Skip to content

dmitryduev/VIP

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

VIP - Vortex Image Processing package

Attribution

Please cite Gomez Gonzalez et al. 2017 (http://iopscience.iop.org/article/10.3847/1538-3881/aa73d7/) whenever you publish data reduced with VIP. Astrophysics Source Code Library reference [ascl:1603.003].

Introduction

VIP is a package/pipeline for angular, reference star and spectral differential imaging for exoplanet/disk detection through high-contrast imaging. VIP is being developed in Python 2.7.

VIP is being developed within the VORTEX team @ University of Liege (Belgium). Most of VIP's functionalities are mature but it doesn't mean it's free from bugs. The code will continue evolving and therefore all the feedback and contributions will be greatly appreciated. If you want to report a bug, suggest or add a functionality please create an issue or send a pull request on the github repository.

Jupyter notebook tutorial

VIP tutorial (Jupyter notebook) is available in this repository and can be visualized online here. If you are new to the Jupyter notebook application read the beginner guide. TL;DR download the tutorial from its repository and from the terminal run:

$ cd <path_to_tutorial_folder>
$ jupyter-notebook

Documentation

Documentation can be found in http://vip.readthedocs.io/.

Quick Setup Guide

Run:

$ pip install git+https://github.com/vortex-exoplanet/VIP.git

Read the documentation for a detailed installation procedure (and optionally, how to install Opencv).

Mailing list

You can subscribe to our mailing list if you want to be informed of the latest developments of the VIP package (new versions and/or updates).

About

VIP is a python package/library for angular, reference star and spectral differential imaging for exoplanet/disk detection through high-contrast imaging.

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages

  • Python 100.0%