Tomotok: Python package for tomography of tokamak plasma radiation

0555264 - ÚFP 2022 RIV GB eng J - Journal Article
Svoboda, Jakub - Cavalier, Jordan - Ficker, Ondřej - Imríšek, Martin - Mlynář, Jan - Hron, Martin
Tomotok: Python package for tomography of tokamak plasma radiation.
Journal of Instrumentation. Roč. 16, č. 12 (2021), č. článku C12015. ISSN 1748-0221. E-ISSN 1748-0221
R&D Projects: GA MŠMT(CZ) EF16_019/0000768
Institutional support: RVO:61389021
Keywords : Data processing methods * Plasma diagnostics interferometry, spectroscopy and imaging
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 1.121, year: 2021
Method of publishing: Limited access
https://iopscience.iop.org/article/10.1088/1748-0221/16/12/C12015

A python package, called Tomotok, focused on performing tomographic inversion of tokamak plasma radiation is being developed at the Institute of Plasma Physics of the Czech Academy of Sciences. It aims at providing multiple inversion algorithms with an user friendly interface. In order to enable and ease performing tomographic inversion on different devices worldwide, it is planned to publish this software as open source in the near future. In this contribution, the package structure allowing an easy implementation of various tokamak and diagnostic geometries is described and an overview of the package contents is given. Apart from inversion methods, overview of Tomotok auxiliary content is given. The package provides tools for creating simple synthetic diagnostic system. These can be used for testing and benchmarking the code. This includes tools for building geometry matrices that describe the view of detectors using single line of sight approximation and artificial data generators capable of creating simple or hollow Gaussian profiles. The implemented inversion methods cover the minimum Fisher regularisation, biorthogonal decomposition and linear algebraic methods. The implementation of each method is explained, example results obtained by inverting phantom models are presented and discussed. The computation speed of implemented algorithms is benchmarked and compared.
Permanent Link: http://hdl.handle.net/11104/0329788