Metadata-Version: 2.1
Name: lintsampler
Version: 0.1.3
Summary: Efficient random sampling via linear interpolation.
Author-email: "Aneesh P. Naik" <aneesh.naik@roe.ac.uk>
Project-URL: Homepage, https://github.com/aneeshnaik/lintsampler
Project-URL: Documentation, https://lintsampler.readthedocs.io
Keywords: python,statistics,numpy,sampling,random
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy<2.0,>=1.17

# lintsampler

**Efficient random sampling via linear interpolation.**

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[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://github.com/aneeshnaik/lintsampler/blob/main/LICENSE)

When you know densities on the 2 endpoints of 1D interval, or the 4 corners of a 2D rectangle, or generally the $2^k$ vertices of a $k$-dimensional hyperbox (or a series of such hyperboxes, e.g., the cells of a $k$-dimensional grid), linear interpolant sampling provides a technique to draw random samples within the hyperbox. `lintsampler` provides a Python implementation of this.

See the [documentation](https://lintsampler.readthedocs.io/) or the linear interpolant sampling paper for further details. 

## Documentation

The documentation, including some example notebooks, is available at [lintsampler.readthedocs.io/](https://lintsampler.readthedocs.io/).

## Installation

Three ways of installing `lintsampler`:

- `pip`:
```
pip install lintsampler
```

- `conda`:
```
conda install -c conda-forge lintsampler
```

- Simply cloning this repository.

## Attribution

If using `lintsampler` for a research publication, please cite our paper: link to come.

## License

`lintsampler` is available under the MIT license. See the LICENSE file for specifics.
