gri-tracksim 0.3.0

Tracking-scenario simulator and replay visualizer over a shared JSONL stream contract

TrackSim (Tracking-Scenario Simulation)

Tracking-scenario simulator and replay visualizer over a shared JSONL stream contract. Generate labeled truth, degrade it into observables across fidelity tiers, replay it through a reference estimator, and scrub the result on a timeline. Requires Python 3.12+.

gri-tracksim is two paired tools joined by one file contract: a simulator that emits truth / input / output streams, and a Dash/Plotly replay viewer that reads them. The boundary is the JSONL contract alone, so the viewer renders the output of any contract-conformant tracker, not just this package's.

The viewer is a 2D replay tool. The 3D analyst app ("Crucible") is a separate, closed product and is not part of this repository.

Key Features

• Scenario simulator: leg-based truth (straight / turn / climb / hold), degraded observables (geo / TDOA / FDOA / AOA / altitude), and a four-tier fidelity ladder (T0..T3) driven from one physical scene.
• Declarative scenarios: author scenarios in YAML from the shipped vocabulary - no source checkout required.
• Reference replay: a single-target IMM replay produces the output stream with forward-prediction horizons.
• Replay viewer: a 2D top-down ENU map plus linked time-series panes, with a play/scrub timeline.
• Self-contained contract: a language-agnostic JSONL wire format you can emit from your own tracker and render here.

Capability Tiers

gri-tracksim is built around three ways to use it:

1. Viz-only consumer - you already have a tracker. Emit the JSONL contract and run gri-tracksim-viz to scrub it.
2. Scenario author - write scenario YAML from the shipped vocabulary (emitters, legs, tiers, collector presets, noise) and generate the truth / input / output triple. Needs the package; no source.
3. Vocabulary developer - add new leg primitives, collector geometries, fidelity tiers, or parametric sweeps. These live in registries in the source and require a checkout (see AGENTS.md).

Installation

pip install gri-tracksim          # simulator + replay viewer

Quick Start

Generate a scenario (CLI)

gri-tracksim --list                  # list bundled example scenarios
gri-tracksim air_racetrack           # render a bundled scenario
gri-tracksim air_racetrack --tier T3 # same scene, harder tier
gri-tracksim air_racetrack --seed 7  # same scene, new realization
gri-tracksim my_scenario.yaml        # render your own YAML

The scene name (air_racetrack) describes the physical scenario; --tier (T0-T3) and --seed are orthogonal run knobs layered over it. Each defaults to the scene's own value (a --seed-less scene with no seed: draws a random one), and the effective tier and seed are folded into the output directory so sweeps never clobber: streams land in _scratch/<name>_<tier>_s<seed>/. The output root defaults to _scratch/ (a gitignored scratch dir); override it with -o <dir>.

Scrub a run (viewer)

gri-tracksim-viz _scratch/air_racetrack_T2_s3

Author a scenario in Python

from gri_tracksim.sim import load_scenario, write_scenario_streams

scenario = load_scenario("my_scenario.yaml")
n_truth, n_input, n_output = write_scenario_streams(
    scenario, "_scratch/my_scenario", predict_horizons_s=(10.0, 30.0),
)

Read the contract from your own tracker

from gri_tracksim.contract import Header, load_stream, write_jsonl

header, records = load_stream("output.jsonl")   # parsed Header + event records

You only need gri_tracksim.contract to emit or read the streams - it has no dependency on the simulator or the viewer.

