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pyFAI: Fast Azimuthal Integration in Python

Main development website: https://github.com/silx-kit/pyFAI

Github Actions Appveyor Status myBinder Launcher Zenodo DOI RTD docs

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.

References

Installation

With PIP

As most Python packages, pyFAI is available via PIP:

pip install pyFAI[gui]

It is advised to run this in a vitural environment . Provide the --user option to perform an installation local to your user-space (not recommended). Under UNIX, you may have to run the command via sudo to gain root access and perform a system wide installation (which is neither recommended).

With conda

pyFAI is also available via conda:

conda install pyfai -c conda-forge

To install conda please see either conda or Anaconda.

From source code

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

unzip pyFAI-main.zip

All files are unpacked into the directory pyFAI-main:

cd pyFAI-main

Install dependencies:

pip install -r requirements.txt

Build it & test it:

python3 run_tests.py

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 (not needed at ESRF):

export http_proxy=http://proxy.site.org:3128

Finally, install pyFAI in the virtualenv after testing it:

pip install .

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

git clone https://github.com/silx-kit/pyFAI.git
cd pyFAI
pip install .

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

Documentation

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

python3 build-doc.py

Dependencies

Python 3.8, ... 3.12 are well tested and officially supported. 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:

  • python3-numpy
  • python3-scipy
  • python3-matplotlib
  • python3-dev
  • python3-fabio
  • python3-pyopencl
  • python3-pyqt5
  • python3-silx
  • python3-numexpr

using apt-get these can be installed as:

sudo apt-get build-dep pyfai

MacOSX

One needs to manually install a recent version of Python (>=3.8) prior to installing pyFAI. Apple provides only an outdated version of Python 2.7 which is now incomatible. If you want to build pyFAI from sources, you will also need Xcode which is available from the Apple store. The compiled extension will use only one core due to the limitation of the compiler. OpenCL is hence greately adviced on Apple systems. Then install the missing dependencies with pip:

pip install -r requirements.txt

Windows

Under Windows, one needs to install Python (>=3.8) prior to pyFAI. The Visual Studio C++ compiler is also needed when building from sources. Then install the missing dependencies with pip:

pip install  -r requirements.txt

Getting help

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

Maintainers

  • Jérôme Kieffer (ESRF)
  • Edgar Gutierrez Fernandez (ESRF)
  • Maciej Jankowski (ESRF)

Contributors

  • Valentin Valls (ESRF)
  • Frédéric-Emmanuel Picca (Soleil)
  • Thomas Vincent (ESRF)
  • Dimitris Karkoulis (Formerly ESRF)
  • Aurore Deschildre (Formerly ESRF)
  • Giannis Ashiotis (Formerly ESRF)
  • Zubair Nawaz (Formerly Sesame)
  • Jon Wright (ESRF)
  • Amund Hov (Formerly 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