Metadata-Version: 1.2
Name: scikit-ued
Version: 1.0.1.0
Summary: Collection of algorithms and functions for ultrafast electron diffraction
Home-page: http://scikit-ued.readthedocs.io
Author: Laurent P. René de Cotret
Author-email: laurent.renedecotret@mail.mcgill.ca
Maintainer: Laurent P. René de Cotret
Maintainer-email: laurent.renedecotret@mail.mcgill.ca
License: MIT
Download-URL: http://github.com/LaurentRDC/scikit-ued
Project-URL: Documentation, http://scikit-ued.readthedocs.io/en/master/
Project-URL: Source, https://github.com/LaurentRDC/scikit-ued
Description: scikit-ued
        ==========
        
        .. image:: https://img.shields.io/appveyor/ci/LaurentRDC/scikit-ued/master.svg
            :target: https://ci.appveyor.com/project/LaurentRDC/scikit-ued
            :alt: Windows Build Status
        .. image:: https://readthedocs.org/projects/scikit-ued/badge/?version=master
            :target: http://scikit-ued.readthedocs.io
            :alt: Documentation Build Status
        .. image:: https://img.shields.io/pypi/v/scikit-ued.svg
            :target: https://pypi.org/project/scikit-ued/
            :alt: PyPI Version
        .. image:: https://img.shields.io/conda/vn/conda-forge/scikit-ued.svg
            :target: https://anaconda.org/conda-forge/scikit-ued
            :alt: Conda-forge Version
        .. image:: https://img.shields.io/pypi/pyversions/scikit-ued.svg
            :alt: Supported Python Versions
        
        Collection of algorithms and functions for ultrafast electron diffraction. It aims to be a fully-tested package
        taking advantage of Python's most recent features.
        
        For examples, see our `tutorials <https://scikit-ued.readthedocs.io/en/latest/tutorials/index.html>`_.
        
        API Reference
        -------------
        
        The `API Reference on readthedocs.io <https://scikit-ued.readthedocs.io>`_ provides API-level documentation, as 
        well as tutorials.
        
        Installation
        ------------
        
        scikit-ued is available on PyPI; it can be installed with `pip <https://pip.pypa.io>`_::
        
            python -m pip install scikit-ued
        
        scikit-ued is also available on the conda-forge channel for the `conda package manager <https://conda.io/docs/>`_::
        
            conda config --add channels conda-forge
            conda install scikit-ued
        
        To install the latest development version from `Github <https://github.com/LaurentRDC/scikit-ued>`_::
        
            python -m pip install git+git://github.com/LaurentRDC/scikit-ued.git
        
        After installing scikit-ued you can use it like any other Python module as ``skued``.
        
        Each version is tested against **Python 3.6**. If you are using a different version, tests can be run
        using the standard library's `unittest` module.
        
        Installation on Windows
        -----------------------
        
        Some of scikit-ued's dependencies require compilation. If you are experiencing problems installing scikit-ued on Windows, here are some potential solutions:
        
        * Install a C/C++ compiler. The easiest way to do so is to install the `Visual Studio Build Tools <https://www.visualstudio.com/downloads/?q=build+tools>`_. More information is available on the `Python Wiki <https://wiki.python.org/moin/WindowsCompilers>`_. Don't forget to upgrade setuptools to the latest version as well to avoid common problems::
        
            pip install --upgrade setuptools
        
        * Download the wheels from scikit-ued's `wheelhouse <https://github.com/LaurentRDC/scikit-ued/tree/master/wheelhouse>`_. These are pre-compiled dependencies that will only work on Windows. To install a wheel, you can use pip::
        
            pip install some-pkg.whl
        
        * Install the dependencies using the `conda package manager <https://conda.io/docs/>`_. Most notably, spglib and pycifrw are both available in the conda-forge channel::
        
            conda config --add channels conda-forge
            conda install spglib pycifrw numpy scipy ...
        
        Optional dependencies
        ---------------------
        
        While it is not strictly required, the Fourier transform routines from ``pyfftw`` will be preferred If
        ``pyfftw`` is installed.
        
        For displaying diffraction images with interactive contrast using the ``skued.diffshow`` function, PyQtGraph is required.
        
        Related projects
        ----------------
        
        Streaming operations on NumPy arrays are available in the `npstreams package <https://pypi.org/pypi/npstreams>`_.
        
        Interactive exploration of ultrafast electron diffraction data with the `iris-ued package <https://pypi.org/project/iris-ued/>`_.
        
        A graphical user interface for the dual-tree complex wavelet transform baseline-removal routine is available as a 
        `separate package <https://pypi.org/pypi/dtgui>`_.
        
        Citations
        ---------
        
        If you are using the baseline-removal functionality of scikit-ued, please consider citing the following publication:
        
            .. [#] L. P. RenÃ© de Cotret and B. J. Siwick, A general method for baseline-removal in ultrafast 
                   electron powder diffraction data using the dual-tree complex wavelet transform, Struct. Dyn. 4 (2017) DOI: 10.1063/1.4972518.
        
        Support / Report Issues
        -----------------------
        
        All support requests and issue reports should be
        `filed on Github as an issue <https://github.com/LaurentRDC/scikit-ued/issues>`_.
        
        License
        -------
        
        scikit-ued is made available under the MIT License. For more details, see `LICENSE.txt <https://github.com/LaurentRDC/scikit-ued/blob/master/LICENSE.txt>`_.
        
Keywords: ultrafast electron diffraction
Platform: UNKNOWN
Classifier: Environment :: Console
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.6
