semvec/pypi-packages huggingface dataset:
mass-driver-plugins 0.4.1
Experimental plugin ecosystem for Mass Driver
Requires Python: >=3.11,<4.0

# Mass Driver Plugins

Experimental plugin ecosystem for Mass Driver

Requires Python 3.10


## Usage

Depends on what the code in there does.

### Run the command

Install the module first:

    make install
    # or
    poetry install

Then inside the virtual environment, launch the command:

    # Run single command inside virtualenv
    poetry run mass-driver-plugins

    # or
    # Load the virtualenv first
    poetry shell
    # Then launch the command, staying in virtualenv
    mass-driver-plugins

## Development

### Python setup

This repository uses Python3.10, using
[Poetry](https://python-poetry.org) as package manager to define a
Python package inside `src/mass_driver_plugins/`.

`poetry` will create virtual environments if needed, fetch
dependencies, and install them for development.


For ease of development, a `Makefile` is provided, use it like this:

	make  # equivalent to "make all" = install lint docs test build
	# run only specific tasks:
	make install
	make lint
	make test
	# Combine tasks:
	make install test

Once installed, the module's code can now be reached through running
Python in Poetry:

	$ poetry run python
	>>> from mass_driver_plugins import main
	>>> main("blabla")


This codebase uses [pre-commit](https://pre-commit.com) to run linting
tools like `flake8`. Use `pre-commit install` to install git
pre-commit hooks to force running these checks before any code can be
committed, use `make lint` to run these manually. Testing is provided
by `pytest` separately in `make test`.

### Documentation

Documentation is generated via [Sphinx](https://www.sphinx-doc.org/en/master/),
using the cool [myst_parser](https://myst-parser.readthedocs.io/en/latest/)
plugin to support Markdown files like this one.

Other Sphinx plugins provide extra documentation features, like the recent
[AutoAPI](https://sphinx-autoapi.readthedocs.io/en/latest/index.html) to
generate API reference without headaches.

To build the documentation, run

    # Requires the project dependencies provided by "make install"
    make docs
	# Generates docs/build/html/

To browse the website version of the documentation you just built, run:

    make docs-serve

And remember that `make` supports multiple targets, so you can generate the
documentation and serve it:

    make docs docs-serve


### Templated repository

This repo was created by the cookiecutter template available at
https://github.com/OverkillGuy/python-template, using commit hash: `a00dd61d7d855303eb607ddfa46a5447d1f042bd`.
speech-intelligibility-index-malcolm-slaney 1.0.0
Code that implements the ANSI standard Speech Intelligibility Index
Requires Python: >=3.7

A Python implementation of American National Standard ANSI S3.5-1997
**"Methods for Calculation of the Speech Intelligibility Index"**
This code is translated from Hannes Muesch's Matlab implementation.

This package includes both Python/Numpy and Python/JAX implementations.

There is a Google Colab that demonstrates how to use this code and shows
typical results.  This colab is a good place to start:
https://colab.research.google.com/drive/1bAnDpUUx5-BYkL3l3ukNVVnsLzEhy-ds.

For more information about the speech intelligibility index (SII) please see
the Acoustical Society of America (ASA) working group's web site at
https://sii.to/index.html.

To use these scripts successfully, you might want to be familiar with the
concepts and definitions of the Speech Intelligibility Index (SII).
You can obtain a copy of the standard at the ANSI webstore
https://webstore.ansi.org/standards/asa/ansiasas31997r2017

You can reach out to the members of the Acoustical Society of America (ASA)
Working Group S3-79, which is in charge of reviewing that standard, at
https://sii.to/index.html.
dreem 0.1.16
DREEM solves RNA structure ensembles using chemical probing data
Requires Python: >=3.10

[![Build docs](https://github.com/rouskinlab/dreem/actions/workflows/documentation.yml/badge.svg)](https://github.com/rouskinlab/dreem/actions/workflows/documentation.yml)
[![Run tests](https://github.com/rouskinlab/dreem/actions/workflows/tests.yaml/badge.svg?branch=main)](https://github.com/rouskinlab/dreem/actions/workflows/tests.yaml)

# DREEM

Prof. Silvi Rouskin's [DREEM algorithm](https://www.nature.com/articles/s41586-020-2253-5).

## Installation

Installation instructions [here](https://rouskinlab.github.io/dreem/dreem/installation.html).

## Documentation

The documentation is available on [Github Pages](https://rouskinlab.github.io/dreem).

## Contributors

Yves Martin, Scott Grote, Matthew Allan, Albéric de Lajarte.

## For developers

Before pushing to main, ensure that the code passes the tests:

```
pytest test/test_pipeline.py
```

and that the docs compile:

```
cd docs
make html
```
yolov8 0.0.2
:warning: The `yolov8` package is a placeholder, not the official Ultralytics version. Please install the official `ultralytics` package via `pip install ultralytics` instead.
Requires Python: >=3.7
Keywords: machine-learning,deep-learning,ml,pytorch,YOLO,object-detection,vision,YOLOv3,YOLOv4,YOLOv5,YOLOv6,YOLOv7,YOLOv8

# :warning: Important Notice for Ultralytics YOLOv8 Users

Please note that the `yolov8` Python package here is a placeholder and not the official package from Ultralytics. For the most reliable and up-to-date features, you should instead install the official `ultralytics` package at https://pypi.org/project/ultralytics/.

To do this, simply run the following command in your Python environment:

```bash
pip install ultralytics
```

This will ensure you are using the most accurate, efficient, and maintained code for your YOLO models. Please see https://docs.ultralytics.com/quickstart/ for full documentation on installing and getting started with Ultralytics YOLOv8. 

Thank you for your understanding and cooperation.
landing-zone 1.0.0
Checks if a user input reaches a specified function; requires gdb
Requires Python: >=3.9,<4.0

# Landing Zone

Landing Zone is a proof of concept tool that checks if specific user inputs can lead to execution of known-vulnerable
functions.

Example use cases:

- you are an exploit researcher trying to determine if a list of inputs can lead to a vulnerable function
- you are a vulnerability management practioner who wants to varify that your security controls can prevent specific
  inputs from reaching known-vulnerable functions in an application.

This tool was created to satisfy classwork for Dakota State University's CSC-842 Security Tool Development.

Demo video: [here]()

## Prerequisites

You must have the following software installed prior to using this tool:

- Python 3 interpreter
- Pip3
- GNU Debugger

## Usage

**Installation**

```bash
pip install landing-zone

miley --help
```

**Scan a hash (MD5, SHA-1, or SHA-256)**

```bash
miley --hash f617abd6a9ccb98e34a6e32184004d5a08ea11d198fca9fed88b04b9dfc96de2
```

**Scan a directory for malware**

```bash
miley --path /path/to/directory/
```

**Scan a remote container image**

```bash
miley --img alpine:latest
```

## Future Work

If I were to maintain this program going forward, I would:

- speed up the program
- support additional malware databases, such as Virus Total
- add a GUI

## Why is it named Miley?

Miley is my beloved family dog, who passed away in 2023 due to old age.

Miley was always good at detecting and alerting to potentially dangerous situations, despite being blind.

I wrote this tool with her memory at the forefront of my mind.

<img src="images/miley.png" alt="miley" width="400" height=auto/>