semvec/pypi-packages huggingface dataset:
ecom-statics 0.1.11
Ecom statics
Requires Python: >=3.8

## VersiÃ³n

- Python 3.12.3

## Limpiar

``` shell
Remove-Item -Recurse -Force build, dist, ecom_statics.egg-info
```

## Actualizar

``` shell
python setup.py sdist bdist_wheel
```

## Publicar en producciÃ³n

``` shell
twine upload dist/*
```

## Instalar

``` shell
pip install ecom-statics
```

## Actualizar a la ultima versiÃ³n

``` shell
pip install --upgrade --no-cache-dir ecom-statics
```
permped-text 0.1.2
A package for text processing.
Requires Python: >=3.8

# permped-text

**A Python library for preprocessing text before sending it to LLMs.**  

This library helps you:

- Reduce the size of your text by cleaning and minimizing it.  
- Extract unique words to avoid repetition.  
- Prepare text efficiently before sending it to large language models (LLMs).  

## Features

- `get_unique_text(text)` – Returns only the unique words from your text.  
- `minimize_text(text)` – Minimizes and simplifies your text.  
- `clean_and_filter_text(text)` – Cleans your text and removes unnecessary characters.  

## Installation

```bash
pip install permped-text
```
```bash
from permped_text import get_unique_text, minimize_text, clean_and_filter_text

text = "This is an example text. Some words repeat words."

unique_text = get_unique_text(text)
clean_text = minimize_text(unique_text)
filtered_text = clean_and_filter_text(clean_text)

print(filtered_text)

```
raptor-stats 0.0.5
Raptor Zonal Statistics
Requires Python: >=3.12

# raptor-stats

[![Docs: latest](https://img.shields.io/badge/Docs-latest-blue)](https://simonpedrogonzalez.github.io/raptor-stats-docs/index.html)
[![PyPI](https://img.shields.io/pypi/v/raptor-stats)](https://pypi.org/project/raptor-stats/)
[![Python  3.12.8+](https://img.shields.io/badge/python-3.12.8+-blue.svg)](https://github.com/simonpedrogonzalez/raptor-stats)
[![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
 
#### Compute zonal statistics using efficient Raptor (Raster+Vector) methods.

## Installation

```bash
pip install raptor-stats
```

## Usage

```python
from raptorstats import zonal_stats

stats = zonal_stats("path/to/vector.shp", "path/to/raster.tif", method="scanline")
```
See the [zonal_stats API docs](https://simonpedrogonzalez.github.io/raptor-stats-docs/raptorstats.api.html#raptorstats.api.zonal_stats) for more details on input types and additional parameters.

## Methods

- `scanline`: Scans the raster file once, line by line, and computes all the intersections with the vector layer in a single pass. Suitable for fast one-time run results.
- `agqt`: Builds a QuadTree with precomputed statistics, then combines it with the scanline method to answer queries more efficiently. Suitable for systems that repeatedly query changing vector layers over the same raster.

## Performance

Raptor methods performance advantage increases with the size of the input raster and number of features. For example, with an ~1.9 billion pixel raster and 50 features (US states):

![See images at https://github.com/simonpedrogonzalez/raptor-stats/blob/main/README.md](assets/total_time_s_states.svg)

For the same raster, on but around 3000 features (US counties):

![See images at https://github.com/simonpedrogonzalez/raptor-stats/blob/main/README.md](assets/total_time_s_counties.svg)


NOTES:
- These tests were made on a i7-8750H (2019) 16GB RAM Linux machine.
- The performance of the agqt method depends on the depth of the tree selected and the size of the features.

## Credits

- Author: [Simon Pedro Gonzalez](https://simonpedrogonzalez.github.io/).
- This package is based on the following <a href="https://simonpedrogonzalez.github.io/raptor-stats-docs/_static/mdml_final_report.pdf" download>project</a>, where you can read more about the zonal stats problem, methods and performance comparison.
- This package API is inspired in the [rasterstats](https://github.com/perrygeo/python-rasterstats) package by [Matthew Perry](https://github.com/perrygeo).