semvec/pypi-packages huggingface dataset:
pylinkwave 3.4.1
A robust and powerful, fully asynchronous Lavalink wrapper built for py-cord in Python.
Requires Python: >=3.10

<div align="center">


![Logo](https://raw.githubusercontent.com/PythonistaGuild/pylinkwave/master/logo.png)

![Python Version](https://img.shields.io/pypi/pyversions/pylinkwave)
[![PyPI - Version](https://img.shields.io/pypi/v/pylinkwave)](https://pypi.org/project/pylinkwave/)
[![Github License](https://img.shields.io/github/license/PythonistaGuild/pylinkwave)](LICENSE)
[![Lavalink Version](https://img.shields.io/badge/Lavalink-v4.0%2B-blue?color=%23FB7713)](https://lavalink.dev)
![Lavalink Plugins](https://img.shields.io/badge/Lavalink_Plugins-Native_Support-blue?color=%2373D673)


</div>


pylinkwave is a robust and powerful Lavalink wrapper for [Discord.py](https://github.com/Rapptz/discord.py)
pylinkwave features a fully asynchronous API that's intuitive and easy to use.


# Migrating from Version 2 to Version 3:

[Migrating Guide](https://pylinkwave.dev/en/latest/migrating.html)


### Features

- Full asynchronous design.
- Lavalink v4+ Supported with REST API.
- discord.py v2.0.0+ Support.
- Advanced AutoPlay and track recommendations for continuous play.
- Object orientated design with stateful objects and payloads.
- Fully annotated and complies with Pyright strict typing.


## Getting Started

**See Examples:** [Examples](https://github.com/PythonistaGuild/pylinkwave/tree/main/examples)

**Lavalink:** [GitHub](https://github.com/lavalink-devs/Lavalink/releases), [Webpage](https://lavalink.dev)


## Documentation

[Official Documentation](https://pylinkwave.dev/en/latest)

## Support

For support using pylinkwave, please join the official [Support Server](https://discord.gg/RAKc3HF) on
[Discord](https://discordapp.com)

[![Discord Banner](https://discordapp.com/api/guilds/490948346773635102/widget.png?style=banner2)](https://discord.gg/RAKc3HF)


## Installation

**pylinkwave 3 requires Python 3.10+**

**Windows**


```sh
py -3.10 -m pip install -U pylinkwave
```

**Linux**

```sh
python3.10 -m pip install -U pylinkwave
```

**Virtual Environments**

```sh
pip install -U pylinkwave
```


## Lavalink

pylinkwave **3** requires **Lavalink v4**.
See: [Lavalink](https://github.com/lavalink-devs/Lavalink/releases)

For spotify support, simply install and use [LavaSrc](https://github.com/topi314/LavaSrc) with your `pylinkwave.Playable`


### Notes

- pylinkwave **3** is compatible with Lavalink **v4+**.
- pylinkwave has built in support for Lavalink Plugins including LavaSrc and SponsorBlock.
- pylinkwave is fully typed in compliance with Pyright Strict, though some nuances remain between discord.py and pylinkwave.
streamlit-cookie-banner 0.1.0
A custom Streamlit component to display a basic cookie banner
Requires Python: >=3.6

# Streamlit Cookie Banner Component

A custom Streamlit component to display a basic cookie banner on your Streamlit web application. This component allows you to display a cookie consent message with customizable text and an optional link to a privacy policy or other relevant information.

## Installation

You can install this component by adding it to your project and running the following command after packaging it:

    pip install streamlit-cookie-banner

## Usage
To use the cookie banner in your Streamlit app, you simply import the cookie_banner function from the component and use it within your Streamlit app like this:

    import streamlit as st
    from streamlit_cookie_banner import cookie_banner

    consent = cookie_banner(
        banner_text="We use cookies to ensure you get the best experience.",
        display=True,  # Set to False if you want to hide the banner
        link_text="Learn more",  # Optional: Link text (e.g., 'Learn more')
        link_url="https://your-privacy-policy-url.com",  # Optional: URL to your privacy policy
        key="cookie_banner"  # Optional: A unique key to control re-rendering
    )

    
## Use the consent

    import streamlit as st
    from streamlit_cookie_banner import cookie_banner
    
    consent = cookie_banner(
        banner_text="This website uses cookies to improve your experience.",
        display=True,
        link_text="Read our Privacy Policy",
        link_url="https://example.com/privacy-policy",
        key="cookie_banner"
    )
    
    if consent:
        st.write("Thank you for accepting cookies!")
    else:
        st.write("Please consider accepting cookies to improve your experience.")

