pytesmo, the Python Toolbox for the Evaluation of Soil Moisture Observations, is a package/python toolbox which aims it is 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.
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
This package should be installable through pip:
pip install pytesmo
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:
- ASCAT
- SMAP
- GLDAS Noah
- ERA5 and ERA5-Land
- Data from the International Soil Moisture Network (ISMN)
- SMOS
- C3S SM
- ESA CCI SM
- MERRA
- H-SAF image products
- ERS
For more details visit the Documentation.
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.
For Development we recommend a conda
environment.
If you want to contribute please follow these steps:
- Fork the pytesmo repository to your account
- Clone the repository, make sure you use
git clone --recursive
to also get the test data repository. - make a new feature branch from the pytesmo master branch
- Add your feature
- please include tests for your contributions in one of the test directories We use py.test so a simple function called test_my_feature is enough
- submit a pull request to our master branch
In order to make a working release for windows the whl
files for windows from appveyor CI have to be uploaded to PyPI. They can be found on the appveyor CI run for the created tag under the jobs/Artifacts
tab. All the .whl
files should be downloaded into a folder. They can then be added to the release on PyPI using e.g. twine upload pytesmo-0.7.1*whl