%0 Journal Article %1 Bilheux_2019 %A Bilheux, Jean-Christophe %A Bilheux, Hassina %A Lin, Jiao %A Lumsden, Ian %A Zhang, Yuxuan %D 2019 %I IOP Publishing %J Journal of Physics Communications %K imported %N 8 %P 083001 %R 10.1088/2399-6528/ab3bea %T Neutron imaging analysis using jupyter Python notebook %U https://doi.org/10.1088%2F2399-6528%2Fab3bea %V 3 %X 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.

@article{Bilheux_2019, abstract = {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.}, added-at = {2019-09-08T16:21:29.000+0200}, author = {Bilheux, Jean-Christophe and Bilheux, Hassina and Lin, Jiao and Lumsden, Ian and Zhang, Yuxuan}, biburl = {https://www.bibsonomy.org/bibtex/2b4fa1f0a4eba9b4101b9a187742ae9bb/linjiao}, doi = {10.1088/2399-6528/ab3bea}, interhash = {950c4e0200b6581a827025e8f74c5fd9}, intrahash = {b4fa1f0a4eba9b4101b9a187742ae9bb}, journal = {Journal of Physics Communications}, keywords = {imported}, month = aug, number = 8, pages = 083001, publisher = {{IOP} Publishing}, timestamp = {2019-09-08T16:21:50.000+0200}, title = {Neutron imaging analysis using jupyter Python notebook}, url = {https://doi.org/10.1088%2F2399-6528%2Fab3bea}, volume = 3, year = 2019 }