semvec/pypi-packages huggingface dataset:
pyaquifer 0.1.1
A high-level library to simplify data operations on AWS data lakes.
Requires Python: >=3.8

# pyaquifer

[![PyPI](https://img.shields.io/pypi/v/pyaquifer.svg)]() [![Build Status](https://img.shields.io/github/actions/workflow/status/yourorg/pyaquifer/ci.yml)]()

**pyaquifer** makes it trivial to read & write AWS S3-Tables Iceberg catalogs using Pandas (and soon Polars/DuckDB).

## Features

- 🔗 Native Iceberg REST integration  
- 🐼 Pandas connector (plus Polars & DuckDB planned)  
- ⚙️ Simple `JobConfig` for bucket/namespace selection  
- 📦 Read, transform, write in 5 lines of code  

## Installation

```bash
pip install pyaquifer[dev]
vipy 1.16.3
Python Tools for Visual Dataset Transformation
Keywords: computer vision machine learning ML CV privacy video image

[![PyPI version](https://badge.fury.io/py/vipy.svg)](https://badge.fury.io/py/vipy)  [![CI](https://github.com/visym/vipy/workflows/vipy%20unit%20tests/badge.svg)](https://github.com/visym/vipy/actions?query=workflow%3A%22vipy+unit+tests%22) [![Python 3.7+](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/downloads/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

VIPY
------------------- 
VIPY: Python Tools for Visual Dataset Transformation    
Documentation: https://visym.github.io/vipy

VIPY is a python package for representation, transformation and visualization of annotated videos and images.  Annotations are the ground truth provided by labelers for training and testing computer vision systems.  VIPY provides tools to easily edit videos and images so that the annotations are transformed along with the pixels.  This enables a clean interface for transforming complex datasets for input to your computer vision pipeline.

VIPY provides:  

* Representation of videos with labeled activities that can be resized, clipped, rotated, scaled, padded, cropped and resampled
* Representation of images with object bounding boxes that can be manipulated as easily as editing an image
* Clean visualization of annotated images and video
* Fluent interface for chaining operations on videos and images
* Lazy loading of images and videos suitable for distributed processing (e.g. dask, spark)
* Straightforward integration into machine learning toolchains (e.g. torch, numpy)
* Visual dataset download, unpack and import (e.g. Imagenet21k, Coco 2014, Visual Genome, Open Images V7, Kinetics700, YoutubeBB, ActivityNet, ... )
* Minimum dependencies for easy installation (e.g. AWS Lambda, Flask)


Requirements
-------------------
python 3.7+  
[ffmpeg](https://ffmpeg.org/download.html) (required for videos)  
numpy, matplotlib, pillow, ffmpeg-python   


Installation
-------------------

```python
pip install vipy
```

Optional dependencies are installable as a complete package:

```python
pip install pip --upgrade
pip install 'vipy[all]'
```

You will receive a friendly warning if attempting to use an optional dependency before installation.


Quickstart
-------------------
```python
import vipy
vipy.image.owl().minsquare().show()
```
<img src="https://raw.githubusercontent.com/visym/vipy/master/docs/vipy_image_owl.jpg" width="980">

```python
import vipy
vipy.dataset.registry('coco_2014').takeone().show()
```
<img src="https://raw.githubusercontent.com/visym/vipy/master/docs/vipy_coco2014_000000290678.jpg" width="980">

```python
v = vipy.dataset.registry('youtubeBB').takeone()
vipy.visualize.montage([im.centersquare().mindim(256).annotate() for t in v.download().trackclip() for im in t.framerate(1)]).show()
```
<img src="https://raw.githubusercontent.com/visym/vipy/master/test/youtubeBB_bear_framerate_29p97.jpg" width="980">


The [tutorials](https://visym.github.io/vipy/#tutorials) provide useful examples to help you get started.
xarrayaita 0.3.5
Python librairy to process AITA data
Requires Python: <4.0,>=3.11

# Librairy xarray_aita

[![PyPI version](https://img.shields.io/badge/license-GPLv3-blue.svg)](https://gricad-gitlab.univ-grenoble-alpes.fr/mecaiceige/tools/lib_python/xarray_aita/-/blob/main/LICENSE)
[![pipeline status](https://gricad-gitlab.univ-grenoble-alpes.fr/mecaiceige/tools/lib_python/xarray_aita/badges/main/pipeline.svg)](https://gricad-gitlab.univ-grenoble-alpes.fr/mecaiceige/tools/lib_python/xarray_aita/-/commits/main)
[![PyPI version](https://badge.fury.io/py/xarrayaita.svg)](https://badge.fury.io/py/xarrayaita)
[![Website shields.io](https://img.shields.io/website-up-down-green-red/http/shields.io.svg)](https://mecaiceige.gricad-pages.univ-grenoble-alpes.fr/tools/lib_python/xarray_aita/)


This package is meant to be use with [xarray](https://github.com/pydata/xarray)
It is a wrapper for AITA output. Therefore It could easily be adapted for over data that are aita-like.

## Installation

### Form pypi

Run this command in your environment :

`pip install xarrayaita`

### From main

You should clone the environnement.

`git clone https://gricad-gitlab.univ-grenoble-alpes.fr/mecaiceige/tools/lib_python/xarray_aita`

Go in the folder.

```
cd xarray_aita
pip install poetry
poetry install
```
mseep-comfyui-llm-party 1.4.1
A set of block-based LLM agent node libraries designed for ComfyUI.This project aims to develop a complete set of nodes for LLM workflow construction based on comfyui. It allows users to quickly and conveniently build their own LLM workflows and easily integrate them into their existing SD workflows.
Requires Python: >=3.6
Keywords: mseep

Package managed by MseeP.ai