stressnet 0.1.0

An adaptable deep-learning model for mechanical stress inference in tissues

StressNET

StressNET: an adaptable deep-learning model for mechanical stress inference in tissues

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What is StressNET?

StressNET is an open-source Python library for data-driven mechanical stress inference in tissues. It implements Graph Neural Network models that infer intercellular stress from tissue geometry, without requiring users to specify a fixed physical model. The package includes pretrained models for stress prediction and utilities to finetune them on in silico and in vivo datasets.

It can be used to:

• preprocess Surface Evolver outputs or skeletonized microscopy images into graph data,
• load pretrained or finetuned StressNET model weights,
• infer stress/tension values on tissue interfaces,
• compare StressNET predictions against ground truth or other method predictions when available,
• plot inferred stresses back on a ForSys frame.

Installation

pip install stressnet

Or with Poetry:

poetry add stressnet

Quick start

import stressnet

print(stressnet.__version__)

From a repository checkout, run the test suite with:

poetry install --with dev
poetry run pytest

Or, if make is available:

make install-dev
make test

Usage

The recommended entry points are the worked example notebooks in examples/:

• examples/inference_in_silico.ipynb: inference on synthetic Surface Evolver data.
• examples/inference_in_vivo.ipynb: inference on experimental skeleton/myosin image data.
• examples/finetuning.ipynb: finetuning a pre-trained StressNET model on user-provided graph data.

These notebooks show the complete workflow, including data loading, model loading, inference, metrics, and plotting.

AI assistants for users

This repository includes AGENTS.md, a user-facing grounding document for AI assistants. It helps assistants write scripts or notebook cells that use StressNET to load data, run inference, compare metrics, and generate plots.

Recommended usage:

• Cursor: add @AGENTS.md to the chat context and ask for help using StressNET on your data. For always-on project context, Cursor users can also create a .cursor/rules/*.mdc rule that points to AGENTS.md.
• GitHub Copilot / VS Code: ask Copilot Chat to read AGENTS.md before writing a StressNET script or notebook cell. If your setup uses repository custom instructions, copy or reference the same content from .github/copilot-instructions.md; VS Code/Copilot also supports AGENTS.md when agent-instruction support is enabled.
• Claude Code / Claude: ask Claude to read AGENTS.md first, then provide your input file paths and desired outputs. If you prefer Claude-specific project memory, copy or reference the same content from CLAUDE.md.

To avoid stale duplicated instructions, treat AGENTS.md as the source document and make tool-specific files point back to it when possible.

Development setup

Requires Poetry and Python 3.10 (TensorFlow 2.8 / NumPy constraints in pyproject.toml).

git clone https://github.com/nicolasaldecoa/StressNET.git
cd StressNET
poetry install --with dev
poetry run pytest

Common development shortcuts are available through the Makefile:

make lint       # run Ruff checks like CI
make lint-diff  # preview Ruff auto-fixes
make lint-fix   # apply Ruff auto-fixes
make test       # run pytest with coverage
make docs       # build local HTML documentation
make build      # build wheel and source distribution
make check      # run lint, tests, and package build

Documentation

• Read the Docs (stable): https://stressnet.readthedocs.io/en/stable/ (development: latest).
• Local HTML build:

poetry install --with docs
poetry run python docs/build_docs.py

Or, with make:

make install-docs
make docs

Open docs/_build/html/index.html in a browser. Maintainer-only release notes live in README_MAINTAINERS.md.

Running examples

The example notebooks assume the repository layout is available. Clone the repo, start Jupyter from the repository root, and open notebooks under examples/ so relative paths such as examples/example_data/... resolve as written.

Installing stressnet from PyPI provides the library package only; running the publication examples also requires this repository's examples/ folder and any downloaded example data.

Example finetuning dataset

The radial_1d_2d_inverted_9v dataset is intentionally not tracked in git because of its size.

Download it to the expected examples path with:

python examples/download_radial_1d_2d_inverted_9v.py

You can also import ensure_dataset_downloaded from examples/download_radial_1d_2d_inverted_9v.py in a notebook.

How to cite

If you use the StressNET software, cite this repository/software package:

@software{stressnet,
  author  = {Aldecoa Rodrigo, Nicolás},
  title   = {{StressNET}: an adaptable deep-learning model for mechanical stress inference in tissues},
  url     = {https://github.com/nicolasaldecoa/StressNET},
  license = {BSD-3-Clause},
}

⚠️ Associated manuscript (unpublished, citation details to be updated after publication):

StressNET: an adaptable deep-learning model for mechanical stress inference in tissues Nicolás Aldecoa Rodrigo, Augusto Borges, Jerónimo R. Miranda-Rodriguez, Guillherme Ventura, Jakub Sedzinski, Hernán López-Schier, Osvaldo Chara

⚠️ Once the paper is published, update CITATION.cff and this section with the journal, year, and DOI.

License

BSD 3-Clause © Nicolás Aldecoa Rodrigo