farn 0.4.0

Python package to generate an n-dimensional case folder structure applying linear and spatial sampling strategies.

farn

farn is an n-dimensional case generator.

Its primary design goal is to parameterize and execute simulation cases. However, at its core, farn is use-case agnostic and can support a wide spectrum of applications.

The name ‘farn’ is inspired by the Barnsley fractal

farn

• runs the sampling of the design space (sampling strategies cover fixed, linSpace, uniformLHS)
• generates the corresponding case folder structure
• copies arbitrary files from a template folder to case folders
• creates case specific parameter files in case folders
• executes user-defined shell command sets in case folders
• builds case specific OSP (co-)simulation files
• runs simulation cases as batch process

Installation

pip install farn

farn requires the following two (sub-)packages:

1. dictIO: foundation package, enabling farn to handle configuration files in dictIO dict file format.
2. ospx: extension package, enabling farn to generate OSP (co-)simulation files.

However, both get installed automatically with farn (just pip install farn and you're done).

Usage Example

farn provides both an API for use inside Python as well as a CLI for shell execution of core functions.

Reading a farnDict file and creating the corresponding case folder structure:

from farn import run_farn

run_farn('farnDict', sample=True, generate=True)

The above task can also be invoked from the command line, using the 'farn' command line script installed with farn:

farn farnDict --sample --generate

For more examples and usage, please refer to farn's documentation.

Further, the farn-demo repository on GitHub is an excellent place for a jumpstart into farn. Simply clone the farn-demo repository to your local machine and click through the demos and related READMEs, by recommendation in the following sequence:

README in root folder -> guides you through installation of farn
\ospCaseBuilder Demo (see README in ospCaseBuilder folder)
\farn Demo (see README in farn folder)
\importSystemStructure  Demo (see README in importSystemStructure folder)

File Format

A farnDict is a file in dictIO dict file format used with farn.

For a documentation of the farnDict file format, see File Format in farn's documentation on GitHub Pages.

For a detailed documentation of the dictIO dict file format used by farn, see dictIO's documentation on GitHub Pages.

Development Setup

1. Install uv

This project uses uv as package manager. If you haven't already, install uv, preferably using it's "Standalone installer" method:
..on Windows:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

..on MacOS and Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

(see docs.astral.sh/uv for all / alternative installation methods.)

Once installed, you can update uv to its latest version, anytime, by running:

uv self update

2. Install Python

This project requires Python 3.10 or later.
If you don't already have a compatible version installed on your machine, the probably most comfortable way to install Python is through uv:

uv python install

This will install the latest stable version of Python into the uv Python directory, i.e. as a uv-managed version of Python.

Alternatively, and if you want a standalone version of Python on your machine, you can install Python either via winget:

winget install --id Python.Python

or you can download and install Python from the python.org website.

3. Clone the repository

Clone the dictIO repository into your local development directory:

git clone https://github.com/dnv-opensource/farn path/to/your/dev/farn

4. Install dependencies

Run uv sync to create a virtual environment and install all project dependencies into it:

uv sync

5. (Optional) Install CUDA support

Run uv sync with option --extra cuda to in addition install torch with CUDA support:

uv sync --extra cuda

Alternatively, you can manually install torch with CUDA support. Note 1: Do this preferably after running uv sync. That way you ensure a virtual environment exists, which is a prerequisite before you install torch with CUDA support using below uv pip install command.

To manually install torch with CUDA support, generate a uv pip install command matching your local machine's operating system using the wizard on the official PyTorch website. Note: As we use uv as package manager, remember to replace pip in the command generated by the wizard with uv pip.

If you are on Windows, the resulting uv pip install command will most likely look something like this:

uv pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124

Hint: If you are unsure which cuda version to indicate in above uv pip install .. /cuXXX command, you can use the shell command nvidia-smi on your local system to find out the cuda version supported by the current graphics driver installed on your system. When then generating the uv pip install command with the wizard from the PyTorch website, select the cuda version that matches the major version of what your graphics driver supports (major version must match, minor version may deviate).

6. (Optional) Activate the virtual environment

When using uv, there is in almost all cases no longer a need to manually activate the virtual environment.
uv will find the .venv virtual environment in the working directory or any parent directory, and activate it on the fly whenever you run a command via uv inside your project folder structure:

uv run <command>

However, you still can manually activate the virtual environment if needed. When developing in an IDE, for instance, this can in some cases be necessary depending on your IDE settings. To manually activate the virtual environment, run one of the "known" legacy commands:
..on Windows:

.venv\Scripts\activate.bat

..on Linux:

source .venv/bin/activate

7. Install pre-commit hooks

The .pre-commit-config.yaml file in the project root directory contains a configuration for pre-commit hooks. To install the pre-commit hooks defined therein in your local git repository, run:

uv run pre-commit install

All pre-commit hooks configured in .pre-commit-config.yaml will now run each time you commit changes.

8. Test that the installation works

To test that the installation works, run pytest in the project root folder:

uv run pytest

Meta

Copyright (c) 2024 DNV SE. All rights reserved.

Frank Lumpitzsch – @LinkedIn – [e-mail]

Claas Rostock – @LinkedIn – [e-mail]

Seunghyeon Yoo – @LinkedIn – [e-mail]

Distributed under the MIT license. See LICENSE for more information.

https://github.com/dnv-opensource/farn

Contributing

1. Fork it (https://github.com/dnv-opensource/farn/fork)
2. Create an issue in your GitHub repo
3. Create your branch based on the issue number and type (git checkout -b issue-name)
4. Evaluate and stage the changes you want to commit (git add -i)
5. Commit your changes (git commit -am 'place a descriptive commit message here')
6. Push to the branch (git push origin issue-name)
7. Create a new Pull Request in GitHub

For your contribution, please make sure you follow the STYLEGUIDE before creating the Pull Request.