semvec/pypi-packages huggingface dataset:
corrupt-tta 0.1.7
A modernized library for image corruptions based on ImageNet-C
Requires Python: >=3.8

# corrupt-tta

`corrupt-tta` is a modernized Python library for applying image corruptions, based on the original ImageNet-C benchmark. It has been rewritten to use modern APIs (OpenCV, Scikit-Image, NumPy) and removes outdated dependencies like `Wand` (ImageMagick).

## Installation

```bash
pip install corrupt-tta
```

## Usage

```python
import numpy as np
from PIL import Image
from corrupt_tta import corrupt

# Load an image
img = np.array(Image.open("example.jpg"))

# Apply a corruption (e.g., Gaussian Noise with severity 3)
corrupted_img = corrupt(img, severity=3, corruption_name="gaussian_noise")

# Save or display the result
Image.fromarray(corrupted_img.astype(np.uint8)).save("corrupted.jpg")
```

### Available Corruptions

- **Noise:** `gaussian_noise`, `shot_noise`, `impulse_noise`, `speckle_noise`
- **Blur:** `gaussian_blur`, `glass_blur`, `defocus_blur`, `motion_blur`, `zoom_blur`
- **Weather:** `fog`, `frost`, `snow`
- **Digital:** `contrast`, `brightness`, `saturate`, `jpeg_compression`, `pixelate`, `elastic_transform`

## Feedback 

Much of this was written by an LLM agent. If you find bugs, please email: yashmali0326@gmail.com

## Citation

If you find this useful in your research, please consider citing the original paper:

```bibtex
@article{hendrycks2019robustness,
  title={Benchmarking Neural Network Robustness to Common Corruptions and Perturbations},
  author={Dan Hendrycks and Thomas Dietterich},
  journal={Proceedings of the International Conference on Learning Representations},
  year={2019}
}
```

If you wish to reference this specific implementation, you may also cite:

```bibtex
@software{corrupt_tta,
  title = {corrupt_tta: Modernized ImageNet-C Corruptions Implementation},
  author = {Yash Mali},
  year = {2025},
  url = {https://github.com/YashM8/robustness}
}
```
xknx 3.15.0
An Asynchronous Library for the KNX protocol. Documentation: https://xknx.io/
Requires Python: >=3.10.0
Keywords: KNX, EIB, Home Assistant, home automation

# XKNX - An asynchronous KNX library written in Python

![PyPI - Python Version](https://img.shields.io/pypi/pyversions/xknx?logo=python)
[![codecov](https://codecov.io/gh/XKNX/xknx/branch/main/graph/badge.svg?token=irWbIygS84)](https://codecov.io/gh/XKNX/xknx)
[![Checked with mypy](http://www.mypy-lang.org/static/mypy_badge.svg)](http://mypy-lang.org/)
[![Pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=f8b424)](https://github.com/pre-commit/pre-commit)
[![HA integration usage](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fanalytics.home-assistant.io%2Fcurrent_data.json&query=%24.integrations.knx&logo=home-assistant&label=integration%20usage&color=41BDF5&cacheSeconds=21600)](https://www.home-assistant.io/integrations/knx/)
[![Discord](https://img.shields.io/discord/338619021215924227?color=7289da&label=Discord&logo=discord&logoColor=7289da)](https://discord.gg/bkZe9m4zvw)

## Documentation

See documentation at: [https://xknx.io/](https://xknx.io/)

## Help

We need your help for testing and improving XKNX. For questions, feature requests, bug reports either open an [issue](https://github.com/XKNX/xknx/issues), join the [XKNX chat on Discord](https://discord.gg/EuAQDXU) or write an [email](mailto:xknx@xknx.io).

## Development

You will need at least Python 3.10 in order to use XKNX.

Setting up your local environment:

1. Install requirements: `pip install -r requirements/testing.txt`
2. Install pre-commit hook: `pre-commit install`

## Testing

To run all tests, linters, formatters and type checker call `tox`

Running only unit tests is possible with `pytest`
Running specific unit tests can be invoked by: `pytest -vv test/management_tests/procedures_test.py -k test_nm_individual_address_serial_number_write_fail`

## Home-Assistant

XKNX is the underlying library for the KNX integration in [Home Assistant](https://home-assistant.io/).

