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"author": "Yuxuan Zhang, Jean C. Bilheux",
"author_email": "zhangy6@ornl.gov, bilheuxjm@ornl.gov",
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"License :: OSI Approved :: BSD License",
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"description": "ResoFit\n=======\n\n.. image:: https://img.shields.io/pypi/v/ResoFit.svg\n :target: https://pypi.python.org/pypi/ResoFit\n\n.. image:: https://travis-ci.org/ornlneutronimaging/ResoFit.svg?branch=master\n :target: https://travis-ci.org/ornlneutronimaging/ResoFit\n\n.. image:: https://codecov.io/gh/ornlneutronimaging/ResoFit/branch/master/graph/badge.svg\n :target: https://codecov.io/gh/ornlneutronimaging/ResoFit\n\n.. image:: https://readthedocs.org/projects/resofit/badge/?version=latest\n :target: http://resofit.readthedocs.io/en/latest/?badge=latest\n :alt: Documentation Status\n\nAbstract\n~~~~~~~~\n\nHere we present an open-source Python library which focuses on\nfitting the neutron resonance signal for neutron imaging\nmeasurements. In this package, by defining the sample information\nsuch as elements and thickness in the neutron path, one can extract\nelemental/isotopic information of the sample. Various sample\ntypes 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.\n\nThe energy dependent cross-section data used in this library are from\n`National Nuclear Data Center `__, a published\nonline database. `Evaluated Nuclear Data File\n(ENDF/B) `__ [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] Periodictable [6], lmfit [7] and ImagingReso [8].\n\nStatement of need\n~~~~~~~~~~~~~~~~~\n\nNeutron imaging is a powerful tool to characterize material\nnon-destructively. And based on the unique resonance features,\nit is feasible to identify elements and/or isotopes resonance with\nincident neutrons. However, a dedicated user-friendly fitting tool\nfor resonance imaging is missing, and **ResoFit** we presented here\ncould fill this gap.\n\nInstallation instructions\n~~~~~~~~~~~~~~~~~~~~~~~~~\n\nPython 3.x is required for installing this package.\n\nInstall **ResoFit** by typing the following command in Terminal:\n\n``pip install ResoFit``\n\nor by typing the following command under downloaded directory in\nTerminal:\n\n``python setup.py``\n\nExample usage\n~~~~~~~~~~~~~\n\nExample of usage is presented at http://resofit.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 [9]-[10]:\n\n.. figure:: https://github.com/ornlneutronimaging/ResoFit/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/ResoFit/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\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, for DOE. Part of this research is supported by the U.S.\nDepartment of Energy, Office of Science, Office of Basic Energy\nSciences, User Facilities under contract number DE-AC05-00OR22725.\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. Newville, A. Nelson, A. Ingargiola, T. Stensitzki, R. Otten,\nD. Allan, Micha\u0142, Glenn, Y. Ram, MerlinSmiles, L. Li, G. Pasquevich,\nC. Deil, D.M. Fobes, Stuermer, A. Beelen, O. Frost, A. Stark, T. Spillane,\nS. Caldwell, A. Polloreno, stonebig, P.A. Brodtkorb, N. Earl, colgan,\nR. Clarken, K. Anagnostopoulos, B. Gamari, A. Almarza, lmfit/lmfit-py 0.9.7,\n(2017). doi:10.5281/zenodo.802298.\n\n[8] Y. Zhang and J. C. Bilheux, \"ImagingReso\".\n\n[9] 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[10] 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\n\n",
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