pybounds

Python implementation of BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems.

Introduction

This repository provides python code to empirically calculate the observability level of individual states for a nonlinear (partially observable) system, and accounts for sensor noise. Below is a graphical example of how pybounds can discover active sensing motifs. Minimal working examples are described below.

Installing

The package can be installed from PyPi:

pip install pybounds

or from source, for development, after cloning the repo:

pip install -e .

Quick Start

To demonstrate pybounds with a simple example we use a downward-pointing camera moving horizontally with acceleration that is controlled directly with control inputs (u). The two states are ground speed g and (constant) altitude d, and the only measurement is the ventral optic flow ratio r = g/d. We use pybounds to understand when g and d are observable.

See notebooks in next section for more detailed usage examples.

import numpy as np
import matplotlib.pyplot as plt
import pybounds

# 1. Define continuous time system dynamics f(X, U) and measurement h(X, U)
def f(X, U):         # states: gap g, distance d — input u drives g
    return [U[0], 0] # returns: d/dt(g), d/dt(d) 

def h(X, U):        # monocular camera measures the g/d ratio
    return [X[0] / X[1]]

# 2. Simulate a trajectory
sim = pybounds.Simulator(f, h, dt=0.01,
                         state_names=['g', 'd'], input_names=['u'],
                         measurement_names=['r'])
t, x, u, _ = sim.simulate(x0={'g': 2.0, 'd': 3.0},
                           u={'u': 0.1 * np.ones(500)},
                           return_full_output=True)

# 3. Set up the observability analysis (nothing is computed yet), then run it
oa = pybounds.ObservabilityAnalysis(sim, t, x, u, w=6, R={'r': 0.1}, lam=1e-8)
oa.run()

# 4. Plot minimum error variance over time for each state
ev = oa.min_error_variance()
ev.set_index('time')[['g', 'd']].plot(logy=True, ylabel='Min. error variance')
plt.show()

• Window: w is the sliding-window length in time-steps. Without it, the whole trajectory is analyzed as one window.
• Noise: R is the measurement noise variance, per sensor.
• Regularization lam (λ): the Fisher information matrix F is inverted as (F + λI)⁻¹. 1e-8 is also the default. 1/λ is the ceiling on the minimum error variance: a state whose error variance sits near 1/λ (1e8 by default) is unobservable, not merely poorly estimated. λ is an absolute value, so it should be small compared to the eigenvalues of F, which depend on the sensor noise R and on the units of each state. When states have very different units, give each its own λ: a dict such as lam={'g': 1e-6, 'd': 1e-10}, or a 1-D array in the order of the selected states, replaces λI with diag(λᵢ). Selected states that the dict leaves out get the default 1e-8. Values must be > 0, and 'limit' is only available as a single value. With a z_function, use the transformed state names. A dict passed to a query may only name selected states. A dict given as the lam setting may also name other states, and those entries are ignored when a query doesn't select them.
• One-call shortcut: pybounds.compute_observability(sim, t, x, u, R={'r': 0.1}, w=6, lam=1e-8) runs steps 3 and 4 in a single call, without keeping the analysis. It picks the backend from the simulator type, like ObservabilityAnalysis. Its finite-difference step defaults to eps=1e-4, while ObservabilityAnalysis defaults to 1e-5. Pass eps=1e-5 to get exactly the result of steps 3 and 4.