Consent is returned as None if user hasn't selected Agree/Disagree, True for Agree & False for Disagree. After selection the value is stored in local session cookies to avoid showing the cookie bar again.
    
## Frontend Assets
The component uses frontend files located in the frontend/public directory. These files are responsible for rendering the banner in the browser. You can edit these to control the styling etc of the banner.

## License
This project is licensed under the MIT License. See the LICENSE file for more details.

## Contributing
Contributions are welcome! Feel free to open an issue or submit a pull request.

## Author
Luke Bowes
image-processing-at 0.0.1
A package to process images
Requires Python: >=3.6

# image_processing_at

Description.
The package image_processing_at is used to:
Processing: - Histogram matching - Structural similarity - Resize image
Utils: - Read image - Save image - Plot image - Plot result - Plot histogram

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name

```bash
pip install image_processing_at
```

## Author

Arthur Ticianeli

## License

[MIT](https://choosealicense.com/licenses/mit/)
puffit 1.0.5
puffit generates directory structure from text templates
Requires Python: <=3.12,>=3.9

# PuffIt

I've always wanted to take an example directory structure for a project structure and pipe that into an application to make it happen. So that's what this is for.

For example I would like to take this:

```
root/
├── tasks/
│   ├── __init__.py
│   ├── task_a.py
│   └── task_b.py
├── core/
│   ├── app.py
│   ├── celery_worker.py
│   └── config.py
├── Dockerfile
├── docker-compose.yml
├── requirements.txt
└── README.md
```

And have it turn into this:

![alt text](/docs/imgs/image.png)


### GitHub Actions Status
![Coverage](https://github.com/thechainercygnus/puffit/actions/workflows/coverage.yml/badge.svg) ![Format](https://github.com/thechainercygnus/puffit/actions/workflows/lint_and_format.yml/badge.svg) ![Publish](https://github.com/thechainercygnus/puffit/actions/workflows/publish.yml/badge.svg) ![Security](https://github.com/thechainercygnus/puffit/actions/workflows/security.yml/badge.svg) ![Version](https://github.com/thechainercygnus/puffit/actions/workflows/version.yml/badge.svg)

### Test Status
![Tests](https://github.com/thechainercygnus/puffit/actions/workflows/tests.yml/badge.svg) ![coverage](./docs/badges/coverage.svg)

## Features

Supports a variety of layout syntaxes.

## Installation

pipx install puffit

## Usage

Create your `template.pft` file containing the textual layout for the project. Then just run `puffit template.pft` where you'd like to create your new project directory. Check out `templates/` for example `.pft` files.

## Contributing

Coming Soon

## License

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

## Roadmap

* Integreate CI/CD release automation
* Improve error handling
* Improve documentation
* Implement logging
cybersentry 1.4.1
Advanced Cybersecurity Intelligence Tool
Requires Python: >=3.6

# CyberSentry

CyberSentry is an Advanced Cybersecurity Intelligence Tool designed for comprehensive domain analysis and threat assessment.

## Installation

1. Ensure you have Python 3.6 or later installed.

2. It's recommended to use a virtual environment:

```bash
python -m venv cybersentry-env
source cybersentry-env/bin/activate  # On Windows use `cybersentry-env\Scripts\activate`
```

3. Install CyberSentry:

```bash
pip install cybersentry
```

If you're installing from the source:

```bash
git clone https://github.com/Lxsanto/cybersentry.git
cd cybersentry
pip install -e .
```

This will install CyberSentry and all its dependencies.

## Usage

```bash
cybersentry example.com
```

## Key Features

- Intelligent Subdomain Discovery
- Deep Web Crawling and Analysis
- Sensitive Directory and File Detection
- Comprehensive DNS Record Analysis
- Advanced IP Intelligence Gathering
- Strategic Port Vulnerability Scanning
- In-depth SSL Certificate Verification
- Automated Email and Phone Number Extraction
- Progress Bar for Real-time Analysis Updates
- JSON and CSV Output

## Configuration

Some features require API keys. Create a `.env` file in the project root with the following content:

```
BEVIGIL_API_KEY=your_bevigil_api_key
VIRUSTOTAL_API_KEY=your_virustotal_api_key
# Add other API keys as needed
```

## Security Notice

CyberSentry is a powerful tool intended for authorized security professionals and ethical hackers. Always obtain explicit permission before analyzing any domain or network you do not own or have the right to test.

