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Tomotok

Tomotok is a framework for the tomographic inversion of fusion plasmas, focusing on inversion methods based on discretisation. It is structured as a namespace package to ease implementation on different experiments and across various diagnostics.

Core

The documentation for the Core can be found on this link.

The Core package of Tomotok implements various discretization algorithms that are used for tomographic reconstruction of tokamak plasmas. It is a dependency for specific packages that automate database access for a given fusion experimental device and facilitate routine tomographic computations. Together with the Core package, a simple GUI for result analysis is distributed.

Inversions

The algorithms accept inputs in the form of numpy.ndarray or scipy.sparse matrix objects, allowing them to run independently of the rest of the package and promoting interoperability with other codes (e.g., ToFu).

Currently implemented algorithms:

  • Minimum Fisher Regularisation for sparse matrices using scipy.sparse.linalg.spsolve
  • Minimum Fisher Regularisation for sparse matrices using Cholesky decomposition from scikit-sparse
  • SVD linear algebraic inversion for dense matrices
  • GEV linear algebraic inversion with optimization for sparse matrices
  • Biorthogonal Basis decomposition for dense matrices
  • Biorthogonal Basis decomposition optimized for sparse matrices (scipy, cholmod)

Auxiliary Features

Apart from the main inversion methods, some auxiliary features are also included.

In order to facilitate routine inversion computations, a database interface was designed using template classes. These template classes can load signals, detector view geometry, and magnetic flux reconstruction in the format usually used for tokamak data.

A simple synthetic diagnostic framework is also implemented. It can be used for testing the implemented algorithms. It uses regular rectangular nodes and assumes toroidal symmetry, as it is the simplest case often used for inversions of tokamak plasma radiation.

Implemented auxiliary features:

  • Template classes for an automated database interface
  • Geometry matrix computation using numerical integration and a single line of sight approximation
  • Smoothing matrix computation, both isotropic and anisotropic (based on magnetic flux surfaces)
  • Simple phantom model generators (isotropic and anisotropic)
  • Other tools for processing

Citing the Code

"J. Svoboda, J. Cavalier, O. Ficker, M. Imrisek, J. Mlynar and M. Hron, Tomotok: python package for tomography of tokamak plasma radiation, Journal of Instrumentation 16.12 (2021): C12015." DOI

About

The core part of Tomotok package, a collection of algorithms used for tokamak plasma tomography

