semvec/pypi-packages huggingface dataset:
bib-watch 1.0.2
A LaTeX bibliography manager that automatically organizes references
Requires Python: >=3.6

# Bibliography Manager

A real-time LaTeX bibliography management tool that automatically organizes and updates your references.

## Features

- Real-time monitoring of .tex file citation changes
- Automatic .bib file organization
- Citation context tracking
- Citation statistics
- Maintains citation order
- Multiple citation commands support

## Prerequisites

- Python 3.x

### Required packages:

- `bibtexparser`
- `watchdog`

## Output Format

The program organizes `references.bib` into:

### Header Information

- Last update time
- Reference counts
- File statistics

### Cited References Section

- Sorted by first appearance
- Citation frequency
- Context information
- Line numbers

### Uncited References Section

- Preserved for future use
- Original entry format

## Usage

### Processing Files in the Current Directory

To process files in the current directory, use:

```bash
bibmanager
```

### Watch Mode

To enable watch mode, which automatically updates the bibliography as changes occur, use:

```bash
bibmanager -w
```

### Specify Directory

To specify a directory for managing LaTeX projects, use:

```bash
bibmanager -d /path/to/latex/project
```

## Future Plans

### Short Term

- DOI lookup and metadata completion
- PDF file management
- Reference format validation

## Common Issues

### Python Not Found

- Ensure Python is installed and in PATH
- Restart terminal after installation

### Package Installation Fails

- Try running with administrator privileges
- Check internet connection
- Update pip: `python -m pip install --upgrade pip`

### File Encoding Issues

- Ensure .tex files are UTF-8 encoded
- Check for special characters

## Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Submit a pull request

## Support

For support:

- Open an issue
- Provide error messages
- Include minimal example

## License

This software is protected by copyright law. All rights reserved.

No part of this software may be reproduced, modified, distributed, or republished without prior written permission from the author.

Commercial use and redistribution are strictly prohibited.

Made with ❤️ for LaTeX users
watch-lite 0.1.1
A simple, lightweight and performant file watcher for python built with rust
Requires Python: >=3.8

## Setup

To install this just run:

    pip install watch-lite

## Usage

```python
import watch_lite

fileHash = watch_lite.getHash("filename.txt")

while True:
    if watch_lite.compareHash(lastHash):
        fileHash = watch_lite.getHash("filename.txt")
    
```
nyx-extras 0.2.5
Nyx Client SDK Extras provides a powerful toolkit for building generative AI applications using data brokered on the Nyx platform.
Requires Python: <4.0,>=3.10
Keywords: client, data, sdk, ai, exchange, nyx, rag, gen-ai, iotics

<div align="center">

# Nyx Extras SDK

[![Version](https://img.shields.io/pypi/v/nyx-extras)](https://pypi.org/project/nyx-extras)
[![License](https://img.shields.io/badge/License-Apache%202.0-yellow.svg)](https://github.com/Iotic-Labs/nyx-sdk/blob/main/LICENSE)
[![Build](https://github.com/Iotic-Labs/nyx-sdk/actions/workflows/build.yaml/badge.svg?branch=main)](https://github.com/Iotic-Labs/nyx-sdk/actions/workflows/build.yaml)
[![Read The Docs](https://readthedocs.org/projects/nyx-extras/badge/?version=stable)](https://nyx-extras.readthedocs.io/en/stable)
[![GitHub Repo stars](https://img.shields.io/github/stars/Iotic-Labs/nyx-sdk)](https://github.com/Iotic-Labs/nyx-sdk)
[![Discord](https://img.shields.io/discord/1285252646554304555)](https://discord.gg/zS8pVHjqSf)


🌟 **Nyx Extras SDK** provides a powerful toolkit for building generative AI applications using data brokered on the Nyx platform.

It enables decentralized data transfer, offering additional context (via a RAG setup) to language models within the trusted IOTICS network.


[![https://iotics.com](https://img.shields.io/badge/Powered%20by-Iotics-blue)](https://iotics.com)
</div>

## 🚧 Status

The Nyx ecosystem is at an early stage of its development, please give us feedback through the [Github issues](https://github.com/Iotic-Labs/nyx-sdk/issues).

# 🔥 Quick Start

## Installation

The Nyx Extras SDK is available on [PyPI](https://pypi.org/project/nyx-extras/) and can be installed via `pip` running the following command.

`pip install nyx-extras[langchain-openai]`

this installs langchain, and openai client (You can always bring your own LLM provider, any of the [LangChain chat models](https://python.langchain.com/docs/integrations/chat/) can be installed and passed in!

## First time set up

See [First time set up](https://github.com/Iotic-Labs/nyx-sdk/blob/main/nyx_client/README.md#first-time-set-up)
torch-incremental-pca 0.0.14
A PyTorch implementation of Incremental PCA
Requires Python: >=3.8

# PyTorch Incremental PCA

[![PyPI Version](https://img.shields.io/pypi/v/torch-incremental-pca.svg)](https://pypi.org/project/torch-incremental-pca/)
[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)

This project provides a PyTorch implementation of the Incremental PCA algorithm, inspired by the `IncrementalPCA` class from scikit-learn and the repository [PCAonGPU](https://github.com/dnhkng/PCAonGPU/tree/main). This implementation has some shortcomings with regards to the precision of operations leading to vastly different results compared to the sklearn implemtation. The IncrementalPCA class in this repo produces outputs which are very close to the sklearn implementation with the added benefit of running on GPU.

Incremental PCA is a valuable technique for dimensionality reduction when dealing with large datasets that cannot fit entirely into memory.

## Features

* **PyTorch Integration:** Seamlessly use incremental PCA within your PyTorch workflows.
* **Memory Efficiency:**  Process large datasets incrementally without loading everything into memory at once.
* **Similar API:**  Familiar interface if you've used scikit-learn's `IncrementalPCA`.
* **Customization:** Easily extend or modify the core functionality to suit your specific needs.

## Installation

```bash
pip install torch-incremental-pca
```

## Usage
```python
import torch_incremental_pca as tip

pca = tip.IncrementalPCA(n_components=32)
```