sensorfusion-tools

Signal-systems utilities for estimation, modeling, and plotting.

Versioning

This library uses calendar-style semantic versions: YYYY.FEATURE.BUGFIX.

• YYYY: release year (for long-term timeline tracking)
• FEATURE: increments for new functionality
• BUGFIX: increments for patches and fixes

Framework And Tooling

• Language: Python 3.14+
• Build backend: uv_build
• Package/dependency workflow: uv
• Numerical stack: numpy, scipy
• Plotting: matplotlib

Testing Framework

• Test runner: pytest
• Property-based testing: hypothesis
• Coverage: pytest-cov (configured for src/sftools)

Run tests:

uv run pytest

Run only sftools tests:

uv run pytest tests/sftools

Code Quality

• Linting: ruff

Run lint:

uv run ruff check .

CI (GitLab)

The pipeline is defined in .gitlab-ci.yml and includes:

• quality stage: ruff lint checks
• test stage: pytest with JUnit artifact output

CI uses python:3.14-slim and installs dependencies with:

uv sync --group dev

License

Distributed under the MIT License. See LICENSE for details.

Project Structure

src/
	sftools/        # Main signal-system models, estimators, helpers, plotting, shared types

tests/
	sftools/          # Pytest suite for src/sftools (one test file per source file)
	fixtures/         # Regression test input/output fixtures

For sftools, tests mirror the source layout:

• src/sftools/model_obs.py -> tests/sftools/test_model.py
• src/sftools/estimator.py -> tests/sftools/test_estimator.py
• src/sftools/helper.py -> tests/sftools/test_helper.py
• src/sftools/plot.py -> tests/sftools/test_plot.py

Shared state/trajectory datatypes are defined in src/sftools/types.py:

• GaussianState
• ParticleState
• StateTrajectory (supports probabilistic trajectories via write_state and deterministic trajectories via write_mean)

Useful StateTrajectory iteration helpers:

• for y in traj yields state columns with shape (n, 1)
• traj.iter_samples(include_cov=True, include_time=True) yields (y, cov, t) tuples when available

Plotting helpers in src/sftools/plot.py use StateTrajectory inputs.

Particle filter helper:

• ParticleFilter.init_state_gaussian(mean, cov, n_particles) initializes a ParticleState from a Gaussian prior using the filter RNG.