Open AccessNeutron imaging analysis using jupyter Python notebook
Author(s) -
Jean-Christophe Bilheux,
Hassina Z. Bilheux,
Jiao Lin,
Ian Lumsden,
Yuxuan Zhang
Publication year - 2019
Publication title -
journal of physics communications
Language(s) - English
Resource type - Journals
ISSN - 2399-6528
DOI - 10.1088/2399-6528/ab3bea
Subject(s) - python (programming language) , preprocessor , computer science , normalization (sociology) , visualization , artificial intelligence , computer graphics (images) , matlab , programming language , database normalization , data mining , image processing , image (mathematics) , pattern recognition (psychology) , sociology , anthropology
Independently of the image modality (x-rays, neutrons, etc), image data analysis requires normalization, a preprocessing step. While the normalization can sometimes easily be generalized, the analysis is, in most cases, specific to an experiment and a sample. Although many tools (MATLAB, ImageJ, VG Studio…) offer a large collection of pre-programmed image analysis tools, they usually require a learning step that can be lengthy depending on the skills of the end user. We have implemented Jupyter Python notebooks to allow easy and straightforward data analysis, along with live interaction with the data. Jupyter notebooks require little programming knowledge and the steep learning curve is bypassed. Most importantly, each notebook can be tailored to a specific experiment and sample with minimized effort. Here, we present the pros and cons of the main methods to analyse data and show the reason why we have found that Jupyter Python notebooks are well suited for imaging data processing, visualization and analysis.