tomotok.github.io/documentation/

Topics

physicsplasmapythontokamaktomography

Resources

Readme
EUPL-1.2 license

Contributing

Contributing
Activity
Custom properties

Stars

12 stars

Watchers

3 watching

Forks

4 forks
Report repository

Releases

Used by

Contributors

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Tomotok
Tomotok is a framework for the tomographic inversion of fusion plasmas, focusing on inversion methods based on discretisation. It is structured as a namespace package to ease implementation on different experiments and across various diagnostics.
Core
The documentation for the Core can be found on this link.
The Core package of Tomotok implements various discretization algorithms that are used for tomographic reconstruction of tokamak plasmas. It is a dependency for specific packages that automate database access for a given fusion experimental device and facilitate routine tomographic computations. Together with the Core package, a simple GUI for result analysis is distributed.
Inversions
The algorithms accept inputs in the form of numpy.ndarray or scipy.sparse matrix objects, allowing them to run independently of the rest of the package and promoting interoperability with other codes (e.g., ToFu).
Currently implemented algorithms:
  • Minimum Fisher Regularisation for sparse matrices using scipy.sparse.linalg.spsolve
  • Minimum Fisher Regularisation for sparse matrices using Cholesky decomposition from scikit-sparse
  • SVD linear algebraic inversion for dense matrices
  • GEV linear algebraic inversion with optimization for sparse matrices
  • Biorthogonal Basis decomposition for dense matrices
  • Biorthogonal Basis decomposition optimized for sparse matrices (scipy, cholmod)
Auxiliary Features
Apart from the main inversion methods, some auxiliary features are also included.
In order to facilitate routine inversion computations, a database interface was designed using template classes. These template classes can load signals, detector view geometry, and magnetic flux reconstruction in the format usually used for tokamak data.
A simple synthetic diagnostic framework is also implemented. It can be used for testing the implemented algorithms. It uses regular rectangular nodes and assumes toroidal symmetry, as it is the simplest case often used for inversions of tokamak plasma radiation.
Implemented auxiliary features:
  • Template classes for an automated database interface
  • Geometry matrix computation using numerical integration and a single line of sight approximation
  • Smoothing matrix computation, both isotropic and anisotropic (based on magnetic flux surfaces)
  • Simple phantom model generators (isotropic and anisotropic)
  • Other tools for processing
Citing the Code
"J. Svoboda, J. Cavalier, O. Ficker, M. Imrisek, J. Mlynar and M. Hron, Tomotok: python package for tomography of tokamak plasma radiation, Journal of Instrumentation 16.12 (2021): C12015."
DOI
About
The core part of Tomotok package, a collection of algorithms used for tokamak plasma tomography
tomotok.github.io/documentation/
Topics
physicsplasmapythontokamaktomography
Resources
Readme
EUPL-1.2 license
Contributing
Contributing
Activity
Custom properties
Stars
12 stars
Watchers
3 watching
Forks
4 forks
Report repository
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Tomotok
Tomotok is a framework for the tomographic inversion of fusion plasmas, focusing on inversion methods based on discretisation. It is structured as a namespace package to ease implementation on different experiments and across various diagnostics.
Core
The documentation for the Core can be found on this link.
The Core package of Tomotok implements various discretization algorithms that are used for tomographic reconstruction of tokamak plasmas. It is a dependency for specific packages that automate database access for a given fusion experimental device and facilitate routine tomographic computations. Together with the Core package, a simple GUI for result analysis is distributed.
Inversions
The algorithms accept inputs in the form of numpy.ndarray or scipy.sparse matrix objects, allowing them to run independently of the rest of the package and promoting interoperability with other codes (e.g., ToFu).
Currently implemented algorithms:
  • Minimum Fisher Regularisation for sparse matrices using scipy.sparse.linalg.spsolve
  • Minimum Fisher Regularisation for sparse matrices using Cholesky decomposition from scikit-sparse
  • SVD linear algebraic inversion for dense matrices
  • GEV linear algebraic inversion with optimization for sparse matrices
  • Biorthogonal Basis decomposition for dense matrices
  • Biorthogonal Basis decomposition optimized for sparse matrices (scipy, cholmod)
Auxiliary Features
Apart from the main inversion methods, some auxiliary features are also included.
