semvec/pypi-packages huggingface dataset:
packaging-legacy 23.0.post0
Core utilities for legacy Python packages

# Legacy Packaging

This library provides support for "legacy" Python Packaging functionality
removed from https://github.com/pypa/packaging.


## Supported APIs

This library includes support for the following deprecated/removed APIs:

### `LegacyVersion`
Removed in https://github.com/pypa/packaging/pull/407, this includes a
`packaging_legacy.version.parse` function that handles legacy versions.

```diff
- from packaging.version import parse, LegacyVersion
+ from packaging_legacy.version import parse, LegacyVersion
```
vendy 0.0.3
Vendy is a tool for vendoring third-party packages into your project
Requires Python: >=2.7,!=3.0,!=3.1,!=3.2,!=3.3

Vendy is a tool for vendoring third-party packages into your project.

## Disclaimer:
This is not an officially supported Google product.

## Example:

In a `pyproject.toml` file in the root of your package, define the target
directory (should be in the same directory as the `pyproject.toml` file), and
the packages to vendor:

```toml
[tool.vendy]
target = 'my_project'
packages = [
    "sampleproject==1.2.0",
]
```

On the command line:

```
$ python -m vendy
[vendy] Using vendor dir: /private/tmp/my_project/my_project/_vendor
[vendy] Cleaning /private/tmp/my_project/my_project/_vendor
[vendy] Installing vendored libraries
Collecting sampleproject==1.3.0
  Using cached https://files.pythonhosted.org/packages/a1/fd/3564a5176430eac106c27eff4de50b58fc916f5083782062cea3141acfaa/sampleproject-1.3.0-py2.py3-none-any.whl
Installing collected packages: sampleproject
Successfully installed sampleproject-1.3.0
[vendy] Detected vendored libraries: bin, my_data, sample
[vendy] Rewriting all imports related to vendored libs
[vendy] Downloading licenses
Collecting sampleproject==1.3.0
  Using cached https://files.pythonhosted.org/packages/a6/aa/0090d487d204f5de30035c00f6c71b53ec7f613138d8653eebac50f47f45/sampleproject-1.3.0.tar.gz
  Saved ./my_project/_vendor/__tmp__/sampleproject-1.3.0.tar.gz
Successfully downloaded sampleproject
[vendy] Extracting sampleproject-1.3.0/LICENSE.txt into my_project/_vendor/sampleproject.LICENSE.txt
[vendy] Revendoring complete
```

Result:

```
$ tree
.
├── my_project
│   ├── __init__.py
│   └── _vendor
│       ├── bin
│       │   └── sample
│       ├── my_data
│       │   └── data_file
│       ├── sample
│       │   ├── __init__.py
│       │   └── package_data.dat
│       └── sampleproject.LICENSE.txt
└── pyproject.toml
```

And you can import from the vendored library like:

```diff
-from sampleproject.foo import bar
+from myproject._vendor.sampleproject.foo import bar
```
phycv 1.2.6
physics-inspired computer vision algorithms
Keywords: python,image processing,computational imaging,computer vision,physics-inspired algorithm

# PhyCV: The First Physics-inspired Computer Vision Library

[![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/playlist?list=PLj--iTBXPaTWABQstUFA6l6_Rf3uiZq9T)
[![Wikipedia](https://img.shields.io/badge/Wikipedia-%23000000.svg?style=for-the-badge&logo=wikipedia&logoColor=white)](https://en.wikipedia.org/wiki/PhyCV)

Welcome to PhyCV ! The First Physics-inspired Computer Vision Python library developed by [Jalali-Lab](https://photonics.ucla.edu/) @ UCLA. This repo is maintained by [Yiming Zhou](https://yiming0416.github.io/).

Please see the release notes of all previous versions [here](https://github.com/JalaliLabUCLA/phycv/blob/main/assets/docs/change_log.md).


## Citation

If you find PhyCV useful in your research, please star :star: this repository and consider citing the following [preprint](https://arxiv.org/abs/2301.12531):
```
@article{zhou2023phycv,
  title={PhyCV: The First Physics-inspired Computer Vision Library},
  author={Zhou, Yiming and MacPhee, Callen and Suthar, Madhuri and Jalali, Bahram},
  journal={arXiv preprint arXiv:2301.12531},
  year={2023}
}
data-alchemy 0.0.2
Package to process documents of any format
Requires Python: >=3.11,<3.12

# Data Processor

## Description


## Installation

```bash
pip install data-alchemy
```

## Data Processing is a multi-step process

A brief overview of the steps involved in data processing:

1. Fetching Information
   1. Scrape data from a website
   2. Fetch data from an API
   3. Fetch data from a database
2. Clean
3. Transform
4. Index
5. Search / Analyze
6. Visualize
7. Store

This packag aims to be a one-stop-shop for all your data processing needs. It provides a simple interface to perform all the above steps and more.

## Documentation

The documentation for this package is hosted [here](https://data-pipleine.vercel.app/).