semvec/pypi-packages huggingface dataset:
send2airgap 0.0.3
A Python package enabling the secure transfer of data from an insecure sender to an air-gapped computer equipped with a camera.
Requires Python: >=3.13
Keywords: air gap, air gapped, air gapping, air wall, air-gap, air-gapping, air-wall, airgap, airgapped, airgapping, airwall, camera, communication security, communicationsecurity, cybersecurity, data transmission, datatransfer, disconnected network, hardwaresecurity, isolatednetwork, network security, networking, networksecurity, physically isolated, qr-code, qrcode, receive data, recvdata, secure computer network, securecomputing, send data, senddata

# Send 2 Air-Gap

[![codecov](https://codecov.io/gh/poing/Send2AirGap/branch/0.0.3/graph/badge.svg?token=LTQYLRKJ6M)](https://codecov.io/gh/poing/Send2AirGap)

[![CI](https://github.com/poing/Send2AirGap/actions/workflows/main.yml/badge.svg)](https://github.com/poing/Send2AirGap/actions/workflows/main.yml)

This is a project to provide data to an air-gapped system.

The air-gapped system uses a camera to receive unidirectional communication.  Creating a "data diode" that can be used to provide data **to** systems in a high security environment.

The *unsecure* system will display the `data` using qr-codes, that are decoded on the secure air-gapped system.

There's even a method to *acknowledge* receipt of the `data`.  The method *involves* inputing an associated timestamp on the *unsecure* system.  This would *typically* be a manual operation, but **does** provide *possible* ways to automate the acknowledgement.  





## Install it from PyPI

```bash
pip install send2airgap
```

## Usage

```py
from send2airgap import BaseClass
from send2airgap import base_function

BaseClass().base_method()
base_function()
```

```bash
$ python -m send2airgap
#or
$ send2airgap
```

## Development

Read the [CONTRIBUTING.md](CONTRIBUTING.md) file.
streamlit-vuesigma-component 0.0.1
Streamlit Vue component to visualize network graph data using Sigma.js.
Requires Python: >=3.7

# Streamlit Vue Sigma Component

A streamlit component to visualize network graph data.

Combines Python, Streamlit, Vue3 and Sigma.js.

## Key Features

- Visualize network data
- Interact with the graph
- Display additional nodes & edges attributes

(screenshot)

## Technology stack

- Python
- Streamlit
- Vue3
- Sigma.js
- Vite (during development)
sbcli-bckp 0.0.4
CLI for managing SimplyBlock cluster


# Simply Block
[![Docker Image Build](https://github.com/simplyblock-io/sbcli/actions/workflows/docker-image.yml/badge.svg)](https://github.com/simplyblock-io/sbcli/actions/workflows/docker-image.yml)

[![Python Unit Testing](https://github.com/simplyblock-io/sbcli/actions/workflows/python-testing.yml/badge.svg)](https://github.com/simplyblock-io/sbcli/actions/workflows/python-testing.yml)


## Install
Add the package repo from AWS CodeArtifact using [awscli](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html)

```bash
aws codeartifact login --tool pip --repository sbcli --domain simplyblock --domain-owner 565979732541 --region eu-west-1
```
Install package
```bash
pip install --extra-index-url https://pypi.org/simple sbcli-dev
```

# Components

## Simply Block Core
Contains core logic and controllers for the simplyblock cluster

## Simply Block CLI
Please see this document
[README.md](../main/simplyblock_cli/README.md)


## Simply Block Web API
Please see this document
[README.md](../main/simplyblock_web/README.md)



### local development

FoundationDB requires a client library (libfdb_c.dylib) for the Python bindings to interact with the database.
Depending on the OS architecture, please install the appropriate version from the official github repo

```
wget https://github.com/apple/foundationdb/releases/download/7.3.3/FoundationDB-7.3.3_arm64.pkg
```

setup the code on a management node and the webApp code can be developed by building the `docker-compose-dev.yml` file.


```
sudo docker compose -f docker-compose-dev.yml up --build -d
```
michelangelo-ai 0.1.0
Michelangelo is an end-to-end model lifecycle management system at large scale
Requires Python: <4.0.0,>=3.9.0

Michelangelo SDK

## User Guide

```
pip install michelangelo
```

TODO: User Guide

## Developer Guide

### Preprequisites

- Python 3.9
- Poetry: https://python-poetry.org

### Cheat Sheet

- Install dependencies: `poetry install`
- Add a new dependency: `poetry add <package-name>`
- Run tests: `poetry run pytest`
- Run examples: `poetry run python ./examples/bert_cola/bert_cola.py`
- Format code: `poetry run black .`
- Run Michelangelo CLI: `poetry run ma --help`

### Environment Setup: Mac

- Install Python 3.9: `brew install python@3.9`
- Install Poetry: `curl -sSL https://install.python-poetry.org | python3.9 -`
- Create Python virtual environment and install packages: `poetry install`

The last step will create a `.venv` directory if it doesn't already exist.
This directory contains a Python virtual environment with all the dependencies installed.
You can activate this virtual environment and use it like any other Python virtual environment, 
or you can run commands via the Poetry CLI, e.g., `poetry run python`, `poetry run pytest`, etc.