semvec/pypi-packages huggingface dataset:
RoboWFuzz 1.1
RoboWFuzz - Generic Robot Framework Library for the WFuzz Fuzzing Framework. Currently, only a Directory Bruter
License: MIT

## RoboWFuzz

Robot Framework Integration for the generic python fuzzing library `wfuzz`

Currently, there's only a single functionality implemented, which is Directory Bruteforcing

`brute directories | url | concur=2 | file_name=exportfile.json | follow=False`

- `url`: mandatory param
- `concur`: concurrency, the number of parallel processes that need to be run. optional param
- `file_name`: export file name, default writes to the same directory that you install. Optional Param
- `follow`: Follow 301 redirects or not, defaults to False. Optional Param

```
*** Settings ***
Library  RoboWFuzz  lists/directory-list-1.0.txt

*** Test Cases ***
Directory Bruter
    [Timeout]  15 minutes
    brute_directories  http://testphp.vulnweb.com/FUZZ  concur=6  follow=True

```
ndreg 1.0.0
Registration package that does affine and LDDMM registration
License: MIT

# ndreg
[![Travis](https://travis-ci.org/neurodata/ndreg.svg?branch=master)](https://travis-ci.org/#)
[![Documentation Status](https://readthedocs.org/projects/ndreg/badge/?version=latest)](http://ndreg.readthedocs.io/en/latest/?badge=latest)
[![DockerHub](https://img.shields.io/docker/pulls/neurodata/ndreg.svg)](https://hub.docker.com/r/neurodata/ndreg)
<br/>
Package that performs affine and LDDMM registration *easily* <br/>

## Sytem Requirements

The recommended way to use this package is to install [Docker](https://store.docker.com/search?offering=community&type=edition). Docker is currently available on Mac OS X El Capitan 10.11 and newer macOS releases, the following Ubuntu versions: Zesty 17.04 (LTS), Yakkety 16.10, Xenial 16.04 (LTS), Trusty 14.04 (LTS), and Windows 10.

### Software Dependencies (with version numbers)

The only software dependency needed if using the recommended method is Docker. The following dependencies are included in the Docker Image.

External libraries: <br/>
- Insight Segmentation and Registration Toolkit (ITK) -- 4.12.2

Python depedencies: <br/>
- jupyter -- (1.0.0)
- numpy -- (1.13.3)
- scikit-image -- (0.13.1)
- scikit-learn -- (0.19.1)
- scipy -- (1.0.0)
- SimpleITK -- (1.0.1)

### Versions tested on
We have tested the Docker image and build on macOS High Sierra (on MacBook Pro with 2.9 GHz Intel Core i7 and 16 GB RAM) and Ubuntu Xenial 16.04.3 LTS (with 64 GB RAM).

## Installation Guide

Once Docker is installed on your machine, pull the `neurodata/ndreg` image from Docker Hub [here](https://hub.docker.com/r/neurodata/ndreg) as follows: <br/>

`docker pull neurodata/ndreg` <br/>

It will typically take around 3 minutes to pull the entire Docker image.

## Demo

### Instructions to run on data

In order to use the functionality built into this Docker image, you need to run the Docker image:

`docker run -p 8888:8888 neurodata/ndreg` <br/>

This should print a link to the terminal console that looks like this: <br/>

`http://0.0.0.0:8888/?token=SOME_TOKEN` <br/>

Go to this link in your browser by copying and pasting it. <br/>

Next, click on `ndreg_demo.ipynb`. Once the notebook opens, you can run all cells by clicking on 'Cell' and then 'Run All'.

The expected run time for this demo is ~ 2 minutes.

### Expected Output

The last 3 cells in the demo notebook display images that should look the same (both the LDDMM registered image and the displacement field warped image) and a mean squared error

### Congrats, you've succesfully run ndreg!