Scenario YAML

A scenario is a single declarative file. All keys but name, origin_lla, dt, and emitters are optional:

name: air_racetrack
origin_lla: [38.0, -77.0, 0.0]   # ENU anchor [lat_deg, lon_deg, alt_m]
dt: 2.0                          # grid step (s)
seed: 3                          # default realization (--seed overrides; random if omitted)
tier: T2                         # default tier (--tier overrides): T0 | T1 | T2 | T3
collectors: diverse              # good | weak | diverse | null
motion: air                      # level (cruise) | air (climb-aware IMM)
noise:
  altitude_std_m: 200.0
emitters:
  E1:
    start_enu: [0.0, 0.0, 1000.0]
    azimuth_deg: 90.0
    speed_mps: 120.0
    legs:
      - {type: straight, duration_s: 30.0}
      - {type: climb, duration_s: 20.0, climb_rate_mps: 10.0}
      - {type: turn, delta_azimuth_deg: 180.0, radius_m: 2000.0}

The leg types (straight, turn, climb, hold), fidelity tiers (T0..T3), collector presets (good, weak, diverse), and replay motion banks (level, level_fx, air) are fixed vocabularies; adding to them is the source-only developer path.

Reference Replay Options

The replay tracker has three orthogonal knobs (defaults are the validated configuration; legacy values remain available for comparison runs):

• Motion bank (motion: in the YAML): level (cv / turn / static), air (adds a climb mode so altitude changes read as a mode), level_fx (adds a fixed +/- nominal-rate turn pair -- instant turn discrimination when the platform's maneuver-rate class is known; harmful off-nominal).
• Segmentation (segmentation=): mode_sequence (default) -- a retrospective MAP decode over the whole track, recomputed each scan, which also emits settled (fixed-lag smoothed) mode probabilities per scan; or locked, the online residual-trigger segmenter.
• Prediction (prediction=): locked (default) -- predict under the current settled segment's model (settle-gated, speed-guarded); evolved or blend for comparison.

Evaluation Harness

Two console scripts score tracker configurations against truth, corpus-wide:

# render + score scenario x seed runs (metrics.json per run, summary.json per sweep)
gri-tracksim-eval --seeds 10 --tier T2 -o _scratch/eval_new
gri-tracksim-eval --seeds 10 --tier T2 --segmentation locked --prediction blend \
    --model-selection mean_ll -o _scratch/eval_legacy   # the legacy stack

# paired (scenario, seed) comparison: median delta, bootstrap CI, sign test
gri-tracksim-compare _scratch/eval_legacy _scratch/eval_new
gri-tracksim-compare _scratch/eval_legacy _scratch/eval_new --scenario boat_racetrack

Per-run metrics cover segmentation (count, boundary error, label accuracy), characterization (instant and settled mode agreement, lock latency, thrash), smoothing (RMS and straight-leg bow, with a truth-boundary oracle bound), and prediction (per-horizon RMS plus the phantom-curve ratio vs a CV extrapolation).

The Stream Contract

Each stream is JSON Lines: a self-describing header record followed by one record per event.

• truth - {"t", "emitter_id", "pos_enu", "vel_enu", "leg_mode"}
• input - {"t", "id", "type", ...} where type is one of geo | tdoa | fdoa | aoa | altitude; the stable id (obs_0000...) is what the output's association references.
• output - the estimator replay (estimate / association / predicted), association keyed back to the input id. Mode-sequence replays also carry settled_t / settled_mode_probabilities: the fixed-lag smoothed mode mix a few scans behind the playhead (the "settled" complement to the instant mode_probabilities); each predicted entry names the model that produced it.
• smoothed (optional) - one retrospective snapshot per scan: the smoothed trajectory the tracker had settled on by that playhead, plus segments ({model, t_start, t_end}) naming the motion model the smoother used per leg. Segments revise as data accrues, so scrubbing replays the boundary back-dating.

The wire format is plain JSON (no gri types on the wire), so any tracker, in any language, can emit a stream the viewer will render.

Dependencies

A top-tier package in the GRI FOSS ecosystem; a sibling consumer of the same mid-tier libraries as gri-multitrack.

• gri-geosim (iterative locate), gri-kalman (reference IMM replay), gri-trajectory (legs), gri-obs, gri-pos, gri-ell, gri-utils
• gri-plot, dash, plotly (the replay viewer)
• numpy, scipy, pyyaml

Other Projects

Current list of other GRI FOSS Projects we are building and maintaining.

License

MIT License. See LICENSE for details.