ImagingReso
===========
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Announcement
------------
A web-based Graphical User Interface (GUI), *Neutron Imaging Toolbox*
(`NEUIT <https://github.com/ornlneutronimaging/NEUIT>`__), is now available at http://isc.sns.gov/.
Abstract
--------
ImagingReso is an open-source Python library that simulates the neutron
resonance signal for neutron imaging measurements. By defining the sample
information such as density, thickness in the neutron path, and isotopic
ratios of the elemental composition of the material, this package plots
the expected resonance peaks for a selected neutron energy range.
Various sample types such as layers of single elements (Ag, Co, etc. in solid form),
chemical compounds (UO\ :sub:`2`, Gd\ :sub:`2`\O\ :sub:`3`, etc.),
or even multiple layers of both types can be plotted with this package.
Major plotting features include display of the transmission/attenuation in
wavelength, energy, and time scale, and show/hide elemental and
isotopic contributions in the total resonance signal.
The energy dependent cross-section data used in this library are from
`National Nuclear Data Center <http://www.nndc.bnl.gov/>`__, a published
online database. `Evaluated Nuclear Data File
(ENDF/B) <http://www.nndc.bnl.gov/exfor/endf00.jsp>`__ [1] is currently
supported and more evaluated databases will be added in future.
Python packages used are: SciPy [2], NumPy [3], Matplotlib [4], Pandas
[5] and Periodictable [6].
Statement of need
-----------------
Neutron imaging is a powerful tool to characterize material
non-destructively. And based on the unique resonance features, it is
feasible to identify elements and/or isotopes which resonance with
incident neutrons. However, a dedicated tool for resonance imaging is
missing, and **ImagingReso** we presented here could fill this gap.
Community guidelines
--------------------
**How to contribute**
Clone the code to your own machine, make changes and do a pull request.
We are looking forward to your contribution to this code!
**How to report issues**
Please use 'Issues' tab on Git to submit issue or bug.
**Support**
You can email authors for support.
Installation instructions
-------------------------
Python 3.5+ is required for installing this package.
Install **ImagingReso** by typing the following command in Terminal:
.. code-block:: bash
$ conda config --add channels conda-forge
$ conda install imagingreso
or
.. code-block:: bash
$ python3 -m pip install ImagingReso
or by typing the following command under downloaded directory in
Terminal:
.. code-block:: bash
$ python setup.py
Example usage
-------------
Example of usage is presented at http://imagingreso.readthedocs.io/ .
Same content can also be found in ``tutorial.ipynb`` under ``/notebooks``
in this repository.
Calculation algorithm
---------------------
The calculation algorithm of neutron transmission *T*\ (*E*),
is base on Beer-Lambert law [7]-[9]:
.. figure:: https://github.com/ornlneutronimaging/ImagingReso/blob/master/documentation/source/_static/Beer_lambert_law_1.png
:alt: Beer-lambert Law 1
:align: center
where
N\ :sub:`i` : number of atoms per unit volume of element *i*,
d\ :sub:`i` : effective thickness along the neutron path of element *i*,
σ\ :sub:`ij` (E) : energy-dependent neutron total cross-section for the isotope *j* of element *i*,
A\ :sub:`ij` : abundance for the isotope *j* of element *i*.
For solid materials, the number of atoms per unit volume can be
calculated from:
.. figure:: https://github.com/ornlneutronimaging/ImagingReso/blob/master/documentation/source/_static/Beer_lambert_law_2.png
:align: center
:alt: Beer-lambert law 2
where
N\ :sub:`A` : Avogadro’s number,
C\ :sub:`i` : molar concentration of element *i*,
ρ\ :sub:`i` : density of the element *i*,
m\ :sub:`ij` : atomic mass values for the isotope *j* of element *i*.
References
----------
[1] M. B. Chadwick et al., “ENDF/B-VII.1 Nuclear Data for Science and
Technology: Cross Sections, Covariances, Fission Product Yields and
Decay Data,” Nuclear Data Sheets, vol. 112, no. 12, pp. 2887–2996, Dec.
2011.
[2] T. E. Oliphant, “SciPy: Open Source Scientific Tools for Python,”
Computing in Science and Engineering, vol. 9. pp. 10–20, 2007.
