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Sakura: A tool to pre-process XAS data
X-ray absorption spectroscopy

Sakura: A tool to pre-process XAS data

Download

To obtain the current software version, please enter an email address in the following field and click 'Subscribe' to obtain download links. Collection of your email address enables us to get an idea of the number of users and is covered by our privacy policy, stated here. We may use this address to inform you of new versions of the software.

The XAS team are interested in any feedback of your experience using the software. Please send bug reports and suggestions to sakura@synchrotron.org.au

sakura screenshot xas

Current Version

1.0

Sakura depends on Python 2.7 and the Python scientific software modules listed here to operate. However, the standalone versions for Windows and Mac OS X do not require a separately installed Python distribution. But if you decide to download the source code version, you probably also need to install a Python distribution first (like EPD free or Canopy) to satisfy the dependencies. 

Windows

Mac OS X (standalone application)

Mac OS X (source code)

Linux (source code)

Features

  • Reads step-mode (.mda file) and mapping-mode data (.mda+.nc files) generated at the Australian Synchrotron's XAS beamline.
  • Exports columnar ASCII.
  • Supports normalisation of data to internally available data channels.
  • Cross-platform. Runs on Windows/Mac OSX/Linux
  • Released under the Modified BSD-license.

Documentation

To learn more, visit the documentation at http://sakura.readthedocs.org.

Software authors

Peter Kappen peter _dot_ kappen _at_ synchrotron.org.au, Australian Synchrotron
Gary Ruben, Victorian eResearch Strategic Initiative (VeRSI)

Recognition of NeCTAR funding

The Australian Synchrotron is proud to be in partnership with the National eResearch Collaboration Tools and Resources (NeCTAR) project to develop eResearch Tools for the synchrotron research community. This will enable our scientific users to have instant access to the results of data during the course of their experiment which will facilitate better decision making and also provide the opportunity for ongoing data analysis via remote access.