Selecting states, and saving settings and results

oa keeps the observability matrices from run(), so you can ask about different selections without recomputing them:

# Drop a state: treat d as known and ask how well g alone can be estimated
ev_g = oa.min_error_variance(states=['g'])

# Other selections and parameters work the same way
ev_short = oa.min_error_variance(time_steps=[0, 1, 2])   # only the first 3 steps of each window
ev_noisy = oa.min_error_variance(R={'r': 1.0})           # a different noise level

# Save every setting to YAML, and load it into another analysis later
oa.save_settings('observability_settings.yaml')
oa2 = pybounds.ObservabilityAnalysis(sim, t, x, u).load_settings('observability_settings.yaml')

# Save results for a selection into a directory: min_error_variance.csv, a YAML sidecar
# (selection, full state/sensor lists, settings) and, optionally, all observability matrices (.npz)
oa.save_results('results_g', states=['g'], include_observability_matrices=True)

• Dropping a state is conditional: the states you leave out are treated as known, so the remaining ones usually look more observable than when every state is estimated together.
• Changing settings: update_settings(...) changes settings before or after run(). Changing anything that affects the observability matrices (e.g. w, eps, z_function) discards the results until you call run() again; changing the query settings R, lam, Q or alignment does not.
• Methods: method picks how each window's Fisher information is computed. It defaults to 'bounds-jax' for a JaxSimulator and 'bounds-empirical' otherwise.
  • 'bounds-empirical' (finite differences) and 'bounds-jax' (autodiff) build the empirical observability matrix, with no process noise. The older names 'empirical' and 'jax' still work.
  • 'stochastic-observability-classic' / '-jax' and 'stochastic-constructability-classic' / '-jax' include process noise Q (see below).
• Memory: run() keeps every window's observability matrix (8·n_windows·w·p·n bytes). For long windows, storage='fisher_per_sensor' keeps each sensor's Fisher information instead, which is smaller when w > (n+1)/2 and still supports selecting states and sensors with a scalar or per-sensor R. With method='bounds-jax', batch_size=... computes windows in chunks to cap JAX's memory. See the storage design note.

Process noise: stochastic observability and constructability

The bounds-* methods assume no process noise, so a longer window always adds information. With process noise Q, measurements far from the state of interest say little about it, and the information saturates. The stochastic methods compute this. They follow Boyacioglu & van Breugel, "Duality of Stochastic Observability and Constructability and their Relation to the Fisher Information", IEEE L-CSS (2025), doi:10.1109/LCSYS.2025.3547297.

oa = pybounds.ObservabilityAnalysis(sim, t, x, u, method='stochastic-constructability-classic',
                                    w=20, R={'r': 0.1}, Q={'g': 1e-3, 'd': 1e-6})
ev = oa.run().min_error_variance()
ev_more_noise = oa.min_error_variance(Q=1e-2)   # Q, R and lam can change without run()

• Observability vs constructability: stochastic observability (Eq. 33) is the Fisher information about the state at the start of each window, the same state the bounds-* methods describe. Stochastic constructability (Eq. 30) is about the state at the end of each window. Its inverse is the posterior Cramér-Rao bound, the quantity a Kalman filter's error covariance tracks.
• Q is the per-step discrete process noise covariance. It can be a scalar, one variance per state (a dict, or a 1-D array in state order), or an (n, n) matrix (an array, or a DataFrame labelled by state name). It must be strictly positive. Give constant parameters a small Q rather than zero.
• Linearization: the model is linearized along the trajectory (Φ = expm(A·dt)). -classic uses finite differences; -jax uses autodiff and needs f and h written with jax.numpy. Because of the linearization, results differ from the bounds-* methods by discretization error even as Q → 0. pybounds.stochastic also exposes the recursions directly, for linear time-varying systems.
• Validation: validation/stochastic_duality_fig2.ipynb checks the recursions against the paper's MATLAB code and redraws its Fig. 2.
• Sweeping the window size: the linearization does not depend on w or on the coordinate transform. Changing only w, z_function or z_state_names keeps it, and the next run() only re-derives the windows. Changing the method or its options linearizes again.
• Which states need a small Q: oa.deterministic_states() lists the states whose row of Φ is exactly eᵢ at every sample, such as constant parameters and clocks. They have no process noise physically. oa.model_state_names gives the names Q is keyed by. These are the model's own names, even when a z_function renames the states.

