parosol-py 0.1.3

Python package and API wrapper for the ParOSol micro-FE solver

parosol-py

parosol-py is the Python package and runtime wrapper for the ParOSol micro-FE solver. It provides Python helpers for creating solver inputs, running the bundled native executable, reading outputs, and mapping label or density images to material stiffness.

The bundled native ParOSol solver was written by Cyril Flaig and is distributed under the GNU General Public License, version 2 or later. The Python package is therefore distributed as GPL-2.0-or-later.

Install

Prebuilt wheels include the native ParOSol executable:

python -m pip install parosol-py

For local development:

python -m pip install -e .[dev]

Source installs require CMake, an MPI C++ compiler/runtime, HDF5 C++ libraries, and Eigen headers. Release wheels are built for Python 3.11, 3.12, and 3.13. macOS wheels currently target macOS 15 or newer because the bundled native solver depends on Homebrew HDF5/OpenMPI libraries built for that target.

Local Check

Before relying on GitHub Actions, run the local verification gate from an environment that has the native build tools:

python scripts/local_check.py

Add --smoke-install to install the built wheel into a temporary virtual environment and import it.

Python API

import numpy as np

from parosol_py import solve

material = np.ones((10, 10, 10), dtype=np.float32) * 1000.0

result = solve(
    material=material,
    spacing=(0.061, 0.061, 0.061),
    material_unit="MPa",
    test="axial",
    test_axis="z",
    strain=-0.01,
    outputs=("sed",),
    export_dir="outputs/example",
)

print(result.summary)
print(result.exported)

Use dry_run=True to write the ParOSol HDF5 input and command without launching the solver:

result = solve(
    material=material,
    spacing=(0.061, 0.061, 0.061),
    material_unit="MPa",
    test="axial",
    test_axis="z",
    strain=-0.01,
    dry_run=True,
    export_dir="outputs/dry_run",
)

Material Mapping

Label images can be mapped through an explicit material table:

import numpy as np

from parosol_py import LinearIsotropicMaterials, labels_to_material_map

labels = np.array([[[100, 127]]], dtype=np.uint16)
table = LinearIsotropicMaterials(
    youngs_modulus_mpa={100: 8748.0, 127: 8748.0},
    poisson_ratio={100: 0.3, 127: 0.3},
)

mapped = labels_to_material_map(labels, table)

Continuous density images can be converted with one of the supported equations:

from parosol_py import density_to_material_map

mapped = density_to_material_map(
    density_image,
    equation="power",
    coefficient=10000.0,
    exponent=1.7,
    reference_density=1000.0,
    poisson_ratio=0.3,
)

The Mulder grayscale BMD law is available as mulder2007:

mapped = density_to_material_map(
    density_image,
    equation="mulder2007",
    active_mask=outer_contour,
    floor_e_mpa=2.0,
    poisson_ratio=0.3,
)