Examples

It is recommended to use a Conda environment to run these examples. Download this yaml file and run the commands below to start using Jupyter Notebook.

$ conda env create --name my-gplately-env --file=env.yaml
$ conda activate my-gplately-env
$ jupyter notebook

Alternatively, you may use Docker to run these examples as well. Use -v option to access the local directory on the host machine. Visit this page for details.

$ docker pull gplates/gplately
$ docker run --rm -ti -v .:/ws -w /ws -p 8888:8888 gplates/gplately

Workflows

• 01 - Getting Started

  A brief overview of how to initialise GPlately’s main objects

• 02 - Plate Reconstructions

  Setting up a gplately.PlateReconstruction object, reconstructing geological data through time.

• 03 - Working with Points

  Setting up a gplately.Points object, reconstructing seed point locations through time. This notebook uses point data from the Paleobiology Database (PBDB).

• 04 - Velocity Basics

  Calculating plate velocities and 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

  Calculating and plotting 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 flowines of points on a tectonic plate to illustrate the plate’s trajectory through geological time.

• 10 - Seafloor Grid

  Defines the parameters needed to set up a gplately.SeafloorGrid object, and demonstrates how to produce age and spreading rate grids from a set of plate reconstruction model files.

• 11 - Andes Fluxes

  Demonstrates how the reconstructed subduction history along the Andean margin can be potentially used in the plate kinematics analysis and data mining.

• 12 - Mutschler World Porphyry Copper Deposits Regional Plots

  Generates regional plots for Mutschler world porphyry copper deposits.

• 13 - Reconstructing Zircon Data

  Demonstrates how to reconstruct and plot Zircon data on a global map through geological time.

• 14 - Rule Based GPML Processing Pipeline

  Demonstrates how to use the rule-based GPML processing pipeline to filter and transform geological data.

• 15 - Convert Grid Reference Frame

  Demonstrates how to convert reference frame of grids.

Note

All the Jupyter Notebook files of these sample workflows are available here in the GPlately GitHub repository.

Basics

• Hello World

  A minimal working example of GPlately.

• Use Plate Model Manager

  Use plate-model-manager to download plate reconstruction models.

• Plot with Cartopy

  Plot a paleo-map using Cartopy.

• Plot with PyGMT

  Plot a paleo-map using PyGMT.

• Reconstruct Files

  Reconstruct and plot shapefiles and other supported files.

• Use Your Own Plate Model

  Use your own plate model to reconstruct points.

• Save Reconstructed Geometries to Files

  Save the reconstructed data to shapefiles.

• Shortcut to Create PlateReconstruction and PlotTopologies Objects

  Easier way to get PlateReconstruction and PlotTopologies objects from the name of a plate reconstruction model.

• Generate Icosahedron Mesh

  Generate and visualize Icosahedron mesh.

Note

The Jupyter Notebook files of these basic examples are available here.