clustering-geodata-cubes 0.8.0

CGC: a Scalable Python Package for Co- and Tri-Clustering of Geodata Cubes

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CGC: Clustering Geo-Data Cubes

The Clustering Geo-Data Cubes (CGC) package focuses on the needs of geospatial data scientists who require tools to make sense of multi-dimensional data cubes. It provides the functionality to perform co-cluster and tri-cluster analyses on both local and distributed systems.

Installation

To install CGC, do:

pip install clustering-geodata-cubes

Alternatively, you can clone this repository and install it using pip:

git clone https://github.com/phenology/cgc.git
cd cgc
pip install .

In order to run tests (including coverage) install the dev package version:

git clone https://github.com/phenology/cgc.git
cd cgc
pip install .[dev]
pytest -v

Documentation

The project’s full API documentation can be found online. Including:

• Co-clustering

• Tri-clustering

• K-means refinement

• Utility Functions

Examples of CGC applications on real geo-spatial data:

• Co-clustering application

• Tri-clustering application

Tutorial

The tutorial of CGC can be found here.

Contributing

If you want to contribute to the development of cgc, have a look at the contribution guidelines.

License

Copyright (c) 2020-2023,

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Credits

The code has been developed as a collaborative effort between the ITC, University of Twente and the Netherlands eScience Center within the generalization of the project High spatial resolution phenological modelling at continental scales.

This package was created with Cookiecutter and the NLeSC/python-template.