[3] S. van der Walt et al., “The NumPy Array: A Structure for Efficient
Numerical Computation,” Computing in Science & Engineering, vol. 13, no.
2, pp. 22–30, Mar. 2011.
[4] J. D. Hunter, “Matplotlib: A 2D Graphics Environment,” Computing in
Science & Engineering, vol. 9, no. 3, pp. 90–95, May 2007.
[5] W. McKinney, “Data Structures for Statistical Computing in Python,”
in Proceedings of the 9th Python in Science Conference, 2010, pp. 51–56.
[6] P. A. Kienzle, “Periodictable V1.5.0,” Journal of Open Source
Software, Jan. 2017.
[7] M. Ooi et al., “Neutron Resonance Imaging of a Au-In-Cd Alloy for
the JSNS,” Physics Procedia, vol. 43, pp. 337–342, 2013.
[8] A. S. Tremsin et al., “Non-Contact Measurement of Partial Gas
Pressure and Distribution of Elemental Composition Using Energy-Resolved
Neutron Imaging,” AIP Advances, vol. 7, no. 1, p. 15315, 2017.
[9] Y. Zhang et al., “The Nature of Electrochemical Delithiation of
Li-Mg Alloy Electrodes: Neutron Computed Tomography and Analytical
Modeling of Li Diffusion and Delithiation Phenomenon,” Journal of the
Electrochemical Society, vol. 164, no. 2, pp. A28–A38, 2017.
Meta
----
Yuxuan Zhang - zhangy6@ornl.gov
Jean Bilheux - bilheuxjm@ornl.gov
Distributed under the BSD license. See ``LICENSE.txt`` for more information
https://github.com/ornlneutronimaging/ImagingReso
Publication
-----------
Yuxuan Zhang and Jean Bilheux, "ImagingReso: A Tool for Neutron Resonance Imaging", *The Journal of Open Source Software*, 2 (2017) 407, doi:10.21105/joss.00407
Acknowledgements
----------------
This work is sponsored by the Laboratory Directed Research and
Development Program of Oak Ridge National Laboratory, managed by
UT-Battelle LLC, under Contract No. DE-AC05-00OR22725 with the U.S.
Department of Energy. The United States Government retains and the
publisher, by accepting the article for publication, acknowledges
that the United States Government retains a non-exclusive, paid-up,
irrevocable, worldwide license to publish or reproduce the published
form of this manuscript, or allow others to do so, for United States
Government purposes. The Department of Energy will provide public
access to these results of federally sponsored research in accordance
with the DOE Public Access Plan(http://energy.gov/downloads/doe-public-access-plan).
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"description": "ImagingReso\n===========\n\n.. image:: https://api.codacy.com/project/badge/Grade/7d9162fa8fb644019b2c336ddc61f9d3\n :alt: Codacy Badge\n :target: https://app.codacy.com/app/JeanBilheux/ImagingReso?utm_source=github.com&utm_medium=referral&utm_content=ornlneutronimaging/ImagingReso&utm_campaign=Badge_Grade_Dashboard\n\n.. image:: https://travis-ci.org/ornlneutronimaging/ImagingReso.svg?branch=master\n :target: https://travis-ci.org/ornlneutronimaging/ImagingReso\n :alt: travis\n\n.. image:: https://readthedocs.org/projects/imagingreso/badge/?version=latest\n :target: http://imagingreso.readthedocs.io/en/latest/?badge=latest\n :alt: readthedocs\n\n.. image:: https://codecov.io/gh/ornlneutronimaging/ImagingReso/branch/master/graph/badge.svg\n :target: https://codecov.io/gh/ornlneutronimaging/ImagingReso\n :alt: codecov\n\n.. image:: http://joss.theoj.org/papers/997d09281a9d76e95f4ec4d3279eeb8c/status.svg\n :target: http://joss.theoj.org/papers/997d09281a9d76e95f4ec4d3279eeb8c\n :alt: JOSS\n\n.. image:: https://img.shields.io/pypi/v/ImagingReso.svg\n :target: https://pypi.python.org/pypi/ImagingReso\n :alt: pypi\n\n.. image:: https://anaconda.org/conda-forge/imagingreso/badges/version.svg\n :target: https://anaconda.org/conda-forge/imagingreso\n :alt: conda\n\n.. image:: https://anaconda.org/conda-forge/imagingreso/badges/latest_release_date.svg\n :target: https://anaconda.org/conda-forge/imagingreso\n :alt: latest_release_date\n\n.. image:: https://anaconda.org/conda-forge/imagingreso/badges/downloads.svg\n :target: https://anaconda.org/conda-forge/imagingreso\n :alt: downloads\n\n.. image:: https://anaconda.org/conda-forge/imagingreso/badges/platforms.svg\n :target: https://anaconda.org/conda-forge/imagingreso\n :alt: platform\n\n.. image:: https://anaconda.org/conda-forge/imagingreso/badges/license.svg\n :target: https://anaconda.org/conda-forge/imagingreso\n :alt: license\n\nAnnouncement\n------------\n\nA web-based Graphical User Interface (GUI), *Neutron Imaging Toolbox*\n(`NEUIT <https://github.com/ornlneutronimaging/NEUIT>`__), is now available at http://isc.sns.gov/.\n\nAbstract\n--------\n\nImagingReso is an open-source Python library that simulates the neutron\nresonance signal for neutron imaging measurements. By defining the sample\ninformation such as density, thickness in the neutron path, and isotopic\nratios of the elemental composition of the material, this package plots\nthe expected resonance peaks for a selected neutron energy range.\nVarious sample types such as layers of single elements (Ag, Co, etc. in solid form),\nchemical compounds (UO\\ :sub:`2`, Gd\\ :sub:`2`\\O\\ :sub:`3`, etc.),\nor even multiple layers of both types can be plotted with this package.\nMajor plotting features include display of the transmission/attenuation in\nwavelength, energy, and time scale, and show/hide elemental and\nisotopic contributions in the total resonance signal.\n\nThe energy dependent cross-section data used in this library are from\n`National Nuclear Data Center <http://www.nndc.bnl.gov/>`__, a published\nonline database. `Evaluated Nuclear Data File\n(ENDF/B) <http://www.nndc.bnl.gov/exfor/endf00.jsp>`__ [1] is currently\nsupported and more evaluated databases will be added in future.\n\nPython packages used are: SciPy [2], NumPy [3], Matplotlib [4], Pandas\n[5] and Periodictable [6].\n\nStatement of need\n-----------------\n\nNeutron imaging is a powerful tool to characterize material\nnon-destructively. And based on the unique resonance features, it is\nfeasible to identify elements and/or isotopes which resonance with\nincident neutrons. However, a dedicated tool for resonance imaging is\nmissing, and **ImagingReso** we presented here could fill this gap.\n\nCommunity guidelines\n--------------------\n\n**How to contribute**\n\nClone the code to your own machine, make changes and do a pull request.\nWe are looking forward to your contribution to this code!\n\n**How to report issues**\n\nPlease use 'Issues' tab on Git to submit issue or bug.\n\n**Support**\n\nYou can email authors for support.\n\nInstallation instructions\n-------------------------\n\nPython 3.5+ is required for installing this package.\n\nInstall **ImagingReso** by typing the following command in Terminal:\n\n.. code-block:: bash\n\n $ conda config --add channels conda-forge\n $ conda install imagingreso\n\nor\n\n.. code-block:: bash\n\n $ python3 -m pip install ImagingReso\n\nor by typing the following command under downloaded directory in\nTerminal:\n\n.. code-block:: bash\n \n $ python setup.py\n\nExample usage\n-------------\n\nExample of usage is presented at http://imagingreso.readthedocs.io/ .\nSame content can also be found in ``tutorial.ipynb`` under ``/notebooks``\nin this repository.\n\nCalculation algorithm\n---------------------\n\nThe calculation algorithm of neutron transmission *T*\\ (*E*),\nis base on Beer-Lambert law [7]-[9]:\n\n.. figure:: https://github.com/ornlneutronimaging/ImagingReso/blob/master/documentation/source/_static/Beer_lambert_law_1.png\n :alt: Beer-lambert Law 1\n :align: center\n\nwhere\n\nN\\ :sub:`i` : number of atoms per unit volume of element *i*,\n\nd\\ :sub:`i` : effective thickness along the neutron path of element\u00a0*i*,\n\n\u03c3\\ :sub:`ij` (E) : energy-dependent neutron total cross-section for the isotope *j* of element *i*,\n\nA\\ :sub:`ij` : abundance for the isotope *j* of element *i*.