PyGPlates 1.0.0rc1 released

PyGPlates enables access to GPlates functionality via the Python programming language.

Install PyGPlates 1.0.0rc1:-

PyGPlates can now be installed using conda or pip.

Please see the installation instructions in the pyGPlates documentation.

Note: The old method of installing a pre-compiled binary package is no longer available. This involved extracting a zip file (or installing a Debian package) and then manually adding the installed location to the PYTHONPATH environment variable.

What's new in PyGPlates 1.0.0rc1:-

Note: This version (1.0.0rc1) is a pre-release of version 1.0.0 (the rc means it's a release candidate). The final version 1.0.0 will have the same functionality (when it is released in January 2025). This release is mainly to ensure there are no issues caused by the introduction of pip support (and to a lesser extent conda support) when the final version 1.0.0 is released.

• Can now install pyGPlates using conda:
  • PyGPlates conda-forge packages can be installed with:
    conda install -c conda-forge pygplates

    Note: For this pre-release (1.0.0rc1) it's actually:

    conda install -c conda-forge/label/pygplates_rc -c conda-forge pygplates
• Can now install pyGPlates using pip:
  • PyGPlates pip wheels can be installed with:
    pip install pygplates
• Added a new Primer chapter in the pyGPlates documentation:
  • Currently documents how to use topologies and deformation.
    • Still a work in progress.
  • Purpose is to show users how to use pyGPlates.
    • Aim to comprehensively cover functionality in pyGPlates.
    • Complements the sample codes.
• Added pickle support:
  • This means pyGPlates can now be used in multi-processing workflows,
    • where a multi-processing module will serialise pyGPlates objects into queues, and
    • de-serialise back into pyGPlates objects for processing on a different CPUs/nodes.
  • Most pyGPlates classes can be pickled.
    • This includes feature collections, features, and feature properties.
    • However, any classes containing reconstructed geometries cannot be pickled.
    • Each class will document whether it can be pickled or not.
• Filenames in pyGPlates can be os.PathLike in addition to strings:
  • Such as pathlib.Path.
• Added ReconstructModel and ReconstructSnapshot classes:
  • Similar to TopologicalModel and TopologicalSnapshot,
    • but for regular reconstruction instead of resolving topologies.
  • Better than using the reconstruct() function.
    • Just like TopologicalModel and TopologicalSnapshot are better than using the resolve_topologies() function.
• Added net rotation:
  • Create a NetRotationModel from a TopologicalModel.
    • And optionally specify your own point distribution (defaults to uniform lat-lon grid of points).
  • Use NetRotationModel to generate a NetRotationSnapshot at a reconstruction time.
  • Use NetRotationSnapshot to calculate a NetRotation.
    • This can be:
      • the total net rotation (over all topologies), or
      • the net rotation of a specific topology.
    • See the sample code.
  • Manually accumulate your own net rotation from points and their finite rotations.
• Can generate statistics along plate boundaries (at uniformly spaced points):
  • TopologicalSnapshot.calculate_plate_boundary_statistics() returns a PlateBoundaryStatistic at each point that contains:
    • boundary normal/length/velocity,
    • convergence velocity/obliquity/etc,
    • left/right plate velocity/etc (including strain rate), and
    • distances to start/end of plate boundary section (eg, trench).
  • See the sample code.
• Improved velocities and deformation:
  • Added Strain and StrainRate classes.
    • Can query strain quantities like dilatation and principal strain.
    • Can query strain rate quantities like dilatation rate and total strain rate.
    • And documented the underlying deformation theory.
  • Can query the deforming triangulation of a resolved network topology.
    • And rigid interior blocks.
  • Added Feature.create_topological_network_feature():
    • Enables rift network parameters to be specified.
  • Can query a TopologicalSnapshot at static points to:
    • Find intersected plates/networks, velocities and strain rates.
  • Can query a ReconstructSnapshot at static points to:
    • Find intersected static polygons and velocities.
  • Improved topologically reconstructed points:
    • Can directly extract crustal thickness, stretching factor, thinning factor and tectonic subsidence.
    • Can calculate velocities.
    • Can query strain rate and strain.
    • See the sample code.
  • Can incrementally reconstruct a point using a deforming network (or using a rigid plate):
    • Same functionality as TopologicalModel.reconstruct_geometry() but at a finer granularity:
      • One network (or plate) topology reconstructs one point over one time step, rather than
      • multiple topologies (eg, a global model) reconstructing multiple points over multiple time steps.
    • Gives user more control over the reconstruction.
  • All reconstructed/resolved geometries can calculate velocities at their vertices:
    • Reconstructed geometries:
      • ReconstructedFeatureGeometry, ReconstructedMotionPath, ReconstructedFlowline.
      • Eg, ReconstructedFeatureGeometry.get_reconstructed_geometry_point_velocities().
    • Resolved topologies:
      • ResolvedTopologicalLine, ResolvedTopologicalBoundary, ResolvedTopologicalNetwork
      • Eg, ResolvedTopologicalLine.get_resolved_geometry_point_velocities().
      • And their sub-segments:
        • ResolvedTopologicalSharedSubSegment, ResolvedTopologicalSubSegment.
        • Eg, ResolvedTopologicalSharedSubSegment.get_resolved_geometry_point_velocities().
  • Fixed quering overriding/subducting plates/networks (at shared boundary segments):

Documentation:-

Documentation and tutorials are available on the User Documentation page.

The pyGPlates Documentation includes:

• an Introduction to pyGPlates,
• a Getting Started chapter with an installation guide and a tutorial,
• a Primer chapter covering the main areas of pyGPlates,
• documented Examples, and
• a detailed Reference of pyGPlates functions and classes.

Note: The Primer chapter is new and is a work in progress.

The pyGPlates Tutorials are Jupyter Notebooks that analyse and visualise real-world data using pyGPlates. These tutorials complement the sample code in the pyGPlates documentation by providing a more research-oriented focus.