Metadata-Version: 2.1
Name: movekit
Version: 0.1.16
Summary: Simple and effective tools for the analysis of movement data
Home-page: https://github.com/dbvis-ukon/movekit
Author: Lukas Weixkler, Arjun Majumdar, Eren Cakmak, Jolle Jolles
Author-email: lukas.weixler@uni-konstanz.de, arjun.majumdar@uni-konstanz.de, eren.cakmak@uni-konstanz.de, j.w.jolles@gmail.com
License: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Project-URL: Documentation, https://pyscaffold.org/
Platform: any
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
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 :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Requires-Python: >=3.5
Description-Content-Type: text/markdown; charset=UTF-8
Provides-Extra: testing
License-File: LICENSE
License-File: AUTHORS.rst

MOVEKIT
======

<p align="center">
    <img src="media/mover.gif" height=150px/>
    <img src="media/voronoi.png" height=150px>
    <img src="media/network.png" height=150px> 
</p>


`movekit` is an open-source software package for the processing and analysis of movement data.

__Features:__

* Data pre-processing (e.g. data checks, smoothing, duplicate removal, interpolation, outlier detection)
* Feature extraction (e.g. speed, acceleration, heading)
* Individual-level movement analysis (e.g. autocorrelation, speed distribution, environment exploration)
* Group-level analysis (e.g. cohesion, polarisation, coordination, leadership)
* Spatial data analysis (Voronoi, delaunay triangulation)
* Network analysis with networkX

---

## Installation

The easiest way to install *movekit* is by using `pip` :

    pip install movekit

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## Demo

You can view a demo of common features here:
[Jupyter Notebooks](examples/).

---

### License

Released under a GNU General Public License. See the [LICENSE](LICENSE) file for details. List of [Authors](AUTHORS.rst)

The package is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy – EXC 2117 – 422037984.


