Skip to main content

Package for astronomical high-contrast image processing.

Project description

VIP - Vortex Image Processing package

.. image::

.. image::

.. image::

.. image::

.. image::

.. image::


``VIP`` is a python package for angular, reference star and spectral
differential imaging for exoplanet/disk detection through high-contrast imaging.
``VIP`` is compatible with Python 2.7 and 3.6.

The goal of ``VIP`` is to incorporate 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 C. Gomez with contributions
made by collaborators from several teams (take a look at the tab contributors 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, suggest or add a functionality please create an issue or send a pull
request `here <>`_.

The documentation for ``VIP`` can be found here:

Jupyter notebook tutorial
A tutorial (Jupyter notebook) showing he use of ``VIP`` for ADI data processing
is available in `this repository <>`_.
Alternatively, it can be visualized online
`here <>`_.
If you are new to the Jupyter notebook application check out the `beginner guide

TL;DR setup guide
.. code-block:: bash

$ 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 which
you can find here:

``VIP`` depends on existing packages from the Python ecosystem, such as
``numpy``, ``scipy``, ``matplotlib``, ``pandas``, ``astropy``, ``scikit-learn``,
``scikit-image``, ``photutils`` and ``emcee``. 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:

.. code-block:: bash

$ pip install vip_hci

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

.. code-block:: bash

$ pip install --upgrade vip_hci

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

.. code-block:: bash

$ 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:

.. code-block:: bash

$ 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):

.. code-block:: bash

$ 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``:

.. code-block:: bash

$ 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).

Also, optionally you can install the Intel Math Kernel Library (MKL)
optimizations provided that you have Anaconda(>v2.5) and ``conda`` on your
system. This is recommended along with ``Opencv`` for maximum speed on ``VIP``
computations. Run:

.. code-block:: bash

$ conda install mkl

Starting from version 0.8.0 ``VIP`` offers the possibility of computing SVDs
on GPU by using ``cupy``. This remains an optional requirement, to be installed
by the user, as it requires having a decent GPU card and a proper CUDA

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

.. code-block:: python

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
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).

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

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
vip_hci-0.9.0-py2.py3-none-any.whl (204.1 kB) Copy SHA256 hash SHA256 Wheel py2.py3
vip_hci-0.9.0.tar.gz (150.4 kB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page