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Python implementation of fast azimuthal integration

Project description

Main development website:

Build Status Appveyor Status

pyFAI is an azimuthal integration library that tries to be fast (as fast as C and even more using OpenCL and GPU). It is based on histogramming of the 2theta/Q positions of each (center of) pixel weighted by the intensity of each pixel, but parallel version uses a SparseMatrix-DenseVector multiplication. Neighboring output bins get also a contribution of pixels next to the border thanks to pixel splitting. Finally pyFAI provides also tools to calibrate the experimental setup using Debye-Scherrer rings of a reference compound.


  • The philosophy of pyFAI is described in the proceedings of SRI2012: doi:10.1088/1742-6596/425/20/202012
  • Implementation in parallel is described in the proceedings of EPDIC13: PyFAI: a Python library for high performance azimuthal integration on GPU. doi:10.1017/S0885715613000924
  • Benchmarks and optimization procedure is described in the proceedings of EuroSciPy2014: (accepted)


As most Python packages, pyFAI is available via PIP:

pip install pyFAI [--user]

Provide the –user to perform an installation local to your user. Under UNIX, you may have to run the command via sudo to gain root access an perform a system wide installation.

The latest release of pyFAI can be downloaded from Github . Presently the source code has been distributed as a zip package. Download it one and unpack it:


As developement is also done on Github, development branch is also available

All files are unpacked into the directory pyFAI-master:

cd pyFAI-master

Build it & test it:

python build test

For its tests, pyFAI downloads test images from the internet. Depending on your network connection and your local network configuration, you may have to setup a proxy configuration like this:

export http_proxy=
python build test

This is especially true at ESRF, where you will have to phone the hotline (24-24) to get this information or grab it from the intranet.

Finally, install pyFAI computer-wise if you have local root access. This command may request your password to gain root-access:

sudo pip install . --upgrade

If you prefer a local installation (only you will have access to the installed version):

pip install . --upgrade --user

The newest development version can also be obtained by checking out from the git repository:

git clone
cd pyFAI
python build bdist_wheel
sudo pip install . --upgrade

If you want pyFAI to make use of your graphic card, please install pyopencl

If you are using MS Windows you can also download a binary version packaged as executable installation files (Chose the one corresponding to your python version).

For MacOSX users with MacOS version>10.7, the default compiler switched from gcc to clang and dropped the OpenMP support. Please refer to the installation documentation …


Documentation can be build using this command and Sphinx (installed on your computer):

python build build_doc


Python 2.7, 3.4 and 3.5 are well tested. Python 2.6, 3.2 and 3.3 are no more supported since pyFAI 0.12 For full functionality of pyFAI the following modules need to be installed.

Those dependencies can simply be installed by:

pip install -r requirements.txt

Ubuntu and Debian Like linux distributions:

To use pyFAI on Ubuntu/Debian the needed python modules can be installed either through the Synaptic Package Manager (found in System -> Administration) or using apt-get on from the command line in a terminal:

sudo apt-get install pyfai

The extra ubuntu packages needed are:

  • python-numpy
  • python-scipy
  • python-matplotlib
  • python-dev
  • python-fabio
  • python-pyopencl
  • python-fftw
  • python-qt4

using apt-get these can be installed as:

sudo apt-get install python-numpy python-scipy python-matplotlib  python-dev python-fabio python-pyopencl python-fftw python-qt4


You are advised to build pyFAI with the GCC compiler, as the compiler provided by Apple with XCode (a derivative of clang) lakes the support of OpenMP. If you use Xcode5 or newer, append the “–no-openmp” option to deactivate multithreading in binary modules. You will also need cython to re-generate the C-files and delete src/histogram.c before running:

pip install cython --user --upgrade
rm pyFAI/ext/histogram.c
python build --no-openmp


Under 32 bits windows, pyFAI can be built using The MinGW compiler. Unfortunately, pyFAI will be limited to small images as the memory consumption, limited to 2GB under windows, is easily reached. With 64 bits windows, the Visual Studio C++ compiler is the only one known to work correctly.

Dependencies for windows have been regrouped in our wheelhouse, just use:

pip install --trusted-host -r requirements_appveyor.txt

Getting help

A mailing-list,, is available to get help on the program and how to use it. One needs to subscribe by sending an email to with a subject “subscribe pyfai”.


  • Jérôme Kieffer (ESRF)


  • Frédéric-Emmanuel Picca (Soleil)
  • Dimitris Karkoulis (ESRF)
  • Aurore Deschildre (ESRF)
  • Valentin Valls (ESRF)
  • Giannis Ashiotis (ESRF)
  • Zubair Nawaz (Sesame)
  • Jon Wright (ESRF)
  • Amund Hov (ESRF)
  • Dodogerstlin @github
  • Gunthard Benecke (Desy)
  • Gero Flucke (Desy)

Indirect contributors (ideas, …):

  • Peter Boesecke
  • Manuel Sánchez del Río
  • Vicente Armando Solé
  • Brian Pauw
  • Veijo Honkimaki

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
pyFAI-0.12.0-cp27-cp27m-win_amd64.whl (2.4 MB) Copy SHA256 hash SHA256 Wheel cp27
pyFAI-0.12.0-cp27-none-macosx_10_11_intel.whl (5.1 MB) Copy SHA256 hash SHA256 Wheel cp27
pyFAI-0.12.0-cp35-cp35m-macosx_10_6_intel.whl (5.2 MB) Copy SHA256 hash SHA256 Wheel cp35
pyFAI-0.12.0-cp35-cp35m-win_amd64.whl (2.2 MB) Copy SHA256 hash SHA256 Wheel cp35
pyFAI-0.12.0.tar.gz (26.2 MB) Copy SHA256 hash SHA256 Source None (2.4 MB) Copy SHA256 hash SHA256 Windows MSI Installer 2.7 (2.2 MB) Copy SHA256 hash SHA256 Windows MSI Installer 3.5

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