Collection of algorithms and functions for ultrafast electron diffraction
Project description
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.
Citations
If you are using the baseline-removal functionality of scikit-ued, please consider citing the following publication:
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.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for scikit_ued-1.0.1.0-py3-none-any.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 84d4d0800504295faa7f116c70e42b460970750f6aab40dbedc22ad069128071 |
|
| MD5 | f8e0524e61d97b6b59886ec60f648cb6 |
|
| BLAKE2b-256 | b96e511c52e5b129f748d161e0b3cbc5b0f222772c3611a1a994bb3f22ac8efd |
