Readers and converters for data from the GLDAS Noah Land Surface Model.
Project description
Readers and converters for data from the GLDAS Noah Land Surface Model. Written in Python.
Works great in combination with pytesmo.
Citation
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.596427 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
Installation
Setup of a complete environment with conda can be performed using the following commands:
conda create -n gldas python=3.9 # or any other supported python version
source activate gldas# Either install required conda packages manually
conda install -c conda-forge numpy netCDF4 pyproj pygrib pyresample -n gldas
# Or use the provided environment file to install all dependencies
conda env update -f environment.yml -n gldas# Install the latest gldas package and its pip-dependencies
pip install gldasSupported Products
At the moment this package supports GLDAS Noah data version 1 in grib format (reading, time series creation) and GLDAS Noah data version 2.0 and version 2.1 in netCDF format (download, reading, time series creation) with a spatial sampling of 0.25 degrees. It should be easy to extend the package to support other GLDAS based products. This will be done as need arises.
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.
Development setup
For Development we also recommend a conda environment. You can create one including test dependencies and debugger by running conda env create -f environment.yml. This will create a new gldas environment which you can activate by using source activate gldas.
Guidelines
If you want to contribute please follow these steps:
Fork the gldas 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 gldas 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
Note
This project has been set up using PyScaffold 2.5.6. For details and usage information on PyScaffold see http://pyscaffold.readthedocs.org/.
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Source Distribution
Built Distribution
Hashes for gldas-0.7.0-py2.py3-none-any.whl
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