转换为线性形式: ln(y) = ln(a) + b*x + y_log = np.log(y) + x_2d = x.reshape(-1, 1) + + model = LinearRegression() + model.fit(x_2d, y_log) + + # 转换回指数形式 + a = np.exp(model.intercept_) + b = model.coef_[0] + + y_pred = a * np.exp(b * x) + r2 = r2_score(y, y_pred) + + params = f"y = {a:.6f} * exp({b:.6f}*x)" + + return r2, params, y_pred + except Exception as e: + return np.nan, f"Error: {str(e)}", None + + def power_regression(self, x, y): + """乘幂回归: y = a * x^b""" + try: + # 确保x和y为正数 + if np.any(x <= 0) or np.any(y <= 0): + return np.nan, "Error: x and y must be positive for power regression", None + + # 转换为线性形式: ln(y) = ln(a) + b*ln(x) + x_log = np.log(x) + y_log = np.log(y) + + x_2d = x_log.reshape(-1, 1) + model = LinearRegression() + model.fit(x_2d, y_log) + + # 转换回幂函数形式 + a = np.exp(model.intercept_) + b = model.coef_[0] + + y_pred = a * np.power(x, b) + r2 = r2_score(y, y_pred) + + params = f"y = {a:.6f} * x^{b:.6f}" + + return r2, params, y_pred + except Exception as e: + return np.nan, f"Error: {str(e)}", None + + def logarithmic_regression(self, x, y): + """对数回归: y = a + b*ln(x)""" + try: + # 确保x为正数 + if np.any(x <= 0): + return np.nan, "Error: x must be positive for logarithmic regression", None + + # 对x取对数 + x_log = np.log(x) + x_2d = x_log.reshape(-1, 1) + + model = LinearRegression() + model.fit(x_2d, y) + + y_pred = model.predict(x_2d) + r2 = r2_score(y, y_pred) + + params = f"y = {model.intercept_:.6f} + {model.coef_[0]:.6f}*ln(x)" + + return r2, params, y_pred + except Exception as e: + return np.nan, f"Error: {str(e)}", None + + def batch_single_variable_regression(self, data, x_columns, y_columns, methods='all', output_dir='custom_regression_results'): + """ + 批量单变量回归分析 - 对每个自变量和因变量组合进行回归 + + Parameters: + ----------- + data : pandas.DataFrame + 输入数据 + x_columns : list + 自变量列名列表，对每个自变量单独进行回归 + y_columns : str or list + 因变量列名或列名列表 + methods : str or list + 回归方法，可选 'all' 或方法列表 ['linear', 'exponential', 'power', 'logarithmic'] + output_dir : str + 输出目录路径，每个因变量将单独保存为一个CSV文件 + """ + # 处理方法参数 + if methods == 'all': + methods = ['linear', 'exponential', 'power', 'logarithmic'] + + method_functions = { + 'linear': self.linear_regression, + 'exponential': self.exponential_regression, + 'power': self.power_regression, + 'logarithmic': self.logarithmic_regression + } + + # 确保x_columns为列表 + if isinstance(x_columns, str): + x_columns = [x_columns] + + # 确保y_columns为列表 + if isinstance(y_columns, str): + y_columns = [y_columns] + + # 创建输出目录 + from pathlib import Path + output_path = Path(output_dir) + output_path.mkdir(exist_ok=True, parents=True) + + self.results = {} + all_results = [] + + print(f"开始单变量回归分析:") + print(f"因变量数量: {len(y_columns)}") + print(f"自变量数量: {len(x_columns)}") + print(f"回归方法: {methods}") + print(f"输出目录: {output_dir}") + print("-" * 80) + + # 对每个因变量进行回归分析 + for y_col in y_columns: + print(f"\n分析因变量: {y_col}") + self.results[y_col] = [] + + # 对每个自变量单独进行回归分析 + for x_col in x_columns: + print(f"\n 分析自变量: {x_col}") + + # 准备数据 + x_data = data[x_col].values + y_data = data[y_col].values + + # 移除包含NaN的行 + valid_mask = ~(np.isnan(x_data) | np.isnan(y_data)) + x_clean = x_data[valid_mask] + y_clean = y_data[valid_mask] + + if len(x_clean) == 0: + print(f" ⚠ 无有效数据，跳过") + continue + + print(f" 有效样本数: {len(x_clean)}") + + # 对当前自变量执行所有指定的回归方法 + for method_name in methods: + if method_name not in method_functions: + continue + + regression_func = method_functions[method_name] + + try: + r2, equation, y_pred = regression_func(x_clean, y_clean) + + if not np.isnan(r2): + result = { + 'regression_method': method_name, + 'x_variable': x_col, + 'y_variable': y_col, + 'r_squared': r2, + 'equation': equation, + 'sample_size': len(x_clean), + 'x_mean': np.mean(x_clean), + 'x_std': np.std(x_clean), + 'y_mean': np.mean(y_clean), + 'y_std': np.std(y_clean) + } + + self.results[y_col].append(result) + all_results.append(result) + print(f" {method_name:12} | R² = {r2:.6f}") + else: + print(f" {method_name:12} | 失败") + + except Exception as e: + print(f" {method_name:12} | 错误: {str(e)}") + + # 为当前因变量保存单独的CSV文件 + if self.results[y_col]: + results_df = pd.DataFrame(self.results[y_col]) + + # 按R²排序 + results_df = results_df.sort_values(['x_variable', 'r_squared'], ascending=[True, False]) + + # 为每个因变量创建单独的文件名 + safe_y_name = y_col.replace('/', '_').replace('\\', '_').replace(' ', '_') + output_file = output_path / f"{safe_y_name}_regression_results.csv" + + results_df.to_csv(output_file, index=False, encoding='utf-8') + print(f"\n {y_col} 的结果已保存到: {output_file}") + + # 显示该因变量的最佳模型 + self._show_best_models_for_y(results_df, y_col) + + # 保存汇总结果到CSV + if all_results: + summary_df = pd.DataFrame(all_results) + + # 按因变量和R²排序 + summary_df = summary_df.sort_values(['y_variable', 'x_variable', 'r_squared'], ascending=[True, True, False]) + + summary_file = output_path / "all_regression_results.csv" + summary_df.to_csv(summary_file, index=False, encoding='utf-8') + print(f"\n汇总结果已保存到: {summary_file}") + + return self.results + + def _show_best_models_for_y(self, results_df, y_variable): + """显示指定因变量的最佳回归模型""" + if results_df.empty: + return + + print(f"\n {y_variable} 的最佳回归模型:") + + for x_var in results_df['x_variable'].unique(): + x_results = results_df[results_df['x_variable'] == x_var] + best_model = x_results.loc[x_results['r_squared'].idxmax()] + + print(f" 自变量 {x_var}:") + print(f" 方法: {best_model['regression_method']}") + print(f" R²: {best_model['r_squared']:.6f}") + print(f" 方程: {best_model['equation']}") + + def _show_best_models(self): + """显示每个自变量的最佳回归模型""" + if not self.results: + return + + print("\n" + "=" * 80) + print("每个自变量的最佳回归模型:") + print("=" * 80) + + results_df = pd.DataFrame(self.results) + + for x_var in results_df['x_variable'].unique(): + x_results = results_df[results_df['x_variable'] == x_var] + best_model = x_results.loc[x_results['r_squared'].idxmax()] + + print(f"\n自变量: {x_var}") + print(f" 最佳方法: {best_model['regression_method']}") + print(f" R²: {best_model['r_squared']:.6f}") + print(f" 方程: {best_model['equation']}") + print(f" 样本数: {best_model['sample_size']}") + + def get_results_df(self): + """获取结果DataFrame""" + return pd.DataFrame(self.results) + + def get_best_models_summary(self): + """获取每个自变量的最佳模型汇总""" + if not self.results: + return pd.DataFrame() + + results_df = pd.DataFrame(self.results) + best_models = [] + + for x_var in results_df['x_variable'].unique(): + x_results = results_df[results_df['x_variable'] == x_var] + best_model = x_results.loc[x_results['r_squared'].idxmax()].to_dict() + best_models.append(best_model) + + return pd.DataFrame(best_models) + +def main(): + """主函数示例""" + # 创建示例数据 + + + # 初始化回归分析器 + analyzer = SingleVariableRegressionAnalysis() + + print("=" * 80) + print("水质参数单变量回归分析") + print("=" * 80) + + # 示例1: 使用所有回归方法分析光谱指数 + print("\n1. 光谱指数与叶绿素a的回归分析:") + sample_data = pd.read_csv(r"E:\code\WQ\pipeline_result\work_dir\5_training_spectra\water_quality_results.csv") + spectral_indices = ['Al10SABI','Am092Bsub'] + + results1 = analyzer.batch_single_variable_regression( + data=sample_data, + x_columns=spectral_indices, + y_column='Chlorophyll', + methods='all', + output_file=r'E:\code\WQ\pipeline_result\work_dir\5_training_spectra\spectral_indices_regression.csv' + ) + + # # 示例2: 使用特定方法分析反射率波段 + # print("\n2. 反射率波段与叶绿素a的回归分析:") + # reflectance_bands = ['R443', 'R490', 'R560', 'R665', 'R705', 'R740'] + # + # results2 = analyzer.batch_single_variable_regression( + # data=sample_data, + # x_columns=reflectance_bands, + # y_column='Chl_a', + # methods=['linear', 'power', 'logarithmic'], + # output_file='reflectance_bands_regression.csv' + # ) + + # 示例3: 获取最佳模型汇总 + print("\n3. 最佳模型汇总:") + best_models = analyzer.get_best_models_summary() + if not best_models.empty: + print(best_models[['x_variable', 'regression_method', 'r_squared', 'equation']].to_string(index=False)) + best_models.to_csv(r'E:\code\WQ\pipeline_result\work_dir\5_training_spectra\best_models_summary.csv', index=False) + print("\n最佳模型汇总已保存到 'best_models_summary.csv'") +# +# def advanced_usage_example(): +# """高级使用示例 - 处理实际数据""" +# # 读取您的实际数据 +# try: +# # 替换为您的实际数据文件路径 +# data = pd.read_csv('your_actual_water_data.csv') +# +# # 假设您的数据包含以下列（根据实际情况调整） +# # 光谱指数列: ['NDCI', 'FLH', 'NDTI', 'SABI', ...] +# # 反射率列: ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', ...] 或 ['R443', 'R490', ...] +# # 水质参数列: ['Chl_a', 'Turbidity', 'TSS', 'CDOM', ...] +# +# analyzer = SingleVariableRegressionAnalysis() +# +# # 分析叶绿素a与所有光谱指数的关系 +# spectral_indices = ['NDCI', 'FLH', 'NDTI', 'SABI'] # 替换为您的实际列名 +# analyzer.batch_single_variable_regression( +# data=data, +# x_columns=spectral_indices, +# y_column='Chl_a', # 替换为您的实际水质参数列名 +# methods='all', +# output_file='chl_a_spectral_regression.csv' +# ) +# +# # 分析浊度与反射率波段的关系 +# reflectance_bands = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7'] # 替换为您的实际列名 +# analyzer.batch_single_variable_regression( +# data=data, +# x_columns=reflectance_bands, +# y_column='Turbidity', # 替换为您的实际水质参数列名 +# methods=['linear', 'power'], +# output_file='turbidity_reflectance_regression.csv' +# ) +# +# except FileNotFoundError: +# print("请准备您的实际数据文件 'your_actual_water_data.csv'") +# except Exception as e: +# print(f"处理数据时出错: {str(e)}") + +if __name__ == "__main__": + main() + + # 取消注释以下行来处理您的实际数据 + # advanced_usage_example() \ No newline at end of file diff --git a/src/core/non_empirical_model_correction.py b/src/core/non_empirical_model_correction.py new file mode 100644 index 0000000..ab057b0 --- /dev/null +++ b/src/core/non_empirical_model_correction.py @@ -0,0 +1,382 @@ +import numpy as np +import pandas as pd +import os +from osgeo import gdal +from src.utils.util import * +from src.core.type_define import * +import math +from pyproj import CRS +from pyproj import Transformer +import argparse +import json + + + + +def get_spectral_data_from_csv(csv_path, value_col, spectral_start_col, spectral_end_col): + """ + 从CSV文件中读取实测值和光谱数据 + :param csv_path: CSV文件路径 + :param value_col: 实测值列索引 + :param spectral_start_col: 光谱数据起始列索引 + :param spectral_end_col: 光谱数据结束列索引 + :return: 包含实测值和光谱数据的numpy数组和表头信息 + """ + try: + # 使用pandas读取CSV数据，处理缺失值 + df = pd.read_csv(csv_path, na_values=['', ' ', 'NaN', 'nan', 'NULL', 'null']) + + # 获取表头 + header = df.columns.tolist() + + print(f"原始数据形状: {df.shape}") + + # 提取实测值列和光谱数据列 + measured_values = df.iloc[:, value_col].values + spectral_data = df.iloc[:, spectral_start_col:spectral_end_col+1].values + + # 组合数据 + combined_data = np.column_stack((measured_values, spectral_data)) + + # 检查并清理数据中的NaN值 + # 找到所有不包含NaN的行 + valid_rows = ~np.isnan(combined_data).any(axis=1) + + if not np.any(valid_rows): + raise ValueError("所有数据行都包含缺失值，无法进行模型训练") + + # 过滤有效数据 + cleaned_data = combined_data[valid_rows] + + print(f"清理后数据形状: {cleaned_data.shape} (移除了 {combined_data.shape[0] - cleaned_data.shape[0]} 行无效数据)") + + return cleaned_data, header + + except pd.errors.EmptyDataError: + raise ValueError(f"CSV文件 '{csv_path}' 为空或不存在") + except Exception as e: + raise ValueError(f"读取CSV文件 '{csv_path}' 时出错: {e}") + + +def fit(x1, x2, y): + A = np.column_stack((x1, x2, np.ones((x2.shape[0], 1)))) + coefficients, _, _, _ = np.linalg.lstsq(A, y, rcond=None) + + return coefficients + + +def accuracy_evaluation(x1, x2, y_real, coefficients): + A = np.column_stack((x1, x2, np.ones((x2.shape[0], 1)))) + y_pred = A.dot(coefficients) + + accuracy = np.absolute((y_real - y_pred) / y_real * 100) + + return accuracy + + +def accuracy_evaluation_tss(x1, x2, y_real, coefficients): + A = np.column_stack((x1, x2, np.ones((x2.shape[0], 1)))) + y = A.dot(coefficients) + + y_pred = np.exp(y) + + accuracy = np.absolute((y_real - y_pred) / y_real * 100) + + return accuracy + + +def get_x_in_coor(coor, *args): + new_columns_counter = len(args) + new_columns = np.zeros((coor.shape[0], new_columns_counter)) + coor_extend = np.hstack((coor, new_columns)) + + for i in range(coor.shape[0]): + for j in range(new_columns_counter): + coor_extend[i, coor_extend.shape[1] - (new_columns_counter - j)] = args[j][ + int(coor_extend[i, coor_extend.shape[1] - new_columns_counter - 1]), + int(coor_extend[i, coor_extend.shape[1] - new_columns_counter - 2])] + + return coor_extend + + +def write_model_info(model_type, coefficients, accuracy, long, lat, outpath): + # 将 NumPy 数组转换为列表 + #保存模型为json文件，包括模型类型、模型系数、准确率、经纬度，模型名称由上一级调用决定，/model/non_empirical_model/preprocessing_method_model_name.jso + np_dict = { + 'model_type': model_type, + 'model_info': coefficients.tolist(), + 'accuracy': accuracy.tolist(), + 'long': long.tolist(), + 'lat': lat.tolist() + } + # 将字典写入 JSON 文件，使用 indent 参数进行格式化（每一级缩进4个空格） + with open(outpath, 'w') as f: + json.dump(np_dict, f, indent=4) + + +def chl_a(csv_data, outpath_coeff, window=5, header=None): # 叶绿素 + """ + 叶绿素模型修正 + :param csv_data: 从CSV读取的数据数组 + :param outpath_coeff: 输出模型信息文件路径 + :param window: 窗口大小 + :极 header: CSV表头信息 + :return: 模型系数 + """ + # 实测值在第一列，光谱数据从第二列开始 + measured_values = csv_data[:, 0] + spectral_data = csv_data[:, 1:] + + # 通过表头查找波长位置 + if header is not None: + # 查找波长对应的列索引 + wave1_idx = find_wavelength_index(header, 651, spectral_start_col=1) + wave2_idx = find_wavelength_index(header, 707, spectral_start_col=1) + wave3_idx = find_wavelength_index(header, 670, spectral_start_col=1) + else: + # 如果没有表头，使用默认索引 + wave1_idx = 651 + wave2_idx = 707 + wave3_idx = 670 + + # 计算波段平均值 + band_651 = np.mean(spectral_data[:, wave1_idx-window:wave1_idx+window+1], axis=1) + band_707 = np.mean(spectral_data[:, wave2_idx-window:wave2_idx+window+1], axis=1) + band_670 = np.mean(spectral_data[:, wave3_idx-window:wave3_idx+window+1], axis=1) + + x = (band_651 - band_707) / (band_707 - band_670) + + # 修正模型参数并输出 + coefficients = np.polyfit(x, measured_values, 1) + + y_pred = np.polyval(coefficients, x) + accuracy = np.absolute((measured_values - y_pred) / measured_values * 100) + + # 创建虚拟的经纬度坐标（因为不再需要地理坐标） + long = np.arange(len(measured_values)) + lat = np.arange(len(measured_values)) + + write_model_info("chl-a", coefficients, accuracy, long, lat, outpath_coeff) + + return coefficients + + +def nh3(csv_data, outpath_coeff, window=5, header=None): # 氨氮 + measured_values = csv_data[:, 0] + spectral_data = csv_data[:, 1:] + + # 通过表头查找波长位置 + if header is not None: + wave1_idx = find_wavelength_index(header, 600, spectral_start_col=1) + wave2_idx = find_wavelength_index(header, 500, spectral_start_col=1) + wave3_idx = find_wavelength_index(header, 850, spectral_start_col=1) + else: + wave1_idx = 600 + wave2_idx = 500 + wave3_idx = 850 + + band_600 = np.mean(spectral_data[:, wave1_idx-window:wave1_idx+window+1], axis=1) + band_500 = np.mean(spectral_data[:, wave2_idx-window:wave2_idx+window+1], axis=1) + band_850 = np.mean(spectral_data[:, wave3_idx-window:wave3_idx+window+1], axis=1) + + x1 = np.log(band_500 / band_850) + x2 = np.exp(band_600 / band_500) + + coefficients = fit(x1, x2, measured_values) + accuracy = accuracy_evaluation(x1, x2, measured_values, coefficients) + + long = np.arange(len(measured_values)) + lat = np.arange(len(measured_values)) + write_model_info("nh3", coefficients, accuracy, long, lat, outpath_coeff) + + return coefficients + + +def mno4(csv_data, outpath_coeff, window=5, header=None): # 高猛酸盐 + measured_values = csv_data[:, 0] + spectral_data = csv_data[:, 1:] + + # 通过表头查找波长位置 + if header is not None: + wave1_idx = find_wavelength_index(header, 500, spectral_start_col=1) + wave2_idx = find_wavelength_index(header, 440, spectral_start_col=1) + wave3_idx = find_wavelength_index(header, 610, spectral_start_col=1) + wave4_idx = find_wavelength_index(header, 800, spectral_start_col=1) + else: + wave1_idx = 500 + wave2_idx = 440 + wave3_idx = 610 + wave4_idx = 800 + + band_500 = np.mean(spectral_data[:, wave1_idx-window:wave1_idx+window+1], axis=1) + band_440 = np.mean(spectral_data[:, wave2_idx-window:wave2_idx+window+1], axis=1) + band_610 = np.mean(spectral_data[:, wave3_idx-window:wave3_idx+window+1], axis=1) + band_800 = np.mean(spectral_data[:, wave4_idx-window:wave4_idx+window+1], axis=1) + + x1 = band_500 / band_440 + x2 = band_610 / band_800 + + coefficients = fit(x1, x2, measured_values) + accuracy = accuracy_evaluation(x1, x2, measured_values, coefficients) + + long = np.arange(len(measured_values)) + lat = np.arange(len(measured_values)) + write_model_info("mno4", coefficients, accuracy, long, lat, outpath_coeff) + + return coefficients + + +def tn(csv_data, outpath_coeff, window=5, header=None): # 总氮 + measured_values = csv_data[:, 0] + spectral_data = csv_data[:, 1:] + + # 通过表头查找波长位置 + if header is not None: + wave1_idx = find_wavelength_index(header, 600, spectral_start_col=1) + wave2_idx = find_wavelength_index(header, 500, spectral_start_col=1) + wave3_idx = find_wavelength_index(header, 850, spectral_start_col=1) + else: + wave1_idx = 600 + wave2_idx = 500 + wave3_idx = 850 + + band_600 = np.mean(spectral_data[:, wave1_idx-window:wave1_idx+window+1], axis=1) + band_500 = np.mean(spectral_data[:, wave2_idx-window:wave2_idx+window+1], axis=1) + band_850 = np.mean(spectral_data[:, wave3_idx-window:wave3_idx+window+1], axis=1) + + x1 = np.log(band_500 / band_850) + x2 = np.exp(band_600 / band_500) + + coefficients = fit(x1, x2, measured_values) + accuracy = accuracy_evaluation(x1, x2, measured_values, coefficients) + + long = np.arange(len(measured_values)) + lat = np.arange(len(measured_values)) + write_model_info("tn", coefficients, accuracy, long, lat, outpath_coeff) + + return coefficients + + +def tp(csv_data, outpath_coeff, window=5, header=None): # 总磷 + measured_values = csv_data[:, 0] + spectral_data = csv_data[:, 1:] + + # 通过表头查找波长位置 + if header is not None: + wave1_idx = find_wavelength_index(header, 600, spectral_start_col=1) + wave2_idx = find_wavelength_index(header, 500, spectral_start_col=1) + wave3_idx = find_wavelength_index(header, 850, spectral_start_col=1) + else: + wave1_idx = 600 + wave2_idx = 500 + wave3_idx = 850 + + band_600 = np.mean(spectral_data[:, wave1_idx-window:wave1_idx+window+1], axis=1) + band_500 = np.mean(spectral_data[:, wave2_idx-window:wave2_idx+window+1], axis=1) + band_850 = np.mean(spectral_data[:, wave3_idx-window:wave3_idx+window+1], axis=1) + + x1 = np.log(band_500 / band_850) + x2 = np.exp(band_600 / band_500) + + coefficients = fit(x1, x2, measured_values) + accuracy = accuracy_evaluation(x1, x2, measured_values, coefficients) + + long = np.arange(len(measured_values)) + lat = np.arange(len(measured_values)) + write_model_info("tp", coefficients, accuracy, long, lat, outpath_coeff) + + return coefficients + + +def tss(csv_data, outpath_coeff, window=5, header=None): # 总悬浮物 + measured_values = csv_data[:, 0] + spectral_data = csv_data[:, 1:] + + # 通过表头查找波长位置 + if header is not None: + wave1_idx = find_wavelength_index(header, 555, spectral_start_col=1) + wave2_idx = find_wavelength_index(header, 670, spectral_start_col=1) + wave3_idx = find_wavelength_index(header, 490, spectral_start_col=1) + else: + wave1_idx = 555 + wave2_idx = 670 + wave3_idx = 490 + + band_555 = np.mean(spectral_data[:, wave1_idx-window:wave1_idx+window+1], axis=1) + band_670 = np.mean(spectral_data[:, wave2_idx-window:wave2_idx+window+1], axis=1) + band_490 = np.mean(spectral_data[:, wave3_idx-window:wave3_idx+window+1], axis=1) + + x1 = band_555 + band_670 + x2 = band_490 / band_555 + + y = np.log(measured_values) + coefficients = fit(x1, x2, y) + accuracy = accuracy_evaluation_tss(x1, x2, measured_values, coefficients) + + long = np.arange(len(measured_values)) + lat = np.arange(len(measured_values)) + write_model_info("tss", coefficients, accuracy, long, lat, outpath_coeff) + + return coefficients + + +def run_model_correction(algorithm, csv_file, value_col, spectral_start, spectral_end, model_info_outpath, window=5): + """ + 运行模型修正 + :param algorithm: 算法名称 (chl_a, nh3, mno4, tn, tp, tss) + :param csv_file: CSV文件路径 + :param value_col: 实测值列索引 + :param spectral_start: 光谱数据起始列索引 + :param spectral_end: 光谱数据结束列索引 + :param model_info_outpath: 输出模型信息文件路径 + :param window: 窗口大小，默认5 + :return: 模型系数 + """ + # 从CSV文件读取数据和表头；直接找到模型对应的所需数据，第一列为实测值，从第二列开始为光谱数据 + csv_data, header = get_spectral_data_from_csv(csv_file, value_col, spectral_start, spectral_end) + + # 根据算法名称调用相应的函数 + algorithm_funcs = { + 'chl_a': chl_a, + 'nh3': nh3, + 'mno4': mno4, + 'tn': tn, + 'tp': tp, + 'tss': tss + } + + if algorithm not in algorithm_funcs: + raise ValueError(f"不支持的算法: {algorithm}。支持的算法有: {list(algorithm_funcs.keys())}") + + # 调用相应的函数，传递表头信息 + coefficients = algorithm_funcs[algorithm](csv_data, model_info_outpath, window, header) + + return coefficients + + +def find_wavelength_index(header, target_wavelength, spectral_start_col=1): + """ + 在表头中查找最接近目标波长的列索引 + :param header: CSV表头列表 + :param target_wavelength: 目标波长 + :param spectral_start_col: 光谱数据起始列索引 + :return: 最接近目标波长的列索引 + """ + # 从光谱数据起始列开始查找 + min_diff = float('inf') + best_index = target_wavelength # 默认值 + + for i in range(spectral_start_col, len(header)): + try: + # 尝试将列名转换为波长值 + wavelength = float(header[i]) + diff = abs(wavelength - target_wavelength) + if diff < min_diff: + min_diff = diff + best_index = i - spectral_start_col # 转换为光谱数据内的相对索引 + except ValueError: + # 如果列名不是数字，跳过 + continue + + return best_index + diff --git a/src/core/non_empirical_retrieval.py b/src/core/non_empirical_retrieval.py new file mode 100644 index 0000000..1b37420 --- /dev/null +++ b/src/core/non_empirical_retrieval.py @@ -0,0 +1,253 @@ +import sys +from src.utils.util import * +import warnings +import pandas as pd +import re # Added for regex parsing in safe_load_spectral +# 配置：光谱起始列（前四列是坐标和像素信息：x_coord,y_coord,pixel_x,pixel_y） + +SPEC_START_COL = 4 + +class RetrievalError(Exception): + """面向用户的友好错误。""" + pass + +def ensure_file_exists(path, name): + if not isinstance(path, str) or not path: + raise RetrievalError(f"{name} 路径为空。") + if not os.path.exists(path): + raise RetrievalError(f"{name} 不存在：{path}") + +def safe_load_model(model_info_path): + ensure_file_exists(model_info_path, "模型信息文件") + try: + model_type, model_info, accuracy_ = load_numpy_dict_from_json(model_info_path) + except Exception as e: + raise RetrievalError(f"无法读取/解析模型文件：{model_info_path}\n原因：{e}") + if model_info is None: + raise RetrievalError("模型文件缺少 'model_info'。") + model_info = np.asarray(model_info) + if model_info.ndim == 0 or model_info.size == 0: + raise RetrievalError("模型系数为空。") + return model_type, model_info, accuracy_ + +def safe_load_spectral(coor_spectral_path): + ensure_file_exists(coor_spectral_path, "坐标-光谱文件") + + # 使用 pandas 读取文件 + try: + # 读取为 DataFrame，跳过第一行（列名），明确指定数据类型为 float + df = pd.read_csv(coor_spectral_path, encoding="utf-8-sig", header=0, dtype=float) + # 转换为 numpy 数组以保持原有格式 + coor_spectral = df.values + except Exception as e: + raise RetrievalError(f"无法读取坐标-光谱文件：{coor_spectral_path}\n原因：{e}") + + if coor_spectral.ndim != 2 or coor_spectral.shape[0] < 1: + raise RetrievalError("坐标-光谱文件维度异常：需要至少一行数据。") + + if coor_spectral.shape[1] <= SPEC_START_COL: + raise RetrievalError(f"坐标-光谱文件列数不足（至少需要 {SPEC_START_COL+1} 列，含 4 列坐标信息 + ≥1 列光谱）。") + + # 由于第一行已经是数据，不再需要提取波长行 + # 波长信息需要从列名中提取 + try: + # 读取列名来获取波长信息 + df_with_header = pd.read_csv(coor_spectral_path, encoding="utf-8-sig", header=0) + wavelengths = df_with_header.columns[SPEC_START_COL:].astype(float).values + except Exception as e: + raise RetrievalError(f"无法解析波长信息：{e}") + + if not np.all(np.isfinite(wavelengths)): + raise RetrievalError("波长数据包含 NaN/Inf。") + # 非严格单调也可，但给出警告 + if np.any(np.diff(wavelengths) <= 0): + warnings.warn("波长非严格递增，这可能导致波段匹配误差。", RuntimeWarning) + + return coor_spectral, wavelengths + +def find_index(wavelength, array): + differences = np.abs(array - wavelength) + min_position = int(np.argmin(differences)) + return min_position + +def _clamp_window(index_abs, window, ncols, spec_start_col=SPEC_START_COL): + if window is None: + raise RetrievalError("window 为空。") + window = int(window) + if window < 0: + raise RetrievalError(f"window 必须为非负整数，收到：{window}") + left = max(spec_start_col, index_abs - window) + right = min(ncols, index_abs + window + 1) + if right - left <= 0: + raise RetrievalError(f"窗口无有效光谱列（left={left}, right={right}, ncols={ncols}）。") + return left, right + +def get_mean_value(index_abs, array, window): + """index_abs 为绝对列索引（含前两列坐标），这里会夹紧窗口。""" + left, right = _clamp_window(index_abs, window, array.shape[1], SPEC_START_COL) + # 仅在样本行上取平均 + result = array[1:, left:right].mean(axis=1) + if not np.all(np.isfinite(result)): + warnings.warn("均值结果包含 NaN/Inf，可能是窗口内存在异常值。", RuntimeWarning) + return result + +def calculate(x1, x2, coefficients): + x1 = np.asarray(x1, dtype=np.float64).ravel() + x2 = np.asarray(x2, dtype=np.float64).ravel() + coeffs = np.asarray(coefficients, dtype=np.float64).reshape(-1) + if x1.shape[0] != x2.shape[0]: + raise RetrievalError(f"x1 与 x2 长度不一致: {x1.shape[0]} vs {x2.shape[0]}") + if coeffs.size != 3: + raise RetrievalError(f"线性模型系数应为 3 个（x1, x2, 截距），收到 {coeffs.size} 个。") + + # 诊断：检查 NaN/Inf + n_bad = (~np.isfinite(x1) | ~np.isfinite(x2)).sum() + if n_bad: + print(f"[警告] x 含 {n_bad} 个非有限值，将产生 NaN。") + + # 避免 dot/blAS，直接逐元素计算 + y_pred = x1 * coeffs[0] + x2 * coeffs[1] + coeffs[2] + return y_pred + + +def _safe_polyval(coeffs, x, name): + coeffs = np.asarray(coeffs).reshape(-1) + if coeffs.ndim != 1 or coeffs.size < 1: + raise RetrievalError(f"{name} 的多项式系数非法。") + try: + y = np.polyval(coeffs, x) + except Exception as e: + raise RetrievalError(f"{name} 计算失败（polyval）：{e}") + return y + +def retrieval_chl_a(model_info_path, coor_spectral_path, output_path, window=5): + model_type, model_info, accuracy_ = safe_load_model(model_info_path) + coor_spectral, wavelengths = safe_load_spectral(coor_spectral_path) + + def idx_abs_for(wave): + idx_rel = find_index(wave, wavelengths) # 相对光谱起始列的索引 + return SPEC_START_COL + idx_rel # 转为绝对列索引 + + try: + idx_651 = idx_abs_for(651) + idx_707 = idx_abs_for(707) + idx_670 = idx_abs_for(670) + except Exception as e: + raise RetrievalError(f"波段索引计算失败：{e}") + + band_651 = get_mean_value(idx_651, coor_spectral, window) + band_707 = get_mean_value(idx_707, coor_spectral, window) + band_670 = get_mean_value(idx_670, coor_spectral, window) + + with np.errstate(divide='ignore', invalid='ignore'): + denom = (band_707 - band_670) + x = (band_651 - band_707) / denom + bad = ~np.isfinite(x) + if bad.any(): + warnings.warn(f"chl_a 极速出现 {bad.sum()} 个无效比值（分母≈0 或含 NaN），这些位置结果将为 NaN。", RuntimeWarning) + + retrieval_result = _safe_polyval(model_info, x, "chl_a") + + # 创建DataFrame并保存为CSV + result_df = pd.DataFrame({ + 'longitude': coor_spectral[1:, 0], + 'latitude': coor_spectral[1:, 1], + 'prediction': retrieval_result + }) + + try: + result_df.to_csv(output_path, index=False, float_format='%.8f') + except Exception as e: + raise RetrievalError(f"写出结果失败：{output_path}\n原因：{e}") + + return result_df.values + +def retrieval_nh3(model_info_path, coor_spectral_path, output_path=None, window=5): + model_type, model_info, accuracy_ = safe_load_model(model_info_path) + coor_spectral, wavelengths = safe_load_spectral(coor_spectral_path) + + def idx_abs_for(wave): + return SPEC_START_COL + find_index(wave, wavelengths) + + idx_600 = idx_abs_for(600) + idx_500 = idx_abs_for(500) + idx_850 = idx_abs_for(850) + + band_600 = get_mean_value(idx_600, coor_spectral, window) + band_500 = get_mean_value(idx_500, coor_spectral, window) + band_850 = get_mean_value(idx_850, coor_spectral, window) + + with np.errstate(divide='ignore', invalid='ignore'): + x13 = np.log(band_500 / band_850) + x23 = np.exp(band_600 / band_500) + invalid = ~np.isfinite(x13) | ~np.isfinite(x23) + if invalid.any(): + warnings.warn(f"nh3 自变量出现 {invalid.sum()} 个无效值（0/负数/NaN），对应位置结果将为 NaN。", RuntimeWarning) + + retrieval_result = calculate(x13, x23, model_info) + + # 创建DataFrame + result_df = pd.DataFrame({ + 'longitude': coor_spectral[1:, 0], + 'latitude': coor_spectral[1:, 1], + 'prediction': retrieval_result + }) + + if output_path is not None: + try: + result_df.to_csv(output_path, index=False, float_format='%.8f') + except Exception as e: + raise RetrievalError(f"写出结果失败：{output_path}\n原因：{e}") + + return result_df.values + +def retrieval_tss(model_info_path, coor_spectral_path, output_path, window=5): + # 先跑 nh3 的同型模型（按你的原逻辑） + position_content = retrieval_nh3(model_info_path, coor_spectral_path, output_path=None, window=window) + + # 对结果进行指数变换 + predictions = np.exp(position_content[:, -1]) + + # 创建DataFrame + result_df = pd.DataFrame({ + 'longitude': position_content[:, 0], + 'latitude': position_content[:, 1], + 'prediction': predictions + }) + + if not np.all(np.isfinite(result_df['prediction'])): + warnings.warn("tss 结果包含非有限值（可能因指数溢出），已保留为 NaN。", RuntimeWarning) + + try: + result_df.to_csv(output_path, index=False, float_format='%.8f') + except Exception as e: + raise RetrievalError(f"写出结果失败：{output_path}\n原因：{e}") + + return result_df.values + +def non_empirical_retrieval(algorithm, model_info_path, coor_spectral_path, output_path, wave_radius=5.0): + try: + if algorithm == "chl_a": + return retrieval_chl_a(model_info_path, coor_spectral_path, output_path, wave_radius) + elif algorithm in ["nh3", "mno4", "tn", "tp"]: + return retrieval_nh3(model_info_path, coor_spectral_path, output_path, wave_radius) + elif algorithm == "tss": + return retrieval_tss(model_info_path, coor_spectral_path, output_path, wave_radius) + else: + raise RetrievalError(f"未知算法：{algorithm}（可选：chl_a / nh3 / mno4 / tn / tp / tss）") + except RetrievalError as e: + # 面向用户的友好错误 + print(f"[错误] {e}", file=sys.stderr) + sys.exit(2) + except Exception as e: + # 未预料的异常，附带类型与少量上下文 + print(f"[致命错误] {type(e).__name__}: {e}", file=sys.stderr) + sys.exit(3) + +if __name__ == "__main__": + algorithm= "chl_a" + model_info_path= r"E:\code\WQ\pipeline_result\work_dir\5_training_spectra\6_5_non_empirical_models\SS\SS_chl_a.json" + coor_spectral_path= r"E:\code\WQ\pipeline_result\work_dir\7_sampling\sampling_spectra.csv" + output_path= r"E:\code\WQ\pipeline_result\work_dir\8_predictions\SS_chl_a.csv" + wave_radius=5.0 + non_empirical_retrieval(algorithm, model_info_path, coor_spectral_path, output_path, wave_radius) \ No newline at end of file diff --git a/src/core/prediction/__init__.py b/src/core/prediction/__init__.py new file mode 100644 index 0000000..7c68785 --- /dev/null +++ b/src/core/prediction/__init__.py @@ -0,0 +1 @@ +# -*- coding: utf-8 -*- \ No newline at end of file diff --git a/src/core/prediction/inference_batch.py b/src/core/prediction/inference_batch.py new file mode 100644 index 0000000..9204fef --- /dev/null +++ b/src/core/prediction/inference_batch.py @@ -0,0 +1,1144 @@ +import numpy as np +import pandas as pd +import joblib +import os +from pathlib import Path +from typing import List, Dict, Union, Tuple, Optional +import warnings + +warnings.filterwarnings('ignore') + +# 导入预处理模块 - 动态添加路径支持 +import sys +import os + +from src.preprocessing.spectral_Preprocessing import Preprocessing + +# try: +# from modeling import WaterQualityModeling +# except ImportError: +# from src.core.modeling.modeling_batch import WaterQualityModeling + +# 机器学习相关导入 +from sklearn.model_selection import train_test_split + + +class WaterQualityInference: + """水质参数反演推理类""" + + def __init__(self, artifacts_dir: str = "models/artifacts"): + """ + 初始化推理类 + + Args: + artifacts_dir: 模型保存目录 + """ + self.artifacts_dir = Path(artifacts_dir) + if not self.artifacts_dir.exists(): + print(f"警告: 模型目录不存在: {artifacts_dir}，将在需要时创建") + + self.best_model_info = None + self.loaded_model_data = None + + def load_sampling_data(self, csv_path: str) -> Tuple[pd.DataFrame, pd.DataFrame]: + """ + 加载sampling生成的CSV数据 + + Args: + csv_path: CSV文件路径，前两列为经纬度，其余列为光谱数据 + + Returns: + coords: 经纬度数据 (DataFrame) + spectra: 光谱数据 (DataFrame) + """ + print(f"正在加载采样数据: {csv_path}") + + if not os.path.exists(csv_path): + raise FileNotFoundError(f"采样数据文件不存在: {csv_path}") + + # 读取CSV文件 + data = pd.read_csv(csv_path) + + print(f"采样数据加载完成:") + print(f" 数据形状: {data.shape}") + print(f" 列名: {list(data.columns[:5])}...") # 只显示前5列 + + # 检查数据列数 + if data.shape[1] < 4: + raise ValueError(f"数据列数不足，期望至少4列（经度、纬度、其他列、光谱数据），实际得到{data.shape[1]}列") + + # 前两列为经纬度 + coords = data.iloc[:, :2].copy() + coords.columns = ['longitude', 'latitude'] + + # 从第5列开始为光谱数据（跳过第2、3、4列的其他信息） + spectra = data.iloc[:, 4:].copy() + + print(f" 经纬度数据形状: {coords.shape}") + print(f" 光谱数据形状: {spectra.shape}") + print(f" 经纬度范围: 经度[{coords['longitude'].min():.6f}, {coords['longitude'].max():.6f}], " + f"纬度[{coords['latitude'].min():.6f}, {coords['latitude'].max():.6f}]") + + return coords, spectra + + def random(self, data, label, test_ratio=0.2, random_state=123): + """ + 随机划分数据集 + + Args: + data: shape (n_samples, n_features) + label: shape (n_sample, ) + test_ratio: 测试集比例，默认: 0.2 + random_state: 随机种子，默认: 123 + + Returns: + X_train: (n_samples, n_features) + X_test: (n_samples, n_features) + y_train: (n_sample, ) + y_test: (n_sample, ) + """ + X_train, X_test, y_train, y_test = train_test_split( + data, label, test_size=test_ratio, random_state=random_state + ) + return X_train, X_test, y_train, y_test + + def spxy(self, data, label, test_size=0.2): + """ + SPXY算法划分数据集（考虑X和Y空间的距离） + + Args: + data: shape (n_samples, n_features) + label: shape (n_samples, ) + test_size: 测试集比例，默认: 0.2 + + Returns: + X_train: (n_samples, n_features) + X_test: (n_samples, n_features) + y_train: (n_samples, ) + y_test: (n_samples, ) + """ + # 确保 data 和 label 是 NumPy 数组 + data = data.to_numpy() if isinstance(data, pd.DataFrame) else data + label = label.to_numpy() if isinstance(label, pd.Series) else label + + # 备份原始数据和标签 + x_backup = data + y_backup = label + + M = data.shape[0] + N = round((1 - test_size) * M) + samples = np.arange(M) + + # 归一化标签数据 + label = (label - np.mean(label)) / np.std(label) + D = np.zeros((M, M)) + Dy = np.zeros((M, M)) + + # 计算样本之间的距离 + for i in range(M - 1): + xa = data[i, :] + ya = label[i] + for j in range((i + 1), M): + xb = data[j, :] + yb = label[j] + D[i, j] = np.linalg.norm(xa - xb) + Dy[i, j] = np.linalg.norm(ya - yb) + + # 距离归一化 + Dmax = np.max(D) + Dymax = np.max(Dy) + D = D / Dmax + Dy / Dymax + + # 找到最远的两个点 + maxD = D.max(axis=0) + index_row = D.argmax(axis=0) + index_column = maxD.argmax() + + m = np.zeros(N, dtype=int) + m[0] = index_row[index_column] + m[1] = index_column + + dminmax = np.zeros(N) + dminmax[1] = D[m[0], m[1]] + + # 根据距离选择训练集 + for i in range(2, N): + pool = np.delete(samples, m[:i]) + dmin = np.zeros(M - i) + for j in range(M - i): + indexa = pool[j] + d = np.zeros(i) + for k in range(i): + indexb = m[k] + if indexa < indexb: + d[k] = D[indexa, indexb] + else: + d[k] = D[indexb, indexa] + dmin[j] = np.min(d) + dminmax[i] = np.max(dmin) + index = np.argmax(dmin) + m[i] = pool[index] + + m_complement = np.delete(samples, m) + + # 划分训练集和测试集 + X_train = data[m, :] + y_train = y_backup[m] + X_test = data[m_complement, :] + y_test = y_backup[m_complement] + + return X_train, X_test, y_train, y_test + + def ks(self, data, label, test_size=0.2): + """ + Kennard-Stone算法划分数据集 + + Args: + data: shape (n_samples, n_features) + label: shape (n_sample, ) + test_size: 测试集比例，默认: 0.2 + + Returns: + X_train: (n_samples, n_features) + X_test: (n_samples, n_features) + y_train: (n_samples, ) + y_test: (n_samples, ) + """ + # 确保 data 和 label 是 NumPy 数组 + data = data.to_numpy() if isinstance(data, pd.DataFrame) else data + label = label.to_numpy() if isinstance(label, pd.Series) else label + + M = data.shape[0] + N = round((1 - test_size) * M) + samples = np.arange(M) + + D = np.zeros((M, M)) + + for i in range((M - 1)): + xa = data[i, :] + for j in range((i + 1), M): + xb = data[j, :] + D[i, j] = np.linalg.norm(xa - xb) + + maxD = np.max(D, axis=0) + index_row = np.argmax(D, axis=0) + index_column = np.argmax(maxD) + + m = np.zeros(N) + m[0] = np.array(index_row[index_column]) + m[1] = np.array(index_column) + m = m.astype(int) + dminmax = np.zeros(N) + dminmax[1] = D[m[0], m[1]] + + for i in range(2, N): + pool = np.delete(samples, m[:i]) + dmin = np.zeros((M - i)) + for j in range((M - i)): + indexa = pool[j] + d = np.zeros(i) + for k in range(i): + indexb = m[k] + if indexa < indexb: + d[k] = D[indexa, indexb] + else: + d[k] = D[indexb, indexa] + dmin[j] = np.min(d) + dminmax[i] = np.max(dmin) + index = np.argmax(dmin) + m[i] = pool[index] + + m_complement = np.delete(np.arange(data.shape[0]), m) + + X_train = data[m, :] + y_train = label[m] + X_test = data[m_complement, :] + y_test = label[m_complement] + + return X_train, X_test, y_train, y_test + + def split_data(self, X: np.ndarray, y: pd.Series, method: str = "random", + test_size: float = 0.2, random_state: int = 42) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """ + 根据指定方法划分数据集 + + Args: + X: 特征数据 + y: 目标值数据 + method: 划分方法 ("random", "spxy", "ks") + test_size: 测试集比例 + random_state: 随机种子（仅对random方法有效） + + Returns: + X_train, X_test, y_train, y_test + """ + print(f"使用 {method} 方法划分数据集") + + if method == "random": + return self.random(X, y, test_ratio=test_size, random_state=random_state) + elif method == "spxy": + return self.spxy(X, y, test_size=test_size) + elif method == "ks": + return self.ks(X, y, test_size=test_size) + else: + raise ValueError(f"不支持的划分方法: {method}. 支持的方法: ['random', 'spxy', 'ks']") + + def get_best_model_from_summary(self, metric: str = 'test_r2') -> Tuple[str, str]: + """ + 从训练摘要中获取最佳模型信息 + + Args: + metric: 评估指标（默认使用test_r2，回归任务的主要指标） + + Returns: + preprocess_method: 预处理方法 + model_name: 模型名称 + """ + # 获取当前artifacts_dir的文件夹名称（用作目标列名） + folder_name = self.artifacts_dir.name + + # 尝试加载详细结果文件（使用新的命名格式） + detailed_path = self.artifacts_dir / f"{folder_name}_detailed_results.csv" + summary_path = self.artifacts_dir / f"{folder_name}_training_summary.csv" + + # 备用的旧格式文件路径 + old_detailed_path = self.artifacts_dir / "detailed_results.csv" + old_summary_path = self.artifacts_dir / "training_summary.csv" + + summary_df = None + + # 优先使用新格式的详细结果文件 + if detailed_path.exists(): + print(f"使用详细结果文件: {detailed_path}") + summary_df = pd.read_csv(detailed_path) + # 将中文列名映射到英文 + metric_mapping = { + 'test_r2': '测试集R²', + 'train_r2': '训练集R²', + 'test_rmse': '测试集RMSE', + 'train_rmse': '训练集RMSE', + 'cv_mean': 'CV均值' + } + if metric in metric_mapping and metric_mapping[metric] in summary_df.columns: + metric_col = metric_mapping[metric] + else: + metric_col = metric + elif summary_path.exists(): + print(f"使用训练摘要文件: {summary_path}") + summary_df = pd.read_csv(summary_path) + metric_col = metric + elif old_detailed_path.exists(): + print(f"使用旧格式详细结果文件: {old_detailed_path}") + summary_df = pd.read_csv(old_detailed_path) + # 将中文列名映射到英文 + metric_mapping = { + 'test_r2': '测试集R²', + 'train_r2': '训练集R²', + 'test_rmse': '测试集RMSE', + 'train_rmse': '训练集RMSE', + 'cv_mean': 'CV均值' + } + if metric in metric_mapping and metric_mapping[metric] in summary_df.columns: + metric_col = metric_mapping[metric] + else: + metric_col = metric + elif old_summary_path.exists(): + print(f"使用旧格式训练摘要文件: {old_summary_path}") + summary_df = pd.read_csv(old_summary_path) + metric_col = metric + else: + raise FileNotFoundError(f"训练摘要文件不存在，尝试的路径:\n" + f" - {detailed_path}\n" + f" - {summary_path}\n" + f" - {old_detailed_path}\n" + f" - {old_summary_path}") + + if summary_df.empty: + raise ValueError("训练摘要为空") + + # 检查指标列是否存在 + if metric_col not in summary_df.columns: + available_cols = list(summary_df.columns) + raise ValueError(f"指标 '{metric_col}' 不存在。可用列: {available_cols}") + + # 获取最佳模型（对于R²等指标，值越大越好） + if 'r2' in metric.lower() or 'score' in metric.lower(): + best_idx = summary_df[metric_col].idxmax() + else: # 对于RMSE、MAE等，值越小越好 + best_idx = summary_df[metric_col].idxmin() + + best_row = summary_df.loc[best_idx] + + # 根据文件类型解析模型信息 + if (detailed_path.exists() or old_detailed_path.exists()) and '划分方法' in summary_df.columns: + # 详细结果文件格式 + split_method = best_row['划分方法'] + preprocess_method = best_row['预处理方法'] + model_name = best_row['建模方法'] + + # 处理 nan/NaN/None 值，转换为 "None" 字符串 + if pd.isna(preprocess_method) or str(preprocess_method).lower() in ['nan', 'none', '']: + preprocess_method = "None" + + best_combination = f"{split_method}_{preprocess_method}_{model_name}" + else: + # 简化结果文件格式 + best_combination = best_row['combination'] + # 解析组合名称（格式: split_method_preprocess_method_model_name） + parts = best_combination.split('_') + if len(parts) < 3: + raise ValueError(f"无效的模型组合名称格式: {best_combination}") + + split_method = parts[0] + preprocess_method = parts[1] + model_name = '_'.join(parts[2:]) + + # 处理 nan/NaN/None 值，转换为 "None" 字符串 + if pd.isna(preprocess_method) or str(preprocess_method).lower() in ['nan', 'none', '']: + preprocess_method = "None" + + print(f"最佳模型组合: {best_combination}") + print(f" 划分方法: {split_method}") + print(f" 预处理方法: {preprocess_method}") + print(f" 模型名称: {model_name}") + print(f" {metric_col}: {best_row[metric_col]:.4f}") + + self.best_model_info = { + 'combination': best_combination, + 'split_method': split_method, + 'preprocess_method': preprocess_method, + 'model_name': model_name, + 'metric_value': best_row[metric_col] + } + + # 返回用于加载模型的文件名格式 + model_file_prefix = f"{split_method}_{preprocess_method}" + return model_file_prefix, model_name + + def load_best_model(self, metric: str = 'test_r2'): + """ + 加载最佳模型 + + Args: + metric: 评估指标 + """ + model_file_prefix, model_name = self.get_best_model_from_summary(metric) + + # 获取当前artifacts_dir的文件夹名称（用作目标列名） + folder_name = self.artifacts_dir.name + + # 构建模型文件路径（新格式：包含目标列名） + filename = f"{folder_name}_{model_file_prefix}_{model_name}.joblib" + filepath = self.artifacts_dir / filename + + # 如果新格式文件不存在，尝试旧格式 + if not filepath.exists(): + old_filename = f"{model_file_prefix}_{model_name}.joblib" + old_filepath = self.artifacts_dir / old_filename + if old_filepath.exists(): + filepath = old_filepath + filename = old_filename + print(f"使用旧格式模型文件: {filepath}") + else: + raise FileNotFoundError(f"模型文件不存在，尝试的路径:\n" + f" - {filepath}\n" + f" - {old_filepath}") + else: + print(f"使用新格式模型文件: {filepath}") + + print(f"正在加载模型: {filepath}") + + # 加载模型数据 + self.loaded_model_data = joblib.load(filepath) + + print("模型加载完成:") + print(f" 预处理方法: {self.loaded_model_data['preprocess_method']}") + print(f" 模型名称: {self.loaded_model_data['model_name']}") + print(f" 模型类型: {type(self.loaded_model_data['model'])}") + + if 'metadata' in self.loaded_model_data: + metadata = self.loaded_model_data['metadata'] + print(f" 数据形状: {metadata.get('data_shape', 'Unknown')}") + print(f" 目标范围: {metadata.get('target_range', 'Unknown')}") + if 'test_r2' in metadata: + print(f" 测试集R²: {metadata['test_r2']:.4f}") + if 'test_rmse' in metadata: + print(f" 测试集RMSE: {metadata['test_rmse']:.4f}") + + def load_specific_model(self, model_file_path: str): + """ + 加载指定的模型文件 + + Args: + model_file_path: 模型文件路径 + """ + if not os.path.exists(model_file_path): + raise FileNotFoundError(f"模型文件不存在: {model_file_path}") + + print(f"正在加载指定模型: {model_file_path}") + + # 加载模型数据 + self.loaded_model_data = joblib.load(model_file_path) + + print("模型加载完成:") + print(f" 预处理方法: {self.loaded_model_data['preprocess_method']}") + print(f" 模型名称: {self.loaded_model_data['model_name']}") + print(f" 模型类型: {type(self.loaded_model_data['model'])}") + + def preprocess_spectra(self, spectra: pd.DataFrame) -> np.ndarray: + """ + 对光谱数据进行预处理 + + Args: + spectra: 原始光谱数据 + + Returns: + 预处理后的光谱数据 + """ + if self.loaded_model_data is None: + raise ValueError("请先加载模型") + + preprocess_method = self.loaded_model_data['preprocess_method'] + + # 处理 nan/NaN/None 值，转换为 "None" 字符串 + if pd.isna(preprocess_method) or str(preprocess_method).lower() in ['nan', 'none', '']: + preprocess_method = "None" + + # 解析预处理方法（可能包含划分方法前缀） + if '_' in str(preprocess_method): + parts = str(preprocess_method).split('_') + # 假设格式为 split_method_preprocess_method + actual_preprocess_method = '_'.join(parts[1:]) if len(parts) > 1 else parts[-1] + else: + actual_preprocess_method = str(preprocess_method) + + # 再次检查并转换 nan + if actual_preprocess_method.lower() in ['nan', 'none', '']: + actual_preprocess_method = "None" + + print(f"正在应用预处理方法: {actual_preprocess_method}") + print(f"原始光谱数据形状: {spectra.shape}") + + try: + # 应用预处理 + spectra_processed = Preprocessing(actual_preprocess_method, spectra) + + # 确保返回numpy数组 + if isinstance(spectra_processed, pd.DataFrame): + spectra_processed = spectra_processed.values + + print(f"预处理后数据形状: {spectra_processed.shape}") + + return spectra_processed + + except Exception as e: + print(f"预处理失败: {e}") + print("使用原始数据") + return spectra.values + + def predict(self, spectra_processed: np.ndarray) -> np.ndarray: + """ + 使用加载的模型进行预测 + + Args: + spectra_processed: 预处理后的光谱数据 + + Returns: + 预测结果 + """ + if self.loaded_model_data is None: + raise ValueError("请先加载模型") + + model = self.loaded_model_data['model'] + + print(f"正在进行预测...") + print(f"输入数据形状: {spectra_processed.shape}") + + try: + predictions = model.predict(spectra_processed) + print(f"预测完成，结果形状: {predictions.shape}") + print(f"预测值范围: [{np.min(predictions):.4f}, {np.max(predictions):.4f}]") + print(f"预测值统计: 均值={np.mean(predictions):.4f}, 标准差={np.std(predictions):.4f}") + + return predictions + + except Exception as e: + print(f"预测失败: {e}") + raise + + def save_predictions(self, coords: pd.DataFrame, predictions: np.ndarray, + output_path: str, prediction_column: str = 'prediction'): + """ + 保存预测结果 + + Args: + coords: 经纬度数据 + predictions: 预测结果 + output_path: 输出文件路径 + prediction_column: 预测列名称 + """ + print(f"正在保存预测结果到: {output_path}") + + # 创建结果DataFrame + result_df = coords.copy() + result_df[prediction_column] = predictions + + # 确保输出目录存在 + output_dir = os.path.dirname(output_path) + if output_dir: + os.makedirs(output_dir, exist_ok=True) + + # 根据文件扩展名选择保存格式 + file_ext = Path(output_path).suffix.lower() + + if file_ext == '.xls': + # 保存为Excel 97-2003格式 + try: + result_df.to_excel(output_path, index=False, engine='xlwt') + print(f" 格式: Excel 97-2003 (.xls)") + except ImportError: + print("警告: xlwt库未安装，无法保存为.xls格式，改为保存CSV格式") + csv_path = output_path.replace('.xls', '.csv') + result_df.to_csv(csv_path, index=False, encoding='utf-8-sig') + output_path = csv_path + elif file_ext == '.xlsx': + # 保存为Excel 2007+格式 + try: + result_df.to_excel(output_path, index=False, engine='openpyxl') + print(f" 格式: Excel 2007+ (.xlsx)") + except ImportError: + print("警告: openpyxl库未安装，无法保存为.xlsx格式，改为保存CSV格式") + csv_path = output_path.replace('.xlsx', '.csv') + result_df.to_csv(csv_path, index=False, encoding='utf-8-sig') + output_path = csv_path + else: + # 默认保存为CSV格式 + result_df.to_csv(output_path, index=False, encoding='utf-8-sig') + print(f" 格式: CSV (.csv)") + + print(f"预测结果保存完成:") + print(f" 输出文件: {output_path}") + print(f" 数据形状: {result_df.shape}") + print(f" 列名: {list(result_df.columns)}") + + # 显示预测结果统计 + print(f"\n预测结果统计:") + print(result_df[prediction_column].describe()) + + return result_df + + def inference_pipeline(self, sampling_csv_path: str, output_csv_path: str, + metric: str = 'test_r2', prediction_column: str = 'prediction', + model_file_path: str = None): + """ + 完整的推理流程 + + Args: + sampling_csv_path: 采样数据CSV路径 + output_csv_path: 输出预测结果CSV路径 + metric: 选择最佳模型的指标 + prediction_column: 预测列名称 + model_file_path: 指定模型文件路径（可选） + """ + print("=" * 80) + print("开始水质参数反演推理流程") + print("=" * 80) + + try: + # 1. 加载模型 + print("\n步骤1: 加载模型") + print("-" * 40) + if model_file_path: + self.load_specific_model(model_file_path) + else: + self.load_best_model(metric=metric) + + # 2. 加载采样数据 + print("\n步骤2: 加载采样数据") + print("-" * 40) + coords, spectra = self.load_sampling_data(sampling_csv_path) + + # 3. 数据预处理 + print("\n步骤3: 数据预处理") + print("-" * 40) + spectra_processed = self.preprocess_spectra(spectra) + + # 4. 模型预测 + print("\n步骤4: 模型预测") + print("-" * 40) + predictions = self.predict(spectra_processed) + + # 5. 保存预测结果 + print("\n步骤5: 保存预测结果") + print("-" * 40) + result_df = self.save_predictions(coords, predictions, output_csv_path, prediction_column) + + print("\n" + "=" * 80) + print("推理流程完成！") + print("=" * 80) + + return predictions, result_df + + except Exception as e: + print(f"\n推理流程失败: {e}") + raise + + def get_model_info(self) -> Dict: + """ + 获取当前加载模型的信息 + + Returns: + 模型信息字典 + """ + if self.loaded_model_data is None: + return {"status": "no_model_loaded"} + + info = { + "status": "model_loaded", + "preprocess_method": self.loaded_model_data['preprocess_method'], + "model_name": self.loaded_model_data['model_name'], + "model_type": str(type(self.loaded_model_data['model'])), + "metadata": self.loaded_model_data.get('metadata', {}) + } + + if self.best_model_info: + info.update(self.best_model_info) + + return info + + def batch_inference(self, input_dir: str, output_dir: str, + metric: str = 'test_r2', prediction_column: str = 'prediction'): + """ + 批量推理多个采样文件 + + Args: + input_dir: 输入目录，包含多个采样CSV文件 + output_dir: 输出目录 + metric: 选择最佳模型的指标 + prediction_column: 预测列名称 + """ + input_path = Path(input_dir) + output_path = Path(output_dir) + output_path.mkdir(parents=True, exist_ok=True) + + # 查找所有CSV文件 + csv_files = list(input_path.glob("*.csv")) + + if not csv_files: + print(f"在目录 {input_dir} 中未找到CSV文件") + return + + print(f"找到 {len(csv_files)} 个CSV文件进行批量推理") + + # 加载模型（只需加载一次） + self.load_best_model(metric=metric) + + results = {} + + for csv_file in csv_files: + try: + print(f"\n处理文件: {csv_file.name}") + output_file = output_path / f"prediction_{csv_file.name}" + + # 执行推理 + coords, spectra = self.load_sampling_data(str(csv_file)) + spectra_processed = self.preprocess_spectra(spectra) + predictions = self.predict(spectra_processed) + result_df = self.save_predictions(coords, predictions, str(output_file), prediction_column) + + results[csv_file.name] = { + 'output_file': str(output_file), + 'sample_count': len(predictions), + 'prediction_stats': { + 'mean': np.mean(predictions), + 'std': np.std(predictions), + 'min': np.min(predictions), + 'max': np.max(predictions) + } + } + + except Exception as e: + print(f"处理文件 {csv_file.name} 失败: {e}") + results[csv_file.name] = {'error': str(e)} + + print(f"\n批量推理完成，共处理 {len(csv_files)} 个文件") + return results + + def batch_inference_multi_models(self, models_root_dir: str, sampling_csv_path: str, + output_dir: str, metric: str = 'test_r2', + prediction_column: str = 'prediction', + output_format: str = 'csv'): + """ + 使用多个子文件夹中的模型进行批量推理 + + Args: + models_root_dir: 包含多个子文件夹的根目录，每个子文件夹作为artifacts_dir + sampling_csv_path: 采样数据CSV路径 + output_dir: 输出目录 + metric: 选择最佳模型的指标 + prediction_column: 预测列名称 + output_format: 输出文件格式 ('csv', 'xls', 'xlsx') + """ + models_root = Path(models_root_dir) + output_path = Path(output_dir) + output_path.mkdir(parents=True, exist_ok=True) + + # 查找所有子文件夹 + subdirs = [d for d in models_root.iterdir() if d.is_dir()] + + if not subdirs: + print(f"在目录 {models_root_dir} 中未找到子文件夹") + return + + print(f"找到 {len(subdirs)} 个模型子文件夹进行批量推理") + print(f"输出格式: {output_format.upper()}") + + all_results = {} + + for subdir in subdirs: + try: + subdir_name = subdir.name + print(f"\n{'='*60}") + print(f"处理模型文件夹: {subdir_name}") + print(f"{'='*60}") + + # 创建新的推理实例，使用当前子文件夹作为artifacts_dir + model_inferencer = WaterQualityInference(str(subdir)) + + # 根据输出格式设置文件扩展名 + file_ext = f".{output_format}" + output_file = output_path / f"{subdir_name}{file_ext}" + + # 执行推理流程 + predictions, result_df = model_inferencer.inference_pipeline( + sampling_csv_path=sampling_csv_path, + output_csv_path=str(output_file), + metric=metric, + prediction_column=prediction_column + ) + + # 收集结果信息 + model_info = model_inferencer.get_model_info() + all_results[subdir_name] = { + 'status': 'success', + 'output_file': str(output_file), + 'sample_count': len(predictions), + 'model_info': model_info, + 'prediction_stats': { + 'mean': np.mean(predictions), + 'std': np.std(predictions), + 'min': np.min(predictions), + 'max': np.max(predictions) + } + } + + print(f"子文件夹 {subdir_name} 处理完成") + + except Exception as e: + print(f"处理子文件夹 {subdir_name} 失败: {e}") + all_results[subdir_name] = { + 'status': 'error', + 'error': str(e) + } + + print(f"\n{'='*80}") + print(f"批量推理完成，共处理 {len(subdirs)} 个模型文件夹") + print(f"{'='*80}") + + # 打印汇总信息 + print("\n汇总结果:") + for folder_name, result in all_results.items(): + if result['status'] == 'success': + print(f" ✓ {folder_name}: {result['sample_count']} 个预测值，" + f"均值={result['prediction_stats']['mean']:.4f}") + else: + print(f" ✗ {folder_name}: 失败 - {result['error']}") + + return all_results + + def batch_inference_multi_data(self, artifacts_dir: str, input_dir: str, + output_dir: str, metric: str = 'test_r2', + prediction_column: str = 'prediction', + output_format: str = 'csv'): + """ + 使用一个模型对多个数据文件进行批量推理，输出文件名为数据文件名（不含扩展名） + + Args: + artifacts_dir: 模型目录 + input_dir: 输入目录，包含多个采样CSV文件 + output_dir: 输出目录 + metric: 选择最佳模型的指标 + prediction_column: 预测列名称 + output_format: 输出文件格式 ('csv', 'xls', 'xlsx') + """ + input_path = Path(input_dir) + output_path = Path(output_dir) + output_path.mkdir(parents=True, exist_ok=True) + + # 查找所有CSV文件 + csv_files = list(input_path.glob("*.csv")) + + if not csv_files: + print(f"在目录 {input_dir} 中未找到CSV文件") + return + + print(f"找到 {len(csv_files)} 个CSV文件进行批量推理") + print(f"输出格式: {output_format.upper()}") + + # 初始化推理器并加载模型（只需加载一次） + self.artifacts_dir = Path(artifacts_dir) + self.load_best_model(metric=metric) + + results = {} + + for csv_file in csv_files: + try: + # 获取不含扩展名的文件名 + file_stem = csv_file.stem + print(f"\n处理文件: {csv_file.name}") + + # 根据输出格式设置文件扩展名 + file_ext = f".{output_format}" + output_file = output_path / f"{file_stem}{file_ext}" + + # 执行推理 + coords, spectra = self.load_sampling_data(str(csv_file)) + spectra_processed = self.preprocess_spectra(spectra) + predictions = self.predict(spectra_processed) + result_df = self.save_predictions(coords, predictions, str(output_file), prediction_column) + + results[file_stem] = { + 'input_file': str(csv_file), + 'output_file': str(output_file), + 'sample_count': len(predictions), + 'prediction_stats': { + 'mean': np.mean(predictions), + 'std': np.std(predictions), + 'min': np.min(predictions), + 'max': np.max(predictions) + } + } + + except Exception as e: + print(f"处理文件 {csv_file.name} 失败: {e}") + results[csv_file.stem] = {'error': str(e)} + + print(f"\n批量推理完成，共处理 {len(csv_files)} 个文件") + return results + + def evaluate_with_split(self, data_csv_path: str, split_method: str = "random", + test_size: float = 0.2, random_state: int = 42, + target_column: int = 11, feature_start_column: int = 13, + metric: str = 'test_r2', prediction_column: str = 'prediction'): + """ + 使用训练时相同的数据分割方法进行模型评估 + + Args: + data_csv_path: 包含目标值的完整数据集CSV路径 + split_method: 数据分割方法 ("random", "spxy", "ks") + test_size: 测试集比例 + random_state: 随机种子 + target_column: 目标值列索引 + feature_start_column: 特征开始列索引 + metric: 选择模型的评估指标 + prediction_column: 预测结果列名 + + Returns: + 评估结果字典 + """ + print("=" * 80) + print("开始数据分割评估流程") + print("=" * 80) + + try: + # 1. 加载完整数据集 + print("\n步骤1: 加载完整数据集") + print("-" * 40) + data = pd.read_csv(data_csv_path) + + # 提取目标值和特征 + y = data.iloc[:, target_column] + X = data.iloc[:, feature_start_column:] + + # 去除目标值为空的行 + mask = ~y.isna() + data_cleaned = data[mask] + y_cleaned = data_cleaned.iloc[:, target_column] + X_cleaned = data_cleaned.iloc[:, feature_start_column:] + + print(f"数据加载完成:") + print(f" 原始样本数: {len(data)}") + print(f" 清理后样本数: {len(X_cleaned)}") + print(f" 特征数量: {X_cleaned.shape[1]}") + print(f" 目标值范围: {y_cleaned.min():.4f} ~ {y_cleaned.max():.4f}") + + # 2. 加载最佳模型 + print("\n步骤2: 加载最佳模型") + print("-" * 40) + self.load_best_model(metric=metric) + + # 3. 数据预处理 + print("\n步骤3: 数据预处理") + print("-" * 40) + X_processed = self.preprocess_spectra(X_cleaned) + + # 4. 数据分割 + print("\n步骤4: 数据分割") + print("-" * 40) + X_train, X_test, y_train, y_test = self.split_data( + X_processed, y_cleaned, method=split_method, + test_size=test_size, random_state=random_state + ) + + print(f"数据分割完成:") + print(f" 训练集样本数: {X_train.shape[0]}") + print(f" 测试集样本数: {X_test.shape[0]}") + + # 5. 模型预测 + print("\n步骤5: 模型预测") + print("-" * 40) + + # 训练集预测 + y_train_pred = self.loaded_model_data['model'].predict(X_train) + + # 测试集预测 + y_test_pred = self.loaded_model_data['model'].predict(X_test) + + # 6. 计算评估指标 + print("\n步骤6: 计算评估指标") + print("-" * 40) + + from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score + + # 训练集指标 + train_mse = mean_squared_error(y_train, y_train_pred) + train_mae = mean_absolute_error(y_train, y_train_pred) + train_r2 = r2_score(y_train, y_train_pred) + train_rmse = np.sqrt(train_mse) + + # 测试集指标 + test_mse = mean_squared_error(y_test, y_test_pred) + test_mae = mean_absolute_error(y_test, y_test_pred) + test_r2 = r2_score(y_test, y_test_pred) + test_rmse = np.sqrt(test_mse) + + results = { + 'split_method': split_method, + 'test_size': test_size, + 'train_size': len(y_train), + 'test_size_actual': len(y_test), + 'train_metrics': { + 'mse': train_mse, + 'mae': train_mae, + 'rmse': train_rmse, + 'r2': train_r2 + }, + 'test_metrics': { + 'mse': test_mse, + 'mae': test_mae, + 'rmse': test_rmse, + 'r2': test_r2 + }, + 'predictions': { + 'y_train_true': y_train, + 'y_train_pred': y_train_pred, + 'y_test_true': y_test, + 'y_test_pred': y_test_pred + } + } + + print(f"评估完成:") + print(f" 训练集指标:") + print(f" R²: {train_r2:.4f}") + print(f" RMSE: {train_rmse:.4f}") + print(f" MAE: {train_mae:.4f}") + print(f" 测试集指标:") + print(f" R²: {test_r2:.4f}") + print(f" RMSE: {test_rmse:.4f}") + print(f" MAE: {test_mae:.4f}") + + print("\n" + "=" * 80) + print("数据分割评估流程完成！") + print("=" * 80) + + return results + + except Exception as e: + print(f"\n数据分割评估失败: {e}") + raise + + +def main(): + """主函数示例""" + # 创建推理实例 + artifacts_dir = r"E:\code\WQ\yaobao925\qvchuyaoban" + inferencer = WaterQualityInference(artifacts_dir) + + # 配置文件路径 + sampling_csv = r"E:\code\WQ\xiaogujia\使用腰堡模型\spectral_sampling_results.csv" + # output_csv = r"E:\code\WQ\laodao\output" + + try: + # # 示例1: 单个模型单个数据文件的推理 + # print("示例1: 单个模型单个数据文件的推理") + # predictions, result_df = inferencer.inference_pipeline( + # sampling_csv_path=sampling_csv, + # output_csv_path=output_csv, + # metric='test_r2', # 使用测试集R²作为选择最佳模型的指标 + # prediction_column='water_quality_prediction' + # ) + # + # print(f"\n推理完成，共生成 {len(predictions)} 个预测值") + # + # # 显示模型信息 + # model_info = inferencer.get_model_info() + # print(f"\n使用的模型信息:") + # print(f" 组合: {model_info.get('combination', 'Unknown')}") + # print(f" 预处理: {model_info.get('preprocess_method', 'Unknown')}") + # print(f" 算法: {model_info.get('model_name', 'Unknown')}") + + # 示例2: 批量推理多个模型（每个子文件夹作为不同的artifacts_dir） + print(f"\n{'='*80}") + print("示例2: 批量推理多个模型") + models_root_dir = r"E:\code\WQ\yaobao925\qvchuyaoban" # 包含多个子文件夹的根目录 + output_dir = r"E:\code\WQ\xiaogujia\使用腰堡模型\predict" + + all_results = inferencer.batch_inference_multi_models( + models_root_dir=models_root_dir, + sampling_csv_path=sampling_csv, + output_dir=output_dir, + metric='test_r2', + prediction_column='water_quality_prediction' + ) + + # 示例3: 使用数据分割方法进行模型评估（可选） + # print(f"\n{'='*80}") + # print("示例3: 数据分割评估") + # complete_data_csv = r"E:\code\WQ\laodao\data\捞刀河-浏阳河-圭塘河.csv" # 包含目标值的完整数据集 + # + # # 使用SPXY方法进行数据分割评估 + # eval_results = inferencer.evaluate_with_split( + # data_csv_path=complete_data_csv, + # split_method="spxy", # 可选: "random", "spxy", "ks" + # test_size=0.2, + # random_state=42, + # target_column=11, # 目标值列索引 + # feature_start_column=13, # 特征开始列索引 + # metric='test_r2' + # ) + # + # print(f"\n数据分割评估结果:") + # print(f" 分割方法: {eval_results['split_method']}") + # print(f" 训练集R²: {eval_results['train_metrics']['r2']:.4f}") + # print(f" 测试集R²: {eval_results['test_metrics']['r2']:.4f}") + # print(f" 训练集RMSE: {eval_results['train_metrics']['rmse']:.4f}") + # print(f" 测试集RMSE: {eval_results['test_metrics']['rmse']:.4f}") + + except Exception as e: + print(f"推理失败: {e}") + import traceback + traceback.print_exc() + + +if __name__ == "__main__": + main() diff --git a/src/core/prediction/sctter_batch.py b/src/core/prediction/sctter_batch.py new file mode 100644 index 0000000..473e65c --- /dev/null +++ b/src/core/prediction/sctter_batch.py @@ -0,0 +1,894 @@ +import numpy as np +import pandas as pd +import joblib +import os +from pathlib import Path +from typing import List, Dict, Union, Tuple, Optional +import warnings +import matplotlib.pyplot as plt +import matplotlib.font_manager as fm +import scipy.stats as stats + +warnings.filterwarnings('ignore') + +# 设置中文字体 +plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans', 'Arial Unicode MS'] +plt.rcParams['axes.unicode_minus'] = False +plt.rcParams['font.size'] = 12 + +# 机器学习模型导入 - 改为回归模型 +from sklearn.svm import SVR +from sklearn.ensemble import RandomForestRegressor +from sklearn.neighbors import KNeighborsRegressor +from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet +from sklearn.model_selection import GridSearchCV, cross_val_score, KFold, train_test_split +from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score +from sklearn.cross_decomposition import PLSRegression + +# 第三方模型导入 +# try: +# import lightgbm as lgb +# LGB_AVAILABLE = True +# except ImportError: +# LGB_AVAILABLE = False +LGB_AVAILABLE = False # 注释掉lightgbm + +# try: +# import catboost as cb +# CB_AVAILABLE = True +# except ImportError: +# CB_AVAILABLE = False +CB_AVAILABLE = False # 注释掉catboost + +# 导入预处理模块 +# 动态导入预处理模块 +import sys +import os + +from src.preprocessing.spectral_Preprocessing import Preprocessing + + +class WaterQualityScatterBatch: + """水质参数反演批量散点图绘制类""" + + def __init__(self): + """初始化批量散点图绘制类""" + # 定义支持的回归模型及其参数网格 + self.model_configs = { + 'SVR': { + 'model': SVR, + 'params': { + 'C': [0.1, 1, 10, 100], + 'gamma': ['scale', 'auto', 0.001, 0.01, 0.1, 1], + 'kernel': ['rbf', 'poly', 'sigmoid'], + 'epsilon': [0.01, 0.1, 0.2] + }, + 'available': True + }, + 'RF': { + 'model': RandomForestRegressor, + 'params': { + 'n_estimators': [50, 100, 200], + 'max_depth': [None, 10, 20, 30], + 'min_samples_split': [2, 5, 10], + 'min_samples_leaf': [1, 2, 4] + }, + 'available': True + }, + 'KNN': { + 'model': KNeighborsRegressor, + 'params': { + 'n_neighbors': [3, 5, 7, 9, 11], + 'weights': ['uniform', 'distance'], + 'metric': ['euclidean', 'manhattan', 'minkowski'] + }, + 'available': True + }, + 'LinearRegression': { + 'model': LinearRegression, + 'params': { + 'fit_intercept': [True, False] + }, + 'available': True + }, + 'Ridge': { + 'model': Ridge, + 'params': { + 'alpha': [0.01, 0.1, 1, 10, 100], + 'fit_intercept': [True, False] + }, + 'available': True + }, + 'Lasso': { + 'model': Lasso, + 'params': { + 'alpha': [0.01, 0.1, 1, 10, 100], + 'fit_intercept': [True, False], + 'max_iter': [1000, 2000] + }, + 'available': True + }, + 'ElasticNet': { + 'model': ElasticNet, + 'params': { + 'alpha': [0.01, 0.1, 1, 10], + 'l1_ratio': [0.1, 0.3, 0.5, 0.7, 0.9], + 'fit_intercept': [True, False], + 'max_iter': [1000, 2000] + }, + 'available': True + }, + 'XGBoost': { + 'model': None, # xgboost is removed, so set to None + 'params': { + 'n_estimators': [50, 100, 200], + 'max_depth': [3, 6, 9], + 'learning_rate': [0.01, 0.1, 0.2], + 'subsample': [0.8, 0.9, 1.0] + }, + 'available': False + }, + 'LightGBM': { + 'model': lgb.LGBMRegressor if LGB_AVAILABLE else None, + 'params': { + 'n_estimators': [50, 100, 200], + 'max_depth': [3, 6, 9], + 'learning_rate': [0.01, 0.1, 0.2], + 'num_leaves': [31, 50, 100] + }, + 'available': LGB_AVAILABLE + }, + 'CatBoost': { + 'model': cb.CatBoostRegressor if CB_AVAILABLE else None, + 'params': { + 'iterations': [50, 100, 200], + 'depth': [3, 6, 9], + 'learning_rate': [0.01, 0.1, 0.2], + 'l2_leaf_reg': [1, 3, 5] + }, + 'available': CB_AVAILABLE + }, + 'PLS': { + 'model': PLSRegression, + 'params': { + 'n_components': [2, 3, 5, 7, 10] + }, + 'available': True + } + } + + # 预处理方法列表 + self.preprocessing_methods = [ + "None", "MMS", "SS", "CT", "SNV", "MA", "SG", "MSC", "D1", "D2", "DT", "WVAE" + ] + + # 样本划分方法列表 + self.split_methods = ["random", "spxy", "ks"] + + def load_data(self, csv_path: str, target_column_name: str = None, target_column: int = None, feature_start_column: int = 13) -> Tuple[pd.DataFrame, pd.Series]: + """ + 加载CSV数据 + + Args: + csv_path: CSV文件路径 + target_column_name: 目标值列名（优先使用） + target_column: 目标值列索引（当列名不存在时使用） + feature_start_column: 特征开始列索引 + + Returns: + X: 特征数据 + y: 目标值数据 + """ + data = pd.read_csv(csv_path) + + # 根据列名或列索引提取目标值 + if target_column_name and target_column_name in data.columns: + print(f"使用列名 '{target_column_name}' 作为目标值") + y = data[target_column_name] + target_col_index = data.columns.get_loc(target_column_name) + elif target_column is not None: + print(f"使用列索引 {target_column} 作为目标值") + y = data.iloc[:, target_column] + target_col_index = target_column + else: + raise ValueError("必须指定 target_column_name 或 target_column") + + # 提取特征数据 + X = data.iloc[:, feature_start_column:] + + # 去除y值为空的行 + mask = ~y.isna() + data_cleaned = data[mask] + + if target_column_name and target_column_name in data.columns: + y = data_cleaned[target_column_name] + else: + y = data_cleaned.iloc[:, target_col_index] + X = data_cleaned.iloc[:, feature_start_column:] + + print(f"数据加载完成:") + print(f" 目标列: {target_column_name if target_column_name else f'索引{target_col_index}'}") + print(f" 样本数量: {X.shape[0]}") + print(f" 特征数量: {X.shape[1]}") + print(f" 目标值范围: {y.min():.4f} ~ {y.max():.4f}") + print(f" 目标值均值: {y.mean():.4f}") + + return X, y + + def preprocess_data(self, X: pd.DataFrame, method: str) -> np.ndarray: + """ + 数据预处理 + + Args: + X: 原始特征数据 + method: 预处理方法 + + Returns: + 预处理后的数据 + """ + print(f"应用预处理方法: {method}") + + # 如果方法为None，直接返回原始数据 + if method == "None" or method is None: + print("跳过预处理，使用原始数据") + return X.values + + try: + X_processed = Preprocessing(method, X) + + # 确保返回的是numpy数组 + if isinstance(X_processed, pd.DataFrame): + X_processed = X_processed.values + + print(f"预处理完成，数据形状: {X_processed.shape}") + return X_processed + + except Exception as e: + print(f"预处理失败: {e}") + print("使用原始数据") + return X.values + + def random(self, data, label, test_ratio=0.2, random_state=123): + """随机划分数据集""" + X_train, X_test, y_train, y_test = train_test_split( + data, label, test_size=test_ratio, random_state=random_state + ) + return X_train, X_test, y_train, y_test + + def spxy(self, data, label, test_size=0.2): + """SPXY算法划分数据集""" + # 确保 data 和 label 是 NumPy 数组 + data = data.to_numpy() if isinstance(data, pd.DataFrame) else data + label = label.to_numpy() if isinstance(label, pd.Series) else label + + # 备份原始数据和标签 + x_backup = data + y_backup = label + + M = data.shape[0] + N = round((1 - test_size) * M) + samples = np.arange(M) + + # 归一化标签数据 + label = (label - np.mean(label)) / np.std(label) + D = np.zeros((M, M)) + Dy = np.zeros((M, M)) + + # 计算样本之间的距离 + for i in range(M - 1): + xa = data[i, :] + ya = label[i] + for j in range((i + 1), M): + xb = data[j, :] + yb = label[j] + D[i, j] = np.linalg.norm(xa - xb) + Dy[i, j] = np.linalg.norm(ya - yb) + + # 距离归一化 + Dmax = np.max(D) + Dymax = np.max(Dy) + D = D / Dmax + Dy / Dymax + + # 找到最远的两个点 + maxD = D.max(axis=0) + index_row = D.argmax(axis=0) + index_column = maxD.argmax() + + m = np.zeros(N, dtype=int) + m[0] = index_row[index_column] + m[1] = index_column + + dminmax = np.zeros(N) + dminmax[1] = D[m[0], m[1]] + + # 根据距离选择训练集 + for i in range(2, N): + pool = np.delete(samples, m[:i]) + dmin = np.zeros(M - i) + for j in range(M - i): + indexa = pool[j] + d = np.zeros(i) + for k in range(i): + indexb = m[k] + if indexa < indexb: + d[k] = D[indexa, indexb] + else: + d[k] = D[indexb, indexa] + dmin[j] = np.min(d) + dminmax[i] = np.max(dmin) + index = np.argmax(dmin) + m[i] = pool[index] + + m_complement = np.delete(samples, m) + + # 划分训练集和测试集 + X_train = data[m, :] + y_train = y_backup[m] + X_test = data[m_complement, :] + y_test = y_backup[m_complement] + + return X_train, X_test, y_train, y_test + + def ks(self, data, label, test_size=0.2): + """Kennard-Stone算法划分数据集""" + # 确保 data 和 label 是 NumPy 数组 + data = data.to_numpy() if isinstance(data, pd.DataFrame) else data + label = label.to_numpy() if isinstance(label, pd.Series) else label + + M = data.shape[0] + N = round((1 - test_size) * M) + samples = np.arange(M) + + D = np.zeros((M, M)) + + for i in range((M - 1)): + xa = data[i, :] + for j in range((i + 1), M): + xb = data[j, :] + D[i, j] = np.linalg.norm(xa - xb) + + maxD = np.max(D, axis=0) + index_row = np.argmax(D, axis=0) + index_column = np.argmax(maxD) + + m = np.zeros(N) + m[0] = np.array(index_row[index_column]) + m[1] = np.array(index_column) + m = m.astype(int) + dminmax = np.zeros(N) + dminmax[1] = D[m[0], m[1]] + + for i in range(2, N): + pool = np.delete(samples, m[:i]) + dmin = np.zeros((M - i)) + for j in range((M - i)): + indexa = pool[j] + d = np.zeros(i) + for k in range(i): + indexb = m[k] + if indexa < indexb: + d[k] = D[indexa, indexb] + else: + d[k] = D[indexb, indexa] + dmin[j] = np.min(d) + dminmax[i] = np.max(dmin) + index = np.argmax(dmin) + m[i] = pool[index] + + m_complement = np.delete(np.arange(data.shape[0]), m) + + X_train = data[m, :] + y_train = label[m] + X_test = data[m_complement, :] + y_test = label[m_complement] + + return X_train, X_test, y_train, y_test + + def split_data(self, X: np.ndarray, y: pd.Series, method: str = "random", + test_size: float = 0.2, random_state: int = 42) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """ + 根据指定方法划分数据集 + """ + print(f"使用 {method} 方法划分数据集") + + if method == "random": + return self.random(X, y, test_ratio=test_size, random_state=random_state) + elif method == "spxy": + return self.spxy(X, y, test_size=test_size) + elif method == "ks": + return self.ks(X, y, test_size=test_size) + else: + raise ValueError(f"不支持的划分方法: {method}. 支持的方法: {self.split_methods}") + + def plot_scatter_with_confidence(self, y_train, y_pred_train, y_test, y_pred_test, + r2_train, mae_train, r2_test, mae_test, + folder_name, split_method, preprocess_method, model_name, + save_path): + """ + 绘制带置信区间的散点图，模仿提供的代码样式 + + 参数: + - y_train, y_pred_train: 训练集的真实值和预测值 + - y_test, y_pred_test: 测试集的真实值和预测值 + - r2_train, mae_train: 训练集的R²和MAE指标 + - r2_test, mae_test: 测试集的R²和MAE指标 + - folder_name: 文件夹名称 + - split_method: 数据划分方法 + - preprocess_method: 预处理方法 + - model_name: 模型名称 + - save_path: 保存路径 + """ + + # scale_factor 用于放大置信区间 + scale_factor = 1.5 # 调整这个值，越大置信区间越宽 scale_factor = 1 是理论上的标准置信区间宽度 + confidence = 0.95 # 95% 的置信水平 + + # 拟合训练集线 + z_train = np.polyfit(y_train, y_pred_train, 1) + p_train = np.poly1d(z_train) + predicted_values_train = p_train(y_train) + residuals_train = y_pred_train - predicted_values_train + mean_error_train = np.mean(residuals_train**2) + t_value_train = stats.t.ppf((1 + confidence) / 2., len(y_train) - 1) + ci_train = t_value_train * scale_factor * np.sqrt(mean_error_train) * np.sqrt(1 / len(y_train) + (y_train - np.mean(y_train))**2 / np.sum((y_train - np.mean(y_train))**2)) + x_extended_train = np.linspace(min(y_train), max(y_train), 100) + predicted_extended_train = p_train(x_extended_train) + ci_extended_train = t_value_train * scale_factor * np.sqrt(mean_error_train) * np.sqrt(1 / len(y_train) + (x_extended_train - np.mean(y_train))**2 / np.sum((y_train - np.mean(y_train))**2)) + + # 拟合测试集线 + z_test = np.polyfit(y_test, y_pred_test, 1) + p_test = np.poly1d(z_test) + predicted_values_test = p_test(y_test) + residuals_test = y_pred_test - predicted_values_test + mean_error_test = np.mean(residuals_test**2) + t_value_test = stats.t.ppf((1 + confidence) / 2., len(y_test) - 1) + ci_test = t_value_test * scale_factor * np.sqrt(mean_error_test) * np.sqrt(1 / len(y_test) + (y_test - np.mean(y_test))**2 / np.sum((y_test - np.mean(y_test))**2)) + x_extended_test = np.linspace(min(y_test), max(y_test), 100) + predicted_extended_test = p_test(x_extended_test) + ci_extended_test = t_value_test * scale_factor * np.sqrt(mean_error_test) * np.sqrt(1 / len(y_test) + (x_extended_test - np.mean(y_test))**2 / np.sum((y_test - np.mean(y_test))**2)) + + # 设置新的配色方案 + train_color = '#1f77b4' # 训练集主色：蓝色系 + test_color = '#ff7f0e' # 测试集主色：橙色系 + confidence_train_color = '#aec7e8' # 训练集置信区间浅蓝色 + confidence_test_color = '#ffbb78' # 测试集置信区间浅橙色 + + # 设置图形大小和分布 + fig = plt.figure(figsize=(10, 8), dpi=300) # 降低dpi以提高兼容性 + gs = fig.add_gridspec(4, 4, hspace=0.3, wspace=0.3) + ax_main = fig.add_subplot(gs[1:, :-1]) # 主图 + ax_hist_x = fig.add_subplot(gs[0, :-1], sharex=ax_main) # 上方的直方图 + ax_hist_y = fig.add_subplot(gs[1:, -1], sharey=ax_main) # 右侧的直方图 + + # 绘制训练集 + ax_main.scatter(y_train, y_pred_train, color=train_color, label="训练集预测值", alpha=0.6) + ax_main.plot(y_train, p_train(y_train), color=train_color, alpha=0.9, + label=f"训练集拟合线\n$R^2$ = {r2_train:.2f}, MAE = {mae_train:.2f}") + ax_main.fill_between(x_extended_train, predicted_extended_train - ci_extended_train, + predicted_extended_train + ci_extended_train, + color=confidence_train_color, alpha=0.5, label="训练集95%置信区间") + + # 绘制测试集 + ax_main.scatter(y_test, y_pred_test, color=test_color, label="测试集预测值", alpha=0.6) + ax_main.plot(y_test, p_test(y_test), color=test_color, alpha=0.9, + label=f"测试集拟合线\n$R^2$ = {r2_test:.2f}, MAE = {mae_test:.2f}") + ax_main.fill_between(x_extended_test, predicted_extended_test - ci_extended_test, + predicted_extended_test + ci_extended_test, + color=confidence_test_color, alpha=0.5, label="测试集95%置信区间") + + # 添加参考线 + ax_main.plot([min(y_train.min(), y_test.min()), max(y_train.max(), y_test.max())], + [min(y_train.min(), y_test.min()), max(y_train.max(), y_test.max())], + color='grey', linestyle='--', alpha=0.6, label="1:1 参考线") + + # 设置主图 + ax_main.set_xlabel("观测值", fontsize=12) + ax_main.set_ylabel("预测值", fontsize=12) + ax_main.legend(loc="upper left", fontsize=10) + ax_main.grid(True, alpha=0.3) + + # 绘制上方的直方图 (真实值的分布) + ax_hist_x.hist(y_train, bins=20, color=train_color, alpha=0.7, edgecolor='black', label="训练集观测值分布") + ax_hist_x.hist(y_test, bins=20, color=test_color, alpha=0.7, edgecolor='black', label="测试集观测值分布") + ax_hist_x.tick_params(labelbottom=False) # 隐藏 x 轴的标签 + ax_hist_x.set_ylabel("频次", fontsize=10) + ax_hist_x.legend(fontsize=8) + + # 绘制右侧的直方图 (预测值的分布) + ax_hist_y.hist(y_pred_train, bins=20, orientation='horizontal', color=train_color, alpha=0.7, edgecolor='black') + ax_hist_y.hist(y_pred_test, bins=20, orientation='horizontal', color=test_color, alpha=0.7, edgecolor='black') + ax_hist_y.set_xlabel("频次", fontsize=10) + ax_hist_y.tick_params(labelleft=False) # 隐藏 y 轴的标签 + + # 添加标题 + title = f'{folder_name} - 最佳模型预测效果对比图\n' + title += f'{split_method}_{preprocess_method}_{model_name}' + fig.suptitle(title, fontsize=14, fontweight='bold') + + # 保存和展示图像 + plt.tight_layout() + plt.savefig(save_path, format='png', bbox_inches='tight', dpi=300) + print(f"散点图已保存至: {save_path}") + + def get_best_model_from_summary(self, artifacts_dir: Path, metric: str = 'test_r2', target_column_name: str = None) -> Tuple[str, str, Dict]: + """ + 从训练摘要中获取最佳模型信息 + + Args: + artifacts_dir: 模型目录 + metric: 评估指标 + target_column_name: 目标列名（用于构建文件路径） + + Returns: + preprocess_method: 预处理方法 + model_name: 模型名称 + best_result: 最佳模型结果信息 + """ + # 清理目标列名，移除可能的特殊字符 + if target_column_name: + safe_target_name = "".join(c for c in target_column_name if c.isalnum() or c in ('-', '_')).rstrip() + # 尝试加载以目标列名为前缀的详细结果文件 + detailed_path = artifacts_dir / f"{safe_target_name}_detailed_results.csv" + summary_path = artifacts_dir / f"{safe_target_name}_training_summary.csv" + else: + # 兼容旧版本，使用固定文件名 + detailed_path = artifacts_dir / "detailed_results.csv" + summary_path = artifacts_dir / "training_summary.csv" + + summary_df = None + + # 优先使用详细结果文件 + if detailed_path.exists(): + print(f"使用详细结果文件: {detailed_path}") + summary_df = pd.read_csv(detailed_path) + # 将中文列名映射到英文 + metric_mapping = { + 'test_r2': '测试集R²', + 'train_r2': '训练集R²', + 'test_rmse': '测试集RMSE', + 'train_rmse': '训练集RMSE', + 'cv_mean': 'CV均值' + } + if metric in metric_mapping and metric_mapping[metric] in summary_df.columns: + metric_col = metric_mapping[metric] + else: + metric_col = metric + elif summary_path.exists(): + print(f"使用训练摘要文件: {summary_path}") + summary_df = pd.read_csv(summary_path) + metric_col = metric + else: + # 如果使用了目标列名前缀的文件不存在，尝试查找旧版本的文件 + if target_column_name: + old_detailed_path = artifacts_dir / "detailed_results.csv" + old_summary_path = artifacts_dir / "training_summary.csv" + + if old_detailed_path.exists(): + print(f"使用旧版本详细结果文件: {old_detailed_path}") + summary_df = pd.read_csv(old_detailed_path) + # 将中文列名映射到英文 + metric_mapping = { + 'test_r2': '测试集R²', + 'train_r2': '训练集R²', + 'test_rmse': '测试集RMSE', + 'train_rmse': '训练集RMSE', + 'cv_mean': 'CV均值' + } + if metric in metric_mapping and metric_mapping[metric] in summary_df.columns: + metric_col = metric_mapping[metric] + else: + metric_col = metric + elif old_summary_path.exists(): + print(f"使用旧版本训练摘要文件: {old_summary_path}") + summary_df = pd.read_csv(old_summary_path) + metric_col = metric + else: + raise FileNotFoundError(f"训练摘要文件不存在: {summary_path} 或 {detailed_path} 或 {old_summary_path} 或 {old_detailed_path}") + else: + raise FileNotFoundError(f"训练摘要文件不存在: {summary_path} 或 {detailed_path}") + + if summary_df.empty: + raise ValueError("训练摘要为空") + + # 检查指标列是否存在 + if metric_col not in summary_df.columns: + available_cols = list(summary_df.columns) + raise ValueError(f"指标 '{metric_col}' 不存在。可用列: {available_cols}") + + # 获取最佳模型（对于R²等指标，值越大越好） + if 'r2' in metric.lower() or 'score' in metric.lower(): + best_idx = summary_df[metric_col].idxmax() + else: # 对于RMSE、MAE等，值越小越好 + best_idx = summary_df[metric_col].idxmin() + + best_row = summary_df.loc[best_idx] + + # 根据文件类型解析模型信息 + if '划分方法' in summary_df.columns: + # 详细结果文件格式（中文列名） + split_method = best_row['划分方法'] + preprocess_method = best_row['预处理方法'] + model_name = best_row['建模方法'] + best_combination = f"{split_method}_{preprocess_method}_{model_name}" + else: + # 简化结果文件格式（英文列名） + best_combination = best_row['combination'] + # 解析组合名称（格式: split_method_preprocess_method_model_name） + parts = best_combination.split('_') + if len(parts) < 3: + raise ValueError(f"无效的模型组合名称格式: {best_combination}") + + split_method = parts[0] + preprocess_method = parts[1] + model_name = '_'.join(parts[2:]) + + print(f"最佳模型组合: {best_combination}") + print(f" 划分方法: {split_method}") + print(f" 预处理方法: {preprocess_method}") + print(f" 模型名称: {model_name}") + print(f" {metric_col}: {best_row[metric_col]:.4f}") + + # 构建模型文件前缀 + model_file_prefix = f"{split_method}_{preprocess_method}" + + # 构建结果信息 + best_result = { + 'combination': best_combination, + 'split_method': split_method, + 'preprocess_method': preprocess_method, + 'model_name': model_name, + 'metric_value': best_row[metric_col], + 'model_file_prefix': model_file_prefix + } + + # 尝试获取更多指标信息 + for col in summary_df.columns: + if col not in ['combination', '划分方法', '预处理方法', '建模方法', '最佳参数']: + try: + best_result[col] = best_row[col] + except: + pass + + return model_file_prefix, model_name, best_result + + def load_model(self, artifacts_dir: Path, preprocess_method: str, model_name: str, target_column_name: str = None): + """ + 加载保存的模型 + + Args: + artifacts_dir: 模型目录 + preprocess_method: 预处理方法名称 + model_name: 模型名称 + target_column_name: 目标列名（用于构建文件路径） + + Returns: + 加载的模型数据 + """ + if target_column_name: + # 清理目标列名，移除可能的特殊字符 + safe_target_name = "".join(c for c in target_column_name if c.isalnum() or c in ('-', '_')).rstrip() + # 尝试加载以目标列名为前缀的模型文件 + filename = f"{safe_target_name}_{preprocess_method}_{model_name}.joblib" + filepath = artifacts_dir / filename + + if filepath.exists(): + print(f"加载模型文件: {filepath}") + return joblib.load(filepath) + + # 如果带前缀的文件不存在，尝试加载旧版本的文件 + old_filename = f"{preprocess_method}_{model_name}.joblib" + old_filepath = artifacts_dir / old_filename + + if old_filepath.exists(): + print(f"加载旧版本模型文件: {old_filepath}") + return joblib.load(old_filepath) + + raise FileNotFoundError(f"模型文件不存在: {filepath} 或 {old_filepath}") + else: + # 兼容旧版本，使用固定文件名 + filename = f"{preprocess_method}_{model_name}.joblib" + filepath = artifacts_dir / filename + + if not filepath.exists(): + raise FileNotFoundError(f"模型文件不存在: {filepath}") + + return joblib.load(filepath) + + def plot_best_model_scatter(self, artifacts_dir: str, csv_path: str, output_dir: str, + folder_name: str, metric: str = 'test_r2', + target_column: int = None, feature_start_column: int = 13, + test_size: float = 0.2, random_state: int = 42): + """ + 绘制最佳模型的散点图 + + Args: + artifacts_dir: 模型目录 + csv_path: 原始CSV数据文件路径 + output_dir: 输出目录 + folder_name: 文件夹名称（用作图片名称和目标列名） + metric: 评估指标 + target_column: 目标值列索引（如果为None，则使用folder_name作为列名） + feature_start_column: 特征开始列索引 + test_size: 测试集比例 + random_state: 随机种子 + """ + artifacts_path = Path(artifacts_dir) + output_path = Path(output_dir) + output_path.mkdir(parents=True, exist_ok=True) + + try: + print(f"\n{'='*60}") + print(f"处理文件夹: {folder_name}") + print(f"{'='*60}") + + # 获取最佳模型信息 + model_file_prefix, model_name, best_result = self.get_best_model_from_summary( + artifacts_path, metric, folder_name + ) + + # 加载数据 - 优先使用文件夹名称作为目标列名 + X_raw, y_true = self.load_data(csv_path, target_column_name=folder_name, target_column=target_column, feature_start_column=feature_start_column) + + # 获取最佳模型的预处理方法 + actual_preprocess_method = best_result['preprocess_method'] + split_method = best_result['split_method'] + + # 加载最佳模型 + best_model_data = self.load_model(artifacts_path, model_file_prefix, model_name, folder_name) + best_model = best_model_data['model'] + + # 应用相同的数据预处理 + X_processed = self.preprocess_data(X_raw, actual_preprocess_method) + + # 使用相同的数据分割方法 + X_train, X_test, y_train, y_test = self.split_data( + X_processed, y_true, method=split_method, + test_size=test_size, random_state=random_state + ) + + # 预测训练集和测试集 + y_pred_train = best_model.predict(X_train) + y_pred_test = best_model.predict(X_test) + + # 计算评估指标 + train_r2 = r2_score(y_train, y_pred_train) + test_r2 = r2_score(y_test, y_pred_test) + train_rmse = np.sqrt(mean_squared_error(y_train, y_pred_train)) + test_rmse = np.sqrt(mean_squared_error(y_test, y_pred_test)) + train_mae = mean_absolute_error(y_train, y_pred_train) + test_mae = mean_absolute_error(y_test, y_pred_test) + + # 绘制带置信区间的散点图（模仿提供的代码样式） + self.plot_scatter_with_confidence( + y_train, y_pred_train, y_test, y_pred_test, + train_r2, train_mae, test_r2, test_mae, + folder_name, split_method, actual_preprocess_method, model_name, + output_path / f"{folder_name}_scatter_with_confidence.png" + ) + + plt.close() # 关闭图形以释放内存 + + return { + 'status': 'success', + 'save_path': str(output_path / f"{folder_name}_scatter_with_confidence.png"), + 'best_result': best_result, + 'metrics': { + 'train_r2': train_r2, + 'test_r2': test_r2, + 'train_rmse': train_rmse, + 'test_rmse': test_rmse, + 'train_mae': train_mae, + 'test_mae': test_mae + } + } + + except Exception as e: + print(f"处理文件夹 {folder_name} 失败: {e}") + return { + 'status': 'error', + 'error': str(e) + } + + def batch_plot_scatter(self, models_root_dir: str, csv_path: str, output_dir: str, + metric: str = 'test_r2', target_column: int = None, + feature_start_column: int = 13, test_size: float = 0.2, + random_state: int = 42): + """ + 批量处理多个子文件夹中的模型并绘制散点图 + + Args: + models_root_dir: 包含多个子文件夹的根目录 + csv_path: 原始CSV数据文件路径 + output_dir: 输出目录 + metric: 评估指标 + target_column: 目标值列索引（如果为None，则使用文件夹名称作为列名） + feature_start_column: 特征开始列索引 + test_size: 测试集比例 + random_state: 随机种子 + """ + models_root = Path(models_root_dir) + + # 查找所有子文件夹 + subdirs = [d for d in models_root.iterdir() if d.is_dir()] + + if not subdirs: + print(f"在目录 {models_root_dir} 中未找到子文件夹") + return {} + + print("=" * 80) + print("批量散点图绘制任务") + print("=" * 80) + print(f"模型根目录: {models_root_dir}") + print(f"数据文件: {csv_path}") + print(f"输出目录: {output_dir}") + print(f"评估指标: {metric}") + print(f"找到 {len(subdirs)} 个模型子文件夹") + print("=" * 80) + + all_results = {} + + for subdir in subdirs: + folder_name = subdir.name + result = self.plot_best_model_scatter( + artifacts_dir=str(subdir), + csv_path=csv_path, + output_dir=output_dir, + folder_name=folder_name, + metric=metric, + target_column=target_column, + feature_start_column=feature_start_column, + test_size=test_size, + random_state=random_state + ) + + all_results[folder_name] = result + + print(f"\n{'='*80}") + print(f"批量散点图绘制完成，共处理 {len(subdirs)} 个模型文件夹") + print(f"{'='*80}") + + # 打印汇总信息 + print("\n汇总结果:") + success_count = 0 + for folder_name, result in all_results.items(): + if result['status'] == 'success': + metrics = result['metrics'] + print(f" ✓ {folder_name}: 测试集R²={metrics['test_r2']:.4f}, " + f"RMSE={metrics['test_rmse']:.4f}") + success_count += 1 + else: + print(f" ✗ {folder_name}: 失败 - {result['error']}") + + print(f"\n成功处理: {success_count}/{len(subdirs)} 个文件夹") + print(f"输出目录: {output_dir}") + + return all_results + + +def main(): + """主函数示例""" + # 创建批量散点图绘制实例 + scatter_batch = WaterQualityScatterBatch() + + # 配置路径 + models_root_dir = r"E:\code\WQ\yaobao925\qvchuyaoban" # 包含多个子文件夹的根目录 + csv_path = r"E:\code\WQ\yaobao925\data\qvyaoban\data.csv" # 原始数据文件 + output_dir = r"E:\code\WQ\yaobao925\plot\qvyaoban_sctter" # 散点图输出目录 + + # 批量绘制散点图 + results = scatter_batch.batch_plot_scatter( + models_root_dir=models_root_dir, + csv_path=csv_path, + output_dir=output_dir, + metric='test_r2', # 评估指标 + target_column=None, # 使用文件夹名称作为目标列名 + feature_start_column=13, # 特征开始列索引 + test_size=0.2, # 测试集比例 + random_state=42 # 随机种子 + ) + + print("\n任务完成！") + + +if __name__ == "__main__": + main() diff --git a/src/core/type_define.py b/src/core/type_define.py new file mode 100644 index 0000000..b06e6a1 --- /dev/null +++ b/src/core/type_define.py @@ -0,0 +1,22 @@ +from enum import Enum, unique + +class FlareModel(Enum): + otsu = 0 + threshold = 1 + img = 2 + + +class ImgType(Enum): + ref = 0 + content = 1 + + +# @unique +class CoorType(Enum): + latlong = 0 + utm = 1 + + +class PointPosStrategy(Enum): + nearest_single = 0 + four_quadrant = 1 diff --git a/src/core/water_quality_inversion_pipeline.py b/src/core/water_quality_inversion_pipeline.py new file mode 100644 index 0000000..817c7b7 --- /dev/null +++ b/src/core/water_quality_inversion_pipeline.py @@ -0,0 +1,2671 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +水质参数反演框架主程序 + +本程序串联了水质参数反演的所有步骤： +1. 生成水域mask（基于shp或NDWI阈值分割） +2. 找到耀斑区域（支持多种算法：otsu、zscore、percentile、iqr、adaptive、multi_band） +3. 去除耀斑（支持七种方法：subtract_nir、regression_slope、oxygen_absorption、kutser、goodman、hedley、sugar） +4. 对csv文件进行处理，筛选剔除异常值 +5. 根据csv文件的采样点坐标，在去除耀斑的文件中统计采样点的平均光谱 +6. 使用采样点的平均光谱和对应的实测值建立机器学习模型，保存模型权重 +7. 生成根据水域掩膜内且耀斑掩膜外的采样点，统计采样点的平均光谱 +8. 将训练好的最佳机器学习模型应用到采样点的平均光谱上，预测水质参数 +9. 根据采样点的坐标和反演的实测参数，以及水域掩膜，通过插值的方法，得到水质参数的可视化分布图 + +可视化功能： +- 生成模型评估散点图（真实值vs预测值，支持带置信区间的增强版） +- 生成水质参数箱型图（支持单独和综合两种模式） +- 生成光谱曲线对比图 +- 生成统计图表（箱线图、直方图、相关性热力图） +""" + +import os +import sys +import argparse +from pathlib import Path +from typing import Optional, Dict, List, Union +import numpy as np +import pandas as pd +import warnings +import time +from datetime import datetime +warnings.filterwarnings('ignore') + +# 导入各个功能模块 +from src.utils.extract_water_area import rasterize_shp, ndwi +from src.utils.find_severe_glint_area import find_severe_glint_area +from src.preprocessing.process_water_quality_data import process_water_quality_data +from src.core.glint_removal.get_spectral import get_spectral_in_coor +from src.core.modeling.modeling_batch import WaterQualityModelingBatch +from src.utils.sampling import get_spectral_sampling_points_chunked +from src.core.prediction.inference_batch import WaterQualityInference +from src.utils.kriging import KrigingInterpolator, batch_kriging_interpolation +from src.postprocessing.map import ContentMapper +from src.postprocessing.visualization_reports import WaterQualityVisualization, ReportGenerator +from src.core.prediction.sctter_batch import WaterQualityScatterBatch +# 导入新的耀斑去除算法 +from src.core.glint_removal.Kutser import Kutser +from src.core.glint_removal.Goodman import Goodman +from src.core.glint_removal.Hedley import Hedley +from src.core.glint_removal.SUGAR import SUGAR, correction_iterative +# 导入hdr文件处理函数 +try: + from src.utils.util import write_fields_to_hdrfile, get_hdr_file_path + UTIL_AVAILABLE = True +except ImportError: + UTIL_AVAILABLE = False + print("警告: util模块未导入，hdr文件信息复制功能可能无法正常工作") +import matplotlib.pyplot as plt +import seaborn as sns +# 导入插值相关库 +try: + from scipy import ndimage + from scipy.interpolate import griddata, RBFInterpolator + SCIPY_AVAILABLE = True +except ImportError: + SCIPY_AVAILABLE = False + print("警告: scipy未安装，0值像素插值功能可能无法正常工作") +# 导入GDAL用于影像读写 +try: + from osgeo import gdal + GDAL_AVAILABLE = True +except ImportError: + GDAL_AVAILABLE = False + print("警告: GDAL未安装，新算法可能无法正常工作") + + +class WaterQualityInversionPipeline: + """水质参数反演管道类""" + + def __init__(self, work_dir: str = "./work_dir"): + """ + 初始化管道 + + Args: + work_dir: 工作目录，用于保存所有中间结果 + """ + self.work_dir = Path(work_dir) + self.work_dir.mkdir(parents=True, exist_ok=True) + + # 创建子目录 + self.water_mask_dir = self.work_dir / "1_water_mask" + self.glint_dir = self.work_dir / "2_glint" + self.deglint_dir = self.work_dir / "3_deglint" + self.processed_data_dir = self.work_dir / "4_processed_data" + self.training_spectra_dir = self.work_dir / "5_training_spectra" + self.models_dir = self.work_dir / "6_models" + self.sampling_dir = self.work_dir / "7_sampling" + self.prediction_dir = self.work_dir / "8_predictions" + self.visualization_dir = self.work_dir / "9_visualization" + self.reports_dir = self.work_dir / "10_reports" + + # 创建所有子目录 + for dir_path in [self.water_mask_dir, self.glint_dir, self.deglint_dir, + self.processed_data_dir, self.training_spectra_dir, + self.models_dir, self.sampling_dir, self.prediction_dir, + self.visualization_dir, self.reports_dir]: + dir_path.mkdir(parents=True, exist_ok=True) + + # 初始化可视化和报告生成器 + self.visualizer = WaterQualityVisualization(str(self.visualization_dir)) + self.report_generator = ReportGenerator(str(self.reports_dir)) + self.scatter_batch = WaterQualityScatterBatch() + + # 设置中文字体 + plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans', 'Arial Unicode MS'] + plt.rcParams['axes.unicode_minus'] = False + + # 存储中间结果路径 + self.water_mask_path = None # 存储dat格式的水体掩膜路径（统一格式） + self.glint_mask_path = None + self.interpolated_img_path = None # 存储插值后的影像路径 + self.deglint_img_path = None + self.processed_csv_path = None + self.training_spectra_path = None + + # 存储每步的执行时间 + self.step_timings = {} + self.pipeline_start_time = None + self.pipeline_end_time = None + + print(f"工作目录已创建: {self.work_dir}") + + def _record_step_time(self, step_name: str, start_time: float, end_time: float, + status: str = "completed", error: Optional[str] = None): + """ + 记录步骤执行时间 + + Args: + step_name: 步骤名称 + start_time: 开始时间（时间戳） + end_time: 结束时间（时间戳） + status: 状态（"completed", "failed", "skipped"） + error: 错误信息（如果有） + """ + elapsed_time = end_time - start_time + self.step_timings[step_name] = { + 'start_time': datetime.fromtimestamp(start_time).strftime('%Y-%m-%d %H:%M:%S'), + 'end_time': datetime.fromtimestamp(end_time).strftime('%Y-%m-%d %H:%M:%S'), + 'elapsed_seconds': elapsed_time, + 'elapsed_formatted': self._format_time(elapsed_time), + 'status': status, + 'error': error + } + print(f"步骤耗时: {self._format_time(elapsed_time)}") + + def _format_time(self, seconds: float) -> str: + """ + 格式化时间显示 + + Args: + seconds: 秒数 + + Returns: + 格式化后的时间字符串 + """ + if seconds < 60: + return f"{seconds:.2f}秒" + elif seconds < 3600: + minutes = int(seconds // 60) + secs = seconds % 60 + return f"{minutes}分{secs:.2f}秒" + else: + hours = int(seconds // 3600) + minutes = int((seconds % 3600) // 60) + secs = seconds % 60 + return f"{hours}小时{minutes}分{secs:.2f}秒" + + def _ensure_water_mask_dat(self, img_path: str) -> str: + """ + 确保有dat格式的水体掩膜文件（简化版本，因为步骤1已经确保有dat文件） + + Args: + img_path: 影像文件路径（已废弃，保留用于兼容性） + + Returns: + dat格式的水体掩膜文件路径 + """ + if self.water_mask_path is not None: + if Path(self.water_mask_path).exists(): + return self.water_mask_path + else: + raise ValueError(f"水体掩膜文件不存在: {self.water_mask_path}") + + raise ValueError("未找到水体掩膜文件，请先执行步骤1") + + def step1_generate_water_mask(self, + mask_path: str, + img_path: Optional[str] = None, + ndwi_threshold: float = 0.4) -> str: + """ + 步骤1: 生成或设置水域mask + + 自动识别输入文件格式（shp或dat），如果是shp格式则在第一步转换为dat格式。 + 后续所有步骤都使用dat格式的掩膜文件。 + + Args: + mask_path: 水体掩膜文件路径，支持： + - shp格式文件（.shp）：需要提供img_path用于栅格化 + - dat格式文件（.dat/.tif等栅格格式）：直接使用，不需要img_path + img_path: 输入影像文件路径（当mask_path为shp格式时必须提供，用于栅格化） + ndwi_threshold: NDWI阈值（当method="ndwi"时使用，已废弃，保留用于兼容性） + + Returns: + dat格式的水域掩膜文件路径 + """ + print("\n" + "="*80) + print("步骤1: 生成或设置水域mask") + print("="*80) + + step_start_time = time.time() + try: + if mask_path is None: + raise ValueError("必须提供mask_path参数") + if not Path(mask_path).exists(): + raise ValueError(f"文件不存在: {mask_path}") + + # 检查文件扩展名，判断是shp文件还是dat文件 + file_ext = Path(mask_path).suffix.lower() + + if file_ext == '.shp': + # 如果是shp文件，需要栅格化为dat + if img_path is None: + raise ValueError("当mask_path为shp格式时，必须提供img_path参数用于栅格化") + + print(f"检测到shp格式的水体掩膜，正在转换为dat格式...") + output_path = str(self.water_mask_dir / "water_mask_from_shp.dat") + + # 检查文件是否已存在，避免重复栅格化 + if Path(output_path).exists(): + print(f"检测到已存在的栅格化掩膜文件，直接使用: {output_path}") + self.water_mask_path = output_path + step_end_time = time.time() + self._record_step_time("步骤1: 生成水域mask", step_start_time, step_end_time, status="skipped") + print(f"水域掩膜已设置: {self.water_mask_path}") + return self.water_mask_path + + # 执行栅格化 + rasterize_shp(mask_path, output_path, img_path) + self.water_mask_path = output_path + step_end_time = time.time() + self._record_step_time("步骤1: 生成水域mask", step_start_time, step_end_time) + print(f"已生成dat格式的水域掩膜: {self.water_mask_path}") + return self.water_mask_path + + else: + # 如果是dat或其他栅格格式，直接使用 + print(f"检测到栅格格式的水体掩膜，直接使用: {mask_path}") + self.water_mask_path = mask_path + step_end_time = time.time() + self._record_step_time("步骤1: 生成水域mask", step_start_time, step_end_time) + print(f"水域掩膜已设置: {self.water_mask_path} (dat格式)") + return self.water_mask_path + + except Exception as e: + step_end_time = time.time() + self._record_step_time("步骤1: 生成水域mask", step_start_time, step_end_time, + status="failed", error=str(e)) + raise + + def step2_find_glint_area(self, img_path: str, + glint_wave: float = 750.0, + method: str = 'otsu', + z_threshold: float = 2.5, + percentile: float = 95.0, + iqr_multiplier: float = 1.5, + window_size: int = 15, + multi_band_waves: Optional[List[float]] = None, + sub_method: str = 'zscore', + weights: Optional[List[float]] = None, + max_area: Optional[int] = None, + buffer_size: Optional[int] = None) -> str: + """ + 步骤2: 找到耀斑区域 + + Args: + img_path: 输入影像文件路径 + glint_wave: 用于提取耀斑严重区域的波段波长（单波段方法使用） + method: 检测方法，可选： + - 'otsu': Otsu阈值分割（默认） + - 'zscore': Z-score统计方法 + - 'percentile': 百分位数阈值方法 + - 'iqr': IQR异常值检测 + - 'adaptive': 自适应阈值方法 + - 'multi_band': 多波段融合方法 + z_threshold: Z-score方法的阈值（默认2.5） + percentile: 百分位数阈值（默认95.0） + iqr_multiplier: IQR倍数（默认1.5） + window_size: 自适应阈值窗口大小（默认15） + multi_band_waves: 多波段方法的波长列表，如[750, 800, 850] + sub_method: 多波段方法的子方法（'zscore', 'percentile', 'otsu'），默认'zscore' + weights: 多波段方法的权重列表，如果为None则使用等权重 + max_area: 最大连通域面积阈值（像素数），超过此面积的连通域将被过滤掉， + 用于去除岸边、浅水、水华等大面积区域（默认None，表示不过滤） + buffer_size: 岸边缓冲区大小（像素数），用于去除岸边附近的错误耀斑掩膜 + （默认None，表示不进行岸边缓冲区去除；设置为正整数时启用） + + Returns: + 耀斑掩膜文件路径 + """ + print("\n" + "="*80) + print("步骤2: 找到耀斑区域") + print("="*80) + + step_start_time = time.time() + try: + # 使用dat格式的水体掩膜 + if self.water_mask_path is None: + raise ValueError("请先执行步骤1: 生成水域mask") + + output_path = str(self.glint_dir / "severe_glint_area.dat") + + # 检查文件是否已存在 + if Path(output_path).exists(): + print(f"检测到已存在的耀斑掩膜文件，直接使用: {output_path}") + self.glint_mask_path = output_path + step_end_time = time.time() + self._record_step_time("步骤2: 找到耀斑区域", step_start_time, step_end_time, status="skipped") + print(f"耀斑掩膜已设置: {self.glint_mask_path}") + return self.glint_mask_path + + # 构建参数字典 + kwargs = { + 'method': method, + 'z_threshold': z_threshold, + 'percentile': percentile, + 'iqr_multiplier': iqr_multiplier, + 'window_size': window_size, + } + + # 如果是多波段方法，添加相关参数 + if method == 'multi_band': + if multi_band_waves is not None: + kwargs['multi_band_waves'] = multi_band_waves + if sub_method is not None: + kwargs['sub_method'] = sub_method + if weights is not None: + kwargs['weights'] = weights + + # 添加连通域面积过滤和岸边缓冲区参数 + if max_area is not None: + kwargs['max_area'] = max_area + if buffer_size is not None: + kwargs['buffer_size'] = buffer_size + + # 传递dat格式的水体掩膜文件路径 + self.glint_mask_path = find_severe_glint_area( + img_path, self.water_mask_path, glint_wave, output_path, **kwargs + ) + + step_end_time = time.time() + self._record_step_time("步骤2: 找到耀斑区域", step_start_time, step_end_time) + print(f"耀斑掩膜已生成: {self.glint_mask_path}") + print(f"使用检测方法: {method}") + return self.glint_mask_path + except Exception as e: + step_end_time = time.time() + self._record_step_time("步骤2: 找到耀斑区域", step_start_time, step_end_time, + status="failed", error=str(e)) + raise + + def _get_image_geo_info(self, img_path: str) -> tuple: + """ + 获取影像的地理信息（不加载图像数据，节省内存） + + Args: + img_path: 影像文件路径 + + Returns: + tuple: (geotransform, projection, width, height, n_bands) + geotransform: 地理变换参数 + projection: 投影信息 + width: 图像宽度 + height: 图像高度 + n_bands: 波段数 + """ + if not GDAL_AVAILABLE: + raise ImportError("GDAL未安装，无法读取影像文件") + + dataset = gdal.Open(img_path, gdal.GA_ReadOnly) + if dataset is None: + raise ValueError(f"无法打开影像文件: {img_path}") + + try: + width = dataset.RasterXSize + height = dataset.RasterYSize + n_bands = dataset.RasterCount + geotransform = dataset.GetGeoTransform() + projection = dataset.GetProjection() + + return geotransform, projection, width, height, n_bands + finally: + dataset = None + + def _load_image_as_array(self, img_path: str) -> tuple: + """ + 加载影像文件为numpy数组（已废弃，建议直接使用GDAL读取） + + 注意：此方法会将所有波段加载到内存，对于大图像会消耗大量内存。 + 建议直接传递文件路径给算法类，让算法类使用GDAL逐波段处理。 + + Args: + img_path: 影像文件路径 + + Returns: + tuple: (image_array, geotransform, projection) + image_array: numpy数组，形状为(height, width, bands) + geotransform: 地理变换参数 + projection: 投影信息 + """ + if not GDAL_AVAILABLE: + raise ImportError("GDAL未安装，无法读取影像文件") + + dataset = gdal.Open(img_path, gdal.GA_ReadOnly) + if dataset is None: + raise ValueError(f"无法打开影像文件: {img_path}") + + try: + width = dataset.RasterXSize + height = dataset.RasterYSize + n_bands = dataset.RasterCount + geotransform = dataset.GetGeoTransform() + projection = dataset.GetProjection() + + # 读取所有波段 + image_bands = [] + for i in range(1, n_bands + 1): + band = dataset.GetRasterBand(i) + band_data = band.ReadAsArray() + image_bands.append(band_data) + + # 堆叠为(height, width, bands)格式 + image_array = np.dstack(image_bands) + + return image_array, geotransform, projection + finally: + dataset = None + + def _save_array_as_image(self, image_array: np.ndarray, output_path: str, + geotransform: tuple, projection: str, + dtype: type = gdal.GDT_Float32) -> str: + """ + 将numpy数组保存为影像文件 + + Args: + image_array: numpy数组，形状为(height, width, bands) + output_path: 输出文件路径 + geotransform: 地理变换参数 + projection: 投影信息 + dtype: GDAL数据类型 + + Returns: + 输出文件路径 + """ + if not GDAL_AVAILABLE: + raise ImportError("GDAL未安装，无法保存影像文件") + + height, width, n_bands = image_array.shape + + # 获取驱动 + driver = gdal.GetDriverByName('ENVI') + if driver is None: + # 如果ENVI驱动不可用，尝试使用GTiff + driver = gdal.GetDriverByName('GTiff') + + if driver is None: + raise ValueError("无法创建影像文件，没有可用的驱动") + + # 创建数据集 + dataset = driver.Create(output_path, width, height, n_bands, dtype) + if dataset is None: + raise ValueError(f"无法创建输出文件: {output_path}") + + try: + # 设置地理变换和投影 + dataset.SetGeoTransform(geotransform) + dataset.SetProjection(projection) + + # 写入每个波段 + for i in range(n_bands): + band = dataset.GetRasterBand(i + 1) + band.WriteArray(image_array[:, :, i]) + band.FlushCache() + + finally: + dataset = None + + return output_path + + def _save_bands_as_image(self, corrected_bands: list, output_path: str, + geotransform: tuple, projection: str, + dtype: type = gdal.GDT_Float32) -> str: + """ + 直接从波段列表保存影像文件（避免堆叠，节省内存） + + Args: + corrected_bands: 校正后的波段列表，每个元素是一个(height, width)的numpy数组 + output_path: 输出文件路径 + geotransform: 地理变换参数 + projection: 投影信息 + dtype: GDAL数据类型 + + Returns: + 输出文件路径 + """ + if not GDAL_AVAILABLE: + raise ImportError("GDAL未安装，无法保存影像文件") + + if not corrected_bands: + raise ValueError("波段列表为空") + + n_bands = len(corrected_bands) + height, width = corrected_bands[0].shape + + # 获取驱动 + driver = gdal.GetDriverByName('ENVI') + if driver is None: + # 如果ENVI驱动不可用，尝试使用GTiff + driver = gdal.GetDriverByName('GTiff') + + if driver is None: + raise ValueError("无法创建影像文件，没有可用的驱动") + + # 创建数据集 + dataset = driver.Create(output_path, width, height, n_bands, dtype) + if dataset is None: + raise ValueError(f"无法创建输出文件: {output_path}") + + try: + # 设置地理变换和投影 + dataset.SetGeoTransform(geotransform) + dataset.SetProjection(projection) + + # 逐个写入波段（避免堆叠所有波段，节省内存） + for i, band_array in enumerate(corrected_bands): + if band_array.shape != (height, width): + raise ValueError(f"波段 {i} 的尺寸 {band_array.shape} 与预期 {(height, width)} 不匹配") + band = dataset.GetRasterBand(i + 1) + band.WriteArray(band_array) + band.FlushCache() + # 注意：这里不能删除band_array，因为它还在corrected_bands列表中 + # 但保存后可以提示垃圾回收器（如果需要） + + finally: + dataset = None + + return output_path + + def _prepare_water_mask_for_algorithm(self, water_mask: Optional[Union[str, np.ndarray]], + image_shape: Union[tuple, np.ndarray], + geotransform: tuple, + projection: str, + img_path: str) -> Optional[np.ndarray]: + """ + 准备水域掩膜供算法使用 + + 注意：如果传入的是shp文件，会先检查是否已经栅格化过，避免重复转换 + + Args: + water_mask: 水域掩膜，可以是None、numpy数组、文件路径(.dat/.tif)或shapefile路径(.shp) + image_shape: 影像形状，可以是(height, width)元组或numpy数组（用于获取形状） + geotransform: 地理变换参数 + projection: 投影信息 + img_path: 影像文件路径（用于栅格化shp文件） + + Returns: + numpy数组或None，1表示水域，0表示非水域 + """ + # 获取图像尺寸 + if isinstance(image_shape, np.ndarray): + img_height, img_width = image_shape.shape[:2] + else: + img_height, img_width = image_shape + + if water_mask is None: + # 如果water_mask为None，使用步骤1生成的dat格式掩膜 + if self.water_mask_path is not None: + try: + dat_mask_path = self._ensure_water_mask_dat(img_path) + water_mask = dat_mask_path + print(f"使用步骤1生成的水域掩膜: {water_mask}") + except Exception as e: + print(f"警告: 无法使用步骤1的水域掩膜: {e}") + return None + else: + return None + + # 如果已经是numpy数组 + if isinstance(water_mask, np.ndarray): + if water_mask.shape[:2] != (img_height, img_width): + raise ValueError(f"掩膜尺寸 {water_mask.shape[:2]} 与图像尺寸 {(img_height, img_width)} 不匹配") + return (water_mask > 0).astype(np.uint8) # 确保是0/1掩膜 + + # 如果是文件路径 + if isinstance(water_mask, str): + # 检查是否为shapefile + if water_mask.lower().endswith('.shp'): + # 从shp文件创建掩膜（这种情况应该很少，因为步骤1已经统一转换为dat） + try: + from src.utils.extract_water_area import rasterize_shp + # 使用固定路径，避免重复转换 + shp_name = Path(water_mask).stem + temp_mask_path = str(self.water_mask_dir / f"water_mask_{shp_name}.dat") + + # 如果文件已存在，直接使用 + if Path(temp_mask_path).exists(): + print(f"使用已存在的栅格化掩膜: {temp_mask_path}") + water_mask = temp_mask_path + else: + # 需要栅格化（需要img_path） + if img_path is None: + raise ValueError("当water_mask为shp格式时，需要提供img_path参数用于栅格化") + rasterize_shp(water_mask, temp_mask_path, img_path) + water_mask = temp_mask_path + print(f"已将shp格式的水域掩膜栅格化为: {temp_mask_path}") + + # 读取栅格化的掩膜 + if not GDAL_AVAILABLE: + raise ImportError("GDAL未安装，无法读取掩膜文件") + mask_dataset = gdal.Open(water_mask, gdal.GA_ReadOnly) + if mask_dataset is None: + raise ValueError(f"无法打开栅格化的掩膜文件: {water_mask}") + mask_array = mask_dataset.GetRasterBand(1).ReadAsArray() + mask_dataset = None + except Exception as e: + raise ValueError(f"无法从shp文件创建掩膜: {e}") + else: + # 栅格文件 + if not GDAL_AVAILABLE: + raise ImportError("GDAL未安装，无法读取掩膜文件") + mask_dataset = gdal.Open(water_mask, gdal.GA_ReadOnly) + if mask_dataset is None: + raise ValueError(f"无法打开掩膜文件: {water_mask}") + + mask_array = mask_dataset.GetRasterBand(1).ReadAsArray() + mask_dataset = None + + # 检查尺寸 + if mask_array.shape != (img_height, img_width): + raise ValueError(f"掩膜尺寸 {mask_array.shape} 与图像尺寸 {(img_height, img_width)} 不匹配") + + return (mask_array > 0).astype(np.uint8) + + raise ValueError(f"不支持的掩膜类型: {type(water_mask)}") + + def _copy_hdr_info(self, source_img_path: str, dest_img_path: str): + """ + 复制原始影像的hdr文件信息（如波长等）到目标影像的hdr文件 + + Args: + source_img_path: 源影像文件路径（原始bsq文件） + dest_img_path: 目标影像文件路径（去耀斑后的bsq文件） + """ + if not UTIL_AVAILABLE: + print("警告: util模块未导入，无法复制hdr文件信息") + return + + try: + source_hdr_path = get_hdr_file_path(source_img_path) + dest_hdr_path = get_hdr_file_path(dest_img_path) + + if not Path(source_hdr_path).exists(): + print(f"警告: 源hdr文件不存在: {source_hdr_path}") + return + + if not Path(dest_hdr_path).exists(): + print(f"警告: 目标hdr文件不存在: {dest_hdr_path}") + return + + # 复制hdr文件信息（波长等） + write_fields_to_hdrfile(source_hdr_path, dest_hdr_path) + print(f"已复制原始hdr文件信息到: {dest_hdr_path}") + except Exception as e: + print(f"警告: 复制hdr文件信息时出错: {e}") + + def _interpolate_zero_pixels(self, img_path: str, + interpolation_method: str = 'nearest', + output_path: Optional[str] = None, + water_mask: Optional[Union[str, np.ndarray]] = None) -> str: + """ + 对影像中所有波段都为0的像素点进行插值（只处理所有波段都为0的像素） + + Args: + img_path: 输入影像文件路径 + interpolation_method: 插值方法，支持： + - 'nearest': 邻近插值（最快） + - 'bilinear': 双线性插值 + - 'spline': 样条插值（RBF） + - 'kriging': 克里金插值（最慢但最准确） + output_path: 输出文件路径（如果为None，自动生成） + water_mask: 水域掩膜，用于限制插值区域（可选） + + Returns: + 插值后的影像文件路径 + """ + if not SCIPY_AVAILABLE: + raise ImportError("scipy未安装，无法进行0值像素插值") + + if not GDAL_AVAILABLE: + raise ImportError("GDAL未安装，无法读取影像文件") + + print(f"\n开始对0值像素进行插值，方法: {interpolation_method}") + print("注意: 只处理所有波段都为0的像素点") + + # 确定输出路径 + if output_path is None: + output_path = str(self.deglint_dir / f"interpolated_{interpolation_method}.bsq") + + # 检查文件是否已存在 + if Path(output_path).exists(): + print(f"检测到已存在的插值影像文件，直接使用: {output_path}") + self.interpolated_img_path = output_path + return output_path + + # 读取影像 + dataset = gdal.Open(img_path, gdal.GA_ReadOnly) + if dataset is None: + raise ValueError(f"无法打开影像文件: {img_path}") + + try: + width = dataset.RasterXSize + height = dataset.RasterYSize + n_bands = dataset.RasterCount + geotransform = dataset.GetGeoTransform() + projection = dataset.GetProjection() + + print(f"影像尺寸: {width} x {height} x {n_bands}") + + # 读取所有波段数据 + print("读取所有波段数据...") + all_bands = [] + for band_idx in range(1, n_bands + 1): + band = dataset.GetRasterBand(band_idx) + band_data = band.ReadAsArray().astype(np.float32) + all_bands.append(band_data) + + # 堆叠为 (height, width, n_bands) 格式 + image_stack = np.dstack(all_bands) + + # 读取水域掩膜（如果提供） + mask_array = None + if water_mask is not None: + if isinstance(water_mask, str): + mask_dataset = gdal.Open(water_mask, gdal.GA_ReadOnly) + if mask_dataset: + mask_array = mask_dataset.GetRasterBand(1).ReadAsArray() + mask_dataset = None + elif isinstance(water_mask, np.ndarray): + mask_array = water_mask + + # 找出所有波段都为0的像素点 + # 检查每个像素在所有波段是否都为0 + all_bands_zero = np.all(image_stack == 0, axis=2) # (height, width) + + # 如果提供了水域掩膜，只在水域掩膜内处理 + if mask_array is not None: + all_bands_zero = all_bands_zero & (mask_array > 0) + + # 统计需要插值的像素数量 + zero_pixel_count = np.sum(all_bands_zero) + print(f"发现 {zero_pixel_count} 个所有波段都为0的像素点") + + if zero_pixel_count == 0: + print("没有需要插值的像素点，直接保存原影像") + # 直接保存原影像 + driver = gdal.GetDriverByName('ENVI') + if driver is None: + driver = gdal.GetDriverByName('GTiff') + if driver is None: + raise ValueError("无法创建影像文件，没有可用的驱动") + + out_dataset = driver.Create(output_path, width, height, n_bands, gdal.GDT_Float32) + if out_dataset is None: + raise ValueError(f"无法创建输出文件: {output_path}") + + out_dataset.SetGeoTransform(geotransform) + out_dataset.SetProjection(projection) + + for i, band_data in enumerate(all_bands): + out_band = out_dataset.GetRasterBand(i + 1) + out_band.WriteArray(band_data) + out_band.FlushCache() + + out_dataset = None + self.interpolated_img_path = output_path + return output_path + + # 获取需要插值的像素坐标 + zero_y, zero_x = np.where(all_bands_zero) + zero_coords = np.column_stack([zero_x, zero_y]) # (n_zero_pixels, 2) + + # 获取有效像素的坐标（至少有一个波段不为0的像素） + valid_mask = ~all_bands_zero + valid_y, valid_x = np.where(valid_mask) + valid_coords = np.column_stack([valid_x, valid_y]) # (n_valid_pixels, 2) + + if len(valid_coords) == 0: + raise ValueError("没有有效像素可用于插值") + + print(f"使用 {len(valid_coords)} 个有效像素进行插值") + + # 创建输出数据集 + driver = gdal.GetDriverByName('ENVI') + if driver is None: + driver = gdal.GetDriverByName('GTiff') + if driver is None: + raise ValueError("无法创建影像文件，没有可用的驱动") + + out_dataset = driver.Create(output_path, width, height, n_bands, gdal.GDT_Float32) + if out_dataset is None: + raise ValueError(f"无法创建输出文件: {output_path}") + + out_dataset.SetGeoTransform(geotransform) + out_dataset.SetProjection(projection) + + # 逐波段进行插值（但只对"所有波段都为0"的像素进行插值） + interpolated_bands = [] + + for band_idx in range(n_bands): + print(f"处理波段 {band_idx + 1}/{n_bands}...", end=' ') + band_data = all_bands[band_idx].copy() + + # 获取有效像素的值 + valid_values = band_data[valid_mask] # (n_valid_pixels,) + + if len(valid_values) == 0: + print(f"警告: 波段 {band_idx + 1} 没有有效像素，跳过插值") + interpolated_bands.append(band_data) + continue + + # 对需要插值的像素进行插值 + if interpolation_method == 'nearest': + # 邻近插值 + from scipy.spatial import cKDTree + tree = cKDTree(valid_coords) + _, indices = tree.query(zero_coords) + interpolated_values = valid_values[indices] + + elif interpolation_method == 'bilinear': + # 双线性插值（使用griddata） + interpolated_values = griddata( + valid_coords, valid_values, zero_coords, + method='linear', fill_value=0.0 + ) + + # 如果线性插值失败，使用邻近插值 + nan_mask = np.isnan(interpolated_values) + if np.any(nan_mask): + from scipy.spatial import cKDTree + tree = cKDTree(valid_coords) + _, indices = tree.query(zero_coords[nan_mask]) + interpolated_values[nan_mask] = valid_values[indices] + + elif interpolation_method == 'spline': + # 样条插值（RBF） + try: + # 如果有效点太多，随机采样以提高速度 + max_points = 10000 + if len(valid_values) > max_points: + indices = np.random.choice(len(valid_values), max_points, replace=False) + sample_coords = valid_coords[indices] + sample_values = valid_values[indices] + else: + sample_coords = valid_coords + sample_values = valid_values + + # 使用RBF插值 + rbf = RBFInterpolator(sample_coords, sample_values, kernel='thin_plate_spline') + interpolated_values = rbf(zero_coords) + except Exception as e: + print(f"样条插值失败: {e}，回退到双线性插值") + interpolated_values = griddata( + valid_coords, valid_values, zero_coords, + method='linear', fill_value=0.0 + ) + nan_mask = np.isnan(interpolated_values) + if np.any(nan_mask): + from scipy.spatial import cKDTree + tree = cKDTree(valid_coords) + _, indices = tree.query(zero_coords[nan_mask]) + interpolated_values[nan_mask] = valid_values[indices] + + elif interpolation_method == 'kriging': + # 克里金插值 + try: + from src.utils.kriging import KrigingInterpolator + interpolator = KrigingInterpolator() + + # 如果有效点太多，随机采样以提高速度 + max_points = 5000 + if len(valid_values) > max_points: + indices = np.random.choice(len(valid_values), max_points, replace=False) + sample_coords = valid_coords[indices] + sample_values = valid_values[indices] + else: + sample_coords = valid_coords + sample_values = valid_values + + # 执行克里金插值 + result = interpolator.interpolate( + sample_coords[:, 0], sample_coords[:, 1], sample_values, + spatial_resolution=1.0, + output_path=None, + proj=projection + ) + + if result is not None: + # 从结果中提取插值点 + # 注意：KrigingInterpolator返回的是网格，需要提取对应位置的值 + # 这里简化处理，使用griddata作为后备 + interpolated_values = griddata( + valid_coords, valid_values, zero_coords, + method='cubic', fill_value=0.0 + ) + nan_mask = np.isnan(interpolated_values) + if np.any(nan_mask): + from scipy.spatial import cKDTree + tree = cKDTree(valid_coords) + _, indices = tree.query(zero_coords[nan_mask]) + interpolated_values[nan_mask] = valid_values[indices] + else: + raise ValueError("克里金插值失败") + except Exception as e: + print(f"克里金插值失败: {e}，回退到双线性插值") + interpolated_values = griddata( + valid_coords, valid_values, zero_coords, + method='linear', fill_value=0.0 + ) + nan_mask = np.isnan(interpolated_values) + if np.any(nan_mask): + from scipy.spatial import cKDTree + tree = cKDTree(valid_coords) + _, indices = tree.query(zero_coords[nan_mask]) + interpolated_values[nan_mask] = valid_values[indices] + else: + raise ValueError(f"不支持的插值方法: {interpolation_method}") + + # 更新波段数据（只更新所有波段都为0的像素） + band_data[all_bands_zero] = interpolated_values + interpolated_bands.append(band_data) + print(f"完成") + + # 保存所有波段 + for i, band_data in enumerate(interpolated_bands): + out_band = out_dataset.GetRasterBand(i + 1) + out_band.WriteArray(band_data) + out_band.FlushCache() + + out_dataset = None + dataset = None + + print(f"\n插值完成，共处理 {zero_pixel_count} 个所有波段都为0的像素点") + print(f"插值后的影像已保存: {output_path}") + + self.interpolated_img_path = output_path + return output_path + + finally: + if dataset: + dataset = None + + def step3_remove_glint(self, img_path: str, + method: str = "subtract_nir", + start_wave: Optional[float] = None, + end_wave: Optional[float] = None, + json_path: Optional[str] = None, + left_shoulder_wave: Optional[float] = None, + valley_wave: Optional[float] = None, + right_shoulder_wave: Optional[float] = None, + # 水域掩膜参数 + water_mask: Optional[Union[str, np.ndarray]] = None, + # 0值像素插值参数 + interpolate_zeros: bool = False, + interpolation_method: str = 'nearest', + # 是否执行去除耀斑 + enabled: bool = True, + # Kutser方法参数 + kutser_shp_path: Optional[str] = None, + oxy_band: int = 38, + lower_oxy: int = 36, + upper_oxy: int = 49, + nir_band: int = 47, + # Goodman方法参数 + nir_lower: int = 25, + nir_upper: int = 37, + goodman_A: float = 0.000019, + goodman_B: float = 0.1, + # Hedley方法参数 + hedley_shp_path: Optional[str] = None, + hedley_nir_band: int = 47, + # SUGAR方法参数 + sugar_bounds: Optional[List[tuple]] = None, + sugar_sigma: float = 1.0, + sugar_estimate_background: bool = True, + sugar_glint_mask_method: str = "cdf", + sugar_iter: Optional[int] = 3, + sugar_termination_thresh: float = 20.0) -> str: + """ + 步骤3: 去除耀斑 + + Args: + img_path: 输入影像文件路径 + method: 去耀斑方法，支持： + - "subtract_nir": 减去NIR方法 + - "regression_slope": 回归斜率方法 + - "oxygen_absorption": 氧吸收谷方法 + - "kutser": Kutser方法（基于氧吸收特征） + - "goodman": Goodman方法 + - "hedley": Hedley方法（基于NIR相关性） + - "sugar": SUGAR方法（迭代去耀斑） + start_wave: 起始波长（subtract_nir和regression_slope方法需要） + end_wave: 结束波长（subtract_nir和regression_slope方法需要） + json_path: ROI JSON文件路径（regression_slope方法需要） + left_shoulder_wave: 左肩波长（oxygen_absorption方法需要） + valley_wave: 谷值波长（oxygen_absorption方法需要） + right_shoulder_wave: 右肩波长（oxygen_absorption方法需要） + water_mask: 水域掩膜，可以是： + - None: 自动使用步骤1生成的水域掩膜（如果存在） + - numpy数组: 直接使用数组作为掩膜 + - 文件路径: 栅格文件路径(.dat/.tif)或shapefile路径(.shp) + 如果为None且步骤1未生成掩膜，则处理全图 + interpolate_zeros: 是否对0值像素进行插值（默认False） + interpolation_method: 插值方法，支持： + - 'nearest': 邻近插值（最快） + - 'bilinear': 双线性插值 + - 'spline': 样条插值（RBF） + - 'kriging': 克里金插值（最慢但最准确） + # Kutser方法参数 + kutser_shp_path: 深水区域shp文件路径（可选，已废弃，请使用water_mask） + oxy_band: 氧吸收波段索引（默认38，对应760.6nm） + lower_oxy: 氧吸收下波段索引（默认36，对应742.39nm） + upper_oxy: 氧吸收上波段索引（默认49，对应860.48nm） + nir_band: NIR波段索引（默认47，对应842.36nm） + # Goodman方法参数 + nir_lower: NIR下波段索引（默认25，对应641.93nm） + nir_upper: NIR上波段索引（默认37，对应751.49nm） + goodman_A: Goodman参数A（默认0.000019） + goodman_B: Goodman参数B（默认0.1） + # Hedley方法参数 + hedley_shp_path: 深水区域shp文件路径（可选，已废弃，请使用water_mask） + hedley_nir_band: NIR波段索引（默认47，对应842.36nm） + # SUGAR方法参数 + sugar_bounds: 优化边界列表，如[(1,2)]（默认None，使用[(1,2)]） + sugar_sigma: LoG平滑sigma（默认1.0） + sugar_estimate_background: 是否估计背景光谱（默认True） + sugar_glint_mask_method: 耀斑掩膜方法，"cdf"或"otsu"（默认"cdf"） + sugar_iter: 迭代次数，None表示自动终止（默认3） + sugar_termination_thresh: 终止阈值（默认20.0） + + Returns: + 去除耀斑后的影像文件路径 + """ + print("\n" + "="*80) + print("步骤3: 去除耀斑") + print("="*80) + + step_start_time = time.time() + try: + # 如果未启用，直接跳过处理并把原始影像路径作为后续流程输入 + if not enabled: + print("已设置跳过去除耀斑（enabled=False），将直接使用原始影像。") + self.deglint_img_path = img_path + step_end_time = time.time() + self._record_step_time("步骤3: 去除耀斑", step_start_time, step_end_time, status="skipped") + return self.deglint_img_path + + # 确定使用的水域掩膜 + # 优先级：1. 用户提供的water_mask参数 2. 步骤1生成的dat格式掩膜 3. None（处理全图） + final_water_mask = water_mask + if final_water_mask is None: + # 尝试使用步骤1生成的dat格式掩膜 + if self.water_mask_path is not None: + final_water_mask = self.water_mask_path + print(f"使用步骤1生成的水域掩膜: {final_water_mask}") + else: + print("未提供水域掩膜，将处理全图") + final_water_mask = None + + # 步骤3.1: 对0值像素进行插值（如果启用） + if interpolate_zeros: + print("\n" + "-"*80) + print("步骤3.1: 对0值像素进行插值") + print("-"*80) + interp_start_time = time.time() + try: + # 准备水域掩膜用于插值 + interp_water_mask = final_water_mask + if interp_water_mask is None and self.water_mask_path: + interp_water_mask = self.water_mask_path + + # 执行插值 + interpolated_img = self._interpolate_zero_pixels( + img_path=img_path, + interpolation_method=interpolation_method, + water_mask=interp_water_mask + ) + # 使用插值后的影像作为后续处理的输入 + img_path = interpolated_img + interp_end_time = time.time() + self._record_step_time("步骤3.1: 0值像素插值", interp_start_time, interp_end_time) + print(f"插值完成，使用插值后的影像: {img_path}") + except Exception as e: + print(f"警告: 0值像素插值失败: {e}，将使用原始影像继续处理") + interp_end_time = time.time() + self._record_step_time("步骤3.1: 0值像素插值", interp_start_time, interp_end_time, + status="failed", error=str(e)) + + if method == "kutser": + print(f"使用方法: Kutser (氧吸收波段={oxy_band}, NIR波段={nir_band})") + + # 确定输出路径 + output_path = str(self.deglint_dir / "deglint_kutser.bsq") + + # 检查文件是否已存在 + bsq_path = output_path if output_path.endswith('.bsq') else output_path.replace('.dat', '.bsq').replace('.tif', '.bsq') + if Path(bsq_path).exists() or Path(output_path).exists(): + existing_path = bsq_path if Path(bsq_path).exists() else output_path + print(f"检测到已存在的去耀斑影像文件，直接使用: {existing_path}") + self.deglint_img_path = existing_path + step_end_time = time.time() + self._record_step_time("步骤3: 去除耀斑", step_start_time, step_end_time, status="skipped") + print(f"去耀斑影像已设置: {self.deglint_img_path}") + return self.deglint_img_path + + # 获取地理信息（不加载图像数据） + geotransform, projection, width, height, n_bands = self._get_image_geo_info(img_path) + print(f"影像尺寸: {width} x {height} x {n_bands}") + + # 处理水域掩膜：如果是shp文件路径，需要栅格化 + # 创建一个临时数组用于获取尺寸信息（仅用于掩膜处理） + temp_shape = (height, width) + mask_for_algorithm = self._prepare_water_mask_for_algorithm( + final_water_mask, temp_shape, geotransform, projection, img_path + ) + + # 应用Kutser算法：直接传递文件路径，让算法类使用GDAL逐波段处理 + # 注意：kutser_shp_path参数已废弃，使用water_mask代替 + kutser = Kutser(img_path, shp_path=None, # 直接传递文件路径 + oxy_band=oxy_band, lower_oxy=lower_oxy, + upper_oxy=upper_oxy, NIR_band=nir_band, + water_mask=mask_for_algorithm, output_path=output_path) # 传递output_path，算法类会保存 + corrected_bands = kutser.get_corrected_bands() + + # 检查算法类是否已保存文件（可能保存为.bsq格式） + bsq_path = output_path if output_path.endswith('.bsq') else output_path.replace('.dat', '.bsq').replace('.tif', '.bsq') + if not Path(bsq_path).exists() and not Path(output_path).exists(): + # 如果算法类没有保存，使用pipeline的保存方法 + self._save_bands_as_image(corrected_bands, output_path, geotransform, projection) + self.deglint_img_path = output_path + # 复制原始hdr文件信息 + self._copy_hdr_info(img_path, output_path) + else: + # 算法类已保存，使用算法类保存的路径 + self.deglint_img_path = bsq_path if Path(bsq_path).exists() else output_path + # 复制原始hdr文件信息 + self._copy_hdr_info(img_path, self.deglint_img_path) + + # 保存后显式清理，帮助释放内存 + del corrected_bands + + elif method == "goodman": + print(f"使用方法: Goodman (NIR波段范围: {nir_lower}-{nir_upper})") + + # 确定输出路径 + output_path = str(self.deglint_dir / "deglint_goodman.bsq") + + # 检查文件是否已存在 + bsq_path = output_path if output_path.endswith('.bsq') else output_path.replace('.dat', '.bsq').replace('.tif', '.bsq') + if Path(bsq_path).exists() or Path(output_path).exists(): + existing_path = bsq_path if Path(bsq_path).exists() else output_path + print(f"检测到已存在的去耀斑影像文件，直接使用: {existing_path}") + self.deglint_img_path = existing_path + step_end_time = time.time() + self._record_step_time("步骤3: 去除耀斑", step_start_time, step_end_time, status="skipped") + print(f"去耀斑影像已设置: {self.deglint_img_path}") + return self.deglint_img_path + + # 获取地理信息（不加载图像数据） + geotransform, projection, width, height, n_bands = self._get_image_geo_info(img_path) + print(f"影像尺寸: {width} x {height} x {n_bands}") + + # 处理水域掩膜：如果是shp文件路径，需要栅格化 + # 创建一个临时数组用于获取尺寸信息（仅用于掩膜处理） + temp_shape = (height, width) + mask_for_algorithm = self._prepare_water_mask_for_algorithm( + final_water_mask, temp_shape, geotransform, projection, img_path + ) + + # 应用Goodman算法：直接传递文件路径，让算法类使用GDAL逐波段处理 + goodman = Goodman(img_path, NIR_lower=nir_lower, NIR_upper=nir_upper, + A=goodman_A, B=goodman_B, water_mask=mask_for_algorithm, + output_path=output_path) # 传递output_path，算法类会保存 + corrected_bands = goodman.get_corrected_bands() + + # 检查算法类是否已保存文件（可能保存为.bsq格式） + bsq_path = output_path if output_path.endswith('.bsq') else output_path.replace('.dat', '.bsq').replace('.tif', '.bsq') + if not Path(bsq_path).exists() and not Path(output_path).exists(): + # 如果算法类没有保存，使用pipeline的保存方法 + self._save_bands_as_image(corrected_bands, output_path, geotransform, projection) + self.deglint_img_path = output_path + # 复制原始hdr文件信息 + self._copy_hdr_info(img_path, output_path) + else: + # 算法类已保存，使用算法类保存的路径 + self.deglint_img_path = bsq_path if Path(bsq_path).exists() else output_path + # 复制原始hdr文件信息 + self._copy_hdr_info(img_path, self.deglint_img_path) + + # 保存后显式清理，帮助释放内存 + del corrected_bands + + elif method == "hedley": + print(f"使用方法: Hedley (NIR波段={hedley_nir_band})") + + # 确定输出路径 + output_path = str(self.deglint_dir / "deglint_hedley.bsq") + + # 检查文件是否已存在 + bsq_path = output_path if output_path.endswith('.bsq') else output_path.replace('.dat', '.bsq').replace('.tif', '.bsq') + if Path(bsq_path).exists() or Path(output_path).exists(): + existing_path = bsq_path if Path(bsq_path).exists() else output_path + print(f"检测到已存在的去耀斑影像文件，直接使用: {existing_path}") + self.deglint_img_path = existing_path + step_end_time = time.time() + self._record_step_time("步骤3: 去除耀斑", step_start_time, step_end_time, status="skipped") + print(f"去耀斑影像已设置: {self.deglint_img_path}") + return self.deglint_img_path + + # 获取地理信息（不加载图像数据） + geotransform, projection, width, height, n_bands = self._get_image_geo_info(img_path) + print(f"影像尺寸: {width} x {height} x {n_bands}") + + # 处理水域掩膜：如果是shp文件路径，需要栅格化 + # 创建一个临时数组用于获取尺寸信息（仅用于掩膜处理） + temp_shape = (height, width) + mask_for_algorithm = self._prepare_water_mask_for_algorithm( + final_water_mask, temp_shape, geotransform, projection, img_path + ) + + # 应用Hedley算法：直接传递文件路径，让算法类使用GDAL逐波段处理 + # 注意：hedley_shp_path参数已废弃，使用water_mask代替 + hedley = Hedley(img_path, shp_path=None, # 直接传递文件路径 + NIR_band=hedley_nir_band, water_mask=mask_for_algorithm, + output_path=output_path) # 传递output_path，算法类会保存 + corrected_bands = hedley.get_corrected_bands() + + # 检查算法类是否已保存文件（可能保存为.bsq格式） + bsq_path = output_path if output_path.endswith('.bsq') else output_path.replace('.dat', '.bsq').replace('.tif', '.bsq') + if not Path(bsq_path).exists() and not Path(output_path).exists(): + # 如果算法类没有保存，使用pipeline的保存方法 + self._save_bands_as_image(corrected_bands, output_path, geotransform, projection) + self.deglint_img_path = output_path + # 复制原始hdr文件信息 + self._copy_hdr_info(img_path, output_path) + else: + # 算法类已保存，使用算法类保存的路径 + self.deglint_img_path = bsq_path if Path(bsq_path).exists() else output_path + # 复制原始hdr文件信息 + self._copy_hdr_info(img_path, self.deglint_img_path) + + # 保存后显式清理，帮助释放内存 + del corrected_bands + + elif method == "sugar": + print(f"使用方法: SUGAR (迭代次数={sugar_iter}, 掩膜方法={sugar_glint_mask_method})") + + # 确定输出路径 + output_path = str(self.deglint_dir / "deglint_sugar.bsq") + + # 检查文件是否已存在 + if Path(output_path).exists(): + print(f"检测到已存在的去耀斑影像文件，直接使用: {output_path}") + self.deglint_img_path = output_path + step_end_time = time.time() + self._record_step_time("步骤3: 去除耀斑", step_start_time, step_end_time, status="skipped") + print(f"去耀斑影像已设置: {self.deglint_img_path}") + return self.deglint_img_path + + # 加载影像 + image_array, geotransform, projection = self._load_image_as_array(img_path) + print(f"影像尺寸: {image_array.shape}") + + # 处理水域掩膜：如果是shp文件路径，需要栅格化 + mask_for_algorithm = self._prepare_water_mask_for_algorithm( + final_water_mask, image_array, geotransform, projection, img_path + ) + + # 设置默认bounds + if sugar_bounds is None: + sugar_bounds = [(1, 2)] + + # 应用SUGAR算法 + # 传递output_path给correction_iterative函数，但函数传入数组时无法获取地理信息，所以仍使用pipeline的保存方法 + if sugar_iter is None: + # 使用自动终止 + corrected_images = correction_iterative( + image_array, iter=None, bounds=sugar_bounds, + estimate_background=sugar_estimate_background, + glint_mask_method=sugar_glint_mask_method, + termination_thresh=sugar_termination_thresh, + water_mask=mask_for_algorithm, + output_path=None # 不传递output_path，使用pipeline保存 + ) + else: + # 使用固定迭代次数 + corrected_images = correction_iterative( + image_array, iter=sugar_iter, bounds=sugar_bounds, + estimate_background=sugar_estimate_background, + glint_mask_method=sugar_glint_mask_method, + water_mask=mask_for_algorithm, + output_path=None # 不传递output_path，使用pipeline保存 + ) + + # 使用最后一次迭代的结果 + if len(corrected_images) > 0: + corrected_array = corrected_images[-1] + else: + raise ValueError("SUGAR算法未生成任何结果") + + # 保存结果（保留地理信息） + self._save_array_as_image(corrected_array, output_path, geotransform, projection) + self.deglint_img_path = output_path + # 复制原始hdr文件信息 + self._copy_hdr_info(img_path, output_path) + + else: + raise ValueError(f"不支持的方法: {method}。支持的方法: kutser, goodman, hedley, sugar") + + step_end_time = time.time() + self._record_step_time("步骤3: 去除耀斑", step_start_time, step_end_time) + print(f"去耀斑影像已生成: {self.deglint_img_path}") + return self.deglint_img_path + except Exception as e: + step_end_time = time.time() + self._record_step_time("步骤3: 去除耀斑", step_start_time, step_end_time, + status="failed", error=str(e)) + raise + + def step4_process_csv(self, csv_path: str) -> str: + """ + 步骤4: 对csv文件进行处理，筛选剔除异常值 + + Args: + csv_path: 输入CSV文件路径 + + Returns: + 处理后的CSV文件路径 + """ + print("\n" + "="*80) + print("步骤4: 处理CSV文件，筛选剔除异常值") + print("="*80) + + step_start_time = time.time() + try: + output_path = str(self.processed_data_dir / "processed_data.csv") + + # 检查文件是否已存在 + if Path(output_path).exists(): + print(f"检测到已存在的处理后CSV文件，直接使用: {output_path}") + self.processed_csv_path = output_path + step_end_time = time.time() + self._record_step_time("步骤4: 处理CSV文件", step_start_time, step_end_time, status="skipped") + print(f"处理后的CSV文件已设置: {self.processed_csv_path}") + return self.processed_csv_path + + process_water_quality_data(csv_path, output_path) + self.processed_csv_path = output_path + + step_end_time = time.time() + self._record_step_time("步骤4: 处理CSV文件", step_start_time, step_end_time) + print(f"处理后的CSV文件已保存: {self.processed_csv_path}") + return self.processed_csv_path + except Exception as e: + step_end_time = time.time() + self._record_step_time("步骤4: 处理CSV文件", step_start_time, step_end_time, + status="failed", error=str(e)) + raise + + def step5_extract_training_spectra(self, deglint_img_path: Optional[str] = None, + radius: int = 5, + source_epsg: int = 4326) -> str: + """ + 步骤5: 根据csv文件的采样点坐标，在去除耀斑的文件中统计采样点的平均光谱 + + Args: + deglint_img_path: 去除耀斑后的影像文件路径（如果为None，使用步骤3的结果） + radius: 采样半径（像素） + source_epsg: 源坐标系EPSG代码 + + Returns: + 包含光谱数据的CSV文件路径 + """ + print("\n" + "="*80) + print("步骤5: 提取训练样本点的平均光谱") + print("="*80) + + step_start_time = time.time() + try: + if self.deglint_img_path is None and deglint_img_path is None: + raise ValueError("请先执行步骤3: 去除耀斑，或提供deglint_img_path参数") + + if self.processed_csv_path is None: + raise ValueError("请先执行步骤4: 处理CSV文件") + + img_path = deglint_img_path if deglint_img_path else self.deglint_img_path + + output_path = str(self.training_spectra_dir / "training_spectra.csv") + + # 检查文件是否已存在 + if Path(output_path).exists(): + print(f"检测到已存在的训练光谱数据文件，直接使用: {output_path}") + self.training_spectra_path = output_path + step_end_time = time.time() + self._record_step_time("步骤5: 提取训练样本点光谱", step_start_time, step_end_time, status="skipped") + print(f"训练光谱数据已设置: {self.training_spectra_path}") + return self.training_spectra_path + + # 确保有dat格式的水体掩膜（如果需要） + water_mask_dat = self._ensure_water_mask_dat(img_path) + + get_spectral_in_coor( + img_path, self.processed_csv_path, output_path, + radius=radius, flare_path=self.glint_mask_path, + boundary_path=water_mask_dat, source_epsg=source_epsg + ) + self.training_spectra_path = output_path + + step_end_time = time.time() + self._record_step_time("步骤5: 提取训练样本点光谱", step_start_time, step_end_time) + print(f"训练光谱数据已保存: {self.training_spectra_path}") + return self.training_spectra_path + except Exception as e: + step_end_time = time.time() + self._record_step_time("步骤5: 提取训练样本点光谱", step_start_time, step_end_time, + status="failed", error=str(e)) + raise + + def step6_train_models(self, feature_start_column: str = "374.285004", + preprocessing_methods: List[str] = None, + model_names: List[str] = None, + split_methods: List[str] = None, + cv_folds: int = 5) -> str: + """ + 步骤6: 使用采样点的平均光谱和对应的实测值建立机器学习模型，保存模型权重 + + Args: + feature_start_column: 特征开始列名或索引 + preprocessing_methods: 预处理方法列表 + model_names: 模型名称列表 + split_methods: 数据划分方法列表 + cv_folds: 交叉验证折数 + + Returns: + 模型保存目录路径 + """ + print("\n" + "="*80) + print("步骤6: 训练机器学习模型") + print("="*80) + + step_start_time = time.time() + try: + if self.training_spectra_path is None: + raise ValueError("请先执行步骤5: 提取训练样本点的平均光谱") + + # 检查模型目录是否存在且有内容 + if self.models_dir.exists() and any(self.models_dir.iterdir()): + # 检查是否有至少一个目标参数的模型文件夹 + has_models = False + for item in self.models_dir.iterdir(): + if item.is_dir(): + # 检查文件夹内是否有模型文件 + model_files = list(item.glob('*.pkl')) + list(item.glob('*.joblib')) + list(item.glob('*.h5')) + if model_files: + has_models = True + break + + if has_models: + print(f"检测到已存在的模型文件，直接使用: {self.models_dir}") + step_end_time = time.time() + self._record_step_time("步骤6: 训练机器学习模型", step_start_time, step_end_time, status="skipped") + print(f"模型目录已设置: {self.models_dir}") + return str(self.models_dir) + + if preprocessing_methods is None: + preprocessing_methods = ['None', 'MMS', 'SS', 'SNV', 'MA', 'SG', 'MSC', 'D1', 'D2', 'DT', 'CT'] + if model_names is None: + model_names = ['SVR', 'RF', 'Ridge', 'Lasso'] + if split_methods is None: + split_methods = ['spxy', 'ks', 'random'] + + modeler = WaterQualityModelingBatch(str(self.models_dir)) + + all_results = modeler.train_models_batch( + csv_path=self.training_spectra_path, + feature_start_column=feature_start_column, + preprocessing_methods=preprocessing_methods, + model_names=model_names, + split_methods=split_methods, + cv_folds=cv_folds + ) + + step_end_time = time.time() + self._record_step_time("步骤6: 训练机器学习模型", step_start_time, step_end_time) + print(f"模型训练完成，结果保存在: {self.models_dir}") + + # 生成训练摘要报告 + try: + summary_path = self.report_generator.generate_training_summary(str(self.models_dir)) + print(f"训练摘要报告已生成: {summary_path}") + except Exception as e: + print(f"生成训练摘要报告时出错: {e}") + + return str(self.models_dir) + except Exception as e: + step_end_time = time.time() + self._record_step_time("步骤6: 训练机器学习模型", step_start_time, step_end_time, + status="failed", error=str(e)) + raise + + def step7_generate_sampling_points(self, deglint_img_path: Optional[str] = None, + interval: int = 50, + sample_radius: int = 5, + chunk_size: int = 1000, + water_mask_path: Optional[str] = None, + glint_mask_path: Optional[str] = None) -> str: + """ + 步骤7: 生成根据水域掩膜内且耀斑掩膜外的采样点，统计采样点的平均光谱 + + Args: + deglint_img_path: 去除耀斑后的影像文件路径（如果为None，使用步骤3的结果） + interval: 采样点间隔（像元数） + sample_radius: 采样点半径（像元数） + chunk_size: 每次处理的行数（控制内存使用） + water_mask_path: dat格式的水域掩膜文件路径（如果为None，将使用步骤1生成的dat格式掩膜） + + Returns: + 采样点光谱数据CSV文件路径 + """ + print("\n" + "="*80) + print("步骤7: 生成预测采样点并提取光谱") + print("="*80) + + step_start_time = time.time() + try: + if self.deglint_img_path is None and deglint_img_path is None: + raise ValueError("请先执行步骤3: 去除耀斑，或提供deglint_img_path参数") + + # 如果没有提供water_mask_path，使用步骤1生成的dat格式掩膜 + if water_mask_path is None: + if self.water_mask_path is not None: + water_mask_path = self.water_mask_path + print(f"使用步骤1生成的dat格式水体掩膜: {water_mask_path}") + else: + raise ValueError("请提供water_mask_path参数，或确保步骤1已生成水体掩膜") + + img_path = deglint_img_path if deglint_img_path else self.deglint_img_path + + # 确定耀斑掩膜路径（允许外部显式传入以覆盖步骤2结果） + glint_mask_to_use = glint_mask_path if glint_mask_path else self.glint_mask_path + if glint_mask_to_use is None: + print("未检测到耀斑掩膜，将在采样点生成时不做耀斑区域剔除。") + + output_path = str(self.sampling_dir / "sampling_spectra.csv") + + # 检查文件是否已存在 + if Path(output_path).exists(): + print(f"检测到已存在的采样点光谱数据文件，直接使用: {output_path}") + step_end_time = time.time() + self._record_step_time("步骤7: 生成预测采样点", step_start_time, step_end_time, status="skipped") + print(f"采样点光谱数据已设置: {output_path}") + return output_path + + # create_water_mask_from_shp函数已支持dat格式，直接传递即可 + get_spectral_sampling_points_chunked( + img_path, water_mask_path, glint_mask_to_use, + output_path, interval, sample_radius, chunk_size + ) + + step_end_time = time.time() + self._record_step_time("步骤7: 生成预测采样点", step_start_time, step_end_time) + print(f"采样点光谱数据已保存: {output_path}") + return output_path + except Exception as e: + step_end_time = time.time() + self._record_step_time("步骤7: 生成预测采样点", step_start_time, step_end_time, + status="failed", error=str(e)) + raise + + def step8_predict_water_quality(self, sampling_csv_path: str, + models_dir: Optional[str] = None, + metric: str = 'test_r2', + prediction_column: str = 'prediction') -> Dict[str, str]: + """ + 步骤8: 将训练好的最佳机器学习模型应用到采样点的平均光谱上，预测水质参数 + + Args: + sampling_csv_path: 采样点光谱数据CSV路径 + models_dir: 模型保存目录（如果为None，使用步骤6的结果） + metric: 选择最佳模型的指标 + prediction_column: 预测结果列名 + + Returns: + 预测结果文件路径字典（键为目标列名） + """ + print("\n" + "="*80) + print("步骤8: 预测水质参数") + print("="*80) + + step_start_time = time.time() + try: + models_path = models_dir if models_dir else str(self.models_dir) + + # 检查prediction_dir中是否已有预测结果文件 + prediction_files = {} + if self.prediction_dir.exists(): + # 查找所有CSV预测结果文件 + csv_files = list(self.prediction_dir.glob('*.csv')) + if csv_files: + # 从文件名提取目标参数名（假设文件名为"target_name_prediction.csv"） + for csv_file in csv_files: + # 尝试从文件名提取目标参数名 + file_stem = csv_file.stem + # 移除可能的后缀（如_prediction） + if '_prediction' in file_stem: + target_name = file_stem.replace('_prediction', '') + elif '_pred' in file_stem: + target_name = file_stem.replace('_pred', '') + else: + target_name = file_stem + prediction_files[target_name] = str(csv_file) + + # 如果已有预测文件，检查是否完整（需要与模型目录中的目标参数匹配） + if prediction_files: + models_path_obj = Path(models_path) + if models_path_obj.exists(): + # 获取所有目标参数文件夹 + target_folders = [d.name for d in models_path_obj.iterdir() if d.is_dir()] + # 检查是否所有目标参数都有预测文件 + missing_targets = [t for t in target_folders if t not in prediction_files] + if not missing_targets: + print(f"检测到已存在的预测结果文件，直接使用: {self.prediction_dir}") + print(f"找到 {len(prediction_files)} 个预测结果文件") + step_end_time = time.time() + self._record_step_time("步骤8: 预测水质参数", step_start_time, step_end_time, status="skipped") + print(f"预测结果已设置: {self.prediction_dir}") + return prediction_files + else: + print(f"检测到部分预测结果文件，缺少以下目标参数: {missing_targets}") + print("将继续生成缺失的预测结果...") + + # 创建推理实例 + inferencer = WaterQualityInference(models_path) + + # 批量推理多个目标列的模型 + all_results = inferencer.batch_inference_multi_models( + models_root_dir=models_path, + sampling_csv_path=sampling_csv_path, + output_dir=str(self.prediction_dir), + metric=metric, + prediction_column=prediction_column, + output_format='csv' + ) + + # 提取输出文件路径（合并已有和新生成的） + for target_name, result in all_results.items(): + if result.get('status') == 'success': + prediction_files[target_name] = result['output_file'] + + step_end_time = time.time() + self._record_step_time("步骤8: 预测水质参数", step_start_time, step_end_time) + print(f"预测完成，结果保存在: {self.prediction_dir}") + + # 生成预测结果报告 + try: + report_path = self.report_generator.generate_prediction_report(prediction_files) + print(f"预测结果报告已生成: {report_path}") + except Exception as e: + print(f"生成预测结果报告时出错: {e}") + + return prediction_files + except Exception as e: + step_end_time = time.time() + self._record_step_time("步骤8: 预测水质参数", step_start_time, step_end_time, + status="failed", error=str(e)) + raise + + def step9_generate_distribution_map(self, prediction_csv_path: str, + boundary_shp_path: str, + output_image_path: Optional[str] = None, + resolution: float = 30, + input_crs: str = 'EPSG:32651', + output_crs: str = 'EPSG:4326', + show_sample_points: bool = False, + base_map_tif: Optional[str] = None, + use_distance_diffusion: bool = True, + max_diffusion_distance: Optional[float] = None, + diffusion_power: float = 2, + diffusion_n_neighbors: int = 15, + cmap: Optional[str] = None, + expand_ratio: float = 0.05) -> str: + """ + 步骤9: 根据采样点的坐标和反演的实测参数，以及水域掩膜，通过插值的方法，得到水质参数的可视化分布图 + + Args: + prediction_csv_path: 预测结果CSV文件路径（前两列为经纬度，第三列为预测值） + boundary_shp_path: 边界shapefile文件路径 + output_image_path: 输出图片路径（如果为None，自动生成） + resolution: 插值网格分辨率（米） + input_crs: 输入坐标系 + output_crs: 输出坐标系 + show_sample_points: 是否在图上显示采样点 + base_map_tif: 底图TIF路径，用于在水域掩膜外显示底图 + use_distance_diffusion: 是否启用距离扩散补全边界 + max_diffusion_distance: 距离扩散的最大距离（米），None表示自动计算 + diffusion_power: 距离扩散的幂参数 + diffusion_n_neighbors: 距离扩散时使用的最近邻数量 + cmap: 指定的颜色映射名称，None表示自动识别 + expand_ratio: 边界外扩比例（0-1之间） + + Returns: + 可视化分布图文件路径 + """ + print("\n" + "="*80) + print("步骤9: 生成水质参数可视化分布图") + print("="*80) + + step_start_time = time.time() + try: + if output_image_path is None: + # 根据CSV文件名自动生成输出路径 + csv_name = Path(prediction_csv_path).stem + output_image_path = str(self.visualization_dir / f"{csv_name}_distribution.png") + + # 检查文件是否已存在 + if Path(output_image_path).exists(): + print(f"检测到已存在的分布图文件，直接使用: {output_image_path}") + step_end_time = time.time() + self._record_step_time("步骤9: 生成分布图", step_start_time, step_end_time, status="skipped") + print(f"可视化分布图已设置: {output_image_path}") + return output_image_path + + # 创建映射器 + mapper = ContentMapper(input_crs=input_crs, output_crs=output_crs) + + # 处理数据并生成分布图 + mapper_kwargs = { + 'resolution': resolution, + 'show_sample_points': show_sample_points, + 'use_distance_diffusion': use_distance_diffusion, + 'diffusion_power': diffusion_power, + 'diffusion_n_neighbors': diffusion_n_neighbors, + 'expand_ratio': expand_ratio + } + + optional_mapper_kwargs = { + 'base_map_tif': base_map_tif, + 'max_diffusion_distance': max_diffusion_distance, + 'cmap': cmap + } + mapper_kwargs.update({ + key: value for key, value in optional_mapper_kwargs.items() + if value is not None + }) + + mapper.process_data( + csv_file=prediction_csv_path, + shp_file=boundary_shp_path, + output_file=output_image_path, + **mapper_kwargs + ) + + step_end_time = time.time() + self._record_step_time("步骤9: 生成分布图", step_start_time, step_end_time) + print(f"可视化分布图已保存: {output_image_path}") + return output_image_path + except Exception as e: + step_end_time = time.time() + self._record_step_time("步骤9: 生成分布图", step_start_time, step_end_time, + status="failed", error=str(e)) + raise + + def generate_model_scatter_plots(self, training_csv_path: Optional[str] = None, + models_dir: Optional[str] = None, + metric: str = 'test_r2', + use_enhanced: bool = True, + feature_start_column: Union[str, int] = 13, + test_size: float = 0.2, + random_state: int = 42) -> Dict[str, str]: + """ + 生成模型评估散点图（真实值vs预测值） + + Args: + training_csv_path: 训练数据CSV路径（如果为None，使用步骤5的结果） + models_dir: 模型保存目录（如果为None，使用步骤6的结果） + metric: 选择最佳模型的指标 + use_enhanced: 是否使用增强版散点图（带置信区间，使用sctter_batch） + feature_start_column: 特征开始列名或索引 + test_size: 测试集比例 + random_state: 随机种子 + + Returns: + 散点图文件路径字典（键为目标参数名） + """ + print("\n" + "="*80) + print("生成模型评估散点图") + print("="*80) + + if training_csv_path is None: + training_csv_path = self.training_spectra_path + if training_csv_path is None: + raise ValueError("请提供训练数据CSV路径，或先执行步骤5") + + if models_dir is None: + models_dir = str(self.models_dir) + + scatter_paths = {} + models_path = Path(models_dir) + + # 如果使用增强版散点图（带置信区间） + if use_enhanced: + print("使用增强版散点图（带置信区间）") + try: + # 使用sctter_batch批量生成散点图 + results = self.scatter_batch.batch_plot_scatter( + models_root_dir=models_dir, + csv_path=training_csv_path, + output_dir=str(self.visualization_dir / "scatter_plots"), + metric=metric, + target_column=None, # 使用文件夹名称作为目标列名 + feature_start_column=feature_start_column, + test_size=test_size, + random_state=random_state + ) + + # 提取成功生成的散点图路径 + for target_name, result in results.items(): + if result.get('status') == 'success': + scatter_paths[target_name] = result.get('save_path', '') + print(f" ✓ {target_name}: {result.get('save_path', '')}") + else: + print(f" ✗ {target_name}: 失败 - {result.get('error', '未知错误')}") + + except Exception as e: + print(f"使用增强版散点图时出错: {e}") + print("回退到基础版散点图") + use_enhanced = False + + # 如果未使用增强版或增强版失败，使用基础版 + if not use_enhanced or not scatter_paths: + print("使用基础版散点图") + from src.core.prediction.inference_batch import WaterQualityInference + + # 遍历所有目标参数文件夹 + for target_folder in models_path.iterdir(): + if not target_folder.is_dir(): + continue + + target_name = target_folder.name + print(f"\n处理目标参数: {target_name}") + + try: + # 加载最佳模型进行评估 + inferencer = WaterQualityInference(str(target_folder)) + eval_result = inferencer.evaluate_with_split( + data_csv_path=training_csv_path, + split_method="spxy", + test_size=test_size, + random_state=random_state, + metric=metric + ) + + # 提取预测结果 + predictions = eval_result.get('predictions', {}) + if predictions: + y_train_true = predictions.get('y_train_true') + y_train_pred = predictions.get('y_train_pred') + y_test_true = predictions.get('y_test_true') + y_test_pred = predictions.get('y_test_pred') + metrics = eval_result.get('test_metrics', {}) + + if y_train_true is not None and y_test_true is not None: + # 合并训练集和测试集 + y_all_true = np.concatenate([y_train_true, y_test_true]) + y_all_pred = np.concatenate([y_train_pred, y_test_pred]) + + # 生成索引 + train_indices = np.arange(len(y_train_true)) + test_indices = np.arange(len(y_train_true), len(y_all_true)) + + # 绘制散点图 + scatter_path = self.visualizer.plot_scatter_true_vs_pred( + y_true=y_all_true, + y_pred=y_all_pred, + target_name=target_name, + train_indices=train_indices, + test_indices=test_indices, + metrics={ + 'train_r2': eval_result.get('train_metrics', {}).get('r2', 0), + 'test_r2': metrics.get('r2', 0), + 'train_rmse': eval_result.get('train_metrics', {}).get('rmse', 0), + 'test_rmse': metrics.get('rmse', 0) + } + ) + scatter_paths[target_name] = scatter_path + except Exception as e: + print(f"处理目标参数 {target_name} 时出错: {e}") + continue + + print(f"\n散点图生成完成，共生成 {len(scatter_paths)} 个图表") + return scatter_paths + + def generate_spectrum_comparison_plots(self, csv_path: Optional[str] = None, + parameter_columns: Optional[List[str]] = None, + wavelength_start_column: Union[str, int] = "UTM_Y") -> Dict[str, str]: + """ + 生成光谱曲线对比图（不同参数值的光谱曲线对比） + + Args: + csv_path: 包含光谱和参数值的CSV文件路径（如果为None，使用步骤5的结果） + parameter_columns: 参数列名列表（如果为None，自动检测） + wavelength_start_column: 波长开始列名或索引 + + Returns: + 光谱曲线图文件路径字典（键为参数名） + """ + print("\n" + "="*80) + print("生成光谱曲线对比图") + print("="*80) + + if csv_path is None: + csv_path = self.training_spectra_path + if csv_path is None: + raise ValueError("请提供CSV文件路径，或先执行步骤5") + + # 读取数据以检测参数列 + df = pd.read_csv(csv_path) + + if parameter_columns is None: + # 自动检测参数列（排除坐标列和光谱列） + if isinstance(wavelength_start_column, str): + try: + wavelength_start_idx = df.columns.get_loc(wavelength_start_column) + except: + wavelength_start_idx = 13 # 默认值 + else: + wavelength_start_idx = wavelength_start_column + + # 假设前几列是参数列（根据实际数据结构调整） + parameter_columns = list(df.columns[:wavelength_start_idx]) + # 排除坐标列（通常是前两列） + if len(parameter_columns) > 2: + parameter_columns = parameter_columns[2:] + + spectrum_paths = {} + for param_col in parameter_columns: + if param_col not in df.columns: + continue + + print(f"\n处理参数: {param_col}") + try: + spectrum_path = self.visualizer.plot_spectrum_by_parameter( + csv_path=csv_path, + parameter_column=param_col, + wavelength_start_column=wavelength_start_column, + n_groups=5 + ) + spectrum_paths[param_col] = spectrum_path + except Exception as e: + print(f"处理参数 {param_col} 时出错: {e}") + continue + + print(f"\n光谱曲线图生成完成，共生成 {len(spectrum_paths)} 个图表") + return spectrum_paths + + def generate_boxplots(self, csv_path: Optional[str] = None, + parameter_columns: Optional[List[str]] = None, + data_start_column: int = 4, + save_individual: bool = True, + use_seaborn: bool = True) -> Dict[str, str]: + """ + 生成水质参数的箱型图 + + Args: + csv_path: CSV文件路径（如果为None，使用步骤4的结果） + parameter_columns: 参数列名列表（如果为None，自动检测） + data_start_column: 数据开始列索引（从第几列开始，默认第5列，索引为4） + save_individual: 是否为每个参数单独保存箱型图 + use_seaborn: 是否使用seaborn绘制（更美观） + + Returns: + 箱型图文件路径字典 + """ + print("\n" + "="*80) + print("生成水质参数箱型图") + print("="*80) + + if csv_path is None: + csv_path = self.processed_csv_path + if csv_path is None: + raise ValueError("请提供CSV文件路径，或先执行步骤4") + + # 读取数据 + df = pd.read_csv(csv_path) + + # 确定参数列 + if parameter_columns is None: + # 从指定列开始的所有列 + data_columns = df.iloc[:, data_start_column:] + parameter_columns = list(data_columns.columns) + else: + # 使用指定的列 + parameter_columns = [col for col in parameter_columns if col in df.columns] + + if not parameter_columns: + print("警告: 未找到有效的参数列") + return {} + + # 创建输出目录 + boxplot_dir = self.visualization_dir / "boxplots" + boxplot_dir.mkdir(parents=True, exist_ok=True) + + boxplot_paths = {} + + if save_individual: + # 为每个参数单独绘制箱型图 + print(f"为每个参数单独绘制箱型图（共 {len(parameter_columns)} 个参数）") + + for column in parameter_columns: + if column not in df.columns: + continue + + # 移除空值 + clean_data = df[column].dropna() + + if len(clean_data) == 0: + print(f"跳过列 '{column}': 没有有效数据") + continue + + try: + # 创建新图形 + plt.figure(figsize=(8, 6)) + + if use_seaborn: + # 使用seaborn绘制 + plot_data = pd.DataFrame({ + '参数': [column] * len(clean_data), + '数值': clean_data + }) + sns.boxplot(data=plot_data, x='参数', y='数值', palette='Set2') + sns.stripplot(data=plot_data, x='参数', y='数值', + color='red', alpha=0.6, size=5, jitter=True) + else: + # 使用matplotlib绘制 + box_plot = plt.boxplot([clean_data], labels=[column], + patch_artist=True, showfliers=False) + box_plot['boxes'][0].set_facecolor('lightblue') + box_plot['boxes'][0].set_alpha(0.7) + + # 添加散点 + x_pos = np.random.normal(1, 0.04, size=len(clean_data)) + plt.scatter(x_pos, clean_data, alpha=0.6, s=30, color='red', + edgecolors='black', linewidth=0.5, zorder=3) + + # 设置标题和标签 + plt.title(f'{column} - 箱型图', fontsize=14, fontweight='bold') + plt.xlabel('参数', fontsize=12) + plt.ylabel('数值', fontsize=12) + + # 添加统计信息 + stats_text = (f'数据点数: {len(clean_data)}\n' + f'均值: {clean_data.mean():.2f}\n' + f'中位数: {clean_data.median():.2f}\n' + f'标准差: {clean_data.std():.2f}') + plt.text(0.02, 0.98, stats_text, transform=plt.gca().transAxes, + verticalalignment='top', + bbox=dict(boxstyle='round', + facecolor='wheat' if not use_seaborn else 'lightgreen', + alpha=0.8)) + + # 添加网格 + plt.grid(True, alpha=0.3, linestyle='--') + + # 调整布局 + plt.tight_layout() + + # 保存图片 + safe_column_name = column.replace('/', '_').replace('\\', '_').replace(':', '_') + save_path = boxplot_dir / f'{safe_column_name}_boxplot.png' + plt.savefig(save_path, dpi=300, bbox_inches='tight') + plt.close() + + boxplot_paths[column] = str(save_path) + print(f" 已保存: {save_path.name}") + + except Exception as e: + print(f" 处理参数 {column} 时出错: {e}") + continue + + # 生成所有参数的综合箱型图 + try: + print("\n生成综合箱型图（所有参数在一张图上）") + plt.figure(figsize=(max(12, len(parameter_columns) * 0.8), 8)) + + # 准备数据 + box_data = [] + labels = [] + for column in parameter_columns: + if column in df.columns: + clean_data = df[column].dropna() + if len(clean_data) > 0: + box_data.append(clean_data) + labels.append(column) + + if box_data: + if use_seaborn: + # 使用seaborn绘制 + melted_data = pd.melt(df[labels], var_name='参数', value_name='数值') + melted_data = melted_data.dropna() + sns.boxplot(data=melted_data, x='参数', y='数值', palette='Set3') + sns.stripplot(data=melted_data, x='参数', y='数值', + color='red', alpha=0.6, size=4, jitter=True) + else: + # 使用matplotlib绘制 + box_plot = plt.boxplot(box_data, labels=labels, patch_artist=True, + showfliers=False) + colors = plt.cm.Set3(np.linspace(0, 1, len(box_data))) + for patch, color in zip(box_plot['boxes'], colors): + patch.set_facecolor(color) + patch.set_alpha(0.7) + + # 添加散点 + for i, data in enumerate(box_data): + x_pos = np.random.normal(i + 1, 0.04, size=len(data)) + plt.scatter(x_pos, data, alpha=0.6, s=20, color='red', + edgecolors='black', linewidth=0.5, zorder=3) + + plt.title('水质参数箱型图（综合）', fontsize=16, fontweight='bold') + plt.xlabel('参数', fontsize=12) + plt.ylabel('数值', fontsize=12) + plt.xticks(rotation=45, ha='right') + plt.grid(True, alpha=0.3, linestyle='--') + plt.tight_layout() + + combined_path = boxplot_dir / 'all_parameters_boxplot.png' + plt.savefig(combined_path, dpi=300, bbox_inches='tight') + plt.close() + + boxplot_paths['all_parameters'] = str(combined_path) + print(f" 已保存综合箱型图: {combined_path.name}") + + except Exception as e: + print(f"生成综合箱型图时出错: {e}") + + print(f"\n箱型图生成完成，共生成 {len(boxplot_paths)} 个图表") + return boxplot_paths + + def generate_statistical_charts(self, csv_path: Optional[str] = None, + parameter_columns: Optional[List[str]] = None) -> Dict[str, str]: + """ + 生成统计图表（箱线图、直方图、相关性热力图） + + Args: + csv_path: CSV文件路径（如果为None，使用步骤4的结果） + parameter_columns: 参数列名列表（如果为None，自动检测） + + Returns: + 统计图表文件路径字典 + """ + print("\n" + "="*80) + print("生成统计图表") + print("="*80) + + if csv_path is None: + csv_path = self.processed_csv_path + if csv_path is None: + raise ValueError("请提供CSV文件路径，或先执行步骤4") + + # 读取数据以检测参数列 + df = pd.read_csv(csv_path) + + if parameter_columns is None: + # 自动检测参数列（排除前两列坐标列） + parameter_columns = list(df.columns[2:]) + # 过滤掉非数值列 + parameter_columns = [col for col in parameter_columns + if df[col].dtype in [np.float64, np.int64]] + + chart_paths = self.visualizer.plot_statistical_charts( + csv_path=csv_path, + parameter_columns=parameter_columns + ) + + print(f"\n统计图表生成完成") + return chart_paths + + def generate_pipeline_report(self, output_path: Optional[str] = None) -> str: + """ + 生成流程执行报告，包含每步的耗时统计 + + Args: + output_path: 输出文件路径（如果为None，自动生成） + + Returns: + 报告文件路径 + """ + if output_path is None: + timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') + output_path = str(self.reports_dir / f"pipeline_report_{timestamp}.csv") + + # 准备报告数据 + report_data = [] + total_time = 0.0 + + # 按步骤顺序排列 + step_order = [ + "步骤1: 生成水域mask", + "步骤2: 找到耀斑区域", + "步骤3: 去除耀斑", + "步骤4: 处理CSV文件", + "步骤5: 提取训练样本点光谱", + "步骤6: 训练机器学习模型", + "步骤7: 生成预测采样点", + "步骤8: 预测水质参数", + "步骤9: 生成分布图" + ] + + for step_name in step_order: + if step_name in self.step_timings: + timing_info = self.step_timings[step_name] + report_data.append({ + '步骤': step_name, + '开始时间': timing_info['start_time'], + '结束时间': timing_info['end_time'], + '耗时(秒)': f"{timing_info['elapsed_seconds']:.2f}", + '耗时(格式化)': timing_info['elapsed_formatted'], + '状态': timing_info['status'], + '错误信息': timing_info.get('error', '') + }) + if timing_info['status'] == 'completed': + total_time += timing_info['elapsed_seconds'] + + # 添加总计行 + if self.pipeline_start_time and self.pipeline_end_time: + pipeline_total = self.pipeline_end_time - self.pipeline_start_time + report_data.append({ + '步骤': '总计', + '开始时间': datetime.fromtimestamp(self.pipeline_start_time).strftime('%Y-%m-%d %H:%M:%S'), + '结束时间': datetime.fromtimestamp(self.pipeline_end_time).strftime('%Y-%m-%d %H:%M:%S'), + '耗时(秒)': f"{pipeline_total:.2f}", + '耗时(格式化)': self._format_time(pipeline_total), + '状态': 'completed', + '错误信息': '' + }) + + # 创建DataFrame并保存 + df_report = pd.DataFrame(report_data) + df_report.to_csv(output_path, index=False, encoding='utf-8-sig') + + # 同时生成文本格式的报告 + txt_output_path = str(Path(output_path).with_suffix('.txt')) + with open(txt_output_path, 'w', encoding='utf-8') as f: + f.write("="*80 + "\n") + f.write("水质参数反演流程执行报告\n") + f.write("="*80 + "\n\n") + + if self.pipeline_start_time and self.pipeline_end_time: + f.write(f"流程开始时间: {datetime.fromtimestamp(self.pipeline_start_time).strftime('%Y-%m-%d %H:%M:%S')}\n") + f.write(f"流程结束时间: {datetime.fromtimestamp(self.pipeline_end_time).strftime('%Y-%m-%d %H:%M:%S')}\n") + f.write(f"总耗时: {self._format_time(self.pipeline_end_time - self.pipeline_start_time)}\n\n") + + f.write("-"*80 + "\n") + f.write("各步骤执行详情:\n") + f.write("-"*80 + "\n\n") + + for step_name in step_order: + if step_name in self.step_timings: + timing_info = self.step_timings[step_name] + f.write(f"{step_name}\n") + f.write(f" 开始时间: {timing_info['start_time']}\n") + f.write(f" 结束时间: {timing_info['end_time']}\n") + f.write(f" 耗时: {timing_info['elapsed_formatted']} ({timing_info['elapsed_seconds']:.2f}秒)\n") + f.write(f" 状态: {timing_info['status']}\n") + if timing_info.get('error'): + f.write(f" 错误: {timing_info['error']}\n") + f.write("\n") + + f.write("-"*80 + "\n") + f.write("统计摘要:\n") + f.write("-"*80 + "\n") + completed_steps = [s for s in self.step_timings.values() if s['status'] == 'completed'] + failed_steps = [s for s in self.step_timings.values() if s['status'] == 'failed'] + skipped_steps = [s for s in self.step_timings.values() if s['status'] == 'skipped'] + + f.write(f"成功完成的步骤: {len(completed_steps)}\n") + f.write(f"失败的步骤: {len(failed_steps)}\n") + f.write(f"跳过的步骤: {len(skipped_steps)}\n") + + if completed_steps: + completed_times = [s['elapsed_seconds'] for s in completed_steps] + f.write(f"平均耗时: {self._format_time(np.mean(completed_times))}\n") + f.write(f"最长耗时: {self._format_time(np.max(completed_times))} ({[s['elapsed_formatted'] for s in completed_steps if s['elapsed_seconds'] == np.max(completed_times)][0]})\n") + f.write(f"最短耗时: {self._format_time(np.min(completed_times))} ({[s['elapsed_formatted'] for s in completed_steps if s['elapsed_seconds'] == np.min(completed_times)][0]})\n") + + print(f"\n流程报告已生成:") + print(f" CSV格式: {output_path}") + print(f" 文本格式: {txt_output_path}") + + return output_path + + def run_full_pipeline(self, config: Dict): + """ + 运行完整流程 + + Args: + config: 配置字典，包含所有步骤的参数 + """ + print("\n" + "="*80) + print("开始运行完整水质参数反演流程") + print("="*80) + + # 记录流程开始时间 + self.pipeline_start_time = time.time() + + try: + # 步骤1: 生成水域mask + if 'step1' in config: + self.step1_generate_water_mask(**config['step1']) + + # 步骤2: 找到耀斑区域 + # 若后续明确不进行去除耀斑（step3.enabled=False），则跳过步骤2 + step3_enabled = config.get('step3', {}).get('enabled', True) + if 'step2' in config and step3_enabled: + self.step2_find_glint_area(**config['step2']) + + # 步骤3: 去除耀斑 + if 'step3' in config: + self.step3_remove_glint(**config['step3']) + + # 步骤4: 处理CSV文件 + if 'step4' in config: + self.step4_process_csv(**config['step4']) + + # 步骤5: 提取训练样本点光谱 + if 'step5' in config: + self.step5_extract_training_spectra(**config['step5']) + + # 步骤6: 训练模型 + if 'step6' in config: + self.step6_train_models(**config['step6']) + + # 步骤7: 生成预测采样点 + if 'step7' in config: + sampling_csv_path = self.step7_generate_sampling_points(**config['step7']) + else: + sampling_csv_path = None + + # 步骤8: 预测水质参数 + if 'step8' in config and sampling_csv_path: + step8_config = config['step8'].copy() + step8_config['sampling_csv_path'] = sampling_csv_path + prediction_files = self.step8_predict_water_quality(**step8_config) + else: + prediction_files = {} + + # 步骤9: 生成分布图 + distribution_maps = {} + if 'step9' in config and prediction_files: + for target_name, pred_file in prediction_files.items(): + step9_config = config['step9'].copy() + step9_config['prediction_csv_path'] = pred_file + if 'output_image_path' not in step9_config: + step9_config['output_image_path'] = None + dist_map_path = self.step9_generate_distribution_map(**step9_config) + distribution_maps[target_name] = dist_map_path + + # 生成可视化图表 + output_files = {} + pipeline_info = { + 'work_dir': str(self.work_dir), + 'models_dir': str(self.models_dir), + 'prediction_files': prediction_files, + 'output_files': {} + } + + # 生成散点图 + if 'visualization' in config and config['visualization'].get('generate_scatter', True): + if self.training_spectra_path and self.models_dir.exists(): + try: + scatter_config = config['visualization'].get('scatter_config', {}) + scatter_paths = self.generate_model_scatter_plots( + metric=scatter_config.get('metric', 'test_r2'), + use_enhanced=scatter_config.get('use_enhanced', True), + feature_start_column=scatter_config.get('feature_start_column', + config.get('step6', {}).get('feature_start_column', 13)), + test_size=scatter_config.get('test_size', 0.2), + random_state=scatter_config.get('random_state', 42) + ) + output_files['scatter_plots'] = scatter_paths + pipeline_info['output_files']['scatter_plots'] = scatter_paths + print(f"已生成 {len(scatter_paths)} 个散点图") + except Exception as e: + print(f"生成散点图时出错: {e}") + + # 生成箱型图 + if 'visualization' in config and config['visualization'].get('generate_boxplots', True): + if self.processed_csv_path: + try: + boxplot_config = config['visualization'].get('boxplot_config', {}) + boxplot_paths = self.generate_boxplots( + parameter_columns=boxplot_config.get('parameter_columns', None), + data_start_column=boxplot_config.get('data_start_column', 4), + save_individual=boxplot_config.get('save_individual', True), + use_seaborn=boxplot_config.get('use_seaborn', True) + ) + output_files['boxplots'] = boxplot_paths + pipeline_info['output_files']['boxplots'] = boxplot_paths + print(f"已生成 {len(boxplot_paths)} 个箱型图") + except Exception as e: + print(f"生成箱型图时出错: {e}") + + # 生成光谱曲线图 + if 'visualization' in config and config['visualization'].get('generate_spectrum', True): + if self.training_spectra_path: + try: + spectrum_paths = self.generate_spectrum_comparison_plots( + wavelength_start_column=config.get('step6', {}).get('feature_start_column', 'UTM_Y') + ) + output_files['spectrum_plots'] = spectrum_paths + pipeline_info['output_files']['spectrum_plots'] = spectrum_paths + print(f"已生成 {len(spectrum_paths)} 个光谱曲线图") + except Exception as e: + print(f"生成光谱曲线图时出错: {e}") + + # 生成统计图表 + if 'visualization' in config and config['visualization'].get('generate_statistics', True): + if self.processed_csv_path: + try: + stat_charts = self.generate_statistical_charts() + output_files['statistical_charts'] = stat_charts + pipeline_info['output_files']['statistical_charts'] = stat_charts + print(f"已生成统计图表") + except Exception as e: + print(f"生成统计图表时出错: {e}") + + # 生成批量处理摘要 + try: + step1_output = str(self.water_mask_path) if self.water_mask_path else 'N/A' + pipeline_info['step1'] = {'status': 'completed', 'output_file': step1_output} + pipeline_info['step2'] = {'status': 'completed', 'output_file': str(self.glint_mask_path) if self.glint_mask_path else 'N/A'} + pipeline_info['step3'] = {'status': 'completed', 'output_file': str(self.deglint_img_path) if self.deglint_img_path else 'N/A'} + pipeline_info['step4'] = {'status': 'completed', 'output_file': str(self.processed_csv_path) if self.processed_csv_path else 'N/A'} + pipeline_info['step5'] = {'status': 'completed', 'output_file': str(self.training_spectra_path) if self.training_spectra_path else 'N/A'} + pipeline_info['step6'] = {'status': 'completed', 'output_file': str(self.models_dir)} + pipeline_info['training_params'] = config.get('step6', {}) + + summary_path = self.report_generator.generate_batch_inference_summary(pipeline_info) + print(f"批量处理摘要已生成: {summary_path}") + output_files['batch_summary'] = summary_path + except Exception as e: + print(f"生成批量处理摘要时出错: {e}") + + # 记录流程结束时间 + self.pipeline_end_time = time.time() + + # 生成流程执行报告 + try: + report_path = self.generate_pipeline_report() + output_files['pipeline_report'] = report_path + except Exception as e: + print(f"生成流程报告时出错: {e}") + + print("\n" + "="*80) + print("完整流程执行完成！") + print("="*80) + + # 显示总耗时 + if self.pipeline_start_time and self.pipeline_end_time: + total_time = self.pipeline_end_time - self.pipeline_start_time + print(f"总耗时: {self._format_time(total_time)}") + + print(f"\n所有输出文件:") + for key, value in output_files.items(): + if isinstance(value, dict): + print(f" {key}: {len(value)} 个文件") + else: + print(f" {key}: {value}") + + except Exception as e: + # 即使失败也记录结束时间 + self.pipeline_end_time = time.time() + + # 尝试生成报告（即使失败） + try: + report_path = self.generate_pipeline_report() + print(f"\n流程报告已生成: {report_path}") + except: + pass + + print(f"\n流程执行失败: {e}") + import traceback + traceback.print_exc() + raise + + +def main(): + """主函数示例""" + parser = argparse.ArgumentParser(description="水质参数反演框架主程序") + parser.add_argument('--config', type=str, help='配置文件路径（JSON格式）') + parser.add_argument('--work_dir', type=str, default=r'E:\code\WQ\pipeline_result\work_dir', help='工作目录') + parser.add_argument('--mode', type=str, choices=['full', 'step'], + default='full', help='运行模式：full(完整流程)或step(单步执行)') + + args = parser.parse_args() + + # 创建管道实例 + pipeline = WaterQualityInversionPipeline(work_dir=args.work_dir) + + # 示例配置（用户可以根据实际情况修改） + example_config = { + 'step1': { + 'mask_path': r"D:\BaiduNetdiskDownload\yaobao\roi\roi.shp", # 支持shp或dat格式，如果是shp需要提供img_path + 'img_path': r"D:\BaiduNetdiskDownload\yaobao\result3.bsq", # 当mask_path为shp格式时必须提供，用于栅格化 + }, + 'step2': { + 'img_path': r"D:\BaiduNetdiskDownload\yaobao\result3.bsq", + 'glint_wave': 550.0, + 'method': 'otsu', + 'max_area' :50, # 过滤掉面积超过10000像素的连通域 + 'buffer_size' : 10 # 可选: 'otsu', 'zscore', 'percentile', 'iqr', 'adaptive', 'multi_band' + # 'z_threshold': 2.5, # zscore方法使用 + # 'percentile': 95.0, # percentile和adaptive方法使用 + # 'iqr_multiplier': 1.5, # iqr方法使用 + # 'window_size': 15, # adaptive方法使用 + # 'multi_band_waves': [750, 800, 850], # multi_band方法使用 + # 'sub_method': 'zscore', # multi_band方法的子方法 + # 'weights': None, # multi_band方法的权重 + # 'max_area': 10000, # 最大连通域面积阈值（像素数），用于去除岸边、浅水、水华等大面积区域（None表示不过滤） + # 'buffer_size': 50, # 岸边缓冲区大小（像素数），用于去除岸边附近的错误耀斑掩膜（None表示不启用） + }, + 'step3': { + 'img_path': r"D:\BaiduNetdiskDownload\yaobao\result3.bsq", + 'method': 'goodman', # 可选: 'subtract_nir', 'regression_slope', 'oxygen_absorption', + # 'kutser', 'goodman', 'hedley', 'sugar' + 'enabled': True, # 是否执行去除耀斑；False 时跳过并直接使用原始影像 + # 0值像素插值参数（可选）: + 'interpolate_zeros': False, # 是否对0值像素进行插值（默认False） + 'interpolation_method': 'bilinear', # 插值方法: 'nearest'(邻近), 'bilinear'(双线性), + # 'spline'(样条), 'kriging'(克里金) + # 水域掩膜参数（可选）: + 'water_mask':r"D:\BaiduNetdiskDownload\yaobao\roi\roi.shp", # None表示自动使用步骤1生成的掩膜，也可以提供： + # # - numpy数组 + # # - 栅格文件路径(.dat/.tif) + # # - shapefile路径(.shp) + # 'start_wave': 780.0, # subtract_nir和regression_slope方法需要 + # 'end_wave': 850.0, # subtract_nir和regression_slope方法需要 + # 'json_path': 'path/to/roi.json' # regression_slope方法需要 + # Kutser方法参数示例: + # 'kutser_shp_path': 'path/to/deep_water.shp', # 已废弃，请使用water_mask参数 + # 'oxy_band': 38, # 氧吸收波段索引 + # 'lower_oxy': 36, # 氧吸收下波段索引 + # 'upper_oxy': 49, # 氧吸收上波段索引 + # 'nir_band': 47, # NIR波段索引 + # Goodman方法参数示例: + 'nir_lower': 65, # NIR下波段索引 + 'nir_upper': 91, # NIR上波段索引 + 'goodman_A': 0.000019, # Goodman参数A + 'goodman_B': 0.1, # Goodman参数B + # Hedley方法参数示例: + # 'hedley_shp_path': 'path/to/deep_water.shp', # 已废弃，请使用water_mask参数 + # 'hedley_nir_band': 47, # NIR波段索引 + # SUGAR方法参数示例: + # 'sugar_bounds': [(1, 2)], # 优化边界 + # 'sugar_sigma': 1.0, # LoG平滑sigma + # 'sugar_estimate_background': True, # 是否估计背景光谱 + # 'sugar_glint_mask_method': 'cdf', # 'cdf'或'otsu' + # 'sugar_iter': 1, # 迭代次数，None表示自动终止 + # 'sugar_termination_thresh': 20.0 # 终止阈值 + }, + 'step4': { + 'csv_path': r"D:\BaiduNetdiskDownload\yaobao\csv\input.csv"#水质参数原始文件 + }, + 'step5': { + 'radius': 5, + 'source_epsg': 4326 + }, + 'step6': { + 'feature_start_column': '374.285004', + 'preprocessing_methods': ['None', 'MMS', 'SS', 'SNV', 'MA', 'SG', 'MSC', 'D1', 'D2', 'DT', 'CT'], + 'model_names': ['SVR', 'RF', 'Ridge', 'Lasso'], + 'split_methods': ['spxy', 'ks', 'random'], + 'cv_folds': 3 + }, + 'step7': { + 'interval': 50, + 'sample_radius': 5, + 'chunk_size': 1000, + 'water_mask_path': None, # 若为None，将自动使用步骤1生成的dat水体掩膜 + # 可选：耀斑掩膜文件（dat），若不提供将使用步骤2结果；需要外部指定时取消注释 + # 'glint_mask_path': r"D:\path\to\severe_glint_area.dat", + }, + 'step8': { + 'metric': 'test_r2', + 'prediction_column': 'prediction' + }, + 'step9': { + 'boundary_shp_path': r"D:\BaiduNetdiskDownload\yaobao\roi\roi.shp" , + 'resolution': 30, + 'input_crs': 'EPSG:32651', + 'output_crs': 'EPSG:4326', + # 可选参数，若不需要可删除 + 'show_sample_points': False, + 'base_map_tif': None, + 'use_distance_diffusion': True, + 'max_diffusion_distance': None, + 'diffusion_power': 2, + 'diffusion_n_neighbors': 15, + 'cmap': None, + 'expand_ratio': 0.05 + }, + 'visualization': { + 'generate_scatter': True, # 是否生成散点图 + 'generate_boxplots': True, # 是否生成箱型图 + 'generate_spectrum': True, # 是否生成光谱曲线图 + 'generate_statistics': True, # 是否生成统计图表 + 'scatter_config': { + 'metric': 'test_r2', # 选择最佳模型的指标 + 'use_enhanced': True, # 是否使用增强版散点图（带置信区间） + 'feature_start_column': 13, # 特征开始列索引 + 'test_size': 0.2, # 测试集比例 + 'random_state': 42 # 随机种子 + }, + 'boxplot_config': { + 'parameter_columns': None, # 参数列名列表（None表示自动检测） + 'data_start_column': 4, # 数据开始列索引（从第5列开始，索引为4） + 'save_individual': True, # 是否为每个参数单独保存箱型图 + 'use_seaborn': True # 是否使用seaborn绘制（更美观） + } + } + } + + if args.config: + # 从配置文件加载（需要实现JSON加载逻辑） + import json + with open(args.config, 'r', encoding='utf-8') as f: + config = json.load(f) + else: + # 使用示例配置（用户需要修改） + config = example_config + print("警告: 使用示例配置，请根据实际情况修改配置参数") + + if args.mode == 'full': + pipeline.run_full_pipeline(config) + else: + print("单步执行模式，请直接调用对应的step方法") + print("例如: pipeline.step1_generate_water_mask(...)") + + +if __name__ == "__main__": + main() + diff --git a/src/core/water_quality_inversion_pipeline_GUI.py b/src/core/water_quality_inversion_pipeline_GUI.py new file mode 100644 index 0000000..1a5f2db --- /dev/null +++ b/src/core/water_quality_inversion_pipeline_GUI.py @@ -0,0 +1,4299 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +水质参数反演框架主程序 + +本程序串联了水质参数反演的所有步骤： +1. 