semvec/pypi-packages huggingface dataset:
zmq-shell-tools 0.1.0
ZeroMQ sockets for the command line
Requires Python: >=3.12
Keywords: zeromq, zmq, 0mq, message queue, decentral, sockets, cli, ci/cd, continuous integration, container, pipeline, pubsub, reqrep, network, architecture

# ZMQ Shell Tools

ZMQ sockets for the command-line, written in Python.
pygdsdesign 0.17.9
pygdsdesign provides some function to more efficiently create gds files.
Requires Python: >=3.7

# PyGDSdesign

PyGDSdesign is a Python library designed to generate GDS files with Python scripts. The library's is built upon code from GDSpy, a popular open-source toolkit for layout design and manipulation. Moreover, PyGDSdesign use the power of Clipper, to execute precise boolean operations.

## ⚙️ Installation

### 🐍 From pypi

The simplest way is to install PyGDSdesign from pypi. Ensure that you have the required dependencies by running
```bash
conda install numpy scipy tqdm typing_extensions
```
Then just run `pip install pygdsdesign`.

### ⚒️ From source

Since PyGDSdesign incorporates C++ code that requires compilation (Clipper), ensure that your development environment supports compilation.
For instance, you can use Microsoft Visual C++ for Windows.

To install pygdsdesign, simply run:
`pip install .`

(You can also add the `-e` flag if you want to install the library in editable mode.)

After installing the library, it is recommended to verify the installation to ensure that all the code functions correctly. To do this, use pytest and run the tests provided in the examples folder.

## 🖼️ Gallery

Example of what you can do with pygdsdesign
### Spiral resonator

![spiral resonator](examples/spiral_resonator.png "spiral resonator")

### CPW resonators

![cpw resonators](examples/cpw_resonators.png "cpw resonators")


## 🛠️ How to start

You can find many scripts in the `Examples/` folder showing different user cases.

## 📜 Licence

The library is shared under the Boost Software License.


## 🙏🏼 Acknowledgment

This library is authored by Étienne Dumur and incorporates significant code from GDSPY, a library written by Lucas H. Gabrielli. For boolean operations, pygdsdesign utilizes Clipper, a library developed by Angus Johnson. Furthermore, the library has been ported from Python 3.8 to 3.11 and beyond by Sacha Wos.
shuttlet_claude_opus 0.1.0
Claude Opus MCP Server - 基于 OpenAI 兼容 API 的 Claude 聊天服务
Requires Python: >=3.10

# Claude Opus MCP Server

基于 OpenAI 兼容 API 的 Claude Opus 聊天服务 MCP Server，支持对话、多轮对话、工具调用和图片分析功能。

## 功能特性

- **简单对话** (`chat`): 与 Claude 进行单轮对话
- **多轮对话** (`chat_with_history`): 支持上下文的多轮对话
- **工具调用** (`chat_with_tools`): 支持函数调用的对话
- **图片分析** (`analyze_image`): 分析图片内容，支持多图
- **图文混合对话** (`chat_with_images`): 带图片的对话
- **模型列表** (`list_models`): 列出所有可用模型及其特性

## 可用模型

| 模型名称 | 描述 | 视觉能力 |
|---------|------|---------|
| `claude-opus-4-20250514` | Claude Opus 4 - 最强大的模型，适合复杂推理 | ✅ |
| `claude-sonnet-4-20250514` | Claude Sonnet 4 - 平衡性能和速度 | ✅ |
| `claude-3-5-sonnet-20241022` | Claude 3.5 Sonnet - 高性能版本 | ✅ |
| `claude-3-5-haiku-20241022` | Claude 3.5 Haiku - 快速响应版本 | ✅ |

## 环境变量

| 变量名 | 必需 | 默认值 | 描述 |
|-------|------|--------|------|
| `CLAUDE_OPUS_API_KEY` | ✅ | - | API 密钥 |
| `CLAUDE_OPUS_API_BASE_URL` | ❌ | `https://api.lightai.io` | API 基础 URL |
| `CLAUDE_OPUS_DEFAULT_MODEL` | ❌ | `claude-opus-4-20250514` | 默认使用的模型 |

