Metadata-Version: 1.1
Name: ImagingReso
Version: 1.5.10
Summary: resonance imaging neutron data
Home-page: https://github.com/ornlneutronimaging/ImagingReso.git
Author: Yuxuan (Shawn) Zhang, Jean Bilheux
Author-email: zhangy6@ornl.gov, bilheuxjm@ornl.gov
License: BSD
Description-Content-Type: UNKNOWN
Description: ImagingReso
        ===========
        
        .. image:: https://img.shields.io/pypi/v/ImagingReso.svg
          :target: https://pypi.python.org/pypi/ImagingReso
        
        .. image:: https://travis-ci.org/ornlneutronimaging/ImagingReso.svg?branch=master
          :target: https://travis-ci.org/ornlneutronimaging/ImagingReso
            
        .. image:: https://codecov.io/gh/ornlneutronimaging/ImagingReso/branch/master/graph/badge.svg
          :target: https://codecov.io/gh/ornlneutronimaging/ImagingReso
          
        .. image:: https://readthedocs.org/projects/imagingreso/badge/?version=latest
          :target: http://imagingreso.readthedocs.io/en/latest/?badge=latest
          :alt: Documentation Status
        
        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/>`__,
        an online database published by Brookhaven National Laboratory.
        `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 the 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.
        
        Installation instructions
        -------------------------
        
        Python 3.x is required for installing this package.
        
        Install **ImagingReso** by typing the following command in Terminal:
        
        .. code-block:: bash
        
           $ pip install ImagingReso
        
        or by typing the following command under downloaded directory in
        Terminal:
        
        .. code-block:: python
           
           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*.
        
        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. 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
        
        
        
        
        
Keywords: neutron,resonance,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
