GPlately was created to accelerate spatio-temporal data analysis by leveraging pyGPlates and PlateTectonicTools 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.
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.
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},
}- pyGPlates >= 1.0.0rc1
- plate-model-manager >= 1.2.2
- Shapely
- NumPy > 1.16
- SciPy > 1.0
- Matplotlib
- Cartopy (for mapping)
- Shapely
- Pooch
- GeoPandas
- netCDF4
- pygmt
- rioxarray
The latest stable public release of GPlately can be installed using conda from the "conda-forge" channel. The following commands will create a new conda environment called "my-gplately-conda-env" and install GPlately within that environment.
conda create -n my-gplately-conda-env
conda activate my-gplately-conda-env
conda install -c conda-forge gplately✏️ If conda gets stuck while solving the environment during the installation of GPlately, you can try using micromamba instead.
GPlately can also be installed using pip.
🟢 Install the latest stable public release from PyPI
pip install gplately🟢 Install from GitHub repository (if you need the latest code changes on GitHub)
pip install git+https://github.com/GPlates/gplately.git🟢 Install from a local folder (if you need local code changes)
git clone https://github.com/GPlates/gplately.git gplately.git
cd gplately.git # go into the folder created by "git clone" command
git checkout master # check out the "master" branch or the name of branch you want
git pull # fetch all recent code changes from the GitHub remote repository
# make your local code changes
pip install . # alternatively, you can use "pip install -e ." to install gplately in editable mode👉 Run GPlately notebooks with Docker
docker pull gplates/gplatelydocker run --rm -ti -p 8888:8888 gplates/gplately- http://localhost:8888
👉 Run GPlately command with Docker
docker run gplates/gplately gplately --versiondocker run gplates/gplately gplately --help
👉 Run your Python script with Docker
docker run -it --rm -v THE_FULL_PATH_TO_YOUR_SCRIPT_FOLDER:/ws -w /ws gplates/gplately python my_script_to_run.py
✏️ Replace THE_FULL_PATH_TO_YOUR_SCRIPT_FOLDER with the full path to the folder containing your script file. In PowerShell, you can use "$PWD" if your script is in the current working directory. On Linux or macOS, you can use `pwd` instead.
Visit this page for more details about using Docker with GPlately.
- Quick start - a brief tutorial to help users get up to speed
- Sample workflows - demonstrations of how GPlately can be used in various workflows
- Examples - code snippets that demonstrate the usage of GPlately's classes and methods
- command-line interface (CLI) - use GPlately in a command-line environment
To see GPlately in action, launch a Jupyter Notebook environment and check out the sample notebooks:
- 01 - Getting Started: A brief overview of how to initialise GPlately's main objects
- 02 - Plate Reconstructions: Setting up a
PlateReconstructionobject, reconstructing geological data through time - 03 - Working with Points: Setting up a
Pointsobject, reconstructing seed point locations through time with. This notebook uses point data from the Paleobiology Database (PBDB). - 04 - Velocity Basics: Calculating plate velocities, plotting velocity vector fields
- 05 - Working with Feature Geometries: Processing and plotting assorted polyline, polygon and point data from GPlates 2.3's sample data sets
- 06 - Rasters: Reading, resizing, resampling raster data, and linearly interpolating point data onto raster data
- 07 - Plate Tectonic Stats: Using PlateTectonicTools to calculate and plot subduction zone and ridge data (convergence/spreading velocities, subduction angles, subduction zone and ridge lengths, crustal surface areas produced and subducted etc.)
- 08 - Predicting Slab Flux: Predicting the average slab dip angle of subducting oceanic lithosphere.
- 09 - Motion Paths and Flowlines: Using pyGPlates to create motion paths and flowlines of points on a tectonic plate to illustrate the plate's trajectory through geological time.
- 10 - SeafloorGrid: Defines the parameters needed to set up a
SeafloorGridobject, and demonstrates how to produce age and spreading rate grids from a set of plate reconstruction model files. - 11 - AndesFluxes: Demonstrates how the reconstructed subduction history along the Andean margin can be potentially used in the plate kinematics analysis and data mining.