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Package for astronomical high-contrast image processing.

Project description
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         Vortex Image Processing package


VIP is a python package for angular, reference star and spectral differential imaging for exoplanet and disk high-contrast imaging. VIP is compatible with Python 3 (Python 2 compatibility dropped with VIP 0.9.9).

Mosaic of S/N maps

The goal of VIP is to integrate open-source, efficient, easy-to-use and well-documented implementations of high-contrast image processing algorithms to the interested scientific community. The main repository of VIP resides on GitHub, the standard for scientific open source code distribution, using Git as a version control system.

VIP started as the effort of Carlos Alberto Gomez Gonzalez, a former PhD student of the VORTEX team (ULiege, Belgium). VIP’s development is led by Dr. Gomez with contributions made by collaborators from several teams (take a look at the contributors tab on VIP’s GitHub repository). Most of VIP’s functionalities are mature but it doesn’t mean it’s free from bugs. The code is continuously evolving and therefore feedback/contributions are greatly appreciated. If you want to report a bug or suggest a functionality please create an issue on GitHub. Pull requests are very welcomed!


The documentation for VIP can be found here:

Jupyter notebook tutorial

Tutorials, in the form of Jupyter notebooks, showcasing VIP’s usage and other resources such as test/dummy datasets are available on the VIP-extras repository. Alternatively, you can execute this repository on Binder. The notebook for ADI processing can be visualized online with nbviewer. If you are new to the Jupyter notebook application check out the beginner’s guide.

TL;DR setup guide

$ pip install vip_hci

Installation and dependencies

The benefits of using a Python package manager (distribution), such as (ana)conda or Canopy, are many. Mainly, it brings easy and robust package management and avoids messing up with your system’s default python. An alternative is to use package managers like apt-get for Ubuntu or Homebrew/MacPorts/Fink for macOS. I personally recommend using Miniconda.

VIP depends on existing packages from the Python ecosystem, such as numpy, scipy, matplotlib, pandas, astropy, scikit-learn, scikit-image, photutils and others. There are different ways of installing VIP suitable for different scenarios.

Using pip

The easiest way to install VIP is through the Python Package Index, aka PyPI, with the pip package manager. Simply run:

$ pip install vip_hci

With pip you can easily uninstall, upgrade or install a specific version of VIP. For upgrading the package run:

$ pip install --upgrade vip_hci

Alternatively, you can use pip install and point to the GitHub repo:

$ pip install git+

Using the file

You can download VIP from its GitHub repository as a zip file. A file (setuptools) is included in the root folder of VIP. Enter the package’s root folder and run:

$ python install

Using Git

If you want to benefit from the git functionalities, you need to clone the repository (make sure your system has git installed):

$ git clone

Then you can install the package by following the previous steps, using the file. Creating a fork with GitHub is recommended to developers or to users who want to experiment with the code.

Other dependencies

OpenCV (Open source Computer Vision) provides fast C++ image processing operations and is used by VIP for basic image transformations. If you don’t have/want the OpenCV python bindings (OpenCV is optional since VIP v0.5.2), VIP will use the much slower ndimage/scikit-image libraries transparently. Fortunately, installing OpenCV library is nowadays and easy process that is done automatically with the VIP installation. Alternatively, you could use conda:

$ conda install opencv

VIP contains a class vip_hci.fits.ds9 that enables, through pyds9, the interaction with a DS9 window (displaying numpy arrays, controlling the display options, etc). pyds9 is an optional requirement and must be installed from the latest development version:

$ pip install git+git://

Also, you can install the Intel Math Kernel Library (MKL) optimizations (provided that you have a recent version of conda) or openblas libraries. Either of them can be installed with conda install. This is recommended along with OpenCV for maximum speed on VIP computations.

VIP offers the possibility of computing SVDs on GPU by using CuPy (starting from version 0.8.0) or PyTorch (from version 0.9.2). These remain as optional requirements, to be installed by the user, as well as a proper CUDA environment (and a decent GPU card).

Loading VIP

Finally, start Python (or IPython or a Jupyter notebook if you prefer) and check that you are able to import VIP:

import vip_hci as vip

If everything went fine with the installation, you will see a welcome message. Now you can start finding exoplanets!

Mailing list

Please subscribe to our mailing list if you want to be informed of VIP’s latest developments (new versions and/or updates).


Please cite Gomez Gonzalez et al. 2017 ( whenever you publish data reduced with VIP. Astrophysics Source Code Library reference [ascl:1603.003].

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