semvec/pypi-packages huggingface dataset:
OAM-KIST 0.2.27
Quantum information and technology using OAM states and SLM for KIST research
Requires Python: >=3.7

<div align="center">
  <img src="https://raw.githubusercontent.com/yjun0915/OAM_KIST/main/assets/logo.png" alt="KIST OAM Logo" width="300">
  <br>
</div>

![Python](https://img.shields.io/badge/python-3670A0?&logo=python&logoColor=ffdd54)
![PyCharm](https://img.shields.io/badge/pycharm-143?&logo=pycharm&logoColor=black&color=black&labelColor=green)
![NumPy](https://img.shields.io/badge/numpy-%23013243.svg?&logo=numpy&logoColor=white)
![OpenCV](https://img.shields.io/badge/opencv-%23white.svg?&logo=opencv&logoColor=white)
![SciPy](https://img.shields.io/badge/SciPy-%230C55A5.svg?&logo=scipy&logoColor=white)  
![KIST](https://img.shields.io/badge/KIST-Research-e44126?labelColor=6d6e67)
![UST](https://img.shields.io/badge/UST-shcool-ef4126?labelColor=4d4d4f)
![Quantum Optics](https://img.shields.io/badge/⚛️-QuantumOptics-9827AC?labelColor=black)
![OAM](https://img.shields.io/badge/🌀-OAM-black?labelColor=00C853)

[![Python Test](https://github.com/yjun0915/OAM_KIST/actions/workflows/test.yml/badge.svg)](https://github.com/yjun0915/OAM_KIST/actions/workflows/test.yml)
[![PyPI Version](https://img.shields.io/pypi/v/OAM_KIST)](https://pypi.org/project/OAM_KIST/)

Imaging and sequence toolkit for Lageurre-Gaussian mode light, i.e. Orbital Angular Montum(OAM) state.  


```bash
.
├── OAM_KIST/                # Main Source Code
│   ├── __init__.py          # Package initialization
│   ├── holography.py        # Core logic for hologram generation
│   ├── utils.py             # Helper functions (math, interpolation)
│   └── outputs/             # Directory for generated images
├── docs/                    # Documentation (Sphinx)
│   ├── source/              # Documentation source files (.rst, conf.py)
│   ├── Makefile             # Build command for Mac/Linux
│   └── make.bat             # Build command for Windows
├── tests/                   # Unit Tests
│   └── test_core.py         # Pytest test cases
├── main.py                  # Execution script
├── README.md                # Project overview
├── requirements.txt         # Dependencies
└── setup.py                 # PyPI distribution setup
```
vicompres 0.1.0
Efficiently compress video files using ffmpeg
Requires Python: >=3.10
Keywords: video, compression, ffmpeg, cli

# Vicompres

Vicompres is a CLI tool to efficiently compress video files using ffmpeg. It provides a simple interface for batch-compressing videos with configurable quality levels.

## Installation

```bash
pip install vicompres
```

**Prerequisite:** [ffmpeg](https://ffmpeg.org/) must be installed and available in your PATH.

## Usage

```bash
vicompres -d /path/to/videos -c 5
```

## Features

Arguments:

- `-d` or `--directory`: Specify the directory containing the video files to be compressed. By default, it uses the current directory.
- `-c` or `--compression`: Set the compression level (1-10). Higher values result in better compression but may take more time. The default value is 5.
  - compression level is ephemeral — it maps to a specific ffmpeg preset, CRF, and bitrate based on the file extension.
- `-p` or `--prefix`: Define a prefix for the input files to be compressed. Only files that start with the specified prefix will be processed. By default, it processes all files in the directory.
- `-o` or `--output`: Specify the output directory where the compressed files will be saved. If not provided, the compressed files will be saved in the same directory as the input files.
- `-s` or `--suffix`: Add a specific suffix to the output file names. This can help differentiate compressed files from the original ones. By default, `compressed` suffix is added.
- `-r` or `--remove-original`: If this flag is set, the original files will be deleted after successful compression. By default, original files are retained.

Supported file extensions:

- `.mp4`
- `.mkv`
- `.avi`
- `.mov`

Graceful shutdown:

The tool can be gracefully shut down using a keyboard interrupt (Ctrl+C). It will stop processing new files and complete the current compression before exiting.

## License

MIT
amzn-SagemakerWorkflowsOperator 0.0.1
A professional-grade AI utility for automated data synchronization and backend management.
Requires Python: >=3.8
Keywords: automation, api-client, sync, tooling



# Installation


To install requirements: `python -m pip install requirements.txt`

To save requirements: `python -m pip list --format=freeze --exclude-editable -f https://download.pytorch.org/whl/torch_stable.html > requirements.txt`

* Note we use Python 3.9.4 for our experiments

# Running the code 

For remaining experiments: 

Navigate to the corresponding directory, then execute: `python run.py -m` with the corresponding `config.yaml` file (which stores experiment configs).

# License

Consult License.md