## License

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

## Contributing

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

## Troubleshooting

If you encounter any issues with missing dependencies, try updating pip and reinstalling:

```bash
pip install --upgrade pip
pip install --upgrade cybersentry
```

If problems persist, please open an issue on the GitHub repository.

## Authors

* **Luca Lorenzi** - *Initial work* - [Lxsanto](https://github.com/Lxsanto)

## Acknowledgments

* Hat tip to anyone whose code was used
* Inspiration
* etc
inspiring-quotes 1.5.1
A simple Python package for retrieving random inspirational quotes.
Requires Python: >=3.6
Keywords: quotes inspirational motivational wisdom positivity self-improvement

# inspiring_quotes

A simple Python package that provides random inspirational quotes. With a collection of 300 quotes, this library is perfect for adding a touch of motivation to your projects.

## Features

- Retrieve a random inspirational quote from a JSON file.
- Simple and easy to retrieve quote and author
- Over 300 inspiring quotes available

## Import the Library

```python
from inspiring_quotes import get_random_quote
```

## Get a Random Quote

```python
quote = get_random_quote()
if quote:
    print(f"Quote: {quote['quote']}\nAuthor: {quote['author']}")
else:
    print("No quotes available.")
```
plz-to-bundesland 0.1.2
A package to convert German postal codes (PLZ) to their corresponding Bundesland
Requires Python: >=3.6

# PLZ to Bundesland Converter

This Python package converts German postal codes (PLZ, Postleitzahl) to their corresponding Bundesland (federal state). It can process individual PLZ inputs or bulk process PLZ data from Excel files.

## Installation

You can install the PLZ to Bundesland Converter using pip:

```
pip install plz-to-bundesland
```

## Usage

### As a Python Module

You can use the `get_bundesland` function in your Python scripts:

```python
from plz_to_bundesland import get_bundesland

bundesland = get_bundesland("10115")
print(bundesland)  # Output: Berlin
```

### Command-line Interface

The package also provides a command-line interface:

1. To convert a single PLZ:
   ```
   plz-to-bundesland -p 10115
   ```

2. To process an Excel file:
   ```
   plz-to-bundesland -f input.xlsx -o output.xlsx
   ```

## Input Excel File Format

The input Excel file should have the following format:

- The file must be in .xlsx format.
- It must contain a column named 'PLZ' (case-sensitive).
- The 'PLZ' column should contain the postal codes to be converted.
- Each PLZ should be in a separate row.

## Output

When processing an Excel file, the script will create a new Excel file with the following columns:
- All original columns from the input file
- A new 'Bundesland' column containing the corresponding federal state for each PLZ

## Contributing

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

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
kpca-deeponet 0.1.1
Nonlinear model reduction for operator learning

===============
KPCA-DeepONet
===============
``kpca_deeponet`` is a library that utilizes nonlinear model reduction for operator learning.

Operator learning provides methods to approximate mappings between infinite-dimensional function spaces. Deep operator networks (DeepONets) are a notable architecture in this field. Recently, an extension of DeepONet based on the combination of model reduction and neural networks, POD-DeepONet, has been able to outperform other architectures in terms of accuracy for several benchmark tests. In this contribution, we extend this idea towards nonlinear model order reduction by proposing an efficient framework that combines neural networks with kernel principal component analysis (KPCA) for operator learning. Our results demonstrate the superior performance of KPCA-DeepONet over POD-DeepONet.

.. image:: https://github.com/HamidrezaEiv/KPCA-DeepONet/blob/main/examples/results.png
   :width: 60%
   :align: center

Comparison of the KPCA-DeepONet (orange) and POD-DeepONet (blue)

More details about the implementation and results are available in our `paper <https://openreview.net/forum?id=Jw6TUpB7Rw>`_.

Installation
------------

Clone the repository and locally install it in editable mode:

.. code::

  git clone https://github.com/HamidrezaEiv/KPCA-DeepONet.git
  cd KPCA-DeepONet
  pip install -e .
  pip install -r requirements.txt

You can also just pip install the library:


.. code::
  
  pip install kpca-deeponet

Citation
--------

If you use ``kpca_deeponet`` in an academic paper, please cite::

   @inproceedings{eivazi2024nonlinear,
                  title={Nonlinear model reduction for operator learning},
                  author={Hamidreza Eivazi and Stefan Wittek and Andreas Rausch},
                  booktitle={The Second Tiny Papers Track at ICLR 2024},
                  year={2024},
                  url={https://openreview.net/forum?id=Jw6TUpB7Rw}
                  }