biomesh

🚀 Installation

biomesh can be installed via pip as

pip install biomesh

If you want to generate a mesh with Gmsh, you also need to install the gmsh python library as

pip install gmsh

📖 Usage

biomesh is composed of multiple utilities for working with complex biomechanical geometries. Below are some common workflows

Generate a mesh from colored STL files

Colored STL-files (e.g., exported from Materialise 3-matic) can be used to generate a volume mesh. Although STL color encoding is not standardized, some software packages embed surface IDs in unused byte fields. biomesh leverages this to extract surface information.

👉 Generating a mesh from stl-files requires the gmsh Python package.

import biomesh

mesh = biomesh.mesh_colored_stl_files(
    "path/to/part1.stl",
    "path/to/part2.stl",
    "path/to/part3.stl",
    mesh_size=2.0
)

Alternatively, you can load the meshes from any format supported by meshio:

import meshio

meshio.read("path/to/mesh.vtu")

Convert linear to quadratic elements

Convert linear elements in your mesh to quadratic ones:

mesh = biomesh.lin_to_quad(mesh)

Reorder mesh nodes

Finite element solvers often benefit from reducing bandwidth in the system matrix. biomesh provides a node reordering algorithm based on Cuthill-McKee's algorithm to improve efficiency:

mesh = biomesh.reorder(mesh)

Merge multiple meshes

Combine several meshes into a single mesh object:

mesh_all = biomesh.merge(mesh1, mesh2, mesh3)

⚠️ Overlapping points are not automatically merged.

Filter a mesh

Extract a subset of a mesh using flexible filters. For example, filtering by cell type:

# keep only hexahedral cells
filter_hex = lambda block : block.type == 'hexahedron'
mesh_filtered = biomesh.filter.by_cellblock(mesh, filter_hex)

# Get point mapping from old -> new IDs
point_mapping = biomesh.filter.points_map_by_cellblock(mesh, filter_hex)

Solve simple Laplace problem

biomesh can also solve basic Laplace problems, commonly used to estimate fiber directions with rule based methods.

dbc_nodes = np.array([0, 1, 2, 4])
dbc_values = np.array([0.0, 0.0, 1.0, 1.0])

phi = biomesh.solve(mesh, dbc_nodes, dbc_values)

# or equivalently
phi = biomesh.solve_onezero(mesh, np.array([2, 4]), np.array([0, 1]))

The result phi is the solution vector of the Laplace problem.

Finite Element Utilities

biomesh.fe provides helper functions for finite element analysis. For example, compute the nodal averaged gradient of a scalar field:

grad_phi = biomesh.fe.grad(mesh, phi)