semvec/pypi-packages huggingface dataset:
gpddatabase 1.1.3
Database for GPD physics
Requires Python: >=3.0

### Database for studying Generalised Parton Distributions (GPDs)

**About:** The project aims to provide a database for studying GPDs. It is a collaborative effort by scientists working in this field.

**Mission:** The mission is to provide open access to GPD datasets for researchers around the world, facilitating easier integration of existing and new data into phenomenological analyses. By improving reproducibility, the project aims to help the research community fully adopt open-science standards. In addition, the database can serve as an aggregation point to store benchmarks for theoretical developments. This open, accessible resource is designed to accelerate research in the field of GPDs.

**Key Features:** The proposed database is a streamlined, efficient resource for storing both experimental and lattice-QCD data relevant to GPD research. Using a lightweight format based on YAML serialization, the database easily captures essential analysis details, including replica values for advanced statistical work. It supports seamless integration with analysis codes through Python and C++ interfaces, making it a practical tool for researchers working on various aspects of GPD phenomenology.

**License:** This project is strictly for the non-profit scientific use, and is distributed under GPL-3.0 license. 

**Resources:**
  * Webpage: [https://opengpd.github.io/gpddatabase](https://opengpd.github.io/gpddatabase)
  * PyPi repository: [https://pypi.org/project/gpddatabase](https://pypi.org/project/gpddatabase)
  * Reference article: [https://arxiv.org/abs/2503.18152](https://arxiv.org/abs/2503.18152)
mcp-mysql-bailian-licai-202509111610-66e8a1 2.0.0
阿里云百炼MCP服务器 - MySQL业务查询服务
Requires Python: >=3.8
Keywords: mcp mysql bailian alibaba cloud server

# 阿里云百炼MCP服务器 - MySQL业务查询服务

这是一个专为阿里云百炼平台设计的MCP（Model Context Protocol）服务器，提供MySQL数据库的业务查询功能。

## 功能特性

- 🚀 支持HTTP传输协议，兼容阿里云百炼
- 🗄️ 提供7个核心业务查询工具
- 🔧 支持跨数据库查询
- 📊 返回中文字段名，便于理解
- 🛡️ 安全的数据库连接管理

## 业务工具

1. **get_user_info** - 获取用户基本信息
2. **get_user_courses** - 获取用户课程列表
3. **get_user_cases** - 获取用户案例分析
4. **get_user_orders** - 获取用户订单信息
5. **get_user_invoices** - 获取用户发票信息
6. **get_user_study_plans** - 获取用户学习计划
7. **get_course_study_plans** - 获取课程学习计划

## 安装

```bash
pip install mcp-mysql-bailian-licai
```

## 配置

### 环境变量

```bash
export MYSQL_HOST="your-mysql-host"
export MYSQL_PORT="3306"
export MYSQL_USER="your-username"
export MYSQL_PASSWORD="your-password"
export MYSQL_DATABASES="database1,database2"
export MCP_SERVICE_PORT="8001"
```

### MCP配置文件

```json
{
  "mcpServers": {
    "licai_mysql_mcp": {
      "type": "http",
      "command": "uvx",
      "args": [
        "main:mcp.run",
        "--transport", "http",
        "--host", "0.0.0.0",
        "--port", "8001"
      ],
      "env": {
        "MCP_SERVICE_NAME": "licai_mysql_mcp",
        "MCP_SERVICE_PORT": "8001",
        "MYSQL_HOST": "your-mysql-host",
        "MYSQL_PORT": "3306",
        "MYSQL_USER": "your-username",
        "MYSQL_PASSWORD": "your-password",
        "MYSQL_DATABASES": "database1,database2"
      }
    }
  }
}
```

## 使用方法

### 直接启动

```bash
mcp-mysql-bailian-licai
```

### 程序化使用

```python
from mcp_mysql_server.server import main

# 设置环境变量后启动
main()
```

## 许可证

MIT License
simple-tracker-redis 0.1.4
Simple Item Count Tracker Using Redis
Requires Python: >=3.8

# Simple Tracker(Redis)

Simple Tracker 是一个基于 Redis 的轻量级计数跟踪系统，支持按分钟、小时、天三种时间粒度对项目进行计数统计。

## 功能特点

- 支持多种时间粒度跟踪：分钟（m）、小时（h）、天（d）
- 自动过期机制，避免数据无限增长
- 同步和异步两种实现方式
- 提供装饰器模式方便集成

## 核心组件

### ItemTracker (同步版本)

```python
from tracker import ItemTracker

# 初始化
tracker = ItemTracker(redis_client, tracker_name="my_tracker")

# 跟踪项目出现次数
tracker.track_item_occurrence("item_name")

# 获取指定时间粒度的计数
count = tracker.get_item_count("item_name", "h")  # 按小时统计

# 获取分布统计
distribution = tracker.get_count_distribution("d")  # 按天获取分布
```

### AsyncItemTracker (异步版本)

```python
from tracker import AsyncItemTracker

# 初始化
async_tracker = AsyncItemTracker(redis_client, tracker_name="my_tracker")

# 异步跟踪项目出现次数
await async_tracker.track_item_occurrence("item_name")

# 异步获取指定时间粒度的计数
count = await async_tracker.get_item_count("item_name", "h")

# 异步获取分布统计
distribution = await async_tracker.get_count_distribution("d")
```

## 使用装饰器模式

### 同步装饰器

```python
from tracker import get_tracker

track_item = get_tracker(redis_client)

@track_item("api_call")
def my_function():
    pass
```

### 异步装饰器

```python
from tracker import get_async_tracker

track_item = get_async_tracker(redis_client)

@track_item("api_call")
async def my_async_function():
    pass
```

## 数据存储结构

Redis 中的数据以以下格式存储：

```
{tracker_name}:{granularity}:{time_period} -> Hash {item_name: count}
```

例如：

```
my_tracker:h:2023120115 -> {"api_call": 25, "user_login": 10}
```

## 过期时间设置

- 分钟级数据保留 60 分钟
- 小时级数据保留 24 小时
- 天级数据保留 14 天

## 返回数据格式

get_count_distribution 方法返回如下格式的数据：

```json
[
  {
    "item_name": "api_call",
    "total_count": 25,
    "freq": "h",
    "period": "2023120115"
  }
]
```