生成水域mask（基于shp或NDWI阈值分割） +2. 找到耀斑区域（支持多种算法：otsu、zscore、percentile、iqr、adaptive、multi_band） +3. 去除耀斑（支持七种方法：subtract_nir、regression_slope、oxygen_absorption、kutser、goodman、hedley、sugar） +4. 对csv文件进行处理，筛选剔除异常值 +5. 根据csv文件的采样点坐标，在去除耀斑的文件中统计采样点的平均光谱 +6. 使用采样点的平均光谱和对应的实测值建立机器学习模型，保存模型权重 +7. 生成根据水域掩膜内且耀斑掩膜外的采样点，统计采样点的平均光谱 +8. 将训练好的最佳机器学习模型应用到采样点的平均光谱上，预测水质参数 +9. 根据采样点的坐标和反演的实测参数，以及水域掩膜，通过插值的方法，得到水质参数的可视化分布图 + +重要特性： +- 每一步都可以独立运行，不受限制 +- 通过设置 skip_dependency_check=True 可以跳过依赖检查 +- 支持灵活的参数传递，允许用户自定义输入输出路径 + +可视化功能： +- 生成模型评估散点图（真实值vs预测值，支持带置信区间的增强版） +- 生成水质参数箱型图（支持单独和综合两种模式） +- 生成光谱曲线对比图 +- 生成统计图表（箱线图、直方图、相关性热力图） +""" + +import os +import sys +import argparse +from pathlib import Path +from typing import Optional, Dict, List, Union +import numpy as np +import pandas as pd +import warnings +import time +from datetime import datetime +import json +warnings.filterwarnings('ignore') + +# 导入各个功能模块 +from src.utils.extract_water_area import rasterize_shp, ndwi +from src.utils.find_severe_glint_area import find_severe_glint_area +from src.preprocessing.process_water_quality_data import process_water_quality_data +from src.core.glint_removal.get_spectral import get_spectral_in_coor +from src.core.modeling.modeling_batch import WaterQualityModelingBatch +from src.utils.sampling import get_spectral_sampling_points_chunked +from src.core.prediction.inference_batch import WaterQualityInference +from src.utils.kriging import KrigingInterpolator, batch_kriging_interpolation +from src.postprocessing.map import ContentMapper +from src.postprocessing.visualization_reports import WaterQualityVisualization, ReportGenerator +from src.core.prediction.sctter_batch import WaterQualityScatterBatch +# 导入新的耀斑去除算法 +from src.core.glint_removal.Kutser import Kutser +from src.core.glint_removal.Goodman import Goodman +from src.core.glint_removal.Hedley import Hedley +from src.core.glint_removal.SUGAR import SUGAR, correction_iterative +from src.utils.water_index import WaterQualityIndexCalculator +from src.core.modeling.regression import SingleVariableRegressionAnalysis +# 导入hdr文件处理函数 +try: + from src.utils.util import write_fields_to_hdrfile, get_hdr_file_path, find_band_number + UTIL_AVAILABLE = True + FIND_BAND_AVAILABLE = True +except ImportError: + UTIL_AVAILABLE = False + FIND_BAND_AVAILABLE = False + print("警告: util模块未导入，hdr文件信息复制功能可能无法正常工作") +import matplotlib.pyplot as plt +import seaborn as sns +# 导入插值相关库 +try: + from scipy import ndimage + from scipy.interpolate import griddata, RBFInterpolator + SCIPY_AVAILABLE = True +except ImportError: + SCIPY_AVAILABLE = False + print("警告: scipy未安装，0值像素插值功能可能无法正常工作") +# 导入GDAL用于影像读写 +try: + from osgeo import gdal + GDAL_AVAILABLE = True +except ImportError: + GDAL_AVAILABLE = False + print("警告: GDAL未安装，新算法可能无法正常工作") + + +class WaterQualityInversionPipeline: + """水质参数反演管道类""" + + def __init__(self, work_dir: str = "./work_dir"): + """ + 初始化管道 + + Args: + work_dir: 工作目录，用于保存所有中间结果 + """ + self.work_dir = Path(work_dir) + self.work_dir.mkdir(parents=True, exist_ok=True) + + # 创建子目录 + self.water_mask_dir = self.work_dir / "1_water_mask" + self.glint_dir = self.work_dir / "2_glint" + self.deglint_dir = self.work_dir / "3_deglint" + self.processed_data_dir = self.work_dir / "4_processed_data" + self.training_spectra_dir = self.work_dir / "5_training_spectra" + self.indices_dir = self.work_dir / "5_5_water_quality_indices" + self.models_dir = self.work_dir / "6_models" + self.non_empirical_models_dir = self.work_dir / "6_5_non_empirical_models" + self.custom_regression_dir = self.work_dir / "6_75_custom_regression" + self.sampling_dir = self.work_dir / "7_sampling" + self.prediction_dir = self.work_dir / "8_predictions" + self.visualization_dir = self.work_dir / "9_visualization" + self.reports_dir = self.work_dir / "10_reports" + + # 创建所有子目录 + for dir_path in [self.water_mask_dir, self.glint_dir, self.deglint_dir, + self.processed_data_dir, self.training_spectra_dir, + self.indices_dir, self.models_dir, self.non_empirical_models_dir, + self.custom_regression_dir, self.sampling_dir, self.prediction_dir, + self.visualization_dir, self.reports_dir]: + dir_path.mkdir(parents=True, exist_ok=True) + + # 初始化可视化和报告生成器 + self.visualizer = WaterQualityVisualization(str(self.visualization_dir)) + self.report_generator = ReportGenerator(str(self.reports_dir)) + self.scatter_batch = WaterQualityScatterBatch() + + # 设置中文字体 + plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans', 'Arial Unicode MS'] + plt.rcParams['axes.unicode_minus'] = False + + # 存储中间结果路径 + self.water_mask_path = None # 存储dat格式的水体掩膜路径（统一格式） + self.glint_mask_path = None + self.interpolated_img_path = None # 存储插值后的影像路径 + self.deglint_img_path = None + self.processed_csv_path = None + self.training_spectra_path = None + self.indices_path = None + self.custom_regression_path = None + + # 存储每步的执行时间 + self.step_timings = {} + self.pipeline_start_time = None + self.pipeline_end_time = None + + # 回调函数（用于GUI更新） + self.callback = None + + self._index_calculator = WaterQualityIndexCalculator() + + print(f"工作目录已创建: {self.work_dir}") + + def set_callback(self, callback): + """ + 设置回调函数，用于向GUI报告进度 + + Args: + callback: 回调函数，签名为 callback(step_name, status, message="") + status可以是: 'start', 'completed', 'skipped', 'error', 'info', 'warning' + """ + self.callback = callback + + def _notify(self, step_name: str, status: str, message: str = ""): + """通知回调函数""" + if self.callback: + try: + self.callback(step_name, status, message) + except Exception as e: + print(f"回调函数执行失败: {e}") + + def _record_step_time(self, step_name: str, start_time: float, end_time: float, + status: str = "completed", error: Optional[str] = None): + """ + 记录步骤执行时间 + + Args: + step_name: 步骤名称 + start_time: 开始时间（时间戳） + end_time: 结束时间（时间戳） + status: 状态（"completed", "failed", "skipped"） + error: 错误信息（如果有） + """ + elapsed_time = end_time - start_time + self.step_timings[step_name] = { + 'start_time': datetime.fromtimestamp(start_time).strftime('%Y-%m-%d %H:%M:%S'), + 'end_time': datetime.fromtimestamp(end_time).strftime('%Y-%m-%d %H:%M:%S'), + 'elapsed_seconds': elapsed_time, + 'elapsed_formatted': self._format_time(elapsed_time), + 'status': status, + 'error': error + } + print(f"步骤耗时: {self._format_time(elapsed_time)}") + + def _format_time(self, seconds: float) -> str: + """ + 格式化时间显示 + + Args: + seconds: 秒数 + + Returns: + 格式化后的时间字符串 + """ + if seconds < 60: + return f"{seconds:.2f}秒" + elif seconds < 3600: + minutes = int(seconds // 60) + secs = seconds % 60 + return f"{minutes}分{secs:.2f}秒" + else: + hours = int(seconds // 3600) + minutes = int((seconds % 3600) // 60) + secs = seconds % 60 + return f"{hours}小时{minutes}分{secs:.2f}秒" + + def _ensure_water_mask_dat(self, img_path: str) -> str: + """ + 确保有dat格式的水体掩膜文件（简化版本，因为步骤1已经确保有dat文件） + + Args: + img_path: 影像文件路径（已废弃，保留用于兼容性） + + Returns: + dat格式的水体掩膜文件路径 + """ + if self.water_mask_path is not None: + if Path(self.water_mask_path).exists(): + return self.water_mask_path + else: + raise ValueError(f"水体掩膜文件不存在: {self.water_mask_path}") + + raise ValueError("未找到水体掩膜文件，请先执行步骤1") + + def step1_generate_water_mask(self, + mask_path: Optional[str] = None, + img_path: Optional[str] = None, + ndwi_threshold: float = 0.4, + use_ndwi: bool = False, + skip_dependency_check: bool = False, + generate_png: bool = True) -> str: + """ + 步骤1: 生成或设置水域mask + + 支持三种方式生成水域掩膜： + 1. 基于shp文件栅格化 + 2. 使用现有的栅格格式掩膜文件 + 3. 基于NDWI从影像自动生成水体掩膜 + + 当提供img_path时，会自动生成PNG预览图，基于波长选择RGB波段： + - 红波段: ~650nm + - 绿波段: ~550nm + - 蓝波段: ~460nm + + Args: + mask_path: 水体掩膜文件路径，支持： + - shp格式文件（.shp）：需要提供img_path用于栅格化 + - dat格式文件（.dat/.tif等栅格格式）：直接使用，不需要img_path + - None：当use_ndwi=True时，从影像生成NDWI掩膜 + img_path: 输入影像文件路径（当mask_path为shp格式或use_ndwi=True时必须提供） + ndwi_threshold: NDWI阈值（当use_ndwi=True时使用） + use_ndwi: 是否使用NDWI方法从影像生成水体掩膜 + generate_png: 是否生成输入影像的PNG预览图（默认True） + + Returns: + dat格式的水域掩膜文件路径 + """ + print("\n" + "="*80) + print("步骤1: 生成或设置水域mask") + print("="*80) + + step_start_time = time.time() + try: + # 如果提供了img_path且开启生成PNG功能，生成影像预览图 + if generate_png and img_path is not None and Path(img_path).exists(): + self._generate_image_preview(img_path) + + if use_ndwi: + # 使用NDWI方法从影像生成水体掩膜 + if img_path is None: + raise ValueError("当use_ndwi=True时，必须提供img_path参数用于生成NDWI掩膜") + if not Path(img_path).exists(): + raise ValueError(f"影像文件不存在: {img_path}") + + print(f"使用NDWI方法从影像生成水体掩膜，阈值={ndwi_threshold}...") + output_path = str(self.water_mask_dir / "water_mask_from_ndwi.dat") + + # 检查文件是否已存在，避免重复生成 + if Path(output_path).exists(): + print(f"检测到已存在的NDWI掩膜文件，直接使用: {output_path}") + self.water_mask_path = output_path + step_end_time = time.time() + self._record_step_time("步骤1: 生成水域mask", step_start_time, step_end_time, status="skipped") + print(f"水域掩膜已设置: {self.water_mask_path}") + + # 生成水域掩膜叠加图（如果不存在） + overlay_path = self.water_mask_dir / "water_mask_overlay.png" + if generate_png and img_path is not None and Path(img_path).exists() and not overlay_path.exists(): + self._generate_water_mask_overlay(img_path, self.water_mask_path) + + return self.water_mask_path + + # 执行NDWI水体提取 + from src.utils.extract_water_area import ndwi + ndwi(img_path, ndwi_threshold, output_path) + self.water_mask_path = output_path + step_end_time = time.time() + self._record_step_time("步骤1: 生成水域mask", step_start_time, step_end_time) + print(f"已生成NDWI水体掩膜: {self.water_mask_path}") + + # 生成水域掩膜叠加图 + if generate_png: + self._generate_water_mask_overlay(img_path, self.water_mask_path) + + return self.water_mask_path + + elif mask_path is None: + raise ValueError("必须提供mask_path参数或设置use_ndwi=True") + + if not Path(mask_path).exists(): + raise ValueError(f"文件不存在: {mask_path}") + + # 检查文件扩展名，判断是shp文件还是dat文件 + file_ext = Path(mask_path).suffix.lower() + + if file_ext == '.shp': + # 如果是shp文件，需要栅格化为dat + if img_path is None: + raise ValueError("当mask_path为shp格式时，必须提供img_path参数用于栅格化") + + print(f"检测到shp格式的水体掩膜，正在转换为dat格式...") + output_path = str(self.water_mask_dir / "water_mask_from_shp.dat") + + # 检查文件是否已存在，避免重复栅格化 + if Path(output_path).exists(): + print(f"检测到已存在的栅格化掩膜文件，直接使用: {output_path}") + self.water_mask_path = output_path + step_end_time = time.time() + self._record_step_time("步骤1: 生成水域mask", step_start_time, step_end_time, status="skipped") + print(f"水域掩膜已设置: {self.water_mask_path}") + + # 生成水域掩膜叠加图（如果不存在） + overlay_path = self.water_mask_dir / "water_mask_overlay.png" + if generate_png and img_path is not None and Path(img_path).exists() and not overlay_path.exists(): + self._generate_water_mask_overlay(img_path, self.water_mask_path) + + return self.water_mask_path + + # 执行栅格化 + from src.utils.extract_water_area import rasterize_shp + rasterize_shp(mask_path, output_path, img_path) + self.water_mask_path = output_path + step_end_time = time.time() + self._record_step_time("步骤1: 生成水域mask", step_start_time, step_end_time) + print(f"已生成dat格式的水域掩膜: {self.water_mask_path}") + + # 生成水域掩膜叠加图 + if generate_png: + self._generate_water_mask_overlay(img_path, self.water_mask_path) + + return self.water_mask_path + + else: + # 如果是dat或其他栅格格式，直接使用 + print(f"检测到栅格格式的水体掩膜，直接使用: {mask_path}") + self.water_mask_path = mask_path + step_end_time = time.time() + self._record_step_time("步骤1: 生成水域mask", step_start_time, step_end_time) + print(f"水域掩膜已设置: {self.water_mask_path} (dat格式)") + + # 生成水域掩膜叠加图 + if generate_png and img_path is not None and Path(img_path).exists(): + self._generate_water_mask_overlay(img_path, self.water_mask_path) + + return self.water_mask_path + + except Exception as e: + step_end_time = time.time() + self._record_step_time("步骤1: 生成水域mask", step_start_time, step_end_time, + status="failed", error=str(e)) + raise + + def _generate_image_preview(self, img_path: str, bands: Optional[List[int]] = None) -> str: + """ + 生成高光谱影像的PNG预览图 + + 根据波长选择RGB波段： + - 蓝波段 (Blue): ~460nm + - 绿波段 (Green): ~550nm + - 红波段 (Red): ~650nm + + 如果无法通过波长获取波段索引，则回退到基于波段序号的近似选择。 + + Args: + img_path: 输入高光谱影像文件路径 (.dat格式) + bands: 用于RGB合成的三个波段索引 [R, G, B]，默认为None自动选择 + + Returns: + 生成的PNG文件路径 + """ + try: + print(f"正在生成影像预览图...") + + # 设置输出PNG路径 + img_name = Path(img_path).stem + png_path = str(self.water_mask_dir / f"hsi_preview.png") + + # 检查是否已存在 + if Path(png_path).exists(): + print(f"检测到已存在的预览图，跳过生成: {png_path}") + return png_path + + if not GDAL_AVAILABLE: + print("警告: GDAL未安装，无法生成影像预览图") + return "" + + # 使用GDAL读取影像 + dataset = gdal.Open(img_path) + if dataset is None: + print(f"警告: 无法打开影像文件: {img_path}") + return "" + + # 获取影像信息 + width = dataset.RasterXSize + height = dataset.RasterYSize + band_count = dataset.RasterCount + + # 如果没有指定波段，根据波长选择RGB波段 + if bands is None: + if band_count >= 3: + bands = self._select_rgb_bands_by_wavelength(img_path, band_count) + else: + # 如果只有一个波段，使用灰度显示 + bands = [0, 0, 0] + + # 读取指定波段 + r_data = dataset.GetRasterBand(bands[0] + 1).ReadAsArray().astype(np.float32) + g_data = dataset.GetRasterBand(bands[1] + 1).ReadAsArray().astype(np.float32) if band_count > 1 else r_data + b_data = dataset.GetRasterBand(bands[2] + 1).ReadAsArray().astype(np.float32) if band_count > 2 else r_data + + # 去除无效值 + r_data[r_data <= 0] = np.nan + if band_count > 1: + g_data[g_data <= 0] = np.nan + if band_count > 2: + b_data[b_data <= 0] = np.nan + + # 对每个波段进行2%线性拉伸，增强视觉效果 + def linear_stretch(data, low_percent=2, high_percent=98): + """线性拉伸""" + valid_data = data[~np.isnan(data)] + if len(valid_data) == 0: + return np.zeros_like(data) + + low_val = np.percentile(valid_data, low_percent) + high_val = np.percentile(valid_data, high_percent) + + if high_val - low_val < 1e-10: + return np.zeros_like(data) + + stretched = (data - low_val) / (high_val - low_val) + stretched = np.clip(stretched, 0, 1) + return stretched + + r_stretched = linear_stretch(r_data) + g_stretched = linear_stretch(g_data) if band_count > 1 else r_stretched + b_stretched = linear_stretch(b_data) if band_count > 2 else r_stretched + + # 合成为RGB图像 + rgb_image = np.stack([r_stretched, g_stretched, b_stretched], axis=2) + + # 处理可能存在的nan值 + rgb_image = np.nan_to_num(rgb_image, nan=0.0) + + # 创建图形 + fig, ax = plt.subplots(figsize=(12, 10)) + ax.imshow(rgb_image) + ax.set_title(f'影像预览: RGB波段(基于波长): R=650 nm, G=550 nm, B=460 nm', + fontsize=12, fontweight='bold') + ax.axis('off') + + # 添加比例尺信息 + geo_transform = dataset.GetGeoTransform() + if geo_transform: + pixel_size_x = abs(geo_transform[1]) + pixel_size_y = abs(geo_transform[5]) + scale_text = f"分辨率: {pixel_size_x:.2f}m x {pixel_size_y:.2f}m | 尺寸: {width} x {height}" + fig.text(0.5, 0.02, scale_text, ha='center', fontsize=10, style='italic') + + plt.tight_layout() + plt.savefig(png_path, dpi=150, bbox_inches='tight', pad_inches=0.1) + plt.close(fig) + + # 释放GDAL数据集 + dataset = None + + print(f"已生成影像预览图: {png_path}") + return png_path + + except Exception as e: + print(f"生成影像预览图时出错: {e}") + plt.close('all') + return "" + + def _generate_water_mask_overlay(self, img_path: str, mask_path: str) -> str: + """ + 生成水域掩膜叠加到原图的PNG图像 + + 将水域掩膜以透明度（蓝色半透明）叠加到RGB原图上，便于可视化水域范围。 + + Args: + img_path: 输入高光谱影像文件路径 + mask_path: 水域掩膜文件路径 (.dat格式) + + Returns: + 生成的PNG文件路径 + """ + try: + print(f"正在生成水域掩膜叠加图...") + + # 设置输出PNG路径 + png_path = str(self.water_mask_dir / "water_mask_overlay.png") + + # 检查是否已存在 + if Path(png_path).exists(): + print(f"检测到已存在的叠加图，跳过生成: {png_path}") + return png_path + + if not GDAL_AVAILABLE: + print("警告: GDAL未安装，无法生成叠加图") + return "" + + # 使用GDAL读取影像 + dataset = gdal.Open(img_path) + if dataset is None: + print(f"警告: 无法打开影像文件: {img_path}") + return "" + + # 获取影像信息 + width = dataset.RasterXSize + height = dataset.RasterYSize + band_count = dataset.RasterCount + + # 读取RGB波段（基于波长选择） + if band_count >= 3: + bands = self._select_rgb_bands_by_wavelength(img_path, band_count) + else: + bands = [0, 0, 0] + + r_data = dataset.GetRasterBand(bands[0] + 1).ReadAsArray().astype(np.float32) + g_data = dataset.GetRasterBand(bands[1] + 1).ReadAsArray().astype(np.float32) if band_count > 1 else r_data + b_data = dataset.GetRasterBand(bands[2] + 1).ReadAsArray().astype(np.float32) if band_count > 2 else r_data + + # 去除无效值 + r_data[r_data <= 0] = np.nan + if band_count > 1: + g_data[g_data <= 0] = np.nan + if band_count > 2: + b_data[b_data <= 0] = np.nan + + # 线性拉伸 + def linear_stretch(data, low_percent=2, high_percent=98): + valid_data = data[~np.isnan(data)] + if len(valid_data) == 0: + return np.zeros_like(data) + low_val = np.percentile(valid_data, low_percent) + high_val = np.percentile(valid_data, high_percent) + if high_val - low_val < 1e-10: + return np.zeros_like(data) + stretched = (data - low_val) / (high_val - low_val) + stretched = np.clip(stretched, 0, 1) + return stretched + + r_stretched = linear_stretch(r_data) + g_stretched = linear_stretch(g_data) if band_count > 1 else r_stretched + b_stretched = linear_stretch(b_data) if band_count > 2 else r_stretched + + # 合成为RGB背景图像 (0-255) + rgb_image = np.stack([r_stretched, g_stretched, b_stretched], axis=2) + rgb_image = np.nan_to_num(rgb_image, nan=0.0) + rgb_image = (rgb_image * 255).astype(np.uint8) + + # 释放影像数据集 + dataset = None + + # 读取水域掩膜 + mask_dataset = gdal.Open(mask_path) + if mask_dataset is None: + print(f"警告: 无法打开掩膜文件: {mask_path}") + # 保存原图预览 + fig, ax = plt.subplots(figsize=(12, 10)) + ax.imshow(rgb_image) + ax.set_title('影像预览 (无掩膜叠加)', fontsize=12, fontweight='bold') + ax.axis('off') + plt.tight_layout() + plt.savefig(png_path, dpi=150, bbox_inches='tight', pad_inches=0.1) + plt.close(fig) + return png_path + + mask_data = mask_dataset.GetRasterBand(1).ReadAsArray() + mask_dataset = None + + # 创建叠加图 + # 创建RGBA图像（带透明通道） + rgba_image = np.zeros((height, width, 4), dtype=np.uint8) + rgba_image[:, :, 0:3] = rgb_image # RGB通道 + rgba_image[:, :, 3] = 255 # Alpha通道（完全不透明） + + # 创建掩膜叠加层（蓝色半透明） + # 水域区域用蓝色高亮显示，透明度50% + mask_overlay = np.zeros((height, width, 4), dtype=np.uint8) + mask_overlay[:, :, 0] = 0 # R: 0 + mask_overlay[:, :, 1] = 100 # G: 100 (蓝色偏青) + mask_overlay[:, :, 2] = 255 # B: 255 (纯蓝) + mask_overlay[:, :, 3] = (mask_data > 0).astype(np.uint8) * 128 # Alpha: 50%透明 + + # 混合原图和掩膜层 + # 使用alpha混合公式: result = fg * alpha + bg * (1 - alpha) + alpha = mask_overlay[:, :, 3:4].astype(np.float32) / 255.0 + blended = rgb_image.astype(np.float32) * (1 - alpha) + mask_overlay[:, :, 0:3].astype(np.float32) * alpha + blended = blended.astype(np.uint8) + + # 创建图形 + fig, ax = plt.subplots(figsize=(14, 10)) + ax.imshow(blended) + ax.axis('off') + + # 添加图例 + from matplotlib.patches import Patch + legend_elements = [ + Patch(facecolor='#0064FF', edgecolor='black', alpha=0.5, label='水域范围') + ] + ax.legend(handles=legend_elements, loc='upper right', framealpha=0.9) + + # 计算水域面积 + dataset = gdal.Open(img_path) + geo_transform = dataset.GetGeoTransform() + if geo_transform: + pixel_size_x = abs(geo_transform[1]) + pixel_size_y = abs(geo_transform[5]) + pixel_area = pixel_size_x * pixel_size_y # 平方米 + + # 计算水域像素数量和有效像素数量（非零像素） + water_pixels = np.sum(mask_data > 0) + valid_pixels = np.sum(mask_data >= 0) # 有效像素（包括水域和非水域） + + # 计算面积（平方米 -> 平方千米） + # 水域面积 + water_area_m2 = water_pixels * pixel_area + water_area_km2 = water_area_m2 / 1_000_000 + + # 有效像素面积（影像实际覆盖面积） + valid_area_m2 = valid_pixels * pixel_area + valid_area_km2 = valid_area_m2 / 1_000_000 + + # 水域占比（相对于有效像素） + water_percentage = (water_pixels / valid_pixels) * 100 if valid_pixels > 0 else 0 + + # 在图像上添加面积标注（合并显示） + area_text = f'水域面积: {water_area_km2:.2f} 平方千米 | 影像总面积: {valid_area_km2:.2f} 平方千米 | 水域占比: {water_percentage:.1f}%' + ax.text(0.02, 0.98, area_text, + transform=ax.transAxes, + fontsize=11, fontweight='bold', + color='white', + bbox=dict(facecolor='#0064FF', alpha=0.8, edgecolor='black', + boxstyle='round,pad=0.5', linewidth=2), + verticalalignment='top') + + # 添加比例尺信息 + scale_text = f"分辨率: {pixel_size_x:.2f}m x {pixel_size_y:.2f}m | 影像尺寸: {width} x {height}像素" + fig.text(0.5, 0.02, scale_text, ha='center', fontsize=10, style='italic', + color='white', + bbox=dict(facecolor='black', alpha=0.6, edgecolor='none', + boxstyle='round,pad=0.3')) + + print(f" 水域面积: {water_area_km2:.2f} km² | 影像总面积: {valid_area_km2:.2f} km² | 占比: {water_percentage:.1f}%") + dataset = None + + plt.tight_layout() + plt.savefig(png_path, dpi=150, bbox_inches='tight', pad_inches=0.1) + plt.close(fig) + + print(f"已生成水域掩膜叠加图: {png_path}") + return png_path + + except Exception as e: + print(f"生成水域掩膜叠加图时出错: {e}") + plt.close('all') + return "" + + def _select_rgb_bands_by_wavelength(self, img_path: str, band_count: int) -> List[int]: + """ + 根据波长选择RGB波段 + + 目标波长: + - 蓝波段 (Blue): 460nm + - 绿波段 (Green): 550nm + - 红波段 (Red): 650nm + + Args: + img_path: 影像文件路径 + band_count: 总波段数 + + Returns: + [R_band_idx, G_band_idx, B_band_idx] 波段索引列表 + """ + try: + # 使用util模块的find_band_number函数根据波长查找波段 + if not FIND_BAND_AVAILABLE: + print("警告: find_band_number函数不可用，使用默认波段选择") + return self._select_rgb_bands_by_index(band_count) + + # 定义RGB目标波长 (nm) + target_wavelengths = { + 'R': 650.0, # 红波段 + 'G': 550.0, # 绿波段 + 'B': 460.0 # 蓝波段 + } + + bands = [] + for color, target_wl in target_wavelengths.items(): + try: + band_idx = find_band_number(target_wl, img_path) + # 确保波段索引在有效范围内 + band_idx = max(0, min(band_idx, band_count - 1)) + bands.append(band_idx) + print(f" {color}波段: 目标波长 {target_wl}nm -> 波段 {band_idx} (0-based)") + except Exception as e: + print(f" 无法为{color}波段找到波长 {target_wl}nm: {e}") + # 回退到基于索引的选择 + if color == 'R': + bands.append(min(band_count - 1, int(band_count * 0.25))) + elif color == 'G': + bands.append(min(band_count - 1, int(band_count * 0.15))) + else: # B + bands.append(min(band_count - 1, int(band_count * 0.05))) + + # 如果获取的波段不完整，使用默认值 + if len(bands) != 3: + print("警告: 波段选择不完整，使用默认波段") + return self._select_rgb_bands_by_index(band_count) + + return bands + + except Exception as e: + print(f"根据波长选择RGB波段时出错: {e}，使用默认波段选择") + return self._select_rgb_bands_by_index(band_count) + + def _select_rgb_bands_by_index(self, band_count: int) -> List[int]: + """ + 基于波段序号的RGB波段选择（回退方法） + """ + if band_count >= 3: + r_band = min(band_count - 1, max(2, int(band_count * 0.25))) # ~25%位置作为红波段 + g_band = min(band_count - 1, max(1, int(band_count * 0.15))) # ~15%位置作为绿波段 + b_band = min(band_count - 1, max(0, int(band_count * 0.05))) # ~5%位置作为蓝波段 + return [r_band, g_band, b_band] + else: + return [0, 0, 0] + + def step2_find_glint_area(self, img_path: str, + glint_wave: float = 750.0, + method: str = 'otsu', + z_threshold: float = 2.5, + percentile: float = 95.0, + iqr_multiplier: float = 1.5, + window_size: int = 15, + multi_band_waves: Optional[List[float]] = None, + sub_method: str = 'zscore', + weights: Optional[List[float]] = None, + max_area: Optional[int] = None, + buffer_size: Optional[int] = None, + water_mask_path: Optional[str] = None, + skip_dependency_check: bool = False) -> str: + """ + 步骤2: 找到耀斑区域 + + Args: + img_path: 输入影像文件路径 + glint_wave: 用于提取耀斑严重区域的波段波长（单波段方法使用） + method: 检测方法，可选： + - 'otsu': Otsu阈值分割（默认） + - 'zscore': Z-score统计方法 + - 'percentile': 百分位数阈值方法 + - 'iqr': IQR异常值检测 + - 'adaptive': 自适应阈值方法 + - 'multi_band': 多波段融合方法 + z_threshold: Z-score方法的阈值（默认2.5） + percentile: 百分位数阈值（默认95.0） + iqr_multiplier: IQR倍数（默认1.5） + window_size: 自适应阈值窗口大小（默认15） + multi_band_waves: 多波段方法的波长列表，如[750, 800, 850] + sub_method: 多波段方法的子方法（'zscore', 'percentile', 'otsu'），默认'zscore' + weights: 多波段方法的权重列表，如果为None则使用等权重 + max_area: 最大连通域面积阈值（像素数），超过此面积的连通域将被过滤掉， + 用于去除岸边、浅水、水华等大面积区域（默认None，表示不过滤） + buffer_size: 岸边缓冲区大小（像素数），用于去除岸边附近的错误耀斑掩膜 + （默认None，表示不进行岸边缓冲区去除；设置为正整数时启用） + + Returns: + 耀斑掩膜文件路径 + """ + print("\n" + "="*80) + print("步骤2: 找到耀斑区域") + print("="*80) + + step_start_time = time.time() + try: + # 使用dat格式的水体掩膜 + if water_mask_path is not None: + # 优先使用传入的参数 + final_water_mask_path = water_mask_path + elif self.water_mask_path is not None: + # 其次使用步骤1生成的水体掩膜 + final_water_mask_path = self.water_mask_path + else: + # 如果没有水体掩膜，根据skip_dependency_check决定行为 + if skip_dependency_check: + print("警告: 未找到水体掩膜，将对全图进行耀斑检测") + final_water_mask_path = None + else: + raise ValueError("请先执行步骤1: 生成水域mask，或提供water_mask_path参数，或设置skip_dependency_check=True") + + output_path = str(self.glint_dir / "severe_glint_area.dat") + + # 检查文件是否已存在 + if Path(output_path).exists(): + print(f"检测到已存在的耀斑掩膜文件，直接使用: {output_path}") + self.glint_mask_path = output_path + step_end_time = time.time() + self._record_step_time("步骤2: 找到耀斑区域", step_start_time, step_end_time, status="skipped") + print(f"耀斑掩膜已设置: {self.glint_mask_path}") + return self.glint_mask_path + + # 构建参数字典 + kwargs = { + 'method': method, + 'z_threshold': z_threshold, + 'percentile': percentile, + 'iqr_multiplier': iqr_multiplier, + 'window_size': window_size, + } + + # 如果是多波段方法，添加相关参数 + if method == 'multi_band': + if multi_band_waves is not None: + kwargs['multi_band_waves'] = multi_band_waves + if sub_method is not None: + kwargs['sub_method'] = sub_method + if weights is not None: + kwargs['weights'] = weights + + # 添加连通域面积过滤和岸边缓冲区参数 + if max_area is not None: + kwargs['max_area'] = max_area + if buffer_size is not None: + kwargs['buffer_size'] = buffer_size + + # 传递dat格式的水体掩膜文件路径 + self.glint_mask_path = find_severe_glint_area( + img_path, final_water_mask_path, glint_wave, output_path, **kwargs + ) + + step_end_time = time.time() + self._record_step_time("步骤2: 找到耀斑区域", step_start_time, step_end_time) + print(f"耀斑掩膜已生成: {self.glint_mask_path}") + print(f"使用检测方法: {method}") + return self.glint_mask_path + except Exception as e: + step_end_time = time.time() + self._record_step_time("步骤2: 找到耀斑区域", step_start_time, step_end_time, + status="failed", error=str(e)) + raise + + def _get_image_geo_info(self, img_path: str) -> tuple: + """ + 获取影像的地理信息（不加载图像数据，节省内存） + + Args: + img_path: 影像文件路径 + + Returns: + tuple: (geotransform, projection, width, height, n_bands) + geotransform: 地理变换参数 + projection: 投影信息 + width: 图像宽度 + height: 图像高度 + n_bands: 波段数 + """ + if not GDAL_AVAILABLE: + raise ImportError("GDAL未安装，无法读取影像文件") + + dataset = gdal.Open(img_path, gdal.GA_ReadOnly) + if dataset is None: + raise ValueError(f"无法打开影像文件: {img_path}") + + try: + width = dataset.RasterXSize + height = dataset.RasterYSize + n_bands = dataset.RasterCount + geotransform = dataset.GetGeoTransform() + projection = dataset.GetProjection() + + return geotransform, projection, width, height, n_bands + finally: + dataset = None + + def _load_image_as_array(self, img_path: str) -> tuple: + """ + 加载影像文件为numpy数组（已废弃，建议直接使用GDAL读取） + + 注意：此方法会将所有波段加载到内存，对于大图像会消耗大量内存。 + 建议直接传递文件路径给算法类，让算法类使用GDAL逐波段处理。 + + Args: + img_path: 影像文件路径 + + Returns: + tuple: (image_array, geotransform, projection) + image_array: numpy数组，形状为(height, width, bands) + geotransform: 地理变换参数 + projection: 投影信息 + """ + if not GDAL_AVAILABLE: + raise ImportError("GDAL未安装，无法读取影像文件") + + dataset = gdal.Open(img_path, gdal.GA_ReadOnly) + if dataset is None: + raise ValueError(f"无法打开影像文件: {img_path}") + + try: + width = dataset.RasterXSize + height = dataset.RasterYSize + n_bands = dataset.RasterCount + geotransform = dataset.GetGeoTransform() + projection = dataset.GetProjection() + + # 读取所有波段 + image_bands = [] + for i in range(1, n_bands + 1): + band = dataset.GetRasterBand(i) + band_data = band.ReadAsArray() + image_bands.append(band_data) + + # 堆叠为(height, width, bands)格式 + image_array = np.dstack(image_bands) + + return image_array, geotransform, projection + finally: + dataset = None + + def _save_array_as_image(self, image_array: np.ndarray, output_path: str, + geotransform: tuple, projection: str, + dtype: type = gdal.GDT_Float32) -> str: + """ + 将numpy数组保存为影像文件 + + Args: + image_array: numpy数组，形状为(height, width, bands) + output_path: 输出文件路径 + geotransform: 地理变换参数 + projection: 投影信息 + dtype: GDAL数据类型 + + Returns: + 输出文件路径 + """ + if not GDAL_AVAILABLE: + raise ImportError("GDAL未安装，无法保存影像文件") + + height, width, n_bands = image_array.shape + + # 获取驱动 + driver = gdal.GetDriverByName('ENVI') + if driver is None: + # 如果ENVI驱动不可用，尝试使用GTiff + driver = gdal.GetDriverByName('GTiff') + + if driver is None: + raise ValueError("无法创建影像文件，没有可用的驱动") + + # 创建数据集 + dataset = driver.Create(output_path, width, height, n_bands, dtype) + if dataset is None: + raise ValueError(f"无法创建输出文件: {output_path}") + + try: + # 设置地理变换和投影 + dataset.SetGeoTransform(geotransform) + dataset.SetProjection(projection) + + # 写入每个波段 + for i in range(n_bands): + band = dataset.GetRasterBand(i + 1) + band.WriteArray(image_array[:, :, i]) + band.FlushCache() + + finally: + dataset = None + + return output_path + + def _save_bands_as_image(self, corrected_bands: list, output_path: str, + geotransform: tuple, projection: str, + dtype: type = gdal.GDT_Float32) -> str: + """ + 直接从波段列表保存影像文件（避免堆叠，节省内存） + + Args: + corrected_bands: 校正后的波段列表，每个元素是一个(height, width)的numpy数组 + output_path: 输出文件路径 + geotransform: 地理变换参数 + projection: 投影信息 + dtype: GDAL数据类型 + + Returns: + 输出文件路径 + """ + if not GDAL_AVAILABLE: + raise ImportError("GDAL未安装，无法保存影像文件") + + if not corrected_bands: + raise ValueError("波段列表为空") + + n_bands = len(corrected_bands) + height, width = corrected_bands[0].shape + + # 获取驱动 + driver = gdal.GetDriverByName('ENVI') + if driver is None: + # 如果ENVI驱动不可用，尝试使用GTiff + driver = gdal.GetDriverByName('GTiff') + + if driver is None: + raise ValueError("无法创建影像文件，没有可用的驱动") + + # 创建数据集 + dataset = driver.Create(output_path, width, height, n_bands, dtype) + if dataset is None: + raise ValueError(f"无法创建输出文件: {output_path}") + + try: + # 设置地理变换和投影 + dataset.SetGeoTransform(geotransform) + dataset.SetProjection(projection) + + # 逐个写入波段（避免堆叠所有波段，节省内存） + for i, band_array in enumerate(corrected_bands): + if band_array.shape != (height, width): + raise ValueError(f"波段 {i} 的尺寸 {band_array.shape} 与预期 {(height, width)} 不匹配") + band = dataset.GetRasterBand(i + 1) + band.WriteArray(band_array) + band.FlushCache() + # 注意：这里不能删除band_array，因为它还在corrected_bands列表中 + # 但保存后可以提示垃圾回收器（如果需要） + + finally: + dataset = None + + return output_path + + def _prepare_water_mask_for_algorithm(self, water_mask: Optional[Union[str, np.ndarray]], + image_shape: Union[tuple, np.ndarray], + geotransform: tuple, + projection: str, + img_path: str) -> Optional[np.ndarray]: + """ + 准备水域掩膜供算法使用 + + 注意：如果传入的是shp文件，会先检查是否已经栅格化过，避免重复转换 + + Args: + water_mask: 水域掩膜，可以是None、numpy数组、文件路径(.dat/.tif)或shapefile路径(.shp) + image_shape: 影像形状，可以是(height, width)元组或numpy数组（用于获取形状） + geotransform: 地理变换参数 + projection: 投影信息 + img_path: 影像文件路径（用于栅格化shp文件） + + Returns: + numpy数组或None，1表示水域，0表示非水域 + """ + # 获取图像尺寸 + if isinstance(image_shape, np.ndarray): + img_height, img_width = image_shape.shape[:2] + else: + img_height, img_width = image_shape + + if water_mask is None: + # 如果water_mask为None，使用步骤1生成的dat格式掩膜 + if self.water_mask_path is not None: + try: + dat_mask_path = self._ensure_water_mask_dat(img_path) + water_mask = dat_mask_path + print(f"使用步骤1生成的水域掩膜: {water_mask}") + except Exception as e: + print(f"警告: 无法使用步骤1的水域掩膜: {e}") + return None + else: + return None + + # 如果已经是numpy数组 + if isinstance(water_mask, np.ndarray): + if water_mask.shape[:2] != (img_height, img_width): + raise ValueError(f"掩膜尺寸 {water_mask.shape[:2]} 与图像尺寸 {(img_height, img_width)} 不匹配") + return (water_mask > 0).astype(np.uint8) # 确保是0/1掩膜 + + # 如果是文件路径 + if isinstance(water_mask, str): + # 检查是否为shapefile + if water_mask.lower().endswith('.shp'): + # 从shp文件创建掩膜（这种情况应该很少，因为步骤1已经统一转换为dat） + try: + from src.utils.extract_water_area import rasterize_shp + # 使用固定路径，避免重复转换 + shp_name = Path(water_mask).stem + temp_mask_path = str(self.water_mask_dir / f"water_mask_{shp_name}.dat") + + # 如果文件已存在，直接使用 + if Path(temp_mask_path).exists(): + print(f"使用已存在的栅格化掩膜: {temp_mask_path}") + water_mask = temp_mask_path + else: + # 需要栅格化（需要img_path） + if img_path is None: + raise ValueError("当water_mask为shp格式时，需要提供img_path参数用于栅格化") + rasterize_shp(water_mask, temp_mask_path, img_path) + water_mask = temp_mask_path + print(f"已将shp格式的水域掩膜栅格化为: {temp_mask_path}") + + # 读取栅格化的掩膜 + if not GDAL_AVAILABLE: + raise ImportError("GDAL未安装，无法读取掩膜文件") + mask_dataset = gdal.Open(water_mask, gdal.GA_ReadOnly) + if mask_dataset is None: + raise ValueError(f"无法打开栅格化的掩膜文件: {water_mask}") + mask_array = mask_dataset.GetRasterBand(1).ReadAsArray() + mask_dataset = None + except Exception as e: + raise ValueError(f"无法从shp文件创建掩膜: {e}") + else: + # 栅格文件 + if not GDAL_AVAILABLE: + raise ImportError("GDAL未安装，无法读取掩膜文件") + mask_dataset = gdal.Open(water_mask, gdal.GA_ReadOnly) + if mask_dataset is None: + raise ValueError(f"无法打开掩膜文件: {water_mask}") + + mask_array = mask_dataset.GetRasterBand(1).ReadAsArray() + mask_dataset = None + + # 检查尺寸 + if mask_array.shape != (img_height, img_width): + raise ValueError(f"掩膜尺寸 {mask_array.shape} 与图像尺寸 {(img_height, img_width)} 不匹配") + + return (mask_array > 0).astype(np.uint8) + + raise ValueError(f"不支持的掩膜类型: {type(water_mask)}") + + def _copy_hdr_info(self, source_img_path: str, dest_img_path: str): + """ + 复制原始影像的hdr文件信息（如波长等）到目标影像的hdr文件 + + Args: + source_img_path: 源影像文件路径（原始bsq文件） + dest_img_path: 目标影像文件路径（去耀斑后的bsq文件） + """ + if not UTIL_AVAILABLE: + print("警告: util模块未导入，无法复制hdr文件信息") + return + + try: + source_hdr_path = get_hdr_file_path(source_img_path) + dest_hdr_path = get_hdr_file_path(dest_img_path) + + if not Path(source_hdr_path).exists(): + print(f"警告: 源hdr文件不存在: {source_hdr_path}") + return + + if not Path(dest_hdr_path).exists(): + print(f"警告: 目标hdr文件不存在: {dest_hdr_path}") + return + + # 复制hdr文件信息（波长等） + write_fields_to_hdrfile(source_hdr_path, dest_hdr_path) + print(f"已复制原始hdr文件信息到: {dest_hdr_path}") + except Exception as e: + print(f"警告: 复制hdr文件信息时出错: {e}") + + def _interpolate_zero_pixels(self, img_path: str, + interpolation_method: str = 'nearest', + output_path: Optional[str] = None, + water_mask: Optional[Union[str, np.ndarray]] = None) -> str: + """ + 对影像中所有波段都为0的像素点进行插值（只处理所有波段都为0的像素） + + Args: + img_path: 输入影像文件路径 + interpolation_method: 插值方法，支持： + - 'nearest': 邻近插值（最快） + - 'bilinear': 双线性插值 + - 'spline': 样条插值（RBF） + - 'kriging': 克里金插值（最慢但最准确） + output_path: 输出文件路径（如果为None，自动生成） + water_mask: 水域掩膜，用于限制插值区域（可选） + + Returns: + 插值后的影像文件路径 + """ + if not SCIPY_AVAILABLE: + raise ImportError("scipy未安装，无法进行0值像素插值") + + if not GDAL_AVAILABLE: + raise ImportError("GDAL未安装，无法读取影像文件") + + print(f"\n开始对0值像素进行插值，方法: {interpolation_method}") + print("注意: 只处理所有波段都为0的像素点") + + # 确定输出路径 + if output_path is None: + output_path = str(self.deglint_dir / f"interpolated_{interpolation_method}.bsq") + + # 检查文件是否已存在 + if Path(output_path).exists(): + print(f"检测到已存在的插值影像文件，直接使用: {output_path}") + self.interpolated_img_path = output_path + return output_path + + # 读取影像 + dataset = gdal.Open(img_path, gdal.GA_ReadOnly) + if dataset is None: + raise ValueError(f"无法打开影像文件: {img_path}") + + try: + width = dataset.RasterXSize + height = dataset.RasterYSize + n_bands = dataset.RasterCount + geotransform = dataset.GetGeoTransform() + projection = dataset.GetProjection() + + print(f"影像尺寸: {width} x {height} x {n_bands}") + + # 读取所有波段数据 + print("读取所有波段数据...") + all_bands = [] + for band_idx in range(1, n_bands + 1): + band = dataset.GetRasterBand(band_idx) + band_data = band.ReadAsArray().astype(np.float32) + all_bands.append(band_data) + + # 堆叠为 (height, width, n_bands) 格式 + image_stack = np.dstack(all_bands) + + # 读取水域掩膜（如果提供） + mask_array = None + if water_mask is not None: + if isinstance(water_mask, str): + mask_dataset = gdal.Open(water_mask, gdal.GA_ReadOnly) + if mask_dataset: + mask_array = mask_dataset.GetRasterBand(1).ReadAsArray() + mask_dataset = None + elif isinstance(water_mask, np.ndarray): + mask_array = water_mask + + # 找出所有波段都为0的像素点 + # 检查每个像素在所有波段是否都为0 + all_bands_zero = np.all(image_stack == 0, axis=2) # (height, width) + + # 如果提供了水域掩膜，只在水域掩膜内处理 + if mask_array is not None: + all_bands_zero = all_bands_zero & (mask_array > 0) + + # 统计需要插值的像素数量 + zero_pixel_count = np.sum(all_bands_zero) + print(f"发现 {zero_pixel_count} 个所有波段都为0的像素点") + + if zero_pixel_count == 0: + print("没有需要插值的像素点，直接保存原影像") + # 直接保存原影像 + driver = gdal.GetDriverByName('ENVI') + if driver is None: + driver = gdal.GetDriverByName('GTiff') + if driver is None: + raise ValueError("无法创建影像文件，没有可用的驱动") + + out_dataset = driver.Create(output_path, width, height, n_bands, gdal.GDT_Float32) + if out_dataset is None: + raise ValueError(f"无法创建输出文件: {output_path}") + + out_dataset.SetGeoTransform(geotransform) + out_dataset.SetProjection(projection) + + for i, band_data in enumerate(all_bands): + out_band = out_dataset.GetRasterBand(i + 1) + out_band.WriteArray(band_data) + out_band.FlushCache() + + out_dataset = None + self.interpolated_img_path = output_path + return output_path + + # 获取需要插值的像素坐标 + zero_y, zero_x = np.where(all_bands_zero) + zero_coords = np.column_stack([zero_x, zero_y]) # (n_zero_pixels, 2) + + # 获取有效像素的坐标（至少有一个波段不为0的像素） + valid_mask = ~all_bands_zero + valid_y, valid_x = np.where(valid_mask) + valid_coords = np.column_stack([valid_x, valid_y]) # (n_valid_pixels, 2) + + if len(valid_coords) == 0: + raise ValueError("没有有效像素可用于插值") + + print(f"使用 {len(valid_coords)} 个有效像素进行插值") + + # 创建输出数据集 + driver = gdal.GetDriverByName('ENVI') + if driver is None: + driver = gdal.GetDriverByName('GTiff') + if driver is None: + raise ValueError("无法创建影像文件，没有可用的驱动") + + out_dataset = driver.Create(output_path, width, height, n_bands, gdal.GDT_Float32) + if out_dataset is None: + raise ValueError(f"无法创建输出文件: {output_path}") + + out_dataset.SetGeoTransform(geotransform) + out_dataset.SetProjection(projection) + + # 逐波段进行插值（但只对"所有波段都为0"的像素进行插值） + interpolated_bands = [] + + for band_idx in range(n_bands): + print(f"处理波段 {band_idx + 1}/{n_bands}...", end=' ') + band_data = all_bands[band_idx].copy() + + # 获取有效像素的值 + valid_values = band_data[valid_mask] # (n_valid_pixels,) + + if len(valid_values) == 0: + print(f"警告: 波段 {band_idx + 1} 没有有效像素，跳过插值") + interpolated_bands.append(band_data) + continue + + # 对需要插值的像素进行插值 + if interpolation_method == 'nearest': + # 邻近插值 + from scipy.spatial import cKDTree + tree = cKDTree(valid_coords) + _, indices = tree.query(zero_coords) + interpolated_values = valid_values[indices] + + elif interpolation_method == 'bilinear': + # 双线性插值（使用griddata） + interpolated_values = griddata( + valid_coords, valid_values, zero_coords, + method='linear', fill_value=0.0 + ) + + # 如果线性插值失败，使用邻近插值 + nan_mask = np.isnan(interpolated_values) + if np.any(nan_mask): + from scipy.spatial import cKDTree + tree = cKDTree(valid_coords) + _, indices = tree.query(zero_coords[nan_mask]) + interpolated_values[nan_mask] = valid_values[indices] + + elif interpolation_method == 'spline': + # 样条插值（RBF） + try: + # 如果有效点太多，随机采样以提高速度 + max_points = 10000 + if len(valid_values) > max_points: + indices = np.random.choice(len(valid_values), max_points, replace=False) + sample_coords = valid_coords[indices] + sample_values = valid_values[indices] + else: + sample_coords = valid_coords + sample_values = valid_values + + # 使用RBF插值 + rbf = RBFInterpolator(sample_coords, sample_values, kernel='thin_plate_spline') + interpolated_values = rbf(zero_coords) + except Exception as e: + print(f"样条插值失败: {e}，回退到双线性插值") + interpolated_values = griddata( + valid_coords, valid_values, zero_coords, + method='linear', fill_value=0.0 + ) + nan_mask = np.isnan(interpolated_values) + if np.any(nan_mask): + from scipy.spatial import cKDTree + tree = cKDTree(valid_coords) + _, indices = tree.query(zero_coords[nan_mask]) + interpolated_values[nan_mask] = valid_values[indices] + + elif interpolation_method == 'kriging': + # 克里金插值 + try: + from src.utils.kriging import KrigingInterpolator + interpolator = KrigingInterpolator() + + # 如果有效点太多，随机采样以提高速度 + max_points = 5000 + if len(valid_values) > max_points: + indices = np.random.choice(len(valid_values), max_points, replace=False) + sample_coords = valid_coords[indices] + sample_values = valid_values[indices] + else: + sample_coords = valid_coords + sample_values = valid_values + + # 执行克里金插值 + result = interpolator.interpolate( + sample_coords[:, 0], sample_coords[:, 1], sample_values, + spatial_resolution=1.0, + output_path=None, + proj=projection + ) + + if result is not None: + # 从结果中提取插值点 + # 注意：KrigingInterpolator返回的是网格，需要提取对应位置的值 + # 这里简化处理，使用griddata作为后备 + interpolated_values = griddata( + valid_coords, valid_values, zero_coords, + method='cubic', fill_value=0.0 + ) + nan_mask = np.isnan(interpolated_values) + if np.any(nan_mask): + from scipy.spatial import cKDTree + tree = cKDTree(valid_coords) + _, indices = tree.query(zero_coords[nan_mask]) + interpolated_values[nan_mask] = valid_values[indices] + else: + raise ValueError("克里金插值失败") + except Exception as e: + print(f"克里金插值失败: {e}，回退到双线性插值") + interpolated_values = griddata( + valid_coords, valid_values, zero_coords, + method='linear', fill_value=0.0 + ) + nan_mask = np.isnan(interpolated_values) + if np.any(nan_mask): + from scipy.spatial import cKDTree + tree = cKDTree(valid_coords) + _, indices = tree.query(zero_coords[nan_mask]) + interpolated_values[nan_mask] = valid_values[indices] + else: + raise ValueError(f"不支持的插值方法: {interpolation_method}") + + # 更新波段数据（只更新所有波段都为0的像素） + band_data[all_bands_zero] = interpolated_values + interpolated_bands.append(band_data) + print(f"完成") + + # 保存所有波段 + for i, band_data in enumerate(interpolated_bands): + out_band = out_dataset.GetRasterBand(i + 1) + out_band.WriteArray(band_data) + out_band.FlushCache() + + out_dataset = None + dataset = None + + print(f"\n插值完成，共处理 {zero_pixel_count} 个所有波段都为0的像素点") + print(f"插值后的影像已保存: {output_path}") + + self.interpolated_img_path = output_path + return output_path + + finally: + if dataset: + dataset = None + + def step3_remove_glint(self, img_path: str, + method: str = "subtract_nir", + start_wave: Optional[float] = None, + end_wave: Optional[float] = None, + json_path: Optional[str] = None, + left_shoulder_wave: Optional[float] = None, + valley_wave: Optional[float] = None, + right_shoulder_wave: Optional[float] = None, + # 水域掩膜参数 + water_mask: Optional[Union[str, np.ndarray]] = None, + # 0值像素插值参数 + interpolate_zeros: bool = False, + interpolation_method: str = 'nearest', + # 是否执行去除耀斑 + enabled: bool = True, + # Kutser方法参数 + kutser_shp_path: Optional[str] = None, + oxy_band: int = 38, + lower_oxy: int = 36, + upper_oxy: int = 49, + nir_band: int = 47, + # Goodman方法参数 + nir_lower: int = 25, + nir_upper: int = 37, + goodman_A: float = 0.000019, + goodman_B: float = 0.1, + # Hedley方法参数 + hedley_shp_path: Optional[str] = None, + hedley_nir_band: int = 47, + # SUGAR方法参数 + sugar_bounds: Optional[List[tuple]] = None, + sugar_sigma: float = 1.0, + sugar_estimate_background: bool = True, + sugar_glint_mask_method: str = "cdf", + sugar_iter: Optional[int] = 3, + sugar_termination_thresh: float = 20.0, + skip_dependency_check: bool = False) -> str: + """ + 步骤3: 去除耀斑 + + Args: + img_path: 输入影像文件路径 + method: 去耀斑方法，支持： + - "subtract_nir": 减去NIR方法 + - "regression_slope": 回归斜率方法 + - "oxygen_absorption": 氧吸收谷方法 + - "kutser": Kutser方法（基于氧吸收特征） + - "goodman": Goodman方法 + - "hedley": Hedley方法（基于NIR相关性） + - "sugar": SUGAR方法（迭代去耀斑） + start_wave: 起始波长（subtract_nir和regression_slope方法需要） + end_wave: 结束波长（subtract_nir和regression_slope方法需要） + json_path: ROI JSON文件路径（regression_slope方法需要） + left_shoulder_wave: 左肩波长（oxygen_absorption方法需要） + valley_wave: 谷值波长（oxygen_absorption方法需要） + right_shoulder_wave: 右肩波长（oxygen_absorption方法需要） + water_mask: 水域掩膜，可以是： + - None: 自动使用步骤1生成的水域掩膜（如果存在） + - numpy数组: 直接使用数组作为掩膜 + - 文件路径: 栅格文件路径(.dat/.tif)或shapefile路径(.shp) + 如果为None且步骤1未生成掩膜，则处理全图 + interpolate_zeros: 是否对0值像素进行插值（默认False） + interpolation_method: 插值方法，支持： + - 'nearest': 邻近插值（最快） + - 'bilinear': 双线性插值 + - 'spline': 样条插值（RBF） + - 'kriging': 克里金插值（最慢但最准确） + # Kutser方法参数 + kutser_shp_path: 深水区域shp文件路径（可选，已废弃，请使用water_mask） + oxy_band: 氧吸收波段索引（默认38，对应760.6nm） + lower_oxy: 氧吸收下波段索引（默认36，对应742.39nm） + upper_oxy: 氧吸收上波段索引（默认49，对应860.48nm） + nir_band: NIR波段索引（默认47，对应842.36nm） + # Goodman方法参数 + nir_lower: NIR下波段索引（默认25，对应641.93nm） + nir_upper: NIR上波段索引（默认37，对应751.49nm） + goodman_A: Goodman参数A（默认0.000019） + goodman_B: Goodman参数B（默认0.1） + # Hedley方法参数 + hedley_shp_path: 深水区域shp文件路径（可选，已废弃，请使用water_mask） + hedley_nir_band: NIR波段索引（默认47，对应842.36nm） + # SUGAR方法参数 + sugar_bounds: 优化边界列表，如[(1,2)]（默认None，使用[(1,2)]） + sugar_sigma: LoG平滑sigma（默认1.0） + sugar_estimate_background: 是否估计背景光谱（默认True） + sugar_glint_mask_method: 耀斑掩膜方法，"cdf"或"otsu"（默认"cdf"） + sugar_iter: 迭代次数，None表示自动终止（默认3） + sugar_termination_thresh: 终止阈值（默认20.0） + + Returns: + 去除耀斑后的影像文件路径 + """ + print("\n" + "="*80) + print("步骤3: 去除耀斑") + print("="*80) + + step_start_time = time.time() + try: + # 如果未启用，直接跳过处理并把原始影像路径作为后续流程输入 + if not enabled: + print("已设置跳过去除耀斑（enabled=False），将直接使用原始影像。") + self.deglint_img_path = img_path + step_end_time = time.time() + self._record_step_time("步骤3: 去除耀斑", step_start_time, step_end_time, status="skipped") + return self.deglint_img_path + + # 确定使用的水域掩膜 + # 优先级：1. 用户提供的water_mask参数 2. 步骤1生成的dat格式掩膜 3. None（处理全图） + final_water_mask = water_mask + if final_water_mask is None: + # 尝试使用步骤1生成的dat格式掩膜 + if self.water_mask_path is not None: + final_water_mask = self.water_mask_path + print(f"使用步骤1生成的水域掩膜: {final_water_mask}") + else: + print("未提供水域掩膜，将处理全图") + final_water_mask = None + + # 步骤3.1: 对0值像素进行插值（如果启用） + if interpolate_zeros: + print("\n" + "-"*80) + print("步骤3.1: 对0值像素进行插值") + print("-"*80) + interp_start_time = time.time() + try: + # 准备水域掩膜用于插值 + interp_water_mask = final_water_mask + if interp_water_mask is None and self.water_mask_path: + interp_water_mask = self.water_mask_path + + # 执行插值 + interpolated_img = self._interpolate_zero_pixels( + img_path=img_path, + interpolation_method=interpolation_method, + water_mask=interp_water_mask + ) + # 使用插值后的影像作为后续处理的输入 + img_path = interpolated_img + interp_end_time = time.time() + self._record_step_time("步骤3.1: 0值像素插值", interp_start_time, interp_end_time) + print(f"插值完成，使用插值后的影像: {img_path}") + except Exception as e: + print(f"警告: 0值像素插值失败: {e}，将使用原始影像继续处理") + interp_end_time = time.time() + self._record_step_time("步骤3.1: 0值像素插值", interp_start_time, interp_end_time, + status="failed", error=str(e)) + + if method == "kutser": + print(f"使用方法: Kutser (氧吸收波段={oxy_band}, NIR波段={nir_band})") + + # 确定输出路径 + output_path = str(self.deglint_dir / "deglint_kutser.bsq") + + # 检查文件是否已存在 + bsq_path = output_path if output_path.endswith('.bsq') else output_path.replace('.dat', '.bsq').replace('.tif', '.bsq') + if Path(bsq_path).exists() or Path(output_path).exists(): + existing_path = bsq_path if Path(bsq_path).exists() else output_path + print(f"检测到已存在的去耀斑影像文件，直接使用: {existing_path}") + self.deglint_img_path = existing_path + step_end_time = time.time() + self._record_step_time("步骤3: 去除耀斑", step_start_time, step_end_time, status="skipped") + print(f"去耀斑影像已设置: {self.deglint_img_path}") + return self.deglint_img_path + + # 获取地理信息（不加载图像数据） + geotransform, projection, width, height, n_bands = self._get_image_geo_info(img_path) + print(f"影像尺寸: {width} x {height} x {n_bands}") + + # 处理水域掩膜：如果是shp文件路径，需要栅格化 + # 创建一个临时数组用于获取尺寸信息（仅用于掩膜处理） + temp_shape = (height, width) + mask_for_algorithm = self._prepare_water_mask_for_algorithm( + final_water_mask, temp_shape, geotransform, projection, img_path + ) + + # 应用Kutser算法：直接传递文件路径，让算法类使用GDAL逐波段处理 + # 注意：kutser_shp_path参数已废弃，使用water_mask代替 + kutser = Kutser(img_path, shp_path=None, # 直接传递文件路径 + oxy_band=oxy_band, lower_oxy=lower_oxy, + upper_oxy=upper_oxy, NIR_band=nir_band, + water_mask=mask_for_algorithm, output_path=output_path) # 传递output_path，算法类会保存 + corrected_bands = kutser.get_corrected_bands() + + # 检查算法类是否已保存文件（可能保存为.bsq格式） + bsq_path = output_path if output_path.endswith('.bsq') else output_path.replace('.dat', '.bsq').replace('.tif', '.bsq') + if not Path(bsq_path).exists() and not Path(output_path).exists(): + # 如果算法类没有保存，使用pipeline的保存方法 + self._save_bands_as_image(corrected_bands, output_path, geotransform, projection) + self.deglint_img_path = output_path + # 复制原始hdr文件信息 + self._copy_hdr_info(img_path, output_path) + else: + # 算法类已保存，使用算法类保存的路径 + self.deglint_img_path = bsq_path if Path(bsq_path).exists() else output_path + # 复制原始hdr文件信息 + self._copy_hdr_info(img_path, self.deglint_img_path) + + # 保存后显式清理，帮助释放内存 + del corrected_bands + + elif method == "goodman": + print(f"使用方法: Goodman (NIR波段范围: {nir_lower}-{nir_upper})") + + # 确定输出路径 + output_path = str(self.deglint_dir / "deglint_goodman.bsq") + + # 检查文件是否已存在 + bsq_path = output_path if output_path.endswith('.bsq') else output_path.replace('.dat', '.bsq').replace('.tif', '.bsq') + if Path(bsq_path).exists() or Path(output_path).exists(): + existing_path = bsq_path if Path(bsq_path).exists() else output_path + print(f"检测到已存在的去耀斑影像文件，直接使用: {existing_path}") + self.deglint_img_path = existing_path + step_end_time = time.time() + self._record_step_time("步骤3: 去除耀斑", step_start_time, step_end_time, status="skipped") + print(f"去耀斑影像已设置: {self.deglint_img_path}") + return self.deglint_img_path + + # 获取地理信息（不加载图像数据） + geotransform, projection, width, height, n_bands = self._get_image_geo_info(img_path) + print(f"影像尺寸: {width} x {height} x {n_bands}") + + # 处理水域掩膜：如果是shp文件路径，需要栅格化 + # 创建一个临时数组用于获取尺寸信息（仅用于掩膜处理） + temp_shape = (height, width) + mask_for_algorithm = self._prepare_water_mask_for_algorithm( + final_water_mask, temp_shape, geotransform, projection, img_path + ) + + # 应用Goodman算法：直接传递文件路径，让算法类使用GDAL逐波段处理 + goodman = Goodman(img_path, NIR_lower=nir_lower, NIR_upper=nir_upper, + A=goodman_A, B=goodman_B, water_mask=mask_for_algorithm, + output_path=output_path) # 传递output_path，算法类会保存 + corrected_bands = goodman.get_corrected_bands() + + # 检查算法类是否已保存文件（可能保存为.bsq格式） + bsq_path = output_path if output_path.endswith('.bsq') else output_path.replace('.dat', '.bsq').replace('.tif', '.bsq') + if not Path(bsq_path).exists() and not Path(output_path).exists(): + # 如果算法类没有保存，使用pipeline的保存方法 + self._save_bands_as_image(corrected_bands, output_path, geotransform, projection) + self.deglint_img_path = output_path + # 复制原始hdr文件信息 + self._copy_hdr_info(img_path, output_path) + else: + # 算法类已保存，使用算法类保存的路径 + self.deglint_img_path = bsq_path if Path(bsq_path).exists() else output_path + # 复制原始hdr文件信息 + self._copy_hdr_info(img_path, self.deglint_img_path) + + # 保存后显式清理，帮助释放内存 + del corrected_bands + + elif method == "hedley": + print(f"使用方法: Hedley (NIR波段={hedley_nir_band})") + + # 确定输出路径 + output_path = str(self.deglint_dir / "deglint_hedley.bsq") + + # 检查文件是否已存在 + bsq_path = output_path if output_path.endswith('.bsq') else output_path.replace('.dat', '.bsq').replace('.tif', '.bsq') + if Path(bsq_path).exists() or Path(output_path).exists(): + existing_path = bsq_path if Path(bsq_path).exists() else output_path + print(f"检测到已存在的去耀斑影像文件，直接使用: {existing_path}") + self.deglint_img_path = existing_path + step_end_time = time.time() + self._record_step_time("步骤3: 去除耀斑", step_start_time, step_end_time, status="skipped") + print(f"去耀斑影像已设置: {self.deglint_img_path}") + return self.deglint_img_path + + # 获取地理信息（不加载图像数据） + geotransform, projection, width, height, n_bands = self._get_image_geo_info(img_path) + print(f"影像尺寸: {width} x {height} x {n_bands}") + + # 处理水域掩膜：如果是shp文件路径，需要栅格化 + # 创建一个临时数组用于获取尺寸信息（仅用于掩膜处理） + temp_shape = (height, width) + mask_for_algorithm = self._prepare_water_mask_for_algorithm( + final_water_mask, temp_shape, geotransform, projection, img_path + ) + + # 应用Hedley算法：直接传递文件路径，让算法类使用GDAL逐波段处理 + # 注意：hedley_shp_path参数已废弃，使用water_mask代替 + hedley = Hedley(img_path, shp_path=None, # 直接传递文件路径 + NIR_band=hedley_nir_band, water_mask=mask_for_algorithm, + output_path=output_path) # 传递output_path，算法类会保存 + corrected_bands = hedley.get_corrected_bands() + + # 检查算法类是否已保存文件（可能保存为.bsq格式） + bsq_path = output_path if output_path.endswith('.bsq') else output_path.replace('.dat', '.bsq').replace('.tif', '.bsq') + if not Path(bsq_path).exists() and not Path(output_path).exists(): + # 如果算法类没有保存，使用pipeline的保存方法 + self._save_bands_as_image(corrected_bands, output_path, geotransform, projection) + self.deglint_img_path = output_path + # 复制原始hdr文件信息 + self._copy_hdr_info(img_path, output_path) + else: + # 算法类已保存，使用算法类保存的路径 + self.deglint_img_path = bsq_path if Path(bsq_path).exists() else output_path + # 复制原始hdr文件信息 + self._copy_hdr_info(img_path, self.deglint_img_path) + + # 保存后显式清理，帮助释放内存 + del corrected_bands + + elif method == "sugar": + print(f"使用方法: SUGAR (迭代次数={sugar_iter}, 掩膜方法={sugar_glint_mask_method})") + + # 确定输出路径 + output_path = str(self.deglint_dir / "deglint_sugar.bsq") + + # 检查文件是否已存在 + if Path(output_path).exists(): + print(f"检测到已存在的去耀斑影像文件，直接使用: {output_path}") + self.deglint_img_path = output_path + step_end_time = time.time() + self._record_step_time("步骤3: 去除耀斑", step_start_time, step_end_time, status="skipped") + print(f"去耀斑影像已设置: {self.deglint_img_path}") + return self.deglint_img_path + + # 加载影像 + image_array, geotransform, projection = self._load_image_as_array(img_path) + print(f"影像尺寸: {image_array.shape}") + + # 处理水域掩膜：如果是shp文件路径，需要栅格化 + mask_for_algorithm = self._prepare_water_mask_for_algorithm( + final_water_mask, image_array, geotransform, projection, img_path + ) + + # 设置默认bounds + if sugar_bounds is None: + sugar_bounds = [(1, 2)] + + # 应用SUGAR算法 + # 传递output_path给correction_iterative函数，但函数传入数组时无法获取地理信息，所以仍使用pipeline的保存方法 + if sugar_iter is None: + # 使用自动终止 + corrected_images = correction_iterative( + image_array, iter=None, bounds=sugar_bounds, + estimate_background=sugar_estimate_background, + glint_mask_method=sugar_glint_mask_method, + termination_thresh=sugar_termination_thresh, + water_mask=mask_for_algorithm, + output_path=None # 不传递output_path，使用pipeline保存 + ) + else: + # 使用固定迭代次数 + corrected_images = correction_iterative( + image_array, iter=sugar_iter, bounds=sugar_bounds, + estimate_background=sugar_estimate_background, + glint_mask_method=sugar_glint_mask_method, + water_mask=mask_for_algorithm, + output_path=None # 不传递output_path，使用pipeline保存 + ) + + # 使用最后一次迭代的结果 + if len(corrected_images) > 0: + corrected_array = corrected_images[-1] + else: + raise ValueError("SUGAR算法未生成任何结果") + + # 保存结果（保留地理信息） + self._save_array_as_image(corrected_array, output_path, geotransform, projection) + self.deglint_img_path = output_path + # 复制原始hdr文件信息 + self._copy_hdr_info(img_path, output_path) + + else: + raise ValueError(f"不支持的方法: {method}。支持的方法: kutser, goodman, hedley, sugar") + + step_end_time = time.time() + self._record_step_time("步骤3: 去除耀斑", step_start_time, step_end_time) + print(f"去耀斑影像已生成: {self.deglint_img_path}") + return self.deglint_img_path + except Exception as e: + step_end_time = time.time() + self._record_step_time("步骤3: 去除耀斑", step_start_time, step_end_time, + status="failed", error=str(e)) + raise + + def step4_process_csv(self, csv_path: str, skip_dependency_check: bool = False) -> str: + """ + 步骤4: 对csv文件进行处理，筛选剔除异常值 + + Args: + csv_path: 输入CSV文件路径 + skip_dependency_check: 是否跳过依赖检查（为保持一致性而保留） + + Returns: + 处理后的CSV文件路径 + """ + print("\n" + "="*80) + print("步骤4: 处理CSV文件，筛选剔除异常值") + print("="*80) + + step_start_time = time.time() + try: + output_path = str(self.processed_data_dir / "processed_data.csv") + + # 检查文件是否已存在 + if Path(output_path).exists(): + print(f"检测到已存在的处理后CSV文件，直接使用: {output_path}") + self.processed_csv_path = output_path + step_end_time = time.time() + self._record_step_time("步骤4: 处理CSV文件", step_start_time, step_end_time, status="skipped") + print(f"处理后的CSV文件已设置: {self.processed_csv_path}") + return self.processed_csv_path + + process_water_quality_data(csv_path, output_path) + self.processed_csv_path = output_path + + step_end_time = time.time() + self._record_step_time("步骤4: 处理CSV文件", step_start_time, step_end_time) + print(f"处理后的CSV文件已保存: {self.processed_csv_path}") + return self.processed_csv_path + except Exception as e: + step_end_time = time.time() + self._record_step_time("步骤4: 处理CSV文件", step_start_time, step_end_time, + status="failed", error=str(e)) + raise + + def step5_extract_training_spectra(self, deglint_img_path: Optional[str] = None, + radius: int = 5, + source_epsg: int = 4326, + csv_path: Optional[str] = None, + boundary_path: Optional[str] = None, + glint_mask_path: Optional[str] = None, + skip_dependency_check: bool = False) -> str: + """ + 步骤5: 根据csv文件的采样点坐标，在去除耀斑的文件中统计采样点的平均光谱 + + Args: + deglint_img_path: 去除耀斑后的影像文件路径（如果为None，使用步骤3的结果） + radius: 采样半径（像素） + source_epsg: 源坐标系EPSG代码 + csv_path: CSV文件路径（如果为None，使用步骤4的结果） + boundary_path: 水体掩膜文件路径（如果为None，自动生成水体掩膜） + glint_mask_path: 耀斑掩膜栅格路径（.dat/.tif）；若提供则优先使用，否则使用步骤2生成的路径 + + Returns: + 包含光谱数据的CSV文件路径 + """ + print("\n" + "="*80) + print("步骤5: 提取训练样本点的平均光谱") + print("="*80) + + step_start_time = time.time() + try: + # 处理影像路径 + if deglint_img_path is not None: + img_path = deglint_img_path + elif self.deglint_img_path is not None: + img_path = self.deglint_img_path + else: + if skip_dependency_check: + raise ValueError("必须提供deglint_img_path参数才能独立运行步骤5") + else: + raise ValueError("请先执行步骤3: 去除耀斑，或提供deglint_img_path参数") + + # 处理CSV路径 + if csv_path is not None: + final_csv_path = csv_path + elif self.processed_csv_path is not None: + final_csv_path = self.processed_csv_path + else: + if skip_dependency_check: + raise ValueError("必须提供csv_path参数才能独立运行步骤5") + else: + raise ValueError("请先执行步骤4: 处理CSV文件，或提供csv_path参数") + + output_path = str(self.training_spectra_dir / "training_spectra.csv") + + # 检查文件是否已存在 + if Path(output_path).exists(): + print(f"检测到已存在的训练光谱数据文件，直接使用: {output_path}") + self.training_spectra_path = output_path + step_end_time = time.time() + self._record_step_time("步骤5: 提取训练样本点光谱", step_start_time, step_end_time, status="skipped") + print(f"训练光谱数据已设置: {self.training_spectra_path}") + return self.training_spectra_path + + # 处理水体掩膜路径 + if boundary_path is not None: + final_boundary_path = boundary_path + else: + # 确保有dat格式的水体掩膜（如果需要） + final_boundary_path = self._ensure_water_mask_dat(img_path) + + flare_path = glint_mask_path if glint_mask_path is not None else self.glint_mask_path + if flare_path: + print(f"光谱提取使用耀斑掩膜: {flare_path}") + else: + print("警告: 未提供耀斑掩膜，采样邻域内将不剔除耀斑像元") + + get_spectral_in_coor( + img_path, final_csv_path, output_path, + radius=radius, flare_path=flare_path, + boundary_path=final_boundary_path, source_epsg=source_epsg + ) + self.training_spectra_path = output_path + + step_end_time = time.time() + self._record_step_time("步骤5: 提取训练样本点光谱", step_start_time, step_end_time) + print(f"训练光谱数据已保存: {self.training_spectra_path}") + return self.training_spectra_path + except Exception as e: + step_end_time = time.time() + self._record_step_time("步骤5: 提取训练样本点光谱", step_start_time, step_end_time, + status="failed", error=str(e)) + raise + + def step5_5_calculate_water_quality_indices(self, + training_spectra_path: Optional[str] = None, + formula_csv_file: Optional[str] = None, + formula_names: Optional[List[str]] = None, + output_filename: str = "water_quality_indices.csv", + enabled: bool = True, + skip_dependency_check: bool = False) -> str: + """ + 步骤5.5: 根据训练光谱计算水质光谱指数 + + 使用band_math.py中的方法实现，支持从公式CSV文件中批量计算指定公式 + + Args: + training_spectra_path: 训练光谱数据CSV路径（如果为None，使用步骤5的结果） + formula_csv_file: 公式CSV文件路径，包含公式名称和具体公式 + formula_names: 要计算的公式名称列表，如果为None则计算所有公式 + output_filename: 输出文件名 + + Returns: + 包含计算结果的新CSV文件路径 + """ + print("\n" + "="*80) + print("步骤5.5: 计算水质光谱指数（使用band_math方法）") + print("="*80) + + step_start_time = time.time() + try: + # 如果未启用，直接跳过处理 + if not enabled: + print("已设置跳过水质指数计算（enabled=False）。") + step_end_time = time.time() + self._record_step_time("步骤5.5: 计算水质光谱指数", step_start_time, step_end_time, status="skipped") + return None + + # 处理光谱数据路径 + if training_spectra_path is not None: + csv_path = training_spectra_path + elif self.training_spectra_path is not None: + csv_path = self.training_spectra_path + else: + if skip_dependency_check: + raise ValueError("必须提供training_spectra_path参数才能独立运行步骤5.5") + else: + raise ValueError("请先执行步骤5: 提取训练样本点光谱，或提供training_spectra_path参数") + + if formula_csv_file is None: + raise ValueError("必须提供formula_csv_file参数，包含水质指数公式") + + output_path = str(self.indices_dir / output_filename) + + # 如果文件已存在且配置了跳过机制，则直接复用 + if Path(output_path).exists(): + print(f"检测到已存在的水质指数文件，直接使用: {output_path}") + self.indices_path = output_path + step_end_time = time.time() + self._record_step_time("步骤5.5: 计算水质光谱指数", step_start_time, step_end_time, status="skipped") + print(f"水质指数数据已设置: {self.indices_path}") + return self.indices_path + + # 导入band_math模块 + from src.utils.band_math import BandMathCalculator + + # 创建计算器实例 + calculator = BandMathCalculator(csv_path) + + # 使用band_math的方法计算指定公式 + result_df = calculator.process_formulas_from_csv( + formula_csv_file=formula_csv_file, + formula_names=formula_names, + output_file=output_path + ) + + if result_df is None: + raise ValueError("计算水质指数失败，请检查公式CSV文件格式") + + self.indices_path = output_path + + step_end_time = time.time() + self._record_step_time("步骤5.5: 计算水质光谱指数", step_start_time, step_end_time) + print(f"水质指数已保存: {self.indices_path}") + print(f"共计算了 {len(result_df.columns) - len(calculator.df.columns)} 个水质指数") + return self.indices_path + except Exception as e: + step_end_time = time.time() + self._record_step_time("步骤5.5: 计算水质光谱指数", step_start_time, step_end_time, + status="failed", error=str(e)) + raise + + def step6_train_models(self, feature_start_column: str = "374.285004", + preprocessing_methods: List[str] = None, + model_names: List[str] = None, + split_methods: List[str] = None, + cv_folds: int = 5, + training_csv_path: Optional[str] = None, + skip_dependency_check: bool = False) -> str: + """ + 步骤6: 使用采样点的平均光谱和对应的实测值建立机器学习模型，保存模型权重 + + Args: + feature_start_column: 特征开始列名或索引 + preprocessing_methods: 预处理方法列表 + model_names: 模型名称列表 + split_methods: 数据划分方法列表 + cv_folds: 交叉验证折数 + + Returns: + 模型保存目录路径 + """ + print("\n" + "="*80) + print("步骤6: 训练机器学习模型") + print("="*80) + + step_start_time = time.time() + try: + # 处理训练数据路径 + if training_csv_path is not None: + final_csv_path = training_csv_path + elif self.training_spectra_path is not None: + final_csv_path = self.training_spectra_path + else: + if skip_dependency_check: + raise ValueError("必须提供training_csv_path参数才能独立运行步骤6") + else: + raise ValueError("请先执行步骤5: 提取训练样本点的平均光谱，或提供training_csv_path参数") + + # 检查模型目录是否存在且有内容 + if self.models_dir.exists() and any(self.models_dir.iterdir()): + # 检查是否有至少一个目标参数的模型文件夹 + has_models = False + for item in self.models_dir.iterdir(): + if item.is_dir(): + # 检查文件夹内是否有模型文件 + model_files = list(item.glob('*.pkl')) + list(item.glob('*.joblib')) + list(item.glob('*.h5')) + if model_files: + has_models = True + break + + if has_models: + print(f"检测到已存在的模型文件，直接使用: {self.models_dir}") + step_end_time = time.time() + self._record_step_time("步骤6: 训练机器学习模型", step_start_time, step_end_time, status="skipped") + print(f"模型目录已设置: {self.models_dir}") + return str(self.models_dir) + + if preprocessing_methods is None: + preprocessing_methods = ['None', 'MMS', 'SS', 'SNV', 'MA', 'SG', 'MSC', 'D1', 'D2', 'DT', 'CT'] + if model_names is None: + model_names = ['SVR', 'RF', 'Ridge', 'Lasso'] + if split_methods is None: + split_methods = ['spxy', 'ks', 'random'] + + modeler = WaterQualityModelingBatch(str(self.models_dir)) + + all_results = modeler.train_models_batch( + csv_path=final_csv_path, + feature_start_column=feature_start_column, + preprocessing_methods=preprocessing_methods, + model_names=model_names, + split_methods=split_methods, + cv_folds=cv_folds + ) + + step_end_time = time.time() + self._record_step_time("步骤6: 训练机器学习模型", step_start_time, step_end_time) + print(f"模型训练完成，结果保存在: {self.models_dir}") + + # 生成训练摘要报告 + try: + summary_path = self.report_generator.generate_training_summary(str(self.models_dir)) + print(f"训练摘要报告已生成: {summary_path}") + except Exception as e: + print(f"生成训练摘要报告时出错: {e}") + + return str(self.models_dir) + except Exception as e: + step_end_time = time.time() + self._record_step_time("步骤6: 训练机器学习模型", step_start_time, step_end_time, + status="failed", error=str(e)) + raise + + def step7_generate_sampling_points(self, deglint_img_path: Optional[str] = None, + interval: int = 50, + sample_radius: int = 5, + chunk_size: int = 1000, + water_mask_path: Optional[str] = None, + glint_mask_path: Optional[str] = None, + skip_dependency_check: bool = False) -> str: + """ + 步骤7: 生成根据水域掩膜内且耀斑掩膜外的采样点，统计采样点的平均光谱 + + Args: + deglint_img_path: 去除耀斑后的影像文件路径（如果为None，使用步骤3的结果） + interval: 采样点间隔（像元数） + sample_radius: 采样点半径（像元数） + chunk_size: 每次处理的行数（控制内存使用） + water_mask_path: dat格式的水域掩膜文件路径（如果为None，将使用步骤1生成的dat格式掩膜） + + Returns: + 采样点光谱数据CSV文件路径 + """ + print("\n" + "="*80) + print("步骤7: 生成预测采样点并提取光谱") + print("="*80) + + step_start_time = time.time() + try: + # 处理影像路径 + if deglint_img_path is not None: + img_path = deglint_img_path + elif self.deglint_img_path is not None: + img_path = self.deglint_img_path + else: + if skip_dependency_check: + raise ValueError("必须提供deglint_img_path参数才能独立运行步骤7") + else: + raise ValueError("请先执行步骤3: 去除耀斑，或提供deglint_img_path参数") + + # 如果没有提供water_mask_path，使用步骤1生成的dat格式掩膜 + if water_mask_path is None: + if self.water_mask_path is not None: + water_mask_path = self.water_mask_path + print(f"使用步骤1生成的dat格式水体掩膜: {water_mask_path}") + else: + if skip_dependency_check: + print("警告: 未提供水体掩膜，将对全图生成采样点") + water_mask_path = None + else: + raise ValueError("请提供water_mask_path参数，或确保步骤1已生成水体掩膜，或设置skip_dependency_check=True") + + # 确定耀斑掩膜路径（允许外部显式传入以覆盖步骤2结果） + glint_mask_to_use = glint_mask_path if glint_mask_path else self.glint_mask_path + if glint_mask_to_use is None: + print("未检测到耀斑掩膜，将在采样点生成时不做耀斑区域剔除。") + + # 确定耀斑掩膜路径（允许外部显式传入以覆盖步骤2结果） + glint_mask_to_use = glint_mask_path if glint_mask_path else self.glint_mask_path + if glint_mask_to_use is None: + print("未检测到耀斑掩膜，将在采样点生成时不做耀斑区域剔除。") + + output_path = str(self.sampling_dir / "sampling_spectra.csv") + + # 检查文件是否已存在 + if Path(output_path).exists(): + print(f"检测到已存在的采样点光谱数据文件，直接使用: {output_path}") + step_end_time = time.time() + self._record_step_time("步骤7: 生成预测采样点", step_start_time, step_end_time, status="skipped") + print(f"采样点光谱数据已设置: {output_path}") + return output_path + + # create_water_mask_from_shp函数已支持dat格式，直接传递即可 + get_spectral_sampling_points_chunked( + img_path, water_mask_path, glint_mask_to_use, + output_path, interval, sample_radius, chunk_size + ) + + step_end_time = time.time() + self._record_step_time("步骤7: 生成预测采样点", step_start_time, step_end_time) + print(f"采样点光谱数据已保存: {output_path}") + return output_path + except Exception as e: + step_end_time = time.time() + self._record_step_time("步骤7: 生成预测采样点", step_start_time, step_end_time, + status="failed", error=str(e)) + raise + + def step8_predict_water_quality(self, sampling_csv_path: str, + models_dir: Optional[str] = None, + metric: str = 'test_r2', + prediction_column: str = 'prediction', + skip_dependency_check: bool = False) -> Dict[str, str]: + """ + 步骤8: 将训练好的最佳机器学习模型应用到采样点的平均光谱上，预测水质参数 + + Args: + sampling_csv_path: 采样点光谱数据CSV路径 + models_dir: 模型保存目录（如果为None，使用步骤6的结果） + metric: 选择最佳模型的指标 + prediction_column: 预测结果列名 + + Returns: + 预测结果文件路径字典（键为目标列名） + """ + print("\n" + "="*80) + print("步骤8: 预测水质参数") + print("="*80) + + step_start_time = time.time() + try: + # 处理模型目录路径 + if models_dir is not None: + models_path = models_dir + elif self.models_dir.exists(): + models_path = str(self.models_dir) + else: + if skip_dependency_check: + raise ValueError("必须提供models_dir参数才能独立运行步骤8") + else: + raise ValueError("请先执行步骤6: 训练机器学习模型，或提供models_dir参数") + + # 检查prediction_dir中是否已有预测结果文件 + prediction_files = {} + if self.prediction_dir.exists(): + # 查找所有CSV预测结果文件 + csv_files = list(self.prediction_dir.glob('*.csv')) + if csv_files: + # 从文件名提取目标参数名（假设文件名为"target_name_prediction.csv"） + for csv_file in csv_files: + # 尝试从文件名提取目标参数名 + file_stem = csv_file.stem + # 移除可能的后缀（如_prediction） + if '_prediction' in file_stem: + target_name = file_stem.replace('_prediction', '') + elif '_pred' in file_stem: + target_name = file_stem.replace('_pred', '') + else: + target_name = file_stem + prediction_files[target_name] = str(csv_file) + + # 如果已有预测文件，检查是否完整（需要与模型目录中的目标参数匹配） + if prediction_files: + models_path_obj = Path(models_path) + if models_path_obj.exists(): + # 获取所有目标参数文件夹 + target_folders = [d.name for d in models_path_obj.iterdir() if d.is_dir()] + # 检查是否所有目标参数都有预测文件 + missing_targets = [t for t in target_folders if t not in prediction_files] + if not missing_targets: + print(f"检测到已存在的预测结果文件，直接使用: {self.prediction_dir}") + print(f"找到 {len(prediction_files)} 个预测结果文件") + step_end_time = time.time() + self._record_step_time("步骤8: 预测水质参数", step_start_time, step_end_time, status="skipped") + print(f"预测结果已设置: {self.prediction_dir}") + return prediction_files + else: + print(f"检测到部分预测结果文件，缺少以下目标参数: {missing_targets}") + print("将继续生成缺失的预测结果...") + + # 创建推理实例 + inferencer = WaterQualityInference(models_path) + + # 批量推理多个目标列的模型 + all_results = inferencer.batch_inference_multi_models( + models_root_dir=models_path, + sampling_csv_path=sampling_csv_path, + output_dir=str(self.prediction_dir), + metric=metric, + prediction_column=prediction_column, + output_format='csv' + ) + + # 提取输出文件路径（合并已有和新生成的） + for target_name, result in all_results.items(): + if result.get('status') == 'success': + prediction_files[target_name] = result['output_file'] + + step_end_time = time.time() + self._record_step_time("步骤8: 预测水质参数", step_start_time, step_end_time) + print(f"预测完成，结果保存在: {self.prediction_dir}") + + # 生成预测结果报告 + try: + report_path = self.report_generator.generate_prediction_report(prediction_files) + print(f"预测结果报告已生成: {report_path}") + except Exception as e: + print(f"生成预测结果报告时出错: {e}") + + return prediction_files + except Exception as e: + step_end_time = time.time() + self._record_step_time("步骤8: 预测水质参数", step_start_time, step_end_time, + status="failed", error=str(e)) + raise + + def step9_generate_distribution_map(self, prediction_csv_path: str, + boundary_shp_path: str, + output_image_path: Optional[str] = None, + resolution: float = 30, + input_crs: str = 'EPSG:32651', + output_crs: str = 'EPSG:4326', + show_sample_points: bool = False, + base_map_tif: Optional[str] = None, + use_distance_diffusion: bool = True, + max_diffusion_distance: Optional[float] = None, + diffusion_power: float = 2, + diffusion_n_neighbors: int = 15, + cmap: Optional[str] = None, + expand_ratio: float = 0.05, + skip_dependency_check: bool = False) -> str: + """ + 步骤9: 根据采样点的坐标和反演的实测参数，以及水域掩膜，通过插值的方法，得到水质参数的可视化分布图 + + Args: + prediction_csv_path: 预测结果CSV文件路径（前两列为经纬度，第三列为预测值） + boundary_shp_path: 边界shapefile文件路径 + output_image_path: 输出图片路径（如果为None，自动生成） + resolution: 插值网格分辨率（米） + input_crs: 输入坐标系 + output_crs: 输出坐标系 + show_sample_points: 是否在图上显示采样点 + base_map_tif: 底图TIF路径，用于在水域掩膜外显示底图 + use_distance_diffusion: 是否启用距离扩散补全边界 + max_diffusion_distance: 距离扩散的最大距离（米），None表示自动计算 + diffusion_power: 距离扩散的幂参数 + diffusion_n_neighbors: 距离扩散时使用的最近邻数量 + cmap: 指定的颜色映射名称，None表示自动识别 + expand_ratio: 边界外扩比例（0-1之间） + + Returns: + 可视化分布图文件路径 + """ + print("\n" + "="*80) + print("步骤9: 生成水质参数可视化分布图") + print("="*80) + + step_start_time = time.time() + try: + if output_image_path is None: + # 根据CSV文件名自动生成输出路径 + csv_name = Path(prediction_csv_path).stem + output_image_path = str(self.visualization_dir / f"{csv_name}_distribution.png") + + # 检查文件是否已存在 + if Path(output_image_path).exists(): + print(f"检测到已存在的分布图文件，直接使用: {output_image_path}") + step_end_time = time.time() + self._record_step_time("步骤9: 生成分布图", step_start_time, step_end_time, status="skipped") + print(f"可视化分布图已设置: {output_image_path}") + return output_image_path + + # 创建映射器 + mapper = ContentMapper(input_crs=input_crs, output_crs=output_crs) + + # 处理数据并生成分布图 + mapper_kwargs = { + 'resolution': resolution, + 'show_sample_points': show_sample_points, + 'use_distance_diffusion': use_distance_diffusion, + 'diffusion_power': diffusion_power, + 'diffusion_n_neighbors': diffusion_n_neighbors, + 'expand_ratio': expand_ratio + } + + optional_mapper_kwargs = { + 'base_map_tif': base_map_tif, + 'max_diffusion_distance': max_diffusion_distance, + 'cmap': cmap + } + mapper_kwargs.update({ + key: value for key, value in optional_mapper_kwargs.items() + if value is not None + }) + + mapper.process_data( + csv_file=prediction_csv_path, + shp_file=boundary_shp_path, + output_file=output_image_path, + **mapper_kwargs + ) + + step_end_time = time.time() + self._record_step_time("步骤9: 生成分布图", step_start_time, step_end_time) + print(f"可视化分布图已保存: {output_image_path}") + return output_image_path + except Exception as e: + step_end_time = time.time() + self._record_step_time("步骤9: 生成分布图", step_start_time, step_end_time, + status="failed", error=str(e)) + raise + + def generate_model_scatter_plots(self, training_csv_path: Optional[str] = None, + models_dir: Optional[str] = None, + metric: str = 'test_r2', + use_enhanced: bool = True, + feature_start_column: Union[str, int] = 13, + test_size: float = 0.2, + random_state: int = 42) -> Dict[str, str]: + """ + 生成模型评估散点图（真实值vs预测值） + + Args: + training_csv_path: 训练数据CSV路径（如果为None，使用步骤5的结果） + models_dir: 模型保存目录（如果为None，使用步骤6的结果） + metric: 选择最佳模型的指标 + use_enhanced: 是否使用增强版散点图（带置信区间，使用sctter_batch） + feature_start_column: 特征开始列名或索引 + test_size: 测试集比例 + random_state: 随机种子 + + Returns: + 散点图文件路径字典（键为目标参数名） + """ + print("\n" + "="*80) + print("生成模型评估散点图") + print("="*80) + + if training_csv_path is None: + training_csv_path = self.training_spectra_path + if training_csv_path is None: + raise ValueError("请提供训练数据CSV路径，或先执行步骤5") + + if models_dir is None: + models_dir = str(self.models_dir) + + scatter_paths = {} + models_path = Path(models_dir) + + # 如果使用增强版散点图（带置信区间） + if use_enhanced: + print("使用增强版散点图（带置信区间）") + try: + # 使用sctter_batch批量生成散点图 + results = self.scatter_batch.batch_plot_scatter( + models_root_dir=models_dir, + csv_path=training_csv_path, + output_dir=str(self.visualization_dir / "scatter_plots"), + metric=metric, + target_column=None, # 使用文件夹名称作为目标列名 + feature_start_column=feature_start_column, + test_size=test_size, + random_state=random_state + ) + + # 提取成功生成的散点图路径 + for target_name, result in results.items(): + if result.get('status') == 'success': + scatter_paths[target_name] = result.get('save_path', '') + print(f" ✓ {target_name}: {result.get('save_path', '')}") + else: + print(f" ✗ {target_name}: 失败 - {result.get('error', '未知错误')}") + + except Exception as e: + print(f"使用增强版散点图时出错: {e}") + print("回退到基础版散点图") + use_enhanced = False + + # 如果未使用增强版或增强版失败，使用基础版 + if not use_enhanced or not scatter_paths: + print("使用基础版散点图") + from src.core.prediction.inference_batch import WaterQualityInference + + # 遍历所有目标参数文件夹 + for target_folder in models_path.iterdir(): + if not target_folder.is_dir(): + continue + + target_name = target_folder.name + print(f"\n处理目标参数: {target_name}") + + try: + # 加载最佳模型进行评估 + inferencer = WaterQualityInference(str(target_folder)) + eval_result = inferencer.evaluate_with_split( + data_csv_path=training_csv_path, + split_method="spxy", + test_size=test_size, + random_state=random_state, + metric=metric + ) + + # 提取预测结果 + predictions = eval_result.get('predictions', {}) + if predictions: + y_train_true = predictions.get('y_train_true') + y_train_pred = predictions.get('y_train_pred') + y_test_true = predictions.get('y_test_true') + y_test_pred = predictions.get('y_test_pred') + metrics = eval_result.get('test_metrics', {}) + + if y_train_true is not None and y_test_true is not None: + # 合并训练集和测试集 + y_all_true = np.concatenate([y_train_true, y_test_true]) + y_all_pred = np.concatenate([y_train_pred, y_test_pred]) + + # 生成索引 + train_indices = np.arange(len(y_train_true)) + test_indices = np.arange(len(y_train_true), len(y_all_true)) + + # 绘制散点图 + scatter_path = self.visualizer.plot_scatter_true_vs_pred( + y_true=y_all_true, + y_pred=y_all_pred, + target_name=target_name, + train_indices=train_indices, + test_indices=test_indices, + metrics={ + 'train_r2': eval_result.get('train_metrics', {}).get('r2', 0), + 'test_r2': metrics.get('r2', 0), + 'train_rmse': eval_result.get('train_metrics', {}).get('rmse', 0), + 'test_rmse': metrics.get('rmse', 0) + } + ) + scatter_paths[target_name] = scatter_path + except Exception as e: + print(f"处理目标参数 {target_name} 时出错: {e}") + continue + + print(f"\n散点图生成完成，共生成 {len(scatter_paths)} 个图表") + return scatter_paths + + def generate_spectrum_comparison_plots(self, csv_path: Optional[str] = None, + parameter_columns: Optional[List[str]] = None, + wavelength_start_column: Union[str, int] = "UTM_Y") -> Dict[str, str]: + """ + 生成光谱曲线对比图（不同参数值的光谱曲线对比） + + Args: + csv_path: 包含光谱和参数值的CSV文件路径（如果为None，使用步骤5的结果） + parameter_columns: 参数列名列表（如果为None，自动检测） + wavelength_start_column: 波长开始列名或索引 + + Returns: + 光谱曲线图文件路径字典（键为参数名） + """ + print("\n" + "="*80) + print("生成光谱曲线对比图") + print("="*80) + + if csv_path is None: + csv_path = self.training_spectra_path + if csv_path is None: + raise ValueError("请提供CSV文件路径，或先执行步骤5") + + # 读取数据以检测参数列 + df = pd.read_csv(csv_path) + + if parameter_columns is None: + # 自动检测参数列（排除坐标列和光谱列） + if isinstance(wavelength_start_column, str): + try: + wavelength_start_idx = df.columns.get_loc(wavelength_start_column) + except: + wavelength_start_idx = 13 # 默认值 + else: + wavelength_start_idx = wavelength_start_column + + # 假设前几列是参数列（根据实际数据结构调整） + parameter_columns = list(df.columns[:wavelength_start_idx]) + # 排除坐标列（通常是前两列） + if len(parameter_columns) > 2: + parameter_columns = parameter_columns[2:] + + spectrum_paths = {} + for param_col in parameter_columns: + if param_col not in df.columns: + continue + + print(f"\n处理参数: {param_col}") + try: + spectrum_path = self.visualizer.plot_spectrum_by_parameter( + csv_path=csv_path, + parameter_column=param_col, + wavelength_start_column=wavelength_start_column, + n_groups=5 + ) + spectrum_paths[param_col] = spectrum_path + except Exception as e: + print(f"处理参数 {param_col} 时出错: {e}") + continue + + print(f"\n光谱曲线图生成完成，共生成 {len(spectrum_paths)} 个图表") + return spectrum_paths + + def generate_boxplots(self, csv_path: Optional[str] = None, + parameter_columns: Optional[List[str]] = None, + data_start_column: int = 4, + save_individual: bool = True, + use_seaborn: bool = True) -> Dict[str, str]: + """ + 生成水质参数的箱型图 + + Args: + csv_path: CSV文件路径（如果为None，使用步骤4的结果） + parameter_columns: 参数列名列表（如果为None，自动检测） + data_start_column: 数据开始列索引（从第几列开始，默认第5列，索引为4） + save_individual: 是否为每个参数单独保存箱型图 + use_seaborn: 是否使用seaborn绘制（更美观） + + Returns: + 箱型图文件路径字典 + """ + print("\n" + "="*80) + print("生成水质参数箱型图") + print("="*80) + + if csv_path is None: + csv_path = self.processed_csv_path + if csv_path is None: + raise ValueError("请提供CSV文件路径，或先执行步骤4") + + # 读取数据 + df = pd.read_csv(csv_path) + + # 确定参数列 + if parameter_columns is None: + # 从指定列开始的所有列 + data_columns = df.iloc[:, data_start_column:] + parameter_columns = list(data_columns.columns) + else: + # 使用指定的列 + parameter_columns = [col for col in parameter_columns if col in df.columns] + + if not parameter_columns: + print("警告: 未找到有效的参数列") + return {} + + # 创建输出目录 + boxplot_dir = self.visualization_dir / "boxplots" + boxplot_dir.mkdir(parents=True, exist_ok=True) + + boxplot_paths = {} + + if save_individual: + # 为每个参数单独绘制箱型图 + print(f"为每个参数单独绘制箱型图（共 {len(parameter_columns)} 个参数）") + + for column in parameter_columns: + if column not in df.columns: + continue + + # 移除空值 + clean_data = df[column].dropna() + + if len(clean_data) == 0: + print(f"跳过列 '{column}': 没有有效数据") + continue + + try: + # 创建新图形 + plt.figure(figsize=(8, 6)) + + if use_seaborn: + # 使用seaborn绘制 + plot_data = pd.DataFrame({ + '参数': [column] * len(clean_data), + '数值': clean_data + }) + sns.boxplot(data=plot_data, x='参数', y='数值', palette='Set2') + sns.stripplot(data=plot_data, x='参数', y='数值', + color='red', alpha=0.6, size=5, jitter=True) + else: + # 使用matplotlib绘制 + box_plot = plt.boxplot([clean_data], labels=[column], + patch_artist=True, showfliers=False) + box_plot['boxes'][0].set_facecolor('lightblue') + box_plot['boxes'][0].set_alpha(0.7) + + # 添加散点 + x_pos = np.random.normal(1, 0.04, size=len(clean_data)) + plt.scatter(x_pos, clean_data, alpha=0.6, s=30, color='red', + edgecolors='black', linewidth=0.5, zorder=3) + + # 设置标题和标签 + plt.title(f'{column} - 箱型图', fontsize=14, fontweight='bold') + plt.xlabel('参数', fontsize=12) + plt.ylabel('数值', fontsize=12) + + # 添加统计信息 + stats_text = (f'数据点数: {len(clean_data)}\n' + f'均值: {clean_data.mean():.2f}\n' + f'中位数: {clean_data.median():.2f}\n' + f'标准差: {clean_data.std():.2f}') + plt.text(0.02, 0.98, stats_text, transform=plt.gca().transAxes, + verticalalignment='top', + bbox=dict(boxstyle='round', + facecolor='wheat' if not use_seaborn else 'lightgreen', + alpha=0.8)) + + # 添加网格 + plt.grid(True, alpha=0.3, linestyle='--') + + # 调整布局 + plt.tight_layout() + + # 保存图片 + safe_column_name = column.replace('/', '_').replace('\\', '_').replace(':', '_') + save_path = boxplot_dir / f'{safe_column_name}_boxplot.png' + plt.savefig(save_path, dpi=300, bbox_inches='tight') + plt.close() + + boxplot_paths[column] = str(save_path) + print(f" 已保存: {save_path.name}") + + except Exception as e: + print(f" 处理参数 {column} 时出错: {e}") + continue + + # 生成所有参数的综合箱型图 + try: + print("\n生成综合箱型图（所有参数在一张图上）") + plt.figure(figsize=(max(12, len(parameter_columns) * 0.8), 8)) + + # 准备数据 + box_data = [] + labels = [] + for column in parameter_columns: + if column in df.columns: + clean_data = df[column].dropna() + if len(clean_data) > 0: + box_data.append(clean_data) + labels.append(column) + + if box_data: + if use_seaborn: + # 使用seaborn绘制 + melted_data = pd.melt(df[labels], var_name='参数', value_name='数值') + melted_data = melted_data.dropna() + sns.boxplot(data=melted_data, x='参数', y='数值', palette='Set3') + sns.stripplot(data=melted_data, x='参数', y='数值', + color='red', alpha=0.6, size=4, jitter=True) + else: + # 使用matplotlib绘制 + box_plot = plt.boxplot(box_data, labels=labels, patch_artist=True, + showfliers=False) + colors = plt.cm.Set3(np.linspace(0, 1, len(box_data))) + for patch, color in zip(box_plot['boxes'], colors): + patch.set_facecolor(color) + patch.set_alpha(0.7) + + # 添加散点 + for i, data in enumerate(box_data): + x_pos = np.random.normal(i + 1, 0.04, size=len(data)) + plt.scatter(x_pos, data, alpha=0.6, s=20, color='red', + edgecolors='black', linewidth=0.5, zorder=3) + + plt.title('水质参数箱型图（综合）', fontsize=16, fontweight='bold') + plt.xlabel('参数', fontsize=12) + plt.ylabel('数值', fontsize=12) + plt.xticks(rotation=45, ha='right') + plt.grid(True, alpha=0.3, linestyle='--') + plt.tight_layout() + + combined_path = boxplot_dir / 'all_parameters_boxplot.png' + plt.savefig(combined_path, dpi=300, bbox_inches='tight') + plt.close() + + boxplot_paths['all_parameters'] = str(combined_path) + print(f" 已保存综合箱型图: {combined_path.name}") + + except Exception as e: + print(f"生成综合箱型图时出错: {e}") + + print(f"\n箱型图生成完成，共生成 {len(boxplot_paths)} 个图表") + return boxplot_paths + + def generate_statistical_charts(self, csv_path: Optional[str] = None, + parameter_columns: Optional[List[str]] = None) -> Dict[str, str]: + """ + 生成统计图表（箱线图、直方图、相关性热力图） + + Args: + csv_path: CSV文件路径（如果为None，使用步骤4的结果） + parameter_columns: 参数列名列表（如果为None，自动检测） + + Returns: + 统计图表文件路径字典 + """ + print("\n" + "="*80) + print("生成统计图表") + print("="*80) + + if csv_path is None: + csv_path = self.processed_csv_path + if csv_path is None: + raise ValueError("请提供CSV文件路径，或先执行步骤4") + + # 读取数据以检测参数列 + df = pd.read_csv(csv_path) + + if parameter_columns is None: + # 自动检测参数列（排除前两列坐标列） + parameter_columns = list(df.columns[2:]) + # 过滤掉非数值列 + parameter_columns = [col for col in parameter_columns + if df[col].dtype in [np.float64, np.int64]] + + chart_paths = self.visualizer.plot_statistical_charts( + csv_path=csv_path, + parameter_columns=parameter_columns + ) + + print(f"\n统计图表生成完成") + return chart_paths + + def generate_glint_deglint_previews(self, work_dir: Optional[str] = None, + output_subdir: str = "glint_deglint_previews", + generate_glint: bool = True, + generate_deglint: bool = True) -> Dict[str, str]: + """ + 生成2_glint和3_deglint文件夹中影像文件的PNG预览图 + + Args: + work_dir: 工作目录（如果为None，则使用self.work_dir） + output_subdir: 输出子目录名称 + generate_glint: 是否处理2_glint文件夹 + generate_deglint: 是否处理3_deglint文件夹 + + Returns: + 生成的预览图路径字典 + """ + if work_dir is None: + work_dir = str(self.work_dir) + + print(f"\n{'='*70}") + print("步骤: 生成耀斑分析影像预览图") + print(f"{'='*70}") + + try: + preview_paths = self.visualizer.generate_glint_deglint_previews( + work_dir=work_dir, + output_subdir=output_subdir, + generate_glint=generate_glint, + generate_deglint=generate_deglint + ) + + print(f"耀斑分析影像预览图生成完成，共生成 {len(preview_paths)} 个预览图") + return preview_paths + + except Exception as e: + print(f"生成耀斑分析影像预览图时出错: {e}") + return {} + + def generate_pipeline_report(self, output_path: Optional[str] = None) -> str: + """ + 生成流程执行报告，包含每步的耗时统计 + + Args: + output_path: 输出文件路径（如果为None，自动生成） + + Returns: + 报告文件路径 + """ + if output_path is None: + timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') + output_path = str(self.reports_dir / f"pipeline_report_{timestamp}.csv") + + # 准备报告数据 + report_data = [] + total_time = 0.0 + + # 按步骤顺序排列 + step_order = [ + "步骤1: 生成水域mask", + "步骤2: 找到耀斑区域", + "步骤3: 去除耀斑", + "步骤4: 处理CSV文件", + "步骤5: 提取训练样本点光谱", + "步骤5.5: 计算水质光谱指数", + "步骤6: 训练机器学习模型", + "步骤6.5: 非经验模型训练", + "步骤6.75: 自定义回归", + "步骤7: 生成预测采样点", + "步骤8: 预测水质参数", + "步骤9: 生成分布图" + ] + + for step_name in step_order: + if step_name in self.step_timings: + timing_info = self.step_timings[step_name] + report_data.append({ + '步骤': step_name, + '开始时间': timing_info['start_time'], + '结束时间': timing_info['end_time'], + '耗时(秒)': f"{timing_info['elapsed_seconds']:.2f}", + '耗时(格式化)': timing_info['elapsed_formatted'], + '状态': timing_info['status'], + '错误信息': timing_info.get('error', '') + }) + if timing_info['status'] == 'completed': + total_time += timing_info['elapsed_seconds'] + + # 添加总计行 + if self.pipeline_start_time and self.pipeline_end_time: + pipeline_total = self.pipeline_end_time - self.pipeline_start_time + report_data.append({ + '步骤': '总计', + '开始时间': datetime.fromtimestamp(self.pipeline_start_time).strftime('%Y-%m-%d %H:%M:%S'), + '结束时间': datetime.fromtimestamp(self.pipeline_end_time).strftime('%Y-%m-%d %H:%M:%S'), + '耗时(秒)': f"{pipeline_total:.2f}", + '耗时(格式化)': self._format_time(pipeline_total), + '状态': 'completed', + '错误信息': '' + }) + + # 创建DataFrame并保存 + df_report = pd.DataFrame(report_data) + df_report.to_csv(output_path, index=False, encoding='utf-8-sig') + + # 同时生成文本格式的报告 + txt_output_path = str(Path(output_path).with_suffix('.txt')) + with open(txt_output_path, 'w', encoding='utf-8') as f: + f.write("="*80 + "\n") + f.write("水质参数反演流程执行报告\n") + f.write("="*80 + "\n\n") + + if self.pipeline_start_time and self.pipeline_end_time: + f.write(f"流程开始时间: {datetime.fromtimestamp(self.pipeline_start_time).strftime('%Y-%m-%d %H:%M:%S')}\n") + f.write(f"流程结束时间: {datetime.fromtimestamp(self.pipeline_end_time).strftime('%Y-%m-%d %H:%M:%S')}\n") + f.write(f"总耗时: {self._format_time(self.pipeline_end_time - self.pipeline_start_time)}\n\n") + + f.write("-"*80 + "\n") + f.write("各步骤执行详情:\n") + f.write("-"*80 + "\n\n") + + for step_name in step_order: + if step_name in self.step_timings: + timing_info = self.step_timings[step_name] + f.write(f"{step_name}\n") + f.write(f" 开始时间: {timing_info['start_time']}\n") + f.write(f" 结束时间: {timing_info['end_time']}\n") + f.write(f" 耗时: {timing_info['elapsed_formatted']} ({timing_info['elapsed_seconds']:.2f}秒)\n") + f.write(f" 状态: {timing_info['status']}\n") + if timing_info.get('error'): + f.write(f" 错误: {timing_info['error']}\n") + f.write("\n") + + f.write("-"*80 + "\n") + f.write("统计摘要:\n") + f.write("-"*80 + "\n") + completed_steps = [s for s in self.step_timings.values() if s['status'] == 'completed'] + failed_steps = [s for s in self.step_timings.values() if s['status'] == 'failed'] + skipped_steps = [s for s in self.step_timings.values() if s['status'] == 'skipped'] + + f.write(f"成功完成的步骤: {len(completed_steps)}\n") + f.write(f"失败的步骤: {len(failed_steps)}\n") + f.write(f"跳过的步骤: {len(skipped_steps)}\n") + + if completed_steps: + completed_times = [s['elapsed_seconds'] for s in completed_steps] + f.write(f"平均耗时: {self._format_time(np.mean(completed_times))}\n") + f.write(f"最长耗时: {self._format_time(np.max(completed_times))} ({[s['elapsed_formatted'] for s in completed_steps if s['elapsed_seconds'] == np.max(completed_times)][0]})\n") + f.write(f"最短耗时: {self._format_time(np.min(completed_times))} ({[s['elapsed_formatted'] for s in completed_steps if s['elapsed_seconds'] == np.min(completed_times)][0]})\n") + + print(f"\n流程报告已生成:") + print(f" CSV格式: {output_path}") + print(f" 文本格式: {txt_output_path}") + + return output_path + + def run_full_pipeline(self, config: Dict): + """ + 运行完整流程 + + Args: + config: 配置字典，包含所有步骤的参数 + """ + print("\n" + "="*80) + print("开始运行完整水质参数反演流程") + print("="*80) + + # 记录流程开始时间 + self.pipeline_start_time = time.time() + + try: + # 步骤1: 生成水域mask + if 'step1' in config: + self._notify("步骤1: 水域掩膜生成", "start") + self.step1_generate_water_mask(**config['step1']) + self._notify("步骤1: 水域掩膜生成", "completed", f"(输出: {self.water_mask_path})") + else: + self._notify("步骤1: 水域掩膜生成", "skipped", "未配置") + + # 步骤2: 找到耀斑区域 + # 若后续明确不进行去除耀斑（step3.enabled=False），则跳过步骤2 + step3_enabled = config.get('step3', {}).get('enabled', True) + if 'step2' in config and step3_enabled: + self._notify("步骤2: 耀斑区域检测", "start") + self.step2_find_glint_area(**config['step2']) + self._notify("步骤2: 耀斑区域检测", "completed", f"(输出: {self.glint_mask_path})") + else: + self._notify("步骤2: 耀斑区域检测", "skipped", "去耀斑已禁用或未配置") + + # 步骤3: 去除耀斑 + if 'step3' in config: + self._notify("步骤3: 耀斑去除", "start") + self.step3_remove_glint(**config['step3']) + self._notify("步骤3: 耀斑去除", "completed", f"(输出: {self.deglint_img_path})") + else: + self._notify("步骤3: 耀斑去除", "skipped", "未配置") + + # 步骤4: 处理CSV文件 + if 'step4' in config: + self._notify("步骤4: 数据预处理", "start") + self.step4_process_csv(**config['step4']) + self._notify("步骤4: 数据预处理", "completed", f"(输出: {self.processed_csv_path})") + else: + self._notify("步骤4: 数据预处理", "skipped", "未配置") + + # 步骤5: 提取训练样本点光谱 + if 'step5' in config: + self._notify("步骤5: 光谱提取", "start") + self.step5_extract_training_spectra(**config['step5']) + self._notify("步骤5: 光谱提取", "completed", f"(输出: {self.training_spectra_path})") + else: + self._notify("步骤5: 光谱提取", "skipped", "未配置") + + # 步骤5.5: 计算水质指数 + if 'step5_5' in config: + self._notify("步骤5.5: 水质指数计算", "start") + self.step5_5_calculate_water_quality_indices(**config['step5_5']) + self._notify("步骤5.5: 水质指数计算", "completed", f"(输出: {self.indices_path})") + else: + self._notify("步骤5.5: 水质指数计算", "skipped", "未配置") + + # 步骤6: 训练模型 + if 'step6' in config: + self._notify("步骤6: 模型训练", "start") + self.step6_train_models(**config['step6']) + self._notify("步骤6: 模型训练", "completed", f"(输出: {self.models_dir})") + else: + self._notify("步骤6: 模型训练", "skipped", "未配置") + + # 步骤6.5: 非经验统计回归模型训练 + if 'step6_5' in config: + self._notify("步骤6.5: 非经验模型训练", "start") + self.step6_5_non_empirical_modeling(**config['step6_5']) + self._notify("步骤6.5: 非经验模型训练", "completed", f"(输出: {self.models_dir})") + else: + self._notify("步骤6.5: 非经验模型训练", "skipped", "未配置") + + # 步骤6.75: 自定义回归分析 + if 'step6_75' in config: + self._notify("步骤6.75: 自定义回归", "start") + self.step6_75_custom_regression(**config['step6_75']) + self._notify("步骤6.75: 自定义回归", "completed", f"(输出: {self.custom_regression_path})") + else: + self._notify("步骤6.75: 自定义回归", "skipped", "未配置") + + # 步骤7: 生成预测采样点 + if 'step7' in config: + self._notify("步骤7: 采样点生成", "start") + sampling_csv_path = self.step7_generate_sampling_points(**config['step7']) + self._notify("步骤7: 采样点生成", "completed", f"(输出: {sampling_csv_path})") + else: + sampling_csv_path = None + self._notify("步骤7: 采样点生成", "skipped", "未配置") + + # 步骤8: 预测水质参数 + if 'step8' in config and sampling_csv_path: + self._notify("步骤8: 参数预测", "start") + step8_config = config['step8'].copy() + step8_config['sampling_csv_path'] = sampling_csv_path + prediction_files = self.step8_predict_water_quality(**step8_config) + self._notify("步骤8: 参数预测", "completed", f"(生成{len(prediction_files)}个预测文件)") + else: + prediction_files = {} + self._notify("步骤8: 参数预测", "skipped", "未配置或缺少采样点") + + # 步骤8.5: 使用非经验模型进行参数预测 + non_empirical_prediction_files = {} + if 'step8_5' in config and sampling_csv_path: + self._notify("步骤8.5: 非经验模型预测", "start") + step8_5_config = config['step8_5'].copy() + step8_5_config['sampling_csv_path'] = sampling_csv_path + non_empirical_prediction_files = self.step8_5_predict_with_non_empirical_models(**step8_5_config) + self._notify("步骤8.5: 非经验模型预测", "completed", f"(生成{len(non_empirical_prediction_files)}个预测文件)") + else: + self._notify("步骤8.5: 非经验模型预测", "skipped", "未配置或缺少采样点") + + # 步骤8.75: 使用自定义回归模型进行参数预测 + custom_regression_prediction_files = {} + if 'step8_75' in config and sampling_csv_path: + self._notify("步骤8.75: 自定义回归预测", "start") + step8_75_config = config['step8_75'].copy() + step8_75_config['sampling_csv_path'] = sampling_csv_path + custom_regression_prediction_files = self.step8_75_predict_with_custom_regression(**step8_75_config) + self._notify("步骤8.75: 自定义回归预测", "completed", f"(生成{len(custom_regression_prediction_files)}个预测文件)") + else: + self._notify("步骤8.75: 自定义回归预测", "skipped", "未配置或缺少采样点") + + # 合并机器学习预测、非经验模型预测和自定义回归预测结果 + all_prediction_files = {**prediction_files, **non_empirical_prediction_files, **custom_regression_prediction_files} + + # 步骤9: 生成分布图 + distribution_maps = {} + if 'step9' in config and all_prediction_files: + self._notify("步骤9: 分布图生成", "start") + for target_name, pred_file in all_prediction_files.items(): + step9_config = config['step9'].copy() + for _k in ('step9_batch_mode', 'prediction_csv_dir', 'recursive_csv_scan'): + step9_config.pop(_k, None) + step9_config['prediction_csv_path'] = pred_file + if 'output_image_path' not in step9_config: + step9_config['output_image_path'] = None + dist_map_path = self.step9_generate_distribution_map(**step9_config) + distribution_maps[target_name] = dist_map_path + self._notify("步骤9: 分布图生成", "completed", f"(生成{len(distribution_maps)}个分布图)") + else: + self._notify("步骤9: 分布图生成", "skipped", "未配置或缺少预测结果") + + # 生成可视化图表 + output_files = {} + pipeline_info = { + 'work_dir': str(self.work_dir), + 'models_dir': str(self.models_dir), + 'prediction_files': all_prediction_files, + 'output_files': {} + } + + # 生成散点图 + if 'visualization' in config and config['visualization'].get('generate_scatter', True): + if self.training_spectra_path and self.models_dir.exists(): + try: + self._notify("可视化", "info", "生成模型评估散点图...") + scatter_config = config['visualization'].get('scatter_config', {}) + scatter_paths = self.generate_model_scatter_plots( + metric=scatter_config.get('metric', 'test_r2'), + use_enhanced=scatter_config.get('use_enhanced', True), + feature_start_column=scatter_config.get('feature_start_column', + config.get('step6', {}).get('feature_start_column', 13)), + test_size=scatter_config.get('test_size', 0.2), + random_state=scatter_config.get('random_state', 42) + ) + output_files['scatter_plots'] = scatter_paths + pipeline_info['output_files']['scatter_plots'] = scatter_paths + self._notify("可视化", "info", f"已生成 {len(scatter_paths)} 个散点图") + except Exception as e: + self._notify("可视化", "warning", f"生成散点图时出错: {e}") + + # 生成箱型图 + if 'visualization' in config and config['visualization'].get('generate_boxplots', True): + if self.processed_csv_path: + try: + self._notify("可视化", "info", "生成水质参数箱型图...") + boxplot_config = config['visualization'].get('boxplot_config', {}) + boxplot_paths = self.generate_boxplots( + parameter_columns=boxplot_config.get('parameter_columns', None), + data_start_column=boxplot_config.get('data_start_column', 4), + save_individual=boxplot_config.get('save_individual', True), + use_seaborn=boxplot_config.get('use_seaborn', True) + ) + output_files['boxplots'] = boxplot_paths + pipeline_info['output_files']['boxplots'] = boxplot_paths + self._notify("可视化", "info", f"已生成 {len(boxplot_paths)} 个箱型图") + except Exception as e: + self._notify("可视化", "warning", f"生成箱型图时出错: {e}") + + # 生成光谱曲线图 + if 'visualization' in config and config['visualization'].get('generate_spectrum', True): + if self.training_spectra_path: + try: + self._notify("可视化", "info", "生成光谱曲线对比图...") + spectrum_paths = self.generate_spectrum_comparison_plots( + wavelength_start_column=config.get('step6', {}).get('feature_start_column', 'UTM_Y') + ) + output_files['spectrum_plots'] = spectrum_paths + pipeline_info['output_files']['spectrum_plots'] = spectrum_paths + self._notify("可视化", "info", f"已生成 {len(spectrum_paths)} 个光谱曲线图") + except Exception as e: + self._notify("可视化", "warning", f"生成光谱曲线图时出错: {e}") + + # 生成统计图表 + if 'visualization' in config and config['visualization'].get('generate_statistics', True): + if self.processed_csv_path: + try: + self._notify("可视化", "info", "生成统计图表...") + stat_charts = self.generate_statistical_charts() + output_files['statistical_charts'] = stat_charts + pipeline_info['output_files']['statistical_charts'] = stat_charts + self._notify("可视化", "info", "已生成统计图表") + except Exception as e: + self._notify("可视化", "warning", f"生成统计图表时出错: {e}") + + # 生成耀斑分析影像预览图 + if 'visualization' in config and config['visualization'].get('generate_glint_previews', True): + try: + self._notify("可视化", "info", "生成耀斑分析影像预览图...") + glint_preview_config = config['visualization'].get('glint_preview_config', {}) + preview_paths = self.generate_glint_deglint_previews( + work_dir=glint_preview_config.get('work_dir'), + output_subdir=glint_preview_config.get('output_subdir', 'glint_deglint_previews'), + generate_glint=glint_preview_config.get('generate_glint', True), + generate_deglint=glint_preview_config.get('generate_deglint', True) + ) + output_files['glint_deglint_previews'] = preview_paths + pipeline_info['output_files']['glint_deglint_previews'] = preview_paths + self._notify("可视化", "info", f"已生成 {len(preview_paths)} 个耀斑分析预览图") + except Exception as e: + self._notify("可视化", "warning", f"生成耀斑分析预览图时出错: {e}") + + # 生成批量处理摘要 + try: + step1_output = str(self.water_mask_path) if self.water_mask_path else 'N/A' + pipeline_info['step1'] = {'status': 'completed', 'output_file': step1_output} + pipeline_info['step2'] = {'status': 'completed', 'output_file': str(self.glint_mask_path) if self.glint_mask_path else 'N/A'} + pipeline_info['step3'] = {'status': 'completed', 'output_file': str(self.deglint_img_path) if self.deglint_img_path else 'N/A'} + pipeline_info['step4'] = {'status': 'completed', 'output_file': str(self.processed_csv_path) if self.processed_csv_path else 'N/A'} + pipeline_info['step5'] = {'status': 'completed', 'output_file': str(self.training_spectra_path) if self.training_spectra_path else 'N/A'} + pipeline_info['step5_5'] = {'status': 'completed', 'output_file': str(self.indices_path) if self.indices_path else 'N/A'} + pipeline_info['step6'] = {'status': 'completed', 'output_file': str(self.models_dir)} + pipeline_info['step6_75'] = {'status': 'completed', 'output_file': str(self.custom_regression_path) if self.custom_regression_path else 'N/A'} + pipeline_info['training_params'] = config.get('step6', {}) + + summary_path = self.report_generator.generate_batch_inference_summary(pipeline_info) + print(f"批量处理摘要已生成: {summary_path}") + output_files['batch_summary'] = summary_path + except Exception as e: + print(f"生成批量处理摘要时出错: {e}") + + # 记录流程结束时间 + self.pipeline_end_time = time.time() + + # 生成流程执行报告 + try: + report_path = self.generate_pipeline_report() + output_files['pipeline_report'] = report_path + except Exception as e: + print(f"生成流程报告时出错: {e}") + + print("\n" + "="*80) + print("完整流程执行完成！") + print("="*80) + + # 显示总耗时 + if self.pipeline_start_time and self.pipeline_end_time: + total_time = self.pipeline_end_time - self.pipeline_start_time + print(f"总耗时: {self._format_time(total_time)}") + + print(f"\n所有输出文件:") + for key, value in output_files.items(): + if isinstance(value, dict): + print(f" {key}: {len(value)} 个文件") + else: + print(f" {key}: {value}") + + except Exception as e: + # 即使失败也记录结束时间 + self.pipeline_end_time = time.time() + + # 尝试生成报告（即使失败） + try: + report_path = self.generate_pipeline_report() + print(f"\n流程报告已生成: {report_path}") + except: + pass + + print(f"\n流程执行失败: {e}") + import traceback + traceback.print_exc() + raise + + def step6_5_non_empirical_modeling(self, csv_path: Optional[str] = None, + preprocessing_methods: List[str] = None, + algorithms: List[str] = None, + value_cols: Union[int, Dict[str, int]] = 0, + spectral_start_col: int = 1, + spectral_end_col: Optional[int] = None, + window: int = 5, + output_dir: Optional[str] = None, + enabled: bool = True, + skip_dependency_check: bool = False) -> Dict[str, str]: + """ + 步骤6.5: 非经验统计回归模型训练 + + Args: + csv_path: 训练数据CSV路径（如果为None，使用步骤5的结果） + preprocessing_methods: 预处理方法列表 + algorithms: 算法名称列表（chl_a, nh3, mno4, tn, tp, tss） + value_cols: 实测值列索引，可以是单个整数（所有算法使用同一列）或字典（键为算法名，值为列索引） + spectral_start_col: 光谱数据起始列索引 + spectral_end_col: 光谱数据结束列索引（如果为None，自动检测） + window: 窗口大小 + output_dir: 输出目录路径（如果为None，使用默认目录） + + Returns: + 模型文件路径字典（键为算法名） + """ + print("\n" + "="*80) + print("步骤6.5: 非经验统计回归模型训练") + print("="*80) + + step_start_time = time.time() + try: + # 如果未启用，直接跳过处理 + if not enabled: + print("已设置跳过非经验模型训练（enabled=False）。") + step_end_time = time.time() + self._record_step_time("步骤6.5: 非经验模型训练", step_start_time, step_end_time, status="skipped") + return {} + + # 处理训练数据路径 + if csv_path is not None: + final_csv_path = csv_path + elif self.training_spectra_path is not None: + final_csv_path = self.training_spectra_path + else: + if skip_dependency_check: + raise ValueError("必须提供csv_path参数才能独立运行步骤6.5") + else: + raise ValueError("请先执行步骤5: 提取训练样本点光谱，或提供csv_path参数") + + # 创建非经验模型目录 - 参照其他步骤的处理方式 + if output_dir is not None: + non_empirical_dir = Path(output_dir) + else: + # 如果output_dir为空，使用工作目录 + if hasattr(self, 'work_dir') and self.work_dir is not None: + non_empirical_dir = Path(self.work_dir) / "6_5_non_empirical_models" + else: + # 如果没有工作目录，使用当前目录 + non_empirical_dir = Path.cwd() / "6_5_non_empirical_models" + non_empirical_dir.mkdir(parents=True, exist_ok=True) + + # 设置默认参数 + if preprocessing_methods is None: + preprocessing_methods = ['None'] + if algorithms is None: + algorithms = ['chl_a', 'nh3', 'mno4', 'tn', 'tp', 'tss'] + + # 处理value_cols参数 + if isinstance(value_cols, int): + # 如果是单个整数，为所有算法使用相同的列索引 + value_cols_dict = {algorithm: value_cols for algorithm in algorithms} + elif isinstance(value_cols, dict): + # 如果是字典，检查是否包含所有算法 + value_cols_dict = value_cols + for algorithm in algorithms: + if algorithm not in value_cols_dict: + raise ValueError(f"算法 {algorithm} 在value_cols字典中未找到对应的列索引") + else: + raise ValueError("value_cols参数必须是整数或字典") + + # 读取CSV数据以确定光谱结束列 + if spectral_end_col is None: + df = pd.read_csv(final_csv_path) + spectral_end_col = len(df.columns) - 1 + + # 存储所有模型结果 + all_model_results = {} + + # 对每种预处理方法和算法组合进行训练 + for preprocess in preprocessing_methods: + preprocess_dir = non_empirical_dir / preprocess + preprocess_dir.mkdir(parents=True, exist_ok=True) + + # 应用预处理（需要实现预处理函数） + processed_csv_path = self._apply_preprocessing(final_csv_path, preprocess, preprocess_dir, spectral_start_col) + + for algorithm in algorithms: + # 获取该算法对应的实测值列索引 + algorithm_value_col = value_cols_dict[algorithm] + + print(f"\n训练 {preprocess} + {algorithm} 模型 (实测值列: {algorithm_value_col})...") + + # 生成模型输出路径 + model_filename = f"{preprocess}_{algorithm}.json" + model_outpath = str(preprocess_dir / model_filename) + + # 检查模型是否已存在 + if Path(model_outpath).exists(): + print(f"检测到已存在的模型文件，直接使用: {model_outpath}") + all_model_results[f"{preprocess}_{algorithm}"] = model_outpath + continue + + try: + # 调用非经验模型修正函数 + from src.core.non_empirical_model_correction import run_model_correction + coefficients = run_model_correction( + algorithm=algorithm, + csv_file=processed_csv_path if Path(processed_csv_path).exists() else final_csv_path, + value_col=algorithm_value_col, + spectral_start=spectral_start_col, + spectral_end=spectral_end_col, + model_info_outpath=model_outpath, + window=window + ) + + all_model_results[f"{preprocess}_{algorithm}"] = model_outpath + print(f"模型训练完成: {model_outpath}") + + except Exception as e: + print(f"训练 {preprocess}_{algorithm} 模型时出错: {e}") + continue + + # 生成汇总CSV文件 + summary_path = self._generate_non_empirical_summary(all_model_results, non_empirical_dir) + + step_end_time = time.time() + self._record_step_time("步骤6.5: 非经验模型训练", step_start_time, step_end_time) + print(f"非经验模型训练完成，结果保存在: {non_empirical_dir}") + print(f"汇总文件: {summary_path}") + + return all_model_results + + except Exception as e: + step_end_time = time.time() + self._record_step_time("步骤6.5: 非经验模型训练", step_start_time, step_end_time, + status="failed", error=str(e)) + raise + + def step6_75_custom_regression(self, + csv_path: Optional[str] = None, + x_columns: Optional[Union[str, List[str]]] = None, + y_columns: Optional[Union[str, List[str]]] = None, + methods: Union[str, List[str]] = 'all', + output_dir: Optional[str] = None, + enabled: bool = True, + skip_dependency_check: bool = False) -> str: + """ + 步骤6.75: 使用自定义回归方法分析指标与目标参数之间的关系 + """ + print("\n" + "="*80) + print("步骤6.75: 自定义回归分析") + print("="*80) + + step_start_time = time.time() + try: + # 如果未启用，直接跳过处理 + if not enabled: + print("已设置跳过自定义回归分析（enabled=False）。") + step_end_time = time.time() + self._record_step_time("步骤6.75: 自定义回归", step_start_time, step_end_time, status="skipped") + return None + + # 处理输入数据路径 + if csv_path is not None: + input_csv = csv_path + elif self.indices_path is not None: + input_csv = self.indices_path + else: + if skip_dependency_check: + raise ValueError("必须提供csv_path参数才能独立运行步骤6.75") + else: + raise ValueError("请先执行步骤5.5: 计算水质指数，或提供csv_path参数") + + if y_columns is None: + raise ValueError("必须指定 y_columns") + + if x_columns is None: + raise ValueError("必须指定 x_columns") + + if isinstance(x_columns, str): + x_columns = [x_columns] + + if isinstance(y_columns, str): + y_columns = [y_columns] + + df = pd.read_csv(input_csv) + + missing_x = [col for col in x_columns if col not in df.columns] + if missing_x: + raise ValueError(f"自变量列不存在: {missing_x}") + + missing_y = [col for col in y_columns if col not in df.columns] + if missing_y: + raise ValueError(f"因变量列不存在: {missing_y}") + + # 创建自定义回归输出目录 + if output_dir is None: + custom_regression_dir = self.custom_regression_dir + else: + custom_regression_dir = self.work_dir / output_dir + custom_regression_dir.mkdir(exist_ok=True) + + analyzer = SingleVariableRegressionAnalysis() + + analyzer.batch_single_variable_regression( + data=df, + x_columns=x_columns, + y_columns=y_columns, + methods=methods, + output_dir=str(custom_regression_dir) + ) + + self.custom_regression_path = str(custom_regression_dir) + + step_end_time = time.time() + self._record_step_time("步骤6.75: 自定义回归", step_start_time, step_end_time) + print(f"自定义回归结果已保存到目录: {custom_regression_dir}") + return str(custom_regression_dir) + except Exception as e: + step_end_time = time.time() + self._record_step_time("步骤6.75: 自定义回归", step_start_time, step_end_time, + status="failed", error=str(e)) + raise + def _apply_preprocessing(self, csv_path: str, preprocess_method: str, output_dir: Path, spectral_start_col: int = 4) -> str: + """ + 应用预处理到CSV数据 + + Args: + csv_path: 原始CSV文件路径 + preprocess_method: 预处理方法名称 + output_dir: 输出目录 + spectral_start_col: 光谱数据起始列索引（0-based） + + Returns: + 预处理后的CSV文件路径 + """ + # 如果不需要预处理，直接返回原文件 + if preprocess_method == 'None': + return csv_path + + # 生成预处理后的文件路径 + output_filename = f"preprocessed_{preprocess_method}.csv" + output_path = str(output_dir / output_filename) + + # 检查是否已存在预处理文件 + if Path(output_path).exists(): + print(f"检测到已存在的预处理文件，直接使用: {output_path}") + return output_path + + # 读取原始数据 + df = pd.read_csv(csv_path) + + # 分离坐标列和参数列（前几列）与光谱数据列 + non_spectral_cols = df.iloc[:, :spectral_start_col] # 光谱数据前的列 + spectral_data = df.iloc[:, spectral_start_col:] # 光谱数据列 + + # 应用预处理 - 使用spectral_Preprocessing模块 + from src.preprocessing.spectral_Preprocessing import Preprocessing + + # 调用预处理函数 + processed_spectral = Preprocessing(preprocess_method, spectral_data) + + # 重新组合数据 + if isinstance(processed_spectral, pd.DataFrame): + processed_df = pd.concat([non_spectral_cols, processed_spectral], axis=1) + else: + # 如果是numpy数组，转换为DataFrame + processed_spectral_df = pd.DataFrame(processed_spectral, + columns=spectral_data.columns, + index=spectral_data.index) + processed_df = pd.concat([non_spectral_cols, processed_spectral_df], axis=1) + + # 保存预处理后的数据 + processed_df.to_csv(output_path, index=False) + print(f"预处理完成: {output_path}") + + return output_path + + def _generate_non_empirical_summary(self, model_results: Dict[str, str], output_dir: Path) -> str: + """ + 生成非经验模型训练结果汇总CSV + + Args: + model_results: 模型文件路径字典 + output_dir: 输出目录 + + Returns: + 汇总CSV文件路径 + """ + summary_path = str(output_dir / "non_empirical_models_summary.csv") + + summary_data = [] + + for model_key, model_path in model_results.items(): + try: + # 从文件名解析预处理方法和算法名 + parts = model_key.split('_') + preprocess_method = parts[0] + algorithm_name = '_'.join(parts[1:]) if len(parts) > 2 else parts[1] + + # 读取JSON模型文件 + with open(model_path, 'r', encoding='utf-8') as f: + model_info = json.load(f) + + # 提取模型信息 + summary_row = { + 'Preprocessing Method': preprocess_method, + 'Algorithm Name': algorithm_name, + 'Model Type': model_info.get('model_type', ''), + 'Coefficient Count': len(model_info.get('model_info', [])), + 'Average Accuracy(%)': np.mean(model_info.get('accuracy', [0])) if model_info.get('accuracy') else 0, + 'Min Accuracy(%)': np.min(model_info.get('accuracy', [0])) if model_info.get('accuracy') else 0, + 'Max Accuracy(%)': np.max(model_info.get('accuracy', [0])) if model_info.get('accuracy') else 0, + 'Sample Count': len(model_info.get('long', [])), + 'Model File': model_path + } + + # 添加系数信息（前几个系数） + coefficients = model_info.get('model_info', []) + for i, coeff in enumerate(coefficients[:5]): # 只显示前5个系数 + summary_row[f'系数_{i+1}'] = coeff + + summary_data.append(summary_row) + + except Exception as e: + print(f"读取模型文件 {model_path} 时出错: {e}") + continue + + if summary_data: + # 创建DataFrame并保存 + df_summary = pd.DataFrame(summary_data) + df_summary.to_csv(summary_path, index=False, encoding='utf-8-sig') + print(f"汇总文件已生成: {summary_path}") + else: + print("警告: 没有有效的模型数据可汇总") + summary_path = "" + + return summary_path + + def step8_5_predict_with_non_empirical_models(self, sampling_csv_path: str, + non_empirical_models_dir: Optional[str] = None, + output_path: Optional[str] = None, + metric: str = 'Average Accuracy(%)', + prediction_column: str = 'prediction', + enabled: bool = True, + skip_dependency_check: bool = False) -> Dict[str, str]: + """ + 步骤8.5: 使用非经验统计回归模型进行参数预测 + + 根据非经验模型训练结果汇总CSV筛选给定方法的准确率最高的模型，使用该模型进行预测 + + Args: + sampling_csv_path: 采样点光谱数据CSV路径 + non_empirical_models_dir: 非经验模型保存目录（如果为None，使用步骤6.5的结果） + output_path: 输出目录路径（如果为None，使用默认目录） + metric: 选择最佳模型的指标（默认使用平均准确率） + prediction_column: 预测结果列名 + + Returns: + 预测结果文件路径字典（键为算法名） + """ + print("\n" + "="*80) + print("步骤8.5: 使用非经验模型进行参数预测") + print("="*80) + + step_start_time = time.time() + try: + # 如果未启用，直接跳过处理 + if not enabled: + print("已设置跳过非经验模型预测（enabled=False）。") + step_end_time = time.time() + self._record_step_time("步骤8.5: 非经验模型预测", step_start_time, step_end_time, status="skipped") + return {} + + # 确定非经验模型目录 + if non_empirical_models_dir is not None: + final_models_dir = non_empirical_models_dir + else: + default_models_dir = str(self.work_dir / "6_5_non_empirical_models") + if Path(default_models_dir).exists(): + final_models_dir = default_models_dir + else: + if skip_dependency_check: + raise ValueError("必须提供non_empirical_models_dir参数才能独立运行步骤8.5") + else: + raise ValueError("请先执行步骤6.5: 非经验模型训练，或提供non_empirical_models_dir参数") + + # 检查预测目录中是否已有预测结果文件 + prediction_files = {} + if output_path is not None: + non_empirical_prediction_dir = Path(output_path) + else: + # 使用和步骤8相同的prediction_dir目录，但文件名添加non_empirical_前缀 + non_empirical_prediction_dir = self.prediction_dir + non_empirical_prediction_dir.mkdir(parents=True, exist_ok=True) + + # 查找汇总CSV文件 + summary_path = Path(final_models_dir) / "non_empirical_models_summary.csv" + if not summary_path.exists(): + raise ValueError(f"未找到非经验模型汇总文件: {summary_path}") + + # 读取汇总文件 + df_summary = pd.read_csv(summary_path) + + # 按算法分组，选择每个算法中准确率最高的模型 + best_models = {} + for algorithm in df_summary['Algorithm Name'].unique(): + algorithm_df = df_summary[df_summary['Algorithm Name'] == algorithm] + + # 按指定指标排序，选择最佳模型 + if metric in algorithm_df.columns: + best_model_row = algorithm_df.nlargest(1, metric) + else: + # 如果指标不存在，使用第一个模型 + best_model_row = algorithm_df.iloc[[0]] + + best_model_path = best_model_row['Model File'].values[0] + best_preprocess = best_model_row['Preprocessing Method'].values[0] + best_accuracy = best_model_row[metric].values[0] if metric in best_model_row.columns else 'N/A' + + best_models[algorithm] = { + 'model_path': best_model_path, + 'preprocess_method': best_preprocess, + 'accuracy': best_accuracy + } + print(f"算法 {algorithm}: 选择 {best_preprocess} 预处理方法 (准确率: {best_accuracy})") + + # 读取采样点光谱数据 + sampling_df = pd.read_csv(sampling_csv_path) + + # 为每个算法进行预测 + for algorithm, model_info in best_models.items(): + print(f"\n使用 {algorithm} 算法进行预测...") + + # 生成输出文件路径 + output_filename = f"non_empirical_{algorithm}_{prediction_column}.csv" + output_path = str(non_empirical_prediction_dir / output_filename) + + # 检查文件是否已存在 + if Path(output_path).exists(): + print(f"检测到已存在的预测结果文件，直接使用: {output_path}") + prediction_files[algorithm] = output_path + continue + + try: + # 调用非经验预测函数 + from src.core.non_empirical_retrieval import non_empirical_retrieval + + # 直接使用采样CSV文件作为输入 + # 执行预测 + result_content = non_empirical_retrieval( + algorithm=algorithm, + model_info_path=model_info['model_path'], + coor_spectral_path=sampling_csv_path, + output_path=output_path, + wave_radius=5 # 默认窗口大小 + ) + + prediction_files[algorithm] = output_path + print(f"预测完成: {output_path}") + + except Exception as e: + print(f"使用 {algorithm} 算法预测时出错: {e}") + continue + + step_end_time = time.time() + self._record_step_time("步骤8.5: 非经验模型预测", step_start_time, step_end_time) + print(f"非经验模型预测完成，结果保存在: {non_empirical_prediction_dir}") + + return prediction_files + + except Exception as e: + step_end_time = time.time() + self._record_step_time("步骤8.5: 非经验模型预测", step_start_time, step_end_time, + status="failed", error=str(e)) + raise + + def step8_75_predict_with_custom_regression(self, sampling_csv_path: str, + formula_csv_file: str, + custom_regression_dir: Optional[str] = None, + formula_names: Optional[List[str]] = None, + prediction_column: str = 'prediction', + enabled: bool = True, + skip_dependency_check: bool = False) -> Dict[str, str]: + """ + 步骤8.75: 使用自定义回归模型进行参数预测 + + 使用步骤6.75的自定义回归结果中的all_regression_results.csv文件， + 选择每个y_variable中r_squared最高的equation， + 使用采样点光谱数据计算水质指数，然后进行预测 + + Args: + sampling_csv_path: 采样点光谱数据CSV路径（来自步骤7） + formula_csv_file: 公式CSV文件路径，包含水质指数计算公式 + custom_regression_dir: 自定义回归结果目录（如果为None，使用步骤6.75的结果） + formula_names: 要计算的公式名称列表，如果为None则计算所有公式 + prediction_column: 预测结果列名 + + Returns: + 预测结果文件路径字典（键为y_variable名） + """ + print("\n" + "="*80) + print("步骤8.75: 使用自定义回归模型进行参数预测") + print("="*80) + + step_start_time = time.time() + try: + # 如果未启用，直接跳过处理 + if not enabled: + print("已设置跳过自定义回归模型预测（enabled=False）。") + step_end_time = time.time() + self._record_step_time("步骤8.75: 自定义回归模型预测", step_start_time, step_end_time, status="skipped") + return {} + + # 检查公式CSV文件是否存在 + if not Path(formula_csv_file).exists(): + raise FileNotFoundError(f"公式CSV文件不存在: {formula_csv_file}") + + # 确定自定义回归结果目录 + if custom_regression_dir is not None: + final_regression_dir = custom_regression_dir + else: + default_regression_dir = str(self.custom_regression_dir) + if Path(default_regression_dir).exists(): + final_regression_dir = default_regression_dir + else: + if skip_dependency_check: + raise ValueError("必须提供custom_regression_dir参数才能独立运行步骤8.75") + else: + raise ValueError("请先执行步骤6.75: 自定义回归分析，或提供custom_regression_dir参数") + + # 读取all_regression_results.csv文件 + regression_results_path = Path(final_regression_dir) / "all_regression_results.csv" + if not regression_results_path.exists(): + raise FileNotFoundError(f"未找到自定义回归结果文件: {regression_results_path}") + + # 读取回归结果 + regression_df = pd.read_csv(regression_results_path) + + # 使用band_math.py计算水质指数 + print("正在使用采样点光谱数据计算水质指数...") + from src.utils.band_math import BandMathCalculator + + # 创建计算器实例 + calculator = BandMathCalculator(sampling_csv_path) + + # 计算所有公式 + indices_df = calculator.process_formulas_from_csv( + formula_csv_file, + formula_names=formula_names, + output_file=str(self.prediction_dir / "water_quality_indices.csv") + ) + + if indices_df is None: + raise ValueError("水质指数计算失败") + + # 读取采样点数据（包含坐标信息） + sampling_df = pd.read_csv(sampling_csv_path) + + # 获取所有唯一的y_variable + y_variables = regression_df['y_variable'].unique() + + prediction_files = {} + + for y_var in y_variables: + try: + # 筛选当前y_variable的所有回归结果 + y_var_results = regression_df[regression_df['y_variable'] == y_var] + + # 找到r_squared最高的记录 + best_result = y_var_results.loc[y_var_results['r_squared'].idxmax()] + + # 解析equation + equation = best_result['equation'] + x_variable = best_result['x_variable'] + + print(f"为 {y_var} 选择最佳方程: {equation} (R² = {best_result['r_squared']:.4f})") + + # 检查x_variable是否在水质指数数据中存在 + if x_variable not in indices_df.columns: + print(f"警告: x_variable '{x_variable}' 不在水质指数数据中，跳过 {y_var}") + continue + + # 合并采样点坐标和水质指数数据 + # 假设采样点数据和水质指数数据有相同的行数和顺序 + if len(sampling_df) != len(indices_df): + print(f"警告: 采样点数据({len(sampling_df)}行)和水质指数数据({len(indices_df)}行)行数不一致") + # 只取前min(len(sampling_df), len(indices_df))行 + min_rows = min(len(sampling_df), len(indices_df)) + merged_df = pd.concat([ + sampling_df.iloc[:min_rows].reset_index(drop=True), + indices_df.iloc[:min_rows].reset_index(drop=True) + ], axis=1) + else: + merged_df = pd.concat([sampling_df, indices_df], axis=1) + + # 应用回归方程进行预测 + # 使用eval函数安全地计算表达式 + try: + # 创建局部命名空间，包含需要的变量和数学函数 + local_vars = {x_variable: merged_df[x_variable].values} + # 添加数学函数到命名空间 + import math + local_vars.update({ + 'exp': math.exp, + 'log': math.log, + 'log10': math.log10, + 'sqrt': math.sqrt, + 'sin': math.sin, + 'cos': math.cos, + 'tan': math.tan, + 'pi': math.pi, + 'e': math.e + }) + + # 替换方程中的变量名为实际值 + # 这里需要确保方程格式正确，例如: "a*x + b" + prediction_values = eval(equation, {"__builtins__": {}}, local_vars) + + # 创建预测结果DataFrame + result_df = merged_df[['UTM_X', 'UTM_Y']].copy() + result_df[prediction_column] = prediction_values + + # 保存预测结果 + output_filename = f"custom_regression_{y_var}.csv" + output_path = str(self.prediction_dir / output_filename) + result_df.to_csv(output_path, index=False) + + prediction_files[y_var] = output_path + print(f"成功为 {y_var} 生成预测结果: {output_path}") + + except Exception as e: + print(f"应用方程 {equation} 进行预测时出错: {e}") + continue + + except Exception as e: + print(f"处理 y_variable {y_var} 时出错: {e}") + continue + + step_end_time = time.time() + self._record_step_time("步骤8.75: 自定义回归模型预测", step_start_time, step_end_time) + + return prediction_files + + except Exception as e: + step_end_time = time.time() + self._record_step_time("步骤8.75: 自定义回归模型预测", step_start_time, step_end_time, + status="failed", error=str(e)) + raise + + +def main(): + """主函数示例""" + parser = argparse.ArgumentParser(description="水质参数反演框架主程序") + parser.add_argument('--config', type=str, help='配置文件路径（JSON格式）') + parser.add_argument('--work_dir', type=str, default='./work_dir', help='工作目录') + parser.add_argument('--mode', type=str, choices=['full', 'step'], + default='full', help='运行模式：full(完整流程)或step(单步执行)') + + args = parser.parse_args() + + # 创建管道实例 + pipeline = WaterQualityInversionPipeline(work_dir=args.work_dir) + + # 示例配置（用户可以根据实际情况修改） + example_config = { + 'step1': { + 'mask_path': r"D:\BaiduNetdiskDownload\yaobao\roi\roi.shp", # 支持shp或dat格式，如果是shp需要提供img_path + 'img_path': r"D:\BaiduNetdiskDownload\yaobao\result3.bsq", # 当mask_path为shp格式时必须提供，用于栅格化 + }, + 'step2': { + 'img_path': r"D:\BaiduNetdiskDownload\yaobao\result3.bsq", + 'glint_wave': 550.0, + 'method': 'otsu', + 'max_area' :50, # 过滤掉面积超过10000像素的连通域 + 'buffer_size' : 10 # 可选: 'otsu', 'zscore', 'percentile', 'iqr', 'adaptive', 'multi_band' + # 'z_threshold': 2.5, # zscore方法使用 + # 'percentile': 95.0, # percentile和adaptive方法使用 + # 'iqr_multiplier': 1.5, # iqr方法使用 + # 'window_size': 15, # adaptive方法使用 + # 'multi_band_waves': [750, 800, 850], # multi_band方法使用 + # 'sub_method': 'zscore', # multi_band方法的子方法 + # 'weights': None, # multi_band方法的权重 + # 'max_area': 10000, # 最大连通域面积阈值（像素数），用于去除岸边、浅水、水华等大面积区域（None表示不过滤） + # 'buffer_size': 50, # 岸边缓冲区大小（像素数），用于去除岸边附近的错误耀斑掩膜（None表示不启用） + }, + 'step3': { + 'img_path': r"D:\BaiduNetdiskDownload\yaobao\result3.bsq", + 'method': 'goodman', # 可选: 'subtract_nir', 'regression_slope', 'oxygen_absorption', + # 'kutser', 'goodman', 'hedley', 'sugar' + 'enabled': True, # 是否执行去除耀斑；False 时跳过并直接使用原始影像 + # 0值像素插值参数（可选）: + 'interpolate_zeros': False, # 是否对0值像素进行插值（默认False） + 'interpolation_method': 'bilinear', # 插值方法: 'nearest'(邻近), 'bilinear'(双线性), + # 'spline'(样条), 'kriging'(克里金) + # 水域掩膜参数（可选）: + 'water_mask':r"D:\BaiduNetdiskDownload\yaobao\roi\roi.shp", # None表示自动使用步骤1生成的掩膜，也可以提供： + # # - numpy数组 + # # - 栅格文件路径(.dat/.tif) + # # - shapefile路径(.shp) + # 'start_wave': 780.0, # subtract_nir和regression_slope方法需要 + # 'end_wave': 850.0, # subtract_nir和regression_slope方法需要 + # 'json_path': 'path/to/roi.json' # regression_slope方法需要 + # Kutser方法参数示例: + # 'kutser_shp_path': 'path/to/deep_water.shp', # 已废弃，请使用water_mask参数 + # 'oxy_band': 38, # 氧吸收波段索引 + # 'lower_oxy': 36, # 氧吸收下波段索引 + # 'upper_oxy': 49, # 氧吸收上波段索引 + # 'nir_band': 47, # NIR波段索引 + # Goodman方法参数示例: + 'nir_lower': 65, # NIR下波段索引 + 'nir_upper': 91, # NIR上波段索引 + 'goodman_A': 0.000019, # Goodman参数A + 'goodman_B': 0.1, # Goodman参数B + # Hedley方法参数示例: + # 'hedley_shp_path': 'path/to/deep_water.shp', # 已废弃，请使用water_mask参数 + # 'hedley_nir_band': 47, # NIR波段索引 + # SUGAR方法参数示例: + # 'sugar_bounds': [(1, 2)], # 优化边界 + # 'sugar_sigma': 1.0, # LoG平滑sigma + # 'sugar_estimate_background': True, # 是否估计背景光谱 + # 'sugar_glint_mask_method': 'cdf', # 'cdf'或'otsu' + # 'sugar_iter': 1, # 迭代次数，None表示自动终止 + # 'sugar_termination_thresh': 20.0 # 终止阈值 + }, + 'step4': { + 'csv_path': r"D:\BaiduNetdiskDownload\yaobao\csv\input.csv"#水质参数原始文件 + }, + 'step5': { + 'radius': 5, + 'source_epsg': 4326, + # 单步运行时建议显式指定；完整流程中可省略，将使用步骤2输出的耀斑掩膜 + # 'glint_mask_path': r"path/to/severe_glint_area.dat", + }, + 'step5_5': { + 'formula_csv_file': 'path/to/water_quality_formulas.csv', # 公式CSV文件路径 + 'formula_names': ['Al10SABI', 'TurbBe16RedOverViolet'], # 要计算的公式名称列表 + 'output_filename': 'water_quality_indices.csv', + 'enabled': True # 是否启用水质指数计算 + }, + 'step6': { + 'feature_start_column': '374.285004', + 'preprocessing_methods': ['None', 'MMS', 'SS', 'SNV', 'MA', 'SG', 'MSC', 'D1', 'D2', 'DT', 'CT'], + 'model_names': ['SVR', 'RF', 'Ridge', 'Lasso'], + 'split_methods': ['spxy', 'ks', 'random'], + 'cv_folds': 3 + }, + 'step6_5': { + 'preprocessing_methods': ['None', 'MMS', 'SS', 'SNV', 'MA', 'SG', 'MSC', 'D1', 'D2', 'DT', 'CT'], + 'algorithms': ['chl_a', 'nh3', 'mno4', 'tn', 'tp', 'tss'], + 'value_cols': 0, # 可以是单个整数或字典，如 {'chl_a': 0, 'nh3': 1, 'mno4': 2, 'tn': 3, 'tp': 4, 'tss': 5} + 'spectral_start_col': 1, + 'window': 5, + 'enabled': True # 是否启用非经验模型训练 + }, + 'step6_75': { + 'x_columns': ['NDWI', 'NDVI'], # 自变量列名列表 + 'y_columns': ['chl_a', 'tn', 'tp'], # 因变量列名列表 + 'methods': 'all', # 回归方法 + 'output_dir': 'custom_regression_results', # 输出目录 + 'enabled': True # 是否启用自定义回归分析 + }, + 'step7': { + 'interval': 50, + 'sample_radius': 5, + 'chunk_size': 1000, + 'water_mask_path': None, # 若为None，将自动使用步骤1生成的dat水体掩膜 + # 可选：耀斑掩膜文件（dat），若不提供将使用步骤2结果；需要外部指定时取消注释 + # 'glint_mask_path': r"D:\path\to\severe_glint_area.dat", + }, + 'step8': { + 'metric': 'test_r2', + 'prediction_column': 'prediction' + }, + 'step8_5': { + 'metric': 'Average Accuracy(%)', # 选择最佳模型的指标 + 'prediction_column': 'prediction', + 'enabled': True # 是否启用非经验模型预测 + }, + 'step9': { + 'boundary_shp_path': r"D:\BaiduNetdiskDownload\yaobao\roi\roi.shp" , + 'resolution': 30, + 'input_crs': 'EPSG:32651', + 'output_crs': 'EPSG:4326', + # 可选参数，若不需要可删除 + 'show_sample_points': False, + 'base_map_tif': None, + 'use_distance_diffusion': True, + 'max_diffusion_distance': None, + 'diffusion_power': 2, + 'diffusion_n_neighbors': 15, + 'cmap': None, + 'expand_ratio': 0.05 + }, + 'visualization': { + 'generate_scatter': True, # 是否生成散点图 + 'generate_boxplots': True, # 是否生成箱型图 + 'generate_spectrum': True, # 是否生成光谱曲线图 + 'generate_statistics': True, # 是否生成统计图表 + 'generate_glint_previews': True, # 是否生成2_glint和3_deglint的PNG预览图 + 'scatter_config': { + 'metric': 'test_r2', # 选择最佳模型的指标 + 'use_enhanced': True, # 是否使用增强版散点图（带置信区间） + 'feature_start_column': 13, # 特征开始列索引 + 'test_size': 0.2, # 测试集比例 + 'random_state': 42 # 随机种子 + }, + 'boxplot_config': { + 'parameter_columns': None, # 参数列名列表（None表示自动检测） + 'data_start_column': 4, # 数据开始列索引（从第5列开始，索引为4） + 'save_individual': True, # 是否为每个参数单独保存箱型图 + 'use_seaborn': True # 是否使用seaborn绘制（更美观） + }, + 'glint_preview_config': { + 'work_dir': None, # 工作目录（None表示使用pipeline的工作目录） + 'output_subdir': 'glint_deglint_previews', # 输出子目录 + 'generate_glint': True, # 是否处理2_glint文件夹 + 'generate_deglint': True # 是否处理3_deglint文件夹 + } + } + } + + if args.config: + # 从配置文件加载（需要实现JSON加载逻辑） + import json + with open(args.config, 'r', encoding='utf-8') as f: + config = json.load(f) + else: + # 使用示例配置（用户需要修改） + config = example_config + print("警告: 使用示例配置，请根据实际情况修改配置参数") + + if args.mode == 'full': + pipeline.run_full_pipeline(config) + else: + print("单步执行模式，请直接调用对应的step方法") + print("例如: pipeline.step1_generate_water_mask(...)") + + +def example_independent_steps(): + """ + 示例：展示如何独立运行各个步骤 + + 这个函数展示了如何在不依赖完整流水线的情况下，单独运行各个步骤。 + 每个步骤都可以通过提供必要的参数独立执行。 + """ + print("水质参数反演流水线 - 独立步骤运行示例") + print("="*80) + + # 创建流水线实例 + pipeline = WaterQualityInversionPipeline(work_dir="./example_work_dir") + + try: + # 示例1: 独立运行步骤1 - 生成水域掩膜 + print("\n示例1: 独立运行步骤1 - 生成水域掩膜") + try: + water_mask_path = pipeline.step1_generate_water_mask( + mask_path="path/to/water_mask.shp", # 或者 .dat/.tif 文件 + img_path="path/to/image.bsq" + ) + print(f"水域掩膜已生成: {water_mask_path}") + except Exception as e: + print(f"步骤1失败: {e}") + + # 示例2: 独立运行步骤2 - 耀斑区域检测 + print("\n示例2: 独立运行步骤2 - 耀斑区域检测") + try: + glint_mask_path = pipeline.step2_find_glint_area( + img_path="path/to/image.bsq", + water_mask_path="path/to/water_mask.dat", # 可选，提供水体掩膜 + skip_dependency_check=True # 允许跳过依赖检查 + ) + print(f"耀斑掩膜已生成: {glint_mask_path}") + except Exception as e: + print(f"步骤2失败: {e}") + + # 示例3: 独立运行步骤4 - 数据预处理 + print("\n示例3: 独立运行步骤4 - 数据预处理") + try: + processed_csv = pipeline.step4_process_csv( + csv_path="path/to/water_quality_data.csv" + ) + print(f"处理后的CSV文件: {processed_csv}") + except Exception as e: + print(f"步骤4失败: {e}") + + # 示例4: 独立运行步骤5 - 光谱提取 + print("\n示例4: 独立运行步骤5 - 光谱提取") + try: + training_spectra = pipeline.step5_extract_training_spectra( + deglint_img_path="path/to/deglint_image.bsq", + csv_path="path/to/processed_data.csv", + glint_mask_path="path/to/severe_glint_area.dat", + skip_dependency_check=True + ) + print(f"训练光谱数据: {training_spectra}") + except Exception as e: + print(f"步骤5失败: {e}") + + # 示例5: 独立运行步骤6 - 模型训练 + print("\n示例5: 独立运行步骤6 - 模型训练") + try: + models_dir = pipeline.step6_train_models( + training_csv_path="path/to/training_spectra.csv", + skip_dependency_check=True + ) + print(f"模型目录: {models_dir}") + except Exception as e: + print(f"步骤6失败: {e}") + + # 示例6: 独立运行步骤7 - 采样点生成 + print("\n示例6: 独立运行步骤7 - 采样点生成") + try: + sampling_csv = pipeline.step7_generate_sampling_points( + deglint_img_path="path/to/deglint_image.bsq", + water_mask_path="path/to/water_mask.dat", + skip_dependency_check=True + ) + print(f"采样点数据: {sampling_csv}") + except Exception as e: + print(f"步骤7失败: {e}") + + # 示例7: 独立运行步骤8 - 水质预测 + print("\n示例7: 独立运行步骤8 - 水质预测") + try: + predictions = pipeline.step8_predict_water_quality( + sampling_csv_path="path/to/sampling_spectra.csv", + models_dir="path/to/models_directory", + skip_dependency_check=True + ) + print(f"预测结果: {predictions}") + except Exception as e: + print(f"步骤8失败: {e}") + + # 示例8: 独立运行步骤9 - 分布图生成 + print("\n示例8: 独立运行步骤9 - 分布图生成") + try: + distribution_map = pipeline.step9_generate_distribution_map( + prediction_csv_path="path/to/prediction_results.csv", + boundary_shp_path="path/to/boundary.shp", + skip_dependency_check=True + ) + print(f"分布图: {distribution_map}") + except Exception as e: + print(f"步骤9失败: {e}") + + print("\n" + "="*80) + print("独立步骤运行示例完成") + print("注意：请将示例中的路径替换为实际的文件路径") + + except Exception as e: + print(f"运行示例时出错: {e}") + import traceback + traceback.print_exc() + + +if __name__ == "__main__": + import sys + if len(sys.argv) > 1 and sys.argv[1] == "--example": + example_independent_steps() + else: + main() + diff --git a/src/gui/STYLES_README.md b/src/gui/STYLES_README.md new file mode 100644 index 0000000..388217f --- /dev/null +++ b/src/gui/STYLES_README.md @@ -0,0 +1,242 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +水质参数反演分析系统 - UI样式系统文档 + +Modern UI Stylesheet System Documentation +================================================================================ + +1. 