\n\nFor solid materials, the number of atoms per unit volume can be\ncalculated from:\n\n.. figure:: https://github.com/ornlneutronimaging/ImagingReso/blob/master/documentation/source/_static/Beer_lambert_law_2.png\n :align: center\n :alt: Beer-lambert law 2\n\nwhere\n\nN\\ :sub:`A` : Avogadro\u2019s number,\n\nC\\ :sub:`i` : molar concentration of element\u00a0*i*,\n\n\u03c1\\ :sub:`i` : density of the element *i*,\n\nm\\ :sub:`ij` : atomic mass values for the isotope *j* of element *i*.\n\nReferences\n----------\n\n[1] M. B. Chadwick et al., \u201cENDF/B-VII.1 Nuclear Data for Science and\nTechnology: Cross Sections, Covariances, Fission Product Yields and\nDecay Data,\u201d Nuclear Data Sheets, vol. 112, no. 12, pp. 2887\u20132996, Dec.\n2011.\n\n[2] T. E. Oliphant, \u201cSciPy: Open Source Scientific Tools for Python,\u201d\nComputing in Science and Engineering, vol. 9. pp. 10\u201320, 2007.\n\n[3] S. van der Walt et al., \u201cThe NumPy Array: A Structure for Efficient\nNumerical Computation,\u201d Computing in Science & Engineering, vol. 13, no.\n2, pp. 22\u201330, Mar. 2011.\n\n[4] J. D. Hunter, \u201cMatplotlib: A 2D Graphics Environment,\u201d Computing in\nScience & Engineering, vol. 9, no. 3, pp. 90\u201395, May 2007.\n\n[5] W. McKinney, \u201cData Structures for Statistical Computing in Python,\u201d\nin Proceedings of the 9th Python in Science Conference, 2010, pp. 51\u201356.\n\n[6] P. A. Kienzle, \u201cPeriodictable V1.5.0,\u201d Journal of Open Source\nSoftware, Jan. 2017.\n\n[7] M. Ooi et al., \u201cNeutron Resonance Imaging of a Au-In-Cd Alloy for\nthe JSNS,\u201d Physics Procedia, vol. 43, pp. 337\u2013342, 2013.\n\n[8] A. S. Tremsin et al., \u201cNon-Contact Measurement of Partial Gas\nPressure and Distribution of Elemental Composition Using Energy-Resolved\nNeutron Imaging,\u201d AIP Advances, vol. 7, no. 1, p. 15315, 2017.\n\n[9] Y. Zhang et al., \u201cThe Nature of Electrochemical Delithiation of\nLi-Mg Alloy Electrodes: Neutron Computed Tomography and Analytical\nModeling of Li Diffusion and Delithiation Phenomenon,\u201d Journal of the\nElectrochemical Society, vol. 164, no. 2, pp. A28\u2013A38, 2017.\n\nMeta\n----\n\nYuxuan Zhang - zhangy6@ornl.gov\n\nJean Bilheux - bilheuxjm@ornl.gov\n\nDistributed under the BSD license. See ``LICENSE.txt`` for more information\n\nhttps://github.com/ornlneutronimaging/ImagingReso\n\nPublication\n-----------\n\nYuxuan Zhang and Jean Bilheux, \"ImagingReso: A Tool for Neutron Resonance Imaging\", *The Journal of Open Source Software*, 2 (2017) 407, doi:10.21105/joss.00407\n\nAcknowledgements\n----------------\n\nThis work is sponsored by the Laboratory Directed Research and\nDevelopment Program of Oak Ridge National Laboratory, managed by\nUT-Battelle LLC, under Contract No. DE-AC05-00OR22725 with the U.S.\nDepartment of Energy. The United States Government retains and the\npublisher, by accepting the article for publication, acknowledges\nthat the United States Government retains a non-exclusive, paid-up,\nirrevocable, worldwide license to publish or reproduce the published\nform of this manuscript, or allow others to do so, for United States\nGovernment purposes. The Department of Energy will provide public\naccess to these results of federally sponsored research in accordance\nwith the DOE Public Access Plan(http://energy.gov/downloads/doe-public-access-plan).\n\n",
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