## Cursor MCP 配置

### uvx 运行（推荐）

在 Cursor 的 `~/.cursor/mcp.json` 中添加以下配置：

```json
{
  "mcpServers": {
    "claude-opus": {
      "command": "uvx",
      "args": [
        "shuttlet_claude_opus"
      ],
      "env": {
        "CLAUDE_OPUS_API_KEY": "your-api-key-here",
        "CLAUDE_OPUS_API_BASE_URL": "https://api.lightai.io",
        "CLAUDE_OPUS_DEFAULT_MODEL": "claude-opus-4-20250514"
      }
    }
  }
}
```

### 本地运行

```json
{
  "mcpServers": {
    "claude-opus": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/shuttlet_claude_opus",
        "run",
        "python",
        "-m",
        "shuttlet_claude_opus"
      ],
      "env": {
        "CLAUDE_OPUS_API_KEY": "your-api-key-here",
        "CLAUDE_OPUS_API_BASE_URL": "https://api.lightai.io"
      }
    }
  }
}
```

> ⚠️ 请将 `your-api-key-here` 替换为你的实际 API 密钥。

## 使用示例

配置完成后，在 Cursor 中可以使用以下工具：

### 简单对话

```
请帮我解释一下什么是机器学习
```

### 带系统提示的对话

```
使用 Claude Opus 作为一个 Python 专家来帮我优化代码
```

### 多轮对话

```
继续之前的对话，深入探讨这个话题
```

### 工具调用

```
使用 Claude 分析数据并调用计算函数
```

### 图片分析

```
分析这张图片: https://example.com/image.jpg
```

### 图文混合对话

```
这两张图片有什么区别？
图片1: https://example.com/image1.jpg
图片2: https://example.com/image2.jpg
```

### 查看可用模型

```
列出所有可用的 Claude 模型
```

## API 参数说明

### 通用参数

| 参数 | 类型 | 描述 |
|-----|------|------|
| `message` | string | 用户消息内容（必需） |
| `model` | string | 模型名称 |
| `system_prompt` | string | 系统提示词 |
| `temperature` | float | 采样温度 (0-2)，较高值输出更随机 |
| `max_tokens` | int | 最大生成令牌数 |
| `top_p` | float | 核采样参数 |

### 图片分析参数

| 参数 | 类型 | 描述 |
|-----|------|------|
| `image_url` | string | 图片 URL 地址（必需），多个 URL 用逗号分隔 |
| `prompt` | string | 针对图片的问题或指令 |
| `image_urls` | string | 图片 URL 列表，多个用逗号分隔 |

## 本地开发

```bash
# 安装依赖
uv sync

# 运行服务
uv run python -m shuttlet_claude_opus
```

## 许可证

MIT
shuttlet_sora2_mcp 0.1.0
Sora2 MCP Server - 基于 Sora2 视频模型的 MCP 服务器实现
Requires Python: >=3.13

# Sora2 MCP Server

基于 Sora2 视频模型的 MCP 服务器实现，支持视频生成功能。

## 功能特性

- **视频生成** (`generate_video`): 根据提示词生成视频
- **基于图片生成视频** (`generate_video_with_image`): 基于参考图片生成视频
- **故事板视频** (`generate_storyboard_video`): 根据故事板格式的提示词生成视频
- **角色客串视频** (`generate_video_with_character`): 生成带有角色客串的视频
- **模型列表** (`list_models`): 列出所有可用模型及其特性
- **任务查询** (`get_video_task`): 查询视频生成任务状态

## 可用模型

| 模型名称 | 描述 |
|---------|------|
| `sora-2` | 标准版，支持基本视频生成功能 |
| `sora-2-pro` | 专业版，支持高清和更长时长 (25秒) |

## 环境变量

| 变量名 | 必需 | 默认值 | 描述 |
|-------|------|--------|------|
| `SORA2_API_KEY` | ✅ | - | API 密钥 |
| `SORA2_API_BASE_URL` | ❌ | `https://api.lightai.io` | API 基础 URL |

