semvec/pypi-packages huggingface dataset:
turtleshell 1.4.0
Convenience wrapper around turtle.Turtle and turtle.Screen
Requires Python: >=3.9

# TurtleShell

Convenience wrapper around Python turtle standard library.

## Development
1. Do all this in a virtual environment, e.g.:
    ```
    python3 -m venv venv
    source /venv/bin/activate
    pip install pytest bumpver build twine
    ```

2. Test:
    ```
    pytest
    ```

3. Push.

4. Update package version:
    ```
    bumpver update --minor
    ```

5. Push.

6. Build/check package:
    ```
    python -m build; twine check dist/*
    ```

7. Upload to Test PyPI, install, and test:
    ```
    twine upload -r testpypi dist/*
    pip install -i https://test.pypi.org/simple turtleshell
    ```

8. Upload to PyPI:
    ```
    twine upload dist/*
    ```
sparkautomapper.fhir 3.0.3
FHIR extensions for SparkAutoMapper
Requires Python: >=3.10

[![Build and Test](https://github.com/imranq2/SparkAutoMapper.FHIR/actions/workflows/build_and_test.yml/badge.svg)](https://github.com/imranq2/SparkAutoMapper.FHIR/actions/workflows/build_and_test.yml)

[![Upload Python Package](https://github.com/imranq2/SparkAutoMapper.FHIR/actions/workflows/python-publish.yml/badge.svg)](https://github.com/imranq2/SparkAutoMapper.FHIR/actions/workflows/python-publish.yml)

[![Known Vulnerabilities](https://snyk.io/test/github/imranq2/SparkAutoMapper.FHIR/badge.svg?targetFile=requirements.txt)](https://snyk.io/test/github/imranq2/SparkAutoMapper.FHIR?targetFile=requirements.txt)

# SparkAutoMapper.FHIR
Add custom FHIR data types to SparkAutoMapper.

## Usage
```shell script
pip install sparkautomapper.fhir
```

## Example
```python
from spark_auto_mapper.automappers.automapper import AutoMapper
from spark_auto_mapper.helpers.automapper_helpers import AutoMapperHelpers as A
mapper = AutoMapper(
    view="members",
    source_view="patients",
    keys=["member_id"]
).withResource(
    resource=F.patient(
        id_=A.column("a.member_id"),
        birthDate=A.date(
            A.column("date_of_birth")
        ),
        name=A.list(
            F.human_name(
                use="usual",
                family=A.column("last_name")
            )
        ),
        gender="female"
    )
)
```

# Publishing a new package
1. Create a new release
2. The GitHub Action should automatically kick in and publish the package
3. You can see the status in the Actions tab
dsd-platformsh 1.1.0
A plugin for django-simple-deploy, supporting deployments to Platform.sh.
Requires Python: >=3.9
Keywords: django, deployment

# dsd-platformsh

A plugin for deploying Django projects to Platform.sh, using django-simple-deploy.

For full documentation, see the documentation for [django-simple-deploy](https://django-simple-deploy.readthedocs.io/en/latest/).
aimanager-hjy 0.3.0
AI服务管理包 - 统一的AI模型调用接口
Requires Python: >=3.8

# aimanager_hjy

AI服务管理包 - 统一的AI模型调用接口

## 简介

`aimanager_hjy` 是一个统一的AI服务管理包，提供标准化的AI模型调用接口。它是 `ai_runner_hjy` 的升级版本，采用更规范的命名和更完善的功能。

## 主要功能

- **统一AI调用接口**: 通过 `run_route()` 方法调用各种AI服务
- **配置管理**: 支持多种配置方式（环境变量、配置文件、数据库）
- **文件上传**: 集成OSS存储，支持文件上传和管理
- **数据库连接**: 内置MySQL连接管理
- **错误处理**: 完善的错误处理和重试机制
- **日志记录**: 集成loguru日志系统

## 安装

```bash
pip install aimanager_hjy
```

## 快速开始

### 基本使用

```python
from aimanager_hjy import AIManager

# 初始化AI管理器
ai_manager = AIManager()

# 调用AI服务
result = ai_manager.run_route("dogvoice.analysis.s2", {
    "audio_url": "https://example.com/audio.mp3",
    "user_id": "user_123"
})

print(result)
```

### 配置管理

```python
from aimanager_hjy import get_config

# 获取配置
config = get_config()
print(config.database.host)
print(config.oss.bucket)
```

### 文件上传

```python
from aimanager_hjy import upload_file

# 上传文件到OSS
file_url = upload_file("audio.mp3", "audio/")
print(f"文件已上传到: {file_url}")
```

## 配置说明

### 环境变量配置

```bash
# 数据库配置
DB_HOST=localhost
DB_PORT=3306
DB_NAME=dogvoice
DB_USER=root
DB_PASSWORD=password

# OSS配置
OSS_ACCESS_KEY_ID=your_access_key
OSS_ACCESS_KEY_SECRET=your_secret_key
OSS_ENDPOINT=oss-cn-hangzhou.aliyuncs.com
OSS_BUCKET=your_bucket

# AI服务配置
AI_SERVICE_URL=http://localhost:8000
AI_SERVICE_TIMEOUT=30
```