Using a linearization computed elsewhere

oa.linearization returns the linearized trajectory as a frozen pybounds.Linearization with fields Phi (N, n, n), C (N, p, n), t_sim, state_names, sensor_names and bounded. Its arrays are read-only and shared with the analysis. It is None for the bounds-* methods. ObservabilityAnalysis.from_linearization wraps such arrays without a simulator. It is the stochastic counterpart of from_sliding:

oa2 = pybounds.ObservabilityAnalysis.from_linearization(
    Phi, C, method='stochastic-constructability', w=20, t_sim=t, state_names=['g', 'd'], sensor_names=['r'],
    R={'r': 0.1}, Q={'g': 1e-3, 'd': 1e-6})
# or round-trip one: from_linearization(**dataclasses.asdict(oa.linearization), method=..., w=...)

• The arrays are kept read-only and are not copied, so analyses built from the same arrays share them.
• A coordinate transform can be given in either of two ways:
  • dxdz_sliding, with shape (n_windows, n, n), already evaluated at each window's bounded state;
  • z_function plus x_sim, which is evaluated exactly as run() does it.
• z_state_names names the transformed states.
• Queries, fisher_information, observability_matrix and save_results behave as they do after run(), with bit-identical results. Only the query settings R, lam, Q and alignment can change afterwards.

Where each window's result is placed: alignment

Each window gives one value per state, and that value has to be placed somewhere along the trajectory.

• alignment='center' (default): at the window's center time-step, w // 2, for every method. Different methods can then be compared on one time axis.
• alignment='bounded_state': at the state the result actually bounds. That is the window's first time-step for bounds-* and stochastic observability, and its last time-step for stochastic constructability.

ev = oa.min_error_variance(alignment='bounded_state')   # or set it once: update_settings(alignment=...)

Observability and constructability viewed at their bounded states are offset by w - 1 time-steps. Centered, they usually line up, especially for short windows. The time column is where each row is placed, and time_initial is the time of the window's first sample.

Notebook examples

Basic Examples

These notebooks provide a more detailed example of pybounds functionality including:

• How to use model predictive control to drive systems along specified trajectories
• Demonstration of what happens inside the pybounds.compute_observability wrapper function, allowing for detailed investigations of the observability calculations

Examples using pybounds with continuous time dynamics, see these notebook examples:

• Monocular camera with optic flow measurements: mono_camera_example.ipynb
• Fly-wind: fly_wind_example.ipynb

JAX Accelerated Examples

pybounds includes a JAX backend (JaxSimulator, JaxSlidingEmpiricalObservabilityMatrix) that replaces the numerical finite-difference Jacobian with exact autodiff via jax.vmap + jax.jacfwd. The simulation and all downstream analysis (Fisher information, plotting) are unchanged.

When JAX helps most: the speedup scales with the number of sliding windows. Short trajectories with few windows see modest gains; long trajectories benefit dramatically.

To use the JAX backend, install JAX and rewrite your dynamics f and measurement h using jax.numpy instead of numpy. See the notebooks below for worked examples.

• Mono-camera — JAX accelerated: mono_camera_example_jax.ipynb
• Fly-wind — JAX accelerated: fly_wind_example_jax.ipynb

Using a Custom Simulator

This has received the least development, however, a working tutorial can be found here.

Citation

If you use the code or methods from this package, please cite the following paper:

Cellini, B., Boyacioglu, B., Lopez, A., & van Breugel, F. (2025). Discovering and exploiting active sensing motifs for estimation (arXiv:2511.08766). arXiv. https://arxiv.org/abs/2511.08766

Additional resources

To learn more about nonlinear observability, its relation to Fisher information, see Boyacioglu and van Breugel

To start with the basics, check out these open source course materials: Nonlinear and Data Driven Estimation.

Related packages

This repository is the evolution of the EISO repo (https://github.com/BenCellini/EISO), and is intended as a companion to the repository directly associated with the paper above.

License

This project utilizes the MIT LICENSE. 100% open-source, feel free to utilize the code however you like.