semvec/pypi-packages huggingface dataset:
virtual-landmark 0.1.2
A Python package for defining custom landmarks in Mediapipe making easy to calculate some new virtual points with a single extention class
Requires Python: >=3.10
Keywords: mediapipe, custom landmarks, ladmarks, pose estimation, computer vision, biomechanics, motion tracking


# Virtual Landmark 

**virtual-landmark** is a modular and extensible system for working with custom landmarks on top of [MediaPipe Pose](https://google.github.io/mediapipe/solutions/pose.html). It enables developers to define additional pose points through geometric relationships, access them like native MediaPipe landmarks, and extend pose rendering and analysis.


The complete documentation can be found on the [documentation page](https://cvpose.github.io/virtual_landmark_python/)

---

## Features

- 🧩 Create virtual landmarks with `@landmark` decorator
- 📐 Measure 2D and 3D joint angles
- 🎨 Extend pose drawings with new connections and styles
- 📚 Seamlessly integrate with MediaPipe’s landmark list
- 🧠 Named access to all landmarks via enum-like interface

---

## How It Works

The system works in three stages:

### 1. **Definition**
Virtual landmarks are declared using `@landmark("NAME", connection=[...])` decorators on methods that return a 3D point.

### 2. **Discovery**
Upon instantiating `VirtualLandmark`, all decorated methods are scanned, executed, and their results added to the landmark list.

### 3. **Access**
Virtual landmarks behave like native landmarks:
- Access index via `.NAME.value`
- Retrieve coordinates via landmark list
- Draw using standard MediaPipe rendering tools

---

## Getting Started

Install MediaPipe:

```bash
pip install mediapipe opencv-python virtual-landmark
```

Clone or copy this repo, then run the examples:

```bash
python examples/get_started.py
python examples/hello_world.py
python examples/video.py
python examples/web_cam.py
```

There are also a [Jupyter](https://jupyter.org) notebook that you can run through the follow command:

```bash
pip install jupyterlab matplotlib
jupyter lab examples
```

---
daak 0.1.2
Asynchronous subprocess runner
Requires Python: >=3.9

# daak

In the Tausug language, daak means to command or order.  daak is a small pure python library that can execute a 
subprocess and grab the stdout and stderr asynchronously.

It has limited support for handling input, mostly for when a sudo password is expected.

## Installation

Through pip

```bash
pip install daak
```

## Usage

The main class to use is called `Run`

```python
import asyncio

from daak.process import Run

def which(prog: str):
    return Run("which", args=[prog]).run(throw=False) 

async def main():
    _, process = await which("poetry")
    print(process.returncode)

if __name__ == "__main__":
    with asyncio.Runner() as runner:
        runner.run(main())
```
pycspemcpserver 0.1.0
Add your description here
Requires Python: >=3.13

# Your MCP Server

这是一个简单的 MCP Server demo, 可以计算两个数的和

## 安装

```bash
pip install pycspemcpserver
```

## 使用

通过 uvx 运行：

```
uvx pycspemcpserver
```
speedy-llm-utils 1.1.10
Utility functions for LLM applications
Requires Python: <4.0,>=3.9

# LLM Utils

A Python library providing utility functions for working with Large Language Models (LLMs).

## Overview

LLM Utils is a collection of utility functions and classes designed to simplify working with language models, particularly for chat applications, message formatting, and model interactions. It provides tools for transforming message formats, managing chat sessions, grouping messages by length, and interacting with various LLM providers.

## Features

- **Message Formatting**: Transform between different chat message formats (ChatML, ShareGPT)
- **Chat Sessions**: Manage conversation history and interactions with LLMs
- **Message Grouping**: Efficiently batch messages based on length for optimal processing
- **Model Integration**: Interface with various LLM backends including OpenAI models
- **Visualization**: Display chat messages and conversations as HTML in Jupyter notebooks
- **Command Line Tools**: VLLM serving and load balancing utilities

## Installation

```bash
# Install from PyPI
pip install speedy_llm_utils

# Install from source
pip install -e .

# Or using Poetry
poetry install
```

Note: While the package is named `speedy_llm_utils` on PyPI, you still import it as `llm_utils` in your code:

```python
import llm_utils
```

## Dependencies

- Python ≥ 3.9
- numpy
- pandas
- google-generativeai
- transformers
- dspy
- langchain
- langchain-core
- langchain-openai
- json-repair

