# xarrayMannKendall
| Conda | Travis CI (Python 3.8) | Code Coverage | Zenodo |
|:-----:|:----------------------:|:-------------:|:------:|
| [](https://anaconda.org/conda-forge/xarrayMannKendall) | [](https://github.com/josuemtzmo/xarrayMannKendall/actions/workflows/test.yml) | [](https://codecov.io/gh/josuemtzmo/xarrayMannKendall) | [](https://zenodo.org/badge/latestdoi/288618695) |
`xarrayMannKendall` is a module to compute linear trends over 2D and 3D arrays.
For 2D arrays `xarrayMannKendall` uses [xarray](http://xarray.pydata.org/) 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](https://doi.org/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](https://doi.org/10.21105/joss.01556)
A useful resource can be found [here](https://vsp.pnnl.gov/help/vsample/Design_Trend_Mann_Kendall.htm). Finally, another library that allows to compute a larger range of Mann-Kendall methods is [pyMannKendall](https://github.com/mmhs013/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](https://doi.org/10.1038/s41558-021-01006-9).
The data analysed with using this module can be found at [`EKE_SST_trends`](https://github.com/josuemtzmo/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
Raw data
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"description": "\n\n\n# xarrayMannKendall\n\n| Conda | Travis CI (Python 3.8) | Code Coverage | Zenodo |\n|:-----:|:----------------------:|:-------------:|:------:|\n| [](https://anaconda.org/conda-forge/xarrayMannKendall) | [](https://github.com/josuemtzmo/xarrayMannKendall/actions/workflows/test.yml) | [](https://codecov.io/gh/josuemtzmo/xarrayMannKendall) | [](https://zenodo.org/badge/latestdoi/288618695) |\n\n`xarrayMannKendall` is a module to compute linear trends over 2D and 3D arrays.\nFor 2D arrays `xarrayMannKendall` uses [xarray](http://xarray.pydata.org/) parallel capabilities to speed up the computation. \n\nFor more information on the Mann-Kendall method please refer to:\n\n> Mann, H. B. (1945). Non-parametric tests against trend, *Econometrica*, **13**, 163-171.\n\n> Kendall, M. G. (1975). Rank Correlation Methods, 4th edition, Charles Griffin, London.\n\n> 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\u2013218. doi:[10.1023/b:warm.0000043140.61082.60](https://doi.org/10.1023/b:warm.0000043140.61082.60)\n\nand\n\n> 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](https://doi.org/10.21105/joss.01556)\n\n\nA useful resource can be found [here](https://vsp.pnnl.gov/help/vsample/Design_Trend_Mann_Kendall.htm). Finally, another library that allows to compute a larger range of Mann-Kendall methods is [pyMannKendall](https://github.com/mmhs013/pyMannKendall).\n\nThis package was primarily developed for the analyisis of ocean Kinetic Energy trends \nover the satellite record period that can be found at doi:[10.1038/s41558-021-01006-9](https://doi.org/10.1038/s41558-021-01006-9).\n\nThe data analysed with using this module can be found at [`EKE_SST_trends`](https://github.com/josuemtzmo/EKE_SST_trends) repository.\n\n## Installation:\n\nYou can install the latest tagged release of this package via `conda-forge` by:\n\n```\nconda install -c conda-forge xarrayMannKendall\n```\n\nAlternatively, you can clone the repository and install. To do so, make sure you\nhave the module requirements (`numpy` & `xarray`):\n\n```\npip install -r requirements.txt \n```\n\n```\nconda install --file ./requirements.txt\n```\n\nNow you can install the module\n\n```\npip install -e .\n```\n\nfor local installation use \n\n```\npip install --ignore-installed --user .\n```\n\n## Cite this code:\n\nThis repository can be cited as:\n\nJosu\u00e9 Mart\u00ednez 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\n",
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