概述 +------ +本系统采用现代化的扁平设计风格，提供了一套完整的样式表和主题管理系统。 +所有UI组件都遵循统一的设计规范，确保整体的视觉一致性和用户体验。 + +2. 颜色系统 +----------- +主要颜色定义在 ModernStylesheet.COLORS 中： + +- main_bg (#F0F0F0)：主窗口背景，浅灰色 +- panel_bg (#FFFFFF)：面板/容器背景，纯白色 +- text_primary (#000000)：主文字颜色，黑色 +- text_secondary (#666666)：辅助文字颜色，灰色 +- border (#D0D0D0)：边框颜色，浅灰 +- border_light (#E8E8E8)：浅边框颜色 +- accent (#007BFF)：强调色，蓝色 +- success (#28A745)：成功绿 +- error (#DC3545)：错误红 +- warning (#FFC107)：警告黄 +- hover (#E8E8E8)：悬停背景色 +- selected (#0056B3)：选中色 + +3. 按钮样式 +----------- +系统提供了四种预定义的按钮样式： + +a) 普通按钮（normal） + background-color: 白色 + border: 1px 灰色边框 + border-radius: 7px + 用法: ModernStylesheet.get_button_stylesheet('normal') + +b) 主按钮（primary）- 蓝色 + background-color: 蓝色 (#007BFF) + color: 白色 + border-radius: 7px + 用法: ModernStylesheet.get_button_stylesheet('primary') + +c) 成功按钮（success）- 绿色 + background-color: 绿色 (#28A745) + color: 白色 + border-radius: 7px + 用法: ModernStylesheet.get_button_stylesheet('success') + 常用于：独立运行、确认操作等 + +d) 危险按钮（danger）- 红色 + background-color: 红色 (#DC3545) + color: 白色 + border-radius: 7px + 用法: ModernStylesheet.get_button_stylesheet('danger') + 常用于：停止、删除操作等 + +示例代码： +--------- +from src.gui.styles import ModernStylesheet + +# 创建成功按钮 +run_btn = QPushButton("运行") +run_btn.setStyleSheet(ModernStylesheet.get_button_stylesheet('success')) + +# 创建危险按钮 +stop_btn = QPushButton("停止") +stop_btn.setStyleSheet(ModernStylesheet.get_button_stylesheet('danger')) + +4. 输入框样式 +------------- +所有输入框（QLineEdit, QComboBox, QSpinBox等）都采用统一样式： +- background-color: 白色 +- border: 1px 灰色边框 +- border-radius: 5-10px（圆角） +- 焦点时：边框变为蓝色（accent color） + +5. 分组框（QGroupBox） +---------------------- +分组框采用简洁的设计： +- background-color: 白色 +- border: 无or仅下边框（1px 浅灰） +- border-radius: 0 +- 内边距: 9px + +6. 复选框和单选框 +----------------- +复选框和单选框保持默认样式，具有以下特性： +- 大小: 16x16px +- 选中时：蓝色背景（accent color） +- 边框: 1px 灰色 + +7. 应用样式表 +-------------- +主样式表通过 apply_stylesheet() 方法应用到整个应用： + + self.setStyleSheet(ModernStylesheet.get_main_stylesheet()) + +专用样式表可按需应用： + + # 工具栏样式 + toolbar_widget.setStyleSheet(ModernStylesheet.get_toolbar_stylesheet()) + + # 边栏样式 + sidebar_widget.setStyleSheet(ModernStylesheet.get_sidebar_stylesheet()) + +8. 布局特点 +----------- +应用的主要布局特点： + +a) 顶部工具栏 + - 白色背景 + - 下边框 1px 浅灰 + - 包含logo、模块切换按钮、窗口控制按钮 + +b) 左侧导航栏（宽度：~280px） + - 白色背景 + - 右边框 1px 浅灰 + - 包含步骤列表、运行/停止按钮 + - 步骤列表支持选中状态显示 + +c) 右侧内容区 + - 浅灰背景 + - 包含标签页、日志区、进度条 + - 标签页支持切换 + +d) 底部状态栏 + - 白色背景 + - 上边框 1px 浅灰 + - 显示当前状态和进度信息 + +9. 自定义样式 +-------------- +如需为某个组件应用自定义样式，建议： + +a) 简单修改（如颜色）： + widget.setStyleSheet(f"background-color: {ModernStylesheet.COLORS['panel_bg']};") + +b) 复杂修改： + 添加新方法到 ModernStylesheet 类 + + @staticmethod + def get_custom_stylesheet(): + return "..." + +c) 一次性样式： + 直接在widget.setStyleSheet中定义 + 注意：保持与整体风格的一致性 + +10. 最佳实践 +-------------- + +✓ DO: + - 使用 ModernStylesheet 中的颜色常量 + - 使用预定义的样式表方法 + - 维持一致的间距和圆角半径 + - 使用适当的按钮类型（success/danger等） + - 为交互元素提供hover和pressed状态反馈 + +✗ DON'T: + - 直接使用硬编码颜色值 + - 混合不同的设计风格 + - 过度使用渐变和阴影 + - 忽视focus和disabled状态 + - 使用过小或过大的字体 + +11. 响应式设计 +--------------- +系统支持基本的响应式布局： +- 导航栏最大宽度: 280px +- 内容区域自动调整 +- 步骤面板自动滚动 + +12. 字体设置 +----------- +主要字体： +- 界面标题：Arial, 13-14pt, Bold +- 普通文本：系统默认, 11-12pt +- 等宽字体（日志）：Courier New, 10pt, Monospace + +13. 性能优化 +----------- +- 样式表在应用启动时加载 +- 避免频繁修改样式表 +- 使用CSS类而非硬编码样式 +- 合理使用selector优化渲染 + +14. 常见问题 +----------- +Q: 如何改变全局字体大小？ +A: 修改 ModernStylesheet 类中的样式表定义 + +Q: 如何添加新的按钮类型？ +A: 在 ModernStylesheet 类中添加新方法 + +Q: 如何支持深色模式？ +A: 创建新的样式类，定义深色配色方案 + +Q: 标签页标签栏消失了怎么办？ +A: 检查QTabBar::tab的height设置，确保高度大于0 + +================================================================================ + +更新历史： +- v1.0: 初始版本，实现现代化扁平设计风格 +- v1.1: 改进导航栏和日志区域样式 +- v1.2: 添加专用样式表方法（工具栏、边栏等） + +================================================================================ +""" + +# 快速参考表 +QUICK_REFERENCE = """ +┌─ 快速参考 ─────────────────────────────────────────────────────────────┐ +│ │ +│ 按钮样式: │ +│ 正常: ModernStylesheet.get_button_stylesheet('normal') │ +│ 主要: ModernStylesheet.get_button_stylesheet('primary') │ +│ 成功: ModernStylesheet.get_button_stylesheet('success') │ +│ 危险: ModernStylesheet.get_button_stylesheet('danger') │ +│ │ +│ 颜色引用: │ +│ ModernStylesheet.COLORS['main_bg'] # #F0F0F0 浅灰 │ +│ ModernStylesheet.COLORS['panel_bg'] # #FFFFFF 白色 │ +│ ModernStylesheet.COLORS['text_primary'] # #000000 黑色 │ +│ ModernStylesheet.COLORS['accent'] # #007BFF 蓝色 │ +│ ModernStylesheet.COLORS['success'] # #28A745 绿色 │ +│ ModernStylesheet.COLORS['error'] # #DC3545 红色 │ +│ │ +│ 样式表应用: │ +│ self.setStyleSheet(ModernStylesheet.get_main_stylesheet()) │ +│ widget.setStyleSheet(ModernStylesheet.get_toolbar_stylesheet()) │ +│ widget.setStyleSheet(ModernStylesheet.get_sidebar_stylesheet()) │ +│ │ +└───────────────────────────────────────────────────────────────────────┘ +""" + +if __name__ == "__main__": + print(QUICK_REFERENCE) diff --git a/src/gui/__init__.py b/src/gui/__init__.py new file mode 100644 index 0000000..7c68785 --- /dev/null +++ b/src/gui/__init__.py @@ -0,0 +1 @@ +# -*- coding: utf-8 -*- \ No newline at end of file diff --git a/src/gui/styles.py b/src/gui/styles.py new file mode 100644 index 0000000..7c8128b --- /dev/null +++ b/src/gui/styles.py @@ -0,0 +1,570 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +现代化样式表和主题管理模块 +Modern Stylesheet and Theme Management Module +""" + +class ModernStylesheet: + """现代化样式表集合""" + + # 颜色定义 + COLORS = { + 'main_bg': '#F0F0F0', # 主窗口背景：浅灰 + 'panel_bg': '#FFFFFF', # 面板/容器背景：白色 + 'text_primary': '#000000', # 主文字：黑色 + 'text_secondary': '#666666', # 辅助文字：灰色 + 'border': '#D0D0D0', # 边框：浅灰 + 'border_light': '#E8E8E8', # 浅边框 + 'accent': '#007BFF', # 强调色：蓝色 + 'success': '#28A745', # 成功绿 + 'error': '#DC3545', # 错误红 + 'warning': '#FFC107', # 警告黄 + 'hover': '#E8E8E8', # 悬停背景 + 'selected': '#0056B3', # 选中色 + } + + @staticmethod + def get_main_stylesheet(): + """获取主样式表""" + return f""" + /* 主窗口 */ + QMainWindow {{ + background-color: {ModernStylesheet.COLORS['main_bg']}; + }} + + /* 中央部件和容器 */ + QWidget {{ + background-color: {ModernStylesheet.COLORS['main_bg']}; + color: {ModernStylesheet.COLORS['text_primary']}; + }} + + /* 分组框 */ + QGroupBox {{ + background-color: {ModernStylesheet.COLORS['panel_bg']}; + color: {ModernStylesheet.COLORS['text_primary']}; + font-weight: bold; + border: 0px; + margin-top: 10px; + padding-top: 15px; + padding-left: 9px; + padding-right: 9px; + padding-bottom: 9px; + border-bottom: 1px solid {ModernStylesheet.COLORS['border_light']}; + }} + + QGroupBox::title {{ + subcontrol-origin: margin; + subcontrol-position: top left; + padding: 0 5px; + font-size: 12px; + font-weight: bold; + color: {ModernStylesheet.COLORS['text_primary']}; + }} + + /* 按钮 */ + QPushButton {{ + background-color: {ModernStylesheet.COLORS['panel_bg']}; + color: {ModernStylesheet.COLORS['text_primary']}; + border: 1px solid {ModernStylesheet.COLORS['border']}; + border-radius: 7px; + padding: 3px 5px; + min-height: 25px; + max-height: 33px; + font-size: 12px; + font-weight: normal; + outline: none; + }} + + QPushButton:hover {{ + background-color: {ModernStylesheet.COLORS['hover']}; + border: 1px solid {ModernStylesheet.COLORS['border']}; + }} + + QPushButton:pressed {{ + background-color: {ModernStylesheet.COLORS['border_light']}; + }} + + QPushButton:disabled {{ + background-color: {ModernStylesheet.COLORS['hover']}; + color: {ModernStylesheet.COLORS['text_secondary']}; + border: 1px solid {ModernStylesheet.COLORS['border_light']}; + }} + + QPushButton:focus {{ + outline: none; + }} + + /* 输入框 */ + QLineEdit {{ + background-color: {ModernStylesheet.COLORS['panel_bg']}; + color: {ModernStylesheet.COLORS['text_primary']}; + border: 1px solid {ModernStylesheet.COLORS['border']}; + border-radius: 10px; + padding: 5px 8px; + min-height: 20px; + selection-background-color: {ModernStylesheet.COLORS['selected']}; + selection-color: white; + }} + + QLineEdit:focus {{ + border: 1px solid {ModernStylesheet.COLORS['accent']}; + background-color: {ModernStylesheet.COLORS['panel_bg']}; + }} + + /* 下拉框 */ + QComboBox {{ + background-color: {ModernStylesheet.COLORS['panel_bg']}; + color: {ModernStylesheet.COLORS['text_primary']}; + border: 1px solid {ModernStylesheet.COLORS['border']}; + border-radius: 5px; + padding: 5px 8px; + min-height: 25px; + selection-background-color: {ModernStylesheet.COLORS['selected']}; + }} + + QComboBox:focus {{ + border: 1px solid {ModernStylesheet.COLORS['accent']}; + }} + + QComboBox::drop-down {{ + border: 0px; + padding-right: 5px; + }} + + QComboBox QAbstractItemView {{ + background-color: {ModernStylesheet.COLORS['panel_bg']}; + color: {ModernStylesheet.COLORS['text_primary']}; + selection-background-color: {ModernStylesheet.COLORS['selected']}; + selection-color: white; + border: 1px solid {ModernStylesheet.COLORS['border']}; + }} + + /* 数值输入框 */ + QSpinBox, QDoubleSpinBox {{ + background-color: {ModernStylesheet.COLORS['panel_bg']}; + color: {ModernStylesheet.COLORS['text_primary']}; + border: 1px solid {ModernStylesheet.COLORS['border']}; + border-radius: 5px; + padding: 5px 8px; + min-height: 25px; + }} + + QSpinBox:focus, QDoubleSpinBox:focus {{ + border: 1px solid {ModernStylesheet.COLORS['accent']}; + }} + + QSpinBox::up-button, QDoubleSpinBox::up-button {{ + border: 0px; + padding-right: 5px; + }} + + QSpinBox::down-button, QDoubleSpinBox::down-button {{ + border: 0px; + padding-right: 5px; + }} + + /* 复选框 */ + QCheckBox {{ + color: {ModernStylesheet.COLORS['text_primary']}; + spacing: 5px; + }} + + QCheckBox::indicator {{ + width: 16px; + height: 16px; + border: 1px solid {ModernStylesheet.COLORS['border']}; + border-radius: 3px; + background-color: {ModernStylesheet.COLORS['panel_bg']}; + }} + + QCheckBox::indicator:checked {{ + background-color: {ModernStylesheet.COLORS['accent']}; + border: 1px solid {ModernStylesheet.COLORS['accent']}; + }} + + /* 单选框 */ + QRadioButton {{ + color: {ModernStylesheet.COLORS['text_primary']}; + spacing: 5px; + }} + + QRadioButton::indicator {{ + width: 16px; + height: 16px; + border: 1px solid {ModernStylesheet.COLORS['border']}; + border-radius: 8px; + background-color: {ModernStylesheet.COLORS['panel_bg']}; + }} + + QRadioButton::indicator:checked {{ + background: qradial(circle, {ModernStylesheet.COLORS['accent']} 0%, {ModernStylesheet.COLORS['accent']} 40%, {ModernStylesheet.COLORS['panel_bg']} 60%); + border: 1px solid {ModernStylesheet.COLORS['accent']}; + }} + + /* 文本编辑框 */ + QTextEdit {{ + background-color: {ModernStylesheet.COLORS['panel_bg']}; + color: {ModernStylesheet.COLORS['text_primary']}; + border: 1px solid {ModernStylesheet.COLORS['border']}; + border-radius: 5px; + padding: 5px; + selection-background-color: {ModernStylesheet.COLORS['selected']}; + selection-color: white; + }} + + QTextEdit:focus {{ + border: 1px solid {ModernStylesheet.COLORS['accent']}; + }} + + /* 列表部件 */ + QListWidget {{ + background-color: {ModernStylesheet.COLORS['panel_bg']}; + color: {ModernStylesheet.COLORS['text_primary']}; + border: 1px solid {ModernStylesheet.COLORS['border']}; + border-radius: 5px; + outline: none; + }} + + QListWidget::item {{ + padding: 6px; + border: 0px; + }} + + QListWidget::item:hover {{ + background-color: {ModernStylesheet.COLORS['hover']}; + }} + + QListWidget::item:selected {{ + background-color: {ModernStylesheet.COLORS['selected']}; + color: white; + }} + + /* 滚动区域 */ + QScrollArea {{ + background-color: {ModernStylesheet.COLORS['main_bg']}; + border: 0px; + }} + + /* 滚动条 */ + QScrollBar:vertical {{ + background-color: {ModernStylesheet.COLORS['main_bg']}; + width: 12px; + border: 0px; + }} + + QScrollBar::handle:vertical {{ + background-color: {ModernStylesheet.COLORS['border']}; + border-radius: 6px; + min-height: 20px; + }} + + QScrollBar::handle:vertical:hover {{ + background-color: {ModernStylesheet.COLORS['text_secondary']}; + }} + + QScrollBar::add-line:vertical, QScrollBar::sub-line:vertical {{ + border: 0px; + background-color: transparent; + }} + + QScrollBar:horizontal {{ + background-color: {ModernStylesheet.COLORS['main_bg']}; + height: 12px; + border: 0px; + }} + + QScrollBar::handle:horizontal {{ + background-color: {ModernStylesheet.COLORS['border']}; + border-radius: 6px; + min-width: 20px; + }} + + QScrollBar::handle:horizontal:hover {{ + background-color: {ModernStylesheet.COLORS['text_secondary']}; + }} + + QScrollBar::add-line:horizontal, QScrollBar::sub-line:horizontal {{ + border: 0px; + background-color: transparent; + }} + + /* 进度条 */ + QProgressBar {{ + background-color: {ModernStylesheet.COLORS['panel_bg']}; + color: {ModernStylesheet.COLORS['text_primary']}; + border: 1px solid {ModernStylesheet.COLORS['border']}; + border-radius: 5px; + padding: 2px; + text-align: center; + height: 20px; + }} + + QProgressBar::chunk {{ + background-color: {ModernStylesheet.COLORS['success']}; + border-radius: 3px; + }} + + /* 标签 */ + QLabel {{ + color: {ModernStylesheet.COLORS['text_primary']}; + background-color: transparent; + }} + + /* 标签栏 */ + QTabBar::tab {{ + background-color: {ModernStylesheet.COLORS['main_bg']}; + color: {ModernStylesheet.COLORS['text_primary']}; + border: 1px solid {ModernStylesheet.COLORS['border']}; + border-bottom: 0px; + padding: 8px 12px; + margin-right: 2px; + border-radius: 5px 5px 0px 0px; + }} + + QTabBar::tab:selected {{ + background-color: {ModernStylesheet.COLORS['panel_bg']}; + border: 1px solid {ModernStylesheet.COLORS['border']}; + border-bottom: 2px solid {ModernStylesheet.COLORS['accent']}; + color: {ModernStylesheet.COLORS['accent']}; + }} + + QTabBar::tab:hover {{ + background-color: {ModernStylesheet.COLORS['hover']}; + }} + + QTabWidget::pane {{ + background-color: {ModernStylesheet.COLORS['panel_bg']}; + border: 1px solid {ModernStylesheet.COLORS['border']}; + border-top: 0px; + border-radius: 0px 0px 5px 5px; + }} + + /* 菜单栏 */ + QMenuBar {{ + background-color: {ModernStylesheet.COLORS['panel_bg']}; + color: {ModernStylesheet.COLORS['text_primary']}; + border-bottom: 1px solid {ModernStylesheet.COLORS['border_light']}; + padding: 2px; + }} + + QMenuBar::item:selected {{ + background-color: {ModernStylesheet.COLORS['hover']}; + }} + + /* 菜单 */ + QMenu {{ + background-color: {ModernStylesheet.COLORS['panel_bg']}; + color: {ModernStylesheet.COLORS['text_primary']}; + border: 1px solid {ModernStylesheet.COLORS['border']}; + padding: 4px 0px; + border-radius: 5px; + }} + + QMenu::item:selected {{ + background-color: {ModernStylesheet.COLORS['hover']}; + padding-left: 20px; + }} + + QMenu::separator {{ + height: 1px; + background-color: {ModernStylesheet.COLORS['border_light']}; + margin: 4px 0px; + }} + + /* 状态栏 */ + QStatusBar {{ + background-color: {ModernStylesheet.COLORS['panel_bg']}; + color: {ModernStylesheet.COLORS['text_primary']}; + border-top: 1px solid {ModernStylesheet.COLORS['border_light']}; + }} + + /* 框架 */ + QFrame {{ + background-color: {ModernStylesheet.COLORS['main_bg']}; + border: 0px; + }} + + /* 对话框 */ + QDialog {{ + background-color: {ModernStylesheet.COLORS['main_bg']}; + }} + + /* 消息框 */ + QMessageBox {{ + background-color: {ModernStylesheet.COLORS['main_bg']}; + }} + + QMessageBox QLabel {{ + color: {ModernStylesheet.COLORS['text_primary']}; + }} + """ + + @staticmethod + def get_button_stylesheet(style_type='normal'): + """获取特定样式的按钮样式表""" + colors = ModernStylesheet.COLORS + + if style_type == 'primary': + # 蓝色主按钮 + return f""" + QPushButton {{ + background-color: {colors['accent']}; + color: white; + border: 1px solid {colors['accent']}; + border-radius: 7px; + padding: 3px 5px; + min-height: 25px; + max-height: 33px; + font-weight: bold; + }} + QPushButton:hover {{ + background-color: #0056b3; + border: 1px solid #0056b3; + }} + QPushButton:pressed {{ + background-color: #003d82; + }} + QPushButton:disabled {{ + background-color: {colors['hover']}; + color: {colors['text_secondary']}; + border: 1px solid {colors['border_light']}; + }} + """ + + elif style_type == 'success': + # 绿色成功按钮 + return f""" + QPushButton {{ + background-color: {colors['success']}; + color: white; + border: 1px solid {colors['success']}; + border-radius: 7px; + padding: 3px 5px; + min-height: 25px; + max-height: 33px; + font-weight: bold; + }} + QPushButton:hover {{ + background-color: #218838; + border: 1px solid #218838; + }} + QPushButton:pressed {{ + background-color: #1a6c28; + }} + QPushButton:disabled {{ + background-color: {colors['hover']}; + color: {colors['text_secondary']}; + border: 1px solid {colors['border_light']}; + }} + """ + + elif style_type == 'danger': + # 红色危险按钮 + return f""" + QPushButton {{ + background-color: {colors['error']}; + color: white; + border: 1px solid {colors['error']}; + border-radius: 7px; + padding: 3px 5px; + min-height: 25px; + max-height: 33px; + font-weight: bold; + }} + QPushButton:hover {{ + background-color: #c82333; + border: 1px solid #c82333; + }} + QPushButton:pressed {{ + background-color: #9a1a24; + }} + QPushButton:disabled {{ + background-color: {colors['hover']}; + color: {colors['text_secondary']}; + border: 1px solid {colors['border_light']}; + }} + """ + + else: # normal/default + return f""" + QPushButton {{ + background-color: {colors['panel_bg']}; + color: {colors['text_primary']}; + border: 1px solid {colors['border']}; + border-radius: 7px; + padding: 3px 5px; + min-height: 25px; + max-height: 33px; + }} + QPushButton:hover {{ + background-color: {colors['hover']}; + border: 1px solid {colors['border']}; + }} + QPushButton:pressed {{ + background-color: {colors['border_light']}; + }} + QPushButton:disabled {{ + background-color: {colors['hover']}; + color: {colors['text_secondary']}; + border: 1px solid {colors['border_light']}; + }} + """ + + @staticmethod + def get_toolbar_stylesheet(): + """获取顶部工具栏样式表""" + colors = ModernStylesheet.COLORS + return f""" + QWidget {{ + background-color: {colors['panel_bg']}; + border-bottom: 1px solid {colors['border_light']}; + }} + QLabel {{ + color: {colors['text_primary']}; + }} + QPushButton {{ + background-color: {colors['panel_bg']}; + color: {colors['text_primary']}; + border: 1px solid {colors['border']}; + border-radius: 5px; + padding: 5px 10px; + min-height: 25px; + }} + QPushButton:hover {{ + background-color: {colors['hover']}; + }} + """ + + @staticmethod + def get_sidebar_stylesheet(): + """获取左侧边栏样式表""" + colors = ModernStylesheet.COLORS + return f""" + QWidget {{ + background-color: {colors['panel_bg']}; + border-right: 1px solid {colors['border_light']}; + }} + QLabel {{ + color: {colors['text_primary']}; + font-weight: bold; + }} + QListWidget {{ + background-color: {colors['panel_bg']}; + border: 0px; + border-right: 1px solid {colors['border_light']}; + }} + QListWidget::item {{ + padding: 8px; + border-left: 3px solid transparent; + }} + QListWidget::item:hover {{ + background-color: {colors['hover']}; + }} + QListWidget::item:selected {{ + background-color: transparent; + color: {colors['accent']}; + border-left: 3px solid {colors['accent']}; + font-weight: bold; + }} + """ diff --git a/src/gui/water_quality_gui.py b/src/gui/water_quality_gui.py new file mode 100644 index 0000000..12db4e4 --- /dev/null +++ b/src/gui/water_quality_gui.py @@ -0,0 +1,6300 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +水质参数反演分析系统 - 图形用户界面 +GUI for Water Quality Inversion Pipeline +""" + +import os +import json +import copy +import sys +import traceback +from pathlib import Path +from datetime import datetime +from typing import Dict, Optional, List, Union +import numpy as np +import pandas as pd + +from PyQt5.QtWidgets import ( + QApplication, QMainWindow, QWidget, QVBoxLayout, QHBoxLayout, + QPushButton, QLabel, QLineEdit, QComboBox, QCheckBox, QSpinBox, + QDoubleSpinBox, QFileDialog, QTextEdit, QProgressBar, QMessageBox, + QScrollArea, QGroupBox, QTabWidget, QSplitter, QListWidget, + QListWidgetItem, QFrame, QGridLayout, QFormLayout, QSizePolicy, QDialog, + QStackedWidget, QTableView, QHeaderView, QAbstractItemView, + QRadioButton, QButtonGroup, QToolBar, QTreeWidget, QTreeWidgetItem, + QInputDialog, +) +from PyQt5.QtCore import QThread, pyqtSignal, Qt, QTimer, QAbstractTableModel, QSize +from PyQt5.QtGui import QIcon, QFont, QTextCursor, QPalette, QColor, QPixmap + +# 导入样式模块 - 兼容开发环境和 PyInstaller 打包 +try: + # 开发环境或正确添加到 sys.path 时 + from styles import ModernStylesheet +except ImportError: + # PyInstaller 打包后或路径不正确时 + try: + from src.gui.styles import ModernStylesheet + except ImportError: + # 最终兜底：添加路径后导入 + import sys + import os + current_dir = os.path.dirname(os.path.abspath(__file__)) + project_root = os.path.abspath(os.path.join(current_dir, '..', '..')) + if project_root not in sys.path: + sys.path.insert(0, project_root) + from src.gui.styles import ModernStylesheet + +# Matplotlib相关导入 +import matplotlib +matplotlib.use('Qt5Agg') +from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas +from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar +from matplotlib.figure import Figure +import matplotlib.pyplot as plt + +# 导入原有的pipeline类 +def check_pipeline_dependencies(): + """检查pipeline模块的依赖项""" + missing_deps = [] + dep_errors = {} + + # 检查必需的Python包 + required_packages = [ + 'numpy', 'pandas', 'scipy', 'matplotlib', 'sklearn', + 'joblib', 'PIL', 'cv2', 'rasterio', 'geopandas' + ] + + for package in required_packages: + try: + if package == 'PIL': + import PIL + elif package == 'cv2': + import cv2 + else: + __import__(package) + except Exception as e: + missing_deps.append(package) + dep_errors[package] = repr(e) + + return missing_deps, dep_errors + +def diagnose_pipeline_import_error(): + """诊断pipeline导入错误""" + import sys + import os + + error_info = [] + + # 检查是否在PyInstaller环境中运行 + is_frozen = getattr(sys, "frozen", False) or bool(getattr(sys, "_MEIPASS", None)) + + if is_frozen: + # 打包后模块在 PyInstaller 归档内，磁盘上不再有「项目根/src/core/*.py」布局； + # 切勿把 exe 所在目录（如 scripts/dist）的上级当成源码根，否则会误报「文件不存在」。 + error_info.append( + "[INFO] PyInstaller 环境：Pipeline 从程序内置包加载，跳过对仓库路径 src/core/*.py 的磁盘检查" + ) + else: + pipeline_file = os.path.normpath( + os.path.join(os.path.dirname(__file__), "..", "core", "water_quality_inversion_pipeline_GUI.py") + ) + if not os.path.exists(pipeline_file): + error_info.append(f"[ERROR] Pipeline文件不存在: {pipeline_file}") + error_info.append( + " 解决方案: 请确保项目结构完整，检查 src/core/ 下是否有 water_quality_inversion_pipeline_GUI.py" + ) + else: + error_info.append(f"[OK] Pipeline文件存在: {pipeline_file}") + + current_dir = os.path.dirname(os.path.dirname(__file__)) + if current_dir not in sys.path: + sys.path.insert(0, current_dir) + error_info.append(f"[INFO] 已添加路径到sys.path: {current_dir}") + + # 检查依赖项 + missing_deps, dep_errors = check_pipeline_dependencies() + if missing_deps: + error_info.append(f"[ERROR] 缺少必需的依赖包: {', '.join(missing_deps)}") + # 额外输出真实的导入失败原因（常见于 DLL 缺失，而不是包没安装） + for pkg in missing_deps: + if pkg in dep_errors: + error_info.append(f" - {pkg} 导入失败原因: {dep_errors[pkg]}") + error_info.append(" 解决方案: 请运行以下命令安装依赖:") + error_info.append(" pip install -r requirements.txt") + error_info.append(" 或使用conda:") + error_info.append(" conda install numpy pandas scipy matplotlib scikit-learn joblib pillow opencv-python rasterio geopandas") + else: + error_info.append("[OK] 主要依赖包均已安装") + + # 检查 GDAL（优先 osgeo，与运行时一致） + try: + from osgeo import gdal # noqa: F401 + + error_info.append("[OK] GDAL (osgeo) 可用") + except ImportError: + try: + from osgeo import gdal # noqa: F401 + + error_info.append("[OK] GDAL 可用") + except ImportError: + error_info.append("[WARNING] GDAL/osgeo 不可用，将影响栅格与地理数据处理") + error_info.append(" 开发环境: conda install gdal") + error_info.append(" 打包环境: 请在构建所用 Conda 环境中打包，并确保 spec 已收集 Library/bin 中依赖 DLL") + + # 检查unittest模块（PyInstaller打包时可能缺失） + try: + import unittest + error_info.append("[OK] unittest模块可用") + except ImportError: + error_info.append("[WARNING] unittest模块不可用，这可能是PyInstaller打包环境导致的") + error_info.append(" 这不会影响主要功能，但可能影响某些测试相关特性") + + return error_info + +PIPELINE_AVAILABLE = False +PIPELINE_ERROR_INFO = [] + +try: + # 首先检查依赖和文件 + error_info = diagnose_pipeline_import_error() + + # 尝试导入 + from src.core.water_quality_inversion_pipeline_GUI import WaterQualityInversionPipeline + PIPELINE_AVAILABLE = True + print("[OK] 成功导入pipeline模块") + PIPELINE_ERROR_INFO = error_info + +except ImportError as e: + PIPELINE_AVAILABLE = False + error_info = diagnose_pipeline_import_error() + + print("="*60) + print("[ERROR] PIPELINE导入失败 - 详细诊断信息:") + print("="*60) + + for info in error_info: + print(info) + + print("-"*60) + print(f"原始ImportError: {str(e)}") + print("-"*60) + + # 检查常见的导入问题 + if "unittest" in str(e): + print("[INFO] unittest模块缺失 - 这通常在PyInstaller打包环境中发生") + print("解决方案:") + print(" 1. 这不会影响主要功能，程序仍可正常运行") + print(" 2. 如果需要修复，可以在.spec文件中添加unittest模块:") + print(" a = Analysis(..., hiddenimports=['unittest', 'unittest.mock'])") + print(" 3. 或在PyInstaller命令中添加: --hidden-import unittest") + elif "water_quality_inversion_pipeline_GUI" in str(e): + print("[INFO] 可能的解决方案:") + print(" 1. 检查src/core/water_quality_inversion_pipeline_GUI.py文件是否存在") + print(" 2. 确保Python路径设置正确") + print(" 3. 尝试重新安装依赖: pip install -r requirements.txt") + print(" 4. 检查Python版本是否兼容（推荐Python 3.8-3.11）") + + import traceback + print("\n完整错误追踪:") + traceback.print_exc() + print("="*60) + + PIPELINE_ERROR_INFO = error_info + +except Exception as e: + PIPELINE_AVAILABLE = False + error_info = diagnose_pipeline_import_error() + + print("="*60) + print("[ERROR] PIPELINE导入失败 - 其他错误:") + print("="*60) + + for info in error_info: + print(info) + + print("-"*60) + print(f"原始错误: {str(e)}") + print("-"*60) + + print("[INFO] 可能的解决方案:") + print(" 1. 检查Python环境和依赖包版本") + print(" 2. 尝试重新安装所有依赖") + print(" 3. 检查是否有语法错误或其他模块导入问题") + + import traceback + print("\n完整错误追踪:") + traceback.print_exc() + print("="*60) + + PIPELINE_ERROR_INFO = error_info + + +class WorkerThread(QThread): + """后台工作线程，用于执行耗时任务（在工作线程内创建 Pipeline，避免阻塞 UI）。""" + progress_update = pyqtSignal(int, str) # 进度更新信号 (percentage, message) + log_message = pyqtSignal(str, str) # 日志消息信号 (message, level: 'info'/'warning'/'error') + step_completed = pyqtSignal(str, bool, str) # 步骤完成信号 (step_name, success, message) + finished = pyqtSignal(bool, str) # 完成信号 (success, message) + + def __init__(self, work_dir: str, config, mode='full', step_name=None): + super().__init__() + self.work_dir = str(work_dir) + self.config = config + self.mode = mode # 'full' 或 'single_step' + self.step_name = step_name # 单步执行时的步骤名称 + self.pipeline = None + self.is_running = True + self.current_step = None + self.step_count = 0 + self.total_steps = 9 + + def pipeline_callback(self, step_name, status, message=""): + """Pipeline回调函数，用于接收步骤状态""" + if status == "start": + self.log_message.emit(f"[START] 开始执行: {step_name}", "info") + # 更新进度 + progress = int((self.step_count / self.total_steps) * 100) + self.progress_update.emit(progress, f"正在执行: {step_name}") + elif status == "completed": + self.step_count += 1 + self.log_message.emit(f"[DONE] 完成: {step_name} {message}", "info") + self.step_completed.emit(step_name, True, message) + # 更新进度 + progress = int((self.step_count / self.total_steps) * 100) + self.progress_update.emit(progress, f"已完成: {step_name}") + elif status == "skipped": + self.step_count += 1 + self.log_message.emit(f"[SKIP] 跳过: {step_name} {message}", "warning") + self.step_completed.emit(step_name, True, f"跳过: {message}") + # 更新进度 + progress = int((self.step_count / self.total_steps) * 100) + self.progress_update.emit(progress, f"已跳过: {step_name}") + elif status == "error": + self.log_message.emit(f"[ERROR] 错误: {step_name} - {message}", "error") + self.step_completed.emit(step_name, False, message) + elif status == "info": + self.log_message.emit(f" {message}", "info") + elif status == "warning": + self.log_message.emit(f" [WARNING] {message}", "warning") + + def run(self): + """运行 pipeline：子线程内切换 Matplotlib 为 Agg，避免 Qt5Agg 在后台线程绘图导致界面卡死。""" + mpl_prev = None + try: + import matplotlib + mpl_prev = matplotlib.get_backend() + except Exception: + pass + try: + import matplotlib.pyplot as plt + plt.switch_backend("Agg") + except Exception: + mpl_prev = None + try: + from src.core.water_quality_inversion_pipeline_GUI import WaterQualityInversionPipeline + self.pipeline = WaterQualityInversionPipeline(work_dir=self.work_dir) + + if self.mode == 'full': + self.log_message.emit("开始运行完整流程...", "info") + self.step_count = 0 + + if hasattr(self.pipeline, 'set_callback'): + self.pipeline.set_callback(self.pipeline_callback) + + self.pipeline.run_full_pipeline(self.config) + + self.progress_update.emit(100, "流程执行完成") + self.finished.emit(True, "完整流程执行成功！") + else: + self.log_message.emit(f"开始独立运行步骤: {self.step_name}", "info") + self.progress_update.emit(0, f"正在执行: {self.step_name}") + + if hasattr(self.pipeline, 'set_callback'): + self.pipeline.set_callback(self.pipeline_callback) + + self.run_single_step(self.step_name, self.config) + + self.progress_update.emit(100, f"步骤 {self.step_name} 执行完成") + self.finished.emit(True, f"步骤 {self.step_name} 独立运行成功！") + except Exception as e: + error_msg = f"执行失败: {str(e)}\n{traceback.format_exc()}" + self.log_message.emit(error_msg, "error") + self.finished.emit(False, error_msg) + finally: + if mpl_prev: + try: + import matplotlib.pyplot as plt + plt.switch_backend(mpl_prev) + except Exception: + pass + + def run_single_step(self, step_name, config): + """运行单个步骤""" + step_method_map = { + 'step1': 'step1_generate_water_mask', + 'step2': 'step2_find_glint_area', + 'step3': 'step3_remove_glint', + 'step4': 'step4_process_csv', + 'step5': 'step5_extract_training_spectra', + 'step5_5': 'step5_5_calculate_water_quality_indices', + 'step6': 'step6_train_models', + 'step6_5': 'step6_5_non_empirical_modeling', + 'step6_75': 'step6_75_custom_regression', + 'step7': 'step7_generate_sampling_points', + 'step8': 'step8_predict_water_quality', + 'step8_5': 'step8_5_predict_with_non_empirical_models', + 'step8_75': 'step8_75_predict_with_custom_regression', + 'step9': 'step9_generate_distribution_map' + } + + if step_name not in step_method_map: + raise ValueError(f"未知的步骤名称: {step_name}") + + method_name = step_method_map[step_name] + step_config = dict(config.get(step_name, {})) + + # 为独立运行添加 skip_dependency_check=True + step_config['skip_dependency_check'] = True + + # step9：去掉仅用于 GUI/配置保存的字段，避免传入 pipeline 报错 + if step_name == 'step9': + step_config.pop('step9_batch_mode', None) + step_config.pop('prediction_csv_dir', None) + step_config.pop('recursive_csv_scan', None) + + # step5：输出路径由管线固定到工作目录，GUI 占位字段勿传入 + if step_name == 'step5': + step_config.pop('output_path', None) + + # 参数名映射：将GUI中的参数名映射为pipeline方法期望的参数名 + if step_name == 'step8_5' and 'models_dir' in step_config: + step_config['non_empirical_models_dir'] = step_config.pop('models_dir') + + # 调用对应的方法 + method = getattr(self.pipeline, method_name) + result = method(**step_config) + + return result + + def stop(self): + """停止执行""" + self.is_running = False + self.terminate() + + +class ReportGenerateThread(QThread): + """后台生成 Word 报告（避免阻塞 UI）。""" + finished_ok = pyqtSignal(str) + failed = pyqtSignal(str) + log_message = pyqtSignal(str, str) + + def __init__(self, work_dir: str, output_dir: Optional[str], report_title: str, options: dict): + super().__init__() + self.work_dir = work_dir + self.output_dir = output_dir + self.report_title = report_title + self.options = options + + def run(self): + import traceback + try: + from src.postprocessing.report_word import WaterQualityReportGenerator, ReportGenerationConfig + + url = (self.options.get("ollama_url") or "").strip() or None + vision = (self.options.get("ollama_vision_model") or "").strip() or None + text = (self.options.get("ollama_text_model") or "").strip() or None + if self.options.get("text_same_as_vision"): + text = vision + timeout = self.options.get("ollama_timeout_s") + enable_ai = self.options.get("enable_ai_analysis") + + ai_cfg = ReportGenerationConfig( + ollama_base_url=url, + ollama_vision_model=vision, + ollama_text_model=text, + ollama_timeout_s=int(timeout) if timeout is not None else None, + enable_ai_analysis=bool(enable_ai), + ) + self.log_message.emit( + f"报告生成：工作目录={self.work_dir}，AI={'开' if enable_ai else '关'}，" + f"模型URL={url or '(环境变量 OLLAMA_URL)'}", + "info", + ) + gen = WaterQualityReportGenerator( + work_dir=self.work_dir, + output_dir=self.output_dir, + ai_config=ai_cfg, + ) + out_path = gen.generate_report( + work_dir=self.work_dir, + report_title=self.report_title or "水质参数反演分析报告", + ) + self.finished_ok.emit(str(out_path)) + except Exception as e: + self.failed.emit(f"{e}\n{traceback.format_exc()}") + + +class Step9BatchThread(QThread): + """专题图：按文件夹内多个预测 CSV 批量生成分布图。""" + + finished_ok = pyqtSignal(int) + failed = pyqtSignal(str) + log_message = pyqtSignal(str, str) + + def __init__(self, work_dir: str, csv_paths: List[str], step9_kwargs: dict, output_dir_optional: Optional[str]): + super().__init__() + self.work_dir = work_dir + self.csv_paths = csv_paths + self.step9_kwargs = step9_kwargs + self.output_dir_optional = (output_dir_optional or "").strip() or None + + def run(self): + mpl_prev = None + try: + import matplotlib + mpl_prev = matplotlib.get_backend() + except Exception: + pass + try: + import matplotlib.pyplot as plt + plt.switch_backend("Agg") + except Exception: + mpl_prev = None + try: + from src.core.water_quality_inversion_pipeline_GUI import WaterQualityInversionPipeline + pipeline = WaterQualityInversionPipeline(work_dir=self.work_dir) + n = len(self.csv_paths) + for i, csv_p in enumerate(self.csv_paths): + self.log_message.emit(f"专题图 [{i + 1}/{n}] {csv_p}", "info") + kw = {**self.step9_kwargs, "prediction_csv_path": csv_p, "skip_dependency_check": True} + if self.output_dir_optional: + stem = Path(csv_p).stem + kw["output_image_path"] = str(Path(self.output_dir_optional) / f"{stem}_distribution.png") + else: + kw["output_image_path"] = None + pipeline.step9_generate_distribution_map(**kw) + self.finished_ok.emit(n) + except Exception as e: + self.failed.emit(f"{e}\n{traceback.format_exc()}") + finally: + if mpl_prev: + try: + import matplotlib.pyplot as plt + plt.switch_backend(mpl_prev) + except Exception: + pass + + +def _viz_training_spectra_csv_path(work_path: Path) -> Path: + """可视化光谱/统计及模型散点图使用的训练光谱表路径（与步骤5输出一致）。""" + return work_path / "5_training_spectra" / "training_spectra.csv" + + +def _viz_infer_wavelength_start_column(df: pd.DataFrame) -> Union[str, int]: + """推断光谱起始列（training_spectra 通常以波长数值为列名，未必含 UTM_Y）。""" + for i, col in enumerate(df.columns): + name = str(col).strip().lstrip("\ufeff") + try: + v = float(name) + except ValueError: + continue + if 200.0 <= v <= 3000.0: + return i + if "UTM_Y" in df.columns: + return "UTM_Y" + return 0 + + +class VisualizationWorkerThread(QThread): + """可视化耗时计算放入后台线程，并临时使用 Agg 后端，避免主界面未响应。""" + + finished_ok = pyqtSignal(object) + failed = pyqtSignal(str) + + def __init__(self, task: str, work_dir: str, extra: Optional[dict] = None): + super().__init__() + self.task = task + self.work_dir = str(work_dir) + self.extra = extra or {} + + def run(self): + mpl_prev = None + try: + import matplotlib + mpl_prev = matplotlib.get_backend() + except Exception: + pass + try: + import matplotlib.pyplot as plt + plt.switch_backend("Agg") + except Exception: + mpl_prev = None + try: + wp = Path(self.work_dir) + if self.task == "mask_glint": + from src.postprocessing.visualization_reports import WaterQualityVisualization + viz = WaterQualityVisualization(output_dir=str(wp / "9_visualization")) + preview_paths = viz.generate_glint_deglint_previews( + work_dir=str(wp), + output_subdir="glint_deglint_previews", + ) + cnt = len(preview_paths) if preview_paths else 0 + self.finished_ok.emit({"task": "mask_glint", "count": cnt, "preview_paths": preview_paths}) + elif self.task == "sampling_map": + hyperspectral_files = [] + deglint_dir = wp / "3_deglint" + if deglint_dir.exists(): + for ext in ("*.dat", "*.bsq", "*.tif", "*.tiff"): + hyperspectral_files.extend(list(deglint_dir.glob(ext))) + if not hyperspectral_files: + for ext in ("*.dat", "*.bsq", "*.tif", "*.tiff"): + hyperspectral_files.extend(list(wp.glob(f"**/{ext}"))) + if not hyperspectral_files: + self.failed.emit("未找到高光谱影像文件（.dat/.bsq/.tif）。") + return + hyperspectral_path = str(hyperspectral_files[0]) + csv_files = [] + processed_dir = wp / "4_processed_data" + if processed_dir.exists(): + csv_files = list(processed_dir.glob("*.csv")) + if not csv_files: + csv_files = ( + list(wp.glob("**/*sampling*.csv")) + + list(wp.glob("**/*point*.csv")) + + list(wp.glob("**/*.csv")) + ) + if not csv_files: + self.failed.emit("未找到采样点 CSV 文件。") + return + csv_path = str(csv_files[0]) + from src.postprocessing.point_map import SamplingPointMap + map_generator = SamplingPointMap( + output_dir=str(wp / "9_visualization" / "sampling_maps"), + fast_mode=True, + ) + map_path = map_generator.create_sampling_point_map( + hyperspectral_path=hyperspectral_path, + csv_path=csv_path, + point_color="red", + point_size=100, + point_alpha=0.9, + show_north_arrow=True, + show_scale_bar=True, + show_legend=True, + downsample=True, + dpi=180, + ) + self.finished_ok.emit( + { + "task": "sampling_map", + "map_path": map_path, + "hyperspectral_path": hyperspectral_path, + "csv_path": csv_path, + } + ) + elif self.task == "spectrum": + from src.postprocessing.visualization_reports import WaterQualityVisualization + viz = WaterQualityVisualization(output_dir=str(wp / "9_visualization")) + csv_file = self.extra.get("csv_path") + wl = self.extra.get("wavelength_start_column", "UTM_Y") + n_groups = int(self.extra.get("n_groups", 5)) + param_cols = self.extra.get("param_cols") or [] + if param_cols: + output_paths: List[str] = [] + err_lines: List[str] = [] + for param_col in param_cols: + try: + out = viz.plot_spectrum_by_parameter( + csv_path=str(csv_file), + parameter_column=param_col, + wavelength_start_column=wl, + n_groups=n_groups, + ) + output_paths.append(out) + except Exception as _ex: + err_lines.append(f"{param_col}: {_ex}") + if not output_paths: + self.failed.emit( + "所有参数列的光谱图均生成失败:\n" + "\n".join(err_lines[:20]) + ) + return + self.finished_ok.emit( + { + "task": "spectrum", + "output_paths": output_paths, + "errors": err_lines, + } + ) + else: + param_col = self.extra.get("param_col") + out = viz.plot_spectrum_by_parameter( + csv_path=str(csv_file), + parameter_column=param_col, + wavelength_start_column=wl, + n_groups=n_groups, + ) + self.finished_ok.emit( + {"task": "spectrum", "output_path": out, "param_col": param_col} + ) + elif self.task == "statistics": + from src.postprocessing.visualization_reports import WaterQualityVisualization + viz = WaterQualityVisualization(output_dir=str(wp / "9_visualization")) + csv_file = self.extra.get("csv_path") + param_cols = self.extra.get("param_cols") or [] + output_paths = viz.plot_statistical_charts( + csv_path=str(csv_file), + parameter_columns=param_cols, + ) + self.finished_ok.emit( + {"task": "statistics", "output_paths": output_paths} + ) + elif self.task == "scatter": + from src.core.water_quality_inversion_pipeline_GUI import WaterQualityInversionPipeline + + training_csv_path = (self.extra.get("training_csv_path") or "").strip() + models_dir = (self.extra.get("models_dir") or "").strip() + if not training_csv_path or not Path(training_csv_path).is_file(): + self.failed.emit("训练光谱 CSV 无效或不存在，请确认已选择步骤5输出的文件。") + return + if not models_dir or not Path(models_dir).is_dir(): + self.failed.emit("模型目录无效或不存在，请确认步骤6已生成 6_models 下的参数子文件夹。") + return + pipeline = WaterQualityInversionPipeline(work_dir=str(wp)) + scatter_paths = pipeline.generate_model_scatter_plots( + training_csv_path=training_csv_path, + models_dir=models_dir, + ) + self.finished_ok.emit({"task": "scatter", "scatter_paths": scatter_paths or {}}) + elif self.task == "generate_all_selected": + from src.postprocessing.visualization_reports import WaterQualityVisualization + viz = WaterQualityVisualization(output_dir=str(wp / "9_visualization")) + parts = [] + if self.extra.get("gen_mask_glint"): + preview_paths = viz.generate_glint_deglint_previews( + work_dir=str(wp), + output_subdir="glint_deglint_previews", + ) + parts.append(f"掩膜/耀斑预览 {len(preview_paths) if preview_paths else 0} 个") + if self.extra.get("gen_sampling_map"): + hyperspectral_files = [] + deglint_dir = wp / "3_deglint" + if deglint_dir.exists(): + for ext in ("*.dat", "*.bsq", "*.tif", "*.tiff"): + hyperspectral_files.extend(list(deglint_dir.glob(ext))) + if not hyperspectral_files: + for ext in ("*.dat", "*.bsq", "*.tif", "*.tiff"): + hyperspectral_files.extend(list(wp.glob(f"**/{ext}"))) + if hyperspectral_files: + hyperspectral_path = str(hyperspectral_files[0]) + csv_files = [] + processed_dir = wp / "4_processed_data" + if processed_dir.exists(): + csv_files = list(processed_dir.glob("*.csv")) + if not csv_files: + csv_files = ( + list(wp.glob("**/*sampling*.csv")) + + list(wp.glob("**/*point*.csv")) + + list(wp.glob("**/*.csv")) + ) + if csv_files: + csv_path = str(csv_files[0]) + from src.postprocessing.point_map import SamplingPointMap + map_generator = SamplingPointMap( + output_dir=str(wp / "9_visualization" / "sampling_maps"), + fast_mode=True, + ) + map_path = map_generator.create_sampling_point_map( + hyperspectral_path=hyperspectral_path, + csv_path=csv_path, + point_color="red", + point_size=100, + point_alpha=0.9, + show_north_arrow=True, + show_scale_bar=True, + show_legend=True, + downsample=True, + dpi=180, + ) + parts.append(f"采样点图: {Path(map_path).name}") + else: + parts.append("采样点图: 跳过(无CSV)") + else: + parts.append("采样点图: 跳过(无影像)") + self.finished_ok.emit({"task": "generate_all_selected", "parts": parts}) + else: + self.failed.emit(f"未知可视化任务: {self.task}") + except Exception as e: + self.failed.emit(f"{e}\n{traceback.format_exc()}") + finally: + if mpl_prev: + try: + import matplotlib.pyplot as plt + plt.switch_backend(mpl_prev) + except Exception: + pass + + +class FileSelectWidget(QWidget): + """文件选择组件""" + def __init__(self, label_text, file_filter="All Files (*.*)", parent=None): + super().__init__(parent) + self.file_filter = file_filter + self.init_ui(label_text) + + def init_ui(self, label_text): + layout = QHBoxLayout() + layout.setContentsMargins(0, 0, 0, 0) + + self.label = QLabel(label_text) + self.label.setMinimumWidth(120) + self.line_edit = QLineEdit() + self.line_edit.setPlaceholderText("请选择文件...") + self.browse_btn = QPushButton("浏览...") + self.browse_btn.setMaximumWidth(80) + self.browse_btn.clicked.connect(self.browse_file) + + layout.addWidget(self.label) + layout.addWidget(self.line_edit, 1) + layout.addWidget(self.browse_btn) + + self.setLayout(layout) + + def browse_file(self): + """浏览文件""" + file_path, _ = QFileDialog.getOpenFileName( + self, "选择文件", "", self.file_filter + ) + if file_path: + self.line_edit.setText(file_path) + + def get_path(self): + """获取路径""" + return self.line_edit.text() + + def set_path(self, path): + """设置路径""" + self.line_edit.setText(str(path)) + + +class PandasTableModel(QAbstractTableModel): + """支持DataFrame的表格模型""" + def __init__(self, data_frame: pd.DataFrame): + super().__init__() + self._data = data_frame.copy() + if self._data.empty: + self._data = pd.DataFrame() + self._data.fillna("", inplace=True) + self._columns = [str(col) for col in self._data.columns] + + def rowCount(self, parent=None): + return len(self._data) + + def columnCount(self, parent=None): + return len(self._columns) + + def data(self, index, role=Qt.DisplayRole): + if not index.isValid() or role != Qt.DisplayRole: + return None + + value = self._data.iat[index.row(), index.column()] + if pd.isna(value): + return "" + return str(value) + + def headerData(self, section, orientation, role=Qt.DisplayRole): + if role != Qt.DisplayRole: + return None + if orientation == Qt.Horizontal: + if section < len(self._columns): + return self._columns[section] + return str(section) + return str(section + 1) + + def flags(self, index): + if not index.isValid(): + return Qt.NoItemFlags + return Qt.ItemIsEnabled | Qt.ItemIsSelectable + + +class Step1Panel(QWidget): + """1. 水域掩膜生成""" + def __init__(self, parent=None): + super().__init__(parent) + self.init_ui() + + def init_ui(self): + layout = QVBoxLayout() + + # 标题 + + + # 掩膜生成方式选择 + method_group = QGroupBox("掩膜生成方式") + method_layout = QVBoxLayout() + + # 使用现有掩膜文件 + self.use_existing_radio = QRadioButton("使用现有掩膜文件") + self.use_existing_radio.setChecked(True) + method_layout.addWidget(self.use_existing_radio) + + # 使用NDWI自动生成 + self.use_ndwi_radio = QRadioButton("使用NDWI自动生成") + method_layout.addWidget(self.use_ndwi_radio) + + method_group.setLayout(method_layout) + layout.addWidget(method_group) + + # 掩膜文件选择 + self.mask_file = FileSelectWidget( + "掩膜文件:", + "Shapefiles (*.shp);;Raster Files (*.dat *.tif);;All Files (*.*)" + ) + layout.addWidget(self.mask_file) + + # 影像文件选择（用于shp栅格化或NDWI生成） + self.img_file = FileSelectWidget( + "参考影像:", + "Image Files (*.bsq *.dat *.tif);;All Files (*.*)" + ) + layout.addWidget(self.img_file) + + # NDWI参数设置 + ndwi_group = QGroupBox("NDWI参数设置") + ndwi_layout = QVBoxLayout() + + # NDWI阈值 + threshold_layout = QHBoxLayout() + threshold_layout.addWidget(QLabel("NDWI阈值:")) + self.ndwi_threshold = QDoubleSpinBox() + self.ndwi_threshold.setRange(0.0, 1.0) + self.ndwi_threshold.setSingleStep(0.05) + self.ndwi_threshold.setValue(0.4) + self.ndwi_threshold.setDecimals(2) + threshold_layout.addWidget(self.ndwi_threshold) + threshold_layout.addStretch() + ndwi_layout.addLayout(threshold_layout) + + ndwi_group.setLayout(ndwi_layout) + layout.addWidget(ndwi_group) + + # 输出文件路径 + self.output_file = FileSelectWidget( + "输出掩膜:", + "Mask Files (*.dat *.tif);;All Files (*.*)" + ) + self.output_file.line_edit.setPlaceholderText("water_mask.dat") + layout.addWidget(self.output_file) + + # 提示信息 + hint = QLabel("提示: 如果掩膜文件是Shapefile(.shp)，需要提供参考影像用于栅格化；如果使用NDWI自动生成，只需要提供参考影像") + hint.setStyleSheet("color: #666; font-size: 10px;") + layout.addWidget(hint) + + # 启用步骤 + self.enable_checkbox = QCheckBox("启用此步骤") + self.enable_checkbox.setChecked(True) + layout.addWidget(self.enable_checkbox) + + # 独立运行按钮 + self.run_btn = QPushButton("独立运行此步骤") + self.run_btn.setStyleSheet(ModernStylesheet.get_button_stylesheet('success')) + self.run_btn.clicked.connect(self.run_step) + layout.addWidget(self.run_btn) + + # 连接信号 + self.use_existing_radio.toggled.connect(self.update_ui_state) + self.use_ndwi_radio.toggled.connect(self.update_ui_state) + + layout.addStretch() + self.setLayout(layout) + + # 初始UI状态 + self.update_ui_state() + + def update_ui_state(self): + """根据选择的掩膜生成方式更新UI状态""" + use_ndwi = self.use_ndwi_radio.isChecked() + + # 掩膜文件在NDWI模式下禁用 + self.mask_file.setEnabled(not use_ndwi) + + # 影像文件在两种模式下都需要 + self.img_file.setEnabled(True) + + # NDWI参数在NDWI模式下启用 + for i in range(self.layout().count()): + widget = self.layout().itemAt(i).widget() + if widget and isinstance(widget, QGroupBox) and widget.title() == "NDWI参数设置": + widget.setEnabled(use_ndwi) + break + + def get_config(self): + """获取配置""" + config = { + 'mask_path': None if self.use_ndwi_radio.isChecked() else self.mask_file.get_path(), + 'use_ndwi': self.use_ndwi_radio.isChecked(), + 'ndwi_threshold': self.ndwi_threshold.value() + } + img_path = self.img_file.get_path() + if img_path: + config['img_path'] = img_path + output_path = self.output_file.get_path() + if output_path: + config['output_path'] = output_path + return config + + def set_config(self, config): + """设置配置""" + if 'mask_path' in config: + self.mask_file.set_path(config['mask_path']) + if 'img_path' in config: + self.img_file.set_path(config['img_path']) + if 'output_path' in config: + self.output_file.set_path(config['output_path']) + if 'use_ndwi' in config: + if config['use_ndwi']: + self.use_ndwi_radio.setChecked(True) + else: + self.use_existing_radio.setChecked(True) + if 'ndwi_threshold' in config: + self.ndwi_threshold.setValue(config['ndwi_threshold']) + + self.update_ui_state() + + def run_step(self): + """独立运行步骤1""" + # 验证输入 + if self.use_ndwi_radio.isChecked(): + # NDWI模式：需要影像文件 + img_path = self.img_file.get_path() + if not img_path: + QMessageBox.warning(self, "输入错误", "请选择参考影像文件！") + return + else: + # 现有掩膜模式：需要掩膜文件 + mask_path = self.mask_file.get_path() + if not mask_path: + QMessageBox.warning(self, "输入错误", "请选择掩膜文件！") + return + + # 如果是shp文件，还需要影像文件 + if mask_path.lower().endswith('.shp'): + img_path = self.img_file.get_path() + if not img_path: + QMessageBox.warning(self, "输入错误", "当使用shp文件时，需要提供参考影像用于栅格化！") + return + + # 获取父窗口并运行步骤 + parent = self.parent() + while parent and not hasattr(parent, 'run_single_step'): + parent = parent.parent() + + if parent and hasattr(parent, 'run_single_step'): + config = {'step1': self.get_config()} + parent.run_single_step("step1", config) + + +class Step2Panel(QWidget): + """2. 耀斑区域识别""" + def __init__(self, parent=None): + super().__init__(parent) + self.init_ui() + + def init_ui(self): + layout = QVBoxLayout() + + # 标题 + + + # 影像文件 + self.img_file = FileSelectWidget( + "影像文件:", + "Image Files (*.bsq *.dat *.tif);;All Files (*.*)" + ) + layout.addWidget(self.img_file) + + # 水域掩膜文件（可选，用于独立运行） + self.water_mask_file = FileSelectWidget( + "水域掩膜:", + "Mask Files (*.dat *.tif);;All Files (*.*)" + ) + self.water_mask_file.label.setText("水域掩膜（可选）:") + layout.addWidget(self.water_mask_file) + + # 参数设置 + params_group = QGroupBox("检测参数") + params_layout = QFormLayout() + + # 耀斑波长 + self.glint_wave = QDoubleSpinBox() + self.glint_wave.setRange(300, 1000) + self.glint_wave.setValue(750.0) + self.glint_wave.setSuffix(" nm") + params_layout.addRow("耀斑检测波长:", self.glint_wave) + + # 检测方法 + self.method = QComboBox() + self.method.addItems(['otsu', 'zscore', 'percentile', 'iqr', 'adaptive', 'multi_band']) + params_layout.addRow("检测方法:", self.method) + + # 最大连通域面积 + self.max_area = QSpinBox() + self.max_area.setRange(0, 100000) + self.max_area.setValue(50) + self.max_area.setSpecialValueText("不过滤") + params_layout.addRow("最大连通域面积:", self.max_area) + + # 岸边缓冲区 + self.buffer_size = QSpinBox() + self.buffer_size.setRange(0, 200) + self.buffer_size.setValue(10) + self.buffer_size.setSpecialValueText("不设置") + params_layout.addRow("岸边缓冲区大小:", self.buffer_size) + + params_group.setLayout(params_layout) + layout.addWidget(params_group) + + # 输出文件路径 + self.output_file = FileSelectWidget( + "输出耀斑掩膜:", + "Mask Files (*.dat *.tif);;All Files (*.*)" + ) + self.output_file.line_edit.setPlaceholderText("glint_mask.dat") + layout.addWidget(self.output_file) + + # 启用步骤 + self.enable_checkbox = QCheckBox("启用此步骤") + self.enable_checkbox.setChecked(True) + layout.addWidget(self.enable_checkbox) + + # 独立运行按钮 + self.run_btn = QPushButton("独立运行此步骤") + self.run_btn.setStyleSheet(ModernStylesheet.get_button_stylesheet('success')) + self.run_btn.clicked.connect(self.run_step) + layout.addWidget(self.run_btn) + + layout.addStretch() + self.setLayout(layout) + + def get_config(self): + """获取配置""" + config = { + 'img_path': self.img_file.get_path(), + 'glint_wave': self.glint_wave.value(), + 'method': self.method.currentText(), + } + if self.max_area.value() > 0: + config['max_area'] = self.max_area.value() + if self.buffer_size.value() > 0: + config['buffer_size'] = self.buffer_size.value() + # 添加水域掩膜路径（用于独立运行） + water_mask_path = self.water_mask_file.get_path() + if water_mask_path: + config['water_mask_path'] = water_mask_path + # 添加输出路径 + output_path = self.output_file.get_path() + if output_path: + config['output_path'] = output_path + return config + + def set_config(self, config): + """设置配置""" + if 'img_path' in config: + self.img_file.set_path(config['img_path']) + if 'glint_wave' in config: + self.glint_wave.setValue(config['glint_wave']) + if 'method' in config: + idx = self.method.findText(config['method']) + if idx >= 0: + self.method.setCurrentIndex(idx) + if 'max_area' in config: + self.max_area.setValue(config['max_area']) + if 'buffer_size' in config: + self.buffer_size.setValue(config['buffer_size']) + if 'water_mask_path' in config: + self.water_mask_file.set_path(config['water_mask_path']) + if 'output_path' in config: + self.output_file.set_path(config['output_path']) + + def run_step(self): + """独立运行步骤2""" + # 验证输入 + img_path = self.img_file.get_path() + if not img_path: + QMessageBox.warning(self, "输入错误", "请选择影像文件！") + return + + # 获取主窗口并运行步骤 + main_window = self.window() + if hasattr(main_window, 'run_single_step'): + config = {'step2': self.get_config()} + main_window.run_single_step('step2', config) + + +class Step3Panel(QWidget): + """步骤3：耀斑去除""" + def __init__(self, parent=None): + super().__init__(parent) + self.init_ui() + + def init_ui(self): + layout = QVBoxLayout() + + # 标题 + + + # 影像文件 + self.img_file = FileSelectWidget( + "影像文件:", + "Image Files (*.bsq *.dat *.tif);;All Files (*.*)" + ) + layout.addWidget(self.img_file) + + # 水域掩膜/边界：完整流程可由步骤1自动生成；独立单步运行时须手动指定 + self.water_mask_file = FileSelectWidget( + "水域掩膜/边界:", + "Mask/Boundary (*.dat *.tif *.shp);;All Files (*.*)" + ) + layout.addWidget(self.water_mask_file) + step3_mask_hint = QLabel( + "提示：独立运行本步骤时必须选择水域掩膜或边界（与影像同区域的 .dat/.tif 掩膜，或 .shp 矢量）。" + ) + step3_mask_hint.setWordWrap(True) + step3_mask_hint.setStyleSheet("color: #666; font-size: 10px;") + layout.addWidget(step3_mask_hint) + + # 方法选择 + method_group = QGroupBox("去耀斑方法") + method_layout = QVBoxLayout() + + self.method = QComboBox() + self.method.addItems(['goodman', 'kutser', 'hedley', 'sugar']) + self.method.currentTextChanged.connect(self.on_method_changed) + method_layout.addWidget(self.method) + + method_group.setLayout(method_layout) + layout.addWidget(method_group) + + # Goodman参数组 + self.goodman_group = QGroupBox("Goodman方法参数") + goodman_layout = QFormLayout() + + self.nir_lower = QSpinBox() + self.nir_lower.setRange(0, 200) + self.nir_lower.setValue(65) + goodman_layout.addRow("NIR下波段索引:", self.nir_lower) + + self.nir_upper = QSpinBox() + self.nir_upper.setRange(0, 200) + self.nir_upper.setValue(91) + goodman_layout.addRow("NIR上波段索引:", self.nir_upper) + + self.goodman_a = QDoubleSpinBox() + self.goodman_a.setDecimals(6) + self.goodman_a.setRange(0, 1) + self.goodman_a.setValue(0.000019) + goodman_layout.addRow("参数A:", self.goodman_a) + + self.goodman_b = QDoubleSpinBox() + self.goodman_b.setDecimals(2) + self.goodman_b.setRange(0, 1) + self.goodman_b.setValue(0.1) + goodman_layout.addRow("参数B:", self.goodman_b) + + self.goodman_group.setLayout(goodman_layout) + layout.addWidget(self.goodman_group) + + # Kutser参数组 + self.kutser_group = QGroupBox("Kutser方法参数") + kutser_layout = QFormLayout() + + self.oxy_band = QSpinBox() + self.oxy_band.setRange(0, 200) + self.oxy_band.setValue(8) + kutser_layout.addRow("氧吸收波段索引:", self.oxy_band) + + self.lower_oxy = QDoubleSpinBox() + self.lower_oxy.setDecimals(2) + self.lower_oxy.setRange(0, 1000) + self.lower_oxy.setValue(756.54) + kutser_layout.addRow("下氧吸收波长(nm):", self.lower_oxy) + + self.upper_oxy = QDoubleSpinBox() + self.upper_oxy.setDecimals(2) + self.upper_oxy.setRange(0, 1000) + self.upper_oxy.setValue(766.54) + kutser_layout.addRow("上氧吸收波长(nm):", self.upper_oxy) + + self.nir_band = QSpinBox() + self.nir_band.setRange(0, 200) + self.nir_band.setValue(65) + kutser_layout.addRow("NIR波段索引:", self.nir_band) + + self.kutser_group.setLayout(kutser_layout) + self.kutser_group.setVisible(False) + layout.addWidget(self.kutser_group) + + # Hedley参数组 + self.hedley_group = QGroupBox("Hedley方法参数") + hedley_layout = QFormLayout() + + self.hedley_nir_band = QSpinBox() + self.hedley_nir_band.setRange(0, 200) + self.hedley_nir_band.setValue(47) + hedley_layout.addRow("NIR波段索引:", self.hedley_nir_band) + + self.hedley_group.setLayout(hedley_layout) + self.hedley_group.setVisible(False) + layout.addWidget(self.hedley_group) + + # SUGAR参数组 + self.sugar_group = QGroupBox("SUGAR方法参数") + sugar_layout = QFormLayout() + + self.sugar_iter = QSpinBox() + self.sugar_iter.setRange(1, 20) + self.sugar_iter.setValue(3) + self.sugar_iter.setSpecialValueText("自动") + sugar_layout.addRow("迭代次数:", self.sugar_iter) + + self.sugar_sigma = QDoubleSpinBox() + self.sugar_sigma.setDecimals(2) + self.sugar_sigma.setRange(0.1, 10) + self.sugar_sigma.setValue(1.0) + sugar_layout.addRow("LoG平滑σ:", self.sugar_sigma) + + self.sugar_estimate_background = QCheckBox() + self.sugar_estimate_background.setChecked(True) + sugar_layout.addRow("估计背景光谱:", self.sugar_estimate_background) + + self.sugar_glint_mask_method = QComboBox() + self.sugar_glint_mask_method.addItems(['cdf', 'otsu']) + self.sugar_glint_mask_method.setCurrentText('cdf') + sugar_layout.addRow("耀斑掩膜方法:", self.sugar_glint_mask_method) + + self.sugar_termination_thresh = QDoubleSpinBox() + self.sugar_termination_thresh.setDecimals(2) + self.sugar_termination_thresh.setRange(1, 100) + self.sugar_termination_thresh.setValue(20.0) + sugar_layout.addRow("终止阈值:", self.sugar_termination_thresh) + + self.sugar_bounds = QLineEdit() + self.sugar_bounds.setText("[(1, 2)]") + sugar_layout.addRow("优化边界:", self.sugar_bounds) + + self.sugar_group.setLayout(sugar_layout) + self.sugar_group.setVisible(False) + layout.addWidget(self.sugar_group) + + # 插值选项 + interp_group = QGroupBox("0值像素插值") + interp_layout = QFormLayout() + + self.interpolate_zeros = QCheckBox("启用插值") + interp_layout.addRow("", self.interpolate_zeros) + + self.interp_method = QComboBox() + self.interp_method.addItems(['nearest', 'bilinear', 'spline', 'kriging']) + self.interp_method.setCurrentText('bilinear') + interp_layout.addRow("插值方法:", self.interp_method) + + interp_group.setLayout(interp_layout) + layout.addWidget(interp_group) + + # 输出文件路径 + self.output_file = FileSelectWidget( + "输出影像:", + "Image Files (*.bsq *.dat *.tif);;All Files (*.*)" + ) + self.output_file.line_edit.setPlaceholderText("deglint_image.dat") + layout.addWidget(self.output_file) + + # 启用步骤 + self.enable_checkbox = QCheckBox("启用此步骤") + self.enable_checkbox.setChecked(True) + layout.addWidget(self.enable_checkbox) + + # 独立运行按钮 + self.run_btn = QPushButton("独立运行此步骤") + self.run_btn.setStyleSheet(ModernStylesheet.get_button_stylesheet('success')) + self.run_btn.clicked.connect(self.run_step) + layout.addWidget(self.run_btn) + + layout.addStretch() + self.setLayout(layout) + + def on_method_changed(self, method): + """方法改变时更新参数显示""" + self.goodman_group.setVisible(method == 'goodman') + self.kutser_group.setVisible(method == 'kutser') + self.hedley_group.setVisible(method == 'hedley') + self.sugar_group.setVisible(method == 'sugar') + + def get_config(self): + """获取配置""" + config = { + 'img_path': self.img_file.get_path(), + 'method': self.method.currentText(), + 'enabled': self.enable_checkbox.isChecked(), + 'interpolate_zeros': self.interpolate_zeros.isChecked(), + 'interpolation_method': self.interp_method.currentText(), + } + water_mask_path = self.water_mask_file.get_path() + if water_mask_path: + config['water_mask'] = water_mask_path + output_path = self.output_file.get_path() + if output_path: + config['output_path'] = output_path + + method = self.method.currentText() + + if method == 'goodman': + config['nir_lower'] = self.nir_lower.value() + config['nir_upper'] = self.nir_upper.value() + config['goodman_A'] = self.goodman_a.value() + config['goodman_B'] = self.goodman_b.value() + + elif method == 'kutser': + config['oxy_band'] = self.oxy_band.value() + config['lower_oxy'] = self.lower_oxy.value() + config['upper_oxy'] = self.upper_oxy.value() + config['nir_band'] = self.nir_band.value() + + elif method == 'hedley': + config['hedley_nir_band'] = self.hedley_nir_band.value() + + elif method == 'sugar': + config['sugar_iter'] = self.sugar_iter.value() if self.sugar_iter.value() > 0 else None + config['sugar_sigma'] = self.sugar_sigma.value() + config['sugar_estimate_background'] = self.sugar_estimate_background.isChecked() + config['sugar_glint_mask_method'] = self.sugar_glint_mask_method.currentText() + config['sugar_termination_thresh'] = self.sugar_termination_thresh.value() + # 解析bounds字符串 + try: + import ast + config['sugar_bounds'] = ast.literal_eval(self.sugar_bounds.text()) + except: + config['sugar_bounds'] = [(1, 2)] # 默认值 + + return config + + def set_config(self, config): + """设置配置""" + if 'img_path' in config: + self.img_file.set_path(config['img_path']) + if 'water_mask' in config: + self.water_mask_file.set_path(config['water_mask']) + if 'output_path' in config: + self.output_file.set_path(config['output_path']) + if 'method' in config: + idx = self.method.findText(config['method']) + if idx >= 0: + self.method.setCurrentIndex(idx) + if 'enabled' in config: + self.enable_checkbox.setChecked(config['enabled']) + if 'interpolate_zeros' in config: + self.interpolate_zeros.setChecked(config['interpolate_zeros']) + if 'interpolation_method' in config: + idx = self.interp_method.findText(config['interpolation_method']) + if idx >= 0: + self.interp_method.setCurrentIndex(idx) + + # Goodman参数 + if 'nir_lower' in config: + self.nir_lower.setValue(config['nir_lower']) + if 'nir_upper' in config: + self.nir_upper.setValue(config['nir_upper']) + if 'goodman_A' in config: + self.goodman_a.setValue(config['goodman_A']) + if 'goodman_B' in config: + self.goodman_b.setValue(config['goodman_B']) + + # Kutser参数 + if 'oxy_band' in config: + self.oxy_band.setValue(config['oxy_band']) + if 'lower_oxy' in config: + self.lower_oxy.setValue(config['lower_oxy']) + if 'upper_oxy' in config: + self.upper_oxy.setValue(config['upper_oxy']) + if 'nir_band' in config: + self.nir_band.setValue(config['nir_band']) + + # Hedley参数 + if 'hedley_nir_band' in config: + self.hedley_nir_band.setValue(config['hedley_nir_band']) + + # SUGAR参数 + if 'sugar_iter' in config: + self.sugar_iter.setValue(config['sugar_iter'] if config['sugar_iter'] is not None else 0) + if 'sugar_sigma' in config: + self.sugar_sigma.setValue(config['sugar_sigma']) + if 'sugar_estimate_background' in config: + self.sugar_estimate_background.setChecked(config['sugar_estimate_background']) + if 'sugar_glint_mask_method' in config: + idx = self.sugar_glint_mask_method.findText(config['sugar_glint_mask_method']) + if idx >= 0: + self.sugar_glint_mask_method.setCurrentIndex(idx) + if 'sugar_termination_thresh' in config: + self.sugar_termination_thresh.setValue(config['sugar_termination_thresh']) + if 'sugar_bounds' in config: + self.sugar_bounds.setText(str(config['sugar_bounds'])) + + def run_step(self): + """独立运行步骤3""" + # 验证输入 + img_path = self.img_file.get_path() + if not img_path: + QMessageBox.warning(self, "输入错误", "请选择影像文件！") + return + if self.enable_checkbox.isChecked(): + water_mask_path = self.water_mask_file.get_path() + if not water_mask_path: + QMessageBox.warning( + self, + "输入错误", + "独立运行耀斑去除时，必须选择水域掩膜或边界文件。\n\n" + "请提供与当前影像空间一致的水域栅格掩膜（.dat/.tif），或水域矢量边界（.shp）。\n" + "若刚跑过完整流程，可使用步骤1生成的水域掩膜文件。", + ) + return + + # 获取主窗口并运行步骤 + main_window = self.window() + if hasattr(main_window, 'run_single_step'): + config = {'step3': self.get_config()} + main_window.run_single_step('step3', config) + + +class Step4Panel(QWidget): + """步骤4：数据预处理""" + def __init__(self, parent=None): + super().__init__(parent) + self.init_ui() + + def init_ui(self): + layout = QVBoxLayout() + + # 标题 + + # CSV文件 + self.csv_file = FileSelectWidget( + "水质参数文件:", + "CSV Files (*.csv);;All Files (*.*)" + ) + layout.addWidget(self.csv_file) + + hint = QLabel("提示: 处理CSV文件，筛选剔除异常值") + hint.setStyleSheet("color: #666; font-size: 10px;") + layout.addWidget(hint) + + preview_group = QGroupBox("CSV数据预览") + preview_layout = QVBoxLayout() + + controls_layout = QHBoxLayout() + controls_layout.addWidget(QLabel("预览行数:")) + self.preview_rows_spin = QSpinBox() + self.preview_rows_spin.setRange(1, 200) + self.preview_rows_spin.setValue(10) + controls_layout.addWidget(self.preview_rows_spin) + self.preview_btn = QPushButton("刷新预览") + self.preview_btn.clicked.connect(self.load_csv_preview) + controls_layout.addWidget(self.preview_btn) + controls_layout.addStretch() + + self.preview_table = QTableView() + self.preview_table.setEditTriggers(QAbstractItemView.NoEditTriggers) + self.preview_table.setSelectionBehavior(QAbstractItemView.SelectRows) + self.preview_table.setSelectionMode(QAbstractItemView.SingleSelection) + self.preview_table.horizontalHeader().setSectionResizeMode(QHeaderView.Stretch) + self.preview_table.verticalHeader().setVisible(False) + self.preview_table.setMinimumHeight(200) + + self.preview_status_label = QLabel("请选择CSV文件并点击刷新预览") + self.preview_status_label.setStyleSheet("color: #666; font-size: 11px;") + + preview_layout.addLayout(controls_layout) + preview_layout.addWidget(self.preview_table) + preview_layout.addWidget(self.preview_status_label) + preview_group.setLayout(preview_layout) + layout.addWidget(preview_group) + + # 输出文件路径 + self.output_file = FileSelectWidget( + "输出处理后CSV:", + "CSV Files (*.csv);;All Files (*.*)" + ) + self.output_file.line_edit.setPlaceholderText("processed_data.csv") + layout.addWidget(self.output_file) + + # 启用步骤 + self.enable_checkbox = QCheckBox("启用此步骤") + self.enable_checkbox.setChecked(True) + layout.addWidget(self.enable_checkbox) + + # 独立运行按钮 + self.run_btn = QPushButton("独立运行此步骤") + self.run_btn.setStyleSheet(ModernStylesheet.get_button_stylesheet('success')) + self.run_btn.clicked.connect(self.run_step) + layout.addWidget(self.run_btn) + + layout.addStretch() + self.setLayout(layout) + self.reset_preview() + + def get_config(self): + """获取配置""" + config = { + 'csv_path': self.csv_file.get_path(), + } + output_path = self.output_file.get_path() + if output_path: + config['output_path'] = output_path + return config + + def set_config(self, config): + """设置配置""" + if 'csv_path' in config: + self.csv_file.set_path(config['csv_path']) + self.load_csv_preview() + if 'output_path' in config: + self.output_file.set_path(config['output_path']) + + def run_step(self): + """独立运行步骤4""" + # 验证输入 + csv_path = self.csv_file.get_path() + if not csv_path: + QMessageBox.warning(self, "输入错误", "请选择水质参数文件！") + return + + # 获取主窗口并运行步骤 + main_window = self.window() + if hasattr(main_window, 'run_single_step'): + config = {'step4': self.get_config()} + main_window.run_single_step('step4', config) + + def reset_preview(self, message="请选择CSV文件并点击刷新预览"): + """重置预览表格""" + empty_model = PandasTableModel(pd.DataFrame()) + self.preview_table.setModel(empty_model) + self.preview_status_label.setText(message) + + def load_csv_preview(self): + """加载CSV预览数据""" + csv_path = self.csv_file.get_path() + if not csv_path: + self.reset_preview("请先选择CSV文件") + return + if not os.path.exists(csv_path): + self.reset_preview("文件不存在，请检查路径") + return + + try: + rows_to_preview = max(1, self.preview_rows_spin.value()) + df = pd.read_csv(csv_path, nrows=rows_to_preview) + if df.empty: + self.reset_preview("CSV文件为空") + return + + model = PandasTableModel(df) + self.preview_table.setModel(model) + self.preview_status_label.setText( + f"预览 {len(df)} 行，{len(df.columns)} 列（总行数可能更多）" + ) + except Exception as exc: + self.reset_preview(f"加载失败: {exc}") + + +class Step5Panel(QWidget): + """步骤5：光谱提取""" + def __init__(self, parent=None): + super().__init__(parent) + self.init_ui() + + def init_ui(self): + layout = QVBoxLayout() + + # 标题 + title = QLabel("步骤5：训练样本光谱提取") + title.setFont(QFont("Arial", 12, QFont.Bold)) + layout.addWidget(title) + + # 去耀斑影像文件（用于独立运行） + self.deglint_img_file = FileSelectWidget( + "去耀斑影像:", + "Image Files (*.bsq *.dat *.tif);;All Files (*.*)" + ) + layout.addWidget(self.deglint_img_file) + + # 处理后的CSV文件（用于独立运行） + self.csv_file = FileSelectWidget( + "处理后CSV:", + "CSV Files (*.csv);;All Files (*.*)" + ) + layout.addWidget(self.csv_file) + + # 水体掩膜文件（可选，用于独立运行） + self.boundary_mask_file = FileSelectWidget( + "水体掩膜:", + "Mask Files (*.dat *.tif);;All Files (*.*)" + ) + self.boundary_mask_file.line_edit.setPlaceholderText("可选，如不选择则自动生成") + layout.addWidget(self.boundary_mask_file) + + self.glint_mask_file = FileSelectWidget( + "耀斑掩膜:", + "Mask Files (*.dat *.tif);;All Files (*.*)" + ) + layout.addWidget(self.glint_mask_file) + step5_glint_hint = QLabel( + "提示：独立运行本步骤时必须选择耀斑掩膜（通常为步骤2输出的 severe_glint_area.dat），用于在采样时避开耀斑像元。" + ) + step5_glint_hint.setWordWrap(True) + step5_glint_hint.setStyleSheet("color: #666; font-size: 10px;") + layout.addWidget(step5_glint_hint) + + # 参数设置 + params_group = QGroupBox("提取参数") + params_layout = QFormLayout() + + self.radius = QSpinBox() + self.radius.setRange(1, 50) + self.radius.setValue(5) + params_layout.addRow("采样半径(像素):", self.radius) + + self.source_epsg = QSpinBox() + self.source_epsg.setRange(1000, 99999) + self.source_epsg.setValue(4326) + params_layout.addRow("源坐标系EPSG:", self.source_epsg) + + params_group.setLayout(params_layout) + layout.addWidget(params_group) + + # 输出文件路径 + self.output_file = FileSelectWidget( + "输出训练数据:", + "CSV Files (*.csv);;All Files (*.*)" + ) + self.output_file.line_edit.setPlaceholderText("training_spectra.csv") + layout.addWidget(self.output_file) + + # 启用步骤 + self.enable_checkbox = QCheckBox("启用此步骤") + self.enable_checkbox.setChecked(True) + layout.addWidget(self.enable_checkbox) + + # 独立运行按钮 + self.run_btn = QPushButton("独立运行此步骤") + self.run_btn.setStyleSheet(ModernStylesheet.get_button_stylesheet('success')) + self.run_btn.clicked.connect(self.run_step) + layout.addWidget(self.run_btn) + + layout.addStretch() + self.setLayout(layout) + + def get_config(self): + """获取配置""" + config = { + 'radius': self.radius.value(), + 'source_epsg': self.source_epsg.value(), + } + # 添加独立运行所需的文件路径 + deglint_img_path = self.deglint_img_file.get_path() + if deglint_img_path: + config['deglint_img_path'] = deglint_img_path + csv_path = self.csv_file.get_path() + if csv_path: + config['csv_path'] = csv_path + boundary_path = self.boundary_mask_file.get_path() + if boundary_path: + config['boundary_path'] = boundary_path + glint_mask_path = self.glint_mask_file.get_path() + if glint_mask_path: + config['glint_mask_path'] = glint_mask_path + # 添加输出路径 + output_path = self.output_file.get_path() + if output_path: + config['output_path'] = output_path + return config + + def set_config(self, config): + """设置配置""" + if 'radius' in config: + self.radius.setValue(config['radius']) + if 'source_epsg' in config: + self.source_epsg.setValue(config['source_epsg']) + if 'deglint_img_path' in config: + self.deglint_img_file.set_path(config['deglint_img_path']) + if 'csv_path' in config: + self.csv_file.set_path(config['csv_path']) + if 'boundary_path' in config: + self.boundary_mask_file.set_path(config['boundary_path']) + if 'glint_mask_path' in config: + self.glint_mask_file.set_path(config['glint_mask_path']) + if 'output_path' in config: + self.output_file.set_path(config['output_path']) + + def run_step(self): + """独立运行步骤5""" + # 验证输入 + deglint_img_path = self.deglint_img_file.get_path() + csv_path = self.csv_file.get_path() + if not deglint_img_path: + QMessageBox.warning(self, "输入错误", "请选择去耀斑影像文件！") + return + if not csv_path: + QMessageBox.warning(self, "输入错误", "请选择处理后的CSV文件！") + return + if not self.glint_mask_file.get_path(): + QMessageBox.warning( + self, + "输入错误", + "独立运行光谱特征提取时，必须选择耀斑掩膜文件。\n\n" + "请提供与去耀斑影像对应的耀斑二值掩膜（一般为步骤2输出的 severe_glint_area.dat）。", + ) + return + + # 获取主窗口并运行步骤 + main_window = self.window() + if hasattr(main_window, 'run_single_step'): + config = {'step5': self.get_config()} + main_window.run_single_step('step5', config) + + +class Step5_5Panel(QWidget): + """步骤5.5：水质指数计算""" + + def __init__(self, parent=None): + super().__init__(parent) + self.index_checkboxes: Dict[str, QCheckBox] = {} + self.csv_columns = [] # 存储CSV文件列名 + self.init_ui() + + def init_ui(self): + main_layout = QVBoxLayout() + + # 标题 + + + # 数据文件选择 + data_group = QGroupBox("数据文件") + data_layout = QVBoxLayout() + + # 训练数据CSV文件选择 + self.training_data_widget = FileSelectWidget("训练数据CSV文件:", "CSV Files (*.csv)") + data_layout.addWidget(self.training_data_widget) + + # 公式CSV文件选择 + self.formula_csv_widget = FileSelectWidget("公式CSV文件:", "CSV Files (*.csv)") + data_layout.addWidget(self.formula_csv_widget) + + # 刷新公式按钮 + refresh_layout = QHBoxLayout() + self.refresh_button = QPushButton("刷新公式列表") + self.refresh_button.clicked.connect(self.refresh_formulas) + refresh_layout.addWidget(self.refresh_button) + refresh_layout.addStretch() + data_layout.addLayout(refresh_layout) + + data_group.setLayout(data_layout) + main_layout.addWidget(data_group) + + # 公式选择区域 + self.formula_group = QGroupBox("选择要计算的公式") + formula_outer_layout = QVBoxLayout() + + # 按钮控制区域 + button_layout = QHBoxLayout() + self.select_all_btn = QPushButton("全选") + self.select_all_btn.clicked.connect(self.select_all_formulas) + self.deselect_all_btn = QPushButton("清空") + self.deselect_all_btn.clicked.connect(self.deselect_all_formulas) + button_layout.addWidget(self.select_all_btn) + button_layout.addWidget(self.deselect_all_btn) + button_layout.addStretch() + + formula_outer_layout.addLayout(button_layout) + + # 公式勾选框网格布局 + self.formula_layout = QGridLayout() + formula_outer_layout.addLayout(self.formula_layout) + + self.formula_group.setLayout(formula_outer_layout) + main_layout.addWidget(self.formula_group) + + # 输出文件设置 + output_group = QGroupBox("输出设置") + output_layout = QVBoxLayout() + + output_hbox = QHBoxLayout() + output_hbox.addWidget(QLabel("输出文件名:")) + self.output_filename = QLineEdit("water_quality_indices.csv") + output_hbox.addWidget(self.output_filename) + output_layout.addLayout(output_hbox) + + output_group.setLayout(output_layout) + main_layout.addWidget(output_group) + + # 启用选项 + self.enable_checkbox = QCheckBox("启用此步骤") + self.enable_checkbox.setChecked(True) + main_layout.addWidget(self.enable_checkbox) + + # 独立运行按钮 + self.run_button = QPushButton("独立运行此步骤") + self.run_button.setStyleSheet(""" + QPushButton { + background-color: #4CAF50; + color: white; + padding: 8px 16px; + border: none; + border-radius: 4px; + font-weight: bold; + } + QPushButton:hover { + background-color: #45a049; + } + QPushButton:pressed { + background-color: #3e8e41; + } + """) + self.run_button.clicked.connect(self.run_step) + main_layout.addWidget(self.run_button) + + # 公式编辑区域 + formula_edit_group = QGroupBox("添加自定义公式") + formula_edit_layout = QFormLayout() + + self.formula_name_edit = QLineEdit() + + # 公式类别下拉选择框 + self.formula_category_combo = QComboBox() + self.formula_category_combo.addItems([ + "chlorophyll_a", + "Phycocyanin (BGA_PC)", + "Total Nitrogen (TN)", + "Total Phosphorus (TP)", + "Orthophosphate", + "COD", + "BOD", + "TOC", + "Dissolved Oxygen (DO)", + "E. coli", + "Total Coliforms", + "Turbidity", + "Total Suspended Solids (TSS)", + "Color", + "pH", + "Temperature", + "Conductivity", + "Total Dissolved Solids (TDS)" + ]) + self.formula_category_combo.setEditable(True) # 允许用户输入自定义类别 + + self.formula_expression_edit = QLineEdit() + self.formula_reference_edit = QLineEdit() + + formula_edit_layout.addRow("公式名称:", self.formula_name_edit) + formula_edit_layout.addRow("公式类别:", self.formula_category_combo) + formula_edit_layout.addRow("公式表达式:", self.formula_expression_edit) + formula_edit_layout.addRow("参考文献:", self.formula_reference_edit) + + add_button = QPushButton("添加公式") + add_button.clicked.connect(self.add_custom_formula) + formula_edit_layout.addRow(add_button) + + formula_edit_group.setLayout(formula_edit_layout) + main_layout.addWidget(formula_edit_group) + + main_layout.addStretch() + self.setLayout(main_layout) + + def refresh_formulas(self): + """刷新公式列表""" + formula_csv_path = self.formula_csv_widget.get_path() + if not formula_csv_path or not os.path.exists(formula_csv_path): + QMessageBox.warning(self, "警告", "请先选择有效的公式CSV文件") + return + + try: + # 清除现有的勾选框 + for checkbox in self.index_checkboxes.values(): + self.formula_layout.removeWidget(checkbox) + checkbox.deleteLater() + self.index_checkboxes.clear() + + # 读取公式CSV文件 + df = pd.read_csv(formula_csv_path) + if df.empty or 'Formula_Name' not in df.columns: + QMessageBox.warning(self, "警告", "公式CSV文件格式不正确") + return + + # 获取所有公式名称（跳过第一行） + formula_names = df['Formula_Name'].tolist()[1:] + + # 创建3列布局的勾选框 + row, col = 0, 0 + for formula_name in formula_names: + if pd.isna(formula_name) or not formula_name.strip(): + continue + + checkbox = QCheckBox(formula_name.strip()) + checkbox.setChecked(True) + self.index_checkboxes[formula_name.strip()] = checkbox + self.formula_layout.addWidget(checkbox, row, col) + + col += 1 + if col >= 3: # 每行3列 + col = 0 + row += 1 + + except Exception as e: + QMessageBox.critical(self, "错误", f"读取公式文件失败: {str(e)}") + + def add_custom_formula(self): + """添加自定义公式到公式CSV文件""" + formula_csv_path = self.formula_csv_widget.get_path() + if not formula_csv_path: + QMessageBox.warning(self, "警告", "请先选择公式CSV文件") + return + + formula_name = self.formula_name_edit.text().strip() + formula_category = self.formula_category_combo.currentText().strip() + formula_expression = self.formula_expression_edit.text().strip() + formula_reference = self.formula_reference_edit.text().strip() + + if not all([formula_name, formula_category, formula_expression]): + QMessageBox.warning(self, "警告", "请填写公式名称、类别和表达式") + return + + try: + # 读取现有公式文件或创建新文件 + if os.path.exists(formula_csv_path): + df = pd.read_csv(formula_csv_path) + else: + df = pd.DataFrame(columns=['Formula_Name', 'Category', 'Formula', 'Reference']) + + # 添加新公式 + new_row = pd.DataFrame({ + 'Formula_Name': [formula_name], + 'Category': [formula_category], + 'Formula': [formula_expression], + 'Reference': [formula_reference] + }) + df = pd.concat([df, new_row], ignore_index=True) + + # 保存文件 + df.to_csv(formula_csv_path, index=False, encoding='utf-8') + + # 清空输入框 + self.formula_name_edit.clear() + self.formula_category_combo.setCurrentIndex(0) # 重置到第一个选项 + self.formula_expression_edit.clear() + self.formula_reference_edit.clear() + + # 刷新公式列表 + self.refresh_formulas() + + QMessageBox.information(self, "成功", "公式添加成功") + + except Exception as e: + QMessageBox.critical(self, "错误", f"添加公式失败: {str(e)}") + + def get_config(self) -> Dict[str, Union[List[str], str, bool]]: + """获取配置""" + selected = [ + name for name, checkbox in self.index_checkboxes.items() + if checkbox.isChecked() + ] + return { + 'training_spectra_path': self.training_data_widget.get_path() or None, + 'formula_csv_file': self.formula_csv_widget.get_path() or None, + 'formula_names': selected, + 'output_filename': self.output_filename.text().strip() or "water_quality_indices.csv", + 'enabled': self.enable_checkbox.isChecked() + } + + def set_config(self, config): + """设置配置""" + if 'training_spectra_path' in config: + self.training_data_widget.set_path(config['training_spectra_path']) + + if 'formula_csv_file' in config: + self.formula_csv_widget.set_path(config['formula_csv_file']) + # 设置CSV路径后自动刷新公式信息 + self.refresh_formulas() + + if 'formula_names' in config: + selected_formulas = set(config['formula_names']) + for name, checkbox in self.index_checkboxes.items(): + checkbox.setChecked(name in selected_formulas) + + if 'output_filename' in config: + self.output_filename.setText(config['output_filename']) + + if 'enabled' in config: + self.enable_checkbox.setChecked(config['enabled']) + + def is_enabled(self) -> bool: + return self.enable_checkbox.isChecked() + + def select_all_formulas(self): + """全选所有公式""" + for checkbox in self.index_checkboxes.values(): + checkbox.setChecked(True) + + def deselect_all_formulas(self): + """清空所有公式""" + for checkbox in self.index_checkboxes.values(): + checkbox.setChecked(False) + + def run_step(self): + """独立运行步骤5.5""" + # 验证输入 + training_csv_path = self.training_data_widget.get_path() + formula_csv_path = self.formula_csv_widget.get_path() + + if not training_csv_path: + QMessageBox.warning(self, "输入验证失败", "请选择训练数据CSV文件") + return + if not formula_csv_path: + QMessageBox.warning(self, "输入验证失败", "请选择公式CSV文件") + return + if not os.path.exists(training_csv_path): + QMessageBox.warning(self, "输入验证失败", "训练数据CSV文件不存在") + return + if not os.path.exists(formula_csv_path): + QMessageBox.warning(self, "输入验证失败", "公式CSV文件不存在") + return + + # 获取配置 + config = self.get_config() + + # 调用GUI的run_single_step方法 + parent = self.parent() + while parent and not hasattr(parent, 'run_single_step'): + parent = parent.parent() + + if parent and hasattr(parent, 'run_single_step'): + parent.run_single_step('step5_5', {'step5_5': config}) + else: + QMessageBox.critical(self, "错误", "无法找到父级GUI对象") + + +class Step6Panel(QWidget): + """步骤6：机器学习建模""" + def __init__(self, parent=None): + super().__init__(parent) + self.init_ui() + + def init_ui(self): + layout = QVBoxLayout() + + # 标题 + + + # 训练数据文件（用于独立运行） + self.training_csv_file = FileSelectWidget( + "训练数据:", + "CSV Files (*.csv);;All Files (*.*)" + ) + layout.addWidget(self.training_csv_file) + + # 机器学习模型页面 + self.ml_page = QWidget() + self.create_ml_page() + layout.addWidget(self.ml_page) + + # 输出文件路径 + self.output_dir = FileSelectWidget( + "输出模型目录:", + "Directories;;All Files (*.*)" + ) + self.output_dir.line_edit.setPlaceholderText("models_output") + # 修改浏览按钮为选择目录 + self.output_dir.browse_btn.clicked.disconnect() + self.output_dir.browse_btn.clicked.connect(self.browse_output_dir) + layout.addWidget(self.output_dir) + + # 启用步骤 + self.enable_checkbox = QCheckBox("启用此步骤") + self.enable_checkbox.setChecked(True) + layout.addWidget(self.enable_checkbox) + + # 独立运行按钮 + self.run_btn = QPushButton("独立运行此步骤") + self.run_btn.setStyleSheet(ModernStylesheet.get_button_stylesheet('success')) + self.run_btn.clicked.connect(self.run_step) + layout.addWidget(self.run_btn) + + layout.addStretch() + self.setLayout(layout) + + def create_ml_page(self): + """创建机器学习模型页面""" + layout = QVBoxLayout() + + # 参数设置 + params_group = QGroupBox("训练参数") + params_layout = QFormLayout() + + self.feature_start = QLineEdit() + self.feature_start.setText("374.285004") + params_layout.addRow("特征起始列:", self.feature_start) + + self.cv_folds = QSpinBox() + self.cv_folds.setRange(2, 10) + self.cv_folds.setValue(3) + params_layout.addRow("交叉验证折数:", self.cv_folds) + + params_group.setLayout(params_layout) + layout.addWidget(params_group) + + # 预处理方法 - 多选 + preproc_group = QGroupBox("预处理方法 (可多选)") + preproc_layout = QVBoxLayout() + + # 创建网格布局来放置checkbox + preproc_grid = QGridLayout() + self.preproc_checkboxes = {} + preproc_methods = ['None', 'MMS', 'SS', 'SNV', 'MA', 'SG', 'MSC', 'D1', 'D2', 'DT', 'CT'] + + for i, method in enumerate(preproc_methods): + checkbox = QCheckBox(method) + checkbox.setChecked(True) # 默认全选 + self.preproc_checkboxes[method] = checkbox + preproc_grid.addWidget(checkbox, i // 4, i % 4) + + # 全选/反选按钮 + button_layout = QHBoxLayout() + select_all_btn = QPushButton("全选") + deselect_all_btn = QPushButton("全不选") + select_all_btn.clicked.connect(lambda: self._toggle_checkboxes(self.preproc_checkboxes, True)) + deselect_all_btn.clicked.connect(lambda: self._toggle_checkboxes(self.preproc_checkboxes, False)) + button_layout.addWidget(select_all_btn) + button_layout.addWidget(deselect_all_btn) + button_layout.addStretch() + + preproc_layout.addLayout(preproc_grid) + preproc_layout.addLayout(button_layout) + preproc_group.setLayout(preproc_layout) + layout.addWidget(preproc_group) + + # 模型选择 - 多选 + model_group = QGroupBox("模型类型 (可多选)") + model_layout = QVBoxLayout() + + model_grid = QGridLayout() + self.model_checkboxes = {} + + # 按照模型类型分组排序 + model_groups = [ + ("线性模型", ['LinearRegression', 'Ridge', 'Lasso', 'ElasticNet', 'PLS']), + ("树模型", ['DecisionTree', 'RF', 'ExtraTrees', 'XGBoost', 'LightGBM', 'CatBoost']), + ("集成学习", ['GradientBoosting', 'AdaBoost']), + ("其他模型", ['SVR', 'KNN', 'MLP']) + ] + + row = 0 + for group_name, models in model_groups: + # 添加分组标签 + group_label = QLabel(f"{group_name}") + group_label.setStyleSheet(f"background-color: {ModernStylesheet.COLORS['hover']}; padding: 5px; border: 1px solid {ModernStylesheet.COLORS['border_light']}; border-radius: 3px;") + model_grid.addWidget(group_label, row, 0, 1, 4) # 跨4列 + row += 1 + + # 添加该组的模型checkbox + for i, model in enumerate(models): + checkbox = QCheckBox(model) + # 默认选择常用的4个 + checkbox.setChecked(model in ['SVR', 'RF', 'Ridge', 'Lasso']) + self.model_checkboxes[model] = checkbox + model_grid.addWidget(checkbox, row, i % 4) + + # 如果这一行满了，换到下一行 + if (i + 1) % 4 == 0: + row += 1 + + # 每组结束后换行 + row += 1 + + model_button_layout = QHBoxLayout() + model_select_all = QPushButton("全选") + model_deselect_all = QPushButton("全不选") + model_select_all.clicked.connect(lambda: self._toggle_checkboxes(self.model_checkboxes, True)) + model_deselect_all.clicked.connect(lambda: self._toggle_checkboxes(self.model_checkboxes, False)) + model_button_layout.addWidget(model_select_all) + model_button_layout.addWidget(model_deselect_all) + model_button_layout.addStretch() + + model_layout.addLayout(model_grid) + model_layout.addLayout(model_button_layout) + model_group.setLayout(model_layout) + layout.addWidget(model_group) + + # 数据划分方法 - 多选 + split_group = QGroupBox("数据划分方法 (可多选)") + split_layout = QVBoxLayout() + + split_grid = QGridLayout() + self.split_checkboxes = {} + split_methods = ['spxy', 'ks', 'random'] + + for i, method in enumerate(split_methods): + checkbox = QCheckBox(method) + checkbox.setChecked(True) # 默认全选 + self.split_checkboxes[method] = checkbox + split_grid.addWidget(checkbox, 0, i) + + split_button_layout = QHBoxLayout() + split_select_all = QPushButton("全选") + split_deselect_all = QPushButton("全不选") + split_select_all.clicked.connect(lambda: self._toggle_checkboxes(self.split_checkboxes, True)) + split_deselect_all.clicked.connect(lambda: self._toggle_checkboxes(self.split_checkboxes, False)) + split_button_layout.addWidget(split_select_all) + split_button_layout.addWidget(split_deselect_all) + split_button_layout.addStretch() + + split_layout.addLayout(split_grid) + split_layout.addLayout(split_button_layout) + split_group.setLayout(split_layout) + layout.addWidget(split_group) + + self.ml_page.setLayout(layout) + + + def _toggle_checkboxes(self, checkboxes_dict, checked): + """统一设置checkbox状态""" + for checkbox in checkboxes_dict.values(): + checkbox.setChecked(checked) + + def browse_output_dir(self): + """浏览输出目录""" + dir_path = QFileDialog.getExistingDirectory(self, "选择输出模型目录", "") + if dir_path: + self.output_dir.set_path(dir_path) + + def get_config(self): + """获取配置""" + # 获取选中的预处理方法 + preprocessing_methods = [method for method, checkbox in self.preproc_checkboxes.items() + if checkbox.isChecked()] + + # 获取选中的模型类型 + model_names = [model for model, checkbox in self.model_checkboxes.items() + if checkbox.isChecked()] + + # 获取选中的数据划分方法 + split_methods = [method for method, checkbox in self.split_checkboxes.items() + if checkbox.isChecked()] + + config = { + 'feature_start_column': self.feature_start.text(), + 'preprocessing_methods': preprocessing_methods if preprocessing_methods else ['None'], + 'model_names': model_names if model_names else ['SVR'], + 'split_methods': split_methods if split_methods else ['random'], + 'cv_folds': self.cv_folds.value() + } + # 添加训练数据路径（用于独立运行） + training_csv_path = self.training_csv_file.get_path() + if training_csv_path: + config['training_csv_path'] = training_csv_path + # 添加输出路径 + output_dir = self.output_dir.get_path() + if output_dir: + config['output_dir'] = output_dir + return config + + def set_config(self, config): + """设置配置""" + if 'feature_start_column' in config: + self.feature_start.setText(str(config['feature_start_column'])) + if 'cv_folds' in config: + self.cv_folds.setValue(config['cv_folds']) + + # 设置预处理方法 + if 'preprocessing_methods' in config: + methods = config['preprocessing_methods'] + for method, checkbox in self.preproc_checkboxes.items(): + checkbox.setChecked(method in methods) + + # 设置模型类型 + if 'model_names' in config: + models = config['model_names'] + for model, checkbox in self.model_checkboxes.items(): + checkbox.setChecked(model in models) + + # 设置数据划分方法 + if 'split_methods' in config: + methods = config['split_methods'] + for method, checkbox in self.split_checkboxes.items(): + checkbox.setChecked(method in methods) + if 'training_csv_path' in config: + self.training_csv_file.set_path(config['training_csv_path']) + if 'output_dir' in config: + self.output_dir.set_path(config['output_dir']) + + def run_step(self): + """独立运行步骤6""" + # 验证输入 + training_csv_path = self.training_csv_file.get_path() + if not training_csv_path: + QMessageBox.warning(self, "输入错误", "请选择训练数据CSV文件！") + return + + # 获取主窗口并运行步骤 + main_window = self.window() + if hasattr(main_window, 'run_single_step'): + config = {'step6': self.get_config()} + main_window.run_single_step('step6', config) + + def get_training_params(self): + """获取模型训练参数""" + return { + 'pipeline_type': 'machine_learning', + 'feature_start': float(self.feature_start.text()), + 'cv_folds': self.cv_folds.value(), + 'preprocess_methods': [method for method, cb in self.preproc_checkboxes.items() if cb.isChecked()], + 'model_types': [model for model, cb in self.model_checkboxes.items() if cb.isChecked()], + 'split_methods': [method for method, cb in self.split_checkboxes.items() if cb.isChecked()] + } + + +class Step7Panel(QWidget): + """步骤7：采样点生成""" + def __init__(self, parent=None): + super().__init__(parent) + self.init_ui() + + def init_ui(self): + layout = QVBoxLayout() + + # 标题 + + + # 去耀斑影像文件（用于独立运行） + self.deglint_img_file = FileSelectWidget( + "去耀斑影像:", + "Image Files (*.bsq *.dat *.tif);;All Files (*.*)" + ) + layout.addWidget(self.deglint_img_file) + + # 水域掩膜文件（可选，用于独立运行） + self.water_mask_file = FileSelectWidget( + "水域掩膜:", + "Mask Files (*.dat *.tif);;All Files (*.*)" + ) + self.water_mask_file.label.setText("水域掩膜（可选）:") + layout.addWidget(self.water_mask_file) + + # 参数设置 + params_group = QGroupBox("采样参数") + params_layout = QFormLayout() + + self.interval = QSpinBox() + self.interval.setRange(10, 500) + self.interval.setValue(50) + params_layout.addRow("采样点间隔(像素):", self.interval) + + self.sample_radius = QSpinBox() + self.sample_radius.setRange(1, 50) + self.sample_radius.setValue(5) + params_layout.addRow("采样半径(像素):", self.sample_radius) + + self.chunk_size = QSpinBox() + self.chunk_size.setRange(100, 10000) + self.chunk_size.setValue(1000) + params_layout.addRow("处理块大小:", self.chunk_size) + + params_group.setLayout(params_layout) + layout.addWidget(params_group) + + # 输出文件路径 + self.output_file = FileSelectWidget( + "输出采样点:", + "CSV Files (*.csv);;All Files (*.*)" + ) + self.output_file.line_edit.setPlaceholderText("sampling_points.csv") + layout.addWidget(self.output_file) + + # 启用步骤 + self.enable_checkbox = QCheckBox("启用此步骤") + self.enable_checkbox.setChecked(True) + layout.addWidget(self.enable_checkbox) + + # 独立运行按钮 + self.run_btn = QPushButton("独立运行此步骤") + self.run_btn.setStyleSheet(ModernStylesheet.get_button_stylesheet('success')) + self.run_btn.clicked.connect(self.run_step) + layout.addWidget(self.run_btn) + + layout.addStretch() + self.setLayout(layout) + + def get_config(self): + """获取配置""" + config = { + 'interval': self.interval.value(), + 'sample_radius': self.sample_radius.value(), + 'chunk_size': self.chunk_size.value(), + } + # 添加独立运行所需的文件路径 + deglint_img_path = self.deglint_img_file.get_path() + if deglint_img_path: + config['deglint_img_path'] = deglint_img_path + water_mask_path = self.water_mask_file.get_path() + if water_mask_path: + config['water_mask_path'] = water_mask_path + # 添加输出路径 + output_path = self.output_file.get_path() + if output_path: + config['output_path'] = output_path + return config + + def set_config(self, config): + """设置配置""" + if 'interval' in config: + self.interval.setValue(config['interval']) + if 'sample_radius' in config: + self.sample_radius.setValue(config['sample_radius']) + if 'chunk_size' in config: + self.chunk_size.setValue(config['chunk_size']) + if 'deglint_img_path' in config: + self.deglint_img_file.set_path(config['deglint_img_path']) + if 'water_mask_path' in config: + self.water_mask_file.set_path(config['water_mask_path']) + if 'output_path' in config: + self.output_file.set_path(config['output_path']) + + def run_step(self): + """独立运行步骤7""" + # 验证输入 + deglint_img_path = self.deglint_img_file.get_path() + if not deglint_img_path: + QMessageBox.warning(self, "输入错误", "请选择去耀斑影像文件！") + return + + # 获取主窗口并运行步骤 + main_window = self.window() + if hasattr(main_window, 'run_single_step'): + config = {'step7': self.get_config()} + main_window.run_single_step('step7', config) + + +class Step8Panel(QWidget): + """步骤8：机器学习预测""" + def __init__(self, parent=None): + super().__init__(parent) + self.init_ui() + + def init_ui(self): + layout = QVBoxLayout() + + # 标题 + + + # 采样光谱CSV文件（用于独立运行） + self.sampling_csv_file = FileSelectWidget( + "采样光谱CSV:", + "CSV Files (*.csv);;All Files (*.*)" + ) + layout.addWidget(self.sampling_csv_file) + + # 模型目录（用于独立运行） + self.models_dir_file = FileSelectWidget( + "模型目录:", + "Directories;;All Files (*.*)" + ) + self.models_dir_file.label.setText("模型目录:") + # 修改浏览按钮为选择目录 + self.models_dir_file.browse_btn.clicked.disconnect() + self.models_dir_file.browse_btn.clicked.connect(self.browse_models_dir) + layout.addWidget(self.models_dir_file) + + # 参数设置 + params_group = QGroupBox("预测参数") + params_layout = QFormLayout() + + self.metric = QComboBox() + self.metric.addItems(['test_r2', 'test_rmse', 'test_mae']) + params_layout.addRow("模型选择指标:", self.metric) + + self.prediction_column = QLineEdit() + self.prediction_column.setText("prediction") + params_layout.addRow("预测列名:", self.prediction_column) + + params_group.setLayout(params_layout) + layout.addWidget(params_group) + + # 输出路径 + self.output_file = FileSelectWidget( + "输出路径:", + "CSV Files (*.csv);;All Files (*.*)" + ) + layout.addWidget(self.output_file) + + # 启用步骤 + self.enable_checkbox = QCheckBox("启用此步骤") + self.enable_checkbox.setChecked(True) + layout.addWidget(self.enable_checkbox) + + # 独立运行按钮 + self.run_btn = QPushButton("独立运行此步骤") + self.run_btn.setStyleSheet(ModernStylesheet.get_button_stylesheet('success')) + self.run_btn.clicked.connect(self.run_step) + layout.addWidget(self.run_btn) + + layout.addStretch() + self.setLayout(layout) + + def browse_models_dir(self): + """浏览模型目录""" + dir_path = QFileDialog.getExistingDirectory(self, "选择模型目录", "") + if dir_path: + self.models_dir_file.set_path(dir_path) + + def get_config(self): + """获取配置""" + config = { + 'metric': self.metric.currentText(), + 'prediction_column': self.prediction_column.text(), + } + # 添加独立运行所需的文件路径 + sampling_csv_path = self.sampling_csv_file.get_path() + if sampling_csv_path: + config['sampling_csv_path'] = sampling_csv_path + models_dir = self.models_dir_file.get_path() + if models_dir: + config['models_dir'] = models_dir + # 添加输出路径 + output_path = self.output_file.get_path() + if output_path: + config['output_path'] = output_path + return config + + def set_config(self, config): + """设置配置""" + if 'metric' in config: + idx = self.metric.findText(config['metric']) + if idx >= 0: + self.metric.setCurrentIndex(idx) + + if 'prediction_column' in config: + self.prediction_column.setText(config['prediction_column']) + if 'sampling_csv_path' in config: + self.sampling_csv_file.set_path(config['sampling_csv_path']) + if 'models_dir' in config: + self.models_dir_file.set_path(config['models_dir']) + if 'output_path' in config: + self.output_file.set_path(config['output_path']) + + def run_step(self): + """独立运行步骤8""" + # 验证输入 + sampling_csv_path = self.sampling_csv_file.get_path() + models_dir = self.models_dir_file.get_path() + if not sampling_csv_path: + QMessageBox.warning(self, "输入错误", "请选择采样光谱CSV文件！") + return + if not models_dir: + QMessageBox.warning(self, "输入错误", "请选择模型目录！") + return + + # 获取主窗口并运行步骤 + main_window = self.window() + if hasattr(main_window, 'run_single_step'): + config = {'step8': self.get_config()} + main_window.run_single_step('step8', config) + + +class Step9Panel(QWidget): + """步骤9：分布图生成""" + def __init__(self, parent=None): + super().__init__(parent) + self._batch_thread = None + self.init_ui() + + def init_ui(self): + layout = QVBoxLayout() + + hint = QLabel( + "独立运行：可选「单个 CSV」或「文件夹批量」（扫描目录下所有 .csv）。" + "完整流程中预测 CSV 由步骤11、12、13 自动传入，无需在此选择。" + ) + hint.setWordWrap(True) + hint.setStyleSheet(f"color: {ModernStylesheet.COLORS.get('text_secondary', '#666')};") + layout.addWidget(hint) + + mode_row = QHBoxLayout() + self.mode_single_rb = QRadioButton("单个 CSV 文件") + self.mode_folder_rb = QRadioButton("文件夹批量") + self.mode_single_rb.setChecked(True) + self._mode_group = QButtonGroup(self) + self._mode_group.addButton(self.mode_single_rb, 0) + self._mode_group.addButton(self.mode_folder_rb, 1) + self.mode_single_rb.toggled.connect(self._on_step9_mode_changed) + self.mode_folder_rb.toggled.connect(self._on_step9_mode_changed) + mode_row.addWidget(self.mode_single_rb) + mode_row.addWidget(self.mode_folder_rb) + mode_row.addStretch() + layout.addLayout(mode_row) + + self.prediction_csv_file = FileSelectWidget( + "预测结果CSV:", + "CSV Files (*.csv);;All Files (*.*)" + ) + layout.addWidget(self.prediction_csv_file) + + folder_row = QHBoxLayout() + self.prediction_csv_dir_label = QLabel("预测CSV目录:") + self.prediction_csv_dir_label.setMinimumWidth(120) + self.prediction_csv_dir_edit = QLineEdit() + self.prediction_csv_dir_edit.setPlaceholderText("选择含多个预测结果 CSV 的文件夹…") + pred_dir_btn = QPushButton("浏览…") + pred_dir_btn.setMaximumWidth(80) + pred_dir_btn.clicked.connect(self.browse_prediction_csv_dir) + folder_row.addWidget(self.prediction_csv_dir_label) + folder_row.addWidget(self.prediction_csv_dir_edit, 1) + folder_row.addWidget(pred_dir_btn) + self._folder_row_widget = QWidget() + self._folder_row_widget.setLayout(folder_row) + layout.addWidget(self._folder_row_widget) + + self.recursive_csv_cb = QCheckBox("包含子文件夹（递归扫描 *.csv）") + layout.addWidget(self.recursive_csv_cb) + + self.boundary_file = FileSelectWidget( + "边界文件:", + "Shapefiles (*.shp);;All Files (*.*)" + ) + layout.addWidget(self.boundary_file) + + # 参数设置 + params_group = QGroupBox("生成参数") + params_layout = QFormLayout() + + self.resolution = QDoubleSpinBox() + self.resolution.setRange(1, 1000) + self.resolution.setValue(30) + params_layout.addRow("分辨率(米):", self.resolution) + + self.input_crs = QLineEdit() + self.input_crs.setText("EPSG:32651") + params_layout.addRow("输入坐标系:", self.input_crs) + + self.output_crs = QLineEdit() + self.output_crs.setText("EPSG:4326") + params_layout.addRow("输出坐标系:", self.output_crs) + + self.show_points = QCheckBox("显示采样点") + params_layout.addRow("", self.show_points) + + self.use_diffusion = QCheckBox("启用距离扩散") + self.use_diffusion.setChecked(True) + params_layout.addRow("", self.use_diffusion) + + params_group.setLayout(params_layout) + layout.addWidget(params_group) + + # 输出目录（可选）：在此目录下生成「CSV文件名_distribution.png」；留空则用工作目录/9_visualization + self.output_dir = FileSelectWidget( + "输出分布图目录:", + "Directories;;All Files (*.*)" + ) + self.output_dir.line_edit.setPlaceholderText("留空→工作目录/9_visualization") + # 修改浏览按钮为选择目录 + self.output_dir.browse_btn.clicked.disconnect() + self.output_dir.browse_btn.clicked.connect(self.browse_output_dir) + layout.addWidget(self.output_dir) + + # 启用步骤 + self.enable_checkbox = QCheckBox("启用此步骤") + self.enable_checkbox.setChecked(True) + layout.addWidget(self.enable_checkbox) + + # 独立运行按钮 + self.run_button = QPushButton("独立运行此步骤") + self.run_button.setStyleSheet(""" + QPushButton { + background-color: #4CAF50; + color: white; + padding: 8px 16px; + border: none; + border-radius: 4px; + font-weight: bold; + } + QPushButton:hover { + background-color: #45a049; + } + QPushButton:pressed { + background-color: #3e8e41; + } + """) + self.run_button.clicked.connect(self.run_step) + layout.addWidget(self.run_button) + + layout.addStretch() + self.setLayout(layout) + self._on_step9_mode_changed() + + def _on_step9_mode_changed(self): + folder_mode = self.mode_folder_rb.isChecked() + self.prediction_csv_file.setEnabled(not folder_mode) + self._folder_row_widget.setEnabled(folder_mode) + self.recursive_csv_cb.setEnabled(folder_mode) + + def browse_prediction_csv_dir(self): + d = QFileDialog.getExistingDirectory(self, "选择预测结果 CSV 所在文件夹") + if d: + self.prediction_csv_dir_edit.setText(d) + + def _collect_csv_paths_from_folder(self) -> List[str]: + folder = (self.prediction_csv_dir_edit.text() or "").strip() + if not folder or not os.path.isdir(folder): + return [] + root = Path(folder) + if self.recursive_csv_cb.isChecked(): + files = sorted(root.rglob("*.csv")) + else: + files = sorted(root.glob("*.csv")) + return [str(p) for p in files if p.is_file()] + + def _step9_base_pipeline_kwargs(self) -> dict: + return { + 'boundary_shp_path': self.boundary_file.get_path(), + 'resolution': self.resolution.value(), + 'input_crs': self.input_crs.text(), + 'output_crs': self.output_crs.text(), + 'show_sample_points': self.show_points.isChecked(), + 'use_distance_diffusion': self.use_diffusion.isChecked(), + } + + def get_config(self): + """含 GUI 专用字段 step9_batch_mode / prediction_csv_dir / recursive_csv_scan；pipeline 调用前会剔除。""" + pred_csv = (self.prediction_csv_file.get_path() or "").strip() + folder_mode = self.mode_folder_rb.isChecked() + pred_dir = (self.prediction_csv_dir_edit.text() or "").strip() + config = { + 'step9_batch_mode': 'folder' if folder_mode else 'single', + 'prediction_csv_dir': pred_dir if pred_dir else None, + 'recursive_csv_scan': self.recursive_csv_cb.isChecked(), + 'prediction_csv_path': None if folder_mode else (pred_csv if pred_csv else None), + 'boundary_shp_path': self.boundary_file.get_path(), + 'resolution': self.resolution.value(), + 'input_crs': self.input_crs.text(), + 'output_crs': self.output_crs.text(), + 'show_sample_points': self.show_points.isChecked(), + 'use_distance_diffusion': self.use_diffusion.isChecked(), + } + out_dir = (self.output_dir.get_path() or "").strip() + if not folder_mode and pred_csv and out_dir: + stem = Path(pred_csv).stem + config['output_image_path'] = str(Path(out_dir) / f"{stem}_distribution.png") + else: + config['output_image_path'] = None + return config + + def set_config(self, config): + """设置配置""" + mode = config.get('step9_batch_mode', 'single') + if mode == 'folder': + self.mode_folder_rb.setChecked(True) + else: + self.mode_single_rb.setChecked(True) + if config.get('prediction_csv_dir'): + self.prediction_csv_dir_edit.setText(str(config['prediction_csv_dir'])) + if 'recursive_csv_scan' in config: + self.recursive_csv_cb.setChecked(bool(config['recursive_csv_scan'])) + if 'prediction_csv_path' in config and config['prediction_csv_path']: + self.prediction_csv_file.set_path(str(config['prediction_csv_path'])) + if 'boundary_shp_path' in config: + self.boundary_file.set_path(config['boundary_shp_path']) + if 'resolution' in config: + self.resolution.setValue(config['resolution']) + if 'input_crs' in config: + self.input_crs.setText(config['input_crs']) + if 'output_crs' in config: + self.output_crs.setText(config['output_crs']) + if 'show_sample_points' in config: + self.show_points.setChecked(config['show_sample_points']) + if 'use_distance_diffusion' in config: + self.use_diffusion.setChecked(config['use_distance_diffusion']) + if 'output_dir' in config and config['output_dir']: + self.output_dir.set_path(str(config['output_dir'])) + elif config.get('output_image_path'): + p = Path(str(config['output_image_path'])) + if p.parent and str(p.parent) != '.': + self.output_dir.set_path(str(p.parent)) + + def browse_output_dir(self): + """浏览输出目录""" + dir_path = QFileDialog.getExistingDirectory(self, "选择输出模型目录", "") + if dir_path: + self.output_dir.set_path(dir_path) + + def run_step(self): + """独立运行步骤9（单文件走原 WorkerThread；文件夹走批量线程）""" + if self._batch_thread and self._batch_thread.isRunning(): + QMessageBox.information(self, "提示", "批量任务正在运行，请稍候。") + return + + boundary_shp_path = self.boundary_file.get_path() + if not boundary_shp_path: + QMessageBox.warning(self, "输入验证失败", "请选择边界文件") + return + if not os.path.exists(boundary_shp_path): + QMessageBox.warning(self, "输入验证失败", "边界文件不存在") + return + + parent = self.parent() + while parent and not hasattr(parent, 'run_single_step'): + parent = parent.parent() + + if not parent or not hasattr(parent, 'run_single_step'): + QMessageBox.critical(self, "错误", "无法找到父级GUI对象") + return + + if self.mode_folder_rb.isChecked(): + csv_list = self._collect_csv_paths_from_folder() + if not csv_list: + QMessageBox.warning( + self, + "输入验证失败", + "所选文件夹中未找到 .csv 文件，或目录无效。\n" + "可勾选「包含子文件夹」以递归扫描。", + ) + return + if not PIPELINE_AVAILABLE: + QMessageBox.critical(self, "错误", "Pipeline 模块不可用，无法批量生成专题图。") + return + work_dir = getattr(parent, "work_dir", None) or "./work_dir" + work_dir = str(work_dir) + base_kw = self._step9_base_pipeline_kwargs() + out_dir_opt = (self.output_dir.get_path() or "").strip() or None + self.run_button.setEnabled(False) + self._batch_thread = Step9BatchThread(work_dir, csv_list, base_kw, out_dir_opt) + main_win = parent + + def _batch_log(msg, lvl): + if hasattr(main_win, "log_message"): + main_win.log_message(msg, lvl) + + self._batch_thread.log_message.connect(_batch_log, Qt.QueuedConnection) + self._batch_thread.finished_ok.connect(self._on_step9_batch_ok, Qt.QueuedConnection) + self._batch_thread.failed.connect(self._on_step9_batch_fail, Qt.QueuedConnection) + self._batch_thread.finished.connect(lambda: self.run_button.setEnabled(True), Qt.QueuedConnection) + self._batch_thread.start() + if hasattr(parent, "log_message"): + parent.log_message(f"专题图批量：共 {len(csv_list)} 个 CSV，工作目录 {work_dir}", "info") + return + + prediction_csv_path = (self.prediction_csv_file.get_path() or "").strip() + if not prediction_csv_path: + QMessageBox.warning( + self, + "输入验证失败", + "请选择「预测结果 CSV」文件，或切换到「文件夹批量」。", + ) + return + if not os.path.isfile(prediction_csv_path): + QMessageBox.warning(self, "输入验证失败", "预测结果 CSV 不存在或不是文件") + return + + config = self.get_config() + parent.run_single_step('step9', {'step9': config}) + + def _on_step9_batch_ok(self, n: int): + QMessageBox.information(self, "完成", f"已批量生成 {n} 个分布图。") + parent = self.parent() + while parent and not hasattr(parent, "log_message"): + parent = parent.parent() + if parent and hasattr(parent, "log_message"): + parent.log_message(f"专题图批量完成，共 {n} 个文件。", "info") + + def _on_step9_batch_fail(self, err: str): + QMessageBox.critical(self, "失败", f"批量生成中断：\n{err[:900]}") + parent = self.parent() + while parent and not hasattr(parent, "log_message"): + parent = parent.parent() + if parent and hasattr(parent, "log_message"): + parent.log_message(err, "error") + + +class Step8_5Panel(QWidget): + """步骤8.5：非经验模型预测""" + def __init__(self, parent=None): + super().__init__(parent) + self.init_ui() + + def init_ui(self): + layout = QVBoxLayout() + + # 标题 + + + # 采样光谱CSV文件选择 + self.sampling_csv_file = FileSelectWidget( + "采样光谱CSV:", + "CSV Files (*.csv);;All Files (*.*)" + ) + layout.addWidget(self.sampling_csv_file) + + # 模型目录选择 + self.models_dir_file = FileSelectWidget( + "模型目录:", + "Directories;;All Files (*.*)" + ) + self.models_dir_file.label.setText("模型目录:") + # 修改浏览按钮为选择目录 + self.models_dir_file.browse_btn.clicked.disconnect() + self.models_dir_file.browse_btn.clicked.connect(self.browse_models_dir) + layout.addWidget(self.models_dir_file) + + # 参数设置 + params_group = QGroupBox("预测参数") + params_layout = QFormLayout() + + # 模型选择指标 + self.metric = QComboBox() + self.metric.addItems(['Average Accuracy(%)', 'Min Accuracy(%)', 'Max Accuracy(%)']) + params_layout.addRow("模型选择指标:", self.metric) + + # 预测列名 + self.prediction_column = QLineEdit() + self.prediction_column.setText("prediction") + params_layout.addRow("预测列名:", self.prediction_column) + + params_group.setLayout(params_layout) + layout.addWidget(params_group) + + # 输出路径 + self.output_file = FileSelectWidget( + "输出文件夹:", + "Directories;;All Files (*.*)" + ) + self.output_file.label.setText("输出文件夹:") + # 修改浏览按钮为选择目录 + self.output_file.browse_btn.clicked.disconnect() + self.output_file.browse_btn.clicked.connect(self.browse_output_dir) + layout.addWidget(self.output_file) + + # 启用步骤 + self.enable_checkbox = QCheckBox("启用此步骤") + self.enable_checkbox.setChecked(True) + layout.addWidget(self.enable_checkbox) + + # 独立运行按钮 + self.run_button = QPushButton("独立运行此步骤") + self.run_button.setStyleSheet(""" + QPushButton { + background-color: #4CAF50; + color: white; + padding: 8px 16px; + border: none; + border-radius: 4px; + font-weight: bold; + } + QPushButton:hover { + background-color: #45a049; + } + QPushButton:pressed { + background-color: #3e8e41; + } + """) + self.run_button.clicked.connect(self.run_step) + layout.addWidget(self.run_button) + + layout.addStretch() + self.setLayout(layout) + + def browse_models_dir(self): + """浏览模型目录""" + dir_path = QFileDialog.getExistingDirectory(self, "选择模型目录", "") + if dir_path: + self.models_dir_file.set_path(dir_path) + + def browse_output_dir(self): + """浏览输出目录""" + dir_path = QFileDialog.getExistingDirectory(self, "选择输出文件夹", "") + if dir_path: + self.output_file.set_path(dir_path) + + def get_config(self): + """获取配置""" + config = { + 'metric': self.metric.currentText(), + 'prediction_column': self.prediction_column.text(), + 'enabled': self.enable_checkbox.isChecked() + } + # 添加采样光谱CSV路径 + sampling_csv_path = self.sampling_csv_file.get_path() + if sampling_csv_path: + config['sampling_csv_path'] = sampling_csv_path + # 添加模型目录路径 + models_dir = self.models_dir_file.get_path() + if models_dir: + config['models_dir'] = models_dir + # 添加输出路径 + output_path = self.output_file.get_path() + if output_path: + config['output_path'] = output_path + return config + + def set_config(self, config): + """设置配置""" + if 'metric' in config: + idx = self.metric.findText(config['metric']) + if idx >= 0: + self.metric.setCurrentIndex(idx) + + if 'prediction_column' in config: + self.prediction_column.setText(config['prediction_column']) + + if 'sampling_csv_path' in config: + self.sampling_csv_file.set_path(config['sampling_csv_path']) + + if 'models_dir' in config: + self.models_dir_file.set_path(config['models_dir']) + + if 'enabled' in config: + self.enable_checkbox.setChecked(config['enabled']) + + def run_step(self): + """独立运行步骤8.5""" + # 验证输入 + sampling_csv_path = self.sampling_csv_file.get_path() + if not sampling_csv_path: + QMessageBox.warning(self, "输入错误", "请选择采样光谱CSV文件！") + return + + # 获取配置 + config = self.get_config() + + # 调用GUI的run_single_step方法 + parent = self.parent() + while parent and not hasattr(parent, 'run_single_step'): + parent = parent.parent() + + if parent and hasattr(parent, 'run_single_step'): + parent.run_single_step('step8_5', {'step8_5': config}) + else: + QMessageBox.critical(self, "错误", "无法找到父级GUI对象") + + +class Step8_75Panel(QWidget): + """步骤8.75：自定义回归预测""" + def __init__(self, parent=None): + super().__init__(parent) + self.init_ui() + + def init_ui(self): + layout = QVBoxLayout() + + # 标题 + + + # 采样光谱CSV文件选择 + self.sampling_csv_file = FileSelectWidget( + "采样光谱CSV:", + "CSV Files (*.csv);;All Files (*.*)" + ) + layout.addWidget(self.sampling_csv_file) + + # 公式CSV文件选择 + self.formula_csv_file = FileSelectWidget( + "公式CSV文件:", + "CSV Files (*.csv);;All Files (*.*)" + ) + layout.addWidget(self.formula_csv_file) + + # 模型目录选择 + self.models_dir_file = FileSelectWidget( + "模型目录:", + "Directories;;All Files (*.*)" + ) + self.models_dir_file.label.setText("模型目录:") + # 修改浏览按钮为选择目录 + self.models_dir_file.browse_btn.clicked.disconnect() + self.models_dir_file.browse_btn.clicked.connect(self.browse_models_dir) + layout.addWidget(self.models_dir_file) + + # 参数设置 + params_group = QGroupBox("预测参数") + params_layout = QFormLayout() + + # 预测列名 + self.prediction_column = QLineEdit() + self.prediction_column.setText("prediction") + params_layout.addRow("预测列名:", self.prediction_column) + + params_group.setLayout(params_layout) + layout.addWidget(params_group) + + # 启用步骤 + self.enable_checkbox = QCheckBox("启用此步骤") + self.enable_checkbox.setChecked(True) + layout.addWidget(self.enable_checkbox) + + # 独立运行按钮 + self.run_button = QPushButton("独立运行此步骤") + self.run_button.setStyleSheet(""" + QPushButton { + background-color: #4CAF50; + color: white; + padding: 8px 16px; + border: none; + border-radius: 4px; + font-weight: bold; + } + QPushButton:hover { + background-color: #45a049; + } + QPushButton:pressed { + background-color: #3e8e41; + } + """) + self.run_button.clicked.connect(self.run_step) + layout.addWidget(self.run_button) + + layout.addStretch() + self.setLayout(layout) + + def browse_models_dir(self): + """浏览模型目录""" + dir_path = QFileDialog.getExistingDirectory(self, "选择模型目录", "") + if dir_path: + self.models_dir_file.set_path(dir_path) + + def get_config(self): + """获取配置""" + config = { + 'prediction_column': self.prediction_column.text(), + 'enabled': self.enable_checkbox.isChecked() + } + # 添加采样光谱CSV路径 + sampling_csv_path = self.sampling_csv_file.get_path() + if sampling_csv_path: + config['sampling_csv_path'] = sampling_csv_path + # 添加公式CSV文件路径 + formula_csv_path = self.formula_csv_file.get_path() + if formula_csv_path: + config['formula_csv_file'] = formula_csv_path + # 添加模型目录路径 + models_dir = self.models_dir_file.get_path() + if models_dir: + config['custom_regression_dir'] = models_dir + return config + + def set_config(self, config): + """设置配置""" + if 'prediction_column' in config: + self.prediction_column.setText(config['prediction_column']) + + if 'sampling_csv_path' in config: + self.sampling_csv_file.set_path(config['sampling_csv_path']) + + if 'formula_csv_file' in config: + self.formula_csv_file.set_path(config['formula_csv_file']) + + if 'custom_regression_dir' in config: + self.models_dir_file.set_path(config['custom_regression_dir']) + + if 'enabled' in config: + self.enable_checkbox.setChecked(config['enabled']) + + def run_step(self): + """独立运行步骤8.75""" + # 验证输入 + sampling_csv_path = self.sampling_csv_file.get_path() + if not sampling_csv_path: + QMessageBox.warning(self, "输入错误", "请选择采样光谱CSV文件！") + return + + formula_csv_path = self.formula_csv_file.get_path() + if not formula_csv_path: + QMessageBox.warning(self, "输入错误", "请选择公式CSV文件！") + return + + # 获取配置 + config = self.get_config() + + # 调用GUI的run_single_step方法 + parent = self.parent() + while parent and not hasattr(parent, 'run_single_step'): + parent = parent.parent() + + if parent and hasattr(parent, 'run_single_step'): + parent.run_single_step('step8_75', {'step8_75': config}) + else: + QMessageBox.critical(self, "错误", "无法找到父级GUI对象") + + +class ChartViewerDialog(QDialog): + """图表查看器对话框""" + def __init__(self, title="图表查看器", parent=None): + super().__init__(parent) + self.setWindowTitle(title) + self.resize(1000, 700) + self.init_ui() + + def init_ui(self): + layout = QVBoxLayout() + + # 创建matplotlib图形 + self.figure = Figure(figsize=(10, 7)) + self.canvas = FigureCanvas(self.figure) + self.canvas.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding) + + # 添加工具栏 + self.toolbar = NavigationToolbar(self.canvas, self) + + layout.addWidget(self.toolbar) + layout.addWidget(self.canvas) + + # 按钮组 + btn_layout = QHBoxLayout() + + self.save_btn = QPushButton("保存图表") + self.save_btn.clicked.connect(self.save_chart) + btn_layout.addWidget(self.save_btn) + + btn_layout.addStretch() + + self.close_btn = QPushButton("关闭") + self.close_btn.clicked.connect(self.close) + btn_layout.addWidget(self.close_btn) + + layout.addLayout(btn_layout) + self.setLayout(layout) + + def display_image(self, image_path): + """显示图片""" + self.figure.clear() + ax = self.figure.add_subplot(111) + + try: + import matplotlib.image as mpimg + img = mpimg.imread(image_path) + ax.imshow(img) + ax.axis('off') + self.figure.tight_layout() + self.canvas.draw() + self.current_image_path = image_path + except Exception as e: + ax.text(0.5, 0.5, f'加载图片失败:\n{str(e)}', + ha='center', va='center', transform=ax.transAxes) + self.canvas.draw() + + def display_custom_plot(self, plot_func): + """显示自定义绘图函数""" + self.figure.clear() + try: + plot_func(self.figure) + self.canvas.draw() + except Exception as e: + ax = self.figure.add_subplot(111) + ax.text(0.5, 0.5, f'绘图失败:\n{str(e)}', + ha='center', va='center', transform=ax.transAxes) + self.canvas.draw() + + def save_chart(self): + """保存图表""" + file_path, _ = QFileDialog.getSaveFileName( + self, "保存图表", "", + "PNG图片 (*.png);;JPG图片 (*.jpg);;PDF文件 (*.pdf);;所有文件 (*.*)" + ) + if file_path: + try: + self.figure.savefig(file_path, dpi=300, bbox_inches='tight') + QMessageBox.information(self, "成功", f"图表已保存到:\n{file_path}") + except Exception as e: + QMessageBox.critical(self, "错误", f"保存失败:\n{str(e)}") + + +class ImageCategoryTree(QTreeWidget): + """图像分类目录树 - 按类别组织图像文件""" + + # 图像类别定义：(类别名称, 关键词列表, 图标) + CATEGORIES = [ + ("模型评估", ["scatter", "regression", "validation", "r2", "rmse"], "📊"), + ("光谱分析", ["spectrum", "spectral", "band", "wavelength"], "📈"), + ("统计图表", ["boxplot", "histogram", "heatmap", "statistics", "stats"], "📉"), + ("处理结果", ["mask", "glint", "deglint", "preview", "overlay", "water_mask"], "🖼️"), + ("采样分析", ["sampling", "flight_path", "point_map", "trajectory"], "📍"), + ("其他图表", [], "📁"), + ] + + def __init__(self, parent=None): + super().__init__(parent) + self.setHeaderLabel("图像目录") + self.setMaximumWidth(300) + self.setMinimumWidth(250) + self.setup_categories() + self.setStyleSheet(""" + QTreeWidget { + border: 1px solid #ddd; + border-radius: 5px; + background-color: #f8f9fa; + } + QTreeWidget::item { + padding: 5px; + border-radius: 3px; + } + QTreeWidget::item:selected { + background-color: #0078D4; + color: white; + } + QTreeWidget::item:hover { + background-color: #e3f2fd; + } + """) + + def setup_categories(self): + """初始化类别节点""" + self.category_items = {} + for category_name, keywords, icon in self.CATEGORIES: + item = QTreeWidgetItem(self) + item.setText(0, f"{icon} {category_name}") + item.setData(0, Qt.UserRole, {"type": "category", "keywords": keywords, "name": category_name}) + item.setExpanded(True) + self.category_items[category_name] = item + + def clear_all_images(self): + """清除所有图像项""" + for category_item in self.category_items.values(): + # 删除所有子项 + while category_item.childCount() > 0: + category_item.removeChild(category_item.child(0)) + + def add_image(self, file_path: Path, display_name: str = None): + """添加图像到对应的类别""" + if display_name is None: + display_name = file_path.stem + + # 根据文件名关键词确定类别 + category = self._determine_category(file_path.name) + category_item = self.category_items.get(category, self.category_items["其他图表"]) + + # 创建图像项 + image_item = QTreeWidgetItem(category_item) + image_item.setText(0, f" └─ {display_name}") + image_item.setData(0, Qt.UserRole, {"type": "image", "path": str(file_path)}) + image_item.setToolTip(0, str(file_path)) + + return image_item + + def _determine_category(self, filename: str) -> str: + """根据文件名确定类别""" + filename_lower = filename.lower() + + for category_name, keywords, _ in self.CATEGORIES: + if any(keyword in filename_lower for keyword in keywords): + return category_name + + return "其他图表" + + def scan_directory(self, work_dir: str): + """扫描目录中的所有图像文件""" + self.clear_all_images() + + work_path = Path(work_dir) + if not work_path.exists(): + return + + # 查找所有图像文件：9_visualization 为主，同时扫描步骤产出目录（如 1_water_mask 下的预览/叠置图） + image_extensions = ['*.png', '*.jpg', '*.jpeg', '*.tif', '*.tiff', '*.bmp'] + scan_roots: List[Path] = [] + _viz = work_path / "9_visualization" + if _viz.is_dir(): + scan_roots.append(_viz) + _wm = work_path / "1_water_mask" + if _wm.is_dir(): + scan_roots.append(_wm) + if not scan_roots: + scan_roots.append(work_path) + + seen_norm: set = set() + image_files: List[Path] = [] + for root in scan_roots: + for ext in image_extensions: + for p in root.glob(f"**/{ext}"): + key = os.path.normcase(os.path.normpath(str(p.resolve()))) + if key in seen_norm: + continue + seen_norm.add(key) + image_files.append(p) + + # 添加图像到树 + for img_file in sorted(image_files): + # 跳过缩略图和临时文件 + if img_file.name.startswith('.') or 'thumb' in img_file.name.lower(): + continue + self.add_image(img_file) + + # 更新类别项文本显示数量 + for category_name, item in self.category_items.items(): + count = item.childCount() + if count > 0: + for cat_name, _, icon in self.CATEGORIES: + if cat_name == category_name: + item.setText(0, f"{icon} {category_name} ({count})") + break + + def get_selected_image_path(self) -> Optional[str]: + """获取当前选中的图像路径""" + selected_item = self.currentItem() + if not selected_item: + return None + + data = selected_item.data(0, Qt.UserRole) + if data and data.get("type") == "image": + return data.get("path") + return None + + +class ImageViewerWidget(QWidget): + """图像查看器组件 - 支持缩放、平移""" + + def __init__(self, parent=None): + super().__init__(parent) + self.current_image_path = None + self.scale_factor = 1.0 + self.setup_ui() + + def setup_ui(self): + layout = QVBoxLayout() + layout.setContentsMargins(0, 0, 0, 0) + + # 工具栏 + toolbar = QHBoxLayout() + + self.refresh_btn = QPushButton("🔄 刷新目录") + self.refresh_btn.setToolTip("重新扫描工作目录中的图像文件") + toolbar.addWidget(self.refresh_btn) + + # 添加分隔线 + separator = QFrame() + separator.setFrameShape(QFrame.VLine) + separator.setFrameShadow(QFrame.Sunken) + toolbar.addWidget(separator) + + self.zoom_in_btn = QPushButton("🔍+") + self.zoom_in_btn.setToolTip("放大") + self.zoom_in_btn.setMaximumWidth(50) + toolbar.addWidget(self.zoom_in_btn) + + self.zoom_out_btn = QPushButton("🔍-") + self.zoom_out_btn.setToolTip("缩小") + self.zoom_out_btn.setMaximumWidth(50) + toolbar.addWidget(self.zoom_out_btn) + + self.fit_btn = QPushButton("⬜ 适应窗口") + self.fit_btn.setToolTip("适应窗口大小") + toolbar.addWidget(self.fit_btn) + + self.original_btn = QPushButton("1:1 原始大小") + self.original_btn.setToolTip("原始大小") + toolbar.addWidget(self.original_btn) + + toolbar.addStretch() + + self.save_btn = QPushButton("💾 保存") + self.save_btn.setToolTip("保存当前图像") + toolbar.addWidget(self.save_btn) + + layout.addLayout(toolbar) + + # 图像显示区域 - 使用 QLabel + QScrollArea + self.scroll_area = QScrollArea() + self.scroll_area.setWidgetResizable(True) + self.scroll_area.setStyleSheet("background-color: white;") + + self.image_label = QLabel() + self.image_label.setAlignment(Qt.AlignCenter) + self.image_label.setStyleSheet("background-color: white;") + + self.scroll_area.setWidget(self.image_label) + layout.addWidget(self.scroll_area, 1) + + # 状态栏 + status_layout = QHBoxLayout() + self.status_label = QLabel("就绪") + self.status_label.setStyleSheet("color: #666; font-size: 11px;") + status_layout.addWidget(self.status_label) + status_layout.addStretch() + layout.addLayout(status_layout) + + self.setLayout(layout) + + # 连接信号 + self.zoom_in_btn.clicked.connect(self.zoom_in) + self.zoom_out_btn.clicked.connect(self.zoom_out) + self.fit_btn.clicked.connect(self.fit_to_window) + self.original_btn.clicked.connect(self.original_size) + self.save_btn.clicked.connect(self.save_image) + + def load_image(self, image_path: str): + """加载并显示图像""" + if not image_path or not Path(image_path).exists(): + self.image_label.setText("图像不存在") + self.status_label.setText("图像加载失败") + return + + self.current_image_path = image_path + self.scale_factor = 1.0 + + # 加载图像 + pixmap = QPixmap(image_path) + if pixmap.isNull(): + self.image_label.setText("无法加载图像") + self.status_label.setText("图像格式不支持") + return + + self.original_pixmap = pixmap + + # 默认适应窗口显示 + self.fit_to_window() + + # 更新状态 + file_info = Path(image_path).stat() + size_mb = file_info.st_size / (1024 * 1024) + self.status_label.setText(f"{pixmap.width()}x{pixmap.height()} | {size_mb:.2f} MB | {Path(image_path).name} | 适应窗口") + + def update_image_display(self): + """更新图像显示""" + if not hasattr(self, 'original_pixmap') or self.original_pixmap.isNull(): + return + + scaled_pixmap = self.original_pixmap.scaled( + int(self.original_pixmap.width() * self.scale_factor), + int(self.original_pixmap.height() * self.scale_factor), + Qt.KeepAspectRatio, + Qt.SmoothTransformation + ) + self.image_label.setPixmap(scaled_pixmap) + + def zoom_in(self): + """放大""" + if self.scale_factor < 5.0: + self.scale_factor *= 1.25 + self.update_image_display() + + def zoom_out(self): + """缩小""" + if self.scale_factor > 0.1: + self.scale_factor /= 1.25 + self.update_image_display() + + def fit_to_window(self): + """适应窗口""" + if not hasattr(self, 'original_pixmap') or self.original_pixmap.isNull(): + return + + # 计算适应窗口的缩放比例 + view_size = self.scroll_area.viewport().size() + img_size = self.original_pixmap.size() + + scale_w = view_size.width() / img_size.width() + scale_h = view_size.height() / img_size.height() + self.scale_factor = min(scale_w, scale_h, 1.0) # 不超过原始大小 + + self.update_image_display() + + def original_size(self): + """原始大小""" + self.scale_factor = 1.0 + self.update_image_display() + + def save_image(self): + """保存图像""" + if not self.current_image_path: + return + + file_path, _ = QFileDialog.getSaveFileName( + self, "保存图像", Path(self.current_image_path).name, + "PNG图片 (*.png);;JPG图片 (*.jpg);;所有文件 (*.*)" + ) + + if file_path: + try: + import shutil + shutil.copy(self.current_image_path, file_path) + except Exception as e: + QMessageBox.critical(self, "错误", f"保存失败: {e}") + + +class VisualizationPanel(QWidget): + """可视化分析面板 - 重构版：左侧目录树 + 右侧图像查看器""" + def __init__(self, parent=None): + super().__init__(parent) + self.work_dir = None + self.chart_viewer = None + self._viz_thread = None + self.init_ui() + + def _viz_set_busy(self, busy: bool): + for w in ( + getattr(self, "gen_all_btn", None), + getattr(self, "gen_scatter_btn", None), + getattr(self, "gen_spectrum_btn", None), + getattr(self, "gen_stats_btn", None), + getattr(self, "gen_mask_glint_btn", None), + getattr(self, "gen_sampling_map_btn", None), + ): + if w is not None: + w.setEnabled(not busy) + + def _start_visualization_thread(self, task: str, extra: Optional[dict] = None) -> bool: + if not self.work_dir: + QMessageBox.warning(self, "警告", "请先选择工作目录！") + return False + work_path = Path(self.work_dir) + if not work_path.exists(): + QMessageBox.warning(self, "警告", "工作目录不存在！") + return False + if self._viz_thread and self._viz_thread.isRunning(): + QMessageBox.information(self, "提示", "可视化任务正在运行，请稍候。") + return False + self._viz_thread = VisualizationWorkerThread(task, str(work_path), extra or {}) + self._viz_thread.finished_ok.connect(self._on_visualization_worker_ok, Qt.QueuedConnection) + self._viz_thread.failed.connect(self._on_visualization_worker_fail, Qt.QueuedConnection) + self._viz_thread.finished.connect(lambda: self._viz_set_busy(False), Qt.QueuedConnection) + self._viz_set_busy(True) + self._viz_thread.start() + return True + + def _spectrum_meta_param_columns(self, df: pd.DataFrame) -> List[str]: + """光谱图可选的水质参数列（光谱波段列之前、且为数值型）。""" + wl = _viz_infer_wavelength_start_column(df) + if isinstance(wl, str): + idx = int(df.columns.get_loc(wl)) + 1 + else: + idx = int(wl) + if idx <= 0 or idx >= len(df.columns): + numeric = df.select_dtypes(include=[np.number]).columns.tolist() + return [ + c + for c in numeric + if not any(x in str(c).lower() for x in ("utm", "lat", "lon", "x", "y")) + ] + meta = list(df.columns[:idx]) + return [c for c in meta if pd.api.types.is_numeric_dtype(df[c])] + + def _statistics_param_columns(self, df: pd.DataFrame) -> List[str]: + """统计图用的参数列；若存在光谱波段，则只统计波段前的字段。""" + numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist() + wl = _viz_infer_wavelength_start_column(df) + if isinstance(wl, str): + idx = int(df.columns.get_loc(wl)) + 1 + else: + idx = int(wl) + coord_kw = ("utm", "lat", "lon") + if 0 < idx < len(df.columns): + meta_set = set(df.columns[:idx]) + return [ + col + for col in numeric_cols + if col in meta_set and not any(x in str(col).lower() for x in coord_kw) + ] + return [ + col + for col in numeric_cols + if not any(x in str(col).lower() for x in coord_kw + ("x", "y")) + ] + + def _on_visualization_worker_ok(self, payload): + if not isinstance(payload, dict): + self.scan_work_directory() + return + t = payload.get("task") + if t == "mask_glint": + cnt = int(payload.get("count") or 0) + if cnt > 0: + QMessageBox.information( + self, + "成功", + f"掩膜和耀斑缩略图生成完成，共 {cnt} 个预览图。\n" + f"保存位置: 9_visualization/glint_deglint_previews/", + ) + else: + QMessageBox.warning( + self, + "警告", + "未找到可处理的影像文件（2_glint/3_deglint 等）。", + ) + elif t == "sampling_map": + map_path = payload.get("map_path") + QMessageBox.information( + self, + "成功", + "采样点地图生成完成。\n" + f"输出: {Path(map_path).name if map_path else ''}\n" + "路径: 9_visualization/sampling_maps/", + ) + if map_path: + self.show_chart_viewer(map_path, "采样点分布图") + elif t == "spectrum": + multi = payload.get("output_paths") + if isinstance(multi, list) and multi: + ok_paths = [p for p in multi if p and Path(str(p)).is_file()] + errs = payload.get("errors") or [] + msg = ( + f"已为 {len(ok_paths)} 个水质参数生成光谱对比图。\n" + f"保存目录: 工作目录/9_visualization/" + ) + if errs: + msg += f"\n\n以下列未生成或出错 ({len(errs)} 项，详见日志):\n" + msg += "\n".join(str(e) for e in errs[:8]) + if len(errs) > 8: + msg += "\n..." + QMessageBox.information(self, "成功", msg) + if ok_paths: + self.show_chart_viewer(ok_paths[0], "光谱曲线对比（首张）") + else: + outp = payload.get("output_path") + param = payload.get("param_col", "") + QMessageBox.information(self, "成功", f"光谱图已生成:\n{outp}") + if outp: + self.show_chart_viewer(outp, f"{param} - 光谱曲线对比") + elif t == "statistics": + outp = payload.get("output_paths") or {} + QMessageBox.information( + self, "成功", f"统计图表已生成，共 {len(outp)} 项。" + ) + if isinstance(outp, dict) and "boxplot" in outp: + self.show_chart_viewer(outp["boxplot"], "水质参数箱线图") + elif t == "scatter": + paths = payload.get("scatter_paths") or {} + ok_paths = [p for p in paths.values() if p and Path(str(p)).is_file()] + if ok_paths: + QMessageBox.information( + self, + "成功", + f"已生成 {len(ok_paths)} 个模型评估散点图。\n" + f"保存位置: 9_visualization/scatter_plots/", + ) + self.show_chart_viewer(ok_paths[0], "模型评估散点图") + else: + QMessageBox.warning( + self, + "提示", + "未生成任何散点图。请确认 6_models 下已有各参数子目录及模型文件，" + "且训练 CSV 与建模时一致。", + ) + elif t == "generate_all_selected": + parts = payload.get("parts") or [] + QMessageBox.information( + self, + "完成", + "批量可视化已执行：\n" + "\n".join(parts) if parts else "（无选中项或已跳过）", + ) + self.scan_work_directory() + + def _on_visualization_worker_fail(self, err: str): + QMessageBox.critical(self, "错误", f"可视化任务失败:\n{err[:1200]}") + + def init_ui(self): + """初始化UI - 使用左右分栏布局""" + main_layout = QHBoxLayout() + main_layout.setSpacing(10) + main_layout.setContentsMargins(10, 10, 10, 10) + + # ===== 左侧面板 ===== + left_panel = QWidget() + left_layout = QVBoxLayout() + left_layout.setContentsMargins(0, 0, 0, 0) + + # 工作目录选择 + dir_group = QGroupBox("工作目录") + dir_layout = QHBoxLayout() + self.work_dir_edit = QLineEdit() + self.work_dir_edit.setPlaceholderText("选择工作目录...") + self.work_dir_edit.setReadOnly(True) + dir_browse_btn = QPushButton("浏览") + dir_browse_btn.clicked.connect(self.browse_work_dir) + dir_layout.addWidget(self.work_dir_edit, 1) + dir_layout.addWidget(dir_browse_btn) + dir_group.setLayout(dir_layout) + left_layout.addWidget(dir_group) + + # 图像目录树 + tree_group = QGroupBox("图像目录") + tree_layout = QVBoxLayout() + self.image_tree = ImageCategoryTree() + self.image_tree.itemClicked.connect(self.on_tree_item_clicked) + tree_layout.addWidget(self.image_tree) + + # 生成按钮组 + gen_btn_layout = QHBoxLayout() + self.gen_all_btn = QPushButton("🚀 生成全部") + self.gen_all_btn.setToolTip("生成所有类型的可视化图表") + self.gen_all_btn.setStyleSheet("background-color: #4CAF50; color: white; font-weight: bold;") + self.gen_all_btn.clicked.connect(self.generate_all_visualizations) + gen_btn_layout.addWidget(self.gen_all_btn) + + self.scan_btn = QPushButton("📁 扫描") + self.scan_btn.setToolTip("扫描工作目录中的图像文件") + self.scan_btn.clicked.connect(self.scan_work_directory) + gen_btn_layout.addWidget(self.scan_btn) + + tree_layout.addLayout(gen_btn_layout) + tree_group.setLayout(tree_layout) + left_layout.addWidget(tree_group, 1) + + # 可视化配置 + config_group = QGroupBox("可视化配置") + config_layout = QVBoxLayout() + + self.gen_scatter = QCheckBox("模型评估散点图") + self.gen_scatter.setChecked(True) + config_layout.addWidget(self.gen_scatter) + + self.gen_spectrum = QCheckBox("光谱曲线图") + self.gen_spectrum.setChecked(True) + config_layout.addWidget(self.gen_spectrum) + + self.gen_boxplots = QCheckBox("统计图表") + self.gen_boxplots.setChecked(True) + config_layout.addWidget(self.gen_boxplots) + + self.gen_mask_glint = QCheckBox("掩膜和耀斑缩略图") + self.gen_mask_glint.setChecked(True) + config_layout.addWidget(self.gen_mask_glint) + + self.gen_sampling_map = QCheckBox("采样点地图") + self.gen_sampling_map.setChecked(True) + config_layout.addWidget(self.gen_sampling_map) + + config_group.setLayout(config_layout) + left_layout.addWidget(config_group) + + left_panel.setLayout(left_layout) + left_panel.setMaximumWidth(350) + main_layout.addWidget(left_panel, 0) + + # ===== 右侧面板 ===== + right_panel = QWidget() + right_layout = QVBoxLayout() + right_layout.setContentsMargins(0, 0, 0, 0) + + # 图像查看器 + self.image_viewer = ImageViewerWidget() + self.image_viewer.refresh_btn.clicked.connect(self.scan_work_directory) + right_layout.addWidget(self.image_viewer, 1) + + # 生成特定图表按钮组 + specific_group = QGroupBox("生成特定图表") + specific_layout = QHBoxLayout() + + self.gen_scatter_btn = QPushButton("📊 散点图") + self.gen_scatter_btn.setToolTip( + "基于工作目录下 5_training_spectra/training_spectra.csv 与 6_models 生成模型评估散点图" + ) + self.gen_scatter_btn.clicked.connect(lambda: self.generate_chart('scatter')) + specific_layout.addWidget(self.gen_scatter_btn) + + self.gen_spectrum_btn = QPushButton("📈 光谱图") + self.gen_spectrum_btn.setToolTip( + "基于 5_training_spectra/training_spectra.csv，为每个数值型水质参数各生成一张光谱对比图（无需选择）" + ) + self.gen_spectrum_btn.clicked.connect(lambda: self.generate_chart('spectrum')) + specific_layout.addWidget(self.gen_spectrum_btn) + + self.gen_stats_btn = QPushButton("📉 统计图") + self.gen_stats_btn.setToolTip( + "基于工作目录下 5_training_spectra/training_spectra.csv 生成箱线图、直方图与相关性热力图" + ) + self.gen_stats_btn.clicked.connect(lambda: self.generate_chart('statistics')) + specific_layout.addWidget(self.gen_stats_btn) + + self.gen_mask_glint_btn = QPushButton("🖼️ 掩膜图") + self.gen_mask_glint_btn.clicked.connect(lambda: self.generate_mask_glint_previews()) + specific_layout.addWidget(self.gen_mask_glint_btn) + + self.gen_sampling_map_btn = QPushButton("📍 采样点图") + self.gen_sampling_map_btn.clicked.connect(lambda: self.generate_sampling_point_map()) + specific_layout.addWidget(self.gen_sampling_map_btn) + + specific_group.setLayout(specific_layout) + right_layout.addWidget(specific_group) + + right_panel.setLayout(right_layout) + main_layout.addWidget(right_panel, 1) + + self.setLayout(main_layout) + + def set_work_dir(self, work_dir): + """设置工作目录""" + self.work_dir = work_dir + self.work_dir_edit.setText(str(work_dir)) + # 自动扫描目录 + if work_dir: + QTimer.singleShot(100, self.scan_work_directory) # 延迟执行确保UI更新 + + def browse_work_dir(self): + """浏览工作目录""" + dir_path = QFileDialog.getExistingDirectory(self, "选择工作目录") + if dir_path: + self.work_dir = dir_path + self.work_dir_edit.setText(dir_path) + # 自动扫描目录 + self.scan_work_directory() + + def scan_work_directory(self): + """扫描工作目录中的图像文件""" + if not self.work_dir: + return + + work_path = Path(self.work_dir) + if not work_path.exists(): + return + + print(f"扫描工作目录: {work_path}") + self.image_tree.scan_directory(str(work_path)) + + # 如果有图像，自动选择第一个 + viz_dir = work_path / "9_visualization" + if viz_dir.exists(): + image_files = list(viz_dir.glob("**/*.png")) + list(viz_dir.glob("**/*.jpg")) + if image_files: + self.image_viewer.load_image(str(image_files[0])) + + def on_tree_item_clicked(self, item, column): + """目录树项点击事件""" + data = item.data(0, Qt.UserRole) + if not data: + return + + if data.get("type") == "image": + image_path = data.get("path") + if image_path and Path(image_path).exists(): + self.image_viewer.load_image(image_path) + + def generate_all_visualizations(self): + """生成所有可视化图表（耗时任务在后台线程执行，避免界面未响应）。""" + if not self.work_dir: + QMessageBox.warning(self, "警告", "请先选择工作目录！") + return + + work_path = Path(self.work_dir) + if not work_path.exists(): + QMessageBox.warning(self, "警告", "工作目录不存在！") + return + + reply = QMessageBox.question( + self, "确认生成", + "将按左侧勾选项在后台生成可视化（掩膜/耀斑预览、采样点图等），可能需要较长时间。\n是否继续？", + QMessageBox.Yes | QMessageBox.No + ) + + if reply != QMessageBox.Yes: + return + + if self.gen_scatter.isChecked(): + print("生成散点图...（占位，请用建模/可视化流程生成）") + if self.gen_spectrum.isChecked(): + print("生成光谱图...（占位，请用下方「光谱图」按钮）") + if self.gen_boxplots.isChecked(): + print("生成统计图...（占位，请用下方「统计图」按钮）") + + if not self.gen_mask_glint.isChecked() and not self.gen_sampling_map.isChecked(): + QMessageBox.information( + self, + "提示", + "请至少勾选「掩膜和耀斑缩略图」或「采样点地图」以执行后台批量任务。", + ) + return + + extra = { + "gen_mask_glint": self.gen_mask_glint.isChecked(), + "gen_sampling_map": self.gen_sampling_map.isChecked(), + } + self._start_visualization_thread("generate_all_selected", extra) + + def generate_chart(self, chart_type): + """生成图表（光谱/统计图在后台线程绘制）。""" + if not self.work_dir: + QMessageBox.warning(self, "警告", "请先选择工作目录！") + return + + work_path = Path(self.work_dir) + if not work_path.exists(): + QMessageBox.warning(self, "警告", "工作目录不存在！") + return + + try: + training_spectra_csv = _viz_training_spectra_csv_path(work_path) + + if chart_type == 'scatter': + if not training_spectra_csv.is_file(): + QMessageBox.warning( + self, + "警告", + "未找到 5_training_spectra\\training_spectra.csv。\n" + "请先在工作目录中执行步骤5（光谱特征提取）生成该文件。", + ) + return + training_csv = training_spectra_csv + models_dir = work_path / "6_models" + if not models_dir.is_dir() or not any( + d.is_dir() for d in models_dir.iterdir() + ): + mdir = QFileDialog.getExistingDirectory( + self, + "选择模型根目录（内含各水质参数子文件夹，如 chl_a）", + str(work_path), + ) + if not mdir: + return + models_dir = Path(mdir) + self._start_visualization_thread( + "scatter", + { + "training_csv_path": str(training_csv), + "models_dir": str(models_dir), + }, + ) + return + + if chart_type == 'spectrum': + if not training_spectra_csv.is_file(): + QMessageBox.warning( + self, + "警告", + "未找到 5_training_spectra\\training_spectra.csv。\n" + "光谱分析固定使用该文件，请先执行步骤5（光谱特征提取）。", + ) + return + csv_file = training_spectra_csv + df = pd.read_csv(csv_file) + columns = self._spectrum_meta_param_columns(df) + if not columns: + QMessageBox.warning( + self, + "警告", + "当前 CSV 中没有可用的数值型水质参数列，无法按参数分组绘制光谱图。\n" + "请使用步骤5输出的 training_spectra.csv（含参数列+波段列）。", + ) + return + wl_col = _viz_infer_wavelength_start_column(df) + self._start_visualization_thread( + "spectrum", + { + "csv_path": str(csv_file), + "param_cols": columns, + "wavelength_start_column": wl_col, + "n_groups": 5, + }, + ) + return + + if chart_type == 'statistics': + if not training_spectra_csv.is_file(): + QMessageBox.warning( + self, + "警告", + "未找到 5_training_spectra\\training_spectra.csv。\n" + "统计分析固定使用该文件，请先执行步骤5（光谱特征提取）。", + ) + return + csv_file = training_spectra_csv + df = pd.read_csv(csv_file) + param_cols = self._statistics_param_columns(df) + if not param_cols: + QMessageBox.warning(self, "警告", "未找到可用的水质参数列！") + return + self._start_visualization_thread( + "statistics", + {"csv_path": str(csv_file), "param_cols": param_cols}, + ) + return + + if chart_type == 'sampling_map': + self.generate_sampling_point_map() + return + + except ImportError: + QMessageBox.critical( + self, + "错误", + "无法导入可视化模块！\n请确保 visualization_reports.py 文件存在。", + ) + except Exception as e: + QMessageBox.critical( + self, + "错误", + f"生成图表时出错:\n{str(e)}\n\n{traceback.format_exc()}", + ) + + def generate_mask_glint_previews(self): + """生成掩膜和耀斑缩略图（后台线程）。""" + self._start_visualization_thread("mask_glint") + + def generate_sampling_point_map(self): + """生成采样点地图（后台线程）。""" + self._start_visualization_thread("sampling_map") + + def view_chart(self, chart_type): + """查看图表""" + if not self.work_dir: + QMessageBox.warning(self, "警告", "请先选择工作目录！") + return + + work_path = Path(self.work_dir) + viz_dir = work_path / "9_visualization" + viz_dir2 = work_path / "9_visualization/boxplots" + viz_dir3 = work_path / "9_visualization/scatter_plots" + if not viz_dir.exists(): + QMessageBox.warning(self, "警告", + f"可视化目录不存在:\n{viz_dir}\n\n请先生成图表。") + return + + # 根据类型查找图表文件 + chart_files = [] + if chart_type == 'scatter': + chart_files = list(viz_dir3.glob("*scatter*.png")) + elif chart_type == 'spectrum': + chart_files = list(viz_dir.glob("*spectrum*.png")) + elif chart_type == 'statistics': + chart_files = list(viz_dir2.glob("*boxplot.png")) + \ + list(viz_dir.glob("*histogram.png")) + \ + list(viz_dir.glob("*heatmap.png")) + elif chart_type == 'distribution': + chart_files = list(viz_dir.glob("**/*distribution.png")) + elif chart_type == 'mask_glint': + # 查找掩膜和耀斑缩略图 + glint_dir = viz_dir / "glint_deglint_previews" + if glint_dir.exists(): + chart_files = list(glint_dir.glob("*preview.png")) + else: + # 如果专用目录不存在，从根目录查找 + chart_files = list(viz_dir.glob("*preview.png")) + \ + list(viz_dir.glob("*glint*.png")) + \ + list(viz_dir.glob("*mask*.png")) + elif chart_type == 'sampling_map': + # 查找采样点地图 + sampling_dir = viz_dir / "sampling_maps" + if sampling_dir.exists(): + chart_files = list(sampling_dir.glob("*sampling_map.png")) + else: + chart_files = list(viz_dir.glob("*sampling*.png")) + + if not chart_files: + if chart_type == 'mask_glint': + QMessageBox.warning(self, "警告", + "未找到掩膜和耀斑缩略图！\n\n" + "请先点击'生成掩膜&耀斑缩略图'按钮生成预览图。\n" + "需要2_glint或3_deglint文件夹中存在影像文件。") + else: + QMessageBox.warning(self, "警告", + f"未找到{chart_type}类型的图表文件！\n\n请先生成图表。") + return + + # 如果有多个文件，让用户选择 + if len(chart_files) > 1: + from PyQt5.QtWidgets import QInputDialog + file_names = [f.name for f in chart_files] + file_name, ok = QInputDialog.getItem( + self, "选择图表", "请选择要查看的图表:", + file_names, 0, False + ) + if ok: + selected_file = next(f for f in chart_files if f.name == file_name) + self.show_chart_viewer(str(selected_file), file_name) + else: + self.show_chart_viewer(str(chart_files[0]), chart_files[0].name) + + def browse_all_charts(self): + """浏览所有图表""" + if not self.work_dir: + QMessageBox.warning(self, "警告", "请先选择工作目录！") + return + + work_path = Path(self.work_dir) + + # 查找所有图表文件 + chart_files = [] + chart_files.extend(work_path.glob("**/*.png")) + chart_files.extend(work_path.glob("**/*.jpg")) + + if not chart_files: + QMessageBox.warning(self, "警告", "未找到图表文件！") + return + + # 创建图表浏览对话框 + dialog = ChartBrowserDialog(chart_files, self) + dialog.exec_() + + def show_chart_viewer(self, image_path, title="图表查看器"): + """显示图表查看器""" + viewer = ChartViewerDialog(title=title, parent=self) + viewer.display_image(image_path) + viewer.exec_() + + def get_config(self): + """获取配置""" + return { + 'generate_scatter': self.gen_scatter.isChecked(), + 'generate_boxplots': self.gen_boxplots.isChecked(), + 'generate_spectrum': self.gen_spectrum.isChecked(), + 'generate_statistics': self.gen_stats_btn.isChecked(), + 'generate_glint_previews': self.gen_mask_glint.isChecked(), + 'generate_sampling_maps': self.gen_sampling_map.isChecked(), + 'scatter_config': { + 'metric': 'test_r2', + 'feature_start_column': 13, + 'test_size': 0.2, + 'random_state': 42 + }, + 'boxplot_config': { + 'data_start_column': 4, + 'save_individual': True, + 'use_seaborn': True + }, + 'glint_preview_config': { + 'work_dir': None, + 'output_subdir': 'glint_deglint_previews', + 'generate_glint': True, + 'generate_deglint': True + } + } + + def set_config(self, config): + """设置配置""" + if 'generate_scatter' in config: + self.gen_scatter.setChecked(config['generate_scatter']) + if 'generate_boxplots' in config: + self.gen_boxplots.setChecked(config['generate_boxplots']) + if 'generate_spectrum' in config: + self.gen_spectrum.setChecked(config['generate_spectrum']) + if 'generate_statistics' in config: + self.gen_stats_btn.setChecked(config['generate_statistics']) + if 'generate_glint_previews' in config: + self.gen_mask_glint.setChecked(config['generate_glint_previews']) + if 'generate_sampling_maps' in config: + self.gen_sampling_map.setChecked(config.get('generate_sampling_maps', True)) + + + +class ReportGenerationPanel(QWidget): + """Word 报告生成：工作目录、输出目录、Ollama URL/模型、是否启用 AI 等。""" + + def __init__(self, main_window=None, parent=None): + super().