semvec/pypi-packages huggingface dataset:
li_queens 0.1.0
Solve a LinkedIn Queens puzzle in an image.
Requires Python: >=3.13

# LinkedIn Queens solver

[Read this blog post](https://mathspp.com/blog/beating-linkedin-queens-with-python) to learn about how this program works.

The CLI provided needs an image of a puzzle, like `puzzle.png` shown here:

![](puzzle.png)

It will then produce a second image `puzzle_solved.png` with the solution:

![](puzzle_solved.png)


## Running with uv

If you're using uv, you can run this CLI with

```bash
uvx --from li_queens queens puzzle.png
```

The argument `puzzle.png` should be a path to an image containing a LinkedIn Queens-like puzzle.
modisci 1.1.2
generates rasters of MODIS clumping index
Requires Python: >=3.10

# `modisci` Python Package

The `modisci` Python package generates rasters of MODIS clumping index.

[Gregory H. Halverson](https://github.com/gregory-halverson-jpl) (they/them)<br>
[gregory.h.halverson@jpl.nasa.gov](mailto:gregory.h.halverson@jpl.nasa.gov)<br>
NASA Jet Propulsion Laboratory 329G

## Installation

This package is available on PyPi as a [pip package](https://pypi.org/project/modisci/) called `modisci`.

```bash
pip install modisci
```

## Usage

Import this package as `modisci`.

```python
import modisci
```

## References

1. He, L., J.M. Chen, J. Pisek, C. Schaaf, and A.H. Strahler. 2017. Global 500-m Foliage Clumping Index Data Derived from MODIS BRDF, 2006. ORNL DAAC, Oak Ridge, Tennessee, USA. .
2. He, LM; Chen, JM; Pisek, J; Schaaf, CB; Strahler, AH (2012). Global clumping index map derived from the MODIS BRDF product. REMOTE SENSING OF ENVIRONMENT, 119, 118-130.