autodynamics 0.3.0a0

Modelling autonomy dynamics over the Autonometrics atlas.

Autodynamics

Layer 2 of 3 in the autonomy research trilogy: Autonometrics (measure) -> Autodynamics (explain) -> Ex-Machina (build / emulate)

Status: Pre-alpha. Recording substrate, algebra of trajectories, generic adapters, and Granger-causal coupling primitives. No theoretical model is claimed.

Vision

Autonometrics quantifies where a system sits on the five autonomy axes (closure, memory, constraint closure, persistence, coherence). This package gives mechanical support for working with how those measurements change over time: record ordered profiles, take differences, and summarise motion in the atlas with explicit primitives. It does not ship a dynamical theory (attractors, phase structure, or predictive claims about autonomy). Interpretation of trajectories as evidence for such a theory is out of scope of the public API as it stands; the code is a reproducible toolkit, not a proof.

What this package contains today

A recording substrate, a small algebra of trajectories, and two generic input adapters. Together they turn "a sequence of autonomy measurements" into "a trajectory in a metric space, queryable axis by axis".

• ProfileTrajectory stores a sequence of AutonomyProfile values and exposes axis-wise time series, pairwise deltas, total path length, and the algebra primitives below.
• Algebra primitives: velocities, accelerations, drift, volatility, path_length_per_axis, rolling_mean, rolling_std, and a one-shot per-axis summary.
• Adapters: CSVTrajectoryAdapter (load from a CSV with canonical axis columns) and BatchTrajectoryAdapter (build several parallel trajectories from grouped profiles, with cross-group mean_summary).
• Granger-causal coupling: granger_graph (directed pairwise Granger graph over admitted axes), CausalCouplingGraph, and four scalar diagnostics (symmetry_ratio, density, max_in_strength, max_out_strength).
• Pre-registered boundary regimes and a saturation theorem documented in docs/TRAJECTORY_DIAGNOSTICS.md; the coupling protocol is pre-registered in docs/COUPLING_DIAGNOSTICS.md.

Install with: pip install autodynamics Import as: import autodynamics

Quick run

pip install autodynamics
autodynamics-demo --n-states-list 3 4 5 6 8 --n-steps 600
autodynamics-demo --n-states-list 3 4 5 6 8 --n-steps 600 --report summary

The first command prints a small table of (closure, memory) profiles measured over a sweep of SimpleAutomaton configurations, the consecutive deltas between them, and the total path length. The second prints a per-axis summary instead of the deltas.

Toy demo: ProfileTrajectory

Disclaimer. This is the recording substrate of Autodynamics, not its theory. The trajectory class lets you collect, traverse, and compute simple geometric quantities over a sequence of AutonomyProfiles. It does not interpret what those movements mean — assigning meaning is outside the scope of the public API. Treat the code as a reproducible toolkit or template, not as evidence for a larger claim.

import autonometrics as anm
from autodynamics import ProfileTrajectory

trajectory = ProfileTrajectory(axes=("closure", "memory"))

for n_states in [3, 4, 5, 6, 8]:
    sys = anm.SimpleAutomaton.demo(n_states=n_states, n_steps=600)
    sys.run()
    profile = anm.measure(sys, axes=["closure", "memory"])
    trajectory.append(profile)

print(trajectory.axis_series("closure"))    # time series of one axis
print(trajectory.deltas())                  # pairwise consecutive movements
print(trajectory.total_path_length())       # sum of delta magnitudes

Trajectory algebra

Beyond the substrate, the package exposes an algebra of trajectories (velocity-style differences, rolling statistics, per-axis summary). Every primitive is mosaic-dropout fielty: None propagates through differences, but never aborts aggregations.

from autodynamics import ProfileTrajectory

trajectory = ProfileTrajectory(axes=("closure", "memory"))
# ... append profiles as above ...

trajectory.velocities("closure")        # first differences, axis by axis
trajectory.accelerations("closure")     # second differences
trajectory.drift("closure")             # net change between first and last defined values
trajectory.volatility("closure")        # sample std of the velocities
trajectory.path_length_per_axis()       # sum of |velocity| per axis
trajectory.rolling_mean("closure", window=10)  # right-aligned rolling mean
trajectory.rolling_std("closure", window=10)   # right-aligned rolling sample std
trajectory.summary()                    # per-axis report: n_total, n_defined, mean, std, drift, volatility, path_length

Calling a primitive without an axis argument returns a dict over every axis configured on the trajectory; calling it with a canonical axis name returns the value for that axis directly. The full list of boundary regimes and the saturation theorem are pre-registered in docs/TRAJECTORY_DIAGNOSTICS.md.

Adapters

Two generic adapters open input paths into ProfileTrajectory without introducing calibration or threshold tuning of their own.

CSV adapter

from autodynamics import CSVTrajectoryAdapter

adapter = CSVTrajectoryAdapter()
trajectory = adapter.load_path("path/to/profiles.csv")
print(trajectory.summary())

The adapter reads columns closure, memory, constraint, persistence, coherence (any subset is fine; missing columns yield fully-undefined axes). Empty or whitespace-only cells become None. Extra columns (class, params, seed, notes, ...) are ignored. Pass order_column="step" (or any integer-valued column name) to sort rows numerically before constructing snapshots.

