Metadata-Version: 2.4
Name: geomad
Version: 1.0.0rc2
Summary: Geomedian and Median Absolute Deviation implementation
Author: Geoscience Australia
License-Expression: Apache-2.0
Project-URL: Homepage, https://github.com/GeoscienceAustralia/geomad
Project-URL: Source, https://github.com/GeoscienceAustralia/geomad
Project-URL: Issues, https://github.com/GeoscienceAustralia/geomad/issues
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX
Classifier: Operating System :: POSIX :: BSD
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: GIS
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Provides-Extra: test
Requires-Dist: pytest; extra == "test"
Requires-Dist: joblib; extra == "test"
Dynamic: license-file

# Geomad

A library for fast calculation of geomedian and median absolute deviation (MAD).

### Assumptions

We assume that the data stack dimensions are ordered so that the spatial dimensions are first (*y*,*x*), followed 
by the spectral dimension of size *p*, finishing with the temporal dimension. 

Algorithms reduce in the last dimension (typically, the temporal dimension).

----

For details of the scientific algorithms implemented, see:

- Roberts, D., Mueller, N., & McIntyre, A. (2017). High-dimensional pixel composites from earth observation time 
  series. IEEE Transactions on Geoscience and Remote Sensing, 55(11), 6254-6264.

- Roberts, D., Dunn, B., & Mueller, N. (2018). Open data cube products using high-dimensional statistics of time 
  series. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 8647-8650).
