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Examples

The source repository includes executable examples in examples/basic/.

Run examples from the repository root after installing PyRecEst or after installing a development checkout. The CI package job smoke-runs the core Kalman and Gaussian examples from an installed wheel so packaging errors are caught outside editable mode.

Choose An Example By Task

Task Example Backend notes Good for
Linear Gaussian filtering examples/basic/kalman_filter.py Portable baseline. First smoke test, API familiarization.
Linear Gaussian filtering with reusable model objects examples/basic/kalman_filter_with_models.py Portable where model-object APIs are supported. Applications with shared transition/measurement models.
Nonlinear additive-noise filtering examples/basic/ukf_with_models.py Follows UnscentedKalmanFilter backend limitations. Nonlinear Euclidean examples.
Particle filtering examples/basic/particle_filter_with_models.py Requires sampleable transition and likelihood models. Monte Carlo filter loops.
Multi-target tracking with clutter examples/basic/multi_target_tracking.py NumPy backend. Missed detections, clutter, and labeled tracks.
Directional distribution algebra examples/basic/von_mises_fisher_multiplication.py Manifold/domain-specific APIs. Unit-sphere probability operations.
User-defined protocols examples/basic/custom_distribution_protocol.py, examples/basic/custom_filter_protocol.py Backend-independent protocol shape. Extending PyRecEst without subclassing.

Scenario Examples

The scenarios/ directory contains data-first examples with TOML configuration and JSON golden outputs. Use these when you want a reproducible regression case rather than an exploratory script. The first scenario is scenarios/linear_gaussian_cv_1d.

Basic Examples

Gaussian Multiplication

examples/basic/gaussian_multiplication.py multiplies several two-dimensional Gaussian distributions and checks the result against the closed-form information representation.

python examples/basic/gaussian_multiplication.py

Kalman Filter

examples/basic/kalman_filter.py runs a one-dimensional constant-velocity Kalman filter.

python examples/basic/kalman_filter.py

Kalman Filter With Model Objects

examples/basic/kalman_filter_with_models.py runs the same one-dimensional constant-velocity Kalman filter as kalman_filter.py, but defines reusable linear-Gaussian transition and measurement model objects and passes them to predict_model() and update_model().

python examples/basic/kalman_filter_with_models.py

Unscented Kalman Filter With Model Objects

examples/basic/ukf_with_models.py demonstrates the additive-noise nonlinear model-object API with an unscented Kalman filter. The transition model stores the state propagation function and process-noise covariance; the measurement model stores the measurement function and measurement-noise covariance.

python examples/basic/ukf_with_models.py

This example follows the current backend limitations of UnscentedKalmanFilter.

Particle Filter With Model Objects

examples/basic/particle_filter_with_models.py demonstrates a particle-filter loop with a sampleable transition model and a likelihood-based measurement model.

python examples/basic/particle_filter_with_models.py

Multi-Target Tracking

examples/basic/multi_target_tracking.py runs a small linear/Gaussian multi-Bernoulli-tracker scenario with two labeled targets, missed detections, and clutter measurements.

python examples/basic/multi_target_tracking.py

This example currently requires the NumPy backend.

von Mises-Fisher Multiplication

examples/basic/von_mises_fisher_multiplication.py multiplies two von Mises-Fisher distributions on the unit sphere and verifies the analytic product relation.

python examples/basic/von_mises_fisher_multiplication.py

Custom Distribution Protocol

examples/basic/custom_distribution_protocol.py shows a small user-defined scalar distribution that satisfies the public dimension protocols without inheriting from a PyRecEst distribution base class.

python examples/basic/custom_distribution_protocol.py

Custom Filter Protocol

examples/basic/custom_filter_protocol.py shows a small user-defined recursive filter that satisfies the public dimension protocol and follows common PyRecEst filter naming conventions.

python examples/basic/custom_filter_protocol.py

Backend Selection

Select a non-default backend by setting PYRECEST_BACKEND before running the script. For example, on a bash-compatible shell:

PYRECEST_BACKEND=pytorch python examples/basic/kalman_filter.py

Install the matching optional dependency extra before using a non-default backend.