landlab-triangle

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This repository adds TriangleModelGrid, a new Landlab grid type that enables unstructured triangular meshes. Unlike Landlab's standard structured grids, TriangleModelGrid allows for complex geometries with irregular boundaries, interior holes, and variable grid resolution.

Uses Jonathan Shewchuk's Triangle software.

Installation

Installing from PyPI

pip install landlab-triangle

A compiled build of Triangle ships inside the wheel, so there is nothing else to install. See NOTICE for the terms of the bundled Triangle, which remains under Jonathan Shewchuk's non-commercial license.

Usage

Option 1: direct initialization

from landlab_triangle import TriangleModelGrid

# Boundary vertices in order. x and y are separate arguments, x first.
x_of_boundary = [0.0, 10.0, 12.0, 6.0, -1.0]
y_of_boundary = [0.0, -1.0, 8.0, 12.0, 7.0]

# Optional interior holes: each ring is a sequence of (x, y) vertices.
interior_rings = [[(4.0, 4.0), (6.0, 4.0), (6.0, 6.0), (4.0, 6.0)]]

grid = TriangleModelGrid(
    x_of_boundary,
    y_of_boundary,
    interior_rings=interior_rings,
    triangle_options="pqa1Devjz",
)

print(f"Number of nodes: {grid.number_of_nodes}")
print(f"Number of cells: {grid.number_of_cells}")

Option 2: from a dictionary

from landlab_triangle import TriangleModelGrid

# Create a grid from a dictionary with "x" and "y" keys
grid_params = {
    "x": [0.0, 10.0, 12.0, 6.0, -1.0],
    "y": [0.0, -1.0, 8.0, 12.0, 7.0],
    "triangle_options": "pqa1Devjz",
}

grid = TriangleModelGrid.from_dict(grid_params)

Any keys beyond "x" and "y" pass through as keyword arguments to the constructor.

Option 3: from a vector file

from landlab_triangle import TriangleModelGrid

# Build from any vector file GeoPandas can read (shapefile, GeoJSON,
# GeoPackage, ...). Interior rings in the file become holes automatically.
grid = TriangleModelGrid.from_vector_file(
    "path/to/polygon.geojson",
    triangle_options="pqDevjz",
    timeout=10,
)

print(f"Number of nodes: {grid.number_of_nodes}")

Triangle options

The triangle_options parameter controls the behavior of the Triangle meshing software (default "pqDevjz"). Common options include:

  • q: Quality mesh generation - ensures no angles smaller than N degrees (defaults to 20)
  • a: Area constraint - limits the maximum area of triangles

Timeout: The timeout parameter (in seconds) prevents the meshing process from running indefinitely if Triangle encounters complex geometries. It defaults to 10.

Example with area constraint

# Reusing the boundary from Option 1, cap the maximum triangle area at 0.1.
grid = TriangleModelGrid(
    x_of_boundary,
    y_of_boundary,
    triangle_options="pqa0.1Devjz",  # 'a0.1' sets max area to 0.1
)

Plotting

Field-aware plotters live in landlab_triangle as free functions that take the grid first and draw on the current axes (or one you pass as ax=). Each returns the matplotlib artist, so a bare call followed by plt.show() just works.

There is one plotter per grid element — plot_node, plot_link, plot_patch, plot_corner, plot_face, plot_cell — plus plot_vector for components and plot_mesh for the bare skeleton. The value argument accepts either a field-name string or a raw array.

import matplotlib.pyplot as plt
from landlab_triangle import TriangleModelGrid, plot_node, plot_cell, plot_vector

grid = TriangleModelGrid(
    [0.0, 10.0, 12.0, 6.0, -1.0], [0.0, -1.0, 8.0, 12.0, 7.0], triangle_options="pqa1Devjz"
)
grid.add_field("elevation", grid.x_of_node + grid.y_of_node, at="node")

# Smooth node field over the full domain, including perimeter nodes
plot_node(grid, "elevation", cmap="terrain", colorbar_label="elevation (m)")
plt.show()

# Flat-colored Voronoi cells from a raw array
import numpy as np
plot_cell(grid, np.arange(grid.number_of_cells, dtype=float))
plt.show()

# Component vectors as arrows colored by magnitude
u = np.ones(grid.number_of_nodes)
plot_vector(grid, u, u, at="node")
plt.show()

Common matplotlib keywords (vmin, vmax, norm, alpha, shading, ...) pass straight through to the underlying call. plot_node defaults to Gouraud shading; pass shading="flat" for per-triangle color instead.

To reconstruct a vector from flux-at-links, map the link components to nodes with Landlab's mappers (e.g. map_link_vector_components_to_node) first, then call plot_vector on the results.

Contributing

Contributions are welcome. See CONTRIBUTING.md for how to set up a development environment and submit changes, and please review the Code of Conduct. If you need help, see SUPPORT.md.

Citation

If you use landlab-triangle in your work, please cite it. Citation metadata lives in CITATION.cff; GitHub renders it as a "Cite this repository" button in the sidebar. Each release is archived on Zenodo:

Pierce, E. landlab-triangle. https://doi.org/10.5281/zenodo.22058174

DOI 10.5281/zenodo.22058174 resolves to the latest version.

Contact

Questions, bugs, and feature requests go to the issue tracker. For private inquiries, email Ethan Pierce at ethan.g.pierce@dartmouth.edu.

License

landlab-triangle is released under the MIT License. It bundles a compiled build of Triangle, which is not MIT-licensed and remains under Jonathan Shewchuk's non-commercial terms; see NOTICE for details.

Acknowledgments

This work was supported by the National Science Foundation under Award 2104102 (OpenEarthScape). See CREDITS.md for the full list of contributors and acknowledgments.

Project links

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Credits

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Author: Ethan Pierce

GitHub Statistics

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MIT
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Requires

Python >=3.13

Tags

grid landlab mesh triangle unstructured

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