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pytesmo, the Python Toolbox for the Evaluation of Soil Moisture Observations, is a package/python toolbox which aims to provide a library that can be used for the comparison and validation of geospatial time series datasets with a (initial) focus on soil moisture.

Documentation & Software Citation

https://zenodo.org/badge/DOI/10.5281/zenodo.596422.svg

To see the latest full documentation click on the docs badge at the top.

If you use the software in a publication then please cite it using the Zenodo DOI. Be aware that this badge links to the latest package version.

Please select your specific version at https://doi.org/10.5281/zenodo.596422 to get the DOI of that version. You should normally always use the DOI for the specific version of your record in citations. This is to ensure that other researchers can access the exact research artefact you used for reproducibility.

You can find additional information regarding DOI versioning at http://help.zenodo.org/#versioning

If you want to contribute, take a look at the developers guide .

Installation

This package should be installable through pip which downloads the package from the python package repository Pypi. However, pytesmo also needs some packages that depend on C or Fortran libraries (like netCDF4). They should be installed first with conda. See http://conda.pydata.org/docs/ on how to use it. We recommend using either Anaconda or Miniconda.

conda install -c conda-forge numpy scipy pandas netCDF4 cython pyresample

Afterwards pytesmo can be installed via pip.

pip install pytesmo

As an alternative (e.g. if you want to contribute to the package), you can clone the Github repository and install from source:

git clone https://github.com/TUW-GEO/pytesmo.git --recursive
cd pytesmo
conda create -n pytesmo python=3.6 # or any supported python version
source activate pytesmo
conda env update -f environment.yml -n pytesmo
pip install -e .

Supported Products

Soil moisture is observed using different methods and instruments, in this version several satellite datasets as well as in situ and reanalysis data are supported through independent and optional (reader) packages:

Related Packages

Some former pytesmo modules are now provided as separate packages.

  • pygeogrids : Creation and handling of Discrete Global Grids or Point collections

  • cadati : Calender, Date and Time functions

  • repurpose : Time series - image conversion and resampling routines

  • colorella : Color maps and color map handling

Contribute

We are happy if you want to contribute. Please raise an issue explaining what is missing or if you find a bug. We will also gladly accept pull requests against our master branch for new features or bug fixes.

Please follow the developers guide.

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.18.1)

2 maintainers

PyPI data
Data sourced directly from PyPI's database.
Avatar for sebhahn from gravatar.com sebhahn Avatar for wpreimes from gravatar.com wpreimes

Credits

Author: TU Wien

License

mit

Requires

Python >=3.6

Classifiers

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Release history Release notifications | RSS feed

0.18.1

23 files

0.18.0

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0.17.0

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0.16.1

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0.16.0

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0.15.2

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