xarrayMannKendall

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xarrayMannKendall is a module to compute linear trends over 2D and 3D arrays. For 2D arrays xarrayMannKendall uses xarray parallel capabilities to speed up the computation.

For more information on the Mann-Kendall method please refer to:

Mann, H. B. (1945). Non-parametric tests against trend, Econometrica, 13, 163-171.

Kendall, M. G. (1975). Rank Correlation Methods, 4th edition, Charles Griffin, London.

Yue, S. and Wang, C. (2004). The Mann-Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. Water Resources Management, 18(3), 201–218. doi:10.1023/b:warm.0000043140.61082.60

and

Hussain, M. and Mahmud, I. (2019). pyMannKendall: a python package for non parametric Mann Kendall family of trend tests. Journal of Open Source Software, 4(39), 1556. doi:10.21105/joss.01556

A useful resource can be found here. Finally, another library that allows to compute a larger range of Mann-Kendall methods is pyMannKendall.

This package was primarily developed for the analyisis of ocean Kinetic Energy trends over the satellite record period that can be found at doi:10.1038/s41558-021-01006-9.

The data analysed with using this module can be found at EKE_SST_trends repository.

Installation:

You can install the latest tagged release of this package via conda-forge by:

conda install -c conda-forge xarrayMannKendall

Alternatively, you can clone the repository and install. To do so, make sure you have the module requirements (numpy & xarray):

pip install -r requirements.txt 

conda install --file ./requirements.txt

Now you can install the module

pip install -e .

for local installation use

pip install --ignore-installed --user .

Cite this code:

This repository can be cited as:

Josué Martínez Moreno, & Navid C. Constantinou. (2021, January 23). josuemtzmo/xarrayMannKendall: Mann Kendall significance test implemented in xarray. (Version v.1.0.0). Zenodo. http://doi.org/10.5281/zenodo.4458777