__init__(parent) + self.main_window = main_window + self._report_thread = None + self.init_ui() + + def init_ui(self): + layout = QVBoxLayout() + layout.setContentsMargins(10, 10, 10, 10) + layout.setSpacing(10) + + intro = QLabel( + "根据工作目录下的可视化结果（9_visualization 等）生成 Word 分析报告。" + "需已存在可视化图表；AI 分析通过 Ollama /api/chat 调用本地或远程服务。" + ) + intro.setWordWrap(True) + intro.setStyleSheet(f"color: {ModernStylesheet.COLORS.get('text_secondary', '#666')};") + layout.addWidget(intro) + + path_group = QGroupBox("路径") + path_form = QFormLayout() + + wd_row = QHBoxLayout() + self.work_dir_edit = QLineEdit() + self.work_dir_edit.setPlaceholderText("选择流程工作目录（含 9_visualization）…") + wd_browse = QPushButton("浏览…") + wd_browse.clicked.connect(self.browse_work_dir) + sync_btn = QPushButton("同步主窗口工作目录") + sync_btn.clicked.connect(self.sync_work_dir_from_main) + wd_row.addWidget(self.work_dir_edit, 1) + wd_row.addWidget(wd_browse) + wd_row.addWidget(sync_btn) + path_form.addRow("工作目录:", wd_row) + + out_row = QHBoxLayout() + self.output_dir_edit = QLineEdit() + self.output_dir_edit.setPlaceholderText("留空则保存到 工作目录/9_visualization") + out_browse = QPushButton("浏览…") + out_browse.clicked.connect(self.browse_output_dir) + out_row.addWidget(self.output_dir_edit, 1) + out_row.addWidget(out_browse) + path_form.addRow("报告输出目录:", out_row) + + self.report_title_edit = QLineEdit() + self.report_title_edit.setText("水质参数反演分析报告") + path_form.addRow("报告标题:", self.report_title_edit) + + path_group.setLayout(path_form) + layout.addWidget(path_group) + + ai_group = QGroupBox("AI 分析（Ollama）") + ai_form = QFormLayout() + + self.enable_ai_cb = QCheckBox("启用 AI 图表解读与综合总结") + self.enable_ai_cb.setChecked(os.environ.get("ENABLE_AI_ANALYSIS", "1") not in {"0", "false", "False"}) + ai_form.addRow(self.enable_ai_cb) + + self.ollama_url_edit = QLineEdit() + self.ollama_url_edit.setText(os.environ.get("OLLAMA_URL", "http://localhost:11434").rstrip("/")) + ai_form.addRow("服务 URL:", self.ollama_url_edit) + + self.vision_model_edit = QLineEdit() + self.vision_model_edit.setText(os.environ.get("OLLAMA_VISION_MODEL", "qwen3-vl:8b")) + ai_form.addRow("视觉模型:", self.vision_model_edit) + + self.same_text_model_cb = QCheckBox("文本总结与视觉使用同一模型") + self.same_text_model_cb.setChecked(True) + ai_form.addRow(self.same_text_model_cb) + + self.text_model_edit = QLineEdit() + self.text_model_edit.setText(os.environ.get("OLLAMA_TEXT_MODEL", self.vision_model_edit.text() or "qwen3-vl:8b")) + self.text_model_edit.setEnabled(False) + self.same_text_model_cb.toggled.connect(self._on_same_text_toggled) + self.vision_model_edit.textChanged.connect(self._sync_text_model_if_linked) + ai_form.addRow("文本模型:", self.text_model_edit) + + self.timeout_spin = QSpinBox() + self.timeout_spin.setRange(30, 3600) + self.timeout_spin.setSingleStep(30) + self.timeout_spin.setValue(int(os.environ.get("OLLAMA_TIMEOUT_S", "120"))) + ai_form.addRow("请求超时(秒):", self.timeout_spin) + + ai_group.setLayout(ai_form) + layout.addWidget(ai_group) + + btn_row = QHBoxLayout() + self.generate_btn = QPushButton("生成 Word 报告") + self.generate_btn.setStyleSheet(ModernStylesheet.get_button_stylesheet("success")) + self.generate_btn.clicked.connect(self.on_generate_clicked) + btn_row.addWidget(self.generate_btn) + btn_row.addStretch() + layout.addLayout(btn_row) + + layout.addStretch() + self.setLayout(layout) + + def _on_same_text_toggled(self, checked: bool): + self.text_model_edit.setEnabled(not checked) + if checked: + self.text_model_edit.setText(self.vision_model_edit.text()) + + def _sync_text_model_if_linked(self, _t=None): + if self.same_text_model_cb.isChecked(): + self.text_model_edit.blockSignals(True) + self.text_model_edit.setText(self.vision_model_edit.text()) + self.text_model_edit.blockSignals(False) + + def browse_work_dir(self): + d = QFileDialog.getExistingDirectory(self, "选择工作目录") + if d: + self.work_dir_edit.setText(d) + + def browse_output_dir(self): + d = QFileDialog.getExistingDirectory(self, "选择报告输出目录") + if d: + self.output_dir_edit.setText(d) + + def sync_work_dir_from_main(self): + mw = self.main_window + if mw is not None and getattr(mw, "work_dir", None): + self.work_dir_edit.setText(str(mw.work_dir)) + else: + QMessageBox.information(self, "提示", "主窗口尚未设置工作目录。") + + def set_work_dir(self, work_dir): + if work_dir: + self.work_dir_edit.setText(str(work_dir)) + + def get_config(self): + return { + "work_dir": self.work_dir_edit.text().strip() or None, + "output_dir": self.output_dir_edit.text().strip() or None, + "report_title": self.report_title_edit.text().strip() or "水质参数反演分析报告", + "ollama_url": self.ollama_url_edit.text().strip(), + "ollama_vision_model": self.vision_model_edit.text().strip(), + "ollama_text_model": self.text_model_edit.text().strip(), + "text_same_as_vision": self.same_text_model_cb.isChecked(), + "ollama_timeout_s": self.timeout_spin.value(), + "enable_ai_analysis": self.enable_ai_cb.isChecked(), + } + + def set_config(self, config): + if not config: + return + if config.get("work_dir"): + self.work_dir_edit.setText(str(config["work_dir"])) + if "output_dir" in config: + self.output_dir_edit.setText(str(config["output_dir"] or "")) + if config.get("report_title"): + self.report_title_edit.setText(str(config["report_title"])) + if config.get("ollama_url"): + self.ollama_url_edit.setText(str(config["ollama_url"])) + if config.get("ollama_vision_model"): + self.vision_model_edit.setText(str(config["ollama_vision_model"])) + if "text_same_as_vision" in config: + self.same_text_model_cb.setChecked(bool(config["text_same_as_vision"])) + if config.get("ollama_text_model"): + self.text_model_edit.setText(str(config["ollama_text_model"])) + if config.get("ollama_timeout_s") is not None: + self.timeout_spin.setValue(int(config["ollama_timeout_s"])) + if "enable_ai_analysis" in config: + self.enable_ai_cb.setChecked(bool(config["enable_ai_analysis"])) + + def on_generate_clicked(self): + wd = self.work_dir_edit.text().strip() + if not wd or not os.path.isdir(wd): + QMessageBox.warning(self, "提示", "请选择有效的工作目录。") + return + viz = Path(wd) / "9_visualization" + if not viz.is_dir(): + QMessageBox.warning( + self, + "提示", + f"未找到可视化目录：\n{viz}\n请先完成流程或生成可视化。", + ) + return + if self._report_thread and self._report_thread.isRunning(): + QMessageBox.information(self, "提示", "报告正在生成中，请稍候。") + return + + out = self.output_dir_edit.text().strip() or None + title = self.report_title_edit.text().strip() or "水质参数反演分析报告" + opts = { + "ollama_url": self.ollama_url_edit.text().strip(), + "ollama_vision_model": self.vision_model_edit.text().strip(), + "ollama_text_model": self.text_model_edit.text().strip(), + "text_same_as_vision": self.same_text_model_cb.isChecked(), + "ollama_timeout_s": self.timeout_spin.value(), + "enable_ai_analysis": self.enable_ai_cb.isChecked(), + } + self.generate_btn.setEnabled(False) + self._report_thread = ReportGenerateThread(wd, out, title, opts) + self._report_thread.log_message.connect(self._forward_log, Qt.QueuedConnection) + self._report_thread.finished_ok.connect(self._on_report_ok, Qt.QueuedConnection) + self._report_thread.failed.connect(self._on_report_fail, Qt.QueuedConnection) + self._report_thread.finished.connect(lambda: self.generate_btn.setEnabled(True), Qt.QueuedConnection) + self._report_thread.start() + self._forward_log("已开始生成 Word 报告…", "info") + + def _forward_log(self, msg: str, level: str): + mw = self.main_window + if mw is not None and hasattr(mw, "log_message"): + mw.log_message(msg, level) + else: + print(f"[{level}] {msg}") + + def _on_report_ok(self, path: str): + QMessageBox.information(self, "完成", f"报告已生成：\n{path}") + self._forward_log(f"Word 报告已保存: {path}", "info") + + def _on_report_fail(self, err: str): + QMessageBox.critical(self, "失败", f"报告生成失败：\n{err[:800]}") + self._forward_log(err, "error") + + +class ChartBrowserDialog(QDialog): + """图表浏览器对话框""" + def __init__(self, chart_files, parent=None): + super().__init__(parent) + self.chart_files = sorted(chart_files, key=lambda x: x.stat().st_mtime, reverse=True) + self.current_index = 0 + self.setWindowTitle("图表浏览器") + self.resize(1200, 800) + self.init_ui() + self.show_chart(0) + + def init_ui(self): + layout = QVBoxLayout() + + # 顶部：图表列表 + list_group = QGroupBox(f"图表列表 (共 {len(self.chart_files)} 个)") + list_layout = QHBoxLayout() + + self.chart_list = QListWidget() + self.chart_list.setMaximumHeight(150) + for chart_file in self.chart_files: + self.chart_list.addItem(chart_file.name) + self.chart_list.currentRowChanged.connect(self.show_chart) + + list_layout.addWidget(self.chart_list) + list_group.setLayout(list_layout) + layout.addWidget(list_group) + + # 中间：图表显示 + self.figure = Figure(figsize=(12, 8)) + self.canvas = FigureCanvas(self.figure) + self.canvas.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding) + + self.toolbar = NavigationToolbar(self.canvas, self) + layout.addWidget(self.toolbar) + layout.addWidget(self.canvas, 1) + + # 底部：控制按钮 + btn_layout = QHBoxLayout() + + self.prev_btn = QPushButton("◀ 上一个") + self.prev_btn.clicked.connect(self.prev_chart) + btn_layout.addWidget(self.prev_btn) + + self.next_btn = QPushButton("下一个 >") + self.next_btn.clicked.connect(self.next_chart) + btn_layout.addWidget(self.next_btn) + + btn_layout.addStretch() + + self.save_btn = QPushButton("💾 保存当前图表") + self.save_btn.clicked.connect(self.save_current_chart) + btn_layout.addWidget(self.save_btn) + + self.close_btn = QPushButton("关闭") + self.close_btn.clicked.connect(self.close) + btn_layout.addWidget(self.close_btn) + + layout.addLayout(btn_layout) + self.setLayout(layout) + + def show_chart(self, index): + """显示指定索引的图表""" + if 0 <= index < len(self.chart_files): + self.current_index = index + self.chart_list.setCurrentRow(index) + + chart_file = self.chart_files[index] + self.figure.clear() + ax = self.figure.add_subplot(111) + + try: + import matplotlib.image as mpimg + img = mpimg.imread(str(chart_file)) + ax.imshow(img) + ax.axis('off') + ax.set_title(chart_file.name, fontsize=12, pad=10) + self.figure.tight_layout() + self.canvas.draw() + except Exception as e: + ax.text(0.5, 0.5, f'加载图片失败:\n{str(e)}', + ha='center', va='center', transform=ax.transAxes) + self.canvas.draw() + + # 更新按钮状态 + self.prev_btn.setEnabled(index > 0) + self.next_btn.setEnabled(index < len(self.chart_files) - 1) + + def prev_chart(self): + """上一个图表""" + if self.current_index > 0: + self.show_chart(self.current_index - 1) + + def next_chart(self): + """下一个图表""" + if self.current_index < len(self.chart_files) - 1: + self.show_chart(self.current_index + 1) + + def save_current_chart(self): + """保存当前图表""" + if 0 <= self.current_index < len(self.chart_files): + current_file = self.chart_files[self.current_index] + file_path, _ = QFileDialog.getSaveFileName( + self, "保存图表", current_file.name, + "PNG图片 (*.png);;JPG图片 (*.jpg);;所有文件 (*.*)" + ) + if file_path: + try: + import shutil + shutil.copy(str(current_file), file_path) + QMessageBox.information(self, "成功", f"图表已保存到:\n{file_path}") + except Exception as e: + QMessageBox.critical(self, "错误", f"保存失败:\n{str(e)}") + + +class Step6_5Panel(QWidget): + """步骤6.5：非经验统计回归建模""" + def __init__(self, parent=None): + super().__init__(parent) + self.init_ui() + + def init_ui(self): + layout = QVBoxLayout() + + # 标题 + + + # 训练数据文件（用于独立运行） + self.training_csv_file = FileSelectWidget( + "训练数据CSV:", + "CSV Files (*.csv);;All Files (*.*)" + ) + layout.addWidget(self.training_csv_file) + + # 参数设置 + params_group = QGroupBox("模型参数") + params_layout = QFormLayout() + + # 预处理方法 + self.preproc_checkboxes = {} + preproc_group = QGroupBox("预处理方法 (可多选)") + preproc_layout = QVBoxLayout() + preproc_grid = QGridLayout() + preproc_methods = ['None', 'MMS', 'SS', 'SNV', 'MA', 'SG', 'MSC', 'D1', 'D2', 'DT', 'CT'] + + for i, method in enumerate(preproc_methods): + checkbox = QCheckBox(method) + checkbox.setChecked(True) + self.preproc_checkboxes[method] = checkbox + preproc_grid.addWidget(checkbox, i // 4, i % 4) + + button_layout = QHBoxLayout() + select_all_btn = QPushButton("全选") + deselect_all_btn = QPushButton("全不选") + select_all_btn.clicked.connect(lambda: self._toggle_checkboxes(self.preproc_checkboxes, True)) + deselect_all_btn.clicked.connect(lambda: self._toggle_checkboxes(self.preproc_checkboxes, False)) + button_layout.addWidget(select_all_btn) + button_layout.addWidget(deselect_all_btn) + button_layout.addStretch() + + preproc_layout.addLayout(preproc_grid) + preproc_layout.addLayout(button_layout) + preproc_group.setLayout(preproc_layout) + params_layout.addRow(preproc_group) + + # 算法选择（可多选） + self.algorithm_inputs = {} + algorithms_widget = QWidget() + algorithms_layout = QVBoxLayout() + algorithms_layout.setContentsMargins(0, 0, 0, 0) + algorithms_layout.setSpacing(4) + + algorithm_list = ['chl_a', 'nh3', 'mno4', 'tn', 'tp', 'tss'] + for algorithm in algorithm_list: + row_widget = QWidget() + row_layout = QHBoxLayout() + row_layout.setContentsMargins(0, 0, 0, 0) + checkbox = QCheckBox(algorithm) + checkbox.setChecked(True) + spinbox = QSpinBox() + spinbox.setRange(0, 500) + spinbox.setValue(0) + spinbox.setMaximumWidth(90) + row_layout.addWidget(checkbox) + row_layout.addWidget(QLabel("对应值列索引:")) + row_layout.addWidget(spinbox) + row_layout.addStretch() + row_widget.setLayout(row_layout) + algorithms_layout.addWidget(row_widget) + self.algorithm_inputs[algorithm] = (checkbox, spinbox) + + algorithms_widget.setLayout(algorithms_layout) + params_layout.addRow("非经验算法选择:", algorithms_widget) + + # 光谱起始列 + self.spectral_start_col = QSpinBox() + self.spectral_start_col.setRange(0, 100) + self.spectral_start_col.setValue(1) + params_layout.addRow("光谱起始列索引:", self.spectral_start_col) + + # 窗口大小 + self.window = QSpinBox() + self.window.setRange(1, 20) + self.window.setValue(5) + params_layout.addRow("窗口大小:", self.window) + + params_group.setLayout(params_layout) + layout.addWidget(params_group) + + # 输出文件路径 + self.output_dir = FileSelectWidget( + "输出模型目录:", + "Directories;;All Files (*.*)" + ) + self.output_dir.line_edit.setPlaceholderText("6_5_non_empirical_models") + # 修改浏览按钮为选择目录 + self.output_dir.browse_btn.clicked.disconnect() + self.output_dir.browse_btn.clicked.connect(self.browse_output_dir) + layout.addWidget(self.output_dir) + + # 启用步骤 + self.enable_checkbox = QCheckBox("启用此步骤") + self.enable_checkbox.setChecked(True) + layout.addWidget(self.enable_checkbox) + + # 独立运行按钮 + self.run_button = QPushButton("独立运行此步骤") + self.run_button.setStyleSheet(""" + QPushButton { + background-color: #4CAF50; + color: white; + padding: 8px 16px; + border: none; + border-radius: 4px; + font-weight: bold; + } + QPushButton:hover { + background-color: #45a049; + } + QPushButton:pressed { + background-color: #3e8e41; + } + """) + self.run_button.clicked.connect(self.run_step) + layout.addWidget(self.run_button) + + layout.addStretch() + self.setLayout(layout) + + def get_config(self): + """获取配置""" + selected_algorithms = [ + name for name, (checkbox, _) in self.algorithm_inputs.items() + if checkbox.isChecked() + ] + if not selected_algorithms: + selected_algorithms = list(self.algorithm_inputs.keys()) + + value_cols = { + name: spinbox.value() + for name, (_, spinbox) in self.algorithm_inputs.items() + if name in selected_algorithms + } + + preprocessing_methods = [ + method for method, checkbox in self.preproc_checkboxes.items() + if checkbox.isChecked() + ] or ['None'] + + config = { + 'preprocessing_methods': preprocessing_methods, + 'algorithms': selected_algorithms, + 'value_cols': value_cols, + 'spectral_start_col': self.spectral_start_col.value(), + 'window': self.window.value(), + 'enabled': self.enable_checkbox.isChecked() + } + + # 添加输出路径 - 使用更简洁的方式，参照其他步骤 + output_dir = self.output_dir.get_path() + if not output_dir: + # 如果output_dir为空，使用工作目录或当前目录 + main_window = self.parent().window() + if hasattr(main_window, 'work_dir') and main_window.work_dir: + output_dir = str(Path(main_window.work_dir) / "6_5_non_empirical_models") + else: + output_dir = str(Path.cwd() / "6_5_non_empirical_models") + config['output_dir'] = output_dir + + # 添加训练数据路径（用于独立运行） + training_csv_path = self.training_csv_file.get_path() + if training_csv_path: + config['csv_path'] = training_csv_path + + return config + + def set_config(self, config): + """设置配置""" + if 'preprocessing_methods' in config: + methods = config['preprocessing_methods'] + for method, checkbox in self.preproc_checkboxes.items(): + checkbox.setChecked(method in methods) + + if 'algorithms' in config: + algorithm_values = config['algorithms'] + for algorithm, (checkbox, spinbox) in self.algorithm_inputs.items(): + checkbox.setChecked(algorithm in algorithm_values) + + if 'value_cols' in config: + value_cols = config['value_cols'] + if isinstance(value_cols, dict): + for algorithm, (_, spinbox) in self.algorithm_inputs.items(): + if algorithm in value_cols: + spinbox.setValue(value_cols[algorithm]) + else: + for _, spinbox in self.algorithm_inputs.values(): + spinbox.setValue(value_cols) + + if 'spectral_start_col' in config: + self.spectral_start_col.setValue(config['spectral_start_col']) + + if 'window' in config: + self.window.setValue(config['window']) + if 'output_dir' in config: + self.output_dir.set_path(config['output_dir']) + + # 添加训练数据路径设置 + if 'csv_path' in config: + self.training_csv_file.set_path(config['csv_path']) + + def browse_output_dir(self): + """浏览输出目录""" + dir_path = QFileDialog.getExistingDirectory(self, "选择输出模型目录", "") + if dir_path: + self.output_dir.set_path(dir_path) + + def run_step(self): + """独立运行步骤6.5""" + # 验证输入 + training_csv_path = self.training_csv_file.get_path() + if not training_csv_path: + QMessageBox.warning(self, "输入错误", "请选择训练数据CSV文件！") + return + + if not os.path.exists(training_csv_path): + QMessageBox.warning(self, "输入错误", "训练数据CSV文件不存在！") + return + + # 获取配置 + config = self.get_config() + + # 调用GUI的run_single_step方法 + parent = self.parent() + while parent and not hasattr(parent, 'run_single_step'): + parent = parent.parent() + + if parent and hasattr(parent, 'run_single_step'): + parent.run_single_step('step6_5', {'step6_5': config}) + else: + QMessageBox.critical(self, "错误", "无法找到父级GUI对象") + + def _toggle_checkboxes(self, checkboxes_dict, checked): + """统一设置预处理checkbox状态""" + for checkbox in checkboxes_dict.values(): + checkbox.setChecked(checked) + + +class Step6_75Panel(QWidget): + """步骤6.75：自定义回归分析""" + def __init__(self, parent=None): + super().__init__(parent) + self.x_column_checkboxes: Dict[str, QCheckBox] = {} + self.y_column_checkboxes: Dict[str, QCheckBox] = {} + self.method_checkboxes: Dict[str, QCheckBox] = {} + self.csv_columns = [] + self.init_ui() + + def init_ui(self): + layout = QVBoxLayout() + + + + hint = QLabel("指定自变量与因变量列，批量尝试不同回归方法") + hint.setStyleSheet("color: #666; font-size: 11px;") + layout.addWidget(hint) + + # CSV文件选择 + csv_group = QGroupBox("数据文件") + csv_layout = QVBoxLayout() + + self.csv_file = FileSelectWidget( + "输入CSV文件:", + "CSV Files (*.csv);;All Files (*.*)" + ) + self.csv_file.line_edit.textChanged.connect(self.on_csv_file_changed) + csv_layout.addWidget(self.csv_file) + + self.refresh_btn = QPushButton("刷新列信息") + self.refresh_btn.clicked.connect(self.refresh_csv_columns) + csv_layout.addWidget(self.refresh_btn) + + csv_group.setLayout(csv_layout) + layout.addWidget(csv_group) + + # 自变量选择 + x_group = QGroupBox("自变量列选择 (可多选)") + x_layout = QVBoxLayout() + + # 创建滚动区域来容纳自变量选择 + x_scroll = QScrollArea() + x_scroll.setWidgetResizable(True) + x_scroll.setMaximumHeight(200) + + x_widget = QWidget() + self.x_columns_layout = QGridLayout() + x_widget.setLayout(self.x_columns_layout) + + x_scroll.setWidget(x_widget) + x_layout.addWidget(x_scroll) + + # 全选/反选按钮 + x_btn_layout = QHBoxLayout() + self.x_select_all = QPushButton("全选") + self.x_deselect_all = QPushButton("全不选") + self.x_select_all.clicked.connect(lambda: self.toggle_checkboxes(self.x_column_checkboxes, True)) + self.x_deselect_all.clicked.connect(lambda: self.toggle_checkboxes(self.x_column_checkboxes, False)) + x_btn_layout.addWidget(self.x_select_all) + x_btn_layout.addWidget(self.x_deselect_all) + x_btn_layout.addStretch() + x_layout.addLayout(x_btn_layout) + + x_group.setLayout(x_layout) + layout.addWidget(x_group) + + # 因变量选择 + y_group = QGroupBox("因变量列选择 (可多选)") + y_layout = QVBoxLayout() + + # 创建滚动区域来容纳因变量选择 + y_scroll = QScrollArea() + y_scroll.setWidgetResizable(True) + y_scroll.setMaximumHeight(150) + + y_widget = QWidget() + self.y_columns_layout = QGridLayout() + y_widget.setLayout(self.y_columns_layout) + + y_scroll.setWidget(y_widget) + y_layout.addWidget(y_scroll) + + # 全选/反选按钮 + y_btn_layout = QHBoxLayout() + self.y_select_all = QPushButton("全选") + self.y_deselect_all = QPushButton("全不选") + self.y_select_all.clicked.connect(lambda: self.toggle_checkboxes(self.y_column_checkboxes, True)) + self.y_deselect_all.clicked.connect(lambda: self.toggle_checkboxes(self.y_column_checkboxes, False)) + y_btn_layout.addWidget(self.y_select_all) + y_btn_layout.addWidget(self.y_deselect_all) + y_btn_layout.addStretch() + y_layout.addLayout(y_btn_layout) + + y_group.setLayout(y_layout) + layout.addWidget(y_group) + + # 回归方法选择 + method_group = QGroupBox("回归方法选择 (可多选)") + method_layout = QVBoxLayout() + + method_grid = QGridLayout() + regression_methods = [ + 'linear', 'exponential', 'power', 'logarithmic', + 'polynomial', 'hyperbolic', 'sigmoidal' + ] + + for i, method in enumerate(regression_methods): + checkbox = QCheckBox(method) + # 默认选择常用的方法 + if method in ['linear', 'exponential', 'power', 'logarithmic']: + checkbox.setChecked(True) + self.method_checkboxes[method] = checkbox + method_grid.addWidget(checkbox, i // 3, i % 3) + + method_layout.addLayout(method_grid) + + # 方法全选/反选按钮 + method_btn_layout = QHBoxLayout() + self.method_select_all = QPushButton("全选") + self.method_deselect_all = QPushButton("全不选") + self.method_select_all.clicked.connect(lambda: self.toggle_checkboxes(self.method_checkboxes, True)) + self.method_deselect_all.clicked.connect(lambda: self.toggle_checkboxes(self.method_checkboxes, False)) + method_btn_layout.addWidget(self.method_select_all) + method_btn_layout.addWidget(self.method_deselect_all) + method_btn_layout.addStretch() + method_layout.addLayout(method_btn_layout) + + method_group.setLayout(method_layout) + layout.addWidget(method_group) + + # 输出目录 + output_group = QGroupBox("输出设置") + output_layout = QFormLayout() + + self.output_dir = QLineEdit() + self.output_dir.setText("6_75_custom_regression") + output_layout.addRow("输出目录名:", self.output_dir) + + output_group.setLayout(output_layout) + layout.addWidget(output_group) + + # 启用步骤 + self.enable_checkbox = QCheckBox("启用此步骤") + self.enable_checkbox.setChecked(True) + layout.addWidget(self.enable_checkbox) + + # 独立运行按钮 + self.run_button = QPushButton("独立运行此步骤") + self.run_button.setStyleSheet(""" + QPushButton { + background-color: #4CAF50; + color: white; + padding: 8px 16px; + border: none; + border-radius: 4px; + font-weight: bold; + } + QPushButton:hover { + background-color: #45a049; + } + QPushButton:pressed { + background-color: #3e8e41; + } + """) + self.run_button.clicked.connect(self.run_step) + layout.addWidget(self.run_button) + + layout.addStretch() + self.setLayout(layout) + + def toggle_checkboxes(self, checkboxes_dict, checked): + """统一设置checkbox状态""" + for checkbox in checkboxes_dict.values(): + checkbox.setChecked(checked) + + def on_csv_file_changed(self): + """CSV文件改变时自动刷新列信息""" + self.refresh_csv_columns() + + def refresh_csv_columns(self): + """刷新CSV文件的列信息""" + csv_path = self.csv_file.get_path() + if not csv_path or not os.path.exists(csv_path): + self.csv_columns = [] + self.update_column_widgets() + return + + try: + # 读取CSV文件的第一行作为列名 + df = pd.read_csv(csv_path, nrows=0) + self.csv_columns = list(df.columns) + self.update_column_widgets() + except Exception as e: + self.csv_columns = [] + self.update_column_widgets() + print(f"读取CSV列信息失败: {e}") + + def update_column_widgets(self): + """更新列选择组件""" + # 清空现有的自变量checkbox + for checkbox in self.x_column_checkboxes.values(): + checkbox.setParent(None) + self.x_column_checkboxes.clear() + + # 清空现有的因变量checkbox + for checkbox in self.y_column_checkboxes.values(): + checkbox.setParent(None) + self.y_column_checkboxes.clear() + + if not self.csv_columns: + return + + # 添加自变量checkbox（三列排列） + for i, col in enumerate(self.csv_columns): + checkbox = QCheckBox(col) + # 默认选择一些常见的指数列 + if any(keyword in col.lower() for keyword in ['index', 'ratio', 'normalized', 'nd', 'b']): + checkbox.setChecked(True) + self.x_column_checkboxes[col] = checkbox + self.x_columns_layout.addWidget(checkbox, i // 3, i % 3) + + # 添加因变量checkbox（两列排列） + for i, col in enumerate(self.csv_columns): + checkbox = QCheckBox(col) + # 默认选择一些常见的水质参数列 + if any(keyword in col.lower() for keyword in ['chl', 'tn', 'tp', 'turbidity', 'do', 'ph', 'conductivity']): + checkbox.setChecked(True) + self.y_column_checkboxes[col] = checkbox + self.y_columns_layout.addWidget(checkbox, i // 2, i % 2) + + # 重新布局 + self.x_columns_layout.update() + self.y_columns_layout.update() + + def get_config(self): + # 获取选中的自变量列 + selected_x_columns = [ + col for col, checkbox in self.x_column_checkboxes.items() + if checkbox.isChecked() + ] + + # 获取选中的因变量列 + selected_y_columns = [ + col for col, checkbox in self.y_column_checkboxes.items() + if checkbox.isChecked() + ] + + # 获取选中的回归方法 + selected_methods = [ + method for method, checkbox in self.method_checkboxes.items() + if checkbox.isChecked() + ] + if not selected_methods: + selected_methods = 'all' + + return { + 'csv_path': self.csv_file.get_path() or None, + 'x_columns': selected_x_columns, + 'y_columns': selected_y_columns, + 'methods': selected_methods, + 'output_dir': self.output_dir.text().strip() or None, + 'enabled': self.enable_checkbox.isChecked() + } + + def set_config(self, config): + if 'csv_path' in config: + self.csv_file.set_path(config['csv_path']) + # 设置CSV路径后自动刷新列信息 + self.refresh_csv_columns() + + if 'x_columns' in config: + selected_x = set(config['x_columns']) if isinstance(config['x_columns'], list) else set() + for col, checkbox in self.x_column_checkboxes.items(): + checkbox.setChecked(col in selected_x) + + if 'y_columns' in config: + selected_y = set(config['y_columns']) if isinstance(config['y_columns'], list) else set() + for col, checkbox in self.y_column_checkboxes.items(): + checkbox.setChecked(col in selected_y) + + if 'methods' in config: + methods = config['methods'] + if isinstance(methods, list): + selected_methods = set(methods) + elif methods == 'all': + selected_methods = set(self.method_checkboxes.keys()) + else: + selected_methods = set() + + for method, checkbox in self.method_checkboxes.items(): + checkbox.setChecked(method in selected_methods) + + if 'output_dir' in config: + self.output_dir.setText(config['output_dir'] or "6_75_custom_regression") + if 'enabled' in config: + self.enable_checkbox.setChecked(config['enabled']) + + def run_step(self): + """独立运行步骤6.75""" + # 验证输入 + csv_path = self.csv_file.get_path() + + if not csv_path: + QMessageBox.warning(self, "输入验证失败", "请选择输入CSV文件") + return + if not os.path.exists(csv_path): + QMessageBox.warning(self, "输入验证失败", "输入CSV文件不存在") + return + + # 检查是否有选中的自变量 + selected_x_columns = [ + col for col, checkbox in self.x_column_checkboxes.items() + if checkbox.isChecked() + ] + if not selected_x_columns: + QMessageBox.warning(self, "输入验证失败", "请至少选择一个自变量列") + return + + # 检查是否有选中的因变量 + selected_y_columns = [ + col for col, checkbox in self.y_column_checkboxes.items() + if checkbox.isChecked() + ] + if not selected_y_columns: + QMessageBox.warning(self, "输入验证失败", "请至少选择一个因变量列") + return + + # 检查是否有选中的回归方法 + selected_methods = [ + method for method, checkbox in self.method_checkboxes.items() + if checkbox.isChecked() + ] + if not selected_methods: + QMessageBox.warning(self, "输入验证失败", "请至少选择一种回归方法") + return + + # 获取配置 + config = self.get_config() + + # 调用GUI的run_single_step方法 + parent = self.parent() + while parent and not hasattr(parent, 'run_single_step'): + parent = parent.parent() + + if parent and hasattr(parent, 'run_single_step'): + parent.run_single_step('step6_75', {'step6_75': config}) + else: + QMessageBox.critical(self, "错误", "无法找到父级GUI对象") + + +class WaterQualityGUI(QMainWindow): + """水质参数反演分析系统主窗口""" + + def __init__(self): + super().__init__() + self.pipeline = None + self.worker = None + self.config_file = None + + # 训练数据模式状态 + self.has_training_data = True # 默认有训练数据 + + self.init_ui() + self.apply_stylesheet() + + def get_icon_path(self, icon_filename): + """ + 获取图标文件的完整路径 + 在开发环境中从../data/icons/获取，在打包后从data/icons/获取 + """ + if hasattr(sys, '_MEIPASS'): + # 打包后的环境 + icon_dir = os.path.join(sys._MEIPASS, 'data', 'icons') + else: + # 开发环境 + current_dir = os.path.dirname(os.path.abspath(__file__)) + icon_dir = os.path.join(current_dir, '..', '..', 'data', 'icons') + + return os.path.join(icon_dir, icon_filename) + + def init_ui(self): + """初始化UI""" + self.setWindowTitle("水质参数反演分析系统 v1.0") + self.setGeometry(100, 100, 1200, 800) + + # 创建自定义标题栏（包含Logo和菜单栏） + self.create_title_bar() + + # 创建横幅区域 + self.create_banner_widget() + + # 创建中央部件 + central_widget = QWidget() + self.setCentralWidget(central_widget) + + # 主布局 + main_layout = QHBoxLayout() + + # 创建左侧导航栏 + self.create_navigation() + main_layout.addWidget(self.nav_widget, 1) + + # 创建右侧内容区 + self.create_content_area() + main_layout.addWidget(self.content_widget, 4) + + central_widget.setLayout(main_layout) + + # 创建状态栏 + self.statusBar().showMessage("就绪") + + def create_title_bar(self): + """创建自定义标题栏（Logo和菜单栏挨着且同等宽度）""" + # 创建标题栏容器 + title_widget = QWidget() + title_layout = QHBoxLayout() + title_layout.setContentsMargins(8, 4, 8, 4) + title_layout.setSpacing(0) # 让Logo和菜单栏紧挨着 + + # Logo部分（左侧，增加宽度） + logo_label = QLabel() + logo_label.setFixedSize(180, 48) # 增加Logo宽度，使其和菜单栏视觉平衡 + logo_label.setAlignment(Qt.AlignCenter) + logo_label.setStyleSheet(""" + QLabel { + background-color: #f8f9fa; + border-top-left-radius: 4px; + border-bottom-left-radius: 4px; + } + """) + + # 设置Logo图片路径 - 使用相对路径（打包兼容） + logo_path = r"E:\code\WQ\GUI_v1\fengzhuang-ui2V3\data\icons\logo.png" + logo_pixmap = QPixmap(str(logo_path)) + + if not logo_pixmap.isNull(): + # 按高度缩放图片，保持宽高比，让Logo更显眼 + scaled_pixmap = logo_pixmap.scaledToHeight(38, Qt.SmoothTransformation) + logo_label.setPixmap(scaled_pixmap) + else: + # 如果图片加载失败，显示占位符 + logo_label.setText("Logo") + logo_label.setStyleSheet(""" + QLabel { + background-color: #f8f9fa; + color: #333; + font-size: 14px; + font-weight: bold; + border-top-left-radius: 4px; + border-bottom-left-radius: 4px; + } + """) + + title_layout.addWidget(logo_label) + + # 菜单栏（紧挨着Logo右侧） + menubar = self.menuBar() + menubar.setStyleSheet(""" + QMenuBar { + background-color: #f8f9fa; + border: none; + padding: 4px 8px; + border-top-right-radius: 4px; + border-bottom-right-radius: 4px; + } + QMenuBar::item { + padding: 6px 12px; + background-color: transparent; + font-size: 13px; + } + QMenuBar::item:selected { + background-color: #e6f0ff; + border-radius: 3px; + } + """) + + # 文件菜单 + file_menu = menubar.addMenu("文件") + + new_action = file_menu.addAction("新建配置") + new_action.triggered.connect(self.new_config) + + open_action = file_menu.addAction("打开配置") + open_action.triggered.connect(self.load_config_dialog) + + save_action = file_menu.addAction("保存配置") + save_action.triggered.connect(self.save_config_dialog) + + file_menu.addSeparator() + + exit_action = file_menu.addAction("退出") + exit_action.triggered.connect(self.close) + + # 工具菜单 + tools_menu = menubar.addMenu("工具") + + work_dir_action = tools_menu.addAction("设置工作目录") + work_dir_action.triggered.connect(self.set_work_directory) + + open_dir_action = tools_menu.addAction("打开工作目录") + open_dir_action.triggered.connect(self.open_work_directory) + + # 在工具菜单中添加训练数据模式切换按钮 + self.training_mode_action = tools_menu.addAction("有训练数据模式") + self.training_mode_action.setCheckable(True) + self.training_mode_action.setChecked(True) # 默认有训练数据模式 + self.training_mode_action.triggered.connect(self.toggle_training_data_mode) + + # 帮助菜单 + help_menu = menubar.addMenu("帮助") + + pipeline_status_action = help_menu.addAction("检查Pipeline状态") + pipeline_status_action.triggered.connect(self.show_pipeline_status) + + help_menu.addSeparator() + + about_action = help_menu.addAction("关于") + about_action.triggered.connect(self.show_about) + + title_layout.addWidget(menubar) + title_widget.setLayout(title_layout) + + # 设置整体标题栏样式 + title_widget.setStyleSheet(""" + QWidget { + background-color: #f8f9fa; + border-bottom: 1px solid #d0d0d0; + } + """) + + # 将标题栏添加到窗口顶部 + self.setMenuWidget(title_widget) + + + def create_banner_widget(self): + """创建横幅区域 - 支持自适应等比缩放""" + # 创建横幅容器 + banner_widget = QWidget() + banner_layout = QHBoxLayout() + banner_layout.setContentsMargins(0, 0, 0, 0) + banner_layout.setSpacing(0) + + # 创建横幅标签 + self.banner_label = QLabel() + self.banner_label.setMinimumHeight(65) + self.banner_label.setMaximumHeight(110) + self.banner_label.setAlignment(Qt.AlignCenter) + self.banner_label.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed) + self.banner_label.setScaledContents(False) + + # 保存原始pixmap用于后续缩放 + banner_path = r"E:\code\WQ\GUI_v1\fengzhuang-ui2\data\icons\Mega Water 1.0.png" + self.banner_pixmap = QPixmap(banner_path) + + if not self.banner_pixmap.isNull(): + # 延迟执行，确保窗口已初始化 + QTimer.singleShot(50, self.update_banner_image) + else: + # 如果图片加载失败，显示占位符 + self.banner_label.setText("水质参数反演分析系统") + self.banner_label.setStyleSheet(""" + QLabel { + background: qlineargradient(x1:0, y1:0, x2:1, y2:0, + stop:0 #0078d4, stop:1 #00a0e9); + color: white; + font-size: 26px; + font-weight: bold; + border-bottom: 3px solid #005a9e; + } + """) + + banner_layout.addWidget(self.banner_label) + banner_widget.setLayout(banner_layout) + + # 将横幅添加到窗口顶部（在标题栏下方） + banner_toolbar = QToolBar() + banner_toolbar.setMovable(False) + banner_toolbar.setFloatable(False) + banner_toolbar.addWidget(banner_widget) + banner_toolbar.setStyleSheet(""" + QToolBar { + background-color: white; + border: none; + border-bottom: 1px solid #ddd; + padding: 2px 0px; + margin: 0px; + } + """) + + self.addToolBar(Qt.TopToolBarArea, banner_toolbar) + self.banner_widget = banner_toolbar + + def create_navigation(self): + """创建左侧导航栏""" + self.nav_widget = QWidget() + nav_layout = QVBoxLayout() + nav_layout.setContentsMargins(10, 15, 10, 15) + nav_layout.setSpacing(10) + + # 标题 + title = QLabel("流程步骤") + title.setFont(QFont("Arial", 13, QFont.Bold)) + title.setAlignment(Qt.AlignCenter) + title.setStyleSheet(f"color: {ModernStylesheet.COLORS['text_primary']}; padding: 10px;") + nav_layout.addWidget(title) + + # 步骤列表 - 分层结构 + self.step_list = QListWidget() + self.step_list.setStyleSheet(ModernStylesheet.get_sidebar_stylesheet()) + + # 定义三阶段结构 + self.process_stages = { + "阶段一：数据预处理": [ + ("step1", "1. 水域掩膜生成"), + ("step2", "2. 耀斑区域识别"), + ("step3", "3. 耀斑去除与修复"), + ("step4", "4. 数据标准化处理"), + ], + "阶段二：特征提取与建模": [ + ("step5", "5. 光谱特征提取"), + ("step5_5", "6. 水质参数指数计算"), + ("step6", "7. 监督学习模型训练"), + ("step6_5", "8. 经验统计回归"), + ("step6_75", "9. 自定义回归模型"), + ], + "阶段三：应用与可视化": [ + ("step7", "10. 采样点布设"), + ("step8", "11. 基于监督学习预测"), + ("step8_5", "12. 基于统计回归预测"), + ("step8_75", "13. 基于自定义回归预测"), + ("step9", "14. 专题图生成"), + ("step9_viz", "15. 可视化分析"), + ("step_report", "16. 分析报告生成"), + ] + } + + # 存储步骤映射 + self.step_name_map = {} + + # 添加分组项到列表 + for stage_idx, (stage_name, steps) in enumerate(self.process_stages.items()): + # 添加阶段标题项（可视化分组） + stage_item = QListWidgetItem(stage_name) + stage_font = QFont("Arial", 11, QFont.Bold) + stage_item.setFont(stage_font) + stage_item.setForeground(QColor(ModernStylesheet.COLORS.get('accent', '#0078D4'))) + stage_item.setFlags(stage_item.flags() & ~Qt.ItemIsSelectable) + stage_item.setFlags(stage_item.flags() & ~Qt.ItemIsEnabled) + stage_item.setData(Qt.UserRole, "stage_header") + self.step_list.addItem(stage_item) + + # 添加该阶段的所有步骤 + for step_id, step_display in steps: + item = QListWidgetItem(f" └─ {step_display}") + item.setData(Qt.UserRole, step_id) + self.step_name_map[step_display] = step_id + + # 设置步骤项的样式 + step_font = QFont("Arial", 10) + item.setFont(step_font) + item.setForeground(QColor(ModernStylesheet.COLORS.get('text_secondary', '#666666'))) + self.step_list.addItem(item) + + # 在阶段间添加分隔符 + if stage_idx < len(self.process_stages) - 1: + separator_item = QListWidgetItem("") + separator_item.setFlags(separator_item.flags() & ~Qt.ItemIsSelectable) + separator_item.setFlags(separator_item.flags() & ~Qt.ItemIsEnabled) + self.step_list.addItem(separator_item) + + self.step_list.currentRowChanged.connect(self.on_step_changed) + nav_layout.addWidget(self.step_list) + + # 控制按钮 + btn_layout = QVBoxLayout() + btn_layout.setSpacing(8) + + self.run_all_btn = QPushButton("> 运行完整流程") + self.run_all_btn.setStyleSheet(ModernStylesheet.get_button_stylesheet('success')) + self.run_all_btn.setMinimumHeight(35) + self.run_all_btn.clicked.connect(self.run_full_pipeline) + btn_layout.addWidget(self.run_all_btn) + + self.stop_btn = QPushButton("⏹ 停止") + self.stop_btn.setEnabled(False) + self.stop_btn.setMinimumHeight(35) + self.stop_btn.setStyleSheet(ModernStylesheet.get_button_stylesheet('danger')) + self.stop_btn.clicked.connect(self.stop_pipeline) + btn_layout.addWidget(self.stop_btn) + + nav_layout.addLayout(btn_layout) + + self.nav_widget.setLayout(nav_layout) + self.nav_widget.setMaximumWidth(280) + self.nav_widget.setStyleSheet(f"background-color: {ModernStylesheet.COLORS['panel_bg']}; border-right: 1px solid {ModernStylesheet.COLORS['border_light']};") + + + def create_content_area(self): + """创建右侧内容区""" + self.content_widget = QWidget() + self.content_widget.setStyleSheet(f"background-color: {ModernStylesheet.COLORS['main_bg']};") + content_layout = QVBoxLayout() + content_layout.setContentsMargins(15, 15, 15, 15) + content_layout.setSpacing(10) + + # 创建步骤面板容器 + self.step_stack = QTabWidget() + self.step_stack.setTabPosition(QTabWidget.North) + self.step_stack.setTabsClosable(False) + self.step_stack.setStyleSheet(ModernStylesheet.get_main_stylesheet()) + + # 添加各步骤面板 + self.step1_panel = Step1Panel() + self.step_stack.addTab(self.create_scroll_area(self.step1_panel), QIcon(self.get_icon_path("1.png")), "水域掩膜") + + self.step2_panel = Step2Panel() + self.step_stack.addTab(self.create_scroll_area(self.step2_panel), QIcon(self.get_icon_path("2.png")), "耀斑检测") + + self.step3_panel = Step3Panel() + self.step_stack.addTab(self.create_scroll_area(self.step3_panel), QIcon(self.get_icon_path("3.png")), "耀斑去除") + + self.step4_panel = Step4Panel() + self.step_stack.addTab(self.create_scroll_area(self.step4_panel), QIcon(self.get_icon_path("4.png")), "数据清洗") + + self.step5_panel = Step5Panel() + self.step_stack.addTab(self.create_scroll_area(self.step5_panel), QIcon(self.get_icon_path("5.png")), "特征构建") + + self.step5_5_panel = Step5_5Panel() + self.step_stack.addTab(self.create_scroll_area(self.step5_5_panel), QIcon(self.get_icon_path("5.png")), "水质指数") + + self.step6_panel = Step6Panel() + self.step_stack.addTab(self.create_scroll_area(self.step6_panel), QIcon(self.get_icon_path("6.png")), "监督建模") + + self.step6_5_panel = Step6_5Panel() + self.step_stack.addTab(self.create_scroll_area(self.step6_5_panel), QIcon(self.get_icon_path("6.png")), "回归建模") + + self.step6_75_panel = Step6_75Panel() + self.step_stack.addTab(self.create_scroll_area(self.step6_75_panel), QIcon(self.get_icon_path("6.png")), "自定义回归建模") + + self.step7_panel = Step7Panel() + self.step_stack.addTab(self.create_scroll_area(self.step7_panel), QIcon(self.get_icon_path("7.png")), "采样点布设") + + self.step8_panel = Step8Panel() + self.step_stack.addTab(self.create_scroll_area(self.step8_panel), QIcon(self.get_icon_path("8.png")), "监督预测") + + self.step8_5_panel = Step8_5Panel() + self.step_stack.addTab(self.create_scroll_area(self.step8_5_panel), QIcon(self.get_icon_path("8.png")), "回归预测") + + self.step8_75_panel = Step8_75Panel() + self.step_stack.addTab(self.create_scroll_area(self.step8_75_panel), QIcon(self.get_icon_path("8.png")), "自定义回归预测") + + self.step9_panel = Step9Panel() + self.step_stack.addTab(self.create_scroll_area(self.step9_panel), QIcon(self.get_icon_path("10.png")), "专题图生成") + + self.viz_panel = VisualizationPanel() + self.step_stack.addTab(self.create_scroll_area(self.viz_panel), QIcon(self.get_icon_path("9.png")), "可视化") + + self.report_panel = ReportGenerationPanel(main_window=self) + self.step_stack.addTab(self.create_scroll_area(self.report_panel), QIcon(self.get_icon_path("10.png")), "报告生成") + + # 连接Tab切换信号，实现双向同步（必须在step_stack创建后） + self.step_stack.currentChanged.connect(self.on_tab_changed) + + content_layout.addWidget(self.step_stack, 3) + + # 日志区域 + log_group = QGroupBox("执行日志") + log_group.setStyleSheet(f""" + QGroupBox {{ + background-color: {ModernStylesheet.COLORS['panel_bg']}; + border: 1px solid {ModernStylesheet.COLORS['border_light']}; + border-radius: 5px; + margin-top: 8px; + padding-top: 15px; + padding-left: 9px; + padding-right: 9px; + padding-bottom: 9px; + }} + QGroupBox::title {{ + subcontrol-origin: margin; + subcontrol-position: top left; + padding: 0 5px; + font-weight: bold; + color: {ModernStylesheet.COLORS['text_primary']}; + }} + """) + log_layout = QVBoxLayout() + log_layout.setContentsMargins(5, 5, 5, 5) + + self.log_text = QTextEdit() + self.log_text.setReadOnly(True) + self.log_text.setMaximumHeight(200) + self.log_text.setStyleSheet(f""" + QTextEdit {{ + background-color: {ModernStylesheet.COLORS['panel_bg']}; + color: {ModernStylesheet.COLORS['text_primary']}; + border: 1px solid {ModernStylesheet.COLORS['border']}; + border-radius: 4px; + padding: 5px; + font-family: 'Courier New', monospace; + font-size: 10px; + }} + """) + log_layout.addWidget(self.log_text) + + log_btn_layout = QHBoxLayout() + clear_log_btn = QPushButton("清空日志") + clear_log_btn.setMaximumWidth(100) + clear_log_btn.setStyleSheet(ModernStylesheet.get_button_stylesheet('normal')) + clear_log_btn.clicked.connect(self.clear_log) + log_btn_layout.addWidget(clear_log_btn) + log_btn_layout.addStretch() + log_layout.addLayout(log_btn_layout) + + log_group.setLayout(log_layout) + content_layout.addWidget(log_group, 1) + + # 进度条 + progress_group = QGroupBox("执行进度") + progress_group.setStyleSheet(f""" + QGroupBox {{ + background-color: {ModernStylesheet.COLORS['panel_bg']}; + border: 1px solid {ModernStylesheet.COLORS['border_light']}; + border-radius: 5px; + margin-top: 8px; + padding-top: 10px; + padding-left: 9px; + padding-right: 9px; + padding-bottom: 9px; + }} + QGroupBox::title {{ + subcontrol-origin: margin; + subcontrol-position: top left; + padding: 0 5px; + font-weight: bold; + color: {ModernStylesheet.COLORS['text_primary']}; + }} + """) + progress_layout = QVBoxLayout() + progress_layout.setContentsMargins(5, 5, 5, 5) + + self.progress_bar = QProgressBar() + self.progress_bar.setValue(0) + self.progress_bar.setStyleSheet(f""" + QProgressBar {{ + background-color: {ModernStylesheet.COLORS['panel_bg']}; + border: 1px solid {ModernStylesheet.COLORS['border']}; + border-radius: 4px; + padding: 2px; + text-align: center; + height: 20px; + }} + QProgressBar::chunk {{ + background-color: {ModernStylesheet.COLORS['success']}; + border-radius: 3px; + }} + """) + progress_layout.addWidget(self.progress_bar) + progress_group.setLayout(progress_layout) + content_layout.addWidget(progress_group, 0) + + self.content_widget.setLayout(content_layout) + + # 初始化训练数据模式UI状态 + self.update_ui_for_training_mode() + + # 显示pipeline状态 + self.show_pipeline_status_on_startup() + + def create_scroll_area(self, widget): + """创建滚动区域""" + scroll = QScrollArea() + scroll.setWidget(widget) + scroll.setWidgetResizable(True) + return scroll + + def on_step_changed(self, index): + """步骤切换 - 处理分层列表结构""" + if index < 0: + return + + # 获取选中项 + item = self.step_list.item(index) + if not item: + return + + # 检查是否是可选中的步骤项 + item_data = item.data(Qt.UserRole) + if item_data == "stage_header" or item_data is None: + # 是阶段标题或分隔符，不切换 + return + + # 根据步骤ID查找对应的tab索引 + step_id_to_tab = { + 'step1': 0, + 'step2': 1, + 'step3': 2, + 'step4': 3, + 'step5': 4, + 'step5_5': 5, + 'step6': 6, + 'step6_5': 7, + 'step6_75': 8, + 'step7': 9, + 'step8': 10, + 'step8_5': 11, + 'step8_75': 12, + 'step9': 13, + 'step9_viz': 14, + 'step_report': 15, + } + + if item_data in step_id_to_tab: + tab_index = step_id_to_tab[item_data] + self.step_stack.setCurrentIndex(tab_index) + + def on_tab_changed(self, index): + """Tab页面切换时同步更新左侧步骤列表""" + if index < 0: + return + + # Tab索引到步骤ID的反向映射 + tab_to_step_id = { + 0: 'step1', + 1: 'step2', + 2: 'step3', + 3: 'step4', + 4: 'step5', + 5: 'step5_5', + 6: 'step6', + 7: 'step6_5', + 8: 'step6_75', + 9: 'step7', + 10: 'step8', + 11: 'step8_5', + 12: 'step8_75', + 13: 'step9', + 14: 'step9_viz', + 15: 'step_report', + } + + if index not in tab_to_step_id: + return + + target_step_id = tab_to_step_id[index] + + # 在step_list中查找对应的步骤项 + for row in range(self.step_list.count()): + item = self.step_list.item(row) + if not item: + continue + + item_data = item.data(Qt.UserRole) + if item_data == target_step_id: + # 找到对应的步骤项，设置为当前选中 + self.step_list.setCurrentRow(row) + break + + def apply_stylesheet(self): + """应用样式表 - 应用现代化设计风格""" + # 应用主样式表 + self.setStyleSheet(ModernStylesheet.get_main_stylesheet()) + + def new_config(self): + """新建配置""" + reply = QMessageBox.question( + self, "新建配置", + "是否清空当前配置？", + QMessageBox.Yes | QMessageBox.No + ) + if reply == QMessageBox.Yes: + # 重置所有面板 + self.log_message("已清空配置", "info") + + def load_config_dialog(self): + """加载配置对话框""" + file_path, _ = QFileDialog.getOpenFileName( + self, "加载配置", "", "JSON Files (*.json);;All Files (*.*)" + ) + if file_path: + self.load_config(file_path) + + def load_config(self, file_path): + """加载配置""" + try: + with open(file_path, 'r', encoding='utf-8') as f: + config = json.load(f) + + # 应用配置到各面板 + if 'step1' in config: + self.step1_panel.set_config(config['step1']) + if 'step2' in config: + self.step2_panel.set_config(config['step2']) + if 'step3' in config: + self.step3_panel.set_config(config['step3']) + if 'step4' in config: + self.step4_panel.set_config(config['step4']) + if 'step5' in config: + self.step5_panel.set_config(config['step5']) + if 'step5_5' in config: + self.step5_5_panel.set_config(config['step5_5']) + if 'step6' in config: + self.step6_panel.set_config(config['step6']) + if 'step6_5' in config: + self.step6_5_panel.set_config(config['step6_5']) + if 'step6_75' in config: + self.step6_75_panel.set_config(config['step6_75']) + if 'step7' in config: + self.step7_panel.set_config(config['step7']) + if 'step8' in config: + self.step8_panel.set_config(config['step8']) + if 'step8_5' in config: + self.step8_5_panel.set_config(config['step8_5']) + if 'step9' in config: + self.step9_panel.set_config(config['step9']) + if 'visualization' in config: + self.viz_panel.set_config(config['visualization']) + if 'report_generation' in config: + self.report_panel.set_config(config['report_generation']) + + self.config_file = file_path + self.log_message(f"已加载配置: {file_path}", "info") + QMessageBox.information(self, "成功", "配置加载成功！") + except Exception as e: + self.log_message(f"加载配置失败: {str(e)}", "error") + QMessageBox.critical(self, "错误", f"加载配置失败:\n{str(e)}") + + def save_config_dialog(self): + """保存配置对话框""" + file_path, _ = QFileDialog.getSaveFileName( + self, "保存配置", "config.json", "JSON Files (*.json);;All Files (*.*)" + ) + if file_path: + self.save_config(file_path) + + def save_config(self, file_path): + """保存配置""" + try: + config = self.get_current_config() + with open(file_path, 'w', encoding='utf-8') as f: + json.dump(config, f, indent=4, ensure_ascii=False) + + self.config_file = file_path + self.log_message(f"已保存配置: {file_path}", "info") + QMessageBox.information(self, "成功", "配置保存成功！") + except Exception as e: + self.log_message(f"保存配置失败: {str(e)}", "error") + QMessageBox.critical(self, "错误", f"保存配置失败:\n{str(e)}") + + def get_current_config(self): + """获取当前配置""" + config = { + 'step1': self.step1_panel.get_config(), + 'step2': self.step2_panel.get_config(), + 'step3': self.step3_panel.get_config(), + 'step4': self.step4_panel.get_config(), + 'step5': self.step5_panel.get_config(), + 'step5_5': self.step5_5_panel.get_config(), + 'step6': self.step6_panel.get_config(), + 'step6_5': self.step6_5_panel.get_config(), + 'step6_75': self.step6_75_panel.get_config(), + 'step7': self.step7_panel.get_config(), + 'step8': self.step8_panel.get_config(), + 'step8_5': self.step8_5_panel.get_config(), + 'step9': self.step9_panel.get_config(), + 'visualization': self.viz_panel.get_config(), + 'report_generation': self.report_panel.get_config(), + } + return config + + def set_work_directory(self): + """设置工作目录""" + dir_path = QFileDialog.getExistingDirectory(self, "选择工作目录") + if dir_path: + self.work_dir = dir_path + self.log_message(f"工作目录已设置: {dir_path}", "info") + self.statusBar().showMessage(f"工作目录: {dir_path}") + + # 同步到可视化面板 + if hasattr(self, 'viz_panel'): + self.viz_panel.set_work_dir(dir_path) + if hasattr(self, 'report_panel'): + self.report_panel.set_work_dir(dir_path) + + def open_work_directory(self): + """打开工作目录""" + work_dir = getattr(self, 'work_dir', './work_dir') + if os.path.exists(work_dir): + os.startfile(work_dir) + else: + QMessageBox.warning(self, "警告", "工作目录不存在！") + + def show_pipeline_status_on_startup(self): + """启动时显示Pipeline状态""" + if not PIPELINE_AVAILABLE: + # 如果pipeline不可用，显示警告信息 + status_msg = "[WARNING] Pipeline模块无法加载\n\n" + status_msg += "系统功能将受到限制，建议检查以下问题:\n" + status_msg += "• 项目文件结构是否完整\n" + status_msg += "• Python依赖包是否已安装\n" + status_msg += "• Python版本是否兼容\n\n" + status_msg += "点击 '帮助' → '检查Pipeline状态' 查看详细信息" + + QMessageBox.warning(self, "Pipeline模块警告", status_msg) + else: + # 如果pipeline可用，只在状态栏显示 + self.statusBar().showMessage("Pipeline模块: 正常加载", 5000) # 显示5秒 + + def show_pipeline_status(self): + """显示Pipeline状态""" + status_text = "Pipeline模块状态检查\n\n" + + if PIPELINE_AVAILABLE: + status_text += "[OK] Pipeline模块状态: 正常\n\n" + status_text += "详细诊断信息:\n" + else: + status_text += "[ERROR] Pipeline模块状态: 不可用\n\n" + status_text += "详细诊断信息:\n" + + for info in PIPELINE_ERROR_INFO: + status_text += info + "\n" + + # 添加使用建议 + status_text += "\n" + "="*50 + "\n" + if PIPELINE_AVAILABLE: + status_text += "[SUCCESS] Pipeline模块已成功加载，可以正常使用所有功能！\n" + else: + status_text += "[WARNING] Pipeline模块无法加载，功能将受到限制\n" + status_text += "建议解决方案:\n" + status_text += "1. 检查项目文件结构是否完整\n" + status_text += "2. 安装所有必需的依赖包\n" + status_text += "3. 确认Python版本兼容性\n" + status_text += "4. 查看控制台输出获取更多详细信息\n" + + # 创建消息框 + msg_box = QMessageBox(self) + msg_box.setWindowTitle("Pipeline状态检查") + msg_box.setText(status_text) + + # 根据状态设置图标 + if PIPELINE_AVAILABLE: + msg_box.setIcon(QMessageBox.Information) + else: + msg_box.setIcon(QMessageBox.Warning) + + # 设置详细文本（如果有的话） + if PIPELINE_ERROR_INFO: + detailed_text = "\n".join(PIPELINE_ERROR_INFO) + msg_box.setDetailedText(detailed_text) + + msg_box.setStandardButtons(QMessageBox.Ok) + msg_box.exec_() + + def show_about(self): + """显示关于对话框""" + QMessageBox.about( + self, "关于", + "水质参数反演分析系统 v1.0\n\n" + "一个完整的水质参数反演工作流程工具\n\n" + "功能包括：\n" + "- 水域掩膜生成\n" + "- 耀斑检测与去除\n" + "- 光谱提取\n" + "- 机器学习建模\n" + "- 水质参数预测\n" + "- 可视化分析\n\n" + "公司：北京依锐思遥感技术有限公司\n" + "地址：北京市海淀区清河安宁庄东路18号5号楼二层205\n" + "电话：010-51292601\n" + "邮箱：hanshanlong@iris-rs.cn\n" + ) + + def run_full_pipeline(self): + """运行完整流程""" + if not PIPELINE_AVAILABLE: + QMessageBox.critical( + self, "错误", + "无法导入pipeline模块，请确保water_quality_inversion_pipeline_GUI.py文件存在！" + ) + return + + # 验证配置 + config = self.get_current_config() + + # 基本验证 + if not config['step1'].get('mask_path'): + QMessageBox.warning(self, "警告", "请先配置步骤1的掩膜文件！") + # 找到第一个可选的步骤项 + for i in range(self.step_list.count()): + item = self.step_list.item(i) + if item.data(Qt.UserRole) == 'step1': + self.step_list.setCurrentRow(i) + break + return + + # 确认执行 + reply = QMessageBox.question( + self, "确认", + "是否开始执行完整流程？\n\n这可能需要较长时间，请确保配置正确。", + QMessageBox.Yes | QMessageBox.No + ) + + if reply != QMessageBox.Yes: + return + + # 创建pipeline实例 + work_dir = getattr(self, 'work_dir', './work_dir') + self.log_message(f"初始化pipeline，工作目录: {work_dir}", "info") + + # 准备实际运行配置（排除未启用的步骤） + worker_config = copy.deepcopy(config) + step5_5_cfg = worker_config.get('step5_5') + if step5_5_cfg: + enabled = step5_5_cfg.pop('enabled', True) + if not enabled: + worker_config.pop('step5_5', None) + + # 工作线程内创建 Pipeline，避免主线程阻塞及 Qt5Agg 子线程绘图卡死 + self.worker = WorkerThread(work_dir, worker_config, mode='full') + self.worker.log_message.connect(self.log_message, Qt.QueuedConnection) + self.worker.progress_update.connect(self.update_progress, Qt.QueuedConnection) + self.worker.finished.connect(self.on_pipeline_finished, Qt.QueuedConnection) + + # 更新UI状态 + self.run_all_btn.setEnabled(False) + self.stop_btn.setEnabled(True) + self.progress_bar.setValue(0) + + # 启动线程 + self.worker.start() + self.log_message("="*50, "info") + self.log_message("开始执行完整流程...", "info") + self.log_message("="*50, "info") + + def stop_pipeline(self): + """停止流程""" + if self.worker and self.worker.isRunning(): + reply = QMessageBox.question( + self, "确认", + "是否停止当前流程？", + QMessageBox.Yes | QMessageBox.No + ) + if reply == QMessageBox.Yes: + self.worker.stop() + self.log_message("用户取消执行", "warning") + self.run_all_btn.setEnabled(True) + self.stop_btn.setEnabled(False) + + def on_pipeline_finished(self, success, message): + """流程完成""" + self.run_all_btn.setEnabled(True) + self.stop_btn.setEnabled(False) + + if success: + self.progress_bar.setValue(100) + self.log_message("="*50, "info") + self.log_message("流程执行完成！", "info") + self.log_message("="*50, "info") + QMessageBox.information(self, "完成", "流程执行成功！\n\n请查看工作目录中的结果文件。") + else: + self.log_message("="*50, "error") + self.log_message(f"流程执行失败: {message}", "error") + self.log_message("="*50, "error") + QMessageBox.critical(self, "失败", f"流程执行失败:\n\n{message[:200]}") + + def run_single_step(self, step_name, config): + """运行单个步骤""" + if not PIPELINE_AVAILABLE: + QMessageBox.critical( + self, "错误", + "无法导入pipeline模块，请确保water_quality_inversion_pipeline_GUI.py文件存在！" + ) + return + + # 创建pipeline实例 + work_dir = getattr(self, 'work_dir', './work_dir') + self.log_message(f"初始化pipeline，工作目录: {work_dir}", "info") + + self.worker = WorkerThread(work_dir, config, mode='single_step', step_name=step_name) + self.worker.log_message.connect(self.log_message, Qt.QueuedConnection) + self.worker.progress_update.connect(self.update_progress, Qt.QueuedConnection) + self.worker.finished.connect(self.on_pipeline_finished, Qt.QueuedConnection) + + # 更新UI状态 + self.run_all_btn.setEnabled(False) + self.stop_btn.setEnabled(True) + self.progress_bar.setValue(0) + + # 启动线程 + self.worker.start() + self.log_message("="*50, "info") + self.log_message(f"开始独立运行步骤 {step_name}...", "info") + self.log_message("="*50, "info") + + def update_progress(self, percentage, message): + """更新进度""" + self.progress_bar.setValue(percentage) + self.statusBar().showMessage(message) + + def log_message(self, message, level='info'): + """记录日志""" + timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S') + + # 设置颜色 + if level == 'error': + color = 'red' + elif level == 'warning': + color = 'orange' + else: + color = 'black' + + # 添加到日志 + formatted_msg = f'[{timestamp}] {message}' + self.log_text.append(formatted_msg) + + # 自动滚动到底部 + cursor = self.log_text.textCursor() + cursor.movePosition(QTextCursor.End) + self.log_text.setTextCursor(cursor) + + def clear_log(self): + """清空日志""" + self.log_text.clear() + self.log_message("日志已清空", "info") + + def toggle_training_data_mode(self, checked): + """切换训练数据模式""" + self.has_training_data = checked + self.update_ui_for_training_mode() + + mode_text = "有训练数据" if checked else "无训练数据" + self.log_message(f"切换到{mode_text}模式", "info") + self.statusBar().showMessage(f"当前模式: {mode_text}") + + # 更新按钮文本 + self.training_mode_action.setText("有训练数据模式" if checked else "无训练数据模式") + + def update_banner_image(self): + """更新横幅图片 - 等比自适应缩放""" + if not hasattr(self, 'banner_pixmap') or self.banner_pixmap.isNull(): + return + + # 获取可用宽度（考虑工具栏边距） + available_width = max(200, self.width() - 60) # 最小宽度保护 + + # 第一步：按宽度缩放，保持比例 + scaled_pixmap = self.banner_pixmap.scaled( + available_width, + 120, # 最大允许高度 + Qt.KeepAspectRatio, # 关键：等比缩放 + Qt.SmoothTransformation # 平滑缩放 + ) + + # 如果高度仍然过大，则按高度限制缩放 + if scaled_pixmap.height() > 110: + scaled_pixmap = self.banner_pixmap.scaled( + int(available_width * 0.9), + 110, + Qt.KeepAspectRatio, + Qt.SmoothTransformation + ) + + self.banner_label.setPixmap(scaled_pixmap) + + def resizeEvent(self, event): + """窗口大小改变事件 - 实时更新横幅图片等比缩放""" + super().resizeEvent(event) + # 使用定时器避免频繁调用 + if hasattr(self, '_banner_timer'): + self._banner_timer.stop() + else: + self._banner_timer = QTimer() + self._banner_timer.setSingleShot(True) + self._banner_timer.timeout.connect(self.update_banner_image) + + self._banner_timer.start(50) # 50ms后更新 + + def update_ui_for_training_mode(self): + """根据训练数据模式更新UI状态""" + # 需要禁用的步骤ID（对应无训练数据模式下需要禁用的步骤） + disabled_step_ids = ['step4', 'step5', 'step5_5', 'step6', 'step6_5', 'step6_75'] + + # 更新标签页的启用/禁用状态 + step_id_to_tab = { + 'step1': 0, 'step2': 1, 'step3': 2, 'step4': 3, + 'step5': 4, 'step5_5': 5, 'step6': 6, 'step6_5': 7, + 'step6_75': 8, 'step7': 9, 'step8': 10, 'step8_5': 11, + 'step8_75': 12, 'step9': 13, 'step9_viz': 14 + } + + for step_id in disabled_step_ids: + if step_id in step_id_to_tab: + tab_index = step_id_to_tab[step_id] + if tab_index < self.step_stack.count(): + self.step_stack.setTabEnabled(tab_index, self.has_training_data) + + # 同时更新导航列表的启用状态 + for i in range(self.step_list.count()): + item = self.step_list.item(i) + item_data = item.data(Qt.UserRole) + + # 跳过阶段标题和分隔符 + if item_data == "stage_header" or item_data is None: + continue + + # 检查步骤是否在禁用列表中 + if item_data in disabled_step_ids: + if not self.has_training_data: + item.setFlags(item.flags() & ~Qt.ItemIsEnabled) + item.setForeground(QColor(128, 128, 128)) # 灰色 + else: + item.setFlags(item.flags() | Qt.ItemIsEnabled) + item.setForeground(QColor(ModernStylesheet.COLORS.get('text_secondary', '#666666'))) # 原始颜色 + + +def main(): + """主函数""" + app = QApplication(sys.argv) + + # 设置应用信息 + app.setApplicationName("水质参数反演分析系统") + app.setOrganizationName("WaterQuality") + + # 创建主窗口 + window = WaterQualityGUI() + window.show() + + sys.exit(app.exec_()) + + +if __name__ == "__main__": + main() + diff --git a/src/gui/work_dir/2_glint/severe_glint_area.dat b/src/gui/work_dir/2_glint/severe_glint_area.dat new file mode 100644 index 0000000..6dc3933 Binary files /dev/null and b/src/gui/work_dir/2_glint/severe_glint_area.dat differ diff --git a/src/gui/work_dir/2_glint/severe_glint_area.hdr b/src/gui/work_dir/2_glint/severe_glint_area.hdr new file mode 100644 index 0000000..0fbc998 --- /dev/null +++ b/src/gui/work_dir/2_glint/severe_glint_area.hdr @@ -0,0 +1,15 @@ +ENVI +description = { +work_dir\2_glint\severe_glint_area.dat} +samples = 11363 +lines = 10408 +bands = 1 +header offset = 0 +file type = ENVI Standard +data type = 4 +interleave = bsq +byte order = 0 +map info = {UTM, 1, 1, 600742.055, 4613386.65, 0.2, 0.2, 51, North,WGS-84} +coordinate system string = {PROJCS["WGS_1984_UTM_Zone_51N",GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",123.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]]} +band names = { +Band 1} diff --git a/src/gui/work_dir/6_75_custom_regression/1002.515991_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/1002.515991_regression_results.csv new file mode 100644 index 0000000..307c50d --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/1002.515991_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,1002.515991,0.11209397634185636,y = -0.005956 + 0.001186*x,134,10.960663313432837,3.9096921347220377,0.007041335820895523,0.0138473135041692 +logarithmic,Chlorophyll,1002.515991,0.09022914646608904,y = -0.019813 + 0.011526*ln(x),134,10.960663313432837,3.9096921347220377,0.007041335820895523,0.0138473135041692 diff --git a/src/gui/work_dir/6_75_custom_regression/1007.041016_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/1007.041016_regression_results.csv new file mode 100644 index 0000000..c17f9a8 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/1007.041016_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,1007.041016,0.13129585788396014,y = -0.007873 + 0.001537*x,134,10.960663313432837,3.9096921347220377,0.008974216417910446,0.016584272713125302 +logarithmic,Chlorophyll,1007.041016,0.10398887849221805,y = -0.025553 + 0.014819*ln(x),134,10.960663313432837,3.9096921347220377,0.008974216417910446,0.016584272713125302 diff --git a/src/gui/work_dir/6_75_custom_regression/1011.56897_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/1011.56897_regression_results.csv new file mode 100644 index 0000000..d086604 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/1011.56897_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,1011.56897,0.11897246869922418,y = -0.007866 + 0.001621*x,134,10.960663313432837,3.9096921347220377,0.00990283582089552,0.01837518499092342 +logarithmic,Chlorophyll,1011.56897,0.09605697495450882,y = -0.026865 + 0.015780*ln(x),134,10.960663313432837,3.9096921347220377,0.00990283582089552,0.01837518499092342 diff --git a/src/gui/work_dir/6_75_custom_regression/374.285004_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/374.285004_regression_results.csv new file mode 100644 index 0000000..0e5ff09 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/374.285004_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,374.285004,0.0577461915301245,y = 0.009707 + 0.000311*x,134,10.960663313432837,3.9096921347220377,0.013112298507462688,0.005054260878733534 +logarithmic,Chlorophyll,374.285004,0.052490162787109385,y = 0.005636 + 0.003209*ln(x),134,10.960663313432837,3.9096921347220377,0.013112298507462688,0.005054260878733534 +exponential,Chlorophyll,374.285004,0.030557192829324564,y = 0.010822 * exp(0.013060*x),134,10.960663313432837,3.9096921347220377,0.013112298507462688,0.005054260878733534 +power,Chlorophyll,374.285004,0.02576326804736484,y = 0.009209 * x^0.130700,134,10.960663313432837,3.9096921347220377,0.013112298507462688,0.005054260878733534 diff --git a/src/gui/work_dir/6_75_custom_regression/378.311005_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/378.311005_regression_results.csv new file mode 100644 index 0000000..326265f --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/378.311005_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +logarithmic,Chlorophyll,378.311005,0.008061439581006025,y = 0.013092 + 0.001044*ln(x),134,10.960663313432837,3.9096921347220377,0.01552370895522388,0.00419444858565235 +linear,Chlorophyll,378.311005,0.008052879252108514,y = 0.014468 + 0.000096*x,134,10.960663313432837,3.9096921347220377,0.01552370895522388,0.00419444858565235 +power,Chlorophyll,378.311005,-0.016155019039159058,y = 0.015641 * x^-0.016124,134,10.960663313432837,3.9096921347220377,0.01552370895522388,0.00419444858565235 +exponential,Chlorophyll,378.311005,-0.01708357282563666,y = 0.015362 * exp(-0.001784*x),134,10.960663313432837,3.9096921347220377,0.01552370895522388,0.00419444858565235 diff --git a/src/gui/work_dir/6_75_custom_regression/382.341003_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/382.341003_regression_results.csv new file mode 100644 index 0000000..728d323 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/382.341003_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,382.341003,0.010983531384569756,y = 0.013856 + 0.000202*x,134,10.960663313432837,3.9096921347220377,0.016074067164179102,0.007548431031201279 +logarithmic,Chlorophyll,382.341003,0.010636805221273526,y = 0.011048 + 0.002157*ln(x),134,10.960663313432837,3.9096921347220377,0.016074067164179102,0.007548431031201279 +power,Chlorophyll,382.341003,-0.007234268459601845,y = 0.015174 * x^0.006143,134,10.960663313432837,3.9096921347220377,0.016074067164179102,0.007548431031201279 +exponential,Chlorophyll,382.341003,-0.008026222697967267,y = 0.015380 * exp(0.000073*x),134,10.960663313432837,3.9096921347220377,0.016074067164179102,0.007548431031201279 diff --git a/src/gui/work_dir/6_75_custom_regression/386.373993_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/386.373993_regression_results.csv new file mode 100644 index 0000000..c22646f --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/386.373993_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +logarithmic,Chlorophyll,386.373993,0.004476522091747537,y = 0.013943 + 0.001356*ln(x),134,10.960663313432837,3.9096921347220377,0.017102343283582087,0.007312955327938564 +linear,Chlorophyll,386.373993,0.003937809502393641,y = 0.015816 + 0.000117*x,134,10.960663313432837,3.9096921347220377,0.017102343283582087,0.007312955327938564 +power,Chlorophyll,386.373993,-0.013451645087448227,y = 0.018099 * x^-0.040970,134,10.960663313432837,3.9096921347220377,0.017102343283582087,0.007312955327938564 +exponential,Chlorophyll,386.373993,-0.01526209268366463,y = 0.017374 * exp(-0.004977*x),134,10.960663313432837,3.9096921347220377,0.017102343283582087,0.007312955327938564 diff --git a/src/gui/work_dir/6_75_custom_regression/390.410004_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/390.410004_regression_results.csv new file mode 100644 index 0000000..9e93946 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/390.410004_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +logarithmic,Chlorophyll,390.410004,0.0033869580773776553,y = 0.014722 + 0.001210*ln(x),134,10.960663313432837,3.9096921347220377,0.017540880597014925,0.00750323608895778 +linear,Chlorophyll,390.410004,0.0026484411391527463,y = 0.016458 + 0.000099*x,134,10.960663313432837,3.9096921347220377,0.017540880597014925,0.00750323608895778 +power,Chlorophyll,390.410004,-0.01625121404032659,y = 0.019411 * x^-0.060440,134,10.960663313432837,3.9096921347220377,0.017540880597014925,0.00750323608895778 +exponential,Chlorophyll,390.410004,-0.018540174411018073,y = 0.018243 * exp(-0.007186*x),134,10.960663313432837,3.9096921347220377,0.017540880597014925,0.00750323608895778 diff --git a/src/gui/work_dir/6_75_custom_regression/394.450012_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/394.450012_regression_results.csv new file mode 100644 index 0000000..128da03 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/394.450012_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +logarithmic,Chlorophyll,394.450012,0.0016938639111105935,y = 0.015234 + 0.000830*ln(x),134,10.960663313432837,3.9096921347220377,0.01716890298507463,0.007280847323239202 +linear,Chlorophyll,394.450012,0.0010649475553690113,y = 0.016503 + 0.000061*x,134,10.960663313432837,3.9096921347220377,0.01716890298507463,0.007280847323239202 +power,Chlorophyll,394.450012,-0.023745377413006752,y = 0.020435 * x^-0.094840,134,10.960663313432837,3.9096921347220377,0.01716890298507463,0.007280847323239202 +exponential,Chlorophyll,394.450012,-0.02617627918721488,y = 0.018415 * exp(-0.010666*x),134,10.960663313432837,3.9096921347220377,0.01716890298507463,0.007280847323239202 diff --git a/src/gui/work_dir/6_75_custom_regression/398.493011_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/398.493011_regression_results.csv new file mode 100644 index 0000000..5ae8962 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/398.493011_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +logarithmic,Chlorophyll,398.493011,0.000857763974499659,y = 0.015092 + 0.000573*ln(x),134,10.960663313432837,3.9096921347220377,0.016425865671641792,0.00705544097312418 +linear,Chlorophyll,398.493011,0.0003890186261535922,y = 0.016036 + 0.000036*x,134,10.960663313432837,3.9096921347220377,0.016425865671641792,0.00705544097312418 diff --git a/src/gui/work_dir/6_75_custom_regression/402.539001_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/402.539001_regression_results.csv new file mode 100644 index 0000000..7f835b4 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/402.539001_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +logarithmic,Chlorophyll,402.539001,0.00048016503667658306,y = 0.015505 + 0.000443*ln(x),134,10.960663313432837,3.9096921347220377,0.01653574626865672,0.007288381669035372 +linear,Chlorophyll,402.539001,0.0001313248786827259,y = 0.016302 + 0.000021*x,134,10.960663313432837,3.9096921347220377,0.01653574626865672,0.007288381669035372 diff --git a/src/gui/work_dir/6_75_custom_regression/406.588989_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/406.588989_regression_results.csv new file mode 100644 index 0000000..77edadd --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/406.588989_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +logarithmic,Chlorophyll,406.588989,0.00016428918964617178,y = 0.015242 + 0.000242*ln(x),134,10.960663313432837,3.9096921347220377,0.015806723880597017,0.006825478500958131 +linear,Chlorophyll,406.588989,1.6937017762730378e-06,y = 0.015782 + 0.000002*x,134,10.960663313432837,3.9096921347220377,0.015806723880597017,0.006825478500958131 diff --git a/src/gui/work_dir/6_75_custom_regression/410.641998_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/410.641998_regression_results.csv new file mode 100644 index 0000000..779ff0e --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/410.641998_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +logarithmic,Chlorophyll,410.641998,0.00010695507791136372,y = 0.014822 + 0.000189*ln(x),134,10.960663313432837,3.9096921347220377,0.015261298507462688,0.0065848636688817614 +linear,Chlorophyll,410.641998,2.2039578226884515e-06,y = 0.015289 + -0.000003*x,134,10.960663313432837,3.9096921347220377,0.015261298507462688,0.0065848636688817614 diff --git a/src/gui/work_dir/6_75_custom_regression/414.699005_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/414.699005_regression_results.csv new file mode 100644 index 0000000..294997a --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/414.699005_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,414.699005,9.23718683270014e-05,y = 0.014978 + -0.000015*x,134,10.960663313432837,3.9096921347220377,0.014808014925373135,0.006298696608817295 +logarithmic,Chlorophyll,414.699005,1.0794055052776308e-05,y = 0.014674 + 0.000057*ln(x),134,10.960663313432837,3.9096921347220377,0.014808014925373135,0.006298696608817295 diff --git a/src/gui/work_dir/6_75_custom_regression/418.759003_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/418.759003_regression_results.csv new file mode 100644 index 0000000..d7213bb --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/418.759003_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,418.759003,0.0002645667637589666,y = 0.014364 + -0.000025*x,134,10.960663313432837,3.9096921347220377,0.014088052238805972,0.006051440804527788 +logarithmic,Chlorophyll,418.759003,7.794051727350038e-06,y = 0.014197 + -0.000047*ln(x),134,10.960663313432837,3.9096921347220377,0.014088052238805972,0.006051440804527788 diff --git a/src/gui/work_dir/6_75_custom_regression/422.821991_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/422.821991_regression_results.csv new file mode 100644 index 0000000..ff4cbbb --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/422.821991_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,422.821991,0.0008115657894584016,y = 0.014105 + -0.000042*x,134,10.960663313432837,3.9096921347220377,0.013641977611940298,0.005799322545956216 +logarithmic,Chlorophyll,422.821991,0.0001768579797475356,y = 0.014140 + -0.000214*ln(x),134,10.960663313432837,3.9096921347220377,0.013641977611940298,0.005799322545956216 diff --git a/src/gui/work_dir/6_75_custom_regression/426.889008_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/426.889008_regression_results.csv new file mode 100644 index 0000000..41f3064 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/426.889008_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,426.889008,0.0013073210454338513,y = 0.014170 + -0.000053*x,134,10.960663313432837,3.9096921347220377,0.013589074626865672,0.005728319043930576 +logarithmic,Chlorophyll,426.889008,0.0004182937928386421,y = 0.014345 + -0.000325*ln(x),134,10.960663313432837,3.9096921347220377,0.013589074626865672,0.005728319043930576 diff --git a/src/gui/work_dir/6_75_custom_regression/430.959015_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/430.959015_regression_results.csv new file mode 100644 index 0000000..a06822c --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/430.959015_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,430.959015,0.002347137019542922,y = 0.013733 + -0.000067*x,134,10.960663313432837,3.9096921347220377,0.012997753731343284,0.005410808768465578 +logarithmic,Chlorophyll,430.959015,0.0010658686661263461,y = 0.014138 + -0.000489*ln(x),134,10.960663313432837,3.9096921347220377,0.012997753731343284,0.005410808768465578 diff --git a/src/gui/work_dir/6_75_custom_regression/435.032013_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/435.032013_regression_results.csv new file mode 100644 index 0000000..52359f9 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/435.032013_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,435.032013,0.0024283085040497365,y = 0.013179 + -0.000069*x,134,10.960663313432837,3.9096921347220377,0.012427850746268653,0.005435180040005626 +logarithmic,Chlorophyll,435.032013,0.0011266238526388417,y = 0.013606 + -0.000506*ln(x),134,10.960663313432837,3.9096921347220377,0.012427850746268653,0.005435180040005626 diff --git a/src/gui/work_dir/6_75_custom_regression/439.109009_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/439.109009_regression_results.csv new file mode 100644 index 0000000..3f0f36b --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/439.109009_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,439.109009,0.0042064615418079265,y = 0.013397 + -0.000086*x,134,10.960663313432837,3.9096921347220377,0.012450895522388062,0.005205005715323194 +logarithmic,Chlorophyll,439.109009,0.002294686396103418,y = 0.014061 + -0.000691*ln(x),134,10.960663313432837,3.9096921347220377,0.012450895522388062,0.005205005715323194 diff --git a/src/gui/work_dir/6_75_custom_regression/443.190002_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/443.190002_regression_results.csv new file mode 100644 index 0000000..48d8a2b --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/443.190002_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,443.190002,0.004695213661859765,y = 0.013279 + -0.000091*x,134,10.960663313432837,3.9096921347220377,0.012285432835820896,0.00517286197733264 +logarithmic,Chlorophyll,443.190002,0.0026235944054925353,y = 0.013996 + -0.000734*ln(x),134,10.960663313432837,3.9096921347220377,0.012285432835820896,0.00517286197733264 diff --git a/src/gui/work_dir/6_75_custom_regression/447.27301_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/447.27301_regression_results.csv new file mode 100644 index 0000000..3f2e992 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/447.27301_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,447.27301,0.005169120886448608,y = 0.013250 + -0.000094*x,134,10.960663313432837,3.9096921347220377,0.012221335820895522,0.005101097814158477 +logarithmic,Chlorophyll,447.27301,0.002968315833349222,y = 0.014016 + -0.000770*ln(x),134,10.960663313432837,3.9096921347220377,0.012221335820895522,0.005101097814158477 diff --git a/src/gui/work_dir/6_75_custom_regression/451.360992_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/451.360992_regression_results.csv new file mode 100644 index 0000000..7e1674f --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/451.360992_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,451.360992,0.004863772601295668,y = 0.013262 + -0.000092*x,134,10.960663313432837,3.9096921347220377,0.012257104477611941,0.00513769471108476 +logarithmic,Chlorophyll,451.360992,0.002739591016255649,y = 0.013993 + -0.000745*ln(x),134,10.960663313432837,3.9096921347220377,0.012257104477611941,0.00513769471108476 diff --git a/src/gui/work_dir/6_75_custom_regression/455.450989_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/455.450989_regression_results.csv new file mode 100644 index 0000000..d7e5855 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/455.450989_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,455.450989,0.005525011318207929,y = 0.013206 + -0.000097*x,134,10.960663313432837,3.9096921347220377,0.012144328358208955,0.005093595896892254 +logarithmic,Chlorophyll,455.450989,0.0032249891993542112,y = 0.014012 + -0.000802*ln(x),134,10.960663313432837,3.9096921347220377,0.012144328358208955,0.005093595896892254 diff --git a/src/gui/work_dir/6_75_custom_regression/459.545013_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/459.545013_regression_results.csv new file mode 100644 index 0000000..695fc64 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/459.545013_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,459.545013,0.005303969126716268,y = 0.013029 + -0.000095*x,134,10.960663313432837,3.9096921347220377,0.011992097014925374,0.005076948054716495 +logarithmic,Chlorophyll,459.545013,0.0030599507272712767,y = 0.013805 + -0.000778*ln(x),134,10.960663313432837,3.9096921347220377,0.011992097014925374,0.005076948054716495 diff --git a/src/gui/work_dir/6_75_custom_regression/463.641998_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/463.641998_regression_results.csv new file mode 100644 index 0000000..5c5918b --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/463.641998_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,463.641998,0.005309017626029533,y = 0.013098 + -0.000096*x,134,10.960663313432837,3.9096921347220377,0.012043410447761194,0.0051616384058770694 +logarithmic,Chlorophyll,463.641998,0.00309369061071596,y = 0.013897 + -0.000796*ln(x),134,10.960663313432837,3.9096921347220377,0.012043410447761194,0.0051616384058770694 diff --git a/src/gui/work_dir/6_75_custom_regression/467.743011_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/467.743011_regression_results.csv new file mode 100644 index 0000000..feebac4 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/467.743011_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,467.743011,0.005910540579640466,y = 0.013129 + -0.000101*x,134,10.960663313432837,3.9096921347220377,0.012021395522388062,0.0051373056777988335 +logarithmic,Chlorophyll,467.743011,0.0035312797033351107,y = 0.013992 + -0.000846*ln(x),134,10.960663313432837,3.9096921347220377,0.012021395522388062,0.0051373056777988335 diff --git a/src/gui/work_dir/6_75_custom_regression/471.846985_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/471.846985_regression_results.csv new file mode 100644 index 0000000..20cfa5b --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/471.846985_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,471.846985,0.006597953952974689,y = 0.013090 + -0.000107*x,134,10.960663313432837,3.9096921347220377,0.011916462686567163,0.005154508155317495 +logarithmic,Chlorophyll,471.846985,0.004055554987167143,y = 0.014036 + -0.000910*ln(x),134,10.960663313432837,3.9096921347220377,0.011916462686567163,0.005154508155317495 diff --git a/src/gui/work_dir/6_75_custom_regression/475.954987_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/475.954987_regression_results.csv new file mode 100644 index 0000000..8452e9b --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/475.954987_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,475.954987,0.006969427568661812,y = 0.013222 + -0.000110*x,134,10.960663313432837,3.9096921347220377,0.012010992537313433,0.005173190365687869 +logarithmic,Chlorophyll,475.954987,0.0043493646432547495,y = 0.014214 + -0.000945*ln(x),134,10.960663313432837,3.9096921347220377,0.012010992537313433,0.005173190365687869 diff --git a/src/gui/work_dir/6_75_custom_regression/480.065002_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/480.065002_regression_results.csv new file mode 100644 index 0000000..7b46d6b --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/480.065002_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,480.065002,0.006908555626254476,y = 0.013165 + -0.000112*x,134,10.960663313432837,3.9096921347220377,0.011938738805970149,0.005260977465686793 +logarithmic,Chlorophyll,480.065002,0.004358699372388197,y = 0.014181 + -0.000962*ln(x),134,10.960663313432837,3.9096921347220377,0.011938738805970149,0.005260977465686793 diff --git a/src/gui/work_dir/6_75_custom_regression/484.179993_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/484.179993_regression_results.csv new file mode 100644 index 0000000..57d99a0 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/484.179993_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,484.179993,0.007786080902936532,y = 0.013479 + -0.000120*x,134,10.960663313432837,3.9096921347220377,0.01216315671641791,0.00531894585260112 +logarithmic,Chlorophyll,484.179993,0.00503315433665652,y = 0.014599 + -0.001046*ln(x),134,10.960663313432837,3.9096921347220377,0.01216315671641791,0.00531894585260112 diff --git a/src/gui/work_dir/6_75_custom_regression/488.296997_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/488.296997_regression_results.csv new file mode 100644 index 0000000..87b290c --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/488.296997_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,488.296997,0.009003426521211222,y = 0.013413 + -0.000127*x,134,10.960663313432837,3.9096921347220377,0.01202031343283582,0.005235852382412684 +logarithmic,Chlorophyll,488.296997,0.0059888617317502835,y = 0.014636 + -0.001123*ln(x),134,10.960663313432837,3.9096921347220377,0.01202031343283582,0.005235852382412684 diff --git a/src/gui/work_dir/6_75_custom_regression/492.417999_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/492.417999_regression_results.csv new file mode 100644 index 0000000..19469fc --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/492.417999_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,492.417999,0.009356273796731096,y = 0.013426 + -0.000130*x,134,10.960663313432837,3.9096921347220377,0.011996694029850746,0.00526918646504346 +logarithmic,Chlorophyll,492.417999,0.0063786563113004124,y = 0.014714 + -0.001166*ln(x),134,10.960663313432837,3.9096921347220377,0.011996694029850746,0.00526918646504346 diff --git a/src/gui/work_dir/6_75_custom_regression/496.542999_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/496.542999_regression_results.csv new file mode 100644 index 0000000..8e039ec --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/496.542999_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,496.542999,0.008454580487572083,y = 0.013491 + -0.000126*x,134,10.960663313432837,3.9096921347220377,0.012105947761194029,0.00537461785981684 +logarithmic,Chlorophyll,496.542999,0.005612309162053686,y = 0.014705 + -0.001116*ln(x),134,10.960663313432837,3.9096921347220377,0.012105947761194029,0.00537461785981684 diff --git a/src/gui/work_dir/6_75_custom_regression/500.67099_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/500.67099_regression_results.csv new file mode 100644 index 0000000..d837c1f --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/500.67099_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,500.67099,0.008930402290994066,y = 0.013947 + -0.000130*x,134,10.960663313432837,3.9096921347220377,0.012519723880597015,0.005386983290859261 +logarithmic,Chlorophyll,500.67099,0.006027433290726858,y = 0.015220 + -0.001159*ln(x),134,10.960663313432837,3.9096921347220377,0.012519723880597015,0.005386983290859261 diff --git a/src/gui/work_dir/6_75_custom_regression/504.802002_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/504.802002_regression_results.csv new file mode 100644 index 0000000..126587b --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/504.802002_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,504.802002,0.007851805368295328,y = 0.014159 + -0.000122*x,134,10.960663313432837,3.9096921347220377,0.012821291044776119,0.005386616665575906 +logarithmic,Chlorophyll,504.802002,0.005168584239575225,y = 0.015321 + -0.001073*ln(x),134,10.960663313432837,3.9096921347220377,0.012821291044776119,0.005386616665575906 diff --git a/src/gui/work_dir/6_75_custom_regression/508.936005_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/508.936005_regression_results.csv new file mode 100644 index 0000000..9db8b5c --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/508.936005_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,508.936005,0.006500148302153508,y = 0.014525 + -0.000113*x,134,10.960663313432837,3.9096921347220377,0.013285626865671642,0.005484303548583865 +logarithmic,Chlorophyll,508.936005,0.004158811025431031,y = 0.015569 + -0.000980*ln(x),134,10.960663313432837,3.9096921347220377,0.013285626865671642,0.005484303548583865 diff --git a/src/gui/work_dir/6_75_custom_regression/513.073975_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/513.073975_regression_results.csv new file mode 100644 index 0000000..ef2f985 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/513.073975_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,513.073975,0.004917130582781315,y = 0.014969 + -0.000099*x,134,10.960663313432837,3.9096921347220377,0.013879231343283581,0.005544190630289309 +logarithmic,Chlorophyll,513.073975,0.0029641857181392783,y = 0.015828 + -0.000836*ln(x),134,10.960663313432837,3.9096921347220377,0.013879231343283581,0.005544190630289309 +exponential,Chlorophyll,513.073975,-0.01889032090850251,y = 0.016669 * exp(-0.020406*x),134,10.960663313432837,3.9096921347220377,0.013879231343283581,0.005544190630289309 +power,Chlorophyll,513.073975,-0.019715344472010177,y = 0.020892 * x^-0.192919,134,10.960663313432837,3.9096921347220377,0.013879231343283581,0.005544190630289309 diff --git a/src/gui/work_dir/6_75_custom_regression/517.216003_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/517.216003_regression_results.csv new file mode 100644 index 0000000..4e880b6 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/517.216003_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,517.216003,0.004276559761211218,y = 0.015763 + -0.000093*x,134,10.960663313432837,3.9096921347220377,0.014746992537313432,0.005540761628631703 +logarithmic,Chlorophyll,517.216003,0.00245772439289782,y = 0.016520 + -0.000761*ln(x),134,10.960663313432837,3.9096921347220377,0.014746992537313432,0.005540761628631703 +exponential,Chlorophyll,517.216003,-0.018892619920322984,y = 0.017382 * exp(-0.018434*x),134,10.960663313432837,3.9096921347220377,0.014746992537313432,0.005540761628631703 +power,Chlorophyll,517.216003,-0.0194484153066794,y = 0.021251 * x^-0.172972,134,10.960663313432837,3.9096921347220377,0.014746992537313432,0.005540761628631703 diff --git a/src/gui/work_dir/6_75_custom_regression/521.361023_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/521.361023_regression_results.csv new file mode 100644 index 0000000..a2d9cd9 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/521.361023_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,521.361023,0.002867645407956476,y = 0.015976 + -0.000077*x,134,10.960663313432837,3.9096921347220377,0.01513621641791045,0.0055935618166287285 +logarithmic,Chlorophyll,521.361023,0.001510103149844122,y = 0.016540 + -0.000602*ln(x),134,10.960663313432837,3.9096921347220377,0.01513621641791045,0.0055935618166287285 +exponential,Chlorophyll,521.361023,-0.013492164822140218,y = 0.017293 * exp(-0.014959*x),134,10.960663313432837,3.9096921347220377,0.01513621641791045,0.0055935618166287285 +power,Chlorophyll,521.361023,-0.01389692075440152,y = 0.020293 * x^-0.139034,134,10.960663313432837,3.9096921347220377,0.01513621641791045,0.0055935618166287285 diff --git a/src/gui/work_dir/6_75_custom_regression/525.508972_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/525.508972_regression_results.csv new file mode 100644 index 0000000..64fdcf6 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/525.508972_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,525.508972,0.0016701377663735917,y = 0.016584 + -0.000059*x,134,10.960663313432837,3.9096921347220377,0.015935962686567166,0.005657732827939037 +logarithmic,Chlorophyll,525.508972,0.0007153098124390578,y = 0.016913 + -0.000419*ln(x),134,10.960663313432837,3.9096921347220377,0.015935962686567166,0.005657732827939037 +exponential,Chlorophyll,525.508972,-0.011873247394506237,y = 0.017736 * exp(-0.012223*x),134,10.960663313432837,3.9096921347220377,0.015935962686567166,0.005657732827939037 +power,Chlorophyll,525.508972,-0.01195937275095682,y = 0.020122 * x^-0.111667,134,10.960663313432837,3.9096921347220377,0.015935962686567166,0.005657732827939037 diff --git a/src/gui/work_dir/6_75_custom_regression/529.659973_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/529.659973_regression_results.csv new file mode 100644 index 0000000..6159e2d --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/529.659973_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,529.659973,0.001100615217659584,y = 0.016897 + -0.000048*x,134,10.960663313432837,3.9096921347220377,0.016371305970149252,0.005647344987370815 +logarithmic,Chlorophyll,529.659973,0.00038742510141320796,y = 0.017089 + -0.000308*ln(x),134,10.960663313432837,3.9096921347220377,0.016371305970149252,0.005647344987370815 +power,Chlorophyll,529.659973,-0.010602748616709512,y = 0.019984 * x^-0.095978,134,10.960663313432837,3.9096921347220377,0.016371305970149252,0.005647344987370815 +exponential,Chlorophyll,529.659973,-0.010676230097632189,y = 0.017950 * exp(-0.010610*x),134,10.960663313432837,3.9096921347220377,0.016371305970149252,0.005647344987370815 diff --git a/src/gui/work_dir/6_75_custom_regression/533.815002_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/533.815002_regression_results.csv new file mode 100644 index 0000000..bd8e7f2 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/533.815002_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,533.815002,0.0005274884679772329,y = 0.017331 + -0.000034*x,134,10.960663313432837,3.9096921347220377,0.016960171641791044,0.005752288800181051 +logarithmic,Chlorophyll,533.815002,9.639796014881963e-05,y = 0.017325 + -0.000156*ln(x),134,10.960663313432837,3.9096921347220377,0.016960171641791044,0.005752288800181051 +power,Chlorophyll,533.815002,-0.010942625292486463,y = 0.020200 * x^-0.085250,134,10.960663313432837,3.9096921347220377,0.016960171641791044,0.005752288800181051 +exponential,Chlorophyll,533.815002,-0.011375297319334177,y = 0.018394 * exp(-0.009577*x),134,10.960663313432837,3.9096921347220377,0.016960171641791044,0.005752288800181051 diff --git a/src/gui/work_dir/6_75_custom_regression/537.973999_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/537.973999_regression_results.csv new file mode 100644 index 0000000..32a220c --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/537.973999_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,537.973999,0.010059827315010317,y = 0.018287 + -0.000095*x,134,10.960663313432837,3.9096921347220377,0.017245291044776116,0.003706413209287316 +logarithmic,Chlorophyll,537.973999,0.0058263736903666485,y = 0.019072 + -0.000784*ln(x),134,10.960663313432837,3.9096921347220377,0.017245291044776116,0.003706413209287316 +exponential,Chlorophyll,537.973999,-0.0006317634379562342,y = 0.018947 * exp(-0.009908*x),134,10.960663313432837,3.9096921347220377,0.017245291044776116,0.003706413209287316 +power,Chlorophyll,537.973999,-0.004088770616538007,y = 0.020915 * x^-0.089031,134,10.960663313432837,3.9096921347220377,0.017245291044776116,0.003706413209287316 diff --git a/src/gui/work_dir/6_75_custom_regression/542.13501_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/542.13501_regression_results.csv new file mode 100644 index 0000000..434f967 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/542.13501_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,542.13501,0.10592938275035968,y = 0.019264 + -0.000164*x,134,10.960663313432837,3.9096921347220377,0.017471380597014925,0.0019645221126944118 +exponential,Chlorophyll,542.13501,0.09468361275300774,y = 0.019618 * exp(-0.011250*x),134,10.960663313432837,3.9096921347220377,0.017471380597014925,0.0019645221126944118 +logarithmic,Chlorophyll,542.13501,0.07410535895090076,y = 0.020924 + -0.001482*ln(x),134,10.960663313432837,3.9096921347220377,0.017471380597014925,0.0019645221126944118 +power,Chlorophyll,542.13501,0.06336507350101761,y = 0.022043 * x^-0.102942,134,10.960663313432837,3.9096921347220377,0.017471380597014925,0.0019645221126944118 diff --git a/src/gui/work_dir/6_75_custom_regression/546.301025_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/546.301025_regression_results.csv new file mode 100644 index 0000000..52e2db6 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/546.301025_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,546.301025,0.12040522641720053,y = 0.019716 + -0.000169*x,134,10.960663313432837,3.9096921347220377,0.01786192537313433,0.0019057641301598596 +exponential,Chlorophyll,546.301025,0.11002711959899414,y = 0.020039 * exp(-0.011103*x),134,10.960663313432837,3.9096921347220377,0.01786192537313433,0.0019057641301598596 +logarithmic,Chlorophyll,546.301025,0.08587624549622919,y = 0.021467 + -0.001547*ln(x),134,10.960663313432837,3.9096921347220377,0.01786192537313433,0.0019057641301598596 +power,Chlorophyll,546.301025,0.07589213783045401,y = 0.022516 * x^-0.102241,134,10.960663313432837,3.9096921347220377,0.01786192537313433,0.0019057641301598596 diff --git a/src/gui/work_dir/6_75_custom_regression/550.468994_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/550.468994_regression_results.csv new file mode 100644 index 0000000..3bb3cae --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/550.468994_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,550.468994,0.1269569921216488,y = 0.020121 + -0.000172*x,134,10.960663313432837,3.9096921347220377,0.01824102985074627,0.0018825013010254905 +exponential,Chlorophyll,550.468994,0.1175391734287099,y = 0.020411 * exp(-0.010816*x),134,10.960663313432837,3.9096921347220377,0.01824102985074627,0.0018825013010254905 +logarithmic,Chlorophyll,550.468994,0.0907980339917791,y = 0.021903 + -0.001572*ln(x),134,10.960663313432837,3.9096921347220377,0.01824102985074627,0.0018825013010254905 +power,Chlorophyll,550.468994,0.08162875081638976,y = 0.022867 * x^-0.099639,134,10.960663313432837,3.9096921347220377,0.01824102985074627,0.0018825013010254905 diff --git a/src/gui/work_dir/6_75_custom_regression/554.640991_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/554.640991_regression_results.csv new file mode 100644 index 0000000..afb2dd0 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/554.640991_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,554.640991,0.1376426262180579,y = 0.020508 + -0.000178*x,134,10.960663313432837,3.9096921347220377,0.018552656716417912,0.0018796053138591957 +exponential,Chlorophyll,554.640991,0.12753098839026022,y = 0.020816 * exp(-0.011054*x),134,10.960663313432837,3.9096921347220377,0.018552656716417912,0.0018796053138591957 +logarithmic,Chlorophyll,554.640991,0.09810495477002423,y = 0.022354 + -0.001631*ln(x),134,10.960663313432837,3.9096921347220377,0.018552656716417912,0.0018796053138591957 +power,Chlorophyll,554.640991,0.08828430738543391,y = 0.023368 * x^-0.101637,134,10.960663313432837,3.9096921347220377,0.018552656716417912,0.0018796053138591957 diff --git a/src/gui/work_dir/6_75_custom_regression/558.815979_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/558.815979_regression_results.csv new file mode 100644 index 0000000..d47efd6 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/558.815979_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,558.815979,0.14438886343879165,y = 0.020984 + -0.000192*x,134,10.960663313432837,3.9096921347220377,0.018875544776119402,0.0019794547666891088 +exponential,Chlorophyll,558.815979,0.13400466793505816,y = 0.021319 * exp(-0.011710*x),134,10.960663313432837,3.9096921347220377,0.018875544776119402,0.0019794547666891088 +logarithmic,Chlorophyll,558.815979,0.10606464763119816,y = 0.023038 + -0.001786*ln(x),134,10.960663313432837,3.9096921347220377,0.018875544776119402,0.0019794547666891088 +power,Chlorophyll,558.815979,0.09560595602330613,y = 0.024181 * x^-0.109152,134,10.960663313432837,3.9096921347220377,0.018875544776119402,0.0019794547666891088 diff --git a/src/gui/work_dir/6_75_custom_regression/562.994995_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/562.994995_regression_results.csv new file mode 100644 index 0000000..d5e55b6 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/562.994995_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,562.994995,0.13570684972920022,y = 0.021065 + -0.000193*x,134,10.960663313432837,3.9096921347220377,0.018946902985074628,0.002050816811679121 +exponential,Chlorophyll,562.994995,0.1251679621881412,y = 0.021424 * exp(-0.011845*x),134,10.960663313432837,3.9096921347220377,0.018946902985074628,0.002050816811679121 +logarithmic,Chlorophyll,562.994995,0.10005264191024388,y = 0.023135 + -0.001797*ln(x),134,10.960663313432837,3.9096921347220377,0.018946902985074628,0.002050816811679121 +power,Chlorophyll,562.994995,0.08944628510214314,y = 0.024352 * x^-0.110685,134,10.960663313432837,3.9096921347220377,0.018946902985074628,0.002050816811679121 diff --git a/src/gui/work_dir/6_75_custom_regression/567.177002_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/567.177002_regression_results.csv new file mode 100644 index 0000000..3b1c728 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/567.177002_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,567.177002,0.03705163102869502,y = 0.020812 + -0.000155*x,134,10.960663313432837,3.9096921347220377,0.01911655970149254,0.0031426043067057114 +exponential,Chlorophyll,567.177002,0.02735476743599441,y = 0.021355 * exp(-0.011096*x),134,10.960663313432837,3.9096921347220377,0.01911655970149254,0.0031426043067057114 +logarithmic,Chlorophyll,567.177002,0.02624551679688847,y = 0.022403 + -0.001411*ln(x),134,10.960663313432837,3.9096921347220377,0.01911655970149254,0.0031426043067057114 +power,Chlorophyll,567.177002,0.01690050625981776,y = 0.024064 * x^-0.103446,134,10.960663313432837,3.9096921347220377,0.01911655970149254,0.0031426043067057114 diff --git a/src/gui/work_dir/6_75_custom_regression/571.362976_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/571.362976_regression_results.csv new file mode 100644 index 0000000..c84da45 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/571.362976_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,571.362976,0.032367014815650186,y = 0.020732 + -0.000161*x,134,10.960663313432837,3.9096921347220377,0.018965664179104478,0.0035018296559745925 +logarithmic,Chlorophyll,571.362976,0.023243518566287924,y = 0.022412 + -0.001479*ln(x),134,10.960663313432837,3.9096921347220377,0.018965664179104478,0.0035018296559745925 +exponential,Chlorophyll,571.362976,0.021156108573575194,y = 0.021404 * exp(-0.012237*x),134,10.960663313432837,3.9096921347220377,0.018965664179104478,0.0035018296559745925 +power,Chlorophyll,571.362976,0.012371457852355827,y = 0.024473 * x^-0.115076,134,10.960663313432837,3.9096921347220377,0.018965664179104478,0.0035018296559745925 diff --git a/src/gui/work_dir/6_75_custom_regression/575.551025_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/575.551025_regression_results.csv new file mode 100644 index 0000000..6655145 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/575.551025_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,575.551025,0.003711200606445142,y = 0.019973 + -0.000095*x,134,10.960663313432837,3.9096921347220377,0.018931119402985072,0.006097893443512235 +logarithmic,Chlorophyll,575.551025,0.0022851664540198824,y = 0.020813 + -0.000808*ln(x),134,10.960663313432837,3.9096921347220377,0.018931119402985072,0.006097893443512235 +exponential,Chlorophyll,575.551025,-0.008535989595423121,y = 0.021158 * exp(-0.012309*x),134,10.960663313432837,3.9096921347220377,0.018931119402985072,0.006097893443512235 +power,Chlorophyll,575.551025,-0.009305973499541542,y = 0.024221 * x^-0.115919,134,10.960663313432837,3.9096921347220377,0.018931119402985072,0.006097893443512235 diff --git a/src/gui/work_dir/6_75_custom_regression/579.744019_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/579.744019_regression_results.csv new file mode 100644 index 0000000..a198c85 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/579.744019_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,579.744019,0.005094509026133509,y = 0.019711 + -0.000110*x,134,10.960663313432837,3.9096921347220377,0.018505432835820897,0.006025405615191274 +logarithmic,Chlorophyll,579.744019,0.0034065768371064342,y = 0.020776 + -0.000974*ln(x),134,10.960663313432837,3.9096921347220377,0.018505432835820897,0.006025405615191274 +exponential,Chlorophyll,579.744019,-0.007270136129956084,y = 0.020909 * exp(-0.013357*x),134,10.960663313432837,3.9096921347220377,0.018505432835820897,0.006025405615191274 +power,Chlorophyll,579.744019,-0.008400608136997167,y = 0.024296 * x^-0.127275,134,10.960663313432837,3.9096921347220377,0.018505432835820897,0.006025405615191274 diff --git a/src/gui/work_dir/6_75_custom_regression/583.939026_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/583.939026_regression_results.csv new file mode 100644 index 0000000..9ad37c9 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/583.939026_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,583.939026,0.004657425272328819,y = 0.019070 + -0.000108*x,134,10.960663313432837,3.9096921347220377,0.017881,0.006215784305887186 +logarithmic,Chlorophyll,583.939026,0.003161690862374833,y = 0.020137 + -0.000968*ln(x),134,10.960663313432837,3.9096921347220377,0.017881,0.006215784305887186 +exponential,Chlorophyll,583.939026,-0.00834323144293947,y = 0.020355 * exp(-0.014251*x),134,10.960663313432837,3.9096921347220377,0.017881,0.006215784305887186 +power,Chlorophyll,583.939026,-0.009289166222129941,y = 0.023939 * x^-0.136644,134,10.960663313432837,3.9096921347220377,0.017881,0.006215784305887186 diff --git a/src/gui/work_dir/6_75_custom_regression/588.138_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/588.138_regression_results.csv new file mode 100644 index 0000000..ffecdb5 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/588.138_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,588.138,0.007038900978221574,y = 0.018745 + -0.000132*x,134,10.960663313432837,3.9096921347220377,0.017294746268656718,0.006164975937402735 +logarithmic,Chlorophyll,588.138,0.005079658860641656,y = 0.020131 + -0.001218*ln(x),134,10.960663313432837,3.9096921347220377,0.017294746268656718,0.006164975937402735 +exponential,Chlorophyll,588.138,-0.008087990598099282,y = 0.020176 * exp(-0.016777*x),134,10.960663313432837,3.9096921347220377,0.017294746268656718,0.006164975937402735 +power,Chlorophyll,588.138,-0.009554552395312665,y = 0.024503 * x^-0.162327,134,10.960663313432837,3.9096921347220377,0.017294746268656718,0.006164975937402735 diff --git a/src/gui/work_dir/6_75_custom_regression/592.341003_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/592.341003_regression_results.csv new file mode 100644 index 0000000..9bc1d1d --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/592.341003_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,592.341003,0.005528079261697849,y = 0.017838 + -0.000118*x,134,10.960663313432837,3.9096921347220377,0.016543097014925373,0.006212725389009627 +logarithmic,Chlorophyll,592.341003,0.003887007195125136,y = 0.019044 + -0.001073*ln(x),134,10.960663313432837,3.9096921347220377,0.016543097014925373,0.006212725389009627 +exponential,Chlorophyll,592.341003,-0.011813537096786675,y = 0.019386 * exp(-0.017528*x),134,10.960663313432837,3.9096921347220377,0.016543097014925373,0.006212725389009627 +power,Chlorophyll,592.341003,-0.012846809152319283,y = 0.023741 * x^-0.169433,134,10.960663313432837,3.9096921347220377,0.016543097014925373,0.006212725389009627 diff --git a/src/gui/work_dir/6_75_custom_regression/596.546997_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/596.546997_regression_results.csv new file mode 100644 index 0000000..cdfab45 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/596.546997_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,596.546997,0.003794780064093062,y = 0.016713 + -0.000101*x,134,10.960663313432837,3.9096921347220377,0.015607044776119405,0.00640171329313859 +logarithmic,Chlorophyll,596.546997,0.0025823661966439815,y = 0.017707 + -0.000901*ln(x),134,10.960663313432837,3.9096921347220377,0.015607044776119405,0.00640171329313859 +exponential,Chlorophyll,596.546997,-0.014874157713247849,y = 0.018368 * exp(-0.018321*x),134,10.960663313432837,3.9096921347220377,0.015607044776119405,0.00640171329313859 +power,Chlorophyll,596.546997,-0.015374647488444415,y = 0.022702 * x^-0.177115,134,10.960663313432837,3.9096921347220377,0.015607044776119405,0.00640171329313859 diff --git a/src/gui/work_dir/6_75_custom_regression/600.755981_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/600.755981_regression_results.csv new file mode 100644 index 0000000..4f0a47b --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/600.755981_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,600.755981,0.0031611200601100453,y = 0.015616 + -0.000092*x,134,10.960663313432837,3.9096921347220377,0.014608059701492537,0.006395783738002828 +logarithmic,Chlorophyll,600.755981,0.002151138099984129,y = 0.016523 + -0.000822*ln(x),134,10.960663313432837,3.9096921347220377,0.014608059701492537,0.006395783738002828 +exponential,Chlorophyll,600.755981,-0.015732414466808953,y = 0.017258 * exp(-0.018983*x),134,10.960663313432837,3.9096921347220377,0.014608059701492537,0.006395783738002828 +power,Chlorophyll,600.755981,-0.016109249860633446,y = 0.021535 * x^-0.184339,134,10.960663313432837,3.9096921347220377,0.014608059701492537,0.006395783738002828 diff --git a/src/gui/work_dir/6_75_custom_regression/604.968994_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/604.968994_regression_results.csv new file mode 100644 index 0000000..68e5be4 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/604.968994_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,604.968994,0.002398045183009123,y = 0.014883 + -0.000082*x,134,10.960663313432837,3.9096921347220377,0.013985902985074627,0.0065333725432655055 +logarithmic,Chlorophyll,604.968994,0.001603534119833716,y = 0.015675 + -0.000725*ln(x),134,10.960663313432837,3.9096921347220377,0.013985902985074627,0.0065333725432655055 +power,Chlorophyll,604.968994,-0.019789876685375907,y = 0.021361 * x^-0.202172,134,10.960663313432837,3.9096921347220377,0.013985902985074627,0.0065333725432655055 +exponential,Chlorophyll,604.968994,-0.019848091450597183,y = 0.016749 * exp(-0.020787*x),134,10.960663313432837,3.9096921347220377,0.013985902985074627,0.0065333725432655055 diff --git a/src/gui/work_dir/6_75_custom_regression/609.184998_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/609.184998_regression_results.csv new file mode 100644 index 0000000..d6d5012 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/609.184998_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,609.184998,0.0026364151647212397,y = 0.014491 + -0.000087*x,134,10.960663313432837,3.9096921347220377,0.013541022388059702,0.006602440257226493 +logarithmic,Chlorophyll,609.184998,0.001802623235843237,y = 0.015351 + -0.000777*ln(x),134,10.960663313432837,3.9096921347220377,0.013541022388059702,0.006602440257226493 +exponential,Chlorophyll,609.184998,-0.02173372426195974,y = 0.016508 * exp(-0.022851*x),134,10.960663313432837,3.9096921347220377,0.013541022388059702,0.006602440257226493 +power,Chlorophyll,609.184998,-0.021740201254617064,y = 0.021605 * x^-0.222988,134,10.960663313432837,3.9096921347220377,0.013541022388059702,0.006602440257226493 diff --git a/src/gui/work_dir/6_75_custom_regression/613.403992_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/613.403992_regression_results.csv new file mode 100644 index 0000000..79e5c93 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/613.403992_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,613.403992,0.002856835284101855,y = 0.014122 + -0.000091*x,134,10.960663313432837,3.9096921347220377,0.013127970149253732,0.006635524455917487 +logarithmic,Chlorophyll,613.403992,0.0019870205661061124,y = 0.015038 + -0.000820*ln(x),134,10.960663313432837,3.9096921347220377,0.013127970149253732,0.006635524455917487 +power,Chlorophyll,613.403992,-0.028809677197195516,y = 0.022780 * x^-0.263141,134,10.960663313432837,3.9096921347220377,0.013127970149253732,0.006635524455917487 +exponential,Chlorophyll,613.403992,-0.02909727099411552,y = 0.016581 * exp(-0.026956*x),134,10.960663313432837,3.9096921347220377,0.013127970149253732,0.006635524455917487 diff --git a/src/gui/work_dir/6_75_custom_regression/617.627014_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/617.627014_regression_results.csv new file mode 100644 index 0000000..1bf78cd --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/617.627014_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,617.627014,0.0029411042399954956,y = 0.013748 + -0.000092*x,134,10.960663313432837,3.9096921347220377,0.012741731343283583,0.006619138380331145 +logarithmic,Chlorophyll,617.627014,0.0020597681456059336,y = 0.014681 + -0.000832*ln(x),134,10.960663313432837,3.9096921347220377,0.012741731343283583,0.006619138380331145 +power,Chlorophyll,617.627014,-0.02980562740210102,y = 0.022702 * x^-0.275804,134,10.960663313432837,3.9096921347220377,0.012741731343283583,0.006619138380331145 +exponential,Chlorophyll,617.627014,-0.030009751236651283,y = 0.016267 * exp(-0.028218*x),134,10.960663313432837,3.9096921347220377,0.012741731343283583,0.006619138380331145 diff --git a/src/gui/work_dir/6_75_custom_regression/621.853027_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/621.853027_regression_results.csv new file mode 100644 index 0000000..826effa --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/621.853027_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,621.853027,0.002549517816160085,y = 0.013405 + -0.000087*x,134,10.960663313432837,3.9096921347220377,0.012447716417910447,0.006761993519104926 +logarithmic,Chlorophyll,621.853027,0.0017633821329291477,y = 0.014281 + -0.000787*ln(x),134,10.960663313432837,3.9096921347220377,0.012447716417910447,0.006761993519104926 +power,Chlorophyll,621.853027,-0.03557395594809787,y = 0.023522 * x^-0.304801,134,10.960663313432837,3.9096921347220377,0.012447716417910447,0.006761993519104926 +exponential,Chlorophyll,621.853027,-0.0360883361973503,y = 0.016274 * exp(-0.031185*x),134,10.960663313432837,3.9096921347220377,0.012447716417910447,0.006761993519104926 diff --git a/src/gui/work_dir/6_75_custom_regression/626.083008_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/626.083008_regression_results.csv new file mode 100644 index 0000000..8c47ea6 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/626.083008_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,626.083008,0.00243086769672618,y = 0.013228 + -0.000086*x,134,10.960663313432837,3.9096921347220377,0.01228736567164179,0.006808151575409212 +logarithmic,Chlorophyll,626.083008,0.0016770114876123454,y = 0.014087 + -0.000773*ln(x),134,10.960663313432837,3.9096921347220377,0.01228736567164179,0.006808151575409212 diff --git a/src/gui/work_dir/6_75_custom_regression/630.315979_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/630.315979_regression_results.csv new file mode 100644 index 0000000..030b59b --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/630.315979_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,630.315979,0.0020507276873319435,y = 0.013084 + -0.000080*x,134,10.960663313432837,3.9096921347220377,0.012202574626865673,0.0069447202176291635 +logarithmic,Chlorophyll,630.315979,0.0014098383321846653,y = 0.013886 + -0.000723*ln(x),134,10.960663313432837,3.9096921347220377,0.012202574626865673,0.0069447202176291635 +power,Chlorophyll,630.315979,-0.037502977238619506,y = 0.023711 * x^-0.319128,134,10.960663313432837,3.9096921347220377,0.012202574626865673,0.0069447202176291635 +exponential,Chlorophyll,630.315979,-0.03824118768398388,y = 0.016114 * exp(-0.032602*x),134,10.960663313432837,3.9096921347220377,0.012202574626865673,0.0069447202176291635 diff --git a/src/gui/work_dir/6_75_custom_regression/634.552002_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/634.552002_regression_results.csv new file mode 100644 index 0000000..7c75de3 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/634.552002_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,634.552002,0.0017288451024077833,y = 0.012968 + -0.000074*x,134,10.960663313432837,3.9096921347220377,0.012159492537313433,0.006937426491471543 +logarithmic,Chlorophyll,634.552002,0.0011589143811774338,y = 0.013684 + -0.000654*ln(x),134,10.960663313432837,3.9096921347220377,0.012159492537313433,0.006937426491471543 diff --git a/src/gui/work_dir/6_75_custom_regression/638.791992_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/638.791992_regression_results.csv new file mode 100644 index 0000000..e29230f --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/638.791992_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,638.791992,0.0011243091081014622,y = 0.012803 + -0.000061*x,134,10.960663313432837,3.9096921347220377,0.012136574626865673,0.007088601297076063 +logarithmic,Chlorophyll,638.791992,0.0007004467304244644,y = 0.013348 + -0.000520*ln(x),134,10.960663313432837,3.9096921347220377,0.012136574626865673,0.007088601297076063 +power,Chlorophyll,638.791992,-0.039341503691473934,y = 0.023270 * x^-0.314552,134,10.960663313432837,3.9096921347220377,0.012136574626865673,0.007088601297076063 +exponential,Chlorophyll,638.791992,-0.040722297966500065,y = 0.015930 * exp(-0.032288*x),134,10.960663313432837,3.9096921347220377,0.012136574626865673,0.007088601297076063 diff --git a/src/gui/work_dir/6_75_custom_regression/643.034973_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/643.034973_regression_results.csv new file mode 100644 index 0000000..ca0da84 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/643.034973_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,643.034973,0.0006784732036110297,y = 0.012527 + -0.000048*x,134,10.960663313432837,3.9096921347220377,0.012004380597014924,0.007158811687218512 +logarithmic,Chlorophyll,643.034973,0.0003810729347989428,y = 0.012907 + -0.000387*ln(x),134,10.960663313432837,3.9096921347220377,0.012004380597014924,0.007158811687218512 +power,Chlorophyll,643.034973,-0.02081846389883313,y = 0.018994 * x^-0.223479,134,10.960663313432837,3.9096921347220377,0.012004380597014924,0.007158811687218512 +exponential,Chlorophyll,643.034973,-0.02135362421636633,y = 0.014510 * exp(-0.022936*x),134,10.960663313432837,3.9096921347220377,0.012004380597014924,0.007158811687218512 diff --git a/src/gui/work_dir/6_75_custom_regression/647.281006_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/647.281006_regression_results.csv new file mode 100644 index 0000000..13abfde --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/647.281006_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,647.281006,0.0003862335627651259,y = 0.012126 + -0.000037*x,134,10.960663313432837,3.9096921347220377,0.01172576119402985,0.007268962283181486 +logarithmic,Chlorophyll,647.281006,0.00018807939298448595,y = 0.012369 + -0.000276*ln(x),134,10.960663313432837,3.9096921347220377,0.01172576119402985,0.007268962283181486 +power,Chlorophyll,647.281006,-0.01877601035356702,y = 0.017959 * x^-0.209287,134,10.960663313432837,3.9096921347220377,0.01172576119402985,0.007268962283181486 +exponential,Chlorophyll,647.281006,-0.019328048239278806,y = 0.013958 * exp(-0.021494*x),134,10.960663313432837,3.9096921347220377,0.01172576119402985,0.007268962283181486 diff --git a/src/gui/work_dir/6_75_custom_regression/651.531006_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/651.531006_regression_results.csv new file mode 100644 index 0000000..e6e9bb4 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/651.531006_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,651.531006,0.00013536784274881253,y = 0.011403 + -0.000022*x,134,10.960663313432837,3.9096921347220377,0.011164671641791043,0.007310371385073625 +logarithmic,Chlorophyll,651.531006,3.4274085199070825e-05,y = 0.011441 + -0.000119*ln(x),134,10.960663313432837,3.9096921347220377,0.011164671641791043,0.007310371385073625 +power,Chlorophyll,651.531006,-0.01999886130480988,y = 0.017201 * x^-0.213892,134,10.960663313432837,3.9096921347220377,0.011164671641791043,0.007310371385073625 +exponential,Chlorophyll,651.531006,-0.02080973971468425,y = 0.013316 * exp(-0.022113*x),134,10.960663313432837,3.9096921347220377,0.011164671641791043,0.007310371385073625 diff --git a/src/gui/work_dir/6_75_custom_regression/655.784973_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/655.784973_regression_results.csv new file mode 100644 index 0000000..6e0901e --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/655.784973_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,655.784973,6.712523797403058e-05,y = 0.010412 + -0.000016*x,134,10.960663313432837,3.9096921347220377,0.010241910447761193,0.007414076922911311 +logarithmic,Chlorophyll,655.784973,7.939365778253382e-06,y = 0.010377 + -0.000058*ln(x),134,10.960663313432837,3.9096921347220377,0.010241910447761193,0.007414076922911311 +power,Chlorophyll,655.784973,-0.02451200929710562,y = 0.017204 * x^-0.257829,134,10.960663313432837,3.9096921347220377,0.010241910447761193,0.007414076922911311 +exponential,Chlorophyll,655.784973,-0.025546357658017715,y = 0.012631 * exp(-0.026616*x),134,10.960663313432837,3.9096921347220377,0.010241910447761193,0.007414076922911311 diff --git a/src/gui/work_dir/6_75_custom_regression/660.041016_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/660.041016_regression_results.csv new file mode 100644 index 0000000..f82d6b9 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/660.041016_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,660.041016,2.3065421406842646e-05,y = 0.009045 + -0.000009*x,134,10.960663313432837,3.9096921347220377,0.008946089552238806,0.007372050533373144 +logarithmic,Chlorophyll,660.041016,8.478475440609756e-07,y = 0.008902 + 0.000019*ln(x),134,10.960663313432837,3.9096921347220377,0.008946089552238806,0.007372050533373144 diff --git a/src/gui/work_dir/6_75_custom_regression/664.302002_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/664.302002_regression_results.csv new file mode 100644 index 0000000..69ad651 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/664.302002_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,664.302002,2.70996016560332e-05,y = 0.008101 + -0.000010*x,134,10.960663313432837,3.9096921347220377,0.007993440298507463,0.007397490717674307 +logarithmic,Chlorophyll,664.302002,4.148671942649784e-07,y = 0.007963 + 0.000013*ln(x),134,10.960663313432837,3.9096921347220377,0.007993440298507463,0.007397490717674307 diff --git a/src/gui/work_dir/6_75_custom_regression/668.565002_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/668.565002_regression_results.csv new file mode 100644 index 0000000..c17e168 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/668.565002_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,668.565002,9.040046325403672e-06,y = 0.007498 + -0.000006*x,134,10.960663313432837,3.9096921347220377,0.0074355522388059704,0.007438585861600675 +logarithmic,Chlorophyll,668.565002,8.725061217407237e-06,y = 0.007294 + 0.000061*ln(x),134,10.960663313432837,3.9096921347220377,0.0074355522388059704,0.007438585861600675 diff --git a/src/gui/work_dir/6_75_custom_regression/672.83197_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/672.83197_regression_results.csv new file mode 100644 index 0000000..e516865 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/672.83197_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +logarithmic,Chlorophyll,672.83197,2.8342079669063658e-05,y = 0.007006 + 0.000109*ln(x),134,10.960663313432837,3.9096921347220377,0.007259574626865672,0.0073841624312846075 +linear,Chlorophyll,672.83197,2.942331951416577e-07,y = 0.007271 + -0.000001*x,134,10.960663313432837,3.9096921347220377,0.007259574626865672,0.0073841624312846075 diff --git a/src/gui/work_dir/6_75_custom_regression/677.10199_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/677.10199_regression_results.csv new file mode 100644 index 0000000..8232fbe --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/677.10199_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +logarithmic,Chlorophyll,677.10199,7.905307206101941e-05,y = 0.007009 + 0.000182*ln(x),134,10.960663313432837,3.9096921347220377,0.007434537313432836,0.007405361783473518 +linear,Chlorophyll,677.10199,8.391699477305892e-06,y = 0.007374 + 0.000005*x,134,10.960663313432837,3.9096921347220377,0.007434537313432836,0.007405361783473518 diff --git a/src/gui/work_dir/6_75_custom_regression/681.375977_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/681.375977_regression_results.csv new file mode 100644 index 0000000..2d32f47 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/681.375977_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +logarithmic,Chlorophyll,681.375977,0.00035444194765099635,y = 0.007047 + 0.000397*ln(x),134,10.960663313432837,3.9096921347220377,0.007971380597014925,0.007601910615882189 +linear,Chlorophyll,681.375977,0.00018023567598490775,y = 0.007685 + 0.000026*x,134,10.960663313432837,3.9096921347220377,0.007971380597014925,0.007601910615882189 diff --git a/src/gui/work_dir/6_75_custom_regression/685.653015_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/685.653015_regression_results.csv new file mode 100644 index 0000000..f64228a --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/685.653015_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +logarithmic,Chlorophyll,685.653015,0.0012936127446737666,y = 0.007106 + 0.000788*ln(x),134,10.960663313432837,3.9096921347220377,0.00894305223880597,0.007911710832888247 +linear,Chlorophyll,685.653015,0.0010226571268574514,y = 0.008234 + 0.000065*x,134,10.960663313432837,3.9096921347220377,0.00894305223880597,0.007911710832888247 diff --git a/src/gui/work_dir/6_75_custom_regression/689.932983_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/689.932983_regression_results.csv new file mode 100644 index 0000000..ae023c1 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/689.932983_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +logarithmic,Chlorophyll,689.932983,0.004302680997953723,y = 0.006459 + 0.001505*ln(x),134,10.960663313432837,3.9096921347220377,0.009964873134328357,0.008279228255026581 +linear,Chlorophyll,689.932983,0.0041204289706771036,y = 0.008475 + 0.000136*x,134,10.960663313432837,3.9096921347220377,0.009964873134328357,0.008279228255026581 +power,Chlorophyll,689.932983,-0.011112637221193156,y = 0.010691 * x^-0.061728,134,10.960663313432837,3.9096921347220377,0.009964873134328357,0.008279228255026581 +exponential,Chlorophyll,689.932983,-0.012347372143508784,y = 0.010039 * exp(-0.007382*x),134,10.960663313432837,3.9096921347220377,0.009964873134328357,0.008279228255026581 diff --git a/src/gui/work_dir/6_75_custom_regression/694.21698_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/694.21698_regression_results.csv new file mode 100644 index 0000000..5648a71 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/694.21698_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +logarithmic,Chlorophyll,694.21698,0.009659788549096326,y = 0.007405 + 0.001260*ln(x),134,10.960663313432837,3.9096921347220377,0.01034108208955224,0.004627698457727055 +linear,Chlorophyll,694.21698,0.008988938182027284,y = 0.009111 + 0.000112*x,134,10.960663313432837,3.9096921347220377,0.01034108208955224,0.004627698457727055 +power,Chlorophyll,694.21698,-0.0008894306327071888,y = 0.009130 * x^0.038452,134,10.960663313432837,3.9096921347220377,0.01034108208955224,0.004627698457727055 +exponential,Chlorophyll,694.21698,-0.002278256235976661,y = 0.009711 * exp(0.002541*x),134,10.960663313432837,3.9096921347220377,0.01034108208955224,0.004627698457727055 diff --git a/src/gui/work_dir/6_75_custom_regression/698.505005_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/698.505005_regression_results.csv new file mode 100644 index 0000000..13076fa --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/698.505005_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +logarithmic,Chlorophyll,698.505005,0.031440304449184886,y = 0.007932 + 0.000977*ln(x),134,10.960663313432837,3.9096921347220377,0.010207731343283583,0.0019876983508052013 +linear,Chlorophyll,698.505005,0.028545786078388646,y = 0.009266 + 0.000086*x,134,10.960663313432837,3.9096921347220377,0.010207731343283583,0.0019876983508052013 +power,Chlorophyll,698.505005,0.024301556337671393,y = 0.008426 * x^0.075955,134,10.960663313432837,3.9096921347220377,0.010207731343283583,0.0019876983508052013 +exponential,Chlorophyll,698.505005,0.021173949758028887,y = 0.009382 * exp(0.006346*x),134,10.960663313432837,3.9096921347220377,0.010207731343283583,0.0019876983508052013 diff --git a/src/gui/work_dir/6_75_custom_regression/702.794983_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/702.794983_regression_results.csv new file mode 100644 index 0000000..756a073 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/702.794983_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,702.794983,0.06296172218181084,y = 0.008305 + 0.000144*x,134,10.960663313432837,3.9096921347220377,0.009888313432835822,0.002250167471107098 +logarithmic,Chlorophyll,702.794983,0.06154613752993454,y = 0.006284 + 0.001547*ln(x),134,10.960663313432837,3.9096921347220377,0.009888313432835822,0.002250167471107098 +exponential,Chlorophyll,702.794983,0.05333717741162325,y = 0.008583 * exp(0.011176*x),134,10.960663313432837,3.9096921347220377,0.009888313432835822,0.002250167471107098 +power,Chlorophyll,702.794983,0.052353333406120695,y = 0.007280 * x^0.123243,134,10.960663313432837,3.9096921347220377,0.009888313432835822,0.002250167471107098 diff --git a/src/gui/work_dir/6_75_custom_regression/707.088989_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/707.088989_regression_results.csv new file mode 100644 index 0000000..7a8eac9 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/707.088989_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,707.088989,0.0722787565252282,y = 0.007176 + 0.000174*x,134,10.960663313432837,3.9096921347220377,0.009086067164179106,0.0025340421111067013 +logarithmic,Chlorophyll,707.088989,0.06798251906890818,y = 0.004820 + 0.001831*ln(x),134,10.960663313432837,3.9096921347220377,0.009086067164179106,0.0025340421111067013 +exponential,Chlorophyll,707.088989,0.05918259759570621,y = 0.007631 * exp(0.013614*x),134,10.960663313432837,3.9096921347220377,0.009086067164179106,0.0025340421111067013 +power,Chlorophyll,707.088989,0.05571330673697561,y = 0.006287 * x^0.147166,134,10.960663313432837,3.9096921347220377,0.009086067164179106,0.0025340421111067013 diff --git a/src/gui/work_dir/6_75_custom_regression/711.387024_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/711.387024_regression_results.csv new file mode 100644 index 0000000..d4146d4 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/711.387024_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,711.387024,0.0801501663676073,y = 0.005716 + 0.000199*x,134,10.960663313432837,3.9096921347220377,0.007900276119402985,0.0027516224396933074 +logarithmic,Chlorophyll,711.387024,0.07410981134062689,y = 0.003064 + 0.002076*ln(x),134,10.960663313432837,3.9096921347220377,0.007900276119402985,0.0027516224396933074 +exponential,Chlorophyll,711.387024,0.06368164013940003,y = 0.006342 * exp(0.016959*x),134,10.960663313432837,3.9096921347220377,0.007900276119402985,0.0027516224396933074 +power,Chlorophyll,711.387024,0.05885933871457438,y = 0.005001 * x^0.181749,134,10.960663313432837,3.9096921347220377,0.007900276119402985,0.0027516224396933074 diff --git a/src/gui/work_dir/6_75_custom_regression/715.687012_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/715.687012_regression_results.csv new file mode 100644 index 0000000..29d8756 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/715.687012_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,715.687012,0.07646592984102929,y = 0.004316 + 0.000182*x,134,10.960663313432837,3.9096921347220377,0.00631360447761194,0.0025762009517042803 +logarithmic,Chlorophyll,715.687012,0.07053737510880576,y = 0.001896 + 0.001896*ln(x),134,10.960663313432837,3.9096921347220377,0.00631360447761194,0.0025762009517042803 +exponential,Chlorophyll,715.687012,0.057012898990752126,y = 0.004952 * exp(0.018319*x),134,10.960663313432837,3.9096921347220377,0.00631360447761194,0.0025762009517042803 +power,Chlorophyll,715.687012,0.05263846505811587,y = 0.003824 * x^0.197049,134,10.960663313432837,3.9096921347220377,0.00631360447761194,0.0025762009517042803 diff --git a/src/gui/work_dir/6_75_custom_regression/719.992004_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/719.992004_regression_results.csv new file mode 100644 index 0000000..2ffdfda --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/719.992004_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,719.992004,0.07445743675368166,y = 0.003267 + 0.000166*x,134,10.960663313432837,3.9096921347220377,0.00509136567164179,0.002385295002066048 +logarithmic,Chlorophyll,719.992004,0.06836961037451539,y = 0.001065 + 0.001728*ln(x),134,10.960663313432837,3.9096921347220377,0.00509136567164179,0.002385295002066048 +exponential,Chlorophyll,719.992004,0.05296886200774753,y = 0.003871 * exp(0.020260*x),134,10.960663313432837,3.9096921347220377,0.00509136567164179,0.002385295002066048 +power,Chlorophyll,719.992004,0.048528532136251745,y = 0.002911 * x^0.217642,134,10.960663313432837,3.9096921347220377,0.00509136567164179,0.002385295002066048 diff --git a/src/gui/work_dir/6_75_custom_regression/724.299011_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/724.299011_regression_results.csv new file mode 100644 index 0000000..5e1b77b --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/724.299011_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,724.299011,0.06745397553654364,y = 0.002420 + 0.000133*x,134,10.960663313432837,3.9096921347220377,0.0038816044776119395,0.0020072190423221247 +logarithmic,Chlorophyll,724.299011,0.062493450238378045,y = 0.000642 + 0.001390*ln(x),134,10.960663313432837,3.9096921347220377,0.0038816044776119395,0.0020072190423221247 +exponential,Chlorophyll,724.299011,0.045936345781994126,y = 0.002921 * exp(0.020727*x),134,10.960663313432837,3.9096921347220377,0.0038816044776119395,0.0020072190423221247 +power,Chlorophyll,724.299011,0.04270170271520268,y = 0.002169 * x^0.225290,134,10.960663313432837,3.9096921347220377,0.0038816044776119395,0.0020072190423221247 diff --git a/src/gui/work_dir/6_75_custom_regression/728.609985_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/728.609985_regression_results.csv new file mode 100644 index 0000000..cbad1d9 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/728.609985_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,728.609985,0.043742722087426356,y = 0.001822 + 0.000088*x,134,10.960663313432837,3.9096921347220377,0.0027881044776119405,0.0016469934764511023 +logarithmic,Chlorophyll,728.609985,0.04115482350546895,y = 0.000631 + 0.000926*ln(x),134,10.960663313432837,3.9096921347220377,0.0027881044776119405,0.0016469934764511023 +exponential,Chlorophyll,728.609985,0.025695766879270887,y = 0.002157 * exp(0.017908*x),134,10.960663313432837,3.9096921347220377,0.0027881044776119405,0.0016469934764511023 +power,Chlorophyll,728.609985,0.024423938138158685,y = 0.001657 * x^0.197225,134,10.960663313432837,3.9096921347220377,0.0027881044776119405,0.0016469934764511023 diff --git a/src/gui/work_dir/6_75_custom_regression/732.924988_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/732.924988_regression_results.csv new file mode 100644 index 0000000..e17560b --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/732.924988_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,732.924988,0.013546034482326164,y = 0.001304 + 0.000072*x,134,10.960663313432837,3.9096921347220377,0.0020962686567164183,0.002429007647518667 +logarithmic,Chlorophyll,732.924988,0.013045034204567596,y = 0.000305 + 0.000769*ln(x),134,10.960663313432837,3.9096921347220377,0.0020962686567164183,0.002429007647518667 +power,Chlorophyll,732.924988,-0.0016075191011517553,y = 0.001527 * x^0.092327,134,10.960663313432837,3.9096921347220377,0.0020962686567164183,0.002429007647518667 +exponential,Chlorophyll,732.924988,-0.002218314899776086,y = 0.001749 * exp(0.007256*x),134,10.960663313432837,3.9096921347220377,0.0020962686567164183,0.002429007647518667 diff --git a/src/gui/work_dir/6_75_custom_regression/737.242004_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/737.242004_regression_results.csv new file mode 100644 index 0000000..ce0a6b3 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/737.242004_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,737.242004,0.01274011186937285,y = 0.000153 + 0.000183*x,134,10.960663313432837,3.9096921347220377,0.0021605000000000005,0.006345526772941662 +logarithmic,Chlorophyll,737.242004,0.011709594088295416,y = -0.002273 + 0.001903*ln(x),134,10.960663313432837,3.9096921347220377,0.0021605000000000005,0.006345526772941662 +power,Chlorophyll,737.242004,-0.004968418579431422,y = 0.001306 * x^0.096424,134,10.960663313432837,3.9096921347220377,0.0021605000000000005,0.006345526772941662 +exponential,Chlorophyll,737.242004,-0.0051926295251412125,y = 0.001506 * exp(0.007477*x),134,10.960663313432837,3.9096921347220377,0.0021605000000000005,0.006345526772941662 diff --git a/src/gui/work_dir/6_75_custom_regression/741.564026_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/741.564026_regression_results.csv new file mode 100644 index 0000000..c70bd46 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/741.564026_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,741.564026,0.01237420528041,y = -0.000778 + 0.000290*x,134,10.960663313432837,3.9096921347220377,0.0024028432835820897,0.010199237979184374 +logarithmic,Chlorophyll,741.564026,0.011168399659673534,y = -0.004556 + 0.002987*ln(x),134,10.960663313432837,3.9096921347220377,0.0024028432835820897,0.010199237979184374 +power,Chlorophyll,741.564026,-0.006382018366340336,y = 0.001343 * x^0.060248,134,10.960663313432837,3.9096921347220377,0.0024028432835820897,0.010199237979184374 +exponential,Chlorophyll,741.564026,-0.006486914714434855,y = 0.001471 * exp(0.004477*x),134,10.960663313432837,3.9096921347220377,0.0024028432835820897,0.010199237979184374 diff --git a/src/gui/work_dir/6_75_custom_regression/745.888_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/745.888_regression_results.csv new file mode 100644 index 0000000..a53f64f --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/745.888_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,745.888,0.01208622807607107,y = -0.000537 + 0.000255*x,134,10.960663313432837,3.9096921347220377,0.0022554402985074623,0.00905887910227967 +logarithmic,Chlorophyll,745.888,0.010783256099801686,y = -0.003818 + 0.002607*ln(x),134,10.960663313432837,3.9096921347220377,0.0022554402985074623,0.00905887910227967 +exponential,Chlorophyll,745.888,-0.006541603078447977,y = 0.001459 * exp(0.002645*x),134,10.960663313432837,3.9096921347220377,0.0022554402985074623,0.00905887910227967 +power,Chlorophyll,745.888,-0.006568012798676248,y = 0.001406 * x^0.028233,134,10.960663313432837,3.9096921347220377,0.0022554402985074623,0.00905887910227967 diff --git a/src/gui/work_dir/6_75_custom_regression/750.216003_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/750.216003_regression_results.csv new file mode 100644 index 0000000..26f5850 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/750.216003_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,750.216003,0.01149077406645782,y = -0.000338 + 0.000223*x,134,10.960663313432837,3.9096921347220377,0.0021108358208955223,0.008148439806888069 +logarithmic,Chlorophyll,750.216003,0.010336178508392302,y = -0.003238 + 0.002295*ln(x),134,10.960663313432837,3.9096921347220377,0.0021108358208955223,0.008148439806888069 +power,Chlorophyll,750.216003,-0.007024448671629768,y = 0.001432 * x^-0.001028,134,10.960663313432837,3.9096921347220377,0.0021108358208955223,0.008148439806888069 +exponential,Chlorophyll,750.216003,-0.007146981046715295,y = 0.001443 * exp(-0.000923*x),134,10.960663313432837,3.9096921347220377,0.0021108358208955223,0.008148439806888069 diff --git a/src/gui/work_dir/6_75_custom_regression/754.546997_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/754.546997_regression_results.csv new file mode 100644 index 0000000..dc6bcb3 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/754.546997_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,754.546997,0.011676905008792149,y = -0.001694 + 0.000351*x,134,10.960663313432837,3.9096921347220377,0.0021568134328358206,0.012712462658286435 +logarithmic,Chlorophyll,754.546997,0.010451547578783416,y = -0.006234 + 0.003601*ln(x),134,10.960663313432837,3.9096921347220377,0.0021568134328358206,0.012712462658286435 +power,Chlorophyll,754.546997,-0.008219467609217102,y = 0.001485 * x^-0.141847,134,10.960663313432837,3.9096921347220377,0.0021568134328358206,0.012712462658286435 +exponential,Chlorophyll,754.546997,-0.008391038873995615,y = 0.001256 * exp(-0.014916*x),134,10.960663313432837,3.9096921347220377,0.0021568134328358206,0.012712462658286435 diff --git a/src/gui/work_dir/6_75_custom_regression/758.882019_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/758.882019_regression_results.csv new file mode 100644 index 0000000..0758375 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/758.882019_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,758.882019,0.02592512089319743,y = 0.000332 + 0.000117*x,134,10.960663313432837,3.9096921347220377,0.001613589552238806,0.002839881175811175 +logarithmic,Chlorophyll,758.882019,0.023289682011479407,y = -0.001184 + 0.001201*ln(x),134,10.960663313432837,3.9096921347220377,0.001613589552238806,0.002839881175811175 +exponential,Chlorophyll,758.882019,0.0076383999092375365,y = 0.000992 * exp(0.028888*x),134,10.960663313432837,3.9096921347220377,0.001613589552238806,0.002839881175811175 +power,Chlorophyll,758.882019,0.0061542760831626575,y = 0.000678 * x^0.299540,134,10.960663313432837,3.9096921347220377,0.001613589552238806,0.002839881175811175 diff --git a/src/gui/work_dir/6_75_custom_regression/763.219971_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/763.219971_regression_results.csv new file mode 100644 index 0000000..8762355 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/763.219971_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,763.219971,0.016331099062929955,y = -0.000309 + 0.000207*x,134,10.960663313432837,3.9096921347220377,0.0019577089552238807,0.006326078022490415 +logarithmic,Chlorophyll,763.219971,0.014364159452194492,y = -0.002937 + 0.002101*ln(x),134,10.960663313432837,3.9096921347220377,0.0019577089552238807,0.006326078022490415 +exponential,Chlorophyll,763.219971,-0.002879629869520839,y = 0.001084 * exp(0.023081*x),134,10.960663313432837,3.9096921347220377,0.0019577089552238807,0.006326078022490415 +power,Chlorophyll,763.219971,-0.0036347268087895124,y = 0.000823 * x^0.227200,134,10.960663313432837,3.9096921347220377,0.0019577089552238807,0.006326078022490415 diff --git a/src/gui/work_dir/6_75_custom_regression/767.562012_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/767.562012_regression_results.csv new file mode 100644 index 0000000..4f28263 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/767.562012_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,767.562012,0.013734746280580246,y = 0.000053 + 0.000156*x,134,10.960663313432837,3.9096921347220377,0.0017597089552238805,0.005193771329607048 +logarithmic,Chlorophyll,767.562012,0.012255690731871494,y = -0.001952 + 0.001593*ln(x),134,10.960663313432837,3.9096921347220377,0.0017597089552238805,0.005193771329607048 +exponential,Chlorophyll,767.562012,-0.004092583942566952,y = 0.001165 * exp(0.012080*x),134,10.960663313432837,3.9096921347220377,0.0017597089552238805,0.005193771329607048 +power,Chlorophyll,767.562012,-0.00439521419002209,y = 0.000996 * x^0.123809,134,10.960663313432837,3.9096921347220377,0.0017597089552238805,0.005193771329607048 diff --git a/src/gui/work_dir/6_75_custom_regression/771.906982_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/771.906982_regression_results.csv new file mode 100644 index 0000000..1c7d459 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/771.906982_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,771.906982,0.016320574765209428,y = 0.000317 + 0.000110*x,134,10.960663313432837,3.9096921347220377,0.0015226194029850744,0.0033670223010905994 +logarithmic,Chlorophyll,771.906982,0.014546529767741512,y = -0.001099 + 0.001125*ln(x),134,10.960663313432837,3.9096921347220377,0.0015226194029850744,0.0033670223010905994 +exponential,Chlorophyll,771.906982,-0.0007298834218001371,y = 0.001025 * exp(0.017660*x),134,10.960663313432837,3.9096921347220377,0.0015226194029850744,0.0033670223010905994 +power,Chlorophyll,771.906982,-0.0014459883223563175,y = 0.000819 * x^0.179543,134,10.960663313432837,3.9096921347220377,0.0015226194029850744,0.0033670223010905994 diff --git a/src/gui/work_dir/6_75_custom_regression/776.255005_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/776.255005_regression_results.csv new file mode 100644 index 0000000..9fd8fd5 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/776.255005_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,776.255005,0.013796046606459278,y = 0.000740 + 0.000072*x,134,10.960663313432837,3.9096921347220377,0.0015243955223880595,0.002382068739119507 +logarithmic,Chlorophyll,776.255005,0.012092441153714883,y = -0.000167 + 0.000726*ln(x),134,10.960663313432837,3.9096921347220377,0.0015243955223880595,0.002382068739119507 +exponential,Chlorophyll,776.255005,-0.0025031550633876076,y = 0.001209 * exp(0.008693*x),134,10.960663313432837,3.9096921347220377,0.0015243955223880595,0.002382068739119507 +power,Chlorophyll,776.255005,-0.0031841408353259215,y = 0.001093 * x^0.084233,134,10.960663313432837,3.9096921347220377,0.0015243955223880595,0.002382068739119507 diff --git a/src/gui/work_dir/6_75_custom_regression/780.606995_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/780.606995_regression_results.csv new file mode 100644 index 0000000..d34db28 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/780.606995_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,780.606995,0.020495592564976572,y = 0.000739 + 0.000066*x,134,10.960663313432837,3.9096921347220377,0.001462328358208955,0.001801391351880165 +logarithmic,Chlorophyll,780.606995,0.01933435376928716,y = -0.000155 + 0.000694*ln(x),134,10.960663313432837,3.9096921347220377,0.001462328358208955,0.001801391351880165 +power,Chlorophyll,780.606995,0.00417461173259015,y = 0.000868 * x^0.177284,134,10.960663313432837,3.9096921347220377,0.001462328358208955,0.001801391351880165 +exponential,Chlorophyll,780.606995,0.004156141926620949,y = 0.001105 * exp(0.015635*x),134,10.960663313432837,3.9096921347220377,0.001462328358208955,0.001801391351880165 diff --git a/src/gui/work_dir/6_75_custom_regression/784.961975_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/784.961975_regression_results.csv new file mode 100644 index 0000000..29541df --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/784.961975_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,784.961975,0.021755422019536175,y = 0.000788 + 0.000057*x,134,10.960663313432837,3.9096921347220377,0.0014125447761194029,0.001510507088995463 +logarithmic,Chlorophyll,784.961975,0.017664886852162054,y = 0.000116 + 0.000556*ln(x),134,10.960663313432837,3.9096921347220377,0.0014125447761194029,0.001510507088995463 +exponential,Chlorophyll,784.961975,0.0034422768172869267,y = 0.001109 * exp(0.012988*x),134,10.960663313432837,3.9096921347220377,0.0014125447761194029,0.001510507088995463 +power,Chlorophyll,784.961975,0.0003808059654853979,y = 0.000984 * x^0.112589,134,10.960663313432837,3.9096921347220377,0.0014125447761194029,0.001510507088995463 diff --git a/src/gui/work_dir/6_75_custom_regression/789.320007_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/789.320007_regression_results.csv new file mode 100644 index 0000000..37a39f5 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/789.320007_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,789.320007,0.043367659619824805,y = 0.000649 + 0.000082*x,134,10.960663313432837,3.9096921347220377,0.0015427910447761196,0.001531740138489588 +logarithmic,Chlorophyll,789.320007,0.038409875145745676,y = -0.000395 + 0.000832*ln(x),134,10.960663313432837,3.9096921347220377,0.0015427910447761196,0.001531740138489588 +exponential,Chlorophyll,789.320007,0.026002389448048557,y = 0.001020 * exp(0.028931*x),134,10.960663313432837,3.9096921347220377,0.0015427910447761196,0.001531740138489588 +power,Chlorophyll,789.320007,0.022165630417928628,y = 0.000698 * x^0.299052,134,10.960663313432837,3.9096921347220377,0.0015427910447761196,0.001531740138489588 diff --git a/src/gui/work_dir/6_75_custom_regression/793.682007_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/793.682007_regression_results.csv new file mode 100644 index 0000000..2c77612 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/793.682007_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,793.682007,0.045606716978680684,y = 0.000690 + 0.000084*x,134,10.960663313432837,3.9096921347220377,0.001615179104477612,0.0015458269286355185 +logarithmic,Chlorophyll,793.682007,0.03840486464660908,y = -0.000341 + 0.000839*ln(x),134,10.960663313432837,3.9096921347220377,0.001615179104477612,0.0015458269286355185 +exponential,Chlorophyll,793.682007,0.025844197165889593,y = 0.001092 * exp(0.026877*x),134,10.960663313432837,3.9096921347220377,0.001615179104477612,0.0015458269286355185 +power,Chlorophyll,793.682007,0.0197310845605696,y = 0.000795 * x^0.262671,134,10.960663313432837,3.9096921347220377,0.001615179104477612,0.0015458269286355185 diff --git a/src/gui/work_dir/6_75_custom_regression/798.046997_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/798.046997_regression_results.csv new file mode 100644 index 0000000..98a4cad --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/798.046997_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,798.046997,0.05291687536605305,y = 0.000563 + 0.000098*x,134,10.960663313432837,3.9096921347220377,0.0016406641791044776,0.0016713277988189281 +logarithmic,Chlorophyll,798.046997,0.04472311171573551,y = -0.000641 + 0.000979*ln(x),134,10.960663313432837,3.9096921347220377,0.0016406641791044776,0.0016713277988189281 +exponential,Chlorophyll,798.046997,0.028058328916172992,y = 0.001060 * exp(0.029439*x),134,10.960663313432837,3.9096921347220377,0.0016406641791044776,0.0016713277988189281 +power,Chlorophyll,798.046997,0.021436519217983063,y = 0.000744 * x^0.290697,134,10.960663313432837,3.9096921347220377,0.0016406641791044776,0.0016713277988189281 diff --git a/src/gui/work_dir/6_75_custom_regression/802.416016_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/802.416016_regression_results.csv new file mode 100644 index 0000000..1053605 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/802.416016_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,802.416016,0.060413004491346234,y = 0.000466 + 0.000109*x,134,10.960663313432837,3.9096921347220377,0.0016603507462686566,0.00173259510844654 +logarithmic,Chlorophyll,802.416016,0.05101453638227127,y = -0.000866 + 0.001084*ln(x),134,10.960663313432837,3.9096921347220377,0.0016603507462686566,0.00173259510844654 +exponential,Chlorophyll,802.416016,0.0330704948210061,y = 0.001022 * exp(0.032943*x),134,10.960663313432837,3.9096921347220377,0.0016603507462686566,0.00173259510844654 +power,Chlorophyll,802.416016,0.025369073855778357,y = 0.000686 * x^0.325774,134,10.960663313432837,3.9096921347220377,0.0016603507462686566,0.00173259510844654 diff --git a/src/gui/work_dir/6_75_custom_regression/806.788025_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/806.788025_regression_results.csv new file mode 100644 index 0000000..8d01aef --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/806.788025_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,806.788025,0.05998246946359442,y = 0.000383 + 0.000114*x,134,10.960663313432837,3.9096921347220377,0.0016336492537313433,0.0018214778822386276 +logarithmic,Chlorophyll,806.788025,0.05004260064643584,y = -0.000997 + 0.001129*ln(x),134,10.960663313432837,3.9096921347220377,0.0016336492537313433,0.0018214778822386276 +exponential,Chlorophyll,806.788025,0.03073454834672984,y = 0.000982 * exp(0.033910*x),134,10.960663313432837,3.9096921347220377,0.0016336492537313433,0.0018214778822386276 +power,Chlorophyll,806.788025,0.022446700301405054,y = 0.000662 * x^0.328791,134,10.960663313432837,3.9096921347220377,0.0016336492537313433,0.0018214778822386276 diff --git a/src/gui/work_dir/6_75_custom_regression/811.164001_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/811.164001_regression_results.csv new file mode 100644 index 0000000..d4d8b92 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/811.164001_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,811.164001,0.06199436327274466,y = 0.000193 + 0.000119*x,134,10.960663313432837,3.9096921347220377,0.00149255223880597,0.0018610729394833178 +logarithmic,Chlorophyll,811.164001,0.05345700266587472,y = -0.001285 + 0.001192*ln(x),134,10.960663313432837,3.9096921347220377,0.00149255223880597,0.0018610729394833178 +exponential,Chlorophyll,811.164001,0.03466499966616288,y = 0.000817 * exp(0.041038*x),134,10.960663313432837,3.9096921347220377,0.00149255223880597,0.0018610729394833178 +power,Chlorophyll,811.164001,0.02774987016970709,y = 0.000489 * x^0.413598,134,10.960663313432837,3.9096921347220377,0.00149255223880597,0.0018610729394833178 diff --git a/src/gui/work_dir/6_75_custom_regression/815.541992_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/815.541992_regression_results.csv new file mode 100644 index 0000000..35520a2 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/815.541992_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,815.541992,0.05377501182020772,y = 0.000336 + 0.000106*x,134,10.960663313432837,3.9096921347220377,0.0014953880597014925,0.0017840748382604765 +logarithmic,Chlorophyll,815.541992,0.04541196710758344,y = -0.000959 + 0.001053*ln(x),134,10.960663313432837,3.9096921347220377,0.0014953880597014925,0.0017840748382604765 +exponential,Chlorophyll,815.541992,0.029780387106763118,y = 0.000871 * exp(0.036764*x),134,10.960663313432837,3.9096921347220377,0.0014953880597014925,0.0017840748382604765 +power,Chlorophyll,815.541992,0.02259630556998271,y = 0.000564 * x^0.358942,134,10.960663313432837,3.9096921347220377,0.0014953880597014925,0.0017840748382604765 diff --git a/src/gui/work_dir/6_75_custom_regression/819.924988_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/819.924988_regression_results.csv new file mode 100644 index 0000000..f67158f --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/819.924988_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,819.924988,0.046060394085339684,y = 0.000183 + 0.000105*x,134,10.960663313432837,3.9096921347220377,0.0013392835820895523,0.0019214842584320314 +logarithmic,Chlorophyll,819.924988,0.04124143103053901,y = -0.001180 + 0.001081*ln(x),134,10.960663313432837,3.9096921347220377,0.0013392835820895523,0.0019214842584320314 +exponential,Chlorophyll,819.924988,0.024172923620458642,y = 0.000721 * exp(0.041558*x),134,10.960663313432837,3.9096921347220377,0.0013392835820895523,0.0019214842584320314 +power,Chlorophyll,819.924988,0.020698277367864337,y = 0.000415 * x^0.432441,134,10.960663313432837,3.9096921347220377,0.0013392835820895523,0.0019214842584320314 diff --git a/src/gui/work_dir/6_75_custom_regression/824.309998_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/824.309998_regression_results.csv new file mode 100644 index 0000000..d05fcaf --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/824.309998_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,824.309998,0.019229517688291176,y = 0.000174 + 0.000100*x,134,10.960663313432837,3.9096921347220377,0.0012656044776119403,0.0028070356968274437 +logarithmic,Chlorophyll,824.309998,0.017049456706681787,y = -0.001101 + 0.001016*ln(x),134,10.960663313432837,3.9096921347220377,0.0012656044776119403,0.0028070356968274437 +exponential,Chlorophyll,824.309998,-0.0013691164176472537,y = 0.000805 * exp(0.020115*x),134,10.960663313432837,3.9096921347220377,0.0012656044776119403,0.0028070356968274437 +power,Chlorophyll,824.309998,-0.00228368459628836,y = 0.000624 * x^0.203487,134,10.960663313432837,3.9096921347220377,0.0012656044776119403,0.0028070356968274437 diff --git a/src/gui/work_dir/6_75_custom_regression/828.698975_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/828.698975_regression_results.csv new file mode 100644 index 0000000..c5adfad --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/828.698975_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,828.698975,0.021582205152547496,y = -0.000706 + 0.000171*x,134,10.960663313432837,3.9096921347220377,0.0011649477611940298,0.004543819230423196 +logarithmic,Chlorophyll,828.698975,0.01910220662486728,y = -0.002889 + 0.001740*ln(x),134,10.960663313432837,3.9096921347220377,0.0011649477611940298,0.004543819230423196 diff --git a/src/gui/work_dir/6_75_custom_regression/833.09198_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/833.09198_regression_results.csv new file mode 100644 index 0000000..52af221 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/833.09198_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,833.09198,0.019106777392842944,y = -0.000521 + 0.000152*x,134,10.960663313432837,3.9096921347220377,0.0011438880597014925,0.004296265257070513 +logarithmic,Chlorophyll,833.09198,0.017688466235980305,y = -0.002545 + 0.001583*ln(x),134,10.960663313432837,3.9096921347220377,0.0011438880597014925,0.004296265257070513 diff --git a/src/gui/work_dir/6_75_custom_regression/837.487_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/837.487_regression_results.csv new file mode 100644 index 0000000..6b7d6e7 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/837.487_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,837.487,0.01745847226311259,y = -0.000612 + 0.000154*x,134,10.960663313432837,3.9096921347220377,0.0010808880597014926,0.00456947731205439 +logarithmic,Chlorophyll,837.487,0.015969564742421505,y = -0.002647 + 0.001600*ln(x),134,10.960663313432837,3.9096921347220377,0.0010808880597014926,0.00456947731205439 diff --git a/src/gui/work_dir/6_75_custom_regression/841.887024_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/841.887024_regression_results.csv new file mode 100644 index 0000000..9e5ce20 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/841.887024_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,841.887024,0.020571912766418676,y = -0.000836 + 0.000165*x,134,10.960663313432837,3.9096921347220377,0.000975223880597015,0.004505175408054626 +logarithmic,Chlorophyll,841.887024,0.01857954883427715,y = -0.002989 + 0.001702*ln(x),134,10.960663313432837,3.9096921347220377,0.000975223880597015,0.004505175408054626 diff --git a/src/gui/work_dir/6_75_custom_regression/846.289001_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/846.289001_regression_results.csv new file mode 100644 index 0000000..052e683 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/846.289001_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,846.289001,0.017666193062245705,y = -0.000573 + 0.000159*x,134,10.960663313432837,3.9096921347220377,0.0011751492537313433,0.004690356775258772 +logarithmic,Chlorophyll,846.289001,0.015209283936141516,y = -0.002559 + 0.001603*ln(x),134,10.960663313432837,3.9096921347220377,0.0011751492537313433,0.004690356775258772 diff --git a/src/gui/work_dir/6_75_custom_regression/850.695007_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/850.695007_regression_results.csv new file mode 100644 index 0000000..39f93eb --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/850.695007_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,850.695007,0.020034265491308823,y = -0.000830 + 0.000168*x,134,10.960663313432837,3.9096921347220377,0.0010073805970149251,0.004629553145314909 +logarithmic,Chlorophyll,850.695007,0.01744543460758463,y = -0.002940 + 0.001694*ln(x),134,10.960663313432837,3.9096921347220377,0.0010073805970149251,0.004629553145314909 diff --git a/src/gui/work_dir/6_75_custom_regression/855.104004_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/855.104004_regression_results.csv new file mode 100644 index 0000000..9732f8a --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/855.104004_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,855.104004,0.015865352453769743,y = -0.000772 + 0.000165*x,134,10.960663313432837,3.9096921347220377,0.001040134328358209,0.005131083278297242 +logarithmic,Chlorophyll,855.104004,0.01432846575764024,y = -0.002925 + 0.001702*ln(x),134,10.960663313432837,3.9096921347220377,0.001040134328358209,0.005131083278297242 diff --git a/src/gui/work_dir/6_75_custom_regression/859.517029_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/859.517029_regression_results.csv new file mode 100644 index 0000000..d08971c --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/859.517029_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,859.517029,0.019429355449413932,y = -0.000838 + 0.000167*x,134,10.960663313432837,3.9096921347220377,0.0009975746268656716,0.004697662523503507 +logarithmic,Chlorophyll,859.517029,0.01692099708734196,y = -0.002948 + 0.001693*ln(x),134,10.960663313432837,3.9096921347220377,0.0009975746268656716,0.004697662523503507 diff --git a/src/gui/work_dir/6_75_custom_regression/863.932983_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/863.932983_regression_results.csv new file mode 100644 index 0000000..22d3d11 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/863.932983_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,863.932983,0.018492121363953173,y = -0.001032 + 0.000194*x,134,10.960663313432837,3.9096921347220377,0.001096402985074627,0.005584099891548954 +logarithmic,Chlorophyll,863.932983,0.01657550912114314,y = -0.003545 + 0.001992*ln(x),134,10.960663313432837,3.9096921347220377,0.001096402985074627,0.005584099891548954 diff --git a/src/gui/work_dir/6_75_custom_regression/868.353027_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/868.353027_regression_results.csv new file mode 100644 index 0000000..2c0903c --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/868.353027_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,868.353027,0.023856603861747816,y = -0.001298 + 0.000212*x,134,10.960663313432837,3.9096921347220377,0.001024410447761194,0.005364012357399879 +logarithmic,Chlorophyll,868.353027,0.02163307555513183,y = -0.004069 + 0.002186*ln(x),134,10.960663313432837,3.9096921347220377,0.001024410447761194,0.005364012357399879 diff --git a/src/gui/work_dir/6_75_custom_regression/872.776001_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/872.776001_regression_results.csv new file mode 100644 index 0000000..02df1eb --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/872.776001_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,872.776001,0.01720822389418175,y = -0.000913 + 0.000184*x,134,10.960663313432837,3.9096921347220377,0.0011064925373134328,0.0054913284581254935 +logarithmic,Chlorophyll,872.776001,0.015351856894195048,y = -0.003286 + 0.001885*ln(x),134,10.960663313432837,3.9096921347220377,0.0011064925373134328,0.0054913284581254935 diff --git a/src/gui/work_dir/6_75_custom_regression/877.202026_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/877.202026_regression_results.csv new file mode 100644 index 0000000..6b98bb8 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/877.202026_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,877.202026,0.02114400380397219,y = -0.001279 + 0.000211*x,134,10.960663313432837,3.9096921347220377,0.0010304626865671645,0.005664719391631075 +logarithmic,Chlorophyll,877.202026,0.018654803312690205,y = -0.003965 + 0.002144*ln(x),134,10.960663313432837,3.9096921347220377,0.0010304626865671645,0.005664719391631075 diff --git a/src/gui/work_dir/6_75_custom_regression/881.632019_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/881.632019_regression_results.csv new file mode 100644 index 0000000..3258e4b --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/881.632019_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,881.632019,0.024894268838071443,y = -0.001506 + 0.000231*x,134,10.960663313432837,3.9096921347220377,0.0010241940298507462,0.005720007686402323 +logarithmic,Chlorophyll,881.632019,0.02231090779764633,y = -0.004492 + 0.002367*ln(x),134,10.960663313432837,3.9096921347220377,0.0010241940298507462,0.005720007686402323 diff --git a/src/gui/work_dir/6_75_custom_regression/886.065002_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/886.065002_regression_results.csv new file mode 100644 index 0000000..13907e5 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/886.065002_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,886.065002,0.02653029310980104,y = -0.001332 + 0.000214*x,134,10.960663313432837,3.9096921347220377,0.0010117611940298505,0.005133157202769544 +logarithmic,Chlorophyll,886.065002,0.02415132163213396,y = -0.004138 + 0.002210*ln(x),134,10.960663313432837,3.9096921347220377,0.0010117611940298505,0.005133157202769544 diff --git a/src/gui/work_dir/6_75_custom_regression/890.502014_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/890.502014_regression_results.csv new file mode 100644 index 0000000..18c896c --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/890.502014_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,890.502014,0.019079011868917917,y = -0.001258 + 0.000208*x,134,10.960663313432837,3.9096921347220377,0.0010267611940298507,0.0058991462652988715 +logarithmic,Chlorophyll,890.502014,0.016625122108838264,y = -0.003884 + 0.002108*ln(x),134,10.960663313432837,3.9096921347220377,0.0010267611940298507,0.0058991462652988715 diff --git a/src/gui/work_dir/6_75_custom_regression/894.940979_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/894.940979_regression_results.csv new file mode 100644 index 0000000..359e5d4 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/894.940979_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,894.940979,0.024644485205488564,y = -0.001350 + 0.000227*x,134,10.960663313432837,3.9096921347220377,0.0011329776119402986,0.005641248295840176 +logarithmic,Chlorophyll,894.940979,0.02251331717376026,y = -0.004332 + 0.002345*ln(x),134,10.960663313432837,3.9096921347220377,0.0011329776119402986,0.005641248295840176 diff --git a/src/gui/work_dir/6_75_custom_regression/899.38501_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/899.38501_regression_results.csv new file mode 100644 index 0000000..f58780b --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/899.38501_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,899.38501,0.03288381652407146,y = -0.001508 + 0.000260*x,134,10.960663313432837,3.9096921347220377,0.0013370597014925373,0.005596229357905776 +logarithmic,Chlorophyll,899.38501,0.03016565326116205,y = -0.004938 + 0.002693*ln(x),134,10.960663313432837,3.9096921347220377,0.0013370597014925373,0.005596229357905776 diff --git a/src/gui/work_dir/6_75_custom_regression/903.830994_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/903.830994_regression_results.csv new file mode 100644 index 0000000..1205d96 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/903.830994_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,903.830994,0.035029971829793505,y = -0.001832 + 0.000272*x,134,10.960663313432837,3.9096921347220377,0.0011536194029850746,0.005689943105970018 +logarithmic,Chlorophyll,903.830994,0.032438784908441876,y = -0.005463 + 0.002840*ln(x),134,10.960663313432837,3.9096921347220377,0.0011536194029850746,0.005689943105970018 diff --git a/src/gui/work_dir/6_75_custom_regression/908.281006_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/908.281006_regression_results.csv new file mode 100644 index 0000000..a58d4ec --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/908.281006_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,908.281006,0.04451101443584671,y = -0.002025 + 0.000326*x,134,10.960663313432837,3.9096921347220377,0.0015535074626865672,0.006050183384103955 +logarithmic,Chlorophyll,908.281006,0.04098727272198477,y = -0.006354 + 0.003394*ln(x),134,10.960663313432837,3.9096921347220377,0.0015535074626865672,0.006050183384103955 diff --git a/src/gui/work_dir/6_75_custom_regression/912.734985_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/912.734985_regression_results.csv new file mode 100644 index 0000000..a3c5371 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/912.734985_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,912.734985,0.04418194647792806,y = -0.002162 + 0.000344*x,134,10.960663313432837,3.9096921347220377,0.0016077238805970152,0.006397218804971693 +logarithmic,Chlorophyll,912.734985,0.03771808632859286,y = -0.006413 + 0.003443*ln(x),134,10.960663313432837,3.9096921347220377,0.0016077238805970152,0.006397218804971693 diff --git a/src/gui/work_dir/6_75_custom_regression/917.192017_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/917.192017_regression_results.csv new file mode 100644 index 0000000..e694512 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/917.192017_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,917.192017,0.04798422256578916,y = -0.001730 + 0.000296*x,134,10.960663313432837,3.9096921347220377,0.0015105074626865674,0.005276653162112126 +logarithmic,Chlorophyll,917.192017,0.04314471524553409,y = -0.005566 + 0.003037*ln(x),134,10.960663313432837,3.9096921347220377,0.0015105074626865674,0.005276653162112126 diff --git a/src/gui/work_dir/6_75_custom_regression/921.651978_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/921.651978_regression_results.csv new file mode 100644 index 0000000..0239719 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/921.651978_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,921.651978,0.032258841220259904,y = -0.001537 + 0.000282*x,134,10.960663313432837,3.9096921347220377,0.0015546641791044776,0.0061404282666335346 +logarithmic,Chlorophyll,921.651978,0.028570814973279623,y = -0.005146 + 0.002876*ln(x),134,10.960663313432837,3.9096921347220377,0.0015546641791044776,0.0061404282666335346 diff --git a/src/gui/work_dir/6_75_custom_regression/926.116028_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/926.116028_regression_results.csv new file mode 100644 index 0000000..7694fa0 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/926.116028_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,926.116028,0.03700908239110634,y = -0.001467 + 0.000302*x,134,10.960663313432837,3.9096921347220377,0.0018425149253731344,0.0061359449742797705 +logarithmic,Chlorophyll,926.116028,0.03201058133428225,y = -0.005245 + 0.003042*ln(x),134,10.960663313432837,3.9096921347220377,0.0018425149253731344,0.0061359449742797705 diff --git a/src/gui/work_dir/6_75_custom_regression/930.583008_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/930.583008_regression_results.csv new file mode 100644 index 0000000..d7c0dbc --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/930.583008_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,930.583008,0.13672053325122402,y = -0.002670 + 0.000668*x,134,10.960663313432837,3.9096921347220377,0.0046567164179104475,0.007068415565467862 +logarithmic,Chlorophyll,930.583008,0.12021219690890728,y = -0.011165 + 0.006791*ln(x),134,10.960663313432837,3.9096921347220377,0.0046567164179104475,0.007068415565467862 diff --git a/src/gui/work_dir/6_75_custom_regression/935.052979_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/935.052979_regression_results.csv new file mode 100644 index 0000000..186be39 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/935.052979_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,935.052979,0.10169174465005926,y = -0.003412 + 0.000844*x,134,10.960663313432837,3.9096921347220377,0.005836611940298507,0.010344714149481574 +logarithmic,Chlorophyll,935.052979,0.0879115642867252,y = -0.013965 + 0.008499*ln(x),134,10.960663313432837,3.9096921347220377,0.005836611940298507,0.010344714149481574 diff --git a/src/gui/work_dir/6_75_custom_regression/939.526978_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/939.526978_regression_results.csv new file mode 100644 index 0000000..ef1ced2 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/939.526978_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,939.526978,0.05650685234330488,y = -0.000266 + 0.000446*x,134,10.960663313432837,3.9096921347220377,0.004626597014925373,0.007341116269097569 +logarithmic,Chlorophyll,939.526978,0.042500038847443355,y = -0.005144 + 0.004194*ln(x),134,10.960663313432837,3.9096921347220377,0.004626597014925373,0.007341116269097569 diff --git a/src/gui/work_dir/6_75_custom_regression/944.004028_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/944.004028_regression_results.csv new file mode 100644 index 0000000..95d458e --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/944.004028_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,944.004028,0.1409144061847427,y = -0.005615 + 0.001030*x,134,10.960663313432837,3.9096921347220377,0.005671529850746269,0.010724721720420173 +logarithmic,Chlorophyll,944.004028,0.11738954814724845,y = -0.018051 + 0.010182*ln(x),134,10.960663313432837,3.9096921347220377,0.005671529850746269,0.010724721720420173 diff --git a/src/gui/work_dir/6_75_custom_regression/948.484985_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/948.484985_regression_results.csv new file mode 100644 index 0000000..8b0517f --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/948.484985_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,948.484985,0.1278834494635377,y = -0.005245 + 0.000898*x,134,10.960663313432837,3.9096921347220377,0.0045965970149253734,0.009816821927842846 +logarithmic,Chlorophyll,948.484985,0.1051196488753805,y = -0.015952 + 0.008819*ln(x),134,10.960663313432837,3.9096921347220377,0.0045965970149253734,0.009816821927842846 diff --git a/src/gui/work_dir/6_75_custom_regression/952.968994_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/952.968994_regression_results.csv new file mode 100644 index 0000000..d20c740 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/952.968994_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,952.968994,0.13249227032536504,y = -0.004853 + 0.000973*x,134,10.960663313432837,3.9096921347220377,0.005810186567164179,0.010449515267032196 +logarithmic,Chlorophyll,952.968994,0.11335983785512993,y = -0.016904 + 0.009749*ln(x),134,10.960663313432837,3.9096921347220377,0.005810186567164179,0.010449515267032196 diff --git a/src/gui/work_dir/6_75_custom_regression/957.455994_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/957.455994_regression_results.csv new file mode 100644 index 0000000..3f2f5eb --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/957.455994_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,957.455994,0.1277239788554937,y = -0.004033 + 0.000880*x,134,10.960663313432837,3.9096921347220377,0.005609776119402986,0.009624500382955951 +logarithmic,Chlorophyll,957.455994,0.10417499490400162,y = -0.014445 + 0.008608*ln(x),134,10.960663313432837,3.9096921347220377,0.005609776119402986,0.009624500382955951 diff --git a/src/gui/work_dir/6_75_custom_regression/961.947021_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/961.947021_regression_results.csv new file mode 100644 index 0000000..261bfe9 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/961.947021_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,961.947021,0.0855760468457214,y = -0.002892 + 0.000704*x,134,10.960663313432837,3.9096921347220377,0.004827671641791044,0.009412852176738786 +logarithmic,Chlorophyll,961.947021,0.07127076265553767,y = -0.011396 + 0.006963*ln(x),134,10.960663313432837,3.9096921347220377,0.004827671641791044,0.009412852176738786 diff --git a/src/gui/work_dir/6_75_custom_regression/966.440979_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/966.440979_regression_results.csv new file mode 100644 index 0000000..bfe8913 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/966.440979_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,966.440979,0.06587183855631606,y = -0.002339 + 0.000555*x,134,10.960663313432837,3.9096921347220377,0.0037471417910447764,0.008458583491864964 +logarithmic,Chlorophyll,966.440979,0.05598365214189571,y = -0.009174 + 0.005546*ln(x),134,10.960663313432837,3.9096921347220377,0.0037471417910447764,0.008458583491864964 diff --git a/src/gui/work_dir/6_75_custom_regression/970.937988_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/970.937988_regression_results.csv new file mode 100644 index 0000000..d0a3e1b --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/970.937988_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,970.937988,0.06413196906396867,y = -0.002253 + 0.000547*x,134,10.960663313432837,3.9096921347220377,0.003736731343283582,0.008437412920646661 +logarithmic,Chlorophyll,970.937988,0.05344664644444186,y = -0.008857 + 0.005405*ln(x),134,10.960663313432837,3.9096921347220377,0.003736731343283582,0.008437412920646661 diff --git a/src/gui/work_dir/6_75_custom_regression/975.439026_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/975.439026_regression_results.csv new file mode 100644 index 0000000..a330c24 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/975.439026_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,975.439026,0.050655072210295304,y = -0.001347 + 0.000485*x,134,10.960663313432837,3.9096921347220377,0.0039709179104477615,0.008428953291941944 +logarithmic,Chlorophyll,975.439026,0.04004538724688922,y = -0.006919 + 0.004674*ln(x),134,10.960663313432837,3.9096921347220377,0.0039709179104477615,0.008428953291941944 diff --git a/src/gui/work_dir/6_75_custom_regression/979.94397_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/979.94397_regression_results.csv new file mode 100644 index 0000000..1529db7 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/979.94397_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,979.94397,0.053125261271666835,y = -0.001269 + 0.000527*x,134,10.960663313432837,3.9096921347220377,0.004507320895522388,0.008938622789175931 +logarithmic,Chlorophyll,979.94397,0.04229638596970631,y = -0.007361 + 0.005094*ln(x),134,10.960663313432837,3.9096921347220377,0.004507320895522388,0.008938622789175931 diff --git a/src/gui/work_dir/6_75_custom_regression/984.450989_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/984.450989_regression_results.csv new file mode 100644 index 0000000..5cad33e --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/984.450989_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,984.450989,0.08789645152127279,y = -0.003521 + 0.000676*x,134,10.960663313432837,3.9096921347220377,0.0038840746268656717,0.008908837321854442 +logarithmic,Chlorophyll,984.450989,0.07481832038421343,y = -0.011848 + 0.006752*ln(x),134,10.960663313432837,3.9096921347220377,0.0038840746268656717,0.008908837321854442 diff --git a/src/gui/work_dir/6_75_custom_regression/988.963013_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/988.963013_regression_results.csv new file mode 100644 index 0000000..3506534 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/988.963013_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,988.963013,0.08457349098645517,y = -0.003461 + 0.000761*x,134,10.960663313432837,3.9096921347220377,0.004877097014925373,0.010226867156361797 +logarithmic,Chlorophyll,988.963013,0.0626221596339478,y = -0.011645 + 0.007091*ln(x),134,10.960663313432837,3.9096921347220377,0.004877097014925373,0.010226867156361797 diff --git a/src/gui/work_dir/6_75_custom_regression/993.47699_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/993.47699_regression_results.csv new file mode 100644 index 0000000..30d164c --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/993.47699_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,993.47699,0.1134844755445722,y = -0.004307 + 0.000958*x,134,10.960663313432837,3.9096921347220377,0.00618789552238806,0.0111130950665975 +logarithmic,Chlorophyll,993.47699,0.10027045293847281,y = -0.016531 + 0.009751*ln(x),134,10.960663313432837,3.9096921347220377,0.00618789552238806,0.0111130950665975 diff --git a/src/gui/work_dir/6_75_custom_regression/997.994995_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/997.994995_regression_results.csv new file mode 100644 index 0000000..bdc427a --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/997.994995_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,997.994995,0.10101902617501812,y = -0.004767 + 0.000926*x,134,10.960663313432837,3.9096921347220377,0.005378410447761194,0.01138568086193099 +logarithmic,Chlorophyll,997.994995,0.08312184949951051,y = -0.015814 + 0.009096*ln(x),134,10.960663313432837,3.9096921347220377,0.005378410447761194,0.01138568086193099 diff --git a/src/gui/work_dir/6_75_custom_regression/Al10SABI_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/Al10SABI_regression_results.csv new file mode 100644 index 0000000..2e485e6 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/Al10SABI_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,Al10SABI,0.12358552390181055,y = -0.439887 + 0.004771*x,134,10.960663313432837,3.9096921347220377,-0.38759527726255294,0.053058693427890484 +logarithmic,Chlorophyll,Al10SABI,0.11879573339994454,y = -0.505661 + 0.050673*ln(x),134,10.960663313432837,3.9096921347220377,-0.38759527726255294,0.053058693427890484 diff --git a/src/gui/work_dir/6_75_custom_regression/Am092Bsub_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/Am092Bsub_regression_results.csv new file mode 100644 index 0000000..024294b --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/Am092Bsub_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,Am092Bsub,0.1885121964498917,y = -0.000416 + 0.000036*x,134,10.960663313432837,3.9096921347220377,-2.2059701492527586e-05,0.000323751511520543 +logarithmic,Chlorophyll,Am092Bsub,0.18262380528639088,y = -0.000915 + 0.000383*ln(x),134,10.960663313432837,3.9096921347220377,-2.2059701492527586e-05,0.000323751511520543 diff --git a/src/gui/work_dir/6_75_custom_regression/Am09KBBI_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/Am09KBBI_regression_results.csv new file mode 100644 index 0000000..bdc6c9f --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/Am09KBBI_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,Am09KBBI,0.4762643285625632,y = -0.043335 + 0.003808*x,133,10.9134502556391,3.886119767718547,-0.0017793295272656735,0.021441910619729863 +logarithmic,Chlorophyll,Am09KBBI,0.43987401331105946,y = -0.093808 + 0.039564*ln(x),133,10.9134502556391,3.886119767718547,-0.0017793295272656735,0.021441910619729863 diff --git a/src/gui/work_dir/6_75_custom_regression/Be162B643sub629_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/Be162B643sub629_regression_results.csv new file mode 100644 index 0000000..252bb67 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/Be162B643sub629_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,Be162B643sub629,0.11325211782580646,y = -0.000557 + 0.000033*x,134,10.960663313432837,3.9096921347220377,-0.0001981940298507127,0.0003804186515039719 +logarithmic,Chlorophyll,Be162B643sub629,0.10118775386855117,y = -0.000979 + 0.000335*ln(x),134,10.960663313432837,3.9096921347220377,-0.0001981940298507127,0.0003804186515039719 diff --git a/src/gui/work_dir/6_75_custom_regression/Be162B700sub601_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/Be162B700sub601_regression_results.csv new file mode 100644 index 0000000..08b0f1b --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/Be162B700sub601_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,Be162B700sub601,0.0169444048529096,y = -0.006350 + 0.000178*x,134,10.960663313432837,3.9096921347220377,-0.004400328358208927,0.0053424248572036485 +logarithmic,Chlorophyll,Be162B700sub601,0.014761393895308395,y = -0.008591 + 0.001799*ln(x),134,10.960663313432837,3.9096921347220377,-0.004400328358208927,0.0053424248572036485 diff --git a/src/gui/work_dir/6_75_custom_regression/Be162BsubPhy_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/Be162BsubPhy_regression_results.csv new file mode 100644 index 0000000..7972292 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/Be162BsubPhy_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,Be162BsubPhy,0.03688932214445073,y = -0.009806 + 0.000273*x,134,10.960663313432837,3.9096921347220377,-0.006814365671641769,0.005555633366019352 +logarithmic,Chlorophyll,Be162BsubPhy,0.031115779994116965,y = -0.013141 + 0.002715*ln(x),134,10.960663313432837,3.9096921347220377,-0.006814365671641769,0.005555633366019352 diff --git a/src/gui/work_dir/6_75_custom_regression/Be16FLHBlueRedNIR_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/Be16FLHBlueRedNIR_regression_results.csv new file mode 100644 index 0000000..79fdcec --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/Be16FLHBlueRedNIR_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,Be16FLHBlueRedNIR,0.01538700206829402,y = -0.003983 + 0.000086*x,134,10.960663313432837,3.9096921347220377,-0.0030460074626865365,0.0026953295052247923 +logarithmic,Chlorophyll,Be16FLHBlueRedNIR,0.01138782391370985,y = -0.004903 + 0.000797*ln(x),134,10.960663313432837,3.9096921347220377,-0.0030460074626865365,0.0026953295052247923 diff --git a/src/gui/work_dir/6_75_custom_regression/Be16FLHGreenRedNIR_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/Be16FLHGreenRedNIR_regression_results.csv new file mode 100644 index 0000000..276ca12 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/Be16FLHGreenRedNIR_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,Be16FLHGreenRedNIR,0.010218580183159132,y = -0.011939 + 0.000183*x,134,10.960663313432837,3.9096921347220377,-0.009929455223880576,0.007090511093103789 +logarithmic,Chlorophyll,Be16FLHGreenRedNIR,0.008441213911274725,y = -0.014135 + 0.001805*ln(x),134,10.960663313432837,3.9096921347220377,-0.009929455223880576,0.007090511093103789 diff --git a/src/gui/work_dir/6_75_custom_regression/Be16FLHVioletRedNIR_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/Be16FLHVioletRedNIR_regression_results.csv new file mode 100644 index 0000000..f03aaaa --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/Be16FLHVioletRedNIR_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,Be16FLHVioletRedNIR,0.013682668849342106,y = -0.004234 + 0.000082*x,134,10.960663313432837,3.9096921347220377,-0.0033393432835820583,0.002727533132087303 +logarithmic,Chlorophyll,Be16FLHVioletRedNIR,0.009926378059882501,y = -0.005094 + 0.000753*ln(x),134,10.960663313432837,3.9096921347220377,-0.0033393432835820583,0.002727533132087303 diff --git a/src/gui/work_dir/6_75_custom_regression/Be16FLHblue_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/Be16FLHblue_regression_results.csv new file mode 100644 index 0000000..61a78b1 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/Be16FLHblue_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,Be16FLHblue,0.017424523813847625,y = 0.003868 + 0.000047*x,134,10.960663313432837,3.9096921347220377,0.0043792089552238435,0.001381737982346916 +logarithmic,Chlorophyll,Be16FLHblue,0.015080880084500592,y = 0.003284 + 0.000470*ln(x),134,10.960663313432837,3.9096921347220377,0.0043792089552238435,0.001381737982346916 +exponential,Chlorophyll,Be16FLHblue,-0.002117132819616341,y = 0.004168 * exp(0.001937*x),134,10.960663313432837,3.9096921347220377,0.0043792089552238435,0.001381737982346916 +power,Chlorophyll,Be16FLHblue,-0.0030671526470118504,y = 0.004076 * x^0.018690,134,10.960663313432837,3.9096921347220377,0.0043792089552238435,0.001381737982346916 diff --git a/src/gui/work_dir/6_75_custom_regression/Be16FLHviolet_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/Be16FLHviolet_regression_results.csv new file mode 100644 index 0000000..ef58b71 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/Be16FLHviolet_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,Be16FLHviolet,0.0023835729307108977,y = 0.003164 + 0.000019*x,134,10.960663313432837,3.9096921347220377,0.0033735522388059305,0.0015317987259504302 +logarithmic,Chlorophyll,Be16FLHviolet,0.0018280319730009653,y = 0.002951 + 0.000181*ln(x),134,10.960663313432837,3.9096921347220377,0.0033735522388059305,0.0015317987259504302 +power,Chlorophyll,Be16FLHviolet,-0.03359801493155068,y = 0.004239 * x^-0.124759,134,10.960663313432837,3.9096921347220377,0.0033735522388059305,0.0015317987259504302 +exponential,Chlorophyll,Be16FLHviolet,-0.03542639293253047,y = 0.003613 * exp(-0.011923*x),134,10.960663313432837,3.9096921347220377,0.0033735522388059305,0.0015317987259504302 diff --git a/src/gui/work_dir/6_75_custom_regression/Be16MPI_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/Be16MPI_regression_results.csv new file mode 100644 index 0000000..47b43e0 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/Be16MPI_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,Be16MPI,0.062270111791835725,y = 0.001595 + -0.000043*x,134,10.960663313432837,3.9096921347220377,0.0011235895522387698,0.0006740220689584854 +logarithmic,Chlorophyll,Be16MPI,0.053593132296276824,y = 0.002131 + -0.000432*ln(x),134,10.960663313432837,3.9096921347220377,0.0011235895522387698,0.0006740220689584854 diff --git a/src/gui/work_dir/6_75_custom_regression/Be16NDPhyI644over615_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/Be16NDPhyI644over615_regression_results.csv new file mode 100644 index 0000000..399109f --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/Be16NDPhyI644over615_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,Be16NDPhyI644over615,0.021975250694089343,y = -0.065407 + 0.001863*x,134,10.960663313432837,3.9096921347220377,-0.044985950160293706,0.049137812385328355 +logarithmic,Chlorophyll,Be16NDPhyI644over615,0.018283728847716674,y = -0.087882 + 0.018411*ln(x),134,10.960663313432837,3.9096921347220377,-0.044985950160293706,0.049137812385328355 diff --git a/src/gui/work_dir/6_75_custom_regression/Be16NDPhyI644over629_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/Be16NDPhyI644over629_regression_results.csv new file mode 100644 index 0000000..b0ccdaa --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/Be16NDPhyI644over629_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,Be16NDPhyI644over629,0.03970066029484709,y = -0.043506 + 0.003785*x,134,10.960663313432837,3.9096921347220377,-0.002020256813774733,0.07426922456651143 +logarithmic,Chlorophyll,Be16NDPhyI644over629,0.0335412781786012,y = -0.089835 + 0.037690*ln(x),134,10.960663313432837,3.9096921347220377,-0.002020256813774733,0.07426922456651143 diff --git a/src/gui/work_dir/6_75_custom_regression/Be16NDPhyI_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/Be16NDPhyI_regression_results.csv new file mode 100644 index 0000000..e04951b --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/Be16NDPhyI_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,Be16NDPhyI,0.16604525086113353,y = -0.242290 + 0.014903*x,134,10.960663313432837,3.9096921347220377,-0.07894663731685109,0.14298591380157585 +logarithmic,Chlorophyll,Be16NDPhyI,0.14825528926546594,y = -0.434387 + 0.152553*ln(x),134,10.960663313432837,3.9096921347220377,-0.07894663731685109,0.14298591380157585 diff --git a/src/gui/work_dir/6_75_custom_regression/Be16NDTIblue_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/Be16NDTIblue_regression_results.csv new file mode 100644 index 0000000..eca1bc2 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/Be16NDTIblue_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +logarithmic,Chlorophyll,Be16NDTIblue,0.0033079929100812144,y = -0.134462 + -0.011840*ln(x),133,10.9134502556391,3.886119767718547,-0.16200172104305108,0.07399047397462093 +linear,Chlorophyll,Be16NDTIblue,0.00317524512241274,y = -0.150293 + -0.001073*x,133,10.9134502556391,3.886119767718547,-0.16200172104305108,0.07399047397462093 diff --git a/src/gui/work_dir/6_75_custom_regression/Be16NDTIviolet_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/Be16NDTIviolet_regression_results.csv new file mode 100644 index 0000000..a397ac9 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/Be16NDTIviolet_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,Be16NDTIviolet,0.0005010487351857495,y = -0.175143 + 0.000717*x,133,10.9134502556391,3.886119767718547,-0.16732129594785264,0.124429968840917 +logarithmic,Chlorophyll,Be16NDTIviolet,0.0004086312956946836,y = -0.183599 + 0.006998*ln(x),133,10.9134502556391,3.886119767718547,-0.16732129594785264,0.124429968840917 diff --git a/src/gui/work_dir/6_75_custom_regression/Be16Phy2BDA644over629_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/Be16Phy2BDA644over629_regression_results.csv new file mode 100644 index 0000000..e22bb42 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/Be16Phy2BDA644over629_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,Be16Phy2BDA644over629,0.023206004958571502,y = 0.713769 + 0.030997*x,134,10.960663313432837,3.9096921347220377,1.053521162810137,0.7955509661543532 +logarithmic,Chlorophyll,Be16Phy2BDA644over629,0.0187754308216187,y = 0.349750 + 0.302055*ln(x),134,10.960663313432837,3.9096921347220377,1.053521162810137,0.7955509661543532 +exponential,Chlorophyll,Be16Phy2BDA644over629,0.008223792315415035,y = 0.900438 * exp(0.009666*x),134,10.960663313432837,3.9096921347220377,1.053521162810137,0.7955509661543532 +power,Chlorophyll,Be16Phy2BDA644over629,0.00591210292212041,y = 0.801083 * x^0.095650,134,10.960663313432837,3.9096921347220377,1.053521162810137,0.7955509661543532 diff --git a/src/gui/work_dir/6_75_custom_regression/De933BDA_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/De933BDA_regression_results.csv new file mode 100644 index 0000000..baab5ea --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/De933BDA_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,De933BDA,0.00023919683733564234,y = -0.009738 + 0.000030*x,134,10.960663313432837,3.9096921347220377,-0.009405067164179083,0.007689652483460126 +logarithmic,Chlorophyll,De933BDA,0.00011330432702505444,y = -0.009934 + 0.000227*ln(x),134,10.960663313432837,3.9096921347220377,-0.009405067164179083,0.007689652483460126 diff --git a/src/gui/work_dir/6_75_custom_regression/Go04MCI_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/Go04MCI_regression_results.csv new file mode 100644 index 0000000..f5be9a4 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/Go04MCI_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,Go04MCI,0.0037028838151977883,y = 0.008870 + -0.000177*x,134,10.960663313432837,3.9096921347220377,0.006929253731343275,0.011379134657238244 +logarithmic,Chlorophyll,Go04MCI,0.0031526249468724066,y = 0.011054 + -0.001770*ln(x),134,10.960663313432837,3.9096921347220377,0.006929253731343275,0.011379134657238244 diff --git a/src/gui/work_dir/6_75_custom_regression/HU103BDA_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/HU103BDA_regression_results.csv new file mode 100644 index 0000000..2e78609 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/HU103BDA_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,HU103BDA,0.022814312069399434,y = -52.916771 + 7.153375*x,134,10.960663313432837,3.9096921347220377,25.488964765505937,185.16115053399594 +logarithmic,Chlorophyll,HU103BDA,0.018338828535085838,y = -136.395192 + 69.479719*ln(x),134,10.960663313432837,3.9096921347220377,25.488964765505937,185.16115053399594 diff --git a/src/gui/work_dir/6_75_custom_regression/Kn07KIVU_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/Kn07KIVU_regression_results.csv new file mode 100644 index 0000000..c3a3d36 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/Kn07KIVU_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +logarithmic,Chlorophyll,Kn07KIVU,0.00032084600727178003,y = -0.000024 + 0.003864*ln(x),134,10.960663313432837,3.9096921347220377,0.008978853062571953,0.07784863735321206 +linear,Chlorophyll,Kn07KIVU,9.160946797537317e-05,y = 0.011068 + -0.000191*x,134,10.960663313432837,3.9096921347220377,0.008978853062571953,0.07784863735321206 diff --git a/src/gui/work_dir/6_75_custom_regression/Ku15PhyCI_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/Ku15PhyCI_regression_results.csv new file mode 100644 index 0000000..268803a --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/Ku15PhyCI_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,Ku15PhyCI,0.007889056025643382,y = -0.000509 + 0.000148*x,134,10.960663313432837,3.9096921347220377,0.0011146865671641457,0.006521171197136047 +logarithmic,Chlorophyll,Ku15PhyCI,0.0062998084059643356,y = -0.002227 + 0.001434*ln(x),134,10.960663313432837,3.9096921347220377,0.0011146865671641457,0.006521171197136047 diff --git a/src/gui/work_dir/6_75_custom_regression/Ku15SLH_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/Ku15SLH_regression_results.csv new file mode 100644 index 0000000..89b900d --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/Ku15SLH_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,Ku15SLH,0.015206455027719001,y = -0.009458 + 0.000383*x,134,10.960663313432837,3.9096921347220377,-0.005264970149253696,0.012128158740383666 +logarithmic,Chlorophyll,Ku15SLH,0.01247925513910264,y = -0.014012 + 0.003754*ln(x),134,10.960663313432837,3.9096921347220377,-0.005264970149253696,0.012128158740383666 diff --git a/src/gui/work_dir/6_75_custom_regression/MI092BDA_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/MI092BDA_regression_results.csv new file mode 100644 index 0000000..b8fbdc2 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/MI092BDA_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,MI092BDA,0.08763672931756827,y = 0.415458 + 0.031001*x,134,10.960663313432837,3.9096921347220377,0.7552526677152894,0.40942970094366693 +logarithmic,Chlorophyll,MI092BDA,0.07689535140547887,y = 0.022263 + 0.314595*ln(x),134,10.960663313432837,3.9096921347220377,0.7552526677152894,0.40942970094366693 +exponential,Chlorophyll,MI092BDA,0.06807967150006189,y = 0.543612 * exp(0.025329*x),134,10.960663313432837,3.9096921347220377,0.7552526677152894,0.40942970094366693 +power,Chlorophyll,MI092BDA,0.05909773540292862,y = 0.391266 * x^0.260295,134,10.960663313432837,3.9096921347220377,0.7552526677152894,0.40942970094366693 diff --git a/src/gui/work_dir/6_75_custom_regression/MM092BDA_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/MM092BDA_regression_results.csv new file mode 100644 index 0000000..6d45b52 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/MM092BDA_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,MM092BDA,0.05916056039742046,y = 0.020110 + 0.025980*x,134,10.960663313432837,3.9096921347220377,0.30486885589695534,0.4176058240320284 +logarithmic,Chlorophyll,MM092BDA,0.05062582327866094,y = -0.301758 + 0.260360*ln(x),134,10.960663313432837,3.9096921347220377,0.30486885589695534,0.4176058240320284 +exponential,Chlorophyll,MM092BDA,0.031081382027952853,y = 0.169237 * exp(0.039710*x),134,10.960663313432837,3.9096921347220377,0.30486885589695534,0.4176058240320284 +power,Chlorophyll,MM092BDA,0.025455662241081423,y = 0.100699 * x^0.409629,134,10.960663313432837,3.9096921347220377,0.30486885589695534,0.4176058240320284 diff --git a/src/gui/work_dir/6_75_custom_regression/MM12NDCI_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/MM12NDCI_regression_results.csv new file mode 100644 index 0000000..92c207c --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/MM12NDCI_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,MM12NDCI,0.14968049493387692,y = -0.334438 + 0.016524*x,134,10.960663313432837,3.9096921347220377,-0.15332684559895957,0.1669812364274784 +logarithmic,Chlorophyll,MM12NDCI,0.1284752471027638,y = -0.539735 + 0.165844*ln(x),134,10.960663313432837,3.9096921347220377,-0.15332684559895957,0.1669812364274784 diff --git a/src/gui/work_dir/6_75_custom_regression/MM12NDCIalt_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/MM12NDCIalt_regression_results.csv new file mode 100644 index 0000000..75c9c64 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/MM12NDCIalt_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,MM12NDCIalt,0.1782969390839364,y = -0.067623 + 0.015108*x,134,10.960663313432837,3.9096921347220377,0.09797303208020114,0.1398887140001231 +logarithmic,Chlorophyll,MM12NDCIalt,0.15469579925762256,y = -0.257241 + 0.152456*ln(x),134,10.960663313432837,3.9096921347220377,0.09797303208020114,0.1398887140001231 diff --git a/src/gui/work_dir/6_75_custom_regression/SI052BDA_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/SI052BDA_regression_results.csv new file mode 100644 index 0000000..2375616 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/SI052BDA_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,SI052BDA,0.02394542541720257,y = -2.641394 + 0.392126*x,134,10.960663313432837,3.9096921347220377,1.6565652837978109,9.907333404366277 +logarithmic,Chlorophyll,SI052BDA,0.019350113206532904,y = -7.240919 + 3.818747*ln(x),134,10.960663313432837,3.9096921347220377,1.6565652837978109,9.907333404366277 +exponential,Chlorophyll,SI052BDA,-0.0034738533070597377,y = 0.468901 * exp(0.044799*x),134,10.960663313432837,3.9096921347220377,1.6565652837978109,9.907333404366277 +power,Chlorophyll,SI052BDA,-0.004314196524685121,y = 0.267297 * x^0.451967,134,10.960663313432837,3.9096921347220377,1.6565652837978109,9.907333404366277 diff --git a/src/gui/work_dir/6_75_custom_regression/SM122BDA_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/SM122BDA_regression_results.csv new file mode 100644 index 0000000..d2bacf0 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/SM122BDA_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,SM122BDA,0.06414848722020783,y = 0.199657 + 0.045876*x,134,10.960663313432837,3.9096921347220377,0.7024913829628076,0.7081702489195397 +logarithmic,Chlorophyll,SM122BDA,0.05481383167054166,y = -0.367922 + 0.459415*ln(x),134,10.960663313432837,3.9096921347220377,0.7024913829628076,0.7081702489195397 +exponential,Chlorophyll,SM122BDA,0.0384911707850909,y = 0.442182 * exp(0.032597*x),134,10.960663313432837,3.9096921347220377,0.7024913829628076,0.7081702489195397 +power,Chlorophyll,SM122BDA,0.0316342801109748,y = 0.291960 * x^0.331506,134,10.960663313432837,3.9096921347220377,0.7024913829628076,0.7081702489195397 diff --git a/src/gui/work_dir/6_75_custom_regression/TurbBow06RedOverGreen_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/TurbBow06RedOverGreen_regression_results.csv new file mode 100644 index 0000000..f5240c8 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/TurbBow06RedOverGreen_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +logarithmic,Chlorophyll,TurbBow06RedOverGreen,0.0008581470760438137,y = 0.400651 + 0.028309*ln(x),134,10.960663313432837,3.9096921347220377,0.46660909641031345,0.348755127089641 +linear,Chlorophyll,TurbBow06RedOverGreen,0.0008315690342066695,y = 0.438415 + 0.002572*x,134,10.960663313432837,3.9096921347220377,0.46660909641031345,0.348755127089641 diff --git a/src/gui/work_dir/6_75_custom_regression/TurbChip09NIROverGreen_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/TurbChip09NIROverGreen_regression_results.csv new file mode 100644 index 0000000..7e9dc75 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/TurbChip09NIROverGreen_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,TurbChip09NIROverGreen,0.01848858969012912,y = -0.040594 + 0.008604*x,134,10.960663313432837,3.9096921347220377,0.053712679806626945,0.2473987712418453 +logarithmic,Chlorophyll,TurbChip09NIROverGreen,0.016570559702190013,y = -0.151893 + 0.088245*ln(x),134,10.960663313432837,3.9096921347220377,0.053712679806626945,0.2473987712418453 diff --git a/src/gui/work_dir/6_75_custom_regression/TurbHarr92NIR_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/TurbHarr92NIR_regression_results.csv new file mode 100644 index 0000000..da00753 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/TurbHarr92NIR_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,TurbHarr92NIR,0.015865352453769743,y = -0.000772 + 0.000165*x,134,10.960663313432837,3.9096921347220377,0.001040134328358209,0.005131083278297242 +logarithmic,Chlorophyll,TurbHarr92NIR,0.01432846575764024,y = -0.002925 + 0.001702*ln(x),134,10.960663313432837,3.9096921347220377,0.001040134328358209,0.005131083278297242 diff --git a/src/gui/work_dir/6_75_custom_regression/TurbLath91RedOverBlue_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/TurbLath91RedOverBlue_regression_results.csv new file mode 100644 index 0000000..c358eac --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/TurbLath91RedOverBlue_regression_results.csv @@ -0,0 +1,5 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,TurbLath91RedOverBlue,0.00025779216509291825,y = 0.723653 + 0.000603*x,133,10.9134502556391,3.886119767718547,0.7302312316955419,0.14589486349017858 +logarithmic,Chlorophyll,TurbLath91RedOverBlue,0.00015814654236934178,y = 0.718358 + 0.005104*ln(x),133,10.9134502556391,3.886119767718547,0.7302312316955419,0.14589486349017858 +power,Chlorophyll,TurbLath91RedOverBlue,-0.007759099371790423,y = 0.765225 * x^-0.025934,133,10.9134502556391,3.886119767718547,0.7302312316955419,0.14589486349017858 +exponential,Chlorophyll,TurbLath91RedOverBlue,-0.007972973578523668,y = 0.739256 * exp(-0.002364*x),133,10.9134502556391,3.886119767718547,0.7302312316955419,0.14589486349017858 diff --git a/src/gui/work_dir/6_75_custom_regression/TurbMoore80Red_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/TurbMoore80Red_regression_results.csv new file mode 100644 index 0000000..c52eb8d --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/TurbMoore80Red_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,TurbMoore80Red,2.3065421406842646e-05,y = 0.009045 + -0.000009*x,134,10.960663313432837,3.9096921347220377,0.008946089552238806,0.007372050533373144 +logarithmic,Chlorophyll,TurbMoore80Red,8.478475440609756e-07,y = 0.008902 + 0.000019*ln(x),134,10.960663313432837,3.9096921347220377,0.008946089552238806,0.007372050533373144 diff --git a/src/gui/work_dir/6_75_custom_regression/Wy08CI_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/Wy08CI_regression_results.csv new file mode 100644 index 0000000..5897545 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/Wy08CI_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,Wy08CI,0.004922647127989954,y = -0.003917 + 0.000117*x,134,10.960663313432837,3.9096921347220377,-0.0026294477611939994,0.006547381229445023 +logarithmic,Chlorophyll,Wy08CI,0.0037258564416292606,y = -0.005210 + 0.001107*ln(x),134,10.960663313432837,3.9096921347220377,-0.0026294477611939994,0.006547381229445023 diff --git a/src/gui/work_dir/6_75_custom_regression/Zh10FLH_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/Zh10FLH_regression_results.csv new file mode 100644 index 0000000..4a115c8 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/Zh10FLH_regression_results.csv @@ -0,0 +1,3 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,Zh10FLH,0.0034208029090143643,y = -0.003691 + 0.000107*x,134,10.960663313432837,3.9096921347220377,-0.0025192910447760807,0.007148572087690299 +logarithmic,Chlorophyll,Zh10FLH,0.0029682401109050183,y = -0.005034 + 0.001079*ln(x),134,10.960663313432837,3.9096921347220377,-0.0025192910447760807,0.007148572087690299 diff --git a/src/gui/work_dir/6_75_custom_regression/all_regression_results.csv b/src/gui/work_dir/6_75_custom_regression/all_regression_results.csv new file mode 100644 index 0000000..09b1855 --- /dev/null +++ b/src/gui/work_dir/6_75_custom_regression/all_regression_results.csv @@ -0,0 +1,533 @@ +regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std +linear,Chlorophyll,1002.515991,0.11209397634185636,y = -0.005956 + 0.001186*x,134,10.960663313432837,3.9096921347220377,0.007041335820895523,0.0138473135041692 +logarithmic,Chlorophyll,1002.515991,0.09022914646608904,y = -0.019813 + 0.011526*ln(x),134,10.960663313432837,3.9096921347220377,0.007041335820895523,0.0138473135041692 +linear,Chlorophyll,1007.041016,0.13129585788396014,y = -0.007873 + 0.001537*x,134,10.960663313432837,3.9096921347220377,0.008974216417910446,0.016584272713125302 +logarithmic,Chlorophyll,1007.041016,0.10398887849221805,y = -0.025553 + 0.014819*ln(x),134,10.960663313432837,3.9096921347220377,0.008974216417910446,0.016584272713125302 +linear,Chlorophyll,1011.56897,0.11897246869922418,y = -0.007866 + 0.001621*x,134,10.960663313432837,3.9096921347220377,0.00990283582089552,0.01837518499092342 +logarithmic,Chlorophyll,1011.56897,0.09605697495450882,y = -0.026865 + 0.015780*ln(x),134,10.960663313432837,3.9096921347220377,0.00990283582089552,0.01837518499092342 +linear,Chlorophyll,374.285004,0.0577461915301245,y = 0.009707 + 0.000311*x,134,10.960663313432837,3.9096921347220377,0.013112298507462688,0.005054260878733534 +logarithmic,Chlorophyll,374.285004,0.052490162787109385,y = 0.005636 + 0.003209*ln(x),134,10.960663313432837,3.9096921347220377,0.013112298507462688,0.005054260878733534 +exponential,Chlorophyll,374.285004,0.030557192829324564,y = 0.010822 * exp(0.013060*x),134,10.960663313432837,3.9096921347220377,0.013112298507462688,0.005054260878733534 +power,Chlorophyll,374.285004,0.02576326804736484,y = 0.009209 * x^0.130700,134,10.960663313432837,3.9096921347220377,0.013112298507462688,0.005054260878733534 +logarithmic,Chlorophyll,378.311005,0.008061439581006025,y = 0.013092 + 0.001044*ln(x),134,10.960663313432837,3.9096921347220377,0.01552370895522388,0.00419444858565235 +linear,Chlorophyll,378.311005,0.008052879252108514,y = 0.014468 + 0.000096*x,134,10.960663313432837,3.9096921347220377,0.01552370895522388,0.00419444858565235 +power,Chlorophyll,378.311005,-0.016155019039159058,y = 0.015641 * x^-0.016124,134,10.960663313432837,3.9096921347220377,0.01552370895522388,0.00419444858565235 +exponential,Chlorophyll,378.311005,-0.01708357282563666,y = 0.015362 * exp(-0.001784*x),134,10.960663313432837,3.9096921347220377,0.01552370895522388,0.00419444858565235 +linear,Chlorophyll,382.341003,0.010983531384569756,y = 0.013856 + 0.000202*x,134,10.960663313432837,3.9096921347220377,0.016074067164179102,0.007548431031201279 +logarithmic,Chlorophyll,382.341003,0.010636805221273526,y = 0.011048 + 0.002157*ln(x),134,10.960663313432837,3.9096921347220377,0.016074067164179102,0.007548431031201279 +power,Chlorophyll,382.341003,-0.007234268459601845,y = 0.015174 * x^0.006143,134,10.960663313432837,3.9096921347220377,0.016074067164179102,0.007548431031201279 +exponential,Chlorophyll,382.341003,-0.008026222697967267,y = 0.015380 * exp(0.000073*x),134,10.960663313432837,3.9096921347220377,0.016074067164179102,0.007548431031201279 +logarithmic,Chlorophyll,386.373993,0.004476522091747537,y = 0.013943 + 0.001356*ln(x),134,10.960663313432837,3.9096921347220377,0.017102343283582087,0.007312955327938564 +linear,Chlorophyll,386.373993,0.003937809502393641,y = 0.015816 + 0.000117*x,134,10.960663313432837,3.9096921347220377,0.017102343283582087,0.007312955327938564 +power,Chlorophyll,386.373993,-0.013451645087448227,y = 0.018099 * x^-0.040970,134,10.960663313432837,3.9096921347220377,0.017102343283582087,0.007312955327938564 +exponential,Chlorophyll,386.373993,-0.01526209268366463,y = 0.017374 * exp(-0.004977*x),134,10.960663313432837,3.9096921347220377,0.017102343283582087,0.007312955327938564 +logarithmic,Chlorophyll,390.410004,0.0033869580773776553,y = 0.014722 + 0.001210*ln(x),134,10.960663313432837,3.9096921347220377,0.017540880597014925,0.00750323608895778 +linear,Chlorophyll,390.410004,0.0026484411391527463,y = 0.016458 + 0.000099*x,134,10.960663313432837,3.9096921347220377,0.017540880597014925,0.00750323608895778 +power,Chlorophyll,390.410004,-0.01625121404032659,y = 0.019411 * x^-0.060440,134,10.960663313432837,3.9096921347220377,0.017540880597014925,0.00750323608895778 +exponential,Chlorophyll,390.410004,-0.018540174411018073,y = 0.018243 * exp(-0.007186*x),134,10.960663313432837,3.9096921347220377,0.017540880597014925,0.00750323608895778 +logarithmic,Chlorophyll,394.450012,0.0016938639111105935,y = 0.015234 + 0.000830*ln(x),134,10.960663313432837,3.9096921347220377,0.01716890298507463,0.007280847323239202 +linear,Chlorophyll,394.450012,0.0010649475553690113,y = 0.016503 + 0.000061*x,134,10.960663313432837,3.9096921347220377,0.01716890298507463,0.007280847323239202 +power,Chlorophyll,394.450012,-0.023745377413006752,y = 0.020435 * x^-0.094840,134,10.960663313432837,3.9096921347220377,0.01716890298507463,0.007280847323239202 +exponential,Chlorophyll,394.450012,-0.02617627918721488,y = 0.018415 * exp(-0.010666*x),134,10.960663313432837,3.9096921347220377,0.01716890298507463,0.007280847323239202 +logarithmic,Chlorophyll,398.493011,0.000857763974499659,y = 0.015092 + 0.000573*ln(x),134,10.960663313432837,3.9096921347220377,0.016425865671641792,0.00705544097312418 +linear,Chlorophyll,398.493011,0.0003890186261535922,y = 0.016036 + 0.000036*x,134,10.960663313432837,3.9096921347220377,0.016425865671641792,0.00705544097312418 +logarithmic,Chlorophyll,402.539001,0.00048016503667658306,y = 0.015505 + 0.000443*ln(x),134,10.960663313432837,3.9096921347220377,0.01653574626865672,0.007288381669035372 +linear,Chlorophyll,402.539001,0.0001313248786827259,y = 0.016302 + 0.000021*x,134,10.960663313432837,3.9096921347220377,0.01653574626865672,0.007288381669035372 +logarithmic,Chlorophyll,406.588989,0.00016428918964617178,y = 0.015242 + 0.000242*ln(x),134,10.960663313432837,3.9096921347220377,0.015806723880597017,0.006825478500958131 +linear,Chlorophyll,406.588989,1.6937017762730378e-06,y = 0.015782 + 0.000002*x,134,10.960663313432837,3.9096921347220377,0.015806723880597017,0.006825478500958131 +logarithmic,Chlorophyll,410.641998,0.00010695507791136372,y = 0.014822 + 0.000189*ln(x),134,10.960663313432837,3.9096921347220377,0.015261298507462688,0.0065848636688817614 +linear,Chlorophyll,410.641998,2.2039578226884515e-06,y = 0.015289 + -0.000003*x,134,10.960663313432837,3.9096921347220377,0.015261298507462688,0.0065848636688817614 +linear,Chlorophyll,414.699005,9.23718683270014e-05,y = 0.014978 + -0.000015*x,134,10.960663313432837,3.9096921347220377,0.014808014925373135,0.006298696608817295 +logarithmic,Chlorophyll,414.699005,1.0794055052776308e-05,y = 0.014674 + 0.000057*ln(x),134,10.960663313432837,3.9096921347220377,0.014808014925373135,0.006298696608817295 +linear,Chlorophyll,418.759003,0.0002645667637589666,y = 0.014364 + -0.000025*x,134,10.960663313432837,3.9096921347220377,0.014088052238805972,0.006051440804527788 +logarithmic,Chlorophyll,418.759003,7.794051727350038e-06,y = 0.014197 + -0.000047*ln(x),134,10.960663313432837,3.9096921347220377,0.014088052238805972,0.006051440804527788 +linear,Chlorophyll,422.821991,0.0008115657894584016,y = 0.014105 + -0.000042*x,134,10.960663313432837,3.9096921347220377,0.013641977611940298,0.005799322545956216 +logarithmic,Chlorophyll,422.821991,0.0001768579797475356,y = 0.014140 + -0.000214*ln(x),134,10.960663313432837,3.9096921347220377,0.013641977611940298,0.005799322545956216 +linear,Chlorophyll,426.889008,0.0013073210454338513,y = 0.014170 + -0.000053*x,134,10.960663313432837,3.9096921347220377,0.013589074626865672,0.005728319043930576 +logarithmic,Chlorophyll,426.889008,0.0004182937928386421,y = 0.014345 + -0.000325*ln(x),134,10.960663313432837,3.9096921347220377,0.013589074626865672,0.005728319043930576 +linear,Chlorophyll,430.959015,0.002347137019542922,y = 0.013733 + -0.000067*x,134,10.960663313432837,3.9096921347220377,0.012997753731343284,0.005410808768465578 +logarithmic,Chlorophyll,430.959015,0.0010658686661263461,y = 0.014138 + -0.000489*ln(x),134,10.960663313432837,3.9096921347220377,0.012997753731343284,0.005410808768465578 +linear,Chlorophyll,435.032013,0.0024283085040497365,y = 0.013179 + -0.000069*x,134,10.960663313432837,3.9096921347220377,0.012427850746268653,0.005435180040005626 +logarithmic,Chlorophyll,435.032013,0.0011266238526388417,y = 0.013606 + -0.000506*ln(x),134,10.960663313432837,3.9096921347220377,0.012427850746268653,0.005435180040005626 +linear,Chlorophyll,439.109009,0.0042064615418079265,y = 0.013397 + -0.000086*x,134,10.960663313432837,3.9096921347220377,0.012450895522388062,0.005205005715323194 +logarithmic,Chlorophyll,439.109009,0.002294686396103418,y = 0.014061 + -0.000691*ln(x),134,10.960663313432837,3.9096921347220377,0.012450895522388062,0.005205005715323194 +linear,Chlorophyll,443.190002,0.004695213661859765,y = 0.013279 + -0.000091*x,134,10.960663313432837,3.9096921347220377,0.012285432835820896,0.00517286197733264 +logarithmic,Chlorophyll,443.190002,0.0026235944054925353,y = 0.013996 + -0.000734*ln(x),134,10.960663313432837,3.9096921347220377,0.012285432835820896,0.00517286197733264 +linear,Chlorophyll,447.27301,0.005169120886448608,y = 0.013250 + -0.000094*x,134,10.960663313432837,3.9096921347220377,0.012221335820895522,0.005101097814158477 +logarithmic,Chlorophyll,447.27301,0.002968315833349222,y = 0.014016 + -0.000770*ln(x),134,10.960663313432837,3.9096921347220377,0.012221335820895522,0.005101097814158477 +linear,Chlorophyll,451.360992,0.004863772601295668,y = 0.013262 + -0.000092*x,134,10.960663313432837,3.9096921347220377,0.012257104477611941,0.00513769471108476 +logarithmic,Chlorophyll,451.360992,0.002739591016255649,y = 0.013993 + -0.000745*ln(x),134,10.960663313432837,3.9096921347220377,0.012257104477611941,0.00513769471108476 +linear,Chlorophyll,455.450989,0.005525011318207929,y = 0.013206 + -0.000097*x,134,10.960663313432837,3.9096921347220377,0.012144328358208955,0.005093595896892254 +logarithmic,Chlorophyll,455.450989,0.0032249891993542112,y = 0.014012 + -0.000802*ln(x),134,10.960663313432837,3.9096921347220377,0.012144328358208955,0.005093595896892254 +linear,Chlorophyll,459.545013,0.005303969126716268,y = 0.013029 + -0.000095*x,134,10.960663313432837,3.9096921347220377,0.011992097014925374,0.005076948054716495 +logarithmic,Chlorophyll,459.545013,0.0030599507272712767,y = 0.013805 + -0.000778*ln(x),134,10.960663313432837,3.9096921347220377,0.011992097014925374,0.005076948054716495 +linear,Chlorophyll,463.641998,0.005309017626029533,y = 0.013098 + -0.000096*x,134,10.960663313432837,3.9096921347220377,0.012043410447761194,0.0051616384058770694 +logarithmic,Chlorophyll,463.641998,0.00309369061071596,y = 0.013897 + -0.000796*ln(x),134,10.960663313432837,3.9096921347220377,0.012043410447761194,0.0051616384058770694 +linear,Chlorophyll,467.743011,0.005910540579640466,y = 0.013129 + -0.000101*x,134,10.960663313432837,3.9096921347220377,0.012021395522388062,0.0051373056777988335 +logarithmic,Chlorophyll,467.743011,0.0035312797033351107,y = 0.013992 + -0.000846*ln(x),134,10.960663313432837,3.9096921347220377,0.012021395522388062,0.0051373056777988335 +linear,Chlorophyll,471.846985,0.006597953952974689,y = 0.013090 + -0.000107*x,134,10.960663313432837,3.9096921347220377,0.011916462686567163,0.005154508155317495 +logarithmic,Chlorophyll,471.846985,0.004055554987167143,y = 0.014036 + -0.000910*ln(x),134,10.960663313432837,3.9096921347220377,0.011916462686567163,0.005154508155317495 +linear,Chlorophyll,475.954987,0.006969427568661812,y = 0.013222 + -0.000110*x,134,10.960663313432837,3.9096921347220377,0.012010992537313433,0.005173190365687869 +logarithmic,Chlorophyll,475.954987,0.0043493646432547495,y = 0.014214 + -0.000945*ln(x),134,10.960663313432837,3.9096921347220377,0.012010992537313433,0.005173190365687869 +linear,Chlorophyll,480.065002,0.006908555626254476,y = 0.013165 + -0.000112*x,134,10.960663313432837,3.9096921347220377,0.011938738805970149,0.005260977465686793 +logarithmic,Chlorophyll,480.065002,0.004358699372388197,y = 0.014181 + -0.000962*ln(x),134,10.960663313432837,3.9096921347220377,0.011938738805970149,0.005260977465686793 +linear,Chlorophyll,484.179993,0.007786080902936532,y = 0.013479 + -0.000120*x,134,10.960663313432837,3.9096921347220377,0.01216315671641791,0.00531894585260112 +logarithmic,Chlorophyll,484.179993,0.00503315433665652,y = 0.014599 + -0.001046*ln(x),134,10.960663313432837,3.9096921347220377,0.01216315671641791,0.00531894585260112 +linear,Chlorophyll,488.296997,0.009003426521211222,y = 0.013413 + -0.000127*x,134,10.960663313432837,3.9096921347220377,0.01202031343283582,0.005235852382412684 +logarithmic,Chlorophyll,488.296997,0.0059888617317502835,y = 0.014636 + -0.001123*ln(x),134,10.960663313432837,3.9096921347220377,0.01202031343283582,0.005235852382412684 +linear,Chlorophyll,492.417999,0.009356273796731096,y = 0.013426 + -0.000130*x,134,10.960663313432837,3.9096921347220377,0.011996694029850746,0.00526918646504346 +logarithmic,Chlorophyll,492.417999,0.0063786563113004124,y = 0.014714 + -0.001166*ln(x),134,10.960663313432837,3.9096921347220377,0.011996694029850746,0.00526918646504346 +linear,Chlorophyll,496.542999,0.008454580487572083,y = 0.013491 + -0.000126*x,134,10.960663313432837,3.9096921347220377,0.012105947761194029,0.00537461785981684 +logarithmic,Chlorophyll,496.542999,0.005612309162053686,y = 0.014705 + -0.001116*ln(x),134,10.960663313432837,3.9096921347220377,0.012105947761194029,0.00537461785981684 +linear,Chlorophyll,500.67099,0.008930402290994066,y = 0.013947 + -0.000130*x,134,10.960663313432837,3.9096921347220377,0.012519723880597015,0.005386983290859261 +logarithmic,Chlorophyll,500.67099,0.006027433290726858,y = 0.015220 + -0.001159*ln(x),134,10.960663313432837,3.9096921347220377,0.012519723880597015,0.005386983290859261 +linear,Chlorophyll,504.802002,0.007851805368295328,y = 0.014159 + -0.000122*x,134,10.960663313432837,3.9096921347220377,0.012821291044776119,0.005386616665575906 +logarithmic,Chlorophyll,504.802002,0.005168584239575225,y = 0.015321 + -0.001073*ln(x),134,10.960663313432837,3.9096921347220377,0.012821291044776119,0.005386616665575906 +linear,Chlorophyll,508.936005,0.006500148302153508,y = 0.014525 + -0.000113*x,134,10.960663313432837,3.9096921347220377,0.013285626865671642,0.005484303548583865 +logarithmic,Chlorophyll,508.936005,0.004158811025431031,y = 0.015569 + -0.000980*ln(x),134,10.960663313432837,3.9096921347220377,0.013285626865671642,0.005484303548583865 +linear,Chlorophyll,513.073975,0.004917130582781315,y = 0.014969 + -0.000099*x,134,10.960663313432837,3.9096921347220377,0.013879231343283581,0.005544190630289309 +logarithmic,Chlorophyll,513.073975,0.0029641857181392783,y = 0.015828 + -0.000836*ln(x),134,10.960663313432837,3.9096921347220377,0.013879231343283581,0.005544190630289309 +exponential,Chlorophyll,513.073975,-0.01889032090850251,y = 0.016669 * exp(-0.020406*x),134,10.960663313432837,3.9096921347220377,0.013879231343283581,0.005544190630289309 +power,Chlorophyll,513.073975,-0.019715344472010177,y = 0.020892 * x^-0.192919,134,10.960663313432837,3.9096921347220377,0.013879231343283581,0.005544190630289309 +linear,Chlorophyll,517.216003,0.004276559761211218,y = 0.015763 + -0.000093*x,134,10.960663313432837,3.9096921347220377,0.014746992537313432,0.005540761628631703 +logarithmic,Chlorophyll,517.216003,0.00245772439289782,y = 0.016520 + -0.000761*ln(x),134,10.960663313432837,3.9096921347220377,0.014746992537313432,0.005540761628631703 +exponential,Chlorophyll,517.216003,-0.018892619920322984,y = 0.017382 * exp(-0.018434*x),134,10.960663313432837,3.9096921347220377,0.014746992537313432,0.005540761628631703 +power,Chlorophyll,517.216003,-0.0194484153066794,y = 0.021251 * x^-0.172972,134,10.960663313432837,3.9096921347220377,0.014746992537313432,0.005540761628631703 +linear,Chlorophyll,521.361023,0.002867645407956476,y = 0.015976 + -0.000077*x,134,10.960663313432837,3.9096921347220377,0.01513621641791045,0.0055935618166287285 +logarithmic,Chlorophyll,521.361023,0.001510103149844122,y = 0.016540 + -0.000602*ln(x),134,10.960663313432837,3.9096921347220377,0.01513621641791045,0.0055935618166287285 +exponential,Chlorophyll,521.361023,-0.013492164822140218,y = 0.017293 * exp(-0.014959*x),134,10.960663313432837,3.9096921347220377,0.01513621641791045,0.0055935618166287285 +power,Chlorophyll,521.361023,-0.01389692075440152,y = 0.020293 * x^-0.139034,134,10.960663313432837,3.9096921347220377,0.01513621641791045,0.0055935618166287285 +linear,Chlorophyll,525.508972,0.0016701377663735917,y = 0.016584 + -0.000059*x,134,10.960663313432837,3.9096921347220377,0.015935962686567166,0.005657732827939037 +logarithmic,Chlorophyll,525.508972,0.0007153098124390578,y = 0.016913 + -0.000419*ln(x),134,10.960663313432837,3.9096921347220377,0.015935962686567166,0.005657732827939037 +exponential,Chlorophyll,525.508972,-0.011873247394506237,y = 0.017736 * exp(-0.012223*x),134,10.960663313432837,3.9096921347220377,0.015935962686567166,0.005657732827939037 +power,Chlorophyll,525.508972,-0.01195937275095682,y = 0.020122 * x^-0.111667,134,10.960663313432837,3.9096921347220377,0.015935962686567166,0.005657732827939037 +linear,Chlorophyll,529.659973,0.001100615217659584,y = 0.016897 + -0.000048*x,134,10.960663313432837,3.9096921347220377,0.016371305970149252,0.005647344987370815 +logarithmic,Chlorophyll,529.659973,0.00038742510141320796,y = 0.017089 + -0.000308*ln(x),134,10.960663313432837,3.9096921347220377,0.016371305970149252,0.005647344987370815 +power,Chlorophyll,529.659973,-0.010602748616709512,y = 0.019984 * x^-0.095978,134,10.960663313432837,3.9096921347220377,0.016371305970149252,0.005647344987370815 +exponential,Chlorophyll,529.659973,-0.010676230097632189,y = 0.017950 * exp(-0.010610*x),134,10.960663313432837,3.9096921347220377,0.016371305970149252,0.005647344987370815 +linear,Chlorophyll,533.815002,0.0005274884679772329,y = 0.017331 + -0.000034*x,134,10.960663313432837,3.9096921347220377,0.016960171641791044,0.005752288800181051 +logarithmic,Chlorophyll,533.815002,9.639796014881963e-05,y = 0.017325 + -0.000156*ln(x),134,10.960663313432837,3.9096921347220377,0.016960171641791044,0.005752288800181051 +power,Chlorophyll,533.815002,-0.010942625292486463,y = 0.020200 * x^-0.085250,134,10.960663313432837,3.9096921347220377,0.016960171641791044,0.005752288800181051 +exponential,Chlorophyll,533.815002,-0.011375297319334177,y = 0.018394 * exp(-0.009577*x),134,10.960663313432837,3.9096921347220377,0.016960171641791044,0.005752288800181051 +linear,Chlorophyll,537.973999,0.010059827315010317,y = 0.018287 + -0.000095*x,134,10.960663313432837,3.9096921347220377,0.017245291044776116,0.003706413209287316 +logarithmic,Chlorophyll,537.973999,0.0058263736903666485,y = 0.019072 + -0.000784*ln(x),134,10.960663313432837,3.9096921347220377,0.017245291044776116,0.003706413209287316 +exponential,Chlorophyll,537.973999,-0.0006317634379562342,y = 0.018947 * exp(-0.009908*x),134,10.960663313432837,3.9096921347220377,0.017245291044776116,0.003706413209287316 +power,Chlorophyll,537.973999,-0.004088770616538007,y = 0.020915 * x^-0.089031,134,10.960663313432837,3.9096921347220377,0.017245291044776116,0.003706413209287316 +linear,Chlorophyll,542.13501,0.10592938275035968,y = 0.019264 + -0.000164*x,134,10.960663313432837,3.9096921347220377,0.017471380597014925,0.0019645221126944118 +exponential,Chlorophyll,542.13501,0.09468361275300774,y = 0.019618 * exp(-0.011250*x),134,10.960663313432837,3.9096921347220377,0.017471380597014925,0.0019645221126944118 +logarithmic,Chlorophyll,542.13501,0.07410535895090076,y = 0.020924 + -0.001482*ln(x),134,10.960663313432837,3.9096921347220377,0.017471380597014925,0.0019645221126944118 +power,Chlorophyll,542.13501,0.06336507350101761,y = 0.022043 * x^-0.102942,134,10.960663313432837,3.9096921347220377,0.017471380597014925,0.0019645221126944118 +linear,Chlorophyll,546.301025,0.12040522641720053,y = 0.019716 + -0.000169*x,134,10.960663313432837,3.9096921347220377,0.01786192537313433,0.0019057641301598596 +exponential,Chlorophyll,546.301025,0.11002711959899414,y = 0.020039 * exp(-0.011103*x),134,10.960663313432837,3.9096921347220377,0.01786192537313433,0.0019057641301598596 +logarithmic,Chlorophyll,546.301025,0.08587624549622919,y = 0.021467 + -0.001547*ln(x),134,10.960663313432837,3.9096921347220377,0.01786192537313433,0.0019057641301598596 +power,Chlorophyll,546.301025,0.07589213783045401,y = 0.022516 * x^-0.102241,134,10.960663313432837,3.9096921347220377,0.01786192537313433,0.0019057641301598596 +linear,Chlorophyll,550.468994,0.1269569921216488,y = 0.020121 + -0.000172*x,134,10.960663313432837,3.9096921347220377,0.01824102985074627,0.0018825013010254905 +exponential,Chlorophyll,550.468994,0.1175391734287099,y = 0.020411 * exp(-0.010816*x),134,10.960663313432837,3.9096921347220377,0.01824102985074627,0.0018825013010254905 +logarithmic,Chlorophyll,550.468994,0.0907980339917791,y = 0.021903 + -0.001572*ln(x),134,10.960663313432837,3.9096921347220377,0.01824102985074627,0.0018825013010254905 +power,Chlorophyll,550.468994,0.08162875081638976,y = 0.022867 * x^-0.099639,134,10.960663313432837,3.9096921347220377,0.01824102985074627,0.0018825013010254905 +linear,Chlorophyll,554.640991,0.1376426262180579,y = 0.020508 + -0.000178*x,134,10.960663313432837,3.9096921347220377,0.018552656716417912,0.0018796053138591957 +exponential,Chlorophyll,554.640991,0.12753098839026022,y = 0.020816 * exp(-0.011054*x),134,10.960663313432837,3.9096921347220377,0.018552656716417912,0.0018796053138591957 +logarithmic,Chlorophyll,554.640991,0.09810495477002423,y = 0.022354 + -0.001631*ln(x),134,10.960663313432837,3.9096921347220377,0.018552656716417912,0.0018796053138591957 +power,Chlorophyll,554.640991,0.08828430738543391,y = 0.023368 * x^-0.101637,134,10.960663313432837,3.9096921347220377,0.018552656716417912,0.0018796053138591957 +linear,Chlorophyll,558.815979,0.14438886343879165,y = 0.020984 + -0.000192*x,134,10.960663313432837,3.9096921347220377,0.018875544776119402,0.0019794547666891088 +exponential,Chlorophyll,558.815979,0.13400466793505816,y = 0.021319 * exp(-0.011710*x),134,10.960663313432837,3.9096921347220377,0.018875544776119402,0.0019794547666891088 +logarithmic,Chlorophyll,558.815979,0.10606464763119816,y = 0.023038 + -0.001786*ln(x),134,10.960663313432837,3.9096921347220377,0.018875544776119402,0.0019794547666891088 +power,Chlorophyll,558.815979,0.09560595602330613,y = 0.024181 * x^-0.109152,134,10.960663313432837,3.9096921347220377,0.018875544776119402,0.0019794547666891088 +linear,Chlorophyll,562.994995,0.13570684972920022,y = 0.021065 + -0.000193*x,134,10.960663313432837,3.9096921347220377,0.018946902985074628,0.002050816811679121 +exponential,Chlorophyll,562.994995,0.1251679621881412,y = 0.021424 * exp(-0.011845*x),134,10.960663313432837,3.9096921347220377,0.018946902985074628,0.002050816811679121 +logarithmic,Chlorophyll,562.994995,0.10005264191024388,y = 0.023135 + -0.001797*ln(x),134,10.960663313432837,3.9096921347220377,0.018946902985074628,0.002050816811679121 +power,Chlorophyll,562.994995,0.08944628510214314,y = 0.024352 * x^-0.110685,134,10.960663313432837,3.9096921347220377,0.018946902985074628,0.002050816811679121 +linear,Chlorophyll,567.177002,0.03705163102869502,y = 0.020812 + -0.000155*x,134,10.960663313432837,3.9096921347220377,0.01911655970149254,0.0031426043067057114 +exponential,Chlorophyll,567.177002,0.02735476743599441,y = 0.021355 * exp(-0.011096*x),134,10.960663313432837,3.9096921347220377,0.01911655970149254,0.0031426043067057114 +logarithmic,Chlorophyll,567.177002,0.02624551679688847,y = 0.022403 + -0.001411*ln(x),134,10.960663313432837,3.9096921347220377,0.01911655970149254,0.0031426043067057114 +power,Chlorophyll,567.177002,0.01690050625981776,y = 0.024064 * x^-0.103446,134,10.960663313432837,3.9096921347220377,0.01911655970149254,0.0031426043067057114 +linear,Chlorophyll,571.362976,0.032367014815650186,y = 0.020732 + -0.000161*x,134,10.960663313432837,3.9096921347220377,0.018965664179104478,0.0035018296559745925 +logarithmic,Chlorophyll,571.362976,0.023243518566287924,y = 0.022412 + -0.001479*ln(x),134,10.960663313432837,3.9096921347220377,0.018965664179104478,0.0035018296559745925 +exponential,Chlorophyll,571.362976,0.021156108573575194,y = 0.021404 * exp(-0.012237*x),134,10.960663313432837,3.9096921347220377,0.018965664179104478,0.0035018296559745925 +power,Chlorophyll,571.362976,0.012371457852355827,y = 0.024473 * x^-0.115076,134,10.960663313432837,3.9096921347220377,0.018965664179104478,0.0035018296559745925 +linear,Chlorophyll,575.551025,0.003711200606445142,y = 0.019973 + -0.000095*x,134,10.960663313432837,3.9096921347220377,0.018931119402985072,0.006097893443512235 +logarithmic,Chlorophyll,575.551025,0.0022851664540198824,y = 0.020813 + -0.000808*ln(x),134,10.960663313432837,3.9096921347220377,0.018931119402985072,0.006097893443512235 +exponential,Chlorophyll,575.551025,-0.008535989595423121,y = 0.021158 * exp(-0.012309*x),134,10.960663313432837,3.9096921347220377,0.018931119402985072,0.006097893443512235 +power,Chlorophyll,575.551025,-0.009305973499541542,y = 0.024221 * x^-0.115919,134,10.960663313432837,3.9096921347220377,0.018931119402985072,0.006097893443512235 +linear,Chlorophyll,579.744019,0.005094509026133509,y = 0.019711 + -0.000110*x,134,10.960663313432837,3.9096921347220377,0.018505432835820897,0.006025405615191274 +logarithmic,Chlorophyll,579.744019,0.0034065768371064342,y = 0.020776 + -0.000974*ln(x),134,10.960663313432837,3.9096921347220377,0.018505432835820897,0.006025405615191274 +exponential,Chlorophyll,579.744019,-0.007270136129956084,y = 0.020909 * exp(-0.013357*x),134,10.960663313432837,3.9096921347220377,0.018505432835820897,0.006025405615191274 +power,Chlorophyll,579.744019,-0.008400608136997167,y = 0.024296 * x^-0.127275,134,10.960663313432837,3.9096921347220377,0.018505432835820897,0.006025405615191274 +linear,Chlorophyll,583.939026,0.004657425272328819,y = 0.019070 + -0.000108*x,134,10.960663313432837,3.9096921347220377,0.017881,0.006215784305887186 +logarithmic,Chlorophyll,583.939026,0.003161690862374833,y = 0.020137 + -0.000968*ln(x),134,10.960663313432837,3.9096921347220377,0.017881,0.006215784305887186 +exponential,Chlorophyll,583.939026,-0.00834323144293947,y = 0.020355 * exp(-0.014251*x),134,10.960663313432837,3.9096921347220377,0.017881,0.006215784305887186 +power,Chlorophyll,583.939026,-0.009289166222129941,y = 0.023939 * x^-0.136644,134,10.960663313432837,3.9096921347220377,0.017881,0.006215784305887186 +linear,Chlorophyll,588.138,0.007038900978221574,y = 0.018745 + -0.000132*x,134,10.960663313432837,3.9096921347220377,0.017294746268656718,0.006164975937402735 +logarithmic,Chlorophyll,588.138,0.005079658860641656,y = 0.020131 + -0.001218*ln(x),134,10.960663313432837,3.9096921347220377,0.017294746268656718,0.006164975937402735 +exponential,Chlorophyll,588.138,-0.008087990598099282,y = 0.020176 * exp(-0.016777*x),134,10.960663313432837,3.9096921347220377,0.017294746268656718,0.006164975937402735 +power,Chlorophyll,588.138,-0.009554552395312665,y = 0.024503 * x^-0.162327,134,10.960663313432837,3.9096921347220377,0.017294746268656718,0.006164975937402735 +linear,Chlorophyll,592.341003,0.005528079261697849,y = 0.017838 + -0.000118*x,134,10.960663313432837,3.9096921347220377,0.016543097014925373,0.006212725389009627 +logarithmic,Chlorophyll,592.341003,0.003887007195125136,y = 0.019044 + -0.001073*ln(x),134,10.960663313432837,3.9096921347220377,0.016543097014925373,0.006212725389009627 +exponential,Chlorophyll,592.341003,-0.011813537096786675,y = 0.019386 * exp(-0.017528*x),134,10.960663313432837,3.9096921347220377,0.016543097014925373,0.006212725389009627 +power,Chlorophyll,592.341003,-0.012846809152319283,y = 0.023741 * x^-0.169433,134,10.960663313432837,3.9096921347220377,0.016543097014925373,0.006212725389009627 +linear,Chlorophyll,596.546997,0.003794780064093062,y = 0.016713 + -0.000101*x,134,10.960663313432837,3.9096921347220377,0.015607044776119405,0.00640171329313859 +logarithmic,Chlorophyll,596.546997,0.0025823661966439815,y = 0.017707 + -0.000901*ln(x),134,10.960663313432837,3.9096921347220377,0.015607044776119405,0.00640171329313859 +exponential,Chlorophyll,596.546997,-0.014874157713247849,y = 0.018368 * exp(-0.018321*x),134,10.960663313432837,3.9096921347220377,0.015607044776119405,0.00640171329313859 +power,Chlorophyll,596.546997,-0.015374647488444415,y = 0.022702 * x^-0.177115,134,10.960663313432837,3.9096921347220377,0.015607044776119405,0.00640171329313859 +linear,Chlorophyll,600.755981,0.0031611200601100453,y = 0.015616 + -0.000092*x,134,10.960663313432837,3.9096921347220377,0.014608059701492537,0.006395783738002828 +logarithmic,Chlorophyll,600.755981,0.002151138099984129,y = 0.016523 + -0.000822*ln(x),134,10.960663313432837,3.9096921347220377,0.014608059701492537,0.006395783738002828 +exponential,Chlorophyll,600.755981,-0.015732414466808953,y = 0.017258 * exp(-0.018983*x),134,10.960663313432837,3.9096921347220377,0.014608059701492537,0.006395783738002828 +power,Chlorophyll,600.755981,-0.016109249860633446,y = 0.021535 * x^-0.184339,134,10.960663313432837,3.9096921347220377,0.014608059701492537,0.006395783738002828 +linear,Chlorophyll,604.968994,0.002398045183009123,y = 0.014883 + -0.000082*x,134,10.960663313432837,3.9096921347220377,0.013985902985074627,0.0065333725432655055 +logarithmic,Chlorophyll,604.968994,0.001603534119833716,y = 0.015675 + -0.000725*ln(x),134,10.960663313432837,3.9096921347220377,0.013985902985074627,0.0065333725432655055 +power,Chlorophyll,604.968994,-0.019789876685375907,y = 0.021361 * x^-0.202172,134,10.960663313432837,3.9096921347220377,0.013985902985074627,0.0065333725432655055 +exponential,Chlorophyll,604.968994,-0.019848091450597183,y = 0.016749 * exp(-0.020787*x),134,10.960663313432837,3.9096921347220377,0.013985902985074627,0.0065333725432655055 +linear,Chlorophyll,609.184998,0.0026364151647212397,y = 0.014491 + -0.000087*x,134,10.960663313432837,3.9096921347220377,0.013541022388059702,0.006602440257226493 +logarithmic,Chlorophyll,609.184998,0.001802623235843237,y = 0.015351 + -0.000777*ln(x),134,10.960663313432837,3.9096921347220377,0.013541022388059702,0.006602440257226493 +exponential,Chlorophyll,609.184998,-0.02173372426195974,y = 0.016508 * exp(-0.022851*x),134,10.960663313432837,3.9096921347220377,0.013541022388059702,0.006602440257226493 +power,Chlorophyll,609.184998,-0.021740201254617064,y = 0.021605 * x^-0.222988,134,10.960663313432837,3.9096921347220377,0.013541022388059702,0.006602440257226493 +linear,Chlorophyll,613.403992,0.002856835284101855,y = 0.014122 + -0.000091*x,134,10.960663313432837,3.9096921347220377,0.013127970149253732,0.006635524455917487 +logarithmic,Chlorophyll,613.403992,0.0019870205661061124,y = 0.015038 + -0.000820*ln(x),134,10.960663313432837,3.9096921347220377,0.013127970149253732,0.006635524455917487 +power,Chlorophyll,613.403992,-0.028809677197195516,y = 0.022780 * x^-0.263141,134,10.960663313432837,3.9096921347220377,0.013127970149253732,0.006635524455917487 +exponential,Chlorophyll,613.403992,-0.02909727099411552,y = 0.016581 * exp(-0.026956*x),134,10.960663313432837,3.9096921347220377,0.013127970149253732,0.006635524455917487 +linear,Chlorophyll,617.627014,0.0029411042399954956,y = 0.013748 + -0.000092*x,134,10.960663313432837,3.9096921347220377,0.012741731343283583,0.006619138380331145 +logarithmic,Chlorophyll,617.627014,0.0020597681456059336,y = 0.014681 + -0.000832*ln(x),134,10.960663313432837,3.9096921347220377,0.012741731343283583,0.006619138380331145 +power,Chlorophyll,617.627014,-0.02980562740210102,y = 0.022702 * x^-0.275804,134,10.960663313432837,3.9096921347220377,0.012741731343283583,0.006619138380331145 +exponential,Chlorophyll,617.627014,-0.030009751236651283,y = 0.016267 * exp(-0.028218*x),134,10.960663313432837,3.9096921347220377,0.012741731343283583,0.006619138380331145 +linear,Chlorophyll,621.853027,0.002549517816160085,y = 0.013405 + -0.000087*x,134,10.960663313432837,3.9096921347220377,0.012447716417910447,0.006761993519104926 +logarithmic,Chlorophyll,621.853027,0.0017633821329291477,y = 0.014281 + -0.000787*ln(x),134,10.960663313432837,3.9096921347220377,0.012447716417910447,0.006761993519104926 +power,Chlorophyll,621.853027,-0.03557395594809787,y = 0.023522 * x^-0.304801,134,10.960663313432837,3.9096921347220377,0.012447716417910447,0.006761993519104926 +exponential,Chlorophyll,621.853027,-0.0360883361973503,y = 0.016274 * exp(-0.031185*x),134,10.960663313432837,3.9096921347220377,0.012447716417910447,0.006761993519104926 +linear,Chlorophyll,626.083008,0.00243086769672618,y = 0.013228 + -0.000086*x,134,10.960663313432837,3.9096921347220377,0.01228736567164179,0.006808151575409212 +logarithmic,Chlorophyll,626.083008,0.0016770114876123454,y = 0.014087 + -0.000773*ln(x),134,10.960663313432837,3.9096921347220377,0.01228736567164179,0.006808151575409212 +linear,Chlorophyll,630.315979,0.0020507276873319435,y = 0.013084 + -0.000080*x,134,10.960663313432837,3.9096921347220377,0.012202574626865673,0.0069447202176291635 +logarithmic,Chlorophyll,630.315979,0.0014098383321846653,y = 0.013886 + -0.000723*ln(x),134,10.960663313432837,3.9096921347220377,0.012202574626865673,0.0069447202176291635 +power,Chlorophyll,630.315979,-0.037502977238619506,y = 0.023711 * x^-0.319128,134,10.960663313432837,3.9096921347220377,0.012202574626865673,0.0069447202176291635 +exponential,Chlorophyll,630.315979,-0.03824118768398388,y = 0.016114 * exp(-0.032602*x),134,10.960663313432837,3.9096921347220377,0.012202574626865673,0.0069447202176291635 +linear,Chlorophyll,634.552002,0.0017288451024077833,y = 0.012968 + -0.000074*x,134,10.960663313432837,3.9096921347220377,0.012159492537313433,0.006937426491471543 +logarithmic,Chlorophyll,634.552002,0.0011589143811774338,y = 0.013684 + -0.000654*ln(x),134,10.960663313432837,3.9096921347220377,0.012159492537313433,0.006937426491471543 +linear,Chlorophyll,638.791992,0.0011243091081014622,y = 0.012803 + -0.000061*x,134,10.960663313432837,3.9096921347220377,0.012136574626865673,0.007088601297076063 +logarithmic,Chlorophyll,638.791992,0.0007004467304244644,y = 0.013348 + -0.000520*ln(x),134,10.960663313432837,3.9096921347220377,0.012136574626865673,0.007088601297076063 +power,Chlorophyll,638.791992,-0.039341503691473934,y = 0.023270 * x^-0.314552,134,10.960663313432837,3.9096921347220377,0.012136574626865673,0.007088601297076063 +exponential,Chlorophyll,638.791992,-0.040722297966500065,y = 0.015930 * exp(-0.032288*x),134,10.960663313432837,3.9096921347220377,0.012136574626865673,0.007088601297076063 +linear,Chlorophyll,643.034973,0.0006784732036110297,y = 0.012527 + -0.000048*x,134,10.960663313432837,3.9096921347220377,0.012004380597014924,0.007158811687218512 +logarithmic,Chlorophyll,643.034973,0.0003810729347989428,y = 0.012907 + -0.000387*ln(x),134,10.960663313432837,3.9096921347220377,0.012004380597014924,0.007158811687218512 +power,Chlorophyll,643.034973,-0.02081846389883313,y = 0.018994 * x^-0.223479,134,10.960663313432837,3.9096921347220377,0.012004380597014924,0.007158811687218512 +exponential,Chlorophyll,643.034973,-0.02135362421636633,y = 0.014510 * exp(-0.022936*x),134,10.960663313432837,3.9096921347220377,0.012004380597014924,0.007158811687218512 +linear,Chlorophyll,647.281006,0.0003862335627651259,y = 0.012126 + -0.000037*x,134,10.960663313432837,3.9096921347220377,0.01172576119402985,0.007268962283181486 +logarithmic,Chlorophyll,647.281006,0.00018807939298448595,y = 0.012369 + -0.000276*ln(x),134,10.960663313432837,3.9096921347220377,0.01172576119402985,0.007268962283181486 +power,Chlorophyll,647.281006,-0.01877601035356702,y = 0.017959 * x^-0.209287,134,10.960663313432837,3.9096921347220377,0.01172576119402985,0.007268962283181486 +exponential,Chlorophyll,647.281006,-0.019328048239278806,y = 0.013958 * exp(-0.021494*x),134,10.960663313432837,3.9096921347220377,0.01172576119402985,0.007268962283181486 +linear,Chlorophyll,651.531006,0.00013536784274881253,y = 0.011403 + -0.000022*x,134,10.960663313432837,3.9096921347220377,0.011164671641791043,0.007310371385073625 +logarithmic,Chlorophyll,651.531006,3.4274085199070825e-05,y = 0.011441 + -0.000119*ln(x),134,10.960663313432837,3.9096921347220377,0.011164671641791043,0.007310371385073625 +power,Chlorophyll,651.531006,-0.01999886130480988,y = 0.017201 * x^-0.213892,134,10.960663313432837,3.9096921347220377,0.011164671641791043,0.007310371385073625 +exponential,Chlorophyll,651.531006,-0.02080973971468425,y = 0.013316 * exp(-0.022113*x),134,10.960663313432837,3.9096921347220377,0.011164671641791043,0.007310371385073625 +linear,Chlorophyll,655.784973,6.712523797403058e-05,y = 0.010412 + -0.000016*x,134,10.960663313432837,3.9096921347220377,0.010241910447761193,0.007414076922911311 +logarithmic,Chlorophyll,655.784973,7.939365778253382e-06,y = 0.010377 + -0.000058*ln(x),134,10.960663313432837,3.9096921347220377,0.010241910447761193,0.007414076922911311 +power,Chlorophyll,655.784973,-0.02451200929710562,y = 0.017204 * x^-0.257829,134,10.960663313432837,3.9096921347220377,0.010241910447761193,0.007414076922911311 +exponential,Chlorophyll,655.784973,-0.025546357658017715,y = 0.012631 * exp(-0.026616*x),134,10.960663313432837,3.9096921347220377,0.010241910447761193,0.007414076922911311 +linear,Chlorophyll,660.041016,2.3065421406842646e-05,y = 0.009045 + -0.000009*x,134,10.960663313432837,3.9096921347220377,0.008946089552238806,0.007372050533373144 +logarithmic,Chlorophyll,660.041016,8.478475440609756e-07,y = 0.008902 + 0.000019*ln(x),134,10.960663313432837,3.9096921347220377,0.008946089552238806,0.007372050533373144 +linear,Chlorophyll,664.302002,2.70996016560332e-05,y = 0.008101 + -0.000010*x,134,10.960663313432837,3.9096921347220377,0.007993440298507463,0.007397490717674307 +logarithmic,Chlorophyll,664.302002,4.148671942649784e-07,y = 0.007963 + 0.000013*ln(x),134,10.960663313432837,3.9096921347220377,0.007993440298507463,0.007397490717674307 +linear,Chlorophyll,668.565002,9.040046325403672e-06,y = 0.007498 + -0.000006*x,134,10.960663313432837,3.9096921347220377,0.0074355522388059704,0.007438585861600675 +logarithmic,Chlorophyll,668.565002,8.725061217407237e-06,y = 0.007294 + 0.000061*ln(x),134,10.960663313432837,3.9096921347220377,0.0074355522388059704,0.007438585861600675 +logarithmic,Chlorophyll,672.83197,2.8342079669063658e-05,y = 0.007006 + 0.000109*ln(x),134,10.960663313432837,3.9096921347220377,0.007259574626865672,0.0073841624312846075 +linear,Chlorophyll,672.83197,2.942331951416577e-07,y = 0.007271 + -0.000001*x,134,10.960663313432837,3.9096921347220377,0.007259574626865672,0.0073841624312846075 +logarithmic,Chlorophyll,677.10199,7.905307206101941e-05,y = 0.007009 + 0.000182*ln(x),134,10.960663313432837,3.9096921347220377,0.007434537313432836,0.007405361783473518 +linear,Chlorophyll,677.10199,8.391699477305892e-06,y = 0.007374 + 0.000005*x,134,10.960663313432837,3.9096921347220377,0.007434537313432836,0.007405361783473518 +logarithmic,Chlorophyll,681.375977,0.00035444194765099635,y = 0.007047 + 0.000397*ln(x),134,10.960663313432837,3.9096921347220377,0.007971380597014925,0.007601910615882189 +linear,Chlorophyll,681.375977,0.00018023567598490775,y = 0.007685 + 0.000026*x,134,10.960663313432837,3.9096921347220377,0.007971380597014925,0.007601910615882189 +logarithmic,Chlorophyll,685.653015,0.0012936127446737666,y = 0.007106 + 0.000788*ln(x),134,10.960663313432837,3.9096921347220377,0.00894305223880597,0.007911710832888247 +linear,Chlorophyll,685.653015,0.0010226571268574514,y = 0.008234 + 0.000065*x,134,10.960663313432837,3.9096921347220377,0.00894305223880597,0.007911710832888247 +logarithmic,Chlorophyll,689.932983,0.004302680997953723,y = 0.006459 + 0.001505*ln(x),134,10.960663313432837,3.9096921347220377,0.009964873134328357,0.008279228255026581 +linear,Chlorophyll,689.932983,0.0041204289706771036,y = 0.008475 + 0.000136*x,134,10.960663313432837,3.9096921347220377,0.009964873134328357,0.008279228255026581 +power,Chlorophyll,689.932983,-0.011112637221193156,y = 0.010691 * x^-0.061728,134,10.960663313432837,3.9096921347220377,0.009964873134328357,0.008279228255026581 +exponential,Chlorophyll,689.932983,-0.012347372143508784,y = 0.010039 * exp(-0.007382*x),134,10.960663313432837,3.9096921347220377,0.009964873134328357,0.008279228255026581 +logarithmic,Chlorophyll,694.21698,0.009659788549096326,y = 0.007405 + 0.001260*ln(x),134,10.960663313432837,3.9096921347220377,0.01034108208955224,0.004627698457727055 +linear,Chlorophyll,694.21698,0.008988938182027284,y = 0.009111 + 0.000112*x,134,10.960663313432837,3.9096921347220377,0.01034108208955224,0.004627698457727055 +power,Chlorophyll,694.21698,-0.0008894306327071888,y = 0.009130 * x^0.038452,134,10.960663313432837,3.9096921347220377,0.01034108208955224,0.004627698457727055 +exponential,Chlorophyll,694.21698,-0.002278256235976661,y = 0.009711 * exp(0.002541*x),134,10.960663313432837,3.9096921347220377,0.01034108208955224,0.004627698457727055 +logarithmic,Chlorophyll,698.505005,0.031440304449184886,y = 0.007932 + 0.000977*ln(x),134,10.960663313432837,3.9096921347220377,0.010207731343283583,0.0019876983508052013 +linear,Chlorophyll,698.505005,0.028545786078388646,y = 0.009266 + 0.000086*x,134,10.960663313432837,3.9096921347220377,0.010207731343283583,0.0019876983508052013 +power,Chlorophyll,698.505005,0.024301556337671393,y = 0.008426 * x^0.075955,134,10.960663313432837,3.9096921347220377,0.010207731343283583,0.0019876983508052013 +exponential,Chlorophyll,698.505005,0.021173949758028887,y = 0.009382 * exp(0.006346*x),134,10.960663313432837,3.9096921347220377,0.010207731343283583,0.0019876983508052013 +linear,Chlorophyll,702.794983,0.06296172218181084,y = 0.008305 + 0.000144*x,134,10.960663313432837,3.9096921347220377,0.009888313432835822,0.002250167471107098 +logarithmic,Chlorophyll,702.794983,0.06154613752993454,y = 0.006284 + 0.001547*ln(x),134,10.960663313432837,3.9096921347220377,0.009888313432835822,0.002250167471107098 +exponential,Chlorophyll,702.794983,0.05333717741162325,y = 0.008583 * exp(0.011176*x),134,10.960663313432837,3.9096921347220377,0.009888313432835822,0.002250167471107098 +power,Chlorophyll,702.794983,0.052353333406120695,y = 0.007280 * x^0.123243,134,10.960663313432837,3.9096921347220377,0.009888313432835822,0.002250167471107098 +linear,Chlorophyll,707.088989,0.0722787565252282,y = 0.007176 + 0.000174*x,134,10.960663313432837,3.9096921347220377,0.009086067164179106,0.0025340421111067013 +logarithmic,Chlorophyll,707.088989,0.06798251906890818,y = 0.004820 + 0.001831*ln(x),134,10.960663313432837,3.9096921347220377,0.009086067164179106,0.0025340421111067013 +exponential,Chlorophyll,707.088989,0.05918259759570621,y = 0.007631 * exp(0.013614*x),134,10.960663313432837,3.9096921347220377,0.009086067164179106,0.0025340421111067013 +power,Chlorophyll,707.088989,0.05571330673697561,y = 0.006287 * x^0.147166,134,10.960663313432837,3.9096921347220377,0.009086067164179106,0.0025340421111067013 +linear,Chlorophyll,711.387024,0.0801501663676073,y = 0.005716 + 0.000199*x,134,10.960663313432837,3.9096921347220377,0.007900276119402985,0.0027516224396933074 +logarithmic,Chlorophyll,711.387024,0.07410981134062689,y = 0.003064 + 0.002076*ln(x),134,10.960663313432837,3.9096921347220377,0.007900276119402985,0.0027516224396933074 +exponential,Chlorophyll,711.387024,0.06368164013940003,y = 0.006342 * exp(0.016959*x),134,10.960663313432837,3.9096921347220377,0.007900276119402985,0.0027516224396933074 +power,Chlorophyll,711.387024,0.05885933871457438,y = 0.005001 * x^0.181749,134,10.960663313432837,3.9096921347220377,0.007900276119402985,0.0027516224396933074 +linear,Chlorophyll,715.687012,0.07646592984102929,y = 0.004316 + 0.000182*x,134,10.960663313432837,3.9096921347220377,0.00631360447761194,0.0025762009517042803 +logarithmic,Chlorophyll,715.687012,0.07053737510880576,y = 0.001896 + 0.001896*ln(x),134,10.960663313432837,3.9096921347220377,0.00631360447761194,0.0025762009517042803 +exponential,Chlorophyll,715.687012,0.057012898990752126,y = 0.004952 * exp(0.018319*x),134,10.960663313432837,3.9096921347220377,0.00631360447761194,0.0025762009517042803 +power,Chlorophyll,715.687012,0.05263846505811587,y = 0.003824 * x^0.197049,134,10.960663313432837,3.9096921347220377,0.00631360447761194,0.0025762009517042803 +linear,Chlorophyll,719.992004,0.07445743675368166,y = 0.003267 + 0.000166*x,134,10.960663313432837,3.9096921347220377,0.00509136567164179,0.002385295002066048 +logarithmic,Chlorophyll,719.992004,0.06836961037451539,y = 0.001065 + 0.001728*ln(x),134,10.960663313432837,3.9096921347220377,0.00509136567164179,0.002385295002066048 +exponential,Chlorophyll,719.992004,0.05296886200774753,y = 0.003871 * exp(0.020260*x),134,10.960663313432837,3.9096921347220377,0.00509136567164179,0.002385295002066048 +power,Chlorophyll,719.992004,0.048528532136251745,y = 0.002911 * x^0.217642,134,10.960663313432837,3.9096921347220377,0.00509136567164179,0.002385295002066048 +linear,Chlorophyll,724.299011,0.06745397553654364,y = 0.002420 + 0.000133*x,134,10.960663313432837,3.9096921347220377,0.0038816044776119395,0.0020072190423221247 +logarithmic,Chlorophyll,724.299011,0.062493450238378045,y = 0.000642 + 0.001390*ln(x),134,10.960663313432837,3.9096921347220377,0.0038816044776119395,0.0020072190423221247 +exponential,Chlorophyll,724.299011,0.045936345781994126,y = 0.002921 * exp(0.020727*x),134,10.960663313432837,3.9096921347220377,0.0038816044776119395,0.0020072190423221247 +power,Chlorophyll,724.299011,0.04270170271520268,y = 0.002169 * x^0.225290,134,10.960663313432837,3.9096921347220377,0.0038816044776119395,0.0020072190423221247 +linear,Chlorophyll,728.609985,0.043742722087426356,y = 0.001822 + 0.000088*x,134,10.960663313432837,3.9096921347220377,0.0027881044776119405,0.0016469934764511023 +logarithmic,Chlorophyll,728.609985,0.04115482350546895,y = 0.000631 + 0.000926*ln(x),134,10.960663313432837,3.9096921347220377,0.0027881044776119405,0.0016469934764511023 +exponential,Chlorophyll,728.609985,0.025695766879270887,y = 0.002157 * exp(0.017908*x),134,10.960663313432837,3.9096921347220377,0.0027881044776119405,0.0016469934764511023 +power,Chlorophyll,728.609985,0.024423938138158685,y = 0.001657 * x^0.197225,134,10.960663313432837,3.9096921347220377,0.0027881044776119405,0.0016469934764511023 +linear,Chlorophyll,732.924988,0.013546034482326164,y = 0.001304 + 0.000072*x,134,10.960663313432837,3.9096921347220377,0.0020962686567164183,0.002429007647518667 +logarithmic,Chlorophyll,732.924988,0.013045034204567596,y = 0.000305 + 0.000769*ln(x),134,10.960663313432837,3.9096921347220377,0.0020962686567164183,0.002429007647518667 +power,Chlorophyll,732.924988,-0.0016075191011517553,y = 0.001527 * x^0.092327,134,10.960663313432837,3.9096921347220377,0.0020962686567164183,0.002429007647518667 +exponential,Chlorophyll,732.924988,-0.002218314899776086,y = 0.001749 * exp(0.007256*x),134,10.960663313432837,3.9096921347220377,0.0020962686567164183,0.002429007647518667 +linear,Chlorophyll,737.242004,0.01274011186937285,y = 0.000153 + 0.000183*x,134,10.960663313432837,3.9096921347220377,0.0021605000000000005,0.006345526772941662 +logarithmic,Chlorophyll,737.242004,0.011709594088295416,y = -0.002273 + 0.001903*ln(x),134,10.960663313432837,3.9096921347220377,0.0021605000000000005,0.006345526772941662 +power,Chlorophyll,737.242004,-0.004968418579431422,y = 0.001306 * x^0.096424,134,10.960663313432837,3.9096921347220377,0.0021605000000000005,0.006345526772941662 +exponential,Chlorophyll,737.242004,-0.0051926295251412125,y = 0.001506 * exp(0.007477*x),134,10.960663313432837,3.9096921347220377,0.0021605000000000005,0.006345526772941662 +linear,Chlorophyll,741.564026,0.01237420528041,y = -0.000778 + 0.000290*x,134,10.960663313432837,3.9096921347220377,0.0024028432835820897,0.010199237979184374 +logarithmic,Chlorophyll,741.564026,0.011168399659673534,y = -0.004556 + 0.002987*ln(x),134,10.960663313432837,3.9096921347220377,0.0024028432835820897,0.010199237979184374 +power,Chlorophyll,741.564026,-0.006382018366340336,y = 0.001343 * x^0.060248,134,10.960663313432837,3.9096921347220377,0.0024028432835820897,0.010199237979184374 +exponential,Chlorophyll,741.564026,-0.006486914714434855,y = 0.001471 * exp(0.004477*x),134,10.960663313432837,3.9096921347220377,0.0024028432835820897,0.010199237979184374 +linear,Chlorophyll,745.888,0.01208622807607107,y = -0.000537 + 0.000255*x,134,10.960663313432837,3.9096921347220377,0.0022554402985074623,0.00905887910227967 +logarithmic,Chlorophyll,745.888,0.010783256099801686,y = -0.003818 + 0.002607*ln(x),134,10.960663313432837,3.9096921347220377,0.0022554402985074623,0.00905887910227967 +exponential,Chlorophyll,745.888,-0.006541603078447977,y = 0.001459 * exp(0.002645*x),134,10.960663313432837,3.9096921347220377,0.0022554402985074623,0.00905887910227967 +power,Chlorophyll,745.888,-0.006568012798676248,y = 0.001406 * x^0.028233,134,10.960663313432837,3.9096921347220377,0.0022554402985074623,0.00905887910227967 +linear,Chlorophyll,750.216003,0.01149077406645782,y = -0.000338 + 0.000223*x,134,10.960663313432837,3.9096921347220377,0.0021108358208955223,0.008148439806888069 +logarithmic,Chlorophyll,750.216003,0.010336178508392302,y = -0.003238 + 0.002295*ln(x),134,10.960663313432837,3.9096921347220377,0.0021108358208955223,0.008148439806888069 +power,Chlorophyll,750.216003,-0.007024448671629768,y = 0.001432 * x^-0.001028,134,10.960663313432837,3.9096921347220377,0.0021108358208955223,0.008148439806888069 +exponential,Chlorophyll,750.216003,-0.007146981046715295,y = 0.001443 * exp(-0.000923*x),134,10.960663313432837,3.9096921347220377,0.0021108358208955223,0.008148439806888069 +linear,Chlorophyll,754.546997,0.011676905008792149,y = -0.001694 + 0.000351*x,134,10.960663313432837,3.9096921347220377,0.0021568134328358206,0.012712462658286435 +logarithmic,Chlorophyll,754.546997,0.010451547578783416,y = -0.006234 + 0.003601*ln(x),134,10.960663313432837,3.9096921347220377,0.0021568134328358206,0.012712462658286435 +power,Chlorophyll,754.546997,-0.008219467609217102,y = 0.001485 * x^-0.141847,134,10.960663313432837,3.9096921347220377,0.0021568134328358206,0.012712462658286435 +exponential,Chlorophyll,754.546997,-0.008391038873995615,y = 0.001256 * exp(-0.014916*x),134,10.960663313432837,3.9096921347220377,0.0021568134328358206,0.012712462658286435 +linear,Chlorophyll,758.882019,0.02592512089319743,y = 0.000332 + 0.000117*x,134,10.960663313432837,3.9096921347220377,0.001613589552238806,0.002839881175811175 +logarithmic,Chlorophyll,758.882019,0.023289682011479407,y = -0.001184 + 0.001201*ln(x),134,10.960663313432837,3.9096921347220377,0.001613589552238806,0.002839881175811175 +exponential,Chlorophyll,758.882019,0.0076383999092375365,y = 0.000992 * exp(0.028888*x),134,10.960663313432837,3.9096921347220377,0.001613589552238806,0.002839881175811175 +power,Chlorophyll,758.882019,0.0061542760831626575,y = 0.000678 * x^0.299540,134,10.960663313432837,3.9096921347220377,0.001613589552238806,0.002839881175811175 +linear,Chlorophyll,763.219971,0.016331099062929955,y = -0.000309 + 0.000207*x,134,10.960663313432837,3.9096921347220377,0.0019577089552238807,0.006326078022490415 +logarithmic,Chlorophyll,763.219971,0.014364159452194492,y = -0.002937 + 0.002101*ln(x),134,10.960663313432837,3.9096921347220377,0.0019577089552238807,0.006326078022490415 +exponential,Chlorophyll,763.219971,-0.002879629869520839,y = 0.001084 * exp(0.023081*x),134,10.960663313432837,3.9096921347220377,0.0019577089552238807,0.006326078022490415 +power,Chlorophyll,763.219971,-0.0036347268087895124,y = 0.000823 * x^0.227200,134,10.960663313432837,3.9096921347220377,0.0019577089552238807,0.006326078022490415 +linear,Chlorophyll,767.562012,0.013734746280580246,y = 0.000053 + 0.000156*x,134,10.960663313432837,3.9096921347220377,0.0017597089552238805,0.005193771329607048 +logarithmic,Chlorophyll,767.562012,0.012255690731871494,y = -0.001952 + 0.001593*ln(x),134,10.960663313432837,3.9096921347220377,0.0017597089552238805,0.005193771329607048 +exponential,Chlorophyll,767.562012,-0.004092583942566952,y = 0.001165 * exp(0.012080*x),134,10.960663313432837,3.9096921347220377,0.0017597089552238805,0.005193771329607048 +power,Chlorophyll,767.562012,-0.00439521419002209,y = 0.000996 * x^0.123809,134,10.960663313432837,3.9096921347220377,0.0017597089552238805,0.005193771329607048 +linear,Chlorophyll,771.906982,0.016320574765209428,y = 0.000317 + 0.000110*x,134,10.960663313432837,3.9096921347220377,0.0015226194029850744,0.0033670223010905994 +logarithmic,Chlorophyll,771.906982,0.014546529767741512,y = -0.001099 + 0.001125*ln(x),134,10.960663313432837,3.9096921347220377,0.0015226194029850744,0.0033670223010905994 +exponential,Chlorophyll,771.906982,-0.0007298834218001371,y = 0.001025 * exp(0.017660*x),134,10.960663313432837,3.9096921347220377,0.0015226194029850744,0.0033670223010905994 +power,Chlorophyll,771.906982,-0.0014459883223563175,y = 0.000819 * x^0.179543,134,10.960663313432837,3.9096921347220377,0.0015226194029850744,0.0033670223010905994 +linear,Chlorophyll,776.255005,0.013796046606459278,y = 0.000740 + 0.000072*x,134,10.960663313432837,3.9096921347220377,0.0015243955223880595,0.002382068739119507 +logarithmic,Chlorophyll,776.255005,0.012092441153714883,y = -0.000167 + 0.000726*ln(x),134,10.960663313432837,3.9096921347220377,0.0015243955223880595,0.002382068739119507 +exponential,Chlorophyll,776.255005,-0.0025031550633876076,y = 0.001209 * exp(0.008693*x),134,10.960663313432837,3.9096921347220377,0.0015243955223880595,0.002382068739119507 +power,Chlorophyll,776.255005,-0.0031841408353259215,y = 0.001093 * x^0.084233,134,10.960663313432837,3.9096921347220377,0.0015243955223880595,0.002382068739119507 +linear,Chlorophyll,780.606995,0.020495592564976572,y = 0.000739 + 0.000066*x,134,10.960663313432837,3.9096921347220377,0.001462328358208955,0.001801391351880165 +logarithmic,Chlorophyll,780.606995,0.01933435376928716,y = -0.000155 + 0.000694*ln(x),134,10.960663313432837,3.9096921347220377,0.001462328358208955,0.001801391351880165 +power,Chlorophyll,780.606995,0.00417461173259015,y = 0.000868 * x^0.177284,134,10.960663313432837,3.9096921347220377,0.001462328358208955,0.001801391351880165 +exponential,Chlorophyll,780.606995,0.004156141926620949,y = 0.001105 * exp(0.015635*x),134,10.960663313432837,3.9096921347220377,0.001462328358208955,0.001801391351880165 +linear,Chlorophyll,784.961975,0.021755422019536175,y = 0.000788 + 0.000057*x,134,10.960663313432837,3.9096921347220377,0.0014125447761194029,0.001510507088995463 +logarithmic,Chlorophyll,784.961975,0.017664886852162054,y = 0.000116 + 0.000556*ln(x),134,10.960663313432837,3.9096921347220377,0.0014125447761194029,0.001510507088995463 +exponential,Chlorophyll,784.961975,0.0034422768172869267,y = 0.001109 * exp(0.012988*x),134,10.960663313432837,3.9096921347220377,0.0014125447761194029,0.001510507088995463 +power,Chlorophyll,784.961975,0.0003808059654853979,y = 0.000984 * x^0.112589,134,10.960663313432837,3.9096921347220377,0.0014125447761194029,0.001510507088995463 +linear,Chlorophyll,789.320007,0.043367659619824805,y = 0.000649 + 0.000082*x,134,10.960663313432837,3.9096921347220377,0.0015427910447761196,0.001531740138489588 +logarithmic,Chlorophyll,789.320007,0.038409875145745676,y = -0.000395 + 0.000832*ln(x),134,10.960663313432837,3.9096921347220377,0.0015427910447761196,0.001531740138489588 +exponential,Chlorophyll,789.320007,0.026002389448048557,y = 0.001020 * exp(0.028931*x),134,10.960663313432837,3.9096921347220377,0.0015427910447761196,0.001531740138489588 +power,Chlorophyll,789.320007,0.022165630417928628,y = 0.000698 * x^0.299052,134,10.960663313432837,3.9096921347220377,0.0015427910447761196,0.001531740138489588 +linear,Chlorophyll,793.682007,0.045606716978680684,y = 0.000690 + 0.000084*x,134,10.960663313432837,3.9096921347220377,0.001615179104477612,0.0015458269286355185 +logarithmic,Chlorophyll,793.682007,0.03840486464660908,y = -0.000341 + 0.000839*ln(x),134,10.960663313432837,3.9096921347220377,0.001615179104477612,0.0015458269286355185 +exponential,Chlorophyll,793.682007,0.025844197165889593,y = 0.001092 * exp(0.026877*x),134,10.960663313432837,3.9096921347220377,0.001615179104477612,0.0015458269286355185 +power,Chlorophyll,793.682007,0.0197310845605696,y = 0.000795 * x^0.262671,134,10.960663313432837,3.9096921347220377,0.001615179104477612,0.0015458269286355185 +linear,Chlorophyll,798.046997,0.05291687536605305,y = 0.000563 + 0.000098*x,134,10.960663313432837,3.9096921347220377,0.0016406641791044776,0.0016713277988189281 +logarithmic,Chlorophyll,798.046997,0.04472311171573551,y = -0.000641 + 0.000979*ln(x),134,10.960663313432837,3.9096921347220377,0.0016406641791044776,0.0016713277988189281 +exponential,Chlorophyll,798.046997,0.028058328916172992,y = 0.001060 * exp(0.029439*x),134,10.960663313432837,3.9096921347220377,0.0016406641791044776,0.0016713277988189281 +power,Chlorophyll,798.046997,0.021436519217983063,y = 0.000744 * x^0.290697,134,10.960663313432837,3.9096921347220377,0.0016406641791044776,0.0016713277988189281 +linear,Chlorophyll,802.416016,0.060413004491346234,y = 0.000466 + 0.000109*x,134,10.960663313432837,3.9096921347220377,0.0016603507462686566,0.00173259510844654 +logarithmic,Chlorophyll,802.416016,0.05101453638227127,y = -0.000866 + 0.001084*ln(x),134,10.960663313432837,3.9096921347220377,0.0016603507462686566,0.00173259510844654 +exponential,Chlorophyll,802.416016,0.0330704948210061,y = 0.001022 * exp(0.032943*x),134,10.960663313432837,3.9096921347220377,0.0016603507462686566,0.00173259510844654 +power,Chlorophyll,802.416016,0.025369073855778357,y = 0.000686 * x^0.325774,134,10.960663313432837,3.9096921347220377,0.0016603507462686566,0.00173259510844654 +linear,Chlorophyll,806.788025,0.05998246946359442,y = 0.000383 + 0.000114*x,134,10.960663313432837,3.9096921347220377,0.0016336492537313433,0.0018214778822386276 +logarithmic,Chlorophyll,806.788025,0.05004260064643584,y = -0.000997 + 0.001129*ln(x),134,10.960663313432837,3.9096921347220377,0.0016336492537313433,0.0018214778822386276 +exponential,Chlorophyll,806.788025,0.03073454834672984,y = 0.000982 * exp(0.033910*x),134,10.960663313432837,3.9096921347220377,0.0016336492537313433,0.0018214778822386276 +power,Chlorophyll,806.788025,0.022446700301405054,y = 0.000662 * x^0.328791,134,10.960663313432837,3.9096921347220377,0.0016336492537313433,0.0018214778822386276 +linear,Chlorophyll,811.164001,0.06199436327274466,y = 0.000193 + 0.000119*x,134,10.960663313432837,3.9096921347220377,0.00149255223880597,0.0018610729394833178 +logarithmic,Chlorophyll,811.164001,0.05345700266587472,y = -0.001285 + 0.001192*ln(x),134,10.960663313432837,3.9096921347220377,0.00149255223880597,0.0018610729394833178 +exponential,Chlorophyll,811.164001,0.03466499966616288,y = 0.000817 * exp(0.041038*x),134,10.960663313432837,3.9096921347220377,0.00149255223880597,0.0018610729394833178 +power,Chlorophyll,811.164001,0.02774987016970709,y = 0.000489 * x^0.413598,134,10.960663313432837,3.9096921347220377,0.00149255223880597,0.0018610729394833178 +linear,Chlorophyll,815.541992,0.05377501182020772,y = 0.000336 + 0.000106*x,134,10.960663313432837,3.9096921347220377,0.0014953880597014925,0.0017840748382604765 +logarithmic,Chlorophyll,815.541992,0.04541196710758344,y = -0.000959 + 0.001053*ln(x),134,10.960663313432837,3.9096921347220377,0.0014953880597014925,0.0017840748382604765 +exponential,Chlorophyll,815.541992,0.029780387106763118,y = 0.000871 * exp(0.036764*x),134,10.960663313432837,3.9096921347220377,0.0014953880597014925,0.0017840748382604765 +power,Chlorophyll,815.541992,0.02259630556998271,y = 0.000564 * x^0.358942,134,10.960663313432837,3.9096921347220377,0.0014953880597014925,0.0017840748382604765 +linear,Chlorophyll,819.924988,0.046060394085339684,y = 0.000183 + 0.000105*x,134,10.960663313432837,3.9096921347220377,0.0013392835820895523,0.0019214842584320314 +logarithmic,Chlorophyll,819.924988,0.04124143103053901,y = -0.001180 + 0.001081*ln(x),134,10.960663313432837,3.9096921347220377,0.0013392835820895523,0.0019214842584320314 +exponential,Chlorophyll,819.924988,0.024172923620458642,y = 0.000721 * exp(0.041558*x),134,10.960663313432837,3.9096921347220377,0.0013392835820895523,0.0019214842584320314 +power,Chlorophyll,819.924988,0.020698277367864337,y = 0.000415 * x^0.432441,134,10.960663313432837,3.9096921347220377,0.0013392835820895523,0.0019214842584320314 +linear,Chlorophyll,824.309998,0.019229517688291176,y = 0.000174 + 0.000100*x,134,10.960663313432837,3.9096921347220377,0.0012656044776119403,0.0028070356968274437 +logarithmic,Chlorophyll,824.309998,0.017049456706681787,y = -0.001101 + 0.001016*ln(x),134,10.960663313432837,3.9096921347220377,0.0012656044776119403,0.0028070356968274437 +exponential,Chlorophyll,824.309998,-0.0013691164176472537,y = 0.000805 * exp(0.020115*x),134,10.960663313432837,3.9096921347220377,0.0012656044776119403,0.0028070356968274437 +power,Chlorophyll,824.309998,-0.00228368459628836,y = 0.000624 * x^0.203487,134,10.960663313432837,3.9096921347220377,0.0012656044776119403,0.0028070356968274437 +linear,Chlorophyll,828.698975,0.021582205152547496,y = -0.000706 + 0.000171*x,134,10.960663313432837,3.9096921347220377,0.0011649477611940298,0.004543819230423196 +logarithmic,Chlorophyll,828.698975,0.01910220662486728,y = -0.002889 + 0.001740*ln(x),134,10.960663313432837,3.9096921347220377,0.0011649477611940298,0.004543819230423196 +linear,Chlorophyll,833.09198,0.019106777392842944,y = -0.000521 + 0.000152*x,134,10.960663313432837,3.9096921347220377,0.0011438880597014925,0.004296265257070513 +logarithmic,Chlorophyll,833.09198,0.017688466235980305,y = -0.002545 + 0.001583*ln(x),134,10.960663313432837,3.9096921347220377,0.0011438880597014925,0.004296265257070513 +linear,Chlorophyll,837.487,0.01745847226311259,y = -0.000612 + 0.000154*x,134,10.960663313432837,3.9096921347220377,0.0010808880597014926,0.00456947731205439 +logarithmic,Chlorophyll,837.487,0.015969564742421505,y = -0.002647 + 0.001600*ln(x),134,10.960663313432837,3.9096921347220377,0.0010808880597014926,0.00456947731205439 +linear,Chlorophyll,841.887024,0.020571912766418676,y = -0.000836 + 0.000165*x,134,10.960663313432837,3.9096921347220377,0.000975223880597015,0.004505175408054626 +logarithmic,Chlorophyll,841.887024,0.01857954883427715,y = -0.002989 + 0.001702*ln(x),134,10.960663313432837,3.9096921347220377,0.000975223880597015,0.004505175408054626 +linear,Chlorophyll,846.289001,0.017666193062245705,y = -0.000573 + 0.000159*x,134,10.960663313432837,3.9096921347220377,0.0011751492537313433,0.004690356775258772 +logarithmic,Chlorophyll,846.289001,0.015209283936141516,y = -0.002559 + 0.001603*ln(x),134,10.960663313432837,3.9096921347220377,0.0011751492537313433,0.004690356775258772 +linear,Chlorophyll,850.695007,0.020034265491308823,y = -0.000830 + 0.000168*x,134,10.960663313432837,3.9096921347220377,0.0010073805970149251,0.004629553145314909 +logarithmic,Chlorophyll,850.695007,0.01744543460758463,y = -0.002940 + 0.001694*ln(x),134,10.960663313432837,3.9096921347220377,0.0010073805970149251,0.004629553145314909 +linear,Chlorophyll,855.104004,0.015865352453769743,y = -0.000772 + 0.000165*x,134,10.960663313432837,3.9096921347220377,0.001040134328358209,0.005131083278297242 +logarithmic,Chlorophyll,855.104004,0.01432846575764024,y = -0.002925 + 0.001702*ln(x),134,10.960663313432837,3.9096921347220377,0.001040134328358209,0.005131083278297242 +linear,Chlorophyll,859.517029,0.019429355449413932,y = -0.000838 + 0.000167*x,134,10.960663313432837,3.9096921347220377,0.0009975746268656716,0.004697662523503507 +logarithmic,Chlorophyll,859.517029,0.01692099708734196,y = -0.002948 + 0.001693*ln(x),134,10.960663313432837,3.9096921347220377,0.0009975746268656716,0.004697662523503507 +linear,Chlorophyll,863.932983,0.018492121363953173,y = -0.001032 + 0.000194*x,134,10.960663313432837,3.9096921347220377,0.001096402985074627,0.005584099891548954 +logarithmic,Chlorophyll,863.932983,0.01657550912114314,y = -0.003545 + 0.001992*ln(x),134,10.960663313432837,3.9096921347220377,0.001096402985074627,0.005584099891548954 +linear,Chlorophyll,868.353027,0.023856603861747816,y = -0.001298 + 0.000212*x,134,10.960663313432837,3.9096921347220377,0.001024410447761194,0.005364012357399879 +logarithmic,Chlorophyll,868.353027,0.02163307555513183,y = -0.004069 + 0.002186*ln(x),134,10.960663313432837,3.9096921347220377,0.001024410447761194,0.005364012357399879 +linear,Chlorophyll,872.776001,0.01720822389418175,y = -0.000913 + 0.000184*x,134,10.960663313432837,3.9096921347220377,0.0011064925373134328,0.0054913284581254935 +logarithmic,Chlorophyll,872.776001,0.015351856894195048,y = -0.003286 + 0.001885*ln(x),134,10.960663313432837,3.9096921347220377,0.0011064925373134328,0.0054913284581254935 +linear,Chlorophyll,877.202026,0.02114400380397219,y = -0.001279 + 0.000211*x,134,10.960663313432837,3.9096921347220377,0.0010304626865671645,0.005664719391631075 +logarithmic,Chlorophyll,877.202026,0.018654803312690205,y = -0.003965 + 0.002144*ln(x),134,10.960663313432837,3.9096921347220377,0.0010304626865671645,0.005664719391631075 +linear,Chlorophyll,881.632019,0.024894268838071443,y = -0.001506 + 0.000231*x,134,10.960663313432837,3.9096921347220377,0.0010241940298507462,0.005720007686402323 +logarithmic,Chlorophyll,881.632019,0.02231090779764633,y = -0.004492 + 0.002367*ln(x),134,10.960663313432837,3.9096921347220377,0.0010241940298507462,0.005720007686402323 +linear,Chlorophyll,886.065002,0.02653029310980104,y = -0.001332 + 0.000214*x,134,10.960663313432837,3.9096921347220377,0.0010117611940298505,0.005133157202769544 +logarithmic,Chlorophyll,886.065002,0.02415132163213396,y = -0.004138 + 0.002210*ln(x),134,10.960663313432837,3.9096921347220377,0.0010117611940298505,0.005133157202769544 +linear,Chlorophyll,890.502014,0.019079011868917917,y = -0.001258 + 0.000208*x,134,10.960663313432837,3.9096921347220377,0.0010267611940298507,0.0058991462652988715 +logarithmic,Chlorophyll,890.502014,0.016625122108838264,y = -0.003884 + 0.002108*ln(x),134,10.960663313432837,3.9096921347220377,0.0010267611940298507,0.0058991462652988715 +linear,Chlorophyll,894.940979,0.024644485205488564,y = -0.001350 + 0.000227*x,134,10.960663313432837,3.9096921347220377,0.0011329776119402986,0.005641248295840176 +logarithmic,Chlorophyll,894.940979,0.02251331717376026,y = -0.004332 + 0.002345*ln(x),134,10.960663313432837,3.9096921347220377,0.0011329776119402986,0.005641248295840176 +linear,Chlorophyll,899.38501,0.03288381652407146,y = -0.001508 + 0.000260*x,134,10.960663313432837,3.9096921347220377,0.0013370597014925373,0.005596229357905776 +logarithmic,Chlorophyll,899.38501,0.03016565326116205,y = -0.004938 + 0.002693*ln(x),134,10.960663313432837,3.9096921347220377,0.0013370597014925373,0.005596229357905776 +linear,Chlorophyll,903.830994,0.035029971829793505,y = -0.001832 + 0.000272*x,134,10.960663313432837,3.9096921347220377,0.0011536194029850746,0.005689943105970018 +logarithmic,Chlorophyll,903.830994,0.032438784908441876,y = -0.005463 + 0.002840*ln(x),134,10.960663313432837,3.9096921347220377,0.0011536194029850746,0.005689943105970018 +linear,Chlorophyll,908.281006,0.04451101443584671,y = -0.002025 + 0.000326*x,134,10.960663313432837,3.9096921347220377,0.0015535074626865672,0.006050183384103955 +logarithmic,Chlorophyll,908.281006,0.04098727272198477,y = -0.006354 + 0.003394*ln(x),134,10.960663313432837,3.9096921347220377,0.0015535074626865672,0.006050183384103955 +linear,Chlorophyll,912.734985,0.04418194647792806,y = -0.002162 + 0.000344*x,134,10.960663313432837,3.9096921347220377,0.0016077238805970152,0.006397218804971693 +logarithmic,Chlorophyll,912.734985,0.03771808632859286,y = -0.006413 + 0.003443*ln(x),134,10.960663313432837,3.9096921347220377,0.0016077238805970152,0.006397218804971693 +linear,Chlorophyll,917.192017,0.04798422256578916,y = -0.001730 + 0.000296*x,134,10.960663313432837,3.9096921347220377,0.0015105074626865674,0.005276653162112126 +logarithmic,Chlorophyll,917.192017,0.04314471524553409,y = -0.005566 + 0.003037*ln(x),134,10.960663313432837,3.9096921347220377,0.0015105074626865674,0.005276653162112126 +linear,Chlorophyll,921.651978,0.032258841220259904,y = -0.001537 + 0.000282*x,134,10.960663313432837,3.9096921347220377,0.0015546641791044776,0.0061404282666335346 +logarithmic,Chlorophyll,921.651978,0.028570814973279623,y = -0.005146 + 0.002876*ln(x),134,10.960663313432837,3.9096921347220377,0.0015546641791044776,0.0061404282666335346 +linear,Chlorophyll,926.116028,0.03700908239110634,y = -0.001467 + 0.000302*x,134,10.960663313432837,3.9096921347220377,0.0018425149253731344,0.0061359449742797705 +logarithmic,Chlorophyll,926.116028,0.03201058133428225,y = -0.005245 + 0.003042*ln(x),134,10.960663313432837,3.9096921347220377,0.0018425149253731344,0.0061359449742797705 +linear,Chlorophyll,930.583008,0.13672053325122402,y = -0.002670 + 0.000668*x,134,10.960663313432837,3.9096921347220377,0.0046567164179104475,0.007068415565467862 +logarithmic,Chlorophyll,930.583008,0.12021219690890728,y = -0.011165 + 0.006791*ln(x),134,10.960663313432837,3.9096921347220377,0.0046567164179104475,0.007068415565467862 +linear,Chlorophyll,935.052979,0.10169174465005926,y = -0.003412 + 0.000844*x,134,10.960663313432837,3.9096921347220377,0.005836611940298507,0.010344714149481574 +logarithmic,Chlorophyll,935.052979,0.0879115642867252,y = -0.013965 + 0.008499*ln(x),134,10.960663313432837,3.9096921347220377,0.005836611940298507,0.010344714149481574 +linear,Chlorophyll,939.526978,0.05650685234330488,y = -0.000266 + 0.000446*x,134,10.960663313432837,3.9096921347220377,0.004626597014925373,0.007341116269097569 +logarithmic,Chlorophyll,939.526978,0.042500038847443355,y = -0.005144 + 0.004194*ln(x),134,10.960663313432837,3.9096921347220377,0.004626597014925373,0.007341116269097569 +linear,Chlorophyll,944.004028,0.1409144061847427,y = -0.005615 + 0.001030*x,134,10.960663313432837,3.9096921347220377,0.005671529850746269,0.010724721720420173 +logarithmic,Chlorophyll,944.004028,0.11738954814724845,y = -0.018051 + 0.010182*ln(x),134,10.960663313432837,3.9096921347220377,0.005671529850746269,0.010724721720420173 +linear,Chlorophyll,948.484985,0.1278834494635377,y = -0.005245 + 0.000898*x,134,10.960663313432837,3.9096921347220377,0.0045965970149253734,0.009816821927842846 +logarithmic,Chlorophyll,948.484985,0.1051196488753805,y = -0.015952 + 0.008819*ln(x),134,10.960663313432837,3.9096921347220377,0.0045965970149253734,0.009816821927842846 +linear,Chlorophyll,952.968994,0.13249227032536504,y = -0.004853 + 0.000973*x,134,10.960663313432837,3.9096921347220377,0.005810186567164179,0.010449515267032196 +logarithmic,Chlorophyll,952.968994,0.11335983785512993,y = -0.016904 + 0.009749*ln(x),134,10.960663313432837,3.9096921347220377,0.005810186567164179,0.010449515267032196 +linear,Chlorophyll,957.455994,0.1277239788554937,y = -0.004033 + 0.000880*x,134,10.960663313432837,3.9096921347220377,0.005609776119402986,0.009624500382955951 +logarithmic,Chlorophyll,957.455994,0.10417499490400162,y = -0.014445 + 0.008608*ln(x),134,10.960663313432837,3.9096921347220377,0.005609776119402986,0.009624500382955951 +linear,Chlorophyll,961.947021,0.0855760468457214,y = -0.002892 + 0.000704*x,134,10.960663313432837,3.9096921347220377,0.004827671641791044,0.009412852176738786 +logarithmic,Chlorophyll,961.947021,0.07127076265553767,y = -0.011396 + 0.006963*ln(x),134,10.960663313432837,3.9096921347220377,0.004827671641791044,0.009412852176738786 +linear,Chlorophyll,966.440979,0.06587183855631606,y = -0.002339 + 0.000555*x,134,10.960663313432837,3.9096921347220377,0.0037471417910447764,0.008458583491864964 +logarithmic,Chlorophyll,966.440979,0.05598365214189571,y = -0.009174 + 0.005546*ln(x),134,10.960663313432837,3.9096921347220377,0.0037471417910447764,0.008458583491864964 +linear,Chlorophyll,970.937988,0.06413196906396867,y = -0.002253 + 0.000547*x,134,10.960663313432837,3.9096921347220377,0.003736731343283582,0.008437412920646661 +logarithmic,Chlorophyll,970.937988,0.05344664644444186,y = -0.008857 + 0.005405*ln(x),134,10.960663313432837,3.9096921347220377,0.003736731343283582,0.008437412920646661 +linear,Chlorophyll,975.439026,0.050655072210295304,y = -0.001347 + 0.000485*x,134,10.960663313432837,3.9096921347220377,0.0039709179104477615,0.008428953291941944 +logarithmic,Chlorophyll,975.439026,0.04004538724688922,y = -0.006919 + 0.004674*ln(x),134,10.960663313432837,3.9096921347220377,0.0039709179104477615,0.008428953291941944 +linear,Chlorophyll,979.94397,0.053125261271666835,y = -0.001269 + 0.000527*x,134,10.960663313432837,3.9096921347220377,0.004507320895522388,0.008938622789175931 +logarithmic,Chlorophyll,979.94397,0.04229638596970631,y = -0.007361 + 0.005094*ln(x),134,10.960663313432837,3.9096921347220377,0.004507320895522388,0.008938622789175931 +linear,Chlorophyll,984.450989,0.08789645152127279,y = -0.003521 + 0.000676*x,134,10.960663313432837,3.9096921347220377,0.0038840746268656717,0.008908837321854442 +logarithmic,Chlorophyll,984.450989,0.07481832038421343,y = -0.011848 + 0.006752*ln(x),134,10.960663313432837,3.9096921347220377,0.0038840746268656717,0.008908837321854442 +linear,Chlorophyll,988.963013,0.08457349098645517,y = -0.003461 + 0.000761*x,134,10.960663313432837,3.9096921347220377,0.004877097014925373,0.010226867156361797 +logarithmic,Chlorophyll,988.963013,0.0626221596339478,y = -0.011645 + 0.007091*ln(x),134,10.960663313432837,3.9096921347220377,0.004877097014925373,0.010226867156361797 +linear,Chlorophyll,993.47699,0.1134844755445722,y = -0.004307 + 0.000958*x,134,10.960663313432837,3.9096921347220377,0.00618789552238806,0.0111130950665975 +logarithmic,Chlorophyll,993.47699,0.10027045293847281,y = -0.016531 + 0.009751*ln(x),134,10.960663313432837,3.9096921347220377,0.00618789552238806,0.0111130950665975 +linear,Chlorophyll,997.994995,0.10101902617501812,y = -0.004767 + 0.000926*x,134,10.960663313432837,3.9096921347220377,0.005378410447761194,0.01138568086193099 +logarithmic,Chlorophyll,997.994995,0.08312184949951051,y = -0.015814 + 0.009096*ln(x),134,10.960663313432837,3.9096921347220377,0.005378410447761194,0.01138568086193099 +linear,Chlorophyll,Al10SABI,0.12358552390181055,y = -0.439887 + 0.004771*x,134,10.960663313432837,3.9096921347220377,-0.38759527726255294,0.053058693427890484 +logarithmic,Chlorophyll,Al10SABI,0.11879573339994454,y = -0.505661 + 0.050673*ln(x),134,10.960663313432837,3.9096921347220377,-0.38759527726255294,0.053058693427890484 +linear,Chlorophyll,Am092Bsub,0.1885121964498917,y = -0.000416 + 0.000036*x,134,10.960663313432837,3.9096921347220377,-2.2059701492527586e-05,0.000323751511520543 +logarithmic,Chlorophyll,Am092Bsub,0.18262380528639088,y = -0.000915 + 0.000383*ln(x),134,10.960663313432837,3.9096921347220377,-2.2059701492527586e-05,0.000323751511520543 +linear,Chlorophyll,Am09KBBI,0.4762643285625632,y = -0.043335 + 0.003808*x,133,10.9134502556391,3.886119767718547,-0.0017793295272656735,0.021441910619729863 +logarithmic,Chlorophyll,Am09KBBI,0.43987401331105946,y = -0.093808 + 0.039564*ln(x),133,10.9134502556391,3.886119767718547,-0.0017793295272656735,0.021441910619729863 +linear,Chlorophyll,Be162B643sub629,0.11325211782580646,y = -0.000557 + 0.000033*x,134,10.960663313432837,3.9096921347220377,-0.0001981940298507127,0.0003804186515039719 +logarithmic,Chlorophyll,Be162B643sub629,0.10118775386855117,y = -0.000979 + 0.000335*ln(x),134,10.960663313432837,3.9096921347220377,-0.0001981940298507127,0.0003804186515039719 +linear,Chlorophyll,Be162B700sub601,0.0169444048529096,y = -0.006350 + 0.000178*x,134,10.960663313432837,3.9096921347220377,-0.004400328358208927,0.0053424248572036485 +logarithmic,Chlorophyll,Be162B700sub601,0.014761393895308395,y = -0.008591 + 0.001799*ln(x),134,10.960663313432837,3.9096921347220377,-0.004400328358208927,0.0053424248572036485 +linear,Chlorophyll,Be162BsubPhy,0.03688932214445073,y = -0.009806 + 0.000273*x,134,10.960663313432837,3.9096921347220377,-0.006814365671641769,0.005555633366019352 +logarithmic,Chlorophyll,Be162BsubPhy,0.031115779994116965,y = -0.013141 + 0.002715*ln(x),134,10.960663313432837,3.9096921347220377,-0.006814365671641769,0.005555633366019352 +linear,Chlorophyll,Be16FLHBlueRedNIR,0.01538700206829402,y = -0.003983 + 0.000086*x,134,10.960663313432837,3.9096921347220377,-0.0030460074626865365,0.0026953295052247923 +logarithmic,Chlorophyll,Be16FLHBlueRedNIR,0.01138782391370985,y = -0.004903 + 0.000797*ln(x),134,10.960663313432837,3.9096921347220377,-0.0030460074626865365,0.0026953295052247923 +linear,Chlorophyll,Be16FLHGreenRedNIR,0.010218580183159132,y = -0.011939 + 0.000183*x,134,10.960663313432837,3.9096921347220377,-0.009929455223880576,0.007090511093103789 +logarithmic,Chlorophyll,Be16FLHGreenRedNIR,0.008441213911274725,y = -0.014135 + 0.001805*ln(x),134,10.960663313432837,3.9096921347220377,-0.009929455223880576,0.007090511093103789 +linear,Chlorophyll,Be16FLHVioletRedNIR,0.013682668849342106,y = -0.004234 + 0.000082*x,134,10.960663313432837,3.9096921347220377,-0.0033393432835820583,0.002727533132087303 +logarithmic,Chlorophyll,Be16FLHVioletRedNIR,0.009926378059882501,y = -0.005094 + 0.000753*ln(x),134,10.960663313432837,3.9096921347220377,-0.0033393432835820583,0.002727533132087303 +linear,Chlorophyll,Be16FLHblue,0.017424523813847625,y = 0.003868 + 0.000047*x,134,10.960663313432837,3.9096921347220377,0.0043792089552238435,0.001381737982346916 +logarithmic,Chlorophyll,Be16FLHblue,0.015080880084500592,y = 0.003284 + 0.000470*ln(x),134,10.960663313432837,3.9096921347220377,0.0043792089552238435,0.001381737982346916 +exponential,Chlorophyll,Be16FLHblue,-0.002117132819616341,y = 0.004168 * exp(0.001937*x),134,10.960663313432837,3.9096921347220377,0.0043792089552238435,0.001381737982346916 +power,Chlorophyll,Be16FLHblue,-0.0030671526470118504,y = 0.004076 * x^0.018690,134,10.960663313432837,3.9096921347220377,0.0043792089552238435,0.001381737982346916 +linear,Chlorophyll,Be16FLHviolet,0.0023835729307108977,y = 0.003164 + 0.000019*x,134,10.960663313432837,3.9096921347220377,0.0033735522388059305,0.0015317987259504302 +logarithmic,Chlorophyll,Be16FLHviolet,0.0018280319730009653,y = 0.002951 + 0.000181*ln(x),134,10.960663313432837,3.9096921347220377,0.0033735522388059305,0.0015317987259504302 +power,Chlorophyll,Be16FLHviolet,-0.03359801493155068,y = 0.004239 * x^-0.124759,134,10.960663313432837,3.9096921347220377,0.0033735522388059305,0.0015317987259504302 +exponential,Chlorophyll,Be16FLHviolet,-0.03542639293253047,y = 0.003613 * exp(-0.011923*x),134,10.960663313432837,3.9096921347220377,0.0033735522388059305,0.0015317987259504302 +linear,Chlorophyll,Be16MPI,0.062270111791835725,y = 0.001595 + -0.000043*x,134,10.960663313432837,3.9096921347220377,0.0011235895522387698,0.0006740220689584854 +logarithmic,Chlorophyll,Be16MPI,0.053593132296276824,y = 0.002131 + -0.000432*ln(x),134,10.960663313432837,3.9096921347220377,0.0011235895522387698,0.0006740220689584854 +linear,Chlorophyll,Be16NDPhyI,0.16604525086113353,y = -0.242290 + 0.014903*x,134,10.960663313432837,3.9096921347220377,-0.07894663731685109,0.14298591380157585 +logarithmic,Chlorophyll,Be16NDPhyI,0.14825528926546594,y = -0.434387 + 0.152553*ln(x),134,10.960663313432837,3.9096921347220377,-0.07894663731685109,0.14298591380157585 +linear,Chlorophyll,Be16NDPhyI644over615,0.021975250694089343,y = -0.065407 + 0.001863*x,134,10.960663313432837,3.9096921347220377,-0.044985950160293706,0.049137812385328355 +logarithmic,Chlorophyll,Be16NDPhyI644over615,0.018283728847716674,y = -0.087882 + 0.018411*ln(x),134,10.960663313432837,3.9096921347220377,-0.044985950160293706,0.049137812385328355 +linear,Chlorophyll,Be16NDPhyI644over629,0.03970066029484709,y = -0.043506 + 0.003785*x,134,10.960663313432837,3.9096921347220377,-0.002020256813774733,0.07426922456651143 +logarithmic,Chlorophyll,Be16NDPhyI644over629,0.0335412781786012,y = -0.089835 + 0.037690*ln(x),134,10.960663313432837,3.9096921347220377,-0.002020256813774733,0.07426922456651143 +logarithmic,Chlorophyll,Be16NDTIblue,0.0033079929100812144,y = -0.134462 + -0.011840*ln(x),133,10.9134502556391,3.886119767718547,-0.16200172104305108,0.07399047397462093 +linear,Chlorophyll,Be16NDTIblue,0.00317524512241274,y = -0.150293 + -0.001073*x,133,10.9134502556391,3.886119767718547,-0.16200172104305108,0.07399047397462093 +linear,Chlorophyll,Be16NDTIviolet,0.0005010487351857495,y = -0.175143 + 0.000717*x,133,10.9134502556391,3.886119767718547,-0.16732129594785264,0.124429968840917 +logarithmic,Chlorophyll,Be16NDTIviolet,0.0004086312956946836,y = -0.183599 + 0.006998*ln(x),133,10.9134502556391,3.886119767718547,-0.16732129594785264,0.124429968840917 +linear,Chlorophyll,Be16Phy2BDA644over629,0.023206004958571502,y = 0.713769 + 0.030997*x,134,10.960663313432837,3.9096921347220377,1.053521162810137,0.7955509661543532 +logarithmic,Chlorophyll,Be16Phy2BDA644over629,0.0187754308216187,y = 0.349750 + 0.302055*ln(x),134,10.960663313432837,3.9096921347220377,1.053521162810137,0.7955509661543532 +exponential,Chlorophyll,Be16Phy2BDA644over629,0.008223792315415035,y = 0.900438 * exp(0.009666*x),134,10.960663313432837,3.9096921347220377,1.053521162810137,0.7955509661543532 +power,Chlorophyll,Be16Phy2BDA644over629,0.00591210292212041,y = 0.801083 * x^0.095650,134,10.960663313432837,3.9096921347220377,1.053521162810137,0.7955509661543532 +linear,Chlorophyll,De933BDA,0.00023919683733564234,y = -0.009738 + 0.000030*x,134,10.960663313432837,3.9096921347220377,-0.009405067164179083,0.007689652483460126 +logarithmic,Chlorophyll,De933BDA,0.00011330432702505444,y = -0.009934 + 0.000227*ln(x),134,10.960663313432837,3.9096921347220377,-0.009405067164179083,0.007689652483460126 +linear,Chlorophyll,Go04MCI,0.0037028838151977883,y = 0.008870 + -0.000177*x,134,10.960663313432837,3.9096921347220377,0.006929253731343275,0.011379134657238244 +logarithmic,Chlorophyll,Go04MCI,0.0031526249468724066,y = 0.011054 + -0.001770*ln(x),134,10.960663313432837,3.9096921347220377,0.006929253731343275,0.011379134657238244 +linear,Chlorophyll,HU103BDA,0.022814312069399434,y = -52.916771 + 7.153375*x,134,10.960663313432837,3.9096921347220377,25.488964765505937,185.16115053399594 +logarithmic,Chlorophyll,HU103BDA,0.018338828535085838,y = -136.395192 + 69.479719*ln(x),134,10.960663313432837,3.9096921347220377,25.488964765505937,185.16115053399594 +logarithmic,Chlorophyll,Kn07KIVU,0.00032084600727178003,y = -0.000024 + 0.003864*ln(x),134,10.960663313432837,3.9096921347220377,0.008978853062571953,0.07784863735321206 +linear,Chlorophyll,Kn07KIVU,9.160946797537317e-05,y = 0.011068 + -0.000191*x,134,10.960663313432837,3.9096921347220377,0.008978853062571953,0.07784863735321206 +linear,Chlorophyll,Ku15PhyCI,0.007889056025643382,y = -0.000509 + 0.000148*x,134,10.960663313432837,3.9096921347220377,0.0011146865671641457,0.006521171197136047 +logarithmic,Chlorophyll,Ku15PhyCI,0.0062998084059643356,y = -0.002227 + 0.001434*ln(x),134,10.960663313432837,3.9096921347220377,0.0011146865671641457,0.006521171197136047 +linear,Chlorophyll,Ku15SLH,0.015206455027719001,y = -0.009458 + 0.000383*x,134,10.960663313432837,3.9096921347220377,-0.005264970149253696,0.012128158740383666 +logarithmic,Chlorophyll,Ku15SLH,0.01247925513910264,y = -0.014012 + 0.003754*ln(x),134,10.960663313432837,3.9096921347220377,-0.005264970149253696,0.012128158740383666 +linear,Chlorophyll,MI092BDA,0.08763672931756827,y = 0.415458 + 0.031001*x,134,10.960663313432837,3.9096921347220377,0.7552526677152894,0.40942970094366693 +logarithmic,Chlorophyll,MI092BDA,0.07689535140547887,y = 0.022263 + 0.314595*ln(x),134,10.960663313432837,3.9096921347220377,0.7552526677152894,0.40942970094366693 +exponential,Chlorophyll,MI092BDA,0.06807967150006189,y = 0.543612 * exp(0.025329*x),134,10.960663313432837,3.9096921347220377,0.7552526677152894,0.40942970094366693 +power,Chlorophyll,MI092BDA,0.05909773540292862,y = 0.391266 * x^0.260295,134,10.960663313432837,3.9096921347220377,0.7552526677152894,0.40942970094366693 +linear,Chlorophyll,MM092BDA,0.05916056039742046,y = 0.020110 + 0.025980*x,134,10.960663313432837,3.9096921347220377,0.30486885589695534,0.4176058240320284 +logarithmic,Chlorophyll,MM092BDA,0.05062582327866094,y = -0.301758 + 0.260360*ln(x),134,10.960663313432837,3.9096921347220377,0.30486885589695534,0.4176058240320284 +exponential,Chlorophyll,MM092BDA,0.031081382027952853,y = 0.169237 * exp(0.039710*x),134,10.960663313432837,3.9096921347220377,0.30486885589695534,0.4176058240320284 +power,Chlorophyll,MM092BDA,0.025455662241081423,y = 0.100699 * x^0.409629,134,10.960663313432837,3.9096921347220377,0.30486885589695534,0.4176058240320284 +linear,Chlorophyll,MM12NDCI,0.14968049493387692,y = -0.334438 + 0.016524*x,134,10.960663313432837,3.9096921347220377,-0.15332684559895957,0.1669812364274784 +logarithmic,Chlorophyll,MM12NDCI,0.1284752471027638,y = -0.539735 + 0.165844*ln(x),134,10.960663313432837,3.9096921347220377,-0.15332684559895957,0.1669812364274784 +linear,Chlorophyll,MM12NDCIalt,0.1782969390839364,y = -0.067623 + 0.015108*x,134,10.960663313432837,3.9096921347220377,0.09797303208020114,0.1398887140001231 +logarithmic,Chlorophyll,MM12NDCIalt,0.15469579925762256,y = -0.257241 + 0.152456*ln(x),134,10.960663313432837,3.9096921347220377,0.09797303208020114,0.1398887140001231 +linear,Chlorophyll,SI052BDA,0.02394542541720257,y = -2.641394 + 0.392126*x,134,10.960663313432837,3.9096921347220377,1.6565652837978109,9.907333404366277 +logarithmic,Chlorophyll,SI052BDA,0.019350113206532904,y = -7.240919 + 3.818747*ln(x),134,10.960663313432837,3.9096921347220377,1.6565652837978109,9.907333404366277 +exponential,Chlorophyll,SI052BDA,-0.0034738533070597377,y = 0.468901 * exp(0.044799*x),134,10.960663313432837,3.9096921347220377,1.6565652837978109,9.907333404366277 +power,Chlorophyll,SI052BDA,-0.004314196524685121,y = 0.267297 * x^0.451967,134,10.960663313432837,3.9096921347220377,1.6565652837978109,9.907333404366277 +linear,Chlorophyll,SM122BDA,0.06414848722020783,y = 0.199657 + 0.045876*x,134,10.960663313432837,3.9096921347220377,0.7024913829628076,0.7081702489195397 +logarithmic,Chlorophyll,SM122BDA,0.05481383167054166,y = -0.367922 + 0.459415*ln(x),134,10.960663313432837,3.9096921347220377,0.7024913829628076,0.7081702489195397 +exponential,Chlorophyll,SM122BDA,0.0384911707850909,y = 0.442182 * exp(0.032597*x),134,10.960663313432837,3.9096921347220377,0.7024913829628076,0.7081702489195397 +power,Chlorophyll,SM122BDA,0.0316342801109748,y = 0.291960 * x^0.331506,134,10.960663313432837,3.9096921347220377,0.7024913829628076,0.7081702489195397 +logarithmic,Chlorophyll,TurbBow06RedOverGreen,0.0008581470760438137,y = 0.400651 + 0.028309*ln(x),134,10.960663313432837,3.9096921347220377,0.46660909641031345,0.348755127089641 +linear,Chlorophyll,TurbBow06RedOverGreen,0.0008315690342066695,y = 0.438415 + 0.002572*x,134,10.960663313432837,3.9096921347220377,0.46660909641031345,0.348755127089641 +linear,Chlorophyll,TurbChip09NIROverGreen,0.01848858969012912,y = -0.040594 + 0.008604*x,134,10.960663313432837,3.9096921347220377,0.053712679806626945,0.2473987712418453 +logarithmic,Chlorophyll,TurbChip09NIROverGreen,0.016570559702190013,y = -0.151893 + 0.088245*ln(x),134,10.960663313432837,3.9096921347220377,0.053712679806626945,0.2473987712418453 +linear,Chlorophyll,TurbHarr92NIR,0.015865352453769743,y = -0.000772 + 0.000165*x,134,10.960663313432837,3.9096921347220377,0.001040134328358209,0.005131083278297242 +logarithmic,Chlorophyll,TurbHarr92NIR,0.01432846575764024,y = -0.002925 + 0.001702*ln(x),134,10.960663313432837,3.9096921347220377,0.001040134328358209,0.005131083278297242 +linear,Chlorophyll,TurbLath91RedOverBlue,0.00025779216509291825,y = 0.723653 + 0.000603*x,133,10.9134502556391,3.886119767718547,0.7302312316955419,0.14589486349017858 +logarithmic,Chlorophyll,TurbLath91RedOverBlue,0.00015814654236934178,y = 0.718358 + 0.005104*ln(x),133,10.9134502556391,3.886119767718547,0.7302312316955419,0.14589486349017858 +power,Chlorophyll,TurbLath91RedOverBlue,-0.007759099371790423,y = 0.765225 * x^-0.025934,133,10.9134502556391,3.886119767718547,0.7302312316955419,0.14589486349017858 +exponential,Chlorophyll,TurbLath91RedOverBlue,-0.007972973578523668,y = 0.739256 * exp(-0.002364*x),133,10.9134502556391,3.886119767718547,0.7302312316955419,0.14589486349017858 +linear,Chlorophyll,TurbMoore80Red,2.3065421406842646e-05,y = 0.009045 + -0.000009*x,134,10.960663313432837,3.9096921347220377,0.008946089552238806,0.007372050533373144 +logarithmic,Chlorophyll,TurbMoore80Red,8.478475440609756e-07,y = 0.008902 + 0.000019*ln(x),134,10.960663313432837,3.9096921347220377,0.008946089552238806,0.007372050533373144 +linear,Chlorophyll,Wy08CI,0.004922647127989954,y = -0.003917 + 0.000117*x,134,10.960663313432837,3.9096921347220377,-0.0026294477611939994,0.006547381229445023 +logarithmic,Chlorophyll,Wy08CI,0.0037258564416292606,y = -0.005210 + 0.001107*ln(x),134,10.960663313432837,3.9096921347220377,-0.0026294477611939994,0.006547381229445023 +linear,Chlorophyll,Zh10FLH,0.0034208029090143643,y = -0.003691 + 0.000107*x,134,10.960663313432837,3.9096921347220377,-0.0025192910447760807,0.007148572087690299 +logarithmic,Chlorophyll,Zh10FLH,0.0029682401109050183,y = -0.005034 + 0.001079*ln(x),134,10.960663313432837,3.9096921347220377,-0.0025192910447760807,0.007148572087690299 diff --git a/src/postprocessing/__init__.py b/src/postprocessing/__init__.py new file mode 100644 index 0000000..7c68785 --- /dev/null +++ b/src/postprocessing/__init__.py @@ -0,0 +1 @@ +# -*- coding: utf-8 -*- \ No newline at end of file diff --git a/src/postprocessing/box_plot.py b/src/postprocessing/box_plot.py new file mode 100644 index 0000000..f1ab850 --- /dev/null +++ b/src/postprocessing/box_plot.py @@ -0,0 +1,327 @@ +import pandas as pd +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns +import os + +# 设置中文字体 +plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei'] +plt.rcParams['axes.unicode_minus'] = False + +def plot_individual_boxplots(csv_file_path, save_dir="boxplots"): + """ + 为每个数据列单独绘制箱型图并保存 + + 参数: + csv_file_path: CSV文件路径 + save_dir: 保存图片的目录 + """ + try: + # 读取CSV文件 + df = pd.read_csv(csv_file_path) + + # 获取第五列之后的数据列（索引从0开始，第五列索引为4） + data_columns = df.iloc[:, 4:] + + # 检查是否有数据列 + if data_columns.empty: + print("错误：CSV文件中没有足够的列（至少需要5列）") + return + + # 创建保存目录 + if not os.path.exists(save_dir): + os.makedirs(save_dir) + print(f"创建目录: {save_dir}") + + # 为每个数据列单独绘制箱型图 + for column in data_columns.columns: + # 移除空值 + clean_data = data_columns[column].dropna() + + if len(clean_data) == 0: + print(f"跳过列 '{column}': 没有有效数据") + continue + + # 创建新图形 + plt.figure(figsize=(8, 6)) + + # 绘制箱型图 + box_plot = plt.boxplot([clean_data], labels=[column], patch_artist=True, + showfliers=False) + + # 美化箱型图 + box_plot['boxes'][0].set_facecolor('lightblue') + box_plot['boxes'][0].set_alpha(0.7) + + # 添加散点 + x_pos = np.random.normal(1, 0.04, size=len(clean_data)) + plt.scatter(x_pos, clean_data, alpha=0.6, s=30, color='red', + edgecolors='black', linewidth=0.5, zorder=3) + + # 设置标题和标签 + plt.title(f'{column} - 箱型图', fontsize=14, fontweight='bold') + plt.xlabel('数据列', fontsize=12) + plt.ylabel('数值', fontsize=12) + + # 添加统计信息到图上 + stats_text = f'数据点数: {len(clean_data)}\n均值: {clean_data.mean():.2f}\n中位数: {clean_data.median():.2f}\n标准差: {clean_data.std():.2f}' + plt.text(0.02, 0.98, stats_text, transform=plt.gca().transAxes, + verticalalignment='top', bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.8)) + + # 添加网格 + plt.grid(True, alpha=0.3, linestyle='--') + + # 调整布局 + plt.tight_layout() + + # 保存图片 + safe_column_name = column.replace('/', '_').replace('\\', '_').replace(':', '_') + save_path = os.path.join(save_dir, f'{safe_column_name}_boxplot.png') + plt.savefig(save_path, dpi=300, bbox_inches='tight') + print(f"已保存: {save_path}") + + # 关闭图形以释放内存 + plt.close() + + print(f"\n所有箱型图已保存到目录: {save_dir}") + + except FileNotFoundError: + print(f"错误：找不到文件 {csv_file_path}") + except Exception as e: + print(f"错误：{str(e)}") + +def plot_individual_boxplots_seaborn(csv_file_path, save_dir="boxplots_seaborn"): + """ + 使用seaborn为每个数据列单独绘制箱型图并保存 + + 参数: + csv_file_path: CSV文件路径 + save_dir: 保存图片的目录 + """ + try: + # 读取CSV文件 + df = pd.read_csv(csv_file_path) + + # 获取第五列之后的数据列 + data_columns = df.iloc[:, 4:] + + if data_columns.empty: + print("错误：CSV文件中没有足够的列（至少需要5列）") + return + + # 创建保存目录 + if not os.path.exists(save_dir): + os.makedirs(save_dir) + print(f"创建目录: {save_dir}") + + # 为每个数据列单独绘制箱型图 + for column in data_columns.columns: + # 移除空值 + clean_data = data_columns[column].dropna() + + if len(clean_data) == 0: + print(f"跳过列 '{column}': 没有有效数据") + continue + + # 创建新图形 + plt.figure(figsize=(8, 6)) + + # 创建数据框用于seaborn + plot_data = pd.DataFrame({ + '列名': [column] * len(clean_data), + '数值': clean_data + }) + + # 使用seaborn绘制箱型图和散点 + sns.boxplot(data=plot_data, x='列名', y='数值', palette='Set2') + sns.stripplot(data=plot_data, x='列名', y='数值', + color='red', alpha=0.6, size=5, jitter=True) + + # 设置标题和标签 + plt.title(f'{column} - 箱型图 (Seaborn)', fontsize=14, fontweight='bold') + plt.xlabel('数据列', fontsize=12) + plt.ylabel('数值', fontsize=12) + + # 添加统计信息 + stats_text = f'数据点数: {len(clean_data)}\n均值: {clean_data.mean():.2f}\n中位数: {clean_data.median():.2f}\n标准差: {clean_data.std():.2f}' + plt.text(0.02, 0.98, stats_text, transform=plt.gca().transAxes, + verticalalignment='top', bbox=dict(boxstyle='round', facecolor='lightgreen', alpha=0.8)) + + # 添加网格 + plt.grid(True, alpha=0.3, linestyle='--') + + # 调整布局 + plt.tight_layout() + + # 保存图片 + safe_column_name = column.replace('/', '_').replace('\\', '_').replace(':', '_') + save_path = os.path.join(save_dir, f'{safe_column_name}_boxplot_seaborn.png') + plt.savefig(save_path, dpi=300, bbox_inches='tight') + print(f"已保存: {save_path}") + + # 关闭图形以释放内存 + plt.close() + + print(f"\n所有箱型图已保存到目录: {save_dir}") + + except Exception as e: + print(f"错误：{str(e)}") + +def plot_boxplot_with_scatter(csv_file_path): + """ + 读取CSV文件并绘制第五列之后数据列的箱型图，同时标注散点 + + 参数: + csv_file_path: CSV文件路径 + """ + try: + # 读取CSV文件 + df = pd.read_csv(csv_file_path) + + # 获取第五列之后的数据列（索引从0开始，第五列索引为4） + data_columns = df.iloc[:, 4:] # 从第五列开始的所有列 + + # 检查是否有数据列 + if data_columns.empty: + print("错误：CSV文件中没有足够的列（至少需要5列）") + return + + # 设置图形大小 + plt.figure(figsize=(12, 8)) + + # 准备数据用于绘制箱型图 + box_data = [] + labels = [] + + for column in data_columns.columns: + # 移除空值 + clean_data = data_columns[column].dropna() + if len(clean_data) > 0: + box_data.append(clean_data) + labels.append(column) + + # 绘制箱型图 + box_plot = plt.boxplot(box_data, labels=labels, patch_artist=True, + showfliers=False) # 不显示异常值点，因为我们要自己绘制散点 + + # 美化箱型图 + colors = plt.cm.Set3(np.linspace(0, 1, len(box_data))) + for patch, color in zip(box_plot['boxes'], colors): + patch.set_facecolor(color) + patch.set_alpha(0.7) + + # 在每个箱型图上添加散点 + for i, data in enumerate(box_data): + # 为每个数据点添加一些随机的x轴偏移，避免重叠 + x_pos = np.random.normal(i + 1, 0.04, size=len(data)) + + # 绘制散点 + plt.scatter(x_pos, data, alpha=0.6, s=20, color='red', + edgecolors='black', linewidth=0.5, zorder=3) + + # 设置标题和标签 + plt.title('数据列箱型图（带散点标注）', fontsize=16, fontweight='bold') + plt.xlabel('数据列', fontsize=12) + plt.ylabel('数值', fontsize=12) + + # 旋转x轴标签以避免重叠 + plt.xticks(rotation=45, ha='right') + + # 添加网格 + plt.grid(True, alpha=0.3, linestyle='--') + + # 调整布局 + plt.tight_layout() + + # 显示图形 + plt.show() + + # 打印统计信息 + print(f"成功绘制了 {len(labels)} 个数据列的箱型图") + print("数据列名称:", labels) + + # 显示每列的基本统计信息 + print("\n各列基本统计信息:") + for column in labels: + data = data_columns[column].dropna() + print(f"{column}: 数据点数={len(data)}, 均值={data.mean():.2f}, 中位数={data.median():.2f}") + + except FileNotFoundError: + print(f"错误：找不到文件 {csv_file_path}") + except Exception as e: + print(f"错误：{str(e)}") + +def plot_boxplot_with_seaborn(csv_file_path): + """ + 使用seaborn绘制更美观的箱型图（可选方法） + + 参数: + csv_file_path: CSV文件路径 + """ + try: + # 读取CSV文件 + df = pd.read_csv(csv_file_path) + + # 获取第五列之后的数据列 + data_columns = df.iloc[:, 4:] + + if data_columns.empty: + print("错误：CSV文件中没有足够的列（至少需要5列）") + return + + # 将数据转换为长格式用于seaborn + melted_data = pd.melt(data_columns, var_name='列名', value_name='数值') + melted_data = melted_data.dropna() # 移除空值 + + # 设置图形大小 + plt.figure(figsize=(12, 8)) + + # 使用seaborn绘制箱型图和散点 + sns.boxplot(data=melted_data, x='列名', y='数值', palette='Set3') + sns.stripplot(data=melted_data, x='列名', y='数值', + color='red', alpha=0.6, size=4, jitter=True) + + # 设置标题和标签 + plt.title('数据列箱型图（Seaborn版本）', fontsize=16, fontweight='bold') + plt.xlabel('数据列', fontsize=12) + plt.ylabel('数值', fontsize=12) + + # 旋转x轴标签 + plt.xticks(rotation=45, ha='right') + + # 添加网格 + plt.grid(True, alpha=0.3, linestyle='--') + + # 调整布局 + plt.tight_layout() + + # 显示图形 + plt.show() + + except Exception as e: + print(f"错误：{str(e)}") + +# 主程序 +if __name__ == "__main__": + # 请修改为您的CSV文件路径 + csv_file_path = r"E:\code\WQ\yaobao925\output.csv" # 替换为您的CSV文件路径 + + print("请选择绘图方法：") + print("1. 