geoh5vista: a Geoh5 <> PyVista (VTK) interface

A PyVista (and VTK) interface for the Geoh5 file format providing Python 3D visualization and useable data structures for processing datasets in the geoh5 specification. This library allows geologists to access the powerful tools available in the PyVista ecosystem for their data stored in the Geoh5 format.

The structure and interfaces of this project are heavily inspired by the 'omfvista' package, which provides a similar interface for the 'omf' format.

omfvista package: https://github.com/OpenGeoVis/omfvista

Geoh5 Python interface package: https://mirageoscience-geoh5py.readthedocs-hosted.com/en/stable/index.html

Documentation is hosted at https://github.com/derek-kinakin/geoh5vista

Installation

From PyPI

pip install geoh5vista

From Github

pip install git+https://github.com/derek-kinakin/geoh5vista.git

Current Status of Supported Geoh5 Entities

This table provides the list of supported entities. Read from and write to Geoh5 support is the goal for each entity.

Block models with uniform spacing within each axis are read as ImageData; U, V, and Z spacing may differ from each other. Models with variable spacing within any axis are read as StructuredGrid with explicit, rotated point coordinates. Use blockmodel_to_vtk to select the representation automatically, or blockmodel_grid_geom_to_image_vtk / blockmodel_grid_geom_to_structured_vtk for geometry only.

StructuredGrid block models support common dataset attributes and filters such as cell_data, user_dict, bounds, slicing, clipping, thresholding, contouring, cell centres, and structured subsets. ImageData-only attributes and filters, such as spacing, origin, direction_matrix, image smoothing, FFT, and morphology, are not available. For positive cell volumes, descending delimiter axes are reversed in StructuredGrid index order; cell data are reordered to remain spatially aligned.

Block-model imports preserve rotation, nonzero delimiter offsets, and descending-axis cell-data alignment. ImageData uses the transformed first delimiter corner as its origin, positive spacing, and signed axis directions. The geometry helper applies no rotation when rotation_matrix=None; blockmodel_to_vtk applies the model's rotation automatically.

Block-model exports preserve horizontal rotation, signed axis directions, and cell-data alignment. Tilted, sheared, or scaled direction matrices are rejected. Export normalizes delimiters to start at zero and moves their offset into the model origin. A reversed U axis is represented by an equivalent rotation and signed V spacing, so geometry and data are preserved, but the original origin, delimiter offsets, and rotation representation may differ after a round trip.

StructuredGrid exports infer rotation and delimiters from the current point coordinates. Points must form an orthogonal rectilinear lattice with horizontal U/V axes and a vertical Z axis; tilted, sheared, warped, or degenerate grids are rejected. The first grid node becomes the model origin.

Geoh5 Entity Metadata Support

The following metadata are read from the geoh5 entities and attached to the PyVista objects in the "user_dict":

• Entity name (ob.user_dict["gh5_name"]) as a string
• Entity colour (ob.user_dict["gh5_colour"]) as a list [R,G,B]
• Entity type (ob.user_dict["gh5_entity_type"]) as "Points", "Curve", "Surface", "Grid2D", "Grid3D", or "Drillhole"
• Entity visibility (ob.user_dict["gh5_visible"]) as True/False. Integer/NumPy visibility values from geoh5py are normalized (0 = hidden); unrecognized values are treated as visible with a warning.

Example Use

import pyvista as pv
import geoh5vista

project = geoh5vista.read_geoh5('test_file.geoh5')
project

Once the data is loaded as a pyvista.MultiBlock dataset from geoh5vista, that object can be directly used for interactive 3D visualization from PyVista:

An interactive scene can be created and manipulated to create a figure. First, grab the elements from the project:

# Grab a few elements of interest and plot em up!
vol = project["Block Model"]
topo = project["Topography"]
dacite = project["Dacite"]

Then create a 3D scene with these spatial data and apply a filtering tool from PyVista to the volumetric data:

# Create a plotting window
p = pv.Plotter(notebook=False)
# Add our datasets
p.add_mesh(topo, cmap="gist_earth", opacity=0.5)
p.add_mesh(dacite, color=dacite.user_dict["gh5_colour"], opacity=0.6)
# Add the volumetric dataset with a thresholding tool
p.add_mesh_threshold(vol)
# Add the bounds axis
p.show_bounds()
# Render the scene in a pop out window
p.show()

Writing PyVista objects to a Geoh5 file can be as simple as:

# Write a single object to geoh5
geoh5vista.write_geoh5(topo, "new_gh5_topo_file.geoh5")

# Write a multiblock to geoh5
new_project = pv.MultiBlock()
new_project["Topo"] = topo
new_project["Dacite"] = dacite

geoh5vista.write_geoh5(new_project, "new_project.geoh5")