semvec/pypi-packages huggingface dataset:
nats-kv-lock 0.0.1
Distributed lock using NATS.io
Requires Python: >=3.11

# nats-kv-lock
A simple, distributed lock using NATS.io

### Note!

- This is a simple PoC and perhaps shouldn't be used in a production environment
- This does NOT guarantee order, i.e. it is an unfair lock.

## Installation

Assuming you already have NATS installed, you can simply `pip install nats-kv-lock`


## Example

All you need to do is create a KV bucket, then initialize an instance of `NatsKvLock` using that bucket with a shared `lock_name`.

```python
import asyncio

from nats import connect
from nats_kv_lock import NatsKvLock

async def main():
    print('connecting...')
    nc = await connect()
    print('connected!')
    
    js = nc.jetstream()
    try:
        kv = await js.key_value(bucket='my_locks')
    except:
        kv = await js.create_key_value(bucket='my_locks')
    
    my_lock = NatsKvLock(kv, 'my_lock')

    print('acquiring lock...')
    
    async with my_lock:
        print('acquired! doing work...')
        await asyncio.sleep(10)
        print('releasing lock...')

    print('released lock!')

if __name__ == '__main__':
    asyncio.run(main())
```
pipeek 0.1.1
A search tool for compressed and uncompressed files.
Requires Python: >=3.8

# 🧪 Pipeek

**Pipeek** is a **fast, stream-based CLI tool** for searching digit or byte patterns inside **large text files** or **compressed archives** — designed with performance and flexibility in mind.

> ⚠️ **Note**
> This project is a work in progress. Some features are still under development. Bug reports and contributions are welcome!

---

## 📦 Installation

> **Requirements**: Python **3.8+**

Install from [PyPI](https://pypi.org/project/pipeek):

```bash
pip install pipeek
```

To install from source (for development):

```bash
git clone https://github.com/danilopatrial/pipeek.git
cd pipeek
pip install -e .
```

---

## 🚀 Quick Start

See the available commands and options:

```bash
pipeek --help
```

Search for a pattern:

```bash
pipeek needle 42 ./data/
```

You can use it on single files, multiple files, or entire folders — including compressed formats like `.gz`, `.bz2`, `.xz`, and `.lzma`.

---

## ⚙️ Configuration

Pipeek uses a configuration file stored in your system’s config directory:

* **Linux/macOS**: `~/.config/pipeek/config.json`
* **Windows**: `%APPDATA%\pipeek\config.json`

To open the config file in your editor (default is `nvim`):

```bash
pipeek conf
```

Specify a different editor:

```bash
pipeek conf -e code
```

Restore default settings:

```bash
pipeek conf --restore
```

---

## 💡 Features

* 🔍 Stream-based search: optimized for **low memory usage**
* 🗜️ Supports compressed files: `.gz`, `.bz2`, `.xz`, `.lzma`
* 🧠 Configurable buffer size, max matches, and context window
* 📁 Accepts multiple files and directories
* 📋 Saves search logs to `pipeek.log` in the config directory
* 🖍️ Optional colored output (via `colorama`)
* 🧪 Easily extensible and built with readability in mind

---

## 📚 Command Overview

All commands support `--help` for usage instructions:

```bash
pipeek conf --help
pipeek needle --help
```

### 🔎 Search for a Pattern

```bash
pipeek needle 31415 data.txt
```

Search through a directory recursively:

```bash
pipeek needle 42 ./data/
```

Force reading as gzip:

```bash
pipeek needle 2718 compressed.gz --force-gzip
```

---

[MIT License](LICENSE) © 2025 [Danilo Patrial](https://github.com/danilopatrial)
nunnpyy 1.0.0
A collection of machine learning algorithms implementations
Requires Python: >=3.6
Keywords: machine learning, algorithms, data science, AI, ML

"""
# ML Algorithms Library

A comprehensive collection of machine learning algorithm implementations.

## Installation

```bash
pip install ml_algorithms
```

## Usage

```python
from ml_algorithms import run
run()
```

This will display all the machine learning algorithm implementations including:
- Candidate Elimination
- ID3 Decision Tree
- Naive Bayes
- Backpropagation Neural Network
- EM Algorithm
- K-Nearest Neighbors
- Locally Weighted Regression
- Random Forest
- Support Vector Machine

## Requirements

- Python 3.6+
- NumPy
- Pandas
- Scikit-learn
- Matplotlib
"""