semvec/pypi-packages huggingface dataset:
wisenations 0.0.0.dev0
A simple library for NationStates RP stats management and evaluation
Requires Python: >=3.10
Keywords: NationStates, Roleplay

# WiseNations

*NOTE*: This project is still in **development phase**, don't use it for actual production.

A simple library for NationStates RP stats management and evaluation.

## Getting started

WiseNations works with stat sheets
``` python
from wisenations import SheetManager

sm = SheetManager()
sm.new_sheet("s1")
my_sheet = sm.get_sheet("s1")

```

Let's add some stats...
``` python
my_stats = {
    "hp": "200",
    "speed": "10",
    "damage": '30',
    "attack_speed": "speed * 2.5"
}
my_sheet.add_stats(my_stats)
```

Output
``` bash
{'attack_speed': 'speed * 2.5', 'damage': '30', 'hp': '200', 'speed': '10'}
```

Evaluate expressions
``` python
result = my_sheet.solve_expressions()
print(result)
```

Output
``` bash
{'attack_speed': '25.0', 'damage': '30', 'hp': '200', 'speed': '10'}
```
#CHANGELOG

## [25.12.2025] 0.0.0.dev0:
Hello, world – and merry christmans!
cugdth 1.1.5
A Python package for GeoTIFF file handling
Requires Python: >=3.7
Keywords: GeoTIFF, GIS, GDAL

## Cug-DeepThinking（cugdth）开源代码包

### 1. Tiff

- tiff文件读取
- tiff文件写入
- tiff文件压缩


```pip install build```

```python -m build```

```pip install twine```

```twine upload dist/*```
mindedge-model-tool 0.1.0
A command line tool for downloading files from MinIO
Requires Python: >=3.10

# mindedge-model-tool

`mindedge-model-tool` 是一个命令行工具，用于辅助mindedge的模型开发，例如从 MinIO 服务器下载文件。

## 功能特性

- 通过命令行从 MinIO 服务器下载文件
- 支持通过配置文件或环境变量设置连接参数
- 灵活的输出路径配置
- 支持指定存储桶名称

## 安装

```bash
# 使用 pip 安装
pip install mindedge-model-tool
```

## 使用方法

### 基本用法

```bash
mindedge-model-tool [OPTIONS] FILE_KEY
```

### 参数说明

- `FILE_KEY`: MinIO 对象路径，格式：`bucket_name/object_key`
- `-o, --output`: 本地保存路径（默认为 `./downloaded_file`）
- `-c, --config`: 配置文件路径（JSON，含 oss.accessKey 等）
- `-b, --bucket`: MinIO 存储桶名称（默认为 `suanpan`）

### 配置

工具需要通过配置文件或环境变量提供 MinIO 连接信息。

#### 配置文件示例 (config.json)

```json
{
  "oss": {
    "internalEndpoint": "your-minio-server.com:9000",
    "accessKey": "your-access-key",
    "accessSecret": "your-access-secret"
  }
}
```

#### 环境变量方式

```bash
export NODE_CONFIG='{"oss":{"internalEndpoint":"your-minio-server.com:9000","accessKey":"your-access-key","accessSecret":"your-access-secret"}}'
mindedge-model-tool bucket-name/file-path
```

### 使用示例

```bash
# 使用配置文件下载文件
mindedge-model-tool my-bucket/my-file.txt -c config.json -o ./local-file.txt

# 使用环境变量下载文件
mindedge-model-tool my-bucket/my-file.txt -o ./local-file.txt

# 指定特定存储桶
mindedge-model-tool my-file.txt -b another-bucket -c config.json
```

## 开发

### 本地运行

```bash
# 克隆项目
git clone <repository-url>

# 安装依赖
cd mindedge-model-tool
pip install -e .
```

## 发布到 PyPI

要发布新版本到 PyPI，请执行以下步骤：

1. 更新 [pyproject.toml](file:///home/luxu/project/mindedge-model-tool/pyproject.toml) 中的版本号
2. 运行测试确保一切正常
3. 使用发布脚本发布：

```bash
# 运行发布脚本
./release.sh
```

发布脚本将：
- 检查环境和依赖
- 构建包
- 询问发布目标（测试PyPI或正式PyPI）
- 执行发布

## 依赖

- Python >= 3.10
- minio >= 7.0.0
light-cache-async 0.1.0
Async caching library with Redis, Memory, L1/L2, AutoSerializer (orjson/msgspec)
Requires Python: >=3.13

# Light Cache

**Light Cache** é uma biblioteca Python para caching assíncrono com suporte a **Redis, memória, L1/L2**, **decorators fáceis**, **orjson** e **msgspec**.  
Projetada para **FastAPI, Granian, ML/BRMS** e qualquer aplicação async Python.

---

## 🔹 Recursos principais

- Cache assíncrono com múltiplos backends:
  - Redis (com senha e TLS)
  - Memória local (L1)
  - Cache em camadas (LayeredCache: L1 + L2)
- Decorator `@cache_response` para funções async
- Serializadores rápidos:
  - `AutoSerializer` (orjson)
  - `MsgspecSerializer` (msgspec.Struct)
- Dogpile protection (evita múltiplas execuções pesadas)
- TTL com jitter (evita stampede)
- Métricas simples: hits / misses
- Suporta versionamento de chaves (`key_version`)
- Seguro: falhas no cache não quebram a aplicação

---

## 🔹 Instalação

```bash
pip install async_cache
```

## Exemplo
```python
import msgspec

from fastapi import FastAPI
import asyncio
import random

import uvicorn

class QuoteResult(msgspec.Struct):
    product_id: str
    price: float
    discount: float

from light_cache.backends import (
    MemoryCacheBackend,
    RedisCacheBackend,
    LayeredCacheBackend,
)
from light_cache.core.decorators import cache_response
from light_cache.core.serializers import MsgspecSerializer    

memory_cache = MemoryCacheBackend()
redis_cache = RedisCacheBackend(
    redis_url="redis://localhost:6379/0",
    prefix="fastapi-demo",
)

cache = LayeredCacheBackend(
    l1=memory_cache,
    l2=redis_cache,
    l1_ttl=2,
    )

    
app = FastAPI(title="Async Cache Demo")    

@app.get("/health")
@cache_response(
    cache=cache,
    key_prefix="health",
    ttl=60,
    use_lock=False,
)
async def health():
    return {
        "status": "ok",
        "value": random.random(),
    }
    
    
@app.get("/quote/{product_id}")
@cache_response(
    cache=cache,
    key_prefix="quote",
    ttl=60,
    serializer=MsgspecSerializer(),
)
async def calculate_quote(product_id: str):
    # simula cálculo pesado
    await asyncio.sleep(1)

    return QuoteResult(
        product_id=product_id,
        price=round(random.uniform(100, 500), 2),
        discount=round(random.uniform(0, 0.3), 2),
    )

    
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)
    
```
mcp-audit 0.9.2
DEPRECATED: This package has been renamed to token-audit.
Requires Python: >=3.10

# mcp-audit (DEPRECATED)

⚠️ **This package has been renamed to token-audit.**

## Migration

```bash
pip uninstall mcp-audit
pip install token-audit
```

See: https://github.com/littlebearapps/token-audit