semvec/pypi-packages huggingface dataset:
hyperquest 0.1.13
A Python package for Hyperspectral quality estimation in hyperspectral imaging (imaging spectroscopy)
Requires Python: >=3.8

# HyperQuest

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`hyperquest`: A Python package for estimating image-wide quality estimation metrics of hyperspectral imaging (imaging spectroscopy). Computations are sped up and scale with number of cpus. Available methods and summaries can be found in [documentation](https://hyperquest.readthedocs.io).

### Important: this package assumes the following about input hyperspectral data:
- Data must be in NetCDF (.nc) or ENVI (.hdr)
- Currently data is expected in Radiance.
  - For smile & striping methods, data must not be georeferenced (typically referred to as L1B before ortho)
- Pushbroom imaging spectrometer, such as, but not limited to:
    - AVIRIS-NG, AVIRIS-3, DESIS, EnMAP, EMIT, GaoFen-5, HISUI, Hyperion EO-1, HySIS, PRISMA, Tanager-1

NOTE: this is under active development. It is important to note that noise methods shown here do not account for spectrally correlated noise. This is a work in progress as I digest literature and translate into python.

## Installation Instructions
The latest release can be installed via pip:

```bash
pip install hyperquest
```

If using Windows PC, [you must have "Build Tools"](https://wiki.python.org/moin/WindowsCompilers) installed to compile cython code,
- Testing on my beat-up Windows PC (Windows11), I did the following to get it to work
  - Installed Visual Studio Build Tools 2022
  - making sure to check the box next to "Desktop development with C++"
  - and then, pip install hyperquest


## Usage example
- see [EMIT example](tutorials/example_using_EMIT.ipynb) which has different methods computed over Libya-4.

## libRadtran install instructions
- Can be installed on Unix type system using the following link:
    - http://www.libradtran.org/doku.php?id=download

## Citation
Brent Wilder. (2025). brentwilder/HyperQuest: v0.XXX (vXXX). Zenodo. https://doi.org/10.5281/zenodo.14890171
scigimage 1.0.0
A lightweight library for generating dynamic star maps and animations.
Requires Python: >=3.6

# scigimage

`scigimage` is a lightweight Python library for generating visually dynamic star maps with twinkling effects and animations. It provides tools for basic image manipulation, gradient generation, and anti-tampering mechanisms.

## Features

- Generate star maps with twinkling stars.
- Add gradients and noise for visual complexity.
- Export animations as GIFs.
- Embed micro-patterns for anti-tampering.
- Modular design for extensibility.

## Installation

Install the library using `pip`:

```bash
pip install scigimage

```

USAGE:
from scigimage.core import Image
from scigimage.shapes import draw_circle, draw_line
from scigimage.effects import twinkle_stars

# Create a blank image

img = Image(512, 512, background_color=(0, 0, 0))

# Draw a white circle

draw_circle(img, 256, 256, 100, (255, 255, 255))

# Add twinkling stars

twinkle_stars(img, [(256, 256)], duration=5, interval=0.1)

# Save the output

img.save("output.png")
sciduck 0.5.5
single cell data analyis unification and communication toolkit
Requires Python: >=3.9

# sciduck: single cell data quality control toolkit

`sciduck` provides a transparent framework for single cell/nuclei quality control and annotation. The package was built following **anndata** and **scanpy** to easily fit into related workflows. It provides tranparency to single cell analysis pipelines for reproducible data analysis and annotation.  The Python-based implementation efficiently deals with
datasets of more than one million cells.

<div align="center">
<img
    src="/logo/sciduck_DALLEv0.png"
    width="200"
>
</div>

## Tutorials:

[Quality Control](https://github.com/AllenInstitute/sciduck/blob/main/tutorials/standard_workflow.ipynb)

In the `Quality Control` tutorial we showcase how to perform various range and group contraints.

[Plotting](https://github.com/AllenInstitute/sciduck/blob/main/tutorials/sciduck_plotting_example.ipynb)

In the `Plotting` tutorial we show how to use the sciduck plotting functions to identify quality control cuttoffs.

[Annotation Table (Taxonomy)](https://github.com/AllenInstitute/sciduck/blob/main/tutorials/annotation_table.ipynb)

In the `Annotation Table` tutorial we show how to build an annotation table for use in taxonomy development. Typically what is hosted on google sheets at the Allen Institute.

## Documentation

[Read the docs](https://sciduck.readthedocs.io/en/latest/sciduck.html)

## Installation

`pip install sciduck`
cbz-merger 0.1.1
Merges multiple CBZ/CBR files into one

# cbz_merger

Merges multiple CBZ/CBR files into one, so that 
```
comic/
├── 01.cbz
├── 02.cbz
└── 03.cbz
```

becomes

```
comic/
├── 01.cbz
├── 02.cbz
├── 03.cbz
└── result.cbz
```

## Installation and usage
Run it with uvx:

```
uvx cbz_merger [-r result_filename] folder_name
```

Or install with pip/pipx:

```
pipx install cbz_merger
```
mamasight 0.1.1
A utility for screen parsing and analysis using YOLO and OCR
Requires Python: >=3.7

# MamaSight

MamaSight is a Python package for analyzing images using YOLO object detection and OCR (Optical Character Recognition). It's designed to detect UI elements and text in screenshots and provide annotated visualizations.

## Installation

```bash
pip install mamasight
```

## Dependencies

MamaSight depends on the following packages:

- torch & torchvision
- ultralytics (v8.3.70)
- numpy (v1.26.4)
- OpenCV
- PaddlePaddle & PaddleOCR (optional for OCR)
- EasyOCR (fallback OCR)
- pandas

## Usage

```python
from mamasight import ScreenParser

# Custom box annotation settings (optional)
box_config = {
    'box_overlay_ratio': 3200,  # Base ratio for scaling
    'text_scale': 1.0,          # Scale factor for text
    'text_thickness': 3,        # Thickness of text
    'text_padding': 5,          # Padding around text
    'thickness': 4,             # Thickness of bounding boxes
    'annotation_style': 'simple', # 'simple' or 'colorful'
}

# Initialize parser with custom settings
parser = ScreenParser(box_config=box_config)

# Setup (will auto-detect GPU/CPU if not specified)
parser.setup(yolo_device='cuda', ocr_device='cuda')  # or 'cpu' for CPU

# Analyze image (with or without OCR)
image, detections = parser.analyze('screenshot.png', use_ocr=True)

# Display the annotated image
image.show()

# View detection results as a pandas DataFrame
print(detections)
```

## Features

- Detect UI elements (icons, buttons, etc.) using YOLO
- Optional text detection with OCR
- Customizable annotation styles
- Auto GPU/CPU detection
- Returns both annotated image and structured detection data

## License

MIT