## 安装

```bash
pip install shuttlet_sora2_mcp
```

或使用 uvx 直接运行（无需安装）：

```bash
uvx shuttlet_sora2_mcp
```

## Cursor MCP 配置

在 Cursor 的 `~/.cursor/mcp.json` 中添加以下配置：

```json
{
  "mcpServers": {
    "sora2": {
      "command": "uvx",
      "args": [
        "shuttlet_sora2_mcp"
      ],
      "env": {
        "SORA2_API_KEY": "your-api-key-here",
        "SORA2_API_BASE_URL": "https://api.lightai.io"
      }
    }
  }
}
```

> ⚠️ 请将 `your-api-key-here` 替换为你的实际 API 密钥。

## 使用示例

配置完成后，在 Cursor 中可以使用以下工具：

### 生成视频

```
生成一个可爱的小猫在花园里玩耍的视频，竖屏，5秒
```

### 基于图片生成视频

```
基于这张图片生成一个视频，展示图片中的场景动态效果
```

### 生成故事板视频

```
生成一个故事板视频：
Shot 1:
duration: 7.5sec
Scene: 飞机起飞

Shot 2:
duration: 7.5sec
Scene: 飞机降落
```

### 生成角色客串视频

```
生成一个视频：@{角色1Username} 在一个舞台上和 @{角色2Username} 牵手跳舞
```

### 查看可用模型

```
列出所有可用的 Sora2 模型
```

### 查询任务状态

```
查询视频生成任务状态，任务 ID: task_123456
```

## 许可证

MIT
lite-bootstrap 0.24.0
Lite package for bootstrapping new microservices
Requires Python: <4,>=3.10
Keywords: python, microservice, bootstrap, opentelemetry, sentry, error-tracing, fastapi, litestar, faststream, structlog

Lite-Bootstrap
==
[![Test Coverage](https://codecov.io/gh/modern-python/lite-bootstrap/branch/main/graph/badge.svg)](https://codecov.io/gh/modern-python/lite-bootstrap)
[![MyPy Strict](https://img.shields.io/badge/mypy-strict-blue)](https://mypy.readthedocs.io/en/stable/getting_started.html#strict-mode-and-configuration)
[![Supported versions](https://img.shields.io/pypi/pyversions/lite-bootstrap.svg)](https://pypi.python.org/pypi/lite-bootstrap)
[![downloads](https://img.shields.io/pypi/dm/lite-bootstrap.svg)](https://pypistats.org/packages/lite-bootstrap)
[![GitHub stars](https://img.shields.io/github/stars/modern-python/lite-bootstrap)](https://github.com/modern-python/lite-bootstrap/stargazers)

`lite-bootstrap` assists you in creating applications with all the necessary instruments already set up.

With `lite-bootstrap`, you receive an application with lightweight built-in support for:
- `sentry`
- `prometheus`
- `opentelemetry`
- `structlog`
- `cors`
- `swagger` - with additional offline version support
- `health-checks`

Those instruments can be bootstrapped for:

- [LiteStar](https://lite-bootstrap.readthedocs.io/integrations/litestar)
- [FastStream](https://lite-bootstrap.readthedocs.io/integrations/faststream)
- [FastAPI](https://lite-bootstrap.readthedocs.io/integrations/fastapi)
- [services and scripts without frameworks](https://lite-bootstrap.readthedocs.io/integrations/free)
---

Usage examples:

- with LiteStar - [litestar-sqlalchemy-template](https://github.com/modern-python/litestar-sqlalchemy-template)
- with FastAPI - [fastapi-sqlalchemy-template](https://github.com/modern-python/fastapi-sqlalchemy-template)

## 📚 [Documentation](https://lite-bootstrap.readthedocs.io)

## 📦 [PyPi](https://pypi.org/project/lite-bootstrap)

## 📝 [License](LICENSE)
ai-edge-torch-nightly 0.8.0.dev20260122
Supporting PyTorch models with the Google AI Edge TFLite runtime.
Requires Python: >=3.10
Keywords: On-Device ML, AI, Google, TFLite, PyTorch, LLMs, GenAI

Library that supports converting PyTorch models into a .tflite format, which can
then be run with TensorFlow Lite and MediaPipe.  This enables applications for
Android, iOS and IOT that can run models completely on-device.

[Install steps](https://github.com/google-ai-edge/ai-edge-torch#installation)
and additional details are in the AI Edge Torch
[GitHub repository](https://github.com/google-ai-edge/ai-edge-torch).