semvec/pypi-packages huggingface dataset:
GeoModelingZ 0.0.6
地理建模与可视化工具库
Requires Python: >=3.6

# GeoModelingZ

这是一个地理建模与可视化工具库，专为Jupyter Notebook环境优化，提供了丰富的交互式地理数据可视化功能。

## 主要功能

- **环境检测**: 自动检测Jupyter Notebook环境
- **交互式图表**: 支持matplotlib交互式图表绘制
- **地理地图可视化**: 强大的交互式地理地图功能
- **GeoJSON支持**: 完整支持GeoJSON数据格式
- **多几何类型**: 支持点、线、多边形和Multi类型几何数据
- **自动聚焦**: 智能聚焦到数据区域
- **多底图支持**: 支持Mapbox和OpenStreetMap等多种底图

## 安装

### 基本安装

```bash
pip install GeoModelingZ
```

### 安装Jupyter支持（推荐）

```bash
pip install GeoModelingZ[jupyter]
```

## 使用方法

### 交互式图表(主版本待开发)

```python
from GeoModelingZ import plot_simple_chart

# 创建简单的交互式图表
fig = plot_simple_chart(title="我的图表")

# 在Jupyter环境中，可以交互调整参数
# 在普通环境中，返回matplotlib图表对象
```

### 地理地图可视化

```python
from GeoModelingZ import create_geo_map

# 创建基本地图
map_obj = create_geo_map(
    width=1000, 
    height=700,
    center=[39.9042, 116.4074],  # 北京坐标
    zoom_start=10
)

# 加载GeoJSON数据
map_with_data = create_geo_map(
    geojson_file="path/to/your/data.geojson",
    mapbox_token="your_mapbox_token",  # 可选
    basemap='dark'
)

# 显示地图（在Jupyter中）
map_with_data
```

## 支持的GeoJSON几何类型

- **Point**: 单点数据
- **MultiPoint**: 多点数据
- **LineString**: 线数据
- **MultiLineString**: 多线数据
- **Polygon**: 多边形数据
- **MultiPolygon**: 复杂多边形数据

## 底图类型

- **streets**: 矢量街道地图（默认）
- **satellite**: 卫星影像地图
- **outdoors**: 地形图
- **light**: 浅色底图
- **dark**: 深色底图
- **OpenStreetMap**: 开源街道地图

## 依赖

- Python >= 3.6
- numpy
- matplotlib
- ipython
- folium

### 可选依赖（用于Jupyter功能）

- ipywidgets >= 7.0.0
- jupyterlab_widgets

## 更新日志

### v0.0.6
- 重构代码结构，移除冗余功能
- 专注于地理数据可视化核心功能
- 优化GeoJSON数据处理逻辑
- 改进地图渲染性能
- 增强错误处理和调试信息

## 贡献

欢迎提交Issue和Pull Request来改进这个库！

## 许可证

MIT License
pyvetic 0.34.0
Internal package for common utils
Requires Python: <3.13,>=3.8

# PyVetic

## Overview

PyVetic is a shared Python utility library designed to provide common functionality across multiple Vetic projects. This library serves as a central repository for reusable code, ensuring consistency and reducing duplication across different repositories.

## Features

- **Unified Logging System**: Advanced logging with Loki integration, colored console output, and file rotation
- **Monitoring Integration**: Prometheus metrics collection and reporting
- **Framework Support**:
  - Django integration
  - FastAPI integration
  - Instrumentation utilities
- **Performance Monitoring**: System resource monitoring with psutil
- **Configuration Management**: Centralized constants and settings

## Installation

### From PyPI

```bash
pip install pyvetic
```

### Development Installation

```bash
git clone https://github.com/vetic-in/pyvetic.git
cd pyvetic
pip install -e .
```

### Optional Dependencies

- For Django support:
  ```bash
  pip install pyvetic[django]
  ```

- For FastAPI support:
  ```bash
  pip install pyvetic[fastapi]
  ```

## Usage

### Basic Logging

```python
from pyvetic import get_logger

logger = get_logger(__name__)
logger.info("Hello, PyVetic!")
```

### Loki Integration

```python
from pyvetic import set_logging_config

config = {
    "handlers": ["loki"],
    "loki": {
        "host": "your-loki-host",
        "port": 3100,
        "username": "your-username",
        "password": "your-password"
    }
}
set_logging_config(config)
```

## Project Structure

```
pyvetic/
├── django/           # Django-specific utilities
├── fastapi/          # FastAPI-specific utilities
├── instrument/       # Monitoring and instrumentation tools
├── logger.py         # Advanced logging system
└── constants.py      # Shared constants and configurations
```

## Contributing

1. Fork the repository
2. Create your feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add some amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request

## Support

For support, please contact techadmin@vetic.in or open an issue in the GitHub repository.

## Acknowledgments

- Thanks to all contributors who have helped make this project better
Rolling-Imax-Processor 0.1.2
Processes raw intensity data stored in a nested Year/Month/Day folder structure. Reads per-minute rainfall breakpoint data (mm/min) from Excel files named dd-mm-yyyy.xlsx. Calculates rolling maximum values for intensity classes I1 to I60 and outputs the results in mm/min.
Requires Python: >=3.6

Rolling_Imax_Processor

This script processes raw intensity measurement data stored in a nested Year to Month to Day folder structure. The data is stored in a nested folder structure organized by Year, then Month, and then Day. Inside the day folders are Excel files named using the format dd-mm-yyyy.xlsx. It reads the per minute rainfall breakpoint data in millimeters per minute (mm/min) from Excel files, calculates rolling maximum values for intensity classes, and outputs results in millimeters per minute (mm/min) for intensity classes I1 to I60.

## Installation

```bash
pip install your_package_name