In order to facilitate routine inversion computations, a database interface was designed using template classes. These template classes can load signals, detector view geometry, and magnetic flux reconstruction in the format usually used for tokamak data.
A simple synthetic diagnostic framework is also implemented. It can be used for testing the implemented algorithms. It uses regular rectangular nodes and assumes toroidal symmetry, as it is the simplest case often used for inversions of tokamak plasma radiation.
Implemented auxiliary features:
  • Template classes for an automated database interface
  • Geometry matrix computation using numerical integration and a single line of sight approximation
  • Smoothing matrix computation, both isotropic and anisotropic (based on magnetic flux surfaces)
  • Simple phantom model generators (isotropic and anisotropic)
  • Other tools for processing
Citing the Code
"J. Svoboda, J. Cavalier, O. Ficker, M. Imrisek, J. Mlynar and M. Hron, Tomotok: python package for tomography of tokamak plasma radiation, Journal of Instrumentation 16.12 (2021): C12015."
DOI
About
The core part of Tomotok package, a collection of algorithms used for tokamak plasma tomography
tomotok.github.io/documentation/
Topics
physicsplasmapythontokamaktomography
Resources
Readme
EUPL-1.2 license
Contributing
Contributing
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Watchers
3 watching
Forks
4 forks
Report repository
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if (__m === '*' || __re.test(location.href)) {
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})();
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Tomotok
Tomotok is a framework for the tomographic inversion of fusion plasmas, focusing on inversion methods based on discretisation. It is structured as a namespace package to ease implementation on different experiments and across various diagnostics.
Core
The documentation for the Core can be found on this link.
The Core package of Tomotok implements various discretization algorithms that are used for tomographic reconstruction of tokamak plasmas. It is a dependency for specific packages that automate database access for a given fusion experimental device and facilitate routine tomographic computations. Together with the Core package, a simple GUI for result analysis is distributed.
Inversions
The algorithms accept inputs in the form of numpy.ndarray or scipy.sparse matrix objects, allowing them to run independently of the rest of the package and promoting interoperability with other codes (e.g., ToFu).
Currently implemented algorithms:
  • Minimum Fisher Regularisation for sparse matrices using scipy.sparse.linalg.spsolve
  • Minimum Fisher Regularisation for sparse matrices using Cholesky decomposition from scikit-sparse
  • SVD linear algebraic inversion for dense matrices
  • GEV linear algebraic inversion with optimization for sparse matrices
  • Biorthogonal Basis decomposition for dense matrices
  • Biorthogonal Basis decomposition optimized for sparse matrices (scipy, cholmod)
Auxiliary Features
Apart from the main inversion methods, some auxiliary features are also included.
In order to facilitate routine inversion computations, a database interface was designed using template classes. These template classes can load signals, detector view geometry, and magnetic flux reconstruction in the format usually used for tokamak data.
A simple synthetic diagnostic framework is also implemented. It can be used for testing the implemented algorithms. It uses regular rectangular nodes and assumes toroidal symmetry, as it is the simplest case often used for inversions of tokamak plasma radiation.
Implemented auxiliary features:
  • Template classes for an automated database interface
  • Geometry matrix computation using numerical integration and a single line of sight approximation
  • Smoothing matrix computation, both isotropic and anisotropic (based on magnetic flux surfaces)
  • Simple phantom model generators (isotropic and anisotropic)
  • Other tools for processing
Citing the Code
"J. Svoboda, J. Cavalier, O. Ficker, M. Imrisek, J. Mlynar and M. Hron, Tomotok: python package for tomography of tokamak plasma radiation, Journal of Instrumentation 16.12 (2021): C12015."
DOI
About
The core part of Tomotok package, a collection of algorithms used for tokamak plasma tomography
tomotok.github.io/documentation/
Topics
physicsplasmapythontokamaktomography
Resources
Readme
EUPL-1.2 license
Contributing
Contributing
Activity
Custom properties
Stars
12 stars
Watchers
3 watching
Forks
4 forks
Report repository
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Used by
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
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})();
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var __re = new RegExp('^' + "youtube\\.com" + '

