Skip to main content

A Python interface for interacting with OpenFOAM

Project description

foamlib

Documentation CI Codecov Checked with mypy Ruff uv PyPI Conda Version PyPI - Python Version OpenFOAM Docker image

foamlib provides a simple, modern and ergonomic Python interface for interacting with OpenFOAM.

It offers the following classes:

  • FoamFile (and FoamFieldFile): read-write access to OpenFOAM configuration and field files as if they were Python dicts, using foamlib's own parser. Supports ASCII and binary field formats (with or without compression).
  • FoamCase: a class for configuring, running, and accessing the results of OpenFOAM cases.
  • AsyncFoamCase: variant of FoamCase with asynchronous methods for running multiple cases at once.
  • AsyncSlurmFoamCase: subclass of AsyncFoamCase used for running cases on a Slurm cluster.

Get started

Install

  • With pip:

    pip install foamlib
    
  • With conda:

    conda install -c conda-forge foamlib
    

Clone a case

import os
from pathlib import Path
from foamlib import FoamCase

pitz_tutorial = FoamCase(Path(os.environ["FOAM_TUTORIALS"]) / "incompressible/simpleFoam/pitzDaily")

my_pitz = pitz_tutorial.clone("myPitz")

Run the case

my_pitz.run()

Access the results

latest_time = my_pitz[-1]

p = latest_time["p"]
U = latest_time["U"]

print(p.internal_field)
print(U.internal_field)

Clean the case

my_pitz.clean()

Edit the controlDict file

my_pitz.control_dict["writeInterval"] = 10

Make multiple file reads and writes in a single go

with my_pitz.fv_schemes as f:
    f["gradSchemes"]["default"] = f["divSchemes"]["default"]
    f["snGradSchemes"]["default"] = "uncorrected"

Run a case asynchronously

import asyncio
from foamlib import AsyncFoamCase

async def run_case():
    my_pitz_async = AsyncFoamCase(my_pitz)
    await my_pitz_async.run()

asyncio.run(run_case())

Parse a field using the FoamFieldFile class directly

from foamlib import FoamFieldFile

U = FoamFieldFile(Path(my_pitz) / "0/U")

print(U.internal_field)

Run an optimization loop in parallel

import os
from pathlib import Path
from foamlib import AsyncFoamCase
from scipy.optimize import differential_evolution

base = AsyncFoamCase(Path(os.environ["FOAM_TUTORIALS"]) / "incompressible/simpleFoam/pitzDaily")
# Replace with `AsyncSlurmFoamCase` if on a cluster and you want cases to be run as Slurm jobs

async def cost(x):
    async with base.clone() as clone:
        clone[0]["U"].boundary_field["inlet"].value = [x[0], 0, 0]
        await clone.run()
        return abs(clone[-1]["U"].internal_field[0][0])

result = differential_evolution(cost, bounds=[(-1, 1)], workers=AsyncFoamCase.map, polish=False)

Use it to create a run (or clean) script

#!/usr/bin/env python3
from pathlib import Path
from foamlib import FoamCase

case = FoamCase(Path(__file__).parent)
# Any additional configuration here
case.run()

Documentation

For more information, check out the documentation.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

foamlib-0.6.7.tar.gz (33.4 kB view hashes)

Uploaded Source

Built Distribution

foamlib-0.6.7-py3-none-any.whl (37.6 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page