semvec/pypi-packages huggingface dataset:
fedcontinuum 0.0.2
FedContinuum: Federated Continual Learning Scenarios Management Tool
Requires Python: >=3.6
Keywords: distributed machine learning,federated learning,continual learning,federated continual learning

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# FedContinuum: Federated Continual Learning Scenarios Management Tool

[![PyPI version](https://badge.fury.io/py/fedcontinuum.svg)](https://badge.fury.io/py/fedcontinuum) [![Python](https://img.shields.io/badge/python-3.10-blue?style=round-square&logo=Python&color=3776AB)](https://www.python.org/) [![PyTorch](https://img.shields.io/badge/pytorch-latest-orange?style=round-square&logo=PyTorch&color=EE4C2C)](https://pytorch.org/)

</div>

## System Environments

### Python Dependent Packages

|      Packagename      | Version |     Note     |
|:---------------------:|:-------:|:------------:|
|        loguru         |  0.6.0  | == to latest |
|         tqdm          | 4.65.0  | == to latest |
|        pyyaml         |   6.0   | == to latest |
|         wandb         | 0.14.0  | == to latest |
|         torch         |  2.0.0  | == to latest |
|      torchvision      | 0.15.1  | == to latest |
py-whoare 0.1.1
A Python library for performing WHOIS queries on multiple domains.

# py-whoare

`py-whoare` is a Python library designed for performing WHOIS queries on multiple domains simultaneously. It provides an easy-to-use interface for fetching domain registration details, including registrar information, registration dates, and more, along with IP geolocation data.

## Features

-   WHOIS query execution for multiple domains.
-   IP address resolution for each domain.
-   Geolocation data retrieval for each IP address.
-   Asynchronous implementation for efficient handling of multiple requests.

## Installation

Install `py-whoare` using pip:

```bash
pip install py-whoare
```

## Usage

`py-whoare` can be used in different environments, supporting both native async environments (like Jupyter notebooks) and standard Python scripts. Here are two examples to illustrate its usage:

### Example 1: Using `asyncio.run` (Recommended for Python 3.7+)

This is the simplest way to run asynchronous code. It's suitable for scripts and environments that don't have a running event loop.

```python
import asyncio
from py-whoare import WhoAre

async def main():
    whoare_instance = WhoAre()
    results = await whoare_instance.handle_multiple_queries(["example.com", "example.org"])
    for result in results:
        # Process and print results as needed

# Run the async function
asyncio.run(main())
```

### Example 2: Manual Event Loop Management

In environments where `asyncio.run` is not suitable (like in some interactive environments or when integrating with applications that manage their own event loop), you can manually create and manage an event loop.

```python
import asyncio
from py-whoare import WhoAre

async def main():
    whoare_instance = WhoAre()
    return await whoare_instance.handle_multiple_queries(["example.com", "example.org"])

# Creating a new event loop and running the main coroutine
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
try:
    results = loop.run_until_complete(main())
    # Process and print results as needed
finally:
    loop.close()
```

## Contributing

Contributions to `py-whoare` are welcome! Please refer to the [Contributing Guidelines](CONTRIBUTING.md) for more information.

## License

`py-whoare` is available under the MIT License. See [LICENSE](LICENSE) for more details.

## Contact

For any questions or feedback, please contact me at engjellavdiu01@gmail.com.

## Acknowledgements

Thanks to all the contributors and users of `py-whoare`. Your support and feedback are greatly appreciated!
hash-tg 0.0.3
Telegram bot for performing various functions against hashes
Requires Python: >=3.12

# hash-tg

![pypi version](https://github.com/Pavel-Sushko/hash-tg/actions/workflows/python-publish.yml/badge.svg) ![python application](https://github.com/Pavel-Sushko/hash-tg/actions/workflows/python-app.yml/badge.svg) ![dependency review](https://github.com/Pavel-Sushko/hash-tg/actions/workflows/dependency-review.yml/badge.svg)

hash-tg is a Telegram bot that allows you to perform various operations agains obtained hashes.

## Installation

1. Install package from PyPI:

    ```bash
    pip install hash-tg
    ```

## Contributing

If you'd like to contribute to the project, please follow these steps:

1. Fork the repository.
2. Create a new branch for your changes: `git checkout -b my-new-feature`.
3. Make your changes and commit them: `git commit -am 'Add some feature'`.
4. Push to the branch: `git push origin my-new-feature`.
5. Submit a pull request.

## License

hash-tg is licensed under the [MIT License](https://opensource.org/licenses/MIT). Feel free to use it for personal or commercial use.
tar-mak-monaco-report 0.2.0
a package that print monaco race report
Requires Python: >=3.8

# tar_mak_monaco_report

This is a simple package that can be used to create a report on the drivers of Formula 1 - Monaco 2018 Racing.
Нhe package has a number of arguments that will help you get the information you need.

-f or --files FILE - an argument that requires specifying the path to the folder which contains the required files(abbreviations.txt, end.log, start.log)

--asc - an argument that follow after --files to specify ascending order.

--desc - an argument that follow after --files to specify descending order.

--driver "driver_name" - an argument that follow after --files to get information about specific raser.


Output of the package is ordered by places racers, their time and cars.

Example: report_mon -f " Folder_path " --desc

In this case In this case, the package will show full report in descending order. If remove --desc, order will be ascending(by default)

Example 2: report_mon --files " Folder_path " --driver "driver_name"

In this case, the package will show information about certain racer