semvec/pypi-packages huggingface dataset:
karva 0.0.1a3
A Python test framework, written in Rust.
Requires Python: <3.15,>=3.10

# Karva (0.0.1-alpha.3)

![PyPI - Version](https://img.shields.io/pypi/v/karva)

A Python test framework, written in Rust.

<div align="center">
  <img src="https://raw.githubusercontent.com/karva-dev/karva/main/docs/assets/benchmark_results.svg" alt="Benchmark results" width="70%">
</div>

**We'd love for you to try Karva!** It's currently in alpha, and your feedback helps shape the project. [Get started](#getting-started) or join us on [Discord](https://discord.gg/XG95vNz4Zu).

## About Karva

Karva aims to be an efficient alternative to `pytest` and `unittest`.

While we do not yet support all of pytest's features, we aim to gradually add support for pytest alternatives as we add features.

## Getting started

### Installation

Karva is available as [`karva`](https://pypi.org/project/karva/) on PyPI.

Use karva directly with `uvx`:

```bash
uvx karva test
uvx karva version
```

Or install karva with `uv`, or `pip`:

```bash
# With uv.
uv tool install karva@latest

# Add karva to your project.
uv add --dev karva

# With pip.
pip install karva
```

### Usage

By default, Karva will respect your `.gitignore` files when discovering tests in specified directories.

To run your tests, try any of the following:

```bash
# Run all tests.
karva test

# Run tests in a specific directory.
karva test tests/

# Run tests in a specific file.
karva test tests/test_example.py
```

#### Example

Here is a small example of using karva, as you can see it works just like pytest.

```py title="tests/test.py"
def test_pass():
    assert True


def test_fail():
    assert False, "This test should fail"
```

Running karva:

```bash
uv run karva test tests/
```

Provides the following output:

```text
test tests.test::test_pass ... ok
test tests.test::test_fail ... FAILED

diagnostics:

error[test-failure]: Test `test_fail` failed
 --> tests/test.py:5:5
  |
5 | def test_fail():
  |     ^^^^^^^^^
6 |     assert False, "This test should fail"
  |
info: Test failed here
 --> tests/test.py:6:5
  |
5 | def test_fail():
6 |     assert False, "This test should fail"
  |     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  |
info: Error message: This test should fail

test result: FAILED. 1 passed; 1 failed; 0 skipped; finished in 8ms
```

## Contributing

Contributions are welcome! See [CONTRIBUTING.md](https://github.com/karva-dev/karva/blob/main/CONTRIBUTING.md) for more information.

You can also join us on [Discord](https://discord.gg/XG95vNz4Zu)
Fast-SSIM 1.2.0
Fast SSIM and PSNR algorithms for Python.
Requires Python: >=3.9
Keywords: ssim, psnr, image quality, image comparison, image processing

<p align="center">
  <h1 align="center">Fast-SSIM</h1>
  <p align="center">
    Speed up your SSIM and PSNR calculations!
    <br />
  </p>
</p>

<br>

## ℹ About

This fork wraps the Fast-SSIM project in an easy to use package that can be readily installed via PyPI.
To further accelerate processing, this version enables multithreading and leverages AVX2/FMA hardware instructions.

## Requirements

- Python 3.9 or higher
- An AVX2/FMA capable CPU (for older CPUs Fast-SSIM v1.0.2 can be used)

## How to Install

```
pip install Fast-SSIM
```

## Usage

The functionalities are explained in the following code snippet that is also provided in this repo:

```python
from skimage.io import imread

import fast_ssim

img1_path = r"test_images/0.jpg"
img2_path = r"test_images/1.jpg"

# Load the images into NumPy arrays
img1 = imread(img1_path)
img2 = imread(img2_path)

ssim_score = fast_ssim.ssim(
    img1, img2,
    data_range=255
)
psnr_score = fast_ssim.psnr(
    img1, img2,
    data_range=255
)

print(f"SSIM Score: {ssim_score}")
print(f"PSNR Score: {psnr_score}")
```

Output:
```
SSIM Score: 0.9886590838432312
PSNR Score: 31.11587142944336
```

## Notes

- The SSIM calculation uses sample covariance (and sample variance) for its statistics. This aligns with the default behavior of `scikit-image`'s `structural_similarity` function, where `use_sample_covariance` is True by default.

- This implementation does not offer an option for applying a Gaussian filter to the images or local windows prior to the SSIM/PSNR calculation.

## Original author

Chen Yu / [@Chen Yu](https://github.com/chinue)
adityassarode.codes 1.0.7
An educational CLI tool by Aditya Sarode designed for learning, experimentation, and code exploration.
Requires Python: >=3.8

## Usage Guidelines

This tool is created to support learning and understanding of concepts.  
It should be used as guidance, similar to books or study materials.  
Users are expected to practice, explore, and learn on their own.

- You **must not** copy, submit, or present the generated code as your own work for marks or academic credit.
- By continuing to use this tool, you confirm that you have read and understood the rules above.
- You accept full responsibility for how you use this tool and its output.

---

## Copyright Notice

All content and generated output are protected under applicable copyright laws.  
This includes the **Copyright Act, 1957 (India)** and its amendments.  
Misuse may also fall under the **Information Technology Act, 2000 (India)** for digital content and online distribution.  
International protections such as the **Berne Convention for the Protection of Literary and Artistic Works** and the **WIPO Copyright Treaty (WCT)** may also apply.  

Unauthorized copying, redistribution, modification, or commercial use is not permitted.  
The content is provided only for personal learning and educational reference.

This tool and its original structure are created and maintained by **Aditya Sarode**.  
All rights not expressly granted are reserved by the author.
chipshot 0.9.0
Set up game-winning headers!
Requires Python: >=3.10

..
    This file is a part of Chipshot <https://github.com/kurtmckee/chipshot>
    Copyright 2022-2026 Kurt McKee <contactme@kurtmckee.org>
    SPDX-License-Identifier: MIT


Chipshot
########

*Set up game-winning headers!*

----

Chipshot helps standardize header information in software development files.

Its target goal is to ensure that copyright dates are standardized
and that licensing information is present.

It supports a wide range of file formats,
including source code and documentation formats.
It purposely does not support configuration file formats by default
(such as TOML, INI, or YAML)
but can be configured to support those, too.


Sample configuration
====================

Create a file named ``.chipshot.toml`` with the following content:

..  code-block:: toml

    [chipshot]
    template = """
    Copyright 2021-{{ year }} Developer or Company
    Released under the terms of the MIT license.
    SPDX-License-Identifier: MIT
    """

You can then run ``chipshot path1 path2`` to see what files will be modified.
If you're satisfied, run ``chipshot --update path1 path2`` to update the files.


Pre-commit hooks
================

Chipshot offers two pre-commit hooks to help you manage your projects:

*   ``check-headers``
*   ``update-headers``

Here's a sample configuration for ensuring your files have correct headers:

..  # [[[cog
..  # import assets.update_readme_pre_commit_example
..  # assets.update_readme_pre_commit_example.main()
..  # ]]]

..  DO NOT EDIT THIS CODE BLOCK!
..  INSTEAD, EDIT assets/update_readme_pre_commit_example.py.

..  code-block:: yaml

    # Filename: .pre-commit-config.yaml
    repos:
      - repo: 'https://github.com/kurtmckee/chipshot'
        rev: 'v0.9.0'
        hooks:
          - id: 'update-headers'

..  # [[[end]]]