### 配置文件

创建 `config.yaml` 文件：

```yaml
database:
  host: localhost
  port: 3306
  name: dogvoice
  user: root
  password: password

oss:
  access_key_id: your_access_key
  access_key_secret: your_secret_key
  endpoint: oss-cn-hangzhou.aliyuncs.com
  bucket: your_bucket

ai_service:
  url: http://localhost:8000
  timeout: 30
  retry_count: 3
```

## API参考

### AIManager类

#### `__init__(config_path: str = None)`
初始化AI管理器

#### `run_route(route_name: str, params: dict = None) -> dict`
调用AI服务路由

- `route_name`: 路由名称，如 "dogvoice.analysis.s2"
- `params`: 参数字典

#### `run_once(route_name: str, params: dict = None) -> dict`
执行一次性AI调用

#### `run(route_name: str, params: dict = None) -> dict`
执行AI调用（别名）

### 工具函数

#### `get_config() -> AppConfig`
获取应用配置

#### `get_db_connection() -> Connection`
获取数据库连接

#### `upload_file(file_path: str, prefix: str = "") -> str`
上传文件到OSS

#### `validate_required_fields(data: dict, required_fields: list) -> bool`
验证必需字段

## 迁移指南

### 从 ai_runner_hjy 迁移

```python
# 旧代码
from ai_runner_hjy import AIRunner, run_route

runner = AIRunner()
result = run_route("dogvoice.analysis.s2", params)

# 新代码
from aimanager_hjy import AIManager, run_route

manager = AIManager()
result = run_route("dogvoice.analysis.s2", params)
```

## 开发

### 安装开发依赖

```bash
pip install -e .[dev]
```

### 运行测试

```bash
pytest
```

### 代码格式化

```bash
black aimanager_hjy/
isort aimanager_hjy/
```

## 许可证

MIT License

## 更新日志

### v0.1.0
- 初始版本
- 基于 ai_runner_hjy 重构
- 统一命名规范
- 完善文档和示例
nonebot-plugin-imagelibrary 1.1.0
Create a shared Image Library

# _✨ ImageLibrary Bot图库 ✨_
### 一个共享给所有人的Bot图库
<img src="https://img.shields.io/badge/python-3.9+-blue.svg" alt="python">

## 安装方法
1. 通过nb-cli安装
```shell
nb plugin install nonebot_plugin_ImageLibrary
```

2. 通过pip安装
```shell
pip install nonebot_plugin_ImageLibrary
```
pip 安装后在 Nonebot2 入口文件(例如 bot.py )增加：

```python
nonebot.load_plugin("onebot_plugin_ImageLibrary")
```

## 使用方式
### `==所有人可用==`
* 添加[XXX]: 可以向图库中添加XXX关键词视频/图片/文字内容
> **添加咖波** --> 发送视频/图片/文字 --> 完成，保存到了咖波关键词下
* 来个/来只/来点[XXX]: 抽取图库中XXX关键词下的内容
> **来只咖波** --> 收到一张咖波关键词下的随机内容
* XXX后面接@[数字]可以选择词条下的指定内容
> **来只咖波@3** --> 收到在咖波关键词下序号为3的内容
* 插画[XXX]: 从网络引擎中获取XXX的随机高清图片
> **插画 伊蕾娜** --> 从网络上获取一张伊蕾娜的插画
### `==管理员可用==`

* @bot 启用/禁用XXX: 允许/禁用群内使用某一词条
* * 设定了禁用的词条不再可以从私聊获取
> **禁用咖波** --> 在群内禁用咖波关键词，并且私聊咖波关键词不再可用
* @bot 删除XXX: 删除某一词条的部分内容
* * 只删[数字]: 删除关键词下指定的内容
* * 彻底删除: 删除整个词条
> **删除咖波** --> 只删5/彻底删除 --> 从图库删除关键词下某一个序号或所有内容
* @bot 图片列表: 查看图库中的所有关键词
> **图片列表** --> 获取机器人下所有的关键词列表

### `==Bot主可用==`
* @bot 独占/取消独占: 让某个群聊独占/取消独占一个关键词，其他群和私聊不可用
> **独占咖波** --> 仅允许在此群内使用某一关键词

## 后台管理
所有图片和视频都会放在工作目录`/imageLibrary/Library`下保存，文字内容直接保存在`/imageLibrary/Library/image.json`中

所有关键词检索可以通过修改`/imageLibrary/Library/image.json`来手动管理

## 额外配置
你可以修改代码中的原始字段来决定相对工作目录的保存位置
```python
dataset = DataSetControl("./image_library/image.json")  # 决定关键词列表的保存路径
base = "./image_library/library/"  # 决定图片视频的存储路径
```
leap-data-management-utils 0.0.19
LEAP / pangeo-forge-recipes extension library for logging data in Google Big Query
Requires Python: >=3.9

# LEAP Data Management Utils

This Repo is intended to be installed via the `requirements.txt` during recipe execution in `pangeo-forge-recipes`. It currently contains beam transforms to log information into BigQuery tables. Additional shared utilities and checks that are commonly used between LEAP pangeo-forge-feedstocks can live here.