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GPlately was created to accelerate spatio-temporal data analysis by leveraging pyGPlates within a simplified Python interface. This object-oriented package enables the reconstruction of data through deep geologic time (such as points, lines, polygons, and rasters), the interrogation of plate kinematic information (plate velocities, rates of subduction and seafloor spreading), the rapid comparison of multiple plate motion models, and the plotting of reconstructed output data on maps. All tools are designed to be parallel-safe, accelerating spatio-temporal analysis over multiple CPU processors.

SeedPointGIF

GPlately can be installed using either pip or conda (via the conda-forge channel). For detailed installation instructions, please refer to the Installation section. Additionally, Docker images are available for your convenience.

Sample data is available from EarthByte servers, which include rasters, seafloor age grids, rotation files, and more to help you get started with plate reconstructions.

Citation

Mather, B.R., Müller, R.D., Zahirovic, S., Cannon, J., Chin, M., Ilano, L., Wright, N.M., Alfonso, C., Williams, S., Tetley, M., Merdith, A. (2023) Deep time spatio-temporal data analysis using pyGPlates with PlateTectonicTools and GPlately. Geoscience Data Journal, 1–8. Available from: https://doi.org/10.1002/gdj3.185

@article{Mather2023,
author = {Mather, Ben R. and Müller, R. Dietmar and Zahirovic, Sabin and Cannon, John and Chin, Michael and Ilano, Lauren and Wright, Nicky M. and Alfonso, Christopher and Williams, Simon and Tetley, Michael and Merdith, Andrew},
title = {Deep time spatio-temporal data analysis using pyGPlates with PlateTectonicTools and GPlately},
year = {2023},
journal = {Geoscience Data Journal},
pages = {1-8},
keywords = {geospatial, plate reconstructions, pyGPlates, python, tectonics},
doi = {https://doi.org/10.1002/gdj3.185},
url = {https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/gdj3.185},
eprint = {https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/gdj3.185},
}

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