## Example

```python
"""Example for switching a light on and off."""
import asyncio

from xknx import XKNX
from xknx.devices import Light


async def main():
    """Connect to KNX/IP bus, switch on light, wait 2 seconds and switch it off again."""
    async with XKNX() as xknx:
        light = Light(
            xknx,
            name='TestLight',
            group_address_switch='1/0/9',
        )
        xknx.devices.async_add(light)

        await light.set_on()
        await asyncio.sleep(2)
        await light.set_off()

asyncio.run(main())
```

## Attributions

Many thanks to [Weinzierl Engineering GmbH](https://weinzierl.de) and [MDT technologies GmbH](https://www.mdt.de) for providing us each an IP Secure Router to support testing and development of xknx.
pymdown-extensions 10.21
Extension pack for Python Markdown.
Requires Python: >=3.9
Keywords: extensions, markdown

[![Donate via PayPal][donate-image]][donate-link]
[![Build][github-ci-image]][github-ci-link]
[![Coverage Status][codecov-image]][codecov-link]
[![PyPI Version][pypi-image]][pypi-link]
[![PyPI Downloads][pypi-down]][pypi-link]
[![PyPI - Python Version][python-image]][pypi-link]
[![License][license-image-mit]][license-link]

# PyMdown Extensions

Extensions for [Python Markdown](https://python-markdown.github.io).

## Documentation

Extension documentation is found here: https://facelessuser.github.io/pymdown-extensions/.

## License

License is MIT.  See [LICENSE](https://github.com/facelessuser/pymdown-extensions/blob/master/LICENSE.md) for more info.

[github-ci-image]: https://github.com/facelessuser/pymdown-extensions/workflows/build/badge.svg?branch=main&event=push
[github-ci-link]: https://github.com/facelessuser/pymdown-extensions/actions?query=workflow%3Abuild+branch%3Amain
[codecov-image]: https://img.shields.io/codecov/c/github/facelessuser/pymdown-extensions/main.svg?logo=codecov&logoColor=aaaaaa&labelColor=333333
[codecov-link]: https://codecov.io/github/facelessuser/pymdown-extensions
[pypi-image]: https://img.shields.io/pypi/v/pymdown-extensions.svg?logo=pypi&logoColor=aaaaaa&labelColor=333333
[pypi-link]: https://pypi.python.org/pypi/pymdown-extensions
[python-image]: https://img.shields.io/pypi/pyversions/pymdown-extensions?logo=python&logoColor=aaaaaa&labelColor=333333
[pypi-down]: https://img.shields.io/pypi/dm/pymdown-extensions.svg?logo=pypi&logoColor=aaaaaa&labelColor=333333
[license-image-mit]: https://img.shields.io/badge/license-MIT-blue.svg?labelColor=333333
[license-link]: https://github.com/facelessuser/pymdown-extensions/blob/main/LICENSE.md
[donate-image]: https://img.shields.io/badge/Donate-PayPal-3fabd1?logo=paypal
[donate-link]: https://www.paypal.me/facelessuser
fintoolkit 0.0.27
Financial Toolkit
Requires Python: >=3.11

# Financial Toolkit 💹

This is an open-source Python 🐍 library that implements the most commonly used functions by an investment professional.

This is still in the early stage of development but I will keep adding new features over time. Here are some of the features implemented so far:

- Getting financial data
- Portfolio optimization
- Peer group analysis
- Factor analysis
- Option pricing

### Installation
```sh
pip install fintoolkit
```

### Documentation

The API doc is hosted on [GitHub Pages](https://chris-kc-cheng.github.io/financial-toolkit/toolkit.html).


### Jupyter Notebooks 📔

Jupyter is browser-based shell that allows user to interact with Python scripts.

In this repository, I have developed some Jupyter notebooks showing the most common use cases in investment management that utilizes the Financial Toolkit.

No Python? No problem. With Binder, you may interact with the Jupyter notebooks in an executable environment even without Python installed in your computer.

Binder: https://mybinder.org/v2/gh/chris-kc-cheng/financial-toolkit/master


## Related Repositories

### Streamlit Apps 👑

Streamlit is a framework that turns Python scripts into interactive web apps. 

In this repository, I have developed some web apps showing the most common use cases in investment management that utilizes the Financial Toolkit.

| App | Description |
|-----|-------------|
| [Index Montior](https://ftk-indices.streamlit.app/) | Tables and charts of asset class returns measured in different currency terms and time horizons.
| [Factor Analysis](https://ftk-factors.streamlit.app/) | Analyzing factor loading of a portfolio using Fama-French model. |
| [Portfolio Optimization](https://ftk-portfolio-optimization.streamlit.app/) | Comparing risk reward and risk contribution of various weighting schemes. |
| [Peer Group Analysis](https://ftk-peers.streamlit.app/) | Comparing fund performance and risk measures against benchmark and peer group. |
| [Option Pricing & Greeks](https://ftk-options.streamlit.app/) | Visualizing payoff and Greeks of various option strategies. |

The source code is hosted on GitHub at: https://github.com/chris-kc-cheng/ftk-streamlit.