scikit-ued - GitLite

KO | EN

gitlite — search

#javascript #hacktoberfest #ai #typescript #rust #docker #nodejs #macos #java #cli #kubernetes #awesome-list #pytorch #open-source

scikit-ued

(★ 158)

Collection of algorithms and routines for (ultrafast) electron diffraction and scattering

File Explorer

Download Latest Version (.zip)

- - ci.yml
  - lint.yml
- ISSUE_TEMPLATE.md
- PULL_REQUEST_TEMPLATE.md
- - baseline.rst
  - image.rst
  - plotting.rst
  - simulation.rst
  - structure.rst
  - time_series.rst
  - tutorials.rst
  - visualization.rst
  - vo2.tif
- api.rst
- cmdline.rst
- conf.py
- index.rst
- installation.rst
- references.txt
- whatsnew.rst
- - __init__.py
  - algorithms.py
  - dtcwt.py
- - __init__.py
  - alignment.py
  - brillouin.py
  - calibration.py
  - center.py
  - indexing.py
  - metrics.py
  - powder.py
  - symmetry.py
- - __init__.py
  - diffshow.py
  - dm.py
  - io.py
  - merlin.py
- - __init__.py
  - form_factors.py
  - kinematic.py
  - potential.py
  - powdersim.py
  - scattering_params.py
  - structure_factors.py
- - __init__.py
  - test_affine.py
  - test_array_utils.py
  - test_eproperties.py
  - test_patterson.py
  - test_plot_utils.py
  - test_potential_map.py
  - test_thin_films.py
  - test_voigt.py
- - __init__.py
  - fitting.py
  - nfft_routines.py
  - robust.py
  - selections.py
  - time_zero.py
- __init__.py
- __main__.py
- affine.py
- array_utils.py
- eproperties.py
- fft.py
- patterson.py
- plot_utils.py
- potential_map.py
- thin_films.py
- utils.py
- voigt.py
.gitattributes
.gitignore
.readthedocs.yml
CHANGELOG.rst
CONTRIBUTING.md
LICENSE.txt
MANIFEST.in
pyproject.toml
README.md
RELEASE-CHECKLIST.rst
release-description.py

# Installation Guide

1. Get the code

git clone https://github.com/LaurentRDC/scikit-ued

Downloads the entire project code from GitHub to your computer.

cd scikit-ued

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

python -m pip install scikit-ued

Installs the Python libraries listed in requirements.txt (or similar).

python -m pip install scikit-ued[diffshow]

Installs the Python libraries listed in requirements.txt (or similar).

python -m pip install git+https://github.com/LaurentRDC/scikit-ued.git

Installs the Python libraries listed in requirements.txt (or similar).

✅ If it runs without errors and prints output in the terminal, it worked.

Pulled directly from this repo's README.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

scikit-ued
==========

[![Documentation Build Status](https://readthedocs.org/projects/scikit-ued/badge/?version=master)](http://scikit-ued.readthedocs.io) [![PyPI Version](https://img.shields.io/pypi/v/scikit-ued.svg)](https://pypi.org/project/scikit-ued/) [![Conda-forge Version](https://img.shields.io/conda/vn/conda-forge/scikit-ued.svg)](https://anaconda.org/conda-forge/scikit-ued) [![DOI badge](https://img.shields.io/badge/DOI-10.1186%2Fs40679--018--0060--y-blue)](https://doi.org/10.1186/s40679-018-0060-y)

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/).

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

To also install optional dependencies required to view diffraction images interactively:

python -m pip install scikit-ued[diffshow]

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+https://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 3.7+**. If you are using a
different version, tests can be run using the `pytest` package.

Optional dependencies
---------------------

For displaying diffraction images with interactive contrast using the
`skued.diffshow` function, PyQtGraph is required.

Contributing
------------

If you want to contribute to `scikit-ued`, take a look at [`CONTRIBUTING.md`](https://github.com/LaurentRDC/scikit-ued/blob/master/CONTRIBUTING.md).

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/).

Crystal structure manipulation (including symmetry-determination) with the [crystals package](https://pypi.org/project/crystals/). (Included
with scikit-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 find this software useful, please consider citing the following
publication:

> L. P. René de Cotret, M. R. Otto, M. J. Stern. and B. J. Siwick, *An open-source software ecosystem for the interactive exploration of ultrafast electron scattering data*, Advanced Structural and Chemical Imaging 4:11 (2018) [DOI: 10.1186/s40679-018-0060-y.](https://ascimaging.springeropen.com/articles/10.1186/s40679-018-0060-y)

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

> 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](https://doi.org/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 GPLv3 License. For more details,
see [LICENSE.txt](https://github.com/LaurentRDC/scikit-ued/blob/master/LICENSE.txt).