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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 high-contrast imaging of exoplanets and circumstellar disks. VIP is compatible with Python 3.8, 3.9, 3.10 and 3.11 (Python 2 compatibility dropped with VIP 0.9.9, and Python 3.7 compatibility dropped with VIP 1.4.3).

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.

Most of VIP’s functionalities are mature but it does not mean it is free from bugs. The code is continuously evolving and therefore feedback/contributions are greatly appreciated. Please refer to these instructions if you want to report a bug, ask a question, suggest a new functionality or contribute to the code (the latter is particularly welcome)!

Mosaic of S/N maps


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 datasets are available in the VIP-extras repository. In order to execute the notebook tutorials, you will have to download or clone the VIP-extras repository, and open each tutorial locally with jupyter notebook. Alternatively, you can execute the notebooks directly on Binder (in the tutorials directory). The first (quick-start) notebook 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, 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. We 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.

Before installing the package, it is highly recommended to create a dedicated conda environment to not mess up with the package versions in your base environment. This can be done easily with (replace vipenv by the name you want for your environment):

$ conda create -n vipenv python=3.10 ipython

For users not planning to contribute:

Once within your new environment, 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

For potential contributors:

If you plan to contribute or experiment with the code you need to make a fork of the repository (click on the fork button in the top right corner) and clone it:

$ git clone<replace-by-your-username>/VIP.git

If you do not create a fork, you can still benefit from the git syncing functionalities by cloning the repository (but will not be able to contribute):

$ git clone

To install VIP, then simply cd into your local VIP directory, and run the installation in editable mode pointing to developer requirements:

$ cd VIP
$  pip install -e . -r requirements-dev.txt

If cloned from your fork, make sure to link your VIP directory to the upstream source, to be able to easily update your local copy when a new version comes out or a bug is fixed:

$ git add remote upstream

If you plan to develop VIP or use it intensively, it is highly recommended to also install the optional dependencies listed below.

Optional dependencies

The following dependencies are not automatically installed upon installation of VIP but may significantly improve your experience:

  • VIP contains a class vip_hci.vip_ds9 that enables, through pyds9, the interaction with a DS9 window (displaying numpy arrays, controlling the display options, etc). To enable this feature, pyds9 must be installed from the latest development version: pip install git+git://

  • VIP image operations (e.g. shifts, rotations, scaling) can be performed using OpenCV instead of the default FFT-based methods. While flux are less well preserved, OpenCV offers a significant speed improvement (up to a factor 50x), in particular for image rotations, which can be useful to get quick results. Installation: pip install opencv-python.

  • 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.

  • 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).

  • Bad pixel correction routines can be optimised with Numba, which converts some Python code, particularly NumPy, into fast machine code. A factor up to ~50x times speed improvement can be obtained on large images compared to NumPy. Numba can be installed with conda install numba.

  • Finally, robust contrast curves and contrast grids can be calculated with applefy (pip install Applefy). Example usage is provided in VIP tutorial 4. See more details in Bonse et al. (2023).

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 should not get any error message upon importation, and you can start finding exoplanets!

Image conventions

By default, VIP routines are compatible with either even- or odd-dimension input frames. For VIP routines that require the star to be centered in the input images (e.g. post-processing routines involving (de)rotation or scaling), the code will assume that it is placed on (zero-based indexing):

  • size/2-0.5 for odd-size input images;

  • size/2 for even-size input images;

i.e. exactly on a pixel in either cases. The VIP recentering routines will place the star centroid at one of these locations accordingly.


Answers to frequently asked questions are provided in the relevant section of the documentation. If you have an issue with VIP, please first check it is not detailed in the FAQ. If you find a bug or experience an unreported issue in VIP, it is recommended to post a new entry in the Issues section on GitHub. Feel free to propose a pull request if you have already identified the source of the bug/issue.

If you have a global comment, inquiry about how to solve a specific task using VIP, or suggestions to improve VIP, feel free to open a new thread in the Discussions section. The ‘Discussions’ section is also used to post VIP-related announcements and discuss recent/on-going changes in VIP. Envisioned future developments are listed in the Projects section. Contributions are very welcome!

If you wish to be kept informed about major VIP updates and on-going/future developments, feel free to click the ‘watch’ button at the top of the GitHub page.


VIP started as the effort of Carlos Alberto Gomez Gonzalez, a former PhD student of PSILab (ULiege, Belgium), who has led the development of VIP from 2015 to 2020. Maintenance and current development is now led by Valentin Christiaens. VIP benefitted from contributions made by collaborators from several teams, including: Ralf Farkas, Julien Milli, Olivier Wertz, Henry Ngo, Alan Rainot, Gary Ruane, Corentin Doco, Miles Lucas, Gilles Orban de Xivry, Lewis Picker, Faustine Cantalloube, Iain Hammond, Christian Delacroix, Arthur Vigan, Dimitri Mawet and Olivier Absil. More details about the respective contributions are available here.

Please cite Gomez Gonzalez et al. (2017) and Christiaens et al. (2023) whenever you publish data reduced with VIP (Astrophysics Source Code Library reference ascl:1603.003). In addition, please cite the relevant publication(s) for the algorithms you use within VIP (usually mentioned in the documentation, e.g. Marois et al. 2006 for median-ADI).

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