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Tomotok
Tomotok is a framework for the tomographic inversion of fusion plasmas, focusing on inversion methods based on discretisation. It is structured as a namespace package to ease implementation on different experiments and across various diagnostics.
Core
The documentation for the Core can be found on this link.
The Core package of Tomotok implements various discretization algorithms that are used for tomographic reconstruction of tokamak plasmas. It is a dependency for specific packages that automate database access for a given fusion experimental device and facilitate routine tomographic computations. Together with the Core package, a simple GUI for result analysis is distributed.
Inversions
The algorithms accept inputs in the form of numpy.ndarray or scipy.sparse matrix objects, allowing them to run independently of the rest of the package and promoting interoperability with other codes (e.g., ToFu).
Currently implemented algorithms:
  • Minimum Fisher Regularisation for sparse matrices using scipy.sparse.linalg.spsolve
  • Minimum Fisher Regularisation for sparse matrices using Cholesky decomposition from scikit-sparse
  • SVD linear algebraic inversion for dense matrices
  • GEV linear algebraic inversion with optimization for sparse matrices
  • Biorthogonal Basis decomposition for dense matrices
  • Biorthogonal Basis decomposition optimized for sparse matrices (scipy, cholmod)
Auxiliary Features
Apart from the main inversion methods, some auxiliary features are also included.
In order to facilitate routine inversion computations, a database interface was designed using template classes. These template classes can load signals, detector view geometry, and magnetic flux reconstruction in the format usually used for tokamak data.
A simple synthetic diagnostic framework is also implemented. It can be used for testing the implemented algorithms. It uses regular rectangular nodes and assumes toroidal symmetry, as it is the simplest case often used for inversions of tokamak plasma radiation.
Implemented auxiliary features:
  • Template classes for an automated database interface
  • Geometry matrix computation using numerical integration and a single line of sight approximation
  • Smoothing matrix computation, both isotropic and anisotropic (based on magnetic flux surfaces)
  • Simple phantom model generators (isotropic and anisotropic)
  • Other tools for processing
Citing the Code
"J. Svoboda, J. Cavalier, O. Ficker, M. Imrisek, J. Mlynar and M. Hron, Tomotok: python package for tomography of tokamak plasma radiation, Journal of Instrumentation 16.12 (2021): C12015."
DOI
About
The core part of Tomotok package, a collection of algorithms used for tokamak plasma tomography
tomotok.github.io/documentation/
Topics
physicsplasmapythontokamaktomography
Resources
Readme
EUPL-1.2 license
Contributing
Contributing
Activity
Custom properties
Stars
12 stars
Watchers
3 watching
Forks
4 forks
Report repository
Releases
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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Tomotok
Tomotok is a framework for the tomographic inversion of fusion plasmas, focusing on inversion methods based on discretisation. It is structured as a namespace package to ease implementation on different experiments and across various diagnostics.
Core
The documentation for the Core can be found on this link.
The Core package of Tomotok implements various discretization algorithms that are used for tomographic reconstruction of tokamak plasmas. It is a dependency for specific packages that automate database access for a given fusion experimental device and facilitate routine tomographic computations. Together with the Core package, a simple GUI for result analysis is distributed.
Inversions
The algorithms accept inputs in the form of numpy.ndarray or scipy.sparse matrix objects, allowing them to run independently of the rest of the package and promoting interoperability with other codes (e.g., ToFu).
Currently implemented algorithms:
  • Minimum Fisher Regularisation for sparse matrices using scipy.sparse.linalg.spsolve
  • Minimum Fisher Regularisation for sparse matrices using Cholesky decomposition from scikit-sparse
  • SVD linear algebraic inversion for dense matrices
  • GEV linear algebraic inversion with optimization for sparse matrices
  • Biorthogonal Basis decomposition for dense matrices
  • Biorthogonal Basis decomposition optimized for sparse matrices (scipy, cholmod)
Auxiliary Features
Apart from the main inversion methods, some auxiliary features are also included.
In order to facilitate routine inversion computations, a database interface was designed using template classes. These template classes can load signals, detector view geometry, and magnetic flux reconstruction in the format usually used for tokamak data.
A simple synthetic diagnostic framework is also implemented. It can be used for testing the implemented algorithms. It uses regular rectangular nodes and assumes toroidal symmetry, as it is the simplest case often used for inversions of tokamak plasma radiation.
Implemented auxiliary features:
  • Template classes for an automated database interface
  • Geometry matrix computation using numerical integration and a single line of sight approximation
  • Smoothing matrix computation, both isotropic and anisotropic (based on magnetic flux surfaces)
  • Simple phantom model generators (isotropic and anisotropic)
  • Other tools for processing
Citing the Code
"J. Svoboda, J. Cavalier, O. Ficker, M. Imrisek, J. Mlynar and M. Hron, Tomotok: python package for tomography of tokamak plasma radiation, Journal of Instrumentation 16.12 (2021): C12015."
DOI
About
The core part of Tomotok package, a collection of algorithms used for tokamak plasma tomography
tomotok.github.io/documentation/
Topics
physicsplasmapythontokamaktomography
Resources
Readme
EUPL-1.2 license
Contributing
Contributing
Activity
Custom properties
Stars
12 stars
Watchers
3 watching
Forks
4 forks
Report repository
Releases
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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Tomotok
Tomotok is a framework for the tomographic inversion of fusion plasmas, focusing on inversion methods based on discretisation. It is structured as a namespace package to ease implementation on different experiments and across various diagnostics.
Core
The documentation for the Core can be found on this link.
The Core package of Tomotok implements various discretization algorithms that are used for tomographic reconstruction of tokamak plasmas. It is a dependency for specific packages that automate database access for a given fusion experimental device and facilitate routine tomographic computations. Together with the Core package, a simple GUI for result analysis is distributed.
Inversions
The algorithms accept inputs in the form of numpy.ndarray or scipy.sparse matrix objects, allowing them to run independently of the rest of the package and promoting interoperability with other codes (e.g., ToFu).
Currently implemented algorithms:
  • Minimum Fisher Regularisation for sparse matrices using scipy.sparse.linalg.spsolve
  • Minimum Fisher Regularisation for sparse matrices using Cholesky decomposition from scikit-sparse
  • SVD linear algebraic inversion for dense matrices
  • GEV linear algebraic inversion with optimization for sparse matrices
  • Biorthogonal Basis decomposition for dense matrices
  • Biorthogonal Basis decomposition optimized for sparse matrices (scipy, cholmod)
Auxiliary Features
Apart from the main inversion methods, some auxiliary features are also included.
In order to facilitate routine inversion computations, a database interface was designed using template classes. These template classes can load signals, detector view geometry, and magnetic flux reconstruction in the format usually used for tokamak data.
A simple synthetic diagnostic framework is also implemented. It can be used for testing the implemented algorithms. It uses regular rectangular nodes and assumes toroidal symmetry, as it is the simplest case often used for inversions of tokamak plasma radiation.
Implemented auxiliary features:
  • Template classes for an automated database interface
  • Geometry matrix computation using numerical integration and a single line of sight approximation
  • Smoothing matrix computation, both isotropic and anisotropic (based on magnetic flux surfaces)
  • Simple phantom model generators (isotropic and anisotropic)
  • Other tools for processing
Citing the Code
"J. Svoboda, J. Cavalier, O. Ficker, M. Imrisek, J. Mlynar and M. Hron, Tomotok: python package for tomography of tokamak plasma radiation, Journal of Instrumentation 16.12 (2021): C12015."
DOI
About
The core part of Tomotok package, a collection of algorithms used for tokamak plasma tomography
tomotok.github.io/documentation/
Topics
physicsplasmapythontokamaktomography
Resources
Readme
EUPL-1.2 license
Contributing
Contributing
Activity
Custom properties
Stars
12 stars
Watchers
3 watching
Forks
4 forks
Report repository
Releases
Used by
Contributors
Languages