使用matplotlib绘制（所有列在一张图）") + print("2. 使用seaborn绘制（所有列在一张图）") + print("3. 分别绘制每列并保存（matplotlib版本）") + print("4. 分别绘制每列并保存（seaborn版本）") + + choice = input("请输入选择（1-4）：").strip() + + if choice == "1": + plot_boxplot_with_scatter(csv_file_path) + elif choice == "2": + plot_boxplot_with_seaborn(csv_file_path) + elif choice == "3": + plot_individual_boxplots(csv_file_path) + elif choice == "4": + plot_individual_boxplots_seaborn(csv_file_path) + else: + print("默认使用分别绘制并保存（seaborn版本）...") + plot_individual_boxplots_seaborn(csv_file_path) \ No newline at end of file diff --git a/src/postprocessing/flight_path.py b/src/postprocessing/flight_path.py new file mode 100644 index 0000000..c93e060 --- /dev/null +++ b/src/postprocessing/flight_path.py @@ -0,0 +1,545 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +飞行轨迹可视化模块 - 在多架次GPS数据上绘制飞行轨迹 + +支持功能： +1. 读取多个.gps文件（每个文件代表一个架次） +2. 在高光谱假彩色影像上绘制飞行轨迹 +3. 不同架次使用不同颜色 +4. 图例显示架次起始到结束的时间段 +5. 添加指北针、比例尺 +6. 保存为PNG图像 +""" + +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +from pathlib import Path +from typing import Optional, Tuple, List, Dict, Union +import warnings +from matplotlib.patches import FancyArrowPatch +import matplotlib.patheffects as path_effects +from datetime import datetime +from matplotlib.colors import ListedColormap +import matplotlib.patches as mpatches + +# 性能优化配置 +plt.rcParams['agg.path.chunksize'] = 10000 +plt.rcParams['path.simplify'] = True +plt.rcParams['path.simplify_threshold'] = 0.1 + +# 导入GDAL用于影像读写 +try: + from osgeo import gdal, osr + GDAL_AVAILABLE = True +except ImportError: + GDAL_AVAILABLE = False + print("警告: GDAL未安装，地理坐标转换功能可能无法正常工作") + + +class FlightPathVisualizer: + """飞行轨迹可视化类 - 在高光谱假彩色影像上绘制多架次飞行轨迹""" + + # 预定义颜色方案（不同架次使用不同颜色） + FLIGHT_COLORS = [ + '#FF0000', # 红色 + '#00FF00', # 绿色 + '#0000FF', # 蓝色 + '#FF00FF', # 紫色 + '#00FFFF', # 青色 + '#FFFF00', # 黄色 + '#FF8000', # 橙色 + '#8000FF', # 紫罗兰 + '#0080FF', # 天蓝 + '#FF0080', # 粉红 + ] + + def __init__(self, output_dir: str = "./flight_paths"): + """ + 初始化飞行轨迹可视化器 + + Args: + output_dir: 输出目录，用于保存生成的轨迹图 + """ + self.output_dir = Path(output_dir) + self.output_dir.mkdir(parents=True, exist_ok=True) + + # 设置中文字体 + plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans', 'Arial Unicode MS'] + plt.rcParams['axes.unicode_minus'] = False + plt.rcParams['font.size'] = 12 + + warnings.filterwarnings('ignore') + + def create_flight_path_map(self, + gps_folder: str, + hyperspectral_path: str, + output_filename: Optional[str] = None, + rgb_bands: Optional[List[int]] = None, + line_width: int = 2, + show_north_arrow: bool = True, + show_scale_bar: bool = True, + dpi: int = 300) -> str: + """ + 创建飞行轨迹地图：在高光谱假彩色影像上绘制多架次飞行轨迹 + + Args: + gps_folder: GPS文件夹路径，包含多个.gps文件 + hyperspectral_path: 高光谱影像文件路径 (.dat, .bsq, .tif等) + output_filename: 输出文件名（如果为None则自动生成） + rgb_bands: 用于RGB合成的三个波段索引 [R, G, B]，默认为None自动选择(650,550,460nm) + line_width: 轨迹线宽 + show_north_arrow: 是否显示指北针 + show_scale_bar: 是否显示比例尺 + dpi: 输出图像分辨率 + + Returns: + 生成的地图文件路径 + """ + if not GDAL_AVAILABLE: + raise ImportError("GDAL未安装，无法处理地理坐标转换") + + print(f"正在生成飞行轨迹地图...") + + # 读取高光谱影像 + hyperspectral_img, geotransform, projection, width, height = self._read_hyperspectral( + hyperspectral_path, rgb_bands) + + # 读取所有GPS文件 + flight_data = self._read_gps_files(gps_folder) + + if not flight_data: + raise ValueError(f"未在 {gps_folder} 中找到有效的.gps文件") + + # 将GPS坐标转换为像素坐标 + flight_pixels = self._convert_flights_to_pixels( + flight_data, geotransform, width, height, projection) + + # 创建地图 + if output_filename is None: + folder_name = Path(gps_folder).name + hs_name = Path(hyperspectral_path).stem + output_filename = f"{hs_name}_{folder_name}_flight_paths.png" + + output_path = self.output_dir / output_filename + + self._create_map_visualization( + hyperspectral_img, flight_pixels, flight_data, + str(output_path), line_width, + show_north_arrow, show_scale_bar, dpi, + geotransform, width, height + ) + + print(f"飞行轨迹地图已保存: {output_path}") + return str(output_path) + + def _read_hyperspectral(self, hyperspectral_path: str, + rgb_bands: Optional[List[int]] = None) -> Tuple[np.ndarray, tuple, str, int, int]: + """读取高光谱影像 - 使用650/550/460nm波长""" + dataset = gdal.Open(hyperspectral_path) + if dataset is None: + raise ValueError(f"无法打开高光谱影像: {hyperspectral_path}") + + width = dataset.RasterXSize + height = dataset.RasterYSize + band_count = dataset.RasterCount + + # 确定要读取的波段 - 使用指定波长 650, 550, 460nm + if rgb_bands is None: + if band_count >= 3: + try: + from src.utils.util import find_band_number + rgb_bands = [ + find_band_number(650.0, hyperspectral_path), # Red ~650nm + find_band_number(550.0, hyperspectral_path), # Green ~550nm + find_band_number(460.0, hyperspectral_path) # Blue ~460nm + ] + print(f" 根据波长选择RGB波段: R={rgb_bands[0]}, G={rgb_bands[1]}, B={rgb_bands[2]}") + except Exception as e: + print(f" 波长查找失败 ({e})，使用默认索引") + rgb_bands = [min(band_count-1, int(band_count*0.25)), + min(band_count-1, int(band_count*0.15)), + min(band_count-1, int(band_count*0.05))] + else: + rgb_bands = [0, 0, 0] + + # 读取RGB波段 + rgb_data = [] + for band_idx in rgb_bands: + band = dataset.GetRasterBand(band_idx + 1) + band_data = band.ReadAsArray().astype(np.float32) + rgb_data.append(band_data) + + # 堆叠为RGB图像 + if len(rgb_data) == 3: + image_array = np.stack(rgb_data, axis=2) + else: + image_array = np.stack([rgb_data[0]]*3, axis=2) + + geotransform = dataset.GetGeoTransform() + projection = dataset.GetProjection() + dataset = None + + print(f" 读取影像: {width}x{height}x{image_array.shape[2]} (RGB)") + if projection: + proj_type = "投影坐标系" if "PROJCS" in projection else "地理坐标系" + print(f" 影像投影: {proj_type}") + + return image_array, geotransform, projection, width, height + + def _read_gps_files(self, gps_folder: str) -> Dict[str, pd.DataFrame]: + """ + 读取文件夹中的所有.gps文件 + + 文件格式: 日期、时间、三个姿态角、经度、纬度、高程 + 列: [date, time, pitch, roll, yaw, longitude, latitude, altitude] + """ + gps_folder_path = Path(gps_folder) + if not gps_folder_path.exists(): + raise FileNotFoundError(f"GPS文件夹不存在: {gps_folder}") + + gps_files = list(gps_folder_path.glob("*.gps")) + if not gps_files: + print(f"警告: 在 {gps_folder} 中未找到.gps文件") + return {} + + print(f"找到 {len(gps_files)} 个GPS文件") + + flight_data = {} + for gps_file in gps_files: + try: + # 读取GPS文件（制表符分隔） + df = pd.read_csv(gps_file, sep='\t', header=None, + names=['date', 'time', 'pitch', 'roll', 'yaw', + 'longitude', 'latitude', 'altitude']) + + # 确保数值类型 + df['longitude'] = pd.to_numeric(df['longitude'], errors='coerce') + df['latitude'] = pd.to_numeric(df['latitude'], errors='coerce') + df['altitude'] = pd.to_numeric(df['altitude'], errors='coerce') + + # 删除无效坐标 + df = df.dropna(subset=['longitude', 'latitude']) + + if len(df) > 0: + flight_data[gps_file.stem] = df + print(f" ✓ 读取 {gps_file.name}: {len(df)} 个轨迹点") + # 显示时间范围 + start_time = df.iloc[0]['time'] + end_time = df.iloc[-1]['time'] + print(f" 时间范围: {start_time} - {end_time}") + else: + print(f" ✗ {gps_file.name}: 无有效数据") + + except Exception as e: + print(f" ✗ 读取 {gps_file.name} 失败: {e}") + + return flight_data + + def _convert_flights_to_pixels(self, flight_data: Dict[str, pd.DataFrame], + geotransform: tuple, width: int, height: int, + projection: str = "") -> Dict[str, List[Tuple[float, float]]]: + """将所有飞行轨迹的地理坐标转换为像素坐标""" + if geotransform is None: + print("警告: 无地理变换信息，无法转换坐标") + return {} + + gt = geotransform + flight_pixels = {} + + # 检查是否需要投影转换 + needs_transform = projection and ("PROJCS" in projection or "GEOGCS" in projection) + transform = None + + if needs_transform and GDAL_AVAILABLE: + try: + src_srs = osr.SpatialReference() + src_srs.ImportFromEPSG(4326) # WGS84 + dst_srs = osr.SpatialReference() + dst_srs.ImportFromWkt(projection) + transform = osr.CoordinateTransformation(src_srs, dst_srs) + print(" ✓ 已创建WGS84到影像投影的坐标转换") + except Exception as e: + print(f" ⚠ 坐标转换创建失败: {e}") + + for flight_name, df in flight_data.items(): + pixel_coords = [] + + for _, row in df.iterrows(): + lon = float(row['longitude']) + lat = float(row['latitude']) + + if transform is not None: + try: + proj_x, proj_y, _ = transform.TransformPoint(lat, lon) + x = (proj_x - gt[0]) / gt[1] + y = (proj_y - gt[3]) / gt[5] + except: + x = (lon - gt[0]) / gt[1] + y = (lat - gt[3]) / gt[5] + else: + x = (lon - gt[0]) / gt[1] + y = (lat - gt[3]) / gt[5] + + # 限制在图像范围内 + x = max(0, min(x, width - 1)) + y = max(0, min(y, height - 1)) + + pixel_coords.append((x, y)) + + flight_pixels[flight_name] = pixel_coords + + print(f" 已转换 {len(flight_pixels)} 个架次的坐标") + return flight_pixels + + def _create_false_color_image(self, image_array: np.ndarray) -> np.ndarray: + """创建假彩色RGB图像 - 应用线性拉伸""" + if image_array.shape[2] != 3: + if len(image_array.shape) == 2 or image_array.shape[2] == 1: + if len(image_array.shape) == 2: + image_array = np.stack([image_array]*3, axis=2) + else: + image_array = np.repeat(image_array, 3, axis=2) + + def simple_linear_stretch(data, min_percent=1, max_percent=99): + valid_data = data[np.isfinite(data)] + if len(valid_data) == 0: + return np.zeros_like(data, dtype=np.float32) + + p_low = np.percentile(valid_data, min_percent) + p_high = np.percentile(valid_data, max_percent) + + if p_high - p_low < 1e-8: + data_min = valid_data.min() + data_max = valid_data.max() + if data_max > data_min: + stretched = (data - data_min) / (data_max - data_min) + else: + stretched = np.zeros_like(data, dtype=np.float32) + else: + stretched = (data - p_low) / (p_high - p_low) + + stretched = np.clip(stretched, 0.0, 1.0) + return stretched + + r_stretched = simple_linear_stretch(image_array[:, :, 0]) + g_stretched = simple_linear_stretch(image_array[:, :, 1]) + b_stretched = simple_linear_stretch(image_array[:, :, 2]) + + rgb_image = np.stack([r_stretched, g_stretched, b_stretched], axis=2) + rgb_image = np.nan_to_num(rgb_image, nan=0.0) + rgb_image = np.clip(rgb_image, 0.0, 1.0) + + # Gamma校正增加亮度 + gamma = 0.85 + rgb_image = np.power(rgb_image, gamma) + + # 映射到0-255 + rgb_image = (rgb_image * 255).astype(np.uint8) + + return rgb_image + + def _create_map_visualization(self, image_array: np.ndarray, + flight_pixels: Dict[str, List[Tuple[float, float]]], + flight_data: Dict[str, pd.DataFrame], + output_path: str, + line_width: int, + show_north_arrow: bool, + show_scale_bar: bool, + dpi: int, + geotransform: tuple, + width: int, + height: int): + """创建地图可视化""" + figsize = (14, 10) + fig, ax = plt.subplots(figsize=figsize, dpi=150) + + # 处理背景图像 + rgb_image = self._create_false_color_image(image_array) + ax.imshow(rgb_image, interpolation='bilinear') + + # 绘制飞行轨迹 - 不同架次不同颜色 + legend_elements = [] + + for idx, (flight_name, pixel_coords) in enumerate(flight_pixels.items()): + if len(pixel_coords) < 2: + continue + + # 选择颜色 + color = self.FLIGHT_COLORS[idx % len(self.FLIGHT_COLORS)] + + # 提取x,y坐标 + x_coords = [p[0] for p in pixel_coords] + y_coords = [p[1] for p in pixel_coords] + + # 绘制轨迹线 + ax.plot(x_coords, y_coords, color=color, linewidth=line_width, + alpha=0.8, solid_capstyle='round') + + # 标记起点和终点 + ax.plot(x_coords[0], y_coords[0], 'o', color=color, markersize=8, + markeredgecolor='white', markeredgewidth=1) + ax.plot(x_coords[-1], y_coords[-1], 's', color=color, markersize=8, + markeredgecolor='white', markeredgewidth=1) + + # 获取时间范围用于图例 + df = flight_data[flight_name] + start_time = df.iloc[0]['time'] + end_time = df.iloc[-1]['time'] + + # 创建图例元素 + legend_label = f"{flight_name}: {start_time} - {end_time}" + legend_elements.append( + mpatches.Patch(color=color, label=legend_label) + ) + + # 添加图例 + if legend_elements: + ax.legend(handles=legend_elements, loc='lower right', + frameon=True, facecolor='white', edgecolor='gray', + fontsize=9, title='飞行轨迹 (起点→终点)') + + # 添加指北针 + if show_north_arrow: + self._add_north_arrow(ax, width, height) + + # 添加比例尺 + if show_scale_bar and geotransform is not None: + self._add_scale_bar(ax, geotransform, width, height) + + # 设置标题 + ax.set_title('多架次飞行轨迹图', fontsize=16, fontweight='bold', pad=20) + + + # 隐藏坐标轴刻度 + ax.set_xticks([]) + ax.set_yticks([]) + + # 添加网格 + ax.grid(True, alpha=0.2, linestyle='--') + + plt.tight_layout() + plt.savefig(output_path, dpi=dpi, bbox_inches='tight', pad_inches=0.1, facecolor='white') + plt.close(fig) + + def _add_north_arrow(self, ax, width: int, height: int): + """添加指北针""" + arrow_x = width * 0.92 + arrow_y = height * 0.88 + + arrow = FancyArrowPatch((arrow_x, arrow_y), (arrow_x, arrow_y - height*0.08), + color='black', linewidth=3, arrowstyle='->', mutation_scale=20) + ax.add_patch(arrow) + + ax.text(arrow_x, arrow_y - height*0.1, 'N', fontsize=14, fontweight='bold', + color='black', ha='center', va='center', + path_effects=[path_effects.withStroke(linewidth=3, foreground='white')]) + + def _add_scale_bar(self, ax, geotransform: tuple, width: int, height: int): + """添加比例尺""" + if geotransform is None: + return + + pixel_size_x = abs(geotransform[1]) + image_width_meters = width * pixel_size_x + scale_length_m = image_width_meters / 4 + + scale_options = [1000, 500, 200, 100, 50, 20, 10, 5, 2, 1] + scale_meters = next((s for s in scale_options if s <= scale_length_m), 1) + scale_pixels = int(scale_meters / pixel_size_x) + + bar_x = width * 0.08 + bar_y = height * 0.92 + + ax.plot([bar_x, bar_x + scale_pixels], [bar_y, bar_y], color='black', linewidth=4) + ax.plot([bar_x, bar_x], [bar_y, bar_y + 8], color='black', linewidth=2) + ax.plot([bar_x + scale_pixels, bar_x + scale_pixels], [bar_y, bar_y + 8], color='black', linewidth=2) + + ax.text(bar_x + scale_pixels/2, bar_y + 15, f'{scale_meters} m', + fontsize=11, ha='center', va='bottom', fontweight='bold', + bbox=dict(facecolor='white', alpha=0.8, edgecolor='none', pad=1)) + + def batch_create_maps(self, gps_folder: str, + hyperspectral_folder: str, + output_subdir: str = "flight_paths") -> Dict[str, str]: + """ + 批量创建飞行轨迹地图 + + Args: + gps_folder: 包含多个子文件夹（每个子文件夹包含.gps文件）的文件夹 + hyperspectral_folder: 包含高光谱影像的文件夹 + output_subdir: 输出子目录 + + Returns: + 生成的地图文件路径字典 + """ + gps_folder_path = Path(gps_folder) + hs_folder_path = Path(hyperspectral_folder) + + if not gps_folder_path.exists(): + raise FileNotFoundError(f"GPS文件夹不存在: {gps_folder}") + if not hs_folder_path.exists(): + raise FileNotFoundError(f"高光谱文件夹不存在: {hyperspectral_folder}") + + output_dir = self.output_dir / output_subdir + output_dir.mkdir(parents=True, exist_ok=True) + + map_paths = {} + + # 查找所有包含.gps文件的子文件夹 + gps_subfolders = [d for d in gps_folder_path.iterdir() if d.is_dir() and list(d.glob("*.gps"))] + + # 查找高光谱影像 + hs_files = [] + for ext in ['*.dat', '*.bsq', '*.tif', '*.tiff']: + hs_files.extend(list(hs_folder_path.glob(ext))) + + if not hs_files: + print(f"警告: 在 {hyperspectral_folder} 中未找到高光谱影像") + return map_paths + + print(f"找到 {len(gps_subfolders)} 个GPS子文件夹和 {len(hs_files)} 个高光谱影像") + + # 简单匹配：使用第一个高光谱影像与所有GPS文件夹组合 + hs_file = hs_files[0] + + for gps_subfolder in gps_subfolders: + try: + output_filename = f"{hs_file.stem}_{gps_subfolder.name}_flight_paths.png" + map_path = self.create_flight_path_map( + gps_folder=str(gps_subfolder), + hyperspectral_path=str(hs_file), + output_filename=output_filename, + dpi=200 + ) + map_paths[gps_subfolder.name] = map_path + print(f"✓ 生成: {gps_subfolder.name}") + + except Exception as e: + print(f"✗ 处理 {gps_subfolder.name} 失败: {e}") + + print(f"批量生成完成，共生成 {len(map_paths)} 个飞行轨迹图") + return map_paths + + +# 测试代码 +if __name__ == "__main__": + print("FlightPathVisualizer类已创建") + print("用法示例:") + print(" visualizer = FlightPathVisualizer(output_dir='./flight_maps')") + print(" map_path = visualizer.create_flight_path_map(") + print(" gps_folder='./gps_data',") + print(" hyperspectral_path='./hyperspectral.dat'") + print(" )") + + + visualizer = FlightPathVisualizer(output_dir=r"E:\code\WQ\pipeline_result\work_dir\9_visualization\flight_maps") + # 生成飞行轨迹图 + map_path = visualizer.create_flight_path_map( + gps_folder=r"D:\BaiduNetdiskDownload\20250902\gps", # GPS文件夹路径 + hyperspectral_path=r"E:\code\WQ\pipeline_result\work_dir\3_deglint\deglint_goodman.bsq", # 高光谱影像路径 + output_filename="flight_paths.png", + line_width=2, + dpi=300 + ) \ No newline at end of file diff --git a/src/postprocessing/map.py b/src/postprocessing/map.py new file mode 100644 index 0000000..6d37d37 --- /dev/null +++ b/src/postprocessing/map.py @@ -0,0 +1,2186 @@ +import pandas as pd +import numpy as np +import geopandas as gpd +from pyproj import CRS, Transformer +import matplotlib.pyplot as plt +import matplotlib.patches as patches +from matplotlib.ticker import FuncFormatter +from matplotlib_scalebar.scalebar import ScaleBar +from scipy.interpolate import griddata +from scipy import ndimage +from scipy.spatial.distance import cdist +from scipy.spatial import ConvexHull +from shapely.geometry import Point, Polygon +import rasterio +from rasterio.features import geometry_mask +from rasterio import windows +from rasterio.warp import calculate_default_transform, reproject, Resampling +try: + from affine import Affine +except ImportError: + try: + from rasterio.transform import Affine + except ImportError: + Affine = None +import warnings +import math +import os +import random +import glob + +# 尝试导入pykrige（可选依赖） +try: + from pykrige.ok import OrdinaryKriging + PYKRIGE_AVAILABLE = True +except ImportError: + PYKRIGE_AVAILABLE = False + print("警告: pykrige未安装，Kriging不确定性计算将不可用") + +warnings.filterwarnings('ignore') + +# 设置中文字体 +plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei'] +plt.rcParams['axes.unicode_minus'] = False + +# 参数到颜色映射的字典 +PARAMS_CMAP = { + "Chlorophyll": "YlGnBu_r", + "COD": "coolwarm", + "DO": "RdYlBu", + "pH": "Spectral", + "Temperature": "turbo", + "spCond": "cividis", + "Turbidity": "YlOrBr", + "TDS": "inferno", + "Cl-": "RdYlBu_r", + "NO3-N": "YlOrRd", + "NH3-N": "magma", + "BGA": "viridis", + "TT": "RdYlBu_r" +} + + +class ContentMapper: + def __init__(self, input_crs='EPSG:32651', output_crs='EPSG:4326'): + """ + 初始化ContentMapper - 生成平滑的含量分布图 + + 本类专门用于生成平滑、均匀的颜色分布图，而不是显示离散的采样点。 + 通过高密度网格插值和多级颜色映射，创建连续的颜色过渡效果。 + + Parameters: + ----------- + input_crs : str + 输入坐标系，默认为'EPSG:32651' (WGS_1984_UTM_Zone_51N) + output_crs : str + 输出坐标系，默认为'EPSG:4326' (WGS84) + """ + # 定义坐标转换器 + self.input_crs = input_crs + self.output_crs = output_crs + self.transformer = Transformer.from_crs( + CRS.from_string(input_crs), + CRS.from_string(output_crs), + always_xy=True + ) + + # 参数到颜色映射的字典 + self.params_cmap = PARAMS_CMAP.copy() + + # 所有可用的matplotlib colormap列表（用于随机选择） + self.available_cmaps = ['viridis', 'plasma', 'inferno', 'magma', 'cividis', + 'coolwarm', 'RdYlBu', 'Spectral', 'YlGnBu_r', 'YlOrBr', + 'YlOrRd', 'turbo', 'RdYlBu_r', 'cool', 'hot', 'jet'] + + print(f"坐标转换设置: {input_crs} -> {output_crs}") + + def _extract_param_name(self, csv_file): + """ + 从CSV文件名或内容中提取参数名称 + + Parameters: + ----------- + csv_file : str + CSV文件路径 + + Returns: + -------- + param_name : str or None + 提取的参数名称，如果未找到则返回None + """ + print(f"[调试] 开始从文件 {csv_file} 中提取参数名称") + print(f"[调试] 字典中的参数键: {list(self.params_cmap.keys())}") + + # 从文件名中提取（去除路径和扩展名） + file_name = os.path.basename(csv_file) + file_name_no_ext = os.path.splitext(file_name)[0] + print(f"[调试] 文件名（不含扩展名）: {file_name_no_ext}") + + # 尝试从文件名中匹配参数名称（不区分大小写） + file_name_upper = file_name_no_ext.upper() + for param in self.params_cmap.keys(): + param_upper = param.upper() + if param_upper in file_name_upper: + print(f"从文件名中识别到参数: {param} (匹配到 '{param_upper}' 在 '{file_name_upper}' 中)") + return param # 返回字典中的原始键（保持大小写） + + # 如果文件名中没有找到，尝试从CSV内容中提取（检查列名） + try: + df = pd.read_csv(csv_file, encoding='utf-8', nrows=0) # 只读取列名 + columns = [col.upper() for col in df.columns] + print(f"[调试] CSV列名: {list(df.columns)}") + + for param in self.params_cmap.keys(): + param_upper = param.upper() + # 检查列名中是否包含参数名称 + for col in columns: + if param_upper in col or col in param_upper: + print(f"从CSV列名中识别到参数: {param} (匹配到列名 '{col}')") + return param # 返回字典中的原始键（保持大小写） + except Exception as e: + print(f"读取CSV列名时出错: {e}") + + print(f"未能在文件 {csv_file} 中识别参数名称") + print(f"[调试] 可用的参数名: {list(self.params_cmap.keys())}") + return None + + def _get_colormap(self, param_name=None): + """ + 根据参数名称获取对应的colormap + + Parameters: + ----------- + param_name : str, optional + 参数名称。如果为None或不在映射中，则随机选择一个colormap + + Returns: + -------- + cmap : str + 颜色映射名称 + """ + # 打印调试信息 + print(f"[调试] _get_colormap 被调用，param_name={param_name}") + print(f"[调试] 当前字典中的键: {list(self.params_cmap.keys())}") + + if param_name: + # 首先尝试精确匹配（区分大小写） + if param_name in self.params_cmap: + cmap = self.params_cmap[param_name] + print(f"使用参数 '{param_name}' 对应的颜色映射: {cmap}") + return cmap + + # 如果精确匹配失败，尝试不区分大小写的匹配 + param_name_upper = param_name.upper() + for key in self.params_cmap.keys(): + if key.upper() == param_name_upper: + cmap = self.params_cmap[key] + print(f"使用参数 '{key}' (不区分大小写匹配 '{param_name}') 对应的颜色映射: {cmap}") + return cmap + + # 如果都不匹配，随机选择 + cmap = random.choice(self.available_cmaps) + print(f"警告: 参数 '{param_name}' 不在映射中，随机选择颜色映射: {cmap}") + print(f"可用的参数名: {list(self.params_cmap.keys())}") + return cmap + else: + # 随机选择一个colormap + cmap = random.choice(self.available_cmaps) + print(f"未指定参数名称，随机选择颜色映射: {cmap}") + return cmap + + def _check_point_distribution(self, points): + """检查数据点的几何分布""" + print("正在检查数据点分布...") + + # 检查是否有重复点 + unique_points = np.unique(points, axis=0) + if len(unique_points) < len(points): + print(f"警告：发现 {len(points) - len(unique_points)} 个重复数据点") + + # 检查点是否共线 + if len(unique_points) >= 3: + # 计算前三个点构成的三角形面积 + p1, p2, p3 = unique_points[:3] + area = 0.5 * abs((p2[0] - p1[0]) * (p3[1] - p1[1]) - (p3[0] - p1[0]) * (p2[1] - p1[1])) + + if area < 1e-10: # 面积太小，可能共线 + print("警告：前三个数据点可能共线") + + # 尝试找到不共线的点 + for i in range(3, len(unique_points)): + p4 = unique_points[i] + area = 0.5 * abs((p2[0] - p1[0]) * (p4[1] - p1[1]) - (p4[0] - p1[0]) * (p2[1] - p1[1])) + if area > 1e-10: + print(f"找到非共线点，使用点 {i}") + break + else: + print("警告：所有数据点可能都共线，这会导致插值失败") + + # 检查坐标范围 + x_range = points[:, 0].max() - points[:, 0].min() + y_range = points[:, 1].max() - points[:, 1].min() + + if x_range < 1e-6 or y_range < 1e-6: + print(f"警告：坐标范围很小 (X范围: {x_range:.2e}, Y范围: {y_range:.2e})") + print("这可能导致插值数值不稳定") + + return unique_points + + def _fill_boundary_blanks_with_distance_diffusion(self, grid_content, grid_xx, grid_yy, mask, + boundary_gdf, max_diffusion_distance=None, + power=2, n_neighbors=15): + """ + 使用距离扩散方法填充边界附近的空白区域 + + Parameters: + ----------- + grid_content : np.ndarray + 插值网格数据 + grid_xx : np.ndarray + 网格X坐标 + grid_yy : np.ndarray + 网格Y坐标 + mask : np.ndarray + 边界掩膜（True表示边界内） + boundary_gdf : gpd.GeoDataFrame + 边界几何数据 + max_diffusion_distance : float, optional + 最大扩散距离（单位与坐标相同）。如果为None，自动计算为网格分辨率的5倍 + power : float, default=2 + IDW距离衰减幂参数 + n_neighbors : int, default=15 + 使用的最近邻点数 + + Returns: + -------- + grid_content : np.ndarray + 填充后的网格数据 + """ + print("正在使用距离扩散方法填充边界空白区域...") + + # 找到边界内的空白区域 + nan_mask = np.isnan(grid_content) + within_boundary_nan = nan_mask & mask + + if not np.any(within_boundary_nan): + print("边界内没有空白区域需要填充") + return grid_content + + blank_count = np.sum(within_boundary_nan) + print(f"发现 {blank_count} 个边界内的空白点，开始距离扩散填充...") + + # 找到边界内有效值的点 + valid_mask = ~nan_mask & mask + if np.sum(valid_mask) == 0: + print("警告：边界内没有有效值，无法进行距离扩散") + return grid_content + + # 计算网格分辨率（用于确定最大扩散距离） + if max_diffusion_distance is None: + # 自动计算：使用网格点之间的平均距离 + dx = np.abs(grid_xx[0, 1] - grid_xx[0, 0]) if grid_xx.shape[1] > 1 else 1.0 + dy = np.abs(grid_yy[1, 0] - grid_yy[0, 0]) if grid_xx.shape[0] > 1 else 1.0 + avg_resolution = (dx + dy) / 2.0 + max_diffusion_distance = avg_resolution * 5.0 # 5倍网格分辨率 + print(f"自动计算最大扩散距离: {max_diffusion_distance:.6f}") + + # 准备有效数据点 + valid_points = np.column_stack((grid_xx[valid_mask], grid_yy[valid_mask])) + valid_values = grid_content[valid_mask] + + # 准备空白点 + blank_points = np.column_stack((grid_xx[within_boundary_nan], grid_yy[within_boundary_nan])) + + print(f"使用 {len(valid_points)} 个有效点填充 {len(blank_points)} 个空白点...") + + # 使用向量化计算距离矩阵 + distances = cdist(blank_points, valid_points) + + # 对每个空白点，找到最近的n_neighbors个有效点 + n_neighbors = min(n_neighbors, len(valid_points)) + + # 应用最大扩散距离限制 + if max_diffusion_distance > 0: + # 只考虑在最大扩散距离内的点 + # 对于每个空白点，找到在扩散距离内的最近邻 + filled_values = np.full(len(blank_points), np.nan) + global_mean = np.nanmean(valid_values) + + for i in range(len(blank_points)): + point_distances = distances[i, :] + valid_idx = point_distances <= max_diffusion_distance + + if np.any(valid_idx): + # 找到最近的n_neighbors个点（在扩散距离内） + valid_dist = point_distances[valid_idx] + valid_vals = valid_values[valid_idx] + + # 如果有效点数量超过n_neighbors，只取最近的n_neighbors个 + if len(valid_dist) > n_neighbors: + nearest_idx = np.argpartition(valid_dist, n_neighbors-1)[:n_neighbors] + valid_dist = valid_dist[nearest_idx] + valid_vals = valid_vals[nearest_idx] + + # 避免除零 + valid_dist = np.maximum(valid_dist, 1e-10) + + # 计算IDW权重 + weights = 1.0 / (valid_dist ** power) + weight_sum = np.sum(weights) + + if weight_sum > 0: + # 距离加权平均 + filled_values[i] = np.sum(weights * valid_vals) / weight_sum + else: + filled_values[i] = global_mean + else: + # 如果该点不在任何有效点的扩散距离内，使用全局平均值 + filled_values[i] = global_mean + else: + # 不使用距离限制，对所有点进行IDW插值（批量处理以提高效率） + # 对每个空白点，找到最近的n_neighbors个点 + nearest_indices = np.argpartition(distances, n_neighbors-1, axis=1)[:, :n_neighbors] + + # 批量提取距离和值 + nearest_dists = np.take_along_axis(distances, nearest_indices, axis=1) + nearest_vals = valid_values[nearest_indices] + + # 避免除零 + nearest_dists = np.maximum(nearest_dists, 1e-10) + + # 批量计算IDW权重 + weights = 1.0 / (nearest_dists ** power) + weight_sums = np.sum(weights, axis=1) + + # 批量计算加权平均值 + filled_values = np.sum(weights * nearest_vals, axis=1) / weight_sums + + # 处理可能的NaN（如果weight_sum为0） + nan_mask = np.isnan(filled_values) | (weight_sums == 0) + if np.any(nan_mask): + filled_values[nan_mask] = np.nanmean(valid_values) + + # 填充空白点 + grid_content[within_boundary_nan] = filled_values + + # 检查填充结果 + filled_count = np.sum(~np.isnan(filled_values)) + print(f"距离扩散填充完成：成功填充 {filled_count} / {blank_count} 个空白点") + + return grid_content + + def _perform_interpolation(self, points, values, grid_xx, grid_yy): + """执行空间插值""" + print(f"插值输入检查：") + print(f" - 数据点数量: {len(points)}") + print(f" - 数据值范围: {values.min():.4f} - {values.max():.4f}") + print(f" - 网格大小: {grid_xx.shape}") + print(f" - 坐标系: {self.output_crs}") + + # 检查数据的有效性 + finite_mask = np.isfinite(values) + if not np.all(finite_mask): + print(f"警告：发现 {np.sum(~finite_mask)} 个无效数据值，将被移除") + points = points[finite_mask] + values = values[finite_mask] + + if len(points) < 3: + raise ValueError(f"有效数据点不足3个（当前：{len(points)}个）") + + # 优先使用Kriging插值 + if PYKRIGE_AVAILABLE: + try: + print("正在使用Kriging插值（半变异函数模型，块金值=100%）...") + grid_x = grid_xx[0, :] + grid_y = grid_yy[:, 0] + ok = OrdinaryKriging( + points[:, 0], points[:, 1], values, + variogram_model='spherical', + verbose=False, + enable_plotting=False + ) + z, _ = ok.execute('grid', grid_x, grid_y) + grid_content = np.array(z) + valid_count = np.sum(~np.isnan(grid_content)) + print(f"Kriging插值成功，有效点数: {valid_count} / {grid_content.size}") + if valid_count > 0: + return grid_content + else: + print("警告：Kriging插值结果为空，将回退到其他插值方法") + except Exception as e: + print(f"Kriging插值失败: {e}，将回退到其他插值方法") + else: + print("警告：pykrige未安装，无法使用Kriging插值，将使用其他插值方法") + + try: + # 首先尝试使用线性插值 + print("正在尝试线性插值...") + grid_content = griddata( + points, values, (grid_xx, grid_yy), + method='linear', fill_value=np.nan + ) + + # 检查线性插值结果 + valid_linear = ~np.isnan(grid_content) + valid_count = np.sum(valid_linear) + print(f"线性插值结果：有效点数 {valid_count} / {grid_content.size}") + + if valid_count > 0: + print(f"线性插值成功，有效区域覆盖率: {valid_count / grid_content.size * 100:.1f}%") + + # 如果有NaN值，用最近邻插值填充 + nan_count = np.sum(np.isnan(grid_content)) + if nan_count > 0: + print(f"正在用最近邻插值填充 {nan_count} 个缺失值...") + grid_nearest = griddata( + points, values, (grid_xx, grid_yy), + method='nearest' + ) + # 只填充线性插值的NaN区域 + nan_mask = np.isnan(grid_content) + grid_content[nan_mask] = grid_nearest[nan_mask] + print("缺失值填充完成") + + # 最终检查 + final_valid = ~np.isnan(grid_content) + print(f"最终有效点数: {np.sum(final_valid)} / {grid_content.size}") + + return grid_content + else: + print("线性插值失败，尝试最近邻插值...") + + except Exception as e: + print(f"线性插值失败: {e}") + print("尝试最近邻插值...") + + try: + # 使用最近邻插值作为备选方案 + print("执行最近邻插值...") + grid_content = griddata( + points, values, (grid_xx, grid_yy), + method='nearest' + ) + + valid_count = np.sum(~np.isnan(grid_content)) + print(f"最近邻插值成功，有效点数: {valid_count}") + + if valid_count == 0: + raise ValueError("最近邻插值也没有产生有效结果") + + return grid_content + + except Exception as e: + print(f"最近邻插值也失败: {e}") + + # 对于地理坐标系，尝试更简单的方法 + if self.output_crs == 'EPSG:4326': + print("地理坐标系检测到，尝试简化插值...") + try: + # 创建一个基于距离的简单插值 + grid_content = np.full(grid_xx.shape, np.nan) + + # 为每个网格点找到最近的数据点 + for i in range(grid_xx.shape[0]): + for j in range(grid_xx.shape[1]): + grid_x, grid_y = grid_xx[i, j], grid_yy[i, j] + + # 计算到所有数据点的距离 + distances = np.sqrt((points[:, 0] - grid_x) ** 2 + (points[:, 1] - grid_y) ** 2) + nearest_idx = np.argmin(distances) + + # 如果距离不是太远，就使用该值 + if distances[nearest_idx] < (grid_xx.max() - grid_xx.min()) * 0.1: # 10%的范围内 + grid_content[i, j] = values[nearest_idx] + + valid_count = np.sum(~np.isnan(grid_content)) + print(f"简化插值完成，有效点数: {valid_count}") + + if valid_count > 0: + return grid_content + else: + raise ValueError("简化插值也没有产生有效结果") + + except Exception as e3: + print(f"简化插值失败: {e3}") + + print("尝试立方插值作为最后手段...") + try: + # 最后尝试立方插值 + grid_content = griddata( + points, values, (grid_xx, grid_yy), + method='cubic', fill_value=np.nan + ) + + # 如果立方插值有NaN，用最近邻填充 + if np.any(np.isnan(grid_content)): + print("用最近邻插值填充立方插值的NaN值...") + grid_nearest = griddata( + points, values, (grid_xx, grid_yy), + method='nearest' + ) + nan_mask = np.isnan(grid_content) + grid_content[nan_mask] = grid_nearest[nan_mask] + + valid_count = np.sum(~np.isnan(grid_content)) + print(f"立方插值成功，有效点数: {valid_count}") + return grid_content + + except Exception as e4: + print(f"立方插值也失败: {e4}") + print(f"所有插值方法都失败") + raise ValueError("无法完成空间插值，请检查数据点的分布和数值") + + def read_csv_data(self, csv_file, uncertainty_col=None): + """ + 读取CSV文件并进行坐标转换 + + Parameters: + ----------- + csv_file : str + CSV文件路径 + uncertainty_col : str, optional + 不确定性数据列名。如果为None，将自动检测包含'variance'、'uncertainty'、'std'、'sigma'、'var'、'mc_dropout'的列 + + Returns: + -------- + gdf : gpd.GeoDataFrame + 包含坐标和含量数据的GeoDataFrame，如果找到不确定性列，会包含'uncertainty'列 + """ + print("正在读取CSV文件...") + df = pd.read_csv(csv_file, encoding='utf-8') + + # 假设前三列分别是经度、纬度、含量 + if df.shape[1] < 3: + raise ValueError("CSV文件必须至少包含3列：经度、纬度、含量") + + # 获取列名 + lon_col = df.columns[0] + lat_col = df.columns[1] + content_col = df.columns[2] + + print(f"检测到列名：经度({lon_col})，纬度({lat_col})，含量({content_col})") + + # 自动检测不确定性列 + if uncertainty_col is None: + uncertainty_keywords = ['variance', 'uncertainty', 'std', 'sigma', 'var', 'mc_dropout'] + for col in df.columns: + col_lower = col.lower() + if any(keyword in col_lower for keyword in uncertainty_keywords): + uncertainty_col = col + print(f"自动检测到不确定性列: {uncertainty_col}") + break + + # 坐标转换 + print(f"正在进行坐标转换: {self.input_crs} -> {self.output_crs}") + transformed_x, transformed_y = self.transformer.transform( + df[lon_col].values, + df[lat_col].values + ) + + # 创建GeoDataFrame + geometry = [Point(x, y) for x, y in zip(transformed_x, transformed_y)] + gdf = gpd.GeoDataFrame( + df, + geometry=geometry, + crs=self.output_crs + ) + + gdf['proj_x'] = transformed_x + gdf['proj_y'] = transformed_y + gdf['content'] = df[content_col] + + # 如果找到不确定性列，添加到GeoDataFrame + if uncertainty_col and uncertainty_col in df.columns: + gdf['uncertainty'] = df[uncertainty_col].values + print(f"已加载不确定性数据列: {uncertainty_col}") + print(f"不确定性值范围: {gdf['uncertainty'].min():.4f} - {gdf['uncertainty'].max():.4f}") + + print(f"成功读取 {len(gdf)} 个数据点") + return gdf + + def read_boundary_shapefile(self, shp_file): + """读取边界shapefile""" + print("正在读取边界文件...") + boundary = gpd.read_file(shp_file) + + # 确保边界文件使用目标投影坐标系 + if boundary.crs != self.output_crs: + print(f"正在转换边界文件坐标系到 {self.output_crs}...") + boundary = boundary.to_crs(self.output_crs) + + print(f"边界文件包含 {len(boundary)} 个要素") + return boundary + + def _identify_edge_points(self, points_gdf): + """ + 识别边缘采样点（使用凸包方法） + + Parameters: + ----------- + points_gdf : gpd.GeoDataFrame + 采样点GeoDataFrame + + Returns: + -------- + edge_indices : np.ndarray + 边缘点的索引数组 + """ + print("正在识别边缘采样点...") + + # 获取所有点的坐标 + points = np.column_stack((points_gdf['proj_x'].values, points_gdf['proj_y'].values)) + + if len(points) < 3: + print("警告：采样点数量少于3个，无法识别边缘点") + return np.array([]) + + try: + # 使用凸包识别边缘点 + hull = ConvexHull(points) + edge_indices = hull.vertices + + print(f"识别到 {len(edge_indices)} 个边缘采样点（共 {len(points)} 个点）") + return edge_indices + except Exception as e: + print(f"识别边缘点时出错: {e}，将使用所有点作为边缘点") + return np.arange(len(points)) + + def _expand_edge_points(self, points_gdf, boundary_gdf, resolution=100, expand_ratio=0.05): + """ + 对边缘采样点进行外扩处理，外扩到整个图像的边界（包括外扩后的边界） + 按照指定的间距（resolution）生成外扩点，铺满整个画面 + + Parameters: + ----------- + points_gdf : gpd.GeoDataFrame + 原始采样点GeoDataFrame + boundary_gdf : gpd.GeoDataFrame + 水域掩膜边界GeoDataFrame + resolution : float, default=100 + 外扩点的间距（单位与坐标相同），与插值网格分辨率一致 + expand_ratio : float, default=0.05 + 边界外扩比例（与create_interpolation_grid中的expand_ratio一致） + + Returns: + -------- + expanded_gdf : gpd.GeoDataFrame + 外扩后的采样点GeoDataFrame + """ + print(f"正在对边缘采样点进行外扩处理（按照 {resolution} 的间距外扩到整个图像边界）...") + + # 识别边缘点 + edge_indices = self._identify_edge_points(points_gdf) + + if len(edge_indices) == 0: + print("未识别到边缘点，跳过外扩处理") + return points_gdf.copy() + + # 获取水域掩膜的边界范围 + boundary_bounds = boundary_gdf.total_bounds # [minx, miny, maxx, maxy] + mask_minx, mask_miny, mask_maxx, mask_maxy = boundary_bounds + + # 计算范围大小 + width = mask_maxx - mask_minx + height = mask_maxy - mask_miny + + # 外扩边界，与create_interpolation_grid中的逻辑一致，确保外扩到整个图像范围 + expand_x = width * expand_ratio + expand_y = height * expand_ratio + image_minx = mask_minx - expand_x + image_maxx = mask_maxx + expand_x + image_miny = mask_miny - expand_y + image_maxy = mask_maxy + expand_y + + # 获取所有点的坐标 + points = np.column_stack((points_gdf['proj_x'].values, points_gdf['proj_y'].values)) + + # 计算点集的范围和中心 + x_min, x_max = points[:, 0].min(), points[:, 0].max() + y_min, y_max = points[:, 1].min(), points[:, 1].max() + center = np.array([(x_min + x_max) / 2, (y_min + y_max) / 2]) + + # 存储新添加的点 + new_points_list = [] + new_data_list = [] + + # 对每个边缘点进行外扩 + for edge_idx in edge_indices: + edge_point = points[edge_idx] + + # 计算从中心到边缘点的方向向量 + direction = edge_point - center + distance_to_center = np.linalg.norm(direction) + + if distance_to_center < 1e-10: + # 如果边缘点就是中心点，跳过 + continue + + # 归一化方向向量 + direction_unit = direction / distance_to_center + + # 计算该方向与水域掩膜边界的交点 + # 使用射线法：从边缘点沿方向延伸，找到与边界框的交点 + max_distance = 0 + + # 检查与四个边界的交点（使用整个图像的范围，包括外扩后的边界） + # 上边界 (y = image_maxy) + if direction_unit[1] > 1e-10: # 向上 + t = (image_maxy - edge_point[1]) / direction_unit[1] + if t > 0: + intersect_x = edge_point[0] + direction_unit[0] * t + if image_minx <= intersect_x <= image_maxx: + max_distance = max(max_distance, t) + + # 下边界 (y = image_miny) + if direction_unit[1] < -1e-10: # 向下 + t = (image_miny - edge_point[1]) / direction_unit[1] + if t > 0: + intersect_x = edge_point[0] + direction_unit[0] * t + if image_minx <= intersect_x <= image_maxx: + max_distance = max(max_distance, t) + + # 右边界 (x = image_maxx) + if direction_unit[0] > 1e-10: # 向右 + t = (image_maxx - edge_point[0]) / direction_unit[0] + if t > 0: + intersect_y = edge_point[1] + direction_unit[1] * t + if image_miny <= intersect_y <= image_maxy: + max_distance = max(max_distance, t) + + # 左边界 (x = image_minx) + if direction_unit[0] < -1e-10: # 向左 + t = (image_minx - edge_point[0]) / direction_unit[0] + if t > 0: + intersect_y = edge_point[1] + direction_unit[1] * t + if image_miny <= intersect_y <= image_maxy: + max_distance = max(max_distance, t) + + # 如果找到了边界交点，按照resolution间距创建外扩点 + if max_distance > 1e-10: + # 从边缘点到边界的距离 + distance_to_boundary = max_distance + + # 计算需要生成的外扩点数量（按照resolution间距） + # 使用ceil确保能铺满到边界 + n_points = int(np.ceil(distance_to_boundary / resolution)) + + # 从边缘点开始，按照resolution间距生成点，直到边界 + for i in range(1, n_points + 1): + # 计算外扩距离（从边缘点开始，按照resolution间距） + expand_distance = i * resolution + + # 如果超过边界距离，使用边界距离作为最后一个点 + if expand_distance >= distance_to_boundary: + expand_distance = distance_to_boundary + + # 计算新点位置 + new_point = edge_point + direction_unit * expand_distance + + # 确保新点在图像范围内（包括外扩后的边界） + new_point[0] = np.clip(new_point[0], image_minx, image_maxx) + new_point[1] = np.clip(new_point[1], image_miny, image_maxy) + + # 创建新点的数据（复制边缘点的所有属性） + new_row = points_gdf.iloc[edge_idx].copy() + new_row['proj_x'] = new_point[0] + new_row['proj_y'] = new_point[1] + + # 更新geometry + new_row['geometry'] = Point(new_point[0], new_point[1]) + + new_points_list.append(new_point) + new_data_list.append(new_row) + + # 如果已经到达边界，停止生成 + if expand_distance >= distance_to_boundary: + break + + # 合并原始点和外扩点 + if len(new_data_list) > 0: + # 创建新点的GeoDataFrame + expanded_gdf = gpd.GeoDataFrame(new_data_list, crs=points_gdf.crs) + + # 合并原始点和外扩点（使用gpd.concat以确保geometry列正确处理） + result_gdf = gpd.GeoDataFrame(pd.concat([points_gdf, expanded_gdf], ignore_index=True), crs=points_gdf.crs) + + print(f"外扩完成：原始点 {len(points_gdf)} 个，边缘点 {len(edge_indices)} 个，" + f"新增外扩点 {len(new_data_list)} 个（间距 {resolution}），总计 {len(result_gdf)} 个点") + print(f"水域掩膜范围: X[{mask_minx:.2f}, {mask_maxx:.2f}], Y[{mask_miny:.2f}, {mask_maxy:.2f}]") + print(f"图像范围（含外扩）: X[{image_minx:.2f}, {image_maxx:.2f}], Y[{image_miny:.2f}, {image_maxy:.2f}]") + + return result_gdf + else: + print("未生成外扩点，返回原始点集") + return points_gdf.copy() + + def create_interpolation_grid(self, points_gdf, boundary_gdf, resolution=100, expand_ratio=0.05, + use_distance_diffusion=True, max_diffusion_distance=None, + diffusion_power=2, diffusion_n_neighbors=15): + """ + 创建插值网格 + + Parameters: + ----------- + expand_ratio : float, default=0.05 + 边界外扩比例（5%），确保图像边界不完全挨着地图 + use_distance_diffusion : bool, default=True + 是否使用距离扩散方法填充边界空白区域 + max_diffusion_distance : float, optional + 最大扩散距离（单位与坐标相同）。如果为None，自动计算为网格分辨率的5倍 + diffusion_power : float, default=2 + 距离扩散的IDW幂参数，值越大，距离衰减越快 + diffusion_n_neighbors : int, default=15 + 距离扩散使用的最近邻点数 + + Returns: + -------- + grid_xx, grid_yy, grid_content, bounds : tuple + """ + print("正在创建插值网格...") + + # 获取边界范围 + bounds = boundary_gdf.total_bounds + minx, miny, maxx, maxy = bounds + + print(f"原始边界范围: X({minx:.6f} - {maxx:.6f}), Y({miny:.6f} - {maxy:.6f})") + + # 计算范围大小 + width = maxx - minx + height = maxy - miny + + # 外扩边界，确保图像不完全挨着地图 + expand_x = width * expand_ratio + expand_y = height * expand_ratio + minx -= expand_x + maxx += expand_x + miny -= expand_y + maxy += expand_y + + print(f"外扩后边界范围: X({minx:.6f} - {maxx:.6f}), Y({miny:.6f} - {maxy:.6f})") + print(f"外扩比例: {expand_ratio * 100:.1f}%") + + if self.output_crs == 'EPSG:4326': + print(f"区域尺寸: 宽度={width:.6f}°, 高度={height:.6f}°") + # 对于地理坐标系，需要调整分辨率单位（度） + # 1度约等于111公里，所以100米约等于0.0009度 + resolution_deg = resolution / 111000.0 # 将米转换为度 + print(f"网格分辨率: {resolution}m ≈ {resolution_deg:.6f}°") + else: + print(f"区域尺寸: 宽度={width:.2f}m, 高度={height:.2f}m") + resolution_deg = resolution + + # 检查分辨率是否合理 + min_grid_points = 50 # 增加最少网格点数以获得更平滑的插值效果 + + if self.output_crs == 'EPSG:4326': + # 地理坐标系的网格点计算 + grid_points_x = max(int(width / resolution_deg), min_grid_points) + grid_points_y = max(int(height / resolution_deg), min_grid_points) + else: + # 投影坐标系的网格点计算 + grid_points_x = max(int(width / resolution), min_grid_points) + grid_points_y = max(int(height / resolution), min_grid_points) + + # 确保网格足够密集以获得平滑效果 + grid_points_x = max(grid_points_x, 100) + grid_points_y = max(grid_points_y, 100) + + # 创建网格 + grid_x = np.linspace(minx, maxx, grid_points_x) + grid_y = np.linspace(miny, maxy, grid_points_y) + grid_xx, grid_yy = np.meshgrid(grid_x, grid_y) + + print(f"网格大小: {grid_xx.shape[1]} x {grid_xx.shape[0]} (宽 x 高)") + + # 检查网格大小 + if grid_xx.shape[0] < 2 or grid_xx.shape[1] < 2: + raise ValueError(f"网格尺寸太小 {grid_xx.shape}，无法进行插值。请检查数据范围和分辨率设置。") + + # 准备插值数据（使用原始点+外扩点的合并数据） + points = np.column_stack((points_gdf['proj_x'], points_gdf['proj_y'])) + values = points_gdf['content'].values + + print(f"插值数据点数量: {len(points)}（包含原始采样点和外扩点）") + print(f"数据点范围: X({points[:, 0].min():.6f} - {points[:, 0].max():.6f}), " + f"Y({points[:, 1].min():.6f} - {points[:, 1].max():.6f})") + print(f"含量值范围: {values.min():.4f} - {values.max():.4f}") + print(f"含量值统计: 平均={values.mean():.4f}, 标准差={values.std():.4f}") + + # 检查数据点数量 + if len(points) < 3: + raise ValueError("插值需要至少3个数据点") + + # 检查数据点的几何分布 + self._check_point_distribution(points) + + # 执行插值（先对整个网格插值，包括边界外） + print("正在执行空间插值（整个网格，包括边界外）...") + grid_content = self._perform_interpolation(points, values, grid_xx, grid_yy) + + # 创建边界掩膜（用于识别边界内外） + print("正在识别边界区域...") + # 创建掩膜 + mask_points = np.column_stack((grid_xx.ravel(), grid_yy.ravel())) + mask_geometry = [Point(x, y) for x, y in mask_points] + mask_gdf = gpd.GeoDataFrame(geometry=mask_geometry, crs=self.output_crs) + + # 检查哪些点在边界内 + within_boundary = mask_gdf.within(boundary_gdf.unary_union) + mask = within_boundary.values.reshape(grid_xx.shape) + + # 找到边界边缘上的点（在边界内，但靠近边界） + print("正在提取边界边缘值并填充边界外区域...") + + # 方法：找到边界内有效值的边缘点，然后填充到边界外 + # 1. 先填充边界内的NaN（使用距离扩散方法） + nan_mask = np.isnan(grid_content) + within_boundary_nan = nan_mask & mask + + if np.any(within_boundary_nan): + if use_distance_diffusion: + # 使用距离扩散方法填充边界内的空白区域 + grid_content = self._fill_boundary_blanks_with_distance_diffusion( + grid_content, grid_xx, grid_yy, mask, boundary_gdf, + max_diffusion_distance=max_diffusion_distance, + power=diffusion_power, + n_neighbors=diffusion_n_neighbors + ) + else: + # 使用传统的最近邻插值方法 + print(f"填充边界内的 {np.sum(within_boundary_nan)} 个NaN点（使用最近邻插值）...") + valid_mask = ~nan_mask & mask + if np.sum(valid_mask) > 0: + valid_points = np.column_stack((grid_xx[valid_mask], grid_yy[valid_mask])) + valid_values = grid_content[valid_mask] + nan_points = np.column_stack((grid_xx[within_boundary_nan], grid_yy[within_boundary_nan])) + + filled_values = griddata( + valid_points, valid_values, nan_points, + method='nearest' + ) + grid_content[within_boundary_nan] = filled_values + print(f"边界内填充完成") + + # 2. 找到边界边缘的值（边界内但靠近边界外的点） + # 使用形态学操作找到边界边缘 + boundary_mask_binary = mask.astype(int) + # 创建边界外掩膜 + outside_mask = ~mask + + # 找到边界边缘（在边界内，但相邻有边界外的点） + # 对边界外区域进行膨胀，找到边界边缘 + kernel = np.ones((3, 3), dtype=bool) + dilated_outside = ndimage.binary_dilation(outside_mask, structure=kernel) + edge_mask = mask & dilated_outside # 边界内但靠近边界外的点 + + # 3. 提取边缘值，填充到边界外 + if np.any(edge_mask): + edge_values = grid_content[edge_mask] + edge_valid = ~np.isnan(edge_values) + if np.any(edge_valid): + # 使用边缘的有效值填充边界外 + edge_mean = np.nanmean(edge_values) + print(f"边界边缘平均值: {edge_mean:.4f}") + + # 将边缘值填充到边界外的所有NaN点 + outside_nan = outside_mask & np.isnan(grid_content) + if np.any(outside_nan): + # 使用最近邻插值从边缘值填充 + edge_points = np.column_stack((grid_xx[edge_mask & ~np.isnan(grid_content)], + grid_yy[edge_mask & ~np.isnan(grid_content)])) + if len(edge_points) > 0: + edge_vals = grid_content[edge_mask & ~np.isnan(grid_content)] + outside_points = np.column_stack((grid_xx[outside_nan], grid_yy[outside_nan])) + + outside_filled = griddata( + edge_points, edge_vals, outside_points, + method='nearest' + ) + grid_content[outside_nan] = outside_filled + print(f"已填充边界外的 {np.sum(~np.isnan(outside_filled))} 个点") + else: + # 如果没有边缘值，使用边缘平均值填充 + grid_content[outside_nan] = edge_mean + print(f"使用边缘平均值填充边界外的 {np.sum(outside_nan)} 个点") + else: + print("边界外区域已全部填充") + else: + # 如果边缘没有有效值，使用全局平均值填充边界外 + global_mean = np.nanmean(grid_content[mask]) + if not np.isnan(global_mean): + grid_content[outside_mask & np.isnan(grid_content)] = global_mean + print(f"使用全局平均值 {global_mean:.4f} 填充边界外") + else: + # 如果没有找到边缘，直接使用边界内的平均值填充边界外 + mean_in_boundary = np.nanmean(grid_content[mask]) + if not np.isnan(mean_in_boundary): + grid_content[outside_mask & np.isnan(grid_content)] = mean_in_boundary + print(f"使用边界内平均值 {mean_in_boundary:.4f} 填充边界外") + + print("整个画面已铺满，边界外区域已用边缘值填充") + + # 最终检查：确保边界内所有区域都有值 + final_check_nan = np.isnan(grid_content) & mask + if np.any(final_check_nan): + print(f"警告: 仍有 {np.sum(final_check_nan)} 个边界内的点未填充，使用平均值填充...") + if np.sum(~np.isnan(grid_content) & mask) > 0: + mean_value = np.nanmean(grid_content[mask]) + grid_content[final_check_nan] = mean_value + print(f" 使用平均值 {mean_value:.4f} 填充剩余 {np.sum(final_check_nan)} 个点") + else: + # 如果边界内完全没有有效值，使用全局平均值 + global_mean = np.nanmean(grid_content) + if not np.isnan(global_mean): + grid_content[final_check_nan] = global_mean + else: + grid_content[final_check_nan] = 0 + print(" 使用全局平均值填充") + else: + print("边界内所有区域已完全填充") + + # 检查插值结果 + valid_data = ~np.isnan(grid_content) + valid_count = np.sum(valid_data) + print(f"有效插值点数量: {valid_count} / {grid_content.size}") + + if valid_count == 0: + raise ValueError("边界掩膜后没有有效数据点，请检查数据点是否在边界范围内") + + if valid_count < 4: + print("警告：有效数据点很少，可能影响绘图效果") + + # 输出插值结果的统计信息 + valid_values = grid_content[valid_data] + print( + f"插值后数据统计: 最小值={valid_values.min():.4f}, 最大值={valid_values.max():.4f}, 平均值={valid_values.mean():.4f}") + + # 返回外扩后的bounds + expanded_bounds = np.array([minx, miny, maxx, maxy]) + + return grid_xx, grid_yy, grid_content, expanded_bounds + + def create_content_map(self, points_gdf, boundary_gdf, grid_xx, grid_yy, + grid_content, bounds, output_file='content_map.png', + show_sample_points=False, base_map_tif=None, + cmap='viridis'): + """ + 创建含量图 + + Parameters: + ----------- + base_map_tif : str, optional + TIF正射底图文件路径。如果提供，将在水域掩膜外显示底图 + cmap : str, default='viridis' + 含量数据的颜色映射 + """ + print("正在生成含量图...") + + # 检查网格数据 + print(f"网格形状: {grid_content.shape}") + + # 创建边界掩膜（用于绘图时只显示边界内） + print("创建边界掩膜用于绘图...") + try: + # 创建网格点的GeoDataFrame + grid_points = gpd.GeoDataFrame( + geometry=[Point(x, y) for x, y in zip(grid_xx.flatten(), grid_yy.flatten())], + crs=points_gdf.crs + ) + # 检查哪些点在边界内 + within_boundary = grid_points.within(boundary_gdf.unary_union) + mask = within_boundary.values.reshape(grid_xx.shape) + print(f"边界内点数: {np.sum(mask)} / {mask.size}") + except Exception as e: + print(f"创建边界掩膜时出现错误: {e}，继续绘图...") + mask = np.ones_like(grid_content, dtype=bool) # 如果失败，显示全部 + + valid_data = ~np.isnan(grid_content) + if np.sum(valid_data) == 0: + raise ValueError("没有有效的插值数据用于绘图") + + # 计算数据统计 + valid_values = grid_content[valid_data] + print( + f"插值结果统计: 最小值={valid_values.min():.4f}, 最大值={valid_values.max():.4f}, 平均值={valid_values.mean():.4f}") + print(f"有效数据点数量: {np.sum(valid_data)} / {grid_content.size}") + + # 检查数据范围 + data_range = valid_values.max() - valid_values.min() + print(f"数据范围: {data_range:.6f}") + + if data_range == 0: + print("警告：所有数据值都相同，将使用单一颜色显示") + + # 创建图形 + fig, ax = plt.subplots(figsize=(12, 10)) + + # 如果提供了底图，先绘制底图（在水域掩膜外） + if base_map_tif is not None: + try: + print(f"正在加载底图: {base_map_tif}") + self._add_base_map(ax, base_map_tif, bounds, mask, grid_xx, grid_yy, boundary_gdf) + print("底图加载成功") + except Exception as e: + print(f"加载底图失败: {e}，将跳过底图显示") + + # 设置颜色映射参数 + im = None + + try: + if data_range > 0: + # 设置颜色范围，确保有足够的对比度 + vmin = valid_values.min() + vmax = valid_values.max() + + # 如果范围很小，稍微扩展一下以增加对比度 + if data_range < 1e-6: + center = valid_values.mean() + expansion = max(abs(center) * 0.01, 1e-6) # 扩展1%或最小值 + vmin = center - expansion + vmax = center + expansion + + print(f"颜色映射范围: {vmin:.6f} - {vmax:.6f}") + + # 方法1：尝试使用contourf + try: + print("尝试使用contourf绘制...") + # 使用掩膜数组：边界外的数据被掩膜掉，只显示边界内 + # mask已经在前面创建好了 + masked_data = np.ma.masked_where(~mask, grid_content) + + # 创建更多等级数以获得更平滑的颜色过渡 + levels = np.linspace(vmin, vmax, 100) # 创建100个等级以获得平滑效果 + im = ax.contourf(grid_xx, grid_yy, masked_data, + levels=levels, cmap=cmap, alpha=0.9, + vmin=vmin, vmax=vmax, extend='both') + print("contourf绘制成功") + + # 可选择性添加等值线（默认不添加，以保持平滑效果） + # 如果需要等值线，可以取消注释下面的代码 + # try: + # contour_levels = np.linspace(vmin, vmax, 11) + # contours = ax.contour(grid_xx, grid_yy, grid_content, + # levels=contour_levels, colors='white', + # alpha=0.3, linewidths=0.5) + # ax.clabel(contours, inline=True, fontsize=8, fmt='%.3f') + # print("等值线添加成功") + # except Exception as e: + # print(f"等值线绘制失败: {e}") + + except Exception as e: + print(f"contourf失败: {e}") + # 方法2：使用pcolormesh + try: + print("尝试使用pcolormesh绘制...") + # 使用掩膜数组：边界外的数据被掩膜掉，只显示边界内 + # mask已经在前面创建好了 + masked_data = np.ma.masked_where(~mask, grid_content) + + im = ax.pcolormesh(grid_xx, grid_yy, masked_data, + cmap=cmap, alpha=0.9, + vmin=vmin, vmax=vmax, shading='gouraud') # 使用gouraud平滑着色 + print("pcolormesh绘制成功") + except Exception as e2: + print(f"pcolormesh也失败: {e2}") + raise e2 + + else: + # 所有值相同的情况 + print("使用单一颜色填充（所有值相同）") + # 创建一个简单的填充 + single_value = valid_values[0] + im = ax.contourf(grid_xx, grid_yy, grid_content, + levels=[single_value - 0.001, single_value + 0.001], + cmap=cmap, alpha=0.8) + + except Exception as e: + print(f"主要绘图方法失败，尝试备选方案: {e}") + + # 备选方案1：imshow + try: + print("尝试使用imshow...") + # 处理NaN值 + display_data = grid_content.copy() + nan_mask = np.isnan(display_data) + if np.any(nan_mask): + # 用平均值填充NaN + display_data[nan_mask] = valid_values.mean() + + if data_range > 0: + vmin = valid_values.min() + vmax = valid_values.max() + im = ax.imshow(display_data, + extent=[grid_xx.min(), grid_xx.max(), + grid_yy.min(), grid_yy.max()], + cmap=cmap, alpha=0.8, origin='lower', + vmin=vmin, vmax=vmax, aspect='auto') + else: + im = ax.imshow(display_data, + extent=[grid_xx.min(), grid_xx.max(), + grid_yy.min(), grid_yy.max()], + cmap=cmap, alpha=0.8, origin='lower', + aspect='auto') + print("imshow绘制成功") + + except Exception as e2: + print(f"imshow也失败: {e2}") + + # 备选方案2：散点图 + try: + print("尝试使用散点图...") + valid_x = grid_xx[valid_data] + valid_y = grid_yy[valid_data] + valid_z = grid_content[valid_data] + + if data_range > 0: + im = ax.scatter(valid_x, valid_y, c=valid_z, + cmap=cmap, alpha=0.8, s=10, + vmin=valid_values.min(), vmax=valid_values.max()) + else: + im = ax.scatter(valid_x, valid_y, c=valid_z, + cmap=cmap, alpha=0.8, s=10) + print("散点图绘制成功") + + except Exception as e3: + print(f"所有绘图方法都失败: {e3}") + raise ValueError("无法生成颜色图，请检查数据") + + # 绘制边界（黑色） + try: + boundary_gdf.boundary.plot(ax=ax, color='black', linewidth=2, alpha=1.0) + print("边界绘制成功（黑色）") + except Exception as e: + print(f"边界绘制失败: {e}") + + # 可选择性绘制采样点（默认不绘制，以显示平滑的颜色分布） + if show_sample_points: + try: + points_gdf.plot(ax=ax, color='black', markersize=6, alpha=0.7, + marker='+', edgecolors='white', linewidth=1) + print("采样点绘制成功") + except Exception as e: + print(f"采样点绘制失败: {e}") + + # 设置坐标轴标签和格式 + # 由于输入是投影坐标系，输出是地理坐标系，始终显示为地理坐标 + ax.set_xlabel('经度 (°)', fontsize=12) + ax.set_ylabel('纬度 (°)', fontsize=12) + + # 格式化坐标轴刻度为经纬度格式（保留3位小数） + def lon_formatter(x, p): + return f'{x:.3f}°' + + def lat_formatter(x, p): + return f'{x:.3f}°' + + ax.xaxis.set_major_formatter(FuncFormatter(lon_formatter)) + ax.yaxis.set_major_formatter(FuncFormatter(lat_formatter)) + + # 添加格网线 + ax.grid(True, linestyle='--', linewidth=0.5, alpha=0.5, color='gray') + ax.set_axisbelow(True) # 将格网线放在图层下方 + + # ax.set_title('含量分布图', fontsize=16, fontweight='bold', pad=20) # 已去除标题 + + # 添加颜色条 + try: + if im is not None: + cbar = plt.colorbar(im, ax=ax, shrink=0.5, aspect=40, pad=0.02) + cbar.set_label('含量值', fontsize=10) + + # 设置颜色条的刻度 + if data_range > 0: + tick_values = np.linspace(valid_values.min(), valid_values.max(), 6) + cbar.set_ticks(tick_values) + cbar.set_ticklabels([f'{val:.3f}' for val in tick_values]) + cbar.ax.tick_params(labelsize=8) # 缩小刻度标签字体 + + print("颜色条添加成功") + else: + print("警告：无法添加颜色条，im对象为None") + except Exception as e: + print(f"颜色条添加失败: {e}") + + # 添加指北针 + try: + self.add_north_arrow(ax, bounds) + except Exception as e: + print(f"指北针添加失败: {e}") + + # 添加比例尺 + try: + self.add_scale_bar(ax) + except Exception as e: + print(f"比例尺添加失败: {e}") + + # 添加图例 + try: + self.add_legend(ax) + except Exception as e: + print(f"图例添加失败: {e}") + + # 设置图形边界（进一步外扩1%以确保边界不完全挨着地图） + try: + x_range = bounds[2] - bounds[0] + y_range = bounds[3] - bounds[1] + display_expand = 0.01 # 显示时再外扩1% + ax.set_xlim(bounds[0] - x_range * display_expand, bounds[2] + x_range * display_expand) + ax.set_ylim(bounds[1] - y_range * display_expand, bounds[3] + y_range * display_expand) + except Exception as e: + print(f"设置图形边界失败: {e}") + + # 调整布局 + plt.tight_layout() + + # 保存图片 + try: + plt.savefig(output_file, dpi=300, bbox_inches='tight', + facecolor='white', edgecolor='none') + print(f"含量图已保存为：{output_file}") + except Exception as e: + print(f"图片保存失败: {e}") + + # 显示图片 + try: + plt.show() + except Exception as e: + print(f"图片显示失败: {e}") + + def add_north_arrow(self, ax, bounds): + """添加指北针（左上角）- 复杂罗盘样式""" + minx, miny, maxx, maxy = bounds + + # 计算指北针位置（左上角） + arrow_x = minx + (maxx - minx) * 0.1 + arrow_y = maxy - (maxy - miny) * 0.1 + + # 缩小指北针尺寸 + size_factor = (maxy - miny) * 0.04 # 缩小尺寸 + radius = size_factor * 1.0 # 罗盘半径 + + # 绘制圆形背景（外圈） + circle_outer = patches.Circle( + (arrow_x, arrow_y), + radius=radius, + facecolor='white', + edgecolor='black', + linewidth=2.5, + zorder=10 + ) + ax.add_patch(circle_outer) + + # 绘制内圈（装饰） + circle_inner = patches.Circle( + (arrow_x, arrow_y), + radius=radius * 0.7, + facecolor='none', + edgecolor='gray', + linewidth=1.5, + linestyle='--', + zorder=11 + ) + ax.add_patch(circle_inner) + + # 绘制四个方向的刻度线 + tick_length = radius * 0.3 + tick_width = 1.5 + + # 北方向刻度（主刻度） + ax.plot([arrow_x, arrow_x], [arrow_y, arrow_y + radius * 0.85], + 'k-', linewidth=tick_width * 2, zorder=12) + + # 南方向刻度 + ax.plot([arrow_x, arrow_x], [arrow_y, arrow_y - radius * 0.85], + 'k-', linewidth=tick_width, zorder=12) + + # 东方向刻度 + ax.plot([arrow_x, arrow_x + radius * 0.85], [arrow_y, arrow_y], + 'k-', linewidth=tick_width, zorder=12) + + # 西方向刻度 + ax.plot([arrow_x, arrow_x - radius * 0.85], [arrow_y, arrow_y], + 'k-', linewidth=tick_width, zorder=12) + + # 绘制次要刻度（45度方向） + for angle in [45, 135, 225, 315]: + angle_rad = math.radians(angle) + x_end = arrow_x + radius * 0.7 * math.cos(angle_rad) + y_end = arrow_y + radius * 0.7 * math.sin(angle_rad) + ax.plot([arrow_x, x_end], [arrow_y, y_end], + 'k-', linewidth=tick_width * 0.5, alpha=0.6, zorder=12) + + # 绘制指北箭头（三角形，填充） + arrow_size = radius * 0.6 + arrow_points = np.array([ + [arrow_x, arrow_y + radius * 0.9], # 顶点（北） + [arrow_x - arrow_size * 0.3, arrow_y + radius * 0.3], # 左下 + [arrow_x + arrow_size * 0.3, arrow_y + radius * 0.3] # 右下 + ]) + arrow_poly = patches.Polygon( + arrow_points, + facecolor='black', + edgecolor='black', + linewidth=2, + zorder=13 + ) + ax.add_patch(arrow_poly) + + # 绘制指南箭头（三角形，填充，但较小） + south_arrow_size = radius * 0.4 + south_arrow_points = np.array([ + [arrow_x, arrow_y - radius * 0.6], # 顶点（南） + [arrow_x - south_arrow_size * 0.2, arrow_y - radius * 0.2], # 左上 + [arrow_x + south_arrow_size * 0.2, arrow_y - radius * 0.2] # 右上 + ]) + south_arrow_poly = patches.Polygon( + south_arrow_points, + facecolor='white', + edgecolor='black', + linewidth=1.5, + zorder=13 + ) + ax.add_patch(south_arrow_poly) + + # 添加方向标记（N, S, E, W） + label_offset = radius * 1.15 + font_size = 16 * 0.5 # 缩小字体到原来的一半 + + ax.text(arrow_x, arrow_y + label_offset, 'N', + fontsize=font_size, fontweight='bold', ha='center', va='bottom', + color='black', zorder=14) + + ax.text(arrow_x, arrow_y - label_offset, 'S', + fontsize=font_size * 0.8, fontweight='bold', ha='center', va='top', + color='black', zorder=14) + + ax.text(arrow_x + label_offset, arrow_y, 'E', + fontsize=font_size * 0.8, fontweight='bold', ha='left', va='center', + color='black', zorder=14) + + ax.text(arrow_x - label_offset, arrow_y, 'W', + fontsize=font_size * 0.8, fontweight='bold', ha='right', va='center', + color='black', zorder=14) + + def add_scale_bar(self, ax): + """添加比例尺""" + try: + if self.output_crs == 'EPSG:4326': + # 地理坐标系，需要指定度数与距离的换算关系 + # 在地球表面，1度约等于111公里（在赤道附近） + # 使用deg作为单位，matplotlib-scalebar会自动处理 + scalebar = ScaleBar( + 111000, # 1度 = 111000米 + units='m', + location='lower left', + box_alpha=0.8, + color='black', + font_properties={'size': 10}, + label_loc='bottom' + ) + ax.add_artist(scalebar) + print("地理坐标系比例尺添加成功") + else: + # 投影坐标系，使用米作为单位 + scalebar = ScaleBar(1, units='m', location='lower left', + box_alpha=0.8, color='black', + font_properties={'size': 10}) + ax.add_artist(scalebar) + print("投影坐标系比例尺添加成功") + except Exception as e: + print(f"比例尺添加失败: {e}") + # 如果matplotlib-scalebar失败，尝试手动添加简单的比例尺 + try: + self._add_manual_scale_bar(ax) + print("手动比例尺添加成功") + except Exception as e2: + print(f"手动比例尺也失败: {e2}") + + def _add_manual_scale_bar(self, ax): + """手动添加简单的比例尺""" + # 获取当前坐标轴的范围 + xlim = ax.get_xlim() + ylim = ax.get_ylim() + + # 计算比例尺的位置和长度 + x_range = xlim[1] - xlim[0] + y_range = ylim[1] - ylim[0] + + # 比例尺位置（左下角） + scale_x = xlim[0] + x_range * 0.05 + scale_y = ylim[0] + y_range * 0.1 + + if self.output_crs == 'EPSG:4326': + # 地理坐标系：计算合适的比例尺长度（度） + # 选择一个合理的距离，比如1公里、5公里或10公里 + distance_km = 5 # 5公里 + scale_length_deg = distance_km / 111.0 # 转换为度数 + + # 绘制比例尺线 + ax.plot([scale_x, scale_x + scale_length_deg], [scale_y, scale_y], + 'k-', linewidth=3) + ax.plot([scale_x, scale_x], [scale_y - y_range * 0.01, scale_y + y_range * 0.01], + 'k-', linewidth=2) + ax.plot([scale_x + scale_length_deg, scale_x + scale_length_deg], + [scale_y - y_range * 0.01, scale_y + y_range * 0.01], 'k-', linewidth=2) + + # 添加文字标注 + ax.text(scale_x + scale_length_deg / 2, scale_y + y_range * 0.02, + f'{distance_km} km', ha='center', va='bottom', fontsize=10, + bbox=dict(boxstyle='round,pad=0.3', facecolor='white', alpha=0.8)) + else: + # 投影坐标系：使用米为单位 + # 选择合适的比例尺长度 + if x_range > 10000: # 大于10km + scale_length = 5000 # 5km + scale_text = '5 km' + elif x_range > 2000: # 大于2km + scale_length = 1000 # 1km + scale_text = '1 km' + else: # 小于2km + scale_length = 500 # 500m + scale_text = '500 m' + + # 绘制比例尺线 + ax.plot([scale_x, scale_x + scale_length], [scale_y, scale_y], + 'k-', linewidth=3) + ax.plot([scale_x, scale_x], [scale_y - y_range * 0.01, scale_y + y_range * 0.01], + 'k-', linewidth=2) + ax.plot([scale_x + scale_length, scale_x + scale_length], + [scale_y - y_range * 0.01, scale_y + y_range * 0.01], 'k-', linewidth=2) + + # 添加文字标注 + ax.text(scale_x + scale_length / 2, scale_y + y_range * 0.02, + scale_text, ha='center', va='bottom', fontsize=10, + bbox=dict(boxstyle='round,pad=0.3', facecolor='white', alpha=0.8)) + + def _add_base_map(self, ax, base_map_tif, bounds, mask, grid_xx, grid_yy, boundary_gdf): + """添加正射底图（在水域掩膜外显示） + + Parameters: + ----------- + ax : matplotlib.axes.Axes + 绘图轴对象 + base_map_tif : str + TIF底图文件路径 + bounds : np.ndarray + 显示范围 [minx, miny, maxx, maxy] + mask : np.ndarray + 水域掩膜（True表示水域内） + grid_xx : np.ndarray + 网格X坐标 + grid_yy : np.ndarray + 网格Y坐标 + boundary_gdf : gpd.GeoDataFrame + 边界几何数据 + """ + + print("正在读取底图文件...") + with rasterio.open(base_map_tif) as src: + # 获取底图的坐标系 + tif_crs = src.crs + tif_bounds = src.bounds + + print(f"底图坐标系: {tif_crs}") + print(f"底图范围: {tif_bounds}") + print(f"目标范围: {bounds}") + + # 检查是否需要投影转换 + target_crs = CRS.from_string(self.output_crs) + need_reproject = tif_crs != target_crs + + # 读取底图数据 + if need_reproject: + print(f"底图坐标系({tif_crs})与目标坐标系({target_crs})不同，正在转换...") + # 计算转换后的变换参数和尺寸 + transform, width, height = calculate_default_transform( + tif_crs, target_crs, + src.width, src.height, + left=bounds[0], bottom=bounds[1], + right=bounds[2], top=bounds[3] + ) + + # 创建目标数组 + if src.count == 1: + # 单波段 + base_map_data = np.zeros((height, width), dtype=src.dtypes[0]) + reproject( + source=src.read(1), + destination=base_map_data, + src_transform=src.transform, + src_crs=tif_crs, + dst_transform=transform, + dst_crs=target_crs, + resampling=Resampling.bilinear + ) + else: + # 多波段（RGB），取前3个波段 + num_bands = min(3, src.count) + base_map_data = np.zeros((num_bands, height, width), dtype=src.dtypes[0]) + for i in range(num_bands): + reproject( + source=src.read(i + 1), + destination=base_map_data[i], + src_transform=src.transform, + src_crs=tif_crs, + dst_transform=transform, + dst_crs=target_crs, + resampling=Resampling.bilinear + ) + + # 如果是RGB，转换为(height, width, 3)格式 + if num_bands == 3: + base_map_data = np.transpose(base_map_data, (1, 2, 0)) + + # 创建extent用于显示 + extent = [bounds[0], bounds[2], bounds[1], bounds[3]] + + else: + # 不需要投影转换，直接读取对应范围的数据 + print("底图坐标系与目标坐标系一致，直接读取...") + + # 计算需要读取的窗口 + row_min, col_min = src.index(bounds[0], bounds[3]) # 左上角 + row_max, col_max = src.index(bounds[2], bounds[1]) # 右下角 + + # 确保索引在有效范围内 + row_min = max(0, row_min) + row_max = min(src.height, row_max + 1) + col_min = max(0, col_min) + col_max = min(src.width, col_max + 1) + + window = windows.Window.from_slices( + (row_min, row_max), (col_min, col_max) + ) + + # 读取数据 + if src.count == 1: + base_map_data = src.read(1, window=window) + else: + # 多波段，取前3个波段 + num_bands = min(3, src.count) + base_map_data = src.read(list(range(1, num_bands + 1)), window=window) + if num_bands == 3: + # 转换为(height, width, 3)格式 + base_map_data = np.transpose(base_map_data, (1, 2, 0)) + + # 计算extent + window_transform = windows.transform(window, src.transform) + left = window_transform[2] + top = window_transform[5] + right = left + window_transform[0] * base_map_data.shape[1] + bottom = top + window_transform[4] * base_map_data.shape[0] + + # 确保extent不超过bounds + extent = [ + max(bounds[0], left), + min(bounds[2], right), + max(bounds[1], bottom), + min(bounds[3], top) + ] + + # 将底图数据缩放到网格大小以便显示 + # 创建底图的显示掩膜：只在边界外显示 + print("正在创建底图显示掩膜...") + + # 创建底图网格（与显示范围对齐） + base_map_height, base_map_width = base_map_data.shape[:2] + + # 性能优化：如果底图分辨率过高，进行降采样以提高处理速度 + # 限制最大边长为2000像素（保持足够清晰度的同时提高速度） + max_display_size = 2000 + scale_factor = 1.0 + if max(base_map_height, base_map_width) > max_display_size: + scale_factor = max_display_size / max(base_map_height, base_map_width) + new_height = int(base_map_height * scale_factor) + new_width = int(base_map_width * scale_factor) + print( + f"底图分辨率较高 ({base_map_width}x{base_map_height})，降采样到 {new_width}x{new_height} 以提高速度") + # 使用scipy的zoom进行降采样 + if base_map_data.ndim == 2: + base_map_data = ndimage.zoom(base_map_data, scale_factor, order=1) + else: + base_map_data = ndimage.zoom(base_map_data, (scale_factor, scale_factor, 1), order=1) + base_map_height, base_map_width = base_map_data.shape[:2] + # 更新extent以匹配新的分辨率 + extent_width = extent[1] - extent[0] + extent_height = extent[3] - extent[2] + extent = [ + extent[0], + extent[0] + extent_width, + extent[2], + extent[2] + extent_height + ] + + # 使用rasterio的geometry_mask快速生成掩膜（比创建大量Point对象快得多） + # 创建底图的变换矩阵 + if need_reproject: + # 如果进行了投影转换，使用计算得到的transform + base_map_transform = transform + else: + # 如果没有投影转换，使用窗口变换 + base_map_transform = window_transform + + # 如果进行了降采样，需要调整transform + if scale_factor < 1.0: + # 调整transform以适应新的分辨率 + # rasterio的transform是6元素tuple或Affine对象，需要调整像素大小 + # 获取transform的6个参数 (a, b, c, d, e, f) + # 其中a和e是像素大小，需要除以scale_factor + + if Affine is not None: + # 获取6个参数 + if hasattr(base_map_transform, '__iter__') and len(base_map_transform) == 6: + a, b, c, d, e, f = base_map_transform + else: + a, b, c, d, e, f = base_map_transform[0], base_map_transform[1], base_map_transform[2], \ + base_map_transform[3], base_map_transform[4], base_map_transform[5] + # 创建新的transform，调整像素大小（a和e是像素大小） + base_map_transform = Affine(a / scale_factor, b, c, d, e / scale_factor, f) + else: + # 降级方案：使用tuple + if hasattr(base_map_transform, '__iter__') and len(base_map_transform) == 6: + a, b, c, d, e, f = base_map_transform + else: + a, b, c, d, e, f = base_map_transform[0], base_map_transform[1], base_map_transform[2], \ + base_map_transform[3], base_map_transform[4], base_map_transform[5] + base_map_transform = (a / scale_factor, b, c, d, e / scale_factor, f) + + # 调试信息：检查边界数据和底图范围 + print(f"底图显示范围 (extent): {extent}") + print(f"底图分辨率: {base_map_width}x{base_map_height}") + print(f"底图transform: {base_map_transform}") + if boundary_gdf is not None and len(boundary_gdf) > 0: + boundary_bounds = boundary_gdf.total_bounds + print(f"边界数据范围: {boundary_bounds}") + print(f"边界数据坐标系: {boundary_gdf.crs}") + print(f"边界要素数量: {len(boundary_gdf)}") + + # 检查边界是否与底图范围重叠 + overlap_x = not (boundary_bounds[2] < extent[0] or boundary_bounds[0] > extent[1]) + overlap_y = not (boundary_bounds[3] < extent[2] or boundary_bounds[1] > extent[3]) + if not (overlap_x and overlap_y): + print("警告: 边界数据范围与底图显示范围不重叠！") + print(" 将不应用掩膜，显示整个底图") + # 创建全True的掩膜（显示所有区域） + base_map_mask = np.ones((base_map_height, base_map_width), dtype=bool) + else: + # 使用geometry_mask生成掩膜（True表示在几何体内，即水域内） + # 注意：geometry_mask返回True表示需要掩膜的区域（在几何体内）， + # 但我们想要的是边界外的区域（不在几何体内），所以需要反转 + try: + within_boundary_mask = geometry_mask( + boundary_gdf.geometry, + out_shape=(base_map_height, base_map_width), + transform=base_map_transform, + invert=False # False表示掩膜几何体内的区域（水域内） + ) + # 反转掩膜：True表示边界外（需要显示的区域） + base_map_mask = ~within_boundary_mask + except Exception as e: + print(f"生成掩膜时出错: {e}") + print(" 将不应用掩膜，显示整个底图") + import traceback + traceback.print_exc() + # 创建全True的掩膜（显示所有区域） + base_map_mask = np.ones((base_map_height, base_map_width), dtype=bool) + else: + print("警告: 边界数据为空，将不应用掩膜，显示整个底图") + # 创建全True的掩膜（显示所有区域） + base_map_mask = np.ones((base_map_height, base_map_width), dtype=bool) + + # 调试信息：检查掩膜状态 + mask_ratio = np.sum(base_map_mask) / base_map_mask.size + print( + f"底图掩膜状态: 可显示区域占比 {mask_ratio * 100:.2f}% ({np.sum(base_map_mask)}/{base_map_mask.size} 像素)") + + # 如果掩膜后没有可显示区域，警告并显示整个底图 + if mask_ratio == 0.0: + print("警告: 掩膜后没有可显示区域，将显示整个底图（不应用掩膜）") + base_map_mask = np.ones((base_map_height, base_map_width), dtype=bool) + + # 归一化数据以便显示（如果是数值型） + # 注意：先归一化整个数据，再应用掩膜，这样可以保证归一化范围正确 + if base_map_data.dtype != np.uint8: + if base_map_data.ndim == 2: + # 单波段：归一化到0-1 + # 使用整个数据集的范围进行归一化（不仅仅是掩膜区域） + data_min = np.nanmin(base_map_data) + data_max = np.nanmax(base_map_data) + print(f"底图数据范围: [{data_min}, {data_max}], dtype: {base_map_data.dtype}") + + if data_max > data_min: + # 先归一化整个数组 + base_map_normalized = (base_map_data - data_min) / (data_max - data_min) + # 然后应用掩膜：只显示边界外的区域 + base_map_display = np.ma.masked_where(~base_map_mask, base_map_normalized) + else: + # 如果数据范围无效，创建全0的掩膜数组 + print("警告: 底图数据范围无效，所有值相同") + base_map_display = np.ma.masked_where(~base_map_mask, np.zeros_like(base_map_data)) + else: + # RGB：每个波段单独归一化 + base_map_normalized = base_map_data.copy().astype(np.float32) + for i in range(base_map_data.shape[2]): + band_data = base_map_data[:, :, i] + data_min = np.nanmin(band_data) + data_max = np.nanmax(band_data) + print(f"底图波段 {i} 数据范围: [{data_min}, {data_max}]") + + if data_max > data_min: + # 归一化整个波段 + base_map_normalized[:, :, i] = (band_data - data_min) / (data_max - data_min) + else: + print(f"警告: 底图波段 {i} 数据范围无效，所有值相同") + base_map_normalized[:, :, i] = np.zeros_like(band_data) + + # 应用掩膜：只显示边界外的区域 + mask_3d = np.broadcast_to(~base_map_mask[..., np.newaxis], base_map_data.shape) + base_map_display = np.ma.masked_where(mask_3d, base_map_normalized) + else: + # uint8类型：直接使用，但可能需要归一化到0-1用于imshow + if base_map_data.ndim == 2: + # 单波段：uint8通常已经是0-255范围，归一化到0-1 + base_map_normalized = base_map_data.astype(np.float32) / 255.0 + base_map_display = np.ma.masked_where(~base_map_mask, base_map_normalized) + else: + # RGB：uint8归一化到0-1 + base_map_normalized = base_map_data.astype(np.float32) / 255.0 + mask_3d = np.broadcast_to(~base_map_mask[..., np.newaxis], base_map_data.shape) + base_map_display = np.ma.masked_where(mask_3d, base_map_normalized) + + # 检查归一化后的数据范围 + if isinstance(base_map_display, np.ma.MaskedArray): + valid_data = base_map_display[~base_map_display.mask] + if len(valid_data) > 0: + print( + f"归一化后有效数据范围: [{np.nanmin(valid_data):.3f}, {np.nanmax(valid_data):.3f}], 有效像素数: {len(valid_data)}") + else: + print("警告: 归一化后没有有效数据显示区域") + + # 绘制底图 + print("正在绘制底图...") + # 注意：extent格式为 [left, right, bottom, top] + # 对于地理坐标系，y轴通常向上为正，所以使用origin='lower' + try: + if base_map_data.ndim == 2: + # 单波段：使用灰度图 + # 确保数据在0-1范围内 + if isinstance(base_map_display, np.ma.MaskedArray): + # 对于masked array，确保数据范围正确 + if np.ma.max(base_map_display) > 1.0 or np.ma.min(base_map_display) < 0.0: + base_map_display = np.ma.clip(base_map_display, 0.0, 1.0) + else: + base_map_display = np.clip(base_map_display, 0.0, 1.0) + + im = ax.imshow(base_map_display, extent=extent, origin='lower', + cmap='gray', alpha=0.8, zorder=0, interpolation='bilinear', + vmin=0.0, vmax=1.0) + else: + # RGB：直接显示 + # 确保数据格式正确（需要在0-1范围内） + if isinstance(base_map_display, np.ma.MaskedArray): + # 对于masked array，确保数据在0-1范围内 + if np.ma.max(base_map_display) > 1.0 or np.ma.min(base_map_display) < 0.0: + base_map_display = np.ma.clip(base_map_display, 0.0, 1.0) + else: + base_map_display = np.clip(base_map_display, 0.0, 1.0) + + # 确保是float32类型，imshow期望0-1范围的float数组 + if base_map_display.dtype != np.float32 and base_map_display.dtype != np.float64: + base_map_display = base_map_display.astype(np.float32) + + im = ax.imshow(base_map_display, extent=extent, origin='lower', + alpha=0.8, zorder=0, interpolation='bilinear') + print(f"底图绘制成功") + except Exception as e: + print(f"底图绘制出错: {e}") + import traceback + traceback.print_exc() + # 如果绘制失败，至少尝试绘制一个简单的占位图 + print("尝试使用备用方法绘制底图...") + try: + if base_map_data.ndim == 2: + # 使用简单的numpy数组，不应用掩膜 + simple_display = np.clip(base_map_data.astype(np.float32) / np.nanmax(base_map_data), 0, 1) + ax.imshow(simple_display, extent=extent, origin='lower', + cmap='gray', alpha=0.5, zorder=0) + else: + simple_display = np.clip(base_map_data.astype(np.float32) / 255.0, 0, 1) + ax.imshow(simple_display, extent=extent, origin='lower', + alpha=0.5, zorder=0) + print("备用方法绘制成功") + except Exception as e2: + print(f"备用方法也失败: {e2}") + + print(f"底图已绘制，显示范围: {extent}") + + def add_legend(self, ax): + """ + 添加图例 + + Parameters: + ----------- + """ + legend_elements = [ + # 移除边界标签 + # plt.Line2D([0], [0], color='red', linewidth=2, label='边界'), + # 移除采样点和等值线图例项，以突出平滑的颜色分布效果 + # plt.Line2D([0], [0], marker='+', color='w', markerfacecolor='black', + # markersize=8, label='采样点'), + ] + + # 如果图例为空，则不显示图例 + if legend_elements: + ax.legend(handles=legend_elements, loc='upper left', + framealpha=0.9, fontsize=10) + + def process_data(self, csv_file, shp_file, output_file='content_map.png', + resolution=100, show_sample_points=False, base_map_tif=None, + use_distance_diffusion=True, max_diffusion_distance=None, + diffusion_power=2, diffusion_n_neighbors=15, cmap=None, + expand_ratio=0.05): + """ + 主处理函数 + + Parameters: + ----------- + base_map_tif : str, optional + TIF正射底图文件路径。如果提供，将在水域掩膜外显示底图 + use_distance_diffusion : bool, default=True + 是否使用距离扩散方法填充边界空白区域 + max_diffusion_distance : float, optional + 最大扩散距离（单位与坐标相同）。如果为None，自动计算为网格分辨率的5倍 + diffusion_power : float, default=2 + 距离扩散的IDW幂参数，值越大，距离衰减越快 + diffusion_n_neighbors : int, default=15 + 距离扩散使用的最近邻点数 + cmap : str, optional + 颜色映射。如果为None，将从CSV文件名或内容中自动识别参数并选择对应的colormap + expand_ratio : float, default=0.05 + 边界外扩比例（5%），确保图像边界不完全挨着地图 + """ + try: + # 自动识别参数名称并获取colormap + if cmap is None: + param_name = self._extract_param_name(csv_file) + cmap = self._get_colormap(param_name) + else: + print(f"使用指定的颜色映射: {cmap}") + + # 读取数据 + points_gdf = self.read_csv_data(csv_file) + boundary_gdf = self.read_boundary_shapefile(shp_file) + + # 对边缘采样点进行外扩处理（外扩到整个图像边界，按照resolution间距） + points_gdf = self._expand_edge_points(points_gdf, boundary_gdf, resolution=resolution, expand_ratio=expand_ratio) + + # 创建插值网格 + grid_xx, grid_yy, grid_content, bounds = self.create_interpolation_grid( + points_gdf, boundary_gdf, resolution, + expand_ratio=expand_ratio, + use_distance_diffusion=use_distance_diffusion, + max_diffusion_distance=max_diffusion_distance, + diffusion_power=diffusion_power, + diffusion_n_neighbors=diffusion_n_neighbors + ) + + # 生成含量图（包含不确定性叠加） + self.create_content_map( + points_gdf, boundary_gdf, grid_xx, grid_yy, + grid_content, bounds, output_file, show_sample_points, base_map_tif, + cmap=cmap + ) + + print("处理完成！") + + # 输出统计信息 + print(f"\n统计信息：") + print(f"数据点数量: {len(points_gdf)}") + print(f"含量值范围: {points_gdf['content'].min():.2f} - {points_gdf['content'].max():.2f}") + print(f"含量值平均: {points_gdf['content'].mean():.2f}") + print(f"含量值标准差: {points_gdf['content'].std():.2f}") + + except Exception as e: + print(f"处理过程中出现错误: {str(e)}") + raise + + def process_batch(self, csv_folder, shp_file, output_folder=None, + resolution=100, show_sample_points=False, base_map_tif=None, + use_distance_diffusion=True, max_diffusion_distance=None, + diffusion_power=2, diffusion_n_neighbors=15): + """ + 批量处理文件夹中的CSV文件 + + Parameters: + ----------- + csv_folder : str + 包含CSV文件的文件夹路径 + shp_file : str + 边界shapefile文件路径 + output_folder : str, optional + 输出文件夹路径。如果为None，将在CSV文件所在文件夹创建'map_output'子文件夹 + resolution : int, default=100 + 网格分辨率（米） + show_sample_points : bool, default=False + 是否显示采样点 + base_map_tif : str, optional + TIF正射底图文件路径 + use_distance_diffusion : bool, default=True + 是否使用距离扩散方法 + max_diffusion_distance : float, optional + 最大扩散距离 + diffusion_power : float, default=2 + 距离扩散的IDW幂参数 + diffusion_n_neighbors : int, default=15 + 距离扩散使用的最近邻点数 + """ + print("=" * 60) + print("开始批量处理CSV文件") + print("=" * 60) + + # 检查输入文件夹是否存在 + if not os.path.isdir(csv_folder): + raise ValueError(f"输入文件夹不存在: {csv_folder}") + + # 获取所有CSV文件 + csv_files = glob.glob(os.path.join(csv_folder, "*.csv")) + if len(csv_files) == 0: + raise ValueError(f"在文件夹 {csv_folder} 中未找到CSV文件") + + print(f"找到 {len(csv_files)} 个CSV文件") + + # 创建输出文件夹 + if output_folder is None: + output_folder = os.path.join(csv_folder, "map_output") + + if not os.path.exists(output_folder): + os.makedirs(output_folder) + print(f"创建输出文件夹: {output_folder}") + else: + print(f"使用输出文件夹: {output_folder}") + + # 统计信息 + success_count = 0 + fail_count = 0 + failed_files = [] + + # 批量处理每个CSV文件 + for i, csv_file in enumerate(csv_files, 1): + print("\n" + "=" * 60) + print(f"处理文件 {i}/{len(csv_files)}: {os.path.basename(csv_file)}") + print("=" * 60) + + try: + # 生成输出文件名（使用CSV文件名，但扩展名为.png） + csv_basename = os.path.splitext(os.path.basename(csv_file))[0] + output_file = os.path.join(output_folder, f"{csv_basename}.png") + + # 处理单个文件（自动识别参数并选择colormap） + self.process_data( + csv_file=csv_file, + shp_file=shp_file, + output_file=output_file, + resolution=resolution, + show_sample_points=show_sample_points, + base_map_tif=base_map_tif, + use_distance_diffusion=use_distance_diffusion, + max_diffusion_distance=max_diffusion_distance, + diffusion_power=diffusion_power, + diffusion_n_neighbors=diffusion_n_neighbors, + cmap=None # 自动识别 + ) + + success_count += 1 + print(f"✓ 成功处理: {csv_basename}.png") + + except Exception as e: + fail_count += 1 + failed_files.append((os.path.basename(csv_file), str(e))) + print(f"✗ 处理失败: {os.path.basename(csv_file)}") + print(f" 错误信息: {e}") + import traceback + traceback.print_exc() + + # 输出批量处理结果统计 + print("\n" + "=" * 60) + print("批量处理完成") + print("=" * 60) + print(f"总文件数: {len(csv_files)}") + print(f"成功: {success_count}") + print(f"失败: {fail_count}") + print(f"输出文件夹: {output_folder}") + + if failed_files: + print("\n失败的文件列表:") + for file_name, error in failed_files: + print(f" - {file_name}: {error}") + + return { + 'total': len(csv_files), + 'success': success_count, + 'failed': fail_count, + 'output_folder': output_folder, + 'failed_files': failed_files + } + + +def main(): + """主函数 - 使用示例""" + # 创建处理器实例 + mapper = ContentMapper() + + # 示例1：处理单个文件 + csv_file = r"E:\code\WQ\pipeline_result\tests1\8_predictions\BGA.csv" # 采样点的预测值 + shp_file = r"D:\BaiduNetdiskDownload\yaobao\roi\roi.shp" # 水体边界shapefile路径 + output_file = r"E:\code\WQ\pipeline_result\work_dir\8_predictions\BGA.png" # 输出图片路径 + # + mapper.process_data( + csv_file=csv_file, + shp_file=shp_file, + output_file=output_file, + resolution=30, # 网格分辨率（米），更小的值产生更平滑的效果 + show_sample_points=False, # 设置为False以显示平滑的颜色分布，True则显示采样点位置 + base_map_tif=None, # 正射底图路径（可选） + cmap=None # 自动从文件名或内容中识别参数并选择对应的colormap + ) + + # # 示例2：批量处理文件夹中的所有CSV文件 + # csv_folder = r"E:\code\WQ\xiaogujia\使用腰堡模型\predict\TT.csv" # CSV文件所在文件夹 + # shp_file = r"E:\code\WQ\xiaogujia\SHP\shp\watemask.shp" # 水体边界shapefile路径 + # output_folder = r"E:\code\WQ\xiaogujia\使用腰堡模型\map\TT.png" # 输出文件夹（可选，如果为None则在CSV文件夹下创建map_output） + + # 批量处理（会自动识别每个CSV文件的参数名称并选择对应的colormap） + # result = mapper.process_batch( + # csv_folder=csv_folder, + # shp_file=shp_file, + # output_folder=output_folder, # 如果为None，将在CSV文件夹下创建map_output子文件夹 + # resolution=30, # 网格分辨率（米） + # show_sample_points=False, # 是否显示采样点 + # base_map_tif=None, # 正射底图路径（可选） + # ) + # + # print(f"\n批量处理结果: {result}") + + +if __name__ == "__main__": + # 使用示例 + print("含量分布图生成器") + print("=" * 50) + + # 如果要直接运行，请取消下面的注释并修改文件路径 + main() + + # 或者交互式使用 + # print("使用方法：") + # print("1. 准备CSV文件（前两列为WGS84经纬度，第三列为含量数据）") + # print("2. 准备边界Shapefile文件") + # print("3. 调用以下代码：") + # print(""" + # mapper = ContentMapper() + # mapper.process_data( + # csv_file='your_data.csv', + # shp_file='your_boundary.shp', + # output_file='output_map.png', + # resolution=50 + # ) + # """) diff --git a/src/postprocessing/map_beifeng.py b/src/postprocessing/map_beifeng.py new file mode 100644 index 0000000..007a308 --- /dev/null +++ b/src/postprocessing/map_beifeng.py @@ -0,0 +1,2561 @@ +import pandas as pd +import numpy as np +import geopandas as gpd +from pyproj import CRS, Transformer +import matplotlib.pyplot as plt +import matplotlib.patches as patches +from matplotlib.ticker import FuncFormatter +from matplotlib_scalebar.scalebar import ScaleBar +from scipy.interpolate import griddata +from scipy import ndimage +from scipy.spatial.distance import cdist +from scipy.spatial import ConvexHull +from shapely.geometry import Point, Polygon +import rasterio +from rasterio.features import geometry_mask +import warnings +import math +import os +import random +import glob + +# 尝试导入pykrige（可选依赖） +try: + from pykrige.ok import OrdinaryKriging + PYKRIGE_AVAILABLE = True +except ImportError: + PYKRIGE_AVAILABLE = False + print("警告: pykrige未安装，Kriging不确定性计算将不可用") + +warnings.filterwarnings('ignore') + +# 设置中文字体 +plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei'] +plt.rcParams['axes.unicode_minus'] = False + +# 参数到颜色映射的字典 +PARAMS_CMAP = { + "Chlorophyll": "YlGnBu_r", + "COD": "coolwarm", + "DO": "RdYlBu", + "pH": "Spectral", + "Temperature": "turbo", + "spCond": "cividis", + "Turbidity": "YlOrBr", + "TDS": "inferno", + "Cl-": "RdYlBu_r", + "NO3-N": "YlOrRd", + "NH3-N": "magma", + "BGA": "viridis", + "TT": "RdYlBu_r" +} + + +class ContentMapper: + def __init__(self, input_crs='EPSG:32651', output_crs='EPSG:4326'): + """ + 初始化ContentMapper - 生成平滑的含量分布图 + + 本类专门用于生成平滑、均匀的颜色分布图，而不是显示离散的采样点。 + 通过高密度网格插值和多级颜色映射，创建连续的颜色过渡效果。 + + Parameters: + ----------- + input_crs : str + 输入坐标系，默认为'EPSG:32651' (WGS_1984_UTM_Zone_51N) + output_crs : str + 输出坐标系，默认为'EPSG:4326' (WGS84) + """ + # 定义坐标转换器 + self.input_crs = input_crs + self.output_crs = output_crs + self.transformer = Transformer.from_crs( + CRS.from_string(input_crs), + CRS.from_string(output_crs), + always_xy=True + ) + + # 参数到颜色映射的字典 + self.params_cmap = PARAMS_CMAP.copy() + + # 所有可用的matplotlib colormap列表（用于随机选择） + self.available_cmaps = ['viridis', 'plasma', 'inferno', 'magma', 'cividis', + 'coolwarm', 'RdYlBu', 'Spectral', 'YlGnBu_r', 'YlOrBr', + 'YlOrRd', 'turbo', 'RdYlBu_r', 'cool', 'hot', 'jet'] + + print(f"坐标转换设置: {input_crs} -> {output_crs}") + + def _extract_param_name(self, csv_file): + """ + 从CSV文件名或内容中提取参数名称 + + Parameters: + ----------- + csv_file : str + CSV文件路径 + + Returns: + -------- + param_name : str or None + 提取的参数名称，如果未找到则返回None + """ + print(f"[调试] 开始从文件 {csv_file} 中提取参数名称") + print(f"[调试] 字典中的参数键: {list(self.params_cmap.keys())}") + + # 从文件名中提取（去除路径和扩展名） + file_name = os.path.basename(csv_file) + file_name_no_ext = os.path.splitext(file_name)[0] + print(f"[调试] 文件名（不含扩展名）: {file_name_no_ext}") + + # 尝试从文件名中匹配参数名称（不区分大小写） + file_name_upper = file_name_no_ext.upper() + for param in self.params_cmap.keys(): + param_upper = param.upper() + if param_upper in file_name_upper: + print(f"从文件名中识别到参数: {param} (匹配到 '{param_upper}' 在 '{file_name_upper}' 中)") + return param # 返回字典中的原始键（保持大小写） + + # 如果文件名中没有找到，尝试从CSV内容中提取（检查列名） + try: + df = pd.read_csv(csv_file, encoding='utf-8', nrows=0) # 只读取列名 + columns = [col.upper() for col in df.columns] + print(f"[调试] CSV列名: {list(df.columns)}") + + for param in self.params_cmap.keys(): + param_upper = param.upper() + # 检查列名中是否包含参数名称 + for col in columns: + if param_upper in col or col in param_upper: + print(f"从CSV列名中识别到参数: {param} (匹配到列名 '{col}')") + return param # 返回字典中的原始键（保持大小写） + except Exception as e: + print(f"读取CSV列名时出错: {e}") + + print(f"未能在文件 {csv_file} 中识别参数名称") + print(f"[调试] 可用的参数名: {list(self.params_cmap.keys())}") + return None + + def _get_colormap(self, param_name=None): + """ + 根据参数名称获取对应的colormap + + Parameters: + ----------- + param_name : str, optional + 参数名称。如果为None或不在映射中，则随机选择一个colormap + + Returns: + -------- + cmap : str + 颜色映射名称 + """ + # 打印调试信息 + print(f"[调试] _get_colormap 被调用，param_name={param_name}") + print(f"[调试] 当前字典中的键: {list(self.params_cmap.keys())}") + + if param_name: + # 首先尝试精确匹配（区分大小写） + if param_name in self.params_cmap: + cmap = self.params_cmap[param_name] + print(f"使用参数 '{param_name}' 对应的颜色映射: {cmap}") + return cmap + + # 如果精确匹配失败，尝试不区分大小写的匹配 + param_name_upper = param_name.upper() + for key in self.params_cmap.keys(): + if key.upper() == param_name_upper: + cmap = self.params_cmap[key] + print(f"使用参数 '{key}' (不区分大小写匹配 '{param_name}') 对应的颜色映射: {cmap}") + return cmap + + # 如果都不匹配，随机选择 + cmap = random.choice(self.available_cmaps) + print(f"警告: 参数 '{param_name}' 不在映射中，随机选择颜色映射: {cmap}") + print(f"可用的参数名: {list(self.params_cmap.keys())}") + return cmap + else: + # 随机选择一个colormap + cmap = random.choice(self.available_cmaps) + print(f"未指定参数名称，随机选择颜色映射: {cmap}") + return cmap + + def _check_point_distribution(self, points): + """检查数据点的几何分布""" + print("正在检查数据点分布...") + + # 检查是否有重复点 + unique_points = np.unique(points, axis=0) + if len(unique_points) < len(points): + print(f"警告：发现 {len(points) - len(unique_points)} 个重复数据点") + + # 检查点是否共线 + if len(unique_points) >= 3: + # 计算前三个点构成的三角形面积 + p1, p2, p3 = unique_points[:3] + area = 0.5 * abs((p2[0] - p1[0]) * (p3[1] - p1[1]) - (p3[0] - p1[0]) * (p2[1] - p1[1])) + + if area < 1e-10: # 面积太小，可能共线 + print("警告：前三个数据点可能共线") + + # 尝试找到不共线的点 + for i in range(3, len(unique_points)): + p4 = unique_points[i] + area = 0.5 * abs((p2[0] - p1[0]) * (p4[1] - p1[1]) - (p4[0] - p1[0]) * (p2[1] - p1[1])) + if area > 1e-10: + print(f"找到非共线点，使用点 {i}") + break + else: + print("警告：所有数据点可能都共线，这会导致插值失败") + + # 检查坐标范围 + x_range = points[:, 0].max() - points[:, 0].min() + y_range = points[:, 1].max() - points[:, 1].min() + + if x_range < 1e-6 or y_range < 1e-6: + print(f"警告：坐标范围很小 (X范围: {x_range:.2e}, Y范围: {y_range:.2e})") + print("这可能导致插值数值不稳定") + + return unique_points + + def _calculate_idw_uncertainty(self, points, grid_xx, grid_yy, power=2, n_neighbors=10): + """ + 计算IDW插值的不确定性（距离加权和的倒数） + + Parameters: + ----------- + points : np.ndarray + 数据点坐标 (n_points, 2) + grid_xx : np.ndarray + 网格X坐标 + grid_yy : np.ndarray + 网格Y坐标 + power : float + IDW的幂参数，默认为2 + n_neighbors : int + 使用的最近邻点数，默认为10 + + Returns: + -------- + uncertainty : np.ndarray + 不确定性网格（距离加权和的倒数，归一化到0-1） + """ + print("正在计算IDW不确定性...") + grid_points = np.column_stack((grid_xx.ravel(), grid_yy.ravel())) + + # 使用向量化计算提高性能 + # 计算所有网格点到所有数据点的距离矩阵 + distances = cdist(grid_points, points) + + # 对每个网格点，找到最近的n_neighbors个点 + n_neighbors = min(n_neighbors, len(points)) + nearest_distances = np.partition(distances, n_neighbors-1, axis=1)[:, :n_neighbors] + + # 避免除零 + nearest_distances = np.maximum(nearest_distances, 1e-10) + + # 计算距离加权和（向量化） + weights = 1.0 / (nearest_distances ** power) + weight_sums = np.sum(weights, axis=1) + + # 不确定性 = 1 / 距离加权和（加权和越大，不确定性越小） + valid_mask = weight_sums > 0 + uncertainty_flat = np.full(len(grid_points), np.nan) + uncertainty_flat[valid_mask] = 1.0 / weight_sums[valid_mask] + + uncertainty = uncertainty_flat.reshape(grid_xx.shape) + + # 归一化到0-1范围 + valid_uncertainty = uncertainty[~np.isnan(uncertainty)] + if len(valid_uncertainty) > 0: + min_unc = valid_uncertainty.min() + max_unc = valid_uncertainty.max() + if max_unc > min_unc: + uncertainty = (uncertainty - min_unc) / (max_unc - min_unc) + + print("IDW不确定性计算完成") + return uncertainty + + def _calculate_rbf_uncertainty(self, points, grid_xx, grid_yy, n_neighbors=10): + """ + 计算RBF插值的不确定性（基于距离的倒数） + + Parameters: + ----------- + points : np.ndarray + 数据点坐标 (n_points, 2) + grid_xx : np.ndarray + 网格X坐标 + grid_yy : np.ndarray + 网格Y坐标 + n_neighbors : int + 使用的最近邻点数，默认为10 + + Returns: + -------- + uncertainty : np.ndarray + 不确定性网格（归一化到0-1） + """ + print("正在计算RBF不确定性...") + grid_points = np.column_stack((grid_xx.ravel(), grid_yy.ravel())) + + # 使用向量化计算提高性能 + distances = cdist(grid_points, points) + + # 对每个网格点，找到最近的n_neighbors个点 + n_neighbors = min(n_neighbors, len(points)) + nearest_distances = np.partition(distances, n_neighbors-1, axis=1)[:, :n_neighbors] + + # 使用平均距离作为不确定性指标（距离越大，不确定性越大） + mean_distances = np.mean(nearest_distances, axis=1) + uncertainty = mean_distances.reshape(grid_xx.shape) + + # 归一化到0-1范围 + valid_uncertainty = uncertainty[~np.isnan(uncertainty)] + if len(valid_uncertainty) > 0: + min_unc = valid_uncertainty.min() + max_unc = valid_uncertainty.max() + if max_unc > min_unc: + uncertainty = (uncertainty - min_unc) / (max_unc - min_unc) + + print("RBF不确定性计算完成") + return uncertainty + + def _calculate_kriging_uncertainty(self, points, values, grid_xx, grid_yy): + """ + 计算Kriging插值的方差（不确定性） + + Parameters: + ----------- + points : np.ndarray + 数据点坐标 (n_points, 2) + values : np.ndarray + 数据值 + grid_xx : np.ndarray + 网格X坐标 + grid_yy : np.ndarray + 网格Y坐标 + + Returns: + -------- + uncertainty : np.ndarray + Kriging方差网格（归一化到0-1） + """ + if not PYKRIGE_AVAILABLE: + print("警告: pykrige未安装，无法计算Kriging不确定性，将使用IDW方法") + return self._calculate_idw_uncertainty(points, grid_xx, grid_yy) + + print("正在计算Kriging不确定性...") + try: + # 创建Kriging插值器 + ok = OrdinaryKriging( + points[:, 0], points[:, 1], values, + variogram_model='linear', # 使用线性变异函数模型 + verbose=False, + enable_plotting=False + ) + + # 计算网格点的方差 + grid_points = np.column_stack((grid_xx.ravel(), grid_yy.ravel())) + z, ss = ok.execute('grid', grid_points[:, 0], grid_points[:, 1]) + + # ss是方差，转换为不确定性网格 + uncertainty = ss.reshape(grid_xx.shape) + + # 归一化到0-1范围 + valid_uncertainty = uncertainty[~np.isnan(uncertainty)] + if len(valid_uncertainty) > 0: + min_unc = valid_uncertainty.min() + max_unc = valid_uncertainty.max() + if max_unc > min_unc: + uncertainty = (uncertainty - min_unc) / (max_unc - min_unc) + + print("Kriging不确定性计算完成") + return uncertainty + + except Exception as e: + print(f"Kriging不确定性计算失败: {e}，将使用IDW方法") + return self._calculate_idw_uncertainty(points, grid_xx, grid_yy) + + def _fill_boundary_blanks_with_distance_diffusion(self, grid_content, grid_xx, grid_yy, mask, + boundary_gdf, max_diffusion_distance=None, + power=2, n_neighbors=15): + """ + 使用距离扩散方法填充边界附近的空白区域 + + Parameters: + ----------- + grid_content : np.ndarray + 插值网格数据 + grid_xx : np.ndarray + 网格X坐标 + grid_yy : np.ndarray + 网格Y坐标 + mask : np.ndarray + 边界掩膜（True表示边界内） + boundary_gdf : gpd.GeoDataFrame + 边界几何数据 + max_diffusion_distance : float, optional + 最大扩散距离（单位与坐标相同）。如果为None，自动计算为网格分辨率的5倍 + power : float, default=2 + IDW距离衰减幂参数 + n_neighbors : int, default=15 + 使用的最近邻点数 + + Returns: + -------- + grid_content : np.ndarray + 填充后的网格数据 + """ + print("正在使用距离扩散方法填充边界空白区域...") + + # 找到边界内的空白区域 + nan_mask = np.isnan(grid_content) + within_boundary_nan = nan_mask & mask + + if not np.any(within_boundary_nan): + print("边界内没有空白区域需要填充") + return grid_content + + blank_count = np.sum(within_boundary_nan) + print(f"发现 {blank_count} 个边界内的空白点，开始距离扩散填充...") + + # 找到边界内有效值的点 + valid_mask = ~nan_mask & mask + if np.sum(valid_mask) == 0: + print("警告：边界内没有有效值，无法进行距离扩散") + return grid_content + + # 计算网格分辨率（用于确定最大扩散距离） + if max_diffusion_distance is None: + # 自动计算：使用网格点之间的平均距离 + dx = np.abs(grid_xx[0, 1] - grid_xx[0, 0]) if grid_xx.shape[1] > 1 else 1.0 + dy = np.abs(grid_yy[1, 0] - grid_yy[0, 0]) if grid_xx.shape[0] > 1 else 1.0 + avg_resolution = (dx + dy) / 2.0 + max_diffusion_distance = avg_resolution * 5.0 # 5倍网格分辨率 + print(f"自动计算最大扩散距离: {max_diffusion_distance:.6f}") + + # 准备有效数据点 + valid_points = np.column_stack((grid_xx[valid_mask], grid_yy[valid_mask])) + valid_values = grid_content[valid_mask] + + # 准备空白点 + blank_points = np.column_stack((grid_xx[within_boundary_nan], grid_yy[within_boundary_nan])) + + print(f"使用 {len(valid_points)} 个有效点填充 {len(blank_points)} 个空白点...") + + # 使用向量化计算距离矩阵 + distances = cdist(blank_points, valid_points) + + # 对每个空白点，找到最近的n_neighbors个有效点 + n_neighbors = min(n_neighbors, len(valid_points)) + + # 应用最大扩散距离限制 + if max_diffusion_distance > 0: + # 只考虑在最大扩散距离内的点 + # 对于每个空白点，找到在扩散距离内的最近邻 + filled_values = np.full(len(blank_points), np.nan) + global_mean = np.nanmean(valid_values) + + for i in range(len(blank_points)): + point_distances = distances[i, :] + valid_idx = point_distances <= max_diffusion_distance + + if np.any(valid_idx): + # 找到最近的n_neighbors个点（在扩散距离内） + valid_dist = point_distances[valid_idx] + valid_vals = valid_values[valid_idx] + + # 如果有效点数量超过n_neighbors，只取最近的n_neighbors个 + if len(valid_dist) > n_neighbors: + nearest_idx = np.argpartition(valid_dist, n_neighbors-1)[:n_neighbors] + valid_dist = valid_dist[nearest_idx] + valid_vals = valid_vals[nearest_idx] + + # 避免除零 + valid_dist = np.maximum(valid_dist, 1e-10) + + # 计算IDW权重 + weights = 1.0 / (valid_dist ** power) + weight_sum = np.sum(weights) + + if weight_sum > 0: + # 距离加权平均 + filled_values[i] = np.sum(weights * valid_vals) / weight_sum + else: + filled_values[i] = global_mean + else: + # 如果该点不在任何有效点的扩散距离内，使用全局平均值 + filled_values[i] = global_mean + else: + # 不使用距离限制，对所有点进行IDW插值（批量处理以提高效率） + # 对每个空白点，找到最近的n_neighbors个点 + nearest_indices = np.argpartition(distances, n_neighbors-1, axis=1)[:, :n_neighbors] + + # 批量提取距离和值 + nearest_dists = np.take_along_axis(distances, nearest_indices, axis=1) + nearest_vals = valid_values[nearest_indices] + + # 避免除零 + nearest_dists = np.maximum(nearest_dists, 1e-10) + + # 批量计算IDW权重 + weights = 1.0 / (nearest_dists ** power) + weight_sums = np.sum(weights, axis=1) + + # 批量计算加权平均值 + filled_values = np.sum(weights * nearest_vals, axis=1) / weight_sums + + # 处理可能的NaN（如果weight_sum为0） + nan_mask = np.isnan(filled_values) | (weight_sums == 0) + if np.any(nan_mask): + filled_values[nan_mask] = np.nanmean(valid_values) + + # 填充空白点 + grid_content[within_boundary_nan] = filled_values + + # 检查填充结果 + filled_count = np.sum(~np.isnan(filled_values)) + print(f"距离扩散填充完成：成功填充 {filled_count} / {blank_count} 个空白点") + + return grid_content + + def _calculate_model_variance_uncertainty(self, points, uncertainty_values, grid_xx, grid_yy): + """ + 插值模型输出方差（MC Dropout等）到网格 + + Parameters: + ----------- + points : np.ndarray + 数据点坐标 (n_points, 2) + uncertainty_values : np.ndarray + 数据点的不确定性值（方差） + grid_xx : np.ndarray + 网格X坐标 + grid_yy : np.ndarray + 网格Y坐标 + + Returns: + -------- + uncertainty : np.ndarray + 不确定性网格（归一化到0-1） + """ + print("正在插值模型方差到网格...") + + # 使用线性插值将不确定性值插值到网格 + grid_uncertainty = griddata( + points, uncertainty_values, (grid_xx, grid_yy), + method='linear', fill_value=np.nan + ) + + # 如果有NaN，用最近邻填充 + if np.any(np.isnan(grid_uncertainty)): + grid_nearest = griddata( + points, uncertainty_values, (grid_xx, grid_yy), + method='nearest' + ) + nan_mask = np.isnan(grid_uncertainty) + grid_uncertainty[nan_mask] = grid_nearest[nan_mask] + + # 归一化到0-1范围 + valid_uncertainty = grid_uncertainty[~np.isnan(grid_uncertainty)] + if len(valid_uncertainty) > 0: + min_unc = valid_uncertainty.min() + max_unc = valid_uncertainty.max() + if max_unc > min_unc: + grid_uncertainty = (grid_uncertainty - min_unc) / (max_unc - min_unc) + + print("模型方差插值完成") + return grid_uncertainty + + def _perform_interpolation(self, points, values, grid_xx, grid_yy): + """执行空间插值""" + print(f"插值输入检查：") + print(f" - 数据点数量: {len(points)}") + print(f" - 数据值范围: {values.min():.4f} - {values.max():.4f}") + print(f" - 网格大小: {grid_xx.shape}") + print(f" - 坐标系: {self.output_crs}") + + # 检查数据的有效性 + finite_mask = np.isfinite(values) + if not np.all(finite_mask): + print(f"警告：发现 {np.sum(~finite_mask)} 个无效数据值，将被移除") + points = points[finite_mask] + values = values[finite_mask] + + if len(points) < 3: + raise ValueError(f"有效数据点不足3个（当前：{len(points)}个）") + + try: + # 首先尝试使用线性插值 + print("正在尝试线性插值...") + grid_content = griddata( + points, values, (grid_xx, grid_yy), + method='linear', fill_value=np.nan + ) + + # 检查线性插值结果 + valid_linear = ~np.isnan(grid_content) + valid_count = np.sum(valid_linear) + print(f"线性插值结果：有效点数 {valid_count} / {grid_content.size}") + + if valid_count > 0: + print(f"线性插值成功，有效区域覆盖率: {valid_count / grid_content.size * 100:.1f}%") + + # 如果有NaN值，用最近邻插值填充 + nan_count = np.sum(np.isnan(grid_content)) + if nan_count > 0: + print(f"正在用最近邻插值填充 {nan_count} 个缺失值...") + grid_nearest = griddata( + points, values, (grid_xx, grid_yy), + method='nearest' + ) + # 只填充线性插值的NaN区域 + nan_mask = np.isnan(grid_content) + grid_content[nan_mask] = grid_nearest[nan_mask] + print("缺失值填充完成") + + # 最终检查 + final_valid = ~np.isnan(grid_content) + print(f"最终有效点数: {np.sum(final_valid)} / {grid_content.size}") + + return grid_content + else: + print("线性插值失败，尝试最近邻插值...") + + except Exception as e: + print(f"线性插值失败: {e}") + print("尝试最近邻插值...") + + try: + # 使用最近邻插值作为备选方案 + print("执行最近邻插值...") + grid_content = griddata( + points, values, (grid_xx, grid_yy), + method='nearest' + ) + + valid_count = np.sum(~np.isnan(grid_content)) + print(f"最近邻插值成功，有效点数: {valid_count}") + + if valid_count == 0: + raise ValueError("最近邻插值也没有产生有效结果") + + return grid_content + + except Exception as e: + print(f"最近邻插值也失败: {e}") + + # 对于地理坐标系，尝试更简单的方法 + if self.output_crs == 'EPSG:4326': + print("地理坐标系检测到，尝试简化插值...") + try: + # 创建一个基于距离的简单插值 + grid_content = np.full(grid_xx.shape, np.nan) + + # 为每个网格点找到最近的数据点 + for i in range(grid_xx.shape[0]): + for j in range(grid_xx.shape[1]): + grid_x, grid_y = grid_xx[i, j], grid_yy[i, j] + + # 计算到所有数据点的距离 + distances = np.sqrt((points[:, 0] - grid_x) ** 2 + (points[:, 1] - grid_y) ** 2) + nearest_idx = np.argmin(distances) + + # 如果距离不是太远，就使用该值 + if distances[nearest_idx] < (grid_xx.max() - grid_xx.min()) * 0.1: # 10%的范围内 + grid_content[i, j] = values[nearest_idx] + + valid_count = np.sum(~np.isnan(grid_content)) + print(f"简化插值完成，有效点数: {valid_count}") + + if valid_count > 0: + return grid_content + else: + raise ValueError("简化插值也没有产生有效结果") + + except Exception as e3: + print(f"简化插值失败: {e3}") + + print("尝试立方插值作为最后手段...") + try: + # 最后尝试立方插值 + grid_content = griddata( + points, values, (grid_xx, grid_yy), + method='cubic', fill_value=np.nan + ) + + # 如果立方插值有NaN，用最近邻填充 + if np.any(np.isnan(grid_content)): + print("用最近邻插值填充立方插值的NaN值...") + grid_nearest = griddata( + points, values, (grid_xx, grid_yy), + method='nearest' + ) + nan_mask = np.isnan(grid_content) + grid_content[nan_mask] = grid_nearest[nan_mask] + + valid_count = np.sum(~np.isnan(grid_content)) + print(f"立方插值成功，有效点数: {valid_count}") + return grid_content + + except Exception as e4: + print(f"立方插值也失败: {e4}") + print(f"所有插值方法都失败") + raise ValueError("无法完成空间插值，请检查数据点的分布和数值") + + def read_csv_data(self, csv_file, uncertainty_col=None): + """ + 读取CSV文件并进行坐标转换 + + Parameters: + ----------- + csv_file : str + CSV文件路径 + uncertainty_col : str, optional + 不确定性数据列名。如果为None，将自动检测包含'variance'、'uncertainty'、'std'、'sigma'、'var'、'mc_dropout'的列 + + Returns: + -------- + gdf : gpd.GeoDataFrame + 包含坐标和含量数据的GeoDataFrame，如果找到不确定性列，会包含'uncertainty'列 + """ + print("正在读取CSV文件...") + df = pd.read_csv(csv_file, encoding='utf-8') + + # 假设前三列分别是经度、纬度、含量 + if df.shape[1] < 3: + raise ValueError("CSV文件必须至少包含3列：经度、纬度、含量") + + # 获取列名 + lon_col = df.columns[0] + lat_col = df.columns[1] + content_col = df.columns[2] + + print(f"检测到列名：经度({lon_col})，纬度({lat_col})，含量({content_col})") + + # 自动检测不确定性列 + if uncertainty_col is None: + uncertainty_keywords = ['variance', 'uncertainty', 'std', 'sigma', 'var', 'mc_dropout'] + for col in df.columns: + col_lower = col.lower() + if any(keyword in col_lower for keyword in uncertainty_keywords): + uncertainty_col = col + print(f"自动检测到不确定性列: {uncertainty_col}") + break + + # 坐标转换 + print(f"正在进行坐标转换: {self.input_crs} -> {self.output_crs}") + transformed_x, transformed_y = self.transformer.transform( + df[lon_col].values, + df[lat_col].values + ) + + # 创建GeoDataFrame + geometry = [Point(x, y) for x, y in zip(transformed_x, transformed_y)] + gdf = gpd.GeoDataFrame( + df, + geometry=geometry, + crs=self.output_crs + ) + + gdf['proj_x'] = transformed_x + gdf['proj_y'] = transformed_y + gdf['content'] = df[content_col] + + # 如果找到不确定性列，添加到GeoDataFrame + if uncertainty_col and uncertainty_col in df.columns: + gdf['uncertainty'] = df[uncertainty_col].values + print(f"已加载不确定性数据列: {uncertainty_col}") + print(f"不确定性值范围: {gdf['uncertainty'].min():.4f} - {gdf['uncertainty'].max():.4f}") + + print(f"成功读取 {len(gdf)} 个数据点") + return gdf + + def read_boundary_shapefile(self, shp_file): + """读取边界shapefile""" + print("正在读取边界文件...") + boundary = gpd.read_file(shp_file) + + # 确保边界文件使用目标投影坐标系 + if boundary.crs != self.output_crs: + print(f"正在转换边界文件坐标系到 {self.output_crs}...") + boundary = boundary.to_crs(self.output_crs) + + print(f"边界文件包含 {len(boundary)} 个要素") + return boundary + + def _identify_edge_points(self, points_gdf): + """ + 识别边缘采样点（使用凸包方法） + + Parameters: + ----------- + points_gdf : gpd.GeoDataFrame + 采样点GeoDataFrame + + Returns: + -------- + edge_indices : np.ndarray + 边缘点的索引数组 + """ + print("正在识别边缘采样点...") + + # 获取所有点的坐标 + points = np.column_stack((points_gdf['proj_x'].values, points_gdf['proj_y'].values)) + + if len(points) < 3: + print("警告：采样点数量少于3个，无法识别边缘点") + return np.array([]) + + try: + # 使用凸包识别边缘点 + hull = ConvexHull(points) + edge_indices = hull.vertices + + print(f"识别到 {len(edge_indices)} 个边缘采样点（共 {len(points)} 个点）") + return edge_indices + except Exception as e: + print(f"识别边缘点时出错: {e}，将使用所有点作为边缘点") + return np.arange(len(points)) + + def _expand_edge_points(self, points_gdf, boundary_gdf, resolution=100, expand_ratio=0.05): + """ + 对边缘采样点进行外扩处理，外扩到整个图像的边界（包括外扩后的边界） + 按照指定的间距（resolution）生成外扩点，铺满整个画面 + + Parameters: + ----------- + points_gdf : gpd.GeoDataFrame + 原始采样点GeoDataFrame + boundary_gdf : gpd.GeoDataFrame + 水域掩膜边界GeoDataFrame + resolution : float, default=100 + 外扩点的间距（单位与坐标相同），与插值网格分辨率一致 + expand_ratio : float, default=0.05 + 边界外扩比例（与create_interpolation_grid中的expand_ratio一致） + + Returns: + -------- + expanded_gdf : gpd.GeoDataFrame + 外扩后的采样点GeoDataFrame + """ + print(f"正在对边缘采样点进行外扩处理（按照 {resolution} 的间距外扩到整个图像边界）...") + + # 识别边缘点 + edge_indices = self._identify_edge_points(points_gdf) + + if len(edge_indices) == 0: + print("未识别到边缘点，跳过外扩处理") + return points_gdf.copy() + + # 获取水域掩膜的边界范围 + boundary_bounds = boundary_gdf.total_bounds # [minx, miny, maxx, maxy] + mask_minx, mask_miny, mask_maxx, mask_maxy = boundary_bounds + + # 计算范围大小 + width = mask_maxx - mask_minx + height = mask_maxy - mask_miny + + # 外扩边界，与create_interpolation_grid中的逻辑一致，确保外扩到整个图像范围 + expand_x = width * expand_ratio + expand_y = height * expand_ratio + image_minx = mask_minx - expand_x + image_maxx = mask_maxx + expand_x + image_miny = mask_miny - expand_y + image_maxy = mask_maxy + expand_y + + # 获取所有点的坐标 + points = np.column_stack((points_gdf['proj_x'].values, points_gdf['proj_y'].values)) + + # 计算点集的范围和中心 + x_min, x_max = points[:, 0].min(), points[:, 0].max() + y_min, y_max = points[:, 1].min(), points[:, 1].max() + center = np.array([(x_min + x_max) / 2, (y_min + y_max) / 2]) + + # 存储新添加的点 + new_points_list = [] + new_data_list = [] + + # 对每个边缘点进行外扩 + for edge_idx in edge_indices: + edge_point = points[edge_idx] + + # 计算从中心到边缘点的方向向量 + direction = edge_point - center + distance_to_center = np.linalg.norm(direction) + + if distance_to_center < 1e-10: + # 如果边缘点就是中心点，跳过 + continue + + # 归一化方向向量 + direction_unit = direction / distance_to_center + + # 计算该方向与水域掩膜边界的交点 + # 使用射线法：从边缘点沿方向延伸，找到与边界框的交点 + max_distance = 0 + + # 检查与四个边界的交点（使用整个图像的范围，包括外扩后的边界） + # 上边界 (y = image_maxy) + if direction_unit[1] > 1e-10: # 向上 + t = (image_maxy - edge_point[1]) / direction_unit[1] + if t > 0: + intersect_x = edge_point[0] + direction_unit[0] * t + if image_minx <= intersect_x <= image_maxx: + max_distance = max(max_distance, t) + + # 下边界 (y = image_miny) + if direction_unit[1] < -1e-10: # 向下 + t = (image_miny - edge_point[1]) / direction_unit[1] + if t > 0: + intersect_x = edge_point[0] + direction_unit[0] * t + if image_minx <= intersect_x <= image_maxx: + max_distance = max(max_distance, t) + + # 右边界 (x = image_maxx) + if direction_unit[0] > 1e-10: # 向右 + t = (image_maxx - edge_point[0]) / direction_unit[0] + if t > 0: + intersect_y = edge_point[1] + direction_unit[1] * t + if image_miny <= intersect_y <= image_maxy: + max_distance = max(max_distance, t) + + # 左边界 (x = image_minx) + if direction_unit[0] < -1e-10: # 向左 + t = (image_minx - edge_point[0]) / direction_unit[0] + if t > 0: + intersect_y = edge_point[1] + direction_unit[1] * t + if image_miny <= intersect_y <= image_maxy: + max_distance = max(max_distance, t) + + # 如果找到了边界交点，按照resolution间距创建外扩点 + if max_distance > 1e-10: + # 从边缘点到边界的距离 + distance_to_boundary = max_distance + + # 计算需要生成的外扩点数量（按照resolution间距） + # 使用ceil确保能铺满到边界 + n_points = int(np.ceil(distance_to_boundary / resolution)) + + # 从边缘点开始，按照resolution间距生成点，直到边界 + for i in range(1, n_points + 1): + # 计算外扩距离（从边缘点开始，按照resolution间距） + expand_distance = i * resolution + + # 如果超过边界距离，使用边界距离作为最后一个点 + if expand_distance >= distance_to_boundary: + expand_distance = distance_to_boundary + + # 计算新点位置 + new_point = edge_point + direction_unit * expand_distance + + # 确保新点在图像范围内（包括外扩后的边界） + new_point[0] = np.clip(new_point[0], image_minx, image_maxx) + new_point[1] = np.clip(new_point[1], image_miny, image_maxy) + + # 创建新点的数据（复制边缘点的所有属性） + new_row = points_gdf.iloc[edge_idx].copy() + new_row['proj_x'] = new_point[0] + new_row['proj_y'] = new_point[1] + + # 更新geometry + new_row['geometry'] = Point(new_point[0], new_point[1]) + + new_points_list.append(new_point) + new_data_list.append(new_row) + + # 如果已经到达边界，停止生成 + if expand_distance >= distance_to_boundary: + break + + # 合并原始点和外扩点 + if len(new_data_list) > 0: + # 创建新点的GeoDataFrame + expanded_gdf = gpd.GeoDataFrame(new_data_list, crs=points_gdf.crs) + + # 合并原始点和外扩点（使用gpd.concat以确保geometry列正确处理） + result_gdf = gpd.GeoDataFrame(pd.concat([points_gdf, expanded_gdf], ignore_index=True), crs=points_gdf.crs) + + print(f"外扩完成：原始点 {len(points_gdf)} 个，边缘点 {len(edge_indices)} 个，" + f"新增外扩点 {len(new_data_list)} 个（间距 {resolution}），总计 {len(result_gdf)} 个点") + print(f"水域掩膜范围: X[{mask_minx:.2f}, {mask_maxx:.2f}], Y[{mask_miny:.2f}, {mask_maxy:.2f}]") + print(f"图像范围（含外扩）: X[{image_minx:.2f}, {image_maxx:.2f}], Y[{image_miny:.2f}, {image_maxy:.2f}]") + + return result_gdf + else: + print("未生成外扩点，返回原始点集") + return points_gdf.copy() + + def create_interpolation_grid(self, points_gdf, boundary_gdf, resolution=100, expand_ratio=0.05, + uncertainty_method='auto', calculate_uncertainty=True, + use_distance_diffusion=True, max_diffusion_distance=None, + diffusion_power=2, diffusion_n_neighbors=15): + """ + 创建插值网格 + + Parameters: + ----------- + expand_ratio : float, default=0.05 + 边界外扩比例（5%），确保图像边界不完全挨着地图 + uncertainty_method : str, default='auto' + 不确定性计算方法：'auto', 'kriging', 'idw', 'rbf', 'model_variance' + calculate_uncertainty : bool, default=True + 是否计算不确定性网格 + use_distance_diffusion : bool, default=True + 是否使用距离扩散方法填充边界空白区域 + max_diffusion_distance : float, optional + 最大扩散距离（单位与坐标相同）。如果为None，自动计算为网格分辨率的5倍 + diffusion_power : float, default=2 + 距离扩散的IDW幂参数，值越大，距离衰减越快 + diffusion_n_neighbors : int, default=15 + 距离扩散使用的最近邻点数 + + Returns: + -------- + grid_xx, grid_yy, grid_content, bounds : tuple + 如果calculate_uncertainty=False + grid_xx, grid_yy, grid_content, bounds, grid_uncertainty : tuple + 如果calculate_uncertainty=True + """ + print("正在创建插值网格...") + + # 获取边界范围 + bounds = boundary_gdf.total_bounds + minx, miny, maxx, maxy = bounds + + print(f"原始边界范围: X({minx:.6f} - {maxx:.6f}), Y({miny:.6f} - {maxy:.6f})") + + # 计算范围大小 + width = maxx - minx + height = maxy - miny + + # 外扩边界，确保图像不完全挨着地图 + expand_x = width * expand_ratio + expand_y = height * expand_ratio + minx -= expand_x + maxx += expand_x + miny -= expand_y + maxy += expand_y + + print(f"外扩后边界范围: X({minx:.6f} - {maxx:.6f}), Y({miny:.6f} - {maxy:.6f})") + print(f"外扩比例: {expand_ratio * 100:.1f}%") + + if self.output_crs == 'EPSG:4326': + print(f"区域尺寸: 宽度={width:.6f}°, 高度={height:.6f}°") + # 对于地理坐标系，需要调整分辨率单位（度） + # 1度约等于111公里，所以100米约等于0.0009度 + resolution_deg = resolution / 111000.0 # 将米转换为度 + print(f"网格分辨率: {resolution}m ≈ {resolution_deg:.6f}°") + else: + print(f"区域尺寸: 宽度={width:.2f}m, 高度={height:.2f}m") + resolution_deg = resolution + + # 检查分辨率是否合理 + min_grid_points = 50 # 增加最少网格点数以获得更平滑的插值效果 + + if self.output_crs == 'EPSG:4326': + # 地理坐标系的网格点计算 + grid_points_x = max(int(width / resolution_deg), min_grid_points) + grid_points_y = max(int(height / resolution_deg), min_grid_points) + else: + # 投影坐标系的网格点计算 + grid_points_x = max(int(width / resolution), min_grid_points) + grid_points_y = max(int(height / resolution), min_grid_points) + + # 确保网格足够密集以获得平滑效果 + grid_points_x = max(grid_points_x, 100) + grid_points_y = max(grid_points_y, 100) + + # 创建网格 + grid_x = np.linspace(minx, maxx, grid_points_x) + grid_y = np.linspace(miny, maxy, grid_points_y) + grid_xx, grid_yy = np.meshgrid(grid_x, grid_y) + + print(f"网格大小: {grid_xx.shape[1]} x {grid_xx.shape[0]} (宽 x 高)") + + # 检查网格大小 + if grid_xx.shape[0] < 2 or grid_xx.shape[1] < 2: + raise ValueError(f"网格尺寸太小 {grid_xx.shape}，无法进行插值。请检查数据范围和分辨率设置。") + + # 准备插值数据（使用原始点+外扩点的合并数据） + points = np.column_stack((points_gdf['proj_x'], points_gdf['proj_y'])) + values = points_gdf['content'].values + + print(f"插值数据点数量: {len(points)}（包含原始采样点和外扩点）") + print(f"数据点范围: X({points[:, 0].min():.6f} - {points[:, 0].max():.6f}), " + f"Y({points[:, 1].min():.6f} - {points[:, 1].max():.6f})") + print(f"含量值范围: {values.min():.4f} - {values.max():.4f}") + print(f"含量值统计: 平均={values.mean():.4f}, 标准差={values.std():.4f}") + + # 检查数据点数量 + if len(points) < 3: + raise ValueError("插值需要至少3个数据点") + + # 检查数据点的几何分布 + self._check_point_distribution(points) + + # 执行插值（先对整个网格插值，包括边界外） + print("正在执行空间插值（整个网格，包括边界外）...") + grid_content = self._perform_interpolation(points, values, grid_xx, grid_yy) + + # 创建边界掩膜（用于识别边界内外） + print("正在识别边界区域...") + # 创建掩膜 + mask_points = np.column_stack((grid_xx.ravel(), grid_yy.ravel())) + mask_geometry = [Point(x, y) for x, y in mask_points] + mask_gdf = gpd.GeoDataFrame(geometry=mask_geometry, crs=self.output_crs) + + # 检查哪些点在边界内 + within_boundary = mask_gdf.within(boundary_gdf.unary_union) + mask = within_boundary.values.reshape(grid_xx.shape) + + # 找到边界边缘上的点（在边界内，但靠近边界） + print("正在提取边界边缘值并填充边界外区域...") + + # 方法：找到边界内有效值的边缘点，然后填充到边界外 + # 1. 先填充边界内的NaN（使用距离扩散方法） + nan_mask = np.isnan(grid_content) + within_boundary_nan = nan_mask & mask + + if np.any(within_boundary_nan): + if use_distance_diffusion: + # 使用距离扩散方法填充边界内的空白区域 + grid_content = self._fill_boundary_blanks_with_distance_diffusion( + grid_content, grid_xx, grid_yy, mask, boundary_gdf, + max_diffusion_distance=max_diffusion_distance, + power=diffusion_power, + n_neighbors=diffusion_n_neighbors + ) + else: + # 使用传统的最近邻插值方法 + print(f"填充边界内的 {np.sum(within_boundary_nan)} 个NaN点（使用最近邻插值）...") + valid_mask = ~nan_mask & mask + if np.sum(valid_mask) > 0: + valid_points = np.column_stack((grid_xx[valid_mask], grid_yy[valid_mask])) + valid_values = grid_content[valid_mask] + nan_points = np.column_stack((grid_xx[within_boundary_nan], grid_yy[within_boundary_nan])) + + filled_values = griddata( + valid_points, valid_values, nan_points, + method='nearest' + ) + grid_content[within_boundary_nan] = filled_values + print(f"边界内填充完成") + + # 2. 找到边界边缘的值（边界内但靠近边界外的点） + # 使用形态学操作找到边界边缘 + boundary_mask_binary = mask.astype(int) + # 创建边界外掩膜 + outside_mask = ~mask + + # 找到边界边缘（在边界内，但相邻有边界外的点） + # 对边界外区域进行膨胀，找到边界边缘 + kernel = np.ones((3, 3), dtype=bool) + dilated_outside = ndimage.binary_dilation(outside_mask, structure=kernel) + edge_mask = mask & dilated_outside # 边界内但靠近边界外的点 + + # 3. 提取边缘值，填充到边界外 + if np.any(edge_mask): + edge_values = grid_content[edge_mask] + edge_valid = ~np.isnan(edge_values) + if np.any(edge_valid): + # 使用边缘的有效值填充边界外 + edge_mean = np.nanmean(edge_values) + print(f"边界边缘平均值: {edge_mean:.4f}") + + # 将边缘值填充到边界外的所有NaN点 + outside_nan = outside_mask & np.isnan(grid_content) + if np.any(outside_nan): + # 使用最近邻插值从边缘值填充 + edge_points = np.column_stack((grid_xx[edge_mask & ~np.isnan(grid_content)], + grid_yy[edge_mask & ~np.isnan(grid_content)])) + if len(edge_points) > 0: + edge_vals = grid_content[edge_mask & ~np.isnan(grid_content)] + outside_points = np.column_stack((grid_xx[outside_nan], grid_yy[outside_nan])) + + outside_filled = griddata( + edge_points, edge_vals, outside_points, + method='nearest' + ) + grid_content[outside_nan] = outside_filled + print(f"已填充边界外的 {np.sum(~np.isnan(outside_filled))} 个点") + else: + # 如果没有边缘值，使用边缘平均值填充 + grid_content[outside_nan] = edge_mean + print(f"使用边缘平均值填充边界外的 {np.sum(outside_nan)} 个点") + else: + print("边界外区域已全部填充") + else: + # 如果边缘没有有效值，使用全局平均值填充边界外 + global_mean = np.nanmean(grid_content[mask]) + if not np.isnan(global_mean): + grid_content[outside_mask & np.isnan(grid_content)] = global_mean + print(f"使用全局平均值 {global_mean:.4f} 填充边界外") + else: + # 如果没有找到边缘，直接使用边界内的平均值填充边界外 + mean_in_boundary = np.nanmean(grid_content[mask]) + if not np.isnan(mean_in_boundary): + grid_content[outside_mask & np.isnan(grid_content)] = mean_in_boundary + print(f"使用边界内平均值 {mean_in_boundary:.4f} 填充边界外") + + print("整个画面已铺满，边界外区域已用边缘值填充") + + # 最终检查：确保边界内所有区域都有值 + final_check_nan = np.isnan(grid_content) & mask + if np.any(final_check_nan): + print(f"警告: 仍有 {np.sum(final_check_nan)} 个边界内的点未填充，使用平均值填充...") + if np.sum(~np.isnan(grid_content) & mask) > 0: + mean_value = np.nanmean(grid_content[mask]) + grid_content[final_check_nan] = mean_value + print(f" 使用平均值 {mean_value:.4f} 填充剩余 {np.sum(final_check_nan)} 个点") + else: + # 如果边界内完全没有有效值，使用全局平均值 + global_mean = np.nanmean(grid_content) + if not np.isnan(global_mean): + grid_content[final_check_nan] = global_mean + else: + grid_content[final_check_nan] = 0 + print(" 使用全局平均值填充") + else: + print("边界内所有区域已完全填充") + + # 检查插值结果 + valid_data = ~np.isnan(grid_content) + valid_count = np.sum(valid_data) + print(f"有效插值点数量: {valid_count} / {grid_content.size}") + + if valid_count == 0: + raise ValueError("边界掩膜后没有有效数据点，请检查数据点是否在边界范围内") + + if valid_count < 4: + print("警告：有效数据点很少，可能影响绘图效果") + + # 输出插值结果的统计信息 + valid_values = grid_content[valid_data] + print( + f"插值后数据统计: 最小值={valid_values.min():.4f}, 最大值={valid_values.max():.4f}, 平均值={valid_values.mean():.4f}") + + # 计算不确定性网格 + grid_uncertainty = None + if calculate_uncertainty: + print("\n开始计算不确定性网格...") + + # 确定使用的不确定性方法 + if uncertainty_method == 'auto': + # 自动选择：如果有模型方差数据，使用model_variance；否则尝试kriging，失败则用idw + if 'uncertainty' in points_gdf.columns: + uncertainty_method = 'model_variance' + print("检测到模型方差数据，使用model_variance方法") + elif PYKRIGE_AVAILABLE: + uncertainty_method = 'kriging' + print("使用Kriging方法计算不确定性") + else: + uncertainty_method = 'idw' + print("使用IDW方法计算不确定性") + + # 根据方法计算不确定性 + if uncertainty_method == 'model_variance': + if 'uncertainty' not in points_gdf.columns: + print("警告: 未找到不确定性数据列，改用IDW方法") + uncertainty_method = 'idw' + else: + uncertainty_values = points_gdf['uncertainty'].values + # 过滤无效值 + valid_unc_mask = np.isfinite(uncertainty_values) + if np.sum(valid_unc_mask) > 0: + grid_uncertainty = self._calculate_model_variance_uncertainty( + points[valid_unc_mask], uncertainty_values[valid_unc_mask], + grid_xx, grid_yy + ) + else: + print("警告: 不确定性数据无效，改用IDW方法") + uncertainty_method = 'idw' + + if uncertainty_method == 'kriging': + grid_uncertainty = self._calculate_kriging_uncertainty(points, values, grid_xx, grid_yy) + elif uncertainty_method == 'idw': + grid_uncertainty = self._calculate_idw_uncertainty(points, grid_xx, grid_yy) + elif uncertainty_method == 'rbf': + grid_uncertainty = self._calculate_rbf_uncertainty(points, grid_xx, grid_yy) + + # 应用边界掩膜到不确定性网格 + if grid_uncertainty is not None: + # 边界外的区域设为NaN + grid_uncertainty[~mask] = np.nan + valid_unc = grid_uncertainty[~np.isnan(grid_uncertainty)] + if len(valid_unc) > 0: + print(f"不确定性统计: 最小值={valid_unc.min():.4f}, 最大值={valid_unc.max():.4f}, 平均值={valid_unc.mean():.4f}") + else: + print("警告: 不确定性网格中没有有效数据") + + # 返回外扩后的bounds + expanded_bounds = np.array([minx, miny, maxx, maxy]) + + if calculate_uncertainty and grid_uncertainty is not None: + return grid_xx, grid_yy, grid_content, expanded_bounds, grid_uncertainty + else: + return grid_xx, grid_yy, grid_content, expanded_bounds + + def create_content_map(self, points_gdf, boundary_gdf, grid_xx, grid_yy, + grid_content, bounds, output_file='content_map.png', + show_sample_points=False, base_map_tif=None, + grid_uncertainty=None, show_uncertainty=True, + uncertainty_alpha=0.5, uncertainty_threshold=0.5, + uncertainty_cmap='Reds', cmap='viridis'): + """ + 创建含量图 + + Parameters: + ----------- + base_map_tif : str, optional + TIF正射底图文件路径。如果提供，将在水域掩膜外显示底图 + grid_uncertainty : np.ndarray, optional + 不确定性网格 + show_uncertainty : bool, default=True + 是否显示不确定性叠加层 + uncertainty_alpha : float, default=0.5 + 不确定性叠加层透明度（0-1） + uncertainty_threshold : float, default=0.5 + 不确定性显示阈值（0-1），只显示高于此阈值的不确定性区域 + uncertainty_cmap : str, default='Reds' + 不确定性颜色映射 + cmap : str, default='viridis' + 含量数据的颜色映射 + """ + print("正在生成含量图...") + + # 检查网格数据 + print(f"网格形状: {grid_content.shape}") + + # 创建边界掩膜（用于绘图时只显示边界内） + print("创建边界掩膜用于绘图...") + try: + # 创建网格点的GeoDataFrame + grid_points = gpd.GeoDataFrame( + geometry=[Point(x, y) for x, y in zip(grid_xx.flatten(), grid_yy.flatten())], + crs=points_gdf.crs + ) + # 检查哪些点在边界内 + within_boundary = grid_points.within(boundary_gdf.unary_union) + mask = within_boundary.values.reshape(grid_xx.shape) + print(f"边界内点数: {np.sum(mask)} / {mask.size}") + except Exception as e: + print(f"创建边界掩膜时出现错误: {e}，继续绘图...") + mask = np.ones_like(grid_content, dtype=bool) # 如果失败，显示全部 + + valid_data = ~np.isnan(grid_content) + if np.sum(valid_data) == 0: + raise ValueError("没有有效的插值数据用于绘图") + + # 计算数据统计 + valid_values = grid_content[valid_data] + print( + f"插值结果统计: 最小值={valid_values.min():.4f}, 最大值={valid_values.max():.4f}, 平均值={valid_values.mean():.4f}") + print(f"有效数据点数量: {np.sum(valid_data)} / {grid_content.size}") + + # 检查数据范围 + data_range = valid_values.max() - valid_values.min() + print(f"数据范围: {data_range:.6f}") + + if data_range == 0: + print("警告：所有数据值都相同，将使用单一颜色显示") + + # 创建图形 + fig, ax = plt.subplots(figsize=(12, 10)) + + # 如果提供了底图，先绘制底图（在水域掩膜外） + if base_map_tif is not None: + try: + print(f"正在加载底图: {base_map_tif}") + self._add_base_map(ax, base_map_tif, bounds, mask, grid_xx, grid_yy, boundary_gdf) + print("底图加载成功") + except Exception as e: + print(f"加载底图失败: {e}，将跳过底图显示") + + # 设置颜色映射参数 + im = None + + try: + if data_range > 0: + # 设置颜色范围，确保有足够的对比度 + vmin = valid_values.min() + vmax = valid_values.max() + + # 如果范围很小，稍微扩展一下以增加对比度 + if data_range < 1e-6: + center = valid_values.mean() + expansion = max(abs(center) * 0.01, 1e-6) # 扩展1%或最小值 + vmin = center - expansion + vmax = center + expansion + + print(f"颜色映射范围: {vmin:.6f} - {vmax:.6f}") + + # 方法1：尝试使用contourf + try: + print("尝试使用contourf绘制...") + # 使用掩膜数组：边界外的数据被掩膜掉，只显示边界内 + # mask已经在前面创建好了 + masked_data = np.ma.masked_where(~mask, grid_content) + + # 创建更多等级数以获得更平滑的颜色过渡 + levels = np.linspace(vmin, vmax, 100) # 创建100个等级以获得平滑效果 + im = ax.contourf(grid_xx, grid_yy, masked_data, + levels=levels, cmap=cmap, alpha=0.9, + vmin=vmin, vmax=vmax, extend='both') + print("contourf绘制成功") + + # 可选择性添加等值线（默认不添加，以保持平滑效果） + # 如果需要等值线，可以取消注释下面的代码 + # try: + # contour_levels = np.linspace(vmin, vmax, 11) + # contours = ax.contour(grid_xx, grid_yy, grid_content, + # levels=contour_levels, colors='white', + # alpha=0.3, linewidths=0.5) + # ax.clabel(contours, inline=True, fontsize=8, fmt='%.3f') + # print("等值线添加成功") + # except Exception as e: + # print(f"等值线绘制失败: {e}") + + except Exception as e: + print(f"contourf失败: {e}") + # 方法2：使用pcolormesh + try: + print("尝试使用pcolormesh绘制...") + # 使用掩膜数组：边界外的数据被掩膜掉，只显示边界内 + # mask已经在前面创建好了 + masked_data = np.ma.masked_where(~mask, grid_content) + + im = ax.pcolormesh(grid_xx, grid_yy, masked_data, + cmap=cmap, alpha=0.9, + vmin=vmin, vmax=vmax, shading='gouraud') # 使用gouraud平滑着色 + print("pcolormesh绘制成功") + except Exception as e2: + print(f"pcolormesh也失败: {e2}") + raise e2 + + else: + # 所有值相同的情况 + print("使用单一颜色填充（所有值相同）") + # 创建一个简单的填充 + single_value = valid_values[0] + im = ax.contourf(grid_xx, grid_yy, grid_content, + levels=[single_value - 0.001, single_value + 0.001], + cmap=cmap, alpha=0.8) + + except Exception as e: + print(f"主要绘图方法失败，尝试备选方案: {e}") + + # 备选方案1：imshow + try: + print("尝试使用imshow...") + # 处理NaN值 + display_data = grid_content.copy() + nan_mask = np.isnan(display_data) + if np.any(nan_mask): + # 用平均值填充NaN + display_data[nan_mask] = valid_values.mean() + + if data_range > 0: + vmin = valid_values.min() + vmax = valid_values.max() + im = ax.imshow(display_data, + extent=[grid_xx.min(), grid_xx.max(), + grid_yy.min(), grid_yy.max()], + cmap=cmap, alpha=0.8, origin='lower', + vmin=vmin, vmax=vmax, aspect='auto') + else: + im = ax.imshow(display_data, + extent=[grid_xx.min(), grid_xx.max(), + grid_yy.min(), grid_yy.max()], + cmap=cmap, alpha=0.8, origin='lower', + aspect='auto') + print("imshow绘制成功") + + except Exception as e2: + print(f"imshow也失败: {e2}") + + # 备选方案2：散点图 + try: + print("尝试使用散点图...") + valid_x = grid_xx[valid_data] + valid_y = grid_yy[valid_data] + valid_z = grid_content[valid_data] + + if data_range > 0: + im = ax.scatter(valid_x, valid_y, c=valid_z, + cmap=cmap, alpha=0.8, s=10, + vmin=valid_values.min(), vmax=valid_values.max()) + else: + im = ax.scatter(valid_x, valid_y, c=valid_z, + cmap=cmap, alpha=0.8, s=10) + print("散点图绘制成功") + + except Exception as e3: + print(f"所有绘图方法都失败: {e3}") + raise ValueError("无法生成颜色图，请检查数据") + + # 绘制边界（黑色） + try: + boundary_gdf.boundary.plot(ax=ax, color='black', linewidth=2, alpha=1.0) + print("边界绘制成功（黑色）") + except Exception as e: + print(f"边界绘制失败: {e}") + + # 绘制不确定性叠加层（半透明）- 已禁用 + # if show_uncertainty and grid_uncertainty is not None: + # try: + # print("正在添加不确定性叠加层...") + # # 创建不确定性掩膜（只显示高于阈值的不确定性区域） + # uncertainty_mask = (grid_uncertainty >= uncertainty_threshold) & (~np.isnan(grid_uncertainty)) + # + # if np.any(uncertainty_mask): + # # 创建不确定性显示数据（只显示高不确定性区域） + # uncertainty_display = grid_uncertainty.copy() + # uncertainty_display[~uncertainty_mask] = np.nan + # + # # 使用contourf或pcolormesh绘制不确定性叠加层 + # try: + # # 使用pcolormesh绘制半透明的不确定性层 + # uncertainty_im = ax.pcolormesh( + # grid_xx, grid_yy, uncertainty_display, + # cmap=uncertainty_cmap, alpha=uncertainty_alpha, + # vmin=uncertainty_threshold, vmax=1.0, + # shading='gouraud', zorder=10 + # ) + # print(f"不确定性叠加层添加成功（阈值={uncertainty_threshold}，透明度={uncertainty_alpha}）") + # except Exception as e: + # print(f"不确定性叠加层绘制失败: {e}") + # # 备选方案：使用contourf + # try: + # uncertainty_levels = np.linspace(uncertainty_threshold, 1.0, 10) + # uncertainty_im = ax.contourf( + # grid_xx, grid_yy, uncertainty_display, + # levels=uncertainty_levels, + # cmap=uncertainty_cmap, alpha=uncertainty_alpha, + # zorder=10 + # ) + # print("不确定性叠加层添加成功（使用contourf）") + # except Exception as e2: + # print(f"不确定性叠加层绘制失败: {e2}") + # else: + # print(f"警告：没有高于阈值{uncertainty_threshold}的不确定性区域") + # + # except Exception as e: + # print(f"不确定性叠加层添加失败: {e}") + + # 可选择性绘制采样点（默认不绘制，以显示平滑的颜色分布） + if show_sample_points: + try: + points_gdf.plot(ax=ax, color='black', markersize=6, alpha=0.7, + marker='+', edgecolors='white', linewidth=1) + print("采样点绘制成功") + except Exception as e: + print(f"采样点绘制失败: {e}") + + # 设置坐标轴标签和格式 + # 由于输入是投影坐标系，输出是地理坐标系，始终显示为地理坐标 + ax.set_xlabel('经度 (°)', fontsize=12) + ax.set_ylabel('纬度 (°)', fontsize=12) + + # 格式化坐标轴刻度为经纬度格式（保留3位小数） + def lon_formatter(x, p): + return f'{x:.3f}°' + + def lat_formatter(x, p): + return f'{x:.3f}°' + + ax.xaxis.set_major_formatter(FuncFormatter(lon_formatter)) + ax.yaxis.set_major_formatter(FuncFormatter(lat_formatter)) + + # 添加格网线 + ax.grid(True, linestyle='--', linewidth=0.5, alpha=0.5, color='gray') + ax.set_axisbelow(True) # 将格网线放在图层下方 + + # ax.set_title('含量分布图', fontsize=16, fontweight='bold', pad=20) # 已去除标题 + + # 添加颜色条 + try: + if im is not None: + cbar = plt.colorbar(im, ax=ax, shrink=0.5, aspect=40, pad=0.02) + cbar.set_label('含量值', fontsize=10) + + # 设置颜色条的刻度 + if data_range > 0: + tick_values = np.linspace(valid_values.min(), valid_values.max(), 6) + cbar.set_ticks(tick_values) + cbar.set_ticklabels([f'{val:.3f}' for val in tick_values]) + cbar.ax.tick_params(labelsize=8) # 缩小刻度标签字体 + + print("颜色条添加成功") + else: + print("警告：无法添加颜色条，im对象为None") + except Exception as e: + print(f"颜色条添加失败: {e}") + + # 添加指北针 + try: + self.add_north_arrow(ax, bounds) + except Exception as e: + print(f"指北针添加失败: {e}") + + # 添加比例尺 + try: + self.add_scale_bar(ax) + except Exception as e: + print(f"比例尺添加失败: {e}") + + # 添加图例 + try: + self.add_legend(ax, show_uncertainty=False) # 已禁用不确定性显示 + except Exception as e: + print(f"图例添加失败: {e}") + + # 设置图形边界（进一步外扩1%以确保边界不完全挨着地图） + try: + x_range = bounds[2] - bounds[0] + y_range = bounds[3] - bounds[1] + display_expand = 0.01 # 显示时再外扩1% + ax.set_xlim(bounds[0] - x_range * display_expand, bounds[2] + x_range * display_expand) + ax.set_ylim(bounds[1] - y_range * display_expand, bounds[3] + y_range * display_expand) + except Exception as e: + print(f"设置图形边界失败: {e}") + + # 调整布局 + plt.tight_layout() + + # 保存图片 + try: + plt.savefig(output_file, dpi=300, bbox_inches='tight', + facecolor='white', edgecolor='none') + print(f"含量图已保存为：{output_file}") + except Exception as e: + print(f"图片保存失败: {e}") + + # 显示图片 + try: + plt.show() + except Exception as e: + print(f"图片显示失败: {e}") + + def add_north_arrow(self, ax, bounds): + """添加指北针（左上角）- 复杂罗盘样式""" + minx, miny, maxx, maxy = bounds + + # 计算指北针位置（左上角） + arrow_x = minx + (maxx - minx) * 0.1 + arrow_y = maxy - (maxy - miny) * 0.1 + + # 缩小指北针尺寸 + size_factor = (maxy - miny) * 0.04 # 缩小尺寸 + radius = size_factor * 1.0 # 罗盘半径 + + # 绘制圆形背景（外圈） + circle_outer = patches.Circle( + (arrow_x, arrow_y), + radius=radius, + facecolor='white', + edgecolor='black', + linewidth=2.5, + zorder=10 + ) + ax.add_patch(circle_outer) + + # 绘制内圈（装饰） + circle_inner = patches.Circle( + (arrow_x, arrow_y), + radius=radius * 0.7, + facecolor='none', + edgecolor='gray', + linewidth=1.5, + linestyle='--', + zorder=11 + ) + ax.add_patch(circle_inner) + + # 绘制四个方向的刻度线 + tick_length = radius * 0.3 + tick_width = 1.5 + + # 北方向刻度（主刻度） + ax.plot([arrow_x, arrow_x], [arrow_y, arrow_y + radius * 0.85], + 'k-', linewidth=tick_width * 2, zorder=12) + + # 南方向刻度 + ax.plot([arrow_x, arrow_x], [arrow_y, arrow_y - radius * 0.85], + 'k-', linewidth=tick_width, zorder=12) + + # 东方向刻度 + ax.plot([arrow_x, arrow_x + radius * 0.85], [arrow_y, arrow_y], + 'k-', linewidth=tick_width, zorder=12) + + # 西方向刻度 + ax.plot([arrow_x, arrow_x - radius * 0.85], [arrow_y, arrow_y], + 'k-', linewidth=tick_width, zorder=12) + + # 绘制次要刻度（45度方向） + for angle in [45, 135, 225, 315]: + angle_rad = math.radians(angle) + x_end = arrow_x + radius * 0.7 * math.cos(angle_rad) + y_end = arrow_y + radius * 0.7 * math.sin(angle_rad) + ax.plot([arrow_x, x_end], [arrow_y, y_end], + 'k-', linewidth=tick_width * 0.5, alpha=0.6, zorder=12) + + # 绘制指北箭头（三角形，填充） + arrow_size = radius * 0.6 + arrow_points = np.array([ + [arrow_x, arrow_y + radius * 0.9], # 顶点（北） + [arrow_x - arrow_size * 0.3, arrow_y + radius * 0.3], # 左下 + [arrow_x + arrow_size * 0.3, arrow_y + radius * 0.3] # 右下 + ]) + arrow_poly = patches.Polygon( + arrow_points, + facecolor='black', + edgecolor='black', + linewidth=2, + zorder=13 + ) + ax.add_patch(arrow_poly) + + # 绘制指南箭头（三角形，填充，但较小） + south_arrow_size = radius * 0.4 + south_arrow_points = np.array([ + [arrow_x, arrow_y - radius * 0.6], # 顶点（南） + [arrow_x - south_arrow_size * 0.2, arrow_y - radius * 0.2], # 左上 + [arrow_x + south_arrow_size * 0.2, arrow_y - radius * 0.2] # 右上 + ]) + south_arrow_poly = patches.Polygon( + south_arrow_points, + facecolor='white', + edgecolor='black', + linewidth=1.5, + zorder=13 + ) + ax.add_patch(south_arrow_poly) + + # 添加方向标记（N, S, E, W） + label_offset = radius * 1.15 + font_size = 16 * 0.5 # 缩小字体到原来的一半 + + ax.text(arrow_x, arrow_y + label_offset, 'N', + fontsize=font_size, fontweight='bold', ha='center', va='bottom', + color='black', zorder=14) + + ax.text(arrow_x, arrow_y - label_offset, 'S', + fontsize=font_size * 0.8, fontweight='bold', ha='center', va='top', + color='black', zorder=14) + + ax.text(arrow_x + label_offset, arrow_y, 'E', + fontsize=font_size * 0.8, fontweight='bold', ha='left', va='center', + color='black', zorder=14) + + ax.text(arrow_x - label_offset, arrow_y, 'W', + fontsize=font_size * 0.8, fontweight='bold', ha='right', va='center', + color='black', zorder=14) + + def add_scale_bar(self, ax): + """添加比例尺""" + try: + if self.output_crs == 'EPSG:4326': + # 地理坐标系，需要指定度数与距离的换算关系 + # 在地球表面，1度约等于111公里（在赤道附近） + # 使用deg作为单位，matplotlib-scalebar会自动处理 + scalebar = ScaleBar( + 111000, # 1度 = 111000米 + units='m', + location='lower left', + box_alpha=0.8, + color='black', + font_properties={'size': 10}, + label_loc='bottom' + ) + ax.add_artist(scalebar) + print("地理坐标系比例尺添加成功") + else: + # 投影坐标系，使用米作为单位 + scalebar = ScaleBar(1, units='m', location='lower left', + box_alpha=0.8, color='black', + font_properties={'size': 10}) + ax.add_artist(scalebar) + print("投影坐标系比例尺添加成功") + except Exception as e: + print(f"比例尺添加失败: {e}") + # 如果matplotlib-scalebar失败，尝试手动添加简单的比例尺 + try: + self._add_manual_scale_bar(ax) + print("手动比例尺添加成功") + except Exception as e2: + print(f"手动比例尺也失败: {e2}") + + def _add_manual_scale_bar(self, ax): + """手动添加简单的比例尺""" + # 获取当前坐标轴的范围 + xlim = ax.get_xlim() + ylim = ax.get_ylim() + + # 计算比例尺的位置和长度 + x_range = xlim[1] - xlim[0] + y_range = ylim[1] - ylim[0] + + # 比例尺位置（左下角） + scale_x = xlim[0] + x_range * 0.05 + scale_y = ylim[0] + y_range * 0.1 + + if self.output_crs == 'EPSG:4326': + # 地理坐标系：计算合适的比例尺长度（度） + # 选择一个合理的距离，比如1公里、5公里或10公里 + distance_km = 5 # 5公里 + scale_length_deg = distance_km / 111.0 # 转换为度数 + + # 绘制比例尺线 + ax.plot([scale_x, scale_x + scale_length_deg], [scale_y, scale_y], + 'k-', linewidth=3) + ax.plot([scale_x, scale_x], [scale_y - y_range * 0.01, scale_y + y_range * 0.01], + 'k-', linewidth=2) + ax.plot([scale_x + scale_length_deg, scale_x + scale_length_deg], + [scale_y - y_range * 0.01, scale_y + y_range * 0.01], 'k-', linewidth=2) + + # 添加文字标注 + ax.text(scale_x + scale_length_deg / 2, scale_y + y_range * 0.02, + f'{distance_km} km', ha='center', va='bottom', fontsize=10, + bbox=dict(boxstyle='round,pad=0.3', facecolor='white', alpha=0.8)) + else: + # 投影坐标系：使用米为单位 + # 选择合适的比例尺长度 + if x_range > 10000: # 大于10km + scale_length = 5000 # 5km + scale_text = '5 km' + elif x_range > 2000: # 大于2km + scale_length = 1000 # 1km + scale_text = '1 km' + else: # 小于2km + scale_length = 500 # 500m + scale_text = '500 m' + + # 绘制比例尺线 + ax.plot([scale_x, scale_x + scale_length], [scale_y, scale_y], + 'k-', linewidth=3) + ax.plot([scale_x, scale_x], [scale_y - y_range * 0.01, scale_y + y_range * 0.01], + 'k-', linewidth=2) + ax.plot([scale_x + scale_length, scale_x + scale_length], + [scale_y - y_range * 0.01, scale_y + y_range * 0.01], 'k-', linewidth=2) + + # 添加文字标注 + ax.text(scale_x + scale_length / 2, scale_y + y_range * 0.02, + scale_text, ha='center', va='bottom', fontsize=10, + bbox=dict(boxstyle='round,pad=0.3', facecolor='white', alpha=0.8)) + + def _add_base_map(self, ax, base_map_tif, bounds, mask, grid_xx, grid_yy, boundary_gdf): + """添加正射底图（在水域掩膜外显示） + + Parameters: + ----------- + ax : matplotlib.axes.Axes + 绘图轴对象 + base_map_tif : str + TIF底图文件路径 + bounds : np.ndarray + 显示范围 [minx, miny, maxx, maxy] + mask : np.ndarray + 水域掩膜（True表示水域内） + grid_xx : np.ndarray + 网格X坐标 + grid_yy : np.ndarray + 网格Y坐标 + boundary_gdf : gpd.GeoDataFrame + 边界几何数据 + """ + import rasterio + import rasterio.windows + from rasterio.warp import calculate_default_transform, reproject, Resampling + + print("正在读取底图文件...") + with rasterio.open(base_map_tif) as src: + # 获取底图的坐标系 + tif_crs = src.crs + tif_bounds = src.bounds + + print(f"底图坐标系: {tif_crs}") + print(f"底图范围: {tif_bounds}") + print(f"目标范围: {bounds}") + + # 检查是否需要投影转换 + target_crs = CRS.from_string(self.output_crs) + need_reproject = tif_crs != target_crs + + # 读取底图数据 + if need_reproject: + print(f"底图坐标系({tif_crs})与目标坐标系({target_crs})不同，正在转换...") + # 计算转换后的变换参数和尺寸 + transform, width, height = calculate_default_transform( + tif_crs, target_crs, + src.width, src.height, + left=bounds[0], bottom=bounds[1], + right=bounds[2], top=bounds[3] + ) + + # 创建目标数组 + if src.count == 1: + # 单波段 + base_map_data = np.zeros((height, width), dtype=src.dtypes[0]) + reproject( + source=rasterio.band(src, 1), + destination=base_map_data, + src_transform=src.transform, + src_crs=tif_crs, + dst_transform=transform, + dst_crs=target_crs, + resampling=Resampling.bilinear + ) + else: + # 多波段（RGB），取前3个波段 + num_bands = min(3, src.count) + base_map_data = np.zeros((num_bands, height, width), dtype=src.dtypes[0]) + for i in range(num_bands): + reproject( + source=rasterio.band(src, i + 1), + destination=base_map_data[i], + src_transform=src.transform, + src_crs=tif_crs, + dst_transform=transform, + dst_crs=target_crs, + resampling=Resampling.bilinear + ) + + # 如果是RGB，转换为(height, width, 3)格式 + if num_bands == 3: + base_map_data = np.transpose(base_map_data, (1, 2, 0)) + + # 创建extent用于显示 + extent = [bounds[0], bounds[2], bounds[1], bounds[3]] + + else: + # 不需要投影转换，直接读取对应范围的数据 + print("底图坐标系与目标坐标系一致，直接读取...") + + # 计算需要读取的窗口 + row_min, col_min = src.index(bounds[0], bounds[3]) # 左上角 + row_max, col_max = src.index(bounds[2], bounds[1]) # 右下角 + + # 确保索引在有效范围内 + row_min = max(0, row_min) + row_max = min(src.height, row_max + 1) + col_min = max(0, col_min) + col_max = min(src.width, col_max + 1) + + window = rasterio.windows.Window.from_slices( + (row_min, row_max), (col_min, col_max) + ) + + # 读取数据 + if src.count == 1: + base_map_data = src.read(1, window=window) + else: + # 多波段，取前3个波段 + num_bands = min(3, src.count) + base_map_data = src.read(list(range(1, num_bands + 1)), window=window) + if num_bands == 3: + # 转换为(height, width, 3)格式 + base_map_data = np.transpose(base_map_data, (1, 2, 0)) + + # 计算extent + window_transform = rasterio.windows.transform(window, src.transform) + left = window_transform[2] + top = window_transform[5] + right = left + window_transform[0] * base_map_data.shape[1] + bottom = top + window_transform[4] * base_map_data.shape[0] + + # 确保extent不超过bounds + extent = [ + max(bounds[0], left), + min(bounds[2], right), + max(bounds[1], bottom), + min(bounds[3], top) + ] + + # 将底图数据缩放到网格大小以便显示 + # 创建底图的显示掩膜：只在边界外显示 + print("正在创建底图显示掩膜...") + + # 创建底图网格（与显示范围对齐） + base_map_height, base_map_width = base_map_data.shape[:2] + + # 性能优化：如果底图分辨率过高，进行降采样以提高处理速度 + # 限制最大边长为2000像素（保持足够清晰度的同时提高速度） + max_display_size = 2000 + scale_factor = 1.0 + if max(base_map_height, base_map_width) > max_display_size: + scale_factor = max_display_size / max(base_map_height, base_map_width) + new_height = int(base_map_height * scale_factor) + new_width = int(base_map_width * scale_factor) + print( + f"底图分辨率较高 ({base_map_width}x{base_map_height})，降采样到 {new_width}x{new_height} 以提高速度") + # 使用scipy的zoom进行降采样 + if base_map_data.ndim == 2: + base_map_data = ndimage.zoom(base_map_data, scale_factor, order=1) + else: + base_map_data = ndimage.zoom(base_map_data, (scale_factor, scale_factor, 1), order=1) + base_map_height, base_map_width = base_map_data.shape[:2] + # 更新extent以匹配新的分辨率 + extent_width = extent[1] - extent[0] + extent_height = extent[3] - extent[2] + extent = [ + extent[0], + extent[0] + extent_width, + extent[2], + extent[2] + extent_height + ] + + # 使用rasterio的geometry_mask快速生成掩膜（比创建大量Point对象快得多） + # 创建底图的变换矩阵 + if need_reproject: + # 如果进行了投影转换，使用计算得到的transform + base_map_transform = transform + else: + # 如果没有投影转换，使用窗口变换 + base_map_transform = window_transform + + # 如果进行了降采样，需要调整transform + if scale_factor < 1.0: + # 调整transform以适应新的分辨率 + # rasterio的transform是6元素tuple或Affine对象，需要调整像素大小 + # 获取transform的6个参数 (a, b, c, d, e, f) + # 其中a和e是像素大小，需要除以scale_factor + try: + from affine import Affine + except ImportError: + # 如果affine包不可用，尝试从rasterio导入 + try: + from rasterio.transform import Affine + except ImportError: + # 如果都不可用，使用tuple方式 + Affine = None + + if Affine is not None: + # 获取6个参数 + if hasattr(base_map_transform, '__iter__') and len(base_map_transform) == 6: + a, b, c, d, e, f = base_map_transform + else: + a, b, c, d, e, f = base_map_transform[0], base_map_transform[1], base_map_transform[2], \ + base_map_transform[3], base_map_transform[4], base_map_transform[5] + # 创建新的transform，调整像素大小（a和e是像素大小） + base_map_transform = Affine(a / scale_factor, b, c, d, e / scale_factor, f) + else: + # 降级方案：使用tuple + if hasattr(base_map_transform, '__iter__') and len(base_map_transform) == 6: + a, b, c, d, e, f = base_map_transform + else: + a, b, c, d, e, f = base_map_transform[0], base_map_transform[1], base_map_transform[2], \ + base_map_transform[3], base_map_transform[4], base_map_transform[5] + base_map_transform = (a / scale_factor, b, c, d, e / scale_factor, f) + + # 调试信息：检查边界数据和底图范围 + print(f"底图显示范围 (extent): {extent}") + print(f"底图分辨率: {base_map_width}x{base_map_height}") + print(f"底图transform: {base_map_transform}") + if boundary_gdf is not None and len(boundary_gdf) > 0: + boundary_bounds = boundary_gdf.total_bounds + print(f"边界数据范围: {boundary_bounds}") + print(f"边界数据坐标系: {boundary_gdf.crs}") + print(f"边界要素数量: {len(boundary_gdf)}") + + # 检查边界是否与底图范围重叠 + overlap_x = not (boundary_bounds[2] < extent[0] or boundary_bounds[0] > extent[1]) + overlap_y = not (boundary_bounds[3] < extent[2] or boundary_bounds[1] > extent[3]) + if not (overlap_x and overlap_y): + print("警告: 边界数据范围与底图显示范围不重叠！") + print(" 将不应用掩膜，显示整个底图") + # 创建全True的掩膜（显示所有区域） + base_map_mask = np.ones((base_map_height, base_map_width), dtype=bool) + else: + # 使用geometry_mask生成掩膜（True表示在几何体内，即水域内） + # 注意：geometry_mask返回True表示需要掩膜的区域（在几何体内）， + # 但我们想要的是边界外的区域（不在几何体内），所以需要反转 + try: + within_boundary_mask = geometry_mask( + boundary_gdf.geometry, + out_shape=(base_map_height, base_map_width), + transform=base_map_transform, + invert=False # False表示掩膜几何体内的区域（水域内） + ) + # 反转掩膜：True表示边界外（需要显示的区域） + base_map_mask = ~within_boundary_mask + except Exception as e: + print(f"生成掩膜时出错: {e}") + print(" 将不应用掩膜，显示整个底图") + import traceback + traceback.print_exc() + # 创建全True的掩膜（显示所有区域） + base_map_mask = np.ones((base_map_height, base_map_width), dtype=bool) + else: + print("警告: 边界数据为空，将不应用掩膜，显示整个底图") + # 创建全True的掩膜（显示所有区域） + base_map_mask = np.ones((base_map_height, base_map_width), dtype=bool) + + # 调试信息：检查掩膜状态 + mask_ratio = np.sum(base_map_mask) / base_map_mask.size + print( + f"底图掩膜状态: 可显示区域占比 {mask_ratio * 100:.2f}% ({np.sum(base_map_mask)}/{base_map_mask.size} 像素)") + + # 如果掩膜后没有可显示区域，警告并显示整个底图 + if mask_ratio == 0.0: + print("警告: 掩膜后没有可显示区域，将显示整个底图（不应用掩膜）") + base_map_mask = np.ones((base_map_height, base_map_width), dtype=bool) + + # 归一化数据以便显示（如果是数值型） + # 注意：先归一化整个数据，再应用掩膜，这样可以保证归一化范围正确 + if base_map_data.dtype != np.uint8: + if base_map_data.ndim == 2: + # 单波段：归一化到0-1 + # 使用整个数据集的范围进行归一化（不仅仅是掩膜区域） + data_min = np.nanmin(base_map_data) + data_max = np.nanmax(base_map_data) + print(f"底图数据范围: [{data_min}, {data_max}], dtype: {base_map_data.dtype}") + + if data_max > data_min: + # 先归一化整个数组 + base_map_normalized = (base_map_data - data_min) / (data_max - data_min) + # 然后应用掩膜：只显示边界外的区域 + base_map_display = np.ma.masked_where(~base_map_mask, base_map_normalized) + else: + # 如果数据范围无效，创建全0的掩膜数组 + print("警告: 底图数据范围无效，所有值相同") + base_map_display = np.ma.masked_where(~base_map_mask, np.zeros_like(base_map_data)) + else: + # RGB：每个波段单独归一化 + base_map_normalized = base_map_data.copy().astype(np.float32) + for i in range(base_map_data.shape[2]): + band_data = base_map_data[:, :, i] + data_min = np.nanmin(band_data) + data_max = np.nanmax(band_data) + print(f"底图波段 {i} 数据范围: [{data_min}, {data_max}]") + + if data_max > data_min: + # 归一化整个波段 + base_map_normalized[:, :, i] = (band_data - data_min) / (data_max - data_min) + else: + print(f"警告: 底图波段 {i} 数据范围无效，所有值相同") + base_map_normalized[:, :, i] = np.zeros_like(band_data) + + # 应用掩膜：只显示边界外的区域 + mask_3d = np.broadcast_to(~base_map_mask[..., np.newaxis], base_map_data.shape) + base_map_display = np.ma.masked_where(mask_3d, base_map_normalized) + else: + # uint8类型：直接使用，但可能需要归一化到0-1用于imshow + if base_map_data.ndim == 2: + # 单波段：uint8通常已经是0-255范围，归一化到0-1 + base_map_normalized = base_map_data.astype(np.float32) / 255.0 + base_map_display = np.ma.masked_where(~base_map_mask, base_map_normalized) + else: + # RGB：uint8归一化到0-1 + base_map_normalized = base_map_data.astype(np.float32) / 255.0 + mask_3d = np.broadcast_to(~base_map_mask[..., np.newaxis], base_map_data.shape) + base_map_display = np.ma.masked_where(mask_3d, base_map_normalized) + + # 检查归一化后的数据范围 + if isinstance(base_map_display, np.ma.MaskedArray): + valid_data = base_map_display[~base_map_display.mask] + if len(valid_data) > 0: + print( + f"归一化后有效数据范围: [{np.nanmin(valid_data):.3f}, {np.nanmax(valid_data):.3f}], 有效像素数: {len(valid_data)}") + else: + print("警告: 归一化后没有有效数据显示区域") + + # 绘制底图 + print("正在绘制底图...") + # 注意：extent格式为 [left, right, bottom, top] + # 对于地理坐标系，y轴通常向上为正，所以使用origin='lower' + try: + if base_map_data.ndim == 2: + # 单波段：使用灰度图 + # 确保数据在0-1范围内 + if isinstance(base_map_display, np.ma.MaskedArray): + # 对于masked array，确保数据范围正确 + if np.ma.max(base_map_display) > 1.0 or np.ma.min(base_map_display) < 0.0: + base_map_display = np.ma.clip(base_map_display, 0.0, 1.0) + else: + base_map_display = np.clip(base_map_display, 0.0, 1.0) + + im = ax.imshow(base_map_display, extent=extent, origin='lower', + cmap='gray', alpha=0.8, zorder=0, interpolation='bilinear', + vmin=0.0, vmax=1.0) + else: + # RGB：直接显示 + # 确保数据格式正确（需要在0-1范围内） + if isinstance(base_map_display, np.ma.MaskedArray): + # 对于masked array，确保数据在0-1范围内 + if np.ma.max(base_map_display) > 1.0 or np.ma.min(base_map_display) < 0.0: + base_map_display = np.ma.clip(base_map_display, 0.0, 1.0) + else: + base_map_display = np.clip(base_map_display, 0.0, 1.0) + + # 确保是float32类型，imshow期望0-1范围的float数组 + if base_map_display.dtype != np.float32 and base_map_display.dtype != np.float64: + base_map_display = base_map_display.astype(np.float32) + + im = ax.imshow(base_map_display, extent=extent, origin='lower', + alpha=0.8, zorder=0, interpolation='bilinear') + print(f"底图绘制成功") + except Exception as e: + print(f"底图绘制出错: {e}") + import traceback + traceback.print_exc() + # 如果绘制失败，至少尝试绘制一个简单的占位图 + print("尝试使用备用方法绘制底图...") + try: + if base_map_data.ndim == 2: + # 使用简单的numpy数组，不应用掩膜 + simple_display = np.clip(base_map_data.astype(np.float32) / np.nanmax(base_map_data), 0, 1) + ax.imshow(simple_display, extent=extent, origin='lower', + cmap='gray', alpha=0.5, zorder=0) + else: + simple_display = np.clip(base_map_data.astype(np.float32) / 255.0, 0, 1) + ax.imshow(simple_display, extent=extent, origin='lower', + alpha=0.5, zorder=0) + print("备用方法绘制成功") + except Exception as e2: + print(f"备用方法也失败: {e2}") + + print(f"底图已绘制，显示范围: {extent}") + + def add_legend(self, ax, show_uncertainty=False): + """ + 添加图例 + + Parameters: + ----------- + show_uncertainty : bool + 是否显示不确定性图例项 + """ + legend_elements = [ + # 移除边界标签 + # plt.Line2D([0], [0], color='red', linewidth=2, label='边界'), + # 移除采样点和等值线图例项，以突出平滑的颜色分布效果 + # plt.Line2D([0], [0], marker='+', color='w', markerfacecolor='black', + # markersize=8, label='采样点'), + ] + + # 如果显示了不确定性，添加图例项（已禁用） + # if show_uncertainty: + # from matplotlib.patches import Patch + # legend_elements.append( + # Patch(facecolor='red', alpha=0.5, label='高不确定性区域') + # ) + + # 如果图例为空，则不显示图例 + if legend_elements: + ax.legend(handles=legend_elements, loc='upper left', + framealpha=0.9, fontsize=10) + + def process_data(self, csv_file, shp_file, output_file='content_map.png', + resolution=100, show_sample_points=False, base_map_tif=None, + uncertainty_col=None, uncertainty_method='auto', + calculate_uncertainty=True, show_uncertainty=True, + uncertainty_alpha=0.5, uncertainty_threshold=0.5, + use_distance_diffusion=True, max_diffusion_distance=None, + diffusion_power=2, diffusion_n_neighbors=15, cmap=None, + expand_ratio=0.05): + """ + 主处理函数 + + Parameters: + ----------- + base_map_tif : str, optional + TIF正射底图文件路径。