semvec/pypi-packages huggingface dataset:
focused-unittest 0.1.1
Decorator to select only one unit test to be run.
Requires Python: >=3.6

# What is it for?

I use it when I need to debug only one test and I don't want to deal with command line parameters or modification of unittest.main().

# Installation

```bash
!pip install focused_unittest
```

# Usage

```
# test_example.py
import unittest
from focused_unittests import FocusedTestCase, focus

class TestExample(FocusedTestCase):

    @focus
    def test_add(self):
        self.assertEqual(2 + 3, 5)

    @focus
    def test_subtract(self):
        self.assertEqual(3 - 2, 1)

    def test_multiply(self):
        self.assertEqual(3 * 2, 6)

    def test_divide(self):
        self.assertEqual(6 / 2, 3)

if __name__ == '__main__':
    unittest.main()
```

Basically I change `unittest.TestCase` to `FocusedTestCase` and I add `@focus` decorator to the methods I need to debug. Now I can debug with just F5 in vscode.
rlate-env 0.0.17
A collection of RL environments
Requires Python: >=3.8

# RL Enviros

RL Enviros is a collection of custom environments compatible with Gymnasium, designed to simulate various games and
scenarios for reinforcement learning experiments. This project aims to provide a variety of environments to help
researchers and enthusiasts develop and test reinforcement learning algorithms.

## Table of Contents

- [Installation](#installation)
- [Environments](#environments)
    - [PickHigh](#pickhigh)
    - [AnotherEnv](#anotherenv)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)

## Installation

### PyPi
- Published on [PyPi](https://pypi.org/project/rlate-env/)
- You can install the `rlate-env` package directly from PyPi:

```bash
pip install rlate-env
```

## Environments

**Shuffler**
Arrange a shuffled list of numbers in ascending order by swapping with the number 5.

**PickHigh**
Choose the higher-valued card from two randomly drawn cards.

**PickLow**
Choose the lower-valued card from two randomly drawn cards.

**Cannon**
Hit a target at a random distance by adjusting the firing angle of a cannon.

**Traffic Light**
Decide to drive, slow down, or stop based on the current traffic light color.

**K-Bandit**
Find and exploit the arm with the highest expected reward in a multi-armed bandit setup.

## Usage

Here is an example of how to use the `Cannon` environment:

```python
import gymnasium_rlate as rlate

# Create the Canon environment
env = rlate.Cannon()

# Reset the environment
obs, info = env.reset()
print(obs)

# Make a step in the environment
obs, reward, terminated, truncated, _ = env.step(23.5)
print(obs, reward, terminated)

# Render the environment
print(env.render())

# Close the environment
env.close()
```

## Contributing

Contributions are welcome! If you have an environment you'd like to add or an improvement to suggest, please open an
issue or submit a pull request.

1. Fork the repository.
2. Create your feature branch (`git checkout -b feature/your-feature`).
3. Commit your changes (`git commit -m 'Add some feature'`).
4. Push to the branch (`git push origin feature/your-feature`).
5. Open a pull request.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

## Repository

GitHub Repository: [https://github.com/RLate-Space/RL-Enviros](https://github.com/RLate-Space/RL-Enviros)
Discord-Anti-Spam 1.9.1
An easy to use package for anti-spam features in python discord libraries.
Requires Python: >=3.8

# DPY Anti-Spam
---

[![Pytest](https://github.com/Skelmis/DPY-Anti-Spam/actions/workflows/pytest.yml/badge.svg?branch=master)](https://github.com/Skelmis/DPY-Anti-Spam/actions/workflows/pytest.yml)
[![Coverage Status](https://coveralls.io/repos/github/Skelmis/Discord-Anti-Spam/badge.svg?branch=master)](https://coveralls.io/github/Skelmis/Discord-Anti-Spam?branch=master)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![PyPI download month](https://img.shields.io/pypi/dm/Discord-Anti-Spam.svg)](https://pypi.python.org/pypi/Discord-Anti-Spam/)
[![Discord](https://img.shields.io/discord/780784732484141077.svg?label=&logo=discord&logoColor=ffffff&color=7389D8&labelColor=6A7EC2)](https://discord.gg/BqPNSH2jPg)

Ever felt the need to handle spammers in your discord, but don't have the time or effort to do so yourself? 
This package aims to help solve that issue by handling all the required logic under the hood, as well as handling the spammers.


---
How to use this right now?

Download the codebase:
```
> pip install Discord-Anti-Spam
```

A basic bot

```python
import discord
from discord.ext import commands
from antispam import AntiSpamHandler
from antispam.enums import Library

intents = discord.Intents.default()
bot = commands.Bot(command_prefix="!", intents=intents)
bot.handler = AntiSpamHandler(bot, library=Library.DPY)


@bot.event
async def on_ready():
    print(f"-----\nLogged in as: {bot.user.name} : {bot.user.id}\n-----")


@bot.event
async def on_message(message):
    await bot.handler.propagate(message)
    await bot.process_commands(message)


if __name__ == "__main__":
    bot.run("Bot Token")
```

And that's it!
Now, there will no doubt be bugs & changes etc. But, you can use this as is now and all I ask is you create an issue for anything you encounter while using this codebase.

#### Docs can be found [here](https://dpy-anti-spam.readthedocs.io/en/latest/?)

---

### Support

Want realtime help? Join the discord [here](https://discord.gg/BqPNSH2jPg).

---

### License
This project is licensed under the MIT license
JsonExtractor 0.0.2
A tool for extracting valid JSON from LLM responses
Requires Python: >=3.6

[![PyPI version](https://badge.fury.io/py/jsonextractor.svg)](https://badge.fury.io/py/jsonextractor)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://static.pepy.tech/badge/jsonextractor)](https://pepy.tech/project/jsonextractor)

# JsonExtractor

`JsonExtractor` is a Python utility designed to extract valid JSON objects from strings. This is particularly useful for processing outputs from various sources where JSON objects may be embedded within larger text blobs.

## Installation

To install `JsonExtractor`, you can use pip:

```bash
pip install jsonextractor
```

## Usage

After installation, `JsonExtractor` can be imported and used in your Python projects to extract JSON objects from strings.

Example:

```python
from json_extractor import JsonExtractor

# Some input string that contains a JSON object
input_string = 'Some text before JSON {"key": "value"} some text after JSON.'

# Extract the valid JSON object
valid_json = JsonExtractor.extract_valid_json(input_string)

if valid_json is not None:
    print("Extracted JSON:", valid_json)
else:
    print("No valid JSON found.")
```

## Features

- Extracts the first valid JSON object found in a given string.
- Handles various formats, including JSON within markdown code blocks.

## Contributing

Contributions, issues, and feature requests are welcome! Feel free to check [issues page](https://github.com/chigwell/jsonextractor/issues).

## License

[MIT](https://choosealicense.com/licenses/mit/)