## Usage Examples

### Transforming Message Formats

```python
from llm_utils import transform_messages, transform_messages_to_chatml

# Transform messages between formats
chatml_messages = transform_messages_to_chatml(messages)
```

### Working with Chat Sessions

```python
from llm_utils import OAI_LM

# Initialize LLM instance
lm = OAI_LM(model_name="gpt-3.5-turbo")

# Create a chat session
session = lm.create_chat_session(
    system_prompt="You are a helpful assistant."
)

# Generate a response
response = session.send_message("Hello, how can you help me today?")
print(response)
```

### Message Grouping

```python
from llm_utils import split_indices_by_length, group_messages_by_len

# Group messages by length for efficient processing
batches = split_indices_by_length(
    lengths=[len(msg) for msg in messages],
    batch_size_by_mean_length=1000,
    random_seed=42,
    verbose=True,
    shuffle=True
)
```

### Visualizing Conversations

```python
from llm_utils import display_chat_messages_as_html, display_conversations

# Display conversations in a Jupyter notebook
display_conversations(conversations)
```

## Command Line Tools

LLM Utils provides command-line utilities:

- `svllm`: Start a VLLM server
- `svllm-lb`: Run a VLLM load balancer

```bash
# Start a VLLM server
svllm --model <model_name> --port <port>

# Run a load balancer
svllm-lb --servers <server1,server2> --port <port>
```

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

[Insert your license information here]

## Author

- Anh Vo
llm-plugin-pdf 0.1
LLM plugin for loading text from PDF files (local or URL)
Requires Python: >=3.9

# llm-plugin-pdf

[![PyPI](https://img.shields.io/pypi/v/llm-plugin-pdf.svg)](https://pypi.org/project/llm-plugin-pdf/)
[![Changelog](https://img.shields.io/github/v/release/agustif/llm-pdf?include_prereleases&label=changelog)](https://github.com/agustif/llm-pdf/releases)
[![Tests](https://github.com/agustif/llm-pdf/actions/workflows/test.yml/badge.svg)](https://github.com/agustif/llm-pdf/actions/workflows/test.yml)
[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/agustif/llm-pdf/blob/main/LICENSE)

LLM plugin for loading text from PDF files (local or URL) using PyMuPDF.

## Installation

Install this plugin in the same environment as [LLM](https://llm.datasette.io/).

```bash
llm install llm-plugin-pdf
```

This plugin requires the `PyMuPDF` and `requests` packages.

## Usage

This plugin adds support for the `pdf:` fragment prefix. You can use it to load the full text content of a PDF from a local file path or a web URL.

```bash
# Load from a local file
llm fragment pdf:/path/to/your/document.pdf

# Load from a URL
llm fragment pdf:https://example.com/some-document.pdf
```

You can pipe the output to LLM commands:

```bash
llm fragment pdf:https://arxiv.org/pdf/2310.06825.pdf | llm -s "Summarize this PDF"
```

## Development

To set up this plugin locally, first checkout the code. Then create a new virtual environment:
```bash
cd llm-pdf
python -m venv venv
source venv/bin/activate
```
Now install the dependencies and test dependencies:
```bash
# Install in editable mode with test dependencies
python -m pip install -e '.[test]'
```
To run the tests:
```bash
pytest
```
aocs-lab 0.0.11
姿轨控工具箱
Requires Python: >=3.8

# 姿轨控工具箱

## 使用说明

安装 (使用清华源)

`pip install -i https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple aocs-lab`

升级到最新版

`pip install -i https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple --upgrade aocs-lab`

使用示例

```py
import aocs_lab.sun_time as st
import numpy as np

if __name__ == "__main__":
    # 太阳矢量与轨道面法线夹角 31 deg
    beta_angle = np.deg2rad(31)
    st.sun_time(beta_angle, 6900e3)
```

## 功能简介

当前功能

1. 重力梯度力矩分析
2. 阳照区时间分析
3. 星敏精测数据处理
4. 飞轮惯滑时间计算
5. IGRF 磁场计算
6. influxdb 数据库读取
7. 星敏和SAR天线精测数据处理
8. 磁棒和磁强计联测分析
9. 飞轮安装配置计算
10. 姿态分析：自定义对日姿态，能源效率分析，机动能力分析，GNSS视场分析，星敏视场分析