scikit-ued 1.0.1.0

Collection of algorithms and functions for ultrafast electron diffraction

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.

API Reference

The API Reference on readthedocs.io provides API-level documentation, as well as tutorials.

Installation

scikit-ued is available on PyPI; it can be installed with pip:

python -m pip install scikit-ued

scikit-ued is also available on the conda-forge channel for the conda package manager:

conda config --add channels conda-forge
conda install scikit-ued

To install the latest development version from Github:

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. More information is available on the Python Wiki. 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. 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. 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.

Interactive exploration of ultrafast electron diffraction data with the iris-ued package.

A graphical user interface for the dual-tree complex wavelet transform baseline-removal routine is available as a separate package.

Citations

If you are using the baseline-removal functionality of scikit-ued, please consider citing the following publication:

[1]

L. P. René 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.

License

scikit-ued is made available under the MIT License. For more details, see LICENSE.txt.