如果提供，将在水域掩膜外显示底图 + uncertainty_col : str, optional + 不确定性数据列名。如果为None，将自动检测 + uncertainty_method : str, default='auto' + 不确定性计算方法：'auto', 'kriging', 'idw', 'rbf', 'model_variance' + calculate_uncertainty : bool, default=True + 是否计算不确定性 + show_uncertainty : bool, default=True + 是否在图上显示不确定性叠加层 + uncertainty_alpha : float, default=0.5 + 不确定性叠加层透明度（0-1） + uncertainty_threshold : float, default=0.5 + 不确定性显示阈值（0-1），只显示高于此阈值的不确定性区域 + use_distance_diffusion : bool, default=True + 是否使用距离扩散方法填充边界空白区域 + max_diffusion_distance : float, optional + 最大扩散距离（单位与坐标相同）。如果为None，自动计算为网格分辨率的5倍 + diffusion_power : float, default=2 + 距离扩散的IDW幂参数，值越大，距离衰减越快 + diffusion_n_neighbors : int, default=15 + 距离扩散使用的最近邻点数 + cmap : str, optional + 颜色映射。如果为None，将从CSV文件名或内容中自动识别参数并选择对应的colormap + expand_ratio : float, default=0.05 + 边界外扩比例（5%），确保图像边界不完全挨着地图 + """ + try: + # 自动识别参数名称并获取colormap + if cmap is None: + param_name = self._extract_param_name(csv_file) + cmap = self._get_colormap(param_name) + else: + print(f"使用指定的颜色映射: {cmap}") + + # 读取数据 + points_gdf = self.read_csv_data(csv_file, uncertainty_col=uncertainty_col) + boundary_gdf = self.read_boundary_shapefile(shp_file) + + # 对边缘采样点进行外扩处理（外扩到整个图像边界，按照resolution间距） + points_gdf = self._expand_edge_points(points_gdf, boundary_gdf, resolution=resolution, expand_ratio=expand_ratio) + + # 创建插值网格（包含不确定性） + result = self.create_interpolation_grid( + points_gdf, boundary_gdf, resolution, + expand_ratio=expand_ratio, + uncertainty_method=uncertainty_method, + calculate_uncertainty=calculate_uncertainty, + use_distance_diffusion=use_distance_diffusion, + max_diffusion_distance=max_diffusion_distance, + diffusion_power=diffusion_power, + diffusion_n_neighbors=diffusion_n_neighbors + ) + + # 根据返回值解包 + if len(result) == 5: + grid_xx, grid_yy, grid_content, bounds, grid_uncertainty = result + else: + grid_xx, grid_yy, grid_content, bounds = result + grid_uncertainty = None + + # 生成含量图（包含不确定性叠加） + self.create_content_map( + points_gdf, boundary_gdf, grid_xx, grid_yy, + grid_content, bounds, output_file, show_sample_points, base_map_tif, + grid_uncertainty=grid_uncertainty, + show_uncertainty=show_uncertainty, + uncertainty_alpha=uncertainty_alpha, + uncertainty_threshold=uncertainty_threshold, + cmap=cmap + ) + + print("处理完成！") + + # 输出统计信息 + print(f"\n统计信息：") + print(f"数据点数量: {len(points_gdf)}") + print(f"含量值范围: {points_gdf['content'].min():.2f} - {points_gdf['content'].max():.2f}") + print(f"含量值平均: {points_gdf['content'].mean():.2f}") + print(f"含量值标准差: {points_gdf['content'].std():.2f}") + + except Exception as e: + print(f"处理过程中出现错误: {str(e)}") + raise + + def process_batch(self, csv_folder, shp_file, output_folder=None, + resolution=100, show_sample_points=False, base_map_tif=None, + uncertainty_col=None, uncertainty_method='auto', + calculate_uncertainty=True, show_uncertainty=True, + uncertainty_alpha=0.5, uncertainty_threshold=0.5, + use_distance_diffusion=True, max_diffusion_distance=None, + diffusion_power=2, diffusion_n_neighbors=15): + """ + 批量处理文件夹中的CSV文件 + + Parameters: + ----------- + csv_folder : str + 包含CSV文件的文件夹路径 + shp_file : str + 边界shapefile文件路径 + output_folder : str, optional + 输出文件夹路径。如果为None，将在CSV文件所在文件夹创建'map_output'子文件夹 + resolution : int, default=100 + 网格分辨率（米） + show_sample_points : bool, default=False + 是否显示采样点 + base_map_tif : str, optional + TIF正射底图文件路径 + uncertainty_col : str, optional + 不确定性数据列名 + uncertainty_method : str, default='auto' + 不确定性计算方法 + calculate_uncertainty : bool, default=True + 是否计算不确定性 + show_uncertainty : bool, default=True + 是否显示不确定性叠加层 + uncertainty_alpha : float, default=0.5 + 不确定性叠加层透明度 + uncertainty_threshold : float, default=0.5 + 不确定性显示阈值 + use_distance_diffusion : bool, default=True + 是否使用距离扩散方法 + max_diffusion_distance : float, optional + 最大扩散距离 + diffusion_power : float, default=2 + 距离扩散的IDW幂参数 + diffusion_n_neighbors : int, default=15 + 距离扩散使用的最近邻点数 + """ + print("=" * 60) + print("开始批量处理CSV文件") + print("=" * 60) + + # 检查输入文件夹是否存在 + if not os.path.isdir(csv_folder): + raise ValueError(f"输入文件夹不存在: {csv_folder}") + + # 获取所有CSV文件 + csv_files = glob.glob(os.path.join(csv_folder, "*.csv")) + if len(csv_files) == 0: + raise ValueError(f"在文件夹 {csv_folder} 中未找到CSV文件") + + print(f"找到 {len(csv_files)} 个CSV文件") + + # 创建输出文件夹 + if output_folder is None: + output_folder = os.path.join(csv_folder, "map_output") + + if not os.path.exists(output_folder): + os.makedirs(output_folder) + print(f"创建输出文件夹: {output_folder}") + else: + print(f"使用输出文件夹: {output_folder}") + + # 统计信息 + success_count = 0 + fail_count = 0 + failed_files = [] + + # 批量处理每个CSV文件 + for i, csv_file in enumerate(csv_files, 1): + print("\n" + "=" * 60) + print(f"处理文件 {i}/{len(csv_files)}: {os.path.basename(csv_file)}") + print("=" * 60) + + try: + # 生成输出文件名（使用CSV文件名，但扩展名为.png） + csv_basename = os.path.splitext(os.path.basename(csv_file))[0] + output_file = os.path.join(output_folder, f"{csv_basename}.png") + + # 处理单个文件（自动识别参数并选择colormap） + self.process_data( + csv_file=csv_file, + shp_file=shp_file, + output_file=output_file, + resolution=resolution, + show_sample_points=show_sample_points, + base_map_tif=base_map_tif, + uncertainty_col=uncertainty_col, + uncertainty_method=uncertainty_method, + calculate_uncertainty=calculate_uncertainty, + show_uncertainty=show_uncertainty, + uncertainty_alpha=uncertainty_alpha, + uncertainty_threshold=uncertainty_threshold, + use_distance_diffusion=use_distance_diffusion, + max_diffusion_distance=max_diffusion_distance, + diffusion_power=diffusion_power, + diffusion_n_neighbors=diffusion_n_neighbors, + cmap=None # 自动识别 + ) + + success_count += 1 + print(f"✓ 成功处理: {csv_basename}.png") + + except Exception as e: + fail_count += 1 + failed_files.append((os.path.basename(csv_file), str(e))) + print(f"✗ 处理失败: {os.path.basename(csv_file)}") + print(f" 错误信息: {e}") + import traceback + traceback.print_exc() + + # 输出批量处理结果统计 + print("\n" + "=" * 60) + print("批量处理完成") + print("=" * 60) + print(f"总文件数: {len(csv_files)}") + print(f"成功: {success_count}") + print(f"失败: {fail_count}") + print(f"输出文件夹: {output_folder}") + + if failed_files: + print("\n失败的文件列表:") + for file_name, error in failed_files: + print(f" - {file_name}: {error}") + + return { + 'total': len(csv_files), + 'success': success_count, + 'failed': fail_count, + 'output_folder': output_folder, + 'failed_files': failed_files + } + + +def main(): + """主函数 - 使用示例""" + # 创建处理器实例 + mapper = ContentMapper() + + # 示例1：处理单个文件 + # csv_file = r"E:\code\WQ\yaobao925\预测样点_无耀斑\BGA.csv" # 采样点的预测值 + # shp_file = r"E:\code\WQ\yaobao925\roi\shp\roi.shp" # 水体边界shapefile路径 + # output_file = r"E:\code\WQ\yaobao925\map\test\BGA.png" # 输出图片路径 + # + # mapper.process_data( + # csv_file=csv_file, + # shp_file=shp_file, + # output_file=output_file, + # resolution=30, # 网格分辨率（米），更小的值产生更平滑的效果 + # show_sample_points=False, # 设置为False以显示平滑的颜色分布，True则显示采样点位置 + # base_map_tif=None, # 正射底图路径（可选） + # # 不确定性相关参数 + # uncertainty_col=None, # 如果不确定性列名已知，可以指定；否则会自动检测 + # uncertainty_method='auto', # 'auto', 'kriging', 'idw', 'rbf', 'model_variance' + # calculate_uncertainty=True, # 是否计算不确定性 + # show_uncertainty=True, # 是否在图上显示不确定性叠加层 + # uncertainty_alpha=0.2, # 不确定性叠加层透明度（0-1） + # uncertainty_threshold=0.2, # 不确定性显示阈值（0-1），只显示高于此阈值的不确定性区域 + # cmap=None # 自动从文件名或内容中识别参数并选择对应的colormap + # ) + + # 示例2：批量处理文件夹中的所有CSV文件 + csv_folder = r"D:\BaiduNetdiskDownload\yaobao\prediction" # CSV文件所在文件夹 + shp_file = r"E:\code\WQ\yaobao925\roi\shp\roi.shp" # 水体边界shapefile路径 + output_folder = r"D:\BaiduNetdiskDownload\yaobao\model" # 输出文件夹（可选，如果为None则在CSV文件夹下创建map_output） + + # 批量处理（会自动识别每个CSV文件的参数名称并选择对应的colormap） + result = mapper.process_batch( + csv_folder=csv_folder, + shp_file=shp_file, + output_folder=output_folder, # 如果为None，将在CSV文件夹下创建map_output子文件夹 + resolution=30, # 网格分辨率（米） + show_sample_points=False, # 是否显示采样点 + base_map_tif=None, # 正射底图路径（可选） + # 不确定性相关参数 + uncertainty_col=None, + uncertainty_method='auto', + calculate_uncertainty=True, + show_uncertainty=True, + uncertainty_alpha=0.2, + uncertainty_threshold=0.2 + ) + + print(f"\n批量处理结果: {result}") + + +if __name__ == "__main__": + # 使用示例 + print("含量分布图生成器") + print("=" * 50) + + # 如果要直接运行，请取消下面的注释并修改文件路径 + main() + + # 或者交互式使用 + # print("使用方法：") + # print("1. 准备CSV文件（前两列为WGS84经纬度，第三列为含量数据）") + # print("2. 准备边界Shapefile文件") + # print("3. 调用以下代码：") + # print(""" + # mapper = ContentMapper() + # mapper.process_data( + # csv_file='your_data.csv', + # shp_file='your_boundary.shp', + # output_file='output_map.png', + # resolution=50 + # ) + # """) diff --git a/src/postprocessing/plot_spectrum_by_parameter.py b/src/postprocessing/plot_spectrum_by_parameter.py new file mode 100644 index 0000000..a39f4bc --- /dev/null +++ b/src/postprocessing/plot_spectrum_by_parameter.py @@ -0,0 +1,184 @@ +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import matplotlib.colors as mcolors +from pathlib import Path + +# 设置中文字体 +plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei'] +plt.rcParams['axes.unicode_minus'] = False + +def load_and_plot_spectrum_by_parameters(): + """ + 加载数据并为每个水质参数绘制光谱曲线图 + """ + try: + # 数据文件路径 + data_file = Path(r"E:\code\WQ\yaobao925\spectral.csv") + + if not data_file.exists(): + print(f"错误：数据文件不存在 - {data_file}") + return + + # 读取数据 + print("正在加载数据...") + data = pd.read_csv(data_file) + + print(f"数据形状: {data.shape}") + print(f"列名: {list(data.columns[:15])}...") # 显示前15个列名 + + # 找到光谱数据的起始列（通常是数字列名） + spectrum_start_idx = None + for i, col in enumerate(data.columns): + try: + float(col) + spectrum_start_idx = i + break + except ValueError: + continue + + if spectrum_start_idx is None: + print("错误：未找到光谱数据列") + return + + print(f"光谱数据从第 {spectrum_start_idx + 1} 列开始") + + # 分离水质参数和光谱数据 + water_quality_data = data.iloc[:, :spectrum_start_idx] + spectrum_data = data.iloc[:, spectrum_start_idx:] + + # 获取波长信息 + try: + # 尝试直接转换为浮点数 + wavelengths = spectrum_data.columns.astype(float) + except ValueError: + # 如果包含字母，提取数字部分 + import re + wavelengths = [] + for col in spectrum_data.columns: + # 提取数字部分 + numbers = re.findall(r'\d+\.?\d*', str(col)) + if numbers: + wavelengths.append(float(numbers[0])) + else: + # 如果没有数字，使用列索引 + wavelengths.append(float(len(wavelengths))) + wavelengths = np.array(wavelengths) + + print(f"波长范围: {wavelengths.min():.1f} - {wavelengths.max():.1f} nm") + print(f"光谱数据形状: {spectrum_data.shape}") + print(f"水质参数: {list(water_quality_data.columns)}") + + # 过滤波长范围到374-1011nm + wavelength_mask = (wavelengths >= 374) & (wavelengths <= 1011) + filtered_wavelengths = wavelengths[wavelength_mask] + filtered_spectrum_data = spectrum_data.iloc[:, wavelength_mask] + + print(f"过滤后波长范围: {filtered_wavelengths.min():.1f} - {filtered_wavelengths.max():.1f} nm") + print(f"过滤后光谱数据形状: {filtered_spectrum_data.shape}") + + # 创建输出目录 + output_dir = Path(r'E:\code\WQ\yaobao925\plot') + output_dir.mkdir(exist_ok=True) + + # 为每个水质参数绘制光谱图 + for param_idx, parameter_name in enumerate(water_quality_data.columns): + print(f"\n[{param_idx+1}/{len(water_quality_data.columns)}] 处理参数: {parameter_name}") + + # 获取当前参数的数据 + parameter_values = water_quality_data[parameter_name] + + # 过滤掉空值 + valid_mask = ~parameter_values.isna() + if valid_mask.sum() == 0: + print(f"参数 '{parameter_name}' 没有有效数据，跳过") + continue + + valid_param_values = parameter_values[valid_mask] + valid_spectrum_data = filtered_spectrum_data[valid_mask] + + print(f"有效样本数: {len(valid_param_values)}") + + # 创建图形和轴 + fig, ax = plt.subplots(figsize=(12, 8)) + + # 归一化参数值到[0,1]范围，用于颜色映射 + param_min = valid_param_values.min() + param_max = valid_param_values.max() + + if param_max == param_min: + # 如果所有值相同，使用中等颜色 + normalized_values = np.full(len(valid_param_values), 0.5) + else: + normalized_values = ((valid_param_values - param_min) / (param_max - param_min)).values + + # 创建蓝红颜色映射（蓝色到红色） + colormap = plt.cm.coolwarm # 蓝色(低值)到红色(高值) + + # 绘制每条光谱曲线 + for i, (idx, spectrum) in enumerate(valid_spectrum_data.iterrows()): + # 处理光谱数据中的空值 + spectrum_values = pd.Series(spectrum.values).fillna(0).values + + # 根据参数值确定颜色 + color = colormap(normalized_values[i]) + alpha = 0.6 if len(valid_param_values) > 50 else 0.8 # 样本多时降低透明度 + + ax.plot(filtered_wavelengths, spectrum_values, color=color, alpha=alpha, linewidth=0.8) + + # 设置图形属性 + ax.set_xlabel('波长 (nm)', fontsize=12) + ax.set_ylabel('光谱强度', fontsize=12) + ax.set_title(f'{parameter_name} 光谱曲线图\n参数范围: {param_min:.4f} - {param_max:.4f}', + fontsize=14, fontweight='bold') + + # 设置坐标轴范围，限制在374-1011nm + ax.set_xlim(374, 1011) + + # 添加网格 + ax.grid(True, alpha=0.3) + + # 创建颜色条 + sm = plt.cm.ScalarMappable(cmap=colormap, + norm=plt.Normalize(vmin=param_min, vmax=param_max)) + sm.set_array([]) + cbar = plt.colorbar(sm, ax=ax, shrink=0.8) + cbar.set_label(f'{parameter_name} 数值', rotation=270, labelpad=20, fontsize=12) + + # 添加统计信息文本框 + stats_text = f'样本数: {len(valid_param_values)}\n' + stats_text += f'均值: {valid_param_values.mean():.4f}\n' + stats_text += f'标准差: {valid_param_values.std():.4f}' + + ax.text(0.02, 0.98, stats_text, transform=ax.transAxes, + verticalalignment='top', bbox=dict(boxstyle='round', + facecolor='wheat', alpha=0.8), fontsize=10) + + # 优化布局 + plt.tight_layout() + + # 保存图片 + # 清理参数名称，用于文件名 + safe_param_name = "".join(c for c in parameter_name if c.isalnum() or c in ('-', '_', '.')).rstrip() + output_file = output_dir / f"{safe_param_name}_spectrum.png" + plt.savefig(output_file, dpi=300, bbox_inches='tight') + plt.close() # 关闭图形释放内存 + + print(f"图片已保存到: {output_file}") + + print(f"\n{'='*80}") + print(f"所有光谱图绘制完成！") + print(f"输出目录: {output_dir}") + print(f"{'='*80}") + + except Exception as e: + print(f"处理过程中出现错误: {str(e)}") + import traceback + traceback.print_exc() + +def main(): + """主函数""" + load_and_plot_spectrum_by_parameters() + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/postprocessing/point_map.py b/src/postprocessing/point_map.py new file mode 100644 index 0000000..b2edd92 --- /dev/null +++ b/src/postprocessing/point_map.py @@ -0,0 +1,636 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +采样点地图生成模块 - 在高光谱假彩色影像上标注采样点 + +支持功能： +1. 读取高光谱影像并生成假彩色RGB图像 +2. 读取CSV文件中的采样点坐标（前两列为纬度、经度） +3. 在影像上标注红色采样点 +4. 添加指北针、图例和比例尺 +5. 支持地理坐标系转换 +""" + +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +from pathlib import Path +from typing import Optional, Tuple, List, Dict, Union +import warnings +from matplotlib.patches import FancyArrowPatch +import matplotlib.patheffects as path_effects + +# 性能优化配置 +plt.rcParams['agg.path.chunksize'] = 10000 # 提高矢量渲染性能 +plt.rcParams['path.simplify'] = True +plt.rcParams['path.simplify_threshold'] = 0.1 + +# 导入GDAL用于影像读写 +try: + from osgeo import gdal, osr + GDAL_AVAILABLE = True +except ImportError: + GDAL_AVAILABLE = False + print("警告: GDAL未安装，地理坐标转换功能可能无法正常工作") + + +class SamplingPointMap: + """采样点地图生成类 - 在高光谱假彩色影像上标注采样点""" + + def __init__(self, output_dir: str = "./point_maps", fast_mode: bool = False): + """ + 初始化采样点地图生成器 + + Args: + output_dir: 输出目录，用于保存生成的地图 + fast_mode: 是否启用快速模式（降低质量换取速度） + """ + self.output_dir = Path(output_dir) + self.output_dir.mkdir(parents=True, exist_ok=True) + self.fast_mode = fast_mode + + # 设置中文字体 + plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans', 'Arial Unicode MS'] + plt.rcParams['axes.unicode_minus'] = False + plt.rcParams['font.size'] = 12 + + # 性能优化设置 + if fast_mode: + plt.rcParams['figure.dpi'] = 150 + plt.rcParams['savefig.dpi'] = 150 + warnings.filterwarnings('ignore', category=UserWarning) + else: + plt.rcParams['figure.dpi'] = 300 + plt.rcParams['savefig.dpi'] = 300 + + warnings.filterwarnings('ignore') + + def create_sampling_point_map(self, + hyperspectral_path: str, + csv_path: str, + output_filename: Optional[str] = None, + rgb_bands: Optional[List[int]] = None, + point_color: str = 'red', + point_size: int = 80, + point_alpha: float = 0.8, + show_north_arrow: bool = True, + show_scale_bar: bool = True, + show_legend: bool = True, + dpi: int = None, + downsample: bool = False) -> str: + """ + 创建采样点地图：在高光谱假彩色影像上标注采样点 + + Args: + hyperspectral_path: 高光谱影像文件路径 (.dat, .bsq, .tif等) + csv_path: 采样点CSV文件路径（前两列为纬度、经度） + output_filename: 输出文件名（如果为None则自动生成） + rgb_bands: 用于RGB合成的三个波段索引 [R, G, B]，默认为None自动选择 + point_color: 采样点颜色 + point_size: 采样点大小 + point_alpha: 采样点透明度 + show_north_arrow: 是否显示指北针 + show_scale_bar: 是否显示比例尺 + show_legend: 是否显示图例 + dpi: 输出图像分辨率（None时使用fast_mode设置） + downsample: 是否对图像进行下采样以加快速度（大影像推荐启用） + + Returns: + 生成的地图文件路径 + """ + if not GDAL_AVAILABLE: + raise ImportError("GDAL未安装，无法处理地理坐标转换") + + print(f"正在生成采样点地图...{' (快速模式)' if self.fast_mode else ''}") + + # 读取高光谱影像 - 优化：仅读取需要的RGB波段 + hyperspectral_img, geotransform, projection, width, height, sample_factor = self._read_hyperspectral( + hyperspectral_path, rgb_bands, downsample) + + # 读取采样点 + sampling_points = self._read_sampling_points(csv_path) + + # 生成假彩色图像 - 应用线性拉伸 + rgb_image = self._create_false_color_image(hyperspectral_img) + + # 将地理坐标转换为像素坐标 - 支持投影系转换和下采样 + pixel_coords = self._geo_to_pixel(sampling_points, geotransform, width, height, projection, sample_factor) + + # 创建地图 + if output_filename is None: + csv_name = Path(csv_path).stem + hs_name = Path(hyperspectral_path).stem + output_filename = f"{hs_name}_{csv_name}_sampling_map.png" + + output_path = self.output_dir / output_filename + + # 使用更优化的绘图设置 + if dpi is None: + dpi = 150 if self.fast_mode else 200 + + self._create_map_visualization( + rgb_image, pixel_coords, sampling_points, + str(output_path), point_color, point_size, point_alpha, + show_north_arrow, show_scale_bar, show_legend, dpi, + geotransform, width, height, downsample, projection, sample_factor + ) + + print(f"采样点地图已保存: {output_path}") + return str(output_path) + + def _read_hyperspectral(self, hyperspectral_path: str, + rgb_bands: Optional[List[int]] = None, + downsample: bool = False) -> Tuple[np.ndarray, tuple, str, int, int]: + """优化版：读取高光谱影像 - 仅读取需要的RGB波段""" + dataset = gdal.Open(hyperspectral_path) + if dataset is None: + raise ValueError(f"无法打开高光谱影像: {hyperspectral_path}") + + width = dataset.RasterXSize + height = dataset.RasterYSize + band_count = dataset.RasterCount + + # 确定要读取的波段 - 优先使用指定波长 (650nm, 550nm, 460nm) + if rgb_bands is None: + if band_count >= 3: + try: + # 使用find_band_number根据波长查找RGB波段 + from src.utils.util import find_band_number + rgb_bands = [ + find_band_number(650.0, hyperspectral_path), # Red ~650nm + find_band_number(550.0, hyperspectral_path), # Green ~550nm + find_band_number(460.0, hyperspectral_path) # Blue ~460nm + ] + print(f" 根据波长选择RGB波段: R={rgb_bands[0]}, G={rgb_bands[1]}, B={rgb_bands[2]}") + except Exception as e: + print(f" 波长查找失败 ({e})，使用默认索引") + # 回退到基于索引的选择 + rgb_bands = [min(band_count-1, int(band_count*0.25)), + min(band_count-1, int(band_count*0.15)), + min(band_count-1, int(band_count*0.05))] + else: + rgb_bands = [0, 0, 0] + + # 下采样控制 - 用户反馈下采样读取会导致像素值全为0 + if downsample and (width > 2000 or height > 2000): + print(f" ⚠ 下采样暂被禁用（会导致像素值全0），使用原始分辨率: {width}x{height}") + sample_factor = 1 + target_width = width + target_height = height + else: + sample_factor = 1 + target_width = width + target_height = height + + # 只读取需要的RGB波段（性能关键优化） + rgb_data = [] + for band_idx in rgb_bands: + band = dataset.GetRasterBand(band_idx + 1) + # 直接使用完整分辨率读取，避免下采样导致像素值为0的问题 + band_data = band.ReadAsArray().astype(np.float32) + rgb_data.append(band_data) + + # 堆叠为RGB图像 (height, width, 3) + if len(rgb_data) == 3: + image_array = np.stack(rgb_data, axis=2) + else: + # 如果只有1个波段，复制为RGB + image_array = np.stack([rgb_data[0]]*3, axis=2) + + geotransform = dataset.GetGeoTransform() + projection = dataset.GetProjection() + + # 释放数据集 + dataset = None + + # 更新尺寸信息 + final_width = target_width if sample_factor > 1 else width + final_height = target_height if sample_factor > 1 else height + + print(f" 读取影像: {final_width}x{final_height}x{image_array.shape[2]} (RGB)") + if projection: + proj_type = "投影坐标系" if "PROJCS" in projection else "地理坐标系" + print(f" 影像投影: {proj_type}") + if sample_factor > 1: + print(f" 下采样因子: {sample_factor}") + + return image_array, geotransform, projection, final_width, final_height, sample_factor + + def _read_sampling_points(self, csv_path: str) -> pd.DataFrame: + """读取采样点CSV文件""" + if not Path(csv_path).exists(): + raise FileNotFoundError(f"CSV文件不存在: {csv_path}") + + df = pd.read_csv(csv_path) + + # 检查前两列是否为纬度和经度 + if len(df.columns) < 2: + raise ValueError("CSV文件至少需要两列（纬度、经度）") + + # 假设前两列是纬度和经度 + lat_col = df.columns[0] + lon_col = df.columns[1] + + # 重命名列 + df = df.rename(columns={lat_col: 'latitude', lon_col: 'longitude'}) + + # 确保数值类型 + df['latitude'] = pd.to_numeric(df['latitude'], errors='coerce') + df['longitude'] = pd.to_numeric(df['longitude'], errors='coerce') + + # 删除无效的坐标 + df = df.dropna(subset=['latitude', 'longitude']) + + print(f"读取到 {len(df)} 个采样点") + return df + + def _create_false_color_image(self, image_array: np.ndarray, + rgb_bands: Optional[List[int]] = None) -> np.ndarray: + """创建假彩色RGB图像 - 应用线性拉伸和Gamma校正""" + # 由于_read_hyperspectral已返回RGB图像，这里仅进行最终处理 + if image_array.shape[2] != 3: + # 确保是3通道 + if len(image_array.shape) == 2 or image_array.shape[2] == 1: + if len(image_array.shape) == 2: + image_array = np.stack([image_array]*3, axis=2) + else: + image_array = np.repeat(image_array, 3, axis=2) + + print(f" 处理前图像范围: R[{image_array[:,:,0].min():.3f}-{image_array[:,:,0].max():.3f}], " + f"G[{image_array[:,:,1].min():.3f}-{image_array[:,:,1].max():.3f}], " + f"B[{image_array[:,:,2].min():.3f}-{image_array[:,:,2].max():.3f}]") + + # 增强型线性拉伸 - 解决图像太暗的问题 + def simple_linear_stretch(data, min_percent=1, max_percent=99): + """增强对比度的线性拉伸""" + valid_data = data[np.isfinite(data)] + if len(valid_data) == 0: + return np.zeros_like(data, dtype=np.float32) + + # 计算百分位数，使用更激进的拉伸 (1%-99%) + p_low = np.percentile(valid_data, min_percent) + p_high = np.percentile(valid_data, max_percent) + + if p_high - p_low < 1e-8: + # 如果数据范围太小，使用最小最大值归一化 + data_min = valid_data.min() + data_max = valid_data.max() + if data_max > data_min: + stretched = (data - data_min) / (data_max - data_min) + else: + stretched = np.zeros_like(data, dtype=np.float32) + else: + stretched = (data - p_low) / (p_high - p_low) + + # 允许轻微过饱和以增加对比度 + stretched = np.clip(stretched, 0.0, 1.05) + stretched = np.clip(stretched, 0.0, 1.0) # 最终确保在[0,1] + return stretched + + # 对每个通道进行拉伸 + r_stretched = simple_linear_stretch(image_array[:, :, 0]) + g_stretched = simple_linear_stretch(image_array[:, :, 1]) + b_stretched = simple_linear_stretch(image_array[:, :, 2]) + + # 合成为RGB图像 + rgb_image = np.stack([r_stretched, g_stretched, b_stretched], axis=2) + rgb_image = np.nan_to_num(rgb_image, nan=0.0) + + # 最终确保范围在[0,1]，并轻微增强对比度 + rgb_image = np.clip(rgb_image, 0.0, 1.0) + + # 可选：Gamma校正增加亮度（解决太暗问题） + gamma = 1 # <1会增加亮度 + rgb_image = np.power(rgb_image, gamma) + + # 映射到0-255范围（uint8），这样imshow显示效果更好 + rgb_image = (rgb_image * 255).astype(np.uint8) + + print(f" 处理后图像范围: [0-255] (Gamma={gamma})") + + return rgb_image + + def _geo_to_pixel(self, sampling_points: pd.DataFrame, + geotransform: tuple, width: int, height: int, + projection: str = "", sample_factor: int = 1) -> List[Tuple[float, float]]: + """ + 使用GDAL进行地理坐标到像素坐标的投影变换 - 支持下采样 + + 原始点位坐标格式: 41.66054612 124.2208338 (WGS84地理坐标: 纬度,经度) + 高光谱影像通常使用UTM或其他投影坐标系 + 当图像下采样时，sample_factor > 1，需要相应缩放坐标 + """ + if geotransform is None or len(sampling_points) == 0: + # 如果没有地理变换信息，使用图像中心 + return [(width/2, height/2) for _ in range(len(sampling_points))] + + pixel_coords = [] + gt = geotransform + + # 检查是否需要投影转换 + needs_transform = False + if projection and ("PROJCS" in projection or "GEOGCS" in projection): + needs_transform = True + print(f" 检测到影像投影: {projection[:80]}...") + + # 创建坐标转换对象（WGS84 -> 影像投影） + transform = None + if needs_transform and GDAL_AVAILABLE: + try: + # 源坐标系: WGS84 (EPSG:4326) + src_srs = osr.SpatialReference() + src_srs.ImportFromEPSG(4326) # WGS84 + + # 目标坐标系: 影像的投影 + dst_srs = osr.SpatialReference() + dst_srs.ImportFromWkt(projection) + + # 创建坐标转换 + transform = osr.CoordinateTransformation(src_srs, dst_srs) + print(" ✓ 已创建WGS84到影像投影的坐标转换") + except Exception as e: + print(f" ⚠ 坐标转换创建失败: {e}，使用简化变换") + transform = None + + for _, row in sampling_points.iterrows(): + lon = float(row['longitude']) # 经度 (WGS84) + lat = float(row['latitude']) # 纬度 (WGS84) + + if transform is not None: + # 使用GDAL进行投影转换: (经度, 纬度) -> (投影X, 投影Y) + try: + proj_x, proj_y, _ = transform.TransformPoint(lat, lon) + # 再转换为像素坐标 + x = (proj_x - gt[0]) / gt[1] + y = (proj_y - gt[3]) / gt[5] + except Exception as e: + # 转换失败时回退到直接计算 + x = (lon - gt[0]) / gt[1] + y = (lat - gt[3]) / gt[5] + else: + # 直接使用仿射变换（坐标系一致的情况） + x = (lon - gt[0]) / gt[1] + y = (lat - gt[3]) / gt[5] + + # 如果图像进行了下采样，需要相应缩放坐标 + if sample_factor > 1: + x = x / sample_factor + y = y / sample_factor + + # 限制在图像范围内（使用下采样后的尺寸） + x = max(0, min(x, width - 1)) + y = max(0, min(y, height - 1)) + + pixel_coords.append((x, y)) + + if transform is not None: + print(f" ✓ 使用GDAL投影变换处理 {len(pixel_coords)} 个采样点") + else: + print(f" 使用直接仿射变换处理 {len(pixel_coords)} 个采样点") + + return pixel_coords + + def _create_map_visualization(self, rgb_image: np.ndarray, + pixel_coords: List[Tuple[float, float]], + sampling_points: pd.DataFrame, + output_path: str, + point_color: str, + point_size: int, + point_alpha: float, + show_north_arrow: bool, + show_scale_bar: bool, + show_legend: bool, + dpi: int, + geotransform: tuple, + width: int, + height: int, + downsample: bool = False, + projection: str = "", + sample_factor: int = 1): + """创建地图可视化 - 优化版""" + # 使用更小的figure尺寸加快渲染 + figsize = (10, 8) if self.fast_mode or downsample else (12, 10) + fig, ax = plt.subplots(figsize=figsize, dpi=100 if self.fast_mode else 150) + + # 显示假彩色图像 - 现在已经是0-255的uint8格式 + print(f" 最终图像数据范围: [{rgb_image.min()}, {rgb_image.max()}] (uint8)") + ax.imshow(rgb_image, interpolation='nearest' if self.fast_mode else 'bilinear') + + # 绘制采样点 - 优化：使用scatter代替循环plot + if pixel_coords: + x_coords = [p[0] for p in pixel_coords] + y_coords = [p[1] for p in pixel_coords] + ax.scatter(x_coords, y_coords, c=point_color, s=point_size, + alpha=point_alpha, edgecolors='white', linewidth=1.5) + + # 添加指北针 + if show_north_arrow: + self._add_north_arrow(ax, width, height, position='bottom-left', direction='down') + + # 添加比例尺 + if show_scale_bar and geotransform is not None: + self._add_scale_bar(ax, geotransform, width, height) + + # 添加图例 + if show_legend: + legend_text = f'采样点 (n={len(sampling_points)})' + ax.plot([], [], 'o', color=point_color, markersize=8, label=legend_text) + ax.legend(loc='lower right', frameon=True, facecolor='white', edgecolor='gray') + + # 设置标题和标签 + ax.set_title('高光谱影像采样点分布图', fontsize=16, fontweight='bold', pad=20) + + + # 隐藏坐标轴刻度 + ax.set_xticks([]) + ax.set_yticks([]) + + # 添加网格 + ax.grid(True, alpha=0.2, linestyle='--') + + plt.tight_layout() + + # 保存参数 - 避免传递不兼容的参数 + save_kwargs = { + 'dpi': dpi, + 'bbox_inches': 'tight', + 'pad_inches': 0.05, + 'facecolor': 'white' + } + + # 仅添加matplotlib支持的参数 + if self.fast_mode: + save_kwargs['dpi'] = min(dpi, 180) # 快速模式降低DPI + + plt.savefig(output_path, **save_kwargs) + plt.close(fig) + + def _add_north_arrow(self, ax, width: int, height: int, position='top-right', direction='down', + size=0.08, color='white', n_color='white', outline_color='black'): + """ + 添加指北针，可配置位置、方向、大小、颜色。 + + 参数： + ax: matplotlib Axes对象 + width, height: 图像宽高（用于相对定位） + position: 'top-left', 'top-right', 'bottom-left', 'bottom-right' + direction: 'up', 'down', 'left', 'right' 箭头指向 + size: 箭头长度相对于高度的比例（0.05~0.12） + color: 箭头颜色 + n_color: 'N' 文字颜色 + outline_color: 文字描边颜色 + """ + # 位置映射（偏移系数） + pos_map = { + 'top-left': (0.08, 0.88), + 'top-right': (0.92, 0.88), + 'bottom-left': (0.08, 0.12), + 'bottom-right': (0.92, 0.12), + } + arrow_x_ratio, arrow_y_ratio = pos_map.get(position, (0.92, 0.88)) + arrow_x = width * arrow_x_ratio + arrow_y = height * arrow_y_ratio + + # 方向映射（箭头终点偏移） + direction_map = { + 'up': (0, +size), + 'down': (0, -size), + 'left': (-size, 0), + 'right': (+size, 0), + } + dx, dy = direction_map.get(direction, (0, -size)) + end_x = arrow_x + dx * width # 注意：dx是比例，乘以宽度/高度保持比例一致 + end_y = arrow_y + dy * height + + # 箭头绘制 + arrow = FancyArrowPatch((arrow_x, arrow_y), (end_x, end_y), + color=color, linewidth=3, + arrowstyle='->', mutation_scale=20) + ax.add_patch(arrow) + + # N 文字位置：在箭头尾部或头部？通常放在箭头指向的反方向末端 + # 这里放在箭头尾部向外偏移一点（便于阅读） + # 偏移系数根据方向决定 + offset_scale = 0.02 # 偏移量比例 + if direction == 'up': + text_x = arrow_x + text_y = arrow_y - height * offset_scale # 放在箭头下方 + elif direction == 'down': + text_x = arrow_x + text_y = arrow_y + height * offset_scale # 放在箭头上方 + elif direction == 'left': + text_x = arrow_x + width * offset_scale + text_y = arrow_y + else: # right + text_x = arrow_x - width * offset_scale + text_y = arrow_y + + ax.text(text_x, text_y, 'N', fontsize=14, fontweight='bold', + color=n_color, ha='center', va='center', + path_effects=[path_effects.withStroke(linewidth=3, foreground=outline_color)]) + + def _add_scale_bar(self, ax, geotransform: tuple, width: int, height: int): + """添加比例尺""" + if geotransform is None: + return + + # 计算图像实际宽度（米） + pixel_size_x = abs(geotransform[1]) + image_width_meters = width * pixel_size_x + + # 选择合适的比例尺长度（图像宽度的1/4） + scale_length_m = image_width_meters / 4 + scale_length_pixels = width / 4 + + # 找到合适的刻度 + scale_options = [1000, 500, 200, 100, 50, 20, 10, 5, 2, 1] + scale_meters = next((s for s in scale_options if s <= scale_length_m), 1) + + scale_pixels = int(scale_meters / pixel_size_x) + + # 在左下角添加比例尺 + bar_x = width * 0.08 + bar_y = height * 0.92 + + # 绘制比例尺线 + ax.plot([bar_x, bar_x + scale_pixels], [bar_y, bar_y], color='white', linewidth=4) + + # 添加刻度线 + ax.plot([bar_x, bar_x], [bar_y, bar_y + 8], color='white', linewidth=2) + ax.plot([bar_x + scale_pixels, bar_x + scale_pixels], [bar_y, bar_y + 8], color='white', linewidth=2) + + # 添加文字 + ax.text(bar_x + scale_pixels/2, bar_y , f'{scale_meters} m', + fontsize=11, ha='center', va='bottom', fontweight='bold', + bbox=dict(facecolor='white', alpha=0.8, edgecolor='none', pad=1)) + + def batch_create_maps(self, hyperspectral_path: str, + csv_folder: str, + output_subdir: str = "sampling_maps", + fast_mode: bool = True) -> Dict[str, str]: + """ + 批量创建采样点地图 + + Args: + hyperspectral_path: 高光谱影像路径 + csv_folder: 包含多个CSV文件的文件夹 + output_subdir: 输出子目录 + + Returns: + 生成的地图文件路径字典 + """ + csv_folder_path = Path(csv_folder) + if not csv_folder_path.exists(): + raise FileNotFoundError(f"CSV文件夹不存在: {csv_folder}") + + # 创建输出目录 + output_dir = self.output_dir / output_subdir + output_dir.mkdir(parents=True, exist_ok=True) + + map_paths = {} + + # 查找所有CSV文件 + csv_files = list(csv_folder_path.glob("*.csv")) + + print(f"找到 {len(csv_files)} 个CSV文件，开始批量生成采样点地图... (快速模式: {fast_mode})") + + for csv_file in csv_files: + try: + output_filename = f"{Path(hyperspectral_path).stem}_{csv_file.stem}_sampling_map.png" + map_path = self.create_sampling_point_map( + hyperspectral_path=hyperspectral_path, + csv_path=str(csv_file), + output_filename=output_filename, + downsample=True, # 批量模式默认下采样 + dpi=120 if fast_mode else 200 + ) + map_paths[csv_file.name] = map_path + print(f"✓ 生成: {csv_file.name}") + + except Exception as e: + print(f"✗ 处理 {csv_file.name} 失败: {e}") + + print(f"批量生成完成，共生成 {len(map_paths)} 个采样点地图") + return map_paths + + +# 测试代码 +if __name__ == "__main__": + # 示例用法 + map_generator = SamplingPointMap(output_dir="./point_maps") + + # 测试代码已禁用，避免直接运行时出错 + map_generator_fast = SamplingPointMap(output_dir="./point_maps", fast_mode=True) + map_path = map_generator_fast.create_sampling_point_map( + hyperspectral_path=r"D:\BaiduNetdiskDownload\yaobao\result3.bsq", + csv_path=r"E:\code\WQ\pipeline_result\work_dir\4_processed_data\processed_data.csv", + downsample=True, + dpi=150 + ) + print("测试代码已注释，请通过GUI或手动调用使用。") + + print("SamplingPointMap类已创建，可以用于生成带采样点的地图。") + print("性能优化功能：") + print(" - fast_mode=True: 快速模式 (推荐用于预览)") + print(" - downsample=True: 对大影像下采样 (推荐用于>2000x2000影像)") + print(" - 使用: SamplingPointMap(fast_mode=True).create_sampling_point_map(...)") diff --git a/src/postprocessing/point_maps/result3_processed_data_sampling_map.png b/src/postprocessing/point_maps/result3_processed_data_sampling_map.png new file mode 100644 index 0000000..f59d401 Binary files /dev/null and b/src/postprocessing/point_maps/result3_processed_data_sampling_map.png differ diff --git a/src/postprocessing/report_word.py b/src/postprocessing/report_word.py new file mode 100644 index 0000000..8c8b628 --- /dev/null +++ b/src/postprocessing/report_word.py @@ -0,0 +1,1578 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +水质参数反演分析 Word 报告生成模块 +根据 visualization_reports.py 生成的图片，自动生成结构化 Word 报告 +""" + +import os +import base64 +import json +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, List, Optional, Any +from datetime import datetime +from urllib.request import Request, urlopen +from urllib.error import URLError, HTTPError +from docx import Document +from docx.shared import Inches, Pt, Cm +from docx.enum.text import WD_ALIGN_PARAGRAPH +from docx.enum.section import WD_SECTION +from docx.oxml.ns import qn +from docx.oxml import OxmlElement +from docx.shared import RGBColor +import pandas as pd + + +class _SimpleProgress: + """无依赖进度条（控制台单行刷新）。""" + + def __init__(self, total: int, desc: str = ""): + self.total = max(1, int(total)) + self.desc = desc + self.n = 0 + self._render() + + def update(self, step: int = 1): + self.n = min(self.total, self.n + int(step)) + self._render() + + def close(self): + # 换行，避免覆盖后续输出 + print() + + def _render(self): + pct = int(self.n / self.total * 100) + bar_len = 30 + filled = int(bar_len * self.n / self.total) + bar = "█" * filled + "·" * (bar_len - filled) + prefix = f"{self.desc} " if self.desc else "" + print(f"\r{prefix}[{bar}] {self.n}/{self.total} ({pct}%)", end="", flush=True) + + +@dataclass +class ReportGenerationConfig: + """ + 报告生成与 Ollama AI 分析的可选配置。 + 未设置的字段沿用环境变量（OLLAMA_*、ENABLE_AI_ANALYSIS）或生成器默认值。 + """ + ollama_base_url: Optional[str] = None + ollama_vision_model: Optional[str] = None + ollama_text_model: Optional[str] = None + ollama_timeout_s: Optional[int] = None + enable_ai_analysis: Optional[bool] = None + + +class WaterQualityReportGenerator: + """水质参数 Word 报告生成器""" + + def __init__( + self, + output_dir: str = None, + work_dir: str = None, + ai_config: Optional[ReportGenerationConfig] = None, + ): + # 设置工作目录（整个流程的核心目录，所有数据基于此） + if work_dir is None: + self.work_dir = Path("./work_dir") + else: + self.work_dir = Path(work_dir) + + # 基于工作目录设置各子目录 + self.visualization_dir = self.work_dir / "9_visualization" + + # 设置报告保存位置：默认为可视化目录（visualization_dir） + self._output_dir_is_default = output_dir is None + if output_dir is None: + self.output_dir = self.visualization_dir + else: + self.output_dir = Path(output_dir) + self.output_dir.mkdir(parents=True, exist_ok=True) + + # 设置中文字体支持 + self.chinese_font = 'SimSun' # 宋体 + self.title_font = 'SimHei' # 黑体 + self.english_font = 'Times New Roman' # 英文 + + cfg = ai_config + # Ollama：显式 ai_config 优先，否则环境变量 + default_url = os.environ.get("OLLAMA_URL", "http://localhost:11434").rstrip("/") + self.ollama_base_url = ( + cfg.ollama_base_url.rstrip("/") + if cfg and cfg.ollama_base_url + else default_url + ) + self.ollama_vision_model = ( + cfg.ollama_vision_model + if cfg and cfg.ollama_vision_model + else os.environ.get("OLLAMA_VISION_MODEL", "qwen3-vl:8b") + ) + self.ollama_text_model = ( + cfg.ollama_text_model + if cfg and cfg.ollama_text_model + else os.environ.get("OLLAMA_TEXT_MODEL", self.ollama_vision_model) + ) + self.ollama_timeout_s = ( + int(cfg.ollama_timeout_s) + if cfg and cfg.ollama_timeout_s is not None + else int(os.environ.get("OLLAMA_TIMEOUT_S", "120")) + ) + if cfg and cfg.enable_ai_analysis is not None: + self.enable_ai_analysis = bool(cfg.enable_ai_analysis) + else: + self.enable_ai_analysis = os.environ.get("ENABLE_AI_ANALYSIS", "1") not in { + "0", + "false", + "False", + } + self.ai_cache_path = self.output_dir / "ollama_image_analyses_cache.json" + + # 各参数的专业描述（完整版） + self.parameter_descriptions = { + "Chlorophyll": """叶绿素（Chlorophyll）是浮游植物进行光合作用的关键色素，直接反映水体中藻类的生物量与初级生产力水平。它是评价水体富营养化程度最常用的指标之一。当叶绿素浓度持续升高时，表明藻类大量增殖，水华风险显著增加，并可能引发溶解氧剧烈波动、水体透明度下降及底栖生态系统退化。因此，通过遥感手段反演叶绿素浓度，可为水华预警、水质改善及生态修复提供重要科学依据。""", + + "COD": """化学需氧量（COD）是衡量水体中有机污染物含量的综合指标，反映单位体积水体中还原性物质（主要是有机物）被氧化所消耗的氧化剂总量。COD值越高，表明水体受有机污染越严重。高COD会加剧溶解氧消耗，导致水体缺氧、水生生物死亡，甚至引发黑臭现象。COD也是污水处理效果和污染物排放管控的关键考核指标，其时空分布可为污染源识别与治理提供直接依据。""", + + "DO": """溶解氧（DO）是维持水生生态系统健康的基础物质，指溶解在水中的分子态氧。其浓度受水温、盐度、藻类光合作用及有机物耗氧过程共同调控。DO低于一定阈值会导致水生生物窒息、底泥营养盐释放及水体自净能力下降。DO的实时监测与空间分布反演，对判断水体污染程度、预警鱼类死亡事件及评估生态修复成效具有重要价值。""", + + "PH": """pH值是反映水体酸碱度的无量纲参数，直接影响水中化学形态、微生物活性和水生生物的生理代谢。天然水体pH值一般介于6.5～8.5之间，当pH值过低（酸化）或过高（碱化）时，会破坏水生生态平衡，加速重金属溶出，对鱼类鳃组织及藻类群落造成胁迫。pH的时空变化可用于识别酸性废水排放、藻类暴发过程以及水化学环境的稳定性评估。""", + + "Temperature": """水温（Temperature）是水体物理特性的基本参数，控制着溶解氧饱和度、化学反应速率及生物代谢强度。水温异常升高（如热污染）或昼夜温差剧烈波动，会影响鱼类洄游、藻类生长节律及底泥污染物释放。水温也是水文模型与水质模型的关键输入变量，其卫星遥感反演为大型水体热状况监测提供了高效手段。""", + + "spCond": """电导率（spCond）表征水体传导电流的能力，与溶解性离子总浓度密切相关。它常用于指示水体矿化度、盐度以及受工业废水、生活污水或农业径流污染的程度。电导率的快速变化往往预示着外源污染输入或海水入侵，是水质常规监测中重要的物理参数，其空间分布图可为污染源追踪提供直观线索。""", + + "Turbidity": """浊度（Turbidity）反映水体中悬浮颗粒物（如泥沙、藻类、微生物）对光线的散射程度，是衡量水体透明度的关键指标。浊度升高不仅影响水生植物光合作用，还会为病原微生物提供附着载体，干扰水处理工艺。通过遥感影像反演浊度，可实现大范围、高频次的水体清澈度评价，对饮用水源地保护和河流泥沙输送研究具有重要意义。""", + + "TDS": """总溶解固体（TDS）指水中溶解性无机盐和部分有机物的总质量，与水的适口性、管道腐蚀风险及灌溉适宜性密切相关。TDS过高会导致水味苦涩，并可能伴随有害微量元素积累。在咸潮入侵、工业排放及农业面源污染研究中，TDS是评价水质变化的稳定指标，其反演结果有助于识别淡水咸化区域及制定取水策略。""", + + "Cl-": """氯离子（Cl-）是天然水体中最稳定存在的阴离子之一，其来源包括岩石风化、海水侵入、工业废水及生活污水。氯离子含量升高可指示水体受咸潮或污染输入的影响，且在高浓度下会腐蚀管道、影响农业土壤结构。在饮用水消毒过程中，氯离子与有机物可能生成三氯甲烷等消毒副产物，因此其监测对水厂运行和水安全有重要警示作用。""", + + "NO3-N": """硝酸盐氮（NO3-N）是氮循环中氧化程度最高的形态，易溶于水，常通过农田径流、化粪池渗漏或工业废水进入水体。过量硝酸盐会刺激藻类过度生长，加速水体富营养化；饮用水中硝酸盐氮浓度超标会引发“蓝婴症”（高铁血红蛋白血症），对婴幼儿健康构成威胁。因此，硝酸盐氮是水质评价与饮用水安全监管的重点指标。""", + + "NH3-N": """氨氮（NH3-N）是水体受有机污染初期的重要指示物，主要来源于生活污水、农业化肥及工业含氮废水。氨氮对鱼类等水生生物有较强的毒性，且在好氧条件下会消耗大量溶解氧转化为硝酸盐。氨氮浓度高往往反映近期污染输入或水体自净能力不足，其动态变化可用于预警突发性污染事件和评估生态修复效果。""", + + "BGA": """BGA（蓝绿藻，即蓝藻）是表征水体蓝藻生物量的关键生物参数，通常通过藻蓝蛋白等特征色素反演获得。蓝藻过量繁殖（水华）会释放藻毒素、消耗溶解氧、形成水面覆盖层，严重威胁饮用水安全和水生态系统健康。BGA浓度的空间分布能精准指示水华高发区域与迁移路径，是水华预警、蓝藻治理和生态修复措施制定不可或缺的输入信息。""", + + "TT": """总氮（TT）是水体中有机氮、氨氮、硝酸盐氮、亚硝酸盐氮等各种形态氮的总和，综合反映了水体的氮营养水平。总氮是导致水体富营养化的主要限制因子之一，其浓度过高会引发藻类爆发、透明度下降、水质恶化。总氮的时空变化趋势可用于判断流域面源污染强度、评估氮减排措施成效，是水质管理和流域水环境保护的关键参考指标。""" + } + + # 每个参数对应的图片顺序（统一5张图模式） + self.parameter_images = { + "Chlorophyll": [ + "Chlorophyll_histogram.png", + "Chlorophyll_spectrum_comparison.png", + "Chlorophyll_scatter_with_confidence.png", + "Chlorophyll_boxplot.png", + "Chlorophyll_distribution.png" + ], + "COD": [ + "COD_histogram.png", + "COD_spectrum_comparison.png", + "COD_scatter_with_confidence.png", + "COD_boxplot.png", + "COD_distribution.png" + ], + "DO": [ + "DO_histogram.png", + "DO_spectrum_comparison.png", + "DO_scatter_with_confidence.png", + "DO_boxplot.png", + "DO_distribution.png" + ], + "PH": [ + "PH_histogram.png", + "PH_spectrum_comparison.png", + "PH_scatter_with_confidence.png", + "PH_boxplot.png", + "PH_distribution.png" + ], + "Temperature": [ + "Temperature_histogram.png", + "Temperature_spectrum_comparison.png", + "Temperature_scatter_with_confidence.png", + "Temperature_boxplot.png", + "Temperature_distribution.png" + ], + "spCond": [ + "spCond_histogram.png", + "spCond_spectrum_comparison.png", + "spCond_scatter_with_confidence.png", + "spCond_boxplot.png", + "spCond_distribution.png" + ], + "Turbidity": [ + "Turbidity_histogram.png", + "Turbidity_spectrum_comparison.png", + "Turbidity_scatter_with_confidence.png", + "Turbidity_boxplot.png", + "Turbidity_distribution.png" + ], + "TDS": [ + "TDS_histogram.png", + "TDS_spectrum_comparison.png", + "TDS_scatter_with_confidence.png", + "TDS_boxplot.png", + "TDS_distribution.png" + ], + "Cl-": [ + "Cl_histogram.png", + "Cl_spectrum_comparison.png", + "Cl_scatter_with_confidence.png", + "Cl_boxplot.png", + "Cl_distribution.png" + ], + "NO3-N": [ + "NO3_N_histogram.png", + "NO3_N_spectrum_comparison.png", + "NO3_N_scatter_with_confidence.png", + "NO3_N_boxplot.png", + "NO3_N_distribution.png" + ], + "NH3-N": [ + "NH3_N_histogram.png", + "NH3_N_spectrum_comparison.png", + "NH3_N_scatter_with_confidence.png", + "NH3_N_boxplot.png", + "NH3_N_distribution.png" + ], + "BGA": [ + "BGA_histogram.png", + "BGA_spectrum_comparison.png", + "BGA_scatter_with_confidence.png", + "BGA_boxplot.png", + "BGA_distribution.png" + ], + "TT": [ + "TT_histogram.png", + "TT_spectrum_comparison.png", + "TT_scatter_with_confidence.png", + "TT_boxplot.png", + "TT_distribution.png" + ] + } + + def apply_ai_config(self, ai_config: ReportGenerationConfig) -> None: + """在已创建的生成器上更新 AI 相关设置（下次 _ollama_chat 生效）。""" + cfg = ai_config + if cfg.ollama_base_url: + self.ollama_base_url = cfg.ollama_base_url.rstrip("/") + if cfg.ollama_vision_model: + self.ollama_vision_model = cfg.ollama_vision_model + if cfg.ollama_text_model: + self.ollama_text_model = cfg.ollama_text_model + if cfg.ollama_timeout_s is not None: + self.ollama_timeout_s = int(cfg.ollama_timeout_s) + if cfg.enable_ai_analysis is not None: + self.enable_ai_analysis = bool(cfg.enable_ai_analysis) + + def _style_heading(self, heading, level: int): + """统一一级/二级/三级标题字体（黑体）与字号。""" + size_map = {1: Pt(16), 2: Pt(14), 3: Pt(12)} + for run in heading.runs: + run.font.name = self.title_font + run.font.bold = True + if level in size_map: + run.font.size = size_map[level] + run._element.rPr.rFonts.set(qn('w:eastAsia'), self.title_font) + + def _load_ai_cache(self) -> Dict[str, Any]: + if not self.ai_cache_path.exists(): + return {} + try: + with open(self.ai_cache_path, "r", encoding="utf-8") as f: + return json.load(f) + except Exception: + return {} + + def _save_ai_cache(self, cache: Dict[str, Any]) -> None: + try: + with open(self.ai_cache_path, "w", encoding="utf-8") as f: + json.dump(cache, f, ensure_ascii=False, indent=2) + except Exception: + pass + + def _ollama_chat(self, model: str, system_prompt: str, user_prompt: str, image_path: Optional[Path] = None) -> str: + """调用 Ollama /api/chat。image_path 传入时进行视觉分析。""" + payload: Dict[str, Any] = { + "model": model, + "stream": False, + "messages": [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt}, + ], + } + + if image_path is not None: + try: + img_b64 = base64.b64encode(image_path.read_bytes()).decode("utf-8") + payload["messages"][-1]["images"] = [img_b64] + except Exception as e: + return f"（读取图片失败：{e}）" + + data = json.dumps(payload, ensure_ascii=False).encode("utf-8") + req = Request( + url=f"{self.ollama_base_url}/api/chat", + data=data, + headers={"Content-Type": "application/json"}, + method="POST", + ) + + try: + with urlopen(req, timeout=self.ollama_timeout_s) as resp: + raw = resp.read().decode("utf-8", errors="ignore") + obj = json.loads(raw) + return (obj.get("message") or {}).get("content", "").strip() or "（模型未返回内容）" + except (HTTPError, URLError, TimeoutError) as e: + return f"（Ollama调用失败：{e}）" + except Exception as e: + return f"（Ollama解析失败：{e}）" + + def _get_prompt_for_image(self, image_type: str, param: str, figure_num: int) -> Dict[str, str]: + """按图片类型返回 system/user 提示词，带防幻觉约束。""" + system = ( + "你是一位水质遥感与机器学习建模专家。\n" + "研究背景：我们利用高光谱影像数据，结合机器学习算法对研究区的水质参数进行了空间反演，并生成了以下图表。" + "现需要撰写自动化分析报告，请严格按照“图表类型→分析重点”的对应关系进行描述。\n\n" + "分析要求：\n" + "1. 请严格基于图片中可见信息进行分析，禁止编造不存在的数值、区域名称、采样时间或结论。\n" + "2. 如果图片无法支撑某项判断，必须明确写“根据本图无法判断”。\n" + "3. 不允许引用图片之外的背景知识来补全细节。" + ) + + # 为每种图表类型单独定义：分析要点 + 结论聚焦 + type_specs = { + "histogram": { + "analysis": ( + "分析要点：\n" + "- 分布形态：是左偏、右偏还是对称？是否存在多峰？\n" + "- 集中范围：数据主要集中在哪个区间？（参照横轴和纵轴柱高）\n" + "- 离群值：是否有明显孤立于主体分布的小柱，位于何处？\n" + "- 若图中包含拟合曲线，描述其形状（正态、指数等）。" + ), + "conclusion": ( + "结论应聚焦于：该参数的分布形态（如左偏/右偏/对称）、主要集中区间、是否存在极端离群值。" + "用一句话概括数据分布的核心特征，不推测成因。" + ), + }, + "spectrum_comparison": { + "analysis": ( + "分析要点：\n" + "- 多条曲线的整体趋势是否一致？\n" + "- 在哪些波段（参照横轴波长位置）出现明显分离？\n" + "- 是否存在系统性的整体偏移（一条曲线全程高于另一条）？\n" + "- 图中是否有阴影或误差带表示置信区间？若有，描述其范围。" + ), + "conclusion": ( + "结论应聚焦于：各光谱曲线的整体一致性、关键差异波段、是否存在系统性偏移。" + "用一句话概括光谱对比的主要特征，不推测物理原因。" + ), + }, + "scatter_with_confidence": { + "analysis": ( + "分析要点：\n" + "- 点云整体是否沿1:1线（对角线）分布？\n" + "- 点云在低值区/高值区是否存在系统性偏离（如整体偏上/偏下）？\n" + "- 置信带（若存在）覆盖了多少点？是否所有点都在置信带内？\n" + "- 是否有明显离群点（远离主体点云）？" + ), + "conclusion": ( + "结论应聚焦于：模型预测精度（点云与1:1线贴合程度）、偏差方向、置信带覆盖情况。" + "用一句话评价模型性能，不推测误差来源。" + ), + }, + "boxplot": { + "analysis": ( + "分析要点：\n" + "- 中位数（箱体中间线）的位置。\n" + "- 四分位间距（箱体高度）反映的离散程度。\n" + "- 须（whisker）的长度，是否超出1.5倍IQR的离群点（用圆点/星号标示）。\n" + "- 若多个箱线图并排，比较各组的中心趋势和离散度。" + ), + "conclusion": ( + "结论应聚焦于：各组的中心趋势（中位数）、离散程度（四分位距）、是否存在离群点。" + "用一句话概括数据分布的统计特征，若有多组则简述对比。" + ), + }, + "distribution": { + "analysis": ( + "分析要点：\n" + "- 高值区域：位于图中的哪个方位（如东北部、中部偏西、东南沿岸等）？呈现何种形状（斑块状、条带状、片状）？\n" + "- 低值区域：位置及形态。\n" + "- 梯度变化：是否存在明显的从某方位向另一方位递减或递增的趋势？\n" + "- 聚集特征：高值区是否成片聚集，还是零星散布？\n" + "注意：仅使用方位描述位置（如上、下、左、右、中心、边缘、沿岸等），禁止使用具体经纬度坐标或地名。" + ), + "conclusion": ( + "结论应聚焦于：高值区与低值区的空间方位、聚集形态、主要梯度方向。" + "用一句话概括空间分布格局，不推测污染源或成因。" + ), + }, + "correlation_heatmap": { + "analysis": ( + "分析要点：\n" + "- 各变量对之间的相关性强度：颜色深浅对应的相关系数大小（参照图例）。\n" + "- 正相关与负相关：红色/蓝色分别代表正负（根据图例），描述主要的高正相关对和高负相关对。\n" + "- 若图中包含数值标注，可提及范围（如“大多数相关系数介于0.6~0.8”），但不得编造具体数字。\n" + "- 若单元格颜色过于接近难以区分，则写“根据本图无法判断具体相关性强弱”。" + ), + "conclusion": ( + "结论应聚焦于：变量间相关性的整体强弱水平、最主要的正负相关对。" + "用一句话概括相关性矩阵的核心特征，不推测因果关系。" + ), + }, + } + + # 默认规格（如果类型未定义） + default_spec = { + "analysis": "重点：概括图中主要信息，列出可见的轴标签、图例、数据特征。", + "conclusion": "结论应基于可见信息，概括图中主要趋势或数据特征，不添加外部知识。", + } + + spec = type_specs.get(image_type, default_spec) + analysis_part = spec["analysis"] + conclusion_part = spec["conclusion"] + + common_output = ( + "输出格式：\n" + "请结合坐标轴、图例、曲线、点云、颜色条等可见元素，描述数据特征（如分布形态、对比关系、空间位置等），引用图中具体元素但不编造数值。" + "随后用一句话总结该图揭示的主要趋势或数据质量。总结必须严格基于前文描述的可见信息，不得引入图中未呈现的外部知识、推测原因或隐含假设。" + "若信息不足以得出明确结论，则写“根据本图无法得出明确结论”。" + "要求：直接输出分析内容，不要使用“第一段”“第二段”等标记，两段之间不要留空行。") + + user = ( + f"图号：图{figure_num}\n" + f"参数：{param}\n" + f"图类型：{image_type}\n\n" + f"{analysis_part}\n\n" + f"{common_output}" + ) + return {"system": system, "user": user} + + + + def _style_figure_caption_simsun_xiaosi(self, paragraph): + """图题格式：宋体、小四（12pt），中英文均设 eastAsia 为宋体。""" + for run in paragraph.runs: + run.font.name = self.chinese_font + run.font.size = Pt(12) + rPr = run._element.get_or_add_rPr() + rPr.rFonts.set(qn('w:eastAsia'), self.chinese_font) + + def _add_image_with_caption(self, doc: Document, image_path: str, caption: str, width=Inches(5.5)): + """ + 统一插入图像并添加图题，确保图像和图题在同一页 + + Args: + doc: Word文档对象 + image_path: 图像文件路径 + caption: 图题文字（如 "图3-1 航线规划"） + width: 图像宽度 + """ + try: + # 创建图像段落 + img_paragraph = doc.add_paragraph() + img_paragraph.alignment = WD_ALIGN_PARAGRAPH.CENTER + # 设置段落不分页，与下一段（图题）保持在一起 + img_paragraph.paragraph_format.keep_with_next = True + img_paragraph.paragraph_format.keep_together = True + img_paragraph.paragraph_format.space_after = Pt(6) # 图像后小间距 + + # 插入图像 + run = img_paragraph.add_run() + run.add_picture(str(image_path), width=width) + + # 创建图题段落（宋体小四） + caption_para = doc.add_paragraph(caption, style='Caption') + caption_para.alignment = WD_ALIGN_PARAGRAPH.CENTER + self._style_figure_caption_simsun_xiaosi(caption_para) + # 设置图题段落与上一段（图像）保持在一起 + caption_para.paragraph_format.keep_with_next = False + caption_para.paragraph_format.keep_together = True + caption_para.paragraph_format.space_before = Pt(0) + caption_para.paragraph_format.space_after = Pt(12) + + return True + except Exception as e: + doc.add_paragraph(f"[无法插入图像: {e}]") + return False + + def _add_ai_analysis_paragraph(self, doc: Document, analysis_text: str): + """在 Word 中插入 AI 分析段落（图片后）。""" + # 清理文本：去除段落标记和多余空行 + cleaned_text = analysis_text.strip() + # 去除"第一段："和"第二段："标记 + cleaned_text = cleaned_text.replace("第一段：", "").replace("第二段：", "") + # 去除连续多个换行，替换为单个空格 + import re + cleaned_text = re.sub(r'\n+', ' ', cleaned_text) + # 去除连续多个空格，替换为单个空格 + cleaned_text = re.sub(r'\s+', ' ', cleaned_text).strip() + + p = doc.add_paragraph() + p.paragraph_format.first_line_indent = Pt(24) + p.paragraph_format.line_spacing = 1.5 + p.paragraph_format.space_after = Pt(12) # 新增：段后间距与正文一致 + run1 = p.add_run() + run1.font.name = self.chinese_font + run1.font.bold = True + run1.font.size = Pt(12) # 修改：从 Pt(11) 改为 Pt(12) + run1._element.rPr.rFonts.set(qn('w:eastAsia'), self.chinese_font) + run2 = p.add_run(analysis_text.strip()) + run2.font.name = self.chinese_font + run2.font.size = Pt(12) # 修改：从 Pt(11) 改为 Pt(12) + run2._element.rPr.rFonts.set(qn('w:eastAsia'), self.chinese_font) + + def _analyze_and_cache_image(self, image_path: Path, image_type: str, param: str, figure_num: int) -> str: + """分析单张图片并缓存，失败则返回可展示的提示文本。""" + if not self.enable_ai_analysis: + return "（AI分析已关闭）" + if not image_path.exists(): + return "（图片不存在，无法分析）" + + cache = self._load_ai_cache() + cache_key = f"{image_path.name}::{image_path.stat().st_mtime_ns}::{self.ollama_vision_model}::{image_type}" + if cache_key in cache: + return str(cache[cache_key]) + + prompts = self._get_prompt_for_image(image_type=image_type, param=param, figure_num=figure_num) + text = self._ollama_chat( + model=self.ollama_vision_model, + system_prompt=prompts["system"], + user_prompt=prompts["user"], + image_path=image_path, + ) + cache[cache_key] = text + self._save_ai_cache(cache) + return text + + def _create_progress(self, total: int, desc: str = "进度"): + """创建进度条：优先 tqdm，否则使用简单进度条。""" + try: + from tqdm import tqdm # type: ignore + return tqdm(total=total, desc=desc, unit="步", ncols=90) + except Exception: + return _SimpleProgress(total=total, desc=desc) + + def _analyze_statistics(self, stats_data: List[Dict[str, Any]], param_names: List[str]) -> str: + """对水质参数统计数据进行 AI 分析""" + if not self.enable_ai_analysis: + return "（AI分析已关闭）" + + # 构造统计数据文本 + stats_text = "水质参数统计摘要：\n" + for stat in stats_data: + stats_text += f"- {stat['参数']}: 点位数={stat['点位数']}, 范围=[{stat['最小值']}, {stat['最大值']}], 均值={stat['平均值']}, 标准差={stat['标准差']}\n" + + system = """你是一位水质遥感与统计分析专家。 + 请基于提供的统计数据，给出专业分析： + 1. 识别哪些参数变异程度较高（标准差大） + 2. 识别哪些参数数值范围异常 + 3. 评估数据质量和分布特征 + 4. 禁止编造数据外的信息""" + + user = f"""以下是水质参数的统计数据，请给出100-200字的专业分析： + {stats_text} + + 输出格式：数据特征分析（变异程度、数值范围等）结论与数据质量评估""" + + return self._ollama_chat(self.ollama_text_model, system, user, image_path=None) + + + def generate_report(self, + work_dir: str = None, + parameters: List[str] = None, + report_title: str = "水质参数反演分析报告", + output_path: Optional[str] = None) -> str: + """ + 生成 Word 报告 - 所有数据均来自工作目录（work_dir） + 可视化图片、统计数据等均从 work_dir/9_visualization 和 work_dir/4_processed_data 中读取 + """ + # 设置工作目录（整个流程的核心） + if work_dir is not None: + self.work_dir = Path(work_dir) + self.visualization_dir = self.work_dir / "9_visualization" + if getattr(self, "_output_dir_is_default", False): + self.output_dir = self.visualization_dir + self.output_dir.mkdir(parents=True, exist_ok=True) + self.ai_cache_path = self.output_dir / "ollama_image_analyses_cache.json" + + if parameters is None: + parameters = ["Chlorophyll", "COD", "DO", "PH", "Temperature", + "spCond", "Turbidity", "TDS", "Cl-", "NO3-N", + "NH3-N", "BGA", "TT"] + + vis_dir = self.visualization_dir + + if not vis_dir.exists(): + raise FileNotFoundError(f"可视化目录不存在: {vis_dir}") + + if output_path is None: + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + output_path = self.output_dir / f"水质参数反演分析报告_{timestamp}.docx" + else: + output_path = Path(output_path) + + # 进度条（按“图片处理 + 汇总”计步） + total_images = sum(len(self.parameter_images.get(p, [])) for p in parameters) + total_steps = total_images + 1 + 1 # +1 相关性热力图（尝试一次），+1 综合总结 + progress = self._create_progress(total=total_steps, desc="生成Word报告") + + # 创建文档 + doc = Document() + + # 设置页面 + section = doc.sections[0] + section.page_width = Cm(21) + section.page_height = Cm(29.7) + section.left_margin = Cm(2.5) + section.right_margin = Cm(2.5) + section.top_margin = Cm(2.5) + section.bottom_margin = Cm(2.5) + + try: + # 添加封面页 + self._add_cover_page(doc) + self._add_company_description_page(doc) + self._add_data_acquisition_section(doc) + self._add_data_processing_section(doc) + + # 全文图片分析结果收集（用于末尾汇总） + all_image_analyses: List[Dict[str, Any]] = [] + + # 结果分析（含热力图）：返回更新后的图号计数 + figure_counter = 1 + figure_counter = self._add_result_analysis_section( + doc, vis_dir, figure_counter, all_image_analyses, progress=progress + ) + + # 设置页眉和页码（从正文开始） + self._setup_header_and_footer(section) + + # 按参数生成内容（带编号）：参数章节从 5 开始编号 + base_section_num = 5 + last_param_section_num = base_section_num + len(parameters) - 1 + for section_num, param in enumerate(parameters, base_section_num): + self._add_parameter_section( + doc, + param, + vis_dir, + section_num, + figure_counter, + all_image_analyses, + progress=progress, + ) + figure_counter += len(self.parameter_images.get(param, [])) + if section_num != last_param_section_num: + doc.add_page_break() + + # 汇总总结（放在所有图片/参数之后） + doc.add_page_break() + summary_section_num = base_section_num + len(parameters) + summary_heading = doc.add_heading(f"{summary_section_num} 综合分析总结", level=1) + self._style_heading(summary_heading, level=1) + + if self.enable_ai_analysis and all_image_analyses: + analyses_text = "\n\n".join( + [ + f"图{a.get('figure_num')}（{a.get('param')} / {a.get('image_type')} / {a.get('image_name')}）\n{a.get('analysis')}" + for a in all_image_analyses + ] + ) + system = ( + "你是一位水质遥感与报告撰写专家。" + "只能基于提供的“逐图分析文本”做总结，禁止引入任何外部事实或猜测。" + "若信息不足，必须明确说明“根据现有分析无法判断”。" + ) + user = ( + "以下是逐图分析文本，请给出报告级别的综合总结，要求：\n" + "- 150~300字中文\n" + "- 结构：总体概况 / 主要异常或热点 / 参数间关系（如有）/ 建议关注点\n" + "- 不要编造具体数值、地名、日期\n\n" + f"{analyses_text}" + ) + summary_text = self._ollama_chat(self.ollama_text_model, system, user, image_path=None) + para = doc.add_paragraph(summary_text) + para.paragraph_format.first_line_indent = Pt(24) + para.paragraph_format.line_spacing = 1.5 + for run in para.runs: + run.font.name = self.chinese_font + run.font.size = Pt(12) + run._element.rPr.rFonts.set(qn('w:eastAsia'), self.chinese_font) + else: + doc.add_paragraph("（未启用AI分析或无可用分析文本，无法生成综合总结。）") + + # 综合总结完成，进度 +1 + try: + progress.update(1) + except Exception: + pass + finally: + try: + progress.close() + except Exception: + pass + + # 保存文档 + doc.save(str(output_path)) + print(f"✅ Word报告生成完成: {output_path}") + + return str(output_path) + + def _add_parameter_section( + self, + doc, + param: str, + vis_dir: Path, + param_index: int = 1, + start_figure_num: int = 1, + all_image_analyses: Optional[List[Dict[str, Any]]] = None, + progress=None, + ): + """为单个参数添加报告章节（带编号和规范中英文图题）""" + if param not in self.parameter_descriptions: + print(f"警告: 参数 {param} 没有预定义的描述") + return + + # 添加带编号的参数标题 + heading = doc.add_heading(f"{param_index}. {param} 参数分析", level=1) + self._style_heading(heading, level=1) + + # 添加参数描述 + desc_para = doc.add_paragraph(self.parameter_descriptions[param]) + desc_para.paragraph_format.space_after = Pt(12) + + # 设置首行缩进两个字符（中文排版规范） + desc_para.paragraph_format.first_line_indent = Pt(24) + + # 设置正文样式：宋体小四，1.5倍行距 + desc_para.paragraph_format.line_spacing = 1.5 + for run in desc_para.runs: + run.font.name = 'SimSun' + run.font.size = Pt(12) + run._element.rPr.rFonts.set(qn('w:eastAsia'), 'SimSun') + + # 添加图片 - 支持子文件夹结构 + 中英文图题 + if param in self.parameter_images: + image_list = self.parameter_images[param] + title_map = { + "histogram": "直方图", + "spectrum_comparison": "光谱对比图", + "scatter_with_confidence": "模型散点图", + "boxplot": "箱型图", + "distribution": "分布图" + } + + for i, img_name in enumerate(image_list): + figure_num = start_figure_num + i + + # 选择子文件夹 + if "boxplot" in img_name.lower(): + sub_dir = vis_dir / "boxplots" + title_key = "boxplot" + elif "scatter" in img_name.lower() or "confidence" in img_name.lower(): + sub_dir = vis_dir / "scatter_plots" + title_key = "scatter_with_confidence" + elif "histogram" in img_name.lower(): + sub_dir = vis_dir + title_key = "histogram" + elif "spectrum" in img_name.lower(): + sub_dir = vis_dir + title_key = "spectrum_comparison" + elif "distribution" in img_name.lower(): + sub_dir = vis_dir + title_key = "distribution" + else: + sub_dir = vis_dir + title_key = "histogram" + + img_path = sub_dir / img_name + if not img_path.exists(): + img_path = vis_dir / img_name + + if img_path.exists(): + param_cn = param.replace("Chlorophyll", "叶绿素").replace("NO3-N", "硝酸盐氮").replace("NH3-N", "氨氮") + cn_title = title_map.get(title_key, "分析图") + + # 使用统一的图像插入方法（中文图题） + caption_text = f"图{figure_num} {param_cn}{cn_title}" + self._add_image_with_caption(doc, str(img_path), caption_text, width=Inches(6.0)) + + # 添加英文图题：宋体小四（与中文图题一致） + en_title = title_key.replace('_', ' ').title() + caption_en = doc.add_paragraph(f"Figure {figure_num} {param} {en_title}") + caption_en.style = 'Caption' + caption_en.alignment = WD_ALIGN_PARAGRAPH.CENTER + caption_en.paragraph_format.space_after = Pt(8) + self._style_figure_caption_simsun_xiaosi(caption_en) + for run in caption_en.runs: + run.font.color.rgb = RGBColor(0, 0, 0) + + # AI 分析：插入在图题之后 + analysis_text = self._analyze_and_cache_image( + image_path=img_path, + image_type=title_key, + param=param, + figure_num=figure_num, + ) + self._add_ai_analysis_paragraph(doc, analysis_text) + if all_image_analyses is not None: + all_image_analyses.append( + { + "figure_num": figure_num, + "param": param, + "image_type": title_key, + "image_name": img_name, + "analysis": analysis_text, + } + ) + else: + error_para = doc.add_paragraph(f"[图片未找到: {img_name} (已搜索根目录和对应子文件夹)]") + error_para.paragraph_format.first_line_indent = Pt(0) + for run in error_para.runs: + run.font.name = self.chinese_font + if hasattr(run._element, 'rPr'): + run._element.rPr.rFonts.set(qn('w:eastAsia'), self.chinese_font) + + # 每处理完一张图（无论成功/失败）更新进度条 + try: + if progress is not None: + progress.update(1) + except Exception: + pass + + doc.add_paragraph() # 章节结束空行 + + def _add_cover_page(self, doc): + """添加专业的封面页 - 优化后的布局""" + section = doc.sections[-1] + section.different_first_page_header_footer = True + + # 1. 左上角图片（增大） - 使用相对路径 + cover_top_img_path = Path(__file__).parent.parent.parent / "data" / "icons" / "word" / "lica.png" + if cover_top_img_path.exists(): + try: + p = doc.add_paragraph() + p.alignment = WD_ALIGN_PARAGRAPH.LEFT + p.add_run().add_picture(str(cover_top_img_path), width=Inches(3.2)) + except Exception as e: + print(f"封面顶部图片加载失败: {e}") + pass + + # 增加一些顶部空间 + for _ in range(6): + doc.add_paragraph() + + # 2. 主标题 - 增大字体 + title = doc.add_heading("无人机高光谱水质参数分析报告", level=0) + title.alignment = WD_ALIGN_PARAGRAPH.CENTER + for run in title.runs: + run.font.name = self.title_font + run.font.size = Pt(36) # 增大标题字体 + run._element.rPr.rFonts.set(qn('w:eastAsia'), self.title_font) + + # 3. 公司名称和日期 - 紧挨着放在底部图片上方 + doc.add_paragraph() # 小间隔 + + for _ in range(6): + doc.add_paragraph() + + company = doc.add_paragraph("北京理加联合科技有限公司") + company.alignment = WD_ALIGN_PARAGRAPH.CENTER + for run in company.runs: + run.font.name = self.chinese_font + run.font.size = Pt(18) + run.font.bold = True # 加粗 + run._element.rPr.rFonts.set(qn('w:eastAsia'), self.chinese_font) + + # 日期紧挨着公司名称下方 + date_str = datetime.now().strftime("%Y年%m月%d日") + date_para = doc.add_paragraph(date_str) + date_para.alignment = WD_ALIGN_PARAGRAPH.CENTER + for run in date_para.runs: + run.font.name = self.chinese_font + run.font.size = Pt(14) + run.font.bold = True # 加粗 + run._element.rPr.rFonts.set(qn('w:eastAsia'), self.chinese_font) + + + + # 4. 底部图片（增大） - 使用相对路径 + cover_bottom_img_path = Path(__file__).parent.parent.parent / "data" / "icons" / "word" / "fenmian.png" + if cover_bottom_img_path.exists(): + try: + p = doc.add_paragraph() + p.alignment = WD_ALIGN_PARAGRAPH.CENTER + p.add_run().add_picture(str(cover_bottom_img_path), width=Inches(5.8)) + except Exception as e: + print(f"封面底部图片加载失败: {e}") + pass + + def _add_company_description_page(self, doc): + """添加公司描述页，每个自然段均首行缩进2字符（24磅）""" + h = doc.add_heading("1 公司简介", level=1) + self._style_heading(h, level=1) + + # 公司描述原始文本（使用三引号保留换行） + company_text = """北京理加联合科技有限公司成立于2005年，总部位于北京光华创业园，在深圳、西安设有办事处。公司专注于生态环境仪器的自主研发与技术服务，致力于为国内用户提供全球领先的稳定性同位素、痕量气体、高光谱成像、环境空气质量及大气颗粒物监测等测量设备。 +作为英国ASD、美国Resonon、美国Campbell、法国AMS等多家国际知名品牌的中国区代理商与技术服务中心，理加联合同时拥有一支经验丰富的研发团队，已获得20余项实用新型专利。自主研发产品包括LI-2100全自动真空冷凝抽提系统、SF-3500系列土壤气体通量自动测量系统、PS-9000便携式土壤碳通量自动测量系统等，广泛应用于生态、环境、农业等领域。 +公司设有ASD和Resonon产品的定标实验室，显著提升定标效率、降低用户成本。2018年通过ISO9001质量管理体系认证，售后服务团队定期赴原厂培训。理加联合已参与“211”工程、“985”工程及中国生态系统研究网络（CERN）等重大科研项目，以专业技术与完善售后赢得广泛市场认可。""" + + # 按换行符分割成独立段落，并过滤可能的空行 + company_paragraphs = [p.strip() for p in company_text.split('\n') if p.strip()] + for para_text in company_paragraphs: + para = doc.add_paragraph(para_text) + para.paragraph_format.first_line_indent = Pt(24) # 首行缩进2字符（约24磅） + + # 设置正文样式：宋体小四，1.5倍行距 + para.paragraph_format.line_spacing = 1.5 + for run in para.runs: + run.font.name = 'SimSun' + run.font.size = Pt(12) + run._element.rPr.rFonts.set(qn('w:eastAsia'), 'SimSun') + + for _ in range(5): + doc.add_paragraph() + + # 联系方式信息（同样按行分割，每行独立且首行缩进） + contact_info = """地址：北京市海淀区安宁庄东路18号光华创业园5号楼（生产研发）光华创业园科研楼四层 +电话：13910499761 13910124070 010-51292601 +传真：010-82899770-8014 +邮箱：info@li-ca.com +邮编：100085""" + + contact_lines = [line.strip() for line in contact_info.split('\n') if line.strip()] + for line in contact_lines: + contact_para = doc.add_paragraph(line) + contact_para.paragraph_format.line_spacing = 1.5 + for run in contact_para.runs: + run.font.name = 'SimSun' + run.font.size = Pt(12) + run._element.rPr.rFonts.set(qn('w:eastAsia'), 'SimSun') + + doc.add_page_break() + + def _add_data_acquisition_section(self, doc): + """添加数据获取章节""" + h = doc.add_heading("2 数据获取", level=1) + self._style_heading(h, level=1) + + # 第一张图片标题 + + + # 第一张图片 - 使用相对路径 + img1_path = Path(__file__).parent.parent.parent / "data" / "icons" / "word" / "屏幕截图 2026-03-31 144131.png" + if img1_path.exists(): + p = doc.add_paragraph() + p.alignment = WD_ALIGN_PARAGRAPH.CENTER + p.add_run().add_picture(str(img1_path), width=Inches(6.0)) + + title1 = doc.add_paragraph("大疆M400无人机及300TC高光谱相机") + title1.alignment = WD_ALIGN_PARAGRAPH.CENTER + for run in title1.runs: + run.font.name = self.title_font + run.font.size = Pt(14) + run.font.bold = True + run._element.rPr.rFonts.set(qn('w:eastAsia'), self.title_font) + doc.add_paragraph() # 图片和文字间空行 + + + + # 数据获取描述文字 + data_text = """本次研究采用大疆M400无人机搭载高光谱成像仪进行数据获取。飞行区域覆盖研究区全部水域及周边参照地，共执行飞行任务____架次，总飞行时间约为____小时，实际有效覆盖面积约____平方公里。飞行前进行航线规划，设置航向重叠率____%、旁向重叠率____%，飞行高度为____米，地面分辨率达到____米。为确保数据质量，选择天气晴朗、风速小于____级、太阳高度角适宜的气象窗口期进行作业，并在水体周边布设____个地面控制点及____个光谱定标参考板。整个数据获取过程严格按照无人机操作规范执行，获取的高光谱原始数据存储于机载固态硬盘，后续用于几何校正、辐射定标等预处理步骤。""" + + para = doc.add_paragraph(data_text) + para.paragraph_format.first_line_indent = Pt(24) + para.paragraph_format.space_after = Pt(12) + para.paragraph_format.line_spacing = 1.5 + + # 设置正文字体：宋体小四 + for run in para.runs: + run.font.name = 'SimSun' + run.font.size = Pt(12) + run._element.rPr.rFonts.set(qn('w:eastAsia'), 'SimSun') + + doc.add_page_break() + + def _add_data_processing_section(self, doc): + """添加数据处理章节""" + h = doc.add_heading("3 数据处理流程", level=1) + self._style_heading(h, level=1) + + # 插入图片 - 使用相对路径 + processing_img_path = Path(__file__).parent.parent.parent / "data" / "icons" / "word" / "liucheng.png" + if processing_img_path.exists(): + p = doc.add_paragraph() + p.alignment = WD_ALIGN_PARAGRAPH.CENTER + p.add_run().add_picture(str(processing_img_path), width=Inches(6.5)) + + # ===== 添加图片下方标题（图注）===== + caption_p = doc.add_paragraph() + caption_p.alignment = WD_ALIGN_PARAGRAPH.CENTER + caption_run = caption_p.add_run("图3-1 水质高光谱反演数据处理流程图") + caption_run.font.name = 'SimSun' + caption_run.font.size = Pt(11) + caption_run._element.rPr.rFonts.set(qn('w:eastAsia'), 'SimSun') + caption_run.font.bold = False + # 设置图注段落格式 + caption_p.paragraph_format.space_before = Pt(6) + caption_p.paragraph_format.space_after = Pt(12) + else: + doc.add_paragraph("[数据处理流程图片占位]") + + doc.add_paragraph() # 图片和文字间空行（可选，因为图注下方已有间距） + + # 数据处理描述文字（暂时留空，供后续填写） + processing_text = """采用基于高光谱遥感的水质反演流程来获取水体参数的空间分布。首先通过无人机或卫星平台获取研究区的高光谱影像，随后进行一系列预处理：几何校正使影像与真实地理坐标匹配，辐射校正将原始数值转换为表观辐亮度，大气校正则去除大气分子与气溶胶的影响以获取真实地表反射率；对于多航带数据还需进行航带自动拼接。针对水面特有的镜面反射，我们执行耀斑识别及去除，并利用BRDF校正消除观测角度变化带来的二向性反射差异。 + 之后采用归一化水体指数或深度学习方法自动分割出纯水域像元，排除陆地与植被干扰。在光谱分析阶段，从预处理后的高光谱数据中提取对叶绿素a、悬浮物、透明度等水质参数敏感的波段、比值或吸收深度等光谱特征，并基于地面同步实测数据构建机器学习模型（如随机森林、支持向量机或偏最小二乘回归）。最终将训练好的模型应用于整景影像，逐像元反演出水质参数浓度，并生成专题图与统计报告，实现从原始高光谱数据到水质空间分布信息的完整技术链。""" + + para = doc.add_paragraph(processing_text) + para.paragraph_format.first_line_indent = Pt(24) + para.paragraph_format.space_after = Pt(12) + para.paragraph_format.line_spacing = 1.5 + + # 设置正文字体：宋体小四 + for run in para.runs: + run.font.name = 'SimSun' + run.font.size = Pt(12) + run._element.rPr.rFonts.set(qn('w:eastAsia'), 'SimSun') + + # 添加高光谱图像、耀斑区域和去耀斑图像展示 + self._add_hyperspectral_images_section(doc) + + doc.add_page_break() + + def _add_hyperspectral_images_section(self, doc): + """添加高光谱图像、耀斑区域和去耀斑图像展示""" + h = doc.add_heading("3.1 高光谱图像处理过程", level=2) + self._style_heading(h, level=2) + + work_dir_path = self.work_dir + vis_dir = self.visualization_dir + + # 0. 航线规划图 + flight_path_img_path = work_dir_path / "9_visualization" / "flight_maps" + h3 = doc.add_heading("航线规划：", level=3) + self._style_heading(h3, level=3) + + # 查找航线图文件 + flight_map_files = [] + if flight_path_img_path.exists(): + flight_map_files = list(flight_path_img_path.glob("*.png")) + list(flight_path_img_path.glob("*.jpg")) + + if flight_map_files: + # 使用最新的航线图文件 + latest_flight_map = max(flight_map_files, key=lambda p: p.stat().st_mtime) + success = self._add_image_with_caption(doc, str(latest_flight_map), "图3-1 航线规划", width=Inches(5.5)) + + if success: + # AI 分析航线规划图 + flight_analysis = self._analyze_flight_path_image(str(latest_flight_map)) + self._add_ai_analysis_paragraph(doc, flight_analysis) + else: + doc.add_paragraph("[航线规划图 - 文件未找到]") + + # 1. 高光谱原始图像 + hyperspectral_img_path = work_dir_path / "1_water_mask" / "hsi_preview.png" + h3 = doc.add_heading("高光谱原始影像：", level=3) + self._style_heading(h3, level=3) + if hyperspectral_img_path.exists(): + self._add_image_with_caption(doc, str(hyperspectral_img_path), "图3-2 高光谱原始影像", width=Inches(5.5)) + else: + doc.add_paragraph("[高光谱原始影像 - 文件未找到]") + + # 2. 水体掩膜叠加图 + water_mask_overlay_path = work_dir_path / "1_water_mask" / "water_mask_overlay.png" + h3 = doc.add_heading("水体区域识别：", level=3) + self._style_heading(h3, level=3) + if water_mask_overlay_path.exists(): + success = self._add_image_with_caption(doc, str(water_mask_overlay_path), + "图3-3 水体区域识别（蓝色半透明区域为水域）", + width=Inches(5.5)) + if success: + water_analysis = self._analyze_water_mask_overlay(str(water_mask_overlay_path)) + self._add_ai_analysis_paragraph(doc, water_analysis) + else: + doc.add_paragraph("[水体区域识别图 - 文件未找到]") + + doc.add_paragraph() + + # 2. 耀斑区域 + glint_img_path = vis_dir / "glint_deglint_previews" / "glint_severe_glint_area_preview.png" + h3 = doc.add_heading("耀斑区域识别结果：", level=3) + self._style_heading(h3, level=3) + if glint_img_path.exists(): + self._add_image_with_caption(doc, str(glint_img_path), "图3-4 耀斑区域识别结果", width=Inches(5.5)) + else: + # 尝试查找其他可能的耀斑预览图 + glint_files = list(vis_dir.glob("glint_deglint_previews/*glint*.png")) + if glint_files: + glint_img_path = glint_files[0] + self._add_image_with_caption(doc, str(glint_img_path), "图3-4 耀斑区域识别结果", width=Inches(5.5)) + else: + doc.add_paragraph("[耀斑区域识别结果 - 文件未找到]") + + doc.add_paragraph() + + # 3. 去除耀斑后的图像 + deglint_img_path = vis_dir / "glint_deglint_previews" / "deglint_deglint_goodman_preview.png" + h3 = doc.add_heading("去除耀斑后的影像：", level=3) + self._style_heading(h3, level=3) + if deglint_img_path.exists(): + self._add_image_with_caption(doc, str(deglint_img_path), "图3-5 去除耀斑后的高光谱影像", width=Inches(5.5)) + else: + # 尝试查找其他去耀斑预览图 + deglint_files = list(vis_dir.glob("glint_deglint_previews/*deglint*.png")) + if deglint_files: + deglint_img_path = deglint_files[0] + self._add_image_with_caption(doc, str(deglint_img_path), "图3-5 去除耀斑后的影像", width=Inches(5.5)) + else: + doc.add_paragraph("[去除耀斑后的影像 - 文件未找到]") + + doc.add_paragraph() + + # 4. AI分析耀斑位置分布 + + self._style_heading(h3, level=3) + glint_analysis = self._analyze_glint_distribution_with_ai( + str(glint_img_path) if 'glint_img_path' in locals() and Path(str(glint_img_path)).exists() else None, + str(hyperspectral_img_path) if hyperspectral_img_path.exists() else None + ) + self._add_ai_analysis_paragraph(doc, glint_analysis) + + # 5. 采样点分布图 + sampling_map_dir = vis_dir / "sampling_maps" + h3 = doc.add_heading("采样点分布：", level=3) + self._style_heading(h3, level=3) + + # 查找采样点分布图文件 + sampling_map_files = [] + if sampling_map_dir.exists(): + sampling_map_files = list(sampling_map_dir.glob("*.png")) + list(sampling_map_dir.glob("*.jpg")) + + if sampling_map_files: + # 使用最新的采样点分布图文件 + latest_sampling_map = max(sampling_map_files, key=lambda p: p.stat().st_mtime) + success = self._add_image_with_caption(doc, str(latest_sampling_map), "图3-6 采样点分布图", width=Inches(5.5)) + + if success: + # AI 分析采样点分布图 + sampling_analysis = self._analyze_sampling_distribution(str(latest_sampling_map)) + self._add_ai_analysis_paragraph(doc, sampling_analysis) + else: + doc.add_paragraph("[采样点分布图 - 文件未找到]") + + def _analyze_glint_distribution_with_ai(self, glint_img_path: str = None, original_img_path: str = None) -> str: + """使用AI分析耀斑的位置分布""" + if not self.enable_ai_analysis: + return "AI分析已禁用。耀斑主要分布在水体表面强反射区域，通常出现在太阳光直射角度较大的位置。" + + try: + analysis_prompt = """请分析这张高光谱影像中的耀斑分布情况。 +请从以下几个方面进行专业分析： +1. 耀斑的主要分布位置（水体中心、边缘、特定方位等） +2. 耀斑面积占比估计 +3. 耀斑分布特征（集中分布还是分散分布） +4. 可能的成因分析 +5. 对水质参数反演的影响评估 + +请用专业且简洁的语言描述，控制在150字以内。""" + + if glint_img_path and Path(glint_img_path).exists(): + return self._ollama_chat(self.ollama_vision_model, "你是一个专业的水质遥感分析专家。", analysis_prompt, Path(glint_img_path)) + elif original_img_path and Path(original_img_path).exists(): + return self._ollama_chat(self.ollama_vision_model, "你是一个专业的水质遥感分析专家。", analysis_prompt, Path(original_img_path)) + else: + return "基于影像分析，耀斑主要分布在水体表面强反射区域，对水质参数反演有一定影响，建议在数据处理时重点关注这些区域。" + + except Exception as e: + return f"AI分析失败: {str(e)}。耀斑主要分布在水体表面强反射区域，通常与太阳入射角和水面粗糙度有关。" + + def _analyze_flight_path_image(self, flight_img_path: str) -> str: + """ + 使用AI分析航线规划图 + + 分析内容： + 1. 架次数量 + 2. 每个架次的飞行方向 + 3. 图例中的飞行起始结束时间 + """ + if not self.enable_ai_analysis: + return "AI分析已禁用。根据航线规划图，可识别多个架次的飞行轨迹，每个架次具有不同的飞行方向和时间安排。" + + try: + if not Path(flight_img_path).exists(): + return "航线图文件不存在，无法进行分析。" + + analysis_prompt = """请详细分析这张航线规划图，并严格按照以下要求输出： + +分析要求： +1. 架次数量：明确指出图中有几个架次（几条不同颜色的轨迹线） +2. 飞行方向：描述每个架次的大致飞行方向（如：东西向、南北向、东北-西南向等） +3. 时间信息：从图例中提取每个架次的起始和结束时间 + +输出格式要求： +- 使用客观、准确的描述 +- 避免推测性语言（如"可能"、"也许"） +- 控制在200字以内 +- 如果看不清具体时间，请明确说明"图例显示时间信息但具体数值不清晰" + +示例输出格式： +"飞行共有X个架次：架次1（红色）：东西向飞行，时间范围XX:XX-XX:XX架次2（蓝色）：南北向飞行，时间范围XX:XX-XX:XX +... +各架次轨迹分布合理，覆盖了目标水体区域。""" + + result = self._ollama_chat( + self.ollama_vision_model, + "你是一位专业的航空摄影测量和遥感专家，擅长分析航线规划图。", + analysis_prompt, + Path(flight_img_path) + ) + + # 如果返回内容为空或太短，使用默认文本 + if not result or len(result) < 20: + return "根据航线图分析，图中包含多个架次的飞行轨迹，各架次采用不同颜色标识，飞行方向各异，图例中标注了各架次的起始和结束时间。" + + return result + + except Exception as e: + return f"AI分析失败: {str(e)}。根据航线规划图，包含多个架次的飞行轨迹，各架次具有不同颜色和飞行方向，图例中标注了时间信息。" + + def _analyze_water_mask_overlay(self, water_mask_path: str) -> str: + """ + 使用AI分析水体区域识别图 + + 分析内容： + 1. 水体的分布情况（集中分布还是分散分布） + 2. 水体的位置和形状特征 + 3. 从图像标注中提取的水域面积和占比 + """ + if not self.enable_ai_analysis: + return "AI分析已禁用。根据水体区域识别图，蓝色半透明区域标识了水域范围，可观察到水体的分布情况和面积占比。" + + try: + if not Path(water_mask_path).exists(): + return "水体区域识别图文件不存在，无法进行分析。" + + analysis_prompt = """【背景说明】 +这是一座水库的遥感影像，水体区域以蓝色半透明标识。水库通常是人工筑坝蓄水形成，具有以下典型特征： +- 水体形态：较宽阔，形状相对规则，边界平滑 +- 大坝位置：通常位于水库最窄处或下游方向 +- 入库方向：上游河流汇入处，通常较窄或有分叉 +- 出水方向：大坝方向，水体在此处收窄 + +【分析维度】 +1. 水体整体形态：描述水库的形状（扇形、狭长形、不规则形、分叉形等），水体是集中还是分散？ +2. 入库特征（重要）：识别水体哪些位置有狭窄的入口或分叉——这些通常是河流入库的方向。描述入库位置（如东北角、西侧等）。 +3. 大坝/出水方向推断（重要）：根据水体形态，判断大坝最可能的位置。通常在水体最窄处、或水体延伸的末端。推断流向是“从XX方向流向大坝（XX方向）”。 +4. 分支情况：是否有多个入库河流？是否有孤立水体？ +5. 面积信息：从图像左上角标注中提取水域面积、影像总面积、水域占比。 + +【输出格式】 + 水体面积X.XX km² ，占比: X.X% ，形态: X。入库方向：XX方向（若有多个，依次列出）。出水/大坝方向：XX方向。流向推断：水体从XX方向汇入，流向大坝（XX方向）补充描述：[简要描述整体分布和形态特征] + +【示例输出】 +水体面积25.60 km² ，占比: 42.3% ，形态: 扇形分叉。入库方向：西北角和东北角各有狭窄水道汇入，为主要入库河流。出水/大坝方向：南侧水体最窄处。流向推断：水体从西北和东北两个方向汇入，向南侧大坝方向流动。补充描述：水库整体呈扇形，库区宽阔，有两个明显入库分支，符合山区水库典型特征""" + + result = self._ollama_chat( + self.ollama_vision_model, + "你是一位专业的水体遥感分析专家，擅长解读水体掩膜图和水域分布特征。", + analysis_prompt, + Path(water_mask_path) + ) + + # 如果返回内容为空或太短，使用默认文本 + if not result or len(result) < 20: + return "根据水体区域识别图分析，蓝色半透明区域标识了水域范围。从图像标注可读取水域面积、影像总面积及水域占比信息，水体分布特征明显，便于后续水质参数反演分析。" + + return result + + except Exception as e: + return f"AI分析失败: {str(e)}。根据水体区域识别图，蓝色半透明区域标识了水域范围，图像左上角标注了水域面积、影像总面积及水域占比数据。" + + def _analyze_sampling_distribution(self, sampling_map_path: str) -> str: + """ + 使用AI分析采样点分布图 + + 分析内容： + 1. 采样点数量 + 2. 采样点在水体中的分布情况（均匀/集中、覆盖范围） + 3. 采样点的空间分布特征 + 4. 对水质反演代表性的评估 + """ + if not self.enable_ai_analysis: + return "AI分析已禁用。根据采样点分布图，红色点标识了采样点位置，可观察采样点在水体中的分布情况和覆盖范围。" + + try: + if not Path(sampling_map_path).exists(): + return "采样点分布图文件不存在，无法进行分析。" + + analysis_prompt = """请详细分析这张采样点分布图，并严格按照以下要求输出： + +【分析要求】 +1. 采样点数量：估算图中有多少个采样点（红色点） +2. 分布情况：描述采样点在水体中的分布是否均匀，是否有聚集或稀疏区域 +3. 覆盖范围：采样点是否覆盖了主要水域，是否有未覆盖的区域 +4. 空间特征：采样点分布在哪些方位（如上下游、左右岸等） +5. 代表性评估：简要评价当前采样点布局对水质参数反演的代表性 + +【输出格式要求】 +- 使用客观、准确的描述 +- 避免推测性语言 +- 控制在200字以内 + +【示例输出格式】 +"图中共有约XX个采样点，分布...，覆盖...，在...区域较为密集，...区域较为稀疏。 +采样点整体覆盖了主要水体区域，但在...区域采样不足。 +当前布局对水质反演具有较好的代表性，建议..." + +请根据图像内容给出专业分析。""" + + result = self._ollama_chat( + self.ollama_vision_model, + "你是一位专业的水质采样设计专家，擅长评估采样点布局的合理性和代表性。", + analysis_prompt, + Path(sampling_map_path) + ) + + # 如果返回内容为空或太短，使用默认文本 + if not result or len(result) < 20: + return "根据采样点分布图分析，红色点标识了采样点位置，分布在水体各个区域。采样点覆盖范围较广，空间布局合理，能够较好地代表整体水质状况，为后续水质参数反演提供了可靠的数据基础。" + + return result + + except Exception as e: + return f"AI分析失败: {str(e)}。根据采样点分布图，红色点标识了采样点位置，分布在水体中，覆盖了主要水域区域，具有较好的代表性。" + + def _setup_header_and_footer(self, section): + """设置页眉：图片在最左侧 + 中间文字""" + header = section.header + + # 清空现有段落 + for paragraph in header.paragraphs: + p = paragraph._element + p.getparent().remove(p) + + # 创建新段落用于页眉 + header_para = header.add_paragraph() + + # 1. 最左侧图片 - 使用相对路径 + header_img_path = Path(__file__).parent.parent.parent / "data" / "icons" / "word" / "lica.png" + if header_img_path.exists(): + try: + run_img = header_para.add_run() + run_img.add_picture(str(header_img_path), width=Inches(1.6)) + except Exception as e: + print(f"页眉图片加载失败: {e}") + header_para.add_run("■ ") + else: + header_para.add_run("■ ") # 图片不存在时的占位 + + # 2. 中间文字 - “水质参数报告” + run_text = header_para.add_run(" 水质参数报告") + run_text.font.name = self.chinese_font + run_text.font.size = Pt(11) + run_text._element.rPr.rFonts.set(qn('w:eastAsia'), self.chinese_font) + + # 左对齐，让图片在最左侧 + header_para.alignment = WD_ALIGN_PARAGRAPH.LEFT + + # 设置页眉边距 + section.header_distance = Cm(0.8) + + # 设置页脚页码 + footer = section.footer + footer_para = footer.paragraphs[0] if footer.paragraphs else footer.add_paragraph() + footer_para.alignment = WD_ALIGN_PARAGRAPH.CENTER + + # 添加页码字段 + run = footer_para.add_run() + fldChar1 = OxmlElement('w:fldChar') + fldChar1.set(qn('w:fldCharType'), 'begin') + run._element.append(fldChar1) + + instrText = OxmlElement('w:instrText') + instrText.text = 'PAGE' + run._element.append(instrText) + + fldChar2 = OxmlElement('w:fldChar') + fldChar2.set(qn('w:fldCharType'), 'end') + run._element.append(fldChar2) + + # 添加 "页" 字 + footer_para.add_run(' / ') + run2 = footer_para.add_run() + fldChar3 = OxmlElement('w:fldChar') + fldChar3.set(qn('w:fldCharType'), 'begin') + run2._element.append(fldChar3) + + instrText2 = OxmlElement('w:instrText') + instrText2.text = 'NUMPAGES' + run2._element.append(instrText2) + + fldChar4 = OxmlElement('w:fldChar') + fldChar4.set(qn('w:fldCharType'), 'end') + run2._element.append(fldChar4) + + footer_para.add_run(' 页') + + # 设置页脚字体 + for run in footer_para.runs: + run.font.size = Pt(9) + run.font.name = self.chinese_font + if hasattr(run, '_element') and hasattr(run._element, 'rPr'): + run._element.rPr.rFonts.set(qn('w:eastAsia'), self.chinese_font) + + + def _add_result_analysis_section( + self, + doc, + vis_dir: Path, + start_figure_num: int = 1, + all_image_analyses: Optional[List[Dict[str, Any]]] = None, + progress=None, + ) -> int: + """添加结果分析章节 - 统计表格 + 相关性热力图（热力图在表格下方）""" + h1 = doc.add_heading("4 结果分析", level=1) + self._style_heading(h1, level=1) + + # 1. 添加统计分析表格（带编号） + h2 = doc.add_heading("4.1 水质参数统计分析", level=2) + self._style_heading(h2, level=2) + + # 从工作目录的4_processed_data文件夹查找CSV文件 + work_dir_path = vis_dir.parent + processed_data_dir = work_dir_path / "4_processed_data" + + if not processed_data_dir.exists(): + doc.add_paragraph(f"未找到数据处理目录: {processed_data_dir}") + doc.add_page_break() + return + + csv_files = list(processed_data_dir.glob("*.csv")) + if not csv_files: + doc.add_paragraph(f"在 {processed_data_dir} 目录下未找到CSV统计数据文件。") + doc.add_page_break() + return + + csv_path = csv_files[0] # 使用找到的第一个CSV文件 + + try: + df_full = pd.read_csv(csv_path, sep=',') + df = df_full.iloc[:, 2:] # 跳过前两列（纬度、经度），直接用列号 + + # 自动统计剩余列 + stats_data = [] + for i in range(df.shape[1]): + col = df.columns[i] + clean_col = str(col).strip() + try: + data = df.iloc[:, i].dropna() + if len(data) > 0: + stats_data.append({ + '参数': clean_col, + '点位数': len(data), + '最大值': f"{data.max():.4f}", + '最小值': f"{data.min():.4f}", + '平均值': f"{data.mean():.4f}", + '标准差': f"{data.std():.4f}" + }) + except Exception as e: + print(f"跳过列 {col}: {e}") + + if stats_data: + # 创建统计表格 + table = doc.add_table(rows=1, cols=6, style='Table Grid') + hdr_cells = table.rows[0].cells + hdr_cells[0].text = '参数' + hdr_cells[1].text = '点位数' + hdr_cells[2].text = '最大值' + hdr_cells[3].text = '最小值' + hdr_cells[4].text = '平均值' + hdr_cells[5].text = '标准差' + + for stat in stats_data: + row_cells = table.add_row().cells + row_cells[0].text = stat['参数'] + row_cells[1].text = str(stat['点位数']) + row_cells[2].text = stat['最大值'] + row_cells[3].text = stat['最小值'] + row_cells[4].text = stat['平均值'] + row_cells[5].text = stat['标准差'] + + else: + doc.add_paragraph("CSV文件中未找到有效的参数数据。") + + except Exception as e: + doc.add_paragraph(f"读取CSV文件时出错: {str(e)}") + #增加空格 + doc.add_paragraph() + # 表格生成完成后，添加 AI 分析 + if stats_data: + analysis_text = self._analyze_statistics(stats_data, [s['参数'] for s in stats_data]) + self._add_ai_analysis_paragraph(doc, analysis_text) + + doc.add_paragraph() # 表格和热力图之间的空行 + + # 2. 添加相关性热力图（放在表格下方） + h3 = doc.add_heading("4.2 水质参数相关性分析", level=2) + self._style_heading(h3, level=2) + heatmap_path = vis_dir / "correlation_heatmap.png" + figure_num = start_figure_num + if heatmap_path.exists(): + try: + # 使用统一的图像插入方法 + caption_text = f"图{figure_num} 水质参数相关性热力图" + self._add_image_with_caption(doc, str(heatmap_path), caption_text, width=Inches(6.0)) + doc.add_paragraph("（颜色越深表示相关性越强，红色为正相关，蓝色为负相关）") + + analysis_text = self._analyze_and_cache_image( + image_path=heatmap_path, + image_type="correlation_heatmap", + param="综合", + figure_num=figure_num, + ) + self._add_ai_analysis_paragraph(doc, analysis_text) + if all_image_analyses is not None: + all_image_analyses.append( + { + "figure_num": figure_num, + "param": "综合", + "image_type": "correlation_heatmap", + "image_name": heatmap_path.name, + "analysis": analysis_text, + } + ) + except Exception as e: + doc.add_paragraph(f"[相关性热力图插入失败: {e}]") + else: + doc.add_paragraph(f"[未找到相关性热力图: {heatmap_path.name}]") + + # 热力图处理结束（无论成功/失败）更新进度条 + try: + if progress is not None: + progress.update(1) + except Exception: + pass + + doc.add_page_break() + return start_figure_num + (1 if heatmap_path.exists() else 0) + +# ==================== 使用示例 ==================== + +def generate_full_water_quality_report( + work_dir: str = "./work_dir", + ai_config: Optional[ReportGenerationConfig] = None, +): + """生成包含所有水质参数的完整报告。""" + generator = WaterQualityReportGenerator(work_dir=work_dir, ai_config=ai_config) + return generator.generate_report( + work_dir=work_dir, + parameters=None, + report_title="水质参数反演分析完整报告", + ) + + +if __name__ == "__main__": + # 默认生成完整报告（包含所有13个水质参数） + report_path = generate_full_water_quality_report() + print(f"完整水质报告已生成: {report_path}") diff --git a/src/postprocessing/reports/水质参数反演分析报告_20260330_164433.docx b/src/postprocessing/reports/水质参数反演分析报告_20260330_164433.docx new file mode 100644 index 0000000..7c90f49 Binary files /dev/null and b/src/postprocessing/reports/水质参数反演分析报告_20260330_164433.docx differ diff --git a/src/postprocessing/reports/水质参数反演分析报告_20260330_164511.docx b/src/postprocessing/reports/水质参数反演分析报告_20260330_164511.docx new file mode 100644 index 0000000..069ef0d Binary files /dev/null and b/src/postprocessing/reports/水质参数反演分析报告_20260330_164511.docx differ diff --git a/src/postprocessing/reports/水质参数反演分析报告_20260330_165245.docx 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+import seaborn as sns +from pathlib import Path +from typing import Dict, List, Optional, Tuple, Union +import json +import warnings +from datetime import datetime +import joblib + +# 导入GDAL用于影像读写 +try: + from osgeo import gdal + GDAL_AVAILABLE = True +except ImportError: + GDAL_AVAILABLE = False + print("警告: GDAL未安装，影像预览图生成功能可能无法正常工作") + +warnings.filterwarnings('ignore') + +# 设置中文字体 +plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans', 'Arial Unicode MS'] +plt.rcParams['axes.unicode_minus'] = False +plt.rcParams['font.size'] = 12 + +# 设置seaborn样式 +sns.set_style("whitegrid") +sns.set_palette("husl") + + +class WaterQualityVisualization: + """水质参数反演可视化类""" + + def __init__(self, output_dir: str = "./visualization_output"): + """ + 初始化可视化类 + + Args: + output_dir: 输出目录 + """ + self.output_dir = Path(output_dir) + self.output_dir.mkdir(parents=True, exist_ok=True) + + def plot_scatter_true_vs_pred(self, y_true: np.ndarray, y_pred: np.ndarray, + target_name: str = "参数", + train_indices: Optional[np.ndarray] = None, + test_indices: Optional[np.ndarray] = None, + metrics: Optional[Dict] = None, + output_path: Optional[str] = None) -> str: + """ + 绘制散点图：真实值vs预测值 + + Args: + y_true: 真实值 + y_pred: 预测值 + target_name: 目标参数名称 + train_indices: 训练集索引（可选） + test_indices: 测试集索引（可选） + metrics: 评估指标字典（可选） + output_path: 输出路径（如果为None，自动生成） + + Returns: + 保存的文件路径 + """ + fig, ax = plt.subplots(figsize=(10, 8)) + + # 计算所有数据的R²和RMSE + from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error + + r2_all = r2_score(y_true, y_pred) + rmse_all = np.sqrt(mean_squared_error(y_true, y_pred)) + mae_all = mean_absolute_error(y_true, y_pred) + + # 如果提供了训练/测试集索引，分别绘制 + if train_indices is not None and test_indices is not None: + y_train_true = y_true[train_indices] + y_train_pred = y_pred[train_indices] + y_test_true = y_true[test_indices] + y_test_pred = y_pred[test_indices] + + # 绘制训练集散点 + ax.scatter(y_train_true, y_train_pred, alpha=0.6, s=50, + label=f'训练集 (n={len(y_train_true)})', color='blue', edgecolors='black', linewidths=0.5) + + # 绘制测试集散点 + ax.scatter(y_test_true, y_test_pred, alpha=0.6, s=50, + label=f'测试集 (n={len(y_test_true)})', color='red', edgecolors='black', linewidths=0.5) + + # 计算训练集和测试集指标 + if metrics is None: + train_r2 = r2_score(y_train_true, y_train_pred) + test_r2 = r2_score(y_test_true, y_test_pred) + train_rmse = np.sqrt(mean_squared_error(y_train_true, y_train_pred)) + test_rmse = np.sqrt(mean_squared_error(y_test_true, y_test_pred)) + else: + train_r2 = metrics.get('train_r2', r2_score(y_train_true, y_train_pred)) + test_r2 = metrics.get('test_r2', r2_score(y_test_true, y_test_pred)) + train_rmse = metrics.get('train_rmse', np.sqrt(mean_squared_error(y_train_true, y_train_pred))) + test_rmse = metrics.get('test_rmse', np.sqrt(mean_squared_error(y_test_true, y_test_pred))) + + metrics_text = f'训练集: R² = {train_r2:.4f}, RMSE = {train_rmse:.4f}\n' + metrics_text += f'测试集: R² = {test_r2:.4f}, RMSE = {test_rmse:.4f}' + else: + # 绘制所有数据 + ax.scatter(y_true, y_pred, alpha=0.6, s=50, color='blue', + edgecolors='black', linewidths=0.5) + metrics_text = f'R² = {r2_all:.4f}, RMSE = {rmse_all:.4f}, MAE = {mae_all:.4f}' + + # 绘制1:1线 + min_val = min(y_true.min(), y_pred.min()) + max_val = max(y_true.max(), y_pred.max()) + ax.plot([min_val, max_val], [min_val, max_val], 'r--', linewidth=2, label='1:1线') + + # 设置图形属性 + ax.set_xlabel(f'真实值 ({target_name})', fontsize=14, fontweight='bold') + ax.set_ylabel(f'预测值 ({target_name})', fontsize=14, fontweight='bold') + ax.set_title(f'{target_name} - 真实值 vs 预测值', fontsize=16, fontweight='bold') + ax.legend(loc='upper left', fontsize=11) + ax.grid(True, alpha=0.3) + + # 添加指标文本框 + ax.text(0.05, 0.95, metrics_text, transform=ax.transAxes, + verticalalignment='top', bbox=dict(boxstyle='round', + facecolor='wheat', alpha=0.8), fontsize=10) + + plt.tight_layout() + + # 保存图片 + if output_path is None: + safe_name = "".join(c for c in target_name if c.isalnum() or c in ('-', '_', '.')) + output_path = self.output_dir / f"{safe_name}_scatter_true_vs_pred.png" + else: + output_path = Path(output_path) + + plt.savefig(output_path, dpi=300, bbox_inches='tight') + plt.close() + + print(f"散点图已保存: {output_path}") + return str(output_path) + + def plot_spectrum_by_parameter(self, csv_path: str, parameter_column: str, + wavelength_start_column: Union[str, int] = "UTM_Y", + output_dir: Optional[str] = None, + wavelength_range: Optional[Tuple[float, float]] = None, + n_groups: int = 5) -> str: + """ + 绘制光谱曲线图：不同参数值的光谱曲线对比 + + Args: + csv_path: 包含光谱和参数值的CSV文件路径 + parameter_column: 参数值列名或索引 + wavelength_start_column: 波长开始列名或索引 + output_dir: 输出目录（如果为None，使用self.output_dir） + wavelength_range: 波长范围（可选，如(374, 1011)） + n_groups: 将参数值分成几组进行对比 + + Returns: + 保存的文件路径 + """ + # 读取数据 + df = pd.read_csv(csv_path) + + # 确定波长开始列 + if isinstance(wavelength_start_column, str): + try: + wavelength_start_idx = df.columns.get_loc(wavelength_start_column) + except KeyError: + try: + wavelength_start_idx = int(wavelength_start_column) + except ValueError: + raise KeyError( + f"未找到波长起始列: {wavelength_start_column!r}" + ) from None + else: + wavelength_start_idx = wavelength_start_column + + # 获取参数值和光谱数据 + param_values = df[parameter_column].values + spectrum_data = df.iloc[:, wavelength_start_idx:].values + + # 获取波长 + wavelength_cols = df.columns[wavelength_start_idx:] + try: + wavelengths = wavelength_cols.astype(float).values + except: + # 如果列名不是数字，使用索引 + wavelengths = np.arange(len(wavelength_cols)) + + # 过滤波长范围 + if wavelength_range: + mask = (wavelengths >= wavelength_range[0]) & (wavelengths <= wavelength_range[1]) + wavelengths = wavelengths[mask] + spectrum_data = spectrum_data[:, mask] + + # 过滤无效值 + valid_mask = ~pd.isna(param_values) & np.all(np.isfinite(spectrum_data), axis=1) + param_values = param_values[valid_mask] + spectrum_data = spectrum_data[valid_mask] + + # 将参数值分成n_groups组 + param_min, param_max = param_values.min(), param_values.max() + group_edges = np.linspace(param_min, param_max, n_groups + 1) + group_labels = [f"{group_edges[i]:.2f}-{group_edges[i+1]:.2f}" + for i in range(n_groups)] + group_indices = np.digitize(param_values, group_edges[1:]) + + # 创建图形 + fig, ax = plt.subplots(figsize=(14, 8)) + + # 为每组选择颜色 + colors = plt.cm.viridis(np.linspace(0, 1, n_groups)) + + # 绘制每组的光谱曲线 + for i in range(n_groups): + group_mask = group_indices == i + if group_mask.sum() == 0: + continue + + group_spectra = spectrum_data[group_mask] + group_mean_spectrum = np.nanmean(group_spectra, axis=0) + group_std_spectrum = np.nanstd(group_spectra, axis=0) + + # 绘制平均光谱 + ax.plot(wavelengths, group_mean_spectrum, + color=colors[i], linewidth=2.5, + label=f'组 {i+1} ({group_labels[i]}, n={group_mask.sum()})') + + # 绘制标准差阴影 + ax.fill_between(wavelengths, + group_mean_spectrum - group_std_spectrum, + group_mean_spectrum + group_std_spectrum, + color=colors[i], alpha=0.2) + + # 设置图形属性 + ax.set_xlabel('波长 (nm)', fontsize=14, fontweight='bold') + ax.set_ylabel('光谱反射率', fontsize=14, fontweight='bold') + ax.set_title(f'{parameter_column} - 不同参数值的光谱曲线对比', + fontsize=16, fontweight='bold') + ax.legend(loc='best', fontsize=10) + ax.grid(True, alpha=0.3) + + if wavelength_range: + ax.set_xlim(wavelength_range) + + plt.tight_layout() + + # 保存图片 + if output_dir is None: + output_dir = self.output_dir + else: + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + safe_name = "".join(c for c in parameter_column if c.isalnum() or c in ('-', '_', '.')) + output_path = output_dir / f"{safe_name}_spectrum_comparison.png" + + plt.savefig(output_path, dpi=300, bbox_inches='tight') + plt.close() + + print(f"光谱曲线图已保存: {output_path}") + return str(output_path) + + def plot_statistical_charts(self, csv_path: str, parameter_columns: List[str], + output_dir: Optional[str] = None) -> Dict[str, str]: + """ + 绘制统计图表：箱线图、直方图、相关性热力图 + + Args: + csv_path: CSV文件路径 + parameter_columns: 参数列名列表 + output_dir: 输出目录 + + Returns: + 保存的文件路径字典 + """ + df = pd.read_csv(csv_path) + + if output_dir is None: + output_dir = self.output_dir + else: + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + output_paths = {} + + # 1. 箱线图 + if len(parameter_columns) > 0: + fig, ax = plt.subplots(figsize=(12, 6)) + data_for_boxplot = [df[col].dropna() for col in parameter_columns if col in df.columns] + if data_for_boxplot: + ax.boxplot(data_for_boxplot, labels=[col for col in parameter_columns if col in df.columns]) + ax.set_ylabel('数值', fontsize=12, fontweight='bold') + ax.set_title('水质参数箱线图', fontsize=14, fontweight='bold') + ax.grid(True, alpha=0.3, axis='y') + plt.xticks(rotation=45, ha='right') + plt.tight_layout() + + boxplot_path = output_dir / "parameter_boxplot.png" + plt.savefig(boxplot_path, dpi=300, bbox_inches='tight') + plt.close() + output_paths['boxplot'] = str(boxplot_path) + + # 2. 直方图 + for col in parameter_columns: + if col not in df.columns: + continue + fig, ax = plt.subplots(figsize=(10, 6)) + data = df[col].dropna() + ax.hist(data, bins=30, edgecolor='black', alpha=0.7, color='skyblue') + ax.set_xlabel(f'{col} 数值', fontsize=12, fontweight='bold') + ax.set_ylabel('频数', fontsize=12, fontweight='bold') + ax.set_title(f'{col} 分布直方图', fontsize=14, fontweight='bold') + ax.grid(True, alpha=0.3, axis='y') + + # 添加统计信息 + mean_val = data.mean() + std_val = data.std() + ax.axvline(mean_val, color='red', linestyle='--', linewidth=2, label=f'均值: {mean_val:.4f}') + ax.legend() + + plt.tight_layout() + + safe_name = "".join(c for c in col if c.isalnum() or c in ('-', '_', '.')) + hist_path = output_dir / f"{safe_name}_histogram.png" + plt.savefig(hist_path, dpi=300, bbox_inches='tight') + plt.close() + output_paths[f'histogram_{col}'] = str(hist_path) + + # 3. 相关性热力图 + if len(parameter_columns) >= 2: + valid_cols = [col for col in parameter_columns if col in df.columns] + if len(valid_cols) >= 2: + corr_matrix = df[valid_cols].corr() + + fig, ax = plt.subplots(figsize=(10, 8)) + sns.heatmap(corr_matrix, annot=True, fmt='.3f', cmap='coolwarm', + center=0, square=True, linewidths=1, cbar_kws={"shrink": 0.8}, + ax=ax, vmin=-1, vmax=1) + ax.set_title('水质参数相关性热力图', fontsize=14, fontweight='bold') + plt.tight_layout() + + heatmap_path = output_dir / "correlation_heatmap.png" + plt.savefig(heatmap_path, dpi=300, bbox_inches='tight') + plt.close() + output_paths['heatmap'] = str(heatmap_path) + + print(f"统计图表已保存到: {output_dir}") + return output_paths + + def plot_distribution_map_enhanced(self, prediction_csv_path: str, + boundary_shp_path: str, + parameter_column: str = 'prediction', + output_path: Optional[str] = None, + resolution: float = 30, + input_crs: str = 'EPSG:32651', + output_crs: str = 'EPSG:4326', + colormap: str = 'viridis') -> str: + """ + 生成增强的含量分布图（彩色填充图） + + 这是对step9的增强版本，使用更丰富的颜色映射 + + Args: + prediction_csv_path: 预测结果CSV文件路径 + boundary_shp_path: 边界shapefile文件路径 + parameter_column: 参数值列名 + output_path: 输出图片路径 + resolution: 插值网格分辨率 + input_crs: 输入坐标系 + output_crs: 输出坐标系 + colormap: 颜色映射方案 + + Returns: + 保存的文件路径 + """ + from map import ContentMapper + + # 使用ContentMapper生成分布图 + mapper = ContentMapper(input_crs=input_crs, output_crs=output_crs) + + if output_path is None: + csv_name = Path(prediction_csv_path).stem + output_path = str(self.output_dir / f"{csv_name}_distribution_enhanced.png") + + # 处理数据并生成分布图 + mapper.process_data( + csv_file=prediction_csv_path, + shp_file=boundary_shp_path, + output_file=output_path, + resolution=resolution, + show_sample_points=False + ) + + print(f"增强分布图已保存: {output_path}") + return output_path + + def generate_glint_deglint_previews(self, work_dir: str, + output_subdir: str = "glint_deglint_previews", + generate_glint: bool = True, + generate_deglint: bool = True) -> Dict[str, str]: + """ + 根据工作目录的2_glint和3_deglint文件夹中的文件生成PNG预览图 + + 功能特点： + - 2_glint文件夹：单波段二值耀斑掩膜，使用红色高亮显示 + - 3_deglint文件夹：多波段去耀斑影像，使用RGB合成显示 + - 自动识别文件类型并应用相应的可视化方案 + - 输出保存至9_visualization/glint_deglint_previews/ + + Args: + work_dir: 工作目录路径 + output_subdir: 输出子目录名称（默认 "glint_deglint_previews"） + generate_glint: 是否处理2_glint文件夹中的文件 + generate_deglint: 是否处理3_deglint文件夹中的文件 + + Returns: + 生成的PNG文件路径字典，键为原始文件名，值为PNG路径 + """ + if not GDAL_AVAILABLE: + print("警告: GDAL未安装，无法生成影像预览图") + return {} + + work_dir_path = Path(work_dir) + if not work_dir_path.exists(): + print(f"错误: 工作目录不存在: {work_dir}") + return {} + + # 创建输出目录 + output_dir = self.output_dir / output_subdir + output_dir.mkdir(parents=True, exist_ok=True) + + preview_paths = {} + processed_count = 0 + + print(f"\n{'='*60}") + print("生成耀斑分析影像预览图") + print(f"{'='*60}") + print(f"输出目录: {output_dir}") + + # 处理2_glint文件夹 + if generate_glint: + glint_dir = work_dir_path / "2_glint" + if glint_dir.exists(): + print(f"正在处理2_glint文件夹: {glint_dir}") + glint_previews = self._process_image_folder( + glint_dir, output_dir, "glint", preview_paths + ) + processed_count += len(glint_previews) + else: + print(f"警告: 2_glint文件夹不存在: {glint_dir}") + + # 处理3_deglint文件夹 + if generate_deglint: + deglint_dir = work_dir_path / "3_deglint" + if deglint_dir.exists(): + print(f"正在处理3_deglint文件夹: {deglint_dir}") + deglint_previews = self._process_image_folder( + deglint_dir, output_dir, "deglint", preview_paths + ) + processed_count += len(deglint_previews) + else: + print(f"警告: 3_deglint文件夹不存在: {deglint_dir}") + + print(f"\n影像预览图生成完成，共处理 {processed_count} 个文件") + print(f"预览图保存至: {output_dir}") + + return preview_paths + + def _process_image_folder(self, input_dir: Path, output_dir: Path, + folder_type: str, preview_paths: Dict[str, str]) -> Dict[str, str]: + """ + 处理指定文件夹中的影像文件并生成预览图 + + Args: + input_dir: 输入文件夹路径 + output_dir: 输出文件夹路径 + folder_type: 文件夹类型 ('glint' 或 'deglint') + preview_paths: 存储预览图路径的字典（会原地修改） + + Returns: + 处理后的预览图路径字典 + """ + if not input_dir.exists(): + return {} + + # 支持的影像文件扩展名 + supported_extensions = {'.dat', '.bsq', '.tif', '.tiff', '.bil', '.img'} + + processed = {} + + for file_path in input_dir.iterdir(): + if file_path.is_file() and file_path.suffix.lower() in supported_extensions: + try: + png_path = self._generate_image_preview_for_visualization( + str(file_path), output_dir, folder_type + ) + + if png_path: + preview_paths[file_path.name] = png_path + processed[file_path.name] = png_path + print(f" ✓ 已生成: {file_path.name} -> {Path(png_path).name}") + + except Exception as e: + print(f" ✗ 处理文件 {file_path.name} 时出错: {e}") + + return processed + + def _generate_image_preview_for_visualization(self, img_path: str, + output_dir: Path, + folder_type: str) -> Optional[str]: + """ + 为可视化模块生成影像预览图 + + 特别处理： + - 耀斑掩膜 (2_glint/*.dat)：单波段二值图，黑底（0）、耀斑区域为白（1） + - 其他影像：多波段RGB合成，使用波长选择RGB波段 + + Args: + img_path: 输入影像文件路径 + output_dir: 输出目录 + folder_type: 文件夹类型 ('glint' 或 'deglint') + + Returns: + 生成的PNG文件路径，如果失败则返回None + """ + try: + img_path_obj = Path(img_path) + img_name = img_path_obj.stem + output_path = output_dir / f"{folder_type}_{img_name}_preview.png" + + # 如果文件已存在，跳过生成 + if output_path.exists(): + return str(output_path) + + # 使用GDAL读取影像 + dataset = gdal.Open(img_path) + if dataset is None: + print(f" 警告: 无法打开影像文件: {img_path}") + return None + + # 获取影像信息 + width = dataset.RasterXSize + height = dataset.RasterYSize + band_count = dataset.RasterCount + + # 检测是否为单波段二值图（耀斑掩膜） + is_binary_mask = (band_count == 1) or (folder_type == 'glint') + + if is_binary_mask: + # 单波段二值图的特殊处理 + binary_data = dataset.GetRasterBand(1).ReadAsArray().astype(np.float32) + + # 单波段二值图 → RGB：耀斑文件夹固定为黑底、耀斑白；其余为灰度拉伸 + if folder_type == 'glint': + # 背景黑色 (0,0,0)，掩膜中大于阈值的像元为耀斑 → 白色 (1,1,1) + rgb_image = np.zeros((height, width, 3), dtype=np.float32) + glint_mask = binary_data > 0.5 + rgb_image[glint_mask] = 255 + title_color_info = "背景黑，白色=耀斑区域" + else: + # 其他单波段：使用灰度 + binary_data = binary_data / (binary_data.max() + 1e-10) if binary_data.max() > 0 else binary_data + rgb_image = np.stack([binary_data, binary_data, binary_data], axis=2) + title_color_info = "灰度显示" + else: + # 多波段影像的正常处理 + # 选择RGB波段 + if band_count >= 3: + bands = self._select_rgb_bands(img_path, band_count) + else: + bands = [0, 0, 0] # 灰度显示 + + # 读取指定波段 + r_data = dataset.GetRasterBand(bands[0] + 1).ReadAsArray().astype(np.float32) + g_data = dataset.GetRasterBand(bands[1] + 1).ReadAsArray().astype( + np.float32) if band_count > 1 else r_data.copy() + b_data = dataset.GetRasterBand(bands[2] + 1).ReadAsArray().astype( + np.float32) if band_count > 2 else r_data.copy() + + # 去除无效值 + r_data[r_data <= 0] = np.nan + if band_count > 1: + g_data[g_data <= 0] = np.nan + if band_count > 2: + b_data[b_data <= 0] = np.nan + + # 2%线性拉伸 + def linear_stretch(data, low_percent=2, high_percent=98): + valid_data = data[~np.isnan(data)] + if len(valid_data) == 0: + return np.zeros_like(data) + + low_val = np.percentile(valid_data, low_percent) + high_val = np.percentile(valid_data, high_percent) + + if high_val - low_val < 1e-10: + return np.zeros_like(data) + + stretched = (data - low_val) / (high_val - low_val) + stretched = np.clip(stretched, 0, 1) + return stretched + + r_stretched = linear_stretch(r_data) + g_stretched = linear_stretch(g_data) if band_count > 1 else r_stretched + b_stretched = linear_stretch(b_data) if band_count > 2 else r_stretched + + # 合成为RGB图像 + rgb_image = np.stack([r_stretched, g_stretched, b_stretched], axis=2) + rgb_image = np.nan_to_num(rgb_image, nan=0.0) + + # ========== 创建图形，禁用格网 ========== + fig, ax = plt.subplots(figsize=(12, 10)) + ax.grid(False) # 显式关闭格网 + ax.imshow(rgb_image) + ax.axis('off') # 可选：关闭坐标轴（连边框都隐藏，更干净） + # 或者用 ax.set_axis_off() 效果相同 + + # 添加影像信息（如果需要，可以取消注释） + # ax.set_title(...) + + plt.tight_layout() + plt.savefig(str(output_path), dpi=150, bbox_inches='tight', pad_inches=0.05) + plt.close(fig) + + # 释放GDAL数据集 + dataset = None + + return str(output_path) + + except Exception as e: + print(f" 生成预览图时出错 {img_path}: {e}") + plt.close('all') + return None + def _select_rgb_bands(self, img_path: str, band_count: int) -> List[int]: + """ + 选择RGB波段（优先使用波长查找，失败则使用默认索引） + + Args: + img_path: 影像文件路径 + band_count: 总波段数 + + Returns: + [R, G, B] 波段索引列表 + """ + try: + # 尝试使用pipeline中的find_band_number函数 + from src.utils.util import find_band_number + target_wavelengths = {'R': 650.0, 'G': 550.0, 'B': 460.0} + bands = [] + + for color, target_wl in target_wavelengths.items(): + try: + band_idx = find_band_number(target_wl, img_path) + band_idx = max(0, min(band_idx, band_count - 1)) + bands.append(band_idx) + except: + # 回退到基于索引的选择 + if color == 'R': + bands.append(min(band_count - 1, int(band_count * 0.25))) + elif color == 'G': + bands.append(min(band_count - 1, int(band_count * 0.15))) + else: + bands.append(min(band_count - 1, int(band_count * 0.05))) + + return bands if len(bands) == 3 else [int(band_count*0.25), int(band_count*0.15), int(band_count*0.05)] + + except ImportError: + # 如果无法导入，使用基于索引的选择 + if band_count >= 3: + return [min(band_count - 1, int(band_count * 0.25)), + min(band_count - 1, int(band_count * 0.15)), + min(band_count - 1, int(band_count * 0.05))] + else: + return [0, 0, 0] + except Exception: + return [min(band_count - 1, int(band_count * 0.25)) if band_count > 0 else 0, + min(band_count - 1, int(band_count * 0.15)) if band_count > 1 else 0, + min(band_count - 1, int(band_count * 0.05)) if band_count > 2 else 0] + + def generate_sampling_point_map(self, hyperspectral_path: Optional[str] = None, + csv_path: Optional[str] = None, + output_subdir: str = "sampling_maps") -> str: + """ + 生成采样点地图 - 在高光谱假彩色影像上标注采样点 + + Args: + hyperspectral_path: 高光谱影像路径（如果为None则自动查找） + csv_path: 采样点CSV文件路径（如果为None则自动查找4_processed_data中的CSV） + output_subdir: 输出子目录名称 + + Returns: + 生成的地图文件路径 + """ + try: + from src.postprocessing.point_map import SamplingPointMap + + # 如果没有提供路径，自动查找 + work_dir = self.output_dir.parent # 9_visualization的父目录就是工作目录 + + if hyperspectral_path is None: + # 查找高光谱影像 + hyperspectral_files = [] + for ext in ['*.dat', '*.bsq', '*.tif', '*.tiff']: + hyperspectral_files.extend(list(work_dir.glob(f"**/{ext}"))) + if hyperspectral_files: + hyperspectral_path = str(hyperspectral_files[0]) + else: + print("警告: 未找到高光谱影像文件") + return "" + + if csv_path is None: + # 查找4_processed_data中的CSV文件 + processed_dir = work_dir / "4_processed_data" + if processed_dir.exists(): + csv_files = list(processed_dir.glob("*.csv")) + if csv_files: + csv_path = str(csv_files[0]) + else: + print(f"警告: 在 {processed_dir} 中未找到CSV文件") + return "" + else: + print("警告: 4_processed_data目录不存在") + return "" + + print(f"生成采样点地图 - 高光谱: {Path(hyperspectral_path).name}, CSV: {Path(csv_path).name}") + + # 创建采样点地图生成器 + map_generator = SamplingPointMap(output_dir=str(self.output_dir / output_subdir)) + + map_path = map_generator.create_sampling_point_map( + hyperspectral_path=hyperspectral_path, + csv_path=csv_path, + point_color='red', + point_size=100, + point_alpha=0.9, + show_north_arrow=True, + show_scale_bar=True, + show_legend=True + ) + + print(f"采样点地图已生成: {map_path}") + return map_path + + except Exception as e: + print(f"生成采样点地图时出错: {e}") + return "" + + def generate_all_visualizations(self, work_dir: Optional[str] = None) -> Dict[str, str]: + """ + 生成所有可视化结果，包括掩膜缩略图、采样点地图等 + + Args: + work_dir: 工作目录（如果为None则使用output_dir的父目录） + + Returns: + 生成的文件路径字典 + """ + if work_dir is None: + work_dir = str(self.output_dir.parent) + + results = {} + + # 生成掩膜和耀斑缩略图 + try: + preview_paths = self.generate_glint_deglint_previews(work_dir=work_dir) + results['glint_deglint_previews'] = preview_paths + except Exception as e: + print(f"生成掩膜缩略图时出错: {e}") + + # 生成采样点地图 + try: + map_path = self.generate_sampling_point_map() + if map_path: + results['sampling_map'] = map_path + except Exception as e: + print(f"生成采样点地图时出错: {e}") + + # 生成航线图 + try: + flight_path = self.generate_flight_path_map(work_dir=work_dir) + if flight_path: + results['flight_path'] = flight_path + except Exception as e: + print(f"生成航线图时出错: {e}") + + return results + + def generate_flight_path_map(self, + work_dir: Optional[str] = None, + gps_folder: Optional[str] = None, + hyperspectral_path: Optional[str] = None, + output_subdir: str = "flight_paths") -> str: + """ + 生成飞行轨迹航线图 - 在高光谱影像上绘制多架次飞行轨迹 + + Args: + work_dir: 工作目录（如果为None则使用output_dir的父目录） + gps_folder: GPS数据文件夹路径（如果为None则自动查找） + hyperspectral_path: 高光谱影像路径（如果为None则自动查找） + output_subdir: 输出子目录名称 + + Returns: + 生成的航线图文件路径 + """ + try: + from src.postprocessing.flight_path import FlightPathVisualizer + + # 如果没有提供路径，自动查找 + if work_dir is None: + work_dir = str(self.output_dir.parent) + work_path = Path(work_dir) + + # 查找GPS文件夹 + if gps_folder is None: + # 首先查找常见的GPS数据文件夹 + possible_gps_dirs = ['gps', 'GPS', 'flight', '轨迹', '航线'] + for gps_dir_name in possible_gps_dirs: + gps_dir = work_path / gps_dir_name + if gps_dir.exists() and list(gps_dir.glob("**/*.gps")): + gps_folder = str(gps_dir) + print(f"找到GPS文件夹: {gps_folder}") + break + + # 如果没找到，查找任何包含.gps文件的文件夹 + if gps_folder is None: + gps_files = list(work_path.glob("**/*.gps")) + if gps_files: + gps_folder = str(gps_files[0].parent) + print(f"使用包含GPS文件的文件夹: {gps_folder}") + + if gps_folder is None or not Path(gps_folder).exists(): + print("警告: 未找到GPS数据文件夹") + return "" + + # 查找高光谱影像 - 优先使用3_deglint + if hyperspectral_path is None: + # 首先查找3_deglint文件夹 + deglint_dir = work_path / "3_deglint" + if deglint_dir.exists(): + hyperspectral_files = [] + for ext in ['*.dat', '*.bsq', '*.tif', '*.tiff']: + hyperspectral_files.extend(list(deglint_dir.glob(ext))) + if hyperspectral_files: + hyperspectral_path = str(hyperspectral_files[0]) + print(f"使用3_deglint中的高光谱影像: {hyperspectral_path}") + + # 如果没找到，再查找整个工作目录 + if hyperspectral_path is None: + hyperspectral_files = [] + for ext in ['*.dat', '*.bsq', '*.tif', '*.tiff']: + hyperspectral_files.extend(list(work_path.glob(f"**/{ext}"))) + if hyperspectral_files: + hyperspectral_path = str(hyperspectral_files[0]) + print(f"使用找到的高光谱影像: {hyperspectral_path}") + + if hyperspectral_path is None or not Path(hyperspectral_path).exists(): + print("警告: 未找到高光谱影像文件") + return "" + + print(f"生成航线图 - GPS: {Path(gps_folder).name}, 影像: {Path(hyperspectral_path).name}") + + # 创建航线图生成器 + flight_visualizer = FlightPathVisualizer( + output_dir=str(self.output_dir / output_subdir) + ) + + map_path = flight_visualizer.create_flight_path_map( + gps_folder=gps_folder, + hyperspectral_path=hyperspectral_path, + line_width=2, + show_north_arrow=True, + show_scale_bar=True, + dpi=300 + ) + + print(f"航线图已生成: {map_path}") + return map_path + + except ImportError as e: + print(f"无法导入flight_path模块: {e}") + return "" + except Exception as e: + print(f"生成航线图时出错: {e}") + return "" + + def batch_generate_flight_paths(self, + work_dir: Optional[str] = None, + gps_parent_folder: Optional[str] = None) -> Dict[str, str]: + """ + 批量生成多个飞行任务的航线图 + + Args: + work_dir: 工作目录 + gps_parent_folder: 包含多个GPS子文件夹的父文件夹 + + Returns: + 生成的航线图文件路径字典 + """ + try: + from src.postprocessing.flight_path import FlightPathVisualizer + + if work_dir is None: + work_dir = str(self.output_dir.parent) + work_path = Path(work_dir) + + # 查找GPS父文件夹 + if gps_parent_folder is None: + # 查找常见的GPS数据文件夹 + possible_gps_dirs = ['gps', 'GPS', 'flight', 'flights', '轨迹', '航线'] + for gps_dir_name in possible_gps_dirs: + gps_dir = work_path / gps_dir_name + if gps_dir.exists(): + gps_parent_folder = str(gps_dir) + break + + if gps_parent_folder is None or not Path(gps_parent_folder).exists(): + print(f"警告: 未找到GPS数据文件夹: {gps_parent_folder}") + return {} + + # 查找高光谱影像 + hyperspectral_files = [] + deglint_dir = work_path / "3_deglint" + if deglint_dir.exists(): + for ext in ['*.dat', '*.bsq', '*.tif', '*.tiff']: + hyperspectral_files.extend(list(deglint_dir.glob(ext))) + + if not hyperspectral_files: + for ext in ['*.dat', '*.bsq', '*.tif', '*.tiff']: + hyperspectral_files.extend(list(work_path.glob(f"**/{ext}"))) + + if not hyperspectral_files: + print("警告: 未找到高光谱影像文件") + return {} + + hyperspectral_path = str(hyperspectral_files[0]) + + print(f"批量生成航线图 - GPS父文件夹: {gps_parent_folder}") + + # 批量生成 + flight_visualizer = FlightPathVisualizer( + output_dir=str(self.output_dir / "flight_paths") + ) + + map_paths = flight_visualizer.batch_create_maps( + gps_folder=gps_parent_folder, + hyperspectral_folder=str(Path(hyperspectral_path).parent), + output_subdir="batch_flight_paths" + ) + + print(f"批量航线图生成完成，共生成 {len(map_paths)} 个") + return map_paths + + except Exception as e: + print(f"批量生成航线图时出错: {e}") + return {} + + +class ReportGenerator: + """报告生成类""" + + def __init__(self, output_dir: str = "./reports"): + """ + 初始化报告生成类 + + Args: + output_dir: 输出目录 + """ + self.output_dir = Path(output_dir) + self.output_dir.mkdir(parents=True, exist_ok=True) + + def generate_training_summary(self, models_dir: str, output_path: Optional[str] = None) -> str: + """ + 生成模型训练摘要报告（training_summary.csv） + + Args: + models_dir: 模型保存目录 + output_path: 输出路径（如果为None，自动生成） + + Returns: + 保存的文件路径 + """ + from modeling_batch import WaterQualityModelingBatch + + modeler = WaterQualityModelingBatch(models_dir) + + # 需要先加载训练结果 + # 这里假设results已经存储在modeler中，或者需要从保存的文件中读取 + # 由于modeling_batch.py的结构，我们需要另一种方式来获取所有结果 + + # 尝试遍历模型目录，查找所有保存的结果 + models_path = Path(models_dir) + all_results = [] + + # 遍历所有目标参数文件夹 + for target_folder in models_path.iterdir(): + if not target_folder.is_dir(): + continue + + target_name = target_folder.name + + # 查找所有模型文件 + for model_file in target_folder.rglob("*.pkl"): + # 从文件名提取信息（假设格式为：{preprocess}_{model}_{split}.pkl） + model_info = { + 'target': target_name, + 'model_file': str(model_file), + 'preprocess': 'Unknown', + 'model': 'Unknown', + 'split_method': 'Unknown' + } + + # 尝试从文件名解析 + parts = model_file.stem.split('_') + if len(parts) >= 3: + model_info['preprocess'] = parts[0] + model_info['model'] = parts[1] + model_info['split_method'] = parts[2] + + all_results.append(model_info) + + # 如果有训练结果数据，使用实际指标 + # 否则创建一个基本的摘要 + summary_data = [] + for result in all_results: + summary_data.append({ + '目标参数': result['target'], + '预处理方法': result['preprocess'], + '模型名称': result['model'], + '划分方法': result['split_method'], + '模型文件': result['model_file'] + }) + + if not summary_data: + print("警告：未找到模型文件，生成空摘要") + summary_data = [{ + '目标参数': 'No Data', + '预处理方法': 'N/A', + '模型名称': 'N/A', + '划分方法': 'N/A', + '模型文件': 'N/A' + }] + + df_summary = pd.DataFrame(summary_data) + + if output_path is None: + output_path = self.output_dir / "training_summary.csv" + else: + output_path = Path(output_path) + + df_summary.to_csv(output_path, index=False, encoding='utf-8-sig') + print(f"训练摘要报告已保存: {output_path}") + return str(output_path) + + def generate_prediction_report(self, prediction_csv_paths: Dict[str, str], + output_path: Optional[str] = None) -> str: + """ + 生成参数反演结果报告（包含预测统计信息） + + Args: + prediction_csv_paths: 预测结果文件路径字典（键为目标参数名） + output_path: 输出路径（如果为None，自动生成） + + Returns: + 保存的文件路径 + """ + report_data = [] + + for target_name, csv_path in prediction_csv_paths.items(): + try: + df = pd.read_csv(csv_path) + + # 假设预测值列名为'prediction'或最后一列 + if 'prediction' in df.columns: + pred_col = 'prediction' + else: + pred_col = df.columns[-1] + + predictions = df[pred_col].dropna() + + stats = { + '目标参数': target_name, + '样本数量': len(predictions), + '均值': predictions.mean(), + '标准差': predictions.std(), + '最小值': predictions.min(), + '最大值': predictions.max(), + '中位数': predictions.median(), + '25%分位数': predictions.quantile(0.25), + '75%分位数': predictions.quantile(0.75), + '文件路径': csv_path + } + + report_data.append(stats) + except Exception as e: + print(f"处理文件 {csv_path} 时出错: {e}") + report_data.append({ + '目标参数': target_name, + '样本数量': 0, + '错误': str(e) + }) + + df_report = pd.DataFrame(report_data) + + if output_path is None: + output_path = self.output_dir / "prediction_report.csv" + else: + output_path = Path(output_path) + + df_report.to_csv(output_path, index=False, encoding='utf-8-sig', float_format='%.6f') + print(f"预测结果报告已保存: {output_path}") + return str(output_path) + + def generate_batch_inference_summary(self, pipeline_info: Dict, + output_path: Optional[str] = None) -> str: + """ + 生成批量处理摘要（batch_inference_summary.json） + + Args: + pipeline_info: 流程信息字典，包含各步骤的执行情况 + output_path: 输出路径（如果为None，自动生成） + + Returns: + 保存的文件路径 + """ + summary = { + '执行时间': datetime.now().strftime('%Y-%m-%d %H:%M:%S'), + '工作目录': str(pipeline_info.get('work_dir', 'Unknown')), + '步骤执行情况': {}, + '模型训练': {}, + '预测结果': {}, + '输出文件': {} + } + + # 添加步骤执行情况 + for step in ['step1', 'step2', 'step3', 'step4', 'step5', 'step6', 'step7', 'step8', 'step9']: + if step in pipeline_info: + summary['步骤执行情况'][step] = { + '状态': pipeline_info[step].get('status', 'completed'), + '输出文件': pipeline_info[step].get('output_file', 'N/A') + } + + # 添加模型训练信息 + if 'models_dir' in pipeline_info: + summary['模型训练']['模型目录'] = pipeline_info['models_dir'] + summary['模型训练']['训练参数'] = pipeline_info.get('training_params', {}) + + # 添加预测结果信息 + if 'prediction_files' in pipeline_info: + summary['预测结果'] = { + '目标参数数量': len(pipeline_info['prediction_files']), + '预测文件': pipeline_info['prediction_files'] + } + + # 添加输出文件列表 + summary['输出文件'] = pipeline_info.get('output_files', {}) + + if output_path is None: + output_path = self.output_dir / "batch_inference_summary.json" + else: + output_path = Path(output_path) + + with open(output_path, 'w', encoding='utf-8') as f: + json.dump(summary, f, ensure_ascii=False, indent=2) + + print(f"批量处理摘要已保存: {output_path}") + return str(output_path) + diff --git a/src/preprocessing/__init__.py b/src/preprocessing/__init__.py new file mode 100644 index 0000000..7c68785 --- /dev/null +++ b/src/preprocessing/__init__.py @@ -0,0 +1 @@ +# -*- coding: utf-8 -*- \ No newline at end of file diff --git a/src/preprocessing/process_water_quality_data.py b/src/preprocessing/process_water_quality_data.py new file mode 100644 index 0000000..4f44cf7 --- /dev/null +++ b/src/preprocessing/process_water_quality_data.py @@ -0,0 +1,223 @@ +import threading # 放在你的其他 import 之前 +if not hasattr(threading.Thread, "isAlive"): + threading.Thread.isAlive = threading.Thread.is_alive # 给旧调试器一个别名 + +import warnings +import os +import numpy as np +import pandas as pd +from scipy import stats + +warnings.filterwarnings("ignore") + + +def detect_outliers_iqr(data: pd.DataFrame, column: str) -> pd.Series: + """使用 IQR 方法检测异常值，返回与 data 同索引的布尔序列""" + s = pd.to_numeric(data[column], errors="coerce") + q1 = s.quantile(0.25) + q3 = s.quantile(0.75) + iqr = q3 - q1 + lower = q1 - 1.5 * iqr + upper = q3 + 1.5 * iqr + mask = (s < lower) | (s > upper) + # 对 NaN 不判为异常 + mask = mask.fillna(False) + mask.index = data.index + return mask + + +def detect_outliers_zscore(data: pd.DataFrame, column: str, threshold: float = 3.0) -> pd.Series: + """使用 Z-score 方法检测异常值，返回与 data 同索引的布尔序列""" + s = pd.to_numeric(data[column], errors="coerce") + z = pd.Series(stats.zscore(s.dropna()), index=s.dropna().index) + mask = (z.abs() > threshold).reindex(data.index).fillna(False) + return mask + + +def read_csv_robust(path, **kwargs): + """ + 尝试多种编码读取 CSV；成功即返回 DataFrame。 + kwargs 会透传给 pd.read_csv（比如 sep、dtype 等）。 + """ + # 按出现概率排序；优先无损 + 常见中文编码 + encodings = [ + "utf-8", "utf-8-sig", + "gbk", "gb18030", "cp936", + "utf-16", "utf-16le", "utf-16be", + "cp1252", "big5", + "latin1", # 最后兜底（能读但中文会变乱码） + ] + errors_modes = ["strict", "replace"] # 先严格，失败再替换非法字符 + + last_err = None + for enc in encodings: + for emode in errors_modes: + try: + return pd.read_csv(path, encoding=enc, **kwargs) + except Exception as e: + last_err = e + continue + # 如果全失败，抛出最后一个错误 + raise last_err + + +def _decimal_len(v) -> float: + """计算数值或字符串小数点后的位数；若无法计算返回 NaN""" + if pd.isna(v): + return np.nan + try: + # 统一成字符串处理 + s = str(v) + if "." not in s: + return 0 + frac = s.split(".", 1)[1] + # 去掉科学计数法中的尾随部分（如 '1.234e-05'） + frac = frac.split("e")[0].split("E")[0] + return len(frac) + except Exception: + return np.nan + + +def process_water_quality_data(input_file: str, output_file: str): + """ + 处理水质数据 CSV 文件 + + 参数: + input_file: 输入 CSV 文件路径 + output_file: 输出 CSV 文件路径 + """ + # 0) 读取 + print("正在读取 CSV 文件...") + df = read_csv_robust(input_file) + print(f"原始数据形状: {df.shape}") + print(f"列名: {list(df.columns)}") + + # 1) 经纬度精度筛选（小数位 >= 7） + print("\n正在筛选经纬度精度(小数位>=7)...") + initial_count = len(df) + + for col in ["经度", "纬度"]: + if col in df.columns: + dec_len = df[col].apply(_decimal_len) + keep_mask = dec_len >= 7 + dropped = (~keep_mask).sum() + df = df[keep_mask].copy() + print(f"列 {col}: 去除了 {int(dropped)} 行（保留 {len(df)} 行）") + + after_coord_filter = len(df) + print(f"经纬度精度筛选后剩余: {after_coord_filter} 行 (去除了 {initial_count - after_coord_filter} 行)") + + # 2) 异常值检测（IQR）- 只删除异常值，不删除整行 + print("\n正在检测异常值(IQR)...") + # 数值列 + numeric_columns = df.select_dtypes(include=[np.number]).columns.tolist() + + # 排除不检测的列 + exclude_columns = ["时间", "测量点", "纬度", "经度"] + if "原始" in df.columns: + exclude_columns.append("原始") + + columns_to_check = [c for c in numeric_columns if c not in exclude_columns] + print(f"将检测以下列的异常值: {columns_to_check}") + + df_clean = df.copy() + total_outliers_removed = 0 + + for column in columns_to_check: + if column in df_clean.columns and df_clean[column].notna().sum() > 0: + col_mask = detect_outliers_iqr(df_clean, column) + outlier_count = int(col_mask.sum()) + print(f'列 "{column}" 检测到 {outlier_count} 个异常值，将其设为 NaN') + # 只将异常值设为 NaN，不删除整行 + df_clean.loc[col_mask, column] = np.nan + total_outliers_removed += outlier_count + + after_outlier_filter = len(df_clean) + print(f"异常值处理完成: 保留 {after_outlier_filter} 行数据，共处理了 {total_outliers_removed} 个异常值") + + # 3) 去除 "原始" 列（若存在） + if "原始" in df_clean.columns: + df_clean = df_clean.drop(columns=["原始"]) + print('已去除 "原始" 列') + + # 4) 字段类型处理：尽量把“时间”转为 datetime + if "时间" in df_clean.columns: + try: + df_clean["时间"] = pd.to_datetime(df_clean["时间"], errors="coerce") + except Exception: + pass + + # 5) 按测量点统计平均值 + print("\n正在按测量点统计平均值...") + if "测量点" not in df_clean.columns: + print('错误：未找到 "测量点" 列') + return + + # 构建聚合字典 + agg_dict = {} + if "时间" in df_clean.columns and np.issubdtype(df_clean["时间"].dtype, np.datetime64): + # 时间取平均（等价于时间戳平均） + agg_dict["时间"] = "mean" + elif "时间" in df_clean.columns: + # 如果不是时间类型，保留最常见值以避免无意义的字符串平均 + agg_dict["时间"] = lambda s: s.mode().iloc[0] if not s.mode().empty else s.dropna().iloc[0] if s.dropna().size else np.nan + + for col in ["纬度", "经度"]: + if col in df_clean.columns: + agg_dict[col] = "mean" + + # 其余数值列取均值 + for col in df_clean.select_dtypes(include=[np.number]).columns: + if col not in ["纬度", "经度"]: + agg_dict[col] = "mean" + + grouped = df_clean.groupby("测量点", as_index=False).agg(agg_dict) + + print(f"统计完成，共 {len(grouped)} 个测量点") + print(f"输出数据形状: {grouped.shape}") + + # 6) 去除"时间"和"测量点"列 + columns_to_drop = [] + if "时间" in grouped.columns: + columns_to_drop.append("时间") + if "测量点" in grouped.columns: + columns_to_drop.append("测量点") + + if columns_to_drop: + grouped = grouped.drop(columns=columns_to_drop) + print(f"已去除列: {columns_to_drop}") + print(f"去除列后数据形状: {grouped.shape}") + + # 7) 保存 + os.makedirs(os.path.dirname(output_file) or ".", exist_ok=True) + grouped.to_csv(output_file, index=False, encoding="utf-8-sig") + print(f"\n处理完成！结果已保存到: {output_file}") + + # 摘要 + print("\n=== 处理结果摘要 ===") + print(f"原始数据行数: {initial_count}") + print(f"经纬度精度筛选后: {after_coord_filter}") + print(f"异常值筛选后: {after_outlier_filter}") + print(f"最终统计结果: {len(grouped)} 个测量点") + + return grouped + + +def main(): + """主函数""" + input_file = r"D:\BaiduNetdiskDownload\yaobao\csv\input.csv" + output_file =r"D:\BaiduNetdiskDownload\yaobao\csv\output_test.csv" + + if not output_file: + output_file = "processed_water_quality.csv" + + try: + _ = process_water_quality_data(input_file, output_file) + except FileNotFoundError as e: + print(f"文件未找到：{e}") + except Exception as e: + print(f"处理失败：{e}") + + +if __name__ == "__main__": + main() diff --git a/src/preprocessing/spectral_Preprocessing.py b/src/preprocessing/spectral_Preprocessing.py new file mode 100644 index 0000000..a207dd3 --- /dev/null +++ b/src/preprocessing/spectral_Preprocessing.py @@ -0,0 +1,157 @@ +import numpy as np +from scipy import signal +from sklearn.linear_model import LinearRegression +from sklearn.preprocessing import MinMaxScaler, StandardScaler +import pandas as pd +import pywt +from copy import deepcopy +import joblib # 用于保存和加载模型 +# 最大最小值归一化 +def MMS(input_spectrum): + output_spectrum = MinMaxScaler().fit_transform(input_spectrum) + return output_spectrum + +# 标准化 +def SS(input_spectrum, save_path=None): + # 初始化 StandardScaler 并拟合数据 + scaler = StandardScaler() + output_spectrum = scaler.fit_transform(input_spectrum) + + # 如果指定了保存路径，保存 scaler 对象 + if save_path: + joblib.dump(scaler, save_path) + print(f"Scaler parameters saved to {save_path}") + + return output_spectrum + +# 均值中心化 +def CT(input_spectrum): + output_spectrum = deepcopy(input_spectrum) + for i in range(output_spectrum.shape[0]): + MEAN = np.mean(output_spectrum[i]) + output_spectrum[i] = output_spectrum[i] - MEAN + return output_spectrum + +# 标准正态变换 +def SNV(input_spectrum): + if not isinstance(input_spectrum, pd.DataFrame): + raise ValueError("Input spectrum must be a Pandas DataFrame") + data_average = input_spectrum.mean(axis=1) + data_std = input_spectrum.std(axis=1) + data_std = data_std.replace(0, 1) + output_spectrum = (input_spectrum.sub(data_average, axis=0)).div(data_std, axis=0) + return output_spectrum + +# 移动平均平滑 +def MA(input_spectrum, WSZ=11): + output_spectrum = deepcopy(input_spectrum) + for i in range(output_spectrum.shape[0]): + out0 = np.convolve(output_spectrum[i], np.ones(WSZ, dtype=int), 'valid') / WSZ + r = np.arange(1, WSZ - 1, 2) + start = np.cumsum(output_spectrum[i, :WSZ - 1])[::2] / r + stop = (np.cumsum(output_spectrum[i, :-WSZ:-1])[::2] / r)[::-1] + output_spectrum[i] = np.concatenate((start, out0, stop)) + return output_spectrum + +# Savitzky-Golay平滑滤波 +def SG(input_spectrum, w=15, p=2): + output_spectrum = signal.savgol_filter(input_spectrum, w, p) + return output_spectrum + +# 一阶导数 +def D1(input_spectrum): + n, p = input_spectrum.shape + output_spectrum = np.ones((n, p - 1)) + for i in range(n): + output_spectrum[i] = np.diff(input_spectrum[i]) + return output_spectrum + +# 二阶导数 +def D2(input_spectrum): + temp2 = (pd.DataFrame(input_spectrum)).diff(axis=1) + temp3 = np.delete(temp2.values, 0, axis=1) + temp4 = (pd.DataFrame(temp3)).diff(axis=1) + output_spectrum = np.delete(temp4.values, 0, axis=1) + return output_spectrum + +# 趋势校正 +def DT(input_spectrum): + lenth = input_spectrum.shape[1] + x = np.asarray(range(lenth), dtype=np.float32) + output_spectrum = np.array(input_spectrum) + l = LinearRegression() + for i in range(output_spectrum.shape[0]): + l.fit(x.reshape(-1, 1), output_spectrum[i].reshape(-1, 1)) + k = l.coef_ + b = l.intercept_ + for j in range(output_spectrum.shape[1]): + output_spectrum[i][j] = output_spectrum[i][j] - (j * k + b) + return output_spectrum + +# 多元散射校正 +def MSC(input_spectrum): + n, p = input_spectrum.shape + output_spectrum = np.ones((n, p)) + mean = np.mean(input_spectrum, axis=0) + for i in range(n): + y = input_spectrum[i, :] + l = LinearRegression() + l.fit(mean.reshape(-1, 1), y.reshape(-1, 1)) + k = l.coef_ + b = l.intercept_ + output_spectrum[i, :] = (y - b) / k + return output_spectrum + +# 小波变换 +def wave(input_spectrum): + def wave_(input_spectrum_row): + w = pywt.Wavelet('db8') + maxlev = pywt.dwt_max_level(len(input_spectrum_row), w.dec_len) + coeffs = pywt.wavedec(input_spectrum_row, 'db8', level=maxlev) + threshold = 0.04 + for i in range(1, len(coeffs)): + coeffs[i] = pywt.threshold(coeffs[i], threshold * max(coeffs[i])) + output_spectrum_row = pywt.waverec(coeffs, 'db8') + return output_spectrum_row + + output_spectrum = None + for i in range(input_spectrum.shape[0]): + if i == 0: + output_spectrum = wave_(input_spectrum[i]) + else: + output_spectrum = np.vstack((output_spectrum, wave_(input_spectrum[i]))) + + return output_spectrum + +# 通用预处理函数 +def Preprocessing(method, input_spectrum): + if isinstance(input_spectrum, np.ndarray): + input_spectrum = pd.DataFrame(input_spectrum) + if method == "None": + output_spectrum = input_spectrum + elif method == 'MMS': + output_spectrum = MMS(input_spectrum.values) + elif method == 'SS': + output_spectrum = SS(input_spectrum.values, r'E:\code\WQ\models/scaler_params.pkl') + elif method == 'CT': + output_spectrum = CT(input_spectrum.values) + elif method == 'SNV': + output_spectrum = SNV(input_spectrum) + elif method == 'MA': + output_spectrum = MA(input_spectrum.values) + elif method == 'SG': + output_spectrum = SG(input_spectrum.values) + elif method == 'MSC': + output_spectrum = MSC(input_spectrum.values) + elif method == 'D1': + output_spectrum = D1(input_spectrum.values) + elif method == 'D2': + output_spectrum = D2(input_spectrum.values) + elif method == 'DT': + output_spectrum = DT(input_spectrum.values) + elif method == 'WVAE': + output_spectrum = wave(input_spectrum.values) + else: + print("No such method of preprocessing!") + output_spectrum = input_spectrum.values + return output_spectrum diff --git a/src/utils/__init__.py b/src/utils/__init__.py new file mode 100644 index 0000000..7c68785 --- /dev/null +++ b/src/utils/__init__.py @@ -0,0 +1 @@ +# -*- coding: utf-8 -*- \ No newline at end of file diff --git a/src/utils/band_math.py b/src/utils/band_math.py new file mode 100644 index 0000000..b5678a8 --- /dev/null +++ b/src/utils/band_math.py @@ -0,0 +1,226 @@ +import pandas as pd +import numpy as np +import re + + +class BandMathCalculator: + def __init__(self, csv_file): + """ + 初始化计算器 + csv_file: 包含光谱反射率的CSV文件路径 + """ + self.df = pd.read_csv(csv_file) + self.wavelengths = self._extract_wavelengths() + + def _extract_wavelengths(self): + """从列名中提取波长信息""" + wavelengths = [] + for col in self.df.columns: + # 尝试从列名中提取数字（波长） + numbers = re.findall(r'\d+\.?\d*', str(col)) + if numbers: + wavelengths.append(float(numbers[0])) + else: + wavelengths.append(None) + return wavelengths + + def _find_closest_wavelength(self, target_wavelength): + """找到最接近目标波长的列索引""" + valid_indices = [i for i, wl in enumerate(self.wavelengths) if wl is not None] + if not valid_indices: + raise ValueError("未找到有效的波长列") + + # 计算与目标波长的差值 + differences = [abs(self.wavelengths[i] - target_wavelength) for i in valid_indices] + min_diff_index = np.argmin(differences) + closest_index = valid_indices[min_diff_index] + closest_wavelength = self.wavelengths[closest_index] + + print( + f"目标波长 {target_wavelength}nm -> 最接近波长 {closest_wavelength}nm (列: {self.df.columns[closest_index]})") + return closest_index + + def _parse_expression(self, expression): + """解析表达式，提取所有波段变量 - 支持大小写""" + # 匹配 w或W后面跟着数字的格式的变量 + pattern = r'[wW](\d+\.?\d*)' + matches = re.findall(pattern, expression) + return matches # 返回字符串列表，如 ['686', '672', '715', '672'] + + def _create_substitution_dict(self, variables, row_index=0): + """创建变量替换字典 - 支持大小写""" + substitution_dict = {} + for var in variables: + wavelength = float(var) # 将字符串转换为浮点数 + col_index = self._find_closest_wavelength(wavelength) + value = self.df.iloc[row_index, col_index] + # 同时添加小写和大写版本的变量 + substitution_dict[f'w{var}'] = value + substitution_dict[f'W{var}'] = value + return substitution_dict + + def calculate(self, expression, row_index=0): + """ + 计算自定义波段表达式 + + 参数: + expression: 波段计算表达式，如 'chl=w560/w760' + row_index: 要计算的数据行索引，默认为第0行 + + 返回: + 计算结果 + """ + try: + # 提取表达式中的计算部分 + if '=' in expression: + # 如果包含赋值，只取等号右边的计算部分 + calc_part = expression.split('=')[1].strip() + var_name = expression.split('=')[0].strip() + else: + calc_part = expression.strip() + var_name = None + + # 解析变量 + variables = self._parse_expression(calc_part) + print(f"解析到的波长变量: {variables}") + + # 创建替换字典 + sub_dict = self._create_substitution_dict(variables, row_index) + print(f"变量值: {sub_dict}") + + # 替换表达式中的变量 - 使用安全的字符串替换 + calc_expression = calc_part + for var_pattern, value in sub_dict.items(): + # 确保替换完整的变量名，避免部分匹配 + calc_expression = re.sub(r'\b' + re.escape(var_pattern) + r'\b', f"({value})", calc_expression) + + print(f"计算表达式: {calc_expression}") + + # 安全地计算表达式 + result = eval(calc_expression) + + # 返回结果 + if var_name: + return {var_name: result} + else: + return result + + except Exception as e: + print(f"计算错误: {e}") + import traceback + traceback.print_exc() + return None + + def calculate_all_rows(self, expression): + """为所有行计算表达式""" + results = [] + for i in range(len(self.df)): + print(f"\n--- 计算第 {i} 行 ---") + result = self.calculate(expression, i) + if result is not None: + if isinstance(result, dict): + results.append(list(result.values())[0]) + else: + results.append(result) + else: + # 如果计算失败，添加NaN值以保持结果数量一致 + results.append(np.nan) + print(f"第 {i} 行计算失败，使用NaN填充") + return results + + def process_formulas_from_csv(self, formula_csv_file, formula_names=None, output_file=None): + """ + 从公式CSV文件中批量计算并添加到数据文件中 + + 参数: + formula_csv_file: 公式CSV文件路径，第一列为公式名称，第三列为具体公式 + formula_names: 要计算的公式名称列表，如果为None则计算所有公式 + output_file: 输出文件路径，如果为None则自动生成 + + 返回: + 包含计算结果的新DataFrame + """ + # 读取公式CSV文件 + try: + formulas_df = pd.read_csv(formula_csv_file) + print(f"读取到 {len(formulas_df)} 个公式") + + # 检查CSV格式，假设第一列为公式名称，第三列为具体公式 + if len(formulas_df.columns) < 3: + raise ValueError("公式CSV文件需要至少3列") + + formula_name_col = formulas_df.columns[0] # 第一列：公式名称 + formula_expr_col = formulas_df.columns[2] # 第三列：具体公式 + + # 创建结果DataFrame的副本 + result_df = self.df.copy() + + # 如果指定了公式名称，则只计算这些公式 + if formula_names is not None: + if isinstance(formula_names, str): + formula_names = [formula_names] # 转换为列表 + + # 筛选出指定的公式 + selected_formulas = formulas_df[formulas_df[formula_name_col].isin(formula_names)] + print(f"找到 {len(selected_formulas)} 个指定的公式") + + if len(selected_formulas) == 0: + print(f"警告: 未找到指定的公式: {formula_names}") + return result_df + + formulas_to_process = selected_formulas + else: + # 计算所有公式 + formulas_to_process = formulas_df + + # 为每个公式计算所有行 + for _, row in formulas_to_process.iterrows(): + formula_name = row[formula_name_col] + formula_expr = row[formula_expr_col] + + if pd.isna(formula_name) or pd.isna(formula_expr): + print(f"跳过空公式: {row}") + continue + + print(f"\n计算公式: {formula_name} = {formula_expr}") + + # 计算所有行的结果 + results = self.calculate_all_rows(formula_expr) + + # 将结果添加到DataFrame + result_df[formula_name] = results + print(f"公式 '{formula_name}' 计算完成，添加到数据中") + + # 保存结果 + if output_file is None: + # 自动生成输出文件名 + import os + base_name = os.path.splitext(os.path.basename(formula_csv_file))[0] + output_file = f"band_math_results_{base_name}.csv" + + result_df.to_csv(output_file, index=False) + print(f"结果已保存到: {output_file}") + + return result_df + + except Exception as e: + print(f"处理公式CSV文件时出错: {e}") + import traceback + traceback.print_exc() + return None + + +# 更新使用示例 +if __name__ == "__main__": + # 创建计算器实例 + calculator = BandMathCalculator(r"E:\code\WQ\pipeline_result\work_dir\5_training_spectra\training_spectra.csv") + + # 示例1: 计算所有公式 + # result_df = calculator.process_formulas_from_csv(r"E:\code\WQ\封装\sub\水质参数.csv", "enhanced_data.csv") + + # 示例2: 计算指定公式 + result_df = calculator.process_formulas_from_csv( + r"E:\code\WQ\封装\sub\水质参数.csv", + formula_names=["BGA_Am09KBBI", "BGA_Be162B643sub629"], + output_file=r"E:\code\WQ\pipeline_result\work_dir\5_training_spectra\enhanced_data.csv" + ) \ No newline at end of file diff --git a/src/utils/extract_water_area.py b/src/utils/extract_water_area.py new file mode 100644 index 0000000..05c67a3 --- /dev/null +++ b/src/utils/extract_water_area.py @@ -0,0 +1,172 @@ +from src.utils.util import * +from osgeo import gdal, ogr +import argparse +gdal.UseExceptions() +ogr.UseExceptions() + +def xml2shp(): + pass + + +def rasterize_envi_xml(shp_filepath): + pass + + +@timeit +def rasterize_shp(shp_filepath, raster_fn_out, img_path, NoData_value=None): + dataset = gdal.Open(img_path) + im_width = dataset.RasterXSize + im_height = dataset.RasterYSize + geotransform = dataset.GetGeoTransform() + imgdata_in = dataset.GetRasterBand(1).ReadAsArray() + del dataset + + # Open the data source and read in the extent + source_ds = gdal.OpenEx(shp_filepath, gdal.OF_VECTOR) + if source_ds is None: + raise ValueError(f"无法打开shapefile: {shp_filepath}") + + # 检查图层数量，如果有多层，指定使用第一层 + layer_count = source_ds.GetLayerCount() + layer_name = None + if layer_count > 1: + print(f"警告: shapefile包含{layer_count}个图层，将使用第一个图层进行栅格化") + # 获取第一个图层 + layer = source_ds.GetLayer(0) + layer_name = layer.GetName() + + # about 25 metres(ish) use 0.001 if you want roughly 100m + pixel_size_x = abs(geotransform[1]) # 像素宽度（X方向） + pixel_size_y = abs(geotransform[5]) # 像素高度（Y方向，通常是负值，需要取绝对值） + raster_fn_out_tmp = append2filename(raster_fn_out, "_tmp_delete") + + # 构建栅格化参数 + rasterize_kwargs = { + 'format': 'envi', + 'outputType': gdal.GDT_Byte, + 'noData': NoData_value, + 'initValues': NoData_value, + 'xRes': pixel_size_x, + 'yRes': pixel_size_y, + 'allTouched': True, + 'burnValues': 1 + } + + # 如果有多层，指定使用第一层 + if layer_name is not None: + rasterize_kwargs['layers'] = [layer_name] + + # 执行栅格化 + gdal.Rasterize(raster_fn_out_tmp, source_ds, **rasterize_kwargs) + + dataset_tmp = gdal.Open(raster_fn_out_tmp) + geotransform_tmp = dataset_tmp.GetGeoTransform() + inv_geotransform_tmp = gdal.InvGeoTransform(geotransform_tmp) + data_tmp = dataset_tmp.GetRasterBand(1).ReadAsArray() + del dataset_tmp + + # 创建和输入影像相同行列号、相同分辨率的水域掩膜，方便后续使用 + water_mask = np.zeros((im_height, im_width)) + for row in range(im_height): + for column in range(im_width): + coor = gdal.ApplyGeoTransform(geotransform, column, row) + + coor_pixel = gdal.ApplyGeoTransform(inv_geotransform_tmp, coor[0], coor[1]) + coor_pixel = [int(num) for num in coor_pixel] + + if coor_pixel[0] < 0 or coor_pixel[0] >= data_tmp.shape[1]: + continue + if coor_pixel[1] < 0 or coor_pixel[1] >= data_tmp.shape[0]: + continue + + if imgdata_in[row, column] == 0: # 当shp区域比影像区域大时，略过 + continue + + water_mask[row, column] = data_tmp[coor_pixel[1], coor_pixel[0]] + + write_bands(img_path, raster_fn_out, water_mask) + + os.remove(raster_fn_out_tmp) + + +def calculate_NDWI(green_bandnumber, nir_bandnumber, filename): + dataset = gdal.Open(filename) # 打开文件 + num_bands = dataset.RasterCount # 栅格矩阵的波段数 + im_geotrans = dataset.GetGeoTransform() # 仿射矩阵 + im_proj = dataset.GetProjection() # 地图投影信息 + + tmp = dataset.GetRasterBand(green_bandnumber + 1) # 波段计数从1开始 + band_green = tmp.ReadAsArray().astype(np.int16) + tmp = dataset.GetRasterBand(nir_bandnumber + 1) # 波段计数从1开始 + band_nir = tmp.ReadAsArray().astype(np.int16) + + ndwi = (band_green - band_nir) / (band_green + band_nir) + + del dataset + + return ndwi + + +def extract_water(ndwi, threshold=0.3, data_ignore_value=0): + water_region = np.where(ndwi > threshold, 1, data_ignore_value) + + return water_region + + +def ndwi(file_path, ndwi_threshold=0.4, output_path=None, data_ignore_value=0): + if output_path is None: + output_path = append2filename(file_path, "_waterarea") + + dataset_in = gdal.Open(file_path) + im_width_in = dataset_in.RasterXSize # 栅格矩阵的列数 + im_height_in = dataset_in.RasterYSize # 栅格矩阵的行数 + num_bands_in = dataset_in.RasterCount # 栅格矩阵的波段数 + geotrans_in = dataset_in.GetGeoTransform() # 仿射矩阵 + proj_in = dataset_in.GetProjection() # 地图投影信息 + del dataset_in + + green_wave = 552.19 + nir_wave = 809.2890 + green_band_number = find_band_number(green_wave, file_path) + nir_band_number = find_band_number(nir_wave, file_path) + + ndwi = calculate_NDWI(green_band_number, nir_band_number, file_path) + + water_binary = extract_water(ndwi, threshold=ndwi_threshold) # 0.4 + + write_bands(file_path, output_path, water_binary) + + return output_path + + +def main(): + parser = argparse.ArgumentParser(description="此程序用于提取水域区域，输出的水域栅格和输入的影像具有相同的行列数。") + + # parser.add_argument("--global_arg", type=str, help="A global argument for all modes", required=True) + + # 创建子命令解析器 + subparsers = parser.add_subparsers(dest="algorithm", required=True, help="Choose a mode") + + rasterize_shp_ = subparsers.add_parser("rasterize_shp", help="Mode 1 description") + rasterize_shp_.add_argument('-i1', '--img_path', type=str, required=True, help='输入影像文件的路径') + rasterize_shp_.add_argument('-i2', '--shp_path', type=str, required=True, help='输入shp文件的路径') + rasterize_shp_.add_argument('-o', '--water_mask_outpath', required=True, type=str, help='输出水体掩膜文件的路径') + rasterize_shp_.set_defaults(func=rasterize_shp) + + ndwi_ = subparsers.add_parser("ndwi", help="Mode 2 description") + ndwi_.add_argument('-i1', '--img_path', type=str, required=True, help='输入影像文件的路径') + ndwi_.add_argument('-i2', '--ndwi_threshold', type=float, required=True, help='输入ndwi水体阈值，大于此值的为水域') + ndwi_.add_argument('-o', '--water_mask_outpath', required=True, type=str, help='输出水体掩膜文件的路径') + ndwi_.set_defaults(func=ndwi) + + # 解析参数 + args = parser.parse_args() + if args.algorithm == "rasterize_shp": + args.func(args.shp_path, args.water_mask_outpath, args.img_path) + elif args.algorithm == "ndwi": + args.func(args.img_path, args.ndwi_threshold, args.water_mask_outpath) + + +# Press the green button in the gutter to run the script. +if __name__ == '__main__': + main() diff --git a/src/utils/find_severe_glint_area.py b/src/utils/find_severe_glint_area.py new file mode 100644 index 0000000..d40f3e7 --- /dev/null +++ b/src/utils/find_severe_glint_area.py @@ -0,0 +1,765 @@ +from src.utils.util import * +from osgeo import gdal, ogr +import argparse +import cv2 + + + +def percentile_stretch(img, data_water_mask, lower_percentile=2, upper_percentile=98, output_range=(0, 255)): + """ + 使用百分位数裁剪进行归一化，适用于低反射率数据 + 通过排除极值，更好地利用数据的动态范围 + + Args: + img: 输入图像数组（反射率值，通常在0-1之间） + data_water_mask: 水域掩膜 + lower_percentile: 下百分位数，用于裁剪最小值（默认2） + upper_percentile: 上百分位数，用于裁剪最大值（默认98） + output_range: 输出范围，默认(0, 255) + + Returns: + 归一化后的图像数组（整数类型） + """ + # 只在水域掩膜区域计算百分位数 + valid_pixels = img[(data_water_mask > 0) & (img > 0) & np.isfinite(img)] + + if len(valid_pixels) == 0: + print("警告: 没有有效像素用于百分位数计算，使用原始值") + return img.astype(np.int32) + + # 计算百分位数 + p_lower = np.percentile(valid_pixels, lower_percentile) + p_upper = np.percentile(valid_pixels, upper_percentile) + + # 如果上下界相同，使用最大值作为上界 + if p_lower >= p_upper: + p_lower = np.percentile(valid_pixels, 1) + p_upper = np.percentile(valid_pixels, 99) + if p_lower >= p_upper: + p_upper = valid_pixels.max() + p_lower = valid_pixels.min() + + print(f"百分位数拉伸: {lower_percentile}%={p_lower:.6f}, {upper_percentile}%={p_upper:.6f}, " + f"数据范围=[{img.min():.6f}, {img.max():.6f}]") + + # 裁剪到百分位数范围 + img_clipped = np.clip(img, p_lower, p_upper) + + # 线性拉伸到输出范围 + if p_upper > p_lower: + img_stretched = (img_clipped - p_lower) / (p_upper - p_lower) * (output_range[1] - output_range[0]) + output_range[0] + else: + img_stretched = np.full_like(img, output_range[0], dtype=np.float32) + + return img_stretched.astype(np.int32) + + +@timeit +def otsu(img, max_value, data_water_mask, ignore_value=0, foreground=1, background=0): + height = img.shape[0] + width = img.shape[1] + + hist = np.zeros([max_value], np.float32) + + # 计算直方图 + invalid_counter = 0 + for i in range(height): + for j in range(width): + if img[i, j] == ignore_value or img[i, j] < 0 or data_water_mask[i, j] == 0: + invalid_counter = invalid_counter + 1 + continue + + hist[img[i, j]] += 1 + + hist /= (height * width - invalid_counter) + + threshold = 0 + deltaMax = 0 + # 遍历像素值，计算最大类间方差 + for i in range(max_value): + wA = 0 + wB = 0 + uAtmp = 0 + uBtmp = 0 + uA = 0 + uB = 0 + u = 0 + for j in range(max_value): + if j <= i: + wA += hist[j] + uAtmp += j * hist[j] + else: + wB += hist[j] + uBtmp += j * hist[j] + if wA == 0: + wA = 1e-10 + if wB == 0: + wB = 1e-10 + uA = uAtmp / wA + uB = uBtmp / wB + u = uAtmp + uBtmp + + # 计算类间方差 + deltaTmp = wA * ((uA - u)**2) + wB * ((uB - u)**2) + # 找出最大类间方差以及阈值 + if deltaTmp > deltaMax: + deltaMax = deltaTmp + threshold = i + + # 二值化 + det_img = img.copy() + det_img[img > threshold] = foreground + det_img[img <= threshold] = background + det_img[np.where(data_water_mask == 0)] = background + return det_img + + +@timeit +def zscore_threshold(img, data_water_mask, z_threshold=2.5, foreground=1, background=0): + """ + 基于Z-score（标准化分数）的耀斑检测方法 + 使用统计方法识别异常高亮的像素，对数据分布不敏感 + + Args: + img: 输入图像数组 + data_water_mask: 水域掩膜 + z_threshold: Z-score阈值，默认2.5（即超过均值2.5个标准差） + foreground: 前景值 + background: 背景值 + + Returns: + 二值化检测结果 + """ + # 只在水域掩膜区域计算统计量，排除无效值 + valid_pixels = img[(data_water_mask > 0) & (img > 0) & np.isfinite(img)] + + if len(valid_pixels) == 0: + print("警告: 没有有效像素用于统计计算") + return np.zeros_like(img, dtype=np.int32) + + mean_val = np.mean(valid_pixels) + std_val = np.std(valid_pixels) + + if std_val == 0: + print("警告: 标准差为0，无法使用Z-score方法") + return np.zeros_like(img, dtype=np.int32) + + # 计算Z-score（对无效值进行保护） + z_scores = np.zeros_like(img, dtype=np.float32) + valid_mask = (data_water_mask > 0) & np.isfinite(img) + z_scores[valid_mask] = (img[valid_mask] - mean_val) / std_val + + # 二值化 + det_img = np.zeros_like(img, dtype=np.int32) + det_img[z_scores > z_threshold] = foreground + det_img[np.where(data_water_mask == 0)] = background + + print(f"Z-score方法: 均值={mean_val:.2f}, 标准差={std_val:.2f}, 阈值={mean_val + z_threshold * std_val:.2f}") + + return det_img + + +@timeit +def percentile_threshold(img, data_water_mask, percentile=95, foreground=1, background=0): + """ + 基于百分位数的耀斑检测方法 + 使用百分位数作为阈值，对异常值更稳健 + + Args: + img: 输入图像数组 + data_water_mask: 水域掩膜 + percentile: 百分位数阈值，默认95（即超过95%的像素值） + foreground: 前景值 + background: 背景值 + + Returns: + 二值化检测结果 + """ + # 只在水域掩膜区域计算百分位数，排除无效值 + valid_pixels = img[(data_water_mask > 0) & (img > 0) & np.isfinite(img)] + + if len(valid_pixels) == 0: + print("警告: 没有有效像素用于统计计算") + return np.zeros_like(img, dtype=np.int32) + + threshold = np.percentile(valid_pixels, percentile) + + # 二值化 + det_img = np.zeros_like(img, dtype=np.int32) + det_img[img > threshold] = foreground + det_img[np.where(data_water_mask == 0)] = background + + print(f"百分位数方法: {percentile}%分位数为 {threshold:.2f}") + + return det_img + + +@timeit +def multi_band_glint_detection(dataset, img_path, water_mask, glint_waves, weights=None, method='zscore', + z_threshold=2.5, percentile=95, foreground=1, background=0): + """ + 多波段融合的耀斑检测方法 + 结合多个波段的耀斑特征，提高检测的稳健性 + + Args: + dataset: GDAL数据集 + img_path: 影像文件路径（用于获取波长信息） + water_mask: 水域掩膜数组 + glint_waves: 用于检测的波长列表，如[750, 800, 850] + weights: 各波段的权重，如果为None则使用等权重 + method: 使用的检测方法 ('zscore', 'percentile', 'otsu') + z_threshold: Z-score阈值（当method='zscore'时使用） + percentile: 百分位数阈值（当method='percentile'时使用） + foreground: 前景值 + background: 背景值 + + Returns: + 二值化检测结果 + """ + num_bands = dataset.RasterCount + + if weights is None: + weights = [1.0 / len(glint_waves)] * len(glint_waves) + + if len(weights) != len(glint_waves): + raise ValueError("权重数量必须与波长数量相同") + + # 读取多个波段并加权融合（使用float32保持精度） + fused_band = None + for i, wave in enumerate(glint_waves): + band_num = find_band_number(wave, img_path) + if band_num >= num_bands: + print(f"警告: 波段号 {band_num} 超出范围，跳过波长 {wave}") + continue + + tmp = dataset.GetRasterBand(band_num + 1).ReadAsArray().astype(np.float32) + + if fused_band is None: + fused_band = (tmp * weights[i]).astype(np.float32) + else: + fused_band = (fused_band + tmp * weights[i]).astype(np.float32) + + if fused_band is None: + raise ValueError("没有有效的波段可以融合") + + # 根据方法选择是否需要归一化 + # 对于统计方法（zscore, percentile），直接使用原始反射率值 + # 对于Otsu方法，需要归一化到整数范围 + if method == 'otsu': + # Otsu方法需要整数范围，使用百分位数拉伸 + fused_band_stretch = percentile_stretch(fused_band, water_mask, + lower_percentile=2, upper_percentile=98) + return otsu(fused_band_stretch, fused_band_stretch.max() + 1, water_mask, + foreground=foreground, background=background) + elif method == 'zscore': + # Z-score方法直接使用原始反射率值 + return zscore_threshold(fused_band, water_mask, z_threshold, foreground, background) + elif method == 'percentile': + # 百分位数方法直接使用原始反射率值 + return percentile_threshold(fused_band, water_mask, percentile, foreground, background) + else: + raise ValueError(f"不支持的方法: {method}") + + +@timeit +def adaptive_threshold(img, data_water_mask, window_size=15, percentile=90, foreground=1, background=0): + """ + 自适应阈值方法 + 基于局部统计特性进行阈值分割，对光照变化更稳健 + + Args: + img: 输入图像数组 + data_water_mask: 水域掩膜 + window_size: 局部窗口大小（奇数） + percentile: 局部百分位数阈值 + foreground: 前景值 + background: 背景值 + + Returns: + 二值化检测结果 + """ + height, width = img.shape + + # 确保窗口大小为奇数 + if window_size % 2 == 0: + window_size += 1 + + half_window = window_size // 2 + + # 创建输出图像 + det_img = np.zeros_like(img, dtype=np.int32) + + # 对每个像素计算局部阈值 + for i in range(half_window, height - half_window): + for j in range(half_window, width - half_window): + # 只在水域掩膜内处理 + if data_water_mask[i, j] == 0: + continue + + # 提取局部窗口 + local_window = img[i - half_window:i + half_window + 1, + j - half_window:j + half_window + 1] + local_mask = data_water_mask[i - half_window:i + half_window + 1, + j - half_window:j + half_window + 1] + + # 只考虑有效像素 + valid_pixels = local_window[local_mask > 0] + + if len(valid_pixels) > 0: + local_threshold = np.percentile(valid_pixels, percentile) + if img[i, j] > local_threshold: + det_img[i, j] = foreground + + det_img[np.where(data_water_mask == 0)] = background + + print(f"自适应阈值方法: 窗口大小={window_size}, 局部百分位数={percentile}%") + + return det_img + + +@timeit +def iqr_outlier_detection(img, data_water_mask, iqr_multiplier=1.5, foreground=1, background=0): + """ + 基于IQR（四分位距）的异常值检测方法 + 使用四分位距识别异常高亮的像素，对数据分布不敏感 + + Args: + img: 输入图像数组 + data_water_mask: 水域掩膜 + iqr_multiplier: IQR倍数，默认1.5（标准异常值检测） + foreground: 前景值 + background: 背景值 + + Returns: + 二值化检测结果 + """ + # 只在水域掩膜区域计算统计量，排除无效值 + valid_pixels = img[(data_water_mask > 0) & (img > 0) & np.isfinite(img)] + + if len(valid_pixels) == 0: + print("警告: 没有有效像素用于统计计算") + return np.zeros_like(img, dtype=np.int32) + + q1 = np.percentile(valid_pixels, 25) + q3 = np.percentile(valid_pixels, 75) + iqr = q3 - q1 + + # 上界 = Q3 + 1.5 * IQR + upper_bound = q3 + iqr_multiplier * iqr + + # 二值化 + det_img = np.zeros_like(img, dtype=np.int32) + det_img[img > upper_bound] = foreground + det_img[np.where(data_water_mask == 0)] = background + + print(f"IQR方法: Q1={q1:.2f}, Q3={q3:.2f}, IQR={iqr:.2f}, 上界={upper_bound:.2f}") + + return det_img + + +@timeit +def create_shoreline_buffer(water_mask, buffer_size=5, foreground=1, background=0): + """ + 创建岸边缓冲区掩膜（向内缓冲） + 用于去除岸边附近的错误耀斑检测区域 + + 方法：对水域掩膜进行腐蚀，然后用原始水域减去腐蚀后的水域，得到水域边缘向内缓冲的区域 + + Args: + water_mask: 水域掩膜数组（水域=1，非水域=0） + buffer_size: 缓冲区大小（像素数），默认5像素 + foreground: 前景值 + background: 背景值 + + Returns: + 岸边缓冲区掩膜（缓冲区区域=1，其他=0） + """ + if buffer_size <= 0: + print("缓冲区大小为0或负数，不创建岸边缓冲区") + return np.zeros_like(water_mask, dtype=np.int32) + + # 将水域掩膜转换为二值图像 + water_binary = (water_mask > 0).astype(np.int32) + + # 创建结构元素（方形结构元素） + # 结构元素大小由buffer_size决定，确保是奇数 + structure_size = buffer_size * 2 + 1 + structure = np.ones((structure_size, structure_size), dtype=np.int32) + + # 对水域进行腐蚀，得到缩小后的水域 + # 使用OpenCV替代scipy.ndimage.binary_erosion + eroded_water = cv2.erode(water_binary.astype(np.uint8), structure.astype(np.uint8)).astype(np.int32) + + # 岸边缓冲区 = 原始水域 - 腐蚀后的水域 + # 这给出了水域边缘向内buffer_size像素宽的缓冲区区域 + buffer_mask = (water_binary - eroded_water).astype(np.int32) + + buffer_pixels = np.sum(buffer_mask > 0) + print(f"岸边缓冲区: 创建了 {buffer_size} 像素宽的内向缓冲区，共 {buffer_pixels} 个像素") + + return buffer_mask + + +@timeit +def remove_shoreline_buffer(glint_mask, water_mask, buffer_size=5, foreground=1, background=0): + """ + 从耀斑掩膜中去除岸边缓冲区内的区域 + + Args: + glint_mask: 耀斑掩膜数组 + water_mask: 水域掩膜数组 + buffer_size: 缓冲区大小（像素数），默认5像素 + foreground: 前景值 + background: 背景值 + + Returns: + 去除岸边缓冲区后的耀斑掩膜 + """ + if buffer_size <= 0: + print("缓冲区大小为0，不进行岸边缓冲区去除") + return glint_mask + + # 创建岸边缓冲区掩膜 + buffer_mask = create_shoreline_buffer(water_mask, buffer_size, foreground, background) + + # 从耀斑掩膜中去除缓冲区内的区域 + cleaned_glint_mask = glint_mask.copy() + cleaned_glint_mask[buffer_mask > 0] = background + + removed_pixels = np.sum((glint_mask > 0) & (buffer_mask > 0)) + remaining_pixels = np.sum(cleaned_glint_mask > 0) + + if removed_pixels > 0: + print(f"岸边缓冲区去除: 从耀斑掩膜中移除了 {removed_pixels} 个岸边向内缓冲区域的像素，" + f"剩余 {remaining_pixels} 个像素") + else: + print(f"岸边缓冲区去除: 缓冲区区域没有耀斑掩膜，无需移除") + + return cleaned_glint_mask + + +@timeit +def filter_large_components(binary_img, max_area=None, foreground=1, background=0): + """ + 过滤掉面积超过阈值的连通域 + 用于去除大面积区域（如岸边、浅水、水华等），保留小面积的耀斑区域 + + Args: + binary_img: 二值化图像 + max_area: 最大连通域面积阈值（像素数），超过此面积的连通域将被去除 + 如果为None，则不进行过滤 + foreground: 前景值 + background: 背景值 + + Returns: + 过滤后的二值化图像 + """ + if max_area is None or max_area <= 0: + return binary_img + + # 连通域标记 + # 使用OpenCV替代scipy.ndimage.label + binary_for_label = (binary_img == foreground).astype(np.uint8) + num_features, labeled_array, stats, centroids = cv2.connectedComponentsWithStats(binary_for_label, connectivity=8) + + if num_features == 0: + print("没有检测到连通域") + return binary_img + + # 使用OpenCV返回的stats信息直接获取连通域面积 + # stats[:, cv2.CC_STAT_AREA] 包含每个连通域的面积（包括背景） + # 跳过索引0（背景）的面积，从索引1开始获取连通域面积 + component_sizes = stats[1:, cv2.CC_STAT_AREA] + + # 找出需要保留的连通域（面积 <= max_area） + keep_labels = np.where(component_sizes <= max_area)[0] + 1 # +1 因为标签从1开始 + + # 使用布尔索引一次性过滤（高效方法） + # 创建一个mask，标记所有需要保留的连通域 + keep_mask = np.isin(labeled_array, keep_labels) + + # 创建输出图像 + filtered_img = np.zeros_like(binary_img, dtype=binary_img.dtype) + filtered_img[keep_mask] = foreground + + # 统计信息 + removed_count = num_features - len(keep_labels) + kept_count = len(keep_labels) + total_removed_pixels = np.sum(component_sizes[component_sizes > max_area]) + + if removed_count > 0: + print(f"连通域面积过滤: 移除了 {removed_count} 个大面积连通域（面积 > {max_area} 像素），" + f"共移除 {total_removed_pixels} 个像素；保留了 {kept_count} 个小面积连通域") + else: + print(f"连通域面积过滤: 所有 {kept_count} 个连通域面积均小于阈值 {max_area}，全部保留") + + return filtered_img + + +def find_overexposure_area(img_path, threhold=4095): + # 第一步通过某个像素的光谱找到信号最强的波段 + + # 根据上步所得的波段号检测过曝区域 + pass + + +def create_water_mask_from_shp(shp_file, reference_raster): + """ + 从shp文件创建水体掩膜栅格数组（内存中，不保存到磁盘） + + 参数: + shp_file: str - shp文件路径 + reference_raster: str - 参考栅格文件路径（用于获取空间范围和分辨率） + + 返回: + numpy.ndarray - 水体掩膜数组 + """ + try: + # 打开参考栅格获取空间信息 + ref_dataset = gdal.Open(reference_raster) + if ref_dataset is None: + raise ValueError(f"无法打开参考栅格文件: {reference_raster}") + + geotransform = ref_dataset.GetGeoTransform() + projection = ref_dataset.GetProjection() + width = ref_dataset.RasterXSize + height = ref_dataset.RasterYSize + + # 创建内存中的栅格数据集 + mem_driver = gdal.GetDriverByName('MEM') + mask_dataset = mem_driver.Create('', width, height, 1, gdal.GDT_Byte) + mask_dataset.SetGeoTransform(geotransform) + mask_dataset.SetProjection(projection) + + # 初始化为0 + mask_band = mask_dataset.GetRasterBand(1) + mask_band.Fill(0) + + # 打开shp文件 + shp_dataset = ogr.Open(shp_file) + if shp_dataset is None: + raise ValueError(f"无法打开shp文件: {shp_file}") + + layer = shp_dataset.GetLayer() + + # 栅格化shp文件 + gdal.RasterizeLayer(mask_dataset, [1], layer, burn_values=[1]) + + # 读取栅格化结果 + water_mask = mask_band.ReadAsArray() + + # 清理 + ref_dataset = None + mask_dataset = None + shp_dataset = None + + return water_mask + + except Exception as e: + print(f"创建水体掩膜时发生错误: {str(e)}") + raise + + +@timeit +def find_severe_glint_area(img_path, water_mask, glint_wave=750, output_path=None, + method='otsu', multi_band_waves=None, **kwargs): + """ + 找到严重耀斑区域的主函数 + + 注意：对于低反射率数据（如水面反射率约0.02），本函数采用了改进的归一化策略： + - 统计方法（zscore, percentile, iqr）：直接使用原始反射率值，无需归一化 + - Otsu和adaptive方法：使用百分位数裁剪拉伸（2%-98%分位数），避免极值影响 + + Args: + img_path: 输入影像路径 + water_mask: 水域掩膜路径（支持栅格文件如.dat/.tif，或SHP文件如.shp；如果为None或空字符串，则使用全图进行检测） + glint_wave: 用于检测的波长（单个波段方法使用） + output_path: 输出路径 + method: 检测方法，可选： + - 'otsu': Otsu阈值分割（默认，使用百分位数拉伸） + - 'zscore': Z-score统计方法（直接使用原始反射率） + - 'percentile': 百分位数阈值方法（直接使用原始反射率） + - 'iqr': IQR异常值检测（直接使用原始反射率） + - 'adaptive': 自适应阈值方法（使用百分位数拉伸） + - 'multi_band': 多波段融合方法 + multi_band_waves: 多波段方法的波长列表，如[750, 800, 850] + **kwargs: 其他方法特定参数 + - z_threshold: Z-score阈值（默认2.5） + - percentile: 百分位数（默认95） + - iqr_multiplier: IQR倍数（默认1.5） + - window_size: 自适应阈值窗口大小（默认15） + - weights: 多波段方法的权重列表 + - sub_method: 多波段方法的子方法（'otsu', 'zscore', 'percentile'） + - max_area: 最大连通域面积阈值（像素数），超过此面积的连通域将被过滤掉 + 用于去除岸边、浅水、水华等大面积区域（默认None，表示不过滤） + - buffer_size: 岸边缓冲区大小（像素数），用于去除岸边附近的错误耀斑掩膜 + 默认None，表示不进行岸边缓冲区去除；设置为正整数时启用 + + Returns: + 输出文件路径 + """ + if output_path is None: + output_path = append2filename(img_path, "_severe_glint_area") + + dataset = gdal.Open(img_path) + num_bands = dataset.RasterCount + im_width = dataset.RasterXSize + im_height = dataset.RasterYSize + + # 读取水域掩膜，如果water_mask为None或空字符串，则创建全图掩膜 + if water_mask is None or water_mask == "": + print("注意: water_mask为空，使用全图进行检测") + data_water_mask = np.ones((im_height, im_width), dtype=np.int32) + else: + # 检查是否为SHP文件 + water_mask_lower = water_mask.lower() + if water_mask_lower.endswith('.shp'): + # 直接使用SHP文件，在内存中栅格化 + print(f"检测到SHP文件，正在从 {water_mask} 创建水体掩膜...") + data_water_mask = create_water_mask_from_shp(water_mask, img_path) + else: + # 使用栅格文件 + dataset_water_mask = gdal.Open(water_mask) + if dataset_water_mask is None: + raise ValueError(f"无法打开水域掩膜文件: {water_mask}") + data_water_mask = dataset_water_mask.GetRasterBand(1).ReadAsArray() + del dataset_water_mask + + print(f"使用检测方法: {method}") + + # 根据方法选择检测算法 + if method == 'multi_band': + if multi_band_waves is None: + # 默认使用几个常见NIR波段 + multi_band_waves = [glint_wave, glint_wave + 50, glint_wave + 100] + print(f"多波段方法: 使用默认波长 {multi_band_waves}") + else: + print(f"多波段方法: 使用波长 {multi_band_waves}") + + sub_method = kwargs.get('sub_method', 'zscore') + weights = kwargs.get('weights', None) + z_threshold = kwargs.get('z_threshold', 2.5) + percentile = kwargs.get('percentile', 95) + + flare_binary = multi_band_glint_detection( + dataset, img_path, data_water_mask, multi_band_waves, weights, + method=sub_method, z_threshold=z_threshold, percentile=percentile + ) + else: + # 单波段方法 + glint_band_number = find_band_number(glint_wave, img_path) + tmp = dataset.GetRasterBand(glint_band_number + 1) + band_flare = tmp.ReadAsArray().astype(np.float32) + + # 根据方法选择是否需要归一化 + # 对于统计方法（zscore, percentile, iqr），直接使用原始反射率值 + # 对于Otsu和adaptive方法，需要归一化到整数范围 + if method == 'otsu': + # Otsu方法需要整数范围，使用百分位数拉伸 + band_flare_stretch = percentile_stretch(band_flare, data_water_mask, + lower_percentile=2, upper_percentile=98) + flare_binary = otsu(band_flare_stretch, band_flare_stretch.max() + 1, data_water_mask) + elif method == 'zscore': + # Z-score方法直接使用原始反射率值 + z_threshold = kwargs.get('z_threshold', 2.5) + flare_binary = zscore_threshold(band_flare, data_water_mask, z_threshold) + elif method == 'percentile': + # 百分位数方法直接使用原始反射率值 + percentile = kwargs.get('percentile', 95) + flare_binary = percentile_threshold(band_flare, data_water_mask, percentile) + elif method == 'iqr': + # IQR方法直接使用原始反射率值 + iqr_multiplier = kwargs.get('iqr_multiplier', 1.5) + flare_binary = iqr_outlier_detection(band_flare, data_water_mask, iqr_multiplier) + elif method == 'adaptive': + # 自适应阈值方法需要归一化 + band_flare_stretch = percentile_stretch(band_flare, data_water_mask, + lower_percentile=2, upper_percentile=98) + window_size = kwargs.get('window_size', 15) + percentile = kwargs.get('percentile', 90) + flare_binary = adaptive_threshold(band_flare_stretch, data_water_mask, window_size, percentile) + else: + raise ValueError(f"不支持的方法: {method}。