Metadata-Version: 1.1
Name: ResoFit
Version: 0.0.5
Summary: Fitting tool for neutron resonance imaging
Home-page: https://github.com/ornlneutronimaging/ResoFit.git
Author: Yuxuan Zhang, Jean C. Bilheux
Author-email: zhangy6@ornl.gov, bilheuxjm@ornl.gov
License: BSD
Description: ResoFit
        =======
        
        .. image:: https://img.shields.io/pypi/v/ResoFit.svg
          :target: https://pypi.python.org/pypi/ResoFit
        
        .. image:: https://travis-ci.org/ornlneutronimaging/ResoFit.svg?branch=master
          :target: https://travis-ci.org/ornlneutronimaging/ResoFit
        
        .. image:: https://codecov.io/gh/ornlneutronimaging/ResoFit/branch/master/graph/badge.svg
          :target: https://codecov.io/gh/ornlneutronimaging/ResoFit
        
        .. image:: https://readthedocs.org/projects/resofit/badge/?version=latest
          :target: http://resofit.readthedocs.io/en/latest/?badge=latest
          :alt: Documentation Status
        
        Abstract
        ~~~~~~~~
        
        Here we present an open-source Python library which focuses on
        fitting the neutron resonance signal for neutron imaging
        measurements. In this package, by defining the sample information
        such as elements and thickness in the neutron path, one can extract
        elemental/isotopic information of the sample. 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.
        
        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] Periodictable [6], lmfit [7] and ImagingReso [8].
        
        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 resonance with
        incident neutrons. However, a dedicated user-friendly fitting tool
        for resonance imaging is missing, and **ResoFit** we presented here
        could fill this gap.
        
        Installation instructions
        ~~~~~~~~~~~~~~~~~~~~~~~~~
        
        Python 3.x is required for installing this package.
        
        Install **ResoFit** by typing the following command in Terminal:
        
        ``pip install ResoFit``
        
        or by typing the following command under downloaded directory in
        Terminal:
        
        ``python setup.py``
        
        Example usage
        ~~~~~~~~~~~~~
        
        Example of usage is presented at http://resofit.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 [9]-[10]:
        
        .. figure:: https://github.com/ornlneutronimaging/ResoFit/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/ResoFit/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*.
        
        Acknowledgements
        ~~~~~~~~~~~~~~~~
        
        This work is sponsored by the Laboratory Directed Research and
        Development Program of Oak Ridge National Laboratory, managed by
        UT-Battelle LLC, for DOE. Part of this research is supported by the U.S.
        Department of Energy, Office of Science, Office of Basic Energy
        Sciences, User Facilities under contract number DE-AC05-00OR22725.
        
        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. Newville, A. Nelson, A. Ingargiola, T. Stensitzki, R. Otten,
        D. Allan, Michał, Glenn, Y. Ram, MerlinSmiles, L. Li, G. Pasquevich,
        C. Deil, D.M. Fobes, Stuermer, A. Beelen, O. Frost, A. Stark, T. Spillane,
        S. Caldwell, A. Polloreno, stonebig, P.A. Brodtkorb, N. Earl, colgan,
        R. Clarken, K. Anagnostopoulos, B. Gamari, A. Almarza, lmfit/lmfit-py 0.9.7,
        (2017). doi:10.5281/zenodo.802298.
        
        [8] Y. Zhang and J. C. Bilheux, "ImagingReso".
        
        [9] M. Ooi et al., “Neutron Resonance Imaging of a Au-In-Cd Alloy for
        the JSNS,” Physics Procedia, vol. 43, pp. 337–342, 2013.
        
        [10] 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.
        
        
Keywords: neutron,resonance,fitting,imaging
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: BSD License
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Natural Language :: English