Batch adapter

from autodynamics import BatchTrajectoryAdapter
import autonometrics as anm

batch = BatchTrajectoryAdapter()
for params in benchmark_configurations:
    for seed in range(K):
        system = build_system(params, seed)
        system.run()
        profile = anm.measure(system)
        batch.add(params, profile)

trajectories = batch.trajectories()        # one ProfileTrajectory per group key
mean_summary = batch.mean_summary()        # per-axis cross-group means of summary metrics

A reproducible end-to-end example using both adapters over a public fixture is shipped at examples/trajectory_demo.py.

Coupling analysis

Beyond intra-axis algebra, the package exposes a cross-axis primitive: a directed Granger-causal coupling graph between every pair of admitted axes of a ProfileTrajectory, with a stationarity gate (Augmented Dickey-Fuller), mosaic-dropout-fielty axis admission, and four scalar diagnostics. The implementation is the standard pairwise Granger test (Granger 1969; Sims 1980; Lütkepohl 2005) applied uniformly to the axes of the autonomy atlas, packaged so it composes with the rest of the algebra without bespoke calibration.

from autodynamics import (
    granger_graph, density, symmetry_ratio,
    max_in_strength, max_out_strength,
)

graph = granger_graph(trajectory)
# Also accepts a Mapping[str, Sequence[float | None]]:
# graph = granger_graph({"closure": [...], "memory": [...]})

graph.axes_used                                # admitted axes
graph.excluded_axes                            # axis -> rejection reason
graph.edge("closure", "memory").f_stat         # F-stat, lag, p-value, status

symmetry_ratio(graph)                          # average min/max F over pairs
density(graph)                                 # fraction of edges above F critical
max_in_strength(graph, "memory")               # max incoming F-stat
max_out_strength(graph, "closure")             # max outgoing F-stat

The protocol (length gate, ADF + up to two differences, AIC-selected VAR up to max_lag, F-test, mosaic-dropout-fielty axis admission) is pre-registered in docs/COUPLING_DIAGNOSTICS.md. It is not a claim that any axis Granger-causes any other axis on any specific Autonometrics zoo; it is a composable primitive that can be fed real or synthetic trajectories without bespoke threshold tuning.

Public validation track

Pre-registered, falsifiable experiments that stress the algebra primitives against the public Autonometrics benchmarks. Each experiment locks its hypothesis in docs/ before any output is generated, and records both confirmations and rejections verbatim. Recent public runs include negative results: they narrow what the current primitives can support and are not written as motivation for a new public release cycle.

• docs/TURBULENCE_RANKING.md — pre-registered ranking of the five Autonometrics zoo classes by cross-seed volatility on the v0.8.0a0 benchmark. Reproducible from examples/turbulence_ranking.py; raw outputs in docs/benchmarks/turbulence_ranking_v0.2.1a0.csv and the full log in docs/benchmarks/turbulence_ranking_v0.2.1a0.log.txt. Verdict: REJECTED.
• docs/COUPLING_VALIDATION.md — pre-registered Granger coupling experiment running granger_graph against every group of the same v0.8.0a0 zoo. Reproducible from examples/coupling_validation.py; raw outputs in docs/benchmarks/coupling_v0.3.0a0.csv and the full log in docs/benchmarks/coupling_v0.3.0a0.log.txt. Verdict: REJECTED, by a single group below the admission threshold (15 of 31 groups admit ≥ 2 axes against a 50 % bar). The pipeline itself is robust (93 % of admitted edges produce finite F-statistics); the bottleneck is the saturation pattern of the source zoo.

Roadmap

• v0.1.0a0 / v0.1.0a1: Toy trajectory recorder. Reserves name, declares vision, ships demo.
• v0.2.0a0: Trajectory algebra (velocities, accelerations, drift, volatility, rolling statistics, summary), generic CSV / batch adapters, pre-registered diagnostics, public validation track.
• v0.2.1a0: Documentation cleanup; PyPI summary description shortened.
• v0.3.0a0 (current): Granger-causal coupling primitives (granger_graph, CausalCouplingGraph, four scalar diagnostics), with pre-registered diagnostics (docs/COUPLING_DIAGNOSTICS.md) and public validation track (docs/COUPLING_VALIDATION.md, verdict: REJECTED).
• v0.4.0a0 (planned): Generic envelope primitives (Envelope, trinary ContainmentVerdict) with the same pre-registration / public-validation discipline.

Beyond v0.4.0a0, no further cycle is pre-declared. Future work will be opened only when motivated by a concrete pre-registered design document or external collaboration.

Position in the trilogy

Layer	Project	Question it answers
1	Autonometrics	Where does a system sit on the autonomy atlas?
2	Autodynamics	How do successive profiles differ, and how do their axes couple (recorded motion plus pairwise Granger coupling, not a dynamical model)?
3	Ex-Machina	Can we build a system that occupies a chosen region?

License

Apache License 2.0 — see LICENSE.

Citation

If you reference this work, please cite Autodynamics directly. A formal citation block will be added when the API stabilises.