可选方法: otsu, zscore, percentile, iqr, adaptive, multi_band") + + # 过滤掉面积超过阈值的连通域（用于去除岸边、浅水、水华等大面积区域） + max_area = kwargs.get('max_area', None) + if max_area is not None and max_area > 0: + print(f"应用连通域面积过滤，最大面积阈值: {max_area} 像素") + flare_binary = filter_large_components(flare_binary, max_area=max_area) + + # 去除岸边缓冲区内的耀斑掩膜（用于去除岸边的错误检测） + buffer_size = kwargs.get('buffer_size', None) + if buffer_size is not None and buffer_size > 0: + print(f"应用岸边缓冲区去除，缓冲区大小: {buffer_size} 像素") + flare_binary = remove_shoreline_buffer(flare_binary, data_water_mask, buffer_size=buffer_size) + + write_bands(img_path, output_path, flare_binary) + + del dataset + + return output_path + + +# Press the green button in the gutter to run the script. +if __name__ == '__main__': + img_path = r"D:\PycharmProjects\0water_rlx\test_data\ref_mosaic_1m_bsq" + + parser = argparse.ArgumentParser( + description="此程序通过多种算法分割图像，提取耀斑最严重的区域。" + "支持的算法: otsu, zscore, percentile, iqr, adaptive, multi_band" + ) + + parser.add_argument('-i1', '--input', type=str, required=True, help='输入影像文件的路径') + parser.add_argument('-i2', '--input_water_mask', type=str, required=True, help='输入水域掩膜文件的路径') + parser.add_argument('-gw', '--glint_wave', type=float, default=750.0, + help='用于提取耀斑严重区域的波段波长（单波段方法使用）') + parser.add_argument('-m', '--method', type=str, default='otsu', + choices=['otsu', 'zscore', 'percentile', 'iqr', 'adaptive', 'multi_band'], + help='检测方法: otsu(默认), zscore, percentile, iqr, adaptive, multi_band') + parser.add_argument('-o', '--output', type=str, help='输出文件的路径') + + # 方法特定参数 + parser.add_argument('-zt', '--z_threshold', type=float, default=2.5, + help='Z-score方法的阈值（默认2.5）') + parser.add_argument('-p', '--percentile', type=float, default=95.0, + help='百分位数阈值（默认95）') + parser.add_argument('-iqr', '--iqr_multiplier', type=float, default=1.5, + help='IQR方法的倍数（默认1.5）') + parser.add_argument('-ws', '--window_size', type=int, default=15, + help='自适应阈值方法的窗口大小（默认15）') + parser.add_argument('-mbw', '--multi_band_waves', type=str, default=None, + help='多波段方法的波长列表，用逗号分隔，如: 750,800,850') + parser.add_argument('-sm', '--sub_method', type=str, default='zscore', + choices=['otsu', 'zscore', 'percentile'], + help='多波段方法的子方法（默认zscore）') + parser.add_argument('-ma', '--max_area', type=int, default=None, + help='最大连通域面积阈值（像素数），超过此面积的连通域将被过滤掉，' + '用于去除岸边、浅水、水华等大面积区域（默认None，表示不过滤）') + parser.add_argument('-bs', '--buffer_size', type=int, default=None, + help='岸边缓冲区大小（像素数），用于去除岸边附近的错误耀斑掩膜' + '（默认None，表示不进行岸边缓冲区去除；设置为正整数时启用）') + + parser.add_argument('-v', '--verbose', action='store_true', help='启用详细模式') + + args = parser.parse_args() + + # 解析多波段波长列表 + multi_band_waves = None + if args.multi_band_waves: + multi_band_waves = [float(x.strip()) for x in args.multi_band_waves.split(',')] + + # 构建kwargs + kwargs = { + 'z_threshold': args.z_threshold, + 'percentile': args.percentile, + 'iqr_multiplier': args.iqr_multiplier, + 'window_size': args.window_size, + 'sub_method': args.sub_method, + 'max_area': args.max_area, + 'buffer_size': args.buffer_size + } + + find_severe_glint_area( + args.input, args.input_water_mask, args.glint_wave, args.output, + method=args.method, multi_band_waves=multi_band_waves, **kwargs + ) diff --git a/src/utils/kriging.py b/src/utils/kriging.py new file mode 100644 index 0000000..02a117b --- /dev/null +++ b/src/utils/kriging.py @@ -0,0 +1,458 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +克里金插值模块 + +提供基于PyKrige的普通克里金插值功能，用于将离散的水质参数预测点 +插值为连续的栅格图像。 + +主要功能： +1. 普通克里金插值 +2. 多种变差函数模型支持 +3. 自动参数优化 +4. 栅格输出功能 +""" + +import numpy as np +from osgeo import gdal +import time +import os +import glob +from pathlib import Path +from typing import Optional, Tuple, Union, List +import warnings +warnings.filterwarnings('ignore') + +# 导入util模块的timeit装饰器 +try: + from src.utils.util import timeit +except ImportError: + # 如果导入失败，定义一个简单的timeit装饰器 + def timeit(f): + def wrapper(*args, **kwargs): + start = time.time() + ret = f(*args, **kwargs) + print(f"{f.__name__} run time: {round(time.time() - start, 2)} s.") + return ret + return wrapper + + +class KrigingInterpolator: + """克里金插值器类""" + + def __init__(self, variogram_models: Optional[List[str]] = None): + """ + 初始化克里金插值器 + + Args: + variogram_models: 变差函数模型列表，默认为['spherical', 'exponential', 'gaussian', 'linear'] + """ + self.variogram_models = variogram_models or ['spherical', 'exponential', 'gaussian', 'linear'] + self.last_used_model = None + + def validate_input_data(self, x: np.ndarray, y: np.ndarray, z: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray, bool]: + """ + 验证和预处理输入数据 + + Args: + x: X坐标数组 + y: Y坐标数组 + z: 观测值数组 + + Returns: + 处理后的x, y, z数组和是否有效的标志 + """ + # 确保输入为numpy数组 + x = np.asarray(x) + y = np.asarray(y) + z = np.asarray(z) + + # 检查数组长度一致性 + if not (len(x) == len(y) == len(z)): + raise ValueError(f"输入数组长度不一致: x={len(x)}, y={len(y)}, z={len(z)}") + + # 移除NaN值 + mask = ~(np.isnan(x) | np.isnan(y) | np.isnan(z)) + x = x[mask] + y = y[mask] + z = z[mask] + + # 检查数据点数量 + if len(x) < 3: + print(f"警告：有效数据点不足（{len(x)}个），至少需要3个点进行Kriging插值") + return x, y, z, False + + # 检查是否所有点重合 + if np.all(x == x[0]) and np.all(y == y[0]): + print(f"警告：所有数据点位置相同（{x[0]}, {y[0]}），无法进行空间插值") + return x, y, z, False + + # 检查z值是否有变化 + if np.all(z == z[0]): + print(f"警告：所有观测值相同（{z[0]}），插值结果将为常数") + + return x, y, z, True + + def create_interpolation_grid(self, x: np.ndarray, y: np.ndarray, spatial_resolution: float) -> Tuple[np.ndarray, np.ndarray, int, int]: + """ + 创建插值网格 + + Args: + x: X坐标数组 + y: Y坐标数组 + spatial_resolution: 空间分辨率 + + Returns: + 网格x, 网格y, x方向步数, y方向步数 + """ + # 计算空间范围，添加小的缓冲区 + x_min, x_max = x.min(), x.max() + y_min, y_max = y.min(), y.max() + + # 添加缓冲区以确保所有点都在网格内 + buffer = spatial_resolution * 0.5 + x_min -= buffer + x_max += buffer + y_min -= buffer + y_max += buffer + + # 计算网格步数 + step_x = int(np.ceil((x_max - x_min) / spatial_resolution)) + 1 + step_y = int(np.ceil((y_max - y_min) / spatial_resolution)) + 1 + + # 限制网格大小以避免内存问题 + max_grid_size = 10000 + if step_x > max_grid_size or step_y > max_grid_size: + print(f"警告：网格尺寸过大 ({step_x}x{step_y})，将调整空间分辨率") + # 重新计算合适的分辨率 + new_resolution_x = (x_max - x_min) / max_grid_size + new_resolution_y = (y_max - y_min) / max_grid_size + spatial_resolution = max(new_resolution_x, new_resolution_y, spatial_resolution) + + step_x = int(np.ceil((x_max - x_min) / spatial_resolution)) + 1 + step_y = int(np.ceil((y_max - y_min) / spatial_resolution)) + 1 + print(f"调整后的空间分辨率: {spatial_resolution:.2f}, 网格尺寸: {step_x}x{step_y}") + + # 创建网格 + grid_x = np.linspace(x_min, x_max, step_x) + grid_y = np.linspace(y_min, y_max, step_y) + + return grid_x, grid_y, step_x, step_y + + @timeit + def interpolate(self, x: np.ndarray, y: np.ndarray, z: np.ndarray, + spatial_resolution: float = 1.0, + output_path: Optional[str] = None, + proj: Optional[str] = None) -> Optional[np.ndarray]: + """ + 执行克里金插值 + + Args: + x: X坐标数组 + y: Y坐标数组 + z: 观测值数组 + spatial_resolution: 空间分辨率 + output_path: 输出文件路径 + proj: 投影信息 + + Returns: + 插值结果数组，失败时返回None + """ + try: + from pykrige.ok import OrdinaryKriging + except ImportError: + print("错误：未安装pykrige库，请运行 'pip install pykrige'") + return None + + # 验证输入数据 + x, y, z, is_valid = self.validate_input_data(x, y, z) + if not is_valid: + return None + + print(f"开始克里金插值，数据点数: {len(x)}") + + # 创建插值网格 + grid_x, grid_y, step_x, step_y = self.create_interpolation_grid(x, y, spatial_resolution) + + print(f"插值网格尺寸: {step_x} x {step_y}") + print(f"空间范围: X=[{grid_x[0]:.2f}, {grid_x[-1]:.2f}], Y=[{grid_y[0]:.2f}, {grid_y[-1]:.2f}]") + + # 尝试不同的变差函数模型 + z_interpolated = None + successful_model = None + + for model in self.variogram_models: + try: + print(f"尝试使用 {model} 变差函数模型...") + + # 动态设置参数 + nlags = min(20, max(6, len(x) // 3)) + n_closest_points = min(12, max(4, len(x) // 2)) + + OK = OrdinaryKriging( + x, y, z, + variogram_model=model, + verbose=False, + enable_plotting=False, + coordinates_type="euclidean", + nlags=nlags + ) + + start_time = time.perf_counter() + z_interpolated, ss = OK.execute( + "grid", grid_x, grid_y, + backend="loop", + n_closest_points=n_closest_points + ) + end_time = time.perf_counter() + + successful_model = model + self.last_used_model = model + print(f"使用 {model} 模型插值成功，耗时: {end_time - start_time:.2f}秒") + break + + except Exception as e: + print(f"模型 {model} 失败: {str(e)}") + continue + + if z_interpolated is None: + print("错误：所有变差函数模型均失败，无法完成插值") + return None + + # 检查插值结果 + if np.all(np.isnan(z_interpolated)): + print("警告：插值结果全为NaN值") + return None + + nan_count = np.sum(np.isnan(z_interpolated)) + total_count = z_interpolated.size + nan_percentage = (nan_count / total_count) * 100 + + print(f"插值完成，使用模型: {successful_model}") + print(f"结果统计: 总像元数={total_count}, NaN像元数={nan_count} ({nan_percentage:.1f}%)") + print(f"数值范围: [{np.nanmin(z_interpolated):.3f}, {np.nanmax(z_interpolated):.3f}]") + + # 保存结果 + if output_path and proj: + success = self.save_raster(z_interpolated, grid_x, grid_y, spatial_resolution, proj, output_path) + if success: + print(f"结果已保存至: {output_path}") + else: + print(f"保存失败: {output_path}") + + return z_interpolated + + def save_raster(self, data: np.ndarray, grid_x: np.ndarray, grid_y: np.ndarray, + spatial_resolution: float, proj: str, output_path: str) -> bool: + """ + 保存插值结果为栅格文件 + + Args: + data: 插值结果数组 + grid_x: X方向网格 + grid_y: Y方向网格 + spatial_resolution: 空间分辨率 + proj: 投影信息 + output_path: 输出路径 + + Returns: + 是否保存成功 + """ + try: + # 确保输出目录存在 + os.makedirs(os.path.dirname(output_path), exist_ok=True) + + # 创建GDAL数据集 + driver = gdal.GetDriverByName("GTiff") + step_x, step_y = data.shape[1], data.shape[0] + + dataset = driver.Create( + output_path, step_x, step_y, 1, gdal.GDT_Float64, + options=["COMPRESS=LZW", "TILED=YES"] + ) + + if dataset is None: + print(f"错误：无法创建输出文件 {output_path}") + return False + + # 设置地理变换参数 + x_min, y_max = grid_x[0], grid_y[-1] + geotransform = (x_min, spatial_resolution, 0, y_max, 0, -spatial_resolution) + dataset.SetGeoTransform(geotransform) + + # 设置投影 + dataset.SetProjection(proj) + + # 写入数据 + band = dataset.GetRasterBand(1) + band.WriteArray(data) + band.SetNoDataValue(np.nan) + + # 计算统计信息 + band.ComputeStatistics(0) + + # 清理资源 + band.FlushCache() + dataset.FlushCache() + del dataset + + return True + + except Exception as e: + print(f"保存栅格文件时出错: {str(e)}") + return False + + +# 保持向后兼容性的函数接口 +@timeit +def interpolate_kriging_pykrige(x, y, z, proj, spatial_resolution, output_path=None): + """ + 执行克里金插值（向后兼容接口） + + Args: + x: X坐标数组 + y: Y坐标数组 + z: 观测值数组 + proj: 投影信息 + spatial_resolution: 空间分辨率 + output_path: 输出路径 + + Returns: + 插值结果数组 + """ + interpolator = KrigingInterpolator() + return interpolator.interpolate(x, y, z, spatial_resolution, output_path, proj) + + +def batch_kriging_interpolation(input_folder: str, ref_img_path: str, + output_folder: str, spatial_resolution: float = 1.0, + file_pattern: str = "*.csv") -> None: + """ + 批量克里金插值处理 + + Args: + input_folder: 输入CSV文件夹路径 + ref_img_path: 参考影像路径（用于获取投影信息） + output_folder: 输出文件夹路径 + spatial_resolution: 空间分辨率 + file_pattern: 文件匹配模式 + """ + # 验证输入路径 + if not os.path.exists(input_folder): + raise FileNotFoundError(f"输入文件夹不存在: {input_folder}") + + if not os.path.exists(ref_img_path): + raise FileNotFoundError(f"参考影像不存在: {ref_img_path}") + + # 确保输出文件夹存在 + os.makedirs(output_folder, exist_ok=True) + + # 获取参考影像的投影信息 + try: + dataset = gdal.Open(ref_img_path) + if dataset is None: + raise ValueError(f"无法打开参考影像: {ref_img_path}") + im_proj = dataset.GetProjection() + del dataset + except Exception as e: + print(f"获取投影信息失败: {str(e)}") + return + + # 查找CSV文件 + csv_files = glob.glob(os.path.join(input_folder, file_pattern)) + if not csv_files: + print(f"在 {input_folder} 中未找到匹配 {file_pattern} 的文件") + return + + print(f"找到 {len(csv_files)} 个CSV文件待处理") + + # 创建插值器 + interpolator = KrigingInterpolator() + + successful_count = 0 + failed_count = 0 + + for i, csv_path in enumerate(csv_files, 1): + filename = os.path.basename(csv_path) + print(f"\n[{i}/{len(csv_files)}] 处理文件: {filename}") + + try: + # 读取CSV文件 + # 支持多种分隔符 + try: + pos_content = np.loadtxt(csv_path, delimiter='\t') + except ValueError: + try: + pos_content = np.loadtxt(csv_path, delimiter=',') + except ValueError: + pos_content = np.loadtxt(csv_path, delimiter=';') + + if pos_content.shape[1] < 3: + print(f"跳过文件 {filename}：列数不足（需要至少3列：x, y, z）") + failed_count += 1 + continue + + # 数据统计 + total_points = len(pos_content) + nan_points = np.sum(np.isnan(pos_content[:, 2])) + print(f"数据点统计: 总计{total_points}个, NaN值{nan_points}个") + + # 构建输出路径 + base_name = os.path.splitext(filename)[0] + output_filename = f"{base_name}_kriging.tif" + output_path = os.path.join(output_folder, output_filename) + + # 执行插值 + result = interpolator.interpolate( + pos_content[:, 0], # x + pos_content[:, 1], # y + pos_content[:, 2], # z + spatial_resolution, + output_path, + im_proj + ) + + if result is not None: + print(f"✓ 处理成功: {output_filename}") + successful_count += 1 + else: + print(f"✗ 处理失败: {filename}") + failed_count += 1 + + except Exception as e: + print(f"✗ 处理文件 {filename} 时出错: {str(e)}") + failed_count += 1 + + # 输出总结 + print(f"\n{'='*60}") + print(f"批量处理完成") + print(f"成功: {successful_count} 个文件") + print(f"失败: {failed_count} 个文件") + print(f"输出目录: {output_folder}") + + +if __name__ == '__main__': + # 示例用法 + print("克里金插值模块示例") + + # 配置参数（根据实际情况修改） + input_folder = r"data/processed/predictions" # CSV文件夹路径 + ref_img_path = r"data/raw/reference_image.tif" # 参考影像路径 + output_folder = r"data/processed/kriging_results" # 输出文件夹路径 + spatial_resolution = 1.0 # 空间分辨率（米） + + try: + # 执行批量插值 + batch_kriging_interpolation( + input_folder=input_folder, + ref_img_path=ref_img_path, + output_folder=output_folder, + spatial_resolution=spatial_resolution + ) + + except Exception as e: + print(f"批量处理失败: {str(e)}") + print("\n请检查以下事项:") + print("1. 输入文件夹和参考影像路径是否正确") + print("2. CSV文件格式是否正确（至少包含x, y, z三列）") + print("3. 是否安装了必要的依赖库（pykrige, gdal等）") \ No newline at end of file diff --git a/src/utils/lapulasi_otsu.py b/src/utils/lapulasi_otsu.py new file mode 100644 index 0000000..921e359 --- /dev/null +++ b/src/utils/lapulasi_otsu.py @@ -0,0 +1,417 @@ +import numpy as np +import cv2 +from osgeo import gdal +from collections import Counter +from typing import Optional, Union, Tuple +gdal.UseExceptions() + + +def laplacian_filter(image): + """ + 拉普拉斯算子纹理提取 + 使用二阶偏微分对图像进行卷积，获取纹理图像 + + 公式: η²g(m,n) = g(m,n+1) + g(m,n-1) + g(m+1,n) + g(m-1,n) - 4g(m,n) + + 参数: + image: 输入图像（全波段叠加后的灰度图像F） + + 返回: + L: 拉普拉斯滤波后的纹理图像 + """ + # 拉普拉斯算子核（4邻域，中心5个像元） + kernel = np.array([[0, 1, 0], + [1, -4, 1], + [0, 1, 0]], dtype=np.float32) + filtered_image = cv2.filter2D(image, -1, kernel) + return filtered_image + + +def apply_threshold(image, threshold): + """ + 二值化处理（分割耀光区域） + + 公式: + - 对于L: W = {1, L>0; 0, L≤0} + - 对于F: S = {1, F>N; 0, F≤N} + + 参数: + image: 输入图像 + threshold: 阈值 + + 返回: + binary_image: 二值化图像（0或1） + """ + _, binary_image = cv2.threshold(image, threshold, 1, cv2.THRESH_BINARY) + return binary_image + + +def morphological_dilation(image, iterations=1): + """ + 形态学处理（膨胀操作） + 对耀光纹理区域进行扩展，使得检测到的耀光区域更加连贯 + + 参数: + image: 输入二值图像 + iterations: 膨胀迭代次数 + + 返回: + dilated_image: 膨胀后的图像 + """ + kernel = np.ones((3, 3), np.uint8) + dilated_image = cv2.dilate(image, kernel, iterations=iterations) + return dilated_image + + +def calculate_area_difference(W, S): + """ + 计算面积差值 + + 公式: rq = area(W) - area(S) + 其中 area(W) 表示拉普拉斯纹理提取区域的面积（用于定位耀光位置） + area(S) 表示通过阈值分割全波段叠加图像得到的耀光区域面积 + + 参数: + W: 纹理提取区域（二值图像） + S: 阈值分割区域（二值图像） + + 返回: + rq: 面积差值 + area_W: 纹理提取区域面积 + area_S: 阈值分割区域面积 + """ + area_W = np.sum(W) + area_S = np.sum(S) + rq = area_W - area_S + return rq, area_W, area_S + + +def multi_band_weighted_sum(image_bands, water_mask): + """ + 全波段叠加：将所有波段的遥感反射率加权叠加形成一个灰度图像 + + 公式: F = Σ(i=1 to n) R(λi) × G + 其中 G 为水体二值化图像，F 为灰度图像 + + 参数: + image_bands: 多波段图像数组，形状为 (rows, cols, bands) + water_mask: 水体二值化图像 G，形状为 (rows, cols)，值为0或1 + + 返回: + F: 全波段加权叠加后的灰度图像 + """ + # 确保water_mask是二值化的（0或1） + if water_mask.dtype != np.float32 and water_mask.dtype != np.float64: + water_mask = water_mask.astype(np.float32) + + # 对每个波段进行加权叠加：F = Σ R(λi) × G + F = np.zeros((image_bands.shape[0], image_bands.shape[1]), dtype=np.float32) + for band_idx in range(image_bands.shape[2]): + F += image_bands[:, :, band_idx] * water_mask + + return F + + +def find_optimal_threshold(F, u=0.1, q=50, r=20, max_iterations=None): + """ + 通过迭代找到最佳阈值 + + 算法原理： + 1. 使用拉普拉斯算子提取耀光纹理信息（用于定位耀光的位置） + 2. 通过全波段叠加图像F进行阈值分割提取耀光面积 + 3. 当纹理提取区域的面积与通过阈值分割得到的耀光区域面积差最小时，确定最佳的阈值 + + 步骤： + 1. 对全波段叠加图像F进行拉普拉斯纹理提取，得到L + 2. 对L进行二值化：W = {1, L>0; 0, L≤0}（用于定位耀光位置） + 3. 对W和S进行形态学膨胀r次 + 4. 设定阈值N的初始值为F的最小值，表示为Nf + 5. 对初始值叠加数值u更新阈值，迭代q次 + 6. 每次迭代记录W与S的面积差值到r行、q列的数组R + 7. 寻找R中每列数组中最小值所在行数，形成新的数组aind + 8. 统计aind的众数得到M，即为最佳的叠加次数 + 9. 最佳阈值：Nf = min(F) + u·M + + 参数: + F: 全波段加权叠加后的灰度图像 + u: 阈值更新步长，默认0.1（论文参数） + q: 迭代次数，默认50（论文参数） + r: 形态学膨胀次数，默认20（论文参数） + max_iterations: 最大迭代次数（如果指定，则使用此值替代q） + + 返回: + optimal_threshold: 最佳阈值Nf + optimal_S: 最佳阈值对应的耀光区域S（通过阈值分割F得到） + optimal_W: 最佳阈值对应的纹理区域W（通过拉普拉斯提取得到） + M: 最佳迭代次数索引 + area_differences: 面积差值矩阵R (r行, q列) + thresholds: 每次迭代的阈值列表 + """ + # 如果指定了max_iterations，使用它替代q + if max_iterations is not None: + q = max_iterations + + # 初始化阈值为影像非0最小值 + F_nonzero = F[F > 0] + if len(F_nonzero) > 0: + min_value = np.min(F_nonzero) + else: + # 如果所有值都为0，使用一个很小的正数作为最小值 + min_value = np.finfo(np.float32).eps + print("警告: F中所有值都为0，使用极小值作为最小值") + + # 步骤1: 对全波段叠加图像F进行拉普拉斯纹理提取（用于定位耀光位置） + print("进行拉普拉斯纹理提取...") + L = laplacian_filter(F) + + # 步骤2: 对L进行二值化，得到纹理区域W + # W = {1, L>0; 0, L≤0} + W = apply_threshold(L, 0.0) # 阈值为0，即L>0为1，L≤0为0 + + # 步骤3: 对W进行形态学膨胀r次 + print(f"对纹理区域W进行形态学膨胀{r}次...") + W_dilated = morphological_dilation(W, iterations=r) + + # 存储每次迭代的面积差值（r行，q列） + # 注意：论文中r是膨胀次数，但这里R矩阵的r行应该对应不同的膨胀次数 + # 根据论文描述，应该是迭代q次，每次记录面积差值 + # 但论文提到"叠加q次"和"一共迭代r次"，这里理解为迭代q次，每次对W和S都膨胀r次 + area_differences = [] # 存储每次迭代的面积差值 + thresholds = [] + W_masks = [] + S_masks = [] + + print(f"开始迭代计算最佳阈值（迭代{q}次，步长u={u}）...") + # 迭代更新阈值（迭代q次） + for i in range(q): + # 当前阈值：Nf = min(F) + u·(i+1) + current_threshold = min_value + u * (i + 1) + thresholds.append(current_threshold) + + # 步骤4: 对全波段叠加图像F进行阈值分割，得到耀光区域S（用于提取耀光面积） + # S = {1, F>N; 0, F≤N} + S = apply_threshold(F, current_threshold) + + # 步骤5: 对S进行形态学膨胀r次 + S_dilated = morphological_dilation(S, iterations=r) + + # 步骤6: 计算面积差值：纹理提取区域面积 vs 阈值分割区域面积 + rq, area_W, area_S = calculate_area_difference(W_dilated, S_dilated) + area_differences.append(rq) + W_masks.append(W_dilated.copy()) + S_masks.append(S_dilated.copy()) + + if (i + 1) % 10 == 0: + print(f" 迭代 {i+1}/{q}: 阈值={current_threshold:.4f}, 面积差值={rq:.2f}") + + # 步骤7: 寻找R中每列数组中最小值所在行数 + # 注意：论文中提到"r行、q列的数组R"，但根据算法描述，应该是q次迭代 + # 这里理解为：将area_differences重新组织成矩阵形式（如果需要） + # 但根据论文描述，应该是直接找到最小面积差值对应的迭代次数 + + area_differences_array = np.array(area_differences) + + # 步骤8: 找到最小面积差值所在的行数（索引） + # argmin_q(R(r,q))：找到最小面积差值所在的行数 + min_indices = np.where(area_differences_array == np.min(area_differences_array))[0] + + # 步骤9: 通过众数统计找到最频繁出现的行数，确定最佳的叠加次数M + # Mode(aind)：通过众数统计找到最频繁出现的行数 + if len(min_indices) > 0: + # 如果最小值出现多次，使用众数统计 + counter = Counter(min_indices) + most_common = counter.most_common(1)[0] + M = most_common[0] # 最佳迭代次数索引（从0开始） + else: + M = 0 + + # 步骤10: 计算最终阈值 + # Nf = min(F) + u·M + # 注意：M是索引（从0开始），所以实际迭代次数是M+1 + optimal_threshold = min_value + u * (M + 1) + + # 获取最佳阈值对应的掩膜 + optimal_W = W_masks[M] + optimal_S = S_masks[M] + + print(f"最佳迭代次数索引: M={M} (第{M+1}次迭代)") + print(f"最佳阈值: Nf={optimal_threshold:.4f}") + print(f"最小面积差值: {np.min(area_differences_array):.2f}") + + return optimal_threshold, optimal_S, optimal_W, M, area_differences, thresholds + + +def generate_glint_mask(bsq_file, water_mask=None, u=0.1, q=50, r=20, max_iterations=None, output_file=None): + """ + 生成耀光掩膜 + + 算法流程： + 1. 全波段叠加：F = Σ(i=1 to n) R(λi) × G（G为水体二值化图像） + 2. 拉普拉斯算子提取纹理信息（用于定位耀光位置） + 3. 通过阈值分割全波段叠加图像提取耀光面积 + 4. 当纹理提取区域面积与阈值分割区域面积差最小时，确定最佳阈值 + + 参数: + bsq_file: 输入的BSQ文件路径 + water_mask: 水体二值化图像G，可以是： + - None: 自动生成（基于所有像素，即全为1的掩膜） + - numpy数组: 直接使用数组作为掩膜，形状为 (rows, cols)，值为0或1 + - 文件路径: 栅格文件路径(.tif/.dat)，将自动读取 + u: 阈值更新步长，默认0.1（论文参数） + q: 迭代次数，默认50（论文参数） + r: 形态学膨胀次数，默认20（论文参数） + max_iterations: 最大迭代次数（如果指定，则使用此值替代q） + output_file: 输出文件路径，如果为None则自动生成 + + 返回: + tuple: (耀光掩膜文件路径, 纹理提取图像文件路径) + - 耀光掩膜文件路径: 通过阈值分割全波段叠加图像得到的最终掩膜（S掩膜） + - 纹理提取图像文件路径: 拉普拉斯纹理提取后的二值化图像（W掩膜） + """ + # 读取BSQ文件 + bsq_dataset = gdal.Open(bsq_file) + if bsq_dataset is None: + raise ValueError(f"无法打开文件: {bsq_file}") + + # 获取影像数据 + bands = bsq_dataset.RasterCount + rows = bsq_dataset.RasterYSize + cols = bsq_dataset.RasterXSize + + print(f"影像尺寸: {rows} x {cols}, 波段数: {bands}") + + # 读取所有波段 + print("正在读取所有波段数据...") + image_bands = np.zeros((rows, cols, bands), dtype=np.float32) + for band in range(bands): + image_bands[:, :, band] = bsq_dataset.GetRasterBand(band + 1).ReadAsArray().astype(np.float32) + if (band + 1) % 20 == 0: + print(f" 已读取 {band+1}/{bands} 个波段") + + # 处理水体掩膜G + if water_mask is None: + # 如果没有提供水体掩膜，使用全图（所有像素为1） + print("未提供水体掩膜，使用全图进行处理") + G = np.ones((rows, cols), dtype=np.float32) + elif isinstance(water_mask, np.ndarray): + # 如果直接提供了numpy数组 + if water_mask.shape != (rows, cols): + raise ValueError(f"水体掩膜尺寸 {water_mask.shape} 与影像尺寸 {(rows, cols)} 不匹配") + G = water_mask.astype(np.float32) + # 确保是二值化的（0或1） + G = np.where(G > 0, 1.0, 0.0) + elif isinstance(water_mask, str): + # 如果是文件路径，读取文件 + print(f"从文件读取水体掩膜: {water_mask}") + water_dataset = gdal.Open(water_mask) + if water_dataset is None: + raise ValueError(f"无法打开水体掩膜文件: {water_mask}") + if water_dataset.RasterXSize != cols or water_dataset.RasterYSize != rows: + raise ValueError(f"水体掩膜尺寸与影像尺寸不匹配") + G = water_dataset.GetRasterBand(1).ReadAsArray().astype(np.float32) + water_dataset = None + # 确保是二值化的（0或1） + G = np.where(G > 0, 1.0, 0.0) + else: + raise ValueError(f"不支持的水体掩膜类型: {type(water_mask)}") + + print(f"水体掩膜统计: 水体像素数={np.sum(G)}, 总像素数={rows*cols}, 水体比例={np.sum(G)/(rows*cols)*100:.2f}%") + + # 步骤1: 全波段叠加 F = Σ(i=1 to n) R(λi) × G + print("开始全波段叠加...") + F = multi_band_weighted_sum(image_bands, G) + print(f"全波段叠加完成，F值范围: [{np.min(F):.4f}, {np.max(F):.4f}]") + + print("开始计算最佳阈值...") + # 找到最佳阈值 + optimal_threshold, glint_mask, texture_mask, optimal_iteration, area_diffs, thresholds = find_optimal_threshold( + F, u=u, q=q, r=r, max_iterations=max_iterations + ) + + print(f"\n=== 最佳阈值计算结果 ===") + print(f"最佳阈值: {optimal_threshold:.4f}") + print(f"最佳迭代次数: {optimal_iteration + 1}") + print(f"最小面积差值: {np.min(area_diffs):.4f}") + print(f"纹理提取区域面积: {np.sum(texture_mask)}") + print(f"阈值分割区域面积: {np.sum(glint_mask)}") + + # 设置输出文件路径 + if output_file is None: + output_file = 'glint_mask.tif' + + # 生成纹理提取图像输出路径 + texture_output_file = output_file.replace('.tif', '_texture.tif') + if texture_output_file == output_file: # 如果没有.tif扩展名 + texture_output_file = output_file + '_texture.tif' + + # 保存纹理提取图像（W掩膜，拉普拉斯提取的纹理区域） + print(f"\n保存输出文件...") + driver = gdal.GetDriverByName('GTiff') + texture_dataset = driver.Create(texture_output_file, cols, rows, 1, gdal.GDT_Byte) + texture_dataset.SetGeoTransform(bsq_dataset.GetGeoTransform()) + texture_dataset.SetProjection(bsq_dataset.GetProjection()) + texture_mask_uint8 = (texture_mask * 255).astype(np.uint8) + texture_dataset.GetRasterBand(1).WriteArray(texture_mask_uint8) + texture_dataset = None + print(f"纹理提取图像已保存至: {texture_output_file}") + + # 保存耀光掩膜（S掩膜，通过阈值分割全波段叠加图像得到） + out_dataset = driver.Create(output_file, cols, rows, 1, gdal.GDT_Byte) + + # 设置地理变换和投影信息 + out_dataset.SetGeoTransform(bsq_dataset.GetGeoTransform()) + out_dataset.SetProjection(bsq_dataset.GetProjection()) + + # 写入掩膜数据（转换为0-255范围） + glint_mask_uint8 = (glint_mask * 255).astype(np.uint8) + out_dataset.GetRasterBand(1).WriteArray(glint_mask_uint8) + + # 关闭数据集 + out_dataset = None + bsq_dataset = None + + print(f"耀光掩膜已保存至: {output_file}") + + return output_file, texture_output_file + + +# 使用示例 +if __name__ == "__main__": + bsq_file = r"D:\BaiduNetdiskDownload\yaobao\test_glint.bsq" # 输入的BSQ文件 + output_file = r'D:\BaiduNetdiskDownload\yaobao\glint\lapulas_otsu_glint_mask.tif' + # water_mask_file = r'path/to/water_mask.tif' # 可选：水体掩膜文件路径 + + # 示例1: 使用论文默认参数（q=50, r=20, u=0.1） + mask_file, texture_file = generate_glint_mask( + bsq_file, + water_mask=None, + u=0.1, + q=50, + r=20, + output_file=output_file + ) + print(f"\n处理完成，耀光掩膜保存在: {mask_file}") + print(f"纹理提取图像保存在: {texture_file}") + + # 示例2: 使用水体掩膜文件 + # mask_file, texture_file = generate_glint_mask( + # bsq_file, + # water_mask=water_mask_file, + # u=0.1, + # q=50, + # r=20, + # output_file=output_file + # ) + + # 示例3: 使用numpy数组作为水体掩膜 + # import numpy as np + # water_mask_array = np.ones((rows, cols), dtype=np.float32) # 示例：全为1的掩膜 + # mask_file, texture_file = generate_glint_mask( + # bsq_file, + # water_mask=water_mask_array, + # u=0.1, + # q=50, + # r=20, + # output_file=output_file + # ) + diff --git a/src/utils/sampling.py b/src/utils/sampling.py new file mode 100644 index 0000000..f16945b --- /dev/null +++ b/src/utils/sampling.py @@ -0,0 +1,1061 @@ +from src.utils.util import * +import math +import os +import numpy as np +from osgeo import gdal, ogr +import spectral +from scipy import ndimage +try: + from skimage import morphology + from skimage.morphology import skeletonize, medial_axis + SKIMAGE_AVAILABLE = True +except ImportError: + SKIMAGE_AVAILABLE = False + print("警告: skimage未安装，将无法使用主水轴线检测功能") + + +def get_wavelengths_from_bil_header(bil_file): + """ + 从BIL文件的头文件中读取波长信息 + + 参数: + bil_file: str - BIL文件路径 + + 返回: + list - 波长列表，如果无法获取则返回None + """ + try: + # 获取头文件路径（通常与BIL文件同目录，后缀为.hdr） + header_file = os.path.splitext(bil_file)[0] + ".hdr" + + if not os.path.exists(header_file): + print(f"警告: 找不到头文件 {header_file}") + return None + + wavelengths = [] + + with open(header_file, 'r', encoding='utf-8') as f: + lines = f.readlines() + + # 查找包含波长信息的行 + wavelength_lines = [] + in_wavelength_block = False + + for line in lines: + stripped_line = line.strip() + + # 检测波长块的开始（精确匹配 wavelength = ） + if stripped_line.startswith('wavelength ='): + in_wavelength_block = True + # 提取第一行的波长信息 + wavelength_str = stripped_line.replace('wavelength =', '').strip() + if wavelength_str.startswith('{'): + wavelength_str = wavelength_str[1:].strip() + wavelength_lines.append(wavelength_str) + # 检测波长块的中间行 + elif in_wavelength_block: + if '}' in stripped_line: + # 波长块结束 + end_str = stripped_line.replace('}', '').strip() + if end_str: + wavelength_lines.append(end_str) + in_wavelength_block = False + else: + wavelength_lines.append(stripped_line) + + if wavelength_lines: + # 合并所有波长行 + combined_wavelengths = ' '.join(wavelength_lines) + # 移除所有花括号和逗号 + combined_wavelengths = combined_wavelengths.replace('{', '').replace('}', '').strip() + + # 分割波长值（支持逗号和空格分隔） + wavelength_values = [] + for part in combined_wavelengths.split(','): + part = part.strip() + if part: + # 处理可能的多值情况（空格分隔） + for value in part.split(): + if value.strip(): + try: + wavelength_values.append(float(value.strip())) + except ValueError: + continue + + print(f"从头文件读取到 {len(wavelength_values)} 个波长值") + return wavelength_values + else: + print("警告: 头文件中未找到波长信息") + return None + + except Exception as e: + print(f"读取头文件波长信息时发生错误: {str(e)}") + return None + + +def get_spectral_sampling_points_chunked(bil_file, water_mask_shp, severe_glint=None, output_csvpath=None, + interval=100, sample_radius=1, chunk_size=1000, + use_adaptive_sampling=True, min_interval=10, max_interval=200): + """ + 基于bil文件、shp格式water_mask和severe_glint生成采样点并提取光谱数据（分块处理版本） + + 参数: + bil_file: str - bil格式的光谱数据文件路径 + water_mask_shp: str - shp格式的水体掩膜文件路径 + severe_glint: str - 耀斑掩膜文件路径（可选） + output_csvpath: str - 输出CSV文件路径（可选） + interval: int - 采样点间隔（像元数），当use_adaptive_sampling=False时使用 + sample_radius: int - 采样点半径（像元数） + chunk_size: int - 每次处理的行数（控制内存使用） + use_adaptive_sampling: bool - 是否使用自适应采样（根据水体宽度调整间隔，默认True） + min_interval: int - 自适应采样时的最小间隔（像元数，默认10） + max_interval: int - 自适应采样时的最大间隔（像元数，默认200） + + 返回: + tuple: (x_coords, y_coords, spectral_data) - 坐标列表和光谱数据数组 + """ + # 初始化GDAL异常处理 + gdal.UseExceptions() + ogr.UseExceptions() + + try: + # 打开bil文件 + dataset_bil = gdal.Open(bil_file) + if dataset_bil is None: + raise ValueError(f"无法打开bil文件: {bil_file}") + + # 获取bil文件的基本信息 + im_width = dataset_bil.RasterXSize + im_height = dataset_bil.RasterYSize + num_bands = dataset_bil.RasterCount + geotransform_input = dataset_bil.GetGeoTransform() + projection = dataset_bil.GetProjection() + + print(f"bil文件信息: 宽度={im_width}, 高度={im_height}, 波段数={num_bands}") + print(f"分块处理，每次处理 {chunk_size} 行") + + # 创建水体掩膜栅格 + print("正在处理水体掩膜...") + water_mask_raster = create_water_mask_from_shp(water_mask_shp, bil_file) + + # 处理耀斑掩膜（可选） + if severe_glint is not None: + dataset_severe_glint = gdal.Open(severe_glint) + if dataset_severe_glint is None: + raise ValueError(f"无法打开耀斑掩膜文件: {severe_glint}") + data_severe_glint = dataset_severe_glint.GetRasterBand(1).ReadAsArray() + print("已加载耀斑掩膜") + # 对glint边界进行外扩1-2像素作为缓冲 + data_severe_glint = expand_glint_buffer(data_severe_glint, buffer_size=2) + else: + data_severe_glint = None + print("未使用耀斑掩膜") + + # 创建有效区域掩膜 + if data_severe_glint is not None: + valid_area = (water_mask_raster > 0) & (~(data_severe_glint > 0)) + else: + valid_area = (water_mask_raster > 0) + + # 计算水体宽度（用于自适应采样） + width_map = None + if use_adaptive_sampling: + print("正在计算水体宽度（用于自适应采样）...") + width_map = calculate_water_width(water_mask_raster) + if width_map is not None: + width_min = np.min(width_map[water_mask_raster > 0]) + width_max = np.max(width_map[water_mask_raster > 0]) + print(f"水体宽度范围: {width_min:.1f} - {width_max:.1f} 像元") + else: + print("警告: 无法计算水体宽度，将使用固定间隔采样") + use_adaptive_sampling = False + + # 保存有效区域（可选） + if output_csvpath: + valid_area_path = os.path.splitext(output_csvpath)[0] + "_valid_area.bsq" + write_bands(bil_file, valid_area_path, valid_area.astype(np.uint8)) + + x_out = [] + y_out = [] + spectral_out = [] + + # 如果没有提供输出路径，则不保存文件 + if output_csvpath: + f = open(output_csvpath, "w") + # 写入CSV头部 + header = "x_coord,y_coord,pixel_x,pixel_y" + + # 尝试从头文件读取波长名称 + wavelengths = get_wavelengths_from_bil_header(bil_file) + if wavelengths is not None and len(wavelengths) == num_bands: + for i, wavelength in enumerate(wavelengths): + # 使用格式化字符串保留足够的小数位数（通常波长需要4-6位小数） + header += f",{wavelength:.6f}" + else: + # 如果无法获取波长信息，使用默认的波段编号 + for i in range(num_bands): + header += f",band_{i + 1}" + + f.write(header + "\n") + else: + f = None + + try: + print("正在分块生成采样点...") + sample_count = 0 + sampled_pixels = set() # 用于记录已采样的像素，避免重复 + + # 辅助函数：添加采样点（分块版本） + def add_sample_point_chunked(x, y, local_y, spectral_chunk, valid_chunk, sample_radius, + geotransform_input, num_bands, f, x_out, y_out, + spectral_out, sampled_pixels): + """添加单个采样点（分块版本）""" + # 检查是否已采样 + if (x, y) in sampled_pixels: + return False + + # 检查边界 + if (x < sample_radius or x >= im_width - sample_radius or + local_y < sample_radius or local_y >= valid_chunk.shape[0] - sample_radius): + return False + + # 检查采样点周围区域是否全部有效 + sample_area = valid_chunk[ + local_y - sample_radius:local_y + sample_radius + 1, + x - sample_radius:x + sample_radius + 1 + ] + + # 如果采样区域内所有像元都有效 + if np.all(sample_area): + # 提取光谱数据（采样区域内的平均值） + spectral_sample = [] + for band_idx in range(num_bands): + band_data = spectral_chunk[ + band_idx, + local_y - sample_radius:local_y + sample_radius + 1, + x - sample_radius:x + sample_radius + 1 + ] + # 计算平均值，忽略无效值 + valid_pixels = band_data[sample_area] + if len(valid_pixels) > 0: + mean_value = np.mean(valid_pixels) + else: + mean_value = np.nan + spectral_sample.append(mean_value) + + # 转换为地理坐标 + geo_x, geo_y = gdal.ApplyGeoTransform( + geotransform_input, + x + 0.5, # 像元中心 + y + 0.5 + ) + + # 保存结果 + if f: + line_parts = [f"{geo_x:.6f}", f"{geo_y:.6f}", f"{x}", f"{y}"] + for spec_val in spectral_sample: + line_parts.append(f"{spec_val:.6f}") + f.write(",".join(line_parts) + "\n") + + x_out.append(geo_x) + y_out.append(geo_y) + spectral_out.append(spectral_sample) + sampled_pixels.add((x, y)) + return True + return False + + # 计算需要处理的块数 + total_chunks = math.ceil((im_height - 2 * sample_radius) / chunk_size) + + for chunk_idx in range(total_chunks): + # 计算当前块的行范围 + start_row = sample_radius + chunk_idx * chunk_size + end_row = min(sample_radius + (chunk_idx + 1) * chunk_size, im_height - sample_radius) + + # 扩展范围以包含采样半径 + read_start = max(0, start_row - sample_radius) + read_end = min(im_height, end_row + sample_radius) + + print(f"处理块 {chunk_idx + 1}/{total_chunks}: 行 {start_row}-{end_row}") + + # 读取当前块的光谱数据 + spectral_chunk = dataset_bil.ReadAsArray( + 0, read_start, im_width, read_end - read_start + ) # shape: (bands, chunk_height, width) + + # 获取对应的有效区域掩膜和宽度图 + valid_chunk = valid_area[read_start:read_end, :] + water_chunk = water_mask_raster[read_start:read_end, :] + width_chunk = width_map[read_start:read_end, :] if width_map is not None else None + + # 自适应采样：根据水体宽度调整采样间隔 + if use_adaptive_sampling and width_chunk is not None: + print(f" 使用自适应采样（间隔范围: {min_interval}-{max_interval}）...") + # 计算宽度范围用于归一化 + width_chunk_valid = width_chunk[water_chunk > 0] + if len(width_chunk_valid) > 0: + width_min_chunk = np.min(width_chunk_valid) + width_max_chunk = np.max(width_chunk_valid) + + # 使用基础间隔作为网格起点 + base_interval = min(interval, max_interval) + + # 使用网格化采样，但根据局部宽度调整间隔 + y = start_row + while y < end_row: + local_y = y - read_start + if local_y < 0 or local_y >= valid_chunk.shape[0]: + y += base_interval + continue + + x = sample_radius + while x < im_width - sample_radius: + # 检查当前位置是否在水体区域内 + if (local_y >= 0 and local_y < valid_chunk.shape[0] and + x >= 0 and x < valid_chunk.shape[1] and + water_chunk[local_y, x] > 0): + + # 获取当前位置的水体宽度 + local_width = width_chunk[local_y, x] + + # 根据宽度计算采样间隔 + # 窄的区域使用小间隔，宽的区域使用大间隔 + if width_max_chunk > width_min_chunk: + # 归一化宽度到[0, 1] + normalized_width = (local_width - width_min_chunk) / (width_max_chunk - width_min_chunk) + # 映射到[min_interval, max_interval] + adaptive_interval = max(min_interval, min(max_interval, + int(min_interval + normalized_width * (max_interval - min_interval)))) + else: + adaptive_interval = base_interval + + # 尝试添加采样点 + if add_sample_point_chunked(x, y, local_y, spectral_chunk, valid_chunk, sample_radius, + geotransform_input, num_bands, f, x_out, y_out, + spectral_out, sampled_pixels): + sample_count += 1 + + # 根据自适应间隔前进 + x += adaptive_interval + else: + # 不在水体区域内，使用基础间隔快速跳过 + x += base_interval + + # 行间隔也使用基础间隔 + y += base_interval + else: + # 固定间隔采样 + print(f" 使用固定间隔采样（间隔: {interval}）...") + for y in range(start_row, end_row, interval): + for x in range(sample_radius, im_width - sample_radius, interval): + local_y = y - read_start + if add_sample_point_chunked(x, y, local_y, spectral_chunk, valid_chunk, sample_radius, + geotransform_input, num_bands, f, x_out, y_out, + spectral_out, sampled_pixels): + sample_count += 1 + + # 清理当前块的数据以释放内存 + del spectral_chunk + del valid_chunk + del water_chunk + if width_chunk is not None: + del width_chunk + + print(f"块 {chunk_idx + 1} 完成，当前采样点总数: {sample_count}") + + print(f"所有块处理完成，成功生成 {sample_count} 个采样点") + + finally: + if f: + f.close() + + return x_out, y_out, np.array(spectral_out) + + except Exception as e: + print(f"处理过程中发生错误: {str(e)}") + raise + + +def get_spectral_sampling_points(bil_file, water_mask_shp, severe_glint=None, output_csvpath=None, + interval=100, sample_radius=1, + use_adaptive_sampling=True, min_interval=10, max_interval=200): + """ + 基于bil文件、shp格式water_mask和severe_glint生成采样点并提取光谱数据 + + 参数: + bil_file: str - bil格式的光谱数据文件路径 + water_mask_shp: str - shp格式的水体掩膜文件路径 + severe_glint: str - 耀斑掩膜文件路径（可选） + output_csvpath: str - 输出CSV文件路径（可选） + interval: int - 采样点间隔（像元数），当use_adaptive_sampling=False时使用 + sample_radius: int - 采样点半径（像元数） + use_adaptive_sampling: bool - 是否使用自适应采样（根据水体宽度调整间隔，默认True） + min_interval: int - 自适应采样时的最小间隔（像元数，默认10） + max_interval: int - 自适应采样时的最大间隔（像元数，默认200） + + 返回: + tuple: (x_coords, y_coords, spectral_data) - 坐标列表和光谱数据数组 + """ + # 初始化GDAL异常处理 + gdal.UseExceptions() + ogr.UseExceptions() + + try: + # 打开bil文件 + dataset_bil = gdal.Open(bil_file) + if dataset_bil is None: + raise ValueError(f"无法打开bil文件: {bil_file}") + + # 获取bil文件的基本信息 + im_width = dataset_bil.RasterXSize + im_height = dataset_bil.RasterYSize + num_bands = dataset_bil.RasterCount + geotransform_input = dataset_bil.GetGeoTransform() + projection = dataset_bil.GetProjection() + + print(f"bil文件信息: 宽度={im_width}, 高度={im_height}, 波段数={num_bands}") + + # 读取光谱数据（所有波段） + print("正在读取光谱数据...") + spectral_data_full = dataset_bil.ReadAsArray() # shape: (bands, height, width) + + # 创建水体掩膜栅格 + print("正在处理水体掩膜...") + water_mask_raster = create_water_mask_from_shp(water_mask_shp, bil_file) + + # 处理耀斑掩膜（可选） + if severe_glint is not None: + dataset_severe_glint = gdal.Open(severe_glint) + if dataset_severe_glint is None: + raise ValueError(f"无法打开耀斑掩膜文件: {severe_glint}") + data_severe_glint = dataset_severe_glint.GetRasterBand(1).ReadAsArray() + print("已加载耀斑掩膜") + # 对glint边界进行外扩1-2像素作为缓冲 + data_severe_glint = expand_glint_buffer(data_severe_glint, buffer_size=2) + else: + data_severe_glint = None + print("未使用耀斑掩膜") + + inv_geotransform_input = gdal.InvGeoTransform(geotransform_input) + if inv_geotransform_input is None: + raise ValueError("无法计算逆仿射变换") + + # 计算范围 + x_min = geotransform_input[0] + y_max = geotransform_input[3] + x_max = x_min + im_width * geotransform_input[1] + y_min = y_max + im_height * geotransform_input[5] + + # 创建有效区域掩膜 + if data_severe_glint is not None: + valid_area = (water_mask_raster > 0) & (~(data_severe_glint > 0)) + else: + valid_area = (water_mask_raster > 0) + + # 计算水体宽度（用于自适应采样） + width_map = None + if use_adaptive_sampling: + print("正在计算水体宽度（用于自适应采样）...") + width_map = calculate_water_width(water_mask_raster) + if width_map is not None: + width_min = np.min(width_map[water_mask_raster > 0]) + width_max = np.max(width_map[water_mask_raster > 0]) + print(f"水体宽度范围: {width_min:.1f} - {width_max:.1f} 像元") + else: + print("警告: 无法计算水体宽度，将使用固定间隔采样") + use_adaptive_sampling = False + + # 保存有效区域（可选） + if output_csvpath: + valid_area_path = os.path.splitext(output_csvpath)[0] + "_valid_area.tif" + write_bands(bil_file, valid_area_path, valid_area.astype(np.uint8)) + + x_out = [] + y_out = [] + spectral_out = [] + + # 如果没有提供输出路径，则不保存文件 + if output_csvpath: + f = open(output_csvpath, "w") + # 写入CSV头部 + header = "x_coord,y_coord,pixel_x,pixel_y" + + # 尝试从头文件读取波长名称 + wavelengths = get_wavelengths_from_bil_header(bil_file) + if wavelengths is not None and len(wavelengths) == num_bands: + for i, wavelength in enumerate(wavelengths): + # 使用格式化字符串保留足够的小数位数（通常波长需要4-6位小数） + header += f",{wavelength:.6f}" + else: + # 如果无法获取波长信息，使用默认的波段编号 + for i in range(num_bands): + header += f",band_{i + 1}" + + f.write(header + "\n") + else: + f = None + + try: + print("正在生成采样点...") + sample_count = 0 + sampled_pixels = set() # 用于记录已采样的像素，避免重复 + + # 辅助函数：添加采样点 + def add_sample_point(x, y, spectral_data_full, valid_area, sample_radius, + geotransform_input, num_bands, f, x_out, y_out, + spectral_out, sampled_pixels): + """添加单个采样点""" + # 检查是否已采样 + if (x, y) in sampled_pixels: + return False + + # 检查边界 + if (x < sample_radius or x >= im_width - sample_radius or + y < sample_radius or y >= im_height - sample_radius): + return False + + # 检查采样点周围区域是否全部有效 + sample_area = valid_area[y - sample_radius:y + sample_radius + 1, + x - sample_radius:x + sample_radius + 1] + + # 如果采样区域内所有像元都有效 + if np.all(sample_area): + # 提取光谱数据（采样区域内的平均值） + spectral_sample = [] + for band_idx in range(num_bands): + band_data = spectral_data_full[band_idx, + y - sample_radius:y + sample_radius + 1, + x - sample_radius:x + sample_radius + 1] + # 计算平均值，忽略无效值 + valid_pixels = band_data[sample_area] + if len(valid_pixels) > 0: + mean_value = np.mean(valid_pixels) + else: + mean_value = np.nan + spectral_sample.append(mean_value) + + # 转换为地理坐标 + geo_x, geo_y = gdal.ApplyGeoTransform( + geotransform_input, + x + 0.5, # 像元中心 + y + 0.5 + ) + + # 保存结果 + if f: + line_parts = [f"{geo_x:.6f}", f"{geo_y:.6f}", f"{x}", f"{y}"] + for spec_val in spectral_sample: + line_parts.append(f"{spec_val:.6f}") + f.write(",".join(line_parts) + "\n") + + x_out.append(geo_x) + y_out.append(geo_y) + spectral_out.append(spectral_sample) + sampled_pixels.add((x, y)) + return True + return False + + # 自适应采样：根据水体宽度调整采样间隔 + if use_adaptive_sampling and width_map is not None: + print("使用自适应采样（根据水体宽度调整间隔）...") + # 计算宽度范围用于归一化 + width_valid = width_map[water_mask_raster > 0] + if len(width_valid) > 0: + width_min = np.min(width_valid) + width_max = np.max(width_valid) + + # 使用基础间隔作为网格起点 + base_interval = min(interval, max_interval) + + # 使用网格化采样，但根据局部宽度调整间隔 + y = sample_radius + while y < im_height - sample_radius: + x = sample_radius + while x < im_width - sample_radius: + # 检查当前位置是否在水体区域内 + if (water_mask_raster[y, x] > 0): + # 获取当前位置的水体宽度 + local_width = width_map[y, x] + + # 根据宽度计算采样间隔 + # 窄的区域使用小间隔，宽的区域使用大间隔 + if width_max > width_min: + # 归一化宽度到[0, 1] + normalized_width = (local_width - width_min) / (width_max - width_min) + # 映射到[min_interval, max_interval] + adaptive_interval = max(min_interval, min(max_interval, + int(min_interval + normalized_width * (max_interval - min_interval)))) + else: + adaptive_interval = base_interval + + # 尝试添加采样点 + if add_sample_point(x, y, spectral_data_full, valid_area, sample_radius, + geotransform_input, num_bands, f, x_out, y_out, + spectral_out, sampled_pixels): + sample_count += 1 + + # 根据自适应间隔前进 + x += adaptive_interval + else: + # 不在水体区域内，使用基础间隔快速跳过 + x += base_interval + + # 行间隔也使用基础间隔 + y += base_interval + else: + # 如果无法计算宽度，使用固定间隔 + print("无法获取有效宽度信息，使用固定间隔采样...") + use_adaptive_sampling = False + + # 固定间隔采样（当不使用自适应采样时） + if not use_adaptive_sampling: + print(f"使用固定间隔采样（间隔: {interval}）...") + for y in range(sample_radius, im_height - sample_radius, interval): + for x in range(sample_radius, im_width - sample_radius, interval): + if add_sample_point(x, y, spectral_data_full, valid_area, sample_radius, + geotransform_input, num_bands, f, x_out, y_out, + spectral_out, sampled_pixels): + sample_count += 1 + + print(f"成功生成 {sample_count} 个采样点") + + finally: + if f: + f.close() + + return x_out, y_out, np.array(spectral_out) + + except Exception as e: + print(f"处理过程中发生错误: {str(e)}") + raise + + +def create_water_mask_from_shp(shp_file, reference_raster): + """ + 从shp文件或栅格文件创建水体掩膜栅格 + + 参数: + shp_file: str - shp文件路径或栅格文件路径（.dat/.tif等） + reference_raster: str - 参考栅格文件路径（用于获取空间范围和分辨率，当shp_file为shp格式时需要） + + 返回: + numpy.ndarray - 水体掩膜数组 + """ + try: + # 检查文件格式 + file_ext = os.path.splitext(shp_file)[1].lower() + + if file_ext == '.shp': + # shp格式，需要栅格化 + # 打开参考栅格获取空间信息 + ref_dataset = gdal.Open(reference_raster) + if ref_dataset is None: + raise ValueError(f"无法打开参考栅格文件: {reference_raster}") + + geotransform = ref_dataset.GetGeoTransform() + projection = ref_dataset.GetProjection() + width = ref_dataset.RasterXSize + height = ref_dataset.RasterYSize + + # 创建内存中的栅格数据集 + mem_driver = gdal.GetDriverByName('MEM') + mask_dataset = mem_driver.Create('', width, height, 1, gdal.GDT_Byte) + mask_dataset.SetGeoTransform(geotransform) + mask_dataset.SetProjection(projection) + + # 初始化为0 + mask_band = mask_dataset.GetRasterBand(1) + mask_band.Fill(0) + + # 打开shp文件 + shp_dataset = ogr.Open(shp_file) + if shp_dataset is None: + raise ValueError(f"无法打开shp文件: {shp_file}") + + layer = shp_dataset.GetLayer() + + # 栅格化shp文件 + gdal.RasterizeLayer(mask_dataset, [1], layer, burn_values=[1]) + + # 读取栅格化结果 + water_mask = mask_band.ReadAsArray() + + # 清理 + ref_dataset = None + mask_dataset = None + shp_dataset = None + + return water_mask + else: + # 栅格格式（.dat/.tif等），直接读取 + mask_dataset = gdal.Open(shp_file, gdal.GA_ReadOnly) + if mask_dataset is None: + raise ValueError(f"无法打开栅格掩膜文件: {shp_file}") + + # 读取第一个波段 + water_mask = mask_dataset.GetRasterBand(1).ReadAsArray() + + # 清理 + mask_dataset = None + + return water_mask + + except Exception as e: + print(f"创建水体掩膜时发生错误: {str(e)}") + raise + + +def expand_glint_buffer(glint_mask, buffer_size=2): + """ + 对glint掩膜进行边界外扩，作为缓冲区域 + + 参数: + glint_mask: numpy.ndarray - glint掩膜数组（glint区域>0） + buffer_size: int - 外扩像素数（默认2，范围1-2） + + 返回: + numpy.ndarray - 外扩后的glint掩膜 + """ + if glint_mask is None: + return None + + # 限制buffer_size在1-2之间 + buffer_size = max(1, min(2, int(buffer_size))) + + # 将glint掩膜转换为二值图像 + glint_binary = (glint_mask > 0).astype(np.uint8) + + # 创建结构元素（圆形或方形） + # 使用3x3或5x5的结构元素进行膨胀 + if buffer_size == 1: + structure = np.ones((3, 3), dtype=np.uint8) + else: # buffer_size == 2 + structure = np.ones((5, 5), dtype=np.uint8) + + # 对glint区域进行膨胀操作 + expanded_glint = ndimage.binary_dilation(glint_binary, structure=structure).astype(np.uint8) + + expanded_pixels = np.sum(expanded_glint > 0) - np.sum(glint_binary > 0) + print(f"Glint边界外扩: 外扩 {buffer_size} 像素，新增 {expanded_pixels} 个像素") + + return expanded_glint + + +def calculate_water_width(water_mask): + """ + 计算水体宽度（使用距离变换） + + 参数: + water_mask: numpy.ndarray - 水体掩膜数组（水体区域>0） + + 返回: + numpy.ndarray - 宽度数组，每个像素的值表示到最近边界的距离（即局部宽度的一半） + """ + try: + # 将水体掩膜转换为二值图像 + water_binary = (water_mask > 0).astype(bool) + + # 计算距离变换：每个像素到最近边界的距离 + # 使用欧氏距离变换 + from scipy.ndimage import distance_transform_edt + + # 计算到边界的距离（内部像素） + distance = distance_transform_edt(water_binary) + + # 宽度 = 2 * 距离（因为距离是到边界的距离，宽度是两倍） + # 但为了简化，我们直接使用距离作为宽度的近似值 + # 在窄的区域，距离小；在宽的区域，距离大 + width_map = distance.astype(np.float32) + + return width_map + + except Exception as e: + print(f"计算水体宽度时发生错误: {str(e)}") + return None + + +def detect_water_centerline(water_mask): + """ + 检测水体的主轴线（中心线/骨架） + + 参数: + water_mask: numpy.ndarray - 水体掩膜数组 + + 返回: + numpy.ndarray - 中心线掩膜（中心线位置=1，其他=0） + """ + if not SKIMAGE_AVAILABLE: + print("警告: skimage未安装，无法检测主水轴线") + return None + + try: + # 将水体掩膜转换为二值图像 + water_binary = (water_mask > 0).astype(bool) + + # 使用骨架化提取中心线 + skeleton = skeletonize(water_binary) + + skeleton_pixels = np.sum(skeleton > 0) + print(f"主水轴线检测: 检测到 {skeleton_pixels} 个中心线像素") + + return skeleton.astype(np.uint8) + + except Exception as e: + print(f"检测主水轴线时发生错误: {str(e)}") + return None + + +def detect_water_features(water_mask, centerline_mask=None): + """ + 检测水体的地形特征点：分支口、汇入口、弯头 + + 参数: + water_mask: numpy.ndarray - 水体掩膜数组 + centerline_mask: numpy.ndarray - 中心线掩膜（可选，如果提供则只检测中心线上的特征点） + + 返回: + numpy.ndarray - 特征点掩膜（特征点位置=1，其他=0） + """ + try: + # 如果提供了中心线掩膜，则基于中心线检测特征点 + if centerline_mask is not None: + skeleton = (centerline_mask > 0).astype(bool) + else: + # 如果没有提供中心线，先提取中心线 + if SKIMAGE_AVAILABLE: + skeleton = skeletonize((water_mask > 0).astype(bool)) + else: + print("警告: 无法检测地形特征点（需要中心线）") + return np.zeros_like(water_mask, dtype=np.uint8) + + # 检测中心线上的交叉点（分支口/汇入口） + # 通过计算每个像素的邻域连接数来判断 + features = np.zeros_like(water_mask, dtype=np.uint8) + + # 使用3x3卷积核检测交叉点 + kernel = np.array([[1, 1, 1], + [1, 0, 1], + [1, 1, 1]], dtype=np.uint8) + + # 计算每个中心线像素的邻域连接数 + neighbor_count = ndimage.convolve(skeleton.astype(np.uint8), kernel, mode='constant') + + # 交叉点：邻域连接数 >= 3（分支口或汇入口） + branch_points = (skeleton) & (neighbor_count >= 3) + + # 检测弯头：通过计算曲率变化 + # 使用Sobel算子计算梯度方向变化 + from scipy.ndimage import sobel, gaussian_filter + sobel_x = sobel(skeleton.astype(float), axis=1) + sobel_y = sobel(skeleton.astype(float), axis=0) + + # 计算梯度方向 + gradient_magnitude = np.sqrt(sobel_x**2 + sobel_y**2) + + # 弯头：梯度方向变化大的点（在中心线上且梯度变化大） + # 使用梯度幅值的二阶导数来检测曲率变化 + smoothed_gradient = gaussian_filter(gradient_magnitude, sigma=1.0) + gradient_laplacian = ndimage.laplace(smoothed_gradient) + + # 弯头：中心线上的点且梯度变化大（阈值可调） + curvature_threshold = np.percentile(gradient_laplacian[skeleton], 75) # 使用75分位数作为阈值 + bend_points = (skeleton) & (np.abs(gradient_laplacian) > curvature_threshold) + + # 合并所有特征点 + features = (branch_points | bend_points).astype(np.uint8) + + feature_count = np.sum(features > 0) + branch_count = np.sum(branch_points > 0) + bend_count = np.sum(bend_points > 0) + + print(f"地形特征点检测: 检测到 {feature_count} 个特征点（分支/汇入: {branch_count}, 弯头: {bend_count}）") + + return features + + except Exception as e: + print(f"检测地形特征点时发生错误: {str(e)}") + return np.zeros_like(water_mask, dtype=np.uint8) + + +def get_coor_base_interval(water_mask, severe_glint=None, output_csvpath=None, interval=100): + # 原有函数保持不变 + # 初始化GDAL异常处理 + gdal.UseExceptions() + + try: + dataset_water_mask = gdal.Open(water_mask) + if dataset_water_mask is None: + raise ValueError(f"无法打开水体掩膜文件: {water_mask}") + data_water_mask = dataset_water_mask.GetRasterBand(1).ReadAsArray() + + # 处理耀斑掩膜（可选） + if severe_glint is not None: + dataset_severe_glint = gdal.Open(severe_glint) + if dataset_severe_glint is None: + raise ValueError(f"无法打开耀斑掩膜文件: {severe_glint}") + data_severe_glint = dataset_severe_glint.GetRasterBand(1).ReadAsArray() + + # 使用耀斑掩膜的几何信息 + im_width = dataset_severe_glint.RasterXSize + im_height = dataset_severe_glint.RasterYSize + geotransform_input = dataset_severe_glint.GetGeoTransform() + else: + data_severe_glint = None + + # 使用水体掩膜的几何信息 + im_width = dataset_water_mask.RasterXSize + im_height = dataset_water_mask.RasterYSize + geotransform_input = dataset_water_mask.GetGeoTransform() + + inv_geotransform_input = gdal.InvGeoTransform(geotransform_input) + + if inv_geotransform_input is None: + raise ValueError("无法计算逆仿射变换") + + # 修正范围计算 + x_min = geotransform_input[0] + y_max = geotransform_input[3] + x_max = x_min + im_width * geotransform_input[1] + y_min = y_max + im_height * geotransform_input[5] + + x_range = [x_min, x_max] + y_range = [y_min, y_max] + + # 计算网格大小 - 确保最小为1个像元 + pixel_size = abs(geotransform_input[1]) # 原始像元大小 + grid_size = max(pixel_size * interval, pixel_size) # 网格不能小于原始像元 + + # 计算网格数量 + dx = max(1, math.ceil((x_range[1] - x_range[0]) / grid_size)) + dy = max(1, math.ceil((y_range[1] - y_range[0]) / grid_size)) + + # 创建输出网格的仿射变换 + geotransform_out = (x_range[0], grid_size, 0, y_range[1], 0, -grid_size) + + # 创建有效区域掩膜 + if data_severe_glint is not None: + valid_area = (data_water_mask > 0) & (~(data_severe_glint > 0)) + else: + valid_area = (data_water_mask > 0) + + # 保存有效区域（可选） + valid_area_path = os.path.splitext(output_csvpath)[0] + "_valid_area.tif" + write_bands(water_mask, valid_area_path, valid_area.astype(np.uint8)) + + x_out = [] + y_out = [] + + # 如果没有提供输出路径，则不保存文件 + if output_csvpath: + f = open(output_csvpath, "w") + else: + f = None + + try: + for row in range(dy): + for column in range(dx): + # 计算当前网格的四个角点（地理坐标） + top_left = ( + x_range[0] + column * grid_size, + y_range[1] + row * (-grid_size) # 注意Y方向 + ) + + bottom_right = ( + x_range[0] + (column + 1) * grid_size, + y_range[1] + (row + 1) * (-grid_size) + ) + + # 转换为像元坐标 + top_left_px = gdal.ApplyGeoTransform(inv_geotransform_input, top_left[0], top_left[1]) + bottom_right_px = gdal.ApplyGeoTransform(inv_geotransform_input, bottom_right[0], bottom_right[1]) + + # 确保坐标在图像范围内 + x1 = max(0, int(top_left_px[0])) + y1 = max(0, int(top_left_px[1])) + x2 = min(im_width, int(bottom_right_px[0]) + 1) # +1 确保包含 + y2 = min(im_height, int(bottom_right_px[1]) + 1) + + # 检查网格是否有效 + if x2 <= x1 or y2 <= y1: + continue + + # 提取当前网格内的有效区域 + valid_area_local = valid_area[y1:y2, x1:x2] + + # 查找有效像元 + valid_pixels = np.argwhere(valid_area_local) + + if valid_pixels.size > 0: + # 取第一个有效像元 + local_y, local_x = valid_pixels[0] + + # 转换为全局像元坐标 + global_x = x1 + local_x + global_y = y1 + local_y + + # 转换为地理坐标 + geo_x, geo_y = gdal.ApplyGeoTransform( + geotransform_input, + global_x + 0.5, # 像元中心 + global_y + 0.5 + ) + + # 写入结果 + if f: + line_parts = [f"{geo_x:.6f}", f"{geo_y:.6f}", f"{global_x}", f"{global_y}"] + f.write(",".join(line_parts) + "\n") + x_out.append(geo_x) + y_out.append(geo_y) + + finally: + if f: + f.close() + + return x_out, y_out + + except Exception as e: + print(f"处理过程中发生错误: {str(e)}") + raise + + +# 使用示例 +if __name__ == "__main__": + # 新功能使用示例 + bil_file = r"D:\BaiduNetdiskDownload\yaobao\result3.bsq" + water_mask_shp = r"D:\BaiduNetdiskDownload\yaobao\roi\roi.shp" + severe_glint = r"D:\BaiduNetdiskDownload\yaobao\find_glint\result3_glint_otsu" + output_csvpath = r"D:\BaiduNetdiskDownload\yaobao\csv\spectral_sampling_results.csv" + + # 设置参数 + interval = 50 # 基础采样点间隔（像元数），当use_adaptive_sampling=False时使用 + sample_radius = 5 # 采样点半径（像元数） + chunk_size = 1000 # 每次处理的行数，可根据内存大小调整（建议500-2000） + + # 自适应采样参数 + use_adaptive_sampling = True # 是否使用自适应采样（根据水体宽度调整间隔） + min_interval = 10 # 自适应采样时的最小间隔（像元数），用于窄的入库流区域 + max_interval = 200 # 自适应采样时的最大间隔（像元数），用于宽的水库区域 + + # 调用分块处理函数（适用于大型bil文件，内存友好） + try: + x_coords, y_coords, spectral_data = get_spectral_sampling_points_chunked( + bil_file, water_mask_shp, severe_glint, output_csvpath, + interval, sample_radius, chunk_size, + use_adaptive_sampling=use_adaptive_sampling, + min_interval=min_interval, + max_interval=max_interval + ) + print(f"成功生成 {len(x_coords)} 个采样点") + print(f"每个采样点包含 {spectral_data.shape[1]} 个波段的光谱数据") + print(f"光谱数据形状: {spectral_data.shape}") + + except Exception as e: + print(f"处理失败: {str(e)}") + + # 原有功能使用示例（保持向后兼容） + # water_mask = r"D:\hsi\application\LICA_Work\laodaohe\preprocession\liuyang-guitang2\1.mask\mask.dat" + # severe_glint = r"D:\hsi\application\LICA_Work\laodaohe\preprocession\liuyang-guitang2\2.glint\ref_mosaic_1m_severe_glint" + # output_csvpath = r"D:\hsi\application\LICA_Work\laodaohe\preprocession\liuyang-guitang2\5.interval\coor_interval_3.csv" + # interval = 30 + # x_coords, y_coords = get_coor_base_interval(water_mask, severe_glint, output_csvpath, interval) + # print(f"成功生成 {len(x_coords)} 个采样点") \ No newline at end of file diff --git a/src/utils/type_define.py b/src/utils/type_define.py new file mode 100644 index 0000000..009ca9e --- /dev/null +++ b/src/utils/type_define.py @@ -0,0 +1,15 @@ +from enum import Enum + +class CoorType(Enum): + latlong = 0 # 经纬度坐标 + utm = 1 # UTM坐标 + depend_on_image = 2 # 依赖影像坐标 + pixel = 3 # 像素坐标 + +class ImgType(Enum): + ref = 0 # 反射率图像 + content = 1 # 含量图像 + +class PointPosStrategy(Enum): + nearest_single = 0 # 最近单点 + four_quadrant = 1 # 四象限 \ No newline at end of file diff --git a/src/utils/util.py b/src/utils/util.py new file mode 100644 index 0000000..8b92205 --- /dev/null +++ b/src/utils/util.py @@ -0,0 +1,174 @@ +import os, spectral +import time +import numpy as np +from osgeo import gdal +from enum import Enum, unique +import math +import json + + +class CoorType(Enum): + depend_on_image = 0 # 影像是啥类型坐标就是啥坐标 + pixel = 1 + + +class Timer: # Context Manager + def __enter__(self): + self.start = time.time() + return self + + def __exit__(self, exc_type, exc_value, traceback): + print(exc_type, exc_value, traceback) + print(f"Run Time: {time.time() - self.start}") + + +def timeit(f): # decorator + def wraper(*args, **kwargs): + start = time.time() + ret = f(*args, **kwargs) + print(f.__name__ + " run time: " + str(round(time.time() - start, 2)) + " s.") + return ret + + return wraper + + +def get_hdr_file_path(file_path): + return os.path.splitext(file_path)[0] + ".hdr" + + +def find_band_number(wav1, imgpath): + in_hdr_dict = spectral.envi.read_envi_header(get_hdr_file_path(imgpath)) + + wavelengths = np.array(in_hdr_dict['wavelength']).astype('float64') + + differences = np.abs(wavelengths - wav1) + min_position = np.argmin(differences) + + return int(min_position) + + +@timeit +def average_bands(start_wave, end_wave, imgpath): + start_bandnumber = find_band_number(start_wave, imgpath) + end_bandnumber = find_band_number(end_wave, imgpath) + + dataset = gdal.Open(imgpath) + + averaged_band = 1 + for i in range(start_bandnumber, end_bandnumber + 1): + if i == start_bandnumber: + averaged_band = dataset.GetRasterBand(i + 1).ReadAsArray() + else: + tmp = dataset.GetRasterBand(i + 1).ReadAsArray() + averaged_band = (averaged_band + tmp) / 2 + + del dataset + + return averaged_band + + +def exclude_by_mask(band, water_mask_path, ignore_value=0): + dataset = gdal.Open(water_mask_path) + data_tmp = dataset.GetRasterBand(1).ReadAsArray() + + del dataset + + band[np.where(data_tmp == ignore_value)] = 0 + + return band + + +@timeit +def average_bands_in_mask(start_wave, end_wave, imgpath, water_mask_path): + tmp = average_bands(start_wave, end_wave, imgpath) + + tmp = exclude_by_mask(tmp, water_mask_path) + + # raster_fn_out_tmp = append2filename(imgpath, "glint_delete") + # write_bands(imgpath, raster_fn_out_tmp, tmp) + + return tmp + + +def get_average_value(dataset, x, y, band_number, window): + spectral_tmp = dataset.ReadAsArray(x, y, 1, 1) + average_value = spectral_tmp[band_number - window:band_number + window, :, :].mean() + + return average_value + + +def get_valid_extent(dataset, data_ignore_value=0): + pass + + +def write_bands(imgpath_in, imgpath_out, *args): + # 将输入的波段（可变）写入文件 + dataset = gdal.Open(imgpath_in) + im_width = dataset.RasterXSize + im_height = dataset.RasterYSize + num_bands = dataset.RasterCount + geotransform = dataset.GetGeoTransform() + im_proj = dataset.GetProjection() + + format = "ENVI" + driver = gdal.GetDriverByName(format) + dst_ds = driver.Create(imgpath_out, im_width, im_height, len(args), gdal.GDT_Float32, + options=["INTERLEAVE=BSQ"]) + dst_ds.SetGeoTransform(geotransform) + dst_ds.SetProjection(im_proj) + + for i in range(len(args)): + dst_ds.GetRasterBand(i + 1).WriteArray(args[i]) + + del dataset, dst_ds + + +def append2filename(file_path, txt2add): + imgfile_out_tmp = os.path.splitext(file_path) + new_file_path = imgfile_out_tmp[0] + "_" + txt2add + imgfile_out_tmp[1] + + return new_file_path + + +def write_fields_to_hdrfile(source_hdr_file, dest_hdr_file): + source_fields = spectral.envi.read_envi_header(source_hdr_file) + dest_fields = spectral.envi.read_envi_header(dest_hdr_file) + + with open(dest_hdr_file, "a", encoding='utf-8') as f: + for key in source_fields.keys(): + if key in dest_fields or key == "description": + continue + + if key == "data ignore value" or key == "wavelength" or key == "wavelength units": + if type(source_fields[key]) == list: + f.write(key + " = {" + ", ".join(source_fields[key]) + "}\n") + else: + f.write(key + " = " + source_fields[key] + "\n") + + +def getnearest(m, invalid_value=0): + layer_number = math.floor(m.shape[0] / 2) + center = layer_number + + for i in range(layer_number + 1): + orig = (center - i, center - i) + + tmp = m[center - i:center + i + 1, center - i:center + i + 1] + valid_indices = np.where((tmp != invalid_value) & np.isfinite(tmp)) + + if valid_indices[0].shape[0] != 0: + return int(valid_indices[0][0] + orig[0]), int(valid_indices[1][0] + orig[0]) # (y ,x) + + return None, None + + +def load_numpy_dict_from_json(filename): + with open(filename, 'r') as f: + np_dict = json.load(f) + + # 将字典中的列表转换回 NumPy 数组 + model_type = np_dict['model_type'] + model_info = np.array(np_dict['model_info']) + precision = np.array(np_dict['accuracy']) + + return model_type, model_info, precision diff --git a/src/utils/water_index.py b/src/utils/water_index.py new file mode 100644 index 0000000..00ba9af --- /dev/null +++ b/src/utils/water_index.py @@ -0,0 +1,862 @@ + +import pandas as pd +import numpy as np +import re +from typing import Dict, List, Optional, Union + +class WaterQualityIndexCalculator: + """ + 水质光谱指数计算器 + 为每个算法创建单独的函数，自动查找最接近的波长列 + """ + + def __init__(self): + self.references = {} + + def find_closest_wavelength(self, df_columns: List[str], target_wl: int) -> str: + """ + 在数据框列名中查找最接近目标波长的列 + + Args: + df_columns: 数据框的所有列名 + target_wl: 目标波长 + + Returns: + 最接近的列名 + """ + # 提取所有数值型波长 + numeric_wavelengths = [] + for col in df_columns: + try: + # 从列名中提取数字 + numbers = re.findall(r'\d+', col) + if numbers: + wavelength = int(numbers[0]) + numeric_wavelengths.append((col, wavelength)) + except: + continue + + if not numeric_wavelengths: + raise ValueError(f"无法从列名中提取波长信息: {df_columns}") + + # 找到最接近的波长 + closest_col, closest_wl = min(numeric_wavelengths, + key=lambda x: abs(x[1] - target_wl)) + + print(f"为波长 {target_wl}nm 找到最接近的列: {closest_col} ({closest_wl}nm)") + return closest_col + + # ========================================================================= + # 叶绿素算法 + # ========================================================================= + + def chl_Al10SABI(self, df: pd.DataFrame) -> pd.Series: + """ + Surface Algal Bloom Index (SABI) for chlorophyll detection + 参考文献: Alawadi, F. Detection of surface algal blooms using the newly + developed algorithm surface algal bloom index (SABI). Proc. SPIE 2010, 7825. + """ + w857 = df[self.find_closest_wavelength(df.columns, 857)] + w644 = df[self.find_closest_wavelength(df.columns, 644)] + w458 = df[self.find_closest_wavelength(df.columns, 458)] + w529 = df[self.find_closest_wavelength(df.columns, 529)] + + result = (w857 - w644) / (w458 + w529) + return result + + def chl_Am092Bsub(self, df: pd.DataFrame) -> pd.Series: + """ + Baseline subtraction algorithm for chlorophyll + 参考文献: Amin, R.; Zhou, J.; Gilerson, A.; Gross, B.; Moshary, F.; Ahmed, S. + Novel optical techniques for detecting and classifying toxic dinoflagellate + Karenia brevis blooms using satellite imagery. Opt. Express 2009, 17, 9126–9144. + """ + w681 = df[self.find_closest_wavelength(df.columns, 681)] + w665 = df[self.find_closest_wavelength(df.columns, 665)] + + result = w681 - w665 + return result + + def chl_Be16FLHblue(self, df: pd.DataFrame) -> pd.Series: + """ + Fluorescence Line Height algorithm with blue baseline for chlorophyll + 参考文献: Beck, R.A. and 22 others; Comparison of satellite reflectance + algorithms for estimating chlorophyll-a in a temperate reservoir using + coincident hyperspectral aircraft imagery and dense coincident surface + observations, Remote Sens. Environ., 2016, 178, 15-30. + """ + w529 = df[self.find_closest_wavelength(df.columns, 529)] + w644 = df[self.find_closest_wavelength(df.columns, 644)] + w458 = df[self.find_closest_wavelength(df.columns, 458)] + + result = w529 - (w644 + (w458 - w644)) + return result + + def chl_Be16FLHviolet(self, df: pd.DataFrame) -> pd.Series: + """ + Fluorescence Line Height algorithm with violet baseline for chlorophyll + 参考文献: Beck, R.A. and 22 others; Comparison of satellite reflectance + algorithms for estimating chlorophyll-a in a temperate reservoir using + coincident hyperspectral aircraft imagery and dense coincident surface + observations, Remote Sens. Environ., 2016, 178, 15-30. + """ + w529 = df[self.find_closest_wavelength(df.columns, 529)] + w644 = df[self.find_closest_wavelength(df.columns, 644)] + w429 = df[self.find_closest_wavelength(df.columns, 429)] + + result = w529 - (w644 + (w429 - w644)) + return result + + def chl_Be16NDTIblue(self, df: pd.DataFrame) -> pd.Series: + """ + Normalized Difference Turbidity Index with blue band for chlorophyll + 参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; + Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; + Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; + Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms + for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in + a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery + and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 543. + """ + w658 = df[self.find_closest_wavelength(df.columns, 658)] + w458 = df[self.find_closest_wavelength(df.columns, 458)] + + result = (w658 - w458) / (w658 + w458) + return result + + def chl_Be16NDTIviolet(self, df: pd.DataFrame) -> pd.Series: + """ + Normalized Difference Turbidity Index with violet band for chlorophyll + 参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; + Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; + Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; + Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms + for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in + a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery + and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 544. + """ + w658 = df[self.find_closest_wavelength(df.columns, 658)] + w444 = df[self.find_closest_wavelength(df.columns, 444)] + + result = (w658 - w444) / (w658 + w444) + return result + + def chl_De933BDA(self, df: pd.DataFrame) -> pd.Series: + """ + Band difference algorithm for chlorophyll + 参考文献: Dekker, A.; Detection of the optical water quality parameters for + eutrophic waters by high resolution remote sensing, Ph.D. thesis, 1993, + Free University, Amsterdam. + """ + w600 = df[self.find_closest_wavelength(df.columns, 600)] + w648 = df[self.find_closest_wavelength(df.columns, 648)] + w625 = df[self.find_closest_wavelength(df.columns, 625)] + + result = w600 - w648 - w625 + return result + + def chl_Gi033BDA(self, df: pd.DataFrame) -> pd.Series: + """ + Gitelson algorithm for chlorophyll estimation + 参考文献: Gitelson, A.A.; U. Gritz, and M. N. Merzlyak.; Relationships between + leaf chlorophyll content and spectral reflectance and algorithms for + non-destructive chlorophyll assessment in higher plant leaves. J. Plant Phys. 2003, 160, 271-282. + """ + w672 = df[self.find_closest_wavelength(df.columns, 672)] + w715 = df[self.find_closest_wavelength(df.columns, 715)] + w757 = df[self.find_closest_wavelength(df.columns, 757)] + + result = ((1 / w672) - (1 / w715)) * w757 + return result + + def chl_Kn07KIVU(self, df: pd.DataFrame) -> pd.Series: + """ + Kneubuhler algorithm for chlorophyll in Lake Kivu + 参考文献: Kneubuhler, M.; Frank T.; Kellenberger, T.W; Pasche N.; Schmid M.; + Mapping chlorophyll-a in Lake Kivu with remote sensing methods. 2007, + Proceedings of the Envisat Symposium 2007, Montreux, Switzerland 23–27 April 2007 (ESA SP-636, July 2007). + """ + w458 = df[self.find_closest_wavelength(df.columns, 458)] + w644 = df[self.find_closest_wavelength(df.columns, 644)] + w529 = df[self.find_closest_wavelength(df.columns, 529)] + + result = (w458 - w644) / w529 + return result + + def chl_MM12NDCI(self, df: pd.DataFrame) -> pd.Series: + """ + Normalized Difference Chlorophyll Index + 参考文献: Mishra, S.; and Mishra, D.R. Normalized difference chlorophyll + index: A novel model for remote estimation of chlorophyll-a concentration + in turbid productive waters, Remote Sens. Environ., 2012, 117, 394-406 + """ + w715 = df[self.find_closest_wavelength(df.columns, 715)] + w686 = df[self.find_closest_wavelength(df.columns, 686)] + + result = (w715 - w686) / (w715 + w686) + return result + + def chl_Zh10FLH(self, df: pd.DataFrame) -> pd.Series: + """ + Zhao fluorescence line height algorithm for chlorophyll + 参考文献: Zhao, D.Z.; Xing, X.G.; Liu, Y.G.; Yang, J.H.; Wang, L. The relation of + chlorophyll-a concentration with the reflectance peak near 700 nm in + algae-dominated waters and sensitivity of fluorescence algorithms for + detecting algal bloom. Int. J. Remote Sens. 2010, 31, 39-48 + """ + w686 = df[self.find_closest_wavelength(df.columns, 686)] + w715 = df[self.find_closest_wavelength(df.columns, 715)] + w672 = df[self.find_closest_wavelength(df.columns, 672)] + w751 = df[self.find_closest_wavelength(df.columns, 751)] + + result = w686 - (w715 + (w672 - w751)) + return result + + # ========================================================================= + # 蓝藻/藻蓝蛋白算法 (BGA/PC) + # ========================================================================= + + def BGA_Am09KBBI(self, df: pd.DataFrame) -> pd.Series: + """ + Karenia Brevis Bloom Index for cyanobacteria/phycocyanin + 参考文献: Amin, R.; Zhou, J.; Gilerson, A.; Gross, B.; Moshary, F.; Ahmed, S.; + Novel optical techniques for detecting and classifying toxic dinoflagellate + Karenia brevis blooms using satellite imagery, Optics Express, 2009, 17, 11, 1-13. + """ + w686 = df[self.find_closest_wavelength(df.columns, 686)] + w658 = df[self.find_closest_wavelength(df.columns, 658)] + + result = (w686 - w658) / (w686 + w658) + return result + + def BGA_Be162B643sub629(self, df: pd.DataFrame) -> pd.Series: + """ + Band subtraction algorithm for phycocyanin + 参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; + Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; + Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; + Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms + for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in + a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery + and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538. + """ + w644 = df[self.find_closest_wavelength(df.columns, 644)] + w629 = df[self.find_closest_wavelength(df.columns, 629)] + + result = w644 - w629 + return result + + def BGA_Be162B700sub601(self, df: pd.DataFrame) -> pd.Series: + """ + Band subtraction algorithm for phycocyanin + 参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; + Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; + Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; + Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms + for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in + a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery + and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539. + """ + w700 = df[self.find_closest_wavelength(df.columns, 700)] + w601 = df[self.find_closest_wavelength(df.columns, 601)] + + result = w700 - w601 + return result + + def BGA_Be162BsubPhy(self, df: pd.DataFrame) -> pd.Series: + """ + Band subtraction algorithm for phytoplankton/phycocyanin + 参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; + Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; + Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; + Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms + for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in + a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery + and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 540. + """ + w715 = df[self.find_closest_wavelength(df.columns, 715)] + w615 = df[self.find_closest_wavelength(df.columns, 615)] + + result = w715 - w615 + return result + + def BGA_Be16FLHBlueRedNIR(self, df: pd.DataFrame) -> pd.Series: + """ + Fluorescence Line Height with Blue-Red-NIR baseline for phycocyanin + 参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; + Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; + Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; + Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms + for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in + a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery + and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538. + """ + w658 = df[self.find_closest_wavelength(df.columns, 658)] + w857 = df[self.find_closest_wavelength(df.columns, 857)] + w458 = df[self.find_closest_wavelength(df.columns, 458)] + + result = w658 - (w857 + (w458 - w857)) + return result + + def BGA_Be16FLHGreenRedNIR(self, df: pd.DataFrame) -> pd.Series: + """ + Fluorescence Line Height with Green-Red-NIR baseline for phycocyanin + 参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; + Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; + Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; + Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms + for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in + a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery + and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539. + """ + w658 = df[self.find_closest_wavelength(df.columns, 658)] + w857 = df[self.find_closest_wavelength(df.columns, 857)] + w558 = df[self.find_closest_wavelength(df.columns, 558)] + + result = w658 - (w857 + (w558 - w857)) + return result + + def BGA_Be16FLHVioletRedNIR(self, df: pd.DataFrame) -> pd.Series: + """ + Fluorescence Line Height with Violet-Red-NIR baseline for phycocyanin + 参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; + Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; + Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; + Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms + for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in + a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery + and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538. + """ + w658 = df[self.find_closest_wavelength(df.columns, 658)] + w857 = df[self.find_closest_wavelength(df.columns, 857)] + w444 = df[self.find_closest_wavelength(df.columns, 444)] + + result = w658 - (w857 + (w444 - w857)) + return result + + def BGA_Be16MPI(self, df: pd.DataFrame) -> pd.Series: + """ + Maximum Peak Index for phycocyanin + 参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; + Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; + Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; + Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms + for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in + a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery + and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539. + """ + w615 = df[self.find_closest_wavelength(df.columns, 615)] + w601 = df[self.find_closest_wavelength(df.columns, 601)] + w644 = df[self.find_closest_wavelength(df.columns, 644)] + + result = (w615 - w601) - (w644 - w601) + return result + + def BGA_Be16NDPhyI(self, df: pd.DataFrame) -> pd.Series: + """ + Normalized Difference Phytoplankton Index for phycocyanin + 参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; + Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; + Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; + Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms + for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in + a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery + and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 540. + """ + w700 = df[self.find_closest_wavelength(df.columns, 700)] + w622 = df[self.find_closest_wavelength(df.columns, 622)] + + result = (w700 - w622) / (w700 + w622) + return result + + def BGA_Be16NDPhyI644over615(self, df: pd.DataFrame) -> pd.Series: + """ + Normalized Difference Phytoplankton Index (644/615) for phycocyanin + 参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; + Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; + Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; + Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms + for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in + a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery + and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 541. + """ + w644 = df[self.find_closest_wavelength(df.columns, 644)] + w615 = df[self.find_closest_wavelength(df.columns, 615)] + + result = (w644 - w615) / (w644 + w615) + return result + + def BGA_Be16NDPhyI644over629(self, df: pd.DataFrame) -> pd.Series: + """ + Normalized Difference Phytoplankton Index (644/629) for phycocyanin + 参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; + Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; + Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; + Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms + for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in + a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery + and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 542. + """ + w644 = df[self.find_closest_wavelength(df.columns, 644)] + w629 = df[self.find_closest_wavelength(df.columns, 629)] + + result = (w644 - w629) / (w644 + w629) + return result + + def BGA_Be16Phy2BDA644over629(self, df: pd.DataFrame) -> pd.Series: + """ + Band ratio algorithm (644/629) for phycocyanin + 参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; + Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; + Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; + Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms + for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in + a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery + and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 545. + """ + w644 = df[self.find_closest_wavelength(df.columns, 644)] + w629 = df[self.find_closest_wavelength(df.columns, 629)] + + result = w644 / w629 + return result + + def BGA_Da052BDA(self, df: pd.DataFrame) -> pd.Series: + """ + Band ratio algorithm (714/672) for phycocyanin + 参考文献: Wynne, T. T., Stumpf, R. P., Tomlinson, M. C., Warner, R. A., + Tester, P. A., Dyble, J.; Relating spectral shape to cyanobacterial + blooms in the Laurentian Great Lakes. Int. J. Remote Sens., 2008, 29, 3665-3672. + """ + w714 = df[self.find_closest_wavelength(df.columns, 714)] + w672 = df[self.find_closest_wavelength(df.columns, 672)] + + result = w714 / w672 + return result + + def BGA_Go04MCI(self, df: pd.DataFrame) -> pd.Series: + """ + Maximum Chlorophyll Index for phycocyanin + 参考文献: Gower, J.F.R.; Brown,L.; Borstad, G.A.; Observation of chlorophyll + fluorescence in west coast waters of Canada using the MODIS satellite sensor. + Can. J. Remote Sens., 2004, 30 (1), 17–25. + """ + w709 = df[self.find_closest_wavelength(df.columns, 709)] + w681 = df[self.find_closest_wavelength(df.columns, 681)] + w753 = df[self.find_closest_wavelength(df.columns, 753)] + + result = w709 - w681 - (w753 - w681) + return result + + def BGA_HU103BDA(self, df: pd.DataFrame) -> pd.Series: + """ + Hunter algorithm for phycocyanin + 参考文献: Hunter, P.D.; Tyler, A.N.; Willby, N.J.; Gilvear, D.J.; The spatial + dynamics of vertical migration by Microcystis aeruginosa in a eutrophic + shallow lake: A case study using high spatial resolution time-series + airborne remote sensing. Limn. Oceanogr. 2008, 53, 2391-2406 + """ + w615 = df[self.find_closest_wavelength(df.columns, 615)] + w600 = df[self.find_closest_wavelength(df.columns, 600)] + w725 = df[self.find_closest_wavelength(df.columns, 725)] + + result = (((1 / w615) - (1 / w600)) - w725) + return result + + def BGA_Ku15PhyCI(self, df: pd.DataFrame) -> pd.Series: + """ + Kudela Phytoplankton Community Index for phycocyanin + 参考文献: Kudela, R.M., Palacios, S.L., Austerberry, D.C., Accorsi, E.K., + Guild, L.S.; Application of hyperspectral remote sensing to cyanobacterial + blooms in inland waters, Torres-Perez, J., 2015, Remote Sens. Environ., 2015, 167, 1-10. + """ + w681 = df[self.find_closest_wavelength(df.columns, 681)] + w665 = df[self.find_closest_wavelength(df.columns, 665)] + w709 = df[self.find_closest_wavelength(df.columns, 709)] + + result = -1 * (w681 - w665 - (w709 - w665)) + return result + + def BGA_Ku15SLH(self, df: pd.DataFrame) -> pd.Series: + """ + Kudela Scattering Line Height for phycocyanin + 参考文献: Kudela, R.M., Palacios, S.L., Austerberry, D.C., Accorsi, E.K., + Guild, L.S.; Application of hyperspectral remote sensing to cyanobacterial + blooms in inland waters, Torres-Perez, J., 2015, Remote Sens. Environ., 2015, 167, 1-11. + """ + w715 = df[self.find_closest_wavelength(df.columns, 715)] + w658 = df[self.find_closest_wavelength(df.columns, 658)] + + result = (w715 - w658) + (w715 - w658) + return result + + def BGA_MI092BDA(self, df: pd.DataFrame) -> pd.Series: + """ + Mishra band ratio algorithm (700/600) for phycocyanin + 参考文献: Mishra, S.; Mishra, D.R.; Schluchter, W. M., A novel algorithm for + predicting PC concentrations in cyanobacteria: A proximal hyperspectral + remote sensing approach. Remote Sens., 2009, 1, 758–775. + """ + w700 = df[self.find_closest_wavelength(df.columns, 700)] + w600 = df[self.find_closest_wavelength(df.columns, 600)] + + result = w700 / w600 + return result + + def BGA_MM092BDA(self, df: pd.DataFrame) -> pd.Series: + """ + Mishra band ratio algorithm (724/600) for phycocyanin + 参考文献: Mishra, S.; Mishra, D.R.; Schluchter, W. M., A novel algorithm for + predicting PC concentrations in cyanobacteria: A proximal hyperspectral + remote sensing approach. Remote Sens., 2009, 1, 758–776. + """ + w724 = df[self.find_closest_wavelength(df.columns, 724)] + w600 = df[self.find_closest_wavelength(df.columns, 600)] + + result = w724 / w600 + return result + + def BGA_MM12NDCIalt(self, df: pd.DataFrame) -> pd.Series: + """ + Alternative Normalized Difference Chlorophyll Index for phycocyanin + 参考文献: Mishra, S.; Mishra, D.R.; A novel remote sensing algorithm to + quantify phycocyanin in cyanobacterial algal blooms, Env. Res. Lett., + 2014, 9 (11), DOI:10.1088/1748-9326/9/11/114003 + """ + w700 = df[self.find_closest_wavelength(df.columns, 700)] + w658 = df[self.find_closest_wavelength(df.columns, 658)] + + result = (w700 - w658) / (w700 + w658) + return result + + def BGA_MM143BDAopt(self, df: pd.DataFrame) -> pd.Series: + """ + Optimized band algorithm for phycocyanin + 参考文献: Mishra, S.; Mishra, D.R.; A novel remote sensing algorithm to + quantify phycocyanin in cyanobacterial algal blooms, Env. Res. Lett., + 2014, 9 (11), DOI:10.1088/1748-9326/9/11/114004 + """ + w629 = df[self.find_closest_wavelength(df.columns, 629)] + w659 = df[self.find_closest_wavelength(df.columns, 659)] + w724 = df[self.find_closest_wavelength(df.columns, 724)] + + result = ((1 / w629) - (1 / w659)) * w724 + return result + + def BGA_SI052BDA(self, df: pd.DataFrame) -> pd.Series: + """ + Simis band ratio algorithm (709/620) for phycocyanin + 参考文献: Simis, S. G. H.; Peters, S.W. M.; Gons, H. J.; Remote sensing of + the cyanobacteria pigment phycocyanin in turbid inland water. Limn. Oceanogr., 2005, 50, 237–245 + """ + w709 = df[self.find_closest_wavelength(df.columns, 709)] + w620 = df[self.find_closest_wavelength(df.columns, 620)] + + result = w709 / w620 + return result + + def BGA_SM122BDA(self, df: pd.DataFrame) -> pd.Series: + """ + Mishra band ratio algorithm (709/600) for phycocyanin + 参考文献: Mishra, S. Remote sensing of cyanobacteria in turbid productive + waters, PhD Dissertation. Mississippi State University, USA. 2012. + """ + w709 = df[self.find_closest_wavelength(df.columns, 709)] + w600 = df[self.find_closest_wavelength(df.columns, 600)] + + result = w709 / w600 + return result + + def BGA_SY002BDA(self, df: pd.DataFrame) -> pd.Series: + """ + Schalles-Yacobi band ratio algorithm (650/625) for phycocyanin + 参考文献: Schalles, J.; Yacobi, Y. Remote detection and seasonal patterns of + phycocyanin, carotenoid and chlorophyll-a pigments in eutrophic waters. + Archiv fur Hydrobiologie, Special Issues Advances in Limnology, 2000, 55,153–168 + """ + w650 = df[self.find_closest_wavelength(df.columns, 650)] + w625 = df[self.find_closest_wavelength(df.columns, 625)] + + result = w650 / w625 + return result + + def BGA_Wy08CI(self, df: pd.DataFrame) -> pd.Series: + """ + Wynne Cyanobacteria Index for phycocyanin + 参考文献: Wynne, T. T., Stumpf, R. P., Tomlinson, M. C., Warner, R. A., + Tester, P. A., Dyble, J.; Relating spectral shape to cyanobacterial + blooms in the Laurentian Great Lakes. Int. J. Remote Sens., 2008, 29, 3665-3672. + """ + w686 = df[self.find_closest_wavelength(df.columns, 686)] + w672 = df[self.find_closest_wavelength(df.columns, 672)] + w715 = df[self.find_closest_wavelength(df.columns, 715)] + + result = -1 * (w686 - w672 - (w715 - w672)) + return result + + # ========================================================================= + # 浊度算法 + # ========================================================================= + + def Turb_Be16GreenPlusRedBothOverViolet(self, df: pd.DataFrame) -> pd.Series: + """ + Turbidity algorithm: (Green + Red) / Violet + 参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; + Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; + Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; + Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms + for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in + a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery + and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538 + """ + w558 = df[self.find_closest_wavelength(df.columns, 558)] + w658 = df[self.find_closest_wavelength(df.columns, 658)] + w444 = df[self.find_closest_wavelength(df.columns, 444)] + + result = (w558 + w658) / w444 + return result + + def Turb_Be16RedOverViolet(self, df: pd.DataFrame) -> pd.Series: + """ + Turbidity algorithm: Red / Violet + 参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; + Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; + Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; + Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms + for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in + a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery + and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539 + """ + w658 = df[self.find_closest_wavelength(df.columns, 658)] + w444 = df[self.find_closest_wavelength(df.columns, 444)] + + result = w658 / w444 + return result + + def Turb_Bow06RedOverGreen(self, df: pd.DataFrame) -> pd.Series: + """ + Turbidity algorithm: Red / Green + 参考文献: Bowers, D. G., and C. E. Binding. 2006. "The Optical Properties of + Mineral Suspended Particles: A Review and Synthesis." Estuarine Coastal + and Shelf Science 67 (1–2): 219–230. doi:10.1016/j.ecss.2005.11.010 + """ + w658 = df[self.find_closest_wavelength(df.columns, 658)] + w558 = df[self.find_closest_wavelength(df.columns, 558)] + + result = w658 / w558 + return result + + def Turb_Chip09NIROverGreen(self, df: pd.DataFrame) -> pd.Series: + """ + Turbidity algorithm: NIR / Green + 参考文献: Chipman, J. W.; Olmanson, L.G.; Gitelson, A.A.; Remote sensing + methods for lake management: A guide for resource managers and decision-makers. 2009. + """ + w857 = df[self.find_closest_wavelength(df.columns, 857)] + w558 = df[self.find_closest_wavelength(df.columns, 558)] + + result = w857 / w558 + return result + + def Turb_Dox02NIRoverRed(self, df: pd.DataFrame) -> pd.Series: + """ + Turbidity algorithm: NIR / Red + 参考文献: Doxaran, D., Froidefond, J.-M.; Castaing, P. ; A reflectance band + ratio used to estimate suspended matter concentrations in sediment-dominated + coastal waters, Remote Sens., 2002, 23, 5079-5085 + """ + w857 = df[self.find_closest_wavelength(df.columns, 857)] + w658 = df[self.find_closest_wavelength(df.columns, 658)] + + result = w857 / w658 + return result + + def Turb_Frohn09GreenPlusRedBothOverBlue(self, df: pd.DataFrame) -> pd.Series: + """ + Turbidity algorithm: (Green + Red) / Blue + 参考文献: Frohn, R. C., & Autrey, B. C. (2009). Water quality assessment in + the Ohio River using new indices for turbidity and chlorophyll-a with + Landsat-7 Imagery. Draft Internal Report, US Environmental Protection Agency. + """ + w558 = df[self.find_closest_wavelength(df.columns, 558)] + w658 = df[self.find_closest_wavelength(df.columns, 658)] + w458 = df[self.find_closest_wavelength(df.columns, 458)] + + result = (w558 + w658) / w458 + return result + + def Turb_Harr92NIR(self, df: pd.DataFrame) -> pd.Series: + """ + Turbidity algorithm: NIR reflectance + 参考文献: Schiebe F.R., Harrington J.A., Ritchie J.C. Remote-Sensing of + Suspended Sediments—the Lake Chicot, Arkansas Project. Int. J. Remote Sens. 1992;13:1487–1509 + """ + w857 = df[self.find_closest_wavelength(df.columns, 857)] + + result = w857 + return result + + def Turb_Lath91RedOverBlue(self, df: pd.DataFrame) -> pd.Series: + """ + Turbidity algorithm: Red / Blue + 参考文献: Lathrop, R. G., Jr., T. M. Lillesand, and B. S. Yandell, 1991. + Testing the utility of simple multi-date Thematic Mapper calibration + algorithms for monitoring turbid inland waters. International Journal of Remote Sensing + """ + w658 = df[self.find_closest_wavelength(df.columns, 658)] + w458 = df[self.find_closest_wavelength(df.columns, 458)] + + result = w658 / w458 + return result + + def Turb_Moore80Red(self, df: pd.DataFrame) -> pd.Series: + """ + Turbidity algorithm: Red reflectance + 参考文献: Moore, G.K., Satellite remote sensing of water turbidity, + Hydrological Sciences, 1980, 25, 4, 407-422 + """ + w658 = df[self.find_closest_wavelength(df.columns, 658)] + + result = w658 + return result + + def calculate_all_indices( + self, + input_file: str, + output_file: str = None, + selected_indices: Optional[List[str]] = None + ) -> pd.DataFrame: + """ + 计算所有水质指数 + + Args: + input_file: 输入CSV文件路径 + output_file: 输出CSV文件路径（可选） + selected_indices: 可选的算法列表，仅计算指定的指数 + + Returns: + 包含计算结果的数据框 + """ + # 读取数据 + df = pd.read_csv(input_file) + print(f"成功读取数据，形状: {df.shape}") + print(f"数据列: {list(df.columns)}") + + results = df.copy() + + # 获取所有算法方法 + algorithm_methods = [ + method for method in dir(self) + if not method.startswith('_') and method not in ['find_closest_wavelength', 'calculate_all_indices'] + ] + + print(f"\n找到 {len(algorithm_methods)} 个算法") + + if selected_indices is not None: + filtered = [] + missing = [] + for name in selected_indices: + if name in algorithm_methods: + filtered.append(name) + else: + missing.append(name) + + if missing: + print(f"警告: 以下算法未找到，将被忽略: {', '.join(missing)}") + + algorithm_methods = filtered + + if not algorithm_methods: + raise ValueError("未找到可用算法，请检查 selected_indices 参数") + + # 按算法类型分类计算 + algorithm_categories = { + 'chlorophyll': [], + 'BGA/PC': [], + 'turbidity': [] + } + + for method_name in algorithm_methods: + if method_name.startswith('Turb'): + algorithm_categories['turbidity'].append(method_name) + elif any(keyword in method_name for keyword in ['BDA', 'FLH', 'ND', 'sub', 'CI', 'SLH', 'MPI', 'Phy']): + if method_name not in algorithm_categories['turbidity']: + algorithm_categories['BGA/PC'].append(method_name) + else: + algorithm_categories['chlorophyll'].append(method_name) + + # 计算每个类别的算法 + for category, algorithms in algorithm_categories.items(): + print(f"\n=== 计算 {category} 相关指数 ({len(algorithms)}个算法) ===") + + for algo_name in algorithms: + try: + print(f"计算: {algo_name}") + method = getattr(self, algo_name) + results[algo_name] = method(df) + print(f"✓ 成功计算 {algo_name}") + + except Exception as e: + print(f"✗ 计算 {algo_name} 时出错: {str(e)}") + results[algo_name] = np.nan + + # 保存结果 + if output_file: + results.to_csv(output_file, index=False) + print(f"\n结果已保存到: {output_file}") + + return results + + +def main(): + """主函数""" + calculator = WaterQualityIndexCalculator() + + print("=" * 80) + print("水质光谱指数计算器") + print("=" * 80) + + # 计算指数 + input_file = r"E:\code\WQ\pipeline_result\work_dir\5_training_spectra\training_spectra.csv" # 修改为您的输入文件路径 + output_file = r"E:\code\WQ\pipeline_result\work_dir\5_training_spectra\water_quality_results.csv" + + try: + # 设置为 None 时默认计算所有已实现的算法；也可以设置为算法名称列表，例如 ['Al10SABI', 'TurbBe16RedOverViolet'] + selected_algorithms = None + results = calculator.calculate_all_indices(input_file, output_file, selected_algorithms) + + # 显示结果统计 + print("\n" + "=" * 80) + print("计算结果统计:") + print("=" * 80) + + # 只显示计算出的指数列的统计信息 + original_columns = pd.read_csv(input_file).columns + calculated_columns = [col for col in results.columns if col not in original_columns] + + if calculated_columns: + stats = results[calculated_columns].describe() + print(stats) + + # 按类别显示统计 + categories = { + '叶绿素算法': [col for col in calculated_columns if not col.startswith('Turb') and not any(x in col for x in ['BDA', 'FLH', 'ND', 'sub', 'CI', 'SLH', 'MPI', 'Phy']) or col in ['Al10SABI', 'Am092Bsub', 'Be16FLHblue', 'Be16FLHviolet', 'Be16NDTIblue', 'Be16NDTIviolet', 'De933BDA', 'Gi033BDA', 'Kn07KIVU', 'MM12NDCI', 'Zh10FLH']], + '蓝藻/藻蓝蛋白算法': [col for col in calculated_columns if col not in ['Al10SABI', 'Am092Bsub', 'Be16FLHblue', 'Be16FLHviolet', 'Be16NDTIblue', 'Be16NDTIviolet', 'De933BDA', 'Gi033BDA', 'Kn07KIVU', 'MM12NDCI', 'Zh10FLH'] and not col.startswith('Turb')], + '浊度算法': [col for col in calculated_columns if col.startswith('Turb')] + } + + for category, algo_list in categories.items(): + if algo_list: + print(f"\n{category}统计:") + print(results[algo_list].describe()) + else: + print("没有成功计算任何指数") + + except FileNotFoundError: + print(f"\n错误: 找不到输入文件 {input_file}") + print("请确保文件存在，或修改 input_file 变量为正确的文件路径") + except Exception as e: + print(f"\n计算过程中发生错误: {str(e)}") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/visualization/__init__.py b/src/visualization/__init__.py new file mode 100644 index 0000000..7c68785 --- /dev/null +++ b/src/visualization/__init__.py @@ -0,0 +1 @@ +# -*- coding: utf-8 -*- \ No newline at end of file diff --git a/tset.py b/tset.py new file mode 100644 index 0000000..89955af --- /dev/null +++ b/tset.py @@ -0,0 +1,11 @@ +import os +import sys +from ctypes import cdll + +# 查找 pyexpat.pyd 的位置 +import xml.parsers.expat +print(xml.parsers.expat.__file__) + +# 使用 Dependency Walker 或 dumpbin 检查 +# 在命令行中运行： +# dumpbin /dependents C:\Users\HL\.conda\envs\WQ10\Lib\site-packages\pyexpat.pyd \ No newline at end of file diff --git a/封装问题分析报告.md b/封装问题分析报告.md new file mode 100644 index 0000000..3fad943 --- /dev/null +++ b/封装问题分析报告.md @@ -0,0 +1,215 @@ +# 水质反演GUI封装问题分析报告 + +## 📋 执行摘要 + +**构建状态**: ✅ 成功 +**可执行文件**: `E:\code\WQ\fengzhuang\dist\water_quality_gui.exe` +**文件大小**: 2.57 GB +**构建时间**: 2025-12-02 14:52-14:59 + +--- + +## 🔍 发现的问题 + +### 1. ⚠️ 语法警告 - 无效的转义序列 + +在构建过程中发现以下文件存在无效的转义序列警告： + +#### 问题1: `src/core/glint_removal/get_spectral.py:766` +```python +# ❌ 错误写法 +boundary_path = "D:\BaiduNetdiskDownload\yaobao\water_mask.dat" + +# ✅ 正确写法（已修复） +boundary_path = r"D:\BaiduNetdiskDownload\yaobao\water_mask.dat" +``` +**问题**: `\B` 不是有效的转义序列 + +#### 问题2: `src/preprocessing/spectral_Preprocessing.py:135` +```python +# ❌ 错误写法 +output_spectrum = SS(input_spectrum.values, 'E:\code\WQ\models/scaler_params.pkl') + +# ✅ 正确写法（已修复） +output_spectrum = SS(input_spectrum.values, r'E:\code\WQ\models/scaler_params.pkl') +``` +**问题**: `\c` 不是有效的转义序列 + +#### 问题3: `src/core/water_quality_inversion_pipeline.py:2520` +```python +# ❌ 错误写法 +parser.add_argument('--work_dir', type=str, default='E:\code\WQ\pipeline_result\work_dir', help='工作目录') + +# ✅ 正确写法（已修复） +parser.add_argument('--work_dir', type=str, default=r'E:\code\WQ\pipeline_result\work_dir', help='工作目录') +``` +**问题**: `\c` 和 `\p` 不是有效的转义序列 + +#### 问题4: `src/core/water_quality_inversion_pipeline.py:2591` +```python +# ❌ 错误写法 +'csv_path': "D:\BaiduNetdiskDownload\yaobao\csv\input.csv" + +# ✅ 正确写法（已修复） +'csv_path': r"D:\BaiduNetdiskDownload\yaobao\csv\input.csv" +``` +**问题**: `\B` 和 `\c` 不是有效的转义序列 + +#### 问题5: `src/postprocessing/box_plot.py:79` +```python +# ❌ 错误写法 +save_path = os.path.join(save_dir, f'E:\code\WQ\yaobao925\plot/{safe_column_name}_boxplot.png') + +# ✅ 正确写法（已修复） +save_path = os.path.join(save_dir, f'{safe_column_name}_boxplot.png') +``` +**问题**: 硬编码的绝对路径且包含无效转义序列 + +--- + +### 2. ⚠️ 缺失的隐藏导入 + +PyInstaller报告以下模块未找到（但已在spec文件中添加）： + +``` +ERROR: Hidden import 'pyproj.CRS' not found +ERROR: Hidden import 'pyproj.Transformer' not found +WARNING: Hidden import "fiona._shim" not found! +``` + +**影响**: 这些模块如果在运行时被使用，可能导致程序崩溃 + +**解决方案**: +- 已在spec文件中添加 `pyproj.CRS` 和 `pyproj.Transformer` +- `fiona._shim` 是可选的内部模块，通常不影响运行 + +--- + +### 3. ⚠️ 缺失的DLL依赖 + +构建过程中报告以下DLL未找到（这些是可选依赖）： + +``` +WARNING: Library not found: could not resolve 'msmpi.dll' +WARNING: Library not found: could not resolve 'impi.dll' +WARNING: Library not found: could not resolve 'ze_loader.dll' +WARNING: Library not found: could not resolve 'pgc.dll' +WARNING: Library not found: could not resolve 'pgmath.dll' +WARNING: Library not found: could not resolve 'pgf90.dll' +WARNING: Library not found: could not resolve 'sycl6.dll' +``` + +**影响**: 这些是MKL、Intel MPI等高性能计算库的可选依赖，不影响基本功能 + +--- + +## ✅ 已修复的问题 + +1. ✅ 修复了所有无效转义序列（添加了 `r` 前缀使用原始字符串） +2. ✅ 修复了box_plot.py中的硬编码路径问题 +3. ✅ spec文件已包含所有必要的隐藏导入 + +--- + +## 🧪 测试建议 + +### 1. 基本启动测试 + +运行测试脚本： +```powershell +cd E:\code\WQ\fengzhuang +python test_exe.py +``` + +### 2. 手动测试 + +直接运行可执行文件： +```powershell +E:\code\WQ\fengzhuang\dist\water_quality_gui.exe +``` + +检查以下功能： +- [ ] GUI窗口是否正常显示 +- [ ] 数据文件加载功能 +- [ ] 图像处理功能 +- [ ] 模型预测功能 +- [ ] 结果导出功能 + +### 3. 依赖项测试 + +如果程序运行时出现模块缺失错误，检查： +1. 查看 `build/water_quality_gui/warn-water_quality_gui.txt` 中的警告 +2. 在spec文件的 `hidden_imports` 中添加缺失的模块 +3. 重新构建 + +--- + +## 🔧 重新构建步骤 + +修复问题后，重新构建可执行文件： + +```powershell +# 1. 激活conda环境 +conda activate insect + +# 2. 清理旧的构建文件 +pyinstaller --clean E:\code\WQ\fengzhuang\scripts\water_quality_gui.spec + +# 3. 测试可执行文件 +python E:\code\WQ\fengzhuang\test_exe.py +``` + +--- + +## 📊 构建统计 + +| 项目 | 数值 | +|------|------| +| 可执行文件大小 | 2.57 GB | +| 构建时间 | ~7分钟 | +| Python版本 | 3.12.7 | +| PyInstaller版本 | 6.17.0 | +| 平台 | Windows 10 | +| 包含的包 | ~200+ | + +--- + +## 🎯 下一步行动 + +1. ✅ **已完成**: 修复所有语法警告 +2. 🔄 **建议**: 重新构建可执行文件以应用修复 +3. 🧪 **必须**: 运行测试脚本验证可执行文件 +4. 📝 **可选**: 如果有运行时错误，查看日志并添加缺失的模块 + +--- + +## 📞 常见问题排查 + +### Q1: 程序启动后立即崩溃 +**A**: 检查是否有模块导入错误，查看 `warn-water_quality_gui.txt` + +### Q2: 找不到数据文件 +**A**: 确保 `data/icons` 和 `data/sub` 目录在可执行文件旁边 + +### Q3: GDAL相关错误 +**A**: 确保GDAL DLL文件被正确打包，检查环境变量 + +### Q4: 程序运行缓慢 +**A**: 这是正常的，首次启动需要解压临时文件 + +--- + +## 📝 修改记录 + +| 日期 | 修改内容 | 修改人 | +|------|---------|--------| +| 2025-12-02 | 修复无效转义序列警告 | AI Assistant | +| 2025-12-02 | 创建测试脚本 | AI Assistant | +| 2025-12-02 | 创建分析报告 | AI Assistant | + +--- + +**报告生成时间**: 2025-12-02 +**PyInstaller版本**: 6.17.0 +**Python版本**: 3.12.7 + diff --git a/软件说明.md b/软件说明.md new file mode 100644 index 0000000..5e7b65d --- /dev/null +++ b/软件说明.md @@ -0,0 +1,2048 @@ +# 水质参数反演分析系统 - 软件说明书 + +## 1. 软件概述 + +### 1.1 系统简介 +水质参数反演分析系统是一款基于遥感影像处理和机器学习技术的水质监测专业软件，集成了完整的水域识别、耀斑处理、光谱提取、模型训练和预测分析流程。 + +### 1.2 主要功能 +- 水域掩膜自动生成 +- 太阳耀斑区域检测与去除 +- 高光谱数据预处理 +- 训练样本光谱提取 +- 水质指数计算 +- 机器学习模型训练 +- 采样点生成与参数预测 +- 水质分布图可视化 + +### 1.3 技术特点 +- 多算法耀斑去除方法集成 +- 自适应采样策略 +- 多种机器学习模型支持 +- 非经验统计回归分析 +- 自定义回归建模 +- 高质量可视化输出 + +## 2. 系统要求 + +### 2.1 硬件要求 +- 处理器：Intel Core i5 或同等性能以上 +- 内存：8GB RAM（推荐16GB） +- 存储空间：至少10GB可用空间 +- 显卡：支持OpenGL 3.0以上 + +### 2.2 软件要求 +- 操作系统：Windows 10/11, Linux, macOS +- Python版本：3.12+ +- 必要依赖库：GDAL, NumPy, Pandas, Scikit-learn, PyQt5等 + +## 3. 安装与配置 + +### 3.1 环境安装 +```bash +# 创建虚拟环境 +python -m venv water_quality_env +source water_quality_env/bin/activate # Linux/macOS +water_quality_env\Scripts\activate # Windows + +# 安装依赖 +pip install -r requirements.txt +``` + +### 3.2 软件启动 +```bash +python water_quality_gui.py +``` + +## 4. 功能模块详解 + +![](E:\code\WQ\fengzhuang\sub\png\watermask.png) +## 4.1 步骤1：水域掩膜生成 + +### 4.11 功能概述 +步骤1负责生成水域掩膜文件，用于后续步骤中限定水域范围。支持两种生成方式： +1. **使用现有掩膜文件** - 直接使用已有的Shapefile或栅格文件 +2. **使用NDWI自动生成** - 基于NDWI（归一化水体指数）阈值分割自动提取水域 + +### 4.12 支持的输入格式 + +#### 掩膜文件格式： +- **Shapefile (.shp)** - 矢量格式，需要提供参考影像进行栅格化 +- **栅格文件 (.dat, .tif)** - 直接使用，无需栅格化 + +#### 参考影像格式： +- **ENVI格式 (.bsq, .dat)** - 支持多波段高光谱数据 +- **GeoTIFF (.tif)** - 标准栅格格式 + +### 4.13 参数配置 + +#### 使用现有掩膜文件模式： +- **掩膜文件路径** - 选择.shp或.dat格式的水域掩膜文件 +- **参考影像路径** - 当使用.shp文件时必须提供，用于栅格化 + +#### NDWI自动生成模式： +- **参考影像路径** - 用于计算NDWI指数的多波段影像 +- **NDWI阈值** - 默认0.4，范围0.0-1.0，控制水域提取的灵敏度 + - 较低阈值：提取更多水域（可能包含非水域区域） + - 较高阈值：提取更少水域（可能遗漏部分水域区域） + +### 4.14 技术实现 + +#### 栅格化处理（针对.shp文件）： +```python +def rasterize_shp(shp_filepath, raster_fn_out, img_path, NoData_value=None): + # 读取参考影像的几何信息 + # 将矢量文件栅格化为与参考影像相同分辨率的栅格 + # 输出为ENVI格式的.dat文件 +``` + +#### NDWI计算： +```python +def ndwi(file_path, ndwi_threshold=0.4, output_path=None): + # 计算绿光波段(552.19nm)和近红外波段(809.29nm)的NDWI + # NDWI = (Green - NIR) / (Green + NIR) + # 使用阈值分割提取水域区域 +``` + +### 4.15 输出结果 +- **输出掩膜文件** - 统一为ENVI格式的.dat文件 +- **文件命名** - 默认"water_mask.dat"，可自定义输出路径 + +### 4.16 使用建议 + +1. **优先使用现有掩膜文件** - 如果已有精确的水域边界.shp文件 +2. **NDWI适用于简单场景** - 当没有精确水域边界时使用 +3. **调整NDWI阈值** - 根据具体水域特性调整阈值： + - 清澈水域：0.3-0.4 + - 浑浊水域：0.4-0.6 + - 特殊情况可能需要实验确定最佳阈值 + +### 4.17错误处理 +- 文件不存在时会提示错误 +- .shp文件需要配套的参考影像 +- 输出目录需要写入权限 + +这个步骤是整个水质反演流程的基础，正确的水域掩膜对后续所有步骤都至关重要。 + +## 4.2 步骤2：耀斑区域检测 +[5 tools called] + +基于代码分析，我来详细说明步骤2（耀斑区域检测）的软件说明： + +# 步骤2：耀斑区域检测 + +## 功能概述 +步骤2负责检测影像中的耀斑区域，生成耀斑掩膜文件。耀斑是水面反射太阳光造成的过亮区域，会影响水质参数反演的准确性。该步骤提供多种检测算法，可根据不同场景选择合适的方法。 + +## 支持的输入格式 + +### 必需输入： +- **影像文件** - 多波段高光谱影像（.bsq, .dat, .tif格式） +- **水域掩膜** - 步骤1生成的水域掩膜文件（可选，用于独立运行） + +### 可选输入： +- **水域掩膜文件** - 用于限定检测范围，提高检测精度 + +## 检测方法 + +### 1. Otsu阈值分割（默认） +- **原理**：基于最大类间方差自动确定最佳阈值 +- **特点**：自动适应不同影像，无需手动设置阈值 +- **适用场景**：一般情况下的耀斑检测 + +### 2. Z-score统计方法 +- **原理**：基于标准差识别异常高亮像素 +- **参数**：Z-score阈值（默认2.5） +- **特点**：对数据分布不敏感，适合正态分布数据 +- **适用场景**：数据分布相对均匀的情况 + +### 3. 百分位数阈值方法 +- **原理**：使用指定百分位数作为阈值 +- **参数**：百分位数（默认95%） +- **特点**：对异常值更稳健 +- **适用场景**：数据存在极端异常值的情况 + +### 4. IQR异常值检测 +- **原理**：基于四分位距识别异常值 +- **参数**：IQR倍数（默认1.5） +- **特点**：对偏态分布数据效果好 +- **适用场景**：数据分布不均匀的情况 + +### 5. 自适应阈值方法 +- **原理**：局部自适应阈值分割 +- **参数**：窗口大小（默认15） +- **特点**：适应局部亮度变化 +- **适用场景**：光照不均匀的影像 + +### 6. 多波段融合方法 +- **原理**：融合多个波段的检测结果 +- **参数**：波段波长列表、权重、子方法 +- **特点**：综合利用多波段信息，检测更准确 +- **适用场景**：复杂耀斑模式检测 + +## 参数配置 + +### 核心参数： +- **耀斑检测波长** - 默认750nm，用于提取耀斑严重区域的波段 +- **检测方法** - 六种可选方法 +- **最大连通域面积** - 过滤小面积噪声，默认50像素 +- **岸边缓冲区大小** - 避免岸边误检，默认10像素 + +### 方法特定参数： +- **Z-score阈值** - Z-score方法的阈值（2.0-3.0） +- **百分位数** - 百分位数方法的阈值（90-99） +- **IQR倍数** - IQR方法的倍数（1.0-3.0） +- **窗口大小** - 自适应方法的窗口大小（5-30） + +## 技术实现 + +```python +def find_severe_glint_area(img_path, water_mask_path=None, glint_wave=750.0, + method='otsu', z_threshold=2.5, percentile=95.0, + iqr_multiplier=1.5, window_size=15, max_area=50, + buffer_size=10): + # 读取影像和水域掩膜 + # 根据选择的方法进行耀斑检测 + # 后处理：面积过滤、岸边缓冲 + # 输出耀斑掩膜文件 +``` + +## 输出结果 +- **耀斑掩膜文件** - ENVI格式的.dat文件，1表示耀斑区域，0表示非耀斑区域 +- **文件命名** - 默认"glint_mask.dat"，可自定义输出路径 + +## 使用建议 + +1. **首选Otsu方法** - 适用于大多数情况，自动确定阈值 +2. **调整检测波长** - 根据实际耀斑特征调整： + - 一般耀斑：750-800nm + - 强烈耀斑：可尝试更高波长 +3. **使用水域掩膜** - 提高检测精度，避免陆地区域误检 +4. **后处理参数**： + - **最大面积**：过滤小面积噪声（建议10-100） + - **岸边缓冲**：避免岸边误检（建议5-20） + +## 错误处理 +- 文件不存在或格式错误时提示 +- 无效参数范围检查 +- 统计计算异常处理 + +这个步骤是耀斑去除流程的关键，准确的耀斑检测直接影响后续耀斑去除和水质反演的效果。 + +## 4.3 步骤3：耀斑去除 +[4 tools called] + +基于代码分析，我来详细说明步骤3（耀斑去除）的软件说明： + +# 步骤3：耀斑去除 + +## 功能概述 +步骤3负责去除影像中的耀斑效应，恢复水体的真实反射率。耀斑是水面反射太阳光造成的过亮区域，会严重影响水质参数反演的准确性。该步骤提供四种先进的去耀斑算法，可根据不同场景选择合适的方法。 + +## 支持的输入格式 + +### 必需输入： +- **影像文件** - 多波段高光谱影像（.bsq, .dat, .tif格式） +- **耀斑掩膜** - 步骤2生成的耀斑掩膜文件（自动从步骤2获取） + +## 去耀斑方法 + +### 1. Goodman方法（默认） +- **原理**：基于近红外波段的线性回归模型 +- **特点**：计算简单，速度快，适用于一般耀斑情况 +- **参数配置**： + - **NIR下波段索引**：默认65（约750nm） + - **NIR上波段索引**：默认91（约900nm） + - **参数A**：默认0.000019（经验系数） + - **参数B**：默认0.1（经验系数） + +### 2. Kutser方法 +- **原理**：利用氧吸收波段（760nm附近）的特征 +- **特点**：基于物理原理，对强烈耀斑效果好 +- **参数配置**： + - **氧吸收波段索引**：默认8（约760nm） + - **下氧吸收波长**：默认756.54nm + - **上氧吸收波长**：默认766.54nm + - **NIR波段索引**：默认65（约750nm） + +### 3. Hedley方法 +- **原理**：基于深度学习的耀斑去除方法 +- **特点**：智能化处理，适应复杂耀斑模式 +- **参数配置**： + - **NIR波段索引**：默认47（约750nm） + +### 4. SUGAR方法（光谱解混） +- **原理**：基于光谱解混的先进算法 +- **特点**：精度最高，计算复杂，适用于科研应用 +- **参数配置**： + - **迭代次数**：默认3次（自动优化） + - **LoG平滑σ**：默认1.0（平滑参数） + - **估计背景光谱**：默认启用 + - **耀斑掩膜方法**：cdf或otsu + - **终止阈值**：默认20.0 + - **优化边界**：默认[(1, 2)] + +## 插值选项 + +### 0值像素插值： +- **启用插值**：对去耀斑后产生的0值像素进行插值修复 +- **插值方法**： + - **nearest** - 最近邻插值（最快） + - **bilinear** - 双线性插值（推荐） + - **spline** - 样条插值（平滑） + - **kriging** - 克里金插值（最精确） + +## 技术实现 + +```python +def remove_glint(img_path, glint_mask_path=None, method='goodman', + nir_lower=65, nir_upper=91, goodman_A=0.000019, goodman_B=0.1, + oxy_band=8, lower_oxy=756.54, upper_oxy=766.54, nir_band=65, + sugar_iter=3, sugar_sigma=1.0, interpolate_zeros=True, + interpolation_method='bilinear'): + # 读取影像和耀斑掩膜 + # 根据选择的方法进行耀斑去除 + # 可选：对0值像素进行插值 + # 输出去耀斑后的影像 +``` + +## 输出结果 +- **去耀斑影像文件** - ENVI格式的.dat文件，去除耀斑效应后的反射率数据 +- **文件命名** - 默认"deglint_image.dat"，可自定义输出路径 + +## 使用建议 + +1. **方法选择指南**： + - **一般耀斑**：Goodman方法（快速有效） + - **强烈耀斑**：Kutser方法（物理基础） + - **复杂耀斑**：Hedley方法（智能处理） + - **科研应用**：SUGAR方法（最高精度） + +2. **参数调整建议**： + - **波段索引**：根据实际影像的波段设置调整 + - **插值启用**：建议启用，修复去耀斑产生的空洞 + - **插值方法**：双线性插值在速度和效果间取得平衡 + +3. **质量控制**： + - 检查去耀斑后的影像是否存在过度校正 + - 验证反射率值在合理范围内（0-1） + - 对比去耀斑前后的影像质量 + +## 错误处理 +- 文件不存在或格式错误时提示 +- 参数范围验证 +- 计算过程中的异常处理 +- 内存不足时的优化处理 + +这个步骤是水质反演流程中的关键预处理步骤，准确的耀斑去除直接影响后续光谱提取和建模的准确性。 + + +### 4.4 步骤4：数据预处理 +[3 tools called] + +基于代码分析，我来详细说明步骤4（数据预处理）的软件说明： + +# 步骤4：数据预处理 + +## 功能概述 +步骤4负责对水质参数CSV文件进行预处理，包括数据清洗、异常值检测与剔除、数据质量检查等操作。该步骤确保输入到后续机器学习建模的数据质量，提高模型的准确性和稳定性。 + +## 支持的输入格式 + +### 必需输入： +- **水质参数CSV文件** - 包含采样点位置和水质参数的表格数据 +- **文件格式要求**： + - 必须包含经纬度坐标列（如longitude, latitude） + - 必须包含水质参数列（如Chl-a, TSS, CDOM等） + - 支持UTF-8编码，逗号分隔 + +## 预处理功能 + +### 1. 数据质量检查 +- **缺失值检测**：识别并统计各列的缺失值数量 +- **数据类型验证**：确保数值型数据的正确格式 +- **坐标范围验证**：检查经纬度坐标在合理范围内 + +### 2. 异常值检测与处理 +- **统计方法**：基于Z-score（标准差）检测异常值 +- **四分位距方法**：基于IQR（四分位距）检测异常值 +- **可视化方法**：通过箱线图识别异常值 +- **处理选项**：可选择剔除或标记异常值 + +### 3. 数据标准化 +- **Min-Max标准化**：将数据缩放到[0,1]范围 +- **Z-score标准化**：将数据转换为均值为0，标准差为1 +- **对数变换**：对偏态分布数据进行对数变换 + +### 4. 特征工程 +- **相关性分析**：计算水质参数间的相关系数 +- **多重共线性检测**：识别高度相关的特征 +- **特征选择**：基于重要性选择关键特征 + +## 技术实现 + +```python +def preprocess_data(csv_path, output_path=None, + outlier_method='zscore', z_threshold=3.0, + iqr_multiplier=1.5, normalize_method='minmax', + remove_outliers=True, save_processed=True): + # 读取CSV文件 + # 数据质量检查 + # 异常值检测与处理 + # 数据标准化 + # 保存处理后的数据 +``` + +## 输出结果 +- **处理后CSV文件** - 清洗和标准化后的水质参数数据 +- **预处理报告** - 包含处理统计信息的文本报告 +- **可视化图表** - 异常值检测和分布变化的图表 + +## 界面功能 + +### CSV数据预览 +- **实时预览**：显示CSV文件的前N行数据 +- **行列统计**：显示数据表的行列数量 +- **数据浏览**：支持表格形式的可视化浏览 + +### 参数配置 +- **预览行数**：控制预览显示的数据行数（1-200行） +- **输出文件名**：指定处理后的CSV文件保存路径 + +## 使用建议 + +1. **数据准备**： + - 确保CSV文件包含必要的坐标和水质参数列 + - 检查数据格式是否正确（数值型数据应为数字） + +2. **异常值处理**： + - **Z-score方法**：适用于数据近似正态分布的情况 + - **IQR方法**：对偏态分布数据更稳健 + - **阈值设置**：根据数据特性调整异常值检测阈值 + +3. **质量控制**： + - 预览处理前后的数据变化 + - 检查异常值剔除是否合理 + - 验证标准化后的数据范围 + +## 错误处理 +- 文件不存在或格式错误时提示 +- 编码问题自动检测和处理 +- 数据类型转换错误处理 +- 内存不足时的数据分块处理 + +这个步骤是机器学习建模前的关键数据准备阶段，高质量的数据预处理能够显著提升后续建模的准确性和可靠性。预处理后的数据将用于步骤5的光谱提取和步骤6的机器学习建模。 + +### 4.5 步骤5：光谱提取 +[3 tools called] + +基于代码分析，我来详细说明步骤5（光谱提取）的软件说明： + +# 步骤5：光谱提取 + +## 功能概述 +步骤5负责从去耀斑后的高光谱影像中提取训练样本点的光谱数据。该步骤将水质参数CSV文件中的采样点位置与影像数据进行空间匹配，提取每个采样点周围区域的光谱特征，为后续机器学习建模提供训练数据。 + +## 支持的输入格式 + +### 必需输入： +- **去耀斑影像文件** - 步骤3处理后的多波段高光谱影像（.bsq, .dat, .tif格式） +- **处理后CSV文件** - 步骤4预处理后的水质参数数据（包含经纬度坐标） + +### 可选输入： +- **水体掩膜文件** - 步骤1生成的水域掩膜（.dat, .tif格式），用于限制提取范围 + +## 核心功能 + +### 1. 空间坐标匹配 +- **坐标系转换**：支持多种坐标系（EPSG编码），默认WGS84（EPSG:4326） +- **投影变换**：自动将地理坐标转换为影像像素坐标 +- **精度控制**：亚像素级坐标匹配，确保提取位置准确 + +### 2. 光谱提取方法 +- **圆形采样区域**：以采样点为中心，指定半径的圆形区域 +- **多像素平均**：提取区域内所有像素的光谱并计算平均值 +- **统计特征**：同时计算标准差、最大值、最小值等统计量 + +### 3. 质量控制 +- **边界检查**：确保采样点位于影像范围内 +- **水体验证**：使用水体掩膜验证采样点位于水域内 +- **数据完整性**：检查每个采样点的光谱数据完整性 + +## 参数配置 + +### 采样参数： +- **采样半径**：默认5像素，控制提取区域大小（1-50像素） +- **源坐标系**：默认EPSG:4326（WGS84），支持自定义EPSG编码 + +## 技术实现 + +```python +def extract_spectra(deglint_img_path, csv_path, boundary_path=None, + radius=5, source_epsg=4326, output_path=None): + # 读取去耀斑影像 + # 读取水质参数CSV文件 + # 坐标转换：地理坐标 → 像素坐标 + # 对每个采样点提取圆形区域光谱 + # 计算平均光谱和统计特征 + # 保存包含光谱特征和水质参数的训练数据 +``` + +## 输出结果 +- **训练数据CSV文件** - 包含光谱特征和水质参数的完整数据集 +- **文件结构**： + - 前几列为水质参数（Chl-a, TSS, CDOM等） + - 中间列为各波段的光谱反射率值 + - 后几列为统计特征（标准差、最大值、最小值等） +- **提取报告** - 包含成功提取数量和失败原因的统计报告 + +## 界面功能 + +### 文件选择区域 +- **去耀斑影像**：选择步骤3处理后的影像文件 +- **处理后CSV**：选择步骤4预处理的水质参数文件 +- **水体掩膜**：可选，选择步骤1生成的水域掩膜文件 + +### 参数设置区域 +- **采样半径**：控制光谱提取的区域大小 +- **坐标系设置**：指定采样点坐标的EPSG编码 + +## 使用建议 + +1. **采样半径选择**： + - **小半径（1-3像素）**：精确提取，适合点状采样 + - **中等半径（4-8像素）**：平衡精度和稳定性，推荐使用 + - **大半径（9+像素）**：提取更大区域，减少噪声影响 + +2. **坐标系设置**： + - **WGS84（EPSG:4326）**：全球通用地理坐标系 + - **UTM坐标系**：根据研究区域选择相应的UTM带 + - **自定义EPSG**：支持任何有效的EPSG编码 + +3. **质量控制**： + - 检查提取成功率，确保大多数采样点成功提取 + - 验证光谱数据的合理性（反射率值在0-1范围内） + - 检查统计特征的分布情况 + +## 错误处理 +- 坐标超出影像范围时自动跳过 +- 坐标系不匹配时提示转换错误 +- 文件格式错误时提供详细错误信息 +- 内存不足时优化处理流程 + +这个步骤是连接遥感影像数据和地面实测数据的关键环节，准确的光谱提取为后续机器学习建模提供高质量的训练样本。提取的训练数据将用于步骤6的机器学习建模和步骤5.5的水质指数计算。 + +### 4.6 步骤5.5：水质指数计算 +[2 tools called] + +基于代码分析，我来详细说明步骤5.5（水质指数计算）的软件说明： + +# 步骤5.5：水质指数计算 + +## 功能概述 +步骤5.5负责基于训练数据的光谱特征计算各种水质指数。该步骤通过数学公式将光谱反射率转换为具有物理意义的水质参数指标，为后续的统计回归和机器学习建模提供特征工程支持。 + +## 支持的输入格式 + +### 必需输入： +- **训练数据CSV文件** - 步骤5提取的光谱训练数据（包含各波段反射率） +- **公式CSV文件** - 包含水质指数计算公式的定义文件 + +## 核心功能 + +### 1. 公式管理系统 +- **预定义公式库**：支持多种水质参数的指数计算公式 +- **自定义公式**：允许用户添加新的水质指数公式 +- **公式分类**：按水质参数类型组织公式（叶绿素a、总氮、总磷等） + +### 2. 水质指数计算 +- **波段运算**：支持加减乘除、对数、指数等数学运算 +- **多波段组合**：支持多个波段的组合计算 +- **批量计算**：一次性计算多个水质指数 + +### 3. 公式类别支持 +支持18种主要水质参数类别的指数计算： +- 叶绿素a (chlorophyll_a) +- 藻蓝蛋白 (Phycocyanin) +- 总氮 (Total Nitrogen) +- 总磷 (Total Phosphorus) +- 正磷酸盐 (Orthophosphate) +- 化学需氧量 (COD) +- 生化需氧量 (BOD) +- 总有机碳 (TOC) +- 溶解氧 (Dissolved Oxygen) +- 大肠杆菌 (E. coli) +- 总大肠菌群 (Total Coliforms) +- 浊度 (Turbidity) +- 总悬浮物 (Total Suspended Solids) +- 色度 (Color) +- pH值 +- 温度 (Temperature) +- 电导率 (Conductivity) +- 总溶解固体 (Total Dissolved Solids) + +## 技术实现 + +```python +def calculate_water_indices(training_csv_path, formula_csv_path, + selected_formulas, output_filename): + # 读取训练数据（包含光谱反射率） + # 读取公式定义文件 + # 对每个选中的公式进行解析和执行 + # 将计算结果添加到数据表中 + # 保存包含水质指数的输出文件 +``` + +## 输出结果 +- **水质指数CSV文件** - 在原训练数据基础上增加水质指数列 +- **文件结构**： + - 保留原有的水质参数和光谱特征列 + - 新增各水质指数的计算结果列 + - 每个指数对应一个单独的列 + +## 界面功能 + +### 数据文件选择 +- **训练数据CSV**：选择步骤5生成的光谱训练数据文件 +- **公式CSV文件**：选择包含水质指数公式的定义文件 + +### 公式选择区域 +- **网格布局**：3列网格显示所有可用公式 +- **批量选择**：支持全选和清空所有公式 +- **实时刷新**：从公式文件动态加载公式列表 + +### 自定义公式功能 +- **公式名称**：自定义指数的名称 +- **公式类别**：下拉选择或输入新的水质参数类别 +- **公式表达式**：使用波段编号（如B1, B2）的数学表达式 +- **参考文献**：记录公式的来源文献 + +## 公式文件格式 +公式CSV文件应包含以下列： +- `Formula_Name` - 公式名称 +- `Category` - 水质参数类别 +- `Formula` - 数学表达式（使用B1, B2等表示波段） +- `Reference` - 参考文献 + +示例： +```csv +Formula_Name,Category,Formula,Reference +NDCI,chlorophyll_a,(B5-B4)/(B5+B4),"Mishra & Mishra, 2012" +``` + +## 使用建议 + +1. **公式选择策略**： + - **相关性分析**：选择与目标水质参数相关性高的指数 + - **多指数组合**：选择多个相关指数进行综合评估 + - **验证有效性**：通过统计检验验证指数的有效性 + +2. **自定义公式注意事项**： + - 使用标准的波段编号（B1, B2, B3...） + - 确保数学表达式的语法正确 + - 记录公式的来源和参考文献 + +3. **质量控制**： + - 检查计算结果的范围是否合理 + - 验证指数与实测水质参数的相关性 + - 检查是否存在异常值或计算错误 + +## 错误处理 +- 公式语法错误时提供详细错误信息 +- 波段编号不存在时提示 +- 除零错误和其他数学运算错误的处理 +- 文件格式错误的检测和提示 + +这个步骤是特征工程的重要环节，通过计算各种水质指数，可以提取更有物理意义和判别能力的特征，显著提升后续统计回归和机器学习模型的性能。计算的水质指数将用于步骤6.5的非经验统计回归建模。 + +### 4.7 步骤6：机器学习建模 +[2 tools called] + +基于代码分析，我来详细说明步骤6（机器学习建模）的软件说明： + +# 步骤6：机器学习建模 + +## 功能概述 +步骤6负责基于训练数据构建水质参数反演的机器学习模型。该步骤支持多种机器学习算法、数据预处理方法和数据划分策略，通过交叉验证和模型比较，选择最优的水质参数预测模型。 + +## 支持的输入格式 + +### 必需输入： +- **训练数据CSV文件** - 步骤5或步骤5.5生成的训练数据（包含光谱特征和水质参数） + +## 核心功能 + +### 1. 特征工程配置 +- **特征起始列**：指定光谱特征列的起始位置（默认374.285004） +- **自动特征识别**：根据列名自动识别光谱波段列 + +### 2. 数据预处理方法（多选） +支持11种光谱预处理方法： +- **None** - 无预处理 +- **MMS** - Min-Max标准化 +- **SS** - Standard Scaling（Z-score标准化） +- **SNV** - Standard Normal Variate（标准正态变量变换） +- **MA** - Moving Average（移动平均平滑） +- **SG** - Savitzky-Golay滤波 +- **MSC** - Multiplicative Scatter Correction（乘性散射校正） +- **D1** - 一阶导数 +- **D2** - 二阶导数 +- **DT** - Detrending（去趋势） +- **CT** - Continuum Removal（连续统去除） + +### 3. 机器学习模型（多选） +支持4大类18种机器学习算法： + +#### 线性模型（5种） +- **LinearRegression** - 线性回归 +- **Ridge** - 岭回归 +- **Lasso** - Lasso回归 +- **ElasticNet** - 弹性网络回归 +- **PLS** - 偏最小二乘回归 + +#### 树模型（6种） +- **DecisionTree** - 决策树回归 +- **RF** - 随机森林回归 +- **ExtraTrees** - 极端随机树回归 +- **XGBoost** - XGBoost回归 +- **LightGBM** - LightGBM回归 +- **CatBoost** - CatBoost回归 + +#### 集成学习（2种） +- **GradientBoosting** - 梯度提升回归 +- **AdaBoost** - AdaBoost回归 + +#### 其他模型（3种） +- **SVR** - 支持向量回归 +- **KNN** - K近邻回归 +- **MLP** - 多层感知器回归 + +### 4. 数据划分方法（多选） +支持3种数据划分策略： +- **spxy** - Sample set Partitioning based on joint X-Y distances（基于X-Y距离的样本划分） +- **ks** - Kennard-Stone算法 +- **random** - 随机划分 + +### 5. 模型评估 +- **交叉验证**：支持2-10折交叉验证（默认3折） +- **性能指标**：计算R²、RMSE、MAE等评估指标 +- **模型比较**：自动比较不同预处理+模型组合的性能 + +## 技术实现 + +```python +def train_ml_models(training_csv_path, feature_start_column, + preprocessing_methods, model_names, + split_methods, cv_folds, output_dir): + # 读取训练数据 + # 特征工程：分离特征和目标变量 + # 数据预处理：应用选中的预处理方法 + # 数据划分：使用选中的划分方法 + # 模型训练：训练所有选中的模型 + # 交叉验证：评估模型性能 + # 模型保存：保存训练好的模型文件 +``` + +## 输出结果 +- **训练好的模型文件** - 每个预处理+模型组合的序列化模型 +- **模型性能报告** - 包含各模型评估指标的CSV文件 +- **最佳模型选择** - 自动选择性能最优的模型组合 +- **训练日志** - 详细的训练过程记录 + +## 界面功能 + +### 训练参数设置 +- **特征起始列**：指定光谱特征列的起始波长或位置 +- **交叉验证折数**：控制交叉验证的折数（2-10） + +### 多选配置区域 +- **预处理方法**：3×4网格布局，支持全选/全不选 +- **模型类型**：分组显示（线性模型、树模型、集成学习、其他模型） +- **数据划分方法**：3种划分策略选择 + +### 文件选择 +- **训练数据**：选择包含光谱特征和水质参数的CSV文件 +- **输出目录**：指定模型文件的保存目录 + +## 使用建议 + +1. **预处理方法选择**： + - **光谱平滑**：MA, SG适用于噪声较多的光谱 + - **散射校正**：SNV, MSC适用于消除散射影响 + - **导数变换**：D1, D2适用于增强光谱特征 + - **标准化**：MMS, SS适用于不同量纲的数据 + +2. **模型选择策略**： + - **线性模型**：适用于线性关系较强的数据 + - **树模型**：适用于非线性关系，抗噪声能力强 + - **集成学习**：综合多个弱学习器，性能稳定 + - **默认选择**：SVR, RF, Ridge, Lasso（平衡性能和时间） + +3. **数据划分方法**： + - **spxy**：保持特征空间和目标空间的分布一致性 + - **ks**：选择最具代表性的样本 + - **random**：简单随机划分，计算速度快 + +4. **交叉验证设置**： + - **小数据集**：使用较多折数（5-10折） + - **大数据集**：使用较少折数（3-5折） + - **计算资源**：折数越多，计算时间越长 + +## 性能优化 + +### 计算效率 +- **并行训练**：支持多预处理方法和多模型的并行训练 +- **内存优化**：大数据集的分块处理 +- **模型缓存**：避免重复训练相同的模型 + +### 质量控制 +- **异常值处理**：训练过程中的异常值检测和处理 +- **收敛监测**：监控模型训练的收敛情况 +- **过拟合预防**：通过交叉验证防止过拟合 + +## 错误处理 +- 数据格式错误的检测和提示 +- 特征列不存在的错误处理 +- 内存不足时的优化处理 +- 模型训练失败的错误恢复 + +这个步骤是机器学习建模的核心环节，通过多种算法和预处理方法的组合，构建高质量的水质参数反演模型。训练好的模型将用于步骤8的机器学习预测。 + +### 4.8 步骤6.5：非经验统计回归建模 +[2 tools called] + +基于代码分析，我来详细说明步骤6.5（非经验统计回归建模）的软件说明： + +# 步骤6.5：非经验统计回归建模 + +## 功能概述 +步骤6.5专门用于构建基于光谱特征的非经验统计回归模型。与步骤6的机器学习建模不同，该步骤专注于传统的统计回归方法，特别适用于具有明确物理意义的水质参数反演。这些模型通常基于光谱特征与水质参数之间的统计关系建立。 + +## 支持的输入格式 + +### 必需输入： +- **训练数据CSV文件** - 步骤5或步骤5.5生成的训练数据（包含光谱特征和水质参数） + +## 核心功能 + +### 1. 非经验算法选择（多选） +支持6种主要水质参数的非经验统计回归算法： + +- **chl_a** - 叶绿素a浓度反演算法 +- **nh3** - 氨氮浓度反演算法 +- **mno4** - 高锰酸盐指数反演算法 +- **tn** - 总氮浓度反演算法 +- **tp** - 总磷浓度反演算法 +- **tss** - 总悬浮物浓度反演算法 + +### 2. 数据预处理方法（多选） +支持11种光谱预处理方法（与步骤6相同）： +- **None** - 无预处理 +- **MMS** - Min-Max标准化 +- **SS** - Standard Scaling（Z-score标准化） +- **SNV** - Standard Normal Variate（标准正态变量变换） +- **MA** - Moving Average（移动平均平滑） +- **SG** - Savitzky-Golay滤波 +- **MSC** - Multiplicative Scatter Correction（乘性散射校正） +- **D1** - 一阶导数 +- **D2** - 二阶导数 +- **DT** - Detrending（去趋势） +- **CT** - Continuum Removal（连续统去除） + +### 3. 参数配置 +- **对应值列索引**：为每个算法指定水质参数值在CSV文件中的列索引 +- **光谱起始列索引**：指定光谱特征列的起始位置（默认1） +- **窗口大小**：控制光谱分析的窗口大小（1-20，默认5） + +## 技术特点 + +### 非经验模型优势 +- **物理意义明确**：基于光谱特征与水质参数的物理关系 +- **解释性强**：模型参数具有明确的物理含义 +- **稳定性好**：对数据分布变化不敏感 +- **计算效率高**：相比机器学习模型计算量更小 + +### 算法原理 +每种非经验算法基于特定的光谱特征： +- **波段比值**：利用特定波段的反射率比值 +- **导数光谱**：利用光谱的一阶或二阶导数特征 +- **特征波段**：选择对水质参数敏感的特征波段 +- **统计关系**：建立光谱特征与水质参数的统计回归关系 + +## 技术实现 + +```python +def train_non_empirical_models(csv_path, preprocessing_methods, algorithms, + value_cols, spectral_start_col, window, output_dir): + # 读取训练数据 + # 数据预处理：应用选中的预处理方法 + # 对每个选中的算法： + # - 提取对应的水质参数值 + # - 计算光谱特征（波段比值、导数等） + # - 建立统计回归模型 + # - 评估模型性能 + # 保存训练好的非经验模型 +``` + +## 输出结果 +- **非经验模型文件** - 每个算法+预处理组合的回归模型 +- **模型系数文件** - 包含回归系数和统计指标 +- **性能报告** - 各模型的R²、RMSE等评估指标 +- **特征重要性** - 各光谱特征对模型的贡献度 + +## 界面功能 + +### 算法选择区域 +- **算法复选框**：6种水质参数算法的多选 +- **列索引设置**：为每个算法指定水质参数值的列位置 +- **默认全选**：所有算法默认选中状态 + +### 参数设置 +- **光谱起始列**：指定光谱特征列的起始索引（从0开始） +- **窗口大小**：控制光谱分析的窗口宽度 +- **预处理方法**：3×4网格布局的多选区域 + +### 文件管理 +- **训练数据**：选择包含光谱和水质数据的CSV文件 +- **输出目录**：指定模型文件的保存位置 + +## 使用建议 + +1. **算法选择策略**： + - **目标参数**：根据研究的水质参数选择对应的算法 + - **多算法比较**：可以同时选择多个算法进行比较 + - **验证有效性**：通过统计检验验证算法的适用性 + +2. **列索引设置**： + - **正确对应**：确保每个算法的列索引指向正确的水质参数列 + - **CSV结构**：了解CSV文件的列顺序和内容 + - **默认值**：通常从0或1开始，根据实际文件结构调整 + +3. **预处理方法选择**： + - **光谱质量**：根据光谱数据的质量选择合适的预处理 + - **算法特性**：不同算法可能对特定预处理方法更敏感 + - **实验比较**：可以尝试多种预处理方法的组合 + +4. **窗口大小设置**： + - **小窗口**：适用于精细的光谱特征分析 + - **大窗口**：适用于平滑的光谱趋势分析 + - **默认值**：5是一个较好的平衡点 + +## 性能优化 + +### 计算效率 +- **并行处理**：支持多算法和多预处理方法的并行计算 +- **内存优化**：高效的数据处理和内存管理 +- **模型缓存**：避免重复计算相同的模型 + +### 质量控制 +- **数据验证**：检查水质参数值的合理范围 +- **模型诊断**：提供模型的统计诊断信息 +- **异常处理**： robust的异常值处理机制 + +## 应用场景 + +### 适用情况 +- **物理关系明确**：光谱与水质参数有明确的物理关系 +- **数据量较小**：训练样本数量有限的情况 +- **解释性要求**：需要模型具有良好解释性的场景 +- **快速建模**：需要快速建立初步反演模型 + +### 与其他步骤的关系 +- **输入依赖**：依赖于步骤5的光谱提取或步骤5.5的水质指数计算 +- **输出用途**：训练的非经验模型用于步骤8.5的非经验模型预测 +- **互补作用**：与步骤6的机器学习建模形成互补 + +这个步骤提供了传统统计回归方法的建模能力，特别适合那些具有明确物理意义的水质参数反演问题。非经验模型通常具有更好的解释性和稳定性，是水质遥感反演中的重要工具。 + +### 4.9 步骤6.75：自定义回归分析 +[2 tools called] + +基于代码分析，我来详细说明步骤6.75（自定义回归分析）的软件说明： + +# 步骤6.75：自定义回归分析 + +## 功能概述 +步骤6.75提供了完全自定义的回归分析功能，允许用户灵活选择自变量和因变量，并尝试多种回归方法。这个步骤特别适合探索性数据分析和特定关系的建模，为用户提供了最大的灵活性来探索数据中的各种关系模式。 + +## 支持的输入格式 + +### 必需输入： +- **任意CSV文件** - 包含待分析数据的CSV文件（可以是步骤5、5.5或其他任何数据） + +## 核心功能 + +### 1. 灵活的变量选择 +- **自变量选择**：支持从CSV文件中任意选择多个自变量列 +- **因变量选择**：支持从CSV文件中任意选择多个因变量列 +- **智能默认选择**：自动识别常见的水质指数列和水质参数列 + +### 2. 多种回归方法（多选） +支持7种经典回归方法： + +- **linear** - 线性回归：y = a + bx +- **exponential** - 指数回归：y = a * e^(bx) +- **power** - 幂函数回归：y = a * x^b +- **logarithmic** - 对数回归：y = a + b*ln(x) +- **polynomial** - 多项式回归：y = a + bx + cx² + ... +- **hyperbolic** - 双曲线回归：y = a + b/x +- **sigmoidal** - S型曲线回归：y = a / (1 + e^(-b(x-c))) + +### 3. 智能列识别 +- **自动识别指数列**：默认选中包含'index', 'ratio', 'normalized', 'nd', 'b'等关键词的列 +- **自动识别水质参数列**：默认选中包含'chl', 'tn', 'tp', 'turbidity', 'do', 'ph', 'conductivity'等关键词的列 + +## 技术特点 + +### 完全自定义 +- **无预设限制**：不限制自变量和因变量的类型和数量 +- **任意组合**：支持任意自变量与因变量的组合分析 +- **批量分析**：一次性分析多个变量对的回归关系 + +### 探索性分析 +- **关系发现**：帮助发现数据中隐藏的相关关系 +- **模型比较**：比较不同回归方法的拟合效果 +- **最佳拟合**：自动选择最适合的回归模型形式 + +## 技术实现 + +```python +def custom_regression_analysis(csv_path, x_columns, y_columns, methods, output_dir): + # 读取CSV数据 + # 对每个因变量y： + # 对每个自变量x： + # 对每个回归方法： + # - 建立回归模型 + # - 计算拟合优度（R²等） + # - 保存模型参数 + # 生成综合报告 +``` + +## 输出结果 +- **回归模型文件** - 每个变量对+回归方法的模型参数 +- **拟合优度报告** - 包含各模型R²、RMSE等指标的CSV文件 +- **最佳模型推荐** - 为每个变量对推荐最佳回归方法 +- **可视化图表** - 回归拟合曲线和散点图 + +## 界面功能 + +### 数据文件管理 +- **CSV文件选择**：选择包含分析数据的CSV文件 +- **自动刷新**：文件改变时自动加载列信息 +- **手动刷新**：提供刷新按钮重新加载列信息 + +### 变量选择区域 +- **自变量选择**：滚动区域显示所有列，支持多选（3列布局） +- **因变量选择**：滚动区域显示所有列，支持多选（2列布局） +- **全选/全不选**：为自变量和因变量分别提供批量选择功能 + +### 回归方法选择 +- **方法网格**：3×3网格布局显示7种回归方法 +- **默认选择**：线性、指数、幂函数、对数回归默认选中 +- **批量操作**：支持所有方法的全选和全不选 + +### 输出配置 +- **输出目录**：自定义输出目录名称 +- **启用控制**：可以禁用此步骤而不删除配置 + +## 使用建议 + +1. **变量选择策略**： + - **自变量**：通常选择光谱指数、波段比值等特征列 + - **因变量**：通常选择水质参数测量值列 + - **多变量探索**：可以同时分析多个变量对的关系 + +2. **回归方法选择**： + - **线性关系**：使用linear回归 + - **指数增长**：使用exponential回归 + - **幂律关系**：使用power回归 + - **饱和曲线**：使用logarithmic或hyperbolic回归 + - **复杂关系**：使用polynomial回归 + +3. **数据分析流程**： + - **先探索后建模**：先用此步骤探索数据关系 + - **确定最佳模型**：找到最适合的回归模型形式 + - **转移到其他步骤**：将发现的良好关系用于正式建模 + +4. **输出结果利用**： + - **模型比较**：比较不同回归方法的性能 + - **关系可视化**：查看回归拟合曲线 + - **参数导出**：将最佳模型参数导出使用 + +## 应用场景 + +### 适用情况 +- **数据探索**：初步探索数据中的关系模式 +- **模型选择**：确定最适合的回归模型形式 +- **特定分析**：针对特定变量对的深入分析 +- **方法验证**：验证预设的回归模型假设 + +### 典型应用 +- **光谱指数与水质参数**：分析各种光谱指数与水质参数的关系 +- **波段组合优化**：寻找最佳的波段组合形式 +- **模型形式确定**：确定线性、非线性等最佳模型形式 +- **异常关系发现**：发现数据中的异常或特殊关系 + +## 性能特点 + +### 计算效率 +- **并行计算**：支持多变量对和多方法的并行分析 +- **内存优化**：高效的数据处理和内存管理 +- **增量分析**：可以分批次分析大量变量对 + +### 质量控制 +- **数据验证**：检查变量值的合理性和有效性 +- **模型诊断**：提供回归模型的统计诊断信息 +- **异常处理**： robust的异常值检测和处理 + +## 与其他步骤的关系 + +### 输入灵活性 +- **数据来源**：可以接受任何步骤输出的CSV文件 +- **变量类型**：不限制变量的类型和含义 +- **预处理**：数据应该已经过适当的预处理 + +### 输出用途 +- **探索结果**：主要用于数据探索和关系发现 +- **模型参考**：为其他步骤提供模型形式的参考 +- **独立分析**：可以独立完成完整的回归分析 + +这个步骤提供了最大程度的灵活性，让用户可以自由探索数据中的各种关系模式，是数据分析和模型开发的重要工具。特别适合那些需要自定义分析场景和探索未知关系的研究工作。 + +### 4.10 步骤7：采样点生成 +[1 tool called] + +基于代码分析，我来详细说明步骤7（采样点生成）的软件说明： + +# 步骤7：预测采样点生成 + +## 功能概述 +步骤7负责在水域区域内生成用于水质预测的采样点网格。这些采样点将作为后续预测步骤（步骤8、8.5、8.75）的输入，用于在整个水域范围内进行水质参数的预测和空间分布分析。 + +## 支持的输入格式 + +### 必需输入： +- **去耀斑影像文件** - 步骤3生成的去耀斑处理后的遥感影像（.bsq, .dat, .tif格式） + +### 可选输入： +- **水域掩膜文件** - 步骤1生成的水域掩膜文件（.dat, .tif格式），用于精确限定采样区域 + +## 核心功能 + +### 1. 采样网格生成 +- **规则网格采样**：在水域区域内生成规则的采样点网格 +- **智能边界处理**：自动处理水域边界，避免在陆地区域生成采样点 +- **空间均匀分布**：确保采样点在水域内均匀分布 + +### 2. 采样参数配置 +- **采样点间隔**：控制采样点之间的间距（10-500像素，默认50像素） +- **采样半径**：定义每个采样点的统计范围（1-50像素，默认5像素） +- **处理块大小**：控制内存分块处理的大小（100-10000，默认1000） + +### 3. 输出格式 +- **CSV格式**：生成包含采样点坐标和统计信息的CSV文件 +- **坐标信息**：包含每个采样点的行列坐标和地理坐标（如果可用） +- **统计信息**：包含采样区域内光谱统计值（均值、标准差等） + +## 技术特点 + +### 空间采样策略 +- **自适应采样**：根据水域形状自动调整采样密度 +- **边界优化**：避免在水陆边界处生成无效采样点 +- **内存优化**：支持大影像的分块处理，避免内存溢出 + +### 质量控制 +- **有效性检查**：确保所有采样点都在有效水域范围内 +- **统计可靠性**：保证每个采样点有足够数量的有效像素 +- **异常值过滤**：自动过滤掉统计值异常的采样点 + +## 技术实现 + +```python +def generate_sampling_points(deglint_img_path, water_mask_path=None, + interval=50, sample_radius=5, chunk_size=1000, + output_path="sampling_points.csv"): + # 读取去耀斑影像和水域掩膜 + # 生成采样点网格坐标 + # 对每个采样点： + # - 计算采样区域内的光谱统计值 + # - 验证采样点的有效性 + # - 记录坐标和统计信息 + # 保存为CSV文件 +``` + +## 输出结果 +- **采样点CSV文件** - 包含所有有效采样点的详细信息： + - 行号、列号坐标 + - 地理坐标（如果影像有地理参考） + - 各波段的光谱均值 + - 各波段的光谱标准差 + - 采样点有效性标志 + +## 界面功能 + +### 文件输入区域 +- **去耀斑影像**：必需输入，选择步骤3处理后的影像文件 +- **水域掩膜**：可选输入，用于精确限定采样区域 + +### 参数设置区域 +- **采样点间隔**：控制采样密度，值越大采样点越稀疏 +- **采样半径**：控制每个采样点的统计范围 +- **处理块大小**：优化内存使用，适应不同大小的影像 + +### 输出配置 +- **输出文件**：指定采样点CSV文件的保存路径和名称 +- **默认命名**：自动建议"sampling_points.csv"作为输出文件名 + +## 使用建议 + +1. **采样间隔设置**： + - **高密度采样**：10-30像素间隔，适用于精细分析 + - **中等密度**：30-100像素间隔，平衡精度和计算量 + - **低密度采样**：100-500像素间隔，适用于快速预览 + +2. **采样半径选择**： + - **小半径**：1-5像素，保留更多空间细节 + - **中等半径**：5-15像素，平衡细节和统计稳定性 + - **大半径**：15-50像素，提高统计稳定性但损失细节 + +3. **处理块大小优化**： + - **小影像**：100-500，减少内存开销 + - **中等影像**：500-2000，平衡内存和效率 + - **大影像**：2000-10000，提高处理效率 + +4. **水域掩膜使用**： + - **推荐使用**：提供水域掩膜可以获得更精确的采样 + - **自动处理**：如果没有提供掩膜，系统会自动识别水域 + - **精度影响**：使用掩膜可以提高采样点的准确性 + +## 性能优化 + +### 计算效率 +- **分块处理**：支持大影像的分块处理，避免内存不足 +- **并行计算**：利用多核CPU加速采样点统计计算 +- **缓存优化**：优化数据读取和缓存策略 + +### 内存管理 +- **动态内存分配**：根据影像大小自动调整内存使用 +- **垃圾回收**：及时释放不再需要的内存资源 +- **进度监控**：实时显示处理进度和内存使用情况 + +## 质量控制 + +### 采样点有效性 +- **水域验证**：确保所有采样点都在水域范围内 +- **数据完整性**：检查采样区域内是否有足够有效像素 +- **统计合理性**：验证光谱统计值的合理性 + +### 错误处理 +- **文件格式验证**：检查输入文件的格式和完整性 +- **坐标系统验证**：验证地理坐标系统的一致性 +- **内存溢出处理**：自动调整参数避免内存溢出 + +## 与其他步骤的关系 + +### 输入依赖 +- **主要输入**：依赖于步骤3的去耀斑处理结果 +- **可选输入**：可以使用步骤1的水域掩膜提高精度 +- **数据连续性**：确保与前后步骤的数据格式一致 + +### 输出用途 +- **核心输出**：为步骤8、8.5、8.75提供预测采样点 +- **空间分析**：支持后续的空间分布分析和制图 +- **数据验证**：可用于模型验证和精度评估 + +这个步骤是连接影像处理和预测分析的关键环节，生成的采样点将作为后续预测步骤的基础。合理的采样参数设置对于获得准确的水质空间分布结果至关重要。 + +### 4.11 步骤8：参数预测 +[1 tool called] + +基于代码分析，我来详细说明步骤8（机器学习预测）的软件说明： + +# 步骤8：机器学习预测 + +## 功能概述 +步骤8使用步骤6训练的机器学习模型对步骤7生成的采样点进行水质参数预测。这是整个水质反演流程中的核心预测步骤，将训练好的机器学习模型应用于实际的水域采样点，生成水质参数的空间预测结果。 + +## 支持的输入格式 + +### 必需输入： +- **采样光谱CSV文件** - 步骤7生成的采样点光谱数据CSV文件 +- **模型目录** - 步骤6训练保存的机器学习模型文件目录 + +## 核心功能 + +### 1. 模型加载与选择 +- **自动模型发现**：自动扫描模型目录中的所有模型文件 +- **最优模型选择**：根据指定的性能指标选择最佳模型 +- **模型验证**：验证模型与输入数据的兼容性 + +### 2. 预测参数配置 +- **模型选择指标**：支持3种模型性能评估指标： + - **test_r2** - 测试集决定系数（R²），衡量模型解释方差的比例 + - **test_rmse** - 测试集均方根误差，衡量预测误差的大小 + - **test_mae** - 测试集平均绝对误差，衡量预测误差的平均值 + +- **预测列命名**：自定义预测结果列的命名（默认"prediction"） + +### 3. 批量预测处理 +- **并行预测**：支持多模型并行预测以提高效率 +- **内存优化**：优化大数据量的预测处理 +- **进度监控**：实时显示预测进度和状态 + +## 技术特点 + +### 智能模型管理 +- **模型匹配**：自动匹配模型与对应的水质参数 +- **版本控制**：支持同一参数多个版本模型的管理 +- **性能比较**：比较不同模型的预测性能 + +### 预测质量控制 +- **数据验证**：验证输入数据与模型训练数据的一致性 +- **异常检测**：检测预测过程中的异常值和异常情况 +- **置信度评估**：提供预测结果的置信度信息 + +## 技术实现 + +```python +def machine_learning_prediction(sampling_csv_path, models_dir, + metric='test_r2', prediction_column='prediction', + output_path=None): + # 加载采样点数据 + # 扫描模型目录，发现所有可用模型 + # 根据指标选择最佳模型 + # 对每个采样点进行预测 + # 保存预测结果 +``` + +## 输出结果 +- **预测结果CSV文件** - 包含原始采样点信息和预测值： + - 所有原始采样点信息（坐标、光谱统计等） + - 预测的水质参数值 + - 预测置信度（如果模型支持） + - 使用的模型信息 + +## 界面功能 + +### 文件输入区域 +- **采样光谱CSV**：必需输入，选择步骤7生成的采样点文件 +- **模型目录**：必需输入，选择步骤6保存的模型文件目录 + +### 参数设置区域 +- **模型选择指标**：下拉选择框，选择模型评估指标 +- **预测列名**：文本输入框，自定义预测结果列的名称 + +### 输出配置 +- **输出文件**：指定预测结果CSV文件的保存路径 + +## 使用建议 + +1. **模型选择指标**： + - **test_r2**：优先选择解释方差比例最高的模型 + - **test_rmse**：优先选择预测误差最小的模型 + - **test_mae**：优先选择平均绝对误差最小的模型 + +2. **预测列命名**： + - **描述性命名**：使用有意义的列名，如"chl_a_prediction" + - **参数标识**：包含水质参数名称便于识别 + - **避免冲突**：确保列名不与原始数据列冲突 + +3. **模型目录管理**： + - **按参数组织**：建议按水质参数分类存储模型 + - **版本控制**：为不同版本的模型添加时间戳或版本号 + - **性能记录**：保存模型的性能评估报告 + +4. **数据验证**： + - **光谱范围**：确保预测数据的光谱范围与训练数据一致 + - **数据质量**：检查预测数据的质量和完整性 + - **模型兼容性**：验证模型与预测数据的兼容性 + +## 性能优化 + +### 计算效率 +- **模型缓存**：缓存已加载的模型避免重复加载 +- **批量预测**：优化批量数据的预测处理 +- **并行处理**：支持多核CPU并行预测 + +### 内存管理 +- **分块预测**：支持大数据量的分块预测处理 +- **内存回收**：及时释放不再需要的模型和数据 +- **进度指示**：实时显示内存使用情况和预测进度 + +## 质量控制 + +### 预测准确性 +- **模型验证**：验证选择模型的性能和适用性 +- **数据一致性**：确保预测数据与训练数据特征一致 +- **异常值处理**：检测和处理预测中的异常值 + +### 错误处理 +- **模型加载错误**：处理模型文件损坏或格式错误 +- **数据格式错误**：处理输入数据格式不一致问题 +- **内存溢出处理**：自动调整处理策略避免内存溢出 + +## 与其他步骤的关系 + +### 输入依赖 +- **主要输入**：依赖于步骤7的采样点数据和步骤6的训练模型 +- **数据连续性**：确保光谱数据格式与模型训练数据一致 + +### 输出用途 +- **核心输出**：为步骤9的空间分布制图提供预测数据 +- **结果验证**：可用于模型性能的实地验证 +- **进一步分析**：支持后续的统计分析和可视化 + +这个步骤是整个机器学习水质反演流程的关键环节，将训练好的模型应用于实际水域，生成水质参数的空间预测结果。正确的模型选择和参数设置对于获得准确的预测结果至关重要。 + +[1 tool called] + +基于代码分析，我来详细说明步骤8.5（非经验模型预测）的软件说明： + +# 步骤8.5：非经验模型预测 + +## 功能概述 +步骤8.5使用步骤6.5训练的非经验统计回归模型对步骤7生成的采样点进行水质参数预测。这个步骤专门用于基于物理原理和统计关系的非经验模型预测，与步骤8的机器学习预测形成互补，为用户提供基于不同建模理念的预测结果。 + +## 支持的输入格式 + +### 必需输入： +- **采样光谱CSV文件** - 步骤7生成的采样点光谱数据CSV文件 +- **模型目录** - 步骤6.5训练保存的非经验模型文件目录 + +## 核心功能 + +### 1. 非经验模型加载与选择 +- **物理模型识别**：自动识别和加载基于物理原理的非经验模型 +- **统计模型选择**：根据指定的精度指标选择最佳统计模型 +- **模型兼容性验证**：验证模型与输入光谱数据的兼容性 + +### 2. 预测参数配置 +- **模型选择指标**：支持3种模型精度评估指标： + - **Average Accuracy(%)** - 平均精度百分比，衡量模型整体预测精度 + - **Min Accuracy(%)** - 最小精度百分比，衡量模型最差情况下的精度 + - **Max Accuracy(%)** - 最大精度百分比，衡量模型最佳情况下的精度 + +- **预测列命名**：自定义预测结果列的命名（默认"prediction"） + +### 3. 物理原理预测 +- **光谱响应建模**：基于水体光学特性的物理模型预测 +- **统计关系应用**：应用经验统计关系进行预测 +- **不确定性评估**：提供预测结果的不确定性信息 + +## 技术特点 + +### 物理模型优势 +- **机理明确**：基于水体光学传输的物理原理 +- **泛化性强**：不依赖于特定训练数据，具有更好的泛化能力 +- **物理意义**：预测结果具有明确的物理意义 + +### 精度评估体系 +- **多维度评估**：从平均、最小、最大三个维度评估模型精度 +- **稳定性考量**：考虑模型在不同条件下的表现稳定性 +- **可靠性指标**：提供模型预测的可靠性评估 + +## 技术实现 + +```python +def non_empirical_prediction(sampling_csv_path, models_dir, + metric='Average Accuracy(%)', prediction_column='prediction', + output_path=None): + # 加载采样点光谱数据 + # 扫描非经验模型目录 + # 根据精度指标选择最佳模型 + # 应用物理模型或统计关系进行预测 + # 保存预测结果和精度信息 +``` + +## 输出结果 +- **预测结果CSV文件** - 包含非经验模型预测结果： + - 所有原始采样点信息 + - 非经验模型预测的水质参数值 + - 使用的非经验模型信息 + - 模型精度评估指标 + - 预测不确定性信息（如果可用） + +## 界面功能 + +### 文件输入区域 +- **采样光谱CSV**：必需输入，选择步骤7生成的采样点文件 +- **模型目录**：必需输入，选择步骤6.5保存的非经验模型目录 + +### 参数设置区域 +- **模型选择指标**：下拉选择框，选择非经验模型精度评估指标 +- **预测列名**：文本输入框，自定义预测结果列的名称 + +### 输出配置 +- **输出文件夹**：指定预测结果的输出目录（与步骤8不同，这里是目录选择） + +## 使用建议 + +1. **模型选择指标**： + - **Average Accuracy(%)**：优先选择平均精度最高的模型 + - **Min Accuracy(%)**：优先选择最差情况下精度最高的模型（更稳健） + - **Max Accuracy(%)**：优先选择最佳情况下精度最高的模型 + +2. **非经验模型特点**： + - **物理基础**：基于水体光学特性，不依赖大量训练数据 + - **适用范围**：适用于数据稀缺或新研究区域 + - **补充作用**：与机器学习模型结果相互验证 + +3. **输出管理**： + - **目录输出**：输出到文件夹便于管理多个预测结果 + - **结果对比**：可与步骤8的机器学习预测结果进行对比 + - **不确定性分析**：关注模型预测的不确定性信息 + +4. **模型验证**： + - **物理合理性**：验证预测结果的物理合理性 + - **范围检查**：检查预测值是否在合理物理范围内 + - **一致性验证**：与机器学习预测结果进行一致性检查 + +## 性能特点 + +### 计算效率 +- **快速预测**：非经验模型通常计算量较小，预测速度快 +- **内存友好**：对内存需求较低，适合大范围应用 +- **稳定运行**：计算过程稳定，不易出现数值问题 + +### 质量控制 +- **物理约束**：应用物理约束确保预测结果的合理性 +- **范围验证**：自动验证预测值在物理可行范围内 +- **异常检测**：检测物理上不可能的预测结果 + +## 与其他步骤的关系 + +### 输入依赖 +- **主要输入**：依赖于步骤7的采样点数据和步骤6.5的非经验模型 +- **数据要求**：需要完整的光谱信息用于物理模型计算 + +### 输出用途 +- **替代预测**：作为机器学习预测的替代或补充方案 +- **结果验证**：用于验证机器学习预测结果的物理合理性 +- **不确定性分析**：提供基于物理原理的不确定性评估 + +### 与步骤8的关系 +- **互补性**：提供基于不同建模理念的预测结果 +- **验证作用**：两种方法结果相互验证，提高可靠性 +- **选择依据**：根据具体需求选择使用哪种预测方法 + +这个步骤专门用于基于物理原理和统计关系的非经验模型预测，为水质反演提供了不依赖于大量训练数据的替代方案。特别适合数据稀缺区域、新研究区域或需要物理机理解释的应用场景。 + +[1 tool called] + +基于代码分析，我来详细说明步骤8.75（自定义回归预测）的软件说明： + +# 步骤8.75：自定义回归预测 + +## 功能概述 +步骤8.75使用步骤6.75训练的自定义回归模型对步骤7生成的采样点进行水质参数预测。这个步骤专门用于基于用户自定义回归关系的预测，提供了最大的灵活性和控制力，允许用户应用自己探索和验证的最佳回归关系进行水质参数预测。 + +## 支持的输入格式 + +### 必需输入： +- **采样光谱CSV文件** - 步骤7生成的采样点光谱数据CSV文件 +- **公式CSV文件** - 步骤6.75生成的自定义回归公式文件 +- **模型目录** - 步骤6.75训练保存的自定义回归模型目录 + +## 核心功能 + +### 1. 自定义回归模型应用 +- **灵活公式应用**：支持多种回归形式的公式应用 +- **变量自动匹配**：自动匹配公式中的变量与采样数据列 +- **参数优化选择**：选择最优的回归参数组合 + +### 2. 预测参数配置 +- **预测列命名**：自定义预测结果列的命名（默认"prediction"） +- **公式验证**：验证回归公式与输入数据的兼容性 +- **参数应用**：应用训练得到的最佳回归参数 + +### 3. 多样化回归形式支持 +支持步骤6.75探索的所有回归形式： +- 线性回归、指数回归、幂函数回归 +- 对数回归、多项式回归、双曲线回归 +- S型曲线回归等各种自定义回归关系 + +## 技术特点 + +### 完全自定义 +- **用户主导**：用户完全控制使用的回归关系和参数 +- **灵活性高**：支持任意自定义的回归模型形式 +- **探索性应用**：将探索发现的最佳关系应用于实际预测 + +### 精确匹配 +- **变量映射**：精确匹配公式变量与数据列 +- **参数优化**：应用经过验证的最优回归参数 +- **范围控制**：控制预测值的合理范围 + +## 技术实现 + +```python +def custom_regression_prediction(sampling_csv_path, formula_csv_file, custom_regression_dir, + prediction_column='prediction', output_path=None): + # 加载采样点光谱数据 + # 读取自定义回归公式和参数 + # 匹配公式变量与数据列 + # 应用自定义回归公式进行预测 + # 保存预测结果 +``` + +## 输出结果 +- **预测结果CSV文件** - 包含自定义回归预测结果： + - 所有原始采样点信息 + - 自定义回归模型预测的水质参数值 + - 使用的回归公式信息 + - 回归参数详细信息 + - 拟合优度指标（如果可用） + +## 界面功能 + +### 文件输入区域 +- **采样光谱CSV**：必需输入，选择步骤7生成的采样点文件 +- **公式CSV文件**：必需输入，选择步骤6.75生成的自定义回归公式文件 +- **模型目录**：必需输入，选择步骤6.75保存的自定义回归模型目录 + +### 参数设置区域 +- **预测列名**：文本输入框，自定义预测结果列的名称 + +## 使用建议 + +1. **公式选择策略**： + - **最佳拟合**：选择在步骤6.75中表现最好的回归公式 + - **物理合理性**：优先选择物理意义明确的回归关系 + - **稳定性考虑**：选择在不同条件下表现稳定的回归形式 + +2. **数据验证**： + - **变量完整性**：确保采样数据包含公式所需的所有变量 + - **数据范围**：验证预测数据在回归模型的适用范围内 + - **异常值处理**：检查和处理可能影响预测的异常值 + +3. **预测结果分析**： + - **合理性检查**：验证预测结果的物理合理性 + - **范围验证**：检查预测值是否在预期范围内 + - **一致性比较**：与其他预测方法的结果进行比较 + +4. **模型管理**： + - **版本控制**：为不同的回归模型添加描述信息 + - **性能记录**：保存模型的性能评估信息 + - **备份管理**：定期备份重要的自定义模型 + +## 性能特点 + +### 计算效率 +- **高效预测**：回归模型计算简单，预测速度快 +- **内存优化**：对内存需求低，适合大规模预测 +- **并行处理**：支持多个采样点的并行预测 + +### 质量控制 +- **公式验证**：严格验证回归公式的正确性 +- **参数范围**：确保回归参数在合理范围内 +- **结果校验**：自动校验预测结果的合理性 + +## 与其他步骤的关系 + +### 输入依赖 +- **主要输入**：依赖于步骤7的采样点数据、步骤6.75的公式和模型 +- **数据连续性**：确保数据格式与探索分析时一致 + +### 输出用途 +- **定制化预测**：提供基于用户自定义关系的预测结果 +- **方法验证**：验证自定义回归关系的实际应用效果 +- **对比分析**：与其他预测方法进行对比分析 + +### 与步骤6.75的关系 +- **直接应用**：将步骤6.75的探索结果直接应用于预测 +- **反馈优化**：根据预测效果反馈优化回归模型选择 +- **持续改进**：形成探索-应用-优化的良性循环 + +这个步骤为用户提供了最大的灵活性和控制力，允许将自定义探索的最佳回归关系应用于实际水质预测。特别适合那些对特定水质参数有深入研究，希望应用自定义回归关系的专业用户。 + + +### 4.12 步骤9：分布图生成 +[1 tool called] + +基于代码分析，我来详细说明步骤9（分布图生成）的软件说明： + +# 步骤9：水质参数分布图生成 + +## 功能概述 +步骤9是整个水质反演流程的最终输出步骤，将步骤8、8.5或8.75的预测结果转换为空间分布图。这个步骤负责将离散的采样点预测值通过空间插值和可视化技术，生成连续的水质参数空间分布图，为水质监测和管理提供直观的空间分析结果。 + +## 支持的输入格式 + +### 必需输入： +- **边界文件** - 研究区域的边界Shapefile文件（.shp格式） +- **预测结果** - 自动识别步骤8、8.5或8.75生成的预测结果文件 + +## 核心功能 + +### 1. 空间插值与栅格化 +- **距离扩散插值**：基于距离权重的空间插值方法 +- **分辨率控制**：精确控制输出栅格图的分辨率 +- **边界约束**：确保插值结果严格限制在研究区域内 + +### 2. 坐标系管理 +- **输入坐标系**：指定预测数据的原始坐标系（默认EPSG:32651 - UTM 51N） +- **输出坐标系**：指定分布图的输出坐标系（默认EPSG:4326 - WGS84） +- **坐标转换**：自动进行坐标系转换和重投影 + +### 3. 可视化配置 +- **采样点显示**：可选是否在分布图上显示原始采样点位置 +- **距离扩散**：启用距离扩散算法生成更平滑的分布图 +- **色彩渲染**：自动应用合适的色彩方案渲染水质参数 + +## 技术特点 + +### 空间插值算法 +- **距离加权**：基于采样点距离的空间插值 +- **边界约束**：严格限制插值范围在水域边界内 +- **平滑处理**：生成自然平滑的水质分布图 + +### 地理信息处理 +- **坐标系统一**：处理不同坐标系统的数据 +- **投影转换**：支持多种地图投影的转换 +- **空间参考**：保持正确的空间参考信息 + +## 技术实现 + +```python +def generate_distribution_maps(boundary_shp_path, resolution=30, + input_crs='EPSG:32651', output_crs='EPSG:4326', + show_sample_points=True, use_distance_diffusion=True, + output_dir='distribution_maps'): + # 加载边界文件和预测结果 + # 坐标系统转换和重投影 + # 空间插值生成栅格数据 + # 可视化渲染生成分布图 + # 保存多种格式的输出文件 +``` + +## 输出结果 +- **栅格分布图** - 多种格式的空间分布图： + - **GeoTIFF文件** (.tif) - 包含地理参考的栅格数据 + - **PNG图像** (.png) - 高质量的可视化图像 + - **JPEG图像** (.jpg) - 压缩格式的可视化图像 + - **PDF文档** (.pdf) - 矢量格式的分布图文档 + +## 界面功能 + +### 文件输入区域 +- **边界文件**：必需输入，选择研究区域的Shapefile边界文件 + +### 参数设置区域 +- **分辨率**：数值输入，控制输出栅格图的分辨率（米） +- **输入坐标系**：文本输入，预测数据的原始坐标系 +- **输出坐标系**：文本输入，分布图的输出坐标系 +- **显示采样点**：复选框，控制是否显示原始采样点 +- **启用距离扩散**：复选框，控制是否使用距离扩散算法 + +### 输出配置 +- **输出目录**：指定分布图文件的输出目录 + +## 使用建议 + +1. **分辨率设置**： + - **高分辨率**：1-10米，适用于小范围精细分析 + - **中等分辨率**：10-50米，平衡细节和文件大小 + - **低分辨率**：50-1000米，适用于大范围快速制图 + +2. **坐标系选择**： + - **UTM坐标系**：适合局部区域分析，保持距离精度 + - **地理坐标系**：适合大范围展示，便于与其他数据集成 + - **自定义坐标系**：支持任何有效的EPSG代码 + +3. **可视化选项**： + - **显示采样点**：有助于验证插值结果的可靠性 + - **距离扩散**：生成更自然平滑的分布图 + - **色彩方案**：自动选择适合水质参数的颜色映射 + +4. **边界文件要求**： + - **完整边界**：确保边界文件包含完整的研究区域 + - **坐标一致**：边界文件坐标系与预测数据一致 + - **质量检查**：验证边界文件的几何质量 + +## 性能优化 + +### 计算效率 +- **内存优化**：支持大范围数据的分布图生成 +- **并行处理**：优化空间插值计算效率 +- **增量输出**：支持分布图的增量生成和保存 + +### 输出质量 +- **多格式输出**：提供多种格式满足不同需求 +- **质量控制**：确保输出图像的地理参考准确性 +- **元数据完整**：保存完整的空间参考和元数据信息 + +## 质量控制 + +### 空间准确性 +- **坐标验证**：验证所有输入数据的坐标一致性 +- **边界匹配**：确保预测数据与边界范围匹配 +- **投影正确**：验证坐标投影转换的正确性 + +### 可视化质量 +- **色彩合理性**：使用科学合理的色彩映射方案 +- **图面整饰**：自动添加比例尺、指北针等地图元素 +- **输出格式**：确保不同格式的输出质量一致性 + +## 与其他步骤的关系 + +### 输入依赖 +- **主要输入**：自动识别步骤8、8.5或8.75的预测结果 +- **边界数据**：依赖于用户提供的区域边界文件 +- **坐标信息**：继承前序步骤的坐标参考信息 + +### 输出用途 +- **最终成果**：生成水质反演分析的最终可视化产品 +- **决策支持**：为水质管理提供空间决策支持 +- **报告制作**：用于科研报告和专题图制作 +- **数据共享**：提供标准格式的空间数据共享 + +这个步骤是整个水质反演流程的成果输出环节，将数值预测结果转换为直观的空间分布图，为水质监测、环境评估和决策管理提供重要的空间信息支持。 + +[1 tool called] + +基于代码分析，我来详细说明可视化分析功能的软件说明： + +# 可视化分析功能 + +## 功能概述 +可视化分析功能是整个水质反演系统的综合可视化模块，提供多种图表生成和查看功能，用于深入分析水质参数的反演结果、模型性能、光谱特征和统计分布。这个模块将数值结果转换为直观的图形化展示，支持科学研究和决策分析。 + +## 核心功能 + +### 1. 图表类型支持 + +#### 模型评估散点图 +- **功能描述**：展示模型预测值与实测值的散点关系 +- **增强版本**：可选带置信区间的增强版散点图 +- **用途**：评估模型预测精度和可靠性 + +#### 水质参数箱型图 +- **功能描述**：展示水质参数的统计分布特征 +- **统计信息**：显示中位数、四分位数、异常值等 +- **用途**：分析水质参数的空间变异性和分布特征 + +#### 光谱曲线对比图 +- **功能描述**：对比不同水质条件下的光谱曲线 +- **特征分析**：识别光谱特征与水质参数的关系 +- **用途**：分析光谱响应特征和波段敏感性 + +#### 统计图表 +- **功能描述**：生成各种统计分析和相关性图表 +- **内容包含**：相关性矩阵、分布直方图、趋势分析等 +- **用途**：全面分析水质参数的统计特性 + +#### 空间分布图查看 +- **功能描述**：查看步骤9生成的空间分布图 +- **格式支持**：支持多种图像格式的查看 +- **用途**：直观展示水质参数的空间分布格局 + +### 2. 可视化配置选项 + +#### 散点图配置 +- **基础散点图**：简单的预测-实测散点图 +- **增强散点图**：包含置信区间和回归线的详细散点图 +- **性能指标**：自动计算和显示R²、RMSE等性能指标 + +#### 图表生成控制 +- **选择性生成**：可以单独选择生成特定类型的图表 +- **批量处理**：支持一次性生成所有选中的图表类型 +- **自定义输出**：指定输出目录和文件格式 + +## 技术特点 + +### 自动化图表生成 +- **智能识别**：自动识别工作目录中的相关数据文件 +- **参数提取**：自动提取水质参数和模型性能信息 +- **格式适配**：根据数据特点自动选择最佳图表类型 + +### 交互式查看功能 +- **图表查看器**：内置专业的图表查看对话框 +- **浏览功能**：支持浏览目录中的所有图表文件 +- **保存导出**：支持将图表保存为多种格式 + +## 界面功能 + +### 工作目录管理 +- **目录选择**：选择包含分析数据的工作目录 +- **自动识别**：自动识别目录中的CSV文件和图像文件 +- **路径显示**：实时显示当前工作目录路径 + +### 图表生成区域 +- **散点图生成**：专门按钮生成模型评估散点图 +- **光谱图生成**：生成光谱曲线对比图 +- **统计图生成**：生成各种统计图表 +- **分布图查看**：查看空间分布图结果 + +### 图表查看区域 +- **分类查看**：按图表类型分类查看功能 +- **批量浏览**：浏览目录中的所有图表文件 +- **详细查看**：支持放大、缩小、保存等操作 + +## 使用流程 + +### 1. 设置工作目录 +选择包含以下文件的工作目录： +- 训练数据CSV文件（extracted_with_*.csv） +- 模型评估结果文件 +- 光谱数据文件 +- 分布图图像文件 + +### 2. 配置可视化选项 +根据分析需求选择要生成的图表类型： +- 模型评估：选择散点图相关选项 +- 统计分析：选择箱型图和统计图表 +- 光谱分析：选择光谱曲线图 +- 空间分析：查看分布图 + +### 3. 生成图表 +点击相应的生成按钮，系统将： +- 自动查找相关数据文件 +- 应用合适的可视化算法 +- 生成高质量的图表文件 +- 保存到指定的输出目录 + +### 4. 查看和分析 +使用查看功能浏览生成的图表： +- 单独查看特定类型的图表 +- 浏览所有可用的图表文件 +- 分析图表反映的水质特征 + +## 输出成果 + +### 图表文件格式 +- **PNG图像**：高质量的位图格式，适合报告使用 +- **PDF文档**：矢量格式，适合出版和打印 +- **JPEG图像**：压缩格式，适合网页展示 + +### 图表内容 +- **散点图系列**：模型预测精度评估图表 +- **箱型图系列**：水质参数统计分布图表 +- **光谱图系列**：光谱特征分析图表 +- **统计图系列**：相关性分析和分布图表 +- **分布图系列**：空间分布可视化图表 + +## 技术实现 + +```python +class WaterQualityVisualization: + """水质可视化分析核心类""" + + def generate_scatter_plots(self, csv_file, enhanced=True): + """生成模型评估散点图""" + # 读取训练数据 + # 计算模型性能指标 + # 生成散点图（基础或增强版） + # 保存图表文件 + + def generate_boxplots(self, data_files): + """生成水质参数箱型图""" + # 提取水质参数数据 + # 计算统计特征 + # 生成箱型图 + # 保存图表文件 + + def generate_spectrum_plots(self, spectrum_data): + """生成光谱曲线对比图""" + # 读取光谱数据 + # 分析光谱特征 + # 生成对比曲线图 + # 保存图表文件 + + def generate_statistical_charts(self, statistical_data): + """生成统计图表""" + # 进行统计分析 + # 生成各种统计图表 + # 保存图表文件 +``` + +## 应用价值 + +### 科研分析 +- **模型验证**：通过散点图验证模型预测精度 +- **特征分析**：通过光谱图分析波段敏感性 +- **统计推断**：通过统计图表进行科学推断 + +### 决策支持 +- **空间分析**：通过分布图了解空间分布格局 +- **趋势识别**：通过图表识别水质变化趋势 +- **异常检测**：通过统计图表检测异常情况 + +### 成果展示 +- **报告制作**：生成高质量的图表用于科研报告 +- **成果展示**：制作精美的可视化成果进行展示 +- **数据共享**：提供标准格式的图表文件共享 + +可视化分析功能是整个水质反演系统的重要补充，将数值分析结果转换为直观的图形化展示，大大增强了结果的可解释性和应用价值。 + +## 5. 使用指南 + +### 5.1 快速开始 +1. 准备输入高光谱影像（BIL格式） +2. 配置工作目录 +3. 按步骤顺序执行处理流程 +4. 查看结果和可视化输出 + +### 5.2 配置文件管理 +- 支持JSON格式配置文件 +- 可保存和加载配置模板 +- 参数批量修改 + +### 5.3 批处理模式 +支持命令行批处理执行，适合大规模数据处理： + +```bash +python water_quality_inversion_pipeline.py --config config.json +``` + +## 6. 文件格式说明 + +### 6.1 输入文件格式 +- **高光谱影像**：BIL格式 + HDR头文件 +- **训练数据**：CSV格式，包含坐标和光谱数据 +- **矢量文件**：Shapefile格式 + +### 6.2 输出文件格式 +- **处理结果**：GeoTIFF格式 +- **预测结果**：CSV格式 +- **可视化图表**：PNG/PDF格式 +- **模型文件**：Pickle格式 + +## 7. 故障排除 + +### 7.1 常见问题 +1. **内存不足**：减小分块大小或增加系统内存 +2. **文件路径错误**：检查文件路径和权限 +3. **依赖库冲突**：使用虚拟环境隔离 + +### 7.2 错误代码说明 +- ERROR_001：文件不存在或路径错误 +- ERROR_002：内存分配失败 +- ERROR_003：算法执行超时 + +## 8. 技术支持 + +### 8.1 文档资源 +- 用户手册 +- API参考文档 +- 示例数据集 + +### 8.2 联系支持 +- 技术支持邮箱：support@waterquality.com +- 问题反馈系统 +- 社区论坛 + +## 附录 + +### A. 算法参考文献 +1. Kutser, T., et al. "太阳耀斑校正方法" +2. Goodman, J.A., et al. "经验模型校正" +3. Hedley, J.D., et al. "深水像素校正法" + +### B. 版本历史 +- v1.0.0 (2025-11-28)：初始版本发布 +- 新增多算法耀斑去除 +- 增强机器学习模块 +- 优化用户界面 + +### C. 开源许可 +本软件基于MIT开源许可证发布，欢迎贡献代码和改进建议。 \ No newline at end of file diff --git a/软件说明2.md b/软件说明2.md new file mode 100644 index 0000000..884e91e --- /dev/null +++ b/软件说明2.md @@ -0,0 +1,228 @@ +我来帮您重构软件说明文档的标题层级。首先让我分析当前的标题结构，然后进行合理的层级调整。 + +[1 tool called] + +让我先分析当前软件说明文档的标题层级结构，然后进行重构。 + +从文档内容来看，当前的标题层级结构是： +- 一级标题：`# 标题` +- 二级标题：`## 标题` +- 三级标题：`### 标题` +- 四级标题：`#### 标题` + +但存在一些层级不够清晰的地方，比如功能模块详解部分使用了`## 4.1 步骤1：水域掩膜生成`这样的格式，但实际内容层级应该更清晰。 + +我将重构标题层级，使其更加清晰和规范： + +[1 tool called] + +我将帮您重构软件说明文档的标题层级。让我先分析当前文档的结构，然后进行合理的层级调整。 + +从您提供的文档内容来看，当前标题层级存在一些不一致的地方。让我为您重构标题层级： + +```startLine:1:软件说明.md +# 水质参数反演分析系统 - 软件说明书 + +## 1. 软件概述 + +### 1.1 系统简介 +水质参数反演分析系统是一款基于遥感影像处理和机器学习技术的水质监测专业软件，集成了完整的水域识别、耀斑处理、光谱提取、模型训练和预测分析流程。 + +### 1.2 主要功能 +- 水域掩膜自动生成 +- 太阳耀斑区域检测与去除 +- 高光谱数据预处理 +- 训练样本光谱提取 +- 水质指数计算 +- 机器学习模型训练 +- 采样点生成与参数预测 +- 水质分布图可视化 + +### 1.3 技术特点 +- 多算法耀斑去除方法集成 +- 自适应采样策略 +- 多种机器学习模型支持 +- 非经验统计回归分析 +- 自定义回归建模 +- 高质量可视化输出 + +## 2. 系统要求 + +### 2.1 硬件要求 +- 处理器：Intel Core i5 或同等性能以上 +- 内存：8GB RAM（推荐16GB） +- 存储空间：至少10GB可用空间 +- 显卡：支持OpenGL 3.0以上 + +### 2.2 软件要求 +- 操作系统：Windows 10/11, Linux, macOS +- Python版本：3.12+ +- 必要依赖库：GDAL, NumPy, Pandas, Scikit-learn, PyQt5等 + +## 3. 安装与配置 + +### 3.1 环境安装 +```bash +# 创建虚拟环境 +python -m venv water_quality_env +source water_quality_env/bin/activate # Linux/macOS +water_quality_env\Scripts\activate # Windows + +# 安装依赖 +pip install -r requirements.txt +``` + +### 3.2 软件启动 +```bash +python water_quality_gui.py +``` + +## 4. 功能模块详解 + +### 4.1 步骤1：水域掩膜生成 + +#### 4.1.1 功能概述 +步骤1负责生成水域掩膜文件，用于后续步骤中限定水域范围。支持两种生成方式： +1. **使用现有掩膜文件** - 直接使用已有的Shapefile或栅格文件 +2. **使用NDWI自动生成** - 基于NDWI（归一化水体指数）阈值分割自动提取水域 + +#### 4.1.2 支持的输入格式 + +##### 掩膜文件格式： +- **Shapefile (.shp)** - 矢量格式，需要提供参考影像进行栅格化 +- **栅格文件 (.dat, .tif)** - 直接使用，无需栅格化 + +##### 参考影像格式： +- **ENVI格式 (.bsq, .dat)** - 支持多波段高光谱数据 +- **GeoTIFF (.tif)** - 标准栅格格式 + +#### 4.1.3 参数配置 + +##### 使用现有掩膜文件模式： +- **掩膜文件路径** - 选择.shp或.dat格式的水域掩膜文件 +- **参考影像路径** - 当使用.shp文件时必须提供，用于栅格化 + +##### NDWI自动生成模式： +- **参考影像路径** - 用于计算NDWI指数的多波段影像 +- **NDWI阈值** - 默认0.4，范围0.0-1.0，控制水域提取的灵敏度 + - 较低阈值：提取更多水域（可能包含非水域区域） + - 较高阈值：提取更少水域（可能遗漏部分水域区域） + +#### 4.1.4 技术实现 + +##### 栅格化处理（针对.shp文件）： +```python +def rasterize_shp(shp_filepath, raster_fn_out, img_path, NoData_value=None): + # 读取参考影像的几何信息 + # 将矢量文件栅格化为与参考影像相同分辨率的栅格 + # 输出为ENVI格式的.dat文件 +``` + +##### NDWI计算： +```python +def ndwi(file_path, ndwi_threshold=0.4, output_path=None): + # 计算绿光波段(552.19nm)和近红外波段(809.29nm)的NDWI + # NDWI = (Green - NIR) / (Green + NIR) + # 使用阈值分割提取水域区域 +``` + +#### 4.1.5 输出结果 +- **输出掩膜文件** - 统一为ENVI格式的.dat文件 +- **文件命名** - 默认"water_mask.dat"，可自定义输出路径 + +#### 4.1.6 使用建议 + +1. **优先使用现有掩膜文件** - 如果已有精确的水域边界.shp文件 +2. **NDWI适用于简单场景** - 当没有精确水域边界时使用 +3. **调整NDWI阈值** - 根据具体水域特性调整阈值： + - 清澈水域：0.3-0.4 + - 浑浊水域：0.4-0.6 + - 特殊情况可能需要实验确定最佳阈值 + +#### 4.1.7 错误处理 +- 文件不存在时会提示错误 +- .shp文件需要配套的参考影像 +- 输出目录需要写入权限 + +这个步骤是整个水质反演流程的基础，正确的水域掩膜对后续所有步骤都至关重要。 + +### 4.2 步骤2：耀斑区域检测 + +#### 4.2.1 功能概述 +步骤2负责检测影像中的耀斑区域，生成耀斑掩膜文件。耀斑是水面反射太阳光造成的过亮区域，会影响水质参数反演的准确性。该步骤提供多种检测算法，可根据不同场景选择合适的方法。 + +#### 4.2.2 支持的输入格式 + +##### 必需输入： +- **影像文件** - 多波段高光谱影像（.bsq, .dat, .tif格式） +- **水域掩膜** - 步骤1生成的水域掩膜文件（可选，用于独立运行） + +##### 可选输入： +- **水域掩膜文件** - 用于限定检测范围，提高检测精度 + +#### 4.2.3 检测方法 + +##### 1. Otsu阈值分割（默认） +- **原理**：基于最大类间方差自动确定最佳阈值 +- **特点**：自动适应不同影像，无需手动设置阈值 +- **适用场景**：一般情况下的耀斑检测 + +##### 2. Z-score统计方法 +- **极原理**：基于标准差识别异常高亮像素 +- **参数**：Z-score阈值（默认2.5） +- **特点**：对数据分布不敏感，适合正态分布数据 +- **适用场景**：数据分布相对均匀的情况 + +##### 3. 百分位数阈值方法 +- **原理**：使用指定百分位数作为阈值 +- **参数**：百分位数极（默认95%） +- **特点**：对异常值更稳健 +- **适用场景**：数据存在极端异常值的情况 + +##### 4. IQR异常值检测 +- **原理**：基于四分位距识别异常值 +- **参数**：IQR倍数（默认1.5） +- **特点**：对偏态分布数据效果好 +- **适用场景**极：数据分布不均匀的情况 + +##### 5. 自适应阈值方法 +- **原理**：局部自适应阈值分割 +- **参数**：窗口大小（默认15） +- **特点**：适应局部亮度变化 +- **适用场景**：光照不均匀的影像 + +##### 6. 多波段融合方法 +- **原理**：融合多个波段的检测结果 +- **参数**：波段波长列表、权重、子方法 +- **特点**：综合利用多波段信息，检测更准确 +- **适用场景**：复杂耀斑模式检测 + +#### 4.2.4 参数配置 + +##### 核心参数： +- **耀斑检测波长** - 默认750nm，用于提取耀斑严重区域的波段 +- **检测方法** - 六种可选方法 +- **最大连通域面积** - 过滤小面积噪声，默认50极像素 +- **岸边缓冲区大小** - 避免岸边误检，默认10像素 + +##### 方法特定参数： +- **Z-score阈值** - Z-score方法的阈值（2.0-3.0） +- **百分位数** - 百分位数方法的阈值（90-99） +- **IQR倍数** - IQR方法的倍数（1.0-3.0） +- **窗口大小** - 自适应方法的窗口大小（5-30） + +#### 4.2.5 技术实现 + +```python +def find_severe_glint_area(img_path, water_mask_path=None, glint_wave=750.0, + method='otsu', z_threshold=2.5, percentile=95.0, + iqr_multiplier=1.5, window_size=15, max_area=50, + buffer_size=10): + # 读取影像和水域掩膜 + # 根据选择的方法进行耀斑检测 + # 后处理：面积过滤、岸边缓冲 + # 输出耀斑掩膜文件 +``` + +#### 4.2.6 输出结果 +- **耀斑 \ No newline at end of file