semvec/pypi-packages huggingface dataset:
quadproj 2.1.0
A package to project onto quadratic hypersurfaces.
Requires Python: >=3.6

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# quadproj

A simple library to project a point onto a quadratic surface, or *quadric*.

## How to install quadproj?

It is a one-liner!

```python3
python3 -m pip install quadproj
```

See [installation page](https://loicvh.gitlab.io/quadproj/installation.html) for further information and the requirements.

## Documentation

The documentation is hosted on GitLab: [https://loicvh.gitlab.io/quadproj](https://loicvh.gitlab.io/quadproj)

## How does quadproj work?

The projection is obtained by computing exhaustively all KKT point from the optimization problem defining the projection. The authors of [[1]](https://loicvh.eu/aca/abstracts/OJMO_2022.html) show that for non-cylindrical central quadrics, the solutions belong to the KKT points that consist in the intersection between:

- a unique root of a nonlinear function on a specific interval;
- a set of closed-form points.

Either set can be empty but for a nonempty quadric, at least one is nonempty and contains (one of the) projections.

The full explanation is provided in [[1]](https://loicvh.eu/aca/abstracts/OJMO_2022.html).


## How to use quadproj?

See the [quickstart](https://loicvh.gitlab.io/quadproj/quickstart.html) page or the [API documentation](https://loicvh.gitlab.io/quadproj/modules.html).



## Dependencies

See [requirements.txt](https://gitlab.com/loicvh/quadproj/-/blob/master/requirements.txt).


## [1]
(2021) L. Van Hoorebeeck, P.-A. Absil and A. Papavasiliou, “Projection onto quadratic hypersurfaces”, submitted. ([preprint](https://loicvh.eu/aca/_downloads/dc8ab520a768f81e13569c647c7553d7/OJMO_2022_preprint.pdf), [abstract/BibTex](https://loicvh.eu/aca/abstracts/OJMO_2022.html))
SiddP6 0.5.0
Siddhartha Purwar portfolio pip package
Requires Python: >=3.6

# Sidd-Package
Siddhartha's resume in the form of python-package 

## Directory stracture

    â”‚   .gitignore
    â”‚   LICENSE
    â”‚   README.md
    â”‚   setup.py
    â”‚
    â””â”€â”€â”€sidd
        â”‚   sidd.py
        â”‚   __init__.py
        â”‚
        â”œâ”€â”€â”€bye
        â”‚       bye.py
        â”‚       __init__.py
        â”‚
        â”œâ”€â”€â”€contact
        â”‚       contact.py
        â”‚       __init__.py
        â”‚
        â”œâ”€â”€â”€education
        â”‚       education.py
        â”‚       __init__.py
        â”‚
        â”œâ”€â”€â”€experience
        â”‚       experience.py
        â”‚       __init__.py
        â”‚
        â”œâ”€â”€â”€hello
        â”‚       hello.py
        â”‚       __init__.py
        â”‚
        â””â”€â”€â”€project
                project.py
                __init__.py


## Run Locally


Install package
```py
  pip install siddp6
```

Import package
```py
  import sidd
```

Print all attributes and methods (functions , modules, strings, lists, dictionaries etc.)
```py
  print(dir(sidd))
```

## Output

<img width="959" alt="image" src="https://user-images.githubusercontent.com/91800813/208263532-9fd01cc9-530b-4758-b5e5-ac364ecc3722.png">


## Tech & Tool

__Language__: [Python](https://www.python.org/)
bh-aws-wrap 0.0.2b0
Add your description here
Requires Python: >=3.12

Wrap aws ec2 commands.

<PACKAGE> = "bh_aws"
The following is a rough description. For a canonical
overview of use, consult the unit tests under [./src].

Users instantiate the class Profile with an aws cli
profile name:

    >>> profile = Profile('bozo')

Users invoke aws ec2 commands as such:

    >>> meta = profile.invoke('create-key --key-name foo')

The invokation invokes an aws invokation:

    subprocess.run( 'aws ec2 create-key --key-name foo --profile bozo'.split() )

The object [meta], an instance of class [Meta], encapsulates
the resulting subprocess.CompletedProcess object, and wraps
the stdout (if any) in the class [CREATE_KEY]

    >>> import <PACKAGE>.aws.ec2.create_key as mod
    >>> root = meta._root
    >>> assert root.__class__ = mod.CREATE_KEY
    >>> assert root.__class__ = mod.ROOT

The object [root] provides access to the json decoded output.
waifutagger 0.1.1
A simple tagger for anime-style images using SmilingWolf models
Requires Python: >=3.7

# WaifuTagger

A Python package for tagging anime-style images using SmilingWolf models. This package provides a simple interface to detect and tag various elements in anime/manga-style illustrations.

## Features

- Support for multiple SmilingWolf models
- Simple and batch image processing
- Flexible tag output formats
- Automatic threshold adjustment (MCut)
- Progress tracking for batch operations

## Installation

```
pip install waifutagger
```

## Quick Start

```
from waifutagger import WaifuTagger

# Initialize tagger
tagger = WaifuTagger("eva02-large-v3")  # or any other available model

# Process single image
results = tagger.predict("image.jpg")

# Print tags
print(results['rating'])           # Image rating
print(results['general_tags'])     # General tags
print(results['character_tags'])   # Character tags

# Get formatted string
from waifutagger.formatters import format_tags
tags_string = format_tags(results)
print(tags_string)  # "rating_general, 1girl, solo, long_hair, ..."
```

## Available Models

- swinv2-v3: SmilingWolf/wd-swinv2-tagger-v3
- convnext-v3: SmilingWolf/wd-convnext-tagger-v3
- vit-v3: SmilingWolf/wd-vit-tagger-v3
- vit-large-v3: SmilingWolf/wd-vit-large-tagger-v3
- eva02-large-v3: SmilingWolf/wd-eva02-large-tagger-v3
- And more...

## Advanced Usage

### Batch Processing

```
# Process multiple images
image_paths = ["img1.jpg", "img2.jpg", "img3.jpg"]
for img_path in image_paths:
    results = tagger.predict(img_path)
    # Save results
    with open(f"{img_path}_tags.txt", "w") as f:
        f.write(format_tags(results))
```

### Custom Thresholds

```
# Use custom thresholds
results = tagger.predict(
    "image.jpg",
    general_threshold=0.4,    # Higher threshold for general tags
    character_threshold=0.9   # Higher threshold for character tags
)
```

### MCut Thresholding

```
# Use automatic threshold adjustment
results = tagger.predict(
    "image.jpg",
    general_use_mcut=True,    # Use MCut for general tags
    character_use_mcut=True   # Use MCut for character tags
)
```

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Acknowledgments

This package is based on the WaifuDiffusion tagger models by [SmilingWolf](https://huggingface.co/SmilingWolf).