semvec/pypi-packages huggingface dataset:
GameBoyLearningEnvironment 1.0.1
A short description of the package
Requires Python: >=3.11

# GameBoy Learning Environment (GLE)

For installing the package you can:

```bash
pip install GameBoyLearningEnvironment
```

We provide also a set of ROM states, available in the `states` folder. For a description of them look at the paper Supplementary Material.

Here a simple example of how to use our library: 

```python
from gle import create_env, PokemonBlueRed

# calling the create_env function
env = create_env('environment_name')
# or by invocaking the environment class
env = PokemonBlueRed(load_path='states/pokemon_red/pokemon_red_squirtle_after_rival_battle.state', window_type=window_type, max_actions=1000)
```

To cite the article please use:

```latex
@article{fazzari2025game,
  title={The Game Boy Learning Environment},
  author={Fazzari, Edoardo and Romano, Donato and Falchi, Fabrizio and Stefanini, Cesare},
  journal={IEEE Transactions on Games},
  year={2025},
  publisher={IEEE}
}
```
rents-weather-project 0.1.4
Rents's test MCP weather server.
Requires Python: >=3.10

# MCP服务开发、测试、发布全流程
## 1、在某个conda环境中(比如llm-mcp)，安装好uv：
pip install uv

## 2、PyCharm项目初始化为uv项目：
uv init .

## 3、创建、激活虚拟环境
uv venv
source .venv/bin/activate

## 4、安装 MCP SDK
uv add openai dotenv mcp

## 5、创建项目源代码
### 创建src/weather 文件夹
#### 添加 server.py 文件

## 6、本地测试（使用Inspector进行）
### 安装nodejs
curl -fsSL https://deb.nodesource.com/setup_20.x | sudo bash -
sudo apt install -y nodejs
### 运行Inspector
npx -y @modelcontextprotocol/inspector uv run ./src/weather/server.py --api_key apikey123abc

## 7、打包
### 添加__init__.py、__main__.py
### 修改pyproject.toml文件
#### 填写内容，注意[project.scripts]中的rents-mcp-weather2名称：
[build-system]
requires = ["setuptools>=61.0", "wheel"]
build-backend = "setuptools.build_meta"

[project.scripts]
rents-mcp-weather2 = "weather:main"
[tool.setuptools]
package-dir = {"" = "src"}
[tool.setuptools.packages.find]
where = ["src"]
### 安装打包/发布(上传)工具包
uv pip install build twine

### 打包（回到项⽬主⽬录）
cd /Users/rents/PycharmProjects/rents-mcp-weather
python -m build

## 8、发布
python -m twine upload dist/*
hawadata 0.17.4
Add your description here
Requires Python: >=3.10

# 文档

## 说明

1. 用于 Hawa 相关的所有服务的统一数据计算（避免重复计算）。

## 依赖

- Python 3.10+

## 流程
### 1. 初始化配置文件
1. 设置MySQL数据库
2. 设置Redis数据库
3. 设置MongoDB数据库
4. 设置 COMPLETED True
````
set_project() # 参数见文档
# or
set_project(build_set_project_params(project))

````
### 2. 启动服务
1. 启动MongoDB数据库
```` 
.connect()
````

### 3. 使用
````
# 学校报告数据
data = SchoolHealthReportData(**{"meta_unit_id": 3707030003, "target_year": 2021})
# mht web 数据
data = SchoolMhtWebData(**{"meta_unit_id": 4107110001, "target_year": 2022})
````


## 版本