Metadata-Version: 2.1
Name: nPDyn
Version: 3.0.0
Summary: Python package for analysis of neutron backscattering data
Home-page: https://github.com/kpounot/nPDyn
Author: Kevin Pounot
Author-email: kpounot@hotmail.fr
License: UNKNOWN
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        nPDyn
        =====
        nPDyn is a Python based API for analysis of neutron backscattering data.
        
        The API aims at providing a lightweight, user-friendly and modular tool
        to process and analyze quasi-elastic neutron scattering (QENS) and
        fixed-window scans (FWS) obtained with backscattering spectroscopy.
        
        nPDyn can be used in combination with other software for neutron data analysis
        such as `Mantid <https://www.mantidproject.org>`_.
        
        An important feature of nPDyn is the modelling interface, which is designed
        to be highly versatile and intuitive for multidimensional dataset with global
        and non-global parameters.
        The modelling in nPDyn is provided by builtin classes,
        ``params.Parameters``, ``model.Model`` and
        ``model.Component``.
        nPDyn provides also some helper functions to use
        `lmfit <https://lmfit.github.io/lmfit-py/>`_ as modelling backend.
        See *Fit data* section in documentation for details.
        
        Eventually, some plotting methods are available to examine processed data,
        model fitting and optimized parameters.
        
        
        Installation:
        -------------
        Prior to building on Windows, the path to Gnu Scientific Library (GSL) should
        be given in setup.cfg file (required by libabsco)
        
        If not, the package can still be installed but paalman-ping corrections won't
        work.
        
        
        Unix and Windows
        ^^^^^^^^^^^^^^^^
        For installation within your python framework:
        
        - with pip:
        
        .. code:: bash
        
            python3 -m pip install nPDyn
        
        - with source code:
        
        .. code:: bash
        
            git clone https://github.com/kpounot/nPDyn npdyn
            cd npdyn
            python3 setup.py install
        
        
        Full documentation
        ------------------
        See https://npdyn.readthedocs.io/en/latest/
        
        
        Support
        -------
        A `google group <https://groups.google.com/g/npdyn>`_ is available for any
        question, discussion on features or comment.
        
        In case of bugs or obvious change to be done in the code use GitHub Issues.
        
        
        Contributions
        -------------
        See `contributing <https://github.com/kpounot/nPDyn/blob/master/contributing.rst>`_.
        
        
        Getting started
        ---------------
        The nPDyn API is organized around a `Sample` class.
        This class inherits from the NumPy ndarray class with some extra
        features added, such as neutron scattering-specific attributes, binning,
        data correction algorithm, automatic error propagation and data fitting.
        
        In a neutron backscattering experiment, there is not only the measurement of
        samples but also some calibration measurements like vanadium, empty cell
        and solvent signal (often D2O). Some methods of the
        `Sample` class can be used to perform normalization or
        absorption correction using the dataset corresponding to vanadium
        or empty cell, respectively. These calibration dataset can be used also
        in the `fit` function to automatically add a background or perform
        a convolution with the resolution function.
        
        Details regarding importation of data are available in the `dataImport`
        section of the documentation.
        
        Importantly, nPDyn provides versatile tools for model building and fitting
        to the data. See the section `dataFitting` for details.
        
        Finally, a `plot` method is provided for easy visualisation
        of the data and the fit results.
        
Platform: Windows
Platform: Linux
Platform: Mac OS X
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering
Description-Content-Type: text/x-rst
