Metadata-Version: 2.4
Name: paves
Version: 0.9.1
Summary: PDF, Analyse et Visualisation avancÉS
Project-URL: Documentation, https://github.com/dhdaines/paves#readme
Project-URL: Issues, https://github.com/dhdaines/paves/issues
Project-URL: Source, https://github.com/dhdaines/paves
Author-email: David Huggins-Daines <dhd@ecolingui.ca>
License-Expression: MIT
License-File: LICENSE.txt
Keywords: graphics,pdf
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.1
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.8
Requires-Dist: pillow
Requires-Dist: playa-pdf<2,>=1
Provides-Extra: detr
Requires-Dist: torchvision; extra == 'detr'
Requires-Dist: transformers[torch]; extra == 'detr'
Description-Content-Type: text/markdown

# PAVÉS: Bajo los adoquines, la PLAYA 🏖️

[**PLAYA**](https://github.com/dhdaines/playa) is intended to get
objects out of PDF, with no dependencies or further analysis.  So,
over top of **PLAYA**, this package provides **P**DF, **A**nalyse et
**V**isualisation simplifi**É**e**S**.

Or, if you prefer, **P**DF **A**nalysis and **V**isualization for
dummi**ES**.

The goal here is not to provide elaborate, enterprise-grade,
battle-tested, cloud and AI-native, completely configurable and
confoundingly complex classes for ETL.  It's to give you some helpful
functions that you can use to poke around in PDFs and get useful
things out of them, often but not exclusively in the context of a
Jupyter notebook.

See the [documentation](https://dhdaines.github.io/paves) for more
information.  There will also be some helpful notebooks soon, to help
you.

## Installation

Install it from PyPI (as `paves`) with `pip` or `uv`, preferably in a
virtual environment.  That's all.  If you want to play around in the
source code you can use `hatch` or `uv` (your choice), for instance:

    # with hatch
    hatch shell
    # with uv
    uv venv
    uv sync
    . .venv/bin/activate

## License

`PAVÉS` is distributed under the terms of the
[MIT](https://spdx.org/licenses/MIT.html) license.