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Tomotok
Tomotok is a framework for the tomographic inversion of fusion plasmas, focusing on inversion methods based on discretisation. It is structured as a namespace package to ease implementation on different experiments and across various diagnostics.
Core
The documentation for the Core can be found on this link.
The Core package of Tomotok implements various discretization algorithms that are used for tomographic reconstruction of tokamak plasmas. It is a dependency for specific packages that automate database access for a given fusion experimental device and facilitate routine tomographic computations. Together with the Core package, a simple GUI for result analysis is distributed.
Inversions
The algorithms accept inputs in the form of numpy.ndarray or scipy.sparse matrix objects, allowing them to run independently of the rest of the package and promoting interoperability with other codes (e.g., ToFu).
Currently implemented algorithms:
  • Minimum Fisher Regularisation for sparse matrices using scipy.sparse.linalg.spsolve
  • Minimum Fisher Regularisation for sparse matrices using Cholesky decomposition from scikit-sparse
  • SVD linear algebraic inversion for dense matrices
  • GEV linear algebraic inversion with optimization for sparse matrices
  • Biorthogonal Basis decomposition for dense matrices
  • Biorthogonal Basis decomposition optimized for sparse matrices (scipy, cholmod)
Auxiliary Features
Apart from the main inversion methods, some auxiliary features are also included.
In order to facilitate routine inversion computations, a database interface was designed using template classes. These template classes can load signals, detector view geometry, and magnetic flux reconstruction in the format usually used for tokamak data.
A simple synthetic diagnostic framework is also implemented. It can be used for testing the implemented algorithms. It uses regular rectangular nodes and assumes toroidal symmetry, as it is the simplest case often used for inversions of tokamak plasma radiation.
Implemented auxiliary features:
  • Template classes for an automated database interface
  • Geometry matrix computation using numerical integration and a single line of sight approximation
  • Smoothing matrix computation, both isotropic and anisotropic (based on magnetic flux surfaces)
  • Simple phantom model generators (isotropic and anisotropic)
  • Other tools for processing
Citing the Code
"J. Svoboda, J. Cavalier, O. Ficker, M. Imrisek, J. Mlynar and M. Hron, Tomotok: python package for tomography of tokamak plasma radiation, Journal of Instrumentation 16.12 (2021): C12015."
DOI
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The core part of Tomotok package, a collection of algorithms used for tokamak plasma tomography
tomotok.github.io/documentation/
